From 238c4a0af83f36858b027e55964d1ce49f102ada Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Laure=CE=B7t?= Date: Thu, 22 Jun 2023 20:35:38 +0200 Subject: [PATCH] init --- TP0.ipynb | 1222 ++++++++++++++++++++++++++++++ TP1.ipynb | 2104 +++++++++++++++++++++++++++++++++++++++++++++++++++ TP3.ipynb | 1673 +++++++++++++++++++++++++++++++++++++++++ TP4.ipynb | 2173 +++++++++++++++++++++++++++++++++++++++++++++++++++++ TP5.ipynb | 1692 +++++++++++++++++++++++++++++++++++++++++ TP6.ipynb | 1701 +++++++++++++++++++++++++++++++++++++++++ TP7.ipynb | 1553 ++++++++++++++++++++++++++++++++++++++ 7 files changed, 12118 insertions(+) create mode 100644 TP0.ipynb create mode 100644 TP1.ipynb create mode 100644 TP3.ipynb create mode 100644 TP4.ipynb create mode 100644 TP5.ipynb create mode 100644 TP6.ipynb create mode 100644 TP7.ipynb diff --git a/TP0.ipynb b/TP0.ipynb new file mode 100644 index 0000000..e2a663b --- /dev/null +++ b/TP0.ipynb @@ -0,0 +1,1222 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "xxuVc", + "launcher_item_id": "X20PE" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "toc_cell": true, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + }, + "colab": { + "name": "TP Réseaux de Neurones avec Numpy - Sujet.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Construire et entraîner un perceptron multi-couches - étape par étape\n", + "\n", + "Dans ce TP, vous allez mettre en œuvre l'entraînement d'un réseau de neurones (perceptron multi-couches) à l'aide de la librairie **numpy**. Pour cela nous allons procéder par étapes successives. Dans un premier temps nous allons traiter le cas d'un perceptron mono-couche, en commençant par la passe *forward* de prédiction d'une sortie à partir d'une entrée et des paramètres du perceptron, puis en implémentant la passe *backward* de calcul des gradients de la fonction objectif par rapport aux paramètrès. A partir de là, nous pourrons tester l'entraînement à l'aide de la descente de gradient stochastique.\n", + "\n", + "Une fois ces étapes achevées, nous pourrons nous atteler à la construction d'un perceptron multi-couches, qui consistera pour l'essentiel en la composition de perceptrons mono-couche. \n", + "\n", + "Dans ce qui suit, nous adoptons les conventions de notation suivantes : \n", + "\n", + "- $(x, y)$ désignent un couple donnée/label de la base d'apprentissage ; $\\hat{y}$ désigne quant à lui la prédiction du modèle sur la donnée $x$.\n", + "\n", + "- L'indice $i$ indique la $i^{\\text{ème}}$ dimension d'un vecteur.\n", + "\n", + "- L'exposant $[l]$ désigne un objet associé à la $l^{\\text{ème}}$ couche.\n", + "\n", + "- L'exposant $(k)$ désigne un objet associé au $k^{\\text{ème}}$ exemple. \n", + " \n", + "Exemple: \n", + "- $a^{(2)[3]}_5$ indique la 5ème dimension du vecteur d'activation du 2ème exemple d'entraînement (2), de la 3ème couche [3].\n", + "\n", + "\n", + "Commençons par importer tous les modules nécessaires : " + ], + "metadata": { + "id": "5b3pjAUEk2LQ" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import math\n", + "import matplotlib.pyplot as plt \n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import datasets" + ], + "metadata": { + "id": "R6LBs_NLla1a" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Perceptron mono-couche\n" + ], + "metadata": { + "id": "3JZIXefJlXSV" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Perceptron mono-couche - passe *forward*\n", + "\n", + "Un perceptron mono-couche est un modèle liant une couche d'entrée (en vert, qui n'effectue pas d'opération) à une couche de sortie. Les neurones des deux couches sont connectés par des liaisons pondérées (les poids synaptiques) $W_{xy}$, et les neurones de la couche de sortie portent chacun un biais additif $b_y$. Enfin, une fonction d'activation $f$ est appliquée à l'issue de ces opérations pour obtenir la prédiction du réseau $\\hat{y}$. \n", + "\n", + "On a donc :\n", + "\n", + "$$\\hat{y} = f ( W_{xy} x + b_y )$$ \n", + "\n", + "On posera pour la suite :\n", + "$$ z = W_{xy} x + b_y $$\n", + "\n", + "La figure montre une représentation de ces opérations sous forme de réseau de neurones (à gauche), mais aussi sous une forme fonctionnelle (à droite) qui permet de bien visualiser l'ordre des opérations.\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "Notez que les paramètres du perceptron, que nous allons ajuster par un processus d'optimisation, sont donc les poids synaptiques $W_{xy}$ et les biais $b_y$. Par commodité dans le code, nous considérerons également comme un paramètre le choix de la fonction d'activation.\n", + "\n", + "**Remarque importante** : En pratique, on traite souvent les données par *batch*, c'est-à-dire que les prédictions sont faites pour plusieurs données simultanément. Ici pour une taille de *batch* de $m$, cela signifie en fait que :\n", + " \n", + "$$ x \\in \\mathbb{R}^{4 \\times m} \\text{ et } y \\in \\mathbb{R}^{5 \\times m}$$ \n" + ], + "metadata": { + "id": "azdcz3QV_k-r" + } + }, + { + "cell_type": "markdown", + "source": [ + "Complétez la fonction *dense_layer_forward* qui calcule la prédiction d'un perceptron mono-couche pour une entrée $x$. " + ], + "metadata": { + "id": "RBtX2euQDSCS" + } + }, + { + "cell_type": "code", + "source": [ + "def dense_layer_forward(x, Wxy, by, activation):\n", + " \"\"\"\n", + " Réalise une unique étape forward de la couche dense telle que décrite dans la figure précédente\n", + "\n", + " Arguments:\n", + " x -- l'entrée, tableau numpy de dimension (n_x, m).\n", + " Wxy -- Matrice de poids multipliant l'entrée, tableau numpy de shape (n_y, n_x)\n", + " by -- Biais additif ajouté à la sortie, tableau numpy de dimension (n_y, 1)\n", + " activation -- Chaîne de caractère désignant la fonction d'activation choisie : 'linear', 'sigmoid' ou 'relu'\n", + "\n", + " Retourne :\n", + " y_pred -- prédiction, tableau numpy de dimension (n_y, m)\n", + " cache -- tuple des valeurs utiles pour la passe backward (rétropropagation du gradient), contient (x, z)\n", + " \"\"\"\n", + " \n", + " \n", + " \n", + " ### A COMPLETER \n", + " # calcul de z\n", + " z = ...\n", + " # calcul de la sortie en appliquant la fonction d'activation\n", + " if activation == 'relu':\n", + " y_pred = ...\n", + " elif activation == 'sigmoid':\n", + " y_pred = ...\n", + " elif activation == 'linear':\n", + " y_pred = ...\n", + " else:\n", + " print(\"Erreur : la fonction d'activation n'est pas implémentée.\")\n", + " \n", + " ### FIN\n", + "\n", + " # sauvegarde du cache pour la passe backward\n", + " cache = (x, z)\n", + " \n", + " return y_pred, cache" + ], + "metadata": { + "id": "YGYbWrRfmIwx" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Exécutez les lignes suivantes pour vérifier la validité de votre code :" + ], + "metadata": { + "id": "1dCFTHOqD_Tp" + } + }, + { + "cell_type": "code", + "source": [ + "np.random.seed(1)\n", + "x_tmp = np.random.randn(3,10)\n", + "Wxy = np.random.randn(2,3)\n", + "by = np.random.randn(2,1)\n", + "activation = 'relu'\n", + "y_pred_tmp, cache_tmp = dense_layer_forward(x_tmp, Wxy, by, activation)\n", + "\n", + "\n", + "print(\"y_pred.shape = \\n\", y_pred_tmp.shape)\n", + "\n", + "print('----------------------------')\n", + "\n", + "print(\"y_pred[1] =\\n\", y_pred_tmp[1])\n", + "\n", + "print('----------------------------')\n", + "\n", + "activation = 'sigmoid'\n", + "\n", + "y_pred_tmp, cache_tmp = dense_layer_forward(x_tmp, Wxy, by, activation)\n", + "print(\"y_pred[1] =\\n\", y_pred_tmp[1])\n", + "\n", + "print('----------------------------')\n", + "\n", + "activation = 'linear'\n", + "\n", + "y_pred_tmp, cache_tmp = dense_layer_forward(x_tmp, Wxy, by, activation)\n", + "print(\"y_pred[1] =\\n\", y_pred_tmp[1])\n" + ], + "metadata": { + "id": "B6wlVU37on1k" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Affichage attendu**: \n", + "```Python\n", + "y_pred.shape = \n", + " (2, 10)\n", + "----------------------------\n", + "y_pred[1] =\n", + " [0. 2.11983968 0.88583246 1.39272594 0. 2.92664609\n", + " 0. 1.47890228 0. 0.04725575]\n", + "----------------------------\n", + "y_pred[1] =\n", + " [0.10851642 0.89281659 0.70802939 0.80102707 0.21934644 0.94914804\n", + " 0.24545321 0.81440672 0.48495927 0.51181174]\n", + "----------------------------\n", + "y_pred[1] =\n", + " [-2.10598556 2.11983968 0.88583246 1.39272594 -1.26947904 2.92664609\n", + " -1.12301093 1.47890228 -0.06018107 0.04725575]\n", + "\n", + "```" + ], + "metadata": { + "id": "YYbiDw8TptiN" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Perceptron mono-couche - passe *backward*\n", + "\n", + "Dans les librairies d'apprentissage profond actuelles, il suffit d'implémenter la passe *forward*, et la passe *backward* est réalisée automatiquement, avec le calcul des gradients (différentiation automatique) et la mise à jour des paramètres. Il est cependant intéressant de comprendre comment fonctionne la passe *backward*, en l'implémentant sur un exemple simple.\n", + "\n", + " \n", + "\n", + "Il faut calculer les dérivées de la fonction objectif par rapport aux différents paramètres, pour ensuite mettre à jour ces derniers pendant la descente de gradient. Les équations de calcul des gradients sont données ci-dessous (c'est un bon exercice que de les calculer à la main). \n", + "\n", + "\\begin{align}\n", + "\\displaystyle {dW_{xy}} &= \\frac{\\partial J}{\\partial W_{xy}} &= (d\\hat{y} * \\frac{\\partial \\hat{y}}{\\partial z}) . x^{T}\\tag{1} \\\\[8pt]\n", + "\\displaystyle db_{y} &= \\frac{\\partial J}{\\partial b_y} &= \\sum_{batch}(d\\hat{y} * \\frac{\\partial \\hat{y}}{\\partial z})\\tag{2} \\\\[8pt]\n", + "\\displaystyle dx &= \\frac{\\partial J}{\\partial x} &= { W_{xy}}^T . (d\\hat{y} * \\frac{\\partial \\hat{y}}{\\partial z}) \\tag{3} \\\\[8pt]\n", + "\\end{align}\n", + "\n", + "\n", + "Ici, $*$ indique une multiplication élément par élément tandis que l'absence de symbole indique une multiplication matricielle. Par ailleurs $d\\hat{y}$ désigne $\\frac{\\partial J}{\\partial \\hat{y}}$, $dW_{xy}$ désigne $\\frac{\\partial J}{\\partial W_{xy}}$, $db_y$ désigne $\\frac{\\partial J}{\\partial b_y}$ et $dx$ désigne $\\frac{\\partial J}{\\partial x}$ (ces noms ont été choisis pour être utilisables dans le code).\n", + "\n", + "Il vous reste à déterminer, par vous même, le terme $\\frac{\\partial \\hat{y}}{\\partial z}$, qui constitue en fait la dérivée de la fonction d'activation évaluée en $z$. Par exemple, pour la fonction d'activation linéaire (l'identité), la dérivée est égale à 1 pour tout $z$. A vous de déterminer, et d'implémenter, la dérivée des fonctions *sigmoid* et *relu*.\n" + ], + "metadata": { + "id": "GypgZ8jBqooR" + } + }, + { + "cell_type": "code", + "source": [ + "def dense_layer_backward(dy_hat, Wxy, by, activation, cache):\n", + " \"\"\"\n", + " Implémente la passe backward de la couche dense.\n", + "\n", + " Arguments :\n", + " dy_hat -- Gradient de la fonction objectif par rapport à la sortie ŷ\n", + " cache -- dictionnaire python contenant des variables utiles (issu de dense_layer_forward())\n", + "\n", + " Retourne :\n", + " gradients -- dictionnaire python contenant les gradients suivants :\n", + " dx -- Gradient de la fonction objectif par rapport aux entrées, de dimension (n_x, m)\n", + " dby -- Gradient de la fonction objectif par rapport aux biais, de dimension (n_y, 1)\n", + " dWxy -- Gradient de la fonction objectif par rapport aux poids synaptiques Wxy, de dimension (n_y, n_x)\n", + " \"\"\"\n", + " \n", + " # Récupérer les informations du cache\n", + " (x, z) = cache\n", + " \n", + " ### A COMPLETER \n", + " # calcul de la sortie en appliquant l'activation\n", + " if activation == 'relu':\n", + " dyhat_dz = ...\n", + " elif activation == 'sigmoid':\n", + " dyhat_dz = ...\n", + " elif activation == 'linear':\n", + " dyhat_dz = ...\n", + " else:\n", + " print(\"Erreur : la fonction d'activation n'est pas implémentée.\")\n", + "\n", + " # calculer le gradient de la perte par rapport à x\n", + " dx = ...\n", + "\n", + " # calculer le gradient de la perte par rapport à Wxy\n", + " dWxy = ...\n", + "\n", + " # calculer le gradient de la perte par rapport à by \n", + " dby = ...\n", + "\n", + " ### FIN\n", + " \n", + " # Stocker les gradients dans un dictionnaire\n", + " gradients = {\"dx\": dx, \"dby\": dby, \"dWxy\": dWxy}\n", + " \n", + " return gradients" + ], + "metadata": { + "id": "wEi_y3W_rCMc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "On peut maintenant créer une classe *DenseLayer*, qui comprend en attribut toutes les informations nécessaires à la description d'une couche dense, c'est-à-dire : \n", + "\n", + "\n", + "* Le nombre de neurones en entrée de la couche dense (input_size)\n", + "* Le nombre de neurones en sortie de la couche dense (output_size)\n", + "* La fonction d'activation choisie sur cette couche (activation)\n", + "* Les poids synaptiques de la couche dense, stockés dans une matrice de taille (output_size, input_size) (Wxy)\n", + "* Les biais de la couche dense, stockés dans un vecteur de taille (output_size, 1) (by)\n", + "\n", + "On ajoute également un attribut cache qui permettra de stocker les entrées de la couche dense (x) ainsi que les calculs intermédiaires (z) réalisés lors de la passe *forward*, afin d'être réutilisés pour la basse *backward*.\n", + "\n", + "A vous de compléter les 4 jalons suivants : \n", + "\n", + "* **L'initialisation des paramètres** Wxy et by : Wxy doit être positionnée suivant [l'initialisation de Glorot](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform), et by est initialisée par un vecteur de zéros.\n", + "* **La fonction *forward***, qui consiste simplement en un appel de la fonction *dense_layer_forward* implémentée précédemment.\n", + "* **La fonction *backward***, qui consiste simplement en un appel de la fonction *dense_layer_backward* implémentée précédemment.\n", + "* Et enfin **la fonction *update_parameters*** qui applique la mise à jour de la descente de gradient en fonction d'un taux d'apprentissage (*learning_rate*) et des gradients calculés dans la passe *forward*.\n" + ], + "metadata": { + "id": "E5KeDgyO-ZPJ" + } + }, + { + "cell_type": "code", + "source": [ + "class DenseLayer:\n", + " def __init__(self, input_size, output_size, activation):\n", + " self.input_size = input_size\n", + " self.output_size = output_size\n", + " self.activation = activation\n", + " self.cache = None # Le cache sera mis à jour lors de la passe forward\n", + " ### A COMPLETER\n", + " self.Wxy = ...\n", + " self.by = ...\n", + "\n", + " def forward(self, x_batch):\n", + "\n", + " y, cache = dense_layer_forward(...)\n", + " self.cache = cache\n", + " return y\n", + "\n", + " def backward(self, dy_hat):\n", + " return dense_layer_backward(...)\n", + "\n", + " def update_parameters(self, gradients, learning_rate):\n", + " self.Wxy = ...\n", + " self.by = ...\n", + " ### FIN" + ], + "metadata": { + "id": "u2K9dp1IL3yM" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Fonction de coût : erreur quadratique moyenne" + ], + "metadata": { + "id": "9GlEB8K3Lani" + } + }, + { + "cell_type": "markdown", + "source": [ + "Pour entraîner notre modèle, nous devons mettre en place un optimiseur. Nous implémenterons la descente de gradient stochastique avec mini-batch. Il nous faut cependant au préalable implanter la fonction de coût que nous utiliserons pour évaluer la qualité de nos prédictions. \n", + "\n", + "Pour le moment, nous allons nous contenter d'une erreur quadratique moyenne, qui associée à une fonction d'activation linéaire (l'identité) permet de résoudre les problèmes de régression. \n", + "\n", + "La fonction de coût prend en entrée deux paramètres : la vérité-terrain *y_true* et la prédiction du modèle *y_pred*. Ces deux matrices sont de dimension $bs \\times output\\text{_}size$. La fonction retourne deux grandeurs : *loss* qui correspond à l'erreur quadratique moyenne des prédictions par rapport aux vérités-terrains, et $d\\hat{y}$ au gradient de l'erreur quadratique moyenne par rapport aux prédictions. Autrement dit : \n", + "$$ d\\hat{y} = \\frac{\\partial J_{mb}}{\\partial \\hat{y}}$$\n", + "\n", + "où $\\hat{y}$ correspond à *y_pred*, et $J_{mb}$ à la fonction objectif calculée sur un mini-batch $mb$ de données." + ], + "metadata": { + "id": "2KMcQzlskdI1" + } + }, + { + "cell_type": "code", + "source": [ + "### A COMPLETER\n", + "def mean_square_error(y_true, y_pred):\n", + " loss = ...\n", + " dy_hat = ...\n", + " return loss, dy_hat" + ], + "metadata": { + "id": "FRDUnhJma6jf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Descente de gradient stochastique" + ], + "metadata": { + "id": "uZRnPbBjQvZc" + } + }, + { + "cell_type": "markdown", + "source": [ + "La descente de gradient stochastique prend en entrée les paramètres suivants : \n", + "* *x_train* et *y_train* respectivement les données et labels de l'ensemble d'apprentissage (que l'on suppose de taille $N$).\n", + "* *model* une instance du modèle que l'on veut entraîner (qui doit implanter les 3 fonctions vues précédemment *forward*, *backward* et *update_parameters*).\n", + "* *loss_function* peut prendre deux valeurs : 'mse' (erreur quadratique moyenne) ou 'bce' (entropie croisée binaire, que nous implémenterons par la suite).\n", + "* *learning_rate* le taux d'apprentissage choisi pour la descente de gradient.\n", + "* *epochs* le nombre de parcours complets de l'ensemble d'apprentissage que l'on veut réaliser.\n", + "* *batch_size* la taille de mini-batch désirée pour la descente de gradient stochastique. \n", + "\n", + "L'algorithme à implémenter est rappelé ci-dessous : \n", + "```\n", + "N_batch = floor(N/batch_size)\n", + "\n", + "Répéter epochs fois\n", + "\n", + " Pour b de 1 à N_batch Faire\n", + "\n", + " - Sélectionner les données x_train_batch et labels y_train_batch du b-ème mini-batch\n", + " - Calculer la prédiction y_pred_batch du modèle pour ce mini-batch\n", + " - Calculer la perte batch_loss et le gradient de la perte batch_grad par rapport aux prédictions sur ce mini-batch\n", + " - Calculer les gradients de la perte par rapport à chaque paramètre du modèle\n", + " - Mettre à jour les paramètres du modèle \n", + "\n", + " Fin Pour\n", + "\n", + "Fin Répéter\n", + "\n", + "```\n", + "Deux remarques additionnelles : \n", + "1. A chaque *epoch*, les *mini-batches* doivent être différents (les données doivent être réparties dans différents *mini-batches*).\n", + "2. Il est intéressant de calculer (et d'afficher !) la perte moyennée sur l'ensemble d'apprentissage à chaque *epoch*. Pour cela, on peut accumuler les pertes de chaque *mini-batch* sur une *epoch* et diviser l'ensemble par le nombre de *mini-batches*." + ], + "metadata": { + "id": "w2XnUBj2n-Df" + } + }, + { + "cell_type": "code", + "source": [ + "def SGD(x_train, y_train, model, loss_function, learning_rate=0.03, epochs=10, batch_size=10):\n", + " # Nombre de batches par epoch\n", + " nb_batches = math.floor(x_train.shape[0] / batch_size)\n", + "\n", + " # Pour gérer le tirage aléatoire des batches parmi les données d'entraînement... \n", + " indices = np.arange(x_train.shape[0])\n", + "\n", + " for e in range(epochs):\n", + "\n", + " running_loss = 0\n", + "\n", + " # Nouvelle permutation des indices pour la prochaine epoch\n", + " indices = np.random.permutation(indices)\n", + "\n", + " for b in range(nb_batches):\n", + "\n", + " # Sélection des données du batch courant\n", + " x_train_batch = x_train[indices[b*batch_size:(b+1)*batch_size]]\n", + " y_train_batch = y_train[indices[b*batch_size:(b+1)*batch_size]]\n", + "\n", + " ### A COMPLETER\n", + " # Prédiction du modèle pour le batch courant\n", + " y_pred_batch = ...\n", + "\n", + " # Calcul de la fonction objectif et de son gradient sur le batch courant\n", + " if loss_function == 'mse':\n", + " batch_loss, batch_dy_hat = mean_square_error(...)\n", + " elif loss_function == 'bce':\n", + " batch_loss, batch_dy_hat = binary_cross_entropy(...)\n", + "\n", + " running_loss += batch_loss \n", + "\n", + " # Calcul du gradient de la perte par rapport aux paramètres du modèle\n", + " param_updates = ...\n", + "\n", + " # Mise à jour des paramètres du modèle\n", + " model.update_parameters(...)\n", + " ### FIN\n", + "\n", + " print(f\"Epoch {e:4d} : Loss {running_loss/nb_batches:.4f}\")\n", + " \n", + " \n", + " return model\n" + ], + "metadata": { + "id": "lk3lypUOLXbv" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Test sur un problème de régression " + ], + "metadata": { + "id": "9bybDhHivjXq" + } + }, + { + "cell_type": "markdown", + "source": [ + "Le bloc de code suivant permet de générer et d'afficher un ensemble de données pour un problème de régression linéaire classique. " + ], + "metadata": { + "id": "N7q44eS0vrrZ" + } + }, + { + "cell_type": "code", + "source": [ + "x, y = datasets.make_regression(n_samples=250, n_features=1, n_targets=1, random_state=1, noise=10)\n", + "\n", + "plt.plot(x, y, 'b.', label='Ensemble d\\'apprentissage')\n", + "\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "id": "nGcIVuALraDG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "A vous de déterminer le nombre de neurones à positionner en entrée et en sortie du perceptron monocouche pour résoudre ce problème. Une fois ceci fait, le code ci-après affiche également la prédiction de votre modèle." + ], + "metadata": { + "id": "q7lfdRFMRFZH" + } + }, + { + "cell_type": "code", + "source": [ + "### A COMPLETER\n", + "model = DenseLayer(..., ..., ...)\n", + "model = SGD(x, y, model, ..., learning_rate=0.1, epochs=10, batch_size=20)\n", + "### FIN\n", + "\n", + "plt.plot(x, y, 'b.', label='Ensemble d\\'apprentissage')\n", + "\n", + "x_gen = np.expand_dims(np.linspace(-3, 3, 10), 1)\n", + "y_gen = np.transpose(model.forward(np.transpose(x_gen)))\n", + "\n", + "plt.plot(x_gen, y_gen, 'g-', label='Prédiction du modèle')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "id": "GKFJ3c2MmomL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Test sur un problème de classification binaire" + ], + "metadata": { + "id": "mA9-6PqLwff4" + } + }, + { + "cell_type": "markdown", + "source": [ + "Afin de pouvoir tester notre perceptron mono-couche sur un problème de classification binaire (i.e. effectuer une régression logistique), il est d'abord nécessaire d'implémenter l'entropie croisée binaire.\n", + "\n", + "Rappel : \n", + "$$ bce(y, \\hat{y}) = -y log(\\hat{y}) - (1-y) log(1-\\hat{y}) $$" + ], + "metadata": { + "id": "K9AHAgGBwjro" + } + }, + { + "cell_type": "code", + "source": [ + "### A COMPLETER\n", + "def binary_cross_entropy(y_true, y_pred):\n", + " loss = ...\n", + " grad = ...\n", + "\n", + " return loss, grad" + ], + "metadata": { + "id": "_xCXP-pQb2oL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Le bloc de code suivant permet de générer et d'afficher un ensemble de données pour un problème de classification binaire classique. " + ], + "metadata": { + "id": "0L3pPIpfSVU7" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn import datasets\n", + "import matplotlib.pyplot as plt \n", + "\n", + "\n", + "x, y = datasets.make_blobs(n_samples=250, n_features=2, centers=2, center_box=(- 3, 3), random_state=1)\n", + "\n", + "plt.plot(x[y==0,0], x[y==0,1], 'b.')\n", + "plt.plot(x[y==1,0], x[y==1,1], 'r.')\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "4AxQRaegdntx" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "A nouveau, vous devez déterminer le nombre de neurones à positionner en entrée et en sortie du perceptron monocouche pour résoudre ce problème. Une fois ceci fait, le code ci-après affiche également la prédiction de votre modèle." + ], + "metadata": { + "id": "X7o-u0kcSk_l" + } + }, + { + "cell_type": "code", + "source": [ + "### A COMPLETER\n", + "model = DenseLayer(..., ..., ...)\n", + "model = SGD(x, y, model, ..., learning_rate=0.3, epochs=50, batch_size=20)\n", + "### FIN\n", + "\n", + "plt.plot(x[y==0,0], x[y==0,1], 'b.')\n", + "plt.plot(x[y==1,0], x[y==1,1], 'r.')\n", + "\n", + "x1_gen = np.linspace(-6, 2, 10)\n", + "x2_gen = -model.Wxy[0,0]*x1_gen/model.Wxy[0,1] - model.by[0,0]/model.Wxy[0,1]\n", + "\n", + "plt.plot(x1_gen, x2_gen, 'g-')\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "TdyntT9zSrum" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Test sur un problème de classification binaire plus complexe" + ], + "metadata": { + "id": "Ypq84RCl0bnI" + } + }, + { + "cell_type": "markdown", + "source": [ + "Testons maintenant un problème de classification plus complexe : " + ], + "metadata": { + "id": "6OPzEofrSrSF" + } + }, + { + "cell_type": "code", + "source": [ + "x, y = datasets.make_gaussian_quantiles(n_samples=250, n_features=2, n_classes=2, random_state=1)\n", + "\n", + "plt.plot(x[y==0,0], x[y==0,1], 'r.')\n", + "plt.plot(x[y==1,0], x[y==1,1], 'b.')\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "_IQdphRV0hsB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Le code ci-dessous vous permettra d'afficher la frontière de décision établie par votre modèle :" + ], + "metadata": { + "id": "8Ol3eqKGSyC5" + } + }, + { + "cell_type": "code", + "source": [ + "def print_decision_boundaries(model, x, y):\n", + " dx, dy = 0.1, 0.1\n", + " y_grid, x_grid = np.mgrid[slice(-4, 4 + dy, dy),\n", + " slice(-4, 4 + dx, dx)]\n", + "\n", + "\n", + " x_gen = np.concatenate((np.expand_dims(np.reshape(y_grid, (-1)),1),np.expand_dims(np.reshape(x_grid, (-1)),1)), axis=1)\n", + " z_gen = model.forward(np.transpose(x_gen)).reshape(x_grid.shape)\n", + "\n", + " z_min, z_max = 0, 1\n", + "\n", + " c = plt.pcolor(x_grid, y_grid, z_gen, cmap='RdBu', vmin=z_min, vmax=z_max)\n", + " plt.colorbar(c)\n", + " plt.plot(x[y==0,0], x[y==0,1], 'r.')\n", + " plt.plot(x[y==1,0], x[y==1,1], 'b.')\n", + " plt.show()" + ], + "metadata": { + "id": "lN8d7YK76MBm" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Complétez le code ci-dessous :" + ], + "metadata": { + "id": "SRNifc8KS_MM" + } + }, + { + "cell_type": "code", + "source": [ + "### A COMPLETER\n", + "model = DenseLayer(..., ..., ...)\n", + "model = SGD(x, y, model, ..., learning_rate=0.3, epochs=50, batch_size=20)\n", + "### FIN\n", + "\n", + "print_decision_boundaries(model, x, y)" + ], + "metadata": { + "id": "E9WV-Az70mR6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Cette fois-ci il n'est pas possible de faire résoudre un problème aussi \"complexe\" à notre simple perceptron monocouche. Nous allons pour cela devoir passer au perceptron multi-couches !" + ], + "metadata": { + "id": "J9jMU_YcTAJl" + } + }, + { + "cell_type": "markdown", + "source": [ + "---" + ], + "metadata": { + "id": "yiGyXLvum0uI" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Perceptron multi-couches" + ], + "metadata": { + "id": "HIEVrFXkDdMD" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Implémentation du perceptron multi-couches" + ], + "metadata": { + "id": "6ZWNGM7vVlCb" + } + }, + { + "cell_type": "markdown", + "source": [ + "A partir du perceptron mono-couche créé précédemment, nous pouvons maintenant implémenter un perceptron multi-couches, qui est un véritable réseau de neurones dans la mesure où il met en jeu plusieurs couches de neurones successives. **Concrètement, le perceptron multi-couches est une composition de perceptron monocouches**, chacun prenant en entrée l'activation de sortie de la couche précédente. Prenons l'exemple ci-dessous : \n", + "\n", + " \n", + "\n", + "\n", + "Ce perceptron multi-couches est la composition de deux perceptrons monocouches, le premier liant deux neurones d'entrée à deux neurones de sortie, et le second deux neurones d'entrée à un neurone de sortie.\n", + "\n", + " \n", + "\n", + "Voici comment nous l'implémenterons : le perceptron multi-couches consiste simplement en une liste de perceptrons monocouches (*DenseLayer*). A l'initialisation, le perceptron multi-couches est une liste vide, dans laquelle il est possible d'ajouter des couches denses (fonction *add_layer()*). \n", + "\n", + "```python\n", + "model = MultiLayerPerceptron()\n", + "model.add_layer(DenseLayer(2, 2, 'relu'))\n", + "model.add_layer(DenseLayer(2, 1, 'sigmoid'))\n", + "```\n", + "\n", + "La fonction *forward()* du perceptron multi-couches consiste en le calcul successif de la sortie des couches denses. Chaque couche dense effectue une prédiction sur la sortie de la couche dense précédente.\n", + "\n", + "La fonction *backward()* implémente l'algorithme de rétro-propagation du gradient. Les gradients des paramètres de la dernière couche sont calculés en premier, et sont utilisés pour calculer les gradients de la couche précédente, comme illustré sur cette figure.\n", + "\n", + " " + ], + "metadata": { + "id": "1a6VuuWODu8G" + } + }, + { + "cell_type": "code", + "source": [ + "class MultiLayerPerceptron:\n", + " def __init__(self):\n", + " # Initialisation de la liste de couches du perceptron multi-couches à la liste vide\n", + " self.layers = []\n", + "\n", + " # Fonction permettant d'ajouter la couche passée en paramètre dans la liste de couches\n", + " # du perceptron multi-couches\n", + " def add_layer(self, layer):\n", + " self.layers.append(layer)\n", + "\n", + " # Fonction réalisant la prédiction du perceptron multi-couches :\n", + " # Elle consiste en la prédiction successive de chacune des couches de la liste de couches,\n", + " # chacune prenant en entrée la prédiction de la couche précédente\n", + " def forward(self, x_batch):\n", + " \n", + " for i in range(len(self.layers)):\n", + " ### A COMPLETER\n", + "\n", + " return ...\n", + "\n", + " # Fonction de calcul des gradients de la fonction objectif par rapport à chaque paramètre \n", + " # du perceptron multi-couches\n", + " # L'entrée dy_hat correspond au gradient de la fonction objectif par rapport à la prédiction\n", + " # finale du perceptron multi-couches (notée dJ/dŷ sur la figure précédente)\n", + " # Cette fonction doit implémenter la rétropropagation du gradient : on parcourt la liste des\n", + " # couches en sens inverse (fonction reversed) et le gradient de la fonction objectif par rapport \n", + " # à l'entrée d'une couche est utilisé pour calculer les gradients de la couche précédente\n", + " # \n", + " # Cette fonction retourne une liste de dictionnaires de gradients, de même dimension que le nombre\n", + " # de couches\n", + " def backward(self, dy_hat):\n", + " gradients = []\n", + " for i in reversed(range(len(self.layers))):\n", + " ### A COMPLETER\n", + "\n", + " return gradients\n", + "\n", + " # Fonction de mise à jour des paramètres en fonction des gradients établis dans la \n", + " # fonction backward et d'un taux d'apprentissage\n", + " def update_parameters(self, gradients, learning_rate):\n", + " for i in range(len(self.layers)):\n", + " ### A COMPLETER" + ], + "metadata": { + "id": "RNhqq0KXm4Jd" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Test sur le problème simple de classification binaire" + ], + "metadata": { + "id": "GyIW025tVcPR" + } + }, + { + "cell_type": "markdown", + "source": [ + "Vous pouvez maintenant tester votre perceptron multi-couches sur le problème précédent. Deux couches suffisent pour résoudre le problème !" + ], + "metadata": { + "id": "JEg5-Z7mVEWd" + } + }, + { + "cell_type": "code", + "source": [ + "x, y = datasets.make_gaussian_quantiles(n_samples=250, n_features=2, n_classes=2, random_state=1)\n", + "\n", + "plt.plot(x[y==0,0], x[y==0,1], 'r.')\n", + "plt.plot(x[y==1,0], x[y==1,1], 'b.')\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "pijGm1ipwrAw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = MultiLayerPerceptron()\n", + "model.add_layer(DenseLayer(2, 10, 'relu'))\n", + "model.add_layer(DenseLayer(10, 1, 'sigmoid'))\n", + "\n", + "model = SGD(x, y, model, 'bce', learning_rate=0.3, epochs=60, batch_size=20)\n", + "\n", + "print_decision_boundaries(model, x, y)" + ], + "metadata": { + "id": "h3He5gXmxQ1j" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Quelques exercices supplémentaires" + ], + "metadata": { + "id": "SMTeraduVplm" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Evanescence du gradient" + ], + "metadata": { + "id": "46K0mq5bVvT1" + } + }, + { + "cell_type": "markdown", + "source": [ + "Testez le réseau suivant sur le problème simple de classification binaire évoqué dans la partie précédente :\n", + "```python\n", + "model.add_layer(DenseLayer(2, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 1, 'sigmoid'))\n", + "```\n", + "\n", + " \n", + "\n", + "1. Qu'observez-vous ?\n", + "2. Comment résoudre ce problème ?\n", + "\n", + "\n" + ], + "metadata": { + "id": "pVBCGX9iVzdL" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Application à un problème de classification d'image\n" + ], + "metadata": { + "id": "YBChCCJREOuP" + } + }, + { + "cell_type": "markdown", + "source": [ + "Le code ci-dessous vous permet de charger l'ensemble de données CIFAR-10 qui regroupe des imagettes de taille $32 \\times 32$ représentant 10 types d'objets différents. \n", + "\n", + "Des images de chat et de chien sont extraites de cet ensemble : à vous de mettre en place un perceptron multi-couches de classification binaire pour apprendre à reconnaître un chien d'un chat dans une image." + ], + "metadata": { + "id": "C7efDmj6WNSg" + } + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "\n", + "# Récupération des données\n", + "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n", + "\n", + "# La base de données CIFAR contient des images issues de 10 classes :\n", + "# 0\tairplane\n", + "# 1\tautomobile\n", + "# 2\tbird\n", + "# 3\tcat\n", + "# 4\tdeer\n", + "# 5\tdog\n", + "# 6\tfrog\n", + "# 7\thorse\n", + "# 8\tship\n", + "# 9\ttruck\n", + "\n", + "# Préparation des données pour la classification binaire : \n", + "\n", + "# Extraction des images des classes de chat et chien\n", + "indices_train = np.squeeze(y_train)\n", + "x_cat_train = x_train[indices_train==3,:]\n", + "x_dog_train = x_train[indices_train==5,:]\n", + "\n", + "indices_test = np.squeeze(y_test)\n", + "x_cat_test = x_test[indices_test==3,:]\n", + "x_dog_test = x_test[indices_test==5,:]\n", + "\n", + "# Création des données d'apprentissage et de test\n", + "# Les images sont redimensionnées en vecteurs de dimension 3072 (32*32*3)\n", + "# On assigne 0 à la classe chat et 1 à la classe chien\n", + "x_train = np.concatenate((np.resize(x_cat_train[0:250],(250, 32*32*3)), np.resize(x_dog_train[0:250],(250, 32*32*3))), axis=0)\n", + "y_train = np.concatenate((np.zeros((250)), np.ones((250))),axis=0)\n", + "\n", + "x_test = np.concatenate((np.resize(x_cat_test,(1000, 32*32*3)), np.resize(x_dog_test,(1000, 32*32*3))), axis=0)\n", + "y_test = np.concatenate((np.zeros((1000)), np.ones((1000))),axis=0)\n", + "\n", + "# Normalisation des entrées\n", + "x_train = x_train/255\n", + "x_test = x_test/255" + ], + "metadata": { + "id": "ZFyeFRYfEN3A" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# A COMPLETER\n", + "model = MultiLayerPerceptron()\n", + "model.add_layer(DenseLayer(..., ..., ...))\n", + "...\n", + "# A vous de tester le nombre de couches qui vous semble adéquat\n", + "\n", + "model = SGD(x_train, y_train, model, ..., learning_rate=0.03, epochs=10, batch_size=10)" + ], + "metadata": { + "id": "VBzhs000JbHT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Prédiction du modèle sur les données de test\n", + "y_pred_test = model.forward(np.transpose(x_test))\n", + "\n", + "# Calcul de la précision : un écart inférieur à 0.5 entre prédiction et label\n", + "# est considéré comme bonne prédiction\n", + "prediction_eval = np.where(np.abs(y_pred_test-y_test)<0.5, 1, 0)\n", + "overall_test_precision = 100*np.sum(prediction_eval)/y_test.shape[0]\n", + "print(f\"Précision de {overall_test_precision:2.1f} %\")" + ], + "metadata": { + "id": "hPUcXM60L0-b" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Si vous obtenez une précision supérieure à 50%, votre réseau est meilleur qu'une prédiction aléatoire, ce qui est déjà bien ! Notez qu'ici nous avons circonscrit l'ensemble d'apprentissage à 500 échantillons (250 de chaque classe) car les calculs de produit matriciel sont longs. C'est tout l'intérêt de porter les calculs sur GPU ou TPU, des dispositifs matériels spécialement conçus et optimisés pour paralléliser ces calculs." + ], + "metadata": { + "id": "A1jASzh3PSKa" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Utilisation de la librairie Keras" + ], + "metadata": { + "id": "YV4WZTfL0KB9" + } + }, + { + "cell_type": "markdown", + "source": [ + "L'utilisation d'une librairie comme Keras permet d'abstraire toutes les difficultés présentées dans ce TP : voici par exemple comment résoudre grâce à Keras le premier problème de régression linéaire présenté dans ce TP." + ], + "metadata": { + "id": "XFR3jwelW1jh" + } + }, + { + "cell_type": "code", + "source": [ + "x, y = datasets.make_regression(n_samples=250, n_features=1, n_targets=1, random_state=1, noise=10)\n", + "\n", + "plt.plot(x, y, 'b.', label='Ensemble d\\'apprentissage')\n", + "\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "id": "ew3_k9uK0P9g" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "\n", + "model = Sequential()\n", + "model.add(Dense(1, activation='linear', input_dim=1)) # input_dim indique la dimension de la couche d'entrée, ici 1\n", + "\n", + "model.summary() # affiche un résumé du modèle" + ], + "metadata": { + "id": "jBQYiUU-XX9a" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from tensorflow.keras import optimizers\n", + "\n", + "sgd = optimizers.SGD(learning_rate=0.1) # On choisit la descente de gradient stochastique, avec un taux d'apprentssage de 0.1\n", + "\n", + "# On définit ici, pour le modèle introduit plus tôt, l'optimiseur choisi, la fonction de perte (ici\n", + "# l'erreur quadratique moyenne pour un problème de régression) et les métriques que l'on veut observer pendant\n", + "# l'entraînement. L'erreur absolue moyenne (MAE) est un indicateur plus simple à interpréter que la MSE.\n", + "model.compile(optimizer=sgd,\n", + " loss='mean_squared_error',\n", + " metrics=['mae'])\n", + "\n", + "# Entraînement du modèle avec des mini-batchs de taille 20, sur 10 epochs. \n", + "# Le paramètre validation_split signifie qu'on tire aléatoirement une partie des données\n", + "# (ici 20%) pour servir d'ensemble de validation\n", + "history = model.fit(x, y, validation_split=0.2, epochs=10, batch_size=20)\n" + ], + "metadata": { + "id": "S0Vqoo26Xfe3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "plt.plot(x, y, 'b.', label='Ensemble d\\'apprentissage')\n", + "\n", + "x_gen = np.expand_dims(np.linspace(-3, 3, 10), 1)\n", + "y_gen = model.predict(x_gen)\n", + "\n", + "plt.plot(x_gen, y_gen, 'g-', label='Prédiction du modèle')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "id": "46LiNDvGYQdK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "S'il vous reste du temps, reprenez les différents problèmes définis précédemment et utilisez la librairie Keras pour les résoudre." + ], + "metadata": { + "id": "kHu5v6lUYqTm" + } + } + ] +} diff --git a/TP1.ipynb b/TP1.ipynb new file mode 100644 index 0000000..d94859b --- /dev/null +++ b/TP1.ipynb @@ -0,0 +1,2104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "5b3pjAUEk2LQ" + }, + "source": [ + "# Construire et entraîner un perceptron multi-couches - étape par étape\n", + "\n", + "Dans ce TP, vous allez mettre en œuvre l'entraînement d'un réseau de neurones (perceptron multi-couches) à l'aide de la librairie **numpy**. Pour cela nous allons procéder par étapes successives. Dans un premier temps nous allons traiter le cas d'un perceptron mono-couche, en commençant par la passe _forward_ de prédiction d'une sortie à partir d'une entrée et des paramètres du perceptron, puis en implémentant la passe _backward_ de calcul des gradients de la fonction objectif par rapport aux paramètrès. A partir de là, nous pourrons tester l'entraînement à l'aide de la descente de gradient stochastique.\n", + "\n", + "Une fois ces étapes achevées, nous pourrons nous atteler à la construction d'un perceptron multi-couches, qui consistera pour l'essentiel en la composition de perceptrons mono-couche.\n", + "\n", + "Dans ce qui suit, nous adoptons les conventions de notation suivantes :\n", + "\n", + "- $(x, y)$ désignent un couple donnée/label de la base d'apprentissage ; $\\hat{y}$ désigne quant à lui la prédiction du modèle sur la donnée $x$.\n", + "\n", + "- L'indice $i$ indique la $i^{\\text{ème}}$ dimension d'un vecteur ⇒ $a_i$\n", + "\n", + "- L'exposant $(k)$ désigne un objet associé au $k^{\\text{ème}}$ exemple ⇒ $a_i^{(k)}$\n", + "\n", + "- L'exposant $[l]$ désigne un objet associé à la $l^{\\text{ème}}$ couche ⇒ $a_i^{(k)[l]}$\n", + "\n", + "Exemple:\n", + "\n", + "- $a_5^{(2)[3]}$ indique la $5^{\\text{ème}}$ dimension du vecteur d'activation du $2^{\\text{ème}}$ exemple d'entraînement (2), de la $3^{\\text{ème}}$ couche [3].\n", + "\n", + "Commençons par importer tous les modules nécessaires :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "R6LBs_NLla1a" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import datasets\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3JZIXefJlXSV" + }, + "source": [ + "# Perceptron mono-couche\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "azdcz3QV_k-r" + }, + "source": [ + "### Perceptron mono-couche - passe _forward_\n", + "\n", + "Un perceptron mono-couche est un modèle liant une couche d'entrée (en vert, qui n'effectue pas d'opération) à une couche de sortie. Les neurones des deux couches sont connectés par des liaisons pondérées (les poids synaptiques) $W_{xy}$, et les neurones de la couche de sortie portent chacun un biais additif $b_y$. Enfin, une fonction d'activation $f$ est appliquée à l'issue de ces opérations pour obtenir la prédiction du réseau $\\hat{y}$.\n", + "\n", + "On a donc :\n", + "\n", + "$$\\hat{y} = f ( W_{xy} x + b_y )$$\n", + "\n", + "On posera pour la suite :\n", + "$$ z = W\\_{xy} x + b_y $$\n", + "\n", + "La figure montre une représentation de ces opérations sous forme de réseau de neurones (à gauche), mais aussi sous une forme fonctionnelle (à droite) qui permet de bien visualiser l'ordre des opérations.\n", + "\n", + "\n", + "\n", + "\n", + "Notez que les paramètres du perceptron, que nous allons ajuster par un processus d'optimisation, sont donc les poids synaptiques $W_{xy}$ et les biais $b_y$. Par commodité dans le code, nous considérerons également comme un paramètre le choix de la fonction d'activation.\n", + "\n", + "**Remarque importante** : En pratique, on traite souvent les données par _batch_, c'est-à-dire que les prédictions sont faites pour plusieurs données simultanément. Ici pour une taille de _batch_ de $m$, cela signifie en fait que :\n", + "\n", + "$$ x \\in \\mathbb{R}^{4 \\times m} \\text{ et } y \\in \\mathbb{R}^{5 \\times m}$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RBtX2euQDSCS" + }, + "source": [ + "Complétez la fonction _dense_layer_forward_ qui calcule la prédiction d'un perceptron mono-couche pour une entrée $x$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "YGYbWrRfmIwx" + }, + "outputs": [], + "source": [ + "def dense_layer_forward(x, Wxy, by, activation):\n", + " \"\"\"\n", + " Réalise une unique étape forward de la couche dense telle que décrite dans la figure précédente\n", + "\n", + " Arguments:\n", + " x -- l'entrée, tableau numpy de dimension (n_x, m).\n", + " Wxy -- Matrice de poids multipliant l'entrée, tableau numpy de shape (n_y, n_x)\n", + " by -- Biais additif ajouté à la sortie, tableau numpy de dimension (n_y, 1)\n", + " activation -- Chaîne de caractère désignant la fonction d'activation choisie : 'linear', 'sigmoid' ou 'relu'\n", + "\n", + " Retourne :\n", + " y_pred -- prédiction, tableau numpy de dimension (n_y, m)\n", + " cache -- tuple des valeurs utiles pour la passe backward (rétropropagation du gradient), contient (x, z)\n", + " \"\"\"\n", + "\n", + " # calcul de z\n", + " z = np.matmul(Wxy, x) + by\n", + " # calcul de la sortie en appliquant la fonction d'activation\n", + " if activation == \"relu\":\n", + " y_pred = np.where(z > 0, z, 0)\n", + " elif activation == \"sigmoid\":\n", + " y_pred = 1 / (1 + np.exp(-z))\n", + " elif activation == \"linear\":\n", + " y_pred = z\n", + " else:\n", + " print(\"Erreur : la fonction d'activation n'est pas implémentée.\")\n", + "\n", + " # sauvegarde du cache pour la passe backward\n", + " cache = (x, z)\n", + "\n", + " return y_pred, cache\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1dCFTHOqD_Tp" + }, + "source": [ + "Exécutez les lignes suivantes pour vérifier la validité de votre code :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "B6wlVU37on1k" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y_pred.shape = \n", + " (2, 10)\n", + "----------------------------\n", + "activation relu : y_pred[1] =\n", + " [0. 2.11983968 0.88583246 1.39272594 0. 2.92664609\n", + " 0. 1.47890228 0. 0.04725575]\n", + "----------------------------\n", + "activation sigmoid : y_pred[1] =\n", + " [0.10851642 0.89281659 0.70802939 0.80102707 0.21934644 0.94914804\n", + " 0.24545321 0.81440672 0.48495927 0.51181174]\n", + "----------------------------\n", + "activation linear : y_pred[1] =\n", + " [-2.10598556 2.11983968 0.88583246 1.39272594 -1.26947904 2.92664609\n", + " -1.12301093 1.47890228 -0.06018107 0.04725575]\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "x_tmp = np.random.randn(3, 10)\n", + "Wxy = np.random.randn(2, 3)\n", + "by = np.random.randn(2, 1)\n", + "\n", + "activation = \"relu\"\n", + "y_pred_tmp, cache_tmp = dense_layer_forward(x_tmp, Wxy, by, activation)\n", + "print(\"y_pred.shape = \\n\", y_pred_tmp.shape)\n", + "\n", + "print(\"----------------------------\")\n", + "\n", + "print(\"activation relu : y_pred[1] =\\n\", y_pred_tmp[1])\n", + "\n", + "print(\"----------------------------\")\n", + "\n", + "activation = \"sigmoid\"\n", + "y_pred_tmp, cache_tmp = dense_layer_forward(x_tmp, Wxy, by, activation)\n", + "print(\"activation sigmoid : y_pred[1] =\\n\", y_pred_tmp[1])\n", + "\n", + "print(\"----------------------------\")\n", + "\n", + "activation = \"linear\"\n", + "y_pred_tmp, cache_tmp = dense_layer_forward(x_tmp, Wxy, by, activation)\n", + "print(\"activation linear : y_pred[1] =\\n\", y_pred_tmp[1])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YYbiDw8TptiN" + }, + "source": [ + "**Affichage attendu**:\n", + "\n", + "```Python\n", + "y_pred.shape =\n", + " (2, 10)\n", + "----------------------------\n", + "activation relu : y_pred[1] =\n", + " [0. 2.11983968 0.88583246 1.39272594 0. 2.92664609\n", + " 0. 1.47890228 0. 0.04725575]\n", + "----------------------------\n", + "activation sigmoid : y_pred[1] =\n", + " [0.10851642 0.89281659 0.70802939 0.80102707 0.21934644 0.94914804\n", + " 0.24545321 0.81440672 0.48495927 0.51181174]\n", + "----------------------------\n", + "activation linear : y_pred[1] =\n", + " [-2.10598556 2.11983968 0.88583246 1.39272594 -1.26947904 2.92664609\n", + " -1.12301093 1.47890228 -0.06018107 0.04725575]\n", + "\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GypgZ8jBqooR" + }, + "source": [ + "### Perceptron mono-couche - passe _backward_\n", + "\n", + "Dans les librairies d'apprentissage profond actuelles, il suffit d'implémenter la passe _forward_, et la passe _backward_ est réalisée automatiquement, avec le calcul des gradients (différentiation automatique) et la mise à jour des paramètres. Il est cependant intéressant de comprendre comment fonctionne la passe _backward_, en l'implémentant sur un exemple simple.\n", + "\n", + "\n", + "\n", + "Il faut calculer les dérivées de la fonction objectif par rapport aux différents paramètres, pour ensuite mettre à jour ces derniers pendant la descente de gradient. Les équations de calcul des gradients sont données ci-dessous (c'est un bon exercice que de les calculer à la main).\n", + "\n", + "\\begin{align}\n", + "\\displaystyle dx &= \\frac{\\partial J}{\\partial x} &= { W*{xy}}^T \\: \\left( d\\hat{y} * \\frac{\\partial \\hat{y}}{\\partial z} \\right) \\\\[8pt]\n", + "\\displaystyle {dW*{xy}} &= \\frac{\\partial J}{\\partial W*{xy}} &= \\left( d\\hat{y} * \\frac{\\partial \\hat{y}}{\\partial z} \\right) \\: x^{T} \\\\[8pt]\n", + "\\displaystyle db*{y} &= \\frac{\\partial J}{\\partial b*y} &= \\sum*{batch} \\left( d\\hat{y} * \\frac{\\partial \\hat{y}}{\\partial z} \\right) \\\\[8pt]\n", + "\\end{align}\n", + "\n", + "Ici, $*$ indique une multiplication élément par élément tandis que l'absence de symbole indique une multiplication matricielle. Par ailleurs $d\\hat{y}$ désigne $\\frac{\\partial J}{\\partial \\hat{y}}$, $dW_{xy}$ désigne $\\frac{\\partial J}{\\partial W_{xy}}$, $db_y$ désigne $\\frac{\\partial J}{\\partial b_y}$ et $dx$ désigne $\\frac{\\partial J}{\\partial x}$ (ces noms ont été choisis pour être utilisables dans le code).\n", + "\n", + "Il vous reste à déterminer, par vous même, le terme $\\frac{\\partial \\hat{y}}{\\partial z}$, qui constitue en fait la dérivée de la fonction d'activation évaluée en $z$. Par exemple, pour la fonction d'activation linéaire (l'identité), la dérivée est égale à 1 pour tout $z$. A vous de déterminer, et d'implémenter, la dérivée des fonctions _sigmoid_ et _relu_. **Attention aux dimensions : $\\frac{\\partial \\hat{y}}{\\partial z}$ est de même dimension que $z$ et $\\hat{y}$ !**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "wEi_y3W_rCMc" + }, + "outputs": [], + "source": [ + "def dense_layer_backward(dy_hat, Wxy, by, activation, cache):\n", + " \"\"\"\n", + " Implémente la passe backward de la couche dense.\n", + "\n", + " Arguments :\n", + " dy_hat -- Gradient de la fonction objectif par rapport à la sortie ŷ, de dimension (n_y, m)\n", + " Wxy -- Matrice de poids multipliant l'entrée, tableau numpy de shape (n_y, n_x)\n", + " by -- Biais additif ajouté à la sortie, tableau numpy de dimension (n_y, 1)\n", + " cache -- dictionnaire python contenant des variables utiles (issu de dense_layer_forward())\n", + "\n", + " Retourne :\n", + " gradients -- dictionnaire python contenant les gradients suivants :\n", + " dx -- Gradient de la fonction objectif par rapport aux entrées, de dimension (n_x, m)\n", + " dby -- Gradient de la fonction objectif par rapport aux biais, de dimension (n_y, 1)\n", + " dWxy -- Gradient de la fonction objectif par rapport aux poids synaptiques Wxy, de dimension (n_y, n_x)\n", + " \"\"\"\n", + "\n", + " # Récupérer les informations du cache\n", + " (x, z) = cache\n", + "\n", + " # calcul de la sortie en appliquant l'activation\n", + " if activation == \"relu\":\n", + " dyhat_dz = np.where(z > 0, 1, 0)\n", + " elif activation == \"sigmoid\":\n", + " dyhat_dz = 1 / (1 + np.exp(-z)) * (1 - 1 / (1 + np.exp(-z)))\n", + " elif activation == \"linear\":\n", + " dyhat_dz = np.ones(z.shape)\n", + " else:\n", + " print(\"Erreur : la fonction d'activation n'est pas implémentée.\")\n", + "\n", + " # calculer le gradient de la perte par rapport à x\n", + " dx = np.matmul(Wxy.T, dy_hat * dyhat_dz)\n", + "\n", + " # calculer le gradient de la perte par rapport à Wxy\n", + " dWxy = np.matmul(dy_hat * dyhat_dz, x.T)\n", + "\n", + " # calculer le gradient de la perte par rapport à by\n", + " # Attention, dby doit être de dimension (n_y, 1), pensez à positionner l'attribut\n", + " # keepdims de la fonction numpy.sum() à True !\n", + " dby = np.sum(dy_hat * dyhat_dz, axis=1, keepdims=True)\n", + "\n", + " # Stocker les gradients dans un dictionnaire\n", + " gradients = {\"dx\": dx, \"dby\": dby, \"dWxy\": dWxy}\n", + "\n", + " return gradients\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qQGZTgx20JVm" + }, + "source": [ + "Exécutez la cellule suivante pour vérifier la validité de votre code :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "gGxKksOd0N2F" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dimensions des différents gradients :\n", + "dx : (3, 10)\n", + "dby : (2, 1)\n", + "dWxy : (2, 3)\n", + "----------------------------\n", + "activation relu : gradients =\n", + " {'dx': array([[ 0. , -0.52166355, -0.25370565, 0.29772356, 0. ,\n", + " -0.87533798, 0. , -0.05523234, 0. , -0.78697273],\n", + " [ 0. , -0.4142952 , -0.20148817, 0.23644635, 0. ,\n", + " -0.43699238, 0. , -0.14103828, 0. , -0.62499867],\n", + " [ 0. , -0.00781663, -0.00380154, 0.0044611 , 0. ,\n", + " -1.15858431, 0. , 0.43029667, 0. , -0.01179203]]), 'dby': array([[1.05545895],\n", + " [1.73350613]]), 'dWxy': array([[-3.41036427, -1.30232405, -0.56109731],\n", + " [-0.03287152, -0.82109488, 0.98388063]])}\n", + "----------------------------\n", + "activation sigmoid : gradients =\n", + " {'dx': array([[-0.12452463, -0.16508708, -0.02939735, 0.18918939, 0.19365898,\n", + " -0.17366309, 0.02947078, 0.03090249, -0.20097835, -0.40773826],\n", + " [-0.07359731, -0.10570831, -0.02843055, 0.1189895 , 0.14755739,\n", + " -0.09647417, 0.02411729, 0.00119749, -0.15435059, -0.27725739],\n", + " [-0.1141027 , -0.11516714, 0.02211421, 0.14152872, 0.03059908,\n", + " -0.18648155, -0.00271799, 0.10403474, -0.02635951, -0.21268142]]), 'dby': array([[0.51620418],\n", + " [0.3562789 ]]), 'dWxy': array([[-0.19619895, -0.04346631, -0.0522999 ],\n", + " [-0.2464412 , -0.23312061, -0.09313104]])}\n", + "----------------------------\n", + "activation linear : gradients =\n", + " {'dx': array([[-1.24957905, -1.03490637, -0.12102053, 0.91166167, 1.48244289,\n", + " -0.87533798, 0.14141685, -0.05523234, -0.84116226, -2.23963678],\n", + " [-0.7391886 , -0.70870384, -0.12537673, 0.58861627, 1.06334861,\n", + " -0.43699238, 0.12006129, -0.14103828, -0.63891076, -1.4582823 ],\n", + " [-1.14209251, -0.51772912, 0.12802262, 0.61441549, 0.52789632,\n", + " -1.15858431, -0.03226814, 0.43029667, -0.1418173 , -1.45503003]]), 'dby': array([[3.97266086],\n", + " [1.34123607]]), 'dWxy': array([[-1.13528086, 0.37477333, -1.77404551],\n", + " [-0.92324845, -1.86932585, -0.37669553]])}\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "x_tmp = np.random.randn(3, 10)\n", + "Wxy = np.random.randn(2, 3)\n", + "by = np.random.randn(2, 1)\n", + "dy_hat = np.random.randn(2, 10)\n", + "activation = \"relu\"\n", + "y_pred_tmp, cache_tmp = dense_layer_forward(x_tmp, Wxy, by, activation)\n", + "gradients = dense_layer_backward(dy_hat, Wxy, by, activation, cache_tmp)\n", + "print(\"dimensions des différents gradients :\")\n", + "print(\"dx : \", gradients[\"dx\"].shape)\n", + "print(\"dby : \", gradients[\"dby\"].shape)\n", + "print(\"dWxy : \", gradients[\"dWxy\"].shape)\n", + "\n", + "print(\"----------------------------\")\n", + "\n", + "print(\"activation relu : gradients =\\n\", gradients)\n", + "\n", + "print(\"----------------------------\")\n", + "\n", + "activation = \"sigmoid\"\n", + "gradients = dense_layer_backward(dy_hat, Wxy, by, activation, cache_tmp)\n", + "print(\"activation sigmoid : gradients =\\n\", gradients)\n", + "\n", + "print(\"----------------------------\")\n", + "\n", + "activation = \"linear\"\n", + "gradients = dense_layer_backward(dy_hat, Wxy, by, activation, cache_tmp)\n", + "print(\"activation linear : gradients =\\n\", gradients)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5-_jk20X0QIt" + }, + "source": [ + "**Affichage attendu**:\n", + "\n", + "```Python\n", + "dimensions des différents gradients :\n", + "dx : (3, 10)\n", + "dby : (2, 1)\n", + "dWxy : (2, 3)\n", + "----------------------------\n", + "activation relu : gradients =\n", + " {'dx': array([[ 0. , -0.52166355, -0.25370565, 0.29772356, 0. ,\n", + " -0.87533798, 0. , -0.05523234, 0. , -0.78697273],\n", + " [ 0. , -0.4142952 , -0.20148817, 0.23644635, 0. ,\n", + " -0.43699238, 0. , -0.14103828, 0. , -0.62499867],\n", + " [ 0. , -0.00781663, -0.00380154, 0.0044611 , 0. ,\n", + " -1.15858431, 0. , 0.43029667, 0. , -0.01179203]]), 'dby': array([[1.05545895],\n", + " [1.73350613]]), 'dWxy': array([[-3.41036427, -1.30232405, -0.56109731],\n", + " [-0.03287152, -0.82109488, 0.98388063]])}\n", + "----------------------------\n", + "activation sigmoid : gradients =\n", + " {'dx': array([[-0.12452463, -0.16508708, -0.02939735, 0.18918939, 0.19365898,\n", + " -0.17366309, 0.02947078, 0.03090249, -0.20097835, -0.40773826],\n", + " [-0.07359731, -0.10570831, -0.02843055, 0.1189895 , 0.14755739,\n", + " -0.09647417, 0.02411729, 0.00119749, -0.15435059, -0.27725739],\n", + " [-0.1141027 , -0.11516714, 0.02211421, 0.14152872, 0.03059908,\n", + " -0.18648155, -0.00271799, 0.10403474, -0.02635951, -0.21268142]]), 'dby': array([[0.51620418],\n", + " [0.3562789 ]]), 'dWxy': array([[-0.19619895, -0.04346631, -0.0522999 ],\n", + " [-0.2464412 , -0.23312061, -0.09313104]])}\n", + "----------------------------\n", + "activation linear : gradients =\n", + " {'dx': array([[-1.24957905, -1.03490637, -0.12102053, 0.91166167, 1.48244289,\n", + " -0.87533798, 0.14141685, -0.05523234, -0.84116226, -2.23963678],\n", + " [-0.7391886 , -0.70870384, -0.12537673, 0.58861627, 1.06334861,\n", + " -0.43699238, 0.12006129, -0.14103828, -0.63891076, -1.4582823 ],\n", + " [-1.14209251, -0.51772912, 0.12802262, 0.61441549, 0.52789632,\n", + " -1.15858431, -0.03226814, 0.43029667, -0.1418173 , -1.45503003]]), 'dby': array([[3.97266086],\n", + " [1.34123607]]), 'dWxy': array([[-1.13528086, 0.37477333, -1.77404551],\n", + " [-0.92324845, -1.86932585, -0.37669553]])}\n", + "\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E5KeDgyO-ZPJ" + }, + "source": [ + "On peut maintenant créer une classe _DenseLayer_, qui comprend en attribut toutes les informations nécessaires à la description d'une couche dense, c'est-à-dire :\n", + "\n", + "- Le nombre de neurones en entrée de la couche dense (input_size)\n", + "- Le nombre de neurones en sortie de la couche dense (output_size)\n", + "- La fonction d'activation choisie sur cette couche (activation)\n", + "- Les poids synaptiques de la couche dense, stockés dans une matrice de taille (output_size, input_size) (Wxy)\n", + "- Les biais de la couche dense, stockés dans un vecteur de taille (output_size, 1) (by)\n", + "\n", + "On ajoute également un attribut cache qui permettra de stocker les entrées de la couche dense (x) ainsi que les calculs intermédiaires (z) réalisés lors de la passe _forward_, afin d'être réutilisés pour la basse _backward_.\n", + "\n", + "A vous de compléter les 4 jalons suivants :\n", + "\n", + "- **L'initialisation des paramètres** Wxy et by : Wxy doit être positionnée suivant [l'initialisation de Glorot](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform), et by est initialisée par un vecteur de zéros de taille (output_size, 1).\n", + "- **La fonction _forward_**, qui consiste simplement en un appel de la fonction _dense_layer_forward_ implémentée précédemment.\n", + "- **La fonction _backward_**, qui consiste simplement en un appel de la fonction _dense_layer_backward_ implémentée précédemment.\n", + "- Et enfin **la fonction _update_parameters_** qui applique la mise à jour de la descente de gradient en fonction d'un taux d'apprentissage (_learning_rate_) et des gradients calculés dans la passe _forward_.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "u2K9dp1IL3yM" + }, + "outputs": [], + "source": [ + "class DenseLayer:\n", + " def __init__(self, input_size, output_size, activation):\n", + " self.input_size = input_size\n", + " self.output_size = output_size\n", + " self.activation = activation\n", + " self.cache = None # Le cache sera mis à jour lors de la passe forward\n", + " limit = np.sqrt(6 / (input_size + output_size))\n", + " self.Wxy = np.random.uniform(-limit, limit, (output_size, input_size))\n", + " self.by = np.zeros((output_size, 1))\n", + "\n", + " def forward(self, x_batch):\n", + " y, cache = dense_layer_forward(x_batch, self.Wxy, self.by, self.activation)\n", + " self.cache = cache\n", + " return y\n", + "\n", + " def backward(self, dy_hat):\n", + " return dense_layer_backward(\n", + " dy_hat, self.Wxy, self.by, self.activation, self.cache\n", + " )\n", + "\n", + " def update_parameters(self, gradients, learning_rate):\n", + " self.Wxy -= learning_rate * gradients[\"dWxy\"]\n", + " self.by -= learning_rate * gradients[\"dby\"]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9GlEB8K3Lani" + }, + "source": [ + "### Fonction de coût : erreur quadratique moyenne\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2KMcQzlskdI1" + }, + "source": [ + "Pour entraîner notre modèle, nous devons mettre en place un optimiseur. Nous implémenterons la descente de gradient stochastique avec mini-batch. Il nous faut cependant au préalable implanter la fonction de coût que nous utiliserons pour évaluer la qualité de nos prédictions.\n", + "\n", + "Pour le moment, nous allons nous contenter d'une erreur quadratique moyenne, qui associée à une fonction d'activation linéaire (l'identité) permet de résoudre les problèmes de régression.\n", + "\n", + "La fonction de coût prend en entrée deux paramètres : la vérité-terrain _y_true_ et la prédiction du modèle _y_pred_ ($\\hat{y}$). Ces deux matrices sont de dimension $output\\_ size \\times bs$. La fonction retourne deux grandeurs : _loss_ qui correspond à l'erreur quadratique moyenne des prédictions par rapport aux vérités-terrains, et $d\\hat{y}$ au gradient de l'erreur quadratique moyenne par rapport aux prédictions. Autrement dit :\n", + "$$ d\\hat{y} = \\frac{\\partial J\\_{mb}}{\\partial \\hat{y}}$$\n", + "\n", + "où $\\hat{y}$ correspond à _y_pred_, et $J_{mb}$ à la fonction objectif calculée sur un mini-batch $mb$ de données.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "FRDUnhJma6jf" + }, + "outputs": [], + "source": [ + "def mean_square_error(y_true, y_pred):\n", + " \"\"\"\n", + " Erreur quadratique moyenne entre prédiction et vérité-terrain\n", + "\n", + " Arguments :\n", + " y_true -- labels à prédire (vérité-terrain), de dimension (m, n_y)\n", + " y_pred -- prédictions du modèle, de dimension (m, n_y)\n", + " Retourne :\n", + " loss -- l'erreur quadratique moyenne entre y_true et y_pred, scalaire\n", + " dy_hat -- dérivée partielle de la fonction objectif par rapport à y_pred, de dimension (m, n_y)\n", + " \"\"\"\n", + " batch_size = y_true.shape[0]\n", + " loss = np.mean(np.square(y_true - y_pred))\n", + " dy_hat = -2 * (y_true - y_pred) / batch_size\n", + " return loss, dy_hat\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eNbVKV5K0hWp" + }, + "source": [ + "Testez votre implémentation avec ce bloc de code :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "Wt-ensXM0jL1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss = 2.0281433227730186\n", + "dy_hat = \n", + " [[-0.46320122 0.04300058 -0.03180019]\n", + " [ 0.0455526 -0.30733075 0.45777482]\n", + " [-0.57242442 0.19912452 0.26815262]\n", + " [ 0.19828291 -0.3307887 0.23450235]\n", + " [-0.08494822 0.41530179 -0.21659234]\n", + " [ 0.09257912 0.07266874 0.59562271]\n", + " [ 0.01558904 0.00687758 0.2801579 ]\n", + " [-0.29939471 -0.40882178 -0.17036741]\n", + " [-0.22195004 0.25407021 0.19237473]\n", + " [ 0.3733743 0.11069508 0.07095714]]\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "y_true = np.random.randn(10, 3)\n", + "y_pred = np.random.randn(10, 3)\n", + "\n", + "loss, dy_hat = mean_square_error(y_true, y_pred)\n", + "print(\"loss = \", loss)\n", + "print(\"dy_hat = \\n\", dy_hat)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2RcgS5JJ0lcY" + }, + "source": [ + "**Affichage attendu**:\n", + "\n", + "```Python\n", + "loss = 2.0281433227730186\n", + "dy_hat =\n", + " [[-0.46320122 0.04300058 -0.03180019]\n", + " [ 0.0455526 -0.30733075 0.45777482]\n", + " [-0.57242442 0.19912452 0.26815262]\n", + " [ 0.19828291 -0.3307887 0.23450235]\n", + " [-0.08494822 0.41530179 -0.21659234]\n", + " [ 0.09257912 0.07266874 0.59562271]\n", + " [ 0.01558904 0.00687758 0.2801579 ]\n", + " [-0.29939471 -0.40882178 -0.17036741]\n", + " [-0.22195004 0.25407021 0.19237473]\n", + " [ 0.3733743 0.11069508 0.07095714]]\n", + "\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uZRnPbBjQvZc" + }, + "source": [ + "### Descente de gradient stochastique\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w2XnUBj2n-Df" + }, + "source": [ + "La descente de gradient stochastique prend en entrée les paramètres suivants :\n", + "\n", + "- _x_train_ et _y_train_ respectivement les données et labels de l'ensemble d'apprentissage (que l'on suppose de taille $N$).\n", + "- _model_ une instance du modèle que l'on veut entraîner (qui doit implanter les 3 fonctions vues précédemment _forward_, _backward_ et _update_parameters_).\n", + "- _loss_function_ peut prendre deux valeurs : 'mse' (erreur quadratique moyenne) ou 'bce' (entropie croisée binaire, que nous implémenterons par la suite).\n", + "- _learning_rate_ le taux d'apprentissage choisi pour la descente de gradient.\n", + "- _epochs_ le nombre de parcours complets de l'ensemble d'apprentissage que l'on veut réaliser.\n", + "- _batch_size_ la taille de mini-batch désirée pour la descente de gradient stochastique.\n", + "\n", + "L'algorithme à implémenter est rappelé ci-dessous :\n", + "\n", + "```\n", + "N_batch = floor(N/batch_size)\n", + "\n", + "Répéter epochs fois\n", + "\n", + " Pour b de 1 à N_batch Faire\n", + "\n", + " - Sélectionner les données x_train_batch et labels y_train_batch du b-ème mini-batch\n", + " - Calculer la prédiction y_pred_batch du modèle pour ce mini-batch\n", + " - Calculer la perte batch_loss et le gradient de la perte batch_grad par rapport aux prédictions sur ce mini-batch\n", + " - Calculer les gradients de la perte par rapport à chaque paramètre du modèle\n", + " - Mettre à jour les paramètres du modèle\n", + "\n", + " Fin Pour\n", + "\n", + "Fin Répéter\n", + "\n", + "```\n", + "\n", + "Deux remarques additionnelles :\n", + "\n", + "1. A chaque _epoch_, les _mini-batches_ doivent être différents (les données doivent être réparties dans différents _mini-batches_).\n", + "2. Il est intéressant de calculer (et d'afficher !) la perte moyennée sur l'ensemble d'apprentissage à chaque _epoch_. Pour cela, on peut accumuler les pertes de chaque _mini-batch_ sur une _epoch_ et diviser l'ensemble par le nombre de _mini-batches_.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "lk3lypUOLXbv" + }, + "outputs": [], + "source": [ + "def SGD(\n", + " x_train, y_train, model, loss_function, learning_rate=0.03, epochs=10, batch_size=10\n", + "):\n", + " \"\"\"\n", + " Implémente la descente de gradient stochastique\n", + "\n", + " Arguments :\n", + " x_train -- Les données d'apprentissage, de dimension (N, n_x) ; ATTENTION ces\n", + " dimensions sont inversées par rapport aux premiers exercices\n", + " y_train -- Les labels d'apprentissage, de dimension (N, n_y)\n", + " model -- Le modèle initialisé, à optimiser.\n", + " loss_function -- la fonction de coût à utiliser pour l'optimisation, qui pourra\n", + " être 'mse' (erreur quadratique moyenne) ou 'bce' (entropie croisée binaire)\n", + " learning_rate -- le taux d'apprentissage pour la descente de gradient\n", + " epochs -- le nombre de parcours complets de l'ensemble d'apprentissage\n", + " batch_size -- le nombre d'éléments considérés dans chaque mini-batch de données\n", + "\n", + " Retourne :\n", + " model -- le modèle obtenu à la fin du processus d'optimisation\n", + " \"\"\"\n", + " # Nombre de batches par epoch\n", + " nb_batches = math.floor(x_train.shape[0] / batch_size)\n", + "\n", + " # Pour gérer le tirage aléatoire des batches parmi les données d'entraînement...\n", + " indices = np.arange(x_train.shape[0])\n", + "\n", + " for e in range(epochs):\n", + "\n", + " running_loss = 0\n", + "\n", + " # Nouvelle permutation des indices pour la prochaine epoch\n", + " indices = np.random.permutation(indices)\n", + "\n", + " for b in range(nb_batches):\n", + "\n", + " # Sélection des données du batch courant\n", + " x_train_batch = x_train[indices[b * batch_size : (b + 1) * batch_size]]\n", + " y_train_batch = y_train[indices[b * batch_size : (b + 1) * batch_size]]\n", + "\n", + " # Prédiction du modèle pour le batch courant\n", + " # ATTENTION, le batch est de dimension (batch_size, n_x) !!!\n", + " y_pred_batch = model.forward(x_train_batch.T)\n", + "\n", + " # Calcul de la fonction objectif et de son gradient sur le batch courant\n", + " if loss_function == \"mse\":\n", + " batch_loss, batch_dy_hat = mean_square_error(\n", + " y_train_batch, y_pred_batch\n", + " )\n", + " elif loss_function == \"bce\":\n", + " batch_loss, batch_dy_hat = binary_cross_entropy(\n", + " y_train_batch, y_pred_batch\n", + " )\n", + "\n", + " running_loss += batch_loss\n", + "\n", + " # Calcul du gradient de la perte par rapport aux paramètres du modèle\n", + " param_updates = model.backward(batch_dy_hat)\n", + "\n", + " # Mise à jour des paramètres du modèle\n", + " model.update_parameters(param_updates, learning_rate)\n", + "\n", + " print(f\"Epoch {e:4d} : Loss {running_loss/nb_batches:.4f}\")\n", + "\n", + " return model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9bybDhHivjXq" + }, + "source": [ + "### Test sur un problème de régression\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N7q44eS0vrrZ" + }, + "source": [ + "Le bloc de code suivant permet de générer et d'afficher un ensemble de données pour un problème de régression linéaire classique.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "nGcIVuALraDG" + }, + "outputs": [ + { + "data": { + "image/png": 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/VtWpAFoAfC962OUAVmZ6L0JKhWxlf2Qy4VhfbwR23Djzmi2BjX/2J57o+b69HZg3jwLvlWzmyd8E4FERuR3ACwB+k8V7ERIosuU1p7pAyU4kEvOoN2wAamuzI7Txzz5lirnfgQOAiAnZeLE1KJOqmeKryKtqK4DW6PdvAxjt5/UJKRUyEWMv1wZinw4KLSbv9OxvvQXce6+595w5iQeYQpggLiS44pWQAiVb2R/pimAu+5vanz0SAe6/H+jqMu8PHEg8wLAwWk9YT56QgJFssVC68f589TdtbTW2WoRCiQeYUlro5AV68oQECDcv3R6jzsQjz0dueUMDUFlpPPhwGHjwwcQ2ZDPUVYxQ5AkJEG5eerzwF5MIpiPapbLQyQsUeUICRLyXXlUFzJ9vvOCurpjwF1sIg6KdPhR5QgKE3eutqjKZKJbAh0Ix4Q9a9glTJt2hyBMSMCyvd8ECI+SWwI8fb7z6oGSf2Esd2FfEBmHQ8hOKPCFFQqreanzoZv782HnFXv/FPsEcCpkByx6OosjHoMgTUgSkk9vuNmEZhOwT+6cRVSP0IsU7aGUTijwhRUC6IRa3Cct0JjILKe4d/ymFRcvcocgTUgQ45banI7q5qCUfiQDNzeb7adNyV/qAOEORJ6QIiBc1IPXwTS5qyUcixr6DB837ZcuAlpbsCT3FPTkUeUKKBLuoWZkzXsI3lve+Y0fq51hestdVss3NMYEHOBFaCFDkCSlwnEIsXkXX7r2XlZmyAID3c+we/7p1sTCM23nLlvXcxonQ/EORJ6SAcRNcrzFpe5gFAGbOBGpqvJ8T74kvX262LV/eO9zT2gocjnZ1FgFGjcpucxHiDYo8IQVMIsH1EpO2PP4DB0ya4fDhQGOjt3PiPyUki8s7ZbxQ4PMPRZ6QAsZLWMYtY8bafu21wH33eWu4Abh/SvCS4cOMl8JDVAund3ZdXZ22tbXl2wxC8o5dPIGe39vTEwH30sJOK0Lt5Q3SEeGmJtNzdcoUM1gErQZOsSIim1W1znGnqhbM18iRI5WQUmfJEtWyMtVQSLVvX9WNG832jRtVKytVzRpP1YoK1R/9SDUcNu/DYdU77zTH3nlnbHsoFLue9d5+Xa9s3GjOC4fNq/3eoZDq2Wenfk3iDwDa1EVX2RmKkAIiEgFmzzYTmF1dsVZ3kUisZLDFoUPm1akLkr07UmUlsHix8eBDoZ41XpJ1kbLT3Ax0dMRi8ta9rWuuXWs8ey/XIrmDMXlCCginVndWaWC7wAOxdEinJf1u8fGWFvOaaslhKz3Siu6GwyZcNG2aGXzWrmWBsEIlY5EXkRMBNAP4EgAF0KSqvxKRfwPwGICBALYDuFhVP870foQEGadWd+3tPUsG19UBxx8PPPss8PDD7gId3wx7zhwzgIRCsYEhUbaMfV4gPj3yyitjx86fD2zYUNxVLYOMH578YQDXq+rzItIPwGYR+ROAKwCsU9W7RORmADcDuMmH+xESWJw88Eikd2piayvwzDPeC5ZZ6Y9dXUakX3jBbHdbHBWfn79wYU8brElfN5tJ4ZCxyKvqBwA+iH6/V0S2AjgBwGQADdHDlgNoBUWekKTE57+7iWh8m78FC9xF1p7+GA6b0Mvhw2YV7MyZvQuJxefEt7cnFnLWkSlcfI3Ji8hAAMMB/BXAl6IDAAD8AyacQwhJg0TC76Uzkv34HTtMmMeK/dfU9D7eKSeeQl6c+JZdIyJHAngCwBxV/Zd9XzTFxzEhX0QaRaRNRNp2797tlzmE5JRUslT8or4emDfPObae6Php05wzcuKPXbcOuO025r8XO7548iJSDiPwv1PVJ6Ob/ykiX1bVD0TkywA+dDpXVZsANAFmMZQf9hCSSzIp4esHdq+7rMx46pGIuw1eY+j03INBxp68iAiA3wDYqqr32XY9DeDy6PeXA1iZ6b0IKUTi49fNzal79al8Eog/1hLtmTNNiuPDDyfPV7e8eop48PHDkx8D4IcAtojIi9FtPwNwF4DHRWQGgHcBXOzDvQgpONwmNTNt5uFUkyZRVUorxz7VFoF2O5ghEzz8yK55DoC47B6X6fUJKXTcJjW9Cq1TdUfAWcwTVYL0WmPeCT9CThwkChOueCXEByxvOhKJ1Vz3WjXSSZxbW82CKHtpA+vYcNhsD4d7Xj+TfPV0G4Xbn4nFygoTijwhPuImtJawO6U7AsDl0dkrK199yxYj5IB5raqK3UOk52v8/dMR10w+BQCpDRL0+HMLRZ4Qn4kXWruXK2JE26rz0tzc0/O3VpK2t8cKf4VC5j0QKy+gal6bmxMLZryguglspqtW02lHSI8/N1DkCfEJNwG1e7mhkAmziBiRA5w9YKuGTbxopjLJawmq1RVq7lzggQfcBdbrpwCn50ynHSGLmeUGijwhSfASXkjkoTq1xbOqRgLOMXw30fQyyWvZu2NHLK7f1QXce6+5RibVIhM9ZyrtCFnMLHdQ5AlJgNfwQrJerIni9E6lgr0MLMOHO7fjs+y1PjHYCYVinyLSEdhMPXEWM8s9FHkSGFKZ0Et2rN0b9iJqDQ1mtWlXl3mNF9BEcfr4wSOVfddeC7z4omnHV19vFklZ9gLA+ecDf/yjeV9Z6TygpIIfnjhX0uYWijwJBHbxKysDpk/vXVnR6dhEsWzLGy6L/pckEzWroYaXtsmJPGKv+w4cAO6/3wwsGzaYnqvxInzjjebLL8+ZnnjxQZEngcAufp2dwJIlJtbtFF5JFnKw7wdMuYCamuQTip2dRuA7OxM34HDLjbdIVIvGvk8k1qDbeo5589xj+U52pAM98SLDrflrPr7YyJuki9VkWiTW6Nre2NrpWKshdXzz6WT7E93f6Ry3fRs3Gvucrr9xo2mUXVHhft6SJanZmc5zkeIACRp505MngcAKIzQ3A0uXmrRCkZ6LiICek51WdyS3a3nJqLEf43aO2ycHewjGuq/dBrdaNPZza2u9e+ZMXyxR3NQ/H1/05IkfLFmiWl6uGgr19oAtT7aiQrWyMn2vNpnnbvfQlyxRLStztqey0nz6qKz07xNFok8H9OSDCejJk1Kivb3nqlLLY7V7slbJANXUvdpIxDSvtnLQ43PU43ujzpkT6606YULsOs3N5hqAeW1uzmwVarIJZU6aliYUeRI43CY141eLisRWi3pNBbSvIrVKDtjPjw+JPPFErIE2AKxcCaxeHatZk4xUJjm9hGM4aVp6UORJ4PCyWtQuyql4tZaQWgI/frzx6t1Wt06ZAqxfH8vUUY1VlZw2zcwfHDoElJfH6tYA6WXBcDUpcULUS1Jvjqirq9O2trZ8m0GIK01NwOzZscVFTima8QI9a5ZJ6bT+1crLjfC7FQzLpIgXKzyWJiKyWVXrnPbRkyfEI01NwDXXGIEPh0283UlI40Mi06aZnH2rUNiDDyau95JJFgzDMSQeijwJDJl4sU1NJn4+ZQrQ2Oh87dmzTQwfMOEaq/xvIluqqsxxVjkB632iRtsMuxA/ociTQJBJiOOmm4C77zbfr1ljXuOF3spZtxDpuRLVyRb75KxVNya+YYjbJwFmwRC/COXbAJI9IhFTsCoSybcl6ePlGewpjfF9UpNdKxKJleC1eOKJ3uda9d2tevChkCnzO25cb9uam4GOjp6dneyZNvE2BuH3RAoXevIBJQgdeLw8Q7KURusYayVsZ2fsWlu2APfcExNjiylTetvipY67da+lS3sWKbNsmjLFFBJzKw1sbwdY7L87Ujhk3ZMXkYki8rqIbBORm7N9P2JwmrwrNrw8g1NKoyWUs2YBF14IjB1rslvs17r7buDqq4Ft22LXCoVMxcba2p6eteVpA6YA2LRppnCYiHmtqoodbw/riAD//u/A7bcbmxobzettt8WE2+kZg/C7I4VDVj15EQkDWAzguwB2AvibiDytqq9m874kGJN3Xp4h/pj582PbDx7sfbzVMOP993tu/9rXjLcPOK9Yjfe0LU+9sxP4yU9Mrns4DPzHf/Qu9ZuoxZ7TM27ZYgYc1eL93ZHCIdvhmtEAtqnq2wAgIo8CmAyAIp9lgjB55+UZnI5ZsMCIrh1L3M85BxgwAPjiF4FNm2L7L7qod9ON+Dh6R4cZCGpqepYVtr7v6gLuuw9YvDh5pyf7+/gFWnPmxPrBuqVpEuKVbIv8CQDes73fCeCbWb4niRKEnOlkz+CUNtnQYBYcWZ58eTkwY4Zpl2f3yqdOBX7/eyPQDzxgQitOK1btteKXLQMWLepZHsESeiCWWjlvXk8bk306sI63Bhmr1k2iNE1CvJD3iVcRaQTQCAA1NTV5toYUE24Ts1as2wq/WB2i4r303buNkCZruvHCC7EVq4cPG+G1H7Nli1kk1dVlMnDiwytu9WycJm6DEGYjhUW2RX4XgBNt76uj27pR1SYATYApa5Ble0iB4Mfy+0QrQ50+ATh56fHZLk7nWitW7cfZj6mv713X3f58Xu9rXavYw2yksMhq7RoRKQPwBoBxMOL+NwD/Q1VfcTqetWtKg3TTO61USCBWzCvV6ySKjSdq6G2tVE235C+Q2n0JSYW81a5R1cMiMhvAagBhAEvdBJ6UDunUZolEembMLFsGtLSk7vXGe+mJYv6JBqNEIu30fPPmeb+vGxwYSDpkPSavqqsArMr2fUjxkE7cubW1Z8aMm3i6kY5AWitX4xuLJPskko24ehAWt5H8kPeJV1J61NebDBOrIJgXsYrPmEmn0UeqYZ1ly3quXLX6xSb7JJKNuHomlSlJaUORJzknEomlEG7YYCYtkwmWlTFz991mIdOMGd4bbNsF8sABs2DK3ujDidbWWMVJwJx7zTXmey+eut/pq8y6IelCkSc5xy0M4oXVq805W7Y4Dw5OXrslkFZ9m7VrzeCSyKO3zrHsBIzoz55tGn7EL2BasCD9blNeYNYNSReKPMkp8QW8ysq8e6VeQhZuk57r1hnvfe3a3s23nbBEtbk5VowMMK/2uQD7oFJWFlsFm424eRAWt5Hcw1LDJKfEF/CaPj31HqbhsHm1FwZzO8Y+gHzlKyau77TPifp64KGHgF//2pxn1YW3nxc/qBw61LMEAiH5hp48ySnxsWV78+pk2EMWVVXuDTguv9y8Witd7d52OAzMnBnb54XaWjMHYL+m0/OUlRmBP3w4VgIhlfsQkg0o8sQXEqUoxu/LJLZshSziSxRY5XjHju09gNi9bcAUGEulMbY9xm8flKznslr7NTQY791eAoFZMCTfUORJxiRbNORWXyYTnLJNmpvN5CpgXpubzX0yyUxxmwdI9Mz2EghWSImTpSRfUORJxiSaEM1WfrfTJwKrT6uXY73iNkDEP1dzc+/SwYlCSoTkCoo8yZhEnnI287vtnwgiEWCVbV11ONwztGIfdOzvvdzDaYCIj8XHtxacN885pESRJ7mGIh9QclnnJJGnnKv87vjFS/FkUhbAKbxkfy63nq9cwEQKAYp8AMlHnZNEcfZkRcDi676nSiRihFYktk01+2Ej67kikd6liK39XMBE8g1FPoAUQ50TS9x/85tY4bGlS3va6uXTiH1As/qiqvZeZJXtsFGiTzKF9rMnpQVFPoAUUpjASagtYbaXDACM2Hut9GhhH9BUjTdvvdpxE2K/wloUc1KoUOQDSKGECdyE2hLm+H415eXO2SvW6lGn57APaFYrP7cc9XghZvleUgpQ5ANKIXiWbmGj+MyUc84BBgzoGZNvaIitIFU1oRynmH2iVbDJPsEUQ1iLkEyhyJNu0g1duJ3nFjaKF+b4tnrW9c45B1i5Mlb0y02E7QNafK/VRDjZx+5LJGhQ5EuIZKUH0u276nZesglJwLkXqr3OTEWFCb14nVtI5RNMvH1O9lDoSbFDkS8Rkol4uqELL12S3K7jdC7Qs87MzJmm1kwmnnWiwc1uHxcvkSBCkS8Rkolxuhk5mWTyuJ0bX6UyE6FN5RNKIWUlEeIXFPkSIZmA1den3nfVOi/dTB7rXHvdda/X8xo7T6X1X6FkJRHiJ6LxeWypnCxyD4DzARwE8BaA6ar6SXTfPAAzAHQC+Imqrk52vbq6Om1ra0vbHpKYbMTkM7UDSK/JttdzrGOt1n9W4w/G20mQEJHNqlrntC/TzlB/AjBEVYcCeAPAvOgNTwPwAwCDAUwE8GsRCWd4L5Ih9fWxtnXxOOWlZ4OmJuCss4Cf/9yIb3Ozc1w+EW6xfCcs73z8eCPw9tZ/hJQCGYm8qq5RVass1F8AVEe/nwzgUVU9oKrvANgGYHQm9yL+Eon0bJ3X0GCyWYBYVyN7Wz2/7jl7tsmW6eqK1X53a9fnRqIWf07U15sQTWVlavchJAj4GZO/EsBj0e9PgBF9i53RbcQnMsnndgt3XHml/12N7Ha2tsayZgDjWU+bZr6snPnmZvOVaMI1nfkDxttJqZJU5EVkLYABDrtuUdWV0WNuAXAYwO9SNUBEGgE0AkBNTU2qp5ckmcbP3TJtpk1zrqbodH8vYhlv58KFxps+cMB41A8+2PP8sWNj3n18sbL461orWzdsMAugvAo9xZ2UGklFXlXHJ9ovIlcAmARgnMZmcXcBONF2WHV0m9P1mwA0AWbiNbnJJFk6ZDIR9rIS1e3cVAaYeDvb292vbx1rYS9WlurzE0JiZBSuEZGJAG4E8G1V3W/b9TSA/yMi9wE4HsApADZlci8SI1E6pBcRzqQ0biKBjR9cnOx0u751rOXJ24uVeXl+liMgxJlMY/IPAqgE8CcxtV3/oqo/UtVXRORxAK/ChHGuUdXOBNchKZBIpL16uemGLtwGGLfBxcsnAyuTZ9Ei4IUXzPfJYvIsR0CINzISeVX9WoJ9dwC4I5PrE3eSecTZWrXpJtxug0uiwSQS6RmHr6joWU9+wQL3wYHlCAjxBle8BoxcZJE4CXdVlannHgp5H1zc4vBAap45yxEQ4g5FPoBkmkWSanzbynbp6jJZMwsXejuvqsoMClZaZShktqU6seo2sDFOTwhFnkSxBDG+8YaX+LYlyl1dxptvb/d2vzlzTE5+KLokT9VsW7gwdc88fmBj1ydCDBR50qsZdmdnz+X/6TTfSHSv1lZgx46eAwNgRD5ZqqVXmGZJiIEiXwIkC1vYBdHyrEW8edHWtRcu7N3hyelYe0OQsuhfX1lZrPtTslRLrzBOT4iBIh9wvIQtrH6qXV0mP33RouSCbV177NjYtVtavOfYAz0bglj7/Yqfs4wBIQaKfIHh92Sh17CFtVZZ1XuZgObmWPrjgQPmfSpZMPG58H4LMcsYEEKRLyiyMVnoJWxhFQ5L1jA7U+hdE5J7KPIFRDYmC70Ia1VV7PuyMu/x62nTTCGxQ4dMmGfaNG/2UNwJyR0U+QIiW5OFyVad/uQnsTi5vRSwl+u2ttIzJ6SQKRmRL4aFMfkIZ8SvOk01XEPPnJDCpiREvlAWxngZaHK9WjWV6o/FTDEM8oRkg5IQ+UJYGJOLgSade9TXm9RHqxJkouqPxUqhDPKE5IOSEPlCWBiTi4Em3XsEPeRSCIM8IfmiJES+EFL3cjHQFMJgVojw50JKGYl17Ms/dXV12tbWlm8zskYu4sKMPTvDnwsJMiKyWVXrHPdR5EsPCh4hwSKRyJdEuIYYrFZ7S5fGioFxEpKQYEORDwBePHMrw6SjI1anhpOQhAQfinyR4zU90MowsQTeaynhTG1jWIiQ/EKRL3K8pgfaM0zKyoDp07ObE8/cdEIKg5AfFxGR60VERaR/9L2IyCIR2SYiL4vICD/uQ3pjiXc4nNgzt9JIZ87MvsADzoMPIST3ZOzJi8iJAM4GsMO2+RwAp0S/vgngoegr8ZlU1wAsX25Ed/ny7HrXzE0npDDwI1xzP4AbAay0bZsMoFlNfuZfRORoEfmyqn7gw/1IHF5XrOZy5WchLEAjhGQo8iIyGcAuVX1JrG7MhhMAvGd7vzO6jSKfR3LtXQe9XAIhxUBSkReRtQAGOOy6BcDPYEI1aSMijQAaAaCmpiaTS5EkuHnXzIIhJLgkFXlVHe+0XURqAZwMwPLiqwE8LyKjAewCcKLt8OroNqfrNwFoAsyK11SMJ6nhJOapZsFwQCCkuEg7XKOqWwAcZ70Xke0A6lR1j4g8DWC2iDwKM+H6adDj8YUufm5inkqcnmmRhBQf2cqTXwXgXADbAOwHMD1L9ykIikH83MQ8lTg9S/YSUnz4JvKqOtD2vQK4xq9rFzrFIH5uYp5KFgzTIgkpPrji1QfyLX5e2wq6ibnXLBimRRJSfLDUsE9kGpO3KkQCqa1GzVWoqNDnHAgpZVhqOAdkkhMeiRjxPHjQvF+2zPRdLZQFTsUw50AIccaX2jVBJhIBFiwwr9mitRU4dCj2PpVaL15r12RqH+vQEFKc0JNPQK482IYGoLw85smnIta5iJPne86BEJI+FPkE5CprxspXTycmb52fzfAJJ1wJKV4o8gnIpQdb6HVeCt0+QogzFPkE0IMlhBQ7FPkkpOPBMt2QEFIoUOR9humGhJBCgimUPsN0Q0JIIUGR95lc5K27kYucfkJIccFwjc/ka7KWYSJCiBMU+SyQj3TDYqiESQjJPQzXBIR8hokIIYULPfkUyEZqpF/XZE4/IcQJirxHshHz9vuaXJVKCImH4RqPZCM1kumWhJBsQ5H3SDZi3oyjE0KyDcM1HslGzJtxdEJItmH7P0IIKXIStf/LOFwjIteKyGsi8oqI3G3bPk9EtonI6yIyIdP7EEIISZ2MwjUiMhbAZACnq+oBETkuuv00AD8AMBjA8QDWisggVe3M1GBCCCHeydSTnwXgLlU9AACq+mF0+2QAj6rqAVV9B8A2AKMzvBchhJAUyVTkBwE4U0T+KiLrRWRUdPsJAN6zHbczuo0QQkgOSRquEZG1AAY47Lolev6/AfgWgFEAHheRr6RigIg0AmgEgJqamlROJYQQkoSkIq+q4932icgsAE+qSdHZJCJdAPoD2AXgRNuh1dFtTtdvAtAEmOwa76YTQghJRqbhmqcAjAUAERkEoALAHgBPA/iBiFSKyMkATgGwKcN7EUIISZFMF0MtBbBURP4O4CCAy6Ne/Ssi8jiAVwEcBnANM2sIIST3ZCTyqnoQwGUu++4AcEcm1/cKG2cTQogzRV/WgB2RCCHEnaIvUMZKjoQQ4k7RizwrORJCiDtFH65hJUdCCHGn6EUeYEckQghxo+jDNYQQQtyhyBNCSIChyBNCSIChyBNCSIChyBNCSIChyBNCSIApqEbeIrIbwLv5tsMD/WGqbZYSpfjMQGk+N5+5+DhJVY912lFQIl8siEibW2f0oFKKzwyU5nPzmYMFwzWEEBJgKPKEEBJgKPLp0ZRvA/JAKT4zUJrPzWcOEIzJE0JIgKEnTwghAYYiTwghAYYinyYico+IvCYiL4vIH0Tk6HzblG1E5Psi8oqIdIlIINPNLERkooi8LiLbROTmfNuTC0RkqYh8KCJ/z7ctuUJEThSRFhF5Nfq3fV2+bfIbinz6/AnAEFUdCuANAPPybE8u+DuAiwD8d74NySYiEgawGMA5AE4DcKmInJZfq3LC/wYwMd9G5JjDAK5X1dMAfAvANUH7XVPk00RV16jq4ejbvwCozqc9uUBVt6rq6/m2IweMBrBNVd9W1YMAHgUwOc82ZR1V/W8AH+Xbjlyiqh+o6vPR7/cC2ArghPxa5S8UeX+4EsCz+TaC+MYJAN6zvd+JgP3jk96IyEAAwwH8Nc+m+Eog2v9lCxFZC2CAw65bVHVl9JhbYD7y/S6XtmULL89MSNAQkSMBPAFgjqr+K9/2+AlFPgGqOj7RfhG5AsAkAOM0IAsOkj1zibALwIm299XRbSSAiEg5jMD/TlWfzLc9fsNwTZqIyEQANwK4QFX359se4it/A3CKiJwsIhUAfgDg6TzbRLKAiAiA3wDYqqr35duebECRT58HAfQD8CcReVFE/me+Dco2InKhiOwEUA/gjyKyOt82ZYPohPpsAKthJuIeV9VX8mtV9hGR3wOIAPi6iOwUkRn5tikHjAHwQwDfif4fvygi5+bbKD9hWQNCCAkw9OQJISTAUOQJISTAUOQJISTAUOQJISTAUOQJISTAUOQJISTAUOQJISTA/H+8kC770GoivgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x, y = datasets.make_regression(\n", + " n_samples=250, n_features=1, n_targets=1, random_state=1, noise=10\n", + ")\n", + "\n", + "plt.plot(x, y, \"b.\", label=\"Ensemble d'apprentissage\")\n", + "\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q7lfdRFMRFZH" + }, + "source": [ + "A vous de déterminer le nombre de neurones à positionner en entrée et en sortie du perceptron monocouche pour résoudre ce problème. Une fois ceci fait, le code ci-après affiche également la prédiction de votre modèle.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "GKFJ3c2MmomL" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0 : Loss 216.6618\n", + "Epoch 1 : Loss 93.8775\n", + "Epoch 2 : Loss 95.6949\n", + "Epoch 3 : Loss 93.6449\n", + "Epoch 4 : Loss 93.2614\n", + "Epoch 5 : Loss 92.0452\n", + "Epoch 6 : Loss 91.8134\n", + "Epoch 7 : Loss 94.1573\n", + "Epoch 8 : Loss 95.9175\n", + "Epoch 9 : Loss 89.3419\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = DenseLayer(1, 1, \"linear\")\n", + "model = SGD(x, y, model, \"mse\", learning_rate=0.1, epochs=10, batch_size=20)\n", + "\n", + "plt.plot(x, y, \"b.\", label=\"Ensemble d'apprentissage\")\n", + "\n", + "x_gen = np.expand_dims(np.linspace(-3, 3, 10), 1)\n", + "y_gen = np.transpose(model.forward(np.transpose(x_gen)))\n", + "\n", + "plt.plot(x_gen, y_gen, \"g-\", label=\"Prédiction du modèle\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mA9-6PqLwff4" + }, + "source": [ + "### Test sur un problème de classification binaire\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K9AHAgGBwjro" + }, + "source": [ + "Afin de pouvoir tester notre perceptron mono-couche sur un problème de classification binaire (i.e. effectuer une régression logistique), il est d'abord nécessaire d'implémenter l'entropie croisée binaire.\n", + "\n", + "Rappel : \n", + "$$ bce(y, \\hat{y}) = -y log(\\hat{y}) - (1-y) log(1-\\hat{y}) $$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "_xCXP-pQb2oL" + }, + "outputs": [], + "source": [ + "def binary_cross_entropy(y_true, y_pred):\n", + " batch_size = y_true.shape[0]\n", + " loss = np.mean(-y_true * np.log(y_pred) - (1 - y_true) * np.log(1 - y_pred))\n", + " # grad = (y_pred - y_true) / (y_pred - np.square(y_true)) / batch_size\n", + " grad = (-y_true / y_pred + (1 - y_true) / (1 - y_pred)) / batch_size\n", + "\n", + " return loss, grad\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0L3pPIpfSVU7" + }, + "source": [ + "Le bloc de code suivant permet de générer et d'afficher un ensemble de données pour un problème de classification binaire classique.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "4AxQRaegdntx" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn import datasets\n", + "import matplotlib.pyplot as plt\n", + "\n", + "x, y = datasets.make_blobs(\n", + " n_samples=250, n_features=2, centers=2, center_box=(-3, 3), random_state=1\n", + ")\n", + "\n", + "plt.plot(x[y == 0, 0], x[y == 0, 1], \"b.\")\n", + "plt.plot(x[y == 1, 0], x[y == 1, 1], \"r.\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X7o-u0kcSk_l" + }, + "source": [ + "A nouveau, vous devez déterminer le nombre de neurones à positionner en entrée et en sortie du perceptron monocouche pour résoudre ce problème. Une fois ceci fait, le code ci-après affiche également la prédiction de votre modèle.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "TdyntT9zSrum" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0 : Loss 0.2542\n", + "Epoch 1 : Loss 0.1595\n", + "Epoch 2 : Loss 0.1338\n", + "Epoch 3 : Loss 0.1184\n", + "Epoch 4 : Loss 0.1177\n", + "Epoch 5 : Loss 0.1132\n", + "Epoch 6 : Loss 0.1035\n", + "Epoch 7 : Loss 0.1009\n", + "Epoch 8 : Loss 0.0909\n", + "Epoch 9 : Loss 0.0946\n", + "Epoch 10 : Loss 0.0869\n", + "Epoch 11 : Loss 0.0920\n", + "Epoch 12 : Loss 0.0801\n", + "Epoch 13 : Loss 0.0883\n", + "Epoch 14 : Loss 0.0847\n", + "Epoch 15 : Loss 0.0839\n", + "Epoch 16 : Loss 0.0796\n", + "Epoch 17 : Loss 0.0812\n", + "Epoch 18 : Loss 0.0788\n", + "Epoch 19 : Loss 0.0773\n", + "Epoch 20 : Loss 0.0772\n", + "Epoch 21 : Loss 0.0776\n", + "Epoch 22 : Loss 0.0771\n", + "Epoch 23 : Loss 0.0749\n", + "Epoch 24 : Loss 0.0678\n", + "Epoch 25 : Loss 0.0735\n", + "Epoch 26 : Loss 0.0749\n", + "Epoch 27 : Loss 0.0752\n", + "Epoch 28 : Loss 0.0741\n", + "Epoch 29 : Loss 0.0726\n", + "Epoch 30 : Loss 0.0710\n", + "Epoch 31 : Loss 0.0718\n", + "Epoch 32 : Loss 0.0712\n", + "Epoch 33 : Loss 0.0704\n", + "Epoch 34 : Loss 0.0704\n", + "Epoch 35 : Loss 0.0715\n", + "Epoch 36 : Loss 0.0703\n", + "Epoch 37 : Loss 0.0713\n", + "Epoch 38 : Loss 0.0702\n", + "Epoch 39 : Loss 0.0679\n", + "Epoch 40 : Loss 0.0628\n", + "Epoch 41 : Loss 0.0691\n", + "Epoch 42 : Loss 0.0700\n", + "Epoch 43 : Loss 0.0696\n", + "Epoch 44 : Loss 0.0689\n", + "Epoch 45 : Loss 0.0689\n", + "Epoch 46 : Loss 0.0686\n", + "Epoch 47 : Loss 0.0666\n", + "Epoch 48 : Loss 0.0669\n", + "Epoch 49 : Loss 0.0681\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = DenseLayer(2, 2, \"sigmoid\")\n", + "model = SGD(x, y, model, \"bce\", learning_rate=0.3, epochs=50, batch_size=20)\n", + "\n", + "plt.plot(x[y == 0, 0], x[y == 0, 1], \"b.\")\n", + "plt.plot(x[y == 1, 0], x[y == 1, 1], \"r.\")\n", + "\n", + "x1_gen = np.linspace(-6, 2, 10)\n", + "x2_gen = -model.Wxy[0, 0] * x1_gen / model.Wxy[0, 1] - model.by[0, 0] / model.Wxy[0, 1]\n", + "\n", + "plt.plot(x1_gen, x2_gen, \"g-\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ypq84RCl0bnI" + }, + "source": [ + "## Test sur un problème de classification binaire plus complexe\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6OPzEofrSrSF" + }, + "source": [ + "Testons maintenant un problème de classification plus complexe :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "id": "_IQdphRV0hsB" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x, y = datasets.make_gaussian_quantiles(\n", + " n_samples=250, n_features=2, n_classes=2, random_state=1\n", + ")\n", + "\n", + "plt.plot(x[y == 0, 0], x[y == 0, 1], \"r.\")\n", + "plt.plot(x[y == 1, 0], x[y == 1, 1], \"b.\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Ol3eqKGSyC5" + }, + "source": [ + "Le code ci-dessous vous permettra d'afficher la frontière de décision établie par votre modèle :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "id": "lN8d7YK76MBm" + }, + "outputs": [], + "source": [ + "def print_decision_boundaries(model, x, y):\n", + " dx, dy = 0.1, 0.1\n", + " y_grid, x_grid = np.mgrid[slice(-4, 4 + dy, dy), slice(-4, 4 + dx, dx)]\n", + "\n", + " x_gen = np.concatenate(\n", + " (\n", + " np.expand_dims(np.reshape(y_grid, (-1)), 1),\n", + " np.expand_dims(np.reshape(x_grid, (-1)), 1),\n", + " ),\n", + " axis=1,\n", + " )\n", + " z_gen = model.forward(np.transpose(x_gen)).reshape(x_grid.shape)\n", + "\n", + " z_min, z_max = 0, 1\n", + "\n", + " c = plt.pcolor(x_grid, y_grid, z_gen, cmap=\"RdBu\", vmin=z_min, vmax=z_max)\n", + " plt.colorbar(c)\n", + " plt.plot(x[y == 0, 0], x[y == 0, 1], \"r.\")\n", + " plt.plot(x[y == 1, 0], x[y == 1, 1], \"b.\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SRNifc8KS_MM" + }, + "source": [ + "Complétez le code ci-dessous :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "E9WV-Az70mR6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0 : Loss 0.8098\n", + "Epoch 1 : Loss 0.7164\n", + "Epoch 2 : Loss 0.6958\n", + "Epoch 3 : Loss 0.6941\n", + "Epoch 4 : Loss 0.6899\n", + "Epoch 5 : Loss 0.6902\n", + "Epoch 6 : Loss 0.6872\n", + "Epoch 7 : Loss 0.6860\n", + "Epoch 8 : Loss 0.6875\n", + "Epoch 9 : Loss 0.6857\n", + "Epoch 10 : Loss 0.6890\n", + "Epoch 11 : Loss 0.6890\n", + "Epoch 12 : Loss 0.6881\n", + "Epoch 13 : Loss 0.6902\n", + "Epoch 14 : Loss 0.6953\n", + "Epoch 15 : Loss 0.6916\n", + "Epoch 16 : Loss 0.6908\n", + "Epoch 17 : Loss 0.6911\n", + "Epoch 18 : Loss 0.6888\n", + "Epoch 19 : Loss 0.6923\n", + "Epoch 20 : Loss 0.6901\n", + "Epoch 21 : Loss 0.6914\n", + "Epoch 22 : Loss 0.6904\n", + "Epoch 23 : Loss 0.6928\n", + "Epoch 24 : Loss 0.6904\n", + "Epoch 25 : Loss 0.6899\n", + "Epoch 26 : Loss 0.6903\n", + "Epoch 27 : Loss 0.6852\n", + "Epoch 28 : Loss 0.6898\n", + "Epoch 29 : Loss 0.6921\n", + "Epoch 30 : Loss 0.6935\n", + "Epoch 31 : Loss 0.6887\n", + "Epoch 32 : Loss 0.6915\n", + "Epoch 33 : Loss 0.6900\n", + "Epoch 34 : Loss 0.6900\n", + "Epoch 35 : Loss 0.6859\n", + "Epoch 36 : Loss 0.6892\n", + "Epoch 37 : Loss 0.6903\n", + "Epoch 38 : Loss 0.6922\n", + "Epoch 39 : Loss 0.6876\n", + "Epoch 40 : Loss 0.6905\n", + "Epoch 41 : Loss 0.6924\n", + "Epoch 42 : Loss 0.6907\n", + "Epoch 43 : Loss 0.6938\n", + "Epoch 44 : Loss 0.6911\n", + "Epoch 45 : Loss 0.6914\n", + "Epoch 46 : Loss 0.6924\n", + "Epoch 47 : Loss 0.6920\n", + "Epoch 48 : Loss 0.6871\n", + "Epoch 49 : Loss 0.6920\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = DenseLayer(2, 1, \"sigmoid\")\n", + "model = SGD(x, y, model, \"bce\", learning_rate=0.3, epochs=50, batch_size=20)\n", + "\n", + "print_decision_boundaries(model, x, y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J9jMU_YcTAJl" + }, + "source": [ + "Cette fois-ci il n'est pas possible de faire résoudre un problème aussi \"complexe\" à notre simple perceptron monocouche. Nous allons pour cela devoir passer au perceptron multi-couches !\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yiGyXLvum0uI" + }, + "source": [ + "---\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HIEVrFXkDdMD" + }, + "source": [ + "# Perceptron multi-couches\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6ZWNGM7vVlCb" + }, + "source": [ + "## Implémentation du perceptron multi-couches\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1a6VuuWODu8G" + }, + "source": [ + "A partir du perceptron mono-couche créé précédemment, nous pouvons maintenant implémenter un perceptron multi-couches, qui est un véritable réseau de neurones dans la mesure où il met en jeu plusieurs couches de neurones successives. **Concrètement, le perceptron multi-couches est une composition de perceptron monocouches**, chacun prenant en entrée l'activation de sortie de la couche précédente. Prenons l'exemple ci-dessous :\n", + "\n", + "\n", + "\n", + "Ce perceptron multi-couches est la composition de deux perceptrons monocouches, le premier liant deux neurones d'entrée à deux neurones de sortie, et le second deux neurones d'entrée à un neurone de sortie.\n", + "\n", + "\n", + "\n", + "Voici comment nous l'implémenterons : le perceptron multi-couches consiste simplement en une liste de perceptrons monocouches (_DenseLayer_). A l'initialisation, le perceptron multi-couches est une liste vide, dans laquelle il est possible d'ajouter des couches denses (fonction _add_layer()_).\n", + "\n", + "```python\n", + "model = MultiLayerPerceptron()\n", + "model.add_layer(DenseLayer(2, 2, 'relu'))\n", + "model.add_layer(DenseLayer(2, 1, 'sigmoid'))\n", + "```\n", + "\n", + "La fonction _forward()_ du perceptron multi-couches consiste en le calcul successif de la sortie des couches denses. Chaque couche dense effectue une prédiction sur la sortie de la couche dense précédente.\n", + "\n", + "La fonction _backward()_ implémente l'algorithme de rétro-propagation du gradient. Les gradients des paramètres de la dernière couche sont calculés en premier, et sont utilisés pour calculer les gradients de la couche précédente, comme illustré sur cette figure.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "RNhqq0KXm4Jd" + }, + "outputs": [], + "source": [ + "class MultiLayerPerceptron:\n", + " def __init__(self):\n", + " # Initialisation de la liste de couches du perceptron multi-couches à la liste vide\n", + " self.layers = []\n", + "\n", + " # Fonction permettant d'ajouter la couche passée en paramètre dans la liste de couches\n", + " # du perceptron multi-couches\n", + " def add_layer(self, layer):\n", + " self.layers.append(layer)\n", + "\n", + " # Fonction réalisant la prédiction du perceptron multi-couches :\n", + " # Elle consiste en la prédiction successive de chacune des couches de la liste de couches,\n", + " # chacune prenant en entrée la prédiction de la couche précédente\n", + " def forward(self, x_batch):\n", + " y = x_batch\n", + " for layer in self.layers:\n", + " y = layer.forward(y)\n", + "\n", + " return y\n", + "\n", + " # Fonction de calcul des gradients de la fonction objectif par rapport à chaque paramètre \n", + " # du perceptron multi-couches\n", + " # L'entrée dy_hat correspond au gradient de la fonction objectif par rapport à la prédiction\n", + " # finale du perceptron multi-couches (notée dJ/dŷ sur la figure précédente)\n", + " # Cette fonction doit implémenter la rétropropagation du gradient : on parcourt la liste des\n", + " # couches en sens inverse (fonction reversed) et le gradient de la fonction objectif par rapport \n", + " # à l'entrée d'une couche est utilisé pour calculer les gradients de la couche précédente\n", + " # \n", + " # Cette fonction retourne une liste de dictionnaires de gradients, de même dimension que le nombre\n", + " # de couches\n", + " def backward(self, dy_hat):\n", + " gradients = []\n", + " for layer in reversed(self.layers):\n", + " grad = layer.backward(dy_hat)\n", + " gradients.append(grad)\n", + " dy_hat = grad[\"dx\"]\n", + "\n", + " return gradients[::-1]\n", + "\n", + " # Fonction de mise à jour des paramètres en fonction des gradients établis dans la \n", + " # fonction backward et d'un taux d'apprentissage\n", + " def update_parameters(self, gradients, learning_rate):\n", + " # print(gradients)\n", + " for i, layer in enumerate(self.layers):\n", + " layer.update_parameters(gradients[i], learning_rate)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GyIW025tVcPR" + }, + "source": [ + "## Test sur le problème simple de classification binaire\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JEg5-Z7mVEWd" + }, + "source": [ + "Vous pouvez maintenant tester votre perceptron multi-couches sur le problème précédent. Deux couches suffisent pour résoudre le problème !\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "pijGm1ipwrAw" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x, y = datasets.make_gaussian_quantiles(\n", + " n_samples=250, n_features=2, n_classes=2, random_state=1\n", + ")\n", + "\n", + "plt.plot(x[y == 0, 0], x[y == 0, 1], \"r.\")\n", + "plt.plot(x[y == 1, 0], x[y == 1, 1], \"b.\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "h3He5gXmxQ1j" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0 : Loss 0.6403\n", + "Epoch 1 : Loss 0.5871\n", + "Epoch 2 : Loss 0.5426\n", + "Epoch 3 : Loss 0.4999\n", + "Epoch 4 : Loss 0.4635\n", + "Epoch 5 : Loss 0.4315\n", + "Epoch 6 : Loss 0.3962\n", + "Epoch 7 : Loss 0.3760\n", + "Epoch 8 : Loss 0.3543\n", + "Epoch 9 : Loss 0.3248\n", + "Epoch 10 : Loss 0.3167\n", + "Epoch 11 : Loss 0.3017\n", + "Epoch 12 : Loss 0.2873\n", + "Epoch 13 : Loss 0.2752\n", + "Epoch 14 : Loss 0.2584\n", + "Epoch 15 : Loss 0.2557\n", + "Epoch 16 : Loss 0.2464\n", + "Epoch 17 : Loss 0.2313\n", + "Epoch 18 : Loss 0.2243\n", + "Epoch 19 : Loss 0.2278\n", + "Epoch 20 : Loss 0.2141\n", + "Epoch 21 : Loss 0.2088\n", + "Epoch 22 : Loss 0.2022\n", + "Epoch 23 : Loss 0.2044\n", + "Epoch 24 : Loss 0.1966\n", + "Epoch 25 : Loss 0.1851\n", + "Epoch 26 : Loss 0.1884\n", + "Epoch 27 : Loss 0.1830\n", + "Epoch 28 : Loss 0.1760\n", + "Epoch 29 : Loss 0.1794\n", + "Epoch 30 : Loss 0.1704\n", + "Epoch 31 : Loss 0.1716\n", + "Epoch 32 : Loss 0.1684\n", + "Epoch 33 : Loss 0.1631\n", + "Epoch 34 : Loss 0.1575\n", + "Epoch 35 : Loss 0.1578\n", + "Epoch 36 : Loss 0.1552\n", + "Epoch 37 : Loss 0.1564\n", + "Epoch 38 : Loss 0.1532\n", + "Epoch 39 : Loss 0.1495\n", + "Epoch 40 : Loss 0.1504\n", + "Epoch 41 : Loss 0.1449\n", + "Epoch 42 : Loss 0.1475\n", + "Epoch 43 : Loss 0.1397\n", + "Epoch 44 : Loss 0.1437\n", + "Epoch 45 : Loss 0.1367\n", + "Epoch 46 : Loss 0.1377\n", + "Epoch 47 : Loss 0.1336\n", + "Epoch 48 : Loss 0.1355\n", + "Epoch 49 : Loss 0.1329\n", + "Epoch 50 : Loss 0.1306\n", + "Epoch 51 : Loss 0.1305\n", + "Epoch 52 : Loss 0.1301\n", + "Epoch 53 : Loss 0.1281\n", + "Epoch 54 : Loss 0.1324\n", + "Epoch 55 : Loss 0.1229\n", + "Epoch 56 : Loss 0.1244\n", + "Epoch 57 : Loss 0.1213\n", + "Epoch 58 : Loss 0.1188\n", + "Epoch 59 : Loss 0.1223\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = MultiLayerPerceptron()\n", + "model.add_layer(DenseLayer(2, 10, \"relu\"))\n", + "model.add_layer(DenseLayer(10, 1, \"sigmoid\"))\n", + "\n", + "model = SGD(x, y, model, \"bce\", learning_rate=0.3, epochs=60, batch_size=20)\n", + "\n", + "print_decision_boundaries(model, x, y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SMTeraduVplm" + }, + "source": [ + "# Quelques exercices supplémentaires\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "46K0mq5bVvT1" + }, + "source": [ + "## Evanescence du gradient\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pVBCGX9iVzdL" + }, + "source": [ + "Testez le réseau suivant sur le problème simple de classification binaire évoqué dans la partie précédente :\n", + "\n", + "```python\n", + "model.add_layer(DenseLayer(2, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 1, 'sigmoid'))\n", + "```\n", + "\n", + "1. Qu'observez-vous ?\n", + "2. Comment résoudre ce problème ?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0 : Loss 0.7070\n", + "Epoch 1 : Loss 0.6948\n", + "Epoch 2 : Loss 0.6994\n", + "Epoch 3 : Loss 0.6982\n", + "Epoch 4 : Loss 0.6959\n", + "Epoch 5 : Loss 0.6980\n", + "Epoch 6 : Loss 0.6959\n", + "Epoch 7 : Loss 0.6919\n", + "Epoch 8 : Loss 0.7023\n", + "Epoch 9 : Loss 0.6971\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = MultiLayerPerceptron()\n", + "model.add_layer(DenseLayer(2, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 10, 'sigmoid'))\n", + "model.add_layer(DenseLayer(10, 1, 'sigmoid'))\n", + "\n", + "model = SGD(x, y, model, \"bce\", learning_rate=0.3, epochs=10, batch_size=20)\n", + "\n", + "print_decision_boundaries(model, x, y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0 : Loss 0.6778\n", + "Epoch 1 : Loss 0.6748\n", + "Epoch 2 : Loss 0.6724\n", + "Epoch 3 : Loss 0.6701\n", + "Epoch 4 : Loss 0.6660\n", + "Epoch 5 : Loss 0.6633\n", + "Epoch 6 : Loss 0.6580\n", + "Epoch 7 : Loss 0.6547\n", + "Epoch 8 : Loss 0.6494\n", + "Epoch 9 : Loss 0.6421\n", + "Epoch 10 : Loss 0.6362\n", + "Epoch 11 : Loss 0.6279\n", + "Epoch 12 : Loss 0.6215\n", + "Epoch 13 : Loss 0.6149\n", + "Epoch 14 : Loss 0.6044\n", + "Epoch 15 : Loss 0.5944\n", + "Epoch 16 : Loss 0.5855\n", + "Epoch 17 : Loss 0.5786\n", + "Epoch 18 : Loss 0.5664\n", + "Epoch 19 : Loss 0.5563\n", + "Epoch 20 : Loss 0.5394\n", + "Epoch 21 : Loss 0.5330\n", + "Epoch 22 : Loss 0.5214\n", + "Epoch 23 : Loss 0.5041\n", + "Epoch 24 : Loss 0.4927\n", + "Epoch 25 : Loss 0.4778\n", + "Epoch 26 : Loss 0.4659\n", + "Epoch 27 : Loss 0.4547\n", + "Epoch 28 : Loss 0.4377\n", + "Epoch 29 : Loss 0.4234\n", + "Epoch 30 : Loss 0.4056\n", + "Epoch 31 : Loss 0.3976\n", + "Epoch 32 : Loss 0.3868\n", + "Epoch 33 : Loss 0.3725\n", + "Epoch 34 : Loss 0.3648\n", + "Epoch 35 : Loss 0.3478\n", + "Epoch 36 : Loss 0.3397\n", + "Epoch 37 : Loss 0.3312\n", + "Epoch 38 : Loss 0.3178\n", + "Epoch 39 : Loss 0.3181\n", + "Epoch 40 : Loss 0.3035\n", + "Epoch 41 : Loss 0.2940\n", + "Epoch 42 : Loss 0.2886\n", + "Epoch 43 : Loss 0.2820\n", + "Epoch 44 : Loss 0.2719\n", + "Epoch 45 : Loss 0.2672\n", + "Epoch 46 : Loss 0.2587\n", + "Epoch 47 : Loss 0.2549\n", + "Epoch 48 : Loss 0.2518\n", + "Epoch 49 : Loss 0.2478\n", + "Epoch 50 : Loss 0.2385\n", + "Epoch 51 : Loss 0.2306\n", + "Epoch 52 : Loss 0.2288\n", + "Epoch 53 : Loss 0.2221\n", + "Epoch 54 : Loss 0.2164\n", + "Epoch 55 : Loss 0.2198\n", + "Epoch 56 : Loss 0.2091\n", + "Epoch 57 : Loss 0.2040\n", + "Epoch 58 : Loss 0.2029\n", + "Epoch 59 : Loss 0.2026\n", + "Epoch 60 : Loss 0.1965\n", + "Epoch 61 : Loss 0.1904\n", + "Epoch 62 : Loss 0.1912\n", + "Epoch 63 : Loss 0.1886\n", + "Epoch 64 : Loss 0.1839\n", + "Epoch 65 : Loss 0.1799\n", + "Epoch 66 : Loss 0.1828\n", + "Epoch 67 : Loss 0.1728\n", + "Epoch 68 : Loss 0.1694\n", + "Epoch 69 : Loss 0.1756\n", + "Epoch 70 : Loss 0.1661\n", + "Epoch 71 : Loss 0.1675\n", + "Epoch 72 : Loss 0.1625\n", + "Epoch 73 : Loss 0.1611\n", + "Epoch 74 : Loss 0.1584\n", + "Epoch 75 : Loss 0.1535\n", + "Epoch 76 : Loss 0.1598\n", + "Epoch 77 : Loss 0.1520\n", + "Epoch 78 : Loss 0.1540\n", + "Epoch 79 : Loss 0.1506\n", + "Epoch 80 : Loss 0.1482\n", + "Epoch 81 : Loss 0.1495\n", + "Epoch 82 : Loss 0.1466\n", + "Epoch 83 : Loss 0.1386\n", + "Epoch 84 : Loss 0.1396\n", + "Epoch 85 : Loss 0.1403\n", + "Epoch 86 : Loss 0.1396\n", + "Epoch 87 : Loss 0.1361\n", + "Epoch 88 : Loss 0.1369\n", + "Epoch 89 : Loss 0.1330\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = MultiLayerPerceptron()\n", + "model.add_layer(DenseLayer(2, 10, 'relu'))\n", + "model.add_layer(DenseLayer(10, 10, 'relu'))\n", + "model.add_layer(DenseLayer(10, 10, 'relu'))\n", + "model.add_layer(DenseLayer(10, 10, 'relu'))\n", + "model.add_layer(DenseLayer(10, 1, 'sigmoid'))\n", + "\n", + "model = SGD(x, y, model, \"bce\", learning_rate=0.03, epochs=90, batch_size=20)\n", + "\n", + "print_decision_boundaries(model, x, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YBChCCJREOuP" + }, + "source": [ + "## Application à un problème de classification d'image\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C7efDmj6WNSg" + }, + "source": [ + "Le code ci-dessous vous permet de charger l'ensemble de données CIFAR-10 qui regroupe des imagettes de taille $32 \\times 32$ représentant 10 types d'objets différents.\n", + "\n", + "Des images de chat et de chien sont extraites de cet ensemble : à vous de mettre en place un perceptron multi-couches de classification binaire pour apprendre à reconnaître un chien d'un chat dans une image.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "id": "ZFyeFRYfEN3A" + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "\n", + "# Récupération des données\n", + "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n", + "\n", + "# La base de données CIFAR contient des images issues de 10 classes :\n", + "# 0\tairplane\n", + "# 1\tautomobile\n", + "# 2\tbird\n", + "# 3\tcat\n", + "# 4\tdeer\n", + "# 5\tdog\n", + "# 6\tfrog\n", + "# 7\thorse\n", + "# 8\tship\n", + "# 9\ttruck\n", + "\n", + "# Préparation des données pour la classification binaire :\n", + "\n", + "# Extraction des images des classes de chat et chien\n", + "indices_train = np.squeeze(y_train)\n", + "x_cat_train = x_train[indices_train == 3, :]\n", + "x_dog_train = x_train[indices_train == 5, :]\n", + "\n", + "indices_test = np.squeeze(y_test)\n", + "x_cat_test = x_test[indices_test == 3, :]\n", + "x_dog_test = x_test[indices_test == 5, :]\n", + "\n", + "# Création des données d'apprentissage et de test\n", + "# Les images sont redimensionnées en vecteurs de dimension 3072 (32*32*3)\n", + "# On assigne 0 à la classe chat et 1 à la classe chien\n", + "x_train = np.concatenate(\n", + " (\n", + " np.resize(x_cat_train[0:250], (250, 32 * 32 * 3)),\n", + " np.resize(x_dog_train[0:250], (250, 32 * 32 * 3)),\n", + " ),\n", + " axis=0,\n", + ")\n", + "y_train = np.concatenate((np.zeros((250)), np.ones((250))), axis=0)\n", + "\n", + "x_test = np.concatenate(\n", + " (\n", + " np.resize(x_cat_test, (1000, 32 * 32 * 3)),\n", + " np.resize(x_dog_test, (1000, 32 * 32 * 3)),\n", + " ),\n", + " axis=0,\n", + ")\n", + "y_test = np.concatenate((np.zeros((1000)), np.ones((1000))), axis=0)\n", + "\n", + "# Normalisation des entrées\n", + "x_train = x_train / 255\n", + "x_test = x_test / 255\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "id": "VBzhs000JbHT" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0 : Loss 0.6907\n", + "Epoch 1 : Loss 0.6785\n", + "Epoch 2 : Loss 0.6762\n", + "Epoch 3 : Loss 0.6715\n", + "Epoch 4 : Loss 0.6657\n", + "Epoch 5 : Loss 0.6516\n", + "Epoch 6 : Loss 0.6528\n", + "Epoch 7 : Loss 0.6504\n", + "Epoch 8 : Loss 0.6356\n", + "Epoch 9 : Loss 0.6410\n", + "Epoch 10 : Loss 0.6463\n", + "Epoch 11 : Loss 0.6261\n", + "Epoch 12 : Loss 0.6174\n", + "Epoch 13 : Loss 0.6252\n", + "Epoch 14 : Loss 0.6196\n", + "Epoch 15 : Loss 0.6162\n", + "Epoch 16 : Loss 0.6057\n", + "Epoch 17 : Loss 0.5784\n", + "Epoch 18 : Loss 0.5908\n", + "Epoch 19 : Loss 0.5903\n", + "Epoch 20 : Loss 0.5652\n", + "Epoch 21 : Loss 0.5556\n", + "Epoch 22 : Loss 0.5439\n", + "Epoch 23 : Loss 0.5201\n", + "Epoch 24 : Loss 0.6409\n", + "Epoch 25 : Loss 0.5271\n", + "Epoch 26 : Loss 0.5146\n", + "Epoch 27 : Loss 0.5608\n", + "Epoch 28 : Loss 0.5149\n", + "Epoch 29 : Loss 0.4972\n", + "Epoch 30 : Loss 0.5159\n", + "Epoch 31 : Loss 0.4962\n", + "Epoch 32 : Loss 0.5198\n", + "Epoch 33 : Loss 0.5027\n", + "Epoch 34 : Loss 0.4672\n", + "Epoch 35 : Loss 0.5406\n", + "Epoch 36 : Loss 0.5124\n", + "Epoch 37 : Loss 0.4502\n", + "Epoch 38 : Loss 0.4163\n", + "Epoch 39 : Loss 0.4734\n", + "Epoch 40 : Loss 0.4098\n", + "Epoch 41 : Loss 0.4418\n", + "Epoch 42 : Loss 0.4128\n", + "Epoch 43 : Loss 0.4661\n", + "Epoch 44 : Loss 0.4381\n", + "Epoch 45 : Loss 0.4079\n", + "Epoch 46 : Loss 0.4724\n", + "Epoch 47 : Loss 0.4054\n", + "Epoch 48 : Loss 0.3555\n", + "Epoch 49 : Loss 0.3965\n" + ] + } + ], + "source": [ + "model = MultiLayerPerceptron()\n", + "model.add_layer(DenseLayer(3072, 100, \"relu\"))\n", + "model.add_layer(DenseLayer(100, 100, \"relu\"))\n", + "model.add_layer(DenseLayer(100, 100, \"relu\"))\n", + "model.add_layer(DenseLayer(100, 100, \"relu\"))\n", + "model.add_layer(DenseLayer(100, 1, \"sigmoid\"))\n", + "\n", + "model = SGD(x_train, y_train, model, \"bce\", learning_rate=0.03, epochs=50, batch_size=20)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "hPUcXM60L0-b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Précision de 57.4 %\n" + ] + } + ], + "source": [ + "# Prédiction du modèle sur les données de test\n", + "y_pred_test = model.forward(np.transpose(x_test))\n", + "\n", + "# Calcul de la précision : un écart inférieur à 0.5 entre prédiction et label\n", + "# est considéré comme bonne prédiction\n", + "prediction_eval = np.where(np.abs(y_pred_test - y_test) < 0.5, 1, 0)\n", + "overall_test_precision = 100 * np.sum(prediction_eval) / y_test.shape[0]\n", + "print(f\"Précision de {overall_test_precision:2.1f} %\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A1jASzh3PSKa" + }, + "source": [ + "Si vous obtenez une précision supérieure à 50%, votre réseau est meilleur qu'une prédiction aléatoire, ce qui est déjà bien ! Notez qu'ici nous avons circonscrit l'ensemble d'apprentissage à 500 échantillons (250 de chaque classe) car les calculs de produit matriciel sont longs. C'est tout l'intérêt de porter les calculs sur GPU ou TPU, des dispositifs matériels spécialement conçus et optimisés pour paralléliser ces calculs.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YV4WZTfL0KB9" + }, + "source": [ + "## Utilisation de la librairie Keras\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XFR3jwelW1jh" + }, + "source": [ + "L'utilisation d'une librairie comme Keras permet d'abstraire toutes les difficultés présentées dans ce TP : voici par exemple comment résoudre grâce à Keras le premier problème de régression linéaire présenté dans ce TP.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "id": "ew3_k9uK0P9g" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x, y = datasets.make_regression(\n", + " n_samples=250, n_features=1, n_targets=1, random_state=1, noise=10\n", + ")\n", + "\n", + "plt.plot(x, y, \"b.\", label=\"Ensemble d'apprentissage\")\n", + "\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "jBQYiUU-XX9a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense_2 (Dense) (None, 1) 2 \n", + " \n", + "=================================================================\n", + "Total params: 2\n", + "Trainable params: 2\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "\n", + "model = Sequential()\n", + "model.add(\n", + " Dense(1, activation=\"linear\", input_dim=1)\n", + ") # input_dim indique la dimension de la couche d'entrée, ici 1\n", + "\n", + "model.summary() # affiche un résumé du modèle\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "S0Vqoo26Xfe3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "10/10 [==============================] - 0s 19ms/step - loss: 245.9282 - mae: 11.5425 - val_loss: 117.3151 - val_mae: 8.8674\n", + "Epoch 2/10\n", + "10/10 [==============================] - 0s 9ms/step - loss: 84.9707 - mae: 7.2142 - val_loss: 129.4701 - val_mae: 9.4206\n", + "Epoch 3/10\n", + "10/10 [==============================] - 0s 7ms/step - loss: 83.3558 - mae: 7.2297 - val_loss: 128.8718 - val_mae: 9.3796\n", + "Epoch 4/10\n", + "10/10 [==============================] - 0s 8ms/step - loss: 82.9053 - mae: 7.2037 - val_loss: 131.2379 - val_mae: 9.4093\n", + "Epoch 5/10\n", + "10/10 [==============================] - 0s 9ms/step - loss: 83.3645 - mae: 7.1756 - val_loss: 136.7746 - val_mae: 9.5448\n", + "Epoch 6/10\n", + "10/10 [==============================] - 0s 9ms/step - loss: 83.7992 - mae: 7.1879 - val_loss: 132.7072 - val_mae: 9.4856\n", + "Epoch 7/10\n", + "10/10 [==============================] - 0s 6ms/step - loss: 83.8195 - mae: 7.1865 - val_loss: 130.4464 - val_mae: 9.4139\n", + "Epoch 8/10\n", + "10/10 [==============================] - 0s 7ms/step - loss: 85.3769 - mae: 7.3006 - val_loss: 127.4360 - val_mae: 9.3335\n", + "Epoch 9/10\n", + "10/10 [==============================] - 0s 5ms/step - loss: 83.1083 - mae: 7.1654 - val_loss: 130.0450 - val_mae: 9.4079\n", + "Epoch 10/10\n", + "10/10 [==============================] - 0s 4ms/step - loss: 84.1076 - mae: 7.2231 - val_loss: 131.9810 - val_mae: 9.4652\n" + ] + } + ], + "source": [ + "from tensorflow.keras import optimizers\n", + "\n", + "sgd = optimizers.SGD(\n", + " learning_rate=0.1\n", + ") # On choisit la descente de gradient stochastique, avec un taux d'apprentssage de 0.1\n", + "\n", + "# On définit ici, pour le modèle introduit plus tôt, l'optimiseur choisi, la fonction de perte (ici\n", + "# l'erreur quadratique moyenne pour un problème de régression) et les métriques que l'on veut observer pendant\n", + "# l'entraînement. L'erreur absolue moyenne (MAE) est un indicateur plus simple à interpréter que la MSE.\n", + "model.compile(optimizer=sgd, loss=\"mean_squared_error\", metrics=[\"mae\"])\n", + "\n", + "# Entraînement du modèle avec des mini-batchs de taille 20, sur 10 epochs.\n", + "# Le paramètre validation_split signifie qu'on tire aléatoirement une partie des données\n", + "# (ici 20%) pour servir d'ensemble de validation\n", + "history = model.fit(x, y, validation_split=0.2, epochs=10, batch_size=20)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "id": "46LiNDvGYQdK" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, y, \"b.\", label=\"Ensemble d'apprentissage\")\n", + "\n", + "x_gen = np.expand_dims(np.linspace(-3, 3, 10), 1)\n", + "y_gen = model.predict(x_gen)\n", + "\n", + "plt.plot(x_gen, y_gen, \"g-\", label=\"Prédiction du modèle\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kHu5v6lUYqTm" + }, + "source": [ + "S'il vous reste du temps, reprenez les différents problèmes définis précédemment et utilisez la librairie Keras pour les résoudre.\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "TP Réseaux de Neurones avec Numpy - Sujet.ipynb", + "provenance": [] + }, + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "xxuVc", + "launcher_item_id": "X20PE" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "toc_cell": true, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/TP3.ipynb b/TP3.ipynb new file mode 100644 index 0000000..72024f1 --- /dev/null +++ b/TP3.ipynb @@ -0,0 +1,1673 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "1qjWcjWFVcs7" + }, + "source": [ + "# Introduction à la librairie Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "77ojmk9zVgUt" + }, + "source": [ + "Dans le TP précédent, vous avez implémenté l'apprentissage et l'inférence d'un réseau de neurones. En pratique, il est plus courant de faire appel à des librairies qui masquent la complexité de ces algorithmes (notamment le calcul des gradients, réalisé par différentiation automatique). Dans la suite, nous utiliserons pour les TPs la librairie ***Keras***. Dans un premier temps, pour ce TP nous allons détailler sur un exemple simple (le même que pour le TP précédent) les différentes étapes à mettre en place pour entraîner un réseau à l'aide de cette librairie." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b2Sq7AygNuNL" + }, + "source": [ + "## Exemple de classification simple" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "luY3XIU7WfWQ", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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Q8Wu3dc1qwis8fXp3CqBPohU/Zdq+mMQJ9Sc3krRyz6YfLNGPEVrjiGSNehv12DWPUemPN9yZyUa/ljhpZfKq/jSY8sWOuD0h/eTiIKNEnx65a0K7gZt089qgxjF66vQAf/z+VXxmax7Hp+aKeb8Nx/5te4Vnz+58fd99Jk7uMtEr7oH7NDIAwvvJFSJN3Zt+0CMPmLU1Mz1MCI2x1tb00p6r9EVM6N/jKn1Db62292uLPK+wrMeY9MhF/PLAl5f98shDBvTIiRXm5oDHHx+6W69/vb+NsVZXMXnlEgRXoLiEW3qrmJ/3wxXL8gqrxM+jmPh99wF/+qc7J1/24Ybixob7m5xdh0JORpNMsQhlbDo/D5naC928BEzsxTs+Oo8bPDd7ddVUZ25tmeeiYry4aMIp8YuAqxt5aaGjUE6ZUKGQk3yyXMRI3A8dcm1hNttBYlldxZ75edwQgJJMTxsRB8zz9HTxz+bFxNssgqkbm+9iwU7TUMhDwsUZnjVJYlzc3/Mef39xgbmCGxumzH5ryzxvbJT7fNru2k53LHIaVj3sXS3YaRoKeSi4OsPTxsm+pSF0iPl5YGrKbnjE5tfV9GnIU6saPdcGkIJkecZt8K53AXfeOfzVNjodeIwA2uDaJgpLHD9eTSQHA1OSPxgM37P5dTV9GrZ1anWOtFSWsg8AtwP4OoALAH5p1PJMP4xRNNfMRVVF3jbbqKrg5I+laOPrauM0DLlgp2nQVPqhiEwA+BiAnwTwHIDPi8gfqepX666785QZp9qq7igTZ88b59aJPfNuViPY/rrSvqY2iozavK3RmVMxTd3LPADMAXg49vowgMN5n6FHvo3tWu1RFHGn4kU9o5av4jqNWmcbU8UHzKhCoqxDW/bw+VZW3wQh7iMyPHIbMfJXAPhW7PVz2++RUbQZEBwMTMx5czM/wHns2PDvvIBtNJo4etQ8x4OyeYwKsvb7Q/kGzPPamrkDWHQbtkgLODtk1CHP+7riX2sRXN6SaYs6++jZqdFe1oqILAJYBICZmZm2Nus3bTXDiBQgqjTp9YpfOLLGuW21lx0MgJtvHi7/2GPtjIE9zIMrcshthSU62yUwRtV99PDUsOKRPw/gutjra7ff24GqnlbVWVWd3bdvn4XNdoS5OdPXs42e3pGIHziw8+zr9wER8wCGf+dljVQdTZRJy1haMvODbW4az3xzE/jIR4ptpy4euqRlD3mVrzWibvZMCFTdRw9PDYjW7JMhIpMAvgHgVhgB/zyAf6uqX8n6zOzsrK6vr9faLilAdCcnOf9X3lkrUrx3Sht3it73PuA3f3P4emICeOKJ5pXFR7cL1Q95ma+V5OPy1BCR86o6u+v9ukK+vfK3ADgFYALAGVX9cN7yFPIWSJ5tp06ZMsFRCuDbL34wAN74RuP+AGZE8Su/MpydoO7FpN/PdlE7k9Lg39caOq5OjSwhtxIjV9WHADxkY11B4fMPPa0FXd7ULG31Til7zObmgN/4DeDuu82+TE0NYwo2XKNjx7KFPLDy/jyWllxb0C18OzVYol8VT4feL1HmTk5yX5rqnVL1mEWt/ZIXgKy7fz5fYFsmOhQHD7a3LR729qGQV8X3phBlMmLa2pc620lzgdIuVkUuFv3+zny86G7g0lKn2gK06Wv47td0HfZaqYpPTSHyQgNFMmLq7EuZhFrbxywt7aBISkFarrpqp0QcaDi7IvG9+5jJMVakVQk1/ehMZacvTSFs9CRpokrTxnaqLF/Gpg73c2mscjFlxSFWSYYIONVbA/h2xyONooHLKvuysgK88EK5ucXKbKfKeL1skVXKXcCuxHobqzdLcb/nDs9xOjeXpKl704/OeORptOWlF+lJ0qSbtLamOjU13O7evfb3ue1eNBpm/43W4UFyBhrstUIiqvYfqUKROG/cc9rcNP+zZdPqKnD5svlbxKQt2nbDHNyHYKy3AONQ9hkYFHKb+KYCkRBGc4c9+qi9C0xcZF/2MmBhwbxvs5tQG4KRsLfytaNjN0pH0kZrCY/xrWkWQys2cTXkjMIpaWGdtTXV225T7fXshyiS2wttyJ1hb6XoWJWbpkU25MsNdfISLk9zZIRWKOS2cfXDGzU9TBtnnoOYdi1s2ltWyIt8J6FdGMcEl6d5lpAztGIbV0POvLBOWzFNn3Lri1DX3jrtBYuE4RyF6rwLG3iGj6e5laZZZWHTrAawWVoXz78DyuWUhZa7Z8vesl2pinxfDsolQ63QbPu0861pFoW8S9g4u+K/5IkJI1CXL4f1q3ZBlfaCRb6vlhXj5EmTdHXlivn6jx/P77XmA6FefKrQaPdD4gk2CpTiw/mtLfNeVPCzshKWt90mVdoLFvm+Wi46C3FmIN/bHrUBhbwsoYUOyjI/b1yxrS3zPDFhPPLJSeDMGfNr6brbU4WOpB+2NfugTUK8+NiGQl6GcRnDxW/evfnNwDXXmNf33++X29PERbXrF+oChNB5Ik6IFx/bUMjLMA5juKhiUxV48UXgwQdNwc+pU827PWVEtImL6rhcqDtIaBcf29RKPxSRd4jIV0RkS0R2BeA7h495R7aJ9jHyyqP4+MZGvRTGUTltZdsbNJGa51tlLiEFqeuRPw3gZwAsW7DFf8ZhDBft48oK8Nu/PcxYifa3ioAn15V2ISg72mkiMMpga6MwatUctYRcVZ8BAIm8t3FgHMZw0T4uLNT75UVedtTqFsgW6bIi2sRFdRwu1I5g1KpZWqvsFJFFEVkXkfWLFy+2tVl31M1i8CELYm7OdE2s+ouLvOxIxEWyRbpK9WkTVbRj3gyqKRi1apaRBUEi8iiAa1L+da+qPri9zCqAX1TVQlU+Y1EQVKVApMjn2x6f1tmPZHHRoUPGy6dIFifl+w4xREGP3A6VC4JU9UAzJpHShPZr8CVUEaLyAanf9wBzQZ0CEb6cCl2FTbNsUqeJUpHPx8enL7xgbiI2QZ39SGanuA5VtDnZh21S4hEhhyhcnwqdJq0lYtEHgLcDeA7AJoC/APBwkc91uo1tRN1JfdM+38b0akXsSLPrxAnV5WU3bVfzWgeX7TnadBviMuvnJMckAdiPvGWaEHJV1bvuUhVprxnyqP2IK8uePc1MYFF0+2nKFv0fGK18TatklfWnCD/nmhhfsoScoZWmqNJEqcjnFxZMpWW8KKnJBtKj9iM+1r9yxUwr1+uZUMz0tH178rYfjzVExwQwwdnoOW9c33Tcosr6U+IRDFGQXaSpe9OPsfDImyTukrkea0fhHhHjkf/ETxhber127Enb/+i9aOSSfERT4xVZV9O2ElICZHjk7LUSIvGipJMn3fd/idITX3wReOKJ0cU/gN1Mkne9yzxHqY3RMVEdNtU+cmR0GmXTqRVM3SANQSEPHddl5aur5iISiWSR4h9baZTJ9SwsmPfrHJOmK3fHoTKYtA6FPHRce3mRaG5umh7mvZ7xgm+6Cfj5n0+3x1YXyaz1pB2TuvcsCPEYTvVG6hOFSaangS9+cXSDrKY88lCqYwipCKd6I82RjNlfvpzvbdsaRYxaT6gVnYSUhEJO7FI0Pm0rVpy1nri3PjkJvPvd7PNCOgvzyNumyZxvH7aZ1sXQxT7H4+ebm8Dycngl+oQUhB55m7iI6dbdZpXwRNxLHgzMZ198Edizp730yGhkEPVCV+3u9Hxk7KFH3iZtdDxKer91tmmj4dTKyjCn+9Kl5hp9JYlGBu99rxH0NqtNCWkZeuRt0nTOd5r3nbbNol526JNNRyODm24C7r7b7Mc99wA33BDWfhAyAgp5mzSd850mvIcP79wmsFPsT50yEyun2WPjwrOwYNIRk0U7Zci78BS5KG1smBz3ra0wL0iEjCKtbr/pB3utNESRXh7xtq69nurkZP7yNlrt1VlH3j4V7V3SZI8TtiIkLQL2WhkDinj8cS+71zPee5qnGvd0Dx+ub1fdeT+j7JN+3zzm5oqHfpoYCQ0GJt4/qviJkBagkHeNUaIZF7XpaRMzToZObGbX1C3KmZ83eeBRaOTRR01jrmT8f3ISePZZs70sMbclstHxiTJiAIZsiFNqZa2IyH0i8jUReVJE/lBEXm7JLtIkUUPrxcX0meuTnu7KSrU88LJZL1n55lH6ILB79HDuHHDnneb/99/fTq54dHyKNAizTdFpA8lYUTf98NMArlfV1wD4BoCaY3DSOmmzFESe7sSE8XTPnCkmxnVSH7NEP+quGJEUzbk5YGZmOLFFGxNZxo/P3r0mxbGtsMqxY81vgwRHrdCKqj4Se/lZAP+mnjke0FR/jpD6fsTDL88+azzdUXHooqmPWayuDjsobm4Ot5MMn6SV2rfdytd1x0lCEtiMkR8C8D8trq99mqq8dFXRGcXB4+mFRS8oUUx5MAA+/vHRIlkk9TFve9PTRsQB8xwV7hQRTRfC2mZf8X5/pycuYp6XlhhqIQAKCLmIPArgmpR/3auqD24vcy+AywA+kbOeRQCLADAzM1PJ2MZpqgCm7cKa6MIR7xE+NWVyxuM3N4tcUIqKZJZXXFTwNjaMnZG9Gxs7bShiZ9VWuPEcex+97ChTBzAi7qD1NPGbkUKuqgfy/i8iPwfgrQBu3c5zzFrPaQCnAdOPvJyZLdHUEL3toX904Yh7uJcuAWfPVrugFBXSOl7x/Ly52LQ501GyQ6KqOTZMJSSBUSu0IiK3A/gQgDep6j/YMckhTQ3R2x76p83as3cvcMcdJnWvilgWCcnUCTe4CI/ER0rRRc/35lqc6YikUGuGIBG5AGAKQDQO/qyq3jXqc5whqAXqxsiT6+riTDz0yElgNDJDkKq+ss7nSYNkecdVvGYXzbPayPJJjgIAP2PkhIyAlZ1kNG3H+PNGALYFPnlho4CTAKGQk9G0Hb/OGgF0NcRDSE0o5KQYbeZNZ/VQ7/eHN3B9viFJSMtQyIl/pMWuk3nxbaUoEhIAFHLiJ/ERwMmTw7z4Xg84cGDYypYQwjk7SQDEm1RNTVHECUlAj5z4D5tUEZILhZyEQZs3WwkJDIZWypI1+QEhhDiCHnkZmMdM2iakPvbEGRTyMrgoVSfjCx0HUhCGVsqQnOKLeczlYWiqOGWmyiNjDT3yMjB7oh70MMvRdo8bEiwU8rIwe6I6DE2Vg44DKQiFnLQHPczy0HEgBaCQk/ao6mEyc4OQXCjkpF3KepiMqxMyklpZKyJyXESeFJEvicgjIvKDtgyzDrMlwoSZG4SMpK5Hfp+qHgUAEflPAH4ZwMg5O1uHXl24MK5OyEjqztn53djL7wFQfSbnJmG2RLgwc4OQkdSOkYvIhwEsAPgbADfnLLcIYBEAZmZm6m62HPTqwoaZG4TkIqr5TrSIPArgmpR/3auqD8aWOwzgZaq6NGqjs7Ozur6+XtbWetTJfGDWBCHEA0TkvKrO7np/lJCX2MAMgIdU9fpRyzoR8qowvk4I8YQsIa+btfKq2Mu3AfhanfV5CbMmCCGeU7dp1q+KyNMi8iSA2wB8wIJNfsFGWe3T77u2gJCgsBZaKUNQoRWAMfK2EQEcnJeE+E5WaKW7lZ02xZdZE4QQj+lmP/LoBuXRo+aZ1Zz+0+8bT1zEvI7+ZpiFkJF0U8h5gzI8+n0TTolCKtHfFHJCRtJNIecNyu7CnjmE7KKbMXKWdYfNUkZNGXP6CUmlm0IO8AZlyGSFU9gzh5BUuhlaId2EITNCUumuR066B0NmhKRCISdhwZAZIbtgaIUQQgKHQk4IIYFDISeEkMChkBNCSOBQyAkhJHAo5IQQEjgUckIICRwKOSGEBI4VIReRD4qIisjVNtZHSCbsfkjILmpXdorIdTDzdT5b3xxCcmD3Q0JSseGR/zqADwHgJIukWThhCCGp1BJyEXkbgOdV9csFll0UkXURWb948WKdzZJxhd0PCUllZGhFRB4FcE3Kv+4FcAQmrDISVT0N4DQAzM7O0nsn5WH3Q0JSGSnkqnog7X0RuQHADwH4spgJc68F8AUReZ2q/rlVKwmJYPdDQnZR+Wanqj4F4Puj1yLyTQCzqvqXFuwihBBSEOaRE0JI4FibWEJV99taFyGEkOLQIyeEkMChkBNCSOBQyAkhJHBEtf2UbhG5CODPMv59NQBfM19oWzVoWzVoW3l8tQuwY9s/V9V9yTedCHkeIrKuqrOu7UiDtlWDtlWDtpXHV7uAZm1jaIUQQgKHQk4IIYHjo5Cfdm1ADrStGrStGrStPL7aBTRom3cxckIIIeXw0SMnhBBSAgo5IYQEjrdCLiL/UUS+JiJfEZGPuLYnQkT6IvK8iHxp+/EW1zYl8XEOVRE5LiJPbh+zR0TkB13bBAAict/2efakiPyhiLzctU0RIvKO7fN/S0S8SKkTkdtF5OsickFEfsm1PREickZEviMiT7u2JYmIXCcij4nIV7e/zw/Y3oaXQi4iNwN4G4AfVdV/CeC/ODYpya+r6o3bj4dcGxPH4zlU71PV16jqjQA+BeCXHdsT8WkA16vqawB8A8Bhx/bEeRrAzwB43LUhACAiEwA+BuDNAF4N4J0i8mq3Vr3E7wC43bURGVwG8EFVfTWAHwfwftvHzUshB/A+AL+qqpsAoKrfcWxPSHg5h6qqfjf28nvgiX2q+oiqXt5++VmYCVK8QFWfUdWvu7YjxusAXFDV/6eqlwD8AYzD5RxVfRzAX7m2Iw1V/baqfmH7778F8AyAV9jchq9C/sMA3iginxOR/yMiP+baoAR3bw/Fz4jIP3VtTESZOVRdICIfFpFvAfh38Mcjj3MIwB+7NsJjXgHgW7HXz8GyIHUdEdkP4CYAn7O5Xmv9yMsyYi7QSQDfBzMM+TEA/0tE/oW2lCs5wrb/AeA4jEd5HMB/hRGAVrA1h2oT5Nmmqg+q6r0A7hWRwwDuBrDkg13by9wLMwT+RBs2lbGNdAMR+V4AZwHckxih1saZkGfNBQoAIvI+AJ/cFu4/EZEtmIYzF13bFkdE7oeJ97aGz3OoFj1uMGL5EFoS8lF2icjPAXgrgFvbchYiShwzH3gewHWx19duv0dGICJ7YET8E6r6Sdvr9zW08r8B3AwAIvLDAPbCk45mIvIDsZdvh7kh5RxVfUpVv19V92/P1vQcgH/ly0TYIvKq2Mu3AfiaK1viiMjtMPcUflpV/8G1PZ7zeQCvEpEfEpG9AH4WwB85tsl7xHhWDwB4RlV/rZFt+FjZuX2SnAFwI4BLAH5RVT/j1KhtROR3YexSAN8E8F5V/bZLm9LwbTJsETkL4EcAbMG0ML5LVZ17cyJyAcAUgI3ttz6rqnc5NOklROTtAP47gH0A/hrAl1T1oGOb3gLgFIAJAGdU9cMu7YkQkd8HMA8zcv8LAEuq+oBTo7YRkX8N4AkAT8Gc/wBwxGbGm5dCTgghpDi+hlYIIYQUhEJOCCGBQyEnhJDAoZATQkjgUMgJISRwKOSEEBI4FHJCCAmc/w/Shfe/FxmxiAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn import datasets\n", + "import matplotlib.pyplot as plt \n", + "\n", + "# Génération des données \n", + "x, y = datasets.make_blobs(n_samples=250, n_features=2, centers=2, center_box=(- 3, 3), random_state=1)\n", + "# Partitionnement des données en apprentissage et test\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=1)\n", + "\n", + "# Affichage des données d'apprentissage\n", + "plt.plot(x_train[y_train==0,0], x_train[y_train==0,1], 'b.')\n", + "plt.plot(x_train[y_train==1,0], x_train[y_train==1,1], 'r.')\n", + "\n", + "# Affichage des données de test\n", + "plt.plot(x_test[y_test==0,0], x_test[y_test==0,1], 'b+')\n", + "plt.plot(x_test[y_test==1,0], x_test[y_test==1,1], 'r+')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "lTGP4a9WXWpU", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense (Dense) (None, 1) 3 \n", + " \n", + "=================================================================\n", + "Total params: 3\n", + "Trainable params: 3\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-03-25 18:31:29.078560: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /applications/opam-2.0.4/default/lib/stublibs/::/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/extras/CUPTI/lib64\n", + "2022-03-25 18:31:29.078622: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", + "Skipping registering GPU devices...\n", + "2022-03-25 18:31:29.079207: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "import tensorflow\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "\n", + "# Définition du modèle, auquel on va ensuite ajouter les différentes couches, dans l'ordre\n", + "# NB: c'est exactement ce que nous avons implémenté avec le perceptron multicouche dans le\n", + "# TP précédent ! \n", + "model = Sequential()\n", + "model.add(Dense(1, activation='sigmoid', input_dim=2)) # input_dim indique la dimension de la couche d'entrée, ici 2\n", + "\n", + "model.summary() # affiche un résumé du modèle" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "nZEDm5I-Lu-p", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/15\n", + "9/9 [==============================] - 0s 26ms/step - loss: 0.2186 - accuracy: 0.9444 - val_loss: 0.2752 - val_accuracy: 0.9111\n", + "Epoch 2/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1951 - accuracy: 0.9500 - val_loss: 0.2616 - val_accuracy: 0.9333\n", + "Epoch 3/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1809 - accuracy: 0.9500 - val_loss: 0.2516 - val_accuracy: 0.9333\n", + "Epoch 4/15\n", + "9/9 [==============================] - 0s 4ms/step - loss: 0.1698 - accuracy: 0.9500 - val_loss: 0.2434 - val_accuracy: 0.9333\n", + "Epoch 5/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1611 - accuracy: 0.9500 - val_loss: 0.2363 - val_accuracy: 0.9333\n", + "Epoch 6/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1543 - accuracy: 0.9556 - val_loss: 0.2300 - val_accuracy: 0.9333\n", + "Epoch 7/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1483 - accuracy: 0.9556 - val_loss: 0.2242 - val_accuracy: 0.9333\n", + "Epoch 8/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1428 - accuracy: 0.9611 - val_loss: 0.2190 - val_accuracy: 0.9333\n", + "Epoch 9/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1382 - accuracy: 0.9667 - val_loss: 0.2145 - val_accuracy: 0.9333\n", + "Epoch 10/15\n", + "9/9 [==============================] - 0s 6ms/step - loss: 0.1341 - accuracy: 0.9667 - val_loss: 0.2104 - val_accuracy: 0.9333\n", + "Epoch 11/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1303 - accuracy: 0.9722 - val_loss: 0.2066 - val_accuracy: 0.9556\n", + "Epoch 12/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1270 - accuracy: 0.9722 - val_loss: 0.2030 - val_accuracy: 0.9556\n", + "Epoch 13/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1239 - accuracy: 0.9722 - val_loss: 0.1997 - val_accuracy: 0.9556\n", + "Epoch 14/15\n", + "9/9 [==============================] - 0s 6ms/step - loss: 0.1210 - accuracy: 0.9722 - val_loss: 0.1967 - val_accuracy: 0.9333\n", + "Epoch 15/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1183 - accuracy: 0.9722 - val_loss: 0.1940 - val_accuracy: 0.9333\n" + ] + } + ], + "source": [ + "from tensorflow.keras import optimizers\n", + "\n", + "# Définition de l'optimiseur\n", + "sgd = optimizers.SGD(learning_rate=0.1) # On choisit la descente de gradient stochastique, avec un taux d'apprentissage de 0.1\n", + "\n", + "# On définit ici, pour le modèle introduit plus tôt, l'optimiseur choisi, la fonction de perte (ici\n", + "# l'entropie croisée binaire pour un problème de classification binaire) et les métriques que l'on veut observer pendant\n", + "# l'entraînement. La précision (accuracy) est un indicateur plus simple à interpréter que l'entropie croisée.\n", + "model.compile(optimizer=sgd,\n", + " loss='binary_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Entraînement du modèle avec des mini-batchs de taille 20, sur 15 epochs. \n", + "# Le paramètre validation_split signifie qu'on tire aléatoirement une partie des données\n", + "# (ici 20%) pour servir d'ensemble de validation\n", + "history = model.fit(x_train, y_train, validation_split=0.2, epochs=15, batch_size=20)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fD8fXJj0WJID" + }, + "source": [ + "La cellule suivante introduit un code permettant de visualiser la frontière de décision du modèle appris. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "8iSYRgNaL6F-", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "def print_decision_boundaries(model, x, y):\n", + " dx, dy = 0.1, 0.1\n", + " y_grid, x_grid = np.mgrid[slice(np.min(x[:,1]), np.max(x[:,1]) + dy, dy),\n", + " slice(np.min(x[:,0]), np.max(x[:,0]) + dx, dx)]\n", + "\n", + "\n", + " x_gen = np.concatenate((np.expand_dims(np.reshape(x_grid, (-1)),1),np.expand_dims(np.reshape(y_grid, (-1)),1)), axis=1)\n", + " z_gen = model.predict(x_gen).reshape(x_grid.shape)\n", + "\n", + " z_min, z_max = 0, 1\n", + "\n", + " c = plt.pcolor(x_grid, y_grid, z_gen, cmap='RdBu', vmin=z_min, vmax=z_max)\n", + " plt.colorbar(c)\n", + " plt.plot(x[y==0,0], x[y==0,1], 'r.')\n", + " plt.plot(x[y==1,0], x[y==1,1], 'b.')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "ltsUweGrMPor", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print_decision_boundaries(model, x_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mSaSWEnoNxqG" + }, + "source": [ + "## Exemple de classification plus \"complexe\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W60rDDAzWTpq" + }, + "source": [ + "Pour manipuler un peu la librairie, voici un second problème légèrement plus complexe. A vous de réutiliser les cellules précédentes pour mettre en place un réseau permettant de résoudre ce problème." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "KvhN3uQaN5ji", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x, y = datasets.make_gaussian_quantiles(n_samples=250, n_features=2, n_classes=2, random_state=1)\n", + "# Partitionnement des données en apprentissage et test\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=1)\n", + "\n", + "# Affichage des données d'apprentissage\n", + "plt.plot(x_train[y_train==0,0], x_train[y_train==0,1], 'b.')\n", + "plt.plot(x_train[y_train==1,0], x_train[y_train==1,1], 'r.')\n", + "\n", + "# Affichage des données de test\n", + "plt.plot(x_test[y_test==0,0], x_test[y_test==0,1], 'b+')\n", + "plt.plot(x_test[y_test==1,0], x_test[y_test==1,1], 'r+')\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense_1 (Dense) (None, 10) 30 \n", + " \n", + " dense_2 (Dense) (None, 10) 110 \n", + " \n", + " dense_3 (Dense) (None, 1) 11 \n", + " \n", + "=================================================================\n", + "Total params: 151\n", + "Trainable params: 151\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Epoch 1/20\n", + "9/9 [==============================] - 0s 24ms/step - loss: 0.8085 - accuracy: 0.4444 - val_loss: 0.7453 - val_accuracy: 0.2667\n", + "Epoch 2/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.7223 - accuracy: 0.3500 - val_loss: 0.6925 - val_accuracy: 0.4889\n", + "Epoch 3/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.6862 - accuracy: 0.5333 - val_loss: 0.6650 - val_accuracy: 0.6000\n", + "Epoch 4/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.6658 - accuracy: 0.6000 - val_loss: 0.6499 - val_accuracy: 0.6444\n", + "Epoch 5/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.6495 - accuracy: 0.6500 - val_loss: 0.6355 - val_accuracy: 0.6889\n", + "Epoch 6/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.6341 - accuracy: 0.6944 - val_loss: 0.6214 - val_accuracy: 0.7333\n", + "Epoch 7/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.6157 - accuracy: 0.7833 - val_loss: 0.6037 - val_accuracy: 0.8222\n", + "Epoch 8/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.5970 - accuracy: 0.8222 - val_loss: 0.5851 - val_accuracy: 0.8444\n", + "Epoch 9/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.5773 - accuracy: 0.8556 - val_loss: 0.5629 - val_accuracy: 0.8444\n", + "Epoch 10/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.5555 - accuracy: 0.8722 - val_loss: 0.5383 - val_accuracy: 0.8444\n", + "Epoch 11/20\n", + "9/9 [==============================] - 0s 4ms/step - loss: 0.5328 - accuracy: 0.8778 - val_loss: 0.5116 - val_accuracy: 0.8667\n", + "Epoch 12/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.5093 - accuracy: 0.8611 - val_loss: 0.4857 - val_accuracy: 0.8889\n", + "Epoch 13/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.4856 - accuracy: 0.8944 - val_loss: 0.4567 - val_accuracy: 0.9111\n", + "Epoch 14/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.4606 - accuracy: 0.8944 - val_loss: 0.4294 - val_accuracy: 0.9111\n", + "Epoch 15/20\n", + "9/9 [==============================] - 0s 6ms/step - loss: 0.4375 - accuracy: 0.8889 - val_loss: 0.4041 - val_accuracy: 0.9111\n", + "Epoch 16/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.4195 - accuracy: 0.8944 - val_loss: 0.3805 - val_accuracy: 0.9333\n", + "Epoch 17/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.3965 - accuracy: 0.9056 - val_loss: 0.3602 - val_accuracy: 0.9333\n", + "Epoch 18/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.3770 - accuracy: 0.9000 - val_loss: 0.3413 - val_accuracy: 0.9333\n", + "Epoch 19/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.3600 - accuracy: 0.9222 - val_loss: 0.3242 - val_accuracy: 0.9333\n", + "Epoch 20/20\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.3447 - accuracy: 0.9167 - val_loss: 0.3099 - val_accuracy: 0.9333\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = Sequential()\n", + "model.add(Dense(10, activation='relu', input_dim=2))\n", + "model.add(Dense(10, activation='relu', input_dim=10))\n", + "model.add(Dense(1, activation='sigmoid', input_dim=10))\n", + "model.summary()\n", + "\n", + "sgd = optimizers.SGD(learning_rate=0.1)\n", + "model.compile(optimizer=sgd, loss='binary_crossentropy', metrics=['accuracy'])\n", + "history = model.fit(x_train, y_train, validation_split=0.2, epochs=20, batch_size=20)\n", + "\n", + "print_decision_boundaries(model, x_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XMMppWbnG3dN" + }, + "source": [ + "# Classification d'images de chiens et de chats\n", + "\n", + "Dans la suite du TP, on s'intéresse au problème simple (en apparence) de reconnaître des chiens et des chats dans des images.\n", + "\n", + "
\n", + "
Figure 1 : Quelques images de la base de données
\n", + "\n", + "Pour cela nous allons utiliser une base de données de 4000 images, réparties en 2000 images d'apprentissage, 1000 images de validation, et 1000 images de test. Compte-tenu de la variabilité possible des représentations de chiens et chats, cette base de données est d'une taille assez réduite et le problème est complexe. Il correspond bien aux problèmes que nous pouvons rencontrer dans la réalité, lorsque les données sont souvent difficiles à obtenir.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m7K-oLcaXkcY" + }, + "source": [ + "Il faut définir une résolution commune à toutes les images, qui sera donc la dimension passée en entrée au réseau de neurones. Pour commencer et simplifier le problème, vous pouvez d'abord considérer des images de taille $64 \\times 64$ ; plus tard, lorsque vos réseaux fonctionneront bien, nous pourrons envisager d'augmenter cette résolution pour améliorer les performances. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "8th8b32kV2kh", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "IMAGE_SIZE = 128\n", + "CLASSES = ['cat', 'dog']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z3mdNJJXc6Wy" + }, + "source": [ + "## Chargement des données\n", + "La base de données est à télécharger depuis Git. Ne passez pas trop de temps à regarder les cellules suivantes (mais exécutez les !)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "n_OkpjrpFXXG", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fatal: le chemin de destination 'iam' existe déjà et n'est pas un répertoire vide.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/axelcarlier/iam.git\n", + "path = \"./iam/tp3/\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KoSVj5OGXa-4" + }, + "source": [ + "Chargement des données dans des tenseurs $x$ et $y$ de dimensions respectives $(N, 64, 64, 3)$ et $(N, 1)$, où $N$ désigne le nombre d'éléments de l'ensemble considéré (apprentissage, validation, ou test)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "VcNp4xl0QfOT", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "import glob\n", + "import PIL\n", + "from PIL import Image\n", + "import numpy as np\n", + "\n", + "def load_data(path, classes, image_size=128):\n", + "\n", + " # Liste les fichiers présents dans le dossier path\n", + " file_path = glob.glob(path)\n", + " \n", + " # Initialise les structures de données\n", + " x = np.zeros((len(file_path), image_size, image_size, 3))\n", + " y = np.zeros((len(file_path), 1))\n", + "\n", + " for i in range(len(file_path)):\n", + " # Lecture de l'image\n", + " img = Image.open(file_path[i])\n", + " # Mise à l'échelle de l'image\n", + " img = img.resize((image_size,image_size), Image.ANTIALIAS)\n", + " # Remplissage de la variable x\n", + " x[i] = np.asarray(img)\n", + "\n", + " img_path_split = file_path[i].split('/')\n", + " img_name_split = img_path_split[-1].split('.')\n", + " class_label = classes.index(img_name_split[-3])\n", + " \n", + " y[i] = class_label\n", + "\n", + " return x, y\n", + "\n", + "x_train, y_train = load_data('./iam/tp3/train/*', CLASSES, image_size=IMAGE_SIZE)\n", + "x_val, y_val = load_data('./iam/tp3/validation/*', CLASSES, image_size=IMAGE_SIZE)\n", + "x_test, y_test = load_data('./iam/tp3/test/*', CLASSES, image_size=IMAGE_SIZE)\n", + "\n", + "# Normalisation des entrées via une division par 255 des valeurs de pixel.\n", + "x_train = x_train/255\n", + "x_val = x_val/255\n", + "x_test = x_test/255" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vwngS1p9V1VN" + }, + "source": [ + "### Visualisation des images" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "YXUxcuIPOS5W", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# Randomisation des indices et affichage de 9 images alétoires de la base d'apprentissage\n", + "indices = np.arange(x_train.shape[0])\n", + "np.random.shuffle(indices)\n", + "plt.figure(figsize=(12, 12))\n", + "for i in range(0, 9):\n", + " plt.subplot(3, 3, i+1)\n", + " plt.title(CLASSES[int(y_train[i])])\n", + " plt.imshow(x_train[i])\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tV5s1T3yWJB6" + }, + "source": [ + "## Première approche : réseau convolutif de base" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "00T5cUGlif9z" + }, + "source": [ + "Les images ont toutes été redimensionnées en $64 \\times 64$. \n", + "Vous devez définir un réseau de neurones convolutif en suivant ce schéma por la base convolutive : \n", + "\n", + "
\n", + "
Figure 2: Vue de l'architecture à implémenter
\n", + "\n", + "Ce réseau alterne dans une première phase les couches de convolution et de Max Pooling (afin de diviser à chaque fois la dimension des tenseurs par 2). \n", + "\n", + "La première couche comptera 32 filtres de convolution, la seconde 64, la troisième 96 et la 4e 128. Enfin, avant la couche de sortie, vous ajouterez une couche dense comptant 512 neurones. Vous aurez donc construit un réseau à 6 couches, sorte de version simplifiée d'AlexNet.\n", + "\n", + "Pour construire ce réseau, vous pouvez utiliser les fonctions Conv2D, Maxpooling2D, et Flatten de Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d (Conv2D) (None, 126, 126, 32) 896 \n", + " \n", + " max_pooling2d (MaxPooling2D (None, 63, 63, 32) 0 \n", + " ) \n", + " \n", + " conv2d_1 (Conv2D) (None, 61, 61, 64) 18496 \n", + " \n", + " max_pooling2d_1 (MaxPooling (None, 30, 30, 64) 0 \n", + " 2D) \n", + " \n", + " conv2d_2 (Conv2D) (None, 28, 28, 96) 55392 \n", + " \n", + " max_pooling2d_2 (MaxPooling (None, 14, 14, 96) 0 \n", + " 2D) \n", + " \n", + " conv2d_3 (Conv2D) (None, 12, 12, 128) 110720 \n", + " \n", + " max_pooling2d_3 (MaxPooling (None, 6, 6, 128) 0 \n", + " 2D) \n", + " \n", + " flatten (Flatten) (None, 4608) 0 \n", + " \n", + " dense_4 (Dense) (None, 512) 2359808 \n", + " \n", + " dense_5 (Dense) (None, 1) 513 \n", + " \n", + "=================================================================\n", + "Total params: 2,545,825\n", + "Trainable params: 2,545,825\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n", + "from tensorflow.keras.layers import Dense, Flatten\n", + "\n", + "model = Sequential()\n", + "model.add(Conv2D(32, 3, activation=\"relu\", input_shape=x_train.shape[1:]))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Conv2D(64, 3, activation=\"relu\"))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Conv2D(96, 3, activation=\"relu\"))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Conv2D(128, 3, activation=\"relu\"))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Flatten())\n", + "model.add(Dense(512, activation=\"relu\"))\n", + "model.add(Dense(1, activation=\"sigmoid\"))\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yWqVtzWZIsOY" + }, + "source": [ + "### Entrainement" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Q9IQIETzLI-" + }, + "source": [ + "Pour l'entraînement, vous pouvez utiliser directement les hyperparamètres suivants." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "IJsJ7mMIjCGm", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "adam = optimizers.Adam(learning_rate=3e-4)\n", + "model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LNbGxTZt4cck" + }, + "source": [ + "... puis lancer l'entraînement. **Attention : si jamais vous voulez relancer l'entraînement, il faut réinitialiser les poids du réseau. Pour cela il faut re-exécuter les cellules précédentes à partir de la définition du réseau !** Sinon vous risquez de repartir d'un entraînement précédent (qui s'est éventuellement bien, ou mal déroulé) et mal interpréter votre nouvel entraînement." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "fjetcQRljJC8", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "200/200 [==============================] - 16s 75ms/step - loss: 0.6965 - accuracy: 0.5125 - val_loss: 0.6850 - val_accuracy: 0.5610\n", + "Epoch 2/10\n", + "200/200 [==============================] - 13s 65ms/step - loss: 0.6547 - accuracy: 0.6320 - val_loss: 0.6452 - val_accuracy: 0.6310\n", + "Epoch 3/10\n", + "200/200 [==============================] - 13s 64ms/step - loss: 0.5868 - accuracy: 0.7025 - val_loss: 0.6075 - val_accuracy: 0.6700\n", + "Epoch 4/10\n", + "200/200 [==============================] - 13s 64ms/step - loss: 0.5296 - accuracy: 0.7360 - val_loss: 0.6445 - val_accuracy: 0.6800\n", + "Epoch 5/10\n", + "200/200 [==============================] - 13s 64ms/step - loss: 0.4848 - accuracy: 0.7745 - val_loss: 0.6199 - val_accuracy: 0.7170\n", + "Epoch 6/10\n", + "200/200 [==============================] - 13s 64ms/step - loss: 0.4319 - accuracy: 0.7950 - val_loss: 0.5907 - val_accuracy: 0.7200\n", + "Epoch 7/10\n", + "200/200 [==============================] - 13s 63ms/step - loss: 0.3770 - accuracy: 0.8250 - val_loss: 0.6193 - val_accuracy: 0.7200\n", + "Epoch 8/10\n", + "200/200 [==============================] - 12s 62ms/step - loss: 0.3217 - accuracy: 0.8540 - val_loss: 0.6377 - val_accuracy: 0.7030\n", + "Epoch 9/10\n", + "200/200 [==============================] - 13s 64ms/step - loss: 0.2562 - accuracy: 0.8920 - val_loss: 0.7223 - val_accuracy: 0.7300\n", + "Epoch 10/10\n", + "200/200 [==============================] - 12s 62ms/step - loss: 0.1914 - accuracy: 0.9230 - val_loss: 0.7326 - val_accuracy: 0.7200\n" + ] + } + ], + "source": [ + "history = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=10, batch_size=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iBPk-patWSYX" + }, + "source": [ + "### Analyse des résultats du modèle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "casoAuXmzWYb" + }, + "source": [ + "Les quelques lignes suivantes permettent d'afficher l'évolution des métriques au cours de l'entraînement, sur les ensembles d'apprentissage et de validation." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "fExCbSI3V6Ur", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "def plot_training_analysis():\n", + " acc = history.history['accuracy']\n", + " val_acc = history.history['val_accuracy']\n", + " loss = history.history['loss']\n", + " val_loss = history.history['val_loss']\n", + "\n", + " epochs = range(len(acc))\n", + "\n", + " plt.plot(epochs, acc, 'b', linestyle=\"--\",label='Training acc')\n", + " plt.plot(epochs, val_acc, 'g', label='Validation acc')\n", + " plt.title('Training and validation accuracy')\n", + " plt.legend()\n", + "\n", + " plt.figure()\n", + "\n", + " plt.plot(epochs, loss, 'b', linestyle=\"--\",label='Training loss')\n", + " plt.plot(epochs, val_loss,'g', label='Validation loss')\n", + " plt.title('Training and validation loss')\n", + " plt.legend()\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "ex3AjPOPu2UN", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_training_analysis()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ochTgkyqwHIe" + }, + "source": [ + "### Correction du surapprentissage" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zXb2ZxKv4gpi" + }, + "source": [ + "Vous devriez reconnaître le problème auquel vous avez affaire : **le surapprentissage**. Ce problème est classique dès lors que l'on travaille sur des bases de données de taille réduite en apprentissage profond.\n", + " En effet, le réseau que vous avez créé compte normalement (si vous avez suivi les indications) plus de trois millions de paramètres. Le problème que vous essayez de résoudre pendant l'entraînement consiste à établir 450 000 paramètres avec seulement 2000 exemples : c'est trop peu !\n", + "\n", + "Afin de limiter ce surapprentissage, nous pouvons appliquer les techniques de régularisation vues pendant le 2nd cours. En traitement d'image, une des techniques les plus couramment utilisées est **l'augmentation de la base de données**.\n", + "\n", + "Nous allons introduire un objet *ImageDataGenerator* pour appliquer des transformations supplémentaires aux images de notre base de données. A vous de chercher dans la documentation à quoi correspondent les différents paramètres présentés ci-dessous." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "90Wlyt6Gwm6v", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "train_datagen = ImageDataGenerator(\n", + " rotation_range=40,\n", + " width_shift_range=0.2,\n", + " height_shift_range=0.2,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TkZEqml-4ccl" + }, + "source": [ + "La cellule suivante vous permet de visualiser des images passées à travers notre boucle d'augmentation de données. Observez comment les valeurs manquantes (par exemple, dans le cas d'une rotation) sont comblées." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "nqDBaNs94ccm", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "example_x, example_y = train_datagen.flow(x_train, y_train, batch_size=1).next()\n", + "for i in range(0,1):\n", + " plt.imshow(example_x[i])\n", + " plt.title(CLASSES[int(example_y[i])])\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KdqsPbKm5TkR" + }, + "source": [ + "Nous pouvons maintenant recréer notre modèle et relancer l'entraînement." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "VJLABQ67yLms", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_4 (Conv2D) (None, 126, 126, 32) 896 \n", + " \n", + " max_pooling2d_4 (MaxPooling (None, 63, 63, 32) 0 \n", + " 2D) \n", + " \n", + " conv2d_5 (Conv2D) (None, 61, 61, 64) 18496 \n", + " \n", + " max_pooling2d_5 (MaxPooling (None, 30, 30, 64) 0 \n", + " 2D) \n", + " \n", + " conv2d_6 (Conv2D) (None, 28, 28, 96) 55392 \n", + " \n", + " max_pooling2d_6 (MaxPooling (None, 14, 14, 96) 0 \n", + " 2D) \n", + " \n", + " conv2d_7 (Conv2D) (None, 12, 12, 128) 110720 \n", + " \n", + " max_pooling2d_7 (MaxPooling (None, 6, 6, 128) 0 \n", + " 2D) \n", + " \n", + " flatten_1 (Flatten) (None, 4608) 0 \n", + " \n", + " dense_6 (Dense) (None, 512) 2359808 \n", + " \n", + " dense_7 (Dense) (None, 1) 513 \n", + " \n", + "=================================================================\n", + "Total params: 2,545,825\n", + "Trainable params: 2,545,825\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Epoch 1/50\n", + "200/200 [==============================] - 16s 78ms/step - loss: 0.6924 - accuracy: 0.5285 - val_loss: 0.6877 - val_accuracy: 0.5130\n", + "Epoch 2/50\n", + "200/200 [==============================] - 14s 72ms/step - loss: 0.6874 - accuracy: 0.5385 - val_loss: 0.6829 - val_accuracy: 0.5470\n", + "Epoch 3/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.6789 - accuracy: 0.5710 - val_loss: 0.6462 - val_accuracy: 0.6600\n", + "Epoch 4/50\n", + "200/200 [==============================] - 14s 72ms/step - loss: 0.6515 - accuracy: 0.6275 - val_loss: 0.6392 - val_accuracy: 0.6290\n", + "Epoch 5/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.6236 - accuracy: 0.6670 - val_loss: 0.6177 - val_accuracy: 0.6560\n", + "Epoch 6/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.5949 - accuracy: 0.6830 - val_loss: 0.6098 - val_accuracy: 0.6720\n", + "Epoch 7/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.5918 - accuracy: 0.6880 - val_loss: 0.5917 - val_accuracy: 0.6870\n", + "Epoch 8/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.5752 - accuracy: 0.7030 - val_loss: 0.5890 - val_accuracy: 0.6820\n", + "Epoch 9/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.5696 - accuracy: 0.7115 - val_loss: 0.5727 - val_accuracy: 0.7010\n", + "Epoch 10/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.5652 - accuracy: 0.7115 - val_loss: 0.5878 - val_accuracy: 0.6940\n", + "Epoch 11/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.5535 - accuracy: 0.7175 - val_loss: 0.5721 - val_accuracy: 0.7160\n", + "Epoch 12/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.5465 - accuracy: 0.7165 - val_loss: 0.5863 - val_accuracy: 0.7080\n", + "Epoch 13/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.5432 - accuracy: 0.7300 - val_loss: 0.5491 - val_accuracy: 0.7330\n", + "Epoch 14/50\n", + "200/200 [==============================] - 14s 68ms/step - loss: 0.5356 - accuracy: 0.7255 - val_loss: 0.5415 - val_accuracy: 0.7170\n", + "Epoch 15/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.5385 - accuracy: 0.7225 - val_loss: 0.5958 - val_accuracy: 0.7020\n", + "Epoch 16/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.5150 - accuracy: 0.7455 - val_loss: 0.5447 - val_accuracy: 0.7210\n", + "Epoch 17/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.5168 - accuracy: 0.7405 - val_loss: 0.5414 - val_accuracy: 0.7290\n", + "Epoch 18/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.5131 - accuracy: 0.7465 - val_loss: 0.5588 - val_accuracy: 0.7350\n", + "Epoch 19/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.5034 - accuracy: 0.7560 - val_loss: 0.5406 - val_accuracy: 0.7500\n", + "Epoch 20/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.4934 - accuracy: 0.7605 - val_loss: 0.5458 - val_accuracy: 0.7630\n", + "Epoch 21/50\n", + "200/200 [==============================] - 13s 67ms/step - loss: 0.4879 - accuracy: 0.7605 - val_loss: 0.5965 - val_accuracy: 0.7190\n", + "Epoch 22/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.4927 - accuracy: 0.7530 - val_loss: 0.5636 - val_accuracy: 0.7440\n", + "Epoch 23/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.4876 - accuracy: 0.7760 - val_loss: 0.5400 - val_accuracy: 0.7260\n", + "Epoch 24/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.4752 - accuracy: 0.7800 - val_loss: 0.5035 - val_accuracy: 0.7510\n", + "Epoch 25/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.4677 - accuracy: 0.7880 - val_loss: 0.5696 - val_accuracy: 0.7060\n", + "Epoch 26/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.4773 - accuracy: 0.7675 - val_loss: 0.4967 - val_accuracy: 0.7710\n", + "Epoch 27/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.4535 - accuracy: 0.7915 - val_loss: 0.4772 - val_accuracy: 0.7650\n", + "Epoch 28/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.4499 - accuracy: 0.7885 - val_loss: 0.5210 - val_accuracy: 0.7610\n", + "Epoch 29/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.4423 - accuracy: 0.7965 - val_loss: 0.4957 - val_accuracy: 0.7740\n", + "Epoch 30/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.4485 - accuracy: 0.7820 - val_loss: 0.4817 - val_accuracy: 0.7820\n", + "Epoch 31/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.4370 - accuracy: 0.8020 - val_loss: 0.4877 - val_accuracy: 0.7730\n", + "Epoch 32/50\n", + "200/200 [==============================] - 14s 72ms/step - loss: 0.4311 - accuracy: 0.8090 - val_loss: 0.4957 - val_accuracy: 0.7600\n", + "Epoch 33/50\n", + "200/200 [==============================] - 15s 72ms/step - loss: 0.4318 - accuracy: 0.7990 - val_loss: 0.5394 - val_accuracy: 0.7420\n", + "Epoch 34/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.4159 - accuracy: 0.8105 - val_loss: 0.5039 - val_accuracy: 0.7820\n", + "Epoch 35/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.4236 - accuracy: 0.7985 - val_loss: 0.4754 - val_accuracy: 0.8000\n", + "Epoch 36/50\n", + "200/200 [==============================] - 14s 68ms/step - loss: 0.4060 - accuracy: 0.8255 - val_loss: 0.4881 - val_accuracy: 0.7840\n", + "Epoch 37/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.4006 - accuracy: 0.8165 - val_loss: 0.4649 - val_accuracy: 0.7770\n", + "Epoch 38/50\n", + "200/200 [==============================] - 14s 72ms/step - loss: 0.3998 - accuracy: 0.8245 - val_loss: 0.4597 - val_accuracy: 0.7870\n", + "Epoch 39/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.4053 - accuracy: 0.8120 - val_loss: 0.4789 - val_accuracy: 0.7800\n", + "Epoch 40/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.4107 - accuracy: 0.8020 - val_loss: 0.5138 - val_accuracy: 0.7660\n", + "Epoch 41/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.3770 - accuracy: 0.8270 - val_loss: 0.4722 - val_accuracy: 0.7890\n", + "Epoch 42/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.3939 - accuracy: 0.8210 - val_loss: 0.4114 - val_accuracy: 0.8150\n", + "Epoch 43/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.3666 - accuracy: 0.8410 - val_loss: 0.4861 - val_accuracy: 0.7800\n", + "Epoch 44/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.3897 - accuracy: 0.8235 - val_loss: 0.4669 - val_accuracy: 0.7810\n", + "Epoch 45/50\n", + "200/200 [==============================] - 14s 68ms/step - loss: 0.3590 - accuracy: 0.8490 - val_loss: 0.4122 - val_accuracy: 0.8120\n", + "Epoch 46/50\n", + "200/200 [==============================] - 14s 71ms/step - loss: 0.3679 - accuracy: 0.8285 - val_loss: 0.4504 - val_accuracy: 0.7910\n", + "Epoch 47/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.3470 - accuracy: 0.8430 - val_loss: 0.4709 - val_accuracy: 0.8110\n", + "Epoch 48/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.3471 - accuracy: 0.8495 - val_loss: 0.4283 - val_accuracy: 0.8070\n", + "Epoch 49/50\n", + "200/200 [==============================] - 14s 70ms/step - loss: 0.3461 - accuracy: 0.8595 - val_loss: 0.4441 - val_accuracy: 0.7980\n", + "Epoch 50/50\n", + "200/200 [==============================] - 14s 69ms/step - loss: 0.3244 - accuracy: 0.8595 - val_loss: 0.4269 - val_accuracy: 0.8070\n" + ] + } + ], + "source": [ + "model = Sequential()\n", + "model.add(Conv2D(32, 3, activation=\"relu\", input_shape=x_train.shape[1:]))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Conv2D(64, 3, activation=\"relu\"))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Conv2D(96, 3, activation=\"relu\"))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Conv2D(128, 3, activation=\"relu\"))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Flatten())\n", + "model.add(Dense(512, activation=\"relu\"))\n", + "model.add(Dense(1, activation=\"sigmoid\"))\n", + "model.summary()\n", + "\n", + "adam = optimizers.Adam(learning_rate=3e-4)\n", + "model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n", + "\n", + "history = model.fit(train_datagen.flow(x_train, y_train, batch_size=10), validation_data=(x_val, y_val), epochs=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vM2CLNX8wbfv" + }, + "source": [ + "### Analyse des résultats" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "9smZiILLyt8g", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_training_analysis()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T_JbNoF46le7" + }, + "source": [ + "On voit clairement sur les courbes que l'on a limité le sur-apprentissage. Notez aussi d'ailleurs, et c'est important, que l'apprentissage est plus lent : le modèle met plus de temps à prédire correctement l'ensemble d'apprentissage. C'est normal, car on a en quelque sorte \"complexifié le problème\" en introduisant toutes ces déformations de nos images.\n", + "Cette forme de régularisation \"par les données\" s'ajoute aux autres méthodes que nous avons vues précédemment comme la régularisation L1/L2 des poids du réseau et le Dropout. \n", + "\n", + "Vous devriez maintenant atteindre des performances autour de 80% de précision sur l'ensemble de validation, ce qui est bien mais pas complètement satisfaisant : il faudrait pour continuer à s'améliorer probablement s'entraîner plus longtemps, mais également disposer de plus de données.\n", + "\n", + "Une autre solution est d'utiliser le **Transfer Learning**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aVFqfXs9GrKe" + }, + "source": [ + "## Transfer learning\n", + "\n", + "L'une des raisons qui peut expliquer le fait que nos résultats soient décevants est que les premières couches de notre réseau convolutif, sensées détecter des caractéristiques utiles pour discriminer chiens et chats, n'ont pas appris de filtres suffisamment généraux à partir des 2000 images d'entraînement. Ainsi, même si ces filtres sont pertinents pour les 2000 images d'entraînement, il y a en fait assez peu de chances que ces filtres puissent bien fonctionner pour la généralisation sur de nouvelles données.\n", + "\n", + "C'est la raison pour laquelle nous avons envie de réutiliser un réseau pré-entrainé sur une large base de données, permettant donc de détecter des caractéristiques qui généraliseront mieux à de nouvelles données.\n", + "\n", + "Dans cette partie, nous allons réutiliser un réseau célèbre, et d'ores et déjà entraîné sur la base de données ImageNet : le réseau VGG-16.\n", + "\n", + "Commençons par récupérer les couches de convolution de ce réseau, et s'en remémorer la composition." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "zRWY8mEQuF9O", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import VGG16\n", + "\n", + "conv_base = VGG16(weights='imagenet', # On utilise les poids du réseau déjà pré-entrainé sur la base de données ImageNet\n", + " include_top=False, # On ne conserve pas la partie Dense du réseau originel\n", + " input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "xv_jCMwkuHY4", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"vgg16\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 128, 128, 3)] 0 \n", + " \n", + " block1_conv1 (Conv2D) (None, 128, 128, 64) 1792 \n", + " \n", + " block1_conv2 (Conv2D) (None, 128, 128, 64) 36928 \n", + " \n", + " block1_pool (MaxPooling2D) (None, 64, 64, 64) 0 \n", + " \n", + " block2_conv1 (Conv2D) (None, 64, 64, 128) 73856 \n", + " \n", + " block2_conv2 (Conv2D) (None, 64, 64, 128) 147584 \n", + " \n", + " block2_pool (MaxPooling2D) (None, 32, 32, 128) 0 \n", + " \n", + " block3_conv1 (Conv2D) (None, 32, 32, 256) 295168 \n", + " \n", + " block3_conv2 (Conv2D) (None, 32, 32, 256) 590080 \n", + " \n", + " block3_conv3 (Conv2D) (None, 32, 32, 256) 590080 \n", + " \n", + " block3_pool (MaxPooling2D) (None, 16, 16, 256) 0 \n", + " \n", + " block4_conv1 (Conv2D) (None, 16, 16, 512) 1180160 \n", + " \n", + " block4_conv2 (Conv2D) (None, 16, 16, 512) 2359808 \n", + " \n", + " block4_conv3 (Conv2D) (None, 16, 16, 512) 2359808 \n", + " \n", + " block4_pool (MaxPooling2D) (None, 8, 8, 512) 0 \n", + " \n", + " block5_conv1 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_conv2 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_conv3 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 \n", + " \n", + "=================================================================\n", + "Total params: 14,714,688\n", + "Trainable params: 14,714,688\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "conv_base.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bKyLfZcwOYwH" + }, + "source": [ + "Nous pouvons extraire les caractéristiques, apprises par le réseau de neurones sur ImageNet, de notre base de données d'image de chiens et de chat. L'intérêt, par rapport à la première partie, est qu'il aurait été presque impossible de déduire ces caractéristiques \"générales\" (trouvées sur une immense base de données) depuis notre base de données trop réduite de 2000 images. En revanche, ces caractéristiques générales devraient se révéler utiles pour notre classifieur.\n", + "\n", + "On peut lire sur la structure du réseau VGG résumée grâce à la fonction *summary* ci-dessus que le tenseur de sortie est de dimension $2 \\times 2 \\times 512$, autrement dit que le réseau prédit des caractéristiques de dimension $2 \\times 2 \\times 512$ à partir d'une image de taille $64 \\times 64$.\n", + "\n", + "On va redimensionner cette sortie dans un vecteur de dimension $2048 = 2 \\times 2 \\times 512$. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "op4vvD9_ugWL", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "cannot reshape array of size 16384000 into shape (2000,4096)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/laurent/Documents/Cours/ENSEEIHT/S8 - Réseau Profond/TP3.ipynb Cell 49'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m train_features \u001b[39m=\u001b[39m conv_base\u001b[39m.\u001b[39mpredict(x_train)\n\u001b[0;32m----> 2\u001b[0m train_features \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mreshape(train_features,(train_features\u001b[39m.\u001b[39;49mshape[\u001b[39m0\u001b[39;49m],\u001b[39m2\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m2\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m1024\u001b[39;49m))\n\u001b[1;32m 4\u001b[0m val_features \u001b[39m=\u001b[39m conv_base\u001b[39m.\u001b[39mpredict(x_val)\n\u001b[1;32m 5\u001b[0m val_features \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mreshape(val_features,(val_features\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m],\u001b[39m2\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m\u001b[39m*\u001b[39m\u001b[39m512\u001b[39m))\n", + "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mreshape\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:298\u001b[0m, in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[39m@array_function_dispatch\u001b[39m(_reshape_dispatcher)\n\u001b[1;32m 199\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mreshape\u001b[39m(a, newshape, order\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mC\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m 200\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 201\u001b[0m \u001b[39m Gives a new shape to an array without changing its data.\u001b[39;00m\n\u001b[1;32m 202\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 296\u001b[0m \u001b[39m [5, 6]])\u001b[39;00m\n\u001b[1;32m 297\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 298\u001b[0m \u001b[39mreturn\u001b[39;00m _wrapfunc(a, \u001b[39m'\u001b[39;49m\u001b[39mreshape\u001b[39;49m\u001b[39m'\u001b[39;49m, newshape, order\u001b[39m=\u001b[39;49morder)\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:57\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[39mreturn\u001b[39;00m _wrapit(obj, method, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[1;32m 56\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 57\u001b[0m \u001b[39mreturn\u001b[39;00m bound(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m 58\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 59\u001b[0m \u001b[39m# A TypeError occurs if the object does have such a method in its\u001b[39;00m\n\u001b[1;32m 60\u001b[0m \u001b[39m# class, but its signature is not identical to that of NumPy's. This\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[39m# Call _wrapit from within the except clause to ensure a potential\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[39m# exception has a traceback chain.\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[39mreturn\u001b[39;00m _wrapit(obj, method, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n", + "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 16384000 into shape (2000,4096)" + ] + } + ], + "source": [ + "train_features = conv_base.predict(x_train)\n", + "train_features = np.reshape(train_features,(train_features.shape[0],2*2*512))\n", + "\n", + "val_features = conv_base.predict(x_val)\n", + "val_features = np.reshape(val_features,(val_features.shape[0],2*2*512))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0A_ayR0dvvwe" + }, + "source": [ + "Nous pouvons maintenant définir un réseau de neurones simple (par exemple, de 2 couches denses, avec 256 neurones sur la couche cachée) qui va travailler directement sur les caractéristiques prédites par VGG." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lmmBYYmtvUUF", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "# A COMPLETER\n", + "model = Sequential()\n", + "model.add(Dense(256, activation=\"relu\", input_dim=train_features.shape[1]))\n", + "model.add(Dense(1, activation=\"sigmoid\"))\n", + "model.summary()\n", + "\n", + "# AJOUTER EGALEMENT LA FONCTION DE COUT\n", + "model.compile(optimizer=optimizers.Adam(lr=3e-4),\n", + " loss='binary_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# COMPLETER AVEC LES TENSEURS SUR LESQUELS EFFECTUER L'APPRENTISSAGE\n", + "history = model.fit(train_features, y_train,\n", + " epochs=50,\n", + " batch_size=16,\n", + " validation_data=(val_features, y_val))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_5xpyZCS4cco", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "plot_training_analysis()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_CFd7e-dJ-cK" + }, + "source": [ + "On observe à nouveau beaucoup de sur-apprentissage. Il faudrait trouver un moyen d'intégrer de l'augmentation de données. \n", + "\n", + "Pour cela, on peut connecter notre petit réseau de neurones à l'extrémité de la base convolutionnelle de VGG. L'idée est qu'en réutilisant notre générateur de données augmentées, nous pourrons calculer les caractéristiques de VGG sur les données augmentées, et ainsi classifier ces caractéristiques plutôt que les caractéristiques de notre base de données uniquement." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8W85VMorXPtP" + }, + "source": [ + "## Intégration de l'augmentation de données" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fCb_itsuXenK" + }, + "source": [ + "### Définition du nouveau modèle et entrainement\n", + "\n", + "On commence par créer un nouveau modèle qui va s'appuyer sur la base convolutive de VGG, à laquelle on adjoint une couche dense et notre couche de sortie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jyZZS-GSKyPZ", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "from tensorflow.keras import layers\n", + "\n", + "model = Sequential()\n", + "model.add(conv_base)\n", + "model.add(layers.Flatten())\n", + "model.add(layers.Dense(256, activation='relu'))\n", + "model.add(layers.Dense(1, activation='sigmoid'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iZr_u4s7K4Fi", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dRiiu2EbBNAv" + }, + "source": [ + "**Attention** : il est important de ne pas commander l'entraînement de la base convolutionnelle de VGG ! Nous ne voulons en aucun cas écraser les bonnes caractéristiques de VGG que nous cherchons justement à réutiliser ! Le réseau aurait en outre un grand nombre de paramètres, ce qui est justement ce que l'on veut éviter ! \n", + "\n", + "Pour cela nous pouvons utiliser l'attribut *trainable* : en le positionnant à *false*, nous pouvons geler les poids et en empêcher la mise à jour pendant l'entraînement." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9h8Fx8P0PId5", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "conv_base.trainable = False\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t7tkMrA14ccp" + }, + "source": [ + "Observez le décompte des poids : le nombre de poids entraînable est maintenant de 500 000, contre 16 millions précédemment ; on ne va entrainer ici que les poids de notre couche dense et de la couche de sortie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "go7Uld7sLRdG", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "model.compile(loss='binary_crossentropy',\n", + " optimizer=optimizers.Adam(learning_rate=3e-4),\n", + " metrics=['accuracy'])\n", + "\n", + "history = model.fit(train_datagen.flow(x_train, y_train, batch_size=10), \n", + " validation_data=(x_val, y_val),\n", + " epochs=10,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N-RNeMcAXu8h" + }, + "source": [ + "### Analyse des résultats du nouveau modèle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tJWBzO-KCCSh" + }, + "source": [ + "L'entraînement est beaucoup plus lent ! Il faut en effet générer les données augmentées, et leur faire traverser les couches de VGG à chaque itération de gradient. Ceci prend du temps !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_DHOFSauLyJa", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "plot_training_analysis()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ac4pNlvJCtkY" + }, + "source": [ + "En revanche, on observe que l'on a bien limité le sur-apprentissage, ce qui était le but recherché. Les résultats sont un peu meilleurs mais pas complètement satisfaisants." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UmykKP9_M1GW" + }, + "source": [ + "### Fine-tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r4pV9QlhFc_8" + }, + "source": [ + "Nous pouvons maintenant tester la dernière technique vue en cours : le **fine-tuning**. Pour cela, nous allons repartir du réseau que nous venons d'entraîner, mais nous allons débloquer l'entraînement des poids de l'ensemble du réseau. **ATTENTION : il est important de choisir un taux d'apprentissage très faible afin de ne pas réduire à néant les bénéfices des entraînements précédents.** L'objectif est simplement de faire évoluer les paramètres du réseau \"à la marge\", et ceci ne peut être fait qu'après la première étape de *transfer learning* précédente. Sans cela, les dernières couches ajoutées à la suite de la base convolutive, après leur initialisation aléatoire, auraient engendré de forts gradients qui auraient complètement détruit les filtres généraux de VGG.\n", + "\n", + "\n", + "\n", + "On commence par réactiver l'entraînement des paramètres de la base convolutive de VGG : " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZeZA3eVYEJDY", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "conv_base.trainable = True\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "p9SvUKcWEPVd", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "model.compile(loss='binary_crossentropy',\n", + " optimizer=optimizers.Adam(learning_rate=1e-5), # Taux d'apprentissage réduit pour ne pas tout casser, ni risquer le sur-apprentissage !\n", + " metrics=['accuracy'])\n", + "\n", + "history = model.fit(train_datagen.flow(x_train, y_train, batch_size=10), \n", + " validation_data=(x_val, y_val),\n", + " epochs=10,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MCK-hm_IN0P4", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "plot_training_analysis()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8hCLsD6tpGhm" + }, + "source": [ + "On atteint un bon résultat, proche des 90% de précision sur l'ensemble de validation, bien au-dessus des performances obtenues sans *transfer learning* ! Vous comprenez maintenant pourquoi en traitement d'image, cette technique est incontournable." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VptFMmkArNqi" + }, + "source": [ + "**S'il vous reste du temps** :\n", + "\n", + "Vous pouvez maintenant reprendre le travail depuis le début en augmentant la résolution des images (par exemple $128 \\times 128$). A l'issue du *transfer learning* et du *fine-tuning*, vous devriez dépasser les 95\\% de précision sur l'ensemble de validation. \n", + "\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "machine_shape": "hm", + "name": "TP3_Classification_de_chiens_et_chats_Sujet.ipynb", + "provenance": [], + "toc_visible": true + }, + "interpreter": { + "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" + }, + "kernelspec": { + "display_name": ".env", + "language": "python", + "name": ".env" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/TP4.ipynb b/TP4.ipynb new file mode 100644 index 0000000..fe8b478 --- /dev/null +++ b/TP4.ipynb @@ -0,0 +1,2173 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XMMppWbnG3dN" + }, + "source": [ + "\n", + "# Estimation de posture dans une image\n", + "\n", + "Pour ce TP ainsi que le suivant, nous allons traiter le problème de la détection du \"squelette\" d'un humain dans une image, tel qu'illustré dans la figure ci-dessous.\n", + "\n", + "![Texte alternatif…](https://drive.google.com/uc?id=1HpyLwzwkFdyQ6APoGZQJL7f837JCHNkh)\n", + "\n", + "Nous allons pour ce faire utiliser le [Leeds Sport Pose Dataset](https://sam.johnson.io/research/lspet.html) qui introduit 10000 images présentant des sportifs dans diverses situations, augmentées d'une annotation manuelle du squelette.\n", + "\n", + "À chaque image est associée une matrice de taille 3x14, correspondant aux coordonnées dans l'image des 14 joints du squelette de la personne décrite dans l'image. La 3e dimension désigne la visibilité du joint (1 s'il est visible, 0 s'il est occulté)\n", + "\n", + "Ces joints sont, dans l'ordre :\n", + "* Cheville droite\n", + "* Genou droit\n", + "* Hanche droite\n", + "* Hanche gauche\n", + "* Genou gauche\n", + "* Cheville gauche\n", + "* Poignet droit\n", + "* Coude droit\n", + "* Épaule droite\n", + "* Épaule gauche\n", + "* Coude gauche\n", + "* Poignet gauche\n", + "* Cou\n", + "* Sommet du crâne\n", + "\n", + "Pour un rappel des notions vues en cours sur ce sujet, vous pouvez regarder la vidéo ci-dessous :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import IFrame\n", + "IFrame(\"https://video.polymny.studio/?v=84ace9c1-f460-4375-9b33-917c3ff82c83/\", width=640, height=360)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Méthodologie \n", + "\n", + "Pour résoudre ce problème, nous allons suivre une méthodologie similaire à celle présentée dans le 2e cours, et rappelée sur la figure suivante : \n", + "\n", + "![Méthodologie de développement d'un algorithme d'apprentissage profond](https://drive.google.com/uc?id=195pkcjca4r_g86KDt2LCe0QdQsMC6iba)\n", + "\n", + "Ainsi nous allons commencer par une modélisation simple du problème, construire un modèle et l'améliorer pas à pas et évaluer sa performance.\n", + "Dans un second temps, nous modifierons la modélisation du problème, et donc l'architecture utilisée, afin d'améliorer les résultats.\n", + "\n", + "Pour chacune de ces deux étapes, je vous suggère de suivre la démarche suivante : \n", + "\n", + "- Simplifier le problème en traitant 10 imagettes (par exemple de dimension $64 \\times 64$) et construire un réseau qui surapprend parfaitement (qui diminue la perte jusqu'à quasiment 0)\n", + "- Ajouter des images (~1000) et recalibrer le réseau pour à nouveau, obtenir un sur-apprentissage\n", + "- Commencer à corriger le sur-apprentissage en ajoutant de la régularisation\n", + "- Et enfin, utiliser l'ensemble de la base de données pour diminuer le sur-apprentissage au maximum" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qjqZNAX2CVi1" + }, + "source": [ + "# Régression de la position des joints" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WFXG64qtCaCb" + }, + "source": [ + "Dans un premier temps, et comme vu en cours, nous allons nous inspirer de l'algorithme DeepPose (**[Toshev et al.] DeepPose : Human Pose Estimation via Deep Neural Networks**) et formuler le problème comme une régression de la position (x,y) des joints dans l'espace de l'image." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z3mdNJJXc6Wy" + }, + "source": [ + "Commencez par télécharger la base de données sur Github\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "3IVjmLKWRDag", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fatal: le chemin de destination 'lsp' existe déjà et n'est pas un répertoire vide.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/axelcarlier/lsp.git" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_-EFIogzdCc9" + }, + "source": [ + "Le bloc suivant contient une fonction qui permet de charger les images de la base de données dans les variables x et y. Par défaut les images sont redimensionnées en taille 128$\\times$128 et la base de données contient 1000 images. Pour commencer et vous permettre de travailler plus efficacement, **je vous suggère très fortement de diminuer la dimension des images** (par exemple 64$\\times$64) **et de ne travailler que sur un ensemble réduit d'images** (par exemple, 10). \n", + "\n", + "\n", + "N'oubliez pas également de diviser les données en images de test et/ou de validation pour obtenir des informations sur le sur-apprentissage éventuel. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "quOHEF__pf36", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((10, 64, 64, 3), (10, 3, 14))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import PIL\n", + "from PIL import Image\n", + "import os, sys\n", + "from scipy.io import loadmat\n", + "\n", + "# Cette fonction permettra plus tard de charger plus ou moins d'images (en modifiant le paramètre num_images)\n", + "# et de modifier la dimension d'entrée\n", + "def load_data(image_size=128, num_images=1000):\n", + "\n", + " path = \"./lsp/images/\"\n", + " dirs = sorted(os.listdir(path))\n", + "\n", + " x = np.zeros((min(num_images,len(dirs)),image_size,image_size,3))\n", + " y = np.zeros((min(num_images,len(dirs)), 3, 14))\n", + " \n", + " #Chargement des joints \n", + " mat_contents = loadmat('./lsp/joints.mat')\n", + " joints = mat_contents['joints']\n", + "\n", + " # Chargement des images, qui sont rangées dans lsp/images\n", + " for i in range(min(num_images,len(dirs))):\n", + " item = dirs[i]\n", + " if os.path.isfile(path+item):\n", + " img = Image.open(path+item)\n", + " # Redimensionnement et sauvegarde des joints\n", + " y[i, 0] = joints[:,0,i]*image_size/img.size[0]\n", + " y[i, 1] = joints[:,1,i]*image_size/img.size[1]\n", + " y[i, 2] = joints[:,2,i]\n", + " # Redimensionnement et sauvegarde des images \n", + " img = img.resize((image_size,image_size))\n", + " x[i] = np.asarray(img)\n", + "\n", + "\n", + " return x, y\n", + "\n", + "# Chargement de seulement 10 images, de taille 64x64\n", + "x, y = load_data(image_size=64, num_images=10) \n", + "x.shape, y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "zRc0B4oxe6h_", + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "labels = {0: 'Cheville droite',\n", + " 1: 'Genou droit',\n", + " 2: 'Hanche droite',\n", + " 3: 'Hanche gauche',\n", + " 4: 'Genou gauche',\n", + " 5: 'Cheville gauche',\n", + " 6: 'Poignet droit',\n", + " 7: 'Coude droit',\n", + " 8: 'Épaule droite',\n", + " 9: 'Épaule gauche',\n", + " 10: 'Coude gauche',\n", + " 11: 'Poignet gauche',\n", + " 12: 'Cou',\n", + " 13: 'Sommet du crâne'}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "meezS1y4G8QO" + }, + "source": [ + "La fonction suivante vous permet de visualiser les données. Vous vous rendrez compte que certaines données sont manquantes ! En effet quand des joints sont occultés dans les images, des valeurs de position aberrantes (négatives) sont indiquées. Dans ce cas, nous n'afficherons pas les articulations." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "JvcqdQIZdCYk", + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Fonction d'affichage d'une image et de son label associé\n", + "def print_data(x,y,i):\n", + " \n", + " if y.shape[1] < 3:\n", + " y_new = np.ones((y.shape[0], 3, y.shape[2]))\n", + " y_new[:,0:2,:] = y\n", + " y = y_new\n", + " \n", + " plt.figure(figsize=(5, 5))\n", + " plt.imshow(x[i]/255)\n", + " for j in range(0,14):\n", + " if y[i, 2, j] == 1:\n", + " plt.scatter(y[i,0,j],y[i,1,j],label=labels.get(j))\n", + "\n", + " # Jambe droite \n", + " if (y[i, 2, 0] + y[i, 2, 1] == 2):\n", + " plt.plot(y[i,0,0:2],y[i,1,0:2],'b')\n", + " # Cuisse droite \n", + " if (y[i, 2, 1] + y[i, 2, 2] == 2):\n", + " plt.plot(y[i,0,1:3],y[i,1,1:3],'b')\n", + " # Bassin \n", + " if (y[i, 2, 2] + y[i, 2, 3] == 2):\n", + " plt.plot(y[i,0,2:4],y[i,1,2:4],'b')\n", + " # Cuisse gauche \n", + " if (y[i, 2, 3] + y[i, 2, 4] == 2):\n", + " plt.plot(y[i,0,3:5],y[i,1,3:5],'b')\n", + " # Jambe gauche \n", + " if (y[i, 2, 4] + y[i, 2, 5] == 2):\n", + " plt.plot(y[i,0,4:6],y[i,1,4:6],'b')\n", + " # Avant-bras droit \n", + " if (y[i, 2, 6] + y[i, 2, 7] == 2):\n", + " plt.plot(y[i,0,6:8],y[i,1,6:8],'b')\n", + " # Bras droit \n", + " if (y[i, 2, 7] + y[i, 2, 8] == 2):\n", + " plt.plot(y[i,0,7:9],y[i,1,7:9],'b')\n", + " # Bras gauche \n", + " if (y[i, 2, 9] + y[i, 2, 10] == 2):\n", + " plt.plot(y[i,0,9:11],y[i,1,9:11],'b')\n", + " # Avant-bras gauche \n", + " if (y[i, 2, 10] + y[i, 2, 11] == 2):\n", + " plt.plot(y[i,0,10:12],y[i,1,10:12],'b') \n", + " # Buste droit\n", + " x1=[y[i,0,2],y[i,0,12]]\n", + " y1=[y[i,1,2],y[i,1,12]]\n", + " if (y[i, 2, 2] + y[i, 2, 12] == 2):\n", + " plt.plot(x1, y1,'b')\n", + " # Buste gauche\n", + " x1=[y[i,0,3],y[i,0,12]]\n", + " y1=[y[i,1,3],y[i,1,12]]\n", + " if (y[i, 2, 3] + y[i, 2, 12] == 2):\n", + " plt.plot(x1, y1,'b')\n", + " # Omoplate droite\n", + " x1=[y[i,0,8],y[i,0,12]]\n", + " y1=[y[i,1,8],y[i,1,12]]\n", + " if (y[i, 2, 8] + y[i, 2, 12] == 2):\n", + " plt.plot(x1, y1,'b')\n", + " # Omoplate gauche\n", + " x1=[y[i,0,9],y[i,0,12]]\n", + " y1=[y[i,1,9],y[i,1,12]]\n", + " if (y[i, 2, 9] + y[i, 2, 12] == 2):\n", + " plt.plot(x1, y1,'b')\n", + " # Tete \n", + " if (y[i, 2, 12] + y[i, 2, 13] == 2):\n", + " plt.plot(y[i,0,12:14],y[i,1,12:14],'b')\n", + "\n", + " plt.axis([0, x.shape[1], x.shape[2], 0])\n", + " plt.show()\n", + " #plt.legend()\n", + "\n", + "# Affichage aléatoire d'une image\n", + "print_data(x,y,np.random.randint(x.shape[0]-1))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Si nous formulons ce problème comme une régression, nous allons utiliser pour évaluer nos réseaux de neurones l'erreur quadratique moyenne (fonction *MSE*). Cette fonction sera parfaite comme fonction de perte, mais elle ne permet pas d'appréhender les résultats de manière satisfaisante.\n", + "\n", + "Une métrique commune en estimation de posture est le **PCK0.5**, pour *Percentage of Correct Keypoints*. *0.5* correspond à un seuil en-deça duquel on considère qu'un joint est correctement estimé. Cette question du seuil est particulièrement sensible car il faut utiliser une valeur qui soit valable pour n'importe quelle image. La personne considérée peut apparaître plus ou moins largement sur l'image, de face ou de profil, ce qui fait qu'une erreur de prédiction sur un joint peut avoir une importance très grande ou très faible selon les cas.\n", + "\n", + "Pour résoudre cette ambiguïté, on considère dans la métrique du **PCK0.5** que la référence est la taille de la tête, définie par la distance entre le joint du cou et le joint de la tête sur la vérité terrain. Un joint prédit par le réseau sera considéré correct s'il est situé à une distance inférieure à la moitié (*0.5*) de la taille de la tête par rapport au joint réel. ([Andriluka et al.] 2D Human Pose Estimation: New Benchmark and State of the Art Analysis)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "import numpy.matlib \n", + "\n", + "# Calcul du \"Percentage of Correct Keypoint\" avec seuil alpha :\n", + "# On compte corrects les joints pour lesquels la distance entre valeurs réelle et prédite \n", + "# est inférieure à alpha fois la dimension de la tête (c'est un peu arbitraire...)\n", + "# On ne comptera pas les joints invisibles.\n", + "# y_true est de dimension Nx3x14 et y_pred Nx2x14 (le réseau ne prédit pas la visibilité)\n", + "def compute_PCK_alpha(y_true, y_pred, alpha=0.5):\n", + " # Calcul des seuils ; la taille de la tête est la distance entre joints 12 et 13\n", + " head_sizes = np.sqrt(np.square(y_true[:,0,13]-y_true[:,0,12])+np.square(y_true[:,1,13]-y_true[:,1,12]))\n", + " thresholds = alpha*head_sizes\n", + " thresholds = np.matlib.repmat(np.expand_dims(thresholds, 1), 1, 14)\n", + "\n", + " # Calcul des distances inter-joints\n", + " joints_distances = np.sqrt(np.square(y_true[:,0,:]-y_pred[:,0,:]) + np.square(y_true[:,1,:]-y_pred[:,1,:]))\n", + "\n", + " # Visibilité des joints de la vérité terrain\n", + " visibility = y_true[:,2,:]\n", + " \n", + " total_joints = np.count_nonzero(visibility==1)\n", + " correctly_predicted_joints = np.count_nonzero(np.logical_and(joints_distances Bx14x3\n", + " y_true = K.permute_dimensions(y_true, (0, 2, 1))\n", + " # Changement de dimension : Bx14x3 -> (B*14)x3\n", + " y_true = K.reshape(y_true, shape=(-1, 3))\n", + " \n", + " # Changement de dimension : Bx2x14 -> Bx14x2\n", + " y_pred = K.permute_dimensions(y_pred, (0, 2, 1))\n", + " # Changement de dimension : Bx14x2 -> (B*14)x2\n", + " y_pred = K.reshape(y_pred, shape=(-1, 2))\n", + " \n", + " # Détermination de l'indices des joints visibles\n", + " visible = K.greater_equal(y_true[:, 2], 1) \n", + " indices = K.arange(0, K.shape(y_true)[0])\n", + " indices_visible = indices[visible]\n", + " \n", + " # Sélection des vérité-terrains et prédictions des joints visibles\n", + " y_true_visible = K.gather(y_true[:,0:2], indices_visible)\n", + " y_pred_visible = K.gather(y_pred, indices_visible)\n", + " \n", + " # Calcul de la MSE\n", + " return K.mean(K.square(y_pred_visible[:,0] - y_true_visible[:,0]) + K.square(y_pred_visible[:,1] - y_true_visible[:,1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Si vous avez bien compris le code de *custom_mse*, vous devriez pouvoir sans trop de problèmes écrire le code pour la fonction *custom_mae* ci-dessous :" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "# y_true : vérité terrain de dimension B x 3 x 14\n", + "# y_pred : une prédiction de dimension B x 2 x 14 (on ne prédit pas la visibilité)\n", + "# B est le nombre d'images considérées (par exemple, pourra être la taille d'un mini-batch)\n", + "def custom_mae(y_true, y_pred):\n", + " # Changement de dimension : Bx3x14 -> Bx14x3\n", + " y_true = K.permute_dimensions(y_true, (0, 2, 1))\n", + " # Changement de dimension : Bx14x3 -> (B*14)x3\n", + " y_true = K.reshape(y_true, shape=(-1, 3))\n", + " \n", + " # Changement de dimension : Bx2x14 -> Bx14x2\n", + " y_pred = K.permute_dimensions(y_pred, (0, 2, 1))\n", + " # Changement de dimension : Bx14x2 -> (B*14)x2\n", + " y_pred = K.reshape(y_pred, shape=(-1, 2))\n", + " \n", + " # Détermination de l'indices des joints visibles\n", + " visible = K.greater_equal(y_true[:, 2], 1) \n", + " indices = K.arange(0, K.shape(y_true)[0])\n", + " indices_visible = indices[visible]\n", + " \n", + " # Sélection des vérité-terrains et prédictions des joints visibles\n", + " y_true_visible = K.gather(y_true[:,0:2], indices_visible)\n", + " y_pred_visible = K.gather(y_pred, indices_visible)\n", + " \n", + " # Calcul de la MAE\n", + " return K.mean(K.abs(y_pred_visible[:,0] - y_true_visible[:,0]) + K.abs(y_pred_visible[:,1] - y_true_visible[:,1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comme d'habitude, on peut monitorer l'entraînement grâce à la fonction suivante (adaptée à nos fonctions *custom_mse* et *custom_mae* définies juste avant) : " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "def plot_training_analysis(history):\n", + " mae = history.history['custom_mae']\n", + " val_mae = history.history['val_custom_mae']\n", + " loss = history.history['loss']\n", + " val_loss = history.history['val_loss']\n", + "\n", + " epochs = range(len(loss))\n", + "\n", + " plt.plot(epochs, mae, 'b', linestyle=\"--\",label='Training MAE')\n", + " plt.plot(epochs, val_mae, 'g', label='Validation MAE')\n", + " plt.title('Training and validation MAE')\n", + " plt.legend()\n", + "\n", + " plt.figure()\n", + "\n", + " plt.plot(epochs, loss, 'b', linestyle=\"--\",label='Training loss')\n", + " plt.plot(epochs, val_loss,'g', label='Validation loss')\n", + " plt.title('Training and validation loss')\n", + " plt.legend()\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A vous de jouer :\n", + "\n", + "Pour tenter de résoudre le problème, vous pouvez suivre les étapes suivantes : \n", + "\n", + "- Simplifier le problème en traitant 10 imagettes (par exemple de dimension $64 \\times 64$) et construire un réseau qui surapprend parfaitement (qui diminue la perte jusqu'à quasiment 0)\n", + "- Ajouter des images (~1000) et éventuellement recalibrer votre réseau pour à nouveau, obtenir un sur-apprentissage\n", + "- Commencer à corriger le sur-apprentissage en ajoutant de la régularisation (notamment sur les couches denses)\n", + "- Et enfin, utiliser l'ensemble de la base de données pour diminuer le sur-apprentissage au maximum\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "import tensorflow\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import InputLayer, Dense, Flatten, Conv2D, MaxPooling2D, Reshape\n", + "from tensorflow.keras import optimizers" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d (Conv2D) (None, 62, 62, 32) 896 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-04-06 17:55:59.634360: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2022-04-06 17:56:00.243575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1537 MB memory: -> device: 0, name: Quadro K620, pci bus id: 0000:03:00.0, compute capability: 5.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " max_pooling2d (MaxPooling2D (None, 31, 31, 32) 0 \n", + " ) \n", + " \n", + " conv2d_1 (Conv2D) (None, 29, 29, 64) 18496 \n", + " \n", + " max_pooling2d_1 (MaxPooling (None, 14, 14, 64) 0 \n", + " 2D) \n", + " \n", + " conv2d_2 (Conv2D) (None, 12, 12, 92) 53084 \n", + " \n", + " max_pooling2d_2 (MaxPooling (None, 6, 6, 92) 0 \n", + " 2D) \n", + " \n", + " conv2d_3 (Conv2D) (None, 4, 4, 128) 106112 \n", + " \n", + " max_pooling2d_3 (MaxPooling (None, 2, 2, 128) 0 \n", + " 2D) \n", + " \n", + " flatten (Flatten) (None, 512) 0 \n", + " \n", + " dense (Dense) (None, 512) 262656 \n", + " \n", + " dense_1 (Dense) (None, 42) 21546 \n", + " \n", + " reshape (Reshape) (None, 3, 14) 0 \n", + " \n", + "=================================================================\n", + "Total params: 462,790\n", + "Trainable params: 462,790\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Epoch 1/500\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-04-06 17:56:01.836865: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8100\n", + "2022-04-06 17:56:02.100433: W tensorflow/stream_executor/gpu/asm_compiler.cc:111] *** WARNING *** You are using ptxas 10.1.243, which is older than 11.1. ptxas before 11.1 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.\n", + "\n", + "You may not need to update to CUDA 11.1; cherry-picking the ptxas binary is often sufficient.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8/8 [==============================] - 3s 51ms/step - loss: 1645.1646 - custom_mae: 52.2486 - accuracy: 0.0833 - val_loss: 1693.5813 - val_custom_mae: 49.8971 - val_accuracy: 0.0000e+00\n", + "Epoch 2/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 1444.6974 - custom_mae: 48.6929 - accuracy: 0.0000e+00 - val_loss: 1508.6548 - val_custom_mae: 46.6334 - val_accuracy: 0.0000e+00\n", + "Epoch 3/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 1282.0558 - custom_mae: 45.4224 - accuracy: 0.0000e+00 - val_loss: 1346.8967 - val_custom_mae: 43.7476 - val_accuracy: 0.0000e+00\n", + "Epoch 4/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 1133.2850 - custom_mae: 42.3140 - accuracy: 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[==============================] - 0s 11ms/step - loss: 0.0021 - custom_mae: 0.0444 - accuracy: 0.0417 - val_loss: 499.7231 - val_custom_mae: 27.0289 - val_accuracy: 0.0000e+00\n", + "Epoch 451/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0020 - custom_mae: 0.0434 - accuracy: 0.0417 - val_loss: 499.7239 - val_custom_mae: 27.0290 - val_accuracy: 0.0000e+00\n", + "Epoch 452/500\n", + "8/8 [==============================] - 0s 12ms/step - loss: 0.0020 - custom_mae: 0.0412 - accuracy: 0.0417 - val_loss: 499.7679 - val_custom_mae: 27.0294 - val_accuracy: 0.0000e+00\n", + "Epoch 453/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0020 - custom_mae: 0.0432 - accuracy: 0.0417 - val_loss: 499.7787 - val_custom_mae: 27.0294 - val_accuracy: 0.0000e+00\n", + "Epoch 454/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0017 - custom_mae: 0.0397 - accuracy: 0.0417 - val_loss: 499.6925 - val_custom_mae: 27.0285 - val_accuracy: 0.0000e+00\n", + "Epoch 455/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0017 - custom_mae: 0.0375 - accuracy: 0.0417 - val_loss: 499.7932 - val_custom_mae: 27.0308 - val_accuracy: 0.0000e+00\n", + "Epoch 456/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0015 - custom_mae: 0.0360 - accuracy: 0.0417 - val_loss: 499.7675 - val_custom_mae: 27.0290 - val_accuracy: 0.0000e+00\n", + "Epoch 457/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0016 - custom_mae: 0.0381 - accuracy: 0.0417 - val_loss: 499.8068 - val_custom_mae: 27.0303 - val_accuracy: 0.0000e+00\n", + "Epoch 458/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0014 - custom_mae: 0.0371 - accuracy: 0.0417 - val_loss: 499.8171 - val_custom_mae: 27.0304 - val_accuracy: 0.0000e+00\n", + "Epoch 459/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0014 - custom_mae: 0.0352 - accuracy: 0.0417 - val_loss: 499.7731 - val_custom_mae: 27.0291 - val_accuracy: 0.0000e+00\n", + "Epoch 460/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0014 - custom_mae: 0.0338 - accuracy: 0.0417 - val_loss: 499.8286 - val_custom_mae: 27.0307 - val_accuracy: 0.0000e+00\n", + "Epoch 461/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0012 - custom_mae: 0.0328 - accuracy: 0.0417 - val_loss: 499.7890 - val_custom_mae: 27.0293 - val_accuracy: 0.0000e+00\n", + "Epoch 462/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0012 - custom_mae: 0.0322 - accuracy: 0.0417 - val_loss: 499.8723 - val_custom_mae: 27.0321 - val_accuracy: 0.0000e+00\n", + "Epoch 463/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0011 - custom_mae: 0.0323 - accuracy: 0.0417 - val_loss: 499.8212 - val_custom_mae: 27.0314 - val_accuracy: 0.0000e+00\n", + "Epoch 464/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0010 - custom_mae: 0.0301 - accuracy: 0.0417 - val_loss: 499.8231 - val_custom_mae: 27.0314 - val_accuracy: 0.0000e+00\n", + "Epoch 465/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 0.0010 - custom_mae: 0.0299 - accuracy: 0.0417 - val_loss: 499.8588 - val_custom_mae: 27.0311 - val_accuracy: 0.0000e+00\n", + "Epoch 466/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 9.8071e-04 - custom_mae: 0.0294 - accuracy: 0.0417 - val_loss: 499.8590 - val_custom_mae: 27.0307 - val_accuracy: 0.0000e+00\n", + "Epoch 467/500\n", + "8/8 [==============================] - 0s 10ms/step - loss: 8.9396e-04 - custom_mae: 0.0285 - accuracy: 0.0417 - val_loss: 499.8817 - val_custom_mae: 27.0321 - val_accuracy: 0.0000e+00\n", + "Epoch 468/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 8.7410e-04 - custom_mae: 0.0278 - accuracy: 0.0417 - val_loss: 499.8451 - val_custom_mae: 27.0314 - val_accuracy: 0.0000e+00\n", + "Epoch 469/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 8.2264e-04 - custom_mae: 0.0261 - accuracy: 0.0417 - val_loss: 499.8533 - val_custom_mae: 27.0314 - val_accuracy: 0.0000e+00\n", + "Epoch 470/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 7.8254e-04 - custom_mae: 0.0256 - accuracy: 0.0417 - val_loss: 499.9111 - val_custom_mae: 27.0325 - val_accuracy: 0.0000e+00\n", + "Epoch 471/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 7.7250e-04 - custom_mae: 0.0271 - accuracy: 0.0417 - val_loss: 499.9024 - val_custom_mae: 27.0323 - val_accuracy: 0.0000e+00\n", + "Epoch 472/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 6.8937e-04 - custom_mae: 0.0260 - accuracy: 0.0417 - val_loss: 499.8756 - val_custom_mae: 27.0321 - val_accuracy: 0.0000e+00\n", + "Epoch 473/500\n", + "8/8 [==============================] - 0s 12ms/step - loss: 7.2580e-04 - custom_mae: 0.0246 - accuracy: 0.0417 - val_loss: 499.8849 - val_custom_mae: 27.0321 - val_accuracy: 0.0000e+00\n", + "Epoch 474/500\n", + "8/8 [==============================] - 0s 12ms/step - loss: 6.5127e-04 - custom_mae: 0.0237 - accuracy: 0.0417 - val_loss: 499.9116 - val_custom_mae: 27.0323 - val_accuracy: 0.0000e+00\n", + "Epoch 475/500\n", + "8/8 [==============================] - 0s 12ms/step - loss: 6.0367e-04 - custom_mae: 0.0234 - accuracy: 0.0417 - val_loss: 499.9120 - val_custom_mae: 27.0326 - val_accuracy: 0.0000e+00\n", + "Epoch 476/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 5.6024e-04 - custom_mae: 0.0224 - accuracy: 0.0417 - val_loss: 499.8936 - val_custom_mae: 27.0323 - val_accuracy: 0.0000e+00\n", + "Epoch 477/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 5.3261e-04 - custom_mae: 0.0217 - accuracy: 0.0417 - val_loss: 499.9124 - val_custom_mae: 27.0324 - val_accuracy: 0.0000e+00\n", + "Epoch 478/500\n", + "8/8 [==============================] - 0s 12ms/step - loss: 5.1553e-04 - custom_mae: 0.0208 - accuracy: 0.0417 - val_loss: 499.8885 - val_custom_mae: 27.0318 - val_accuracy: 0.0000e+00\n", + "Epoch 479/500\n", + "8/8 [==============================] - 0s 12ms/step - loss: 4.8770e-04 - custom_mae: 0.0196 - accuracy: 0.0417 - val_loss: 499.9443 - val_custom_mae: 27.0334 - val_accuracy: 0.0000e+00\n", + "Epoch 480/500\n", + "8/8 [==============================] - 0s 12ms/step - loss: 4.5954e-04 - custom_mae: 0.0202 - accuracy: 0.0417 - val_loss: 499.9435 - val_custom_mae: 27.0328 - val_accuracy: 0.0000e+00\n", + "Epoch 481/500\n", + "8/8 [==============================] - 0s 10ms/step - loss: 4.4328e-04 - custom_mae: 0.0201 - accuracy: 0.0417 - val_loss: 499.9374 - val_custom_mae: 27.0330 - val_accuracy: 0.0000e+00\n", + "Epoch 482/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 4.2739e-04 - custom_mae: 0.0189 - accuracy: 0.0417 - val_loss: 499.8960 - val_custom_mae: 27.0326 - val_accuracy: 0.0000e+00\n", + "Epoch 483/500\n", + "8/8 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27.0339 - val_accuracy: 0.0000e+00\n", + "Epoch 488/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 3.0632e-04 - custom_mae: 0.0169 - accuracy: 0.0417 - val_loss: 499.9485 - val_custom_mae: 27.0333 - val_accuracy: 0.0000e+00\n", + "Epoch 489/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 2.8342e-04 - custom_mae: 0.0154 - accuracy: 0.0417 - val_loss: 499.9747 - val_custom_mae: 27.0340 - val_accuracy: 0.0000e+00\n", + "Epoch 490/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 2.6139e-04 - custom_mae: 0.0145 - accuracy: 0.0417 - val_loss: 499.9545 - val_custom_mae: 27.0334 - val_accuracy: 0.0000e+00\n", + "Epoch 491/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 2.6569e-04 - custom_mae: 0.0145 - accuracy: 0.0417 - val_loss: 499.9816 - val_custom_mae: 27.0340 - val_accuracy: 0.0000e+00\n", + "Epoch 492/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 2.5165e-04 - custom_mae: 0.0148 - accuracy: 0.0417 - val_loss: 499.9815 - val_custom_mae: 27.0341 - val_accuracy: 0.0000e+00\n", + "Epoch 493/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 2.3426e-04 - custom_mae: 0.0149 - accuracy: 0.0417 - val_loss: 499.9771 - val_custom_mae: 27.0339 - val_accuracy: 0.0000e+00\n", + "Epoch 494/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 2.4409e-04 - custom_mae: 0.0143 - accuracy: 0.0417 - val_loss: 499.9797 - val_custom_mae: 27.0339 - val_accuracy: 0.0000e+00\n", + "Epoch 495/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 2.0566e-04 - custom_mae: 0.0129 - accuracy: 0.0417 - val_loss: 499.9780 - val_custom_mae: 27.0340 - val_accuracy: 0.0000e+00\n", + "Epoch 496/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 2.3338e-04 - custom_mae: 0.0148 - accuracy: 0.0417 - val_loss: 499.9927 - val_custom_mae: 27.0345 - val_accuracy: 0.0000e+00\n", + "Epoch 497/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 2.0089e-04 - custom_mae: 0.0131 - accuracy: 0.0417 - val_loss: 499.9764 - val_custom_mae: 27.0341 - val_accuracy: 0.0000e+00\n", + "Epoch 498/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 1.7758e-04 - custom_mae: 0.0119 - accuracy: 0.0417 - val_loss: 500.0060 - val_custom_mae: 27.0346 - val_accuracy: 0.0000e+00\n", + "Epoch 499/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 1.6596e-04 - custom_mae: 0.0118 - accuracy: 0.0417 - val_loss: 499.9808 - val_custom_mae: 27.0343 - val_accuracy: 0.0000e+00\n", + "Epoch 500/500\n", + "8/8 [==============================] - 0s 11ms/step - loss: 1.5378e-04 - custom_mae: 0.0112 - accuracy: 0.0417 - val_loss: 500.0090 - val_custom_mae: 27.0350 - val_accuracy: 0.0000e+00\n" + ] + } + ], + "source": [ + "x, y = load_data(image_size=64, num_images=10)\n", + "\n", + "model = Sequential([\n", + " InputLayer(input_shape=x.shape[1:]),\n", + " \n", + " Conv2D(32, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + " \n", + " Conv2D(64, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + "\n", + " Conv2D(92, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + "\n", + " Conv2D(128, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + "\n", + " Flatten(),\n", + "\n", + " Dense(512, activation=\"relu\"),\n", + " Dense(y.shape[1] * y.shape[2], activation=\"linear\"),\n", + " Reshape(y.shape[1:])\n", + "])\n", + "\n", + "model.summary()\n", + "\n", + "adam = optimizers.Adam(learning_rate=1e-5)\n", + "model.compile(optimizer=adam, loss=custom_mse, metrics=[custom_mae, \"accuracy\"])\n", + "history = model.fit(x, y, epochs=300, validation_split=0.2, batch_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.08333333333333333\n" + ] + } + ], + "source": [ + "plot_training_analysis(history)\n", + "\n", + "y_pred = model.predict(x)\n", + "pck = compute_PCK_alpha(y, y_pred)\n", + "print(pck)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_12 (Conv2D) (None, 62, 62, 32) 896 \n", + " \n", + " max_pooling2d_12 (MaxPoolin (None, 31, 31, 32) 0 \n", + " g2D) \n", + " \n", + " conv2d_13 (Conv2D) (None, 29, 29, 64) 18496 \n", + " \n", + " max_pooling2d_13 (MaxPoolin (None, 14, 14, 64) 0 \n", + " g2D) \n", + " \n", + " conv2d_14 (Conv2D) (None, 12, 12, 92) 53084 \n", + " \n", + " max_pooling2d_14 (MaxPoolin (None, 6, 6, 92) 0 \n", + " g2D) \n", + " \n", + " conv2d_15 (Conv2D) (None, 4, 4, 128) 106112 \n", + " \n", + " max_pooling2d_15 (MaxPoolin (None, 2, 2, 128) 0 \n", + " g2D) \n", + " \n", + " flatten_3 (Flatten) (None, 512) 0 \n", + " \n", + " dense_6 (Dense) (None, 512) 262656 \n", + " \n", + " dense_7 (Dense) (None, 42) 21546 \n", + " \n", + " reshape_3 (Reshape) (None, 3, 14) 0 \n", + " \n", + "=================================================================\n", + "Total params: 462,790\n", + "Trainable params: 462,790\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Epoch 1/20\n", + "900/900 [==============================] - 7s 7ms/step - loss: 436.1250 - custom_mae: 22.9435 - accuracy: 0.1063 - val_loss: 301.4426 - val_custom_mae: 19.5207 - val_accuracy: 0.1067\n", + "Epoch 2/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 306.5551 - custom_mae: 19.7242 - accuracy: 0.0911 - val_loss: 282.6898 - val_custom_mae: 18.9662 - val_accuracy: 0.0867\n", + "Epoch 3/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 293.9691 - custom_mae: 19.2866 - accuracy: 0.0796 - val_loss: 282.8599 - val_custom_mae: 18.9778 - val_accuracy: 0.0667\n", + "Epoch 4/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 286.9001 - custom_mae: 19.0547 - accuracy: 0.0752 - val_loss: 277.5684 - val_custom_mae: 18.7558 - val_accuracy: 0.0600\n", + "Epoch 5/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 279.4478 - custom_mae: 18.8183 - accuracy: 0.0733 - val_loss: 276.7022 - val_custom_mae: 18.8043 - val_accuracy: 0.0300\n", + "Epoch 6/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 275.4402 - custom_mae: 18.6737 - accuracy: 0.0785 - val_loss: 276.4867 - val_custom_mae: 18.7703 - val_accuracy: 0.0567\n", + "Epoch 7/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 270.3714 - custom_mae: 18.5070 - accuracy: 0.0737 - val_loss: 273.4456 - val_custom_mae: 18.5880 - val_accuracy: 0.0633\n", + "Epoch 8/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 265.8038 - custom_mae: 18.3447 - accuracy: 0.0700 - val_loss: 274.5800 - val_custom_mae: 18.6840 - val_accuracy: 0.0400\n", + "Epoch 9/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 261.2702 - custom_mae: 18.1500 - accuracy: 0.0693 - val_loss: 271.1825 - val_custom_mae: 18.5663 - val_accuracy: 0.1067\n", + "Epoch 10/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 257.5489 - custom_mae: 18.0389 - accuracy: 0.0737 - val_loss: 272.6388 - val_custom_mae: 18.5681 - val_accuracy: 0.0367\n", + "Epoch 11/20\n", + "900/900 [==============================] - 6s 6ms/step - loss: 253.8484 - custom_mae: 17.8498 - accuracy: 0.0659 - val_loss: 275.5805 - val_custom_mae: 18.5730 - val_accuracy: 0.0600\n", + "Epoch 12/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 248.3715 - custom_mae: 17.6936 - accuracy: 0.0730 - val_loss: 270.3711 - val_custom_mae: 18.5194 - val_accuracy: 0.0600\n", + "Epoch 13/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 242.3419 - custom_mae: 17.4627 - accuracy: 0.0737 - val_loss: 269.5866 - val_custom_mae: 18.4398 - val_accuracy: 0.0567\n", + "Epoch 14/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 239.1807 - custom_mae: 17.3445 - accuracy: 0.0696 - val_loss: 273.4536 - val_custom_mae: 18.5913 - val_accuracy: 0.0500\n", + "Epoch 15/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 234.5071 - custom_mae: 17.1719 - accuracy: 0.0748 - val_loss: 281.4737 - val_custom_mae: 18.8920 - val_accuracy: 0.0400\n", + "Epoch 16/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 231.7038 - custom_mae: 17.0464 - accuracy: 0.0670 - val_loss: 272.9087 - val_custom_mae: 18.5188 - val_accuracy: 0.0367\n", + "Epoch 17/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 226.5649 - custom_mae: 16.8362 - accuracy: 0.0774 - val_loss: 268.8194 - val_custom_mae: 18.3130 - val_accuracy: 0.0367\n", + "Epoch 18/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 221.8425 - custom_mae: 16.6449 - accuracy: 0.0793 - val_loss: 269.8996 - val_custom_mae: 18.4402 - val_accuracy: 0.0333\n", + "Epoch 19/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 219.2012 - custom_mae: 16.5254 - accuracy: 0.0715 - val_loss: 279.0634 - val_custom_mae: 18.6853 - val_accuracy: 0.0600\n", + "Epoch 20/20\n", + "900/900 [==============================] - 6s 7ms/step - loss: 215.3356 - custom_mae: 16.4273 - accuracy: 0.0759 - val_loss: 267.8714 - val_custom_mae: 18.2929 - val_accuracy: 0.0400\n" + ] + } + ], + "source": [ + "x, y = load_data(image_size=64, num_images=1000)\n", + "\n", + "model = Sequential([\n", + " InputLayer(input_shape=x.shape[1:]),\n", + " \n", + " Conv2D(32, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + " \n", + " Conv2D(64, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + "\n", + " Conv2D(92, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + "\n", + " Conv2D(128, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + "\n", + " Flatten(),\n", + "\n", + " Dense(512, activation=\"relu\"),\n", + " Dense(y.shape[1] * y.shape[2], activation=\"linear\"),\n", + " Reshape(y.shape[1:])\n", + "])\n", + "\n", + "model.summary()\n", + "\n", + "adam = optimizers.Adam(learning_rate=1e-5)\n", + "model.compile(optimizer=adam, loss=custom_mse, metrics=[custom_mae, \"accuracy\"])\n", + "history = model.fit(x, y, epochs=20, validation_split=0.1, batch_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.11499959306584194\n" + ] + } + ], + "source": [ + "plot_training_analysis(history)\n", + "\n", + "y_pred = model.predict(x)\n", + "pck = compute_PCK_alpha(y, y_pred)\n", + "print(pck)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "from tensorflow.keras.regularizers import L1" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_8\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_32 (Conv2D) (None, 62, 62, 32) 896 \n", + " \n", + " max_pooling2d_32 (MaxPoolin (None, 31, 31, 32) 0 \n", + " g2D) \n", + " \n", + " conv2d_33 (Conv2D) (None, 29, 29, 64) 18496 \n", + " \n", + " max_pooling2d_33 (MaxPoolin (None, 14, 14, 64) 0 \n", + " g2D) \n", + " \n", + " conv2d_34 (Conv2D) (None, 12, 12, 92) 53084 \n", + " \n", + " max_pooling2d_34 (MaxPoolin (None, 6, 6, 92) 0 \n", + " g2D) \n", + " \n", + " conv2d_35 (Conv2D) (None, 4, 4, 128) 106112 \n", + " \n", + " max_pooling2d_35 (MaxPoolin (None, 2, 2, 128) 0 \n", + " g2D) \n", + " \n", + " flatten_8 (Flatten) (None, 512) 0 \n", + " \n", + " dense_16 (Dense) (None, 512) 262656 \n", + " \n", + " dense_17 (Dense) (None, 42) 21546 \n", + " \n", + " reshape_8 (Reshape) (None, 3, 14) 0 \n", + " \n", + "=================================================================\n", + "Total params: 462,790\n", + "Trainable params: 462,790\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Epoch 1/50\n", + "900/900 [==============================] - 7s 7ms/step - loss: 1600.6188 - custom_mae: 24.7274 - accuracy: 0.0856 - val_loss: 1313.9059 - val_custom_mae: 19.2149 - val_accuracy: 0.0567\n", + "Epoch 2/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 1291.3328 - custom_mae: 19.5816 - accuracy: 0.0663 - val_loss: 1230.8826 - val_custom_mae: 18.9220 - val_accuracy: 0.0633\n", + "Epoch 3/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 1207.2291 - custom_mae: 19.2404 - accuracy: 0.0696 - val_loss: 1155.3102 - val_custom_mae: 18.8178 - val_accuracy: 0.0267\n", + "Epoch 4/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 1132.5310 - custom_mae: 19.0138 - accuracy: 0.0659 - val_loss: 1093.0542 - val_custom_mae: 18.9194 - val_accuracy: 0.0433\n", + "Epoch 5/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 1062.8149 - custom_mae: 18.7545 - accuracy: 0.0681 - val_loss: 1035.3274 - val_custom_mae: 18.8979 - val_accuracy: 0.0733\n", + "Epoch 6/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 1001.9334 - custom_mae: 18.5588 - accuracy: 0.0737 - val_loss: 978.5314 - val_custom_mae: 18.7171 - val_accuracy: 0.0367\n", + "Epoch 7/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 948.2893 - custom_mae: 18.3561 - accuracy: 0.0678 - val_loss: 935.9355 - val_custom_mae: 18.7541 - val_accuracy: 0.0533\n", + "Epoch 8/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 902.5227 - custom_mae: 18.1901 - accuracy: 0.0730 - val_loss: 912.6291 - val_custom_mae: 19.1317 - val_accuracy: 0.0700\n", + "Epoch 9/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 866.1888 - custom_mae: 17.9962 - accuracy: 0.0719 - val_loss: 868.8121 - val_custom_mae: 18.6186 - val_accuracy: 0.0567\n", + "Epoch 10/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 835.9440 - custom_mae: 17.8430 - accuracy: 0.0696 - val_loss: 843.2394 - val_custom_mae: 18.5577 - val_accuracy: 0.0700\n", + "Epoch 11/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 809.4442 - custom_mae: 17.6539 - accuracy: 0.0696 - val_loss: 847.6819 - val_custom_mae: 19.3826 - val_accuracy: 0.0400\n", + "Epoch 12/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 785.1916 - custom_mae: 17.4793 - accuracy: 0.0715 - val_loss: 803.3231 - val_custom_mae: 18.3979 - val_accuracy: 0.0400\n", + "Epoch 13/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 764.9724 - custom_mae: 17.2881 - accuracy: 0.0741 - val_loss: 790.0184 - val_custom_mae: 18.4058 - val_accuracy: 0.0800\n", + "Epoch 14/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 747.8378 - custom_mae: 17.1618 - accuracy: 0.0785 - val_loss: 778.4919 - val_custom_mae: 18.3664 - val_accuracy: 0.0400\n", + "Epoch 15/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 731.6422 - custom_mae: 16.9957 - accuracy: 0.0767 - val_loss: 774.0274 - val_custom_mae: 18.5825 - val_accuracy: 0.0267\n", + "Epoch 16/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 717.2520 - custom_mae: 16.8505 - accuracy: 0.0704 - val_loss: 753.8166 - val_custom_mae: 18.3133 - val_accuracy: 0.0567\n", + "Epoch 17/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 704.8784 - custom_mae: 16.7288 - accuracy: 0.0726 - val_loss: 747.3119 - val_custom_mae: 18.3142 - val_accuracy: 0.0333\n", + "Epoch 18/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 692.4477 - custom_mae: 16.5168 - accuracy: 0.0741 - val_loss: 736.3395 - val_custom_mae: 18.2599 - val_accuracy: 0.0400\n", + "Epoch 19/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 681.8674 - custom_mae: 16.3894 - accuracy: 0.0719 - val_loss: 732.4158 - val_custom_mae: 18.3323 - val_accuracy: 0.0567\n", + "Epoch 20/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 671.8062 - custom_mae: 16.2239 - accuracy: 0.0685 - val_loss: 723.9401 - val_custom_mae: 18.2230 - val_accuracy: 0.0700\n", + "Epoch 21/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 663.5496 - custom_mae: 16.1147 - accuracy: 0.0707 - val_loss: 717.6505 - val_custom_mae: 18.2588 - val_accuracy: 0.0567\n", + "Epoch 22/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 655.1087 - custom_mae: 16.0022 - accuracy: 0.0778 - val_loss: 711.7806 - val_custom_mae: 18.1043 - val_accuracy: 0.0467\n", + "Epoch 23/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 648.0977 - custom_mae: 15.8962 - accuracy: 0.0770 - val_loss: 724.2146 - val_custom_mae: 18.7435 - val_accuracy: 0.0867\n", + "Epoch 24/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 638.8377 - custom_mae: 15.6779 - accuracy: 0.0741 - val_loss: 702.2473 - val_custom_mae: 18.1418 - val_accuracy: 0.0833\n", + "Epoch 25/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 631.8398 - custom_mae: 15.5892 - accuracy: 0.0744 - val_loss: 699.0881 - val_custom_mae: 18.1864 - val_accuracy: 0.0800\n", + "Epoch 26/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 625.5166 - custom_mae: 15.5084 - accuracy: 0.0815 - val_loss: 700.0754 - val_custom_mae: 18.2896 - val_accuracy: 0.0867\n", + "Epoch 27/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 618.1070 - custom_mae: 15.3338 - accuracy: 0.0774 - val_loss: 696.2742 - val_custom_mae: 18.4092 - val_accuracy: 0.0633\n", + "Epoch 28/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 611.0324 - custom_mae: 15.2123 - accuracy: 0.0819 - val_loss: 693.2403 - val_custom_mae: 18.3400 - val_accuracy: 0.0433\n", + "Epoch 29/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 605.3654 - custom_mae: 15.1036 - accuracy: 0.0785 - val_loss: 689.4489 - val_custom_mae: 18.3240 - val_accuracy: 0.0867\n", + "Epoch 30/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 598.5970 - custom_mae: 14.9257 - accuracy: 0.0830 - val_loss: 697.1451 - val_custom_mae: 18.6957 - val_accuracy: 0.0700\n", + "Epoch 31/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 593.0287 - custom_mae: 14.8324 - accuracy: 0.0837 - val_loss: 682.5284 - val_custom_mae: 18.2608 - val_accuracy: 0.1067\n", + "Epoch 32/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 586.8223 - custom_mae: 14.6828 - accuracy: 0.0919 - val_loss: 676.1535 - val_custom_mae: 18.1355 - val_accuracy: 0.0600\n", + "Epoch 33/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 580.5615 - custom_mae: 14.5288 - accuracy: 0.0893 - val_loss: 679.8427 - val_custom_mae: 18.3359 - val_accuracy: 0.0700\n", + "Epoch 34/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 575.5278 - custom_mae: 14.4210 - accuracy: 0.0848 - val_loss: 673.6619 - val_custom_mae: 18.2497 - val_accuracy: 0.0467\n", + "Epoch 35/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 569.3248 - custom_mae: 14.2640 - accuracy: 0.0837 - val_loss: 674.8303 - val_custom_mae: 18.3349 - val_accuracy: 0.0567\n", + "Epoch 36/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 563.3799 - custom_mae: 14.1157 - accuracy: 0.0863 - val_loss: 678.8046 - val_custom_mae: 18.4942 - val_accuracy: 0.0667\n", + "Epoch 37/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 558.2557 - custom_mae: 13.9926 - accuracy: 0.0881 - val_loss: 669.0584 - val_custom_mae: 18.3795 - val_accuracy: 0.0700\n", + "Epoch 38/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 553.7178 - custom_mae: 13.9113 - accuracy: 0.0867 - val_loss: 664.1337 - val_custom_mae: 18.2757 - val_accuracy: 0.0667\n", + "Epoch 39/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 547.0479 - custom_mae: 13.6922 - accuracy: 0.0893 - val_loss: 665.8436 - val_custom_mae: 18.4226 - val_accuracy: 0.0700\n", + "Epoch 40/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 542.4849 - custom_mae: 13.6235 - accuracy: 0.0881 - val_loss: 667.3710 - val_custom_mae: 18.5683 - val_accuracy: 0.0700\n", + "Epoch 41/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 537.6252 - custom_mae: 13.4894 - accuracy: 0.0885 - val_loss: 662.0100 - val_custom_mae: 18.4422 - val_accuracy: 0.0633\n", + "Epoch 42/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 532.7130 - custom_mae: 13.3705 - accuracy: 0.0941 - val_loss: 658.4034 - val_custom_mae: 18.3899 - val_accuracy: 0.0467\n", + "Epoch 43/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 526.7731 - custom_mae: 13.1849 - accuracy: 0.0948 - val_loss: 663.8610 - val_custom_mae: 18.7139 - val_accuracy: 0.0933\n", + "Epoch 44/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 521.6479 - custom_mae: 13.0322 - accuracy: 0.0937 - val_loss: 659.8156 - val_custom_mae: 18.6054 - val_accuracy: 0.0500\n", + "Epoch 45/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 517.1684 - custom_mae: 12.9476 - accuracy: 0.0937 - val_loss: 655.1570 - val_custom_mae: 18.5126 - val_accuracy: 0.0633\n", + "Epoch 46/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 511.0065 - custom_mae: 12.7268 - accuracy: 0.0915 - val_loss: 654.9119 - val_custom_mae: 18.5737 - val_accuracy: 0.0500\n", + "Epoch 47/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 506.4033 - custom_mae: 12.6392 - accuracy: 0.0911 - val_loss: 653.8961 - val_custom_mae: 18.5993 - val_accuracy: 0.0600\n", + "Epoch 48/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 502.6320 - custom_mae: 12.5451 - accuracy: 0.0996 - val_loss: 658.9085 - val_custom_mae: 18.7675 - val_accuracy: 0.0433\n", + "Epoch 49/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 497.2288 - custom_mae: 12.3731 - accuracy: 0.0970 - val_loss: 652.7715 - val_custom_mae: 18.6982 - val_accuracy: 0.0667\n", + "Epoch 50/50\n", + "900/900 [==============================] - 6s 7ms/step - loss: 492.1422 - custom_mae: 12.1980 - accuracy: 0.0948 - val_loss: 650.8259 - val_custom_mae: 18.7055 - val_accuracy: 0.0400\n" + ] + } + ], + "source": [ + "x, y = load_data(image_size=64, num_images=1000)\n", + "\n", + "model = Sequential([\n", + " InputLayer(input_shape=x.shape[1:]),\n", + " \n", + " Conv2D(32, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + " \n", + " Conv2D(64, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + "\n", + " Conv2D(92, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + "\n", + " Conv2D(128, 3, activation=\"relu\"),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + "\n", + " Flatten(),\n", + "\n", + " Dense(512, activation=\"relu\", kernel_regularizer=L1(0.1)),\n", + " Dense(y.shape[1] * y.shape[2], activation=\"linear\", kernel_regularizer=L1(0.1)),\n", + " Reshape(y.shape[1:])\n", + "])\n", + "\n", + "model.summary()\n", + "\n", + "adam = optimizers.Adam(learning_rate=1e-5)\n", + "model.compile(optimizer=adam, loss=custom_mse, metrics=[custom_mae, \"accuracy\"])\n", + "history = model.fit(x, y, epochs=50, validation_split=0.1, batch_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.11475543257101001\n" + ] + } + ], + "source": [ + "plot_training_analysis(history)\n", + "\n", + "y_pred = model.predict(x)\n", + "pck = compute_PCK_alpha(y, y_pred)\n", + "print(pck)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "machine_shape": "hm", + "name": "IAM2020 - TP4 - Estimation de Posture.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": ".env", + "language": "python", + "name": ".env" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/TP5.ipynb b/TP5.ipynb new file mode 100644 index 0000000..d14df08 --- /dev/null +++ b/TP5.ipynb @@ -0,0 +1,1692 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XMMppWbnG3dN" + }, + "source": [ + "\n", + "# Estimation de posture dans une image\n", + "\n", + "Pour ce TP ainsi que le suivant, nous allons traiter le problème de la détection du \"squelette\" d'un humain dans une image, tel qu'illustré dans la figure ci-dessous.\n", + "\n", + "![Texte alternatif…](https://drive.google.com/uc?id=1HpyLwzwkFdyQ6APoGZQJL7f837JCHNkh)\n", + "\n", + "Nous allons pour ce faire utiliser le [Leeds Sport Pose Dataset](https://sam.johnson.io/research/lspet.html) qui introduit 10000 images présentant des sportifs dans diverses situations, augmentées d'une annotation manuelle du squelette.\n", + "\n", + "À chaque image est associée une matrice de taille 3x14, correspondant aux coordonnées dans l'image des 14 joints du squelette de la personne décrite dans l'image. La 3e dimension désigne la visibilité du joint (1 s'il est visible, 0 s'il est occulté)\n", + "\n", + "Ces joints sont, dans l'ordre :\n", + "* Cheville droite\n", + "* Genou droit\n", + "* Hanche droite\n", + "* Hanche gauche\n", + "* Genou gauche\n", + "* Cheville gauche\n", + "* Poignet droit\n", + "* Coude droit\n", + "* Épaule droite\n", + "* Épaule gauche\n", + "* Coude gauche\n", + "* Poignet gauche\n", + "* Cou\n", + "* Sommet du crâne\n", + "\n", + "Pour un rappel des notions vues en cours sur ce sujet, vous pouvez regarder la vidéo ci-dessous :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "COfRON-Mp1Iu" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import IFrame\n", + "IFrame(\"https://video.polymny.studio/?v=84ace9c1-f460-4375-9b33-917c3ff82c83/\", width=640, height=360)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z3mdNJJXc6Wy" + }, + "source": [ + "Commencez par télécharger la base de données sur Github\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "3IVjmLKWRDag" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fatal: le chemin de destination 'lsp' existe déjà et n'est pas un répertoire vide.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/axelcarlier/lsp.git" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "quOHEF__pf36" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((10, 64, 64, 3), (10, 3, 14))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import PIL\n", + "from PIL import Image\n", + "import os, sys\n", + "from scipy.io import loadmat\n", + "\n", + "# Cette fonction permettra plus tard de charger plus ou moins d'images (en modifiant le paramètre num_images)\n", + "# et de modifier la dimension d'entrée\n", + "def load_data(image_size=128, num_images=1000):\n", + "\n", + " path = \"./lsp/images/\"\n", + " dirs = sorted(os.listdir(path))\n", + "\n", + " x = np.zeros((min(num_images,len(dirs)),image_size,image_size,3))\n", + " y = np.zeros((min(num_images,len(dirs)), 3, 14))\n", + " \n", + " #Chargement des joints \n", + " mat_contents = loadmat('./lsp/joints.mat')\n", + " joints = mat_contents['joints']\n", + "\n", + " # Chargement des images, qui sont rangées dans lsp/images\n", + " for i in range(min(num_images,len(dirs))):\n", + " item = dirs[i]\n", + " if os.path.isfile(path+item):\n", + " img = Image.open(path+item)\n", + " # Redimensionnement et sauvegarde des joints\n", + " y[i, 0] = joints[:,0,i]*image_size/img.size[0]\n", + " y[i, 1] = joints[:,1,i]*image_size/img.size[1]\n", + " y[i, 2] = joints[:,2,i]\n", + " # Redimensionnement et sauvegarde des images \n", + " img = img.resize((image_size,image_size))\n", + " x[i] = np.asarray(img)\n", + "\n", + "\n", + " return x, y\n", + "\n", + "# Chargement de seulement 10 images, de taille 64x64\n", + "x, y = load_data(image_size=64, num_images=10) \n", + "x.shape, y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "zRc0B4oxe6h_" + }, + "outputs": [], + "source": [ + "labels= {0: 'Cheville droite',\n", + " 1: 'Genou droit',\n", + " 2: 'Hanche droite',\n", + " 3: 'Hanche gauche',\n", + " 4: 'Genou gauche',\n", + " 5: 'Cheville gauche',\n", + " 6: 'Poignet droit',\n", + " 7: 'Coude droit',\n", + " 8: 'Épaule droite',\n", + " 9: 'Épaule gauche',\n", + " 10: 'Coude gauche',\n", + " 11: 'Poignet gauche',\n", + " 12: 'Cou',\n", + " 13: 'Sommet du crâne'}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "meezS1y4G8QO" + }, + "source": [ + "La fonction suivante vous permet de visualiser les données. Vous vous rendrez compte que certaines données sont manquantes ! En effet quand des joints sont occultés dans les images, des valeurs de position aberrantes (négatives) sont indiquées. Dans ce cas, nous n'afficherons pas les articulations." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "JvcqdQIZdCYk" + }, + "outputs": [ + { + "data": { + "image/png": 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gaQCLlVI7lVJXIzN5rVRKvQngnVlbEAQLVZVdePeFn4PjprDqya9iYGi6/0ZCzvjeiWmtLzvAR2dPsi+CcMhSU92Gd1/wOdx7/5fx6FM345/f9RyaawJIeAMYSr+IUXdroV0sWaQooiDkibranXjfmT9AKlWJWx86HZvaogi7Naj13oay9JxCu1ey5LkDuItgXQ1q4/R5vDw9bKzbVEmLDW7uZQXa2LN3wMthPmYij8e0BWUEncIIb+SdpXk3b1sjBK6BGc0UmHDkWYvA8Yp0VJ/wmO+ORd8LGF2yWSAv36Olw7Pn8q7hAb4CMZMBcwyHaUseOx5p5olrqaAaf+XPxI4NU5218m2X0vUtQd1cAwwwnS1VNnYCNABUN8whdtsWWhTxtw/9ktjXnn05ms9/Ed/6zQm49eFj8PW/ewKOE0Bw+Ej87MmfAACSc2k85zlXfoTYCUvWtNL09+IxWzH917UUKuCFIyNMA3NY9/Kg5TJ12H5f2fozYg+OdFJ7wx5w5k6rM5aNhdyJCUIeqY40YG7TAM46ZjviyQBe3tYAAKiK1BfYs9JFJjFByCMDsS4AwEUnbkZTdBgPPDcfaU9hMNZdYM9KF5nEBCGPPLnhbiRTcbiOxrtWbEZ7XwX+sr4BT228139jwUpeNbGU56JjoAb1URb34pmaWGuUPnC/2UNjR4Kax1qZQoGhT3HNixVw4w1qAUv0GdPRuH5li4viCeDcL5fFkTlm91QzXs3vz4/Fj7TRtJY1deD6niXhmVfO0+x4GIfQksvBC/K5XDNk58V1TT8Gt28kdlU/jdeqPPUSYvekzDFcj8VBcV3V4fqf+WXiAZrwXLnwdGLXRGmBly1dz+LR1x2csuD/YdkcjZkN3bj/2emYO/dVhMOZn+Mzv7mDbHPkEbQY48wTzjD8GE3S5iraoTaXLhVoYxkARiQdv4RyiQEMshCvytnLiN3WTjWyaNRsNl0xrdkceAyknpgg5Jk325/Gm+1PAwBU6GTEEvegu+dKNDbcVmDPShN5nBSEAlJe9gwqKx9FV/c/IpU2744Ef2QSE4QC09T4DXheJbq6PlxoV0qSvD5OxtNBbB1qwpwm+iamL9FrrNsUZfl0oA/bGjz3y7+tvPkI71+M0Ijx4j1KfffqD9dabNqLESZm7Nm/4aj/NoZnfm5AGevwDqy2Aic8bm7sfaQtcVGVS4+n66x6nNjTYrT43nDITL5OMe0xwX4OrN4hXJgNLGJJeh/QvPAIYi846Z3EXvuIKeCXl72Omqr70NN7FWqrf4LuNho7dcsNHyX2x7/+v8YYLcfTSrJpVjTSyNfVli4f4PmV7B6HbaKUeQ+UYoeo+YgTiL3khFOJHUj2GWMsnDfH4tuBkTsxQSgCGutvBgB09nyisI6UICLsC4ckT23pw93r2tE9kkR9eRBnLg/gmPmthXbrgASDu1Bb8zP09P096qI/Qjj8hv9GAgC5ExMOQdarWfjRs7vQNZxEsrsC3SNJ3P/Uy3hx065CuzYm9XXfheMMo7P7XwvtSkkhk5hwyPGUswyJtMbg83Ox5yenYejVViTTaaxau6HQro1JwO1Dfe33MTT8ToyMGv0whAOQ18fJRDqArQONiNXTzixloaSxbtTtInZVmD4KDLNNlC3yzgcedGpN3vZZx7TH7cYhjfVwjPMY6bT5cqB81pHErj/l3L3/P/h8pgx6xdG7MLKxCd0PHotkRxXwjvWY3/nU3vV6WleQMRKsaCYPDk5YtPAwuw1IscDcU99zBbFfetJsrBsb2VfQsDz8XbjuVejovAGtze+FUoAeoUVD7/j2fxljXH/rr+gClxZI8OtqZsPYhhcusLwc4AHjSYcGAzfPnEvs0Y43jTGi0fGFmsidmHDIURvK/Ljc8gSa3v8sqo7fioHn5qP31ydiYND/LXYhcZxR1EVvRiy+HMOjKwvtTkkgk5hwyHF+awyhbKki5WrUrXwV085/CcNb6/Gha4/Bli1lPiMUlurKuxEMbEJ37w3Qurgn3WJAJjHhkGN5QxJXRDehzo0B0KhzY7j+ii58539ewciIi2v/cRn+/OT4alblE6XSqK/9GpLJhRgcem+h3Sl68qqJpbWDwVgZ2kdp67ZmxyxDEkrSBkr1UZoUOtTOu3mP/5lfKA2sDVwSVPMZXnwxsZeFViOTepwGEADWZPISv3ZlHW66+3rc+OkjcOUTD+IDKx/ZK/WoKG2rFq+hzWqmLznR8GNzLy0uWNVKt5m5iAa/ts6aY4zRtpF2CR+Mx6Dc3yAYvBZdvZ9EsOluOGrffl557lljjC0vPEfseafQRHQe/DoRPF4k0yIA854u3jA9Pv2dNJB39vSoMUbfOPvTyp2YcFjRUN2DL1/1Bbxj6Z/x04cvxJd/8g8YjYf8N8wzSgHVVf8Jz5uOoaGrC+1OUSOTmHDYEQ4m8Yl3fxfXXHQfnn55GT51yyfR1l18j5fh8NMIh1dhYOif4XmSHH4gZBITDkuUAt575mP44rXfR1dfLa67+V/w4mtmD8RCU131FWhdjYGhjxfalaIlr5qYBpBQCruHaePXhkpTE3PS9Lm4tYo+j29r565bkpV5YjFLWM0ppos/4/PahHwMW0FDo1LgeBOxJ4It3Z2/6WJxP8anOegovM6kEVrkX2gyyQbhDU3cHE7UsK4k9ugCqpE1VDaYfjx1C46b0YabP/w6/vOOf8PnvnExrjn927hg2b1QCmjzGsn6r/51jTHGLx5cRez6mfOI/Xd/Tx8FlWdmsw/FqG6UjO+/zhpEwndicOhDCAW/B9fdhXDAfPzd8cKTxF7wNqqJpdh5sd29aONcsQYuiifAmwnxUPQaiw31EbuMNR+prjbvMAOB8b09ljsx4bBnen07vvnhz2LF3L/gtsc/if9Z/WkkU0H/DfNERfmXASgMj3y60K4UJTKJCQKA8nAMn77gs/jAiT/G6tcuwGd+/W30D9f6b5gHXHcHyiL/i1j8g0illhTanaJDJjFByOIojctP/jFuPP+z2NY9D1+9++vY0raw0G4BACrKvwGlhjA08vlCu1J05LkUj4J2AtgxTJ+Dj7W4ERvsIXZlaIDYboBqHPYoMT/t6XCLLcuHFufP+I+6Rd8zmq2wxsBJqr30tpxijFFzJtVewk9+HwDw9umdmL7w6/jPn/wT/vu+L+Oa836A05c9jlVrnjLGSA1SPXfby/S6feyBamJ3d9L4RwBwAvTRNcwKgKY8D44zhIryb2No+P/D8OgKBIM0VuypR39P7JMvo1ViA5X07esEUikNuTdtKUTK19m18TViN8+gd7eu5T5KtSwal19yJyYIFua27MJ//t2NWDxjPb7/0Mfwsz9eBc8rbApQefltcJw2DA1/fkKT0KGKTGKCcACqywdx4we+hPOWP4SHn78QT6/9GhKJav8NpwhHjaCy4mtIpU5GInFewfwoNmQSE4QxcB0PV678Ca694Lvo7lmKPz39ffQPzvXfcIooi9wB130TwyP/Dm3pk3o4IkdBEHLgjGV/wqknfQKeF8ITz3wPu9tOK4gfSqVRUf4lpNNL8PfuNXisbhruijbgpNGY/8aHKPkV9hWglIv2VJQs7huOGKtWOFQgdWO0tHBVhAqVfaOWZFQzEnVs96xBleMtiugvlueyjt82fsUYbUXwJrJfP4z9+AS/2hfyzuTMtgzCl5nfjepXCR6lDKCv8Thi15xN47Ciu6l4HnziJixfehleWv9NPLfuP3Hmkv/Boxd/Aa2VaewZdvCtdWW4ewPd75OP0SKIw8P0BRUA1FZXETvt0nDgENPizi9/DM8lX8TPeq7DZTW/R7MbxxX9Axjo68IT2QDUvg5aSLGhpp7ug0e/wuwXZhxjfm4tndnj/YPE7ttKhf2mKtpnPBBpMsYobz3aWDYWcicmCOMgHO7E8UuvwXGz7sVj6/8ZH3/opxiKV6K10sOXTh7GxfMS/oMcJFcGI/jXxm+iLdWCD+3MtG+LKIUrVPEE6OYT30lMKTVTKfWYUuo1pdSrSqnrssvrlFKrlFJvZv8tjshAQZhiXCeB373/Q/jvc2/Abzech+ZvbsSjW05DWQC4cTnvhzr5NCgHFc4wAI0XRo9HOnuX2VCgkJlCk8udWArA9VrrIwGcDOCjSqkjAdwIYLXWeiGA1VlbEA4LppWlcd3J38c3zvkcEukg3nvXz/HHzWdgeuXUxz68lpiGD+/IxLRdHr0DbraWXtdhF/eYwVcT01rvAbAn+/+DSqnXAbQCuBjAGdnVbgfwJwA3+I3nKI2kR4MMd46YyblH1FPtoGKIBgk2VNJgxr5R81ba1EkO/iQfnn/rDkyx/mzS7NyH0mYyu8NS0btr6FtHHaWFCipn/3bv/3el2jAtOIpPnvI9XLjoYVxy1y9w3s/vxefP/CyiFfft1SiHhofIGOGwqf/G47TrjRugfpWrfQnhaa8S/9z+QwzrCgAKZ1RmOp8nHQcvL5qHC5oyv6WquhYyRsrjHcFtyf1j39No9jkvgAgAnRtfIvacaTRZfXodLW5atfDtxhiPPvdXYl907BHGOvszLk1MKTUHwHEAngXQlJ3gAKANgKnQCcIhys+6FiOWFdwX1m/B01evxN8s/D3+/dGvYlfbl+F5k69PaR3Ajq7/RXtsLhbXPgoXKSwrW4d+B3hi0RxsajJvBg4Hcn47qZSqBHAvgE9orQf2v8vRWmul+LuLvdtdC+BaAKisL756TYIwEf48lLmWL6t+Cc3lHgZTIzhq9j/h2e5t6Oz+KOKJeZjV+nEAQ2MPlCNaA7t7bsJw7Ay8bfHnsantIkSr1uM7rR6ACOoO0wkMyPFOTCkVRGYCu0Nr/evs4nalVEv28xYAZlIYAK31bVrr5Vrr5WXV9bZVBKEk+fPQDJz/YD2Ov7sR5z9Yj9/vCKO58RbMmv7PGI0dgU1b70U8sXRS9tU18DH0DX8QDdX/jblND6Nz4Gg01bwwKWOXOr53Yipzy/UjAK9rrb+130cPALgKwE3Zf+/3HQsZHcJjesXOmEUTw1Zil4epbjA9OErsjY5ZSE2xIm7GraIIXJNOLhqZmYbOizPSv625tLgwx/T3xNP08o+kacHC4WAFsc/58H8YY2x++mFiP3v/T1BT+xTqGq7G6+u/hT0d96O16V8Qrcr8PEIwHzPjQ7QAaNCjbziD7hXo6P8sptfej2Nmfwdh92/g6TCOmb0e86dliiksPv4Ysk15DY2jHPXob8Gx3L94euzYQ352nYRZ4LGbxYWdtIA2vZ7eTDWxXc1mTNiR7ixj2Vjkcif2dgBXADhLKbUu+9/5yExeK5VSbwJ4Z9YWBAFAZcUbOGbZFSgLv4idbd9BW9cNE0oTGo2fhJe2fR21Fc9h6axPQylgW1fm7m5Ww8uT7XZJksvbySdx4HuWsyfXHUE4dAgFezFnxmXY0/FFdPV+DPH4EjQ1Xw/XHfTfGEAiORe7u+5AJLQbJ8z7CFwnE0i7rfMYTKvegvKwGf1/OCIR+4IwhTgqidamT6Ol8TMYHHkHtu+4F4nEHN/t0ula7O66GwoaK+Zfg1CgDwCgtYMdXUdjVuNLYw9wGJHnoojIdNZgOVldcbNZQFrTBrvhINXEatJ9xA4FzJZbRs6dMWXTBZYeH7k1E/HBL2dxIvmXllEmYYzxw5Un30YqAJSP9sLPg5eDvmVkTmqus9kauLD9eqyRTJKuUD1jgTHG2z5IuxAN9dGcxafv+zkAoLHuDpRFNmHb7u9j+877MHfW9aiu+gsAIJWg13ZZqBybdv4SqfQMLJj5fixd5AGYCQDo6FuIeKoCrY2vI672xZyFI1QTLnOp76Npdjwsj7Zce+RHLODSJYN7thtjRDz6NraBN70O0di7YRU1xqibNb72eXInJgh5orL8GSxZeCmCwTZs3PIDtHdeaeTCa62wve1bGB49CbOaP4GKMtphaU93pq/5zEbaNfxwRiYxQcgj4dAuLJ7/QdRUP4pde27Etp1fhjqyAuFPViHyhRoMLP4s+gbfjZaGL6O2+rfG9ru7l6Kmog3V5V0F8L44kUlMEPKM645g3uxPoKXpO+jpvQS7NtyJNJrQ9+dL0LXqw6g97VeoP/WHxnZaA7u7lmFWo7yV3J/8a2KCIEApjZam7yFwwUbs/PlX8MYND0AnylF59FOYftVXgOEy4HVa1qdvaCZGE7WY2fBqgbwuTgoyiXGxdyRdZayzm4n9S8o6iR10+4ldV26WQOkcooGFhW3zsI98ie7CPqyvBnzfF9AVvKS5xhCTv4+75EPE3v461a56tr1B7JrT/ojQ7J3Y9Pm7AaUx4yPXQwVSQLWDhvooACDhZK7j7T2ZIo7zFvUgVDubjHPE0uOpXw79PXSnwsR2rF/epyM8u2y7tpq63Jx6+itTIfqw11dJk+w97+AfBuVOTBAKiBoEymZvQPmS55HqbUKgfBgA4FhSLtu6j0ZZuBf10fY8e1nciCYmCAUk+JQHndAI1bdBp7JPDkmNyufMO6W2rqVorn9lUsJ+DiVkEhOEAhLcoKF/OwjH7Ud6pBq6L43qJzTKN9H1hkYaMTjSjBZJNTLI++OkUsp42E465ly6uZ8GvM2P7CF2OE47L9eXmZpYB2tAwnUAzaIdbX/hxtsYxKZ3jbfJRy5jjPfzA2xFTR60NIEhzaYe/oMY9xw+jUTs+/HbwoQfM7PpCTtPljFYXjXKmucQ+6rP30Ls2/79I8YYgR2vYXBPD3pGq1DzfzEsaGoEKvd9ntYudndl4sMa61/D+/7hamOM2XOpRvZ6D/296Aj1ayLVLGMDNM1pZPd6Y53px9DfbayMBrsOB6J0A2sFr/FdeHInJghFQCCQeVGVsrzkAoC2rqMRDAyjrmZLPt0qCWQSE4QiIOBm7nJSaTMFD8iI+k31r8FRuRQmOryQSUwQigA3GzKUTlcbn8Xi1egbnI3mekk1spH35rlQZhE85ZoBON3xRmLHFI0/iXi0KGI0RO3MwLSLnNEcQd7y+FCYJPLCnZbx7zmgqSiWZpdyom4OsT/w+W8bY+x86HZs2zYb67cAS096H0Y2fIt8ritPAgAcMWcbmqK18OJmKZ+O3duIHa2hWlSYdfVIWmLe+B2NwwIrO7duIHZjhXm8QkHaGKQnNJPYaaYzOmCiIgA9zmlJ7sQEoQiIRDJ/hGMxsxvSjvbFcN0EWho259utkkAmMUEoAsoiMQBALGaWWd/RvgStjRsRcM1y0IJMYoJQFESyk9joKJ3EkulytHXPwcymDbbNBOQ9TkxBOY4Rj+NYqhEOslfNAzHaKakSO4ldk6JxYwBQHqRNClIprr74z+Hjjb+aSLyWURTQEgNljMsSUG0FHY0xjCU8Loyu4Wkz29R4O8Zj7XLQlXiRQ14kkZ8Wx+a58YXHbpSsLNUZjbAwtmPebITHFQKAx4oLKl4VkmlP0db5xhjbkwquSsJx0hgdiaAxti8eq3PwGGjtohGrMLw1E5f188/9qzFGVV2U2PNWnErs+qW0Sa238G3GGOkI/c0pJvAN76AvFhY3030CgA7RZUPh6fRzvr6lOKPZ5GXsa0ruxAShCFAqo4vxx8nO4RVQKoWWailHfSBkEhOEIiESiRnCfufwiWis3IBQYOQAWwkyiQlCkcAnsbQXQvfIcWiN/rWAXhU/MokJQpGQmcT2PU72jC6Fp8NolU7fY1KQemJcpHYtbiQcKiruTFKRflmQBve5CbPGUn2EvpJuG6T74cGvStnm9PFlyk4keVsoFczz6LFlPEC0nDYRR/8WWhQRAF5+ahUAID54GvqHFiJUl3l07BnKFEE8qvE5VIT2VXmNhs0A0cFOmpz9+K/vJHb9k48T+/TPfNcYY3gu7cYd27mL2GFN91EXNVOk+irYyzSHBr+ayf0H/9uQooiCUCQEgwNIpvalHXUMn4ya8HpUhPombR9bqo/Ai42n4xf3bUNdmYuLj4ripJmV/hsWMTKJCUKREAwMIZmqhNaZcI+OkRMxt+Y+Y71XFr8Nj739Axioqkf1YDfOfOpOzH7+ccuIlC3VR+C5lvOQ1CEgBfSMpnHHCz0AUNITmUxiglAkBAOD0DqIlC7HQHwekl41plU8TdZ544hT8Ng7r0EqmKmZP1DdiIfeeS3OHklj0StPYjRVg8H4NOwcasJIsgnDiWkYSUxDatssbMdRiA9UwhsOI3raBtS8bRMSaY37X+2TSWw8WPUhS6SmYgnf24YaiL20iWb7h2JmsGt9iD7Dt6nxdRYGAMfWwnqc+BdO5MUZD15Xcyz6nt8IRk1E2wa8gKNRWJGanmeWjjGPKQsqzckRHyahoKNf4UUACDANLNlJg7B/+R3aei29wywkOLQts8wb2Jj5N12JrpETAQDTy59CpGKfBvbMGZciFQxj4DvTkd4WgapMw+sO4pedtwJdDpLpsDF+JNCPqkg3dL1GZGYPRtZPRzq2r4lOz2gKyV1vIjKfamJ73qSNe2fWsOu43NTEBlkjkDQ7/e7Ypz47sGXZGMidmCAUCQG3DwCQSNegffhtqAxuQ2VoF4CmvesMVtVj5MF6jP66EXAAZ3ocbkMS7tGjWNnxMGoruhAt60bf8FZUhjtRFe5E0I3Bcctwz+JPYDgUxc6ddfBG9k12tUx7LzVkEhOEIiHo9AEAYqkatA2fjNaqR8nnWgPJ/63B4J0zETqxH9HPb4Mqy9zq1A5344P337p33Vd29BnjH9+2Gn+Z8S4EGweQ6KzK7lPjXTPMt52lhExiglAkBJxMYcS++BLE0tPQXP7M3s887eC+dZ9Dz5b5KF/Zicp/3QWV/fWG0glcuO63vuPP6880GXm4fhG6tx2BqAtcNDuNFQ2lXS22KCYxW/JykLk2EKfpGP2a6ltB701jjLCiDXfdQC1b4+BjfScjBiyXZiNC4dG2RPQU7dL9sy/R5OxnHryL2MuaTR3p2JmZa7k/5uH53cBAPKOHLYq+gNpQBdSIxi9e+BZeaj8XZ8+/FWdevBu/TJ2NbrcG9fF+XL5lNU6fMx2Yc9HeMft30MKJWx/L+DGz60m8PRbFA963cGl8M2YMdKA3Kx3XLF1Jv1s/jcWcvYT+5oZCtPAiAMQc+v0cPfWFSItiEhMEAQgHMrNJd2wJygOdqA1vRixVjVuf+z9s6VmOS478D7xj7u04vusYnN7FSlVXn5LzfqZVZcr67NjTjBnNHZPmf6GQtCNBKBJC2WYhvfG5aK18DkPJZtz1xr3Y1nssrjjuOrxj7u2Tsp+68i1w3RR27GnyX7kE8J3ElFIRpdRzSqkXlVKvKqX+I7t8rlLqWaXURqXUXUqpEn/HIQiFpWuRggoPIZ6uweCKnfj55t9gINGKD5/4Dzh++kOTth/XSaF1Wid27DEfB0uRXB4n4wDO0loPKaWCAJ5USj0M4FMAbtZa36mU+j6AqwHcOtZAQEbv4eE3Dm/gAQCg8S4JttGuIfrsPb/cLOBXMbSb2GVBWpBuNM4K2lljwtg6Po1ubemXyhm7SS/XWmyamFHO0UdH05YAHN+ChbzAoeMv+Jq+089tcXbm1+MHhJ1LSzHC8TbPtR4PZ3x5fG7A/LxrS6bufcvzT2DRA3fg2z0d6PI8/HRkCI8nYqgL0wuiMmKWn26OVmH7bA+bTwacB5JIx4GOJy+HE4zj/e+7Bu+bsQfAjL3rJ1Pm/cLIYqpnVc6nwavuX6jw31y3FW9sW4Khrra9y9Ib15J1Wqrp9FBRScdsLzcLPPICl+a55jGAB/8w6DuJ6czVMpQ1g9n/NICzAFyeXX47gC8gh0lMEA41Wp5/Akf94lYEknFAKUxzXXyssgoYAv6AzBvHzJwbQjIdRVpXIOVVwNNlSHkV2NDZiudaw4g9VY70SAUAjUBVL+b8y4exqXInsJG/kDp4Wht34rlXT8HQaAUqy4Ynffx8kpOwrzLh82sBLADwXQCbAPRprd8qE7ETQOsBtr0WwLUAUNUw07aKIJQ0ix64A4FkHF/tuAFbEnMx4pVjxCvHoFeGPckQNCqhUQEgiN07zO1faAewbv8lGnM/cyWC0W4Mja+ISs60TstkFuzumIFFs0u7fn9Ok5jWOg3gWKVUFMB9AJbkugOt9W0AbgOApvnHTdEpEYTCEentAgC0pZrQk65DuRpBY6ATs5xhdKfboNQwFIbgYAjNNQ5cNQzXGYarRuA6IzhhdghPrxxBsm4EPX9+Fzrvuw6J9jkIRrtRaWmnOhlMb8xMYrs6D5NJ7C201n1KqccAnAIgqpQKZO/GZgDYNfbWgnBoEqttQFlvJ26e/imyvCOdxss7NpFlM6unGdtPr56GFW0e/joHaDzvDnQ/fA16n7wYNfPX4pRXXS4PTwrVFQOoLB/Aro4Z/isXOb6TmFKqEUAyO4GVAVgJ4KsAHgPwPgB3ArgKwP257tRhWp6ydNXxmHCbdKndNkovhvll5pkOJmhSeHU5LbQ4lKBCZcDSjZh3XuGFE7mgzgV32zp+WNf3SRqfGiYhO3dC5LKPyfj+4yzQZ9llIBTExvdciSN/9h24ifje5TGt8fNkEtEqKsKfMquBD4GmQDWadgE1z8fxzLIYqlf8AQPPnYtzVnwXJ8QclEXoNTe69BxjjMFmmrwdSsaJ7dbTrkOJgW5Mr92CnW3NiPdnfieprTQ5/agl9PcxUknfZsaCUcOPQpDLq4EWAI8ppV4C8DyAVVrrBwHcAOBTSqmNAOoB/Gjq3BSE4qX9pDPw2hUfw2hdIzyt0eF5+F48jj+nxpeTuGh7GFc+WIPrqp6CF6/A8JqV/hsdBC3127CnZyY8r7TDRXN5O/kSgOMsyzcDOHEqnBKEUqP9pDPQftIZ+O7lZx30WEuaX0FT1W48vnElTl+4ehK8szO9fitS6TC6BpoxLbrbf4MipbSnYEE4BHGUxmkL/ohXdx+LrqHGKdvPKS2ZuLDjUoO4uuEpLBotza5KBcmdNOMUbVoEvRVXTBPrSlJNLA4zlqYyQBPAa1QXsXe55dQvy5zOm4lwHC7wWbWasbUXI1DVEsjpF9yak+5m1BrkY/gXZ+QYfbhz0v/y/5J6MnLqU5YCj9Uts4jdPGs2sctepzEVJ86m1xwABMpoAKxygQuO/CPueeFKPLvlnWh6D9W39EmXgxNIB4ntRVhTnCYamNra8zjOm92OT6oUXm4/Cpce9Ru8c/Be1IQ9bK/KPHhVVlBNbE+QvgSI8yYgAELsGPEgYx5wbbvWbWHaYyF3YoJQhDRXt2Hp9BexesNKyx/9g2dl425Uh2JY3PAmXmzPvBQI6CSW9T4y+TubYmQSE4QiZeUpv8Wu/hnY1b4aVbU/QDD02qSNHQ1m3tQva3oVL3cctXd5eapv0vaRL2QSE4QiRM0cxDv+9leIREaw6pH3wHUHUF71h0mbyPqSmcfPb53zGbz8T/vK+IwEopMyfj7Jqyam1IE0E0szCbZa2KOujrLn5IG0KYA2V/UTuypGG+xWOHSbhGadTgEoPs/7iSvWODFzVPq5FEEsBRybfhOkWtTpV32M2OmXjyJ2sIEWUQSAUDcNiFUrLoHX/BMEAkNYseJxrFp1CS677HtobtmNSN1fMeqZSeSaJbNH2DVW2UD1rN9vnov3Nm5ES9W+30TaCWLn/IswoymjhQWraWzZSJDatsdcbSTrT/21LXdiglCMuJnKrEcetRaJRASPPpap2uqk2sbaKmfWDU3DvZ0L0JsMQ2ugNxnGxoWXoatpxaSMn0+ksqsgFCPpKiAwiOUnPIkfAGhpzuQ6eoHJqwG2bmga1g3te8t/0dmlN4EBcicmCMVJ/ymAF0BdfSYsqK+vHlpFMFp3XYEdKz7yfCemJqz/OGy+1awBwfa4mZM2v4beekdTVCOr8KgviYSZw6ldGq9my430I5eih+P5HDDjbcyCh5ZoG+Ux26+Anb9vZpwYt21+jB0z4FtID7acVrYFsz1r8b2xC17yL+dY/NBJekwXrnw/sSPnvo/Y8Q1/MsYI/+ErxE5FZwKYCSTqUFnxBzhOGt0Ds9HX9BWMVl+MSNLUkD12LXtcU66npagbFi82xohW0Gu9O9hC7CTLRA9YTyOLA/M5L5MhmcnjpCAUK6njoVLHo6reRVv6KoxW+29yOCKPk4JQ5FTXAwNd/usdrsgkJghFTlUDMNDtv97hikxiglDkyJ3Y2ORdE7OL1v7qHhcElabzb3vMFPZ1Aw0KrGfCZQ3r5tNnxiGa+/UR6ScjbnVCidc5DcwzwHP16GCYiuKFtmXjFJQt2/AxzTH8r91kil5T6SB9WTRaO88Yob+3l9gtXbTzdrRBY7AbeKvZUtrykiLIX3R00sTz7p204OGKI2iiOgCoCC2i0BOiwr5xxK01M5lvLCLWTAg3Ge8VI3diglDkVDcAo0MKiVihPSlOZBIThCKnpj7z72BPYf0oVmQSE4QipzqrlPSLLmalSDSx8W/H9YnRNO0IDgDbR2hy7jSXNgmNuqxIokOLwGXgXcLH9sv2kO+fM14cCeCmH0XcYc/3mBXHMeW9Z4KVpnY7XE37sQa6txK7YX4cQASx9jgiSQ8JmIUVwbqTdzz9O/rxYAex57bQxiIA0FexiNgjThWxfWKUJ43xnjm5ExOEIqemPjN79HcXycRcZMgkJghFzt5JrEsmMRsyiQlCkROpAEJhLXdiB6BkNDEOn33TvIoigO2jVGtoqqK6QFTRBPFQaI4xRiI1vgYdPA4ml21yigubhOM2GX6YY/okUU8CZnK7LdH84OP3pmKK4DqSLq8y1ik/4lRiR7peJXY4OYSa+jAGO1MIJYaQDJmaWJoVFg0O0gKgi+fTooi60tR/e1nRw7zc49g05HGeCbkTE4QSIFrnob9bfq425KgIQglQU++hr0d+rjbkqAhCCRCtlzuxA1EkjUImMhg1HR6QA6AjwUr5Bmmzz0o9SO1gyhijN80KJfokLdq+3ri1p5zSS32CdnKIV5vIqfAdw8jPnMhO+BiW7+obnzeBoCbfQKgJaIbMTpp1N5GuoHrVhmceJHZtXyeqKhz0dYeR7O2Enk4bRwOAm6SJv5UxWvZi9kwanxYvMxvrjLK4MFfzRriTcC6nAJnaBaEECDqdSCUdPHPfA3jzzpvRv/GlQrtUNMgkJghFTs+ubUgObgAADA1VIjXcjz1P/lYmsiwyiQlCkbN7wysY6M+EVWzdmimho9NJdKxZXUi3igaZxAShyEnGRtDUlIn7Sqf3ydip4f4DbXJYUZBGIbmI+7xbCxdqefeagGcqpgOaBgUOJeqIXaao+FkXMC+KHtYlnLcm9+vunRNM/FSWwF2zq9DYnYusu/EV9rkIa616R9fgvhuO+v+dVODfhb6k0ZYxjAKFzHb4GJZ21X4BsZMRDGy8+LGsEmqghRLb4/Tz8vJyzJixCwDg7XedBypr8JbpjdCXUuEy+hIrVEMLHu6JzDf88BzLW4f9cMC7LPmL9r7H1HJEpCiiIBxiLDv2GFRUJBEMJtDfn2l5pAJBNKw4u8CeFQc534kppVwAawDs0lpfqJSaC+BOAPUA1gK4QmttKfAsCMLBMHvuXABANDqIgYEaBCpr0LDibNQsXFZgz4qD8dyJXQfg9f3srwK4WWu9AEAvgKsn0zFBEPYxe+5cLFgYQTA0F/Mv/6RMYPuR052YUmoGgAsAfBnAp1TmwfYsAJdnV7kdwBcA3OozEhwnt3nTlIX4szQbxzVvAlOKFkXcNUyDX5dW0iTZatVnjBFwmSY2BcnbRhK174iFYyoaoxzKeKwZTThhHjBvzkJiDyaonrfpj/cAAMq9C7Bh6zxELb/aHbs3Ebvl6AXEDs6h9qBbb4xhxKWWyLnN9U7svwH8G7BX2asH0Ke1fktN3AmgdXJdEwRhfxpqB9E7UIlUstCeFBe+k5hS6kIAHVrrtRPZgVLqWqXUGqXUmpH+zokMIQgCgIbaAWit0NPuv+7hRC53Ym8HcJFSaisyQv5ZAG4BEFVKvXVjOwPALtvGWuvbtNbLtdbLy2vMfC1BEHKjoXYAANC1p8COFBm+mpjW+tMAPg0ASqkzAPyL1vqDSqlfAXgfMhPbVQDuz2WHSilrzE4u2+0Pn321ZT522Da7UrQZ6FHqDWLXerRIIgCUBeYSezRFD5nRDNQiEvHva8bK+OMXj5ZbDNPkJ+NORvFBMy6OxaJZrhe+zDHi98avVfrFNNkuW/84KIZtkCoawxVZdhqxN637CwBgtGcXgA9gcEsMlUfRuLBA+2vErptLG+d0B2YxP6henHE2D6IYG3IyrsiDiRO7ARmRfyMyGtmPJsEfQRAOQLQi052rc0+JKO55YlwR+1rrPwH4U/b/NwM4cfJdEgTBRlloGKHAKDr2jB1Zf7ghEfuCUCIoBUQru9C5R362+5P3ooiOo6yyAIfnxhlaAxtEg+aKAUCZpvE2vU41sfs8WgQuFDPfTZSHY8QedWg+pmPkDk4gbiynwnHja/Jhc2O8WqQtpm9KmmlwO4dYQq6BFSsO+3Yph+cfAuE0/b6t536A2Jtve2Hv/1eWd2LP1iiGttIyPA1hmvdbVjWH2JtCtGkOz099y9tSpDS9FoTDlOqKTnR3mX+wD2dkEhOEEqK6vAs93SGkUqVxJ5oPZBIThBKipqIzE/DabQmROEyRSUwQSojqt8IsOsIF9qR4yH9RREfbA/4YfqXStLGCKZjylwNJRb9uR4wWSZweMIX9qO4idqdDE2ddvl/TMSOY07dRj62LOLcV/74+bZgOMO7+GGK5sQ9TdDcKGhoFDifSenvswGbrMn5N8aR6i6BuHMMJFUFk35c7xoZwLaeAux6ZfiSxZ604Z+//O631wCqgY0s/Fk/ftnf5vAY6qfWEadHDuEM/D2rzePDzbX59yzZ8CLaRy4PUje+fQwSxD3InJgglxBHBzB/i0AutOOrFVtR2lftscehTkPLUgiCMn+ahOhwx0oqKUAptg2GEEgHM3JZ9Mmg4fOvty52YIJQIC/pmIAAX06sS2D2YeTx0PQfTd9X6bHlok/c7sVyDXSdjfuXP5wG2410eLYE2J2D28QsnaVJ4JESbOqQ9egitHcB9voriAbPWdfiY4z8+PHjVNwF6igJK/ZKmzfUtOqNv8vaEXBs35jEau9lKLo0xXI+ep2BXHwAgks7EhzVXxbFncF+sWDDhwm2eTrbpD9Au4bkppsVRFXG8504eJwWhRIirGCK6DF85ZyPCgX0ie9IdLaBXhUceJwWhRNgc3Ig00phTG0NLVaYce1qlsLv6NZ8tD21kEhOEEqEj2IYNodeQcEegoZFwR7Ajug69FdZ6pIcNRds8d7x1E3MZk8esdKGB2MOKJoQDQJ1L48Sq1AixexVNKrfGq7HYMbOAXw5Na82utOY642QiuhJPIuc6ihEmZT0vY59cM0He3w+/45GLvOfXxMaqdxqJ+ex4GE19LUn1bNyUS9fxQvtWaAu1IxxcRz6vTgCjIarvDjj0WjZ1V+uXGZNcfmP8GPrdJVn1TiOYzOfc+nolCIJQxMgkJghCSSOTmCAIJU1+NTGVaz5aduXxDG0NxuL5dNROK1qXqTs02xhhZnw9scuZJtbn0IYMVt94oxCuifmOMP7YqtzGGNu2jzG2NjcRPydS4NHMJaU218C4NgWYubV+2OPV/DzLQdxlm6STtAlIqKWJ2LVN1AaANkUbQ3uGNkXHtDXWKQT2U8B15bF9LY5vIgiCMEFkEhMEoaSRSUwQhJJGJjFBEEqa/HY7goLjODl13Rm/bm0pJMjG8Jj4H0rTFdpYh3AAmO3SlI5wikZHuwEqsrq2btWK9gl0WPBrLsnckyLs8yBCxQNX2ccT8GNyOm/zLWzHlG3DlHzNCvxpa6vG8QYQm344RoI3737Fd2GOEWAr9e/eTux5VfRn6i041hhjMEhfMDms05dR79KsTgi/lxBGYHMOgbtGUC0vvGjxwxqIOwZyJyYIQkkjk5ggCCWNTGKCIJQ0eU8AV47KKf5vUvbFtRfDF6ob9KWjxhjpskZi18R2EDscOJbYKWWKLzzQcjKCTCdS9G/8xQj9dUY/P+wBouPsmm4V57i+xx3jel8OhRXH9sKq1ThGcw3WKINtY2u14bGmHd6eV4jd0FJJ7K6Aqd1q1gRHgXf4ziEId5ynYULXoE/CPAAoS7OdsZA7MUEQShqZxARBKGlkEhMEoaQpQAK4zlsTB/7cz/UJBKhuEINZFLEnRRNrK+LPE7ssPEDsAZc25AUssUQT0sSY7fD4pBzG4Nv4ikCWhrNG3A83+T7Mv5NGgUJDF+FxdDZtju/H7xjnoon5FGu0/Mn3i5Nz+AGyjDHSu4fY05I7ie01HE3s/iBtApLd87j8mogwzePCcjmmPKCPH0N7LKJoYoIgHEbIJCYIQkmT0+OkUmorgEEAaQAprfVypVQdgLsAzAGwFcClWuveqXFTEATBzng0sTO11vt3zbgRwGqt9U1KqRuz9g1+g9jiQgqGChIzaHlC3+PR3MhFIXrIKlgu5VAwau6G6wI55bGxdQyNZ5yxVtYx+Bpj51bat6EYjUKsOhLfj88+bH5wbcU4HjmIL8agYx9Ta3FGn4PKVUXXksM5sI3GhR3ZXE7soTLasNlTYdOPcXbWsV4vfnmx49rDW2NMYKNxcjCPkxcDuD37/7cDePdBeyMIgjBOcp3ENIBHlFJrlVLXZpc1aa3feq3SBsCsmQtAKXWtUmqNUmrNcF/nQborCIJAyfVx8lSt9S6l1DQAq5RSpPC81lqrAzwnaq1vA3AbALQuWV5Ez5KCIBwK5HQnprXelf23A8B9AE4E0K6UagGA7L8dU+WkIAjCgfC9E1NKVQBwtNaD2f8/B8AXATwA4CoAN2X/vd93LNi7zuQLI6+Ux75qM7izF1Fip0K043dFop2O4R5p7jg9tkCsmQjt2ERatpFRFDCX+n6+nbWNNw7+Y7CXAZrfkNuGMGwuIPsHiPrVM+SBzdbnBL6NkczNVrceDx4ASj/2mJDvDQ0bQ5T3byZ2aP5cYu9xaXCr9c7DP3udmrl0buLC/vgbN/kGJVuLmY7zFUIuj5NNAO7LfukAgF9orX+vlHoewN1KqasBbANw6bj2LAiCMAn4TmJa680AjrEs7wZw9lQ4JQiCkCsSsS8IQkmT96KItuYC+cLUa6jtWZ75k6qC2AMpmuDdoOj7jHYnbowxChqcaOoCvJCeqc2lmV4TMLbhQpsxhH9bDCO40aKbGK77NMqw6Iz8PJiXhH/hPP4FudbqGX5Z4MnahiP+wa6OSrF1uJ5HRbHOXbSjPAAcWUV9HSqfQ+xkgP5MHY8XPLR0M/fRTK2SqTEoP4b+wa9m0UN6fIwCoZYx3HHK5nInJghCSSOTmCAIJY1MYoIglDT5L4roALkVZJv8eDJD8dBcRzH3mQrQrXqTNBm3ynmD2NWpPmOMeNn0Mf0IGPnQlixhthHfxvhrlEN8Fu/Zm0uxRv9Cijmsz33jfjDbzUXf45IgG8N2xfHijEG/3iOWMfx+QsEk1YS8bWuNdaqOoo0/2kKz6DZsfWvDEr7ATxObSO5MDhuZifk+xRptY0hRREEQDidkEhMEoaSRSUwQhJIm/81zlcZUdM/N5SmaN1DlXgQtz/wptlZPiFYcamG5ldHO54wx2kdoA96ReIzYTjJB7QqzYYlRTc+lf394uqUttirN4ouSbL+JBLU9j+o5NhwmrIXDYfa5RWdk3z/N9qvZMffSSct+6biVlbTBbEWAHi8jjg5AMkX36zpJZrO8SEv8nsOWRcrKiJ3ooE0/5juDxhjhppOJHYjQMWrYbtOWiz3FQse0pgtyKZpoazBMxlBjx+bZ1jFya/l1yoVZjL+QotyJCYJQ0sgkJghCSSOTmCAIJY1MYoIglDR5FfYV3hJk/SPvTCGSCdlqbMEQADQTfwM8iJKJjuWeKbpGQEVoN0hV1rI4FeHbdz5ljJGO0EpGQ100aXyop4fYrUcZlY+QjNH98sKJXpp+7mkzSTidokJ9PE6T1WMxaieTprDPRdcAS05OhPyFfX6q+AsHPmYgSLtSAUA4RMXvSJCOESqnfgQtY1QFaYFLN0i7DIXYdwmFzTFQxtYJ0XXeXL2B2HFtXmNb1tOOWe1drxFbs2u/rNHsAF47ZyFdp44GWCdZdYOU+Y7CTN5nL4/MTva2blhjJ9Wbnx98ULvciQmCUNLIJCYIQkkjk5ggCCVNgRLAOeaztQuWOIsQtVlBw5BrGXi0j5h966lelW5/ldi9qW5jCC81Quwg029Ga+khPOWM040xQtXvIvYLa58mdstCqs1MW3y8MUZy7DqKRjKz7XCMOwHcdq58/MgpaZr77uOHrUE6d80M9rXteGzfjCRyNmYqh4T40QS9XiKpUWL3DlD9EwD6dq0m9mCcNhMZGKK/hYHBAWMMDarnzTviOGIfe/4lxC5rnGmMEdeswCMvPMm709jOCzsgvJRBTs1GxnlrJXdigiCUNDKJCYJQ0sgkJghCSVOARiHa1EQsz8kph8bbJJmnlSwneODVJ40xBl96gNjBVCexa2ppE5CKulZjjLRTQ+xwmM77SU0d2dNlxgHFdBexy2poQnhtLY37SVvyro2YHKZPcLkibfnz5Ciq52lvbEHL3qCDF7XzaRRirazIG+4yP42kYP94JFOcswRCmYOwEcZuLmJtnMK/C0uArz/6HcQuC5knpjpNL27FxiiL0GtwuI9exwCw67U1xF73xCPE3vDK88S+4MprjTEajj+N2Cw/PvPb3Q+jGTUscWBc7zQ2MMewdKOxrLTfPsb8VBAEociRSUwQhJJGJjFBEEqaPOdOarjKlo9nPvMa+XOpfmK/8Yc7iJ3c/oQxRnML1ZqiS1YSOzKTxtKggjZsAACX6QKaaS0qQLW7N1JDxhiI1NPdhGkskROJ0PUtIqHLciFddszSTHzgeaNALo1A/HPjuDzhFwdkqXln69JKt+HF96xFNMfW3pRNaOVuGDFtY2tivPlIZgyWs8qCnMrnLyd2Spk/uRA7VzzrNcG+SvU0M8YruvhYYs855Wxi//WuHxL7wW9/1RjjtCtpTNv8s88hdjLJrkHLPRC/7Pwah9jQRsNlS+Oc/ffpO6IgCEIRI5OYIAgljUxigiCUNDKJCYJQ0uRX2FdAOKyQ4s1rLNnKKkkF8hd/9T1iB3v+SuzWI5cZY9Qtv5TYgegcYsdBgwrTFi04SGvvIeWwJFmmXHoRs1MRT2ZXLJqVC/s2PT3I9NAK6jqGWAHDlDLFUD6uMsR/LrCbfpjr8IRfHjBrjuAn7k4kidxIGs+hA7ixjc/ntg7x/CUE74jksVHDts5NRjcjup8U63SVskSZ8tje8ub5xD7ro58h9gsP0BdjAPDHn95C7FCEFl2Ye+oZxE4mzYBiM3mbB2kb7e4NrC+DxkDuxARBKGlymsSUUlGl1D1KqfVKqdeVUqcopeqUUquUUm9m/62damcFQRA4ud6J3QLg91rrJQCOAfA6gBsBrNZaLwSwOmsLgiDkFV9NTClVA+B0AH8HAFrrBICEUupiAGdkV7sdwJ8A3OA3ntYayqXP0uVO3Fjvyd/dTuyy3teJPe+Us4hdddz7jTES4Tpip1mb5AALvA3Z9BtmayPikTVTUGaDDs0K4w149PtXM80j7VkUHCYUTKumY9QkqL1zyPz75LpjJzxzbNoVT4I2NJBcMny5jmRUI/T3w28MvgUPDrZhBPdyvc+q79Fz5/f1UwHzJ2ckTTNdLWSEv/ofD49dQ4kA1WqP/39XG0MMDdKioA9+/xvEvmIGDbKtmU11NwBwWMMazaRZhxcdsMADiP3I5U5sLoBOAP+nlHpBKfVDpVQFgCat9Z7sOm0Amsa1Z0EQhEkgl0ksAOB4ALdqrY8DMAz26Ki11rC/AIJS6lql1Bql1Jqh3i7bKoIgCBMml0lsJ4CdWutns/Y9yExq7UqpFgDI/tth21hrfZvWernWenllbcNk+CwIgrAXX01Ma92mlNqhlFqstd4A4GwAr2X/uwrATdl/7/cbK5TqQ2vXb1DduJgsf/7xvxjrxtfTom4LTz+T2BUnXUnXd2iBQ8CSjBxgRd2MDXJ4Xmc2T062JV67ARps1jJ/Ad2Gxc6Y8VumryHm65w6+vdo2NL4djhNk9X9Ep4dy984Q3viCfFcz7DGVrFVDBGMmq4lmZvHZ7l+gWH25wRmjr2R7epw7cF0ezEbuNi61nJdlX2cQ+ydZ6biE0t79Hqw+X3eFf9E7PYttKnv7374XWL/7Rf+y3TEpbFlfk1gcugb4kuuwa4fB3CHUioEYDOAv0fmLu5updTVALYBuHSM7QVBEKaEnCYxrfU6AMstH51tWSYIgpA3JGJfEISSJq+5k+GAxqK6OHZ37yTLX3xqtbHuCcfRXMjT33UesdcNU33HlvfIn79d3lzD0BqsXQuI5bCCbQHefMOSB5pifys8FlvksjggW/yWZr730tAzzKmk9qwa87u80Uv3EwpSP3haGyzxai6P+zE0H/9jytfhY2i232DA1JF4zmYgSI9xmnW65XFTGef4fsf+3KYjGQ1m+Xdjl4NryWlNs9gpfoztDVtMTwgsFjHo0jEiIdOPwSCNqzzv72gzkR9+5l+Jve7Rh40xTrzw3cROJliMm4/eZ13JB7kTEwShpJFJTBCEkkYmMUEQShqZxARBKGnyKuxrOEioKtz728fI8khNvbHuuRfSsLNFLbQLsttFk1XX9ZnZAEnFgjtZUKHZrdriNFsWYAJ7Od0FnKApwo4m6bK05sI+xfJuwOiKPcA6z7zZQ+3pNcwxAJEQD0RlvnJh2xJkagReMjttaPDmGOk0D1T1eRmQsp0YOsZo3EdAtlXaM04477LE3DC/HFyPByrza2xMt7Ju0KU8TlnnJOxTKpiQP72c/tR7LAUNR9j3m3YEfbl23DtoN/Mn77vPGGPZmTTqKhguJ7YR2J1LxUsf5E5MEISSRiYxQRBKGpnEBEEoaRR/Rp1Kli9frtesWZO3/QmCcOiglFqrtTbSH+VOTBCEkiavd2JKqUEAG/K2w4nTAKBUKjiWiq+l4idQOr6Wip/A5Pg6W2vdyBfmNcQCwAbb7WCxoZRaUwp+AqXja6n4CZSOr6XiJzC1vsrjpCAIJY1MYoIglDT5nsRuy/P+Jkqp+AmUjq+l4idQOr6Wip/AFPqaV2FfEARhspHHSUEQSpq8TGJKqfOUUhuUUhuVUjf6b5E/lFI/Vkp1KKVe2W9ZnVJqlVLqzey/tYX0MevTTKXUY0qp15RSryqlritiXyNKqeeUUi9mff2P7PK5Sqlns9fBXdnGMwVHKeVmG0M/mLWL1c+tSqmXlVLrlFJrssuK8fxHlVL3KKXWK6VeV0qdMpV+TvkkpjJ93r8L4G8AHAngMqXUkVO933HwEwDnsWU3AlittV4IYDVYs+ACkQJwvdb6SAAnA/ho9jgWo69xAGdprY8BcCyA85RSJwP4KoCbtdYLAPQCuLpwLhKuA/D6fnax+gkAZ2qtj90vXKEYz/8tAH6vtV4C4Bhkju3U+am1ntL/AJwC4A/72Z8G8Omp3u84fZwD4JX97A0AWrL/34JMfFvB/WQ+3w9gZbH7CqAcwF8BnIRMsGPAdl0U0L8Z2R/VWQAeRKYOTNH5mfVlK4AGtqyozj+AGgBbkNXb8+FnPh4nWwHs2M/emV1WzDRprfdk/78NQFMhneEopeYAOA7AsyhSX7OPaOuQ6Qy/CsAmAH1a67eqZRXLdfDfAP4N+6qp1aM4/QQyBc8eUUqtVUq91cWj2M7/XACdAP4v+4j+Q6VUBabQTxH2fdCZPx1F8wpXKVUJ4F4An9BaD+z/WTH5qrVOa62PReZO50QASwrrkYlS6kIAHVrrtYX2JUdO1Vofj4w081Gl1On7f1gk5z8A4HgAt2qtjwMwDPboONl+5mMS2wVg5n72jOyyYqZdKdUCANl/OwrsDwBAKRVEZgK7Q2v96+ziovT1LbTWfQAeQ+axLKqUeivVrRiug7cDuEgptRXAncg8Ut6C4vMTAKC13pX9twPAfcj8cSi2878TwE6t9bNZ+x5kJrUp8zMfk9jzABZm3/iEAHwAwAN52O/B8ACAq7L/fxUy+lNBUZkaxj8C8LrW+lv7fVSMvjYqpaLZ/y9DRrt7HZnJ7H3Z1Qruq9b601rrGVrrOchcl49qrT+IIvMTAJRSFUqpqrf+H8A5AF5BkZ1/rXUbgB1KqcXZRWcDeA1T6WeexL7zAbyBjC7y2UIKjxbffglgD4AkMn9FrkZGF1kN4E0AfwRQVwR+norMLfhLANZl/zu/SH1dBuCFrK+vAPj37PJ5AJ4DsBHArwCEC+3rfj6fAeDBYvUz69OL2f9efet3VKTn/1gAa7Ln/zcAaqfST4nYFwShpBFhXxCEkkYmMUEQShqZxARBKGlkEhMEoaSRSUwQhJJGJjFBEEoamcQEQShpZBITBKGk+f8BkeQ9c3h177MAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Fonction d'affichage d'une image et de son label associé\n", + "def print_data(x,y,i):\n", + " \n", + " if y.shape[1] < 3:\n", + " y_new = np.ones((y.shape[0], 3, y.shape[2]))\n", + " y_new[:,0:2,:] = y\n", + " y = y_new\n", + " \n", + " plt.figure(figsize=(5, 5))\n", + " plt.imshow(x[i]/255)\n", + " for j in range(0,14):\n", + " if y[i, 2, j] == 1:\n", + " plt.scatter(y[i,0,j],y[i,1,j],label=labels.get(j))\n", + "\n", + " # Jambe droite \n", + " if (y[i, 2, 0] + y[i, 2, 1] == 2):\n", + " plt.plot(y[i,0,0:2],y[i,1,0:2],'b')\n", + " # Cuisse droite \n", + " if (y[i, 2, 1] + y[i, 2, 2] == 2):\n", + " plt.plot(y[i,0,1:3],y[i,1,1:3],'b')\n", + " # Bassin \n", + " if (y[i, 2, 2] + y[i, 2, 3] == 2):\n", + " plt.plot(y[i,0,2:4],y[i,1,2:4],'b')\n", + " # Cuisse gauche \n", + " if (y[i, 2, 3] + y[i, 2, 4] == 2):\n", + " plt.plot(y[i,0,3:5],y[i,1,3:5],'b')\n", + " # Jambe gauche \n", + " if (y[i, 2, 4] + y[i, 2, 5] == 2):\n", + " plt.plot(y[i,0,4:6],y[i,1,4:6],'b')\n", + " # Avant-bras droit \n", + " if (y[i, 2, 6] + y[i, 2, 7] == 2):\n", + " plt.plot(y[i,0,6:8],y[i,1,6:8],'b')\n", + " # Bras droit \n", + " if (y[i, 2, 7] + y[i, 2, 8] == 2):\n", + " plt.plot(y[i,0,7:9],y[i,1,7:9],'b')\n", + " # Bras gauche \n", + " if (y[i, 2, 9] + y[i, 2, 10] == 2):\n", + " plt.plot(y[i,0,9:11],y[i,1,9:11],'b')\n", + " # Avant-bras gauche \n", + " if (y[i, 2, 10] + y[i, 2, 11] == 2):\n", + " plt.plot(y[i,0,10:12],y[i,1,10:12],'b') \n", + " # Buste droit\n", + " x1=[y[i,0,2],y[i,0,12]]\n", + " y1=[y[i,1,2],y[i,1,12]]\n", + " if (y[i, 2, 2] + y[i, 2, 12] == 2):\n", + " plt.plot(x1, y1,'b')\n", + " # Buste gauche\n", + " x1=[y[i,0,3],y[i,0,12]]\n", + " y1=[y[i,1,3],y[i,1,12]]\n", + " if (y[i, 2, 3] + y[i, 2, 12] == 2):\n", + " plt.plot(x1, y1,'b')\n", + " # Omoplate droite\n", + " x1=[y[i,0,8],y[i,0,12]]\n", + " y1=[y[i,1,8],y[i,1,12]]\n", + " if (y[i, 2, 8] + y[i, 2, 12] == 2):\n", + " plt.plot(x1, y1,'b')\n", + " # Omoplate gauche\n", + " x1=[y[i,0,9],y[i,0,12]]\n", + " y1=[y[i,1,9],y[i,1,12]]\n", + " if (y[i, 2, 9] + y[i, 2, 12] == 2):\n", + " plt.plot(x1, y1,'b')\n", + " # Tete \n", + " if (y[i, 2, 12] + y[i, 2, 13] == 2):\n", + " plt.plot(y[i,0,12:14],y[i,1,12:14],'b')\n", + "\n", + " plt.axis([0, x.shape[1], x.shape[2], 0])\n", + " plt.show()\n", + " #plt.legend()\n", + "\n", + "# Affichage aléatoire d'une image\n", + "print_data(x,y,np.random.randint(x.shape[0]-1))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WueEErACp1Iy" + }, + "source": [ + "Pour comparer nos résultats avec ceux du TP 5, nous redéfinissons le **PCK0.5** (*Percentage of Correct Keypoints*). Pour rappel, *0.5* correspond à un seuil en-deça duquel on considère qu'un joint est correctement estimé. Cette question du seuil est particulièrement sensible car il faut utiliser une valeur qui soit valable pour n'importe quelle image. La personne considérée peut apparaître plus ou moins largement sur l'image, de face ou de profil, ce qui fait qu'une erreur de prédiction sur un joint peut avoir une importance très grande ou très faible selon les cas.\n", + "\n", + "Pour résoudre cette ambiguïté, on considère dans la métrique du **PCK0.5** que la référence est la taille de la tête, définie par la distance entre le joint du cou et le joint de la tête sur la vérité terrain. Un joint prédit par le réseau sera considéré correct s'il est situé à une distance inférieure à la moitié (*0.5*) de la taille de la tête par rapport au joint réel. ([Andriluka et al.] 2D Human Pose Estimation: New Benchmark and State of the Art Analysis)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "NZjjvx9cp1Iz" + }, + "outputs": [], + "source": [ + "import numpy.matlib \n", + "\n", + "# Calcul du \"Percentage of Correct Keypoint\" avec seuil alpha :\n", + "# On compte corrects les joints pour lesquels la distance entre valeurs réelle et prédite \n", + "# est inférieure à alpha fois la dimension de la tête (c'est un peu arbitraire...)\n", + "# On ne comptera pas les joints invisibles.\n", + "# y_true est de dimension Nx3x14 et y_pred Nx2x14 (le réseau ne prédit pas la visibilité)\n", + "def compute_PCK_alpha(y_true, y_pred, alpha=0.5):\n", + " # Calcul des seuils ; la taille de la tête est la distance entre joints 12 et 13\n", + " head_sizes = np.sqrt(np.square(y_true[:,0,13]-y_true[:,0,12])+np.square(y_true[:,1,13]-y_true[:,1,12]))\n", + " thresholds = alpha*head_sizes\n", + " thresholds = np.matlib.repmat(np.expand_dims(thresholds, 1), 1, 14)\n", + "\n", + " # Calcul des distances inter-joints\n", + " joints_distances = np.sqrt(np.square(y_true[:,0,:]-y_pred[:,0,:]) + np.square(y_true[:,1,:]-y_pred[:,1,:]))\n", + "\n", + " # Visibilité des joints de la vérité terrain\n", + " visibility = y_true[:,2,:]\n", + " \n", + " total_joints = np.count_nonzero(visibility==1)\n", + " correctly_predicted_joints = np.count_nonzero(np.logical_and(joints_distances" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.ndimage import gaussian_filter\n", + "\n", + "def heat_point(ind_x, ind_y, heatmap_size):\n", + " heat_point=np.zeros((heatmap_size,heatmap_size))\n", + " heat_point[ind_x][ind_y] = 1\n", + "\n", + " return gaussian_filter(heat_point, round(heatmap_size/20))\n", + "\n", + "h_m = heat_point(14, 44, 64)\n", + "plt.imshow(h_m)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a1vQKbeBGI9g" + }, + "source": [ + "On peut ensuite retrouver la position du point le plus \"chaud\", en utilisant la commande suivante : \n", + "```python\n", + "np.unravel_index(np.argmax(h_m), h_m.shape) \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kiLiDhPjGieb" + }, + "source": [ + "On peut donc définir les 2 fonctions suivantes permettant de générer les cartes de chaleur à partir des coordonnées de joints, et vice-versa." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "fD3PoNusGhC2" + }, + "outputs": [], + "source": [ + "def coord2heatmap(y, heatmap_size):\n", + " heatmap = np.zeros((y.shape[0], 14, heatmap_size, heatmap_size))\n", + " for img in range(y.shape[0]):\n", + " for j in range(14):\n", + " ind_x = int(y[img][1][j])\n", + " ind_y = int(y[img][0][j])\n", + " #print(ind_x, ind_y)\n", + " if ind_x >= 0 and ind_y >= 0 and ind_x < heatmap_size and ind_y < heatmap_size:\n", + " heatmap[img][j] = heat_point(ind_x, ind_y, heatmap_size)\n", + " heatmap = np.transpose(heatmap, (0, 2, 3, 1))\n", + " heatmap = heatmap / np.max(heatmap)\n", + " return heatmap\n", + "\n", + "def heatmap2coord(heatmap):\n", + " y = np.ones((heatmap.shape[0], 3, 14))\n", + "\n", + " heatmap = np.transpose(heatmap, (0, 3, 1, 2))\n", + " for img in range(y.shape[0]):\n", + " for j in range(14): \n", + " max_heat = np.unravel_index(np.argmax(heatmap[img][j]),heatmap[img][j].shape) \n", + " y[img][0][j] = max_heat[1]\n", + " y[img][1][j] = max_heat[0]\n", + " if max_heat[0] == 0 and max_heat[1] == 0:\n", + " y[img][2][j] = 0 # Le joint est invisible\n", + " return y" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TG0BlJ3LH2kP" + }, + "source": [ + "La fonction ci-dessous nous permettra d'afficher les cartes de chaleur associées à une image. On affichera d'abord la somme de toutes les cartes de chaleur, afin d'avoir une vue globale de la position de la personne, puis les cartes associées à 3 joints : tête, coude droit, et genou gauche (choix arbitraire, que vous pouvez aisément modifier si vous le voulez). " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "dCfWl8ncH5JQ" + }, + "outputs": [], + "source": [ + "def print_heatmap(x, y, rows=3):\n", + " hm = np.transpose(y, (0,3,1,2))\n", + "\n", + " for i in range(rows):\n", + " j=np.random.randint(1,x.shape[0])\n", + " plt.figure(figsize=(12, 12))\n", + " # Affichage de l'image\n", + " plt.subplot(rows,5,5*i+1)\n", + " plt.imshow(x[j])\n", + " plt.title('Image originale')\n", + " # Affichage simultané de tous les joints\n", + " plt.subplot(rows,5,5*i+2)\n", + " joints=np.zeros((y.shape[1], y.shape[2]))\n", + " for k in range(14):\n", + " joints+= hm[j][k]\n", + " plt.imshow(joints)\n", + " plt.title('Tous les joints')\n", + "\n", + " plt.subplot(rows, 5, 5*i+3)\n", + " plt.imshow(hm[j][13])\n", + " plt.title('Tête')\n", + " plt.subplot(rows, 5, 5*i+4)\n", + " plt.imshow(hm[j][7])\n", + " plt.title('Coude droit')\n", + " plt.subplot(rows, 5, 5*i+5)\n", + " plt.imshow(hm[j][4])\n", + " plt.title('Genou gauche')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a9QKb9YLJRr4" + }, + "source": [ + "Les lignes suivantes vous permettront de démarrer simplement : " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "fwcCfcazJQL7" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Chargement des données : on choisit une dimension d'image et de carte de chaleur de 64x64\n", + "image_size = 64\n", + "heatmap_size = 64\n", + "\n", + "# Chargement de seulement 1000 images\n", + "# Chargement des données : 1000 images d'apprentissage, 100 de validation, 100 de test \n", + "x, y = load_data(image_size=image_size, num_images=1000) \n", + "# Normalisation des données\n", + "x = x/255\n", + "x_train, x_gen, y_train, y_gen = train_test_split(x, y, test_size=1/10, random_state=2)\n", + "x_val, x_test, y_val, y_test = train_test_split(x_gen, y_gen, test_size=1/2, random_state=1)\n", + "\n", + "# Calcul des cartes de chaleur pour chaque ensemble (prend un peu de temps)\n", + "y_hm_train = coord2heatmap(y_train, heatmap_size)\n", + "y_hm_val = coord2heatmap(y_val, heatmap_size)\n", + "y_hm_test = coord2heatmap(y_test, heatmap_size)\n", + "\n", + "# Affichage de quelques exemples\n", + "print_heatmap(x_train, y_hm_train)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QZVbag1MOphZ" + }, + "source": [ + "Pour vous aider pour la suite, voici ci-dessous une implémentation du réseau UNet, vu en cours sur la segmentation.\n", + "\n", + "![Texte alternatif…](https://raw.githubusercontent.com/zhixuhao/unet/master/img/u-net-architecture.png)\n", + "\n", + "A vous de l'adapter afin qu'il soit pertinent pour le problème courant (dimensions d'entrée et de sortie, **fonction d'activation de sortie**)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "O_qMkG8ROoky" + }, + "outputs": [], + "source": [ + "import keras\n", + "from keras.layers import *\n", + "from keras import *\n", + "\n", + "def create_unet(image_size=572):\n", + " input_layer=Input((image_size, image_size, 3))\n", + "\n", + " conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(input_layer)\n", + " conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\n", + " pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n", + " \n", + " conv2 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\n", + " conv2 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\n", + " pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n", + " \n", + " conv3 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)\n", + " conv3 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)\n", + " pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n", + " \n", + " conv4 = Conv2D(216, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)\n", + " conv4 = Conv2D(216, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)\n", + " drop4 = Dropout(0.5)(conv4)\n", + " pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)\n", + "\n", + " conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)\n", + " conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)\n", + " drop5 = Dropout(0.5)(conv5)\n", + "\n", + " up6 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))\n", + " merge6 = concatenate([drop4,up6], axis = 3)\n", + " conv6 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)\n", + " conv6 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)\n", + "\n", + " up7 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))\n", + " merge7 = concatenate([conv3,up7], axis = 3)\n", + " conv7 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)\n", + " conv7 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)\n", + " \n", + " up8 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))\n", + " merge8 = concatenate([conv2,up8], axis = 3)\n", + " conv8 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)\n", + " conv8 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)\n", + "\n", + " up9 = Conv2D(32, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))\n", + " merge9 = concatenate([conv1,up9], axis = 3)\n", + " conv9 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)\n", + " conv9 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)\n", + " conv10 = Conv2D(len(labels), 1, activation = 'sigmoid')(conv9)\n", + "\n", + " model = Model(input_layer, conv10)\n", + "\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "vgFsh0DRp1I3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 64, 64, 3)] 0 [] \n", + " \n", + " conv2d (Conv2D) (None, 64, 64, 32) 896 ['input_1[0][0]'] \n", + " \n", + " conv2d_1 (Conv2D) (None, 64, 64, 32) 9248 ['conv2d[0][0]'] \n", + " \n", + " max_pooling2d (MaxPooling2D) (None, 32, 32, 32) 0 ['conv2d_1[0][0]'] \n", + " \n", + " conv2d_2 (Conv2D) (None, 32, 32, 64) 18496 ['max_pooling2d[0][0]'] \n", + " \n", + " conv2d_3 (Conv2D) (None, 32, 32, 64) 36928 ['conv2d_2[0][0]'] \n", + " \n", + " max_pooling2d_1 (MaxPooling2D) (None, 16, 16, 64) 0 ['conv2d_3[0][0]'] \n", + " \n", + " conv2d_4 (Conv2D) (None, 16, 16, 128) 73856 ['max_pooling2d_1[0][0]'] \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-04-12 21:27:08.105989: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2022-04-12 21:27:08.962948: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1538 MB memory: -> device: 0, name: Quadro K620, pci bus id: 0000:03:00.0, compute capability: 5.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " conv2d_5 (Conv2D) (None, 16, 16, 128) 147584 ['conv2d_4[0][0]'] \n", + " \n", + " max_pooling2d_2 (MaxPooling2D) (None, 8, 8, 128) 0 ['conv2d_5[0][0]'] \n", + " \n", + " conv2d_6 (Conv2D) (None, 8, 8, 216) 249048 ['max_pooling2d_2[0][0]'] \n", + " \n", + " conv2d_7 (Conv2D) (None, 8, 8, 216) 420120 ['conv2d_6[0][0]'] \n", + " \n", + " dropout (Dropout) (None, 8, 8, 216) 0 ['conv2d_7[0][0]'] \n", + " \n", + " max_pooling2d_3 (MaxPooling2D) (None, 4, 4, 216) 0 ['dropout[0][0]'] \n", + " \n", + " conv2d_8 (Conv2D) (None, 4, 4, 512) 995840 ['max_pooling2d_3[0][0]'] \n", + " \n", + " conv2d_9 (Conv2D) (None, 4, 4, 512) 2359808 ['conv2d_8[0][0]'] \n", + " \n", + " dropout_1 (Dropout) (None, 4, 4, 512) 0 ['conv2d_9[0][0]'] \n", + " \n", + " up_sampling2d (UpSampling2D) (None, 8, 8, 512) 0 ['dropout_1[0][0]'] \n", + " \n", + " conv2d_10 (Conv2D) (None, 8, 8, 256) 524544 ['up_sampling2d[0][0]'] \n", + " \n", + " concatenate (Concatenate) (None, 8, 8, 472) 0 ['dropout[0][0]', \n", + " 'conv2d_10[0][0]'] \n", + " \n", + " conv2d_11 (Conv2D) (None, 8, 8, 256) 1087744 ['concatenate[0][0]'] \n", + " \n", + " conv2d_12 (Conv2D) (None, 8, 8, 256) 590080 ['conv2d_11[0][0]'] \n", + " \n", + " up_sampling2d_1 (UpSampling2D) (None, 16, 16, 256) 0 ['conv2d_12[0][0]'] \n", + " \n", + " conv2d_13 (Conv2D) (None, 16, 16, 128) 131200 ['up_sampling2d_1[0][0]'] \n", + " \n", + " concatenate_1 (Concatenate) (None, 16, 16, 256) 0 ['conv2d_5[0][0]', \n", + " 'conv2d_13[0][0]'] \n", + " \n", + " conv2d_14 (Conv2D) (None, 16, 16, 128) 295040 ['concatenate_1[0][0]'] \n", + " \n", + " conv2d_15 (Conv2D) (None, 16, 16, 128) 147584 ['conv2d_14[0][0]'] \n", + " \n", + " up_sampling2d_2 (UpSampling2D) (None, 32, 32, 128) 0 ['conv2d_15[0][0]'] \n", + " \n", + " conv2d_16 (Conv2D) (None, 32, 32, 64) 32832 ['up_sampling2d_2[0][0]'] \n", + " \n", + " concatenate_2 (Concatenate) (None, 32, 32, 128) 0 ['conv2d_3[0][0]', \n", + " 'conv2d_16[0][0]'] \n", + " \n", + " conv2d_17 (Conv2D) (None, 32, 32, 64) 73792 ['concatenate_2[0][0]'] \n", + " \n", + " conv2d_18 (Conv2D) (None, 32, 32, 64) 36928 ['conv2d_17[0][0]'] \n", + " \n", + " up_sampling2d_3 (UpSampling2D) (None, 64, 64, 64) 0 ['conv2d_18[0][0]'] \n", + " \n", + " conv2d_19 (Conv2D) (None, 64, 64, 32) 8224 ['up_sampling2d_3[0][0]'] \n", + " \n", + " concatenate_3 (Concatenate) (None, 64, 64, 64) 0 ['conv2d_1[0][0]', \n", + " 'conv2d_19[0][0]'] \n", + " \n", + " conv2d_20 (Conv2D) (None, 64, 64, 32) 18464 ['concatenate_3[0][0]'] \n", + " \n", + " conv2d_21 (Conv2D) (None, 64, 64, 32) 9248 ['conv2d_20[0][0]'] \n", + " \n", + " conv2d_22 (Conv2D) (None, 64, 64, 14) 462 ['conv2d_21[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 7,267,966\n", + "Trainable params: 7,267,966\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model = create_unet(image_size=64)\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-04-12 21:27:32.068602: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8100\n", + "2022-04-12 21:27:32.858066: W tensorflow/stream_executor/gpu/asm_compiler.cc:111] *** WARNING *** You are using ptxas 10.1.243, which is older than 11.1. ptxas before 11.1 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.\n", + "\n", + "You may not need to update to CUDA 11.1; cherry-picking the ptxas binary is often sufficient.\n", + "2022-04-12 21:27:35.131334: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.21GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n", + "2022-04-12 21:27:40.157301: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.21GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "28/29 [===========================>..] - ETA: 0s - loss: 0.7098 - accuracy: 0.0273" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-04-12 21:27:54.195414: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n", + "2022-04-12 21:27:56.609297: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29/29 [==============================] - ETA: 0s - loss: 0.7096 - accuracy: 0.0273" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-04-12 21:27:58.438753: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29/29 [==============================] - 31s 663ms/step - loss: 0.7096 - accuracy: 0.0273 - val_loss: 0.6456 - val_accuracy: 0.0313\n" + ] + } + ], + "source": [ + "from tensorflow.keras import optimizers\n", + "\n", + "adam = optimizers.Adam(learning_rate=1e-4)\n", + "model.compile(optimizer=adam, loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n", + "history = model.fit(x_train, y_hm_train, validation_data=(x_val, y_hm_val), epochs=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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1plnqWrZsGeeffz69e/fmwAMP5Nxzz61Z98ILL3DKKacwYsQI7r77biorKxutZ+3atQwdOpThw4cDcMkll7B06dKa9VOmTAFg9OjRNRdCa8gTTzzBF77wBaD+yxnfeuutbN26le7duzNmzBjuuOMO5syZw/PPP0+fPn0a3Xc+fERvZrU0duTdliZPnsysWbNYuXIlu3btYvTo0WzYsIF58+axfPly+vbty/Tp09m9e3eL9j99+nQWLlzIiSeeyJ133smSVl5Lef+ljltzmeP2upyxj+jNrFMoKipi4sSJzJgxo+Zofvv27XzkIx/hoIMO4s033+Shhx5qdB+nnnoqCxcu5J133mHHjh38/ve/r1m3Y8cODj/8cPbs2VNzaWGAPn36sGPHjvft65hjjmHjxo2sW7cOgF//+tecdtppLRpbR1/O2Ef0ZtZpTJs2jfPPP79mCmf/ZX2PPfZYBg8ezPjx4xvdftSoUXz+85/nxBNPZODAgYwZM6Zm3Y033si4ceMYMGAA48aNqwn3qVOnMnPmTG699daak7AAvXr14o477uBzn/sce/fuZcyYMVx++eUtGtf+37IdOXIkvXv3rnU548WLF/OhD32I448/njPPPJOysjJuvvlmevToQVFREb/61a9a9Ji5fJliM/NlirsYX6bYzMxqcdCbmaWcg97MAOhs07hWv5a8TnkFvaRJktZKWidpdj3rfyRpVXJ7SdLWnHWXSHo5uV3S7ArNrM316tWL6upqh30nFxFUV1fTq1evZm3X5KduJHUD5gOnA1XAcknlEVHzrYWImJXT/ytAaXK/H3ADkAECWJFsu6VZVZpZmyouLqaqqorNmzd3dCnWhF69elFcXNysbfL5eOVYYF1ErAeQVAZMBhr6eto0suEOcAbwSES8nWz7CDAJ+E2zqjSzNtWjRw+GDh3a0WVYG8ln6mYQ8FrOclXS9j6SjgKGAo83Z1tJl0qqkFThIwozs8Iq9MnYqcD9EbGvORtFxIKIyEREZsCAAQUuyczsgy2foN8EDM5ZLk7a6jOV2tMyzdnWzMzaQD5BvxwYJmmopAPIhnl53U6SjgX6Ak/nNC8CPi2pr6S+wKeTNjMzaydNnoyNiL2SriQb0N2AX0ZEpaS5QEVE7A/9qUBZ5Hw+KyLelnQj2TcLgLn7T8yamVn78LVuzMxSwNe6MTP7AHPQm5mlnIPezCzlHPRmZinnoDczSzkHvZlZyjnozcxSzkFvZpZyDnozs5Rz0JuZpZyD3sws5Rz0ZmYp56A3M0s5B72ZWco56M3MUs5Bb2aWcg56M7OUc9CbmaWcg97MLOUc9GZmKeegNzNLOQe9mVnK5RX0kiZJWitpnaTZDfS5UNJqSZWS7slp/4GkF5Lb5wtVuJmZ5ad7Ux0kdQPmA6cDVcBySeURsTqnzzDgOmB8RGyRNDBpPxsYBZQAPYElkh6KiO0FH4mZmdUrnyP6scC6iFgfEe8BZcDkOn1mAvMjYgtARPw9aT8OWBoReyPiH8BzwKTClG5mZvnIJ+gHAa/lLFclbbmGA8MlPSnpGUn7w/yvwCRJvSX1ByYCg+s+gKRLJVVIqti8eXPzR2FmZg1qcuqmGfsZBkwAioGlkkZExMOSxgBPAZuBp4F9dTeOiAXAAoBMJhMFqsnMzMjviH4TtY/Ci5O2XFVAeUTsiYgNwEtkg5+IuCkiSiLidEDJOjMzayf5BP1yYJikoZIOAKYC5XX6LCR7NE8yRTMcWC+pm6RDkvaRwEjg4cKUbmZm+Why6iYi9kq6ElgEdAN+GRGVkuYCFRFRnqz7tKTVZKdmromIakm9gGWSALYD/y0i9rbVYMzM7P0U0bmmxDOZTFRUVHR0GWZmXYqkFRGRqW+dvxlrZpZyDnozs5Rz0JuZpZyD3sws5Rz0ZmYp56A3M0s5B72ZWco56M3MUs5Bb2aWcg56M7OUc9CbmaWcg97MLOUc9GZmKeegNzNLOQe9mVnKOejNzFLOQW9mlnIOejOzlHPQm5mlnIPezCzlHPRmZimXV9BLmiRpraR1kmY30OdCSaslVUq6J6f9h0nbi5JulaRCFW9mZk3r3lQHSd2A+cDpQBWwXFJ5RKzO6TMMuA4YHxFbJA1M2k8GxgMjk65PAKcBSwo5CDMza1g+R/RjgXURsT4i3gPKgMl1+swE5kfEFoCI+HvSHkAv4ACgJ9ADeLMQhZuZWX7yCfpBwGs5y1VJW67hwHBJT0p6RtIkgIh4GlgMvJ7cFkXEi60v28zM8tXk1E0z9jMMmAAUA0sljQD6Ax9N2gAekXRKRCzL3VjSpcClAEceeWSBSjIzM8jviH4TMDhnuThpy1UFlEfEnojYALxENvjPB56JiJ0RsRN4CDip7gNExIKIyEREZsCAAS0Zh5mZNSCfoF8ODJM0VNIBwFSgvE6fhWSP5pHUn+xUznrg/wGnSeouqQfZE7GeujEza0dNBn1E7AWuBBaRDen7IqJS0lxJ5ybdFgHVklaTnZO/JiKqgfuBV4Dngb8Cf42I37fBOMzMrAGKiI6uoZZMJhMVFRUdXYaZWZciaUVEZOpb52/GmpmlnIPezCzlHPRmZinnoDczSzkHvZlZyjnozcxSzkFvZpZyDnozs5Rz0JuZpZyD3sws5Rz0ZmYp56A3M0s5B72ZWco56M3MUs5Bb2aWcg56M7OUc9CbmaWcg97MLOUc9GZmKeegNzNLOQe9mVnKOejNzFIur6CXNEnSWknrJM1uoM+FklZLqpR0T9I2UdKqnNtuSecVsH4zM2tC96Y6SOoGzAdOB6qA5ZLKI2J1Tp9hwHXA+IjYImkgQEQsBkqSPv2AdcDDhR6EmZk1LJ8j+rHAuohYHxHvAWXA5Dp9ZgLzI2ILQET8vZ79XAA8FBG7WlOwmZk1Tz5BPwh4LWe5KmnLNRwYLulJSc9ImlTPfqYCv2lZmWZm1lJNTt00Yz/DgAlAMbBU0oiI2Aog6XBgBLCovo0lXQpcCnDkkUcWqCQzM4P8jug3AYNzlouTtlxVQHlE7ImIDcBLZIN/vwuB30XEnvoeICIWREQmIjIDBgzIv3ozM2tSPkG/HBgmaaikA8hOwZTX6bOQ7NE8kvqTncpZn7N+Gp62MTPrEE0GfUTsBa4kO+3yInBfRFRKmivp3KTbIqBa0mpgMXBNRFQDSBpC9i+CP7VB/WZm1gRFREfXUEsmk4mKioqOLsPMrEuRtCIiMvWt8zdjzcxSzkFvZpZyDnozs5Rz0JuZpZyD3sws5Rz0ZmYp56A3M0s5B72ZWco56M3MUs5Bb2aWcg56M7OUc9CbmaWcg97MLOUc9GZmKeegNzNLOQe9mVnKOejNzFLOQW9mlnIOejOzlHPQm5mlnIPezCzlHPRmZimXV9BLmiRpraR1kmY30OdCSaslVUq6J6f9SEkPS3oxWT+kQLWbmVkeujfVQVI3YD5wOlAFLJdUHhGrc/oMA64DxkfEFkkDc3bxK+CmiHhEUhHwz4KOwMzMGpXPEf1YYF1ErI+I94AyYHKdPjOB+RGxBSAi/g4g6Tige0Q8krTvjIhdBavezMyalE/QDwJey1muStpyDQeGS3pS0jOSJuW0b5X0n5KelXRz8hdCLZIulVQhqWLz5s0tGYeZmTWgUCdjuwPDgAnANOB2SQcn7acAVwNjgKOB6XU3jogFEZGJiMyAAQMKVJKZmUF+Qb8JGJyzXJy05aoCyiNiT0RsAF4iG/xVwKpk2mcvsBAY1eqqzcwsb/kE/XJgmKShkg4ApgLldfosJHs0j6T+ZKds1ifbHixp/2H6J4DVmJlZu2ky6JMj8SuBRcCLwH0RUSlprqRzk26LgGpJq4HFwDURUR0R+8hO2zwm6XlAwO1tMRAzM6ufIqKja6glk8lERUVFR5dhZtalSFoREZn61vmbsWZmKeegNzNLOQe9mVnKOejNzFLOQW9mlnIOejOzlHPQm5mlnIPezCzlHPRmZinnoDczSzkHvZlZyjnozcxSzkFvZpZyDnozs5Rz0JuZpZyD3sws5Rz0ZmYp56A3M0s5B72ZWco56M3MUs5Bb2aWcg56M7OUyyvoJU2StFbSOkmzG+hzoaTVkiol3ZPTvk/SquRWXqjCzcwsP92b6iCpGzAfOB2oApZLKo+I1Tl9hgHXAeMjYoukgTm7eCciSgpbtpmZ5SufI/qxwLqIWB8R7wFlwOQ6fWYC8yNiC0BE/L2wZZqZWUvlE/SDgNdylquStlzDgeGSnpT0jKRJOet6SapI2s+r7wEkXZr0qdi8eXNz6jczsyY0OXXTjP0MAyYAxcBSSSMiYitwVERsknQ08Lik5yPildyNI2IBsAAgk8lEgWoyMzPyO6LfBAzOWS5O2nJVAeURsSciNgAvkQ1+ImJT8u96YAlQ2sqazcysGfIJ+uXAMElDJR0ATAXqfnpmIdmjeST1JzuVs15SX0k9c9rHA6sxM7N20+TUTUTslXQlsAjoBvwyIiolzQUqIqI8WfdpSauBfcA1EVEt6WTgZ5L+SfZN5fu5n9YxM7O2p4jONSWeyWSioqKio8swM+tSJK2IiEx96/zNWDOzlHPQm5mlnIPezCzlHPRmZinnoDczSzkHvZlZyjnozcxSzkFvZpZyDnozs5Rz0JuZpZyD3sws5Rz0ZmYp56A3M0s5B72ZWco56M3MUs5Bb2aWcg56M7OUc9CbmaVcp/spQUmbgVc7uo4W6A+81dFFtDOP+YPBY+4ajoqIAfWt6HRB31VJqmjo9xrTymP+YPCYuz5P3ZiZpZyD3sws5Rz0hbOgowvoAB7zB4PH3MV5jt7MLOV8RG9mlnIOejOzlHPQN4OkfpIekfRy8m/fBvpdkvR5WdIl9awvl/RC21fceq0Zs6Tekh6QtEZSpaTvt2/1+ZM0SdJaSeskza5nfU9J9ybr/yxpSM6665L2tZLOaNfCW6GlY5Z0uqQVkp5P/v1EuxffQq15nZP1R0raKenqdiu6ECLCtzxvwA+B2cn92cAP6unTD1if/Ns3ud83Z/0U4B7ghY4eT1uPGegNTEz6HAAsA87s6DHVU3834BXg6KTOvwLH1enzJeCnyf2pwL3J/eOS/j2Bocl+unX0mNp4zKXAEcn9E4BNHT2eth5zzvr7gf8Aru7o8TTn5iP65pkM3JXcvws4r54+ZwCPRMTbEbEFeASYBCCpCLgK+F7bl1owLR5zROyKiMUAEfEesBIobvuSm20ssC4i1id1lpEdd67c5+F+4JOSlLSXRcS7EbEBWJfsr7Nr8Zgj4tmI+FvSXgl8WFLPdqm6dVrzOiPpPGAD2TF3KQ765jk0Il5P7r8BHFpPn0HAaznLVUkbwI3A/wJ2tVmFhdfaMQMg6WDgM8BjbVBjazVZf26fiNgLbAMOyXPbzqg1Y871WWBlRLzbRnUWUovHnBykfRP4H+1QZ8F17+gCOhtJjwKH1bPq+tyFiAhJeX82VVIJ8C8RMavuvF9Ha6sx5+y/O/Ab4NaIWN+yKq2zkXQ88APg0x1dSzuYA/woInYmB/hdioO+joj4VEPrJL0p6fCIeF3S4cDf6+m2CZiQs1wMLAFOAjKSNpJ93gdKWhIRE+hgbTjm/RYAL0fELa2vtk1sAgbnLBcnbfX1qUreuA4CqvPctjNqzZiRVAz8Drg4Il5p+3ILojVjHgdcIOmHwMHAPyXtjogft3nVhdDRJwm60g24mdonJn9YT59+ZOfx+ia3DUC/On2G0HVOxrZqzGTPR/wW+FBHj6WRMXYnewJ5KP91ku74On2+TO2TdPcl94+n9snY9XSNk7GtGfPBSf8pHT2O9hpznT5z6GInYzu8gK50Izs/+RjwMvBoTphlgJ/n9JtB9qTcOuDf69lPVwr6Fo+Z7BFTAC8Cq5LbFzt6TA2M8yzgJbKfyrg+aZsLnJvc70X20xbrgL8AR+dse32y3Vo64aeKCj1m4NvAP3Je01XAwI4eT1u/zjn76HJB70sgmJmlnD91Y2aWcg56M7OUc9CbmaWcg97MLOUc9GZmKeegNzNLOQe9mVnK/X+suaOzkGTCeAAAAABJRU5ErkJggg==", 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", 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9T7z5i6C8H6R9O4WIfqaqkI4goc7+HiR0x3W34yuJ6EeSlB8POYX9qetux035AV9+OETqclNuyk25KTflprznQkT/HBF9QE0TfhEkLOxSwvtFW96v4Uq+DqLKIYgq6hv4CyMCvwUxdXgNoh75rZAYfjflpgAAiOhPA/hy3ISvuik35abclJvy/MpXoMWP/j4AP5vfR6Et35PZAxH9dIidTwTw/3ifRAy4Ke+i3NDKTXmackMnN+Vpyw2t3JSnKTd0clP2lXcNfknivf1DSBaiTwH4mwB+HkuyhptyU2q5oZWb8jTlhk5uytOWG1q5KU9TbujkplxU3ovZw48H8I+Y+fsAgIj+KMSc4EKiWg09Hx4OKMwgIoQYEIg0zhiBaB4o0CLQTVPCNCXkVFCYESgABFB99tkKL8MRMnYi4s5qpsU1srbpFUL9lHZT/fTXdyrUdtj5ww4iXFPwaWuLtLjdp9d9N+y3J4RaJN8XbTeRu67fa18Z+m55HzOjFMaUClLmpx3+Z6KV1d01r1+5g22O4EJAaW3ZKbycJ94f3XhZlvfsoYG95Xkax7yXuva1dXnNxuOaSqMhnl07/0evv8nML13wmC/PzFO624fcvXRP6OSJDVyue/fMjPE8RUuftvxA9Sees6DLf/N/+7F51r7R8jvLHLB8TW+9g3xyeiU8ZbhzwOsP3EakgsIBmT3vZqFd3iWDZRcD8ex6eWoWeD2F3yXx0qKns/1s5zcGPeNr2u26x13SThtTAtf7lu985x+8eXU8ZX3Ew537KFFZxpN4AwNU2u8chKyN3VBxdSzX1GXlPZDWkm4rzeqXGW279f1Ur3yWdX/Jvc+8PdEFw2fjzwxigANV1r49fRtps5+nvBfw+0HMw2l9CntCghHRL4Ok88PhwYB//if9CJxPGwzrFQ6OjzB0EX2M6Az8Ru2ZgVuK+Ozrb+Czn30T77z9GJvzCQerA8QY0XUdKAAhkALHUgEkXbA6DVja90Vb6+fyewjSqBgJFAgxBoRACPr+ECBgXn8jInRdnD27r12llAooSynIOSOlhJwLpikj54JxO6EURkpyX2YgpwwujFKsHq71tL7JqpM+ABTk/THIoaPrAmJgxAh0HSMGYBikDx2iUFohpFyQcsF2LEipYLMd8Y8/eYpnKE+klRmdvHKMr/49Pw/f/8YLmLYd+KwDCkCZZgsVrNdsGgPAgcEdz105960YWnAAz618eTebmwcJewG7NqHQkxf/BSCXyXEqQuvP4jsFexnv1AE4oLrnPWR17bTfbWU8vzcQI3YFMRaEIMT597/uP/7EE3pp5Zl5SvfiHbz2Lb8C+dEgPwaWw5KPTmzjEt1YLOd1IhDrcwHgy+59lnLZ/C/rvXDH2vNpvy8RWmM1QiP++rLui8o+WvFriKCs5QnjUqSPlVYjN/oMQpts6zcFfPZbfsfl9c3LM/GU1Su38NXf9vPRx4xH2zXeOTvA0CX0saALBYG4gi4PbFmv5SIM5fZ6g44KEgcUJoy5Jbyy+21NBf28jBXYO4Mb8yWgtjbQnrXI7l7f3qcpvr4wO7AyoutDIEbUMQKASGV2/7KOfX0JxAhgBCrut4ACQtCRKSAUJqQSEIgxhIzEAakErGNCF3J99k99zbddGU/pb93Dl/7CX4PxLoMKgRKacMnhEzBACQgJ6M7bPdMtoAwATQLKujMgZICynK6IgRIJ7MG1P4c/w1pl2v87u/3PpoOX/ILm1zzQnz3j62GA8hzse95EzDt1+fqWfIwqcNU2W320GAdqv9etWusIEzCcFFABQmbknlA6Agfg73/3t+4bNgDX4PDGzN8O4NsB4IV7t7nrVojMiHHQf1FAo4Jf2AAEAlEAUUDXrdB3KwQ6B5AqIPWSXwGVco1VsnxR8fcswfK+T/+vSX1nNbr+CqANgZAzQBTAHEDEO21rEt7lPwUVDIDt3ah1kLWd5DTM3NobQpMMN8ANV0cbp9o/iDQ9REIgQminD13oAQ1EEyg8/yAhnk7ufeXLvM1dBb7xJIAygcpisUKYDykwLj2DO/0MbVHRQirIHiB68Lso+3DfTj0X3LsXSy8X/vI6zf9slS/qmAEcAIHbuJACN/seFsd5D5KX7/L3EitwNppv48VMQCGhs6K/dwUAoejvQpNXE0zG08rqSz7E+dGA4Y1YGWR3RohboHTS/KK4GI5h5zUjr/SgRED/MCBMhDC2Z4xOjOlSxi6dEO8waWnknmv2fbZZuJ/2Sa95977al+Ca4zeN4NoNd81X6zaSneIORLP1ZuNnYIyVvljWZqW9QlJFQZPuEGRdKo/nnsF9mdPccy6eTm59xQf4PPV44/QIj08OkN9e4eQgI6wyKKg018AvA1xIgLlpnnRIHhwfInZZ7yPk3AaW7T7tT90n9ADFbt5bG3Usg7/4tP3TdVgWzIZbe+thw/+2IKZ6yCVULRvFUp+nwAihgAgIgXUf4vbsrC79Hdg5UERi9F0W6bk+b/0vTMiFlHcQulhwNIxIRcDvEDNikIPKVRRPK+vXPszjHSAdMspQwCvdeEwAo/RNCehORQudi4LCLAC5dKrdTjKGJQLoqK5ljk8+O+7sKXvYqef7y3t2wC4wX/MBF+9hBn61PiptPw1JeaE9l5V8MsCmvi8ihb0IeNt1T7qlAzjY4gFKjwq2OboxM36kv8UtI0/Cg0oW0Fv3/0vKewG/n8Y8Hd6H8IQUq4ULzjZbnI1bpMIoFDB2AV0MiFAgG0xKKSArUMB2OyGl0jbUGSC1BejB3vzTige8TwuS2+++zt3rAM1AZwWwEImu1GXvtftM8iufTWpbW9AAL+m7DJM6MK69q+8lcOVKbF/dM+1ve1IVW9rm2aFtcYJ7lxvVM9EKA+hCwbCeMBYCb0Ltnz/5cdGFw9QeXJ5ITZrn2i8/LbnCnnbs7hPtPXT5vTsn3Ys6uueenSFeMiXHwKi+HG0D0785sEjeZgDDNmgPdPe8z+rUvxk26Eq/tsGqNJ4d1y2BAcS9kqonlGfmKSZRLAPAHaOsGBwCSk/gTgAg+2z0Ss/cC/DlXsYhrwS4GYNlBc7GbJlQVWuzd7vPizaSBlB3D1wzOtnjf2GsrapOK127G5a0aMDWVbIDfuNT9MMflBQQWmOJXXOZ60bKEaAiB/iqlTGajW6d2D+rs/iGPFV5JlohiPTyYJhw1g1IkUGREWOpElMuAsYq6E20M74MoBQ3UPsOzTvraQ58Z9JZAweFnmm9eKBe2+LrnPEM2qE7fy9bo909IrCBrnmAEQBiOXOF9v65iZNH/jyTYBOaZDqrdLwBX6kvl6DLQK5vUieS4Bxm0vTuSahmXp6ZpxADcStjUiZCTtSknjr3TCLNDWMDWVQUGCaotldK6R14JN1jHV+xZ5dt2CnLa1Snp13ap/Fi926/7xdcuOJmkl19dxsD97v9bb8teNhFAN9LrGdPaB1MpMIGNKDttyx9JyWWA0ZtlwKePXv0srwX8Ps3AXw5EX0JhJi+AfM0wjsl5YIHj8+wGTfoNiPW44RoZgLa1kBiMhAiEEJACBGnJ+cYt0knK4BUPOxtVe1vABcC2n3Ad2n6sATES8kvXVL/0nbXQG8IodbbgDr2SHyLmi80ECr9ksUfAsCFG5A1JKf3ATybd2ZhVFXiK41fjJs1XhhT2QPgCpRBwRHqM+1Tz04rfch44fYp3iZgcx7lMGQJNZWBUCbQ2K55VQgHHe/S+lEZT3CdW3bW94sXi7faou15zn5erPb613Ix7hlndt+X0uAZUNb5m4EtGECFak8YCE61VpmtcRBqL1y2yzH4eVtIicFxrazfPbMHwF3Rdj4TAH5mOgEYNGTk4wBeZQy3RoyHA9ImAB2LZNE6Elkal93gDQUUxJSoZGr9UdW8HSKsv1WND+yAxicWP9ZLILIEtK17Yh7hpM4mVW31XtyOmZbD3ScSW1TTC7b+Ltvp63TvrOBHAS4zqrkIm7lD5hlY4KHI36MHj/qaKTxrIu1nppVVTDjoJpyve2yHFeKQMawSxm3XAHAmYBt105UDFPrimCqByx6dMlD5sad51n6ahm6neBzqTCaeRFPVDjcIiLkYZeypizEbeywlxwqYTEoPJhQqYmLIjOKAOnPTSgLYkfzuSni1CSU0kxL7rQQVBAWU0H6r49IBqy5dOU+hAvQnQNwQuIOo0G39KOsoPSNkQrfR/gQBvnHL6M4JuTTtkYHf4PaqquK3uTEAze5vv15p8Tscu9gzx6YlNQloBZC0e6+9i8Mc4O4TevnDa7uX29/+Hv+s8TdXz15gzSztJUYBgQojZIDTvH1BNb5h4mo6IWYPqqjZ189Fedfgl5kTEf1KSGrdCOD3aergSwrJCRIR4ICSFcQxVcGVLhVQZlW3ANOUUTIrEwkzySfVTjbQug+cXgR8lwD4InMHbzYwe7f2qw0MhEE6MF6KmSc00wF7pUh7eQZ6/XgtKZBIcAab+cHMxnfZpqa6aoDZPv3Y7bZdwDcrM2I9nescesb5FOVZacUYZ5lJTGhmfmmglgkItri1XTQJ5glJwJqdUlkPAshAlXT4vu/bJPZ8t1nx0vgdKS7tecb2Nfs0BsOtmsvea0xixvT0ehXUusZRAYr+aCDIDkFNGqzAyg+ujYWBETVt2LnnMr2dnATwLA4474anDH3GvfsnePv8rgCBypBJQYFrZ25toaz9zhFMjHgupjXWdI6o0swZO2E9dNaKLgArSxMG4kVFmAHgfWYztXrbjJbPus3DPzN7LTCndXtPaKYJQnvUNtnlgeiSd1a7dftNQYs/iNZ2mC22vddt8M+qJHg3PAUAtrnDduqAbUAKohLIKQjonUJtI2UCTaTA3mk1IgOq+jezg/mLZJ6XAM14JtedGbs8lABvpz0zm5hV5viiSal5/vs+4FLf6efF7iWavbseZtWkihliox1t32B3qwJfHZdmGoGdcQhMM+kuIJJ0M3doAiSx/yXtfyDstTN+Unk3PIUy0D9mpCMCTyJA0mZU0FrUhCFuUcc0TlwBMBUxewSAOBoAtc4BHBf7j1vfBgArgNtDJ/NOQmmHquR1+bwzl25aIAc6KyBfXn/Sex3obe3YBcgX1WEatR2TC9ujbM+j+fMGej3vI9i2IweTHRC+KO/J5peZvxvAdz/LMxTEjhfQkx/Ld7BGNMiAbJomNYVEeSgK7mZS3zmAu0zye5kD3PK3mY0v2nvmZgb2m/vbJk23xwZk2dnlLqI06H3N6Y2dva+9p72r7aF+C+Za95y63HOGX+ogtToqiFNgK7iHlVkDhRnF5oetaU8PaqSPz0Ir4mRhDHH/LTSXFDn7wpB1DpIBTLcKbDgDduZhZ7NeXPP3NZMR/Xt5/57n/D1mr2R9qWD0osV6QTuW7an9MyaUdecK0A0OAoIr2LH3LpixkdKMeblNtUohF5umPivmN5d16IJuPiNPWccJr956jLfjncYlDWQV6ElRmkGJqlMkktjjcVKusnV2q0Gcb+q4LQ63c5Ug7YDOvRtHZVLLDmMvap2BBqYddSb7oTXw4evU53bAudorzmx1l/USdmnB6lMw1CRURgd6i7ddpfaY3Iu2IdocGQ0+O655NlrRNkw5IqWIMAaUyMiBwSkAmUT6bLSSCWEiYTE2R8TgngRCmQAjLye/rau5Ha/a5nrNidVt7VO+tHOY2XdosjZ50xK7fckbd57dz1NnPMSp6GXsgtIMK89wu0+waBnGty8Gv0YfnveaxNfzev97dBLkd1OeladQAfpzrr4CMyGFHohLJ20NiavU0RzBxBSiaT3ipoE4aRBEQ+LtUj0dGI8htLXmnq9RKBbPMTVAuHzev2dmx6sbOgdq13le7077ZoO7HLxFm9X296LCpMDcjw8aH9ohVb0WEuphxN+r2xsYjCeZh19rhrcYIw4Pj0Eg9H2Hg4M1ur5D7DoY4gtqoEeBxfkqEAgdUioYxwSkdoTZ54C25BP7Ijj4iA/7nN78/UspbyDAIjjMTRgcICqQGQisKpu2m3h7X98W7+RmtmeuF3Z3XRwhUONvLOoBkyizViQSC2NCEImFVbcYN5Hwehyk/dKIEvZ75mfVTr6bIqf+KvldLJ7KaBjCYDwDWe4T2u/lxs5Y3LvY7GdRDHabt/N8nVbCE/lF/a3ioT3cYQ9TeeJJ3G9e9R/PGYlTgc9U3bUOdvW4Aa1cZb7ZVvC8UJvWcbtMOvwcSpUQ9SI+yDmAtgHdWUDpeGbr2gBbqNKGPMgY2AZxmbpsRxUID1Dsgq6fvRLBRf11qHn/ONmc+3lYYFnfiGrH6+qihQnL0gl0SVbyqnZI2ukDGhkADtxUaSa1e5cLQIFd6QBEBkf1ps/Nvu86SgjN1ptzENveTAgbo23ZvTlyc8CuYEP+oFgaoDUwb/tB3QfcmHjAv3dN2Hj5+tpa3Xd7lVJ7WlvwsRkt7AFM9VZ7j63tJV3bdwP1/pW6r4hTbAO+0Gueh1oEmKKmIzMT+pnUm5tEmcReuw8Fka7O6c2XEoG0piakWKxdG9bSk9r+Mkh9C/KKULq5wxZltMOf9fGicYZ7H7Dg3wr4FmDRSpWcx8U1H6XCXqHa0srW9/knK03sSGah+/Dy/W5/nYHZMH+29ZFQMPchaAfp3Wdmh3OGWAgAEE0nV7ZD1uZLeMq1gl8iQt/3mGJE3/UYuh5dP6DrOzAXyByJ61tQm80YA4bhXMKakYcJxrxpB6DMwTDNPp/UPv99VwLcXm2S1gZs27PeuUHUY5iZOizBL9h/LupYtK8dukV6pxC11r+vm+0ETrWprqf1vWztaa0Tia+2r2Aef/gqy8xZwqmFZ4IstyhnqhFfFozjvZSdHtPiB12Qy/v2YPLZ/ReVC/t0WRsd89lfqf6+T7W9ALB1kC+Uvl/2jnfR+HdZihNFciFQIgkzBALNIiJIm0zSsCNhMzq7SFRR2kaxDwhaIZ5Xsfcce0FZYslZ0Snbazuqv/s66jUGKFzwG4Q/zJY0XTJ9fPnvl0270Vzd9B3Y2hvp4ooKEc+dQJlUWkrONlLtfX3xHSOAoAfLmdkBwYlLpRjgtU/72W/OZmZQ3ADWexkzXuHeOQuX6O9h65dvv/t9NiDu92W5kHntPg4HdPftRbumIKimDheFc/OObgaI3435w7MWDmLva4IVXh6C/L3k7tHPGnGABeDtSK4XIPCiOudtQt3LLto6HEnL347WfJtqG/Tfzv64+D4TQrk1POsGz7+bCUYj52aWsWw0+3c6qfWs3kUbmdCku81J6qnLtYJfAIhE6ILE9Y0sDegBcCfu1cTq/BbM7IFc7FwAVJqpgzNDmIPRXdDrpb4m7V3G+/UmEAZ6gxr8zE+yS0C9/PQUt0upDIjjWn333N630iRj4czQmCCpDYTEOFapLZl9cQPCzNyIhvyQNSryDnbVixgN9GZmMeAvBoavlvkQMbpQkHIQCYGdmt0GyeTmZGHXW+9xG76dAk1dTEuzhyWoXggX/PvsRi6zPy9efIvr9US6A75cO7w62UnRqJBoFOpL97+vAprL7tt5roHIeYMv+dvR1u5zV79JZSZsUg/eSqgzdKV5AkOHVCV43LOoLkYFyJWpsoTYCWIrhpkNOVcQsTR3qH126u1Z3Gl/G6sN2sy2T7/spUN3/4Ie5zbI/nujmZmU2miVMHd8cXVYlwxvgYTWKtB3EiJAJbaY11HrpRYebikp2/tuk2Di6gqBazzfEBjoxFEydIyi8YZ5S9IvG/OJhN8RVU1TnggoQTCRRYTwgNajfwWgM4C/ABEN/KIBG18fPAhQ3kxcTRtI6XV2yIejG15cm1fYxocMnMjfEiZQBSyBq3kFc6g0YXRYojq3qhpJtmapKKvWNOgaMVvpoiHavK2v2RILuJUoHMHVxUw1XvBVFg4S/aX0sl6iC+0VtwzK4sxGBYgbx8+1UGaUDshrGaCQ5Jk4QemAZ2YG7UHsSjvdnBo9BN0PZ3a8i7VWH6m0NwfPABqfXDrHmTkE0EKPab0cXdt4RkIIqYHdpSZNyJlrm+w3UuYj76c6/nUsWGL32vu4qRTV7ME63eI3sZr4oTAuYyrXDH4ZXApKSphKARUBsjGQhjYjifYA6OLSa4GaiQMAkHc6aeYHSzvdfZ9PUy6y+dVX73m3fHc1YA589yq2HeiGQ7yYEde8dmsPVOqgmzOaN3EzxfCh3PSe2j+ryp7HbHdiRYhzkwz395MG8D0WAhDQvIYbwNqHKqzRF3xf/F2XTh2rPfcu50AX85JZzZ5fGO3v2V/m9e/5zWGx+e8GnGrbROa/A24d4GucwAgDC9WS1uA3YLKG2w7oNnH/ImOo7Ox93WZ9HaC3FQmKX9XEzyBBtEOROSPNxpdZAWMDQ9V/1CQ8RhvunbON0C0v/XNeygXXUaeszumMtuyh5aar15aAmQmi9sT8fl+h0bdfHxcfrhwdXDbcFcy5Sw7AzTt09aVKD0MBIlcHLYlRHdQevAGdWSHXTLbdGjO+WYtbt/Z3BbT2jDvQ+ucINJ9XamPmD0b79gjPq/aOKV1wvb67tU/CTc3rnX1XwFoPRmAN1yZza0KUAKElw90hoALfouNYzfyC+SLMG+kjSVxHYRLgawKPmtgCAEbMaH7mTO1BYXHgz9U7k2Je2IA6rHKrPVPa584cu7mlxXxV4Y87JM3oxPMaBmaJKvxLVNDmD9c1LJk66Vlb6j3BkY/rU62/wRPXEICx4MnWL33fTiHZzwwf+gPAReVawS8zMI0jTh4/RhpHTJsNXnj5Rdy9fxcHt47RDQNCjFU6GYKEPItdRNdHxIAKiESVb45o2At2L7L3tU/7t8/ud389Oz1Co6aL+rxrlsF195rXNaNnlivyBnkHUUEAkJtYdwHqfXtKM7Ugx0D0TtrTZsY8jI8wLa7RHnJh5HwNkl8w1t2Egz7hfEhIQ4/qaeCM9avnuC40n9iiRnmoC6hxknpKJlRGMmM0akdXF54tSAfuWBB6W5gLW74dFR+1duyV+MHR157NqwKgIuCregvbJuo74MIi+0NOlSoR2sFo4fA076drD83/NglnlQwZFyOrgLEXGFxBYQCUqPrO1rkoGnIpqQRTacKigIDlucCszirqJKkgCNEBAlh4Hemv/V4bYHToNyqjESs2hn6TXACdGV0tf+dWTY1x7efPvZtMhR8gmgIP7GabNM02QQ5CP/UwDdiWIu9ebrjuOgO7IZysPQQXNWTR3kVdV1EkzpBIFbtQEPqM2Gd0XUEphBIDpiEicABpKDaThqFzTsg6nrXNHs3sOK3qGiDh5eQX+HKMlv3X35tD6n6evXN4XRTP66wP5E0rFtuHSacridj7l4eV2hUSJ3XVRC41ZCb5hfrBcGmSXzDqwbOJCxfdI5YspNRMHnyWuKsoHIHpljSkRCAfoUV/CZIIp0lJWSXFhKDRHvaZEHAESkBdmzVxzsJfhSOq5mmpIdxxjoNbb45X+H3LC03IvWdWX63M6hT7WQZm9s6UGYEJIWn4sdpPeTAkm3/FJrYXB6c1qv4VrROz1NAEleY2h8CQbc8WW+pZUic3DqJBcr/t2Ud9uVbwm1PCw4ePMG4npHHEdrPFNE5IKYMoIIaIkrNKGrOqSwhpKijmwa9xgIUVNPAL+M/9wNf+buCNZ9d3bZRo53sD2/ssuFupALeCAXKTRWDdtVpbTJwEaMgLnT/XRrjTTf27bVT1dcxt8yL/NGq9ldD1mFbjMKLRDdhFW/D8+opBDQMizYMwvwp4HVM2IFPDpCw2Yg8evGSO0ew62d17aY9MQlcHsG1orWKvFsZ8jMi8cC8Avg47Lgst73PfPTAhP5/YZQ4zXFrbt++FwN4IDsuyr/2z368YzbiXDzGDD3Oj6Yia4lpUdfPDCkNxinP+MrBIRbtT2ma+PEDVJeGneN6k+cZlIGpR9tLCEpMsAbBdWz4HNDo1QLuY/7ZmqEV72DfNy3c5MD5nImimGRXg7h+fZb1i1uEWNF26T73nwiCMJWKTOkw5iqq9BOSManNaHe9MYuUA6MwmWcdyLgHWTgBuMVKlL39AqnXsmffZtaBc2655s5sFj5vRKRaAwrXb88OqKLYP386i7y6otAL4T9dmkNvv3KajYFb2KLnOwfYW1LBvbTuWTauaQgC4MNrPVRYGwiRfa2QFhhw4s/tnArMi5hBh0ueMLLT/VpfVPZOQepBLuv86M75Fs+QxnQZvxhBmgh73LvvkhhwajdgXwwGLNnpa1akVgC//JIXwXCspGiRuoJWpmngKJmltMEAL4sZ3uY3rxZGe3L5vIMdpXvcpVfaVawW/23HEpz71WRz2PdI0YjzfYrudMI0ZMfbo+hVOH50j54yUSu3B2UnGtIVKJS3Umf7TupemD0vQuyxmayu3eI7eIjTsN3+w99oznkp8XTovsFBI5GjLpLx+RwmYUR9nzLCzMZxKrwZ8m3RXvhTQjGrr4LTWGXHVnxoDylyXiL4yCPFWg/JnDXL27KVwwFi6NqpG1WrvCqBFfNDEF8WrpqSZjdEXVw25f0+52y4lwHIR7UIFt7sYUJrimM7yncYwZvW69i2nUqei2kwtX+j6twOA3TsbYe0fBA9O9oczM4RjjpeLjfGaChFwa9ji3ouPsRl7bM4G8MA1KxMTUDSigzSbEJVmBCSKVK9mZkoQ8FyAyvEdY4deNimzVOrWkku3DdYcGxE1tW9bc9gxCZjRgs13dhsF5kDH2lLHIs+1FT7NsYU+Chq7lsiFaKs0w/UdckLQ352GpdKWX0vazgqIwyWLa9HfOihXXAoTzqYBj87X2G47cCZkBJRM1f4/jISQCCETCmm2Py/jYNfxpGu6ahGUNwVUT/tqj+vs93dU2X4eGTNJ2zL9rC+V3+yxCa9AVptFwe0d7PghIKrswDV9vIHVkJxGx+zlQRoTHJjxKfdeUTkbI6RGV04yzAXgHFroN1dXHeYiqaMpFlhEl+sqoQDdKSMdEqBRGyjpLj0q0LW0xR1acostI26LAMIIbEsEB4n/C6DasBIDJcpBII5cgVw9SPKcVurYRJr/3aHxJpVC7xRHZx6sz85nJoWudTmprdtHKAHdtiCMAn7TYUDuW11GP2ABvmFsTJNVYGQp571zoAfX9n7QYk83fqVVhuz3GQ1rRtYfozf3/J5y7Q5vzIycMwiEfhhQCmOz2eDhw0foNxs8fniKlDKmMVfwuN1usd1ukHNBCJbkwv4ZIN11VrPve1qhoJHqd7t+GfCV0tAF6f31eDUbae95ZadY34L2dxc7rIZDECICRZyePsI4blF4CayVePXddpISvqITDoJwcxFfyHmPXNN8I1Sdp4PpYG/tElduRNgxGrqiQiQJLsbUSf/6IlmXKhhDXUBFFx9lzE7C7TsjVK7quq6by3JI2gAsuc/8PvJ/P4ExXyhE5fk9S2ZXP63PjsnspOwFAAd26sFJ96EKmDyjAeZHGad23LHJvASg7ADj69unAAiVHwwiXmEmbDYRCCSYw7WFo/RW4ncKkLnogFAdRPRWuZ0rWDNpJ6M9b0upsgS4OTXGbssIjtk7Oqp22Pr7jsZAD1q+XrvH0nzWTTZAQnjZ/YRqz2qbUO2fNbZuVphtnLP74GirOs1QpR+T4rT2kkoQ2z3y0/URCkMAlDm8mX1082+gOdhkjReeAe5dRcYbVIVtnanSuAvWwuwyOf3dYvntHOD9n74uvybJTd2Sx8FdUzqa0SZDADyhqdztEUcEVVtA8+uzbc/zQ7tmANroarmdLQ7gNUNckHm6jsgOO6U4aa1bN7Z2OEIzLqKtuQjkgcDBHAKp2Q0vNEaA2hSrY5YHmkUBbctYZul7GaRoTcAz4G1fmxTVLrj+LLexgCZdNS2mmUWpn0PWVORF3ykRchg8kYRy6xlpHVA6XdcsQN7q36E9a4pJoNn4G9f+tvTn8nCJ5mgsQMS0IAZHCPbuxhM5GJ/BzFlvX7l28AswSikIRBhWKwW/W4RHjxC6iEcPTzBNGZuNGM8QAnJOKCUjxCLOb2EJUDEzW3iSs9ssjSTacy1VY3iqegCT5tquJtf85xLw7n4nhNhhvT5EFwd0scc4bjFNU2NC/o2EBli5/U31i3DlBmR3gV9DVKgC3eBWCIMbLmSLjIFKdYz9DnzPu4xJ1JMUxSMboLk6L6CmmZydUlUqZ0Iadidrj90rSADmuN4DiMvMy7wa8rJywWBdyNeXpOTqqKDMbTpVMG62nbv7at0dlyohv+E2dfWiYQzsOLc9qUP7Dg9XUGx9rbsk00eMTT+AQ5QoD74JFiWjV0dgpvkhAphtHlXo6ySgBBdLMqE6SfkxNGC1t76ls0oFAAtQswAslb4dkKgSF5Xa1fTf+t4q0c6tD8t1sJQ+76UPnvexHqDsu6lprePLMVUzDMosksbYXnJt2IZF+htDcbb1XmvWAEAdkwRQp5tx7SyEv8QlUtVi4Lj+UV/VJGNw98zGtKmQZ/NgXxYH3guXGO8mXqmvdLQkJ8EGPErHVTMANy5V4u275V/Arj+erut4zNvNy2dtfJXZyj4/T2xxXSHOAOlKsLWk+0SYJFNbBYpk94kDlpgANPAKQIHhfL2bxij3xnvmjmJmZmHJMpCbqUEe0LZuM8tyUSOMfoG2b3lQWPeLInxkNg22Zyr/4ij/8krooTuXikpkpVNCWlEF8TI1buNx7/LRI6RN1qj59RLCLIFHiQK6KVu35Tm3fHUvVNMK2QAuDUvny7WC3/V6jS//8i/FeHaGQAFdF7BarzCsB8ShB4WA46MjNYgPCBQQY4ezs1OcnZ3iwYO3sN1upPMGeMPldr/7JL8hBN00hZuY7ebOof0Cu+Hd0kb6oigCu1ESpNFEhEAdQugwTQmb8xHbcUTK2cXYbbyHwa291N6364TW2lxKQeGCzWYLArBadRI+rqJ+80UxwAyUojaUXFAY+k8sla/DDqswIeWAaYoom4iwDcKYbX5rbm+x0wtJ1STOW9ucmHaycnhQu9inqoTsafjsAgxU8I1F/f4+e9TXv28frcAGdaPzn/CS7gXY8Pt0LZbow0sz3X0m1dmf+AKYxQUmyObvN1H7/epJY1YSR3zm5DbeevsYITJW6wnYRsQtoUQF/JaFi0Kde0qQFJhrno3fjqc/O3oyu0+30devjHroWkqEQEBwzl4lLsa47lBoQAOo4zqzxwMaIHYAymsyTBviD3HWjnogjC1TW3HA2N5PQLXPrGBsaXNq4HkGjNUgi93fBpC5dW0Gvq39V0g7tmxWfcLZZgCfx/o+7ouyY25mCwYA7BBsob500kOfUSiAk5rAOfvxeoi09KzagMo2fT/t3sgyRlN7vobbs3neo6na4VO2Fmf8Ze7UWAFUcvwkaB91LkonDTc7+AKtI6AlU8H8HdURcPmzzjuBxATDnJJmYEnfFyQsWgilHmYB1KRHAK4cBHOAAE3rS10zRu8kavcMjScukWFMCzIeE/JAyAfybLcxh1oHVpnV7AGzkGB+zIxfp7X8mFfa/4mVj1AFvMH5K1jxB04srsNe5wColyRnDWlmod26c7nH2mD32m9BQTpHkvYyXOa2FqFhxmt1zdiY7IBWbU5IUu9sn6vv53pYkGdF0zuLvnFBuVbw23cdXnzhPk66iECErosY1gP6oQNCAIWAGHuIU1uHGDp0XY9HjzvEjnFy8gDbrXBggiFe2gN8cSH4JSKUoh4NOrpm/zu/D5eaTvgQYsbZ3F+ucP3/PKKExnBQBz5mYJombDYjpmlCznrcqWceAnNRENzeUCXBDnxLCfVaYUbOBdtRpMldJ7t0CKExyrrh2iYs1wy0F0ZNcnHVhZmQi8T45UzAFIRRzzxPm7SrghmYw4BIKasdm2MkflM2dYn9WcHbk7q4D6xecF/dhxa4aVbXYrFb8fZfF0mg24bqKub2adM5r3hPHTsA1zVqHzCxTXvnxMh7Onl1pRTC6WZAedyjrAq6Psu8J1sBLqkFUB2+qvMXFuPjAYK7VjeMBVjkxf11rhkzsFeZM6FK8Lx2eFZ8fSSba/0D7t3+lObot4IuBdwVxNUFgiYt8s/WdNuuLa6BVdLo21fV/5cUXnx6etrR9V9NMbOHSGraNirvCxBbSpcFcjY29mWxTikAZI6US9CCdt/s09/j58vagWX0HtQDCRtS2d+5+p4drQ/P75vhbh/zWXlMtRM3u+VE+hs3emOg2dIs+rt/E8RMpE3LRs47HELZa/JwXXa/rGvG5nwWzkwvl7hwMiPjLeIIltcCoJtNtkZIcKaGJQqwq3zaUrEDjhapSprzCm3BehoCcNmaBS/mygFe37/ATnNoB40JNUZxUdMOX6iIuUNIAn5LD5D2m/KS4ObNMJKu/fR9cd+95srPUSUnlSyrYYQzT7t8H7reDG+BsFp12A4RIQT0fcT6oMewGkAxiLkBRRDke4wB/RCxnSKGTUTsxDnBmz0E8pLfXbvffREbQogAMzbbpEkhAC4F23GLruuwXq9mJhVS73z0W2Y3mamaoQ37eaEHrCKtlT6uhhVSKvjcZz+Ht996gLfffoD1ukM3RNy5fYQYo0p+JdEEQ8KO2cLkIse9nLO+IQLa7pQKUs7YbjeYpoTHj89qyuhh6NF1fW3jdrtFzhkhRIRAWHWhLfSi4LegSqGvsmQmnE4DmIGSAsJ5qM4oJWq6ZrcZkS4QkzBxp/Nja37Jox0g3Qd8LBxW9eo11aAzKWDAny/as17tZAtz0b+deMHs2sHtWX96XhZWCZWkiEVtmDkLLPFS8/B39XkAYmdBwjyUkw8dU9PnQoLew9XTtWcoiM0ePwkUPYeSS8D5yQphEyTnwBRni7BiBhKJKNSOjrL0oUSAe5HIEIR2ijmoOTWijJm6qOpGh0JqjwYxpTBgG1CD4NsmOgvBx6j2yDN+URl5Sy7hs0XBhWSr0RWIWvgyapuCmQTVCBY6v2Gr68I7k9jUZdsIqb4bhOpY4wF8XQsKZkpsB0nA2qCgroNGUtB6HeAjtgQHV8tUmMWMqo8FOUXEDaEM2g5nu+vbL05OJHzPDg99AfUFscsABeTIRhrNJnY2EDo1NmdJ+BiggOYoy7oaClBUraxagMBOUwHU9VQVBXvA9UzNrb9Rlhi80KWxl6cYXwgCfKu03tawPsMqbDL+uVOs/6bJcPzXTOeo8pICRnDRdETj0MWi5imMQMAys9tVg+B6fg8G7vQH22ecw5jtQzE1JzGL+pBXre9LAQYVFeTru0qgBi51bYXEKBko/VzCG7dt8gx4Go8ukXZMr6RdrPxBeJbXJPp44AL81X8gibPeTruVf9oBqT8tksgjMbgjhBQkLFpilF5NQkwDZrbSE8s9U0E+iEhrNdFy7aGsEm1VV1WpuU1/Ucl0PXRg7hT4hHK94BfQ2L2hZm4LUUCuxPcV8AsSVVKMATGS/gsNWEDAjUl/LwK+9n3WBiJwKciFMW4nTCkjEFC4YLvdYhgK+r4DUdTsbuxA8EUD6ygNBm6xy2BqHYSu6xRoRuSccHp6jrPzDTabLWIHhEgVaM6STQBN2sRAyQWlFKQpAUQI0cYkYEoJ05QwjgkpJeQsnqgGYoGW6W5KGWmaEGORlNJxkMVS38m1b8bsr6qYTTHpCbSFNZnd1PYYXTANlMyZkz9R7pgb2D2A7fltI/D/7HksNp1FnXVsPODZ7eDseSszQL8AS7PMdR607iuLNs8kQn48rA0GRvZsprNiD+v9M0fKq92PLi9GI5oxqh56ZsgSOyr2PSb8DfD5w8ByzPY1AYBJzLiCy/aQl1Q8S78aQSyayo50HJ2qZUfrg990L3v3Erha36lOeXuP0T1kre6sq1qR0AkTAcGZnyz746WBV1j2+r/soYGZJJTR7Cvtln1aFv+8N1Xwp2WlU5/NroJ/s0eXzag2zPOjGb/bQ9u1CW7+qjntk/ig3W8H6ovmanltyVAqH+E2lnoP2XcAFgWJAtc1CwW6+4DvdZfZ4cLx6zquATV1uh9vP1RVK7Ksi6GRGWjG8wE0p0sFqjNzNQOnYT7sVObPWfuXS0wh5Az4ys26/vbwmCpU0veE1MKYFaPRLMCXmMUUJCtgNjreoUWuAHx5YPY8ZrZ/LsZgtne6/XbHh+OScu3gt4uE0An4pS4gdhGx69BFsfEtIcLSCocQECMQO0I3BASP+gHN/KZ1K9rcG5ZsAYTPzzc4P9/g9dffwtnpRs0OClKecHi4xosv3sPt27dw69YxWCMumOR3n9r/QknoAtxYG2LscO/eiyAQTk/PsDmf8Oab74CIcPfuHawPOvS9HJVyKZpYQjaanANyyQgCPXB2eoZxO+H09BwxRhzfviMMlAIePz7B6ekZgno/xhjQdR36YUDUdNK5FKQk4Huz2aLv5d1D3yHoEd+EW/Z51ayIwOhDEbuvWMAdwOp9S47Q4TZ1L5UiRo3JCMBnn23vIMATk7dTrPE+gaoynp2QF84BswEpc6Yxs+ey+j24sPYD1W4MQEthaV7H3QLAQjdlh8xmTkdWNaHZ6fmfjFlGnkus3AYlKU1pfs0+g2o7/G/u9+uKzxkCo1tPKDwAaMkIvIq+HoyWzTQaMTW35/8e5C3XcG6VVMlsxygDgw8KEBg59QgTI55T2yyzzGfpxPaRfcQFwG1gChYYMgeLBtS2L4Y4jkKv6QhV8hvPCWF09Ky0WwGxPyiaXav2Ka9lfikDNJGaEWnoNP/uohJlBU8zRkEyNpYeF4BskKD9tqNXVAIxDnpZTLHLmNYstO+1GQ7YgNVOWyOCGE8ok6Q2LpFRUgASNakubPy0Mq/50LVMqZnhEACcBqGbta41Sx7j4k6TxZw1sAG3wveBLl3XRvs+jBmAJuF3a7QERl4zeGi2QOIQynVeObBIjyMLUzXGqpJsu0bOodEvqJpdTxvJrOHO9D4KjBCLJLXQ+7yt73UVM3soGuXDa05M61gGQpiA7kx5QAz12bwWR7AyyL1pDUQSO2Ex0VNJvDvBChikOmd2LwAgy0HA7G3zyrXVzAWotbtKgYEmPd1jV2zOeWYjK9EVpE1WT3/G1dyQioQv4yjS3JgV8FKLG27Jl8SBMqIMpCHL5MUlavQlWHKQgDxIfTXkW00exNpOQu6pmVwYMAarpJdn/M2iPTypXG+0ByKEEBFDBEVCiCL5jDGiixEUAoJKfkUyHBBjFMmwQ76LrQAt/eEc+Ppr9j3GiK7r0XUZJTNSyohBQoIxc5WilmIZ0p40isaKHJLBHBBXwMjQUG0C9JkJ0ySpUlarlRsH4Y4+G52ZHUxJJLldCAhEyFkysIEle844TgARCpFIfHNBhwBy9kSs7ZOsbQUpF+RcNJMbN/MGvc9LoM3x7Sq3LSIghoIYS4tRWQeTaifI/ZsVu/5u1O6OAdmfFXAbE9ChbJsI2ibkwFeV3IbWBX9i9/Z5S2nyzpi40321SdNKlv2c1eXJ111vKuvFb74NJpGx72DsMJXa9+sDMr4QJCRS0Y3EAufvPaU5uqjgAmiAWcGwaBAE+FQnozomvj43Jiz1scV21bmvKsmu1VMPRa6amabCSYREwtdMVirQMjpy9OJNJCR2q7zfIj6AXYgluPfZnJqTiNHFgoeZycJsHPz4GD17eqnjpO+6gEyuGt8QAX3MyCWIet3Yil8jHrARqtkGAjczH+37Enya2ZDXoNSUxmT05iS/Nt9J9qXiDwd+zdmcA5jZabrmzDu6+302tq7uGteXlCY6P+GNOVUp6LLuyjsWO/I+vmJ8Quut+2pgsQHyj7j6viChzkjMD0qPpnXUMSAA9YBIqOG0qmZHpaMcqEWM0DprlIhCTaiS2/vYokN4IYu+D4D8TkpXdl0lrtWhzObFzc8sGoSbl9xT0yraHBvNav1mUtDaRDUSBKDzuAKoi4hjUVrxIFWywgHaTsMhlTYbbyAFHDamocDxa7WZDlSxlMl9zAK1Av+nLNcr+SVC3w3ohwEUCLGL6IcBQz+g7zsEimDqpDOhKPgNGPpeTRECLPRtY6Iykh4AX+TsFmPEer0GOCCGHjG8AS6E2Ms7YwECBYzjhJyL1kt7pb3zfgW0rGr7ncJYVw1RQAgSvzalgtPTDYCAl158BV0n/X306B2M40Yd86SvJTOmBJyebfH45AzrvkffRZQkR7o+dCjMODs9Q2LGVCSjXimMoguKWexschHCSoUxTgXjOGHKBZkZkQkFhFI9clGjTggw5tquqyoi+c3oY7bIbXVBensgYjRHuD1gx2x1Z1KdS5i2fy6kxhQqUzPbLw05w2lepwfKjXHo387JaQZOrdlWhwM2s2aazV0ni7wMzdbXvLYttM6O6sdjEC9tW2zW/rqA/cYIa5anqs5FsyOG24xtsyefi/DqChGj64oInxjgFBCT2GybxzrQ5rDOiweFFk+SZY6rY2Vl9Crt1CQSZt9qYE4kg/p7kTifEhqJ0J3JvFTspBseEQnYqBuVACyvNW6dRAtFuKBpC7IPiKSp9CKBpgzEDSGeSxuscAfshA+3jYObShRAja5SJUlde7eFF8wr1nTbaPdivibbBqYvozkNXkcJylPqa53N4r7CJAeF0oktYUA7fHByEsvoDgQ2j/a3ywpnUv+ZEy6AmGRjz1PzsjNwY4cxm9+dcHUBIol1S9gOtf5gLSH+qNoO14OfjgEPDO4Y3BfQFEQyb2AntjqtfbURdt3Ce1lEE0B8c8xUhKkeMEtpJy8igCIjZ66Es8/65artwZfFtCfpmBG2VA/EQJsT+DVhe1Nm1dRJquO8onqglAgSVPlPUT5uEsvplgPEFp5QozkETZIxHQIGog2Ux63QR+4BH3axCkRI7ImhvgwVUIcmSabEdX16STGx2uYmSd6RVwHj7VjbUEzKSyKpXr/DdfxsrMLIKjEuIGakg6i2u2bHTO09SgZzZ0N9v2k/luHjlE/LoZ4qcJbfLmcu1wp+Sy44PTnD6ek5EMSuNaUJ22FAHyMCBWRDCSGrpJbw+OQUJ49PaxrkZucLOIgw66sHoDFG9H2P4+Nj3Lt3F5vzCdvthNc/8xbGsUCyqTFC6NB1ndrj+kVKe797yax97gW/jjlS/U/smQ/WhzUMTM4Tcp7qe+pBn4FSGNttwjRmTFMBlYScCoYY1ImtA5eMnBJykUxttV2WMYUZJWdstyPSNGEcI6ZpQkrm+CcmGTF2NYIEc2kh1wqr41250qgPBKiDgzC+3AlD8GDGJG0BuscwFvZSbrPw3v42FQzx+vagmdFUjbnV6Z3eANQUliW6Op0Ku51i3UblTtj1IbT7/Z5ibQ76D3BMi2wjbZvobDOsZglaZ3C/+QE24FvrdUCsDrEb7z0idh/poZqNuf7VJDBXWAIxVv2E04MCdIwwZJR1RJqcx7oD8FQk+QUVNAciM6Fx89Y6BqG1wjV8WcxN1UjcTFSokCTA0k2odKjJJHwIteo8RXBz4YbebVx2b1GpHBUISK5qTIZJ8WTTINnMEgl4hYZtsjapQ56XvtR+QtthG6HSTQWJSsfB9X+21hw9kAJFuLHVodS2zAHwdeAbiyLDTG0OvPoe2D/3vBvVgTOplkH+Fmme7d5QkxpqzlzJ+S4ADQDbXDizLQDtwO/+NQkwUGNWQ58x+tULpr6u0jGTsALVlKHGLzamaCc0Pz++XcYzNFQfxTIfMgaoK4iddJQIKDmAiUXzuK/ofay8noiRC1W+tHR0C7gGG2AVdMRzqkCUHK8wYUQ9JNt1J6EEiVmEza8XhISsobuABiRrQgwBs90GyINEjrDDjwlkauxbvS7JNQyE6hpzYJbUPCvAYYoCkOcpaPuThTkLGmWBCOCOkNcB09H8QEcF6LYsod9MkxB0zQTpoHyqWUhNQIG2XxYLrUitHUA192FNowwvJTaeovykzY9sRE+z7Vwr+M0549HDx3h8fgomRqGCg9WAdd8jKiScWEwQQEk20wBszrfYnG8xjROIzNp12bt2cjQHuBa9i3B4eIi7d+/itddew7jN2G4nfPz7X8f52YTz81MwF4QY0fcdhmGQKAszcWKrz5cl4GWPWNGAjzSvcRGigC4EHB0e69+E09PH2GzOtc0B4FxF/DkXbLcTttuEcSwoVBADoT88EMfAIPF4cxqRWcwXCKTmFajS2oSC8/Nz6xJKKWrXLO8UkxBJLczM4FKaKQRQw6bNd4nnXYTBSaafAu4LSg5ig1ZVQQyKBEyoag9jMnWDVWmUl/ix0lRlDm6TMYmvSQZhTCDN77W4gzaGsH0lLurzm5c12wEG/zwBswUblLlRUjrqHdNR5uylMxWEeEBiUt563YFVs/O1z33Fru+7x4N4G4oKgK8ByVjTiHHQJ9BRAkVGPyRs1xHZN8EDCp3jhsTahlbNEQyQ2OMFANEsLioHi+hA1S7T1MhmWlB68Xb2mgRTGRYfJquaG6BJdg1XBbUvddmdiATEG0Aw2jQv/ZAINAFhK/XkFcA9KvivpbG2mfmDSXUD9ADhIkOQAbVunlBlZr/LCgxMxa8HA6nc2dmqhJKuS/wLYCpBHRK52a5eYntcD0nV5EjXYw6oaY1tzet9lvKYpgZ4JSSjB8towLSQPGP2tAp0CfN7q8SRgaVtv+c7ABroNjxuIIYh/gMKjJoqXtpb22DtSGbLyu1wHRnUqUkaUD8BIOgarOPNABcj8IsGWcG4JrYQ6XARB8XZAVtG5KrBL7GAz9o0o2M3viHJtarJcfuJAc44KhA0G281H/DmcsYTDPyKJJfRn9mCFGkysdj0lwjVWjHiRp5N68W7jZflZoJh7/dax6J7ivk9ZLfHhMxijaIS2tKLpHi8TfVd3ZmA3v6sgJJ8t9jDdgicRYxBG7+Z8EdjIFukC7H/FYkxIO2cSa0ZNb6vmEHM10E1yXgCW7leyS8zxmlEzhkFBYUYORbkUCS+bwjoIGJ1ihJyq4sBARGEDicnG4xb8wDShbe3g22GuxhxsD7AB15+BQeHhyAEpDRiHCe88MILCLHHO2+/DeaCw8MVVqseR8drSPpgbhJUw7SOw7RTlFByTWRhWIEIKAKat+OIcZpw61YE0CEVrqrhs9MzvPXWOzg/l2QeUeMcrlY9KBBKBtKUMY0JuYjpREEB54IxJYmHW7ja7YIIMYhzW+w6Ge9SkKYJXFjsjK392j87WE1jAhgYul4ld2Lny0ya4AJIaf9B4HmVQIwhJFjGPZM0zEwGgnVALxhgMMmWK9U71jax4D51ruom5SQyxkAMsARjKCOalMc2/xrQW//eAw5bg+Z7QXVG4XaqF/U719N3BR9LCSLa7xWYWf3eLIEXbXJtqYcz22wKmsq2AjQHlq3Ci0CLbmRcCFedC7CjgvsHZ/hcfxshFgxDwhglQn3texLCqZtDppk0h6k5f+wwTHeoqiDT0RcTwJ2o/i3BgdkSh4kQN+25YpnlIsSZNLRwYpfhPy90F2cYAeKl16xPBozsfjODWKEm1DAJdNwIALZwW2AI4AktVFNRMFjXUp1LVBu7mW0hcGn7qwMZjOfYuFKzi73q85IeyghAjIwUMQNdgK5pm8N6gBaTADN5ActmG/oMLiQ23rYBKziUeLDNWQx2UOEGfGzeS89qTiJ12CGBOwUtZl+pPEmALFeeZO+oSShseGt7d3EnQ+oqA7c2mbNjLE0a7heDfVWkbOmHAdkbmqDpgn15VtdcelsT7LCZRQgxx6Ahz4AqDAnvypHjGQtDhQ5UwamERnS/++8sIBNATfJQeiAdyP4QRwOiqGr9rGBSTJWA6chCBcqa2CYFxJ2T4hclow7IkeqBGGj71iyMWQFKF2CSVs/zqlDIumFrmVE1HV6LZ6DWzGVCYnTnjG7LFfiGsQCd8hYCJPOdjEmYBFCHSei6mlzoWMexyGGiI+ReQa8PW6bSXDENEQc5E+qE7PY3akDZWTntLdfr8GYqcwNVpYgqvRDQBVCIiBRBBIQuanSCiFIIpZAzRbAF5rmvhSRr1whADBGrfsCd23ckri1LkouUM45vHyPEDiklMBfcOj7Eat3j6GiNcXuGcbsRT1NW0KucW0Cu4zQs5gGsfVROC/Ma4yxh1c43W6zXGbFnBbFiznC+2eLNN9/CZnOOzWaD9Sqg7y0EXEDJQM4CbtnE+gVgZqScxVavmDOavF4y6HXoepeYXu2XU25gHsyVPxMBORUEyiglOhtmUic3cQAs+WrBrzE7aTI3NZy52rJTu9ozbBtDA8nkKmS0TXumpjXvNccwgMrjZf7UziiMwhgkWLlj2tSczipPXwDwvf3079TN1tRZlJua3e8j9V6gOjfNJMk2FrVPhBkiX4a+sN89lnUSKhs/+6y2vcooZ/2h9ll/vgQUPY8SqeCwGxFiQdeJnfgsTjGhqt9JVdFVEllNB5SpLlXP1i8PIpbDF1ikMUE2Hi/prHbFxTymqTm0ZHmm0tcCTM7oW80uADRpqtG7xiQO1ACMaThKz9WO0EyGwpbqoU7qRl0fJnHkjpv2xK8lD3QXNLc8NOwI52qDLyAIA8FXXAxEtQgngAFjNjMIUAVj1TzCQKuRlh0QAoTpBl1Hfh7N9Ej3Jn+IauAVDhjrezUCCwcxF7DDAwc3LwFtnggiIHDZLL1JVOVjZr+r9RXVVElKY+cEWCXh2npv/gRU7SppRAYupo3Vp5iqCUO7tnje8SGyMZzNE/YAX65zeJXFxox0/osJPhxwhMafJTengK01dZjTLHHeXtjWI3cKYtdqHjUYEITs96tmA4y+xeZlbgdZmEOemScwgKyh81i1Np21ixBHdiARc/C7MwiONwaImQZBzfE0qUXiatOLwqBUEChUgRGBwTHI4S5DDtCapIBj0M4IfwzqLFcQqunGzLbX2hkWUmAFvFUL5QByTXl8QblW8Bu7iLv372C13SCnjGmacHx8hMODAwyrFaJGdCCScGgmvRz6Dfp4jr57c/dEuadzBn272OGlF17C8fExDldH2I5bvP3wHQGMhfHCi/dw517BmEacnp7hjbfewjB0uLM9RAyELhBi6AQwFtOVZjnVwyTCRRauZl8DoIxOZoJ1o9uejTh5fIajW/fRF8JmzEjTFp/91GdwcnKKRw9PkLkgF8LJ+YiwZSAG9F2HQD2mibGdsjs8iD5umxJiCBi6CFbdlkZARskJCaw2vQU1qDwcYiKgcUlUk4bzjUizQiSRhjAhZ6AUYByvFvxmJpylAdsUkVIEJgnXFCZUmyFZhDQz0G+nXcycWbwae+YQ5qSB3QYzlXOVpKLVa9/t9NoAClfnhCWI9jZfs+tYMB/bmBjV1tdAjDeRMIYMoKmgTaINCG2SczUzAKySKXkPzyU79qkHq7nUdz7RbE5wBpAdYGZNblH7R7hSOgEkvfE7m0NM5z3yENDFAt5GxPMgIbYU8FlbPZA14TVH1rBGJCYCNk4KMqpE1EIDmfKJZNNKtzPCeUAcCdHCiulcp0MBNVV1uVXASpBkPQWiNYDOdc/goQ07FQWrowOHpOGO9LBkG1RUa6Z0iApcaRIbZdswu3OqZj22EWagOXEG3ZDc5mciW84EREimRzMpWhzg/KER0GcNvCk9VftYk5L6A9oVlVwIZ5NqBFjMDBhQqSy3zXQh6SZWmkdbRtBhqTbDhKYt8Zu1A0W16MHDYtZHdSoMW0IZGOmwbdjcyVhTjxp3lxhAEtvXQtRMEYAqIDC+UA851EBZjbdsQAGo0m3uuB0cLXTVAvgCMh5F4/STZWIzYUWVBss4N2mDvC+EXVDs7xXH8TktFCbzBMIQMw7M++mKCvvxMtaoe0ow8ybdN+JWU/tmrhLRtI7IQPUJMQCde1Hpl05V+V0Dr3Era707A7pzYDgRrY44eJHa3Go9a9S1MzwidKdtjsKExpfZm+yVusRKR22davtikbbnlbRNDseM8TjUgzcHQrfhyj+no4B0APRnhLhldOe5HmKqGVaWg59Iay00pElxhQ7jNoC7Uv8OiZuAitUEA4KrSH2TTKpbVCpPue15rCZBFyWIsnLt0R6GYUAuGUl3xr7v0Q8D+r5HjLHeF6Mkmei6DillpD7VpBO+PgKECS9QMUHCqh0eHOJwfYAQAkphjNtRQqqFgG7oEPR0kEvG6dkZpqlDCMCqjxh6Tbzh0dBM5MWYSYX1w4e2gqqE2GzHFIzklDCOI05OHuP8bIOck5qOiWQ6l4JxymAW041ULBRZAxRMaPbGREAgzX6HGtBdUiKLg1oINFNRAbvW06z2vSlnFYKIgwhK0LBq0NBq75UaLi4MwlSimnOQ2lUSLDh83SvdNLSMV6iL86naaPeZxI7nmM5v7F797J/ft29XMMC1SfCUU98NzCaAXHvAgMvC3QCRDT/Px4NdHSb5rQD0ovGYXdtp4f77vWR42flLTtpXUWo2wClIrNISgCzMseikLZ2LaN94qIq7dB74z981k86S2u1GFvtXHzmggmpxHqmB4pMwcgMD3mvctBtV2uMPS6UNcz2vmM2wK177wLoezEQigCrrIkfrBDnQ+vHwtOT7Tb7/vq9uHZJ98vxeJgV8/lkDYFWrgysthQldKG1t6IGtRksgG1OqzePS1M4zjYrvg//U7wwL8QTUQ7abR1vjIdmLMDOnqeMSNTtWPb3v5zeeYbGXw3pmRmZjrT8FubNm+7Plb3xDD8z2bAWxxgOApplzbdpxcjUN26Kw7oVWp39un99AIJaoG9dQqmOWm1+LmgRGTWZTDxaR5umB3T8OpLbuLUxYMcm/7StJHNvi1rK2cVPt20Hcr5fgnnfr3niNHdprbOHS6phplazOSoNishH0x3Qgh9VlRCXTOgEQMwduY1VDr3ma0Fi/FkGn8VAdk6pOgIBts1NerjfjX0pwpM51MzPCuk4vp5Xrj/OriRaYGSmkmsyi6zrEGKvpgoHfvu+RUsLUTxojty0QwXwW5cH3Hui6gPW6xyuvvIShHzCNW6RpBJeCw6MjrA8P8eDRYzx6dILv/d7vxaNHj7HZjCAC4uc1uUYEbt++hfXBGnfv3EHXRVGVQnYXkwBDbX2LnnpylRIaARAODg4RYo+D1RpdCDh7/BibszOcPXyAXBirIYqUFgzOAVwKHm/OEQIhxhVKYeQgGxUXIEYB6UMkRAKGTsLAcd8BJCBY5p/RxwCOInkvDGzGVM0mjKKbKktW4zQmhAiwiW44YBwTplSQk0ifr6oUJmxTp4cGahtFJpGEuIVrG4ZtIlVSlxpT2NlUPak4W04PLv1CjxtZbMb40sHCFsyZInjmuBc8BcxCk3kG2yQKjWGVAU2ivLzXri1it1Y1uYIYy0RUHaTsAGQbnZn2RKtQ275MfmGbnpcMm+e3BbgPzlExR1whmQAAxtzhjYfHoLMIzoTzwKBRAZ+uvTAuQpQZM6/jKP0xdaR9b7/rNZ3THMSkIB0xylrS3RJHUII6Upk6U36PZwH8ThAHmJGqPXlQpxqz+2tRGbim8w6jvD8fOBW39gEk7zLnR5MkdWdzb2cqqA6ZYkcIkIZIi+f6uW2pQ21ssqrZvYQc7KLqLIGvgRlqwLw5ShGKqvJnjmYMsZl9CjOh91IiMY76EdvctfYXpX0S+q2HDh0zmlCjyZizGhk79EIEWwfeca4AvAliX52BMIr9tz8QUBEpH6BOiZFAmVF6BvoCJvHl4EjwWipJlKI2u7pGSQFI1TABc+ALtNCFCo7KAUsChUkBSUFNbMMxayIWGxCIikDjaHMGQOz2Xol+wQykLLGUTVoQ4NaTSalVm8iA7neaIjtrNA6W5BaRGKsuoSOx/z1LA8biT5pXUIKuSRctJq9kvFlDnxVN2T0duXlRU4Aas1efLysomGNd51TNHWyv6U9kn1m/IxKe0hHGW4TpVgO0cSv3hlFMKqZbsv7TAWF4zIijgk6gHqxLRxVo55UA2+6cHRimqiUCgPE2kI4Y/WOhW+vf8MDtj7nZOAPAdChJKPqTzplUMUoXKuBPZuOroe48COdIKENoNKKS3H0lToCF6TEJ8o7tcgXIl0/ztWd4k6QVEaWUCnjt0wAv9D4DuwaQ98Xu3fsesmQWEV0v/zJE6nx4eIj1eo1hGDBuR5ydnSEnMYOwmL7jKJ7jFBh0do6xZKwPDrDCgD4GkBoLtsAONHN2M45D9U9C7Dr0IqpFTgnTZou03aIjQgiQ07pJsjkgIyCXgsIEpiTMYhFyLRD0nzoWEaOztMjgmrTCgGoIAQSxDywGair4pQqAg4LnGAP6vqvMKMSCUBjUReTiI3hfXbFYkQSodFc2V2/HWTfhevJse/K+7zPJli0UpwKye2VIeecEaupwJo0vvTxN7+uHvZN321zjNlYG6tpDqDbFs1BpmE1fbSNfMA7edGL2eVnxwNf+xuK70jQvxgjAlZs82Du4iFlBieqAZOKtxfsrOPPPq9qYlf53TFI9gLBNwjaBIhsgTwFhktBi1c42GujD3MGygkJnmmwHJpdkYKllII2hysGlBbU5V5q1zTZuWt/8vxJZ7Sup2TtbH422L6Nj1/b696Kdy7EjBUvNPIIAi6HtzQSumlZIbUirWlYPCGrigEIaQq61qa4noGqdzO73Um2KoxcUoS3v2FidcYqaUBGpaYPY3/JgYbBYGTxkjWEe3nC23o2PJTWTmVBNZgBdpoUbLQKwEGQ1JGDWcSCW8FK+L54uZkxlPgAGaA3dNAc4I3ZPaIvCqNJfMwG2UGfe5veqM74xMLPF9qEHl6VifBNaJGmzmNagSc5h65Ca1LZDNZ8waaYc0sVEwg7bFvlnac9t/8wpupozMGpsYDDJobebJ9agDLDGBrZIE4Bcs7YVCE/iAqEHx3eqs7gzE7SoErZn1cHEfNptvOwHkyKzOwTPAK1fO0DTa+hzhUjiHNv9KhGvMX8vKNcLfkPAMAyqgpfsbQcHB1iv1zXDmZk+AKhg1wPfuZMbgCrekr8sMcZq1WO1GiqAO1wPOAbh/guS/aww8ODBQ3zuc5/HalgBxwHjmDFOE07PzyRCQmacvvOO2L52EcdHR3jh7l0EVmNqzrKJsEpklYKIfTuFGXT9gK4npGnEuN3g/PEJSkq4e7BGLuK4lkNADoTzsWBMwNl200wdEMHo9KRG0qbACn6L2PQAWHUBqRRMKSONGVMqQABCIBwcrgEQpphRSKTM4ujAmlGP1MmOJLHI0OHo+ECkzrkgxgnTmBBDh7fefnSltBJDQRd1x3FZrcTRoKlhxIsUDYCifdbTn2Pa1XJFSzDAOQnIS+u2UZsdl9VdT9O9Lm57p20oERJ6zW+ahArSgzINs8tqi1Ta0W3kVB63XJlJtYMaVaI4tMbXMTCAY17h1AaBWOylvGpLxqbZ8y0UpbulHpTQzDCMOZnk1FTIQXYL0h32OuL8gjSNMBPyKso4xzY+rDFRSf/HhHqIrglDNDEGnMmBlTZPupFnsRntTgllYuSJ0J+Kvd72BU38YIeZjQDjGfD181VEolMiUFYMmlo6Yh/BwcKWVboLQrfMkoaYIxCDJLboT2xDIaRDkVrZPbJhMbrU6qkgXaXWlmShgfa2aMxW1YYneMk4CberZhKZAKV1kQjpOtboB0y6uYamOr2qIod+xhCybOITqbSXwGMAT4SwCepwSm5z1n4aYCACRTSNVJWmOjpXJiOmKQTqBNTkkerP3RnUTlIOJWUlau/uTO07NewTwC7+rtQXkpr5MzlnXFLbUQFdcXTNscOVAq/xtkYnYV2zAyOMQZwhdT2nCJH61kQsnpFKfYX1sADld9bv0mK8UNDH1TmuRYjQ/1kYNLetSzxfWdsxFPQhVxDc6fcrLQHV3jYkD25R4/7GJDTRn3Ad724rySBKL5LQ0quzV7Lxb//KIHwiTMKLWAHqdKBaoDXVvWL1DiOMkgijxDngjiPQnTLSEWHqWxsP3zAbX3EuS6plDBlVSjweB6QDFk2mjn9esYZobKZSRMJDKk+DcnhNTFHjeEcCmOcgVtsZnJk2FUacRKNhph1prZJfIt2LG66TSBPOqQ3t+nQk6y+uUMF4W4qMy7a1a05ykXFycoLtdoucM3IW7p5SmiWWWCaV2Gw22Gw2GMetgM7laRPKdN3lnDPGcYs33ngDQ7/C0K3Fvnh1gDgMCDHizp27YA74wMuv4fTkFN/zD/4PMBeshhWmkpA4oaQJhQvON1vEEMF3gHoEAZpRvxtli4og8lj5tz44wGpY4WC9AgF44zOfRh4nxJKRc8E0JZQgwHwVCWPqQYUx5iyxEtFOVBJKRyI/FE0J1vdRJLYUwBlIpahDAlXgMk1iG2CxiEOAghTWFNJilhIDYVgNGIYew2qFNCXkPIG5SNpnuF35CoptUhbyLUdbKBDwoqpXYsVgdkKHmwZunxUE+03abHKDs78KspmYZzepTR4bA9eFRT5MkltwXpJmZhje6WEOwlv4JGMOcSvAt9tyDVxuJ/i4VecHBdslohr0s3+HMSM7KS8Xv5niEFeV77JUYBz1fu/NbmM7q1iZzM71qy8hMNbrCWcHjLJm0CprqtjQwKMdhiwaggLiEFwouGpygh0b26KS3BZaaM5/6saYWx3dmbw0HcnYlIERt1RV1t50p9ahUjtRUeozZ9Q0AXqAqjSl7aOJYFmoTLKDCGRLsFEajdYA+R7gs6uMuYY6A/SdE83vgyMbB+ibU6f2o+NZCK6ZqY4BXw2zViNtXHHZ5g7FpJtztl37Z2tdDjlyMJQxU/vCDKUxaiZAfluKUBtPAX/moc89VQmed6I0cwszaah1mlOgAtIaDcKWIisIpzYplW9N2DlAlQro1OHQNGmlgbqKKzNkzQvKlkGxVPMeAzOqxrCCXn9wVIfCeqCyplY+vgtQ6jAq4PWJLq6lOHAJoAo+xBSpjakf20rTlmWMUOdZ/rnIEElMlYS/NzMKkwxbV7uNtCVuIWYANoa2bw0S4zccNzMKQOY+r8iZPrQ9AkD1a5huEdJaDsa167ZP2T6Bxo/CCHSTNiMKAOZO2xbEAY70UOPtjIsLyxYn7GwRXE1CzFyr2cgv50USXrQxuKwsNZHLcq3gN6WEt956C+M4ohQBUmdnZ+g1HNcyO5pJesdxxDRNYqJQsvu91c12yoGsxilNyDnh49//CcTYYT0c4ujoGHfv3cfR7TtYHx3hlVdexcsvfQBf+tEvwVtvvYVPfP/HwaUghIhtGrFNAalIW09OziSwQxGzgIp2zG6pSnnbrub3lqPjY9y7ex8ffOVlrLoO3zONGM/O0LFkZZvGSYgJwNm4xpgSkIHNOOLRZqNmCh67MMAFU5kQCaChE+Aag5xcIeHUInNVIW02I6pjCYmkN5BkiDPwGwnouigROIYBB+sDnPMG282EkgtyTjvz9LxLIMZBN6GLBbHLSH1BGRpTkF1DmXwQ+7O6+ZpqyDWvhgxjVDVhIbeANDSNMYoAiE2bqSWNmSmj54SaxWsWL7aqgrglNTCVZ2ibltkPW7uCpsWMowQ3784Lyp0IRGV8DHQbrjZiHPXgUt+rpi5mt0fGSHUA3DrxDL3+5g8MDAH/RE0NbyLj4AjQ/imzagOrmxpYD4bvggCeoUQquHuwweO7R6C+YH04YgOghNhAxFCkHVMQAJEaCPUq35pYxECwSWlIxr6s1WZ1G9r86bPeZAVEWL0t854OBDjnAPSPCXHTHFlmh6EiUrsSAe5F+sKBQTmCmCqYCQmgXqM9QNoYRwFm05HY01roozK0jVsC74tk2Eej8KYzdiAsvXza5kkKfut0Lw6S3qSDcgMIFiLK1l6T5CgwHljXBYsq+wrBr0l+z6ZeHGnNsTD4juinAl/R0KiHuZkTJIiWxKUjlv6Rk4Cx1NtBnZGBUgLywOgsvbGOfV4Jj6gxSf24dwWhK8KrikioBVhLW21O3ZbjaEmkhd2m8a/xWD7DKO9EaX0ysx0LAeeBNgBQNK3OnrFVIGvj59e9xfsuJkBwDEHartEdZr488j2Ggo7KzNHtukBw5QFofN4c0rxztDRKaJo6oQPJ7EjV9yQkruMRku4NnQyuCTDE5hvgW6LFGh6LVFnsc7mp8AmqrQLyYQEQwAYaSSW2EzA9onqYyQPVsHZURKqdV4TNCybp1aoZNSJD7b/uJflAFi+dKm/pAfTWJ8lFUJ3sJgmrZntWcfwqjkCsKVnhDqBU22lmjSb48dra3KO2VypA9W/xh9eqqb2kPBH8EtGHAXwHgFe0+m9n5t9BRPcB/DEAHwPwcQA/l5nfuawuZkbWuLSllCrxBVAlv0uHtxijOLxNE8ZxRErZ1ScDR3veIaCUwEjosgTDPzvbovAj3Lr7El64/zJeeCEAROgBUCnoidGTMGMOAegixjGCueBgdYDjg2Pcu3sPKAWnDx8iMVA4NzsnBcUmnCYi3L5zFy+/8gF81Vd+FT760Y/h9Y9/HI/feRtHfY/1wRoHXQC4oCRJYJEz482Hj3GSMnomTEyIIAQQQhdhx2xiec4YGIOQ7aQFAkJA7Hv0ISIXsf/NmnCjns65qGrJbI2l7pIz3nrroTgcDp1En0gJ45g0D/tCL/yc6UQ2KnfsM3UR2uZbJTFeYmLPax+DBzJAIxS/IWu3LdaqN7oPSU6quZcVZeGqJPWkGPqXHjXMWbDQNzU3O8CBq9OBZf4KW0IoIuUV1ZV8789Ky6U+BlUVm8RAOAD1urGY3SALGDKpj8UktXEzwN+kcjYO880NaMDYvOBn0nSTAJuZg1235wtVp58QGSEUAeU+o5hV9xxppaOC26sNaCjo+oz1MGEcO2SKjWuOQQ4y9pmoqgdZc8Kb5LVmfzPpgm423LfYsNwxkAHOjf44iJMbJUIsyuxJ7W+DeTQD6dA5uxQ1u5lQzTQCoM4+8lx3SujOgXgu786rJvmI54TutG2ccSvgNk4FxGKGUWyz2BCCHY4YlUa9w6fGE6pZBaummytrmB0w7Z65OpJrTFDuUTfdyi0ILU25SS0vAb3Pk1ZSCZhSREkBsQI9QjanTRsLJxmX9UP1WogNIApa0HXE3OybUwBnbnbafWk8LLrPLOYPgAKQFaGYenkM4EzIxDNbZAOpVsQh0pIIAN2J8JPhxKRvFl4KGE6AskE9rE13tBIm129pM5kDcXTzo3bRNpklBUEQKgiZmTjx3CGQi5iZUGg4t0Z7ULqRrY2rj4nV1zkUU5j2AuDnuv8UoDtd7AdujdTvto9kMXkw/pl7cUhLR7pOt2oe5A6b8Rx66JDxsXWaDrmGN8srQtwShocS/cHzDV8M+FazNjVn8Yd4c3TlAIx3RPI/3s/qQ0Ai8EiiXRATiyJY4lBeFk+Cgm6q7zQeEkYR4MTNXJDTnRfkPlbzEZOMl66F2xOzCZnPOIpQZzYXdojSBAZm4tj2JuVVjpfZ4f1Jh+mnkfwmAN/EzH+biG4B+FtE9BcA/GIA/x9m/i1E9OsA/DoA/95lFTFQTR08EAYE/Jrjm0V/6LquRXuYJrzz9js4D+f1+Vop2YISW1yG2ckKOEUISLGglAnTdIYYe9y5cx8Hx8cIRHjwuddBXNAT0BOjkMV9lNxyhQnrfo3D9SFuH99GThM2p2eSdIJN2tukIy0KRcStW3fw0Y9+CX7Ej/xR+Kqv+ir8j2+/jUdvvol1L2K82weDgL2SMU0F05Rx8vgMGwYiCBFBhBMB6AKBojj/sYJfzmLGIOmMIf1nAiggdEAXApDl9JinrM4aEieYcwEigRBQSOw0C4BSXArkAMRINQYzIBEX9gh+nxudEIAuLHZYkw4Ywyk026i8bW6lCbcgQGJeVifJfwItzq4WCeStIWcU3EYN6C0n+SCZfAaqQAdmvqB2KqxSAFLHhRrUHnJvnATc9ueMeF7QP24IXsCvSArBBihI8sIzgQs0bJZsekVVlKS0UoWxdhIuomKsWcjA1ZxjhkzqgOhgelOHHbQMzE4QxCAFviGwc6rcKc+NViIVHPdbhE7A76pPCKEIaNATk5gFKGgoTvWfSCOztI3C9tnionVYIglb5jUqhs6jZTTKanMHVWOCgDiSOqPIeEscTdmISO0yKXOzh7U6IRtY3Ega0e5c6pQYn/JctxFQvHlBpPRhpJZNilkd36jGJzUNCbB/M7cMiDX7mwoWmAUcmmYhZEcDShz1XKnjY5oR7uzAhCodrlEj3PnqkvLcaKUwIZXQDi2qpqeVO+wpb/E8opomZRlD4ScOADupNTHASUze7ACM3vqNCrjtwNBt5HCb2NkIF6FZ6DzYwZ6D0rId0NAASHcuWqr+lBEnoD/JwvNMTRxQU92a+ZwVs9v0ds5GD3IQbmMxc1LUDHdFTaJ2sIbyqYXv0/wWd5C2CBEW97eoWcQS7F7g8Pb89p8ivhd5TTUsWT0YsX1vxEsZGmlB26c223ltwLTVK/HbuQpLwii/j7dlDMsBi+ldEKAZJgXKCupK1IgHaPXuK2xmarruSi/S2zIw8kEBOkY4THJ+23Q1ykcYhd7G2wVYF8R1kkPO6VBNBAEdBxMQjVzHQA7zjLgpiJuMcCQRT7wUWbSUukGZiQQDgNBndcCzOJ12/ipqKkhtLswG2gQ7PkTck8oTwS8zvw7gdf3+mIi+B8AHAXwdgJ+st/1BAH8JTyCqLkbcu3cPzIztdovT01McHh7i4OAAR0dH6Pt+Jvm1SBApJYkDbJLP1jbAIiSgheApXEDoQBQQw4AQehA63L59H6984IN47dWP4v4LH8D59hyb7Qbb8w04F3zwlZfw4OEDfP7NN2EZagYigCKO10e4dXiMg/WhTBgTHj9+hIcPH2BKE0oRB7kQIl548T5eeOFF/Jgf/WPxyquv4ku+9Mvwyssv4+7du7h1fITjgzU2gxDTQRfAOSGXhO35KR49PsXZ6Sk25yLlzqUgQUwtYheE3xa5XorYIpdcWnzfPqKw2AKH0IFCREEAEyH2K5UsZZSUkfIWBIllzKwh2kpB8SEDQChZmNjQd+j6iNVqjc324ZXRicwhIWtmP6SAsA0a0smpHx1gqfa4tsBU0loDylMDJJ4h1XA0cJvcBD3Fym8GZuNGdgdWm6mQAGxVzazSruAAdzG72iLt7VRNbg4HcZR0j91ZRtD4ePF8QtgkUGbwEFC6IJLjvqnTAKh2lmaSt6rCV7BPCdXmW3yWyAG4JjEwIOLV2dVEBLrZkfvBGmC/zyaONEWplj1M6HnSSgHh0bhGPuuwLYRNnzCdDogPOsSNptr0kpIADamkDDkw0LHmuWeULTVasfbauU8PXGFqnwJOUUMbTcdN9QhukmQ7rIUMYAtgrB2ASTKqLWYEwgagHET1uRGAHFikQGZqIOYDrS1CH9LePBCm23Iwy0PTWljJg0pakh4qG5YXabgeHmfnHYbGLlZpC6OmjhaHPK7jVSrQ04OD0UJpkkvWAyOhRTG4KlpJJeLhdo2cg/CTEbVj3AuPk0Q6pAkHpP8Wo90wrnnCU190aYg2QVKzyrxYwougDm48xQr6Lf6pzUUexI64xDbmZmtsmR0oyR5XIprSRtsXt6aO5+qcVSIw3o6gLI5F3XlBGAvSUZRQV1uVjhV3sANpGmelg5WGpLOpi6piZzvMSJQTLoQwZA2TWcCl2XdRFJMHtsxzVXHZeEidcgZyCqBAGIZcQS8zYSwd1nGqJhD7yvPkKTa21YbW/Dc6dxCxCEtAdUQ2Dd72NlpCnIxKTyZPgIYXK5FQDpRmIHMS7m2RtxEcOnQcJNxhFsC7frPFE2e11RZpKst9RcKf5chIh6R26cB0BKRjRv7AFse3z7E5H8T0h9EcNwGgY+RDRikEmgKQNTwht74b6DVta0is/RMeWnoAa0IcxFk7r1wyD6BKtSWUWTv4AHoQnAo261j3cDENYSMXmQ8zRyG1Rc4teVDqqZofipDq4uP1M9n8EtHHAPwYAH8dwCtKcADwWYi6Yd8zvwzALwOAo0OJ7GA2o9vtFl3fo+t7DJrowkd4MAmw1qNmEQDgbE559rJ6oQJiMRgAUcTB+ggvvvAyjm/dwTCscbY5R04FaUoIYNy/cxucJzx6+A4oFzBn9CECkbAeBqyHFbrYgUji9o7jiK7vMeWMUhIoBHR9j/v3X8Brr72Gr/yqr8JLL7+MD374IzhYr9F3HYa+R9dFRCL1JRCgyiUhTVtst+dIaldbIP9swYQQIJlaCnLJKLlIApBcaozeWA/mDDbQAwJTQAhdPVWL1C/oGMlDzKyS49JGkIPUVQAMIs3uuu7SkHPvlU4OP3CEVKJKOIOzqWwbh4/TWlWUjhyqTZ8SQ1Vh+WZTu6dCfWV8Fhwc0MXOkr9cMF9zVLN4soVVbVeasX8zNWCRlqnEII6MblsQNJh53Oa6SGmbge2IsOlQ0AGd6AnNkWJm5E/YwQvVUUMPAlWCY/dX57j5k17IW+sy8wfSG+oRfN8EtoeNxp6mPA9a2eYOSAGcGKkEYAoiBT2napdrc106qCOfx/Gy0Rcn5aqRM2qnMDcTsA1ATV0yqKmzo2jyzEmlqe4AS2xQD2DuHaRmB0xcTTCqVIMBSlxTf4Ikvmfu2hyzOoaa42bpdbPpWKSI7l0mDZS+cDWfqiYK1ebBtY/na0hkD6bu17ZX5yj3OLl/zsNf/iQ0W6zLy7PSiqeT4eXbGJPwFLPpre/UtKuzuXXSJJv/2m/tE5mq2bRSCkQsY53FzyaV/vPAO2tMlBPt8NFoq623KnF09GKHdPnHNfOY0UYJJGGHmSXt7JhB6wCKajphTpTVHIPdKWd+4PF9X2rgvBnE7oS5Oqp5iPwwi/dbHTB2qyqgKukVx7eFzn/fa98jTxkO7zbzMFsTThO01AoAjj1mbnx2IVDwERBsnirI0yHo+yxj4t8HAMyzWNxxhGgB9ECKGlFCx7UTO2sOcpApHWN9OOKVWyf4PI4xpYhx289ZHLHYqYMl8kkiZNVqIUpMIBsPk36T00DmobWZssYVNqGQHW4ta56tJfddbOvbWBXo2Hse6XhF5XVmdqMHFC/IucxE/KnBLxEdA/gTAP6vzPxonmyCmfalZJHfvh3AtwPAiy/eZQOzIs3t0HfyzyS90ex+Hfi18bHf2HZ2kANhtkCiQF79LKUgBmBYrfHiSy/jh/+wH4E49Jg2W5Qpi+qfE44OVvhxP/ZH4u0338ALtw7wubfewefeeoD1cAimiFdefAm3bh0jsIDQw8NDABKP+FOf/hTOzjf40Ac/jFdeeRlf//U/By+//DI+9KEPS/a61RpdlGMvhQgg4sGjE+TNGco6AJyR84jz7QZjnnBw6wjxVsTnX/8cGITVSjLUBYsxXBjjOGIzTmLHC4iElBkIjK6PGFaD2vqiqbjMk7QQuhAQ+gGKemHJOkpKQqNRNgrRCjMKCqYkwHgY9po9PDc6ufuVL/N56rGdOuQxgrbKrM3ByJixW4hSiV6OomYmk4r5kMTsmA/U/jAAZjfXnwNxw/VUawkndoBQac4LgJziQXp6zcYYJJSLgB2uDK0/K4jbopIviakZTxPi2yfQmHngPiAfdJiOo0S6UEl2WstJ2BJleIGsJOMQxlN6SMpajfdpqU1ZI2bUOKfEs8MEgCaRmwhQe9f6g2P8AnKsAZhtDmaacRkIfh60cusrPsDvnB2IFCQTxrFrkl53uJHEE9w2G+tAkbBlptKv42m2ZYBqHDQUFANpLcAtjqi2kWVQkwRuzxID/am+P0DDqen3BbioYGsCupE03BHj7AOiirz9Ca5mN2kVJPh8FpOItCZgaOHM8go1MghlsQ02cJQOUR14mppfQFQ+gCZV0YOYrhM2tW+iCvjKoNe1/20NKh9S6aeAXKU1C5lGLhYytygGl5V3QyueTo5+6KvMTEiTiFj9IZI7FpvuTszmDLT6uM71MM1y4EkpAN4unnXAtPtkAoYsjowggEeZC/Hel9tF8g6Mh7KRh0kTHmypxmatNGmJSVTb0J9wUzVrStjxVlC1fal2lwBQVlHmOgHDlAFE9I8C8iEj3SrAGMSZS0v/WNpTBgmVWDZV5NhMH5RIasIKtfGvdv5McuhyphJ5inWuORM4hQagO1lXOQekEDAVSZYRKSCVgECMlC+HLM+Dpxzf/zCDUJPHhKTATqW55tDsbX0t6UNeB6RDainGAWzvtegFYWLwqHM/aYQD5UkEYHs6ACcdVm/EOsZ5AChLKDB7bngA3MoB2/vA9n4Gd6FpDbLQiEikUWkopYDzqcertx+BmfCpB3cwbnukrd4QhBdSamnaOXY19XUZ1GzGCwZK2zvSASEkxuqRjsUgh8puA/TquDdlWXghNc2V7LM6D1EFT7oG48ToNqVqLXPf/GtCaJJwM1OzvY8DaTKqixnLU4FfIuqVoL6Tmf+kXv4cEb3KzK8T0asAPv/EemC2o0AXA6LG5O1irP+805tJfzkGRG7Z3cRhjivwI4u3UiQ8ijBbrhLN2AHrg0McHh7h8PAAqRTkKYFzBnJGKSIaO1j3OD5c4d7xAc7PznG2WqFHhxI6HKxWGPq+nVJIss8dHBzi+NYtFAAf/NCH8Nqrr+HVV1/F3bt30XWSsY5LkSCcEOBeQDjbjEibEevYAyyhxLLGyBsOVujiCgeHB8ghYEpTFaQQCOY1UNXYWgKJLDdSQB8jYoDECYamKDYepDZ8IQax/S3OZq/NuYw3i42v9ZkWWfaugk526nWn54tOcjPTVLvmpK913pwkw9TH1WTAhSGqqpYgux8V1IUkp0+agRbzRhYpDc+AIGUF5SpNCBMjjKV6BNNUQFMGpgQJKUbgGLCUVNdsPCrJnZkq6KZszLkC1QW4apVp1SoJq85H7pWsksj5g5cUOT9VAPQE4PtcaIUZGFOHMAbxx0kRFkKuHnScBqD23Q5OJmXTAa0HRQPOBuoZzRnOqquZ+mhmzy20ojeVNvYzJyrfHmrPkdtMCjdHKwCoYYNUortzKCPM2msAyzZNe8eyyCGqnQgtdqv/vdGQjlWNWeXq1XfvHIztUFrMvGHPTZeQ2POgFQJjhnv8Cdg1YS/JGq8wkG9mDsZQfcbDPc9CD5OmwvZzUNPo7omNWsdU65lJtGYaJtcvxwss/B3UX6AmXHAArqiNutlAz8A8a5SLgAZoL1rSjJZgBtjhP7PBNV7t3+erYlSb31wCcggoHCSs5yXlufEUMrOdOd80bQlH2RMDAUUPhNWGnZvjmYU+XI6Tl9B7HsWAJMwZ5cBrplSlJxSLGKFka6Z/tX5fL9QJNmg0B+NNavOeldAqf+4YmKjFGJ+oOVKOosWqTpsRqOHt9GBg0SjE0YxqJs3K84AmRJpNmIxzqGnf3dpgJ3wy8zQAZMlX3Hgu+Xqrm/avZy1PE+2BAPzXAL6HmX+b++nPAPhFAH6Lfv53T1EXVoOYNqBkTKsVDlcrHK7lX9/3LdavgqwYYz0JBpKT+ZSFqhjbyohrn1ngZVAHMOoO8MJwgNc++lG88PJLGIYO+eQU+fwMvDkHT1uMaURJG4T0ED22+ODtA/TTXRyix2nXIcUOR7cP0a0GFBW3BZXIHqyPcP/FV9ANA772a78Wr776Ku7fu4ucMz7/+c9jtVrh+OgI4fAQfbdCQsQ5B3z6ncdImzP0/R2AE/J2g0SEuDrE3VdeQn94jHHo8eDRY3zvP/44wEBHAQgR6DpEJsQ4IW8TqBT0zIgAeiKsug5Hq0HHMFTziSklMZUgyWiHCOTMKKmIxBHi3MYAJH5aQKFODhmZ0esBQMwero5OAjH6mDF0GZuuzNTJPgGB7V8EVCZAMBstkjiJqrJaxgQ0ZxwDOKVXpyM4VfYgDm01tqOCBJPsAuZ0xxUoCpCmGrOwqYYU9CZG/3BEmDLyUQ+aCro3T0AaxYT7Dug78CCpIeNGowysQwW1pgYNSerOE7XsTWibZ+ssqm2lDR4lqvdWgEYatk+98UMicf40BuMBg82/gWZDc0XVUJqgZJ+g5XnSSuGA87MB68+LxGVkoDsP1STBIjpQgdhgEtSRTMOd9TIqeSV9LkOjhxouzoPJysxF+iZpRk3aYLsknOradxxNzazfASBbIpMEhK2FpwKYCMMJN6neUcDmhcW8FdSUzHWeExAzwNo+cyAhoKbYzQMqHVUcm71NYwPzYWpAptruabghMZ0Qm8NKGrV/XA+XxKhppimLOnsm1bxAff48aaWPBSEW0YCoVHZ2gNQ5qg5OGUDXpEo2rxEkoc4U1DbVpAAEc1Sz9ZgHRndO6E8bH4ECnulY5tnC0vmIECJd53oY4cCwjG+SNlZ5lNEkC+2ExOjOithAHsU6n1ZMwmuxa+N50PjC7YCX11rfmYSxGtcKnjX28FzTo+0zeveApLj4xJBIMAzU6A/s7K3IQsRBgNmYIhJFbFKHsBaThyHmKmjw5XnSCQdU6W2YBBtK3G1Uv44SpYPdRnhpmFhs6DXMF050vFic56zJUdf3dCjauJAlFnbpZa3QWVRfBWA6YKRDRty0vYBKOwAnUT6jOwvoTsURcrotPgdnrymdW6bJKD4DJ5sVHp4eoBRCTrIAV8dbbB+sMbwTJILMufEzWZd5DZRjgHtrj6zVuAWG0yIapQ6Ybit9I6A7k7CdHoxyFM0lGOiYkXupu9vKfkuqBTEBRLfherAz08M8CPLOKt0tvfKWCfUgyFH2raIZ7C4qTyP5/WcB/EIAf4+I/q5e+w+UmP44Ef1SAJ8A8HOfoi6RfKrUVjKbSTayzXaLKQln0EOnrB8iiYObJpycnmGzHTEmGc2UudoAA1T/T2DNEBMwRELoV7j/4ss4OL6FVBibccTp+QZnmy3GNKGw2AR3sQNiRAyEGEXyPKzWiP0gJgKxQ9YTbAwdgkqr77/8Eu7cvYtXPvAq7t9/AUMfsd1uMI6jqDI6SUCRuyzana7HwfEtTDEAXY+SCsZCoL5HNwy4decuDm7fxhuPTnC2HRFDkBi7KYMp15NlFzugF+ATUwJBwpaJWUTC4eEhVusVTs5OkZLo54lIJNIQfFtKVMkvg7iFQktMSAXYTDLGIUi0iT4SIvI+CexzoxPWE2otxmiVmdg1w1tVbVSlIG3zqSYS+q8KH3zIIRKe2wClqWUtPqIMOJuE1IjTNns9hROXGhnCO0xZvXEsYrc5JtA2SxKSwkAXZcHHIN9jVHWSMi6Ig0BIwlhNfW62yXFUZ6ehLrI6RgaSat8ZTcJja8xJFCqz8PeY7S9IgXLbSautYNWRo6EpxkXS3+dGKwQGedBZqAI3SyZRVgIcilr5UGp04CWt4hXN9bvZQtp4eccNOYQox1EzG7ZoDywHh2oSoU5SltlPYsWiMufmUId6cLHA9lWdqt7j1bTCgIq2PwDoTwLCVsxqSqcqz0DIg0YDSK1fIbcpW0qeZ/bf7nc5zKGCQL+xVCllgmqNGDWBgr/HADRD/lfthbndeEW0YtEDKi8I3KS2S35GMmcSdxn1nnr4qWvCFZNoG5/S+qu02IC+LiWLAV4PWX4cbazNvhYCRKwuk06qqAhx4mruYIChJkRSTZZFerBxFoddaXeNUW1d2jYQMpNe6v6nAwoQgc2srGpQXF8X0t1KVvvPOs0WmCFOWcSiNAVaeuP9hPJccQoVrrbRQTO4EaOZrPTSmaDOXhaysNsUcAzq0KXtNjtWarRvEt2QxBQtHXKNylLDEGaqvMr2MjvUAwK8Sw8UjT8ctgoIB2ByNtwcGNQX5BRwfjqgTLK/IJMcZg6ohtPrzoH+MSvtK72BMLnDrwHZPABpJdqMPBDSgWoTTufaKRP8eAJY+sVwAFhthP3BGlAaZrE5trCiVRBlB8O6x9NFfGSnPE20h7+Ci6v755/uNa3kAqScsU0ZYy4I2xEFhPPtCJDYApfC+llQcq4xft948208enyCcRKQFmJU8whpnqnj5VpEjD1urzt0B7fw4S/9ITgYVtikjAen53jnnUc4HSdMpaBHh1VcY91tMY0jpkASRzwA/eERuoMjUN8DFDAVgCmijyuEvkM/9PjSL/0h+NiXfAwf+cjHcHR0iPOzRxjHLbbbLUpOAGeM2xExynCvDg7w4gdew/bsBIFHTAycl1McDAdY3bqFl179IO7cv49Pv/kAJ+db9LHDmEeM2y1SychcENeHGLoVhk4YLp+fAcwCaDPj9GSDW7fu4M7tu3j48DHOzs6xWq/FzGTdo4uEVd9SR3ca61exM07PGWebCZt3TkEkfld9BFaRMZh9wBXRSWaSTEzcCFtUvnJ6NckCoW0ahQxcWCifBmzhNpjqENLpAh9EqmZRAcSzHhqupnlj1+xgRU39fLpF1k0nFUy3O5Qo6S0BVDuvMEr4lzAWhLMRtJ1A4wR0EeVwaJ2v3tTqYFdkQ+AQEAOL9EiBcZxUJcRiQrHt3cI3hqkOeeaMUDdXp463+y2TWU01y8KA2Wyw9Lmm5uXqkV5LafXN1KCuPE9aIWJ0XRYppTJF7tgi94hk/EBjZ64KaCJ0Jy3JgUg7dQ2pvayNC0ehEVJv7bLi2sfqMATdyFZAXhd0ZxJJICodmQQvr9VWM7fQQCmQYL9qS9g2g9XDgv5xwni3Q1pJGs90KJIdazNrWKIwSRv7x2KvfvDmhOk4Ih2Kd3+6xZp+WbzAAQhIVQBrUuYaLcCiorhDEwcAtplNQBnUE9xIwZtYqIq/pPnhqChgoAwJ/6WmRmGiFkruimhF4qC7Q5sdOHySCw/0dd6KZqEzlWoN/ZUBizMk/hJoz0dxNuYgQgaLDqI313slKgeadFy9+rnoP1tvttmXBiYAPexO8nM4E9vI4cEEDoTpuKsAppuKaLJWEBMIXbPxXEBTD3lPyFxDo8Wttm8N8SexttdU8835CpU36oHAO88Zv1D76BrvlwQ4XzhfKgAJ9fQu4DeVsDfU2XPlKaxAVg+M5gPCoUUBMs1Jt2mH5f6UMTxKCLlD7qkeQkwLyJGqcKWo1BNb2WfKvQkohPjZXuLAaxa4qNjG7OvFWZoRIyGPug91hP4Roz+TNueVXJP9j5DuZHTrCdPDFeg8orM01srzcwoI5wEoAnyPPp81xBthczeA7hA2GuPZUrXnNUu65V4k0nlFSLcywpbQP5a1Vg/xLKE8RQsqGEjC7qFqIEGi2coDVX+G/rRFM7KptcyncRR6snkxfl4dgoG21i4o15rhbbMd8Y+//+OSengasd1s0EVxdjOytdi/2Rx/CtfIBidn50jZ7a5op8QKfJUDFUSE0OP2nXu4ffsu+mEAM+Ps7BRn56c425zi8WbElDLW2KJQwthNyGNGYaBbr3F0p8d4eIg8rAXuMSNNBQGMLvZYr9Y4OjrEvXv3cP/+ffSdJMQouYCZJXMdS8zcNGWEGMEAhtUKH/zwR5DGDULaYLs5w9Gje3jxlZfx4ssvoTs4wCYVvPnW23jrrbcAkqxrCISQCSlnpCmhJMlGF0Bij1wYOUvikNUw4M6du7h37z4+8clPYbMZkQvQ9x1ur48BYoxqBpFzwdARYiCEIIznfAtsx4yck5qSMI7Xx7h76wi3BuD1t0+ujE4iFdwatjifemyHDtt1h6xxcrjj2UYiqRF14wgC3ptkR6nEb+AG6ljoxtSzVDSkjC6q3DdP1RIJAZqqtZj9L5o9MKuaug+YDkhPwXKvZWhr9kvSeI5BzBu6gDKoM0oWFVIFlAwQWuzPOMpOaB6vQUOwkQFt24P9fq42wlafFdtTQpYNa2bfNgPFPLMVrJIsj2vtHpO6a/xPXAB+n2fpQsG9W2f43ItHAjpWEvuacpN6lV5BeiehmDxgiwXgBIQDMdfw9rVUCKSJI0JqDnM1daya1KRDzVYWVUXem5NKA5N5xdWxTlKPMtIaM9W/1C0JCgAgH0SklYYLcg5mM0kim7RINAL9OSNsM2IvILw7EzoNGlpNkrKgxoo1gF41IcGrPG1+23hZYH5KAEWAhwZ4vcSde9TYxhJySMMbOdqUaDTtUHnZRvVeS6SCW6sRU44Yh4zSx2ZqEQBLR8xstoty2K5RMZRfSOMVsDNEJ25jVP8JkOdetUVo5h52rzki2UHDaMmu13mIEMmdvhOQ66RSLipc1zcTIa9iawcb/QmwT6tQbT9LRw1U5xZrNW5ljoTemknFTH1MCvjdQdvGpd2jvxdNM25JDQJ7wfD8+dDqJGJ0oVRJMBGjo4JN6ZB8rt4rKExzzV2NiuIOpzNHZ9hhyiV4MdMUap8y7+rIqIl1qtZSzt9yUGK0pBRRBA82Pybdz6uA6ZAQt4zhgZjUdFvGeDuI46qtPx2qtO1AozjtmmmTmdUAre68ImxvhwaOdS3E09DSqIdGq7LhqPlBFme5oFJy4yOlJ3S3omZLnY9lfU/XohkV5Z0+oYeB8UrX7nrdYoKkdC+9aGzDeDlDuVbwu91u8X3f//EaozarKt5Cn9V/7hm/dVbbJmhMXwsPZfdKEEGAAjJHROpx685d3LpzG0M/IG+3ODs7w9m5/Ds522BKGQUjmAqmYUQeEwoTutUah0MH9IeY4oCUxEEuTRldYMTYY706wK3j27h7V8Bv10WglArgh2HAuN1gu9kgxYRAUUDxMOC1D30YJSfk7Rm223McnTzCRz72UXz4Ix/Bp17/DN54600Fv2+L5LULCAginQEwbhPGXNDFHl2MGA4OwFwq+D08Psbt23dw7959AITtZsSUClbrAXdjBDhhO04Yt/JvPQR0HSHqBrDZRoyjSN4DJObi8cGAF+8c4f5Rj+H7XsdVlUiM426Lx/0K50OPcS1Z9DJCSzlq0rlOAWBqlOBDx1Q+6aQ6M6cbY7iqPo6jOAjkQR3ODBBAA65nkWJVMO02AA6EtCbkNdVsX6a2piSS4TAVpVECdwHcR5QhgpKJeEqzg2TngMWssYIzzLY4ZJYDz1p3Jts7nFDeDgA7RSU1lCD2Lya94jl4NvUwmcTHQIqv1CRo3JgTrgn89iHjlcMTfP6F2yJVAgR8FwKHoCBYwDl1LCpaUrzuHFLCLbS4yYDcoClu40YAnwWtJ8sQZ4DlsMBnfysg0MCVzkov4LiMmr5zkOt5BSA0G0yQvGd4nJFXAekgIK+oJcYIDQCRmyezN7SUonGbUYYontJnALGoxQHUQPmdRQtRu9aZram+K5pK0oHfarue5YDDpKZnBS6xhdi5mrd7tflNBgwY/lAlwPJq6SSSZAI8m3qc90USkgTR6JhdZA1Rp1LomUmCW1Ni5qQH4ij7ETtzCtLYqNxxjbU6UxkTqlqYTX2uyQ7qPHRyaOPolhuj+p+SvSurhNHA1WrXyJEjIUfhTT5FetV82EGILZEPMEGl8RoCb36i3jPARiNMsi0H4a2zec/NdIHLop3EmgHOzOwYfad8v5CGOWN1fLtaWoECVq/1IJ8RkbmCedsDKuB1dOMTjNTizJwqkFTgDA71UNrCFOpzzmQonmv2zAg94LLY3mZG6YPE9l4x0KnNLzF4ExEV+IZJ2p4PStsfte4yANOxB7Ayd92pmEWY1qbSKDsAr7TUstYR0AtuS2cBoW9CFklGofRCqOaCBrgp2hw4O+dB5iE48yxvMlYM+A6LMb+gXCv4LaXg8el5ixYwy9JmqxlVnSS3NUKPZveiVw3sBo1MIFWIkxcBWHcBH/nAS3jl3m3k03cwnp9h++AdbB98DuODtzA+foSUM9Z37uCo73A0DBg54zz2mBjY5IKSJQtKXwpCydiO58gl4exBwIt3jvHaKy/hzq1bWK1WoEDgnLHdbpDShGEYUHLCFoScCzKXGpNuM04gAg5u3cHR/ft4dfUluH3nNvqj29imT+Px6TkePz7FyemZ2B8zQFxAIPR9hxWTqNmnhCklbIOMQ9BYw6vVAUAB2zGBWUKsxdiBmfDwwWMFyhNySsg5oZQgkl+dm3HKSJklUkWQfWroAtZDh4Mh1vuuothp/3QckLKiNxJmKgGyUTeUZWYiL1aodlJFScxsOyujhsQzZGB4pOlgY7OvFI9fqdACyjOpVLgzGlYmRs3j35ggAc1EYVsQtxk0JnAIwCDAV0LULE6o6k5bYlC7Jq4MRYCEqs6iAOh0QEirZiu1DO12WWkOXW3g6iPGXAIgFleEejRViRS60sBvceC3ELCw97uqEqi0jRNAHmNLCVuAkIJItzuqdpM1tSjmm5a3rxRbv7mHPgEYHisYHoC0BnCcgG0ApYCyKqCeEMegdTdpFgdNpmHv0fdaliKTBNUMUYMCFtsITVKndF8l+yZtVZvOfNghr4LSh0mxpBHm+V36XdONKuXUa96G3CTOTCa9EslSSE4iqRnoSs9VamUSMNOY1PGOrQ8z57MrKgzCNnWYUhQzolUBJpkL7thsmVR1qo6fEU4iLOOQLVmIO9dVVb8BuzHAQugJoBDpndlrW6GiFkTUxn1mHqGmPEZ/zc4f7dCmSQa8+ZKv3/iEP/x7EzAD1VHjBdv7LY2s2ZVWe149NFdzDIIAf0CBnRMosDvsqMAiJ+HnM5MoB3wDSYZIAJKKmlvUh8I0i/t7ZYXaekC0tnIdE4nzzrL3uP3GaNhrcqqjNsv8Vcijc54OVKM0BcDiL6c252XjgOgAJBCm2xHTQZAMdDqPcSJ051wT3QAAdwX97RFp24HPIrrTgP5x69swhWp73J0RhkdNg8QREidYE7dQloQ+67fFWS8dEvoT1HBteaUHsUzVHE/AcMv65mlTQrzJBQvVFzWJVX8CpTOudVABeHJjrAc+A/JgIK8J07HOxVJAs6dcK/hlZkxTEjtTkGSf8sygglhyAHjeAwEU7MAvtD44yS+JljMS7t06wu2DATyeIm9OMZ09Qj5/hLJ5BGweIuSM1Z1DrEPAqhuQp4AMQmYgMcBFKDYwg0sG0gjmjLTp0Efg7p3bWK9XiGqVX5gxTRNyzogxIMQIUABnMYewduZSJCnGeo2DwwPcuXcXQ98jdD1yAaYpYxwnTNMEok72ypI1HnKHvpNTURonTXaRJA5yjGLv3EWUwhjHSbXREjsZAM7PRzAkiUcpuYrUixv3lBm5ABpVWeyoIWYFV8x6pC0gpBxQNNajd9QwyjZw6cuML6rK37CzSPT8zain+bjVFJVBjOqz2c8uQRGjLtoKRGwzDHtO+iwSWsoFUOmvnGhVzaP6rp3FWkGpvtekO5odhzKL/V5QMN7rSbm0zehCMMGt3p3JdH/Pxty3Cw7U7ZkXc3xbOrtcVSkcWhOC2o2oaUCVbgeLKepAhm7gzVnCwhS1uJwWTsiPZdg6+8iOEfqCPAapr9fxZ9hJpT24oCf2fxf3nRRImurTBYY34DsDkprFr/TS39IFjQagEhUdjqrit/eS0hXmcW/NSS0PSkdFBAvV/o78pjhvO3cthm9N5wtUlS7I0Q7ac7MD2xUUCzFZgVN0scoVvLJGCKopgC0aTGjzyR0qaJ/1xffJwKFKfbkDSlaJltHdshgOXJiayLW501y9Rw9MlHlmb+0P/7X/ATN6azSvYEJ9CADM10ASrQ9Zkgriuq4kCgxrFknMpN+t8XKQN57jYwDX560dgABgtQnOLPGDSyEkDnX+rhz8uvEi3x/j0YTKE7QL2tW21m0+xFdFpfN7eCH38g8pgKZ5lJoA9x7lCWAgrUUjZIfiltnR9p92GFmtJzlw5E4iTZyrBktpRbQQ4ujmo1JI3S5UGMuBvz+1ukkii4xAIhEs+P3Q0h5bFlMLB+ml5O0wpVpMPVhWTVj9DTWEHLnxAEETT1GdM8vquneNLcq1gl8iqhnSAIUvppYlqp8AEMh0Yu55/Qwm0VsAZQCwUGddDDiKwL014VacwI8+h/z4EbbvfB799jHu0Cleut+j69a4d+8YfdcjgHE+TvjM599BOLqN9dFtnD5+iGncIE2SUpi3I2i1Qn+0wv27R/iyL/so+qHDycmpRHTICWdn50h5AopIao+Pj3B2coZxnHBwcACiiPPNCBChG9Zgijg5O0fXjRLveOhx+84d3Lp1jM35GQpl7Rchxg5d7LE6WIFCxFsAxu2IlMVco+8CxjTh5OQUU0p48+23cb45B0hiEzIrI0PUf8JVbXMIOtLDoJIrBjgnlDThwcPH4JzwoJeIGVdVzNkhF0cDUQJt00SVMc/U76Qn2qKbjTrHEdBs4jy1G6PWYvaz6cCApNtY1OO327ACWWEMYoeniQcOxFif1ZbOJDXdlhG2jKAh6TgSECTdNDGAXFT1QwLQoiAhVtMGKNAunUZ8yKw2v6biasHPKTnmmzELWm852WvaXZMke9WzSf4MdBsYBBQk8i6grd919+qLZhO7jiMSsM0dPnt6C2kbZePsCJwIlEIN4RQ0w1ZOtqE327IyyNgUzYhmO5o5bZEC5BzNI5uRjqI4dOmmk8cA2sr7oA6JwwOVdA0k0SaigD9zUAMkFSlHCSflN1aJ0EDIB1TjdVb1tIHyjBoH1DJ8pQNUk510GLC9p3y2ONV2kvkNY9uc8kr6aeGCRHorzn+ygGRzs6RjYAHGZqsY2Emmi0m4JAqAOOS0wxjrQZzV7KH+Nl7tQclwya31VqxyUkAZoxwcTPKr667owddC/rla6rovqyLJX1aqdYgFnCXdLywyioLnrGAJ54ZwUSWCdsgyRypKEsKqKCgqqtqujm52APFNI70PkpTI6KfaSUL2RXHgo1kotZn9ZKBZfFtmVO96GQtD1aIBCsqLTcqPHqhhc+y+jkUhZI5JhUCREXrxTKr+CASEWNB1GUNn5pACfhFQnd2AZjpxVaWCMxg4hOyfPdf4ySZ4yytUDUt6TFh1VP1FxG6X1P5fFo9peExCbAq37qGEOBseMcxO1nhSVt7NZ6L1TAf6vjUqDx+PqZpZzA7qxChTRH8aNBU2i6NtJ0DYzL/CiFnov3RIc82PtVkFAnHTElOY05kdYNOBnKhID0mAalN79YXJQLcRnjOcFHRnBd15VmFCQJhCjbRkc2DF7Nfr3CjoDpou2dYUMD/87SvXCn6BZqLQrkhnqokDybV6C9X/6f/VSYIc+J3VJ0W1tYgoCCWBp3Nw2oDSBj0SQmQc9FHsdJGRErCdJpxuNtiMCf0qo8+MMk3I2211YgsE9F3E0eEBjo6PcHTrCIWihBJjcTib0oScE1AKAhG6rtfMbhJmrO87hCAyVXGCY4zTCFYjmkCkST/EGZBZ0hfHINe6rkfX9QgU0fc9Si4YxyQy2kAoXDClEfk8g7YBOWc3Vm6U7EQ+H2yEQOg70aEzF0m+QRljKjjbTMhTRs5PcbR6D8VO+HsD5psExCSYHgAb7lpirwUe27HZ1bqM+fv3AcYkWEOaWRBvqvdUFZcNrQJnUQsXtem1d5Pa2dbOzv+uyTRaW2dDQARSlahvq41HBe0ad9MEkMyYRa2YSYYI7UDwrHvLBfdXSdAVFosHiiwbJcfczC1M+qugoUrPVBo1k5oYADKwZ7zIHQpYD2BmGwpo3dsoziDJxp7a3OlBTGzv5gCvSuBs3hy4MVMGc4gy4Fs33ySbYpXmkdJV0DiX3dycQl5YsX3byHR8Wupm3ZF9O209OZOIWSGe0SybpFzfn1XiM7u/ista+6+jdKGII5XFlKW2/xh4rzaYtKBhVpDHjV4oSl0hSBZMIDRJlEpFhWaadN1nU7RxD0tplZuCmdR3H/+z5tk+Sk4SGOfvW/LKKgkONJfIW/117vcs8mJOWzqO1hjTZNj3Wk9De4Ekc9wslJtKgU2xltmuyQ2F6cqBby00/84BQCdOsZV/2nVn3mAA1A6x9XsUIAyX7GEmVVWTFlvH1XbYzCaAJq1VOgoJs/mrsckj6oEu5VD5YC1u7RufDCpYqfRk2jvH5+SfxvRWE4SQ1ZQLqDRq+6DUz/O+ujGV+OAmUGJATQlDEq1McXui0CrN9tjZHHl+Zn17Ak+5XskvgD40sDrDLQbAdLEFYKfxBAC8CG1GNKfTak8s9rrb08fY9kAZMmI6w1FfwCEC/RqpANMEfPpTn8XZdsI7b7+lFgAR46MzlMcTpu0JSh7R9wP6rsPR7SPcf+lFfMWP+hH46Jd9KW7dv4+zsw222xEPHz7AdrvFZrNBzgl5mnCwXuPw1m1shxHTlCRWby4gFkDaD5LB7fHJYxyu14jrFVASAhcQuHqBdn2Po+MDDF2PVT8gJyAn4ODwELHrMD0agRDQrweUApyfn2HKCblkZBT0qw6xk5206IlaHBNkrCiQOhoE9F3EnVvHKKVgu9lghJiAbEoQm7dtQdq3CT6nwiCMpUNKESlFlDECUwCNVGMv7sSeBKqKyFTDlcF7kKvMovRqi5eb8xJgp3xURzXb9MXZSE67FkLG7CYBKOBoYDho7MX+cUZ3lsTWt5cYvtwH1Jhy1izmGdA3O2DWWItxyrCMfnJCjjU1pr3TgEzcioSqZHbAjUAdwLoRUplLO0WaRPCZeAwkmb0jMQHJAbtlSCMANQwaIAz4qsEvIBqCKQCBUSgogOS2cRsgUGUHqxQ7dFTtU8u6aIgmHYcJ+hAkjNlKUuAa/RA0yPtjQshddYgpKwG7EgaIMb6Y1B4UoCxhjIzGjEmbNLg71xBoa6qObnkt49ydEkJBDXQvEloLdk8IvUn3CdNRQO6pbY7a95p8Au2QR0W0E+VE6ssrcpsr1fjFSPO1EjemxmRY+myjIbNXtLVnUR+kUtTIGADENjtBU6deIZ0wYcqx8r5Kln7NWbpU9YBvNNRu4kGt3nsGuoLQMULMGIaMaYrImZFTO31zYGBgZABTkSQCcfRrtYEQjpJimgNAk/i4WOzXKnnLEqKMCte/WYGnmWTVcdS5j3oIF+95y/jVpLzpUMJVgSRZQyiiC7RIOmzgl6kCKePDpiEpHZA0fB11RWxYLdlHZEEaBopZ5qOmQnZAgBnImi45lSCHlViq0/VA6eoBsILVKgk1oKuRMohE0yKaP0goyOzAq5NaekHDdCQ+BNiwhNFctTm1d27vNy0Mu/VqZgdxy+hPMDOxkCgfGmv3iJGOGOGeOKicPTwApoC8loQosacagciir9he1Z23OkvHKEqnHIGpSKjE/rRoem/S1M6M8ThKX3Qubez6s6ICATnRl45RTgPssG/JpACgDAHj3Q55oBYTWPseMiPHICHhzIlQ7aijmqDFkZHP/v/E/UuvJMmSJoh9oqpm5u7nEREZ+bqPulXVVdPVPdPTzZ4huOGGmA0BckBwwQ3RILggMFsCBEFifgJXbK5IDMgFdwOQG/4CDhcECFQXZtiY6WLXrVtdde/NvDcjMyLOy93NTB/ChYioqnuciMzqGxFliZMnjh8/5mZqqqKfiHzyCWO8a3uwjM3bH/PHjfyqZ9rh+Rb57X4PWGqje0Hern/fKBL2vX5EBdYOpQD3Dw8YPXDlE/I6Yz3s4Yng4FDchOI9ylhAFDFeJXgQRhcQs0PKhDQ6lJIwThNCGDBeXuDp80/x/PMvsLu6BsPVz885I6WEGBO4ZHApVcGCnIP3XiPf5vkyclxRijSocPplQo/OCdDnzCoPF3VsHFJk5CQR4zWuyKrxK5FkIe2Rk/t00OpjaToPIgeGlS4p7cG8TWYUhvTN7pQryDlkJUgSP9Zj5/0dDDRel0WhHotwnB909p7+9XNv0Xyt0hY4IIbrzQvSaI8SHy31UiWmrJjHojKAVuAzwpzh1gykLELJfbRVeeLUR9FJVsWj90XG4aTq7dcFZUC//6ofhNOInN63ARD7O3lPkzLTCdGA7/lDP3suJ/+u/L8Pu1ERTtOgZF2NWKr4uVial1U2SMbX1kMtbDkbrxZ2Q+Nx9wbVxrtAgbI6VaofnU1miCAOgSlHrKibvc21cECjNaBxuCXqyzUyKBQOnEb4uxSfFWZVqbJyulbqe3QOlqGlwGsUsUbp9N5U3cQlqht+neek16iFqADeKCq1H0hVfNrYnc6LDwl87UjF1a+SCUjKSXBojqeplJA06WDjrPZz+Wxet3iLSvwlV51v8ZIkY1AC4DRyZcCxAl9zUnv7xASUVu/AHtBasAqazmsFSiCl8RhdAeDYDUIHxuyceQRq8x4WfmrP0bQAQG31SDgBZfW8SocgxzgtfINQus4yQTXoq7alFN3vNOtXCiETwZXG1w6ufJzobxdMOFVlgEbUuT6388CKvSZFcQQYKDa6ylndAWkwhtVJdivVdWQ0Ayqy9p0Wo0nnvJZREMdVzzMKfSQnL22LE51ko2zt5knHfZAb9qupKHWOlWvX3hev+hXwS6kBp5Ohs3EiKZFuNoXetFsaSIJv0qJ9tNlohmAGk6uZ3TqW3PZeow3Vvf17img/Mu0BcLL1AGf/r4VVaJSHNwCOImR5Tw+h+/eSforDWhhf/eYF9re3iHcTkFbk5YCL7RU2mx381WfAeInxasDggOcuYwJjx4zlGLEcEyIBhUgaRISA4WKH519+gT/8+/8uLp8+RSySInXOSbe6GLEsM5gLBuekYUeW4rZhHLG4AwgCVDln7B9u5HeOMHpgcIDjAs4Jg1If8nxETAnHOGMzjlinCWllpFRwd3ePGBMKCsg7ie5SQS7ANAq/+rgcFZRHiQQMg+6SUvTGyvYtDJTCkPBYksj5fAC7AfAjEjxSdmAOb+uy82GOHvgaH+384w1QPDJn+kiIfLfFLZW0Lmq16hkVwM4rYudimDgQ8uTgEmO8ywJ8NW3jo0TjARU8fygYXs+g4wqaV2AcwJPTjUD4TWJoTOOwQypnlITinfL0nL6vN0gtxQ0n4KlqiHabo/CC0cCwpu+rMSlqo2zz16gHpQaw61jbZldpBPZ7lshZHfMPDH6JEbSVMrwUnxWSjaSshkpkXHhgpX7oXl/HsbtGU4mAgVlUKay+XXGNPkAq5SXywtJQY2ggiTRjEfaE8ZYw3HMFwMtT2Wy2L4tuQsIPjJckVeBbrqoT4SBR3+lOZI5KkE3SqVSWcRPZSdcl2UQVlFqmI8g52QMlywXGrev4pwJyy9QAGmdIFbqK/te5ZpxIlX+rEaRVP2toYypqKzqHVHbuxHwoCPuQJoUh4He/jFjmAWXxoEV0T0vhCmbEJlC9JrKgwFBaPv783Arc0hLAs5yXGCi7DHiGnzIygJJJlCBAtZMXAAUv3fkUtNYW2R0ortm6TqIMaDYxbqkC2p72QAai0ZZkbWainHSpR5A5J1xzquogbhDZMRTh1MMUJHRwCajZH6cqMOY7gyWA0x9EWn9CjFLEWSjZIwEIQQBwzs0ISnMLB0ePtzd+7wdrxF1VCPLYeLiArh8GythsTAmaBTGVjgQwsXQWNEBcREHDFDDEvhDyptQ150KzNZSB4UFlDA9yjvVKgK9fNeI7EdZrWdv5KoM2GdOYcCwEmr20sF5Qi3cBmWPrE7VtAIZ7ap3lukBO0fu2yO565RCOjPE+wy+C0F0MCpRtL7OsYpP6tLoKAJVCyA5IW1f3oz4bZkoPVFiKLi2aZY6gw4nzaJFsvxpgF/WLR2laenx02gO6aG69YQO8xCAqABEcZGVSdR111ZPAZIlouO68dkIACrCZC168usHDfsDxuMHTq0t88fyn8GEEQoAbAtwAPL3agZwsLF8KhpTAFEA+IUAzueRAwWHabbC92OHq+hrjtAEzwzsPGkZspgE5BazLAjAjbCaUkhFTFI84OI23SlMJlIK4vwcRwQeHxBlJi5lcGODHDfwwgflQMYVnQmDSSDEwOQ/nWfhRzLi/vwdDtRGtU10GXC3TB4pGcyUyXQBmjKPoBZdS4EnEza0NtfOEcQi6gRG22wvs715/0HniyKLVgPWHr1XPeh8nf8PdH9vPfSTLDt3gKPNJAOdk87W/s1PqRmMLzq8FbmWEY67pp/NFJlxfRuWHaEMLiRaypKCrh82CQoNqC59nO2Abons0Mm3tbSvf2XRbezWVs7Fge39PD9BvFoFo0R2qF0L9+YTiiPoLq3K33/cP5AMeRCyFdjU1SxXEkuqUcoHwclnAvM0PiTRQLYIjLdY74cHafFEngAcrgNN04V6Mu/OErDfuj66Ot4uik2l6wZVSAzqJYsgGalQHtCK0jg9aAtXug1LApGcqNucaAM0TnawD4QrrszQANHTz5hGHso/CnEb225phQHitTK3VLWCDe7oms1JKDAxkKHf0AztJgAItErCVda4WkvXYKRJYdsAAIxGqcsP5YW14U4JEfS1ayIA7eOk2CPl7k34Tx8TUZWQcfdccgbzuOVYMGKmCEaeSZC3rhPo7KKgoCq7ZM0rHHzVntrbVrjxVdbLUbjQaGNc23SX61ka8syE1e9Q5NLV7GzGQnT5fjfyezCE+sXVcgEKugl6h/AkIjtkjscOcBrh3IZr3fNT5b2Ns13q+Xmwf6bIrfZDCYIwBO9HJlmglZ7VNnUpDGVukVzIsJAW3hCorBuKavZFW1YSUlU4CICeP4YFadJbQONgwyoY6ygp8wyLqDDJXCWU05RO1KSNhOIhspzuKDKwoOkgDLqAB3zywBI6sG2k3T2uwyqmHVNprQh9UjNhFiaUo/VSZpjag0gBSXcPgU7WKR46PHvk18GrVkvJa/RXgEghONiSdNWQhKYsbG/rvit0YqIiHNGpVOOOrFy8RvMe32y3+6A+v8Qef/TEcJxBnuIEQAuHJ0wuRGMsZJSWUZcEUEsKYxGNn1eX1hGm3xe7qEtdPn4LIC2bxA+ACttsJKc1YlxlcGJtxVLrCAvIeCB5MAsrH4MCJ8XB7AyJg8B4xZ4kkMwT8Tjv4aQfGPcAkwJw9RiZkSCooB49EwFKAWApubu9AzoP8AE8ET4QheFXPIDAKOFllDYNU5mwzjJimCSklECDC1IWRCmNyhGkMwocmwrMnV/jumw85dZqFZNWLpUS1+03dlB/bL6uRobaRue60ZoCyzjduC7ZWxQMnhHmXuAEWlgicXzLCPiofzjbvttjsbyhnifROA9i5xvW1zZ4ZFLNEUjTyW9PpTowCOwIPToDPoKoP2jXLIm5cGpDpZW/6wOuJg1CND1q61S4ptD84TV9359JWprWlqf6ekhjejxGcscO7AjdlmdIFNfVcQaOaD7eaQkcD9taYgaLakhp5IEn1J4nyVODoWJQOHJAvCsbXDpuXkHai3cYSDloAp88nHAB/5NpEoB4OKoUEoQYEh7SDAMNCFdBaMYpFh9NOtai1Ittla1crbW6jl05Ptf2xgaMEFMgzY6ed6N7xrCxbYGn6qlPqu3mje07RYt23bji6CZv6hoBfahSBDzlnSBzqnJXy0DlGPTCzqL9x3StoUOfoRNJM1x9nQi5e+PCWYs6i4FA8IUMdStc4+H5lDAeuWRqgK1QcxCk1iTt7fn5VQLQCPpouql63RlapEBx3POzKX24AukXm0DjvSb7smVZQnRk+ErA6cCig2tGOxa4ZZcx1r8P2cqEd1fbHUPUGnSCypduEoiqDllSL2TmuUmdZOcDppNXcRzrqnqF6s0SV5lN/3xV21cK3LqDCDq2oUZ0QSuJQO33GpowimQCu6jA2Z0zXtzYqMXDnOic5yXiXImom443OKbUTxSs9gGUeWVbB5tSp8gJhuZLNoAxyrXmS87glwx0iqBSEeYc0iQPFHk1FRwvhXCzaWY7RFwEaRccaRwHqL2lNBBPVVt7g1oBF9jWqY52VUtM7eNB98F3T5e8A/Lq3qz3Yd8lRdj8DUEVpIgen64dI3E6FHZ3RZTCKcoYciALcdIXx8hmuPvkcnGYgrUDegznhm6+/wpoybu72IABB2pyBCmNZV6ScsaSEzW6HZz/5CfwwCB0hF6SYsKSEnBL2hzsc5wcsyywAnuwGGI4cnA9ALsjrivn2NebDHvHmJXJOuM8J/NkXGILEdJ0ZS2I1BMAUAgbv4UFwzqMEJ80m1HlijVgwA8i5OljPP/kE0zTh5e1r4QivraUU6x9vpi0uLy+QteXxuiwoheGdw3Yc8eTyAtvdFtO0waefPsff/PVff9Bp4kiK/UTjt4GrXvewb7t70viCZPNwZnQ7HmLzPKlydc89erJNT9MugG4Yg6gsUFaveJDWxGUUybE8iMGoOqyFwV55vpbtiIK6+TyF6hxKsOg8i4YvUTUWvVF1ii5l4+5AN0MjOr2D2I0Luu8d4D0PpFQOcLE36OZZAQJq8QfXSkHd9C06+pHQr2Q5XLsvu1+N6hm9AwSp0Ne2x067tNVOZApsDPz06UFS6oefZcO3cRbuhHxgODD8TCiTZF3KyMAo4FKaEMh7+0xDvJLrGh5sjlL9vD46Q0k/xkOccWt6YfOZZFPDBnVuFY/a9MRAb+UPkmxOoiFMNQtgDpuL1EX1UaOEhdtn10hyB1x7PrBIKso9UeLajMOOCjLVsSiPkvjf75GLQ8ktIt/hsGoT6twBKs+92htbB04KcOAZ7FzNTlmxZ+Uesow1KSAB2nOQZ0Z17JyqdxjdxVq8WiGly0DYS8Q3zPqcuoLGPLbNsmaTQMAi75fOla1WoN63k2JOF4GyUqU+VKWQWuUvvyOfwd7J3NE0t4F6CTcq8FIHowd7IGgEXAuu9RLINWvBTEjRi9bvkOFcgXOMyWcEV6Ro8SNkkwDLqlD1+JxyYAms2uFSnGo219CUZWRqBsDagReTy2wRXZOcrA5okQxBnuxvmi030CuFb9LZrfHBrZBWcMN8HIHVNbm0SdY1ZcBVOoA8H5lPqDaH7Xwmi2e23kk2KW4dhosBQ8xAkp/TBSFeSFGwdCZ0dmG1YU9fW2BrgB1qLYqts74znnSRRCuiVafbJbl3kVXTvwkNcIOl0PNdx0eXOgOo0hXqq53cjBSxObACWxGtN2aw/I+qYoRGkSGbLRliqUAQ2hXGA2GCH7eYLq5E3zF65HlGXlfc3t5if1zwmxev4H3AtN3WqOl8PCCuCUvJuIwJLniEEITjm6RF87IsiHHFsh6xxiNSTghO3mPSbl47rBEzOCfEwx5xf498uMMaVxzmGdvdFvHJNRBGifo5gByqBEzwej4dMwGHpB61GA6JrEt0GRoV2G62uLy8xMPxAGYW8MvicRYQmBnDMGAzbZB9lnuaZwAssmdDwHYa8fT6ChcXF/j02VNxED7gLDmbMnJ0G2ztLoQOrOrXyaYGVMNL3TlqZKdLt9QvNdauVz5wOgeJtQqbwN5V4GvV7qYEYNXYsIYWwYncWZLoHhkQ6tenSnv02UErMrBrK5bi4nYv50cPfCu1iNtYvTE+Bli6DfEkgk4tXYtunM+fjYF+Lp1U4Qc+6rzXw6L5ZIV7PdBx7WeJUgMUZL2c8pa729K/EWqJGVo+mUeAVe0zloVaW1oH5Mssjs1Bi9hie6Z5y8rn7vw4+7wunV2dE4IC9S71p8+N0DbcmnoV6n59r3Dt2jwtgZucEAEwYFZsHG0jlM+FR7cZtmvr6TEtMoMG4PUaytSuw35XOX7na+F9H4zWIKGf/OfZDTQ7AaBmDyrHWQGwZTg4oBbJkTlDlYsowLDpp1KTs+vXjn4Ok4yRRWRDourAQyO/fuXaQctF66RHJ/qnNjedom8XGX5hUXsxBzqo0wwA3rpFokXzu+uiDPG6KlpVJ9A+rrebTBX41i5u1Zjp93L2qDtqjEXSC2CNLjH4LNkdYnyUDm+dg1oYGvBAA/BocxpADcTYZeVR/tYBralONqpKN+ct09hljOqaG4so+th6cxp9zYBfJKtkOu5MUI59mwN5FvlFocvZPEGltcj1C+id7koFvGJjCObonRT0aaYoT4S8cQiDFxqNZRE2jDIVYChIyp33RwIVV7OSdt8+8htroA5/T8npaHUuoTpwwWpwTPrMAxzUAejA9buOj1/w5lrkt30HKvp1jRXNrqC4CCCDSAn3IDieYL0f9RUYl1YQIwM+AOTg/QRyDvuHO9zfvsbNyxcYHSM4RsyEWAKWHHBcV9zczyByGOeEi90FLnZbRBoRnYMfHLaXT/B7v/cHeP7pZ1iXGcd5xf5wRCkRuSTEtAIE/OHf+0NM4waff/pZBaPTdIEQJszffod0d494f4/4cAM/P4DiirIccXh4hde3I3affI5pHHB1OeDhIcC5iJIyjuuK7DxKCMiJkTNjZUJ2QSZgKZoG06JCrc4NwWMYB2y3WwDAfv9Qx985ApPDsizY7w+42O3gHcE7ArzHbjshOEKOK5bjHsQZL9KKGD9ck4sCqoLm9eiBHnXgQAFabQBg0QqzRAZ0+1ORvNehpabtoNyKC/pK+PbZpI0luBqMHrAYSd8+h4MDagYAIl7pABCDsvRjRyogZvhKcgbKZgAP1KgQ3gri9DIsTQ6NYHdoU7xi49G3e67vcKiNPHoDUkG1geezsT8xVv24dsax7xV/Inv2gQ/SCAIXNDBSn4lchzuregd0M2JpKGHV6OwEkIp+q9qoDoT6WYxs3kpVewkS3XTZwIWMTQkMBEbZMOKlRIbDrB/vJI1IAKZb4WUs15qqDgAtqE0NiIHhAG1ly0gboWQUT/IsFbTkSf42adGT045OvovsWEV1GQVZxEvdhHOLmthGZSL4QPteI9EaeXaLXoNvKUk5iW66JJs19DNL4JpCdasWEIVT/PMhDoZIaJ0c5hlaoGGRjIBbHnFM9SSUIevXOZW/szCx3PN5pzpKBN/ZkkppuGnnIzRQ0steSYSdZYyhFBWt8vdLgZ8L8sYBowBGS4mDLQ3eaDbSnEMiluO+wCXCei/ZmjJ2jqKumeEgYG04FAxHB3Ye8cpjfeJNCQ4uUbUXPQDm6CoQlvnb2a0kmc28eFlf2h6dSNabSXsCwlm1bm9zCsja7fNjqD2YvSMFgS5KQbMVCNthtrbaTLUfvbJOscJJJ8/EsiwtKyVrgYs2jskEv3qEg8jihb0EYtJOa50UUJcOvYUjgCNjuHNI0eTSqDphTAqcV9T51e8lZSSsFwR2Dn4khLnAryILWwagTIy8BdZroSkMB4/NC4Y7LNi+ygA89j8F4Bhhl5AKISbC8EAoR91XM2O8LzUTKxQuGTuTWDSN7N7ht0Yiztv66WxjEtvfS8vVPa1LCD52/B1IndHbwW8fFfZRUh4Dw3tRUyiZZYJEBooR0DrNxj504zyIPMIwSlAjRszHA+7uXuNyM2I7DoipIGZGzEDMwJoYjIRYAOdH+GFCzNJPewwjwrjB7uIK07RBKQWlZDCXGnUmJ2oL24trbMYtLi4vUZiRc8Fms8M4TBj9CE8OHCPKuoJyBHJESSvWOOM4H7ApCWEYMU0DtpsRIXgUZphcUC6MmDNSKijOafZVVmrTOeZmjHR0QggIoT1y6kCZyLRFVfURp8I5wjQOCEHQXYoRC4CcEnLO+JDHG979+WZke06hFp0x480KgjoDRGdGq57TvtPp6z2wqzhav5ueqqUCz6PGfdraxrcHn6bpK4VuRbMBgLADIc9ukiyB6M7Ryee/MSYG8liDE15uuFIrOsDaLuLsy6KDRTIBdulvMx4WOT990b7eNegf9rAocJ8JqLJBWtxhkmfymr6vT/PbPRhv+owaU+WG0ACNcfOg9IlalV9ln/TUWsUsG6Fca5i5pVjRhoy6SKyLkur2q3BDUwJ8afrMxrWza2KiFl3qlAHs7+v9qONWI7xo4L2O4dnacEUiMlaUx0DT7e3fqvPhhFaj4w9rl6scWlo/zjyRrYbfeJFJmyeZA9vde0+Xcllsiz3vCqjPzRWhyZIl7a5pqVxTViCuXF2Q1g3Y+VQ+rqeKsHIbja7lckGphcACwqRzZHsPdanfGjDQ7JVfdO6mFpG2QwonGf4oi2e6kYfZc1lPov79GJw5GULdM+OpX1mdjjrZRSKN0NawdXhjFmoTQaLAH+XoggAnGS8A5iBXe297rb3kJXJdO225thZ6qcGaZclCSWCWeQIDbRYlXUzFoGuWQmhqHUTwizWLoNqZUmoF0OTkbW8ivGGWK63Ok3CtDdB3EW12XAtp06R7XykIR3GQ3Cq0r7pvehZaVYAk5NkKiWV+gpSWEahSgGq2ku0z1Vba+NkaqvO8sz+WiXKP3+P58XcS+QXwCADWfLFLQCgI2wM2Fx5PPpnw9Oklnjy5xM2r1zg8zHjxNxFxBlCcEpydRPFAwksjhgsCXp9/8hwpJrz+9ht88+Jr/Is/nfGHP/s9/Ojzz3GYE5aY8fp+xmFOyG7EPM94eHiNV7d7jOMNpu0GwzjgR0+fY3P1FOO0gw8jcmaMw4RhmJDyilySKCJwwWa8grcWXKWAi2jrOu+lGA0CZkthuFKQ44rD4QHxboPZjbh49jmG6QJfPP8cA4043h0Q14iSEnJMAkLjAWtOYPbV6ME5hODqpCgkGsPzfIQPHuMwSqe6Ih620za7XBgpJsw0o1xewTlRk5jGAddXl3DOwTuHh4cHLMsCZkb8wO2NTQanT5/3gMb1ldUW8U2y2K0lrG34NTXSGa9qwOy8GnEpIyCR1rbQRN+07QytW5OrHDrhNdkbGlihVFSlwYGpSPo0ifg3DwCpE0elyDVk+dkNHoUDfAE4MaiIFc1TI/pLQV0bHIvwpEzI2sTD9JvpLXuGZSX7lFTvQ9p41fHTN1fQYoBSvQV2rI0kbEP48FEaoAFxIuUPqqG3jd0yAtY1rUZBdJNLs7TzLJMAEKcV9iCZR854t2RpaYAHRtoB83MgbCWqm0c+TYMmAq0iqVUBaJRnJWL1wHQTkSeP5drVzwpH4fTZBjY+lDqXjc9rmQavKUBKAoLCzHVeliDSd8O+IMyM8UEjvORalFaBrkWQ63XOMn/SDjWKbgdlAXe5UfukSXqmN+ZOPwWoQOTfWCPjfYLnA04V086mkxS7Tuyi5DnufDfu3uLb2vDCBhN5xC1hHhk8MjAU0fLV3yGj6T9rAQ8IyNsCHhlpE+CPwKgNBzhL8ZuPQDZbNBXwQPCzZTG6GgWtkJdC3IISfMerJikyUhvk1tLskFOpRgeMd5oRsLHRtV+CZRkKwj7BzxkuDhgfPIa9tKgtoxQmcZDiLDhuXRIdN6NierhqKyTCSeI8OYCtoolQI7qcDQuY4o/o+26HiMl/hCYX3ECU0ZLEV2lFiTyaky33Z/bERRbe9kgqJdZF43M31rpvuQzQDGxeinJDvCLEK0b8JIPYw68a/Z0L2DmUkbE80TVGTRIxfyvzJG0ZZVNUbYbgFkmmkxW/rqpg4tp6TlpI51dRewgzY7120ppd7cxwJ8XWecvIG+Dhp4TtywnbY8Rws8DFgutfTFifBBw/99LfqMjYxctGR8ijR5gZm5e50r9EwQZ1DoZjC07kEYheucoaZZeAhLzHnHnbD90BrSZCM2NvOz6y1NljUd/G3wUBFArCyLh46jHtCNNlxuaqYHvNKBgwbgruvxP3Oy1ZF1kfw5d/eR8wDiMuL68QlwU3ROBSEOOCZZkxz0fkEgAKGMYNRnYI4wYuZuTCWFNCAZAADDnjOC9YlhXZdH19aF4qAa54AbpcEPwoxj1lJG13XIoS6erGqGls7+FCQBgmDMMGw7iBd9K6+MnVUxAC5p8uSFHKzlNMiHHFq5ev8PCwx+3DA2JKeuvF6DqoAu0gxJywxohhGBtPmNozAQGlZJGYIWhLZmnIkWIEESERIcaIlBKcf8eMei/HO4ybRaQ63pRtzNYIwKIrJ13g3nIYgDCgY21hVWmveZvmkXbXYNEWcBelUTBQucKh6fIiOP2YrNdv2Yt3XZ/8nhKDAreCGYtqgmuq1WkXLiYHKkBmat21zHs/C+f2XOCzYT4PZukfnL35kTdVMfvHWqJ+gKPoWDB3ckF2dJEbEuEN5SKiFoABxoEjYNQNuI/4nB0nkX2HypUsJk+WmgIGW1e0zkkzQMUOQg/QORQWFnkyU1fIFrXlBsY0QlIpNt2ctBS36ZL2Y1AjKBppCnuu57JUrddInnWeqoelcc+jKb2T9IhDaRmYPgEg/DxWfehTKbYPfVRuuDlKdR0r79sijl306Q0+stmaKM/dLQ7FlQZwzVb0XEm0v7X1Yw4UCAK+M6qsWOWXekj0zLdIHHsBK4FFfaHdh4KwXjbOnpfZegPMUdIcXnWgw6G77oRWYQ9U2hUVkcAKR1PGkDVUWK67OIDHcnrPbwQbOqtiY0piwwCcrl2GgGRVerDfBCoI7i2e/Ps6HpnHFmGtBYPGa+9pKl7GHxCagkvKXVaUlX37ABlncW7BrWYgb0jTd1J0aPxhEIk2bjIZu3aNPUfWbC57nWMjtQhxXxAG+VzydnN6Zapb75ST65YuK6DOdy0gd1LLAgAUC6Y7Vdnqis7s/iwg5ZXCUzOmOnd7lZW+1qYq4XR7fZ0ljNqNsMqgKjYQfnD/5jePj0578FoodQqC9d+OMe4KdteEP/j3rsF+wYJbjJeAv2L86PkWjjdYHhi3r1a8/GYBp4CKOooVgzlsN1vsLq7wk5/8GMfDHi9++2sMY8BmM2JZjnj1+iWunv0U290lNk9HzMuKmQlwDjf3t0jMSOuK5eEAKSBjgAj7wxEXV5fYbncoqSDGpKCScCyEnBM8i2za8bDHmlbM6wHTdoctXahYr0TopN3tFmEYcb3Z4eL5j3D56RfYbZ5gdFv8wU//CAzCP/6H/xREgA8OhTNKyfjLv/g5fvPV1/jTP/tTvHz9Cj5FENjqqwTgOofMwHw8opSC6+snIiTgTpGhI0JKCVwKiEhaKV9cYD4e8eKbFzAvM4SA4D12ux2cv/lw04QAE0E/wYZ1A2pRNPtODOU4snIStcDIor5oG3q1yWzeuvxcPADlwlahA9sTdWOQjVAAp9MiEmszXEXQuW1A+cJK7kWShQYHt0CK3zJrykwfmjycOgjEDERtPV3svVDgrM01uF2/XwrggDD5ygdNW/Hg18uu/XI/np0zIZ+LFrkEasTXaA4nsnFAF+EFGFb9DTHCgdugf8CjFCfRIiepUyOQykbF4EGbNMyoKglmlMMs8yVe6mauDSLKyDV92HMpiSFyU7aEKqCRL6+bhRWYgB38SlUw3yUtGBlEqowykDcOlBmbV9LCOm8IbhWOoYtcwYg0L5BIfpi7Z6Cbaphl0xrvhatXOX+wv6Madd6+FDQrxTLQtqKyER0+1wIVzXBYUwtL5dY5ALRx1jlTi3cYLXpu+6uTanCeiursMmh1pyD+Ax7VpqgySXWgoxYPnxU1mUNdtXG718XRIIyBENkhjbIzC8CQG3bWZAbd5p01jb8B4hVh2MuWEJYCXhjDXuaH2zrkqwyEgjI40dpVWbQ8OYx3DMpeU90Knp1wdAHUAjjSIIsbHFyUmpCwzxotI4SFEGaqUb4qqwf5vHgpEME6ew2HIjzhGUgbAVe1U9gOde3B2hbbEUpVWpHxsALiNq7ONRDMhcDJoXiPnLModTBhEyLGc2L1+z4UKBroM3qHBEa6rCADPsvY5BGgSRp5+lW51bcSzZ0/FXuSVQ5MosRSNDm9lj1rfCgqZakKEpmQdwVlJEw3TlVYLNKPGlGuKg9mm3XjKhtuRdhBgHC8kmdqDj+pgRdqhWSaBIgXhIOQ0f06II+EeClzxGxiBadjgGnPX3y1YDs6bF+G6lDHC4e0BQ4/BvKmAORRBsLmpgO70aK6uifP3Bw4zXaauklu22mN/Dp1REqNAMvvvQW23nJ8dNrDYxFf2/eJROx7GAi7C48I4LgkrHnB8UgIKAguYHPJWCMB3yWUzAAGEETLlq0Fowh+IsWIFCO4FKQUcTgesd1ssMkZzjkMYQBNE5gIwzAgDAEheOSckFNBzhE5F7x+/RqbacLXX38NR8DzT56iZEaOGSVlOAeUJNHZXCKWecbLF99gSQfM8QHLcouX323x6tVXmNdbfPHjz5CWS5T1gJgzDiljuvoE09UT7C4uMA5bkB9BzsOHIA0rgqs46dWL7/Bwe4vNEDB5V+89F6FTlKZ9hlwyUs5CvdCCQ5FEM62MtkcVZhQuKkUr3GJ7VuQEHM/LAi4/IKz6Ox7cRYXOC28sjdTz82qUtEAKy8LpQiUzEDXihspBYiJJG1ILXtSCqf4arDGF6Tqqx9+3yWUPpJ2DC6TRWP2qO6EUFgjDS73noue0L+PvdUDDoi/1cE2mCgDK4Cp4rbzn7vdykg6s2Ot9wIXO/n1eiaRc4jeaHui5T4DvRzpIwzNUJ0m7PyZUmRwDO87SykqBcGiV0+lCfle55GdRIC4SbXBZoiLoolJUBBhTAvLW5lejXYDEeMedAAbTcY0XrgJXiXpIEVL0bbNkJzSF9UrtZx9NVmfIr1zbbUuUDrUhwnAQlQCLtFi4X9QezABDuXTN6Tvn4AHdv80x6gJ+rB3CTt5fI39nz+0j8sEJfNqgjbtn0/EajR5D2ehOaE5Gt2Ys8l5bTi9Oo6c6jp5bh0XIfPILqjoDqEXeZWzo5BpcAsriWnMNalQXeO3stXGgY5GsUJamR2lrVCygZFF1cBHACi2wZInmeovmCgiozvtgznzrziXzV3nGiaUYGtL86ESaz4wRt3VY+b/ENSv2xmMnPjEx3E08Iob3BUTSyCAVB/cYwfw9HzXgUZ1oNAWVnofdcXBtjlNqhWW5aGGrI9CACnx7p/ncFhs1SOaIfiYRXJKF5jJJRiWLfrhlFkVfVxxMGVMhy7pI8EdgeKBq54AucFQYzigPhwJ/SELxcIRwDGILvDzXMojdyiNwfO5Rxk0tNqv1CFMDtoDcjz8SKDtZK48BUta1gdOxONn/uwxr5bPr78/nlEWQ33X8ndIeTovfoLImDtPocHntcYiEvCQclwPiuoDLgikE7J5uhM/2qwUpSY5aOsIFSV8VAnNCyRHH4x7zfEDOCfOScXufJSq8vYDzHuM0YthtQd5j2oyYphHjNGJZClJakdOKdY148XCPtC74i3/9r5FixI9//CNIe1fW/gQMLkIizAAOD7f45V//JdZ0jyXfoLD2Cl0TAgh/9A/+AJ6Ah7s7xJwxpwz2O1DYYhovEfyEaXMJ7wOGaQJ56TA3hIAheHz79W9w9+olLqYRh0G0hzIz1pgRU8FSCpR2L40ryMM5D++F61tqSNVWoRwlC/0hZ0bKDfx674EsYC3uD8j5w4JfVyO/ah0KnYBY40ZaBA9oQLW+L3eva6qyphsht11BBBjFpF10M+8pESfA14Cfg3bbok5DUwDVcuVVyF65d6zqlCzGikgMmFSNe42+qgJE6VLSNawq0QSPDGuFLHqIVHlNOVA1nnLfqkhhigXMrWAFbxpdA7sE3b/8I00uDBA/Vuijmz6GIm2Go3vTKr3no3I5tVL85HIJldtmABEKeKpCwobAUaIvLhHiEwGzMrfaPVrlMBngS2LQW4RXNyOlwpdRKBii+aoA1gtHb30ikb88SqHLcu3AnrH5LoKKB1j0Xi1STxp55EBYnsk89keL9EoGQnjCWkmtQHi8Y22GwBgepB1psS6D1k57kk2CIyEql9NSh7apn0TAgCb7BVQH4UQXWUF5LwVVmWk6fvUE3bP6kAcBWkNgqN5oUiTpUaK6Pg3U+gWnYI3aF2XAg8FHUt6i8DGtu6KBkdpgZW7toVkHsM9Klb4QKgNuJQSzFSzrKo/NBqZEiNq21uUCXsX5LZpVtHbtLhKC/pGvc1OoWLYm3FqQhyBgbRKN6HiBrnCKa1pebGqRyG3V/raxU+CbH3mYXTpexlSACysWl8CKdODrYgRwvmAKue4HawkfXuoMOAGzUPBXwFXD1+hnRinoHUIfgeEoi8VFErBLQJkkMxSUl1pBdE+l0JS9X2Qtcieb6Ff5kDxynb+2F67XYlN4l0FTFvUPHaewJ4x3wvH2Sq2S7BG3OaBBoPFmRXi1l4Zcg0fYBDhtGERF+N55A6QLxv7HhHn1tUB2uOfqCNW9U9fKdCPzUu7tdF+xJZk1gzoEgrfMZsdzrzxsb3Qw3eMq17N7DsD3ZpQ+Ou2hlzoD0P1bIsAlFuSVkCLDu4An108w7wuWY8bsAB4KwqZgw8D1swH7O8btfobjAM+DNJMgh7TMWIlw3D9gXRehAOSM/fGIu4d7eO/x6RczNmmFyxNKSShFZNW0GRuKB8bBgdjBMSGtC/6r//K/xO3NLZ4+fYYn11d4ev0Ey+EWcdljnh8kYhwZ+/0dvvrq59jsGE+eEogiCAkXT3bYhAnPPxlB5LDZOTwcZhxf3uI43+O43mG3mTGOW1wxYxgnkC8INGAMF/DOwZGvdQW7weN6HGpaMm83OK4ZD7OoSaQYkXICOY9hGOG9RLw5FzDL7DRMIxrAEWBGLpKCHabWdJ5BSEUoIB+6iUExykM1KnKVxu3pq+Blc21zqvIbdROvHm5uTpb2UKkG3YriTrx7kjF+I/JVP6ebx0UiXgas0xYAUfPyFaEKfcFV6gI7QrrQZcgMvxZQLLXJBVl7ZL0/iRBRjchRlhamFaQALVpnUbzS+GeiG42TMexTj5Wn6Rq+bQDZToyaCqxp4hNQQ4+O14c4HBjBN0eMXAtDUm4Atp8jTkFANqDHGr0r2qgi8KlToNmCvkHBCcqGzKe8aRQUa2VrQyYRYa7AsDpPAyNeaxSYB5QAjPeM4+eEdMndhmgoSYC3pErFuQoHif7VMdDsjwE3A0QorkZp8yAFLGnjqnNnhSe2ATcueVtTOiTV4WovnP2sRqXPhoAhwMju5WwD/KCHflY+a3JRfHNorZkD2bMZUTvk+aWBlVpTAEnRFqXVEAic2vwpffaDoa2lbR7Jv0uAtqIX6kmejKPJ1X4JT1ojv9VJBUpwCEehSvhY9Lk51A5jjoCdRIOFBy60Fz8L6qypZXWW/CyFshUVEJoKCQGmKJSVElNBjl6ntQg36kvt/mg214ri7HmwUBt4KIC2iIbx9okr/W3Vjm82XT6G1JllAOuzZqgGElqARTNA9nxckjVeAhB3roLL6UZeyw9t70oy7KLksDQnqHZ0XKWQkKeMtPHwW3FM+nqDnoJmkWY6StMV2mTwUJC3hOHOY7xlbG4ywj5j/6NR9aTbnmLnAU0YN14pBIS8lc6icesQLyFfV4x8WVC8k6zSnqoyTWvCInuzUQHz1D6DiVR6D9W+SiACtaDQaZbCIr0ZHWUmd8/F9j4LbsDmPqpk49uOj097cKLKcPIaEWxqleSQIyFHhtsEXF5cIc0L9uuC1QHIBdNTxkTAxZOAlDIyryo9BgQXQA5IcYUjh/l40MgnIRfprrY/HBC8xxpXBb3yxVkK6bwnkYLxhMETKDggO+QY8Rf/+i+wLhF/8Ad/Dz/58Y+wnSbc3nyH/f1r3N+9koK644LjvMe33/0Sn366wRefPYF3K7xLeH59hd004eoqgNyA6WKSYoHXr3Fc9ri5OyDFjGncYQgDCic4XwDaYIMLAEBRAEpcsPEeuzHAOUEsxU/wPiEyYc4JHIGSC8gVhBDALNQH6XGvQEYeAphFzoz0MwAgDKOAXWaUUuR7Lt9Xp/V+jkdm7kk01v7t0FryArA0ogFeOZcAxdoRzzR8zaM88cJZlJgy1VTpSY2XUR76dKmBST13GUmKIz0qk0FaE+sFQlKIJQjH0/QNWTWW7bw+n4Jeu7/aJrnecFv8tcOb3rfLkBa9BImQahTdPOPzR1nlY+yz+sfQRe0kPa7FQnUc6I3GEx/yENmmlhIQIK/zOre5AqCmy6R3/am2qbWIdisAJuSJ7ZQtcqlZBktG9EfxDOoKPXq1EMB4bd08sXH3QNyhFna4VSgKByJk5e1RkehNzWCowP2wLxhvE8LDCmRG2Q0nG1lN1RYBusI/Vk7oQMijQ9xqC2XdtHtlA2vnKsBYN0vqrv2xR9zjvR78Om6OgylCfJwpcnp5XWTRgFqfKaqFqrq2XRR1DmjXvN6hcFmegzg9Yj8tRS2FaoDRhkpBa0XdZatq9zyQ8oW7eaNrjIPZjwYcjWeaXyggi0UKqHJAtkjlKIVGLhJyBNhJkduURW2GYqkAxEUpuIwXgGkcWtaohFZlz97Ar/DOTyJ9iaRID0p9KaSNNXQtkf67KzzkohkwD7Bqu9fGTq6ASDrz5aJ8TjCCrfcPdJxQvyyQEsQ+1N/ZGkkMqzKnJJJk1vTBr7KGxnuzIVwdTONkO+X19xmF6rQTQGNBmVqdQF/rd0LVScpmmR2yA2grqlk8isM7PhRMryL8w4rDF0N9jiBUrV1Zj746TtXJ8tp5cEPIO0beFfAuS9+ThQT8mnpFEt65W1l0qLfSBKq2NtY1VALVwjbuMqgmA8iajQGgOsmowYv6N147PKhNqkEqb2uU3mljPjrtwaGL/JK8BgKcEYJKQV4Zd68Kds8Crp9eIM0z8rJgPRywHhOePh8wbjx+8ocen33p8JMfj3j1Ysa3X++RFkaKGYWLtA2+vUXhUqW9gIBpt8PF9VNcXj/FxdVT7NeI47zi4XDEsqwgAKP3GILH4ER9dfVSPLY+3OF4f4v72xsMv/8T/ORnX+LTz3ZY5z1+/Ve/xMPdHV6vfwMKBZ9eEa7GDB/3ePbJhOvrC+y2wBASpjGBCyFFh/nhFl//m5/j5es7vHp9j5I9uDiEsIFz0qAijBtsr55WCbdf/dUv8PKbb/CwrEgUsK4LcomY04JjLHhYEpZUADgFrhmHozS3GIYAoCCvTfVf5MxEgi3ljDWuSClhiREhBAzjJA4GF+Rl+aCBPcF2NptRowgWLbFNtd8gek1feY0bsOm4ewWiOSm6ifp29UylIrWlZFwWMCI8Sl18WgnbIlsKpmcBn3lDWpGNGjoVT1cDIpWCABSUFkHhBjTs93LtYu24OCniC07Sm6qf2EefOTZQDv2sMojRtD7n9fP1s5x1AXJAA42E805z9lxq/IWaQe/fwNqWW3i49MEzBPWwcVNKhjlFRQuQrPGJn4FhBuioERctkrD0ZThoWjNSjcKcZAQUMDEJdy9DhelL2wCYgLSVKFeLhslz9lGaFngPrE8BHhjrJwx/IPiFMC2M8T7DL04KsVQ2yq26mSZg2DO2rzLG1yvC3Yx8OSFvAtYnoTpx64XD8TPdvRjwVlhmhZjdhudnqeI32SXhdTfAa5XaNRXcbygdiBU+Ncm4T6gRUFr1j4w/3vtsqmRQ6RMf8HAkBZH1+p1wngmApajBFu2Ue7ZIftjLekjbbs07VcbIkEK1UQFsUBCsDTBEgq5lrdA5MU67tknnvU5/2YBQIXDPkSRZURwYZZLiqDx5BAWyFtE31RqbuwU2f9v6zRstPmexW3lEDRr4pUXv5LkqcLN17W0s0MBvNEpRF9Wz71W/GOK0Z504xmdnhvNizF0HbolYO7wp55fdmw2QPsRBjRrFQfeQImuwp8GY/bYIrl8Yx08d8kZk8cIsTiqgEUkFwMszwvqE4Y+EUPV69XOHNq6c3MneRpkxLI1b66I4GuNd0TnpkC495iEAgUHbhLz1SFtCuJtBv/wttp//EagErNeW/Wl73XBkTK9TrVMJd9KVJ1+MSJcD5mcBaeOQN04oN4nhkmQd/FJqDQ0IyFuRUnWRMezVMRjEhoW5qBMvRX2yR7lOOYcQUhbazsJ1nw/l7Bkpdz0sGkhSQG9z9l3bz99Jwdu50oME47RbGzuUXDAfCsZLQggjxqFgMzHmhxlxFTDhgsPl9YCyDbja7lAy4/52xiEx8qqRSmvlC/EcyBEYDmEYMW42GKcNwjghz1EaXsSElGX3cvX6gAzx7lJhkftaFyzzEQBje7HFNBFy3OD1NzfIS8K98+oxOwxUwHGBKx7BeYAXlFKQ4hE5R8wHwuH+DrevvsPd6zvc39xjngtygvShJ4LzHn4YMV5cg8IIN0wCfO9uscSExIQ5Cmg9RAG9SyrIVkXIAHPBuq6nXOtuE3JaDFe4AAVIuUjxHFQ6yzkBRHANAH2sg7svO/rIkUU9zya6RSX6vuVV37CPCJIYuDc0ARkt+lW5RVT5jUAHWk2qSKPIlQvan46oFmVJqoxOwOtbj8LKmaMG1nSDM06gNXyx6668wx6w2f1141ZbP3cRTdel1t4Yj/7nd126LOY3sjwf4ngsDVqLGy0Cp2DOGT83yaPMFqE1Lc5urph0HjaoYKfyzjpwUtMgOkcJ+uw9ZHPvsJ98hipJMFCcKARYK1IqDLeUE0k74/VREvDsoxbHJdFvzZuAtPNYL129xrTtNlHdUCVF24BbcxxFXcLlLo3LaMonnWxaH2169NBxZ8cngLn/ff0Am7JOweIPfuL/9kfHuKuccJNxq6/XaHV71l6lqcqgHGG7DcN06kwXoBYMvkENYVQHyVrMWvRLlFs6R9aCVnYukjHtr7GonZEoItXXK/WgRpUVc3rAdTagyV6Z1ipV5+Rcnq/eA+xaOqcZ5vxpx8m+0Ki33WoTzg2HtEPvnej2bzoDwwA+OOf3ZG5Tmw91XAwY21qoUU8tKrXf1+yiBhWKnNCpkajPz2gkQLPdgHyA0XC6wyVG7oIolEWbl5gRjqLUgkxAYLihII+QIrTMKIcD/Fq0vTBV21cpcUXtylrgYoa73cu+v27glhGUNsiTQxnFZtT6FL0uu2zT73WJIa25bR+V+VFt2CoZa1Gi0W2ZunnFFriQsTFAW4YWmSYmUC5AoNOs5/ccHzfySwTvwin4JcCgr6wN8WQOdwXjBYNzxnZHGMaA+UjIifGwXxBzwnYCfCgInvD8ixGefoS//osbvHw4Co+qsOjSEiEQgcmDncN2d4nrJ5/ADRtkeCyxYIkFayxIieUJJAatGUNKCCXBgZAcQGOA54ybly9we/MKD/cHTGOA8zsMfsLot7genmNJHpzvEG8f8JtXL/GbX/4ScEeETYHzBIfPkFaH714sePXqNX75N38DZo8CKUEndqIkURgpzkjlAXP6DscccUgr4roipYy4euQCLGkVpQaIUkOGl6ICMIgKwIz9/l40e1NEzlm1NmUx+2FACAHHWZwFFzzCOGBzsUOMEfOyIEb5O+f96W7x3o/eeNLJS3XDsq8goGA4oFsMACohXgGdStDkJHSEChyV11hMpzGTTckabRPvuhmHijF9Sw9JOqrpFUonHIkGDYeC4T5LW2RPNSotOomEeOFg9ATvRcy8fkYYa1WryLcJTzOPrcpYQBLVwoM+5Z1rdW7b2HqeVV+BfZLus58N6Bk9pIKpLopX91CuG36N9Hxgft7gCp5MM74dLuEcYxgy1rGgTA0ImuYzbeT5mlYlGTANQLxotAOfWVK6OofShlCmBkYNOJqmdJn0njtqhJ3bAHLP1QxH2SiWZzLAeSuRk3Bk+donuDjqxkb1Oqhw487BI15s4T/d4OHHHumCEHdyjZtXKmf2LesG2xxdi2CnTQNQMvcIWFQmUDdtK47sqT21hbVROPqCUGrR3j4azAbcDDwkqs6YZArwJkh+3wdL+lwuSq/HU5WwNNk66tYGB4mIRm7tbYe9RItzHT+q117lF60dsX12B5Lcqpqu6njWoh3lOuYRtWof7hRI9oWVrE5DHiQKx/fihFnEXtQggHhhEUmC28qCTRsCsfA4WwZHgVrWTndQ7DWSVv67qmpjEVFb2i7BGvY1oNuGvTo45BhcNRTRvAQLCBSAHMF7AbzeS5Fwyh7eJXiS1sYfo8lF5bmzgVaIA22O9NDGzSUARAhHKSgb77hmDNKGkDahjrM41IxwACYiXW9dgeLSnG6/EHJwlU9rtICsUpZ5ROUfe127taB7YPhNxvXlEa+fD9gfBuz+6BoX7g9x/HRAvHB63aouozUL88HBL4PsealgCE8BAHkTKqC1PSdvnP5bnkcZxc5YsMgvrA1SCtiNEvU9FHESjkXXmABfoxJWR88TyuRqFqEPQFW7RVBnXu9B1Xtsfg6HdxuUv7PIb6M+KBCGRbBE2HqdGWllcCbh4HqHaXJYR1k8JTNyJnDKWPcL5nlAjARmB6IAcg5ESrEg0o+Rxe9DQBhGgKiqHlQuaxZeq1OFAzcOylPWDnpFMpjLfMR8nHE8zHDYYPAeJUeUvCKuGTk6EO/AKSGtIzJmCRjOK0AFMb7GugAvvz3i/v4Bx3kB0QgiD3YejICYElJkxIURc8EcMw5pxT4dkUtC4YKUB5TikJSnKTZcEa3adRvvWPm80uXNnge6+Jy1LXbBwzmHcRwb17cU+b1p033A41ED90hwwCIklGWzsGpzK1CCRWAs8sKN8wowzgMOffq158VWGgXa7+2aKu2CUDfR/pBmBQWFndaCqeRQfw8MgPvXCDX6RAKWmsKDbdDdjmMbd93MmjHoZdjOC7b66NXJa/qz/P7NaE19fz8m+l4rVPk4vF/ZDK2QkeqGihZBsXeegaw+qnUSFe8iVbbZnZyH2lypX2+MA07GpD0rjfYxpB3ogJMiTrkm48dxPaVco/BOKTS93zJIBbZoruKkSM2vmqoeWqrfomY2Z5vzdDpOJzQPuzU+W/b6t4w2Xjh7/8k/7LNLN+/O59wHPNrc4HpdLRvTAF99ZoQKJk+UGJil8YKCn36d1ttVesPJGkEDK5atarUCvV1CW3v+9O/td3at5thyoNqgwhqknHCYFbTSSFU1Io/t2ts9NLtT/47oJB1t/67FeEDbfOgtz7IvgrXxPnufKPvYvgT4zjh7Vz4417ceZkN1LkjxKtXxPpG3VARVlOtdNZfNibQ5AnkelqCjJAWGrWGE2nnXnoecGKoYc7pX2VqrqnLFMkpqAzKhmOqGY5RB1EJ4Guq9nDjzQN0rZX7IZtLTZIwDLpkzkdazQML5fkTdoxJ7ybXeoto4UP2dqPAwyLV5Ulssn+2N7byne+T5vPs+m/LRwa/3/kTtwY6qN8sBKSbc3yZsrgNKDNhsgXELPHseME0DyAdwIRxuGA+3Eb/+65c43HrsX3u4co3NeI1pDAjeIQTlNalGETNjGCZMmw1KAWIU9QNHouaQ4oqcE6ZxwMXFBtPTSwQAkYUKcHM4wI8Dbu9u8fK7V/jtVy/w6bNr7LYjHh6+xt3dd/jN16+A7HA5/Qyu7EH5GlM4wPkZd4ff4Lg84Ovf3mKeI+73R6SckTAhpwG5eIzbazi3wev7G8xzwnJAlf1KEGvLOICxCtfeAyMNKGCkonaXhdvK3BqLHA4HHWsB+waKpQCuACkJQCbCuJkQQsDFbgcuBUf9W2bGuiydVNr7PwjA6BK8Z6Szjl0VaFoxkwIR6VakBkcBgHGF+pbAdeE3TwEgMTCFKrsAAFqLYotwjcJhrQZAq6QBII8tessaNagFTpnhlgxsfE0RMUs/dqjMjOiyFhUzL4gXod4LgcDEMIF701VsURt1HLMCZIs6T22DOTFwnWE/AXZ107P2uKhAsv6tPQ4mWFvY2uhCnVfyGc6zDO8HRzWEwlQF8nN2kkaFXbtVIp85E0UoBCkB5AkmHSryVMJ/zVowVEZo5JIanaJYG046KZ6rPMFBO3OlFtGTiG9LD063QpVZtLq5au164QymXRFOdm6RoaIVzhJ9lrFNO71u5TsHlUELh6IdniRKVLymprVhgkUR/SrRQXu21hWqjG0DkQ1YQImFNHvw1zsLpxJibTOvGyILaCibUucU/RD6z+9wEDE2ISEEkYEqruMkWaRTo5nG+7dId9E2vuxQW0dHqNzgiLZWoGNh9km1WsmKVjtHVGyLcB6t4Kl4Qji0qDJrgZs5R04r+vtOkmkHLNeE6cYjlITxNsOvDiU45EU0XiUaKFHsEgBKTni+I7VoZu8QaiZJsibiQKVde4/ZDivcq89XQdC5TQEDiAqk+hqB+nuqmuRgoKj6ham4EDEmn6W18UcgxzChSv7liZG3XOUK81bGshYmBgGxbpXBcavY8FykMYTRGvwCjDfasIaFC+wiV3sSr2RNJ7KsIddsiZ8lsxms+YMOQdo2oOiXgmGfMN4GoBDS1iNdOTwMGYga4feEMjihPMyous5ZVU3GO7m2tBUb4pcCtySZz6mAtgF54xEOGf6YYHKJZZQIbd7Kg5cMoRVIipdQa03U8c+Tr5lFKD83LIxcmoNoxaMt8ovqOPHQgkAOrT2yOf99ZuJtxw8Gv0TkAfwLAF8x839MRH8I4D8H8BzAnwH4nzHz+j3neDPya0f1CgXNlRxQokc8kgBZ57EZNygT4e4uYV0KDncZh4eE/S2QZg+HCWMY4WlECA7evflZRARHojhRuCAXdceYUXLS5hYJKwqoJETK8GCkIrq3a4yAD8L9jRElZazzAa4s4LzAuYiLywHxSLi/T5iPCQ/3jHFwCMOA++OI4zLi9XeMwxxx83AQA+mBXCQqGznBuYx5cVjXgFJG4bVAZNw8MWSoIzIxJF9kT18Mm1P1BtLvdYjNUwQE9OtXr+YAADFGPZ3D8XislIf6nreA3/cxTwAgsT+ROtNgzBven6VZxNMWjrJVe9ofvoG/1CBbEIiBWoRiKW2g/f7kM6mFck7UF8h+hxox6rvwGF+4T/E0zpgaQU8Qh1uI/8UTMNl9eqU8NE5Tz0VjAI6pfq4Zn/PI2kmEUsfPHIiKa8+B72ORJ/sdPf66FbzROzar9zVXJPLLyg+0oqZH+Mb23C0aptEK47WeRHSXdm8VDNhcOONBnkdOKwCiVgRX0/xGJ+jmIBNQPNeKbqMpUCZQpNZIoQDQaF2ezj6PJd1uFeYAKh80TVR1RYtVU1dOnc67Tt6tDB2I17lsvNHKd7bP7u77nDZj8+M84t5HSX/I8T7mSWHCmn2nHd5/AKtDIc5DfdY1QoyaZvZrX13e8SZts9X544yyYLUVNZqlILhQzb7J651z5lFVEWR+sERZO23tagNIQEzeEIAgjoum4nllBKjjlBtHMm/kJFbIWGlANfPQAVm2Ir7uefbrXh2ZE56xa+NWHeNqG3RMeqeaOpUWtR0tMXxKc3B9SPGR433ZlPocM4lSTmnPF9zoKNb10mgmeStOlVEX2KL8fSDEKHKqemJt0evz7yO/0OtITZpOlFrohPqWtg4ueZ2HUsBaBiBrwVzNYAxOeL1Z5nulVcXGyxVAS8jFw61BaTmqDd01MGFVhRCeMFA6ZSU7aldRWzM2bsZxVjqDzTlHXHm7vZ06yXacZeKAZtMBiR7bGn3X8beJ/P4vAfw5gGv9+X8H4H/PzP85Ef2fAPwvAPwfv+8k5+2N5XJlcROo6puWvEVaBhxuHHZTwHDtcbEBPC/41b/5Bnc3C777zYx1JiwPAdOwxXa6xmbYIbhRF4lZHJUGUxqEcx5ETrqeJcuNS1OLFBfEKNSFPWdwWcFcpKkDAwzCtjAu5hlpjUDOONzfYqUElD2mMeHLLy9x8zLhl7+4w82rGd/9ZsFuw5hGj2PcYI4Zv/4t42GOeHF/g2F0uLweUYoY5rC5gHOEh71HySNGXArwhYOjA7wLAC9gXpFZGk+IPSE4Eq6vKFycAmACOqMlE9l7oTf0wLYw43g44oADbm5uTmgP/P0R3995njCAVBxy8hLFO+P92kFsaT7Rx2Un0jp5ojcqm9/4e7PDanQs8ouON9unJ8ENHNUoV7ewT1IvBNF9VZ5WHgll9C1KZl6sphm9ysCkDcFlMSZ5kIhi2lDjOQ0iN2OptXpvBqLUEPeC+T1ArrfebZ59tKZeu9PNl/o/0O9OIxJeN+VzAOwFfJJjpYq8E+W8F5viSLpAAUBwBeT5dAOxyJ69X9tUWwq3DMKNNJkvv1Dl7BFD+K06VgBqZ8FeOaMfW4mCSdbFJanyt5RxCVwbolTHJbB0dLsGwpGQB1Fm8AvBHwluFe1fAIhsIvOnY0BFFAf8LNxUAEiTQ9wR0q5X+0CdS0D3c9e21aKZVVJolJQ8nxWJohuTBnAaOLZ5ZbSLyns2x8poAY9sZmfH7zxPcnG4P04CIEs3n/vn5mQvqprKXUS4TEBibV9dne02hq01sP6OhdZicmdmN+rY2+frmFWFBo3S8qBAhAVAErdUtWQxOgdnBNZLB68NGJhQpfV4lihlHkja2wbRapUulCzrmQA/E8KxuaqmbpCdgttBHLRaOEvdfdhz9wp2DQD3tkFfJ8sG1DmjQNc1u+GccH4N+JqUobYJ+j76w3uxKZIR0UtXZ4KSATRIJNuKZZ1o364zIRxt7fLJ8zYAa3QkD7n3PJB2fmtA+BwAS5ORBhrXC3F24qXOl5Ex3MsiEoeGEPby/TgHWbtBit7SNui9CWeFvTRKCbNEmG0+rxcObgLAg2SLPFVHTrISDmkjEd3hkATAZo0wdwGeHFwtfLPiTrkp1IivNbPwq3LRazFbt+/b+vRUM5/NGUGlEFlWjL7fpvwAswOAiH4K4H8I4P+sPxOA/wjA/13f8n8F8D/+AeeB974CrvrdeTjvQJ7gA8EHD+9GpMXh9YsVv/3VAb/6xR2+/usDfvPLGTffMB5eebj8BBv3CZ5efoGr7VPsNhsMAyGErAtIo7ya2nfOI4QBpQDrmsFFmjXEuCLGtbZAPh6PeHh4wOu7W7y6u8er+zv5urvDq5tb3N7fYZ5ncCmYwoh5f4/XL7/Gun6Lwq/AmAGsKCXDO4fNdgdPz8DxM+T5C5T5C3j+EgP9CJvhR9hOX+Bi8xyX22tcbrbgmDEfZ3AGqDi44uFLgOcAxx7EUh5YI1vmMTsIP1nv9/y7/dugQM/jPYnodiHX82j9Y5SV9z1PbNWL1qOmj9vLLTpnvDrgpHOZncKK2OqG3aUnTxaObcoduO0Leao+oZ1aBeKFv9R3oeEa7a0pX4u0abFA8e183C1u67IjUUDbYLnxjTvP+TRyiQo4asROi3AspVbsmnSjtChgH5E78S9qRMa+9PNNJuqxKeC4Ad/u5bf5Su9rrmSN6NlGaYUyGAooEZwCWUqoz6PSQjZoigh2mzqnTObMLw3U1KI/O09XXGFzraa2VbbJUuB+Rk01959dVAfVuOoW5BKNVq7XVSkYfWcoG0vV/RVZvlMnjFhpFh3gFe5d5wQEuZ60lTR62rF8bVm0hg34nMyJs3nQOU7onCfGmXN19n6ZJHjrvHp/NkX20sIkDnXlQ7bnVekE/TXWaD2qIkIDqVRbVNOZjalUAEbl8vZRVbMXlkmowGiWzoHuqDlw7saznpvrc6xUFZV5sgI8v2rjCi2Esuhau2euF31iT6j76hw0SkrN6bMc1H5/SodSgF7fZ4MDQHXA34jKFdJoOFWq1OAzRp8xuALvSqU8vE3t4X3OlRpp9Or46NqQ5hDcot36XpN+mz9xlVNtyizn3Ou0JaxXDvMTdU6nLhNT2hdU3tPUfSply4B0bcAiSglOm+iA1b6MYn/cQhjuCHDAeu2ryoc0SCmYbhnDg9AOALUT1SlzQmsYpIDYrUVtIInCzLXH8nTA8nRAvPRYLz3ipUPcib6vBX/kS86VJ6drhzSjIkBalFR071PJNMuKnmfaDDT3jAGriQB173nHI/6hkd9/DuB/A+BKf34O4IbZ2JX4NYCfPPaHRPSfAPhPAGCaxhr5tYN1Jdgm6SGRX8aEvCS8+mbF4SHh5lVCWhNizLh9wYiLxxiuMYQJ2+2Vbn4FQJbOZcU3Mri6AY48gh/ABYgxKW+VJeKbFoAzcko4Hg9Y1hnLOiOzFJYVLYRDksjovMwopWAMA14f7nB38xvsLr9DcBHMW0mvIcF7j+32AjxfgNcNyjogrwd4XjHQPaYR2EwZF1uAywGcj9gfM5Z5BngDgoOHh4OHI+XPwAkNogbnOrAqN3wa8T0DrNyRx98ZzSUIxO7VOeoze/T453gP82T3xaW8Zilsiyb04FRBSrWrvhnbE1pAdxSTQelAb3+v1aba+eu5geaFcvceruAYlm6xja/bBNmdAmAqLIbNnplG2KhwTSUJH0wiA/09GafJXhP+sUyE0umP2jWyRm7q36gXXTtu1chNnUwnKet+o+PufXxOqPIsDTRcoyDUiPHjxz/He5grmy+usOQAghTFDL7Ah4I0lFpdbym+rNHPfiPpK9e5BypJIlKcqEp+1VtyMi7ViTCaTY22c/23gE+JyAIKvEeqILxKoik31DbJYpxYiEh9qQVub4Jfv6LpxfbzQ6+TcnOAvAGvpGNhBTwaQeLQRWpzU6k4o963yO/5w3Es0dWe6qBzh4BuHnV/zDhbjCfHP8d7mCfDZ9ftwguq/vKJfFg6na9G/ZF5QnAKeq06XpwCfXMPIP3ZMzJb1cuH9d8ZVT88zIy0kHQanMTxqK2BnVJ5qoPerrd2YgOqJF69XdUEb0EAPgW29pxds3vGa69LXiP85GQyMLpnehZIoCK1EbUeoDu4j7ozi3KP/kzKvbIMqIHeQAUmhVbYvave5J/jPcyV8OSZXqyMQ96Iga62lACK3bwhgD0j7cQehwNAR3kO7AAX2tiYk5I3VLMFANr+VoGv2puCSksyFQ6XJHtkzisrb99FCaBkGPiVi3MLYbiHRnQJ417eH46io2t7kksSHTYlJCaR9rMsmV8FUEKnWtoQ4oXoTEOdaNt//Co1LZbRMFqRqdWUINlqbwoOHi0jxs1uyvkaXumDUxaEEPpRW3v27N6go50d3wt+ieg/BvCCmf+MiP573/f+84OZ/zMA/xkAXF9dskUhrWuYFaLJZ7GkFMjB8QBQAJLH+rAiHR1KkbTV1fgMHBy82wqgdR6gCKDoplzAkG5mYKgnSSDnQeTh/YBhmOCcB4NwnPc4HvdIccE6H3B/f4s1JcSUUCjLQlZD4MmDfEAYBhyOe3z1m79BXg4YB4/BTSAwbu+/wrh5iv/ov/+P8frbhF//9YzvvmLcfMvY8BW8nzCnO7jgseYFw7jAYUEpM0o2KyP3QShwyPBEGAIhu4LkClIhlOJApWidi9Af1JWwsT95FqXn6vbA5m3PvkZgddGpSKRzDnE9TT29z3nyyT/8jGPxiEsQkW+gRuMqaGWd9F5SetiiFRM95u7x6e8s6mrSZZQIvkCK5ap3Kf82YFPTNgp4uRqJDuB2kdg+0uxiARVxWmoEu+c/kdyHV+3DPLkqWF/5hOXN80ravAHkymNWcGEG9I3hoMeHSS6s8Rp7h6L6UAxNw+p4qnGqAWoWxRYQP1rI9D7nytWffMlzCjgcJoxTxG6MGIaEnByyRfKsCMIz8gSsV6fXRBodqf/OUCcENW1ZnQVAaAXcUWXUNiCjRQ9rkZJ8t02EWQpXUIDcFawIj1D+WQYnzzHKRhoOzaj7BcCKyt8FoTZiqc0MOqfPgK38gBN5pJ4/J2lundOEWphUHZ4zDnk9fzj9PPscOz8BrTBV56FUvdOpE/XIZHyf82T6ez/h42EEJwdaPMKBms7qVrJLpbBwrE/4tu0+k0rlSSMHVJpMHU+LbvcOqGURVNfZ+JUusjbfMWCrhYlTe9bVMTLwO7JQcDJJVkOpCcUrmFL7lLVpQE2xa3Fd2uiYBwGaBp4N+CLI/YMFONXX7fmYPemcZpkrairsWrn/G26OkI6H/K7ZYsmmG+0BtXg1FgfqKE1OA1yP6nq/x7my+cnvcRmUBqC2P++KUM7UYRkOrmZQ5HaEPuDnRpeofHm1CXmiuk+kLU604NNGh2qF7lUkzqhGlF1sz9scJz+zNj5Cpe+V0AC6ROyleU44ci3AzgPA5DC9jHAJyLNEaNcrh/W60SmsDTyxNtTIjDwNdc6kLdXslcsiA3gCUoFqC4oXx0mk8+w9Ygdr8yWdD94yqgm19qWuMVsbNndqS+P2eT/0+CGR3/8ugP8REf0PIHLv1wD+DwCeElFQr+qnAL76vhMREYZhOGmXq7/RaJFUbRMRnEY4SwFKZkiBdADBYfITyHs4GmRBooCRYU4xQChOUwZ11Zn0mYP3ASEM0rjBaA/rCi4JKUXM84xUCmLJ1ThLMM/BBwI5B+c91rTg5vYlJpoxOAbpbF7W73B5MeHv/fEneHERsc4PON4fcX+7YMgBBMI4TMgUMdAonCZewcV4tVC/WVxAE/r2ngBXwE5kRhwr+YHFeJxG4xQQwQDJowbj8cjwW57d296vx3ubJ4CmJ5MTse4aUUBdTDXiq8ZXUjkdJaDjHQENMLf7aRXdrMCWuwjH+efUgekMe+NuUjNydPrFKgtH5ZHxt9ccUEgLCnIDvydpnE6CzT6/XlYXFbd76q/5fK8wQMM43eBPzv2Yc9S/jyGRnbMCFMl4dOmmxyN6722uMANr8sirRw5ZukERw/mCdMads8hAmVCfZd1QDJiZk2HvtaJF1+aQbVAn80nnSvbdzxZhZTXa3Ws+ysbmQpvf5mz1ETkDSkn1Ll3qOqIZANPrKJ7g+oKUzilrFwoU1zkyFm3KiksYoA1OVTrMrtIjc8U/8hrOxseaGHDb+LjIxkeuc6LePN6fTWFCWb0+c4ms0iBdw8qWJZrp5ZrYE6DZmQpm9blaR7cazTa7pODjzTXSALNFrKocVs+h1mixSRnWU3TFY9YMBLC50D5MWsC2okzj3FpAwGt0kAOBJmiGQ3fLjBNHiBjSfIVkj6kAuXeIbZ6ezDPGif3rrrvaExMEbghI5kX9u5Y5MnrDacHbW9HN+5srZPeizoJlclwR5YTYHJkqhZmh4JdRM24162dfzaEWUIfmhAZuY4POhhOrWowVqGlbaTbnm6U9WHWicDJ/+m6CNaukeyWYRQ8/WvOWBsjzKANhvO4yakSeqGY8azG3Pv+xNJqCZSWJVfLS6eMP2klSKT9U2m7RA1rrRsk16tKee80Anz2zOk+/9wHL8b3gl5n/UwD/KQCoR/W/ZuZ/RkT/NwD/E0gl5f8cwP/j+8612+3wJ3/yJ/jFL36BkguKK3DklKcKVMRRrU0BUUCBQ8EEwgRCgMsFYNYIJ4OlaS2AAQwPkdKWAS9F0/bFIfgB07TF02fP8cWXP0FiRkwJcV0R1wVpnaXgLUcUtfYtUqeGggjBe2wvdohpxjff/Q3Swz3KMuMnnz3BNE4ofA9GwXH5r3Bx/Rx/8o9/jMh7HMsdXvzygHlZ4cJrEN9j//A1QAv8vID5COYF9zNwTA7erwiOMLkIcglMDHIHhDDDlywdU4toFbMrXdS37fgNhLQFAhiV4VQNor4VbQLxW97zIeeJAyNQAVYHWhy8cpaGB6BWh9b0MHUL6TR9UltPrqcFJwZkiFt60DrGGGhxWUW612ZUmsYuNWNmG4C+7hJQugYA1vEnbaUtZNoQhoNsgK2qX2VlktxLGV0tjnNWkGYPsnsM9XpsFVuap7vP87/pizRMy/EEAD8GQmz+v4Vr107e5lgv0fjG297jXEnZ4/buArw60AXwbDpgv47YH0fwVJDZtQ1FOYvsGG7VNN7SjbHeQh9BKR09wUVUEAO0NJ8UyckGY6nwmr6zyF+Nouh8IpETAqTdsVutWE3HT+dP2sk8s/a6PWUgbwSMJZYLH/aoG4U1YqgREctI9N+7DdwiWOz1WvX+jf9ZzXPn/AEdONMIjgG+elTHrb366Lx0b3pp73Oe2DXS7DHcEbYvGn/9/vcd0rUAHB4YuQd49qfBNvxOdgzdWKKNY4HgJMsW+LnNAbEpwrf08dSOpUmibmnLHV/ToRj46oC4rWG/AOGg8otOI/sFksXSoEBYpKnA9iWQ7wlzdNL1a6t7RG8XswErAVtFC++gBVmV3vOYI1Qj34yTqH7/WM//piIeoRkJb59rQwsAJyD4bXzf9zlX2AnvnQphvS7wT1eEkOF9wTKPyKvDwgNcbEoJbtHx8VJYaDdqVCUb59qwJskzqnJxBiI7h6IfH/ZANklFXWTFSzTWrUKXcUtpXP4EYAR4FO5+vGx7FSCOU1imyhMeHgq2366ytznC8bMBcUtYnsj+MN5yo/lRt8fq/ZGTzIKLkhkzwF4jwXVetf3bigDZi9rVuUpD30RKuqHySTF3e2DNhr+RfXnHlnV+mr/N8b8F8L8ior+EcGv+L9/3B9M04fd+9jM8//RTXF1fYQgDvDZTcBpNtX97R/DOwTnAa1Gcd0G/CN5R7fYii8aByIMQQBi0uMuJ5ExX9GZR32EYtN0xA4XBXFBKQWHhwJ6sV91cGIB1BBL94IIYFxwORzzcH/Bwn3Dci4ICuYx5/g4xv4YP97h8kvH8S4+LJ4xpl0B+BugI5gNSPmJZj1jWFXPMWHPGmhNika9MGRkJiRdkXpDLgsKp8nUbt7ntPOeNRKzQjc7+3b9Xvtr7Qe09dSzeUfD2vuZJPYrxJVH7pvuFKwihLvrQRyDOj0aTOPtFv/lXgXDWYgLV+FWJmRoR7v+8/9xzo9Vx/IAeONtFoaZ6eq6x/a5eewf037yx9t5+T+C3bU5nf9PfBz/y+luP+pn86PkeeesPPf7Wc4UZyOokxSjhOVF/YGltHLgZxJM/xOmmfDY3TgqXlPvZp7jrMzGDnlGjvG9epIDe00yE/CybAJ1EB5uj0+ZMrTYvqBy3c/pC3RgMwPdFlfWDu3XSOzz9POodordF2nrgSm2Nfe/xmJP1tzYp/xY2hQEkV4sgx3vGdCfduPxCcKurzqM1L2BVXeDAldct/27znh8ZT3OsxYluqfK+AYXZmZr9oe5zLXXdbeQ1pMVo1+n4BFCAUdVLrLi2Bwt+EZDkZ67OWi3kPJfzKoB1qzwFFY885BOn6Nz7eeQ4f0+1IwyL/PbH79jO+N96/yn67AnAOCZcbReEIcENBTyyaABPjDxC1TB0HYaOKtVH8fu94ZwD3tmU3sGFa5rrpX+2oVGU3lh75zbKzmnZLD1P2hDSpIEWFi36cL9iuJkR5lIVTYryagHUoJJ1E61z5mxd1z0Fvc1kPNas4uQaz53kfl+1iMo7jjfs3TuOv1WTC2b+LwD8F/rvvwLw3/nb/P0nz5/jf/rP/hn+wZ/9Gf7yL/8Sf/Zn/wLzfMS6LK35hYaxpAN2llQcAZ5INfOEA0tcQNqf16nlYB4hxXJQl6qgFFQOog/C9QVI+LxF8lXOSWy5lAQuWcA0CEwOrDQEKCCOeQXnjGkcMYQBDh5xKTg8RHwVX+FiG/CP/vHPMAwrfv31XyHnXyLGf4mf/P4/wb/3H/4x/r//71/h6795hT/701fYr68Q/A1iXPEwz0hFojj3EVgLQPkBwa+YwohAGWs6oOAeiW+xlizUDPZgcvAD9N4FnIOFL9VHbFk5u6w8Vy6PRXV/J0Mj5/8d50kB4ZgGUBGpp813hOmGMT6UCiKLcoHymdxOvQtGU0Y4B77UjI4pNkhL4RZ1s77jfi1C6PfyIXZeW6S9bqpFYdmJZFWYgeEgiz3tnFRi8+mGaVw/MRAMpD6SrZI5pG1PQxPzPjduRKipVymSQSvG6e67B7mVr9cVaJ0+yG5MqyWz83TAtx/cbqP6IV3efte5AgCYPS5+6XFIG3zzyRW8K/jkao/f7EdwJE1nn11Hv0lRlxbUe65FbDZXssgH+ZWbPNzUzimKECLXw45AFwpMkgHUJo3kZ+VmDnRKh9AIXNq4lp2wDMIi84NVb7YMwHDHmCJEymw4BT5WvETmHNdCI7mnEiSy5bjxhGv7WuUacjiN3PUFUTWsr0DZ5m+t8rd5omN9Mg0sDe7wTs5vf/yu84QSIdx4cGBMN8DT/+YWZTugTB7L0y1cciJ3F1g4wHaNevi9E45skXs+cT6MO2xmQf/OqSxaBZqrRH0NhFISGyNRPwEheSNNFcpFk/6goYhCxdFLxuKoTnOX8XEQWbJ4IevZ7ZTruQdI2zr7meHngulW+KclOFQ1i8EcH7N/Osc1A1WGNxFLxTu5wzPqIIjN7fYW180lBXVvaPvaKR4BwEHVHn5Ie+Pffa4Aw4NDvBIDm+5GTNcH/P1nL/Dn5UvcMWEdiqCnTUaKDvnoMb52CDOJsktRbj+jNRMJUHUG1vFHk080Hr85PV6ANW8zOIhMZrpAXcPG5Q97KY6WQjJf16U0UCGEO4/hnjA8sFAXOlWk5YmTzOadzI/Io7RWX1KlZpkqijlT2XjjTtuxHxhJF7pfbL81uUCu+5Q9Mqf3y2T3q4XgWhDOBDgni8s0tN98QKiTr1jrdn0/Omfgdy54e5+Hcw5PnjzFz37/95FLwd39Lb795rd4/foV1nVFyUWVGQhEKmzCAaaHCkAXlROAaikBZeSagDkDzQo5lVZRoZRUspAkHGEKEk0WZnBpZAFmnLpTukAhvF95T0ZOEXGZkdMKLhGHhZA44qsXGeO4wtGCECKmIcK5ewz+FuN0wLRdsN0Ax4UQAkC5IHFCZIdUHMhDGnS4AkcJsexRkJHKEYUXJI7ICsad94ACN9NTt7xUv0fB/l2rksyLkneZ6gbaHcv3D9jJ7W0HMyHr9VEmjVSI4SbVv7U7M3BhEZXTE+k3TcdWYEPCMzwJPpTO09T3usSgWECD8nEVTLjMKB0/rgpAdJuHfX5NpwMtXemhc1V/5xuX2nEn7WIRZ5I0dtUutEdS9HL7azkDEdRf29n91gjfO57Fo0cPiusJ/7YneU8HC7ffJQEbN/stgi8IPoOTFAbJ9SmQYOl6VR0FU4BA51B0zkm9r9JFeBkVsFpkmApL4dkCeIdGQQEq0KuAUI2+fY6ZGmldTBVQlcB6f6iRF3NoDEATN/BVsw2lOWpiQ+XzqwybAU903Qp7vd6zo0a8Oym28znzQ7IMlQvYO2FtKX/Yo0ghUiZVUjmu8KmAloDhYaNdIFvHNngCD+X0+sx+OJYOUx34LZ7huiYUAGBOtzgwKk1l0V79nYsFnEmcqk6p4zGHHmjjdkK36pzcGiDWzmx5A5SjBApCKXIdGtFzuaMq99Ffm1cMdR5xyj22++6BSb99nD9PfuQBs9YG6K+kWJt1j5J9vHCTPStM3wt639chBbAALgF/dHD3wP0nE+anA6aQsJki4jiAGRi3EXnwSASk2UmRnD5b2zP68/YBGXlNHpiLEtxjBw3ySf0HJ3cilQa0Z15GoERUaTLLULrM0pHOqFpKSTpvXQ2IPYkXBPZCyvVHAqUCFwv8KqAZEKArevWNsuAX+axziVEDsVTXCxpop3YPMvEeMzgyHx7dU7p5dkLJ6n9PeNwenR0fFfwCwOXVFf79/9Z/gJ/+7Gf44z/+Q/zpn/5/8Of/6r/Gb7/+GsfDAZ4mBcBBwCoHmLA+e+H6OvKg6va2u7RCOlYXXJi/sohAhAzGfplRqGCYAi42AaUE/IoyqCR4JxuXA5C4qDqCfIYjaYE8eofgCHldsezvcRcy4rIHlwU3y4L4APz1tws224Kf/Cji1RAdFAAAexFJREFUy88C/uhnG6B8hfn+AMIR47Tgk2cBRBO+23scM7AgIXJAhMMweUzegagAvOIwvwC4gBGRS0TmWDWSLzYjfPBIOXWaqsqTAVWTY3QNcwyk4PAUHp/j3NoZ7iMD4MKEmL1EMBIkPXmfMewTinfClb3wwvHVRe/jKWApXnlvQK20l4hte09PN3BJN4vRACjDpQI/J5TRg0x+rDD8ksHkxXCRFPOUIN69iaBboVoe1UEjVA5pTsJfsiYJJaCCHMA2IFZuoV4zueq0GPhyUMkia7ZhHq9uXCf+G3W/61KaAERuyH7g7uttxqeGaICTFGZF7frW3y1d+cMORq1QD0fC4cUFsMnwY4F7CCIZNXCLpGYg7FGrlMskPDLnNUXdAxCdK72mdM0kkMwZ0a8EhjtguhdFj7QAyyedCkhPoSDhWuYRVbjeCqjyVqLU7Ej0dTcFxE61e0mjPwR/ZGxfFcSdQ9pI04I8MTavqFKDTJfWQHcetalBt/lQkbVd5d0cWiV/lzGxwju/dmvGNq8uHX6iDHB2nGdeJFLDp3PpAx5UJErGJCl/Oi7A/ggPYPdiB5c81iMhT4R4RcgjI2+dRETHUsfIuPhpbAC2RrihnTVtrulcMqAw7rnxHVV5x++FPMyeEC8cUHQOJlKlB6g8mGYwGFVHllg5vluqtQkUJWuZR67qJn4hzRxIbUEZnHLYGVCFD9c7QN0yptSl8PU+DSBXB0YRtMwnPYfTfYNJQuKmq8x2km4/ssMTuDiUQshEyMWhuNIA8EfysClLw5j4hDDcEi6/Ynx7tcPX109wPc14ujniL1fBH18+vUcsDg/zhHu/wzwMdY34WZzy8CD1Bf7Y0UyYxRE3GkxkVWnQP3baUIVd1fCmiLrmigfShitlSiKvRbV6rThOHpBLMmeqrKG2bg4HySQdvhDbOd0QxrsEOiwY7ge4yBju9HISI14GlMFj2DPGh1z3AhddzX4Wj6o0YnuqFZTL+9t+SIyq7FEDDz3Vx56HRpOJIepB3aRhCzQAlUJk6knfd3x08GtGYrPd4NPPP8c/+If/Lq6uLvGv//zP8erlS7x68RopZgAOtUDeDCU0Ync2AFbMxSChKZBsvAToKGuVIhhcCpwWrT179hTeAdvtDuM0YRgGeB9ARPAKdgFRVPAuwBEgjU4IpWRsNhM+++w5vln32N8/YI0BawbmNWNZI0q6hyuX+PLZFhMluDLj5u4GN3czcl5RkHU+SAOO68srTLsnGEYP7wk5Z+SU8XD7gJgi1pjhvGiahhDgfcC03cJ7j3w8grlImhNUo7xvdHnraA7nlIe3/dy/zvr6ox7bezqIGIPPMNmcMBf4ucAdEzB6IEtquURJF1pxGoCaNnZabAL0xubM82TUanmrdq1g1SS6MkuBZcQp8FSOsGURgFNv02WcAghAdGZr5EbmMnstFFBZIgNcJlbvYtdC1QJLyh0sev2sepJ2bSaJ1m9i5nX3xTnyD8W6J4AWbxoPG7z+9e7cJ8bqHc/2fR/kJUUs6hgKhGNAIWC4J/hV+XFONhe3CrfWM1QTl+C0A9NJ9MpuhLSgI6jIvRp2c7CM5lIG0ciUFqFSwFaBs/KB2UCE/o2PDZTJJqiNDiLDLQ5uFvCVtgLYaxV2sc5/qJHqnlrQR4ShKexwEBBrkU12ep1WxKebj0sAVkLx3FQidIJQRk2TP/osLFbwNl/ZsG6B2I8CvEPp4b0e7IG8FUAyHAv4cACcB3knoGEW5zLPAEDwAyGv4jiV4KX7VF9UE85ukoC+sYNuOzW9a1kfc57KoHUVCoLrs5+FwkIscxahwIWinel8RQ3sGnCsTm61Le0aoOlxaawiBsDko94oCOrWcQXudHZ+vdfardJeKmjUGkPG5hyfPIjzz7QPVLWdd8wF99aJ9X4Pgjyr+CSDskfaAmDC/Tzh1cNOwHnyCEPG5BMKDyhMoFBQthkWaMjkUFRPOkcgDIRUOqk8J6C2b1h38uwABbUiIyZth8UeiCa3du+bCHHn4LJvVCod5zJZxooxqEqNtMIGhgOQR8VVXpvoeGoeDAF540HM8FG6uLnU7bFJHLm0cZWOVyk0XvbQNu9F+SEPjbbIC0DFnWYxbO8xp5JsKklm4LEM5rkz1vON33V8ZPBLutYzNpsNdrsf4cn1Ff7k7/872Gw2+NXf/BL/9f2/xLEcwSwyZ05vGl0+uWja+PSQ9xQi6dymQtlCHyENxwGFCzwRxsHj008/xWYccHl5gYe7DYZxRAhBorzOA97DWiIPPgjhoSQBvzljt9viRz/6Eq++/RWO6z3mFBCzw+FIyGnBt799icDA73/5HIETOB7x8tVLvHx1QEwOhZPcmyO4EPDsk2f4/MufYhgIzgHzPGNdVxAzjvOMuF/hyYH8gCEMCCFge3EJ5xzmNYJzBlf/WCf1W4DvG0/mHPjaGd54/cMbIEeM0WdAuWP+WBAOEX6/gsqAMniEHMGDA/sgUj5HmRvEDMrS474qM/R8M705a0whnNvGpzWJFx9lFKkUUFRKTI1eWARYwmWlT0L0ALXz8i2DYNdjacMSCGlLtZDPFrB5zF71Z63y1aKXLurzLYCJ1rMWLFo69iSqZsak45KeZCOpXmI7zjaqfslVnV9z4SuC/jgblB3eFwxXC/JmkEzB4rRAUqNdJkul2prWFcmeCRUTYe+cg1PMVyPHYInKVR3PLqJbtHPb5nUBlQJ/DAK2E2oUhD2hdGnqokoT4y3qxmJducIRyEeN4DmqneiylCwgapT6RKZKr9tlrhw9k7gab2VTilfGGyd9Lyqlw5IBboUUAw+t8NdSnfXoN5kTznf3dXZUipAhqqL25Yxf+yEODkC8LNh97TDsM8rtHdxuB95u4OeEYe8QZgWlxVdQUTmSdq/6vI3HWbutoXM69GZtPdbuisrxJEh0jLTQiFKBdYwcDqriwQA8ww3SsTBnpxkb+zBTq2gDR6zZJOrlprQ7oBY3QT+7NjLonSZuj80l+eE8dQ+czjVbQ2bTaoDBTvZDn6tGhB+zHrV741srf9/zwfKsxk9mxLJFfCkA8HgckV5u4BYCf77ATQXbELEWj5Q8vC+gy1SzJ4WCqDHtAIrSyUxuqK2n8Z4ABZHcOTD9uIUjY7wvoOwQLwjzp60Is4xynvWKwM5XO2FDlTeqJpMEQPuVEXfi6I33BXEnXloJEoThYOkfeSTxwoEKY3iAOsdiC9NWalqotKBABdwB4Nj2JZDaTELVsc6TrKtKparzpvlNMlZUo7sEanuYzjWTC6zc+06P/PsA8EeP/IKUMwuhFYRhwMXFFf7xP/mn+Nnv/wEGCvj2xXf4xc//DUYfcHFxhZhWxLxijTNyltF6w4FUoOcU2Ei3HpboGIlkmVBjGZ4Ax4ztNOHy4gK/93s/AwH4+c9/DlFyCKAQ4LxplBA2wyStkkvGNE4AgDVG3D884DAfMccFh/mAlB2ILwVwlx3ubgi/+Ms73H1a8PR6xd3DgjmuWBJjTStiTsilAES4vNzh88+eYV0OWOOCh/sbHI9HzMsBqWSEcdB9hTFMG0zjiGm700jsvYKRN3eenr7Qf731EaHZLQPA7TwfeJeCePjbEDsgaYuRQGuCXxN4DCjZY1DdQbcI30A67GTwDPmZIA0LlO+UJ0KaugXnIItfJdJqAYiHUiyUE5C50gvaBifncKb1WESZoqa4O+5vrcq3oiIArKmoMqJ163FSxGeRPel33iIFdi7XRQ+sArcWEYzNkNQ0fXcdAE5S1D3/8sRrPgPPDbygRex64PMIJ+9DQ+FABcOQ4R+AeEmIuwzKrnLdjGd5wm3VVDJBKrXLiFqkYrdBWcaSkvLxLIrbLQyjQ9jv+psd77XAZVVQohkGkzayQihiBbR6vuIJcUe1mMmvTdy+bnbGGdR5atdbo46qIkLFOjlZxIVqi+SqmHJO6bCHxvpaP096CsfZBq3ptvpenP3qZM2cv+dj+EvEKBNrhTyBc5bgxmaCySkVtRPDkVFUMsoaFNSGEl2kXzi1LbrVeIyqxmFRMLVBZdBmA6vOhcQouxEUxatwUQqInDVhKTJPU/Hg3Gnhcr2lOndCP7/VoaBuvdcW59kAfQeaS2ud7VecqpYQqtyffWb/7KsJKN3zt6POHYbJ4NU/eCx7QG+ZHsr/LXBwlH9X9YfvP0ju94un9/gtA8f7C5SpgKNHeHDwB0LKGxy3I/7VGpBWD9yL8w0HSStV8M/VSUpDe3CUHJAIy5EQJmuEA6xXSkOqoI6qkzMcudkatPeUwCgTIaljVLm9XiPLsdkzyVzJdeRJMkh5w7U2YnnqEX70VOQ2g6vvjbuAtJN2zHb4FUINXHBKKQTV9ssit9ico7p/Wdvnbp7VQrUewig9ROYyV7peHy2ugRy089dM6DuOjw5+JZIK0ablAu8Cwujxez/7GT77/Av89tdfw4cBv/zrX2GaNvjkk08wL0fMyxGHI7DGFVT5uO3umKXtMEi5WU4jv9WrEA8GTnQkoK2Jt9sNPnn+HA8P9zAf3nsPHwa4MNRnMAwDHBE8ewxB+hWmFHE8HrHGiJQz1jgjZcLgd+KllAnz0eHbFwvAHjEWHI6raAuXjFRW5FqA5zCOA64uN7grC1JkzMc9DvsDYlxRQHDeVxzmw4AwThgUiLNz4NysEp+19XoX6H2DCoEzAPyROb+OGBsf6+ytk9iTbBRZnAUT9CdmUMxC2g8OPvdVBlRbBpfBAVDVhfp7nETOGk/TPEhtllHO9uhqx9TIaeTCr1wjQ1VE3hZ0ByAk9YkaWerbFLNvkT4rhqvXY/db+dqom3cF1mcFCCdGoPdfOuB7Akxw+p4f5O98eJ/o0SO4guAL3CzpQB6KzANzFLJsACCuus9iYLk6P7VTlxUAlUYrcQC4biqnn12NtBlmmxNFojW1mpssndgAp8utFbFtbqI6oiBFnyGpPmwFMYNoFBOfzqWTQqUzikzVuSZJl0pHqCbBVTccy1Y/IusnJ8Nb0Ak1x6jbzPoFcxrNeQQkf2gT4wAeuCpagFkKPLxXLltzGvxaKo/SqV2QDlmszQY0TTxIBF5aQ6N11+ruGQTwiOpUMxGCs+I2Qpl8k5zMTYKspR1IKQ/deHHnhAdG3lArxiyQ/rbB5ie1+/dU7/8NyoPrwO9ZcRVIPof669C/rc/cnm///M/RBwFV9eEdyKTSXj9Sgdv5wZAx+mRzwP5iwKsnGyBI8Zk/UqUQ+dlh8RPc6hAerOU1gyeTw+N2z45BmuEgYpTogUjIO6pZaQ5AuhDnKBypXYzanrAUhKCqI/0zUOekb3dtNoAy1Qwkk9CZLNCSTSEmaP1IIsQtIV6Htr/p8y4TaX2DzT2uiT7JEuj+5OXLAjRZr+8ku9DZq/7+Tuwp49Te1n327FnZONj5TXLlB0ydj8/5ZanoFDqDjB4rH9MRY3u5w7PPPsE/+Cf/CF9++WP803/638ayzJiXGb/5za9xc3uD33z9FfaHA169/A4xCgCVkwt4gyN4jdhx/SIUknbBcT5if3eLcZxwff0Ul5dXGDdbHJcFBcD19VMMCixTSVoUIg8kEMMTYT4c8Oq7GwRy2B8A8tcgygAS4joDxcEHj83mEk+efoqb25f47TevkNcZXDK2boswOJAnOHYYxgF39zf4q7/6ORwVcC6I6xElr2Cy1sWu4n3nA8IwwvlQI7IMVPk262kr9/4Dor1ddPds3zp5z8cAwhsX8ePtHf7llJUnKZsEO4I/RCAVBcFSYEIxg44rVPAZSBmUC3gawN6DNwEcHCi6iir6yKfJrFjPdRAjWzvSNQEIgCPQoH+kqcrhgZG2AUzmTVNbyNlkXriCE/u8mj5VRy0cmkxM7ZqjoCxt5A/yBBgXuEak9Fy1IAXt+/lRAZF9Z9SIRN3gjD7hgTeiMx1QfgMQ6890/vqbP77348of8e88/xZ/6Z51wEzXg3Jy/QPEFniGn4HNTdbIA2O+DZVzDQLWa+rGQP7O9QBRD0snG1AWh0WoMH4u8Is/MeR+aXrDdm4D4F65eMIlRW0B6hbZaFujFRKuqV5XCcIBNEfJimfSRtrb5okQGSiDx/JUaB/ZgFg8fZ6MFuWsAMnGQKk1VaHiLMoJAFadfQKA+sdRfzj9+5pB+MBmxfsMf72ijAFp6+CfPQNdXaJcblC8q6AXkHXpu/VLBRhvYi1+lUiuQxkd0tYhT9K8xiToTHouj2hrIwlv22gqZZL5Mn8ywi+iwlCmzjnLkr3kIpOGMsHN1ACok/mciUAKTEWeCkCWjEGx4iayqCLBba1bWHu+5vBLYaUCIqJqWyqfs3N8KliBXEf/fCvABU6jv/aGbkIYTeuxo9SIr34n+jiqD+oIfHO4RMwedJEksJRJoqyDAErOJKCYpWGRn6UZU7ySLEPZtboV697GSbPSA4OmgvVZrmCWh4LhyYK0BOT7ATwWwDPyVyOIGeNtgj867H4zIm9V1s5s9dkaK5M8Iz8bzUsc7l7NxkeAj8B0o3NnBsIigZa0dShefjb6i9mEminiBqrfcKbsMIe/P4c7nUvA475QDUIRqoN6GrjpPrC3zzU99+7jo4JfUWIozQsAwAp+OReUkhGGgO3FDl/86Av8+Cc/we/9we9jXVesy4JhGnB98wpEhPv7ezgizPOMEO6RckKMCSVnkUzTUB1bullDW1yAFFcc9nss84xlWbDGiBgjcikgIkyTAN9hGOGzQ+YCTkUpE6yc34J5WXF3t0eMDGCEuD1yH1QYhADnHcIwYY2Mu/sZHquk9cemZUhEcJ6Qc8IyHzAEpYZYEwtwpXUw1HMi0vbMOrb6euEiMLhSHfBO4HsCeu3f7YGdvO/7eMPv63Ao2PoVTtOHTEJBkK5JykHLDNLxQWZQ7Mr0YwJyBpUCBC8GOHu4wlJRHHqSvao7FEmvPLoJa+qFW0hC1pc1wCgMl5tRZ6Lm3Z57sefnZwXKfQFaFyUqurEUiw73wMGdvv+xqNsbwMMAS1d1+2g0T4HJG2PyA9Hsx0oWDJTxbDxq2pY15ai/tE3aHJAsToqfRb+ZYsE4Ou2sJ3MraZpObkK+arrOIg/n46TRWrCktms3tz7Nl1r0vhgg6n4PoNFtTMs5N85wzUjYM0T3uRWFyLfiqTZKKCqBJlGpN68dOAPB/b+dnLumXs/ee3qudzzw81/1882+PjSeIcYwZC1gA2gaweMAHnzNDPUNJwBo9ystRlsSaIlwSlHj4ODUmZbouQDU0lFkepDvkvH05eAgDkPait6uj4w8dvrOGZKK1qggijZC0TlYI66uZQsckxTmatTNnFyxn0qHUp3oN9ZxN7cFbLQ596ZdkM81BYe3shDqc+Yaze3bR/XY5V2FbhUEn6QPPtxhNnJNXgrnnTl4aAVcXYEsG81FHWUBxnqiLovCDGm0kgkcFBhPpVLJaCjYbCJmADE60FCkoFcdEOOFh6PY7zzq5xgvu99rqt2R/aVmDfuEsAb13CJgvnY7DerIeapdCPvI7UlGpzrB7bXWxRKntQLcvj9qLs73LJsU3Tz+wYyXH2BPPir4LTnj+PCAMHgE7xAGDzCDOWN/f4d5PmK7m7C92OIf/qN/HxcXV5i2O4ybLRiM62efoJSM/+A/PCLGiNevX2H/8IDf/va3ePXqFV68+BbfffsCd3d3eLi9Q4pRQDAAMKqCw+tXL3HY7/H/+n9ucXX9BH/51z/Hy5ffgZzHZrvD4MU6EEu3ucLigREDjjPGIWAIA+bjiq/3L+H8AKINgC0IHswJhTOoLIh5wpoXSLZ+gFMxzrisiGtCKRJyCsHj8mKD5588BXJCSQljCALoOaEwIysALtopxQEoyhlOcUWOUZyLs/zADwGrPbht3L02084L5z7k4Yix8ytIJe4AgAMhBw8XvXKAc7tGB7nmlMExAmsUr2dehOayH4EQwNMAP43w20EAsHea2yb4ZUCeCGF2NVUMQDh5WYybKAqITqzwllQ3c2UMpcBaKxvnrxaUWAoqiT6rCX3bZlsL4wqDyZ0CXe3iU0b7TAFyxsPLA+m5IAYCOAVq9vzINjN1uN7hHBNDOLHngF2jdO+s0NdrID5zpD7Q4cD48eYG+z/M4E3GxbMjDofLqrBQx5pbSlfaRxNoFCpUmCE8cQIoh5P0sFVP11QxlLdLwgVOWyBetQ5geRPg5wDT3Bz2TcHB5O2KguPhUAAC8qQFViqBlie95iIbF4oAlzxBKrxH4ZrmjUaftTucpT/BEt1LWwFQ4ciVbuEXVGpD5aEbSO42J4uUAzjNYNsG9IYT13tcbc6fpCR70NZ9jvzuHE2/38M7xuV2wauLC+EtTiN4CiibAfHSI26p0lAErDLCoUjb8bXItXsvYNmoUAoejCYRvMyV8UGcoOOnrmZorNDS5lK8sGfq6rMBodKW/IFqy9yagYhA30SkeEhDjJHhvAR28oCacq7tlkcga8TypBBWwZGfW/agghkDz/1BfJoRkFSrFOLZs2TgJCyswLduQd1Jq84vUKUZWf8t30XvPRcnBW8fy6PWj3EE5OxQZo/pesHFdsHN7Seq8iNjFW6DZnMYSdcjawGtv/eN5w999voRKRS4DcNPa20GxIVwOExie4cioJuB5RNpwjQcRH7MRwbtGz2mFnuRODeUgelGOLfhyEhbQryg6syFWTDReqXZVI32GqUhjxIEkHVANfMFBvyRW12MOvx5BGr799LqGli58n5llQkF6IBmD4z20B2nFL3TIE318R8zE53jbkpJ/Ngc7o6PX/DGBVyAgoJCBSmtyCliPuyxLDO20wZ+GHB9fY1x3FZZLSJIQwdmTGNAzhnOe+x2F/A+4OLyCheX17i6usLd3R1effst5uMR+/sHpJSwLAtKLmDOSDkBy4IX37zA3d0DXr58ifuHe/ggXJfggixArTB00Cgrq/6rc/DeIcaMZUnwgUAugCggBEZehXglnWoKWDWvxMNTwocudtLdgFmkb4L3lmmEd6I0wQVauKcRXL045oKcEnLJ2omuiDPxGDmmOx4DrydRXeOgveM9HxL+MgiZHZxjrY6nZhwdVQoDm+RagfCAc0bTx+vOVwoopRpgcoCAX+ckOkwa+Umu22gUZAYHhwImQtpJpx3Rb2QFq7oorbGAcTB1vLLJoGm02OXTa6tRHrs3pxdom4JF+/rI7skJUAn+J/dMXRTg0UjdW37Xv+f88x576DXMoLbqPCf6gQ8ixkQJPBTQUOBdEa3cM8Bq0S8pLJFKe8oCUsQxcfWSXWLlbgs/s7aK1bS/S9J0pPgWFeVuQyDj1NbXTyV6amGdFWZYdMR4u/Z77hwjjQJTbLJocjE6/N2SNzBbiyTRAFEtnOoOu/4Tri+jqptIerNzsqi+5exE735WvQ/VO0n8fX/4Hg4CYwwJPLLo3A5BimYHhzQJRUSeBZ9SlKzzlKfq0JHSnhCcNKlQbi6yzAXn5P1JK9qZUB2gPFCNwJfASDuqILVSEM5kxOo99F367GdbezrPgMYH77NE6M5bnZpElc9+0oQFOKU8dYD2pJKeoNlLarbE3t+F6975fMmEObuPY9IiN/nZCtw+eKGbXRJk7q/Jo+i8d46xGRLKWFAGLxq8BbLfeoADVSfRcEPl06c2dnXtZUJeXY/vZFtPXTaXZXyNorJeOi3gBYCmE1+H18kPQrMSG+HUTpSg+rsRVZmhBmbCI8/P5r4Dis4xA5Unn2n/7oGpTUs+e+/Z+U9e60/1tr3ukfc/+hbb977n7R+3wxsBgwOYM3LMyEvCYX+P+XjA/e1rlJzx49//A0y7C2yunoBIaAQytgYJAUcOIXg8ffocT548x5df/lQ9Rcb+4QHzfMC/+atf4PXrl/jzf/WvcHtzi9989WvEGFGWLEVmhfEXP/85SgGO6YBcMja7rXyEGoVKyWBroQzxfrzHOAQsy4r9YQ+4CHIBnzzbIrgN0iypDPIZzjPIN9fYk0eAg4em28FAKcglASXBEYmaRBkw+BGeInJehHrhCDBaQ0ngHLHEBSlncI5AsdDF3w742uvnkd83sM9HivwyEyJ7OFeQgrQGls2fUSYjt3L9TqWA11U9hAIE5egGmd6cEjhlIEZgXUHHpWkVpwQ4h2G9RBgDwmGQcfaE4h3yxgOLbGb7L6TBxngr3vP4WCTCAJSKlpt6g2kvUsJJT3bb+Ii5AhaL5kqEl2A92S31Y1wrm49mZOumaSi/94ZNY9gu03PlmLaoML7XuLw16qsbXs+x/xjw14Gx84sUlBDDuwIElgrmTJUnTSxcTK4FPXITSav1w5Yq+AsLY3oZ4WKGm5M4SsEh7QYBy46QNw5l0HmZCcUoTApW80a/r20T6YEt0OgV1jKZCpDTKdCUAiiW687AuBUe8PAgnxFD4/qaPFXSqLCoG8jGJpxyc8QeifTpUcFtAcLRXiNpnpC6jcmAUjeX5abav+vz7yfCI84VVcT14Q5PBZfDCt4lpAuPcrVDuhwRLwOWJ4S0MyUGwua2yLMYHUpguEGdniXDxQKUIvOCCDR5wDlpFayHS0LtGx5EIoE6O7FeDYgXDsfPCDwAy5MMKsIVtR27jNzaCXObCzpYZ8+LhJM9iMjl8CDjb/zjvOVTJRDPyBu9Tm2IULJEjMvENQVe7VVpjpM1SThJAwAVKL8BoMwxdo87w+fqFdWpUvDLGvUdnO37fPL9gx1FouH7gxFnGSFkkd/cZuTVYbwljXAK7z5eNJUGZ5k+s9EOEsTp1po/OPDcHO4yNrvKgSXTPEjG2dQaHn4qMo7Xf5Nb4xvdG2rBqzrK44NmlcbW+rxy0Wdf1RnKACxPlK6hSh/DoSDtXM0uUtHorl5/LdpVh86vbW7Y9Vj0156rFXRXwA4do0efpdF7+Pzl0zmmr53bFKG5NSrI246PzPmVNL0UFK2Iy4yHu1scHu7BOcE7Ah9nFHbItJGGDsMkUVdvM6e3vOozuuY7brYb+ODx5Y9+jKvra4QQcH93h08//QR3t3d49d13ODwcMB9npKyqERBgRybD5sRbZRBcIalYVN6gJ6qc31IySknILGB0WQH2HuOwQXEOcV2VpytRWes+x4BcO1vjjYyYIpZlwTLPcMMETyQRZqcFWhpakzJBAcAOjAwGsRhkUwvo7YndG97xszwb7oZXDa5Fq9GA78c4GGitLUlAgoMY97TxcJ4QWDYZaOSWhkEnWIcu7D5IwCzgpLq750qnDHAEHQIQAzxLignBSSrRCUjmQNWrN6+4jN15PFo6FGiGrNsMSH+utAZd/C5BOH0mL9AvaOpAitfP9QxnagCsTU0smtZ54W8c1L5qxOf7jr+tn9MXs7zjUt7n4aGbhT0Oz1KYEmQzSju5kLSVcTepKaA9HybZ5NgREoB4FUDZw+0GjaJRBXn+mJVG4VAWhp8JmAQA91FaazcMaLTFDgUVXjsGii1oht0ihZRVnYD0+drccDiNavvuM1iKXUx9wLi+bBXQdt8KYmsltkZqau0M4YTrC33tjfmgNuyNg9/x/D+OGTk5ChMSO+FQBoAnjzI6lIGQt4S0kyIxaJTeFFhc1u+aCRJusIbwSBVkzOFVeyTAggQoA2K3i6ScByc6wuHgUEZCuhBAKpQEkZwyp/RksJRD3DfSMCe40jXMTnTFSOcSfGACW1cFpVYVD7DOk17xBD2AK+gmCto8RDcPLcPRbdGP2o+OElG/G8g/e6vo+55OmI8VAXa6IDgIIF+zl2yObzQi4+e7SE1to1/qZFuq7t81YKH3q88n7KmC5zJIZ758KU0z1k8K4rU6sUdRmRDgStWWyIeJk+uzZi7VlviZEEJzsK2exA7f0TLEXqgUnmvP1v5OmrNI0MEegxV7m054UyVq11X59DY3IHvXCQf5bNxM47fKmel7en7+SRAIwN9manxk8FuQ1gXeAXE5Yv9wh9ffvcDd61d4/uwZhs0W+fYe8bCCViBMWwwXDjQOIKeCltXN7E8MBX2EcZwwjhMuLi4BMP7kH/x9HA57fPWrX+GrX/8af/H/+3N89auv8O03L5C0QM57r8BBB9RTjfoSSYe3wsL7cs6BmJBiUspBxMorMjOmQ0YeRlxvnyKnAfPxATkzShEOcClZCRBmORTUlSRSbocDHu73cBfaVMN5eB8q8LUdykH0jEXaUSTeiIuAYHKnw/IDgK/84nRMT3HwxwO+dhQm5Cz3kkdNJxKjXDpQUf1BAtxRKAnYTmJcsgJi5fLCpE+cA8YBleKQ1VlICZySRICHES5KJJh92+DSJxe6AQLmDRQPxJ1rHqqOq6k2AM3AnBS9qdGyzY0yoWjBm4sthW5/V1PqBnAC4FSvs08vnQOYE6/bppvjep5qKDpj9EZ6qhqzrvDNNjTuvoPqB1tA3XhsH/ogAAMluFDqZ1IoKBtG0UYg67WMlXBmOzknM7zm0GgRiTQBCKgydnob431BOBaE2wVuCQhXDkyu2/y1rWdmYFX96QT03ZPYQ7l30mGJdGNg/V3t7Kc6mVJ5L9dr/GMDtfa5RYF/nuRv0046N5WJkSfhB1vxpR2WIj+P8Anfyt7U7UfdvDwZf0bnQbSvfrN6w1bjLa99wKPAIWYPH4pGRAPy5JBHQrwE4iVj2EvVu0XqW5tWwCWRSHRZ6gRoo1klp89qyUAqcGsSO0QEVnUYKiLFSHPEsCb4KWD6TLjly3NI1HYU4OuzyGSVsbS1VsRO8FHWYM/BpCT7Hg/CKS+a2nZRzZ61Uqfu+VOzK8UzKGg750nmu9N5XGk4/fwAqi2xDYKZq42BKT8QcKL6YIdlSN5oPNT4v0IXbJkc70rt7lbYPcZse68H6WWGofGAmAnHdRAw3IFfVn6+NdQx1Rjj0rOwJatDYZmivqMiJWB4sCYnjLSRFtvHLxxWx7j86R2ebmes2ePusMG9u9JnIY63W+oQgm5svrLMQy3mdck6XbYsoD1Pf0TrAjgRooJ6K76VYs1mx0T+TycUoxbFgag6A3V+qg01ul/usgTnNMATFQigOvjFC06qEraWRWOuQaO6552d413Hx6U9OI9puwPnBfF+xs133yAe9/DEGMlhYgI97ME4YLnbYx1H+O0OfjPBbyeE7RY0jHDjhSAAM8bKhe2dLtIYvXMDNtMFvvjyJ7i4vMKnn3+Kb77+Gt99+wK/+Pkv8fr1DX774huskSUKqIfxZskBjkn4oZBObMUxDjFhThkxswRdQXjYz1hCxBh2CI7w7PkzXF5t4d2IUhzWKJzhBFnYMUZdBFJcx/DINGA/JzhKEkkKLejN5EH633GekVJGYRZ5M3IgB1jlqXlOtR86U1VdIfSWkFt02tCThCS7IhRVuFA1jNJHVz/AUSC0B71siZYq3xKQiS8pY6+RPKcd2DLYyqyh3qbjFg3W72RV18zAOIC8rcgCfthrZz9qgHYzAI6wuSkSTSxauBQMCzeQa53j+qhHD2grWLU9oovOnFTkdhtUr+bQp3L6qPIJqOg3uRol5Aq+gQam6+VTu2a2n20qGPC1P6Czf7+jAO5Dw18HxkBZi0QydtOKh2GDFIz3y0pVANKO1UCT8OFszAlI5lvrGLvaWKUZXNFu1qxAKti9WJEHocasV066Hh3FEB8+p9plULIXqDzkDNn8/OK0EA41E2DjWlsyd6+XgNbVzZ6rdkG0+SPXaZQbiYCbdBpYqQt1mXdFUAWt63DACefTgM2JfnSdl20y9vOcW0D0xJnq5/TpGukn7vs/mIFYXOVwFk9a2COSY2wSVlHkrOp1d2tIAA+3TV3vW1oTa2t5allIN6t9XyMo5apC49eAi99OGA4eVLzIkQF1fYpsmQLa7hmzKnsUQi1yrPYE3ZxgABrlcwtUzaKlot1ENS3vsoLlQZOHAcimR5sIpJ25BDzph+medZJFOBnsM4+nbSktTmBvqeTMN5+9ZcidAmHr8vZR9H8ZNfhSmJCSAxDA2UlGRKPzaafmL0HiTq7Zc+PxwwO+aHRYXz+hCajEJuu6s3XmIsHNDlPIeLo54tvDBbwvOF5nje4LNS90wRFRkgDWo1cHuvG07SgqH+pX1iwTwzr5VSdZ13y6oKqVnrfAei2AO8xiD10kBA1AleBAHa0BAKzhRtH54LR43KTLepvyeMQY8Fo8XG2yRYJrYIdO9reqe/89m89HBb/kHIZpg7RE5Lji/uYliBkejAGEEYA7HME5I+YiKeppxHCxxXCxBZ48g99uQX4CkYP2hwTAWgwmxQcESz8TyHkMU8CzaYtnzz/BT3//p/j2R1/g1ctvkQsw/HrA67vXNTJr0mIERlHoSEp1IBLwy5wxp4glFaQM5ScBx7hg8Q6XFxG7zYgn11fYbkY4F1CKQ0wAuCBzwUCMlHIL6ELBLzwOawJKFl1iD3gvBqcoLYNAWJaIZZF8RS2kcwAj68SVCCfrRivv60oLWO7QjBSf50/gq1Y04VTm7GNwflNdzY0nVCySmll0NlFEysxrJDeipVegG7MCdgBSFNdTI5wTXrBz4viUDJ4X/VsF0ESg+RJu8Bhvfe2OlCcn3Nw+LQR0API0ZVy5uucRr37YzSfpAaa+3vO67Hz2Ox2mPojSGQY0b/gxQGLn6oHv245zgP090+BxPtf7PcSOiiV0jrEJCWHISEGaXbDKfhXPyNsCKk5oNF0Kt4JPbpF7q76Hk8irRExUAig4uDUhvDzqxuMxXo9IW/GGOAg/vGqknjsyanXTHigrnchSkcnqKcWlGnQrnOpVGgyUdSoFALTgjWFqIWVo88ovqKCDnYA+ZxJaaNdnG0x9zv3cAeqcqhuWxg1O5uX5HAPkQhx0XXcg8wMfDOGPsjYC4KCUhwmta5tF1bUJjjk+UthqixOnGQHGmU0BrByYYhbN8eMC5AxOGZQSEAI23wwYHkYQTx0IN11vAEV42+xVUcwKn4CadpagCU6okewZXLromCoTwDcqjotozrTVHFS5PwYPAqysQZSth8brPntgPwBokN4DnU2KvuDr0b9792k/zGHOpHHkC6Fkh8i6l+qzLwHIkxaY6ZUWSLzlpHkM0Oo0lD+ddhIFtcxLm4ftjqXBDcG7gl1YwXwpr+8SUGTsCgfk4oQj7lAVQtYLgl+BwRz87iiDZaDks43LXzoKAQDNfigA9RA1i52ugSK1LMET3KJBpatzI4Fmk/S+Kh1D6Vx9dsDew2cNg+QauYJ0U6apsQJqa9Z+Rv/1luOj0x7W9Yh13mNZj8gpITiHQB4lF6xrRkmSsi76fo4r8r5gWWeE4wwaRwwXD/DDhPHyAi4M8JsNyDuQ7yp+Os3a9jwdCAFPnn2Oi4snuLh4iof7e/z8L/4CL797iT//b/4V7m5u8e033wmIrCJ9MqEYjJgzCgtHtzCkBbLF67180u3dHeI6YjddYTMOCGEEk0MqQIwRjjOIHHLOSJlRlND5sD8if/MtxuARnMO0dfBDwDgMEMWjEbK8SpU7YzXEAkqdFn/LfzEWpFJQsnB3A0vk2DsSYOIARyT1CEoopK7A8MNqOrz9yHA45uH0Rd0T/CpSLyYzVtt0OicLKXSupPKBMWiI1naJYuEpFtqDfgcbb1rnzrqCc4Z78RLuZsDm1Q48BJSrjUhaXUtxXA9Uwj7LIvUSYUxbockQW5MDiRQRCY/QwEy7ZlRKB6BTK7fNrIG15qUX3xwE0qgPnPw7WzSiCJXnsSITC8DUiK9rvzsZf40ItU3fzkMwvrkdol7y4cFvgSiDwDGcYwxOHcrk6vVRkuyNicmXqaWOKaHy+ACcgED5R5O9CzPDrwUcHPI4Ij/3oMRwa5ZGCSwGGolx9VWWlPpOI4y79vwyAFNpcBkYDsb9lWhKnpoBt4I82zDyAIkat8uDK7JJDnfynnShoPsyISmSHe8IfgbGe5Pto+oYWDFbfVyxA79AS5kzTrjG9bC5gZYerYVx/ZRRMFnnypkD9yGPUghLDHBDQZkY8cIhbkXlAd28Zi/AhImq00CFRKIwOowPRZVeUCNqLjqki1ClpPoIvlszwk0A1iggWC5GucHl/9/em8TKsmXned/aO5rM09xz77uvrUYluiRSkmlJlGgBhgTBhmDB9oQ2YAjSwJBtAfTAEmTAAwkaeSgYtgGNDNC2DA3kRrDakWzDsCceyGpAiSxSFKvIIusVX3+702RmROy9PFhr74g899z3XpH3nNdULuDec042kTsjVuz977X+9S/a80RsA83WFUVaYXdmVJ042JxiLZQ9mg81uqtVMtHBKxahRQ3Ii1Dlp4DaKTBvF5Hf0dLe6qns8Z4aDcM3WI1TLcRT32GcNwbLFHP1j+XcAJTMkBV9szdPKNQiTAPC6ku3RXr9lrDGFhSt38DMV7od0wC5g66fGHYN+apB+0TwAjSjmNj5aM9dI9fbo0tmbhazKCotxavl+MDsQ+xfp3LvhwnaC+HRP3uNj+Q1mktBG0VfT8gqcXRvyyb0JGkJW2uCElzn2YoahfaqrDEKnW3g4073mlXYpsiiq/1TpbvITMcN08o6zkmJWmfjHEu2eWM8EQjQXgZk0pmDviiGK/UtVS+4ZrHtHJWum1JAus4bhbDktwhVdaesoxps07A8tzD74ues4E2ZpoFh2DKNIzknkEAIYvOBeHRTsKgeYl9SFaZM3m6RcUSzELuOKApdR1hU9tvNE+fJDKpuoIG7QNet6bsVq9WKcdwCyv3793n60SO6pmFzecl2u2O32zJkmElGxv3NOZNSNjppCAamRAhEVDO7cSQGrO3xlEAMSCbFKAM5MYhaQw7HWyFEpilztdmiXUtuGvqjpsqqke17afWcvKAfLHbUPhvlnK0oL9lYS6ozSCAQDPjWdy3ULPyxZQz4rk0RxmU+ZAHWahQiay32UnGCfiFYFe62I0BtbLWQ0va4hEvKlywqEdfJZFnRaSJfXSFDY40z2tawYepdK1i801bwKlclpExW82MDPHOlPQs5opsyvfWx8rjaf2VRq+djCS702mHK+2X+qstj19deA8H71If5iNbg4xpg+ZyYAqNGJMypUS00jRvCUQp1AoV9UFfNXWk5iYr6piRTmyJM67CIZJQNt53gAmrU0jHkbh5Ldr3PGo0dck2pS9mY+RisI5vM0b1rKUwwAGF6s74oTzYOidk3RlSgbalOqVqlyLxoL31uKXu2jMLcxPst48WBYonmVA9armHF/4qPvuh4L9nqx4lWkFk5meU1ZQqJi+5o9T61SHkaZPaN8r4gSKvosGgIIOV8CaH3OWOcFtknkMl1hCclJSuwzRNMvRB75jlPbeGfjrX6xV4qWxfjKWnrsPh96cdFUzhQucHWaluY9VnVsyP7NA5K5BMDqCoLV7xhLqvRcfx8XJ8/RG+6RfFMuZds3F1zi6VpwJRjACaBzqXMou5tlKMrMVSOtCoSZC+gUKK79ib2z5VfI1kEQcpLgiu8tBdGPWg2hQ8cavOjuvFcXhvF6VI+zqxeuImDape4K0DT5wZR2+A3VwmZotHdFvdHAcHl/OQOpmzrX6jHs7GYr9m5oBS2uT/VbNbyPiknvp6f/UWtFh0vgO916pR9d/3UQbs7Bb8pTTz66F0uHr3P5uLCTka2SF+iJcWe9ev30ZzZXJwT25bV8RFt39P0PZuLp0y7LVw+Qy+V8eqSqWkZ2hVxtSaujmmOjpCuh3VvRUuE+f5Th3a+PY2xIYQjvvk7fxff+O3f5Hf/rt/NRx9+wK9+5zt859v27/vvvsezi0sDQlkZRy9cS5YXjbGxKHUprBIgC7th5N1332caR9arjqvdlkEd0itsspKTcjUlUowcHa3tcogXUuRZb6bSLvxK3whIy7qblHGa2O52TDmTyrjwatEIXbeiiZGua0jTSJoGI44rzj12oIPe2eJ0k6nOAKAUnlTQG8SjMwGRbJJDbYR1i0wWVRGPjkvXWLRlKguPeke45JFzz+tcyyFKa5Jpoe8tepwyqgPy7IK4Gwi7kXzUkdYtk8yTdG6CdWsqOom+mNbvJb64erc6KyjDgFURkXcLNsy6MJXIkhXjaAUt5ZzADDCWBQT22D6I9b2O6+JaCnwJqufx3gB8ry1iezcZ3EnUF2CnDe+NZw5+IeXANEbCNti5CaUJhZLX9oA2UlUQyjCnlV27GCwCUuTRgJlvpmLAspwLsSYFaWWFU6kV+vNE3Cpxl4hDpr2EaR3ZXcXKJb56zfjBYfBMxiZ7Wi8juaEUraoYEEoLoFOr9z0y1Gykan/GnfuOR4Kni5bmWaR7KhYZKeAHk/RCmSv73UrUpQC8AghrFXvxnVJEVRbckiIWXyBH5x4vaRmegDFgXTaDfrxb9pc2Zh4eX/Hu2FCkVGXyqH6A3FlEXws1QAvoZE9CsNQeyAI84CHPIgNWK+mdU52PWrQNhDZau/QpGUgYJtoPJ4hCOumtkU8bCGOkf+ab6gZ2Z8EamqwwVYbeMlp7Sg7LYOhiw1bpNLn8M1pNoTsUGa0KitVqPbTL5OAbBL+PtPFir+yvy7MyhqFVnTdn5d4JupfaDtE4Pqpi6YvFPBKbRAhW5NbERBMTbciV82tfUz5mB/byTDIMQ8M0RiQJ3dHAV195yvcfnbGbFo1JvFBsPK3vpLmiNraA/XMM2LI6+Pkf7JrUyOXiesVRZyUP7Gs3G0W+bW21h7N79JMVae4eZsb7iTA2SIb1udaNedgq7flIPGpIXSDuPCu5KoWdy+9txZv9UwPMpQi7zi3YvJk79YwEvvE3TeFCH5NsGdqyBk79/gIiCe+s6t9NmCkfAqV4ThwI51a8ENmeb7YKE0bZyjqf78XcpPsf+ZzdbYe3nNhuLhnHHWkayTkz5URmJBOQtqM5OQWMA0wQUmxp2p7Qr2mmCQmRmCckJzRP6KgeubPUobUi9gbnTUSaDuPDWpxzcfoBayLR9S1d19N3LU3TMk2JaTIZtM04MuXMxfmGSZNvSG6YqOt1c6CpBkKnaSKlRMp2PAmRGAMnqyNUldR1TCEwdi27ITHs7A6p0RHfMctiQllaaV1cZNSMSpE8Mu1KEDgoUweOqhCE2DSkNDGlTHBQWbO9wXdpd4Nh9kywwgaxS2Q3l2uYFh7bHK7xX65nwpzzbOF5ra04914brcMXORsnT9WK3xwgI75baBsIJU/knzklS2W2EWmjid1LqPSH3Dvnuizuy0KACiCoC6pk9fR72bVTd8BSQG/5ykb6e+7Gvs6//JgTPBcm+Tmu0eTly7QAmgJ+df8gsEgZLygVS6LbLVtAWS1XmhusqmW4/1QOfDT/UJGqV1ujpiWC55YL2BAHesKe6HvpCJcbQVr70CIyL7lw1uaf+0Vrgia11OE0h1fLqSz1C8toSZEuWwKgMBm9KZRGGM7jXNoeiGUGSfVcLZ57jr+bZ2CMLiTOSqT9+kKzAMsfm0K66b0v2WxOUULI84bQNwP1HiivzcWnpW4kcwTUi+OC1OIbe33hy9rve4VrjSLaEEbTig5tNC6w2OZdctHCU9vkBoUhIyqkLlTfyWk+x9o6r7f49PXzt/ATWWyAa3HQ9euq87lY8rzVZeH2+Nwvup5Ol6o87hvmp3mO8CBDdqqaDyg4dckK27hR5uzOTJnVagS6buKs2/AbclapArIAptkb6xCMGlA3mrq4Z0uR8RKUlWtTNpthvg5Vu9steXFq7diYZ3m16UR8U28dIEeX7hO1jbCl09WzS0UqzOexbl5XNQi5i/Uah1rwaBm13GCb2EDl3uZGTBZ03vPNc0oJpNy0XynPLc6F8XepvK5SlPkckC1zuc/Vla5WnqMA5xdf4jsFv+M48Pij9+gD5Glg2u242GwYBjh77Ru0pw84/vpvI7YNR5sNF8/Oee/ddzlZBYgdR69/hTYK06N3ycOW8fLSqAPDJTpcoefCWHRcT06g61nde0DoeuLRiQGYEGeHw2cPBSES2zX3X11z7+Gb/I7f9ePsdhv+zt/6m/z8z/8cv/CtX2IcB2okWcA4t+UalrNckKpgCqSmEzypMmnm+PiY46MjfuLHfy9933O523E1DjzebXj713+D73/vN6BoAtcctYMQ9aI+tEqP5Wz0h+12S85qwFch6TyWOvlh9IftuEPaQOw7xs2Gi80OSaZdumpbk3MTWX70nZqg9CHRtomxtQm4yFPFwXiXVUOTQEiZsJvshk4l+iDkvjFOY1KkUWteUjTisBJr2SUkZwMd4EA4W2R4nKxAJTqBDjsuhUaxMz2h6NciTIFp3ZB6YfOK3b0lzVzbTxbAE+a0qlWSe/edEmnCvm97sViQArVd6pJTWcB14T/5adkDtks+r332Aky3cypvnq1vsLh4rvDzvAK8ShcVPFgWulu205D5fetfB/0jNsSQiTEzNQqDL0IBSBAvA82V0F7ae60VsA2ytPM0HxNkN8s3GQdXKhBovKGBSV9ZN7gCUlJnHN/cRCQrzUbr9RHNxhH2SO14Ys0VciN055nu8ZbQhXp9c+HD5dl3ykJVCyAdkKtYcw4U+scW3ZuObZOUVlRueVlMczsfo5gufKvwfGuB3jinRk1thRrVAfbAdG60dpnT4jNKjYxW/10Sl2/ZkgZ2U2PgEpsT4ojdz0Hdtx1wFECMuj6484CDcaVrm+ggMzViQccqVhZlmSJxUJqdWkHl5JuiZFG25S0XBrsgKoGomTBJbSKAQG4VOZrI0kAOpvqgBjrLpkzBNcH92pX4QMesTbu4N5uN0bUabA5KSWCdQL1JxgRx0WpZI/V8VZQRQdvMHAH2OUHF+Ix+3psm1XkhJSUlQZ3qE6Pdu03MNeq7pDJ53oZwfUf3kq1woUNQYpOZ+sz99ZavHT3hW/ktZBOtffCkTEfGCwijkFprroOfR+PWCsOZ4fzSBrg0GIKZQxuH+TrFnbVFT73POe5HqTRbmQwAn7ybXP1FkdQy3I9sX7cI8PYNobkIHH0/0LmSRBh1LuT1+Sx3OL/c5NaGk0DqW3b3TK0mePS5uVIvyvMGOo3N/drAcGKqE7BYl7A5MySbi7LL4ZXMiWWTZI8KM63K2jc7ZxjtXIh6lsbPwZ4yRTFVQsYyFZ/C7ri9sWveZmV7dcXjR4+YpoacG5p+xfrklNC2hBiIfc/EOY/Pz9lMiYvtjq9+5U369THx5AwZV2hoyOOO6QokjTCNkEZrM7kBxh1DzkjbEccdoe0IqzUhFt3gZQjE/xMQhLa3ZhkPXnnAq6++wqrv2G13TIudmAXmtGIQhCp9VSLAWfG2ygZYx5wZs7I6PuXk+Jg+J+LmiovHidhE4xD768u4ymRRP6twWl12LHnUMmsmebS3BHjKV6vupJlpGhl2gavLS4Zx8PfabnzIGZvbSmhwcfXuMAwcZNGqtgiJu3RLGDKzyLVaJM+jvOKVGkaLdt3ZlGceVBBTAfAJQEIRl19w8UYLdVQ5oyKFBvsgOAbbaMmsNhFipihF1Ju9vNUX12XArACXstbWxUXnHXSN/C2sphsXx7EPWTzG/FgpVtPFvzqo6weuvy6eq9SG5SZvAXzrnk+RkCvIuAsLZILrgXYuHClJTANzsArlSgkpVe2+CYgDkP2nzEAzdVAirHsd+QKkwqtvQMc5Iryki9QoW5yvVeHhFT5cMy1AkwIh1JR53WM0XujhTSu0cVki/yxRW1jLZid4CrUEw3Ojzh90Af3SrCXM45Lr/ljGDLPvuA5xnRaqjJx/9yXfsLzuuppD4QK7n8xasdw6vUpQmpCr1JnxehfcwcVYl1n1es8283NlLllSlLT+J/X71uhdTf9ag5o4KuoNTpbFhFIyTsxR/Kz2/jhSeblTKeb0ASolw+SR2tIhUOeXoU7ziZD7MnfOmxeL/Dn3d1LSwieknJPi49c3tcJ+dmhvzfLX+JhSCp5QM4ex7N78uwBNTERZyJuxT3u4CyaeKOQsxCYhJ8rrR+d8tX9i85r6BmJ5IsTutbxOjKd2rRsvLCz3iPq9VOaTEkALaY7il/uvbLDiYnO7/AkY8HVlhLgrmr+ecTxOTArTcSRMMG0DDZmgTocAioRlWZNyBBzgDmcWTGk2NoeGYXFfK3vdHst9YJ1KQXYWEAijul6Wb8yW9CmozinV542LXDOXspgb/V8I81xe104DRXZIX9eNkvXxa9Adg1+sm9k48vTxY379V3+NprtHuzpldXqPs9ffQBqLzMa+Z5sSv/7uewQJxBC498oD7r36Ks1qhaaJ5uqcaXsJj99HNxeQNkiaLHp3bp3iLvUDtGmIJ6c0J6f0D16hXZ/SrI4QWfDrfHyWFRVCbGhiw5tvvsn26hm/8PP/nM1mwzTZFk2w9HiJwtbIbzn5GsiYrmRK2WgPwGY3Is3I+t59zh68Quxb4qMPef/8qXW0KxFXVx0wykO5wnkeqU9EBfxmp1Uk5xVnpNIYRLHJw98zDhPTOLDbXmHLAiSLC5PSRNDAumntq+Rr4PuOAHArmb6doM0GRNQiW81mIuwSRT+wRGw1BhOSLxFcEUJjIDfsJgOkfUSjtaWtlARvlRZGY93LlKtuME4TIZqecL2Zcjag0rVo26AhIKPxjMOYjXf1WmP6jR5tqc0PsEWnprgKwHU+aPYdeUjUooTlgrNMo+VW8A7gflw/eYs5uUy4sy4ri4W+AGLluUXthgVubxflqeECfIMXnCFKjHpnTS7srgg0baJvJ46awarDB6F/bBGS7SszgK0RFjFgGS4dLFYagvGxp2OpH1ALo6IByBIlz60QhmUacb4GBVDmZt4EhQkrzs1KHITuYlYHCIOSu7hX8ayY+sO0No3i0uAiXitmKulWDcBkrU2nIzuWVaZnxFMGqZea1q9SaT61LCO/Bk79JPumJ5dNpVjmwJQRsi9S8/naW9zcX4X9hfu5ToZRn/e5l2hNyKyakWmy+33yqJr6hkKaXMdTZZN8MxRGi5Cpj1uVOYNSfCMs/cQXc98YxcEjqr0DmmFWegl98M23LdZhcvqLawdHIK1tvgo7T1xeRa7LjdmGRZFeoDRXKU9W0GRgZ1rIVdVagsmAVthB6IW0iNaWDdASSLP8vc4NMwBWhXmnPW8OprEhlEIycGUH+71sYFv/CUZ9iIvXA674cPuWUuD4aMfrpxf8/rO3+Yn1d/nr7U+yA+PsTzpn1gKko0x7tmNolGEX6D+MyCQeDTYr/lE1fq2UZN6ABgOxGoT+sdJsFxsPcJDttAvPvuVGLOtzbi2M0UB4Y8PYNgwX9ma7zoE2eZHlAHIarAi2iJC0tsnWAJs3Mvk40TxtrKvczuY/jZ4BuvLXFtAfTe83eMMmy3BlRENtCQ/UTUwNGPjvtUDOgbyoVlpZXXsVw1Zhlv2zgfuhFoVwda7/GLtbnV9gGkeefPghjz56xJMnT3jjK6/w4OFDulVPaEIFjwCn9075sR/70aqOcnR87IudoCES+jUxBDoyul6j6550eUne7cijFdMFzWiayJtLxjQyDVua1TFNf0R3dEZse8Kqt0iwp/sDVpw3pokHr9wnTV/nd/zOb3J0fMw//8VfZprmm7EUhxVMqItdSrm62VuYqlqqZzckHj1+RggtD159CNIQm4YYIzEEa6esoDnPEcry2ILysOT71g9XrYGUWP+5U4l/vzaaxFmMTDkz5swg8/GzaxEH32XfNe83E9jVvpFzdKlEeSWZEJsK0AQWsyfaRpiyRYDHZO40pnkib21DoI1YY4SSI0mztiBB7Li5cZBtx698tlzCIQ6OaxMNnGaRaTdKHvHCqZnYD+V7zF23asGJ81GXIHZZ1LaM6taUtdrxlJnfWVPh5QNL1cvCSgFc2ZVXGTOuvXQJepf52bohMu6n+ueIL3x3QXkAGFR5dzqzIrcj5a3VU2LMploXFmk2oUY0xXZ71SWW8jnW/MJbdcri1PkGRZzv51++/l66J1k6T+s5rNxLP3VaFq6stJeWWh6PXXN2Fa1drpssP9fVPoI6UCpV1cV/dF6ctHaJK/w5jza19ndQqdJKe/1qFtdaKRFt3aPs1NeVX0uUcVl8de3a37Qpe47qcMtzjICnz/HvZIt36v3enkK9/6xwh6rjXQr+auFSZh/Iw3PfuWwQyLZ4h1iaFZlsmXR2PeNgIKrZlqKd509EGJSmsQ50iDCdlvmuvForetJgNR5716puxHytKlzNqDWrUAqwmq3NjeN9Py/RNrG1cegL5wbqzVXvfVHTVa7ziqDOsQ/XAC1Qo71g80oBvXfVzrhYKbLWZGN4uLrkw/GE/+/qm1w8OaI9D0Z1KvN2cCrCRWSUlV8O0wQP0e7XvfOUpW4UrKhLYcLu56JD7RSbyu5TK/IqxaLmg+Y7cUxoNGpKGAPDqfD0YV/rXAp1oL3MNBubc7S0Wy/zR1GE8DGGQdAuELdGi2kvAVHiYBvy3MHotBrzIaN8abIxGW4wp3mOM04Bu8y1LDIrZJSMq82nJSpuc50FExbBhuL6Mn+fT2t3G/lVmIaRRx9+yKNHH/Hs6TO++o2GVx4+pFv3lV9V0Nbp6Sk/+mM/WiObBfza9Q/Ebk1oW+KqRbdr8qYnS0AJaB5QTd4+OJO3l0zbS9KzJ4TVMbFbc/Ig0a5P6EIwNejQ4VoHjGliN2y5/+CMvg9883d8k7bt+Oe/+MvknAjBgHpd+mWBeVUKrKA0wCigeMoKDn5j03P28FUkBGLTzuDXq9uydyiby/RsUb0J/OoC+JZXtwINYh1SgBYhSmDVdqYJLIENI1tVEkISixyLBKNBLI4F3Hpzi2JZhZ3LI0iw1p8q801aQb6T+MtNkpvg4MAoDIW+wDiZWoYqGVNwMKBm75cFuq9C263nM8MSjPhnp4WfqsKy+EXs89vLZDvpRuoGpgCIufJ6MQk4sA8JsksRgVX8LjdSNf21TM0W4n/pF194TyWqN2OxWmtS10sxBQjRhc7mooCpKn/4oqYllFeGVEB0xn21nBqpp+g2bdTAe+MZaQrEkGtqsoil5EWKUYsecpnwHbhqEZcXEG9hLaXoMMznek8qSi1SVtJ4JYpomzSZC1XqZG9/52hRP1ForiyDMZxGNCqpDxW42mRiqXE7nrtbllkbWhfj0hnsF2URGQU6hcY6vRWeb1EP0QRSgL9nELRElcsYgoG1cv7KIlRdxG8xmZaIuJzzeYFanosbZVpvfYO9KJ6SAnAsqo5iclblfpxso1DSuSWlGxZAZE9mSRZ/l5f4ZgtwlYT5/g2N1O+rW/UCxf0NMlDPUxgzcQfNpXcn9JbZGlh2s7fjWcqqym2VjVfZmNsGzEF5aandilMrlLj1DXtpixx9nXEQu0eRKd/zpqK0JQ1iORf4brJyrxfvtcjvxzvCXcieaXBOfBZiUF7tLnk6rnn76j48bWkuTCsXFnOC2PUJY7QsTWvtxXUS27Tgmw+PUlZubONBHHX9d51VZqzJidTCN7miUrbKvCNTJm4Tki1SuvoIhrOGi2/E6n9hhPYq015OxM1EWpWJyp/3dus1Uir2u+6EuDH1iu7CBp63wri2sU0rseK3xru99aDTQu2kXMriAsI8/6plOeLOFVGi7o3JroPUIuLynrKexnFeP6GAZptn97plfozdsdpD5upyw6VLh52d3eP45JjVcU+IHvVdzIIxCH3fotqiqsRoTRgcFlHzUNJBHwjNmq49Jd4fmC7PycOO8dkTZBxothtiypAUna5Aduyutgxtx+70lND3NPfuE/oVzckJabdh3F6RpgHNM60AmTcZ9fxWEGCoonBzkWC8raRo8gknQ0qZ9z94hEjD17/xDUQaVqs1q35Fv1oxDQOjGgVB3CcQiyBb0doMfsFAaeH9BoUuNqzbjuOmYR0iPUIUaEr1cs61UO+0WzM1gQ8uzrkcd2xKUV3O7kTz7Lrs8nabJijrOPJgteHqtOPylYbpo8YW9tZWahtbuXsLeFFyEKKDXy1R4aagFzFQuZSlC2Kd4+KsFyzJUjZ0RdvFALKYUPNMrQCjUzTBQmgZtPXoc422uKxZ4xXiexOB/QuhLERqwuJH9rwk2N43WZ20nu/m2hrTv0KVX1qCjJtu/gJKYG9iqh38quC8/yhtXhXIrg1ZOb4zkCipS8f4+x95ywvVpfb802dfQx/1vBfv8Y9Ov8H2omO1E7pnLiFUCw6FZgOrR7lycC2CM7dAnqV6ShjdJvlSeALzwlUmX2uCYYWtNSLkG4yyeTEQaz+Da7s2lyMahfYqVvBkXeRmcNRsnA7T2fk3Go+J6qfOIrlpbRvA7YNAs1HaS6W5UrqnwqiBKVhUKG6kdkpqrmb/y8vGGsWUyiMu7WxrijyDeOOVVOanJSgqoMePt9cW2Z+qGbIFSL5NG3PkYug5Pd7y6HjFeNIQd0p7BbtXwtwSumxiWFBkfOFFDSjOmxPnQoaZA2nn0MHektvpf2ehRl4lw4RFhcNkXbpSH4i7TNxm03/ORbLKosO5g7h1ykarxmFXB0qF1x/8nDt1JUyl6h9QqaB3OrG0tD5zHdltpjsPhAmGZ4G0KhewgA+8aNju+bkw1wNAN7ARpPK6bWyhsSK2aYq1mUWhTOVsO68hRZqQ6aPtTpf0qcIBvlUTu6def+0Z/9LZR/zY0bv8/OVXuBjvoUeJ4b4Qd3ZOh1ObJ+uc3LikYpNB7L6e1K5N4Vovh1+v2doeTKLIEIiu9FE3l1lIK6ntza1To0ssrmPNPInCcBzIvdfLhMDuvnC5jYxHgWbbekEu9Z4v2axmm5lWgdRjT2RrcqFRuHp9lndLvTfJ8etdkrSDb5riTml2tixWYD1a45RCZTCFI89KZ8u2LefA4m+1oK9u9HXeOLAAvXVdnX2lNsB6gd0t+FVlt90y7AZQWPU9Xd/Sto0VHgEswlQShBCaha/YjBPKr8WKikOzIjQrJE1obGC3IYyDAZRpRDQh4+RFdyNp2KIhkMYtoe/JKM3RCbFvyeOONBjwLSoLOWeuq+xeP726/Ck2yee8KGBT0KxcXFxyfHRJ8va7XdvRth1NY+fC3qeL48/c4hl8ao3GKoAqAaEVYd00HLcdx7HlSIRGhMaB7zQMxu9V0Nigbcdl3DBNIztyLX5bRpmBPeB7m9OPiHF+j9sd627kcp3IbVN5l4W3CyxSJLYLZXKnn8Sue2AG8IVHrfP5KiBaXRotN4Xq4Y95ikVUjde7oDwAVjzXBAMGonMxnZtFgKQWSATneC6jwATbFJXq7Nz6JitadydJWieYWqy1iDrZ9+cGAMLNBW6ymCyWA70+o9Tn/F/GZKhrUcsMgO26lc3Yp7/Wv1Ubc+Sj7THNRhgvW97bnMLOijxKxXXhq6FWtdxuctWMjDsvzEh484MloJ2/f25lb9NSTIp8UDI+b/DJGnDAoVXyrJzvZpMIQzLN18a7I/n1K8ClRHMtgiyLRdCjkNMcKSk8vWltz3fnFk1sLyH3QhplL2WPUlvcVqpGO3/X675ROOl72rYTFnfwnzUZMOP2xWOzz8vyycJnq3//5v3gkyxpYMyBdTtBl8mtpXLbSztX2bMmuhinjHNq276InQz1LJKKlJ41z0V+gbnTVy0AfP6+xaPu2TdnuSi/lKKeDIEMI8Qxujas3as5yDy+jv0bz4GvRizCL9RizzAJWZS81tqFTLL5atzZ94qbuVirBmDK5Vps4G+0xfygi125BK10h+yV/qFwqPFAdw4kMdWWJQUi33a495pphFePLnm1v+AsmjzMdmqRLpGOAtOR6XbnfsHfdS6q3bj2t4RFsKLVOpdKkkp906I2EkyVSFWMq6/MzqhUCcO4o1IAZp6rnZ/6ONj1iZY5nY5mOb7kG/1SjF025pXCFed7PjfAShlPrPCt2WrdPNl7qfdtbszPjG4ldZ0StNLJyn2znHts3C4vGWTft1QXDTTsHqmb5r0Ldo3uwyf4KHctdbbb8uid70Ea0Tx5tBJn8e9HnW42v1h1NS7R4hkAamwgNLRnDW1OrO7dQ8cBvTgnX25Izy5IF5ekzYaQd2gaSJdPSJvAeG6UiO3jVxjbhrGNtihl2Fxt2FxtKh2gfKAwb3hzYWGLzSYqiSyZCa31CZZOzDx68hGxjVxuNoRGuH92n3v37nFy74Tz86dMmhhTRBSmSUmTafjOkd/CrQzE2LBarYxPNVnr44uLK0JjfeRDaOiQyoXVcUBUaUWsQKvPvB56jlct49UTtjlRZdwW0eW7ivwGMg+7C/ow8nh3xIfDWY2mTUcRWZUtJ8SdR0cmk5EiK9pG+7dYDJaV1EuzdLTN5uJlzZVbTImkCIrUjYZpJRqtQtto/OEpOxDKECK5s2ry6ELjyx2v7i1SfpNGrNjNmy8UTq81+Njn8Bko8kVT5sV5WQy35GiK3yq1KEfKg/7aqNeiNv5ck2dQUo6bC/+vcAHLAmWL1HUZottudpERhhxt5RyFx1drS/cDw6kJucdRkZ1W0JijMK0C05EVriwjtSXCV1UNxAs8Cr9XS+W+gwZ/r7XddlBacGlS4jZZJO9qQNtILjqvkzdVaCO5t4hOdzGCNKQu7kXxKy0BakeuSnNo52YSaSVerQ/dZSaOlikZj+fdT+r8oP4jd5B7L8wsoNivdeqYtaejkhYR3NrUol0AZZ3PY110Fs9VWwJsBwo33Zsv0wRlTJGLTY+cN3RPPUK+UfrHgThEuqe2GeouzFdmWUVPt5amDwX8+mYZX7hLsU+x1FmUNbXUVGylC3jmBvehZqt1w9NeZtpng22QpmxgtCkb/sh4bFHA1M/nu/C5Z8lC/1fROVXGrbm096ZjS8VPay9kmrwILyndMyGMwu6BVtBegW9RLFjSYfyzJCqhNfUVox8FSqtzCUrbuo69zxfld5C5nDsFRCK79Dw8ySq3r/1bzpUov7E54+9c/QTfe/aAp+drdDKK5HiqVUs7blyK0/m6qQ9WpxLKPKzOzW/qZqXSVnT+TPxek9KwpswxXiAddlQfiTu/xzfZO7JZwbXGwOqoYbjfk6ONKwyWNUBtHWq2ft1qgWYZi0dhk1EdJAnj8Vxo21wqRx8khhPjFTcbG3p3rg7KtSoyXee7lcZCKqEGGXIrpD5SmgjNnVvxQjpAxVvR25pc6GS2yfBod/KsSLufBV0C85vsU4FfEbkP/PfAj/vh/mPgl4D/FfjtwHeBP6Gqjz/Wp3Jm3G2QnAkiNG1L03Y0bUe4Tl6SedaU5d/q/9WI5/WQg33zEFskNlbx1XZO1WwgQxondBi8ml4hTZBAh4msMLYd03pFCj2NNIjCOE6M40iZVQqrF49U28QQCo7yMSsqpqJQqBD2LmUYd+yGLcM40MeOdb/m+PiY09NTHq16ttuNrQdplrxZXkn1SJ2IWAtkon1WFlBrdDEwERW6YOclTErQbDq2GMU6TJkYEn0TyTHQEIhkkn+vAnjnz32xN70sPxGglURftqKpEPypUk0yXQNWyW64srO8vuNTHLRdX2QL4PEPDsnoC14ZMw8IZt5eoUZktZu6CUgbKY01chvqYgn7gLQer/xbAsuyEw5A6VITZW8oFdSW4y13uovI0vJYteNNUPYK25ZjAZap6hI1KBHfvVNWALBoLXiTsH89Pgn0vkxfaUI2EKHCbmwQj5AUiztqUVGprM+tkksziDxTFUrV8TLyW3RRw6AV9Fi0rkTSyoSsi9/dRyYljImwnYxe04S6GSEEyxQEAwdLMZeZUyl7f1//8nuRjoX0lqRZ8qwWnJQoo6fd9boflNug4Hd93qfqy72or0y5z12Um8wjgbL8PgUA33DP1re9rPVHhWkKhEHcJ+zeL92r4tYKesKI819LZXwiFIUYgSKrqNEaCxhYskW/dqAUITaCNsGK6oQa8aqb4EAtiC2cX+Mc51l6ESxg4Y+HyYqtltE38E0YzIBrOb8srktN8Czua22oHSfL81I6ahYc4xFaocwtBRR/wn0e7AC1gUWN/FqqI+ewVyRb9HxTlufUYu5sTvFN4PnQk/Kai13H06dH5IvWzo2DUeukaOcpbqggLkyzhm6Zw2tktdR1eDR4Llr2a+Gb7VC0nT34Vte8kq3UxX3tvGAWG6zSvS9uC6ic77lloXXdqDvXfZbnM5+o8mU+xjD4fe/z27J2xXSErbtl3RjahTHf902j1IEUcO9jLpvnF1yTMvblgyozR9r8ujgSzwHw6/ZpI79/Bfj7qvrvi0gHHAF/Cfi/VPUvi8hfBP4i8Bc+7iCimZgGcs6sVmtO7t/j1Tff4uEbb9L2vb/oOnHophkxPvfIcg3fezSsoOug7WlOE83DV9mG77EZE5JHk+SQRFClnRJpNzBeXjKJMjRCDOalV5cbri43IIKEaFO4n/WSJo8avVGPzxpBySR2aWLMpsFbvG07XHG5Pefx08c8bB/yxiuvE1Dun50gorzzzjt89OGHDONU28HH6BziRVR2TjUH4+AFU9QYMjxNifNxZBciKwmcaUMnwlHX0iB04tW3m4H18TFNjKybjpSEjaabF9xqNzrWS/GTwuu6yh0Xu57mWfQiD5+ZsxBynitFl0PKcwoZZvCwOLidr0J69p2mTFblo6O9ptysVDF/2RPoDqNx0NMqMK0CIcWqo6oRxqNQu37Vzl8lYueSUja+fRBbpXD8O6R+MUmW5x3ElMmqitYvjmnpVrXIXMA7U82fUwFtdjAS1G4rT70hFsVBQVOYU3ClGjni94IdN4ZUWwyHkJfMkBfZS/GVPky8dfSU7xx/BQ3K5nzF6lFg/f6sNbn6aCJuJ+LFznxGFe0achdNA3rZPc83NqUNLTlD1xp/PLvfDC6iG4L5UwjkrkEXSg25syYX8WJnkd5xgnVrjyuQA9oFchNqCnI6soxB4SM/Bzo9WlPa3pYmEtbBCSptojUuc3OVaDbBileiktYmhWQLtl3bMNq1DMVPSope3HeD1ujukgNemivs+VyJP/hrarFgeV2kgsbaGTCan+lis3IbviIYeMopErewepqcZiA0l0oYoH9WIljZo2XJNMXHBEPR/oZa6CrifuFzyJgq999e6yC1a3zDFCgd1nIf94rgKkAaM2HKaBtJRy0IxM18csqmZrmRsfnI6VHOnSzyWpXHL5D6hbJLgHjpc1arjPeEq9SY6sTkRcAqVeZtWukcZe50jjAvLRlgTQo5BkJUmnaiaTJdk+palbypRSkGXwJgsNOXs9FU6ubWXz/lcCO3+GX5iZ0z6B8L3/3V15FNpHsSOP0IuqfK9lVTcWiuqB0bZ5UQA45xq3OWbUEv2PuMogBRQGXZ/EwzMM2LyKxd+3Id7Wduhd1Zw3gkFaRmLz7bPsSLJG0cw5HQbHxO9M8qkduQoL3I9I8HNHZoDISd0GToH/txHQjnThhO7fiFhmHUPMtQxh2sHpeMxwxMl8Db5h7n/I6FFja/psx30YG2+Lxb1SHUxqFBCL4jS73sbajVgyEfZ58IfkXkDPijwH8IoKoDMIjITwH/ur/srwH/D5/gVCbZJaSsrFdHPHzzK9x/+BonZ/eJzczt/VhVAfnYP81KOqWkrRGQYIBBlLDqCUdr8rixwi6skKwoPdj8ZioRmq1KLU2JaTLvM+UAn+1nDHzjUMuxZkkyQwVJM1NKjJNNbH3f0/UdXdexXq9Zr9eGzRZ84Zp292+3/Pb1OcFaOcdIVuPvbp3HGxWjP2ikkwXiwnR+k0IIDRHFyuUXp/TaGJ77ri/RTwTlKO54SzIn/Y7H3rIxdT7JZ1tZJat3vFJkCnvE+GVEtwDg2rZ0+VmlSUaN/opHNMS5ukLuQo30Fd5xaMtNZ0UHOoE0/ppo/dIL6FVP89jYmfVFo41Tm/lUZ+9ohxeKpN4W0NK0oADbml2wE7Y3SYID6KbwDnWmNpQ31UuvdTGctVctRVk7NJGpBW8FzDjQRZSbIr4i+/65d85foq8MOfLe1T3ilVGg0hDonkH/1AuGEsRdMs3J7YgVLCYYG+IuwpSMqlJ8pXTvGycT4EzJfpbHsxqQLeaNTkLOaIqURhXiRY+ym2q3QJzvjRpNJ7kOdaW9tIHUhRoVLNd7r7nE4pTOVA3mRcIj12GntOcjzVVDcxWY1tTCnBKutWilvV6mstFj9qdlpHD5swByfX5M88DmMZXfn1N5KGDqYzZJL8tXbB62yTq3tjktQKDZKclbxaJW/AiQR4F21vy2B9U3Qa7eIpYFUS+irevvAsmVjbVkL8L1qFml10RxlQ91WS8Dg7kRe61rE88ZJfb4kjed0xvXpGuR2mWix0DaPK8UX6qf1eZ5OQjX3iz7BzR1yP0BZBVPdmSCKNnnh0qJWMwbcm1OuW43SZ+9zDlFxfjz4cgywbmTOVLfGk1oLLz8VIAfc5pd2ANiecGnrxbm11ik2Y8zztHUAjFMDYEaSEmdAU40MK2FtKZGcctaM55mciu0z2SuJSlyaj7O1Eml66jPP7kVpl7q+qSNkUwLLWrqbZ6tWQH3m1KfkiM0m7Dvn3Ltu/t3tgwJEIS8fMEy21XvI8sw7mVNy1wkMDf+mQHzy4j8/gjwAfA/isjvA/4x8OeBN1T1HX/Nu8AbN71ZRH4a+GmAh2cnEBqGaeTslVf5vX/wD/Hw6z/Cvde/UkHnx2P136wp2dSBkRBoTo5ZpcTVODIS0N2FOZsqSIC2Mf5lSvZvSgy7HeOwo55xweSQVAgB18ss1IZyZWYPV1XX7bVa1SklpnHylsnK0dERlxc9MUZOTk44Ozvj7bffJqW52iKIpYSoVIQygTgXFwMxEgOha0iSyZK5mBIhK1c60opwllv6EDmRhiiB6Hq/KU80bUenATY2rheD3ueu1Evzk1e+0vNm85Svtx/xvfuv8N17rzOeB3Ybq2avHNgysWSTPqnHSoWDZ+eqhCHnyXy+UUI5vc/dnFrBbuqlgloNNmnEQWlbb2XbekvcpBXsptUMelNnP0uq1dpEenRG2atKTStFe89LqTDec0mYrcwLW5b9xc1drbYsVp+kO60LUuXl3QRUHBgXFQfxQo3ShQlM8D2PYQGetRYy1ffJfvX2xzS5eGm+0r52j19++3VOf6PoQAr3fm3i+LvnVYJOV43pODsI1c1mPlhK5JRhNAeSztpe55QgBCTazS0hoFNCr/fOFHuNDCMSA9p3SNuQTjqLVmx3sBvQ7RY5O7GNzJCQ7YR4FFiDWPvT1jjK01rmzIR4pNcX0FljGPfxeeEQT5c2m0z3dKD9/iOOV6+T247Lt4Tx1Bf11tQi4k6IY7YPUbUFtcW4vUKlyNRFXcVlu9xvyqQN8wK3SGnWxhehSBXOz5FdsqvIEt2Cryz9pHv9Hlmtve7uXuLiaw3Hv5FZf5DoYObkR2FcS6W9WCrXqSpZq2KLeBv1KsCfvBA72HN79DAvalaCbWrD/F0LdWI8Ch7xW3THqhtny0KllQGTorOaO2bqQtmQ+OmV0oGrABClFkKFxVwJNp9UjqUHc4qqgDY+j6yy3efBZc+yWGbI54v6weWYwaTlyrKRckBDphEgZBogZzEKVcwVMIsobUxV8iz4YykHsgpJw/ylX5KfXPeV5uwBm7cSP/71d3j/8oT3+zO2agT47UMlnSTkaLJ7JAkMgVAUW0pEXCFe2TVNvflMmOY5XGtQwv4uHRjjbi5iDMm+alrbMcYHGW0zYT2xuWwZ3mtYdkAFPJIP4asbhk1LmLpF0aYQGmvgU0BrGEGe4M7SsL0f2D2QGnWe1g64j5TmshToCe3F3K65FE1OJ95lzmmKkubN076sp98/DuzLZq3WXrg/53Y+jmUjILty06w9Xu5bp4t4wXGpE/w4+zTgtwH+APDnVPUfiMhfwVIH1VRV5QVbNVX9GeBnAL7x1qs65UxsWrr1MSdnD2i7FeWuuQ3wO8/PdpIUaNZrxCMf0+aKzQdKHraM44YggjQNijKNI9u8YRwmU6kYBtv51+3dcrdSP8KeFQOMgkXMrBNbRp2nopqZ0sTFxQWPHz/h3Xff4dmzJ2w2G2KMHB0dmZYwzJFjFJO5nSfX61q/pRhOBJePsxLMnDNDzhbdJbPN1sq4JdKSyRNkDQziWr/s61rMAPuF1+jl+cmPn+qgka221uxiyYUsEc4y36b55tiLgimz1iplN6h7u/KSSqmOsriWGnxH7C1AJSsM+zdyXSjSYjyKRXtcb7AuSJ6yqlG8AirKm3xQkkwupq4aQRdqEf6+ZLxVqRxkrI3tcjfcqGtI+sBuihKJ1oivBEVipkRuEPvuey+PWvumV+DrPl84fYtTSAj5RQD4pfnK8Y++paHxNN62SJvB7rUjj9hLvfbNWe+8ycx17rck5yJ6K2vxSJ6Cd14TjxCziOqVc1siFT7HxMBw39Bq7l8jbBPxfMfm66dcvtlw3Ajt+WSFMd18YcpCknv3g+wLTIPJJCnkSaqqw7SyzVLq1Dc/FqkZjwO569k9fIvNw8j2FakqFtpYF8ESdQyTganUUwst00rngqxyMR0M70WhgedaGC9vpGuLWaU6COaXBSy4v73AftO+svST1Te/queXK1BBxlCLlMKoxN1Uv4MGIfWBMOR9VQ4vuKkSeMUKv0d1ziKUgMXSx2KwrELXom0kDHORd24CYWz9/pVKfZjWje3dJ5vPcy7V/jYnSDfPJWEERqnASvoF8Chz4lK1ghngFn5vqdKn8+eX3NPl5c0GfmtDjT0eps8Nzt012oU5TM7iXF9musOiYLZyfl3yLLoCVHBHDKJEyZUGcc1e2pyy+srXNW4C3/nwIdurDp629E+E1UdGMcpdQ26ayg0Oo6nIzNlBG691KpsBnhXMUmkELNaH0nRmVorRCoCnlRVNTu9FtImkrjXqzmOt9KfUS10XcwsbOaIboHsyr5FFhrFcp9wUkK1V8SEORukogLZkE9sLIW6tuA1srZlWNq8kjxRzIZUzP0dfFz+VefPoe+ebWhCXtbWu6X65iwSaXTBfe0unUddWL5vGT9MD+9OA37eBt1X1H/jf/xvmVO+JyFuq+o6IvAW8/0kHyqoMUyK2K/r1Maf3X6Ht1wWbzAv+x9j1gqsb0/DPRbfsBlQ1ikNzdES7PqJbHTNttkxXl4wqDLohInRtJKfEOIyMaWS3HdhcXTFsdxb5yVbE9twnl9SNj0vEQig5ZXJSUs4Oxm1imMaJp0+f0TSRVd8yjTvGcUfbthwfHxOjp7uc+qB+Dj8RAKOEWlwjpGDgd5psEh9SosnKRYZeIn1okDFDigwBJk8vmP4rNuI9ysWN9tL8RBG22nGZezappUjCALbIU6K6M+8qDrOOar0ce6BU97l4ZbNyU3pElsUkClgaxwpNYEqhpqOLRmEBRJbOlFpUsNc+1t9TT+I18r54AwN2MoOPaJSF3EGJ4IZBCAjqgDq3Ohc7BU9t+/vmSoEbTrQfv0R0JO77dK55bx9fmCM5ZbEqEd8SJS4RnOAp3BesNS/NV6JkYpMQtQXo6L2R4azh8q2O4Z5tGkpnJEnNvHHB/KDqYy6KwmQx4ZaImUb2iuIKoK6n158Loz2wuxfsOrwRabZK/7Tn/KuRzZsCNKxWoe6hy97HKDbCtPJFNVnkJXeeJfA2yupp8ulEmdaKHk9Im5lSIPcN43Fgs4Lta2I0ms42SzX659FbbYQBiyJOa62JqtK6eM6Jz/6yzCCo2AargtcCcB0ckQwYFe5dLbZ0fyMu/Cm/cGZ5Kb6ikzA+64nHE3EntJdai9maJ1tkN7DkcDMlZDegmy1stjbX5jk5K7HIb/i4s9bsHq65Xi0r0rVI1yFHa6Rrjf8LyG4kxkC8Oq7qH0VP3IB4JA6m+BCcjtFelU2KUcE0zFml0gBjSiXdrfNm2/V+c2tji8Ocsq/gt6HSPkJSwk4IHbXrqE1i7hPq2ca0DDn7pgbfPIosegAJmmdUEmLeyy6VjXJSgRwwMh7G88XAbxMyzXPdQF6en+BfobkUNm+fErdCeyGs31eO3xk5/Z6BlTAki9JvRmSYkM3ONjeNKQ3VdHChimWQlKA0aWoiVYoTqnZ80abXJthjydSLKKDaueMyJuLFrtYu7B6uSOtQ7/Hu3HSCm6u8aIoz87aBPS5ymcfiTmllnvNSb6By/ZEpn8StWgAhKcO9htQHtvftWHEzr8X1mublZ+yroZRA4XPn3yVeNc8bt9J2voLoUfeOp8GArwHmUFVZbvwAt08Ev6r6roh8T0R+TFV/CfhjwC/4vz8N/GX/+Xc/8Vg5M44j3fqYlJXNZos2a7qmhxg/DfZdjuvjucHPWdmBSgWgdL3tKmOLSmRSA62hbdltrnj69BnDZmC32dE1Dcfr9fyZBYBmnSdEsaPnxSSZUmYYJlSVJgbUyuIJbctq3aOaDRznzINXXuHs3gnf//7350irKuM4VvC7wHB74Ld+S1UvB7TKakWI3kAhNUb4s12eyaKN6lW1qpAndlltx15SYDJvMAzQ3yx19jL95DRs+cPr7zBq4I3+GbJO7B5YZXzRqpxTwra4FqAbljdXuTFkBi17ygsyg5ZyTR3r1jRKBQxiURCgytgs9TtLh6sKZDoDCqU1c43KUsCqol0BzGJRsuT6jo1Cmy2t6NzfPIYKKFKjpGOYRrH3FKH01l/fKNJkYpNvwi9+LWdagkVx54rspb4mi9cvH1vKFIWgNItFrLQlDdzMz3uZvrIbWvQ7x7zx/z4lXO2Q7Y5V10LXsvnaKWkdiBuTq2ouBkoxo6TsmxPZUwepepmlEcrCZEz1PSWqV1Le2obFwiVo7EidMB7Nx+suFP1QaDc2UafeuITDsUUqcgNk6J4x89ezuBKBpVDjbnEeL0yOSp+2XiVuC1D/LLH+UDn71cx0HBmOA8OZRYyHe5YmtUJK9U5eM31CPBqsk5+Sa/cNJX3mvxZ+et1wlaI4f+/+hfefnjZH5yKnF+2qX5av9KuRf+1f/jZ/+MG3+Z8f/qs8evYmKhFJHWkVCelo/swghCkTdsmu+ZhqoWTlhsfoy8li4IsNsHgkuJqDoty4wkcbbV5yP9I2kGMgrVxVooFxbRzfONiMPpzMfjqeSr2WVuA2b9jUKVW63JQskEbRm41XNqdIgvEY4n2p3NUixzWeKukoI00mRK3ZHMVoUaXBDbDXsjhGVxRyvd7r80Z5fbjhumeFGJTom+iykQZeWPD2UnHKUeb0D33An/zat/jO1av8/Adv8cHX7vHko3bBzfXXXt8s+2PL2ozniowpQZDFuu2R9Kr53KqtC3l+f9ziTSTKm05dVUIrBaG5svM0nmVkgvY8uMQjVfmlANvxxMYQRtfwdTaYivlTbqzZB8Dm9YBMwQvcFv4FtSANNdrGdC5zkMHXVKn1TuX7L+8b+1EaYoTSLXLpGyXI4EEH098HqRQt2+jv6+DLC+cV+PRqD38O+OteQfkrwH/kl+RviMifAX4N+BOfdBBVJU0TePvc3W4krhJxysQQ+NiRXjvO9d9l+UUXk+78kD1fo5kiSOP93CWiPiYVIcTIOIxcPjtne7llu9nRhEjf9SxacYDqvu5vfaYABUg5M44GfkNw8BsCXWdNLewd1sr4+OiY119/nadPn3J+fl5pD9be2UG2+HdhsXAsvms5i4W2oCK2zkiZp30Cz4qGTKrd59Qzd8mLHqSC33p+P9leip+sRPhGI7yXRu41W0KXSEeRAb8xCk1xyXPFgWyS/b9hlu/RBfj1oo2izbi30C95Ss57gxn8LhUXCgdSxrkRQYnGzhEyanq3RsuiElemc61TQCcxDUkHvHE91cVFVZhitNdtowHjNtvfGQMTQgXMscmEmKumpoiSPDqzbCtqKcblYqQ1agvsLTqFe6cqVlEO9f0Gfot+p+wB5Y/R5HwpvkISVh8J8i++S3YVBokBYmTV/DbSSU+4GpExEZ6cGzjtWyt6S9kidyZR4ZGVUEEMIlbQVtLZpdAthvnGyBmGET1eQ99V1YcwtYviJK9Mniw6Lcmicck549PRzGEvjTnKHBYHre/FIx6FBlP4v6WQUrLLde0y7dOR9t2n5PvHdGc956GrXPcsft9I0a2d/bbUN4YyjaktzIZ7LTuxmOKQ1tUbMjMdKWAbsuuXfgHAyrWr9vFtbX/LvnLa7PjjD7/Fnzh5m19/4yF/8/7rtBeB8dI7memMRVQ8grWNlAYlVfu7tKluZ/77c3rivtjLUFIM7gMLubTcucTdojBOo/G9c7ugokRLJSOYXjPUuoG0XkToK6hQiKD9tRCbn2MRCE1Gs5C0qdczZWFKStyJp/K9dmFtm/QQlBBTnVMAmmjFawX0LlsTx5AreIWbN8FLYLt8Pi3mnDLvlI100vACyi/wstafduTf+Mov858//Cf809OOv9X9JD979DXeeXiPYWjQJORdBIFmPRe/qnqmw9eWpklV/aZeisVG4Tq2i6K0TaKLiZNux5gjY4r1HH14fswwNGyfefVZl4ldpmkn2mBz8/bJChTa04E0BXarDhnE5f1cnaEzBZfxgY1ddoG4DaRL4+GHBNORBW7Mx+yfZKPk5bORo3tbxjGSU2Q6by34k5xeleb5qASlpGQMoCphXKdEmISgb8qVvYLOoqghZT5adGktfPXnamHgtw5+VfVngZ+84ak/9mnePx8nk6aJvu9RhfOLC7Rdo03HOh7RhPCxgy32gzdaKJB1T50XzYmUJnZXG4arDXmytr8hNjx59IRf+Rff5v69+zRNa4VhEp7fjNSIrFEhqnZEhilDmjLjbiQlpWlblzuzcTRNw8OHDzk+OgKUEIS2bVmvTfP33r17bLdbri4vjW4hy8aOixlPTOvXMJa1OI64XBcz+E0hoGKAXaIBMEoUOGVElSZZKqtOqj8A+H1ZfvJh6viZJ7+H98Z7/OKzNwkhk5psETW1s1d4RXErzvdhsVgvbjzYj+yyAMGLq3kTRqtAOc5gGGXm7wZqMVJ5f1oZsLEuTFTqQ4kEFzkxglpXKQWd/AKVxUMhD5EcPDWuYq9JHumNBjA1iQNfBxpbiyZNXSZ0s7TQTVb4dZ9kZfFaHmtJabhOlZhfY2nNF/UueFm+0q1Hrv7gFR/8yd/L0UeJo+9dsnu4ZncWTYUjCHFY2WfG+/WaLSPxy9/LZFrkxvINM+RyE1XkibIXNU5HBiwLjQAUGS16l3prfdo+DsSdRe5yp2hnyiphCHMUv9GFPyzOcI2kiPuTv85BK2Pg6q0WyS3oUVV4mE6yZRpa43WThKwwnWHjbPLzQczyuWXCXFITCv1hOS4wgOUgS5OdzBunasFk8uq9KS+c+1+Gryjwrauv8tOPfg8/9/5bAOweYJHWMsf5vd1sMNDrEVfRRdvqytNfDHuxGSivLwW2exvuAoIXi3ptXuIFajlKLZKdjoySMlyTvdNGfT7BZMeabBvqJed+wcWuyi0+4Fwi79HWrrALaGe+qX0wjoO/V32jLnWPaNSDpTpDDDNIBZybe+0xny/KfJJyILryQ7HAYoPtJ7iseOW9H9fg4mXNKZttx9/95X+FTWp5f3fKrzx5SBsTr9+7cAAunG/7vc19EGXKgWmKniCQ2uBDPXri5ThzgMDPKb6BCEHZjQ05C+/nE9LkAY8x2Ny/sxbD7SDeCGP/hhGFI1+bpnVDl4ynWwveip9Pdq/FTWvR4YuZPmgDNYkzFdDGm1L0xT+Bj3qy9LQLeTVJsHrksm+oZ0XlucYv88UqQLb80+fUMjTOfl+iwvU7LKhn9rwB4iqZJjNgfpHdaYc3MAAcgrn0OE5MUyK51NCnwL0/oMkNv9kfS8pAnkzRwR4yjxyGgcuLC06PTmib1nCgzHhwb11wnq2y4OFi6RvJ2aqxpUikzfOkiNB1PU3T2Pv8NSEEmqap/xZn79p32l8wxI9ZxijMKd2iW2t/q92JEcQheylcKPw8kfId53GV83XbttOWX9+9wvvbU86H3icI5jSS4/4iM1N4a8Vq8cByV7kAwLL4Ds9FbRZWFqq9bjw6f5YKSLcE09SodD11JWWcmeWpy6SnGCdqocVZP7tG2PxnKlvc5QDZcwmZHBAlq6LWBfAoXNwy8X4S8F0uXC96Hrix49IylXlTWvNlWhcmXjm75OLNIyDSPenZPmjYPpg3H6U1cVHgqO1Il9Jw18GvRydy5bQuPrRM0IGapitd1qbTBK0iHnkrC1eaBPpMXCXG3JG2gfRgRNpMbGwjlHfRgEZjUfvYZFIFkFI/WrOgSTwNrcQmETzyM46RYQgQ1NLbvsiG9USMi7mpALto2YKizQzsA1KoaW4tGQY8SxDMubPaeMr5Kwt65uYonYnq6N59cl1E42WbIjybVnz7yatceuFbbpWpbHR8sSwp1zDJvn8UznfaB7R+8L20d0nxXqemLsHvXMQzP7e38YoyN5/ooETdNcwpbvNBB6etqzHccMvWtsJ+HeuGxLmoqB+nzf54Weh0Rvcyq7mIGM0p+bx1HfjWW2mZ1nfg2ojpLmX/Oy5O0jISLDrLoIFp/d56Z7diWdg96/n2+Ws826242PScHW9YNWMdX8rBFShK8kjBtegty6ZkDQ6CZ7CgHokqhch2H4jJZKplgHM2ZR0dA0wB2RndQEaZi+wG78K2pBfAPC/loh4xg8BCkQmuMVzoG+3FLK9WfLBQ/orm/DSaP6aOWYFhNPA5nlitSv/UNzadf59W61p83ew+0vlYC631HMUKPN2XjKbGnhYwMNdbONiqNT/BC/OrOMHNJncBZuqHiXwAXAIf3tmHvhx7lcOYr9s3VPW12zjwwU/u1O5izAdfed4OvvK8Hfzkefsi+gkcfOWzsC+ir3xmfnKn4BdARP6Rqt6Umvjc2mHMd29fxPEfxvzZ2BfxOxzGfPf2RRz/F3HM8MUdd7Ev4vgPY/7B7FOooR3sYAc72MEOdrCDHexgXw47gN+DHexgBzvYwQ52sIP90NhnAX5/5jP4zN+qHcZ89/ZFHP9hzJ+NfRG/w2HMd29fxPF/EccMX9xxF/sijv8w5h/A7pzze7CDHexgBzvYwQ52sIN9VnagPRzsYAc72MEOdrCDHeyHxg7g92AHO9jBDnawgx3sYD80dmfgV0T+LRH5JRH5toj8xbv63B/EROTrIvJ/i8gviMi3ROTP++P/hYh8X0R+1v/9O5/1WJcmIt8VkZ/zsf0jf+wVEfk/ReSX/eeDz3qcn9YOvnJ79mXylYOf3J59mfwEDr5ym/Zl8pWDn9yufZ585U44vyISgX8B/JvA28A/BP6Uqv7CrX/4D2Ai8hbwlqr+ExE5Bf4x8O9i/cAvVPW/+izH9yITke8CP6mqHy4e+y+BR6r6l/0mfqCqf+GzGuOntYOv3K59WXzl4Ce3a18WP4GDr9y2fVl85eAnt2+fJ1+5q8jvHwK+raq/oqoD8L8AP3VHn/2pTVXfUdV/4r+fA78IfPWzHdVv2n4K+Gv++1/Dbo4vgh185e7ti+grBz+5e/si+gkcfOWzsC+irxz85LOxz8RX7gr8fhX43uLvt/mcXywR+e3ATwD/wB/6syLyz0Tkr34OUzgK/B8i8o9F5Kf9sTdU9R3//V3gjc9maD+wHXzldu3L4isHP7ld+7L4CRx85bbty+IrBz+5ffvc+Mqh4O0GE5ET4G8C/5mqPgP+W+CbwO8H3gH+689udDfaH1HVPwD828B/KiJ/dPmkGrfloGl3C3bwlYN9Gjv4ycE+rR185WCfxr6AfgKfI1+5K/D7feDri7+/5o997kxEWsyh/rqq/i0AVX1PVZOqZuC/w9IjnxtT1e/7z/eBv42N7z3nBhWO0Puf3Qh/IDv4yi3al8hXDn5yi/Yl8hM4+Mqt2pfIVw5+csv2efKVuwK//xD4nSLyIyLSAX8S+Ht39Nmf2kREgP8B+EVV/W8Wj7+1eNm/B/z8XY/tRSYix056R0SOgT+Oje/vAX/aX/angb/72YzwB7aDr9ySfcl85eAnt2RfMj+Bg6/cmn3JfOXgJ7donzdfae7iQ1R1EpE/C/zvQAT+qqp+6y4++we0Pwz8B8DPicjP+mN/CfhTIvL7sXD8d4H/5LMY3AvsDeBv2/1AA/xPqvr3ReQfAn9DRP4M8GtYJejn3g6+cqv2pfGVg5/cqn1p/AQOvnLL9qXxlYOf3Lp9rnzl0N74YAc72MEOdrCDHexgPzR2KHg72MEOdrCDHexgBzvYD40dwO/BDnawgx3sYAc72MF+aOwAfg92sIMd7GAHO9jBDvZDYwfwe7CDHexgBzvYwQ52sB8aO4Dfgx3sYAc72MEOdrCD/dDYAfwe7GAHO9jBDnawgx3sh8YO4PdgBzvYwQ52sIMd7GA/NPb/A2lfHa/G8ZNPAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.043383751471786976\n" + ] + } + ], + "source": [ + "mae = history.history['accuracy']\n", + "val_mae = history.history['val_accuracy']\n", + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "\n", + "epochs = range(len(loss))\n", + "\n", + "plt.plot(epochs, mae, 'b', linestyle=\"--\",label='Training accuracy')\n", + "plt.plot(epochs, val_mae, 'g', label='Validation accuracy')\n", + "plt.title('Training and validation accuracy')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(epochs, loss, 'b', linestyle=\"--\",label='Training loss')\n", + "plt.plot(epochs, val_loss,'g', label='Validation loss')\n", + "plt.title('Training and validation loss')\n", + "plt.legend()\n", + "\n", + "plt.show()\n", + "\n", + "y_pred_hm = model.predict(x_train)\n", + "print_heatmap(x_train, y_pred_hm)\n", + "y_pred_coords = heatmap2coord(y_pred_hm)\n", + "pck = compute_PCK_alpha(y_train, y_pred_coords)\n", + "print(pck)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LPamKJXZsCff" + }, + "source": [ + "## Travail à faire \n", + "\n", + "Commencez par adapter le réseau Unet à votre problème, puis à l'entraîner sur le petit ensemble de données x_train. \n", + "\n", + "Plusieurs remarques (**à lire attentivement**) : \n", + "\n", + "\n", + "\n", + "* Vous devriez observer beaucoup moins de sur-apprentissage ! Si vos prédictions sur l'ensemble de validation sont aberrantes, c'est que vous avez un bug !!\n", + "* Se pose la question de la formulation du problème : dans la mesure où \n", + "les cartes de chaleur sont assimilables à des cartes de probabilité (entre 0 et 1), on peut certes choisir de conserver une formulation du problème basée sur la régression, mais l'expérience montre que l'apprentissage se passe mieux si l'on formule le problème comme de la classification binaire.\n", + "* Il y a cependant toujours du sur-apprentissage. Vous pouvez donc augmenter la taille de la base de données à 10000, puis tester différentes possibilités pour diminuer ce sous-apprentissage, notamment l'augmentation de données. Les cellules suivantes vous fournissent des éléments permettant de mettre en place cette augmentation.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JzghttyGTPvq" + }, + "source": [ + "## Code fourni pour l'augmentation de données (à n'envisager que dans un second temps)\n", + "\n", + "Je vous fournis ici quelques éléments qui vous permettront de mettre en place de l'augmentation de données. \n", + "Attention l'augmentation rend l'apprentissage plus difficile et en fait plus lent, et vous devrez peut-être augmenter un peu la capacité de votre UNet et le nombre d'epochs pour éviter le sous-apprentissage." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n2U4RIeNTmq_" + }, + "source": [ + "Albumentation est une librairie implémentant un grand nombre d'opérations d'augmentation de données. Dans le code suivant, deux types d'augmentation sont définies : des transformations spatiales (ShiftScaleRotate), et des transformations colorimétriques. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "4E-o7eMQTtE5" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/deepl/.env/lib/python3.8/site-packages/albumentations/augmentations/transforms.py:1826: FutureWarning: This class has been deprecated. Please use RandomBrightnessContrast\n", + " warnings.warn(\n", + "/tmp/deepl/.env/lib/python3.8/site-packages/albumentations/augmentations/transforms.py:1800: FutureWarning: This class has been deprecated. Please use RandomBrightnessContrast\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import cv2 as cv\n", + "from albumentations import (Compose, RandomBrightness, RandomContrast, RandomGamma, ShiftScaleRotate)\n", + "\n", + "AUGMENTATIONS_TRAIN = Compose([\n", + " ShiftScaleRotate(p=0.5),\n", + " RandomContrast(limit=0.2, p=0.5),\n", + " RandomGamma(gamma_limit=(80, 120), p=0.5),\n", + " RandomBrightness(limit=0.2, p=0.5)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ac98mnGJTxNu" + }, + "source": [ + "La classe Sequence permet de définir l'accès aux données d'entraînement de manière personnalisée, afin par exemple d'implanter des augmentations particulières (c'est le cas ici), ou d'avoir un contrôle plus fin sur la manière de constituer les *mini-batches*.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "XaGczO0QT0H8" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.utils import Sequence\n", + "\n", + "class LSPDSequence(Sequence):\n", + " # Initialisation de la séquence avec différents paramètres\n", + " def __init__(self, x_set, y_set, batch_size,augmentations):\n", + " self.x, self.y = x_set, y_set # Données et labels d'apprentissage \n", + " self.batch_size = batch_size # Taille de mini-batch\n", + " self.augment = augmentations # Fonction d'augmentation à appliquer\n", + " self.indices1 = np.arange(x_set.shape[0]) \n", + " np.random.shuffle(self.indices1) # Les indices permettent d'accéder\n", + " # aux données et sont retirés aléatoirement à chaque epoch pour varier \n", + " # la composition des batches au cours de l'entraînement\n", + "\n", + " # Fonction calculant le nombre de pas de descente du gradient par epoch\n", + " def __len__(self):\n", + " return int(np.ceil(len(self.x) / float(self.batch_size)))\n", + "\n", + " # Application de l'augmentation de données à chaque image du batch et aux\n", + " # cartes de probabilités associées\n", + " def apply_augmentation(self, bx, by):\n", + "\n", + " batch_x = np.zeros(bx.shape)\n", + " batch_y = np.zeros(by.shape)\n", + " # Pour chaque image du batch\n", + " for i in range(len(bx)):\n", + " masks = []\n", + " # Les 14 masques associés à l'image sont rangés dans une liste pour \n", + " # pouvoir être traités par la librairie Albumentation\n", + " for n in range(by.shape[3]):\n", + " masks.append(by[i,:,:,n])\n", + "\n", + " img = bx[i]\n", + " # Application de l'augmentation à l'image et aux masques\n", + " transformed = self.augment(image=img, masks=masks)\n", + " batch_x[i] = transformed['image']\n", + " batch_y_list = transformed['masks']\n", + "\n", + " # Reconstitution d'un tenseur à partir des masques augmentés\n", + " for k in range(by.shape[3]):\n", + " batch_y[i,:,:,k] = batch_y_list[k]\n", + "\n", + " return batch_x, batch_y\n", + "\n", + " # Fonction appelée pour chaque nouveau batch : sélection et augmentation des données\n", + " def __getitem__(self, idx):\n", + " batch_x = self.x[self.indices1[idx * self.batch_size:(idx + 1) * self.batch_size]]\n", + " batch_y = self.y[self.indices1[idx * self.batch_size:(idx + 1) * self.batch_size]]\n", + " \n", + " batch_x, batch_y = self.apply_augmentation(batch_x, batch_y)\n", + "\n", + " return np.array(batch_x), np.array(batch_y)\n", + "\n", + " # Fonction appelée à la fin d'un epoch ; on randomise les indices d'accès aux données\n", + " def on_epoch_end(self):\n", + " np.random.shuffle(self.indices1)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "NGBI2gXVT399" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Instanciation d'une Sequence\n", + "train_gen = LSPDSequence(x_train.astype(np.float32), y_hm_train.astype(np.float32), 16, augmentations=AUGMENTATIONS_TRAIN)\n", + "\n", + "# Pour tester la séquence, nous sélectionnons les éléments du premier batch et les affichons\n", + "batch_x, batch_y = train_gen[0]\n", + "\n", + "print_heatmap(batch_x, batch_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "id": "yT-ySEuOUCyh" + }, + "outputs": [], + "source": [ + "import keras\n", + "from keras.layers import *\n", + "from keras import *\n", + "\n", + "def create_unet(image_size=572):\n", + " input_layer=Input((image_size, image_size, 3))\n", + "\n", + " conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(input_layer)\n", + " conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\n", + " pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n", + " \n", + " conv2 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\n", + " conv2 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\n", + " pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n", + " \n", + " conv3 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)\n", + " conv3 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)\n", + " pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n", + " \n", + " conv4 = Conv2D(216, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)\n", + " conv4 = Conv2D(216, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)\n", + " drop4 = Dropout(0.5)(conv4)\n", + " pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)\n", + "\n", + " conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)\n", + " conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)\n", + " drop5 = Dropout(0.5)(conv5)\n", + "\n", + " up6 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))\n", + " merge6 = concatenate([drop4,up6], axis = 3)\n", + " conv6 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)\n", + " conv6 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)\n", + "\n", + " up7 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))\n", + " merge7 = concatenate([conv3,up7], axis = 3)\n", + " conv7 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)\n", + " conv7 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)\n", + " \n", + " up8 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))\n", + " merge8 = concatenate([conv2,up8], axis = 3)\n", + " conv8 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)\n", + " conv8 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)\n", + "\n", + " up9 = Conv2D(32, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))\n", + " merge9 = concatenate([conv1,up9], axis = 3)\n", + " conv9 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)\n", + " conv9 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)\n", + " conv10 = Conv2D(len(labels), 1, activation = 'sigmoid')(conv9)\n", + "\n", + " model = Model(input_layer, conv10)\n", + "\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_1\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_2 (InputLayer) [(None, 64, 64, 3)] 0 [] \n", + " \n", + " conv2d_23 (Conv2D) (None, 64, 64, 32) 896 ['input_2[0][0]'] \n", + " \n", + " conv2d_24 (Conv2D) (None, 64, 64, 32) 9248 ['conv2d_23[0][0]'] \n", + " \n", + " max_pooling2d_4 (MaxPooling2D) (None, 32, 32, 32) 0 ['conv2d_24[0][0]'] \n", + " \n", + " conv2d_25 (Conv2D) (None, 32, 32, 64) 18496 ['max_pooling2d_4[0][0]'] \n", + " \n", + " conv2d_26 (Conv2D) (None, 32, 32, 64) 36928 ['conv2d_25[0][0]'] \n", + " \n", + " max_pooling2d_5 (MaxPooling2D) (None, 16, 16, 64) 0 ['conv2d_26[0][0]'] \n", + " \n", + " conv2d_27 (Conv2D) (None, 16, 16, 128) 73856 ['max_pooling2d_5[0][0]'] \n", + " \n", + " conv2d_28 (Conv2D) (None, 16, 16, 128) 147584 ['conv2d_27[0][0]'] \n", + " \n", + " max_pooling2d_6 (MaxPooling2D) (None, 8, 8, 128) 0 ['conv2d_28[0][0]'] \n", + " \n", + " conv2d_29 (Conv2D) (None, 8, 8, 216) 249048 ['max_pooling2d_6[0][0]'] \n", + " \n", + " conv2d_30 (Conv2D) (None, 8, 8, 216) 420120 ['conv2d_29[0][0]'] \n", + " \n", + " dropout_2 (Dropout) (None, 8, 8, 216) 0 ['conv2d_30[0][0]'] \n", + " \n", + " max_pooling2d_7 (MaxPooling2D) (None, 4, 4, 216) 0 ['dropout_2[0][0]'] \n", + " \n", + " conv2d_31 (Conv2D) (None, 4, 4, 512) 995840 ['max_pooling2d_7[0][0]'] \n", + " \n", + " conv2d_32 (Conv2D) (None, 4, 4, 512) 2359808 ['conv2d_31[0][0]'] \n", + " \n", + " dropout_3 (Dropout) (None, 4, 4, 512) 0 ['conv2d_32[0][0]'] \n", + " \n", + " up_sampling2d_4 (UpSampling2D) (None, 8, 8, 512) 0 ['dropout_3[0][0]'] \n", + " \n", + " conv2d_33 (Conv2D) (None, 8, 8, 256) 524544 ['up_sampling2d_4[0][0]'] \n", + " \n", + " concatenate_4 (Concatenate) (None, 8, 8, 472) 0 ['dropout_2[0][0]', \n", + " 'conv2d_33[0][0]'] \n", + " \n", + " conv2d_34 (Conv2D) (None, 8, 8, 256) 1087744 ['concatenate_4[0][0]'] \n", + " \n", + " conv2d_35 (Conv2D) (None, 8, 8, 256) 590080 ['conv2d_34[0][0]'] \n", + " \n", + " up_sampling2d_5 (UpSampling2D) (None, 16, 16, 256) 0 ['conv2d_35[0][0]'] \n", + " \n", + " conv2d_36 (Conv2D) (None, 16, 16, 128) 131200 ['up_sampling2d_5[0][0]'] \n", + " \n", + " concatenate_5 (Concatenate) (None, 16, 16, 256) 0 ['conv2d_28[0][0]', \n", + " 'conv2d_36[0][0]'] \n", + " \n", + " conv2d_37 (Conv2D) (None, 16, 16, 128) 295040 ['concatenate_5[0][0]'] \n", + " \n", + " conv2d_38 (Conv2D) (None, 16, 16, 128) 147584 ['conv2d_37[0][0]'] \n", + " \n", + " up_sampling2d_6 (UpSampling2D) (None, 32, 32, 128) 0 ['conv2d_38[0][0]'] \n", + " \n", + " conv2d_39 (Conv2D) (None, 32, 32, 64) 32832 ['up_sampling2d_6[0][0]'] \n", + " \n", + " concatenate_6 (Concatenate) (None, 32, 32, 128) 0 ['conv2d_26[0][0]', \n", + " 'conv2d_39[0][0]'] \n", + " \n", + " conv2d_40 (Conv2D) (None, 32, 32, 64) 73792 ['concatenate_6[0][0]'] \n", + " \n", + " conv2d_41 (Conv2D) (None, 32, 32, 64) 36928 ['conv2d_40[0][0]'] \n", + " \n", + " up_sampling2d_7 (UpSampling2D) (None, 64, 64, 64) 0 ['conv2d_41[0][0]'] \n", + " \n", + " conv2d_42 (Conv2D) (None, 64, 64, 32) 8224 ['up_sampling2d_7[0][0]'] \n", + " \n", + " concatenate_7 (Concatenate) (None, 64, 64, 64) 0 ['conv2d_24[0][0]', \n", + " 'conv2d_42[0][0]'] \n", + " \n", + " conv2d_43 (Conv2D) (None, 64, 64, 32) 18464 ['concatenate_7[0][0]'] \n", + " \n", + " conv2d_44 (Conv2D) (None, 64, 64, 32) 9248 ['conv2d_43[0][0]'] \n", + " \n", + " conv2d_45 (Conv2D) (None, 64, 64, 14) 462 ['conv2d_44[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 7,267,966\n", + "Trainable params: 7,267,966\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model = create_unet(image_size=64)\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "57/57 [==============================] - 16s 260ms/step - loss: 0.0492 - accuracy: 0.0355\n", + "Epoch 2/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0311 - accuracy: 0.0376\n", + "Epoch 3/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0276 - accuracy: 0.0376\n", + "Epoch 4/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0249 - accuracy: 0.0408\n", + "Epoch 5/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0230 - accuracy: 0.0474\n", + "Epoch 6/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0221 - accuracy: 0.0686\n", + "Epoch 7/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0220 - accuracy: 0.0830\n", + "Epoch 8/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0209 - accuracy: 0.1399\n", + "Epoch 9/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0205 - accuracy: 0.2018\n", + "Epoch 10/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0202 - accuracy: 0.2163\n", + "Epoch 11/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0201 - accuracy: 0.2323\n", + "Epoch 12/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0196 - accuracy: 0.2354\n", + "Epoch 13/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0196 - accuracy: 0.2590\n", + "Epoch 14/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0194 - accuracy: 0.2388\n", + "Epoch 15/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0192 - accuracy: 0.2503\n", + "Epoch 16/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0191 - accuracy: 0.2396\n", + "Epoch 17/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0191 - accuracy: 0.2664\n", + "Epoch 18/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0190 - accuracy: 0.2490\n", + "Epoch 19/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0190 - accuracy: 0.2233\n", + "Epoch 20/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0189 - accuracy: 0.2491\n", + "Epoch 21/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0187 - accuracy: 0.2485\n", + "Epoch 22/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0189 - accuracy: 0.2488\n", + "Epoch 23/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0189 - accuracy: 0.2710\n", + "Epoch 24/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0187 - accuracy: 0.2643\n", + "Epoch 25/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0187 - accuracy: 0.2373\n", + "Epoch 26/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0187 - accuracy: 0.2581\n", + "Epoch 27/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0187 - accuracy: 0.2337\n", + "Epoch 28/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0186 - accuracy: 0.2480\n", + "Epoch 29/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0187 - accuracy: 0.2294\n", + "Epoch 30/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0184 - accuracy: 0.2626\n", + "Epoch 31/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0184 - accuracy: 0.2467\n", + "Epoch 32/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0186 - accuracy: 0.2497\n", + "Epoch 33/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0183 - accuracy: 0.2340\n", + "Epoch 34/100\n", + "57/57 [==============================] - 15s 262ms/step - loss: 0.0183 - accuracy: 0.2401\n", + "Epoch 35/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0183 - accuracy: 0.2517\n", + "Epoch 36/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0184 - accuracy: 0.2479\n", + "Epoch 37/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0183 - accuracy: 0.2323\n", + "Epoch 38/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0182 - accuracy: 0.2647\n", + "Epoch 39/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0182 - accuracy: 0.2566\n", + "Epoch 40/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0183 - accuracy: 0.2436\n", + "Epoch 41/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0182 - accuracy: 0.2486\n", + "Epoch 42/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0182 - accuracy: 0.2625\n", + "Epoch 43/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0181 - accuracy: 0.2717\n", + "Epoch 44/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0181 - accuracy: 0.2351\n", + "Epoch 45/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0182 - accuracy: 0.2538\n", + "Epoch 46/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0181 - accuracy: 0.2486\n", + "Epoch 47/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0179 - accuracy: 0.2432\n", + "Epoch 48/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0180 - accuracy: 0.2379\n", + "Epoch 49/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0181 - accuracy: 0.2535\n", + "Epoch 50/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0179 - accuracy: 0.2460\n", + "Epoch 51/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0179 - accuracy: 0.2618\n", + "Epoch 52/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0179 - accuracy: 0.2349\n", + "Epoch 53/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0178 - accuracy: 0.2481\n", + "Epoch 54/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0179 - accuracy: 0.2255\n", + "Epoch 55/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0179 - accuracy: 0.2463\n", + "Epoch 56/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0177 - accuracy: 0.2260\n", + "Epoch 57/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0179 - accuracy: 0.2415\n", + "Epoch 58/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0178 - accuracy: 0.2606\n", + "Epoch 59/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0177 - accuracy: 0.2402\n", + "Epoch 60/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0176 - accuracy: 0.2527\n", + "Epoch 61/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0177 - accuracy: 0.2505\n", + "Epoch 62/100\n", + "57/57 [==============================] - 15s 261ms/step - loss: 0.0176 - accuracy: 0.2685\n", + "Epoch 63/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0178 - accuracy: 0.2562\n", + "Epoch 64/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0177 - accuracy: 0.2271\n", + "Epoch 65/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0176 - accuracy: 0.2306\n", + "Epoch 66/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0176 - accuracy: 0.2553\n", + "Epoch 67/100\n", + "57/57 [==============================] - 15s 259ms/step - loss: 0.0176 - accuracy: 0.2250\n", + "Epoch 68/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0175 - accuracy: 0.2556\n", + "Epoch 69/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0174 - accuracy: 0.2524\n", + "Epoch 70/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0175 - accuracy: 0.2578\n", + "Epoch 71/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0175 - accuracy: 0.2579\n", + "Epoch 72/100\n", + "57/57 [==============================] - 15s 261ms/step - loss: 0.0174 - accuracy: 0.2663\n", + "Epoch 73/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0173 - accuracy: 0.2718\n", + "Epoch 74/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0176 - accuracy: 0.2350\n", + "Epoch 75/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0172 - accuracy: 0.2704\n", + "Epoch 76/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0173 - accuracy: 0.2347\n", + "Epoch 77/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0173 - accuracy: 0.2553\n", + "Epoch 78/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0174 - accuracy: 0.2309\n", + "Epoch 79/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0173 - accuracy: 0.2484\n", + "Epoch 80/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0174 - accuracy: 0.2431\n", + "Epoch 81/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0173 - accuracy: 0.2418\n", + "Epoch 82/100\n", + "57/57 [==============================] - 15s 261ms/step - loss: 0.0172 - accuracy: 0.2434\n", + "Epoch 83/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0172 - accuracy: 0.2410\n", + "Epoch 84/100\n", + "57/57 [==============================] - 15s 261ms/step - loss: 0.0171 - accuracy: 0.2544\n", + "Epoch 85/100\n", + "57/57 [==============================] - 15s 261ms/step - loss: 0.0170 - accuracy: 0.2382\n", + "Epoch 86/100\n", + "57/57 [==============================] - 15s 261ms/step - loss: 0.0171 - accuracy: 0.2422\n", + "Epoch 87/100\n", + "57/57 [==============================] - 15s 261ms/step - loss: 0.0171 - accuracy: 0.2537\n", + "Epoch 88/100\n", + "57/57 [==============================] - 15s 261ms/step - loss: 0.0170 - accuracy: 0.2672\n", + "Epoch 89/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0169 - accuracy: 0.2552\n", + "Epoch 90/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0170 - accuracy: 0.2544\n", + "Epoch 91/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0171 - accuracy: 0.2409\n", + "Epoch 92/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0169 - accuracy: 0.2201\n", + "Epoch 93/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0169 - accuracy: 0.2288\n", + "Epoch 94/100\n", + "57/57 [==============================] - 15s 261ms/step - loss: 0.0167 - accuracy: 0.2568\n", + "Epoch 95/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0169 - accuracy: 0.2724\n", + "Epoch 96/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0166 - accuracy: 0.2498\n", + "Epoch 97/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0168 - accuracy: 0.2345\n", + "Epoch 98/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0167 - accuracy: 0.2457\n", + "Epoch 99/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0168 - accuracy: 0.2588\n", + "Epoch 100/100\n", + "57/57 [==============================] - 15s 260ms/step - loss: 0.0167 - accuracy: 0.2716\n" + ] + } + ], + "source": [ + "from tensorflow.keras import optimizers\n", + "\n", + "adam = optimizers.Adam(learning_rate=1e-4)\n", + "model.compile(optimizer=adam, loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n", + "history = model.fit(train_gen, epochs=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.33928086224073906\n" + ] + } + ], + "source": [ + "mae = history.history['accuracy']\n", + "# val_mae = history.history['val_accuracy']\n", + "loss = history.history['loss']\n", + "# val_loss = history.history['val_loss']\n", + "\n", + "epochs = range(len(loss))\n", + "\n", + "plt.plot(epochs, mae, 'b', linestyle=\"--\",label='Training accuracy')\n", + "# plt.plot(epochs, val_mae, 'g', label='Validation accuracy')\n", + "plt.title('Training and validation accuracy')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(epochs, loss, 'b', linestyle=\"--\",label='Training loss')\n", + "# plt.plot(epochs, val_loss,'g', label='Validation loss')\n", + "plt.title('Training and validation loss')\n", + "plt.legend()\n", + "\n", + "plt.show()\n", + "\n", + "y_pred_hm = model.predict(x_train)\n", + "print_heatmap(x_train, y_pred_hm)\n", + "y_pred_coords = heatmap2coord(y_pred_hm)\n", + "pck = compute_PCK_alpha(y_train, y_pred_coords)\n", + "print(pck)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "machine_shape": "hm", + "name": "TP5_Estimation_de_Posture_Sujet.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3.10.6 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "vscode": { + "interpreter": { + "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/TP6.ipynb b/TP6.ipynb new file mode 100644 index 0000000..9000e86 --- /dev/null +++ b/TP6.ipynb @@ -0,0 +1,1701 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Kd4SVfDxz5aw" + }, + "source": [ + "

Localisation et détection d'objet

\n", + "\n", + "Dans ce TP, nous allons mettre en pratique certaines des méthodes présentées en cours pour localiser des objets dans une image.\n", + "\n", + "En localisation et détection, on cherche à déterminer la position d'un objet, ainsi que sa classe, sous la forme d'une boîte englobante de largeur $b_w$ et hauteur $b_h$, et dont le centre a pour coordonnées le point $(b_x, b_y)$. \n", + "\n", + "
\n", + "
Figure 1: Modèle de boîte englobante utilisé pour la localisation
\n", + "\n", + "Le problème de localisation considère qu'un seul objet est présent sur l'image, alors que le problème de détection cherche à déterminer l'ensemble des objets présents sur l'image.\n", + "\n", + "\n", + "\n", + "Pour commencer, récupérez les images de la base de données :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "2ZjveWpbuNeV" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fatal: le chemin de destination 'mangeoires_loc' existe déjà et n'est pas un répertoire vide.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/axelcarlier/mangeoires_loc.git" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LF6aRZLE2-Yl" + }, + "source": [ + "La base de données consiste en des photographies prises par une caméra reliée à une Raspberry Pi, cachée dans une mangeoire. Plusieurs mangeoires sont disséminées dans la nature en Occitanie, et l'objectif de [ce projet](https://econect.cnrs.fr/) est la reconnaissance des espèces et le comptage des individus qui viennent se poser devant le caméra, afin de suivre l'évolution des populations d'oiseaux et ainsi monitorer la biodiversité.\n", + "\n", + "La base de données qui vous est fournie regroupe 11 espèces d'animaux, majoritairement des oiseaux, désignés par un code : \n", + "\n", + "1. Mésange charbonnière (**MESCHA**)\n", + "2. Verdier d'Europe (**VEREUR**)\n", + "3. Écureuil roux (**ECUROU**)\n", + "4. Pie bavarde (**PIEBAV**)\n", + "5. Sittelle torchepot (**SITTOR**)\n", + "6. Pinson des arbres (**PINARB**)\n", + "7. Mésange noire (**MESNOI**)\n", + "8. Mésange nonnette (**MESNON**)\n", + "9. Mésange bleue (**MESBLE**)\n", + "10. Rouge-gorge (**ROUGOR**)\n", + "11. Accenteur mouchet (**ACCMOU**)\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "\n", + "
\n", + "
Figure 2: Exemples d'images de la base de données
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kxZ6cVouz9Lp" + }, + "source": [ + "# Localisation et classification d'objet\n", + "\n", + "Dans cette partie, nous allons nous concentrer sur le problème de la localisation d'un seul objet par classe. La base de données a été épurée pour se concentrer uniquement sur ce cas.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "SBA3fa8RSDpt" + }, + "outputs": [], + "source": [ + "import PIL\n", + "from PIL import Image\n", + "import csv\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import tensorflow as tf\n", + "\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "from tensorflow.keras import models\n", + "from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, Input\n", + "from tensorflow.keras.models import Model, Sequential\n", + "from tensorflow.keras.optimizers import Adam\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HKRm5oT-_Qsw" + }, + "source": [ + "## Préparation des données\n", + "\n", + "Le code ci-dessous permet de charger les données et les formater pour la classification. Prenez le temps de regarder un peu le format des labels $y$.\n", + "Notez que les images sont rendues carrées lors du chargement." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "Q54zSuMvGM-5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['MESCHA', 'VEREUR', 'ECUROU', 'PIEBAV', 'SITTOR', 'PINARB', 'MESNOI', 'MESNON', 'MESBLE', 'ROUGOR', 'ACCMOU']\n" + ] + } + ], + "source": [ + "# Lecture du CSV contenant les informations relatives à la base de données\n", + "dataset = []\n", + "with open('mangeoires_loc/bd_mangeoires_equilibre.csv', newline='') as csvfile:\n", + "\tfilereader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n", + "\tfor row in filereader:\n", + "\t\tdata = row[0].split(',')\n", + "\t\tif data[0] != 'Data':\n", + "\t\t\tbox = [float(data[5]), float(data[6]), float(data[7]), float(data[8])]\n", + "\t\t\tnew_entry = {'type': data[0], 'specie': data[1], 'path': data[2], 'shape': [float(data[3]), float(data[4])], 'box': box}\n", + "\t\t\tdataset.append(new_entry)\n", + "\n", + "# Nombre de classes de la base de données et intitulé des classes\n", + "class_labels = list(dict.fromkeys([item['specie'] for item in dataset]))\n", + "num_classes = len(class_labels)\n", + "\n", + "# Extraction des données d'apprentissage et de test \n", + "dataset_train = [item for item in dataset if item['type']=='TRAIN']\n", + "dataset_test = [item for item in dataset if item['type']=='TEST']\n", + "\n", + "print(class_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "JPzqdJBVJLWJ" + }, + "outputs": [], + "source": [ + "def build_localization_tensors(image_size, dataset, num_classes):\n", + " # Préparation des structures de données pour x et y\n", + " x = np.zeros((len(dataset), image_size, image_size, 3))\n", + " y = np.empty((len(dataset), num_classes + 5)) # 1 + 4 + num_classes : présence / boîte englobante / classes\n", + "\n", + " # Compteur de parcours du dataset\n", + " i = 0\n", + "\n", + " for item in dataset:\n", + " # Lecture de l'image\n", + " img = Image.open('mangeoires_loc/' + item['path'])\n", + " # Mise à l'échelle de l'image\n", + " img = img.resize((image_size,image_size), Image.ANTIALIAS)\n", + " # Remplissage de la variable x\n", + " x[i] = np.asarray(img)\n", + "\n", + " y[i, 0] = 1 # Un objet est toujours présent !\n", + "\n", + " # Coordonnées de boîte englobante\n", + " img_shape = item['shape']\n", + " box = item['box']\n", + " bx = (box[0] + (box[2] - box[0])/2)/img_shape[0]\n", + " by = (box[1] + (box[3] - box[1])/2)/img_shape[1]\n", + " bw = (box[2] - box[0])/img_shape[0]\n", + " bh = (box[3] - box[1])/img_shape[1]\n", + " y[i, 1] = bx\n", + " y[i, 2] = by\n", + " y[i, 3] = bw\n", + " y[i, 4] = bh\n", + "\n", + " # Probabilités de classe, sous la forme d'une one-hot vector\n", + " label = class_labels.index(item['specie'])\n", + " classes_probabilities = keras.utils.to_categorical(label, num_classes=num_classes)\n", + " y[i, 5:] = classes_probabilities\n", + "\n", + " i = i+1\n", + "\n", + " return x, y\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fnlljZWc_i1L" + }, + "source": [ + "Séparation des données d'entraînement pour extraire un ensemble de validation, et pré-traitement des données." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "FJLRiuFX_VPL" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3171614/3333676307.py:13: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.\n", + " img = img.resize((image_size,image_size), Image.ANTIALIAS)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "\n", + "# Pour la suite du TP on considèrera des images de taille 64x64x3\n", + "# Augmenter cette valeur donnerait de meilleurs résultats mais nécessiterait des calculs plus long.\n", + "IMAGE_SIZE = 64\n", + "\n", + "# Lecture des données d'entraînement et de test\n", + "x, y = build_localization_tensors(IMAGE_SIZE, dataset_train, num_classes)\n", + "x_test, y_test = build_localization_tensors(IMAGE_SIZE, dataset_test, num_classes)\n", + "\n", + "#Extraction d'un ensemble de validation\n", + "x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.10, random_state=42)\n", + "\n", + "# Pour améliorer l'entraînement, on peut centrer-réduire les coordonnées des bounding boxes...\n", + "y_std = np.std(y_train, axis=0)\n", + "y_mean = np.mean(y_train, axis=0)\n", + "y_train[...,1:5] = (y_train[...,1:5] - y_mean[1:5])/y_std[1:5]\n", + "y_val[...,1:5] = (y_val[...,1:5] - y_mean[1:5])/y_std[1:5]\n", + "y_test[...,1:5] = (y_test[...,1:5] - y_mean[1:5])/y_std[1:5]\n", + "\n", + "# ... et normaliser les valeurs de couleur\n", + "x_train = x_train/255\n", + "x_val = x_val/255\n", + "x_test = x_test/255" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DE4wQYq3AKnA" + }, + "source": [ + "## Fonctions utiles" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n6NBpMtaM-C0" + }, + "source": [ + "Une fonction de calcul de l'intersection sur union, qui nous sera utile pour les métriques d'évaluation de nos méthodes :\n", + "\n", + "$$ IoU (R_1, R_2) = \\frac{\\mathcal{A} (R_1 \\cap R_2)}{\\mathcal{A} (R_1 \\cup R_2)} = \\frac{\\mathcal{A} (R_1 \\cap R_2)}{\\mathcal{A} (R_1) + \\mathcal{A} (R_2) - \\mathcal{A} (R_1 \\cap R_2)} $$ \n", + "\n", + "
\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "rr-O1XuwLoL3" + }, + "outputs": [], + "source": [ + "### A COMPLETER\n", + "def intersection_sur_union(box1, box2):\n", + " \"\"\"\n", + " Calcul de l'intersection sur union entre deux rectangles box1 et box2\n", + "\n", + " Arguments:\n", + " box1, box2 -- les coordonnées des deux rectangles, chacun sous la forme [cx, cy, w, h]\n", + " où (cx, cy) désigne les coordonnées du centre du rectangle, \n", + " w sa largeur et h sa hauteur\n", + "\n", + " Retourne :\n", + " iou -- la valeur d'intersection sur union entre les deux rectangles \n", + " \"\"\"\n", + "\n", + " # unpacking\n", + " cx1, cy1, w1, h1 = box1\n", + " cx2, cy2, w2, h2 = box2\n", + "\n", + " # haut gauche R1\n", + " x1 = cx1 - w1/2\n", + " y1 = cy1 - h1/2\n", + " \n", + " # bas droite R1\n", + " x2 = cx1 + w1/2\n", + " y2 = cy1 + h1/2\n", + "\n", + " # haut gauche R2\n", + " x3 = cx2 - w2/2\n", + " y3 = cy2 - h2/2\n", + " \n", + " # bas droite R2\n", + " x4 = cx2 + w2/2\n", + " y4 = cy2 + 2/2\n", + "\n", + " # haut gauche intersection\n", + " xi1 = max(x1, x3)\n", + " yi1 = max(y1, y3)\n", + "\n", + " # bas droite intersection\n", + " xi2 = min(x2, x4)\n", + " yi2 = min(y2, y4)\n", + "\n", + " # dimension de l'intersection\n", + " wi = abs(xi2 - xi1)\n", + " hi = abs(yi2 - yi1)\n", + "\n", + " # calcul des aires\n", + " aR1iR2 = wi * hi\n", + " aR1 = w1 * h1\n", + " aR2 = w2 * h2\n", + "\n", + " return aR1iR2 / (aR1 + aR2 - aR1iR2)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "SdqAFWFxRx8l" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2\n", + "0.0\n" + ] + } + ], + "source": [ + "print(intersection_sur_union([2.5, 2, 1, 4], [2, 3, 4, 2])) # Résultat attendu : 0.2\n", + "print(intersection_sur_union([2.5, 2, 1, 4], [5, 3, 4, 2])) # Résultat attendu : 0.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RLpMJGi3QC9i" + }, + "source": [ + "Calcul des différentes métriques : \n", + "\n", + "$$ P = \\frac{TP}{TP + FP} $$\n", + "\n", + "$$ R = \\frac{TP}{TP + FN} $$\n", + "\n", + "$$ F1 = \\frac{2}{\\frac{1}{P} + \\frac{1}{R}} $$\n", + "\n", + "où $TP$ désigne le nombre de vrais positifs, $FP$ le nombre de faux positifs, $FN$ le nombre de faux négatifs, $P$ la précision, $R$ le rappel et $F1$ le F1-score.\n", + "\n", + "On considère souvent qu'une détection est correcte si la classification est valide et que l'intersection sur union entre vérité terrain et prédiction est supérieure à 0.5 (on utilisera un seuil modifiable *iou_thres*)." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "id": "5e_aIvjXLq32" + }, + "outputs": [], + "source": [ + "# A COMPLETER\n", + "def global_accuracy(y_true, y_pred, iou_thres=0.5):\n", + " \"\"\"\n", + " Calcul, pour chaque classe de la précision, du rappel et du F1-score ainsi \n", + " que du pourcentage global de bonnes détections.\n", + "\n", + " Arguments:\n", + " y_true -- les labels de la vérité terrain, de dimension (M, 1+4+N) où M désigne\n", + " le nombre d'éléments du dataset et N le nombre de classes (11 dans notre cas)\n", + " y_pred -- les labels prédits par un modèle, de dimension (M, 1+4+N) \n", + " iou_thres -- seuil d'intersection sur union entre une boîte \"vérité-terrain\" et \n", + " une boite prédite au-dessus duquel on considère que la prédiction est correcte \n", + "\n", + " Retourne :\n", + " class_res -- liste de longueur N contenant des dictionnaires sous la forme \n", + " {\"Précision\": p, \"Rappel\": r, \"F-score\": f} résumant les métriques\n", + " précision, rappel et F1-score pour chacune des classes.\n", + " accuracy -- pourcentage global de bonnes détections\n", + " \"\"\"\n", + " # Initialisation des métriques : nombre de vrais positifs (TP), faux positifs (FP)\n", + " # et faux négatifs (FN) pour chaque classe\n", + " class_metrics = []\n", + " for i in range(num_classes):\n", + " class_metrics.append({'TP': 0, 'FP': 0, 'FN': 0})\n", + "\n", + " # Nombres de détections correctes et de détections incorrectes\n", + " total_correct_detections = 0\n", + " total_incorrect_detections = 0\n", + " for i in range(y_true.shape[0]):\n", + " # Labels vérité-terrain et prédits\n", + " groundtruth_label = np.argmax(y_true[i,5:])\n", + " predicted_label = np.argmax(y_pred[i,5:])\n", + "\n", + " # Coordonnées de boîtes englobantes réelles et prédites\n", + " bx_true = (y_true[i,1]*y_std[1] + y_mean[1])\n", + " by_true = (y_true[i,2]*y_std[2] + y_mean[2])\n", + " bw_true = (y_true[i,3]*y_std[3] + y_mean[3])\n", + " bh_true = (y_true[i,4]*y_std[4] + y_mean[4]) \n", + " bx_pred = (y_pred[i,1]*y_std[1] + y_mean[1])\n", + " by_pred = (y_pred[i,2]*y_std[2] + y_mean[2])\n", + " bw_pred = (y_pred[i,3]*y_std[3] + y_mean[3])\n", + " bh_pred = (y_pred[i,4]*y_std[4] + y_mean[4]) \n", + "\n", + " # Calcul de l'intersection sur union\n", + " iou = intersection_sur_union([bx_true, by_true, bw_true, bh_true], [bx_pred, by_pred, bw_pred, bh_pred])\n", + " \n", + " # Si la détection est correcte : \n", + " if groundtruth_label == predicted_label and iou > iou_thres:\n", + " total_correct_detections += 1\n", + " class_metrics[predicted_label][\"TP\"] += 1\n", + " else:\n", + " total_incorrect_detections += 1\n", + " class_metrics[predicted_label][\"FP\"] += 1\n", + " class_metrics[groundtruth_label][\"FN\"] += 1\n", + "\n", + "# for i in range(num_classes):\n", + "# print(class_metrics[i])\n", + "\n", + " class_res = []\n", + " for i in range(num_classes):\n", + " TP = class_metrics[i][\"TP\"]\n", + " FP = class_metrics[i][\"FP\"]\n", + " FN = class_metrics[i][\"FN\"]\n", + " if (TP or FP) and (TP or FN): \n", + " P = TP / (TP + FP)\n", + " R = TP / (TP + FN)\n", + " F_score = 2 / ( 1/P + 1/R )\n", + " else:\n", + " P = 0\n", + " R = 0\n", + " F_score = 0\n", + " class_res.append({'Precision': P, 'Rappel': R, 'F-score': F_score})\n", + "\n", + " accuracy = total_correct_detections / (total_correct_detections + total_incorrect_detections)\n", + " return class_res, accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "id": "AOOWhuk4VBZE" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "La précision globale est de 66.7%\n", + "\n", + "--------------------------------------------\n", + "| Classe | Précision | Rappel | F1-score |\n", + "--------------------------------------------\n", + "| Classe 1 | 1.00 | 1.00 | 1.00 |\n", + "--------------------------------------------\n", + "| Classe 2 | 0.00 | 0.00 | 0.00 |\n", + "--------------------------------------------\n", + "| Classe 3 | 0.50 | 1.00 | 0.67 |\n", + "--------------------------------------------\n" + ] + } + ], + "source": [ + "num_class_test = 3\n", + "class_labels_test = ['Classe 1', 'Classe 2', 'Classe 3']\n", + "y_true_test = np.ones((num_class_test,8))\n", + "y_true_test[0,:2] = [0.5, 0.5]\n", + "y_true_test[0, 5:] = [1, 0, 0]\n", + "y_true_test[1,:2] = [0.5, 0.5]\n", + "y_true_test[1, 5:] = [0, 1, 0]\n", + "y_true_test[2,:2] = [0.5, 0.5]\n", + "y_true_test[2, 5:] = [0, 0, 1]\n", + "y_pred_test = np.ones((num_class_test,8))\n", + "y_pred_test[0,:2] = [0.6, 0.6]\n", + "y_pred_test[0, 5:] = [1, 0, 0]\n", + "y_pred_test[1,:2] = [2.5, 2.5]\n", + "y_pred_test[1, 5:] = [0, 0, 1]\n", + "y_pred_test[2,:2] = [0.6, 0.6]\n", + "y_pred_test[2, 5:] = [0, 0, 1]\n", + "\n", + "class_res_test, acc_test = global_accuracy(y_true_test, y_pred_test)\n", + "\n", + "print(f\"La précision globale est de {100 * acc_test:.1f}%\")\n", + "\n", + "print()\n", + "print(\"--------------------------------------------\")\n", + "print(\"| Classe | Précision | Rappel | F1-score |\")\n", + "print(\"--------------------------------------------\")\n", + "for i in range(num_class_test):\n", + " print(f\"| {class_labels_test[i]:9s}| {class_res_test[i]['Precision']:.2f} | {class_res_test[i]['Rappel']:.2f} | {class_res_test[i]['F-score']:.2f} |\")\n", + " print(\"--------------------------------------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q1Tg4VFVVB-w" + }, + "source": [ + "**Affichage attendu :**\n", + "```\n", + "La précision globale est de 66.7%\n", + "\n", + "--------------------------------------------\n", + "| Classe | Précision | Rappel | F1-score |\n", + "--------------------------------------------\n", + "| Classe 1 | 1.00 | 1.00 | 1.00 |\n", + "--------------------------------------------\n", + "| Classe 2 | 0.00 | 0.00 | 0.00 |\n", + "--------------------------------------------\n", + "| Classe 3 | 0.50 | 1.00 | 0.67 |\n", + "--------------------------------------------\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cMntCEgkANMg" + }, + "source": [ + "La fonction ci-dessous permet de calculer l'intersection sur union sur des tenseurs (et non des tableaux numpy), elle sera donc utilisable comme métrique pendant l'entraînement." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "id": "tVk9cB1WAMUK" + }, + "outputs": [], + "source": [ + "def compute_iou(y_true, y_pred):\n", + " ### \"Dénormalisation\" des coordonnées des boîtes englobantes\n", + " pred_box_xy = y_pred[..., 0:2]* y_std[0:2] + y_mean[0:2]\n", + " true_box_xy = y_true[..., 0:2]* y_std[0:2] + y_mean[0:2]\n", + "\n", + " ### \"Dénormalisation\" des largeur et hauteur des boîtes englobantes\n", + " pred_box_wh = y_pred[..., 2:4] * y_std[2:4] + y_mean[2:4]\n", + " true_box_wh = y_true[..., 2:4] * y_std[2:4] + y_mean[2:4]\n", + " \n", + " # Calcul des coordonnées minimales et maximales des boiptes englobantes réelles\n", + " true_wh_half = true_box_wh / 2.\n", + " true_mins = true_box_xy - true_wh_half\n", + " true_maxes = true_box_xy + true_wh_half\n", + " \n", + " # Calcul des coordonnées minimales et maximales des boiptes englobantes prédites\n", + " pred_wh_half = pred_box_wh / 2.\n", + " pred_mins = pred_box_xy - pred_wh_half\n", + " pred_maxes = pred_box_xy + pred_wh_half \n", + " \n", + " # Détermination de l'intersection des boîtes englobantes\n", + " intersect_mins = tf.maximum(pred_mins, true_mins)\n", + " intersect_maxes = tf.minimum(pred_maxes, true_maxes)\n", + " intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)\n", + " intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]\n", + " \n", + " # Aire des boîtes englobantes prédites et réelles\n", + " true_areas = true_box_wh[..., 0] * true_box_wh[..., 1]\n", + " pred_areas = pred_box_wh[..., 0] * pred_box_wh[..., 1]\n", + "\n", + " # Aire de l'union des boîtes prédites et réelles\n", + " union_areas = pred_areas + true_areas - intersect_areas\n", + "\n", + " iou_scores = tf.truediv(intersect_areas, union_areas)\n", + " return iou_scores\n", + "\n", + "def iou():\n", + " def iou_metrics(y_true, y_pred):\n", + " return compute_iou(y_true, y_pred)\n", + " iou_metrics.__name__= \"IoU\"\n", + " return iou_metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vidE3XHlAkst" + }, + "source": [ + "Visualisation des données et labels" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "id": "8yotZHKgAiV1" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Si seuls x et y sont indiqués, on tire au hasard un numéro d'image et on affiche le label y associé à l'image\n", + "# Si un 2e y, nommé y_pred, est indiqué, alors les deux labels sont affichés côte à côte, afin de pouvoir les comparer\n", + "# Enfin on peut également indiquer l'id de l'image que l'on souhaite visualiser.\n", + "def print_data_localisation(x, y, y_pred=[], id=None, image_size=64):\n", + " if id==None:\n", + " # Tirage aléatoire d'une image dans la base\n", + " num_img = np.random.randint(x.shape[0]-1)\n", + " else:\n", + " num_img = id\n", + "\n", + " img = x[num_img]\n", + " lab = y[num_img]\n", + "\n", + " colors = [\"blue\", \"yellow\", \"red\", \"orange\", \"coral\", \"gold\", \"ivory\", \"fuchsia\", \"purple\", \"cyan\", \"navy\"] # Différentes couleurs pour les différentes classes\n", + " classes = ['MESCHA', 'VEREUR', 'ECUROU', 'PIEBAV', 'SITTOR', 'PINARB', 'MESNOI', 'MESNON', 'MESBLE', 'ROUGOR', 'ACCMOU']\n", + "\n", + " if np.any(y_pred):\n", + " plt.subplot(1, 2, 1)\n", + "\n", + " # Affichage de l'image\n", + " plt.imshow(img)\n", + " # Détermination de la classe\n", + " class_id = np.argmax(lab[5:])\n", + "\n", + " # Détermination des coordonnées de la boîte englobante dans le repère image\n", + " ax = (lab[1]*y_std[1] + y_mean[1]) * image_size\n", + " ay = (lab[2]*y_std[2] + y_mean[2]) * image_size\n", + " width = (lab[3]*y_std[3] + y_mean[3]) * image_size\n", + " height = (lab[4]*y_std[4] + y_mean[4]) * image_size\n", + " #print(\"x: {}, y: {}, w: {}, h:{}\".format(ax,ay,width, height))\n", + " # Détermination des extrema de la boîte englobante\n", + " p_x = [ax-width/2, ax+width/2]\n", + " p_y = [ay-height/2, ay+height/2]\n", + " # Affichage de la boîte englobante, dans la bonne couleur\n", + " plt.plot([p_x[0], p_x[0]],p_y,color=colors[class_id])\n", + " plt.plot([p_x[1], p_x[1]],p_y,color=colors[class_id])\n", + " plt.plot(p_x,[p_y[0],p_y[0]],color=colors[class_id])\n", + " plt.plot(p_x,[p_y[1],p_y[1]],color=colors[class_id])\n", + " plt.title(\"Vérité Terrain : Image {} - {}\".format(num_img, classes[class_id]))\n", + "\n", + " if np.any(y_pred):\n", + " plt.subplot(1, 2, 2)\n", + " # Affichage de l'image\n", + " plt.imshow(img)\n", + " lab = y_pred[num_img]\n", + " # Détermination de la classe\n", + " class_id = np.argmax(lab[5:])\n", + "\n", + " # Détermination des coordonnées de la boîte englobante dans le repère image\n", + " ax = (lab[1]*y_std[1] + y_mean[1]) * image_size\n", + " ay = (lab[2]*y_std[2] + y_mean[2]) * image_size\n", + " width = (lab[3]*y_std[3] + y_mean[3]) * image_size\n", + " height = (lab[4]*y_std[4] + y_mean[4]) * image_size\n", + " #print(\"x: {}, y: {}, w: {}, h:{}\".format(ax,ay,width, height))\n", + " # Détermination des extrema de la boîte englobante\n", + " p_x = [ax-width/2, ax+width/2]\n", + " p_y = [ay-height/2, ay+height/2]\n", + " # Affichage de la boîte englobante, dans la bonne couleur\n", + " plt.plot([p_x[0], p_x[0]],p_y,color=colors[class_id])\n", + " plt.plot([p_x[1], p_x[1]],p_y,color=colors[class_id])\n", + " plt.plot(p_x,[p_y[0],p_y[0]],color=colors[class_id])\n", + " plt.plot(p_x,[p_y[1],p_y[1]],color=colors[class_id])\n", + " plt.title(\"Prédiction : Image {} - {}\".format(num_img, classes[class_id]))\n", + "\n", + " plt.show()\n", + "\n", + "for i in range(10):#x.shape[0]):\n", + " print_data_localisation(x_train, y_train, image_size=IMAGE_SIZE, id=i)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mE9_B3yuR4OH" + }, + "source": [ + "Fonction d'affichage des courbes d'apprentissage et de validation" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "id": "-6O7R3qnCtlN" + }, + "outputs": [], + "source": [ + "def plot_training_analysis(history, metric='loss'): \n", + "\n", + " loss = history.history[metric]\n", + " val_loss = history.history['val_' + metric]\n", + "\n", + " epochs = range(len(loss))\n", + "\n", + " plt.plot(epochs, loss, 'b', linestyle=\"--\",label='Training ' + metric)\n", + " plt.plot(epochs, val_loss, 'g', label='Validation ' + metric)\n", + " plt.title('Training and validation ' + metric)\n", + " plt.legend()\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8F_l_yNlDapa" + }, + "source": [ + "## Travail à faire\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E6D31NGPNyJp" + }, + "source": [ + "
\n", + "
Figure 3: Illustration de l'architecture du réseau à construire.
\n", + "\n", + "Complétez les codes qui vous sont fournis pour obtenir un algorithme de localisation. \n", + "Vous pouvez utiliser n'importe quelle base convolutive de votre choix (**commencez par exemple par réutiliser celle que vous avez implémenté au TP3**), en revanche vous devrez porter une attention particulière à la couche de sortie.\n", + "\n", + "Vous allez en fait produire 3 sorties différentes : une caractérisant la présence d'un objet, une autre fournissant les coordonnées de la boîte englobante, et enfin une dernière effectuant la classification." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "id": "vPOXiJ7hDcbr" + }, + "outputs": [], + "source": [ + "def create_model_localisation(input_shape=(64, 64, 3)):\n", + "\n", + " input_layer = Input(shape=input_shape)\n", + "\n", + " conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(input_layer)\n", + " conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\n", + " pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n", + "\n", + " conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\n", + " conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\n", + " pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n", + "\n", + " flat = Flatten()(pool2)\n", + " dense = Dense(128, activation = 'relu')(flat)\n", + "\n", + " prev_layer = dense\n", + "\n", + " output_p = Dense(1, activation='sigmoid', name='p')(prev_layer) # Sortie caractérisant la présence d'un objet\n", + " output_coord = Dense(4, activation='linear', name='coord')(prev_layer) # Sortie caractérisant les coordonnées de boîte englobante\n", + " output_class = Dense(len(class_labels), activation='softmax', name='classes')(prev_layer) # Sortie caractérisant les probabilités de classe\n", + " \n", + " output= [output_p, output_coord, output_class]\n", + " model = Model(input_layer, output)\n", + "\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MYoVYfQpotjy" + }, + "source": [ + "
\n", + "
Figure 4: Illustration des fonctions de coût à utiliser pour l'entraînement.
\n", + "\n", + "Pour entraîner votre réseau, vous allez donc devoir associer une fonction de coût à chacune des sorties du réseau. La fonction de coût totale sera la somme des trois fonctions de coût précédemment définies, pondérées par des poids définis dans la variable *loss_weights*.\n", + "\n", + "**Prenez le temps de tester différentes valeurs de *loss_weights* en fonction de l'évolution des métriques que vous observerez pendant l'entraînement.**\n", + "\n", + "Vous évaluerez vos résultats de manière qualitative (en affichant les boîtes englobantes prédites et réelles) mais aussi quantitatives grâce aux fonctions définies dans la section précédente. **N'hésitez pas à modifier un peu le paramètre iou_threshold positionné par défaut à 0.5** (une valeur de 0.4 vous permettra d'obtenir de meilleurs résultats !)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "id": "s_ewlCn5Rovm" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_6\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_7 (InputLayer) [(None, 64, 64, 3)] 0 [] \n", + " \n", + " conv2d_22 (Conv2D) (None, 64, 64, 64) 1792 ['input_7[0][0]'] \n", + " \n", + " conv2d_23 (Conv2D) (None, 64, 64, 64) 36928 ['conv2d_22[0][0]'] \n", + " \n", + " max_pooling2d_11 (MaxPooling2D (None, 32, 32, 64) 0 ['conv2d_23[0][0]'] \n", + " ) \n", + " \n", + " conv2d_24 (Conv2D) (None, 32, 32, 128) 73856 ['max_pooling2d_11[0][0]'] \n", + " \n", + " conv2d_25 (Conv2D) (None, 32, 32, 128) 147584 ['conv2d_24[0][0]'] \n", + " \n", + " max_pooling2d_12 (MaxPooling2D (None, 16, 16, 128) 0 ['conv2d_25[0][0]'] \n", + " ) \n", + " \n", + " flatten_3 (Flatten) (None, 32768) 0 ['max_pooling2d_12[0][0]'] \n", + " \n", + " dense_5 (Dense) (None, 128) 4194432 ['flatten_3[0][0]'] \n", + " \n", + " p (Dense) (None, 1) 129 ['dense_5[0][0]'] \n", + " \n", + " coord (Dense) (None, 4) 516 ['dense_5[0][0]'] \n", + " \n", + " classes (Dense) (None, 11) 1419 ['dense_5[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 4,456,656\n", + "Trainable params: 4,456,656\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n", + "Epoch 1/50\n", + "118/118 [==============================] - 15s 104ms/step - loss: 1.5293 - p_loss: 0.0235 - coord_loss: 1.1148 - classes_loss: 0.3911 - p_accuracy: 1.0000 - coord_IoU: 0.4255 - classes_accuracy: 0.1787 - val_loss: 0.8456 - val_p_loss: 0.0010 - val_coord_loss: 0.5451 - val_classes_loss: 0.2995 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5131 - val_classes_accuracy: 0.2810\n", + "Epoch 2/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.7562 - p_loss: 0.0018 - coord_loss: 0.4862 - classes_loss: 0.2681 - p_accuracy: 1.0000 - coord_IoU: 0.5129 - classes_accuracy: 0.4040 - val_loss: 0.7039 - val_p_loss: 8.6872e-04 - val_coord_loss: 0.4485 - val_classes_loss: 0.2545 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5107 - val_classes_accuracy: 0.4048\n", + "Epoch 3/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.6387 - p_loss: 6.8169e-04 - coord_loss: 0.4034 - classes_loss: 0.2346 - p_accuracy: 1.0000 - coord_IoU: 0.5332 - classes_accuracy: 0.5032 - val_loss: 0.6252 - val_p_loss: 0.0010 - val_coord_loss: 0.3901 - val_classes_loss: 0.2341 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5327 - val_classes_accuracy: 0.4762\n", + "Epoch 4/50\n", + "118/118 [==============================] - 10s 87ms/step - loss: 0.5672 - p_loss: 5.0588e-04 - coord_loss: 0.3520 - classes_loss: 0.2147 - p_accuracy: 1.0000 - coord_IoU: 0.5354 - classes_accuracy: 0.5292 - val_loss: 0.5895 - val_p_loss: 2.2709e-04 - val_coord_loss: 0.3826 - val_classes_loss: 0.2066 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5358 - val_classes_accuracy: 0.5000\n", + "Epoch 5/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.4793 - p_loss: 2.0704e-04 - coord_loss: 0.2845 - classes_loss: 0.1946 - p_accuracy: 1.0000 - coord_IoU: 0.5772 - classes_accuracy: 0.5610 - val_loss: 0.5538 - val_p_loss: 2.4165e-04 - val_coord_loss: 0.3575 - val_classes_loss: 0.1961 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5256 - val_classes_accuracy: 0.5286\n", + "Epoch 6/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.4431 - p_loss: 1.6858e-04 - coord_loss: 0.2604 - classes_loss: 0.1825 - p_accuracy: 1.0000 - coord_IoU: 0.6001 - classes_accuracy: 0.5732 - val_loss: 0.5183 - val_p_loss: 1.1366e-04 - val_coord_loss: 0.3382 - val_classes_loss: 0.1800 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5732 - val_classes_accuracy: 0.5333\n", + "Epoch 7/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.3859 - p_loss: 1.0205e-04 - coord_loss: 0.2163 - classes_loss: 0.1695 - p_accuracy: 1.0000 - coord_IoU: 0.6207 - classes_accuracy: 0.5944 - val_loss: 0.5467 - val_p_loss: 1.3684e-04 - val_coord_loss: 0.3726 - val_classes_loss: 0.1740 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5229 - val_classes_accuracy: 0.5762\n", + "Epoch 8/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.3621 - p_loss: 9.5203e-05 - coord_loss: 0.1990 - classes_loss: 0.1630 - p_accuracy: 1.0000 - coord_IoU: 0.6330 - classes_accuracy: 0.6108 - val_loss: 0.4850 - val_p_loss: 6.6984e-05 - val_coord_loss: 0.3162 - val_classes_loss: 0.1688 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5435 - val_classes_accuracy: 0.5476\n", + "Epoch 9/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.3252 - p_loss: 8.0633e-05 - coord_loss: 0.1702 - classes_loss: 0.1549 - p_accuracy: 1.0000 - coord_IoU: 0.6402 - classes_accuracy: 0.6257 - val_loss: 0.4909 - val_p_loss: 5.5699e-05 - val_coord_loss: 0.3324 - val_classes_loss: 0.1584 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5603 - val_classes_accuracy: 0.5762\n", + "Epoch 10/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.2911 - p_loss: 5.4550e-05 - coord_loss: 0.1445 - classes_loss: 0.1466 - p_accuracy: 1.0000 - coord_IoU: 0.6725 - classes_accuracy: 0.6495 - val_loss: 0.4471 - val_p_loss: 4.2831e-05 - val_coord_loss: 0.2914 - val_classes_loss: 0.1556 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5763 - val_classes_accuracy: 0.5714\n", + "Epoch 11/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.2748 - p_loss: 4.0582e-05 - coord_loss: 0.1337 - classes_loss: 0.1411 - p_accuracy: 1.0000 - coord_IoU: 0.6778 - classes_accuracy: 0.6617 - val_loss: 0.5055 - val_p_loss: 2.8487e-05 - val_coord_loss: 0.3523 - val_classes_loss: 0.1532 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5665 - val_classes_accuracy: 0.5571\n", + "Epoch 12/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.2577 - p_loss: 3.7545e-05 - coord_loss: 0.1205 - classes_loss: 0.1371 - p_accuracy: 1.0000 - coord_IoU: 0.6899 - classes_accuracy: 0.6691 - val_loss: 0.4531 - val_p_loss: 1.9267e-05 - val_coord_loss: 0.3026 - val_classes_loss: 0.1505 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5872 - val_classes_accuracy: 0.6048\n", + "Epoch 13/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.2374 - p_loss: 3.0101e-05 - coord_loss: 0.1064 - classes_loss: 0.1309 - p_accuracy: 1.0000 - coord_IoU: 0.7021 - classes_accuracy: 0.6909 - val_loss: 0.4378 - val_p_loss: 2.6943e-05 - val_coord_loss: 0.2922 - val_classes_loss: 0.1455 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5917 - val_classes_accuracy: 0.6238\n", + "Epoch 14/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.2291 - p_loss: 3.2337e-05 - coord_loss: 0.1018 - classes_loss: 0.1273 - p_accuracy: 1.0000 - coord_IoU: 0.7118 - classes_accuracy: 0.7052 - val_loss: 0.4363 - val_p_loss: 2.6610e-05 - val_coord_loss: 0.2911 - val_classes_loss: 0.1452 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5864 - val_classes_accuracy: 0.6238\n", + "Epoch 15/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.2152 - p_loss: 2.7226e-05 - coord_loss: 0.0912 - classes_loss: 0.1239 - p_accuracy: 1.0000 - coord_IoU: 0.7252 - classes_accuracy: 0.7169 - val_loss: 0.4317 - val_p_loss: 3.0600e-05 - val_coord_loss: 0.2906 - val_classes_loss: 0.1411 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5881 - val_classes_accuracy: 0.6381\n", + "Epoch 16/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.2030 - p_loss: 2.6618e-05 - coord_loss: 0.0832 - classes_loss: 0.1198 - p_accuracy: 1.0000 - coord_IoU: 0.7442 - classes_accuracy: 0.7397 - val_loss: 0.4165 - val_p_loss: 1.5708e-05 - val_coord_loss: 0.2784 - val_classes_loss: 0.1381 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5916 - val_classes_accuracy: 0.6238\n", + "Epoch 17/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.2058 - p_loss: 3.1942e-05 - coord_loss: 0.0886 - classes_loss: 0.1172 - p_accuracy: 1.0000 - coord_IoU: 0.7303 - classes_accuracy: 0.7386 - val_loss: 0.4309 - val_p_loss: 6.8455e-05 - val_coord_loss: 0.2904 - val_classes_loss: 0.1405 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5980 - val_classes_accuracy: 0.6190\n", + "Epoch 18/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1907 - p_loss: 2.9955e-05 - coord_loss: 0.0776 - classes_loss: 0.1130 - p_accuracy: 1.0000 - coord_IoU: 0.7450 - classes_accuracy: 0.7609 - val_loss: 0.4112 - val_p_loss: 1.6949e-05 - val_coord_loss: 0.2780 - val_classes_loss: 0.1332 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5937 - val_classes_accuracy: 0.6571\n", + "Epoch 19/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1740 - p_loss: 2.3655e-05 - coord_loss: 0.0654 - classes_loss: 0.1086 - p_accuracy: 1.0000 - coord_IoU: 0.7620 - classes_accuracy: 0.7662 - val_loss: 0.4295 - val_p_loss: 7.7834e-06 - val_coord_loss: 0.2988 - val_classes_loss: 0.1308 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5796 - val_classes_accuracy: 0.6905\n", + "Epoch 20/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.1670 - p_loss: 2.2241e-05 - coord_loss: 0.0623 - classes_loss: 0.1047 - p_accuracy: 1.0000 - coord_IoU: 0.7619 - classes_accuracy: 0.7821 - val_loss: 0.4024 - val_p_loss: 2.5984e-05 - val_coord_loss: 0.2725 - val_classes_loss: 0.1298 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6069 - val_classes_accuracy: 0.6810\n", + "Epoch 21/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1687 - p_loss: 2.1902e-05 - coord_loss: 0.0664 - classes_loss: 0.1023 - p_accuracy: 1.0000 - coord_IoU: 0.7641 - classes_accuracy: 0.7884 - val_loss: 0.4067 - val_p_loss: 9.9703e-06 - val_coord_loss: 0.2822 - val_classes_loss: 0.1245 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5956 - val_classes_accuracy: 0.7000\n", + "Epoch 22/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1523 - p_loss: 1.5818e-05 - coord_loss: 0.0550 - classes_loss: 0.0973 - p_accuracy: 1.0000 - coord_IoU: 0.7689 - classes_accuracy: 0.7990 - val_loss: 0.4198 - val_p_loss: 1.6219e-05 - val_coord_loss: 0.2917 - val_classes_loss: 0.1281 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5777 - val_classes_accuracy: 0.7095\n", + "Epoch 23/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1517 - p_loss: 1.6976e-05 - coord_loss: 0.0574 - classes_loss: 0.0944 - p_accuracy: 1.0000 - coord_IoU: 0.7685 - classes_accuracy: 0.8070 - val_loss: 0.3936 - val_p_loss: 1.5322e-05 - val_coord_loss: 0.2671 - val_classes_loss: 0.1265 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5896 - val_classes_accuracy: 0.7048\n", + "Epoch 24/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1395 - p_loss: 1.3186e-05 - coord_loss: 0.0486 - classes_loss: 0.0909 - p_accuracy: 1.0000 - coord_IoU: 0.7819 - classes_accuracy: 0.8261 - val_loss: 0.4031 - val_p_loss: 1.2730e-05 - val_coord_loss: 0.2800 - val_classes_loss: 0.1230 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6027 - val_classes_accuracy: 0.7524\n", + "Epoch 25/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1389 - p_loss: 1.2866e-05 - coord_loss: 0.0517 - classes_loss: 0.0872 - p_accuracy: 1.0000 - coord_IoU: 0.7672 - classes_accuracy: 0.8399 - val_loss: 0.4047 - val_p_loss: 2.1689e-05 - val_coord_loss: 0.2824 - val_classes_loss: 0.1223 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5614 - val_classes_accuracy: 0.7286\n", + "Epoch 26/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.1326 - p_loss: 1.3932e-05 - coord_loss: 0.0485 - classes_loss: 0.0841 - p_accuracy: 1.0000 - coord_IoU: 0.7796 - classes_accuracy: 0.8478 - val_loss: 0.4003 - val_p_loss: 9.8319e-06 - val_coord_loss: 0.2825 - val_classes_loss: 0.1178 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6046 - val_classes_accuracy: 0.7524\n", + "Epoch 27/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.1250 - p_loss: 1.0477e-05 - coord_loss: 0.0449 - classes_loss: 0.0801 - p_accuracy: 1.0000 - coord_IoU: 0.7908 - classes_accuracy: 0.8579 - val_loss: 0.3921 - val_p_loss: 6.6715e-06 - val_coord_loss: 0.2763 - val_classes_loss: 0.1158 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5834 - val_classes_accuracy: 0.7476\n", + "Epoch 28/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.1239 - p_loss: 1.2673e-05 - coord_loss: 0.0470 - classes_loss: 0.0769 - p_accuracy: 1.0000 - coord_IoU: 0.7836 - classes_accuracy: 0.8680 - val_loss: 0.3843 - val_p_loss: 1.1446e-05 - val_coord_loss: 0.2687 - val_classes_loss: 0.1156 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5992 - val_classes_accuracy: 0.7524\n", + "Epoch 29/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1191 - p_loss: 1.0588e-05 - coord_loss: 0.0452 - classes_loss: 0.0739 - p_accuracy: 1.0000 - coord_IoU: 0.7808 - classes_accuracy: 0.8818 - val_loss: 0.3907 - val_p_loss: 1.1466e-05 - val_coord_loss: 0.2760 - val_classes_loss: 0.1148 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6082 - val_classes_accuracy: 0.7429\n", + "Epoch 30/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.1193 - p_loss: 1.0438e-05 - coord_loss: 0.0486 - classes_loss: 0.0707 - p_accuracy: 1.0000 - coord_IoU: 0.7825 - classes_accuracy: 0.8881 - val_loss: 0.3991 - val_p_loss: 1.1293e-05 - val_coord_loss: 0.2860 - val_classes_loss: 0.1131 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5731 - val_classes_accuracy: 0.7762\n", + "Epoch 31/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.1125 - p_loss: 8.7969e-06 - coord_loss: 0.0445 - classes_loss: 0.0680 - p_accuracy: 1.0000 - coord_IoU: 0.7844 - classes_accuracy: 0.8940 - val_loss: 0.3903 - val_p_loss: 5.9613e-06 - val_coord_loss: 0.2771 - val_classes_loss: 0.1132 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5902 - val_classes_accuracy: 0.7714\n", + "Epoch 32/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1079 - p_loss: 8.5401e-06 - coord_loss: 0.0434 - classes_loss: 0.0644 - p_accuracy: 1.0000 - coord_IoU: 0.7930 - classes_accuracy: 0.9062 - val_loss: 0.3907 - val_p_loss: 3.9751e-06 - val_coord_loss: 0.2794 - val_classes_loss: 0.1113 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6132 - val_classes_accuracy: 0.7810\n", + "Epoch 33/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1041 - p_loss: 6.3921e-06 - coord_loss: 0.0421 - classes_loss: 0.0619 - p_accuracy: 1.0000 - coord_IoU: 0.7967 - classes_accuracy: 0.9168 - val_loss: 0.3941 - val_p_loss: 9.8752e-06 - val_coord_loss: 0.2780 - val_classes_loss: 0.1160 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5973 - val_classes_accuracy: 0.7333\n", + "Epoch 34/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.0981 - p_loss: 8.5369e-06 - coord_loss: 0.0395 - classes_loss: 0.0585 - p_accuracy: 1.0000 - coord_IoU: 0.8023 - classes_accuracy: 0.9226 - val_loss: 0.3971 - val_p_loss: 3.0896e-06 - val_coord_loss: 0.2855 - val_classes_loss: 0.1116 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5973 - val_classes_accuracy: 0.7524\n", + "Epoch 35/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0926 - p_loss: 5.3509e-06 - coord_loss: 0.0377 - classes_loss: 0.0549 - p_accuracy: 1.0000 - coord_IoU: 0.8007 - classes_accuracy: 0.9348 - val_loss: 0.3745 - val_p_loss: 6.3115e-06 - val_coord_loss: 0.2608 - val_classes_loss: 0.1137 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6280 - val_classes_accuracy: 0.7619\n", + "Epoch 36/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0863 - p_loss: 4.5172e-06 - coord_loss: 0.0340 - classes_loss: 0.0523 - p_accuracy: 1.0000 - coord_IoU: 0.8094 - classes_accuracy: 0.9380 - val_loss: 0.3905 - val_p_loss: 1.8461e-06 - val_coord_loss: 0.2769 - val_classes_loss: 0.1136 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5988 - val_classes_accuracy: 0.7476\n", + "Epoch 37/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0808 - p_loss: 3.7010e-06 - coord_loss: 0.0318 - classes_loss: 0.0489 - p_accuracy: 1.0000 - coord_IoU: 0.8073 - classes_accuracy: 0.9507 - val_loss: 0.3798 - val_p_loss: 4.9560e-06 - val_coord_loss: 0.2730 - val_classes_loss: 0.1068 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5770 - val_classes_accuracy: 0.8000\n", + "Epoch 38/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0803 - p_loss: 3.8230e-06 - coord_loss: 0.0338 - classes_loss: 0.0465 - p_accuracy: 1.0000 - coord_IoU: 0.8084 - classes_accuracy: 0.9560 - val_loss: 0.3729 - val_p_loss: 2.7241e-06 - val_coord_loss: 0.2624 - val_classes_loss: 0.1105 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6056 - val_classes_accuracy: 0.7857\n", + "Epoch 39/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0754 - p_loss: 3.1915e-06 - coord_loss: 0.0323 - classes_loss: 0.0431 - p_accuracy: 1.0000 - coord_IoU: 0.8181 - classes_accuracy: 0.9581 - val_loss: 0.3831 - val_p_loss: 4.8808e-06 - val_coord_loss: 0.2767 - val_classes_loss: 0.1064 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6058 - val_classes_accuracy: 0.7952\n", + "Epoch 40/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.0741 - p_loss: 3.3675e-06 - coord_loss: 0.0332 - classes_loss: 0.0409 - p_accuracy: 1.0000 - coord_IoU: 0.8164 - classes_accuracy: 0.9661 - val_loss: 0.3762 - val_p_loss: 2.6131e-06 - val_coord_loss: 0.2668 - val_classes_loss: 0.1094 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6179 - val_classes_accuracy: 0.7952\n", + "Epoch 41/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.0717 - p_loss: 2.9842e-06 - coord_loss: 0.0330 - classes_loss: 0.0388 - p_accuracy: 1.0000 - coord_IoU: 0.8165 - classes_accuracy: 0.9708 - val_loss: 0.3781 - val_p_loss: 2.0115e-06 - val_coord_loss: 0.2716 - val_classes_loss: 0.1064 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6109 - val_classes_accuracy: 0.8048\n", + "Epoch 42/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0667 - p_loss: 2.6782e-06 - coord_loss: 0.0311 - classes_loss: 0.0356 - p_accuracy: 1.0000 - coord_IoU: 0.8156 - classes_accuracy: 0.9719 - val_loss: 0.3922 - val_p_loss: 2.0544e-06 - val_coord_loss: 0.2870 - val_classes_loss: 0.1051 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5667 - val_classes_accuracy: 0.8000\n", + "Epoch 43/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.0719 - p_loss: 2.8606e-06 - coord_loss: 0.0370 - classes_loss: 0.0349 - p_accuracy: 1.0000 - coord_IoU: 0.7998 - classes_accuracy: 0.9756 - val_loss: 0.3827 - val_p_loss: 3.2613e-06 - val_coord_loss: 0.2734 - val_classes_loss: 0.1093 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6156 - val_classes_accuracy: 0.8048\n", + "Epoch 44/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0692 - p_loss: 2.8178e-06 - coord_loss: 0.0368 - classes_loss: 0.0324 - p_accuracy: 1.0000 - coord_IoU: 0.8033 - classes_accuracy: 0.9814 - val_loss: 0.3922 - val_p_loss: 1.6154e-06 - val_coord_loss: 0.2802 - val_classes_loss: 0.1120 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5931 - val_classes_accuracy: 0.7952\n", + "Epoch 45/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0639 - p_loss: 2.7500e-06 - coord_loss: 0.0331 - classes_loss: 0.0308 - p_accuracy: 1.0000 - coord_IoU: 0.8090 - classes_accuracy: 0.9783 - val_loss: 0.3965 - val_p_loss: 1.2561e-06 - val_coord_loss: 0.2813 - val_classes_loss: 0.1152 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5977 - val_classes_accuracy: 0.7810\n", + "Epoch 46/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.0593 - p_loss: 2.0790e-06 - coord_loss: 0.0309 - classes_loss: 0.0284 - p_accuracy: 1.0000 - coord_IoU: 0.8117 - classes_accuracy: 0.9852 - val_loss: 0.3921 - val_p_loss: 9.9823e-07 - val_coord_loss: 0.2795 - val_classes_loss: 0.1125 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5989 - val_classes_accuracy: 0.8048\n", + "Epoch 47/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.0582 - p_loss: 2.2083e-06 - coord_loss: 0.0316 - classes_loss: 0.0266 - p_accuracy: 1.0000 - coord_IoU: 0.8052 - classes_accuracy: 0.9857 - val_loss: 0.4020 - val_p_loss: 1.3179e-06 - val_coord_loss: 0.2929 - val_classes_loss: 0.1091 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6036 - val_classes_accuracy: 0.8048\n", + "Epoch 48/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0585 - p_loss: 2.2486e-06 - coord_loss: 0.0332 - classes_loss: 0.0253 - p_accuracy: 1.0000 - coord_IoU: 0.8175 - classes_accuracy: 0.9878 - val_loss: 0.3783 - val_p_loss: 8.4552e-07 - val_coord_loss: 0.2659 - val_classes_loss: 0.1125 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6222 - val_classes_accuracy: 0.7905\n", + "Epoch 49/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0535 - p_loss: 1.7950e-06 - coord_loss: 0.0302 - classes_loss: 0.0233 - p_accuracy: 1.0000 - coord_IoU: 0.8218 - classes_accuracy: 0.9894 - val_loss: 0.3774 - val_p_loss: 2.5507e-06 - val_coord_loss: 0.2659 - val_classes_loss: 0.1115 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6127 - val_classes_accuracy: 0.7857\n", + "Epoch 50/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.0472 - p_loss: 1.3065e-06 - coord_loss: 0.0260 - classes_loss: 0.0212 - p_accuracy: 1.0000 - coord_IoU: 0.8263 - classes_accuracy: 0.9931 - val_loss: 0.3771 - val_p_loss: 1.0610e-06 - val_coord_loss: 0.2671 - val_classes_loss: 0.1100 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6085 - val_classes_accuracy: 0.8095\n" + ] + } + ], + "source": [ + "batch_size=16\n", + "model = create_model_localisation()\n", + "model.summary()\n", + "opt = Adam(learning_rate=3e-4) \n", + "\n", + "# Ici mettre, dans l'ordre, les fonctions de coût associées à chacune des sorties\n", + "loss=['binary_crossentropy', 'mean_squared_error', 'binary_crossentropy']\n", + "# On va associer une métrique à chaque sortie : l'accuracy pour les deux classifications, \n", + "# et l'IoU définie plus tôt pour la qualité des boîtes englobantes. \n", + "metrics=[ ['accuracy'], [iou()], ['accuracy']]\n", + "loss_weights = [1, 1, 1]\n", + "\n", + "model.compile(loss=loss,\n", + " optimizer=opt,\n", + " metrics=metrics,\n", + " loss_weights=loss_weights\n", + ")\n", + "history = model.fit(\n", + " x_train,\n", + " [y_train[:,0], y_train[:,1:5], y_train[:,5:]],\n", + " epochs=50,\n", + " batch_size=batch_size, \n", + " validation_data=(x_val, [y_val[:,0], y_val[:,1:5], y_val[:,5:]])\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "id": "Jc-sGe8LNzA-" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Analyse des résultats : courbes d'évolution de la fonction de perte, et de l'IoU des boîtes englobantes ainsi que de la précision des classes prédites\n", + "plot_training_analysis(history, metric='loss')\n", + "plot_training_analysis(history, metric='coord_IoU')\n", + "plot_training_analysis(history, metric='classes_accuracy')\n", + "\n", + "# Prédiction des données de test\n", + "y_pred_presence, y_pred_coords, y_pred_classes = model.predict(x_test)\n", + "y_pred = np.zeros(y_test.shape)\n", + "for i in range(y_pred.shape[0]):\n", + " y_pred[i, 0] = y_pred_presence[i]\n", + " y_pred[i, 1:5] = y_pred_coords[i]\n", + " y_pred[i, 5:] = y_pred_classes[i]\n", + "\n", + "# Affichage des résultats sur plusieurs images\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=1, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=2, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=3, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=4, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=5, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=6, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=7, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=8, image_size=IMAGE_SIZE)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "id": "UVU1bR_YEIMs" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "La précision globale est de 56.2%\n", + "\n", + "------------------------------------------\n", + "| Classe | Précision | Rappel | F1-score |\n", + "------------------------------------------\n", + "| MESCHA | 0.14 | 0.09 | 0.11 |\n", + "------------------------------------------\n", + "| VEREUR | 0.52 | 0.59 | 0.55 |\n", + "------------------------------------------\n", + "| ECUROU | 0.86 | 0.86 | 0.86 |\n", + "------------------------------------------\n", + "| PIEBAV | 0.91 | 0.91 | 0.91 |\n", + "------------------------------------------\n", + "| SITTOR | 0.55 | 0.27 | 0.36 |\n", + "------------------------------------------\n", + "| PINARB | 0.71 | 0.77 | 0.74 |\n", + "------------------------------------------\n", + "| MESNOI | 0.36 | 0.57 | 0.44 |\n", + "------------------------------------------\n", + "| MESNON | 0.29 | 0.36 | 0.32 |\n", + "------------------------------------------\n", + "| MESBLE | 0.30 | 0.27 | 0.29 |\n", + "------------------------------------------\n", + "| ROUGOR | 0.71 | 0.71 | 0.71 |\n", + "------------------------------------------\n", + "| ACCMOU | 0.71 | 0.77 | 0.74 |\n", + "------------------------------------------\n" + ] + } + ], + "source": [ + "class_res, accuracy = global_accuracy(y_test, y_pred)\n", + "\n", + "print(f\"La précision globale est de {100 * accuracy:.1f}%\")\n", + "\n", + "print()\n", + "print(\"------------------------------------------\")\n", + "print(\"| Classe | Précision | Rappel | F1-score |\")\n", + "print(\"------------------------------------------\")\n", + "for i in range(num_classes):\n", + " print(f\"| {class_labels[i]:7s}| {class_res[i]['Precision']:.2f} | {class_res[i]['Rappel']:.2f} | {class_res[i]['F-score']:.2f} |\")\n", + " print(\"------------------------------------------\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eiFO6fBns-OI" + }, + "source": [ + "En pratique, il est délicat de trouver une bonne combinaison des fonctions de perte tel que vous l'avez fait sur les cellules précédentes. L'entropie croisée et l'erreur quadratique moyenne donnent des valeurs trop différentes pour être combinables efficacement.\n", + "\n", + "Une variante, peut-être plus simple à faire fonctionner, est d'utiliser uniquement l'erreur quadratique moyenne comme perte pour toutes les sorties. C'est cette variante qui est implémentée dans l'algorithme YOLO, dont nous implémenterons une version simplifiée dans le prochain TP.\n", + "Testez cette solution ci-dessous. Comme sur l'exercice précédent, n'hésitez pas à faire varier le poids des différents éléments de la fonction de coût." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "id": "ttGCeqz0Dc3y" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "118/118 [==============================] - 11s 88ms/step - loss: 1.4310 - p_loss: 0.0151 - coord_loss: 1.3283 - classes_loss: 0.0876 - p_accuracy: 0.9989 - coord_IoU: 0.4143 - classes_accuracy: 0.0859 - val_loss: 0.6519 - val_p_loss: 5.9735e-04 - val_coord_loss: 0.5653 - val_classes_loss: 0.0860 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.4729 - val_classes_accuracy: 0.1286\n", + "Epoch 2/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.5781 - p_loss: 0.0011 - coord_loss: 0.4942 - classes_loss: 0.0828 - p_accuracy: 1.0000 - coord_IoU: 0.5069 - classes_accuracy: 0.1729 - val_loss: 0.5192 - val_p_loss: 5.8288e-04 - val_coord_loss: 0.4374 - val_classes_loss: 0.0812 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5086 - val_classes_accuracy: 0.2143\n", + "Epoch 3/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.4808 - p_loss: 7.0716e-04 - coord_loss: 0.4026 - classes_loss: 0.0774 - p_accuracy: 1.0000 - coord_IoU: 0.5380 - classes_accuracy: 0.2964 - val_loss: 0.4908 - val_p_loss: 9.9283e-04 - val_coord_loss: 0.4128 - val_classes_loss: 0.0770 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5183 - val_classes_accuracy: 0.3143\n", + "Epoch 4/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.4210 - p_loss: 4.7277e-04 - coord_loss: 0.3479 - classes_loss: 0.0726 - p_accuracy: 1.0000 - coord_IoU: 0.5572 - classes_accuracy: 0.3881 - val_loss: 0.4603 - val_p_loss: 2.6880e-04 - val_coord_loss: 0.3883 - val_classes_loss: 0.0718 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.4756 - val_classes_accuracy: 0.3476\n", + "Epoch 5/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.3553 - p_loss: 2.5311e-04 - coord_loss: 0.2874 - classes_loss: 0.0676 - p_accuracy: 1.0000 - coord_IoU: 0.5813 - classes_accuracy: 0.4316 - val_loss: 0.3913 - val_p_loss: 1.5167e-04 - val_coord_loss: 0.3239 - val_classes_loss: 0.0672 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5942 - val_classes_accuracy: 0.3952\n", + "Epoch 6/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.3121 - p_loss: 2.1208e-04 - coord_loss: 0.2483 - classes_loss: 0.0636 - p_accuracy: 1.0000 - coord_IoU: 0.6031 - classes_accuracy: 0.4708 - val_loss: 0.3978 - val_p_loss: 1.8132e-04 - val_coord_loss: 0.3330 - val_classes_loss: 0.0647 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5796 - val_classes_accuracy: 0.4429\n", + "Epoch 7/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.2712 - p_loss: 1.4645e-04 - coord_loss: 0.2110 - classes_loss: 0.0600 - p_accuracy: 1.0000 - coord_IoU: 0.6217 - classes_accuracy: 0.5048 - val_loss: 0.3656 - val_p_loss: 9.9904e-05 - val_coord_loss: 0.3042 - val_classes_loss: 0.0613 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6001 - val_classes_accuracy: 0.4762\n", + "Epoch 8/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.2488 - p_loss: 1.8330e-04 - coord_loss: 0.1911 - classes_loss: 0.0576 - p_accuracy: 1.0000 - coord_IoU: 0.6303 - classes_accuracy: 0.5223 - val_loss: 0.3516 - val_p_loss: 2.8725e-04 - val_coord_loss: 0.2916 - val_classes_loss: 0.0597 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6061 - val_classes_accuracy: 0.4952\n", + "Epoch 9/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.2226 - p_loss: 1.1546e-04 - coord_loss: 0.1673 - classes_loss: 0.0552 - p_accuracy: 1.0000 - coord_IoU: 0.6481 - classes_accuracy: 0.5451 - val_loss: 0.3517 - val_p_loss: 9.4986e-05 - val_coord_loss: 0.2937 - val_classes_loss: 0.0579 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5699 - val_classes_accuracy: 0.5048\n", + "Epoch 10/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.2028 - p_loss: 1.1011e-04 - coord_loss: 0.1493 - classes_loss: 0.0533 - p_accuracy: 1.0000 - coord_IoU: 0.6572 - classes_accuracy: 0.5551 - val_loss: 0.3346 - val_p_loss: 6.5773e-05 - val_coord_loss: 0.2787 - val_classes_loss: 0.0558 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6171 - val_classes_accuracy: 0.5571\n", + "Epoch 11/50\n", + "118/118 [==============================] - 10s 85ms/step - loss: 0.1809 - p_loss: 7.7123e-05 - coord_loss: 0.1293 - classes_loss: 0.0515 - p_accuracy: 1.0000 - coord_IoU: 0.6801 - classes_accuracy: 0.5870 - val_loss: 0.3483 - val_p_loss: 3.8490e-05 - val_coord_loss: 0.2935 - val_classes_loss: 0.0547 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5973 - val_classes_accuracy: 0.5333\n", + "Epoch 12/50\n", + "118/118 [==============================] - 10s 86ms/step - loss: 0.1652 - p_loss: 7.5254e-05 - coord_loss: 0.1155 - classes_loss: 0.0496 - p_accuracy: 1.0000 - coord_IoU: 0.6897 - classes_accuracy: 0.5960 - val_loss: 0.3261 - val_p_loss: 7.5853e-05 - val_coord_loss: 0.2731 - val_classes_loss: 0.0529 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6050 - val_classes_accuracy: 0.5619\n", + "Epoch 13/50\n", + " 86/118 [====================>.........] - ETA: 2s - loss: 0.1487 - p_loss: 5.7952e-05 - coord_loss: 0.0999 - classes_loss: 0.0488 - p_accuracy: 1.0000 - coord_IoU: 0.6981 - classes_accuracy: 0.6054" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/laurent/Documents/Cours/ENSEEIHT/S8 - Réseau Profond/TP6.ipynb Cell 33'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m loss_weights \u001b[39m=\u001b[39m [\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m]\n\u001b[1;32m 9\u001b[0m model\u001b[39m.\u001b[39mcompile(loss\u001b[39m=\u001b[39mloss,\n\u001b[1;32m 10\u001b[0m optimizer\u001b[39m=\u001b[39mopt,\n\u001b[1;32m 11\u001b[0m metrics\u001b[39m=\u001b[39mmetrics,\n\u001b[1;32m 12\u001b[0m loss_weights\u001b[39m=\u001b[39mloss_weights\n\u001b[1;32m 13\u001b[0m )\n\u001b[0;32m---> 14\u001b[0m history \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit(x_train, [y_train[:,\u001b[39m0\u001b[39;49m], y_train[:,\u001b[39m1\u001b[39;49m:\u001b[39m5\u001b[39;49m], y_train[:,\u001b[39m5\u001b[39;49m:]],\n\u001b[1;32m 15\u001b[0m epochs\u001b[39m=\u001b[39;49m\u001b[39m50\u001b[39;49m,\n\u001b[1;32m 16\u001b[0m batch_size\u001b[39m=\u001b[39;49mbatch_size, \n\u001b[1;32m 17\u001b[0m validation_data\u001b[39m=\u001b[39;49m(x_val, [y_val[:,\u001b[39m0\u001b[39;49m], y_val[:,\u001b[39m1\u001b[39;49m:\u001b[39m5\u001b[39;49m], y_val[:,\u001b[39m5\u001b[39;49m:]]))\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 64\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 65\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 66\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py:1389\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1387\u001b[0m logs \u001b[39m=\u001b[39m tmp_logs \u001b[39m# No error, now safe to assign to logs.\u001b[39;00m\n\u001b[1;32m 1388\u001b[0m end_step \u001b[39m=\u001b[39m step \u001b[39m+\u001b[39m data_handler\u001b[39m.\u001b[39mstep_increment\n\u001b[0;32m-> 1389\u001b[0m callbacks\u001b[39m.\u001b[39;49mon_train_batch_end(end_step, logs)\n\u001b[1;32m 1390\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstop_training:\n\u001b[1;32m 1391\u001b[0m \u001b[39mbreak\u001b[39;00m\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:438\u001b[0m, in \u001b[0;36mCallbackList.on_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[39m\"\"\"Calls the `on_train_batch_end` methods of its callbacks.\u001b[39;00m\n\u001b[1;32m 432\u001b[0m \n\u001b[1;32m 433\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[1;32m 434\u001b[0m \u001b[39m batch: Integer, index of batch within the current epoch.\u001b[39;00m\n\u001b[1;32m 435\u001b[0m \u001b[39m logs: Dict. Aggregated metric results up until this batch.\u001b[39;00m\n\u001b[1;32m 436\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 437\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_call_train_batch_hooks:\n\u001b[0;32m--> 438\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_hook(ModeKeys\u001b[39m.\u001b[39;49mTRAIN, \u001b[39m'\u001b[39;49m\u001b[39mend\u001b[39;49m\u001b[39m'\u001b[39;49m, batch, logs\u001b[39m=\u001b[39;49mlogs)\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:297\u001b[0m, in \u001b[0;36mCallbackList._call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call_batch_begin_hook(mode, batch, logs)\n\u001b[1;32m 296\u001b[0m \u001b[39melif\u001b[39;00m hook \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mend\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m--> 297\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_end_hook(mode, batch, logs)\n\u001b[1;32m 298\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 299\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 300\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mUnrecognized hook: \u001b[39m\u001b[39m{\u001b[39;00mhook\u001b[39m}\u001b[39;00m\u001b[39m. Expected values are [\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbegin\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\u001b[39mend\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m'\u001b[39m)\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:318\u001b[0m, in \u001b[0;36mCallbackList._call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m 315\u001b[0m batch_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_start_time\n\u001b[1;32m 316\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_times\u001b[39m.\u001b[39mappend(batch_time)\n\u001b[0;32m--> 318\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_hook_helper(hook_name, batch, logs)\n\u001b[1;32m 320\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_times) \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_batches_for_timing_check:\n\u001b[1;32m 321\u001b[0m end_hook_name \u001b[39m=\u001b[39m hook_name\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:356\u001b[0m, in \u001b[0;36mCallbackList._call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[39mfor\u001b[39;00m callback \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallbacks:\n\u001b[1;32m 355\u001b[0m hook \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(callback, hook_name)\n\u001b[0;32m--> 356\u001b[0m hook(batch, logs)\n\u001b[1;32m 358\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_timing:\n\u001b[1;32m 359\u001b[0m \u001b[39mif\u001b[39;00m hook_name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_hook_times:\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:1034\u001b[0m, in \u001b[0;36mProgbarLogger.on_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m 1033\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mon_train_batch_end\u001b[39m(\u001b[39mself\u001b[39m, batch, logs\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m-> 1034\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_batch_update_progbar(batch, logs)\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:1106\u001b[0m, in \u001b[0;36mProgbarLogger._batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m 1102\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mseen \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m add_seen\n\u001b[1;32m 1104\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 1105\u001b[0m \u001b[39m# Only block async when verbose = 1.\u001b[39;00m\n\u001b[0;32m-> 1106\u001b[0m logs \u001b[39m=\u001b[39m tf_utils\u001b[39m.\u001b[39;49msync_to_numpy_or_python_type(logs)\n\u001b[1;32m 1107\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprogbar\u001b[39m.\u001b[39mupdate(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mseen, \u001b[39mlist\u001b[39m(logs\u001b[39m.\u001b[39mitems()), finalize\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py:563\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m 560\u001b[0m \u001b[39mreturn\u001b[39;00m t\n\u001b[1;32m 561\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mitem() \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39mndim(t) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39melse\u001b[39;00m t\n\u001b[0;32m--> 563\u001b[0m \u001b[39mreturn\u001b[39;00m tf\u001b[39m.\u001b[39;49mnest\u001b[39m.\u001b[39;49mmap_structure(_to_single_numpy_or_python_type, tensors)\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py:914\u001b[0m, in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m 910\u001b[0m flat_structure \u001b[39m=\u001b[39m (flatten(s, expand_composites) \u001b[39mfor\u001b[39;00m s \u001b[39min\u001b[39;00m structure)\n\u001b[1;32m 911\u001b[0m entries \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mflat_structure)\n\u001b[1;32m 913\u001b[0m \u001b[39mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> 914\u001b[0m structure[\u001b[39m0\u001b[39m], [func(\u001b[39m*\u001b[39mx) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m entries],\n\u001b[1;32m 915\u001b[0m expand_composites\u001b[39m=\u001b[39mexpand_composites)\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py:914\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 910\u001b[0m flat_structure \u001b[39m=\u001b[39m (flatten(s, expand_composites) \u001b[39mfor\u001b[39;00m s \u001b[39min\u001b[39;00m structure)\n\u001b[1;32m 911\u001b[0m entries \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mflat_structure)\n\u001b[1;32m 913\u001b[0m \u001b[39mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> 914\u001b[0m structure[\u001b[39m0\u001b[39m], [func(\u001b[39m*\u001b[39;49mx) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m entries],\n\u001b[1;32m 915\u001b[0m expand_composites\u001b[39m=\u001b[39mexpand_composites)\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py:557\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type.._to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 554\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_to_single_numpy_or_python_type\u001b[39m(t):\n\u001b[1;32m 555\u001b[0m \u001b[39m# Don't turn ragged or sparse tensors to NumPy.\u001b[39;00m\n\u001b[1;32m 556\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(t, tf\u001b[39m.\u001b[39mTensor):\n\u001b[0;32m--> 557\u001b[0m t \u001b[39m=\u001b[39m t\u001b[39m.\u001b[39;49mnumpy()\n\u001b[1;32m 558\u001b[0m \u001b[39m# Strings, ragged and sparse tensors don't have .item(). Return them as-is.\u001b[39;00m\n\u001b[1;32m 559\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(t, (np\u001b[39m.\u001b[39mndarray, np\u001b[39m.\u001b[39mgeneric)):\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1223\u001b[0m, in \u001b[0;36m_EagerTensorBase.numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1200\u001b[0m \u001b[39m\"\"\"Copy of the contents of this Tensor into a NumPy array or scalar.\u001b[39;00m\n\u001b[1;32m 1201\u001b[0m \n\u001b[1;32m 1202\u001b[0m \u001b[39mUnlike NumPy arrays, Tensors are immutable, so this method has to copy\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1220\u001b[0m \u001b[39m NumPy dtype.\u001b[39;00m\n\u001b[1;32m 1221\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1222\u001b[0m \u001b[39m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[39;00m\n\u001b[0;32m-> 1223\u001b[0m maybe_arr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_numpy() \u001b[39m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m 1224\u001b[0m \u001b[39mreturn\u001b[39;00m maybe_arr\u001b[39m.\u001b[39mcopy() \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(maybe_arr, np\u001b[39m.\u001b[39mndarray) \u001b[39melse\u001b[39;00m maybe_arr\n", + "File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1189\u001b[0m, in \u001b[0;36m_EagerTensorBase._numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1187\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_numpy\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m 1188\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1189\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_numpy_internal()\n\u001b[1;32m 1190\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e: \u001b[39m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m 1191\u001b[0m \u001b[39mraise\u001b[39;00m core\u001b[39m.\u001b[39m_status_to_exception(e) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "batch_size=16\n", + "model = create_model_localisation()\n", + "opt = Adam(learning_rate=3e-4) \n", + "\n", + "loss = ['mean_squared_error', \"mean_squared_error\", 'mean_squared_error']\n", + "metrics =[ ['accuracy'], [iou()], ['accuracy']]\n", + "loss_weights = [1, 1, 1]\n", + "\n", + "model.compile(loss=loss,\n", + " optimizer=opt,\n", + " metrics=metrics,\n", + " loss_weights=loss_weights\n", + " )\n", + "history = model.fit(x_train, [y_train[:,0], y_train[:,1:5], y_train[:,5:]],\n", + " epochs=50,\n", + " batch_size=batch_size, \n", + " validation_data=(x_val, [y_val[:,0], y_val[:,1:5], y_val[:,5:]]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gKlN9-clJMrI" + }, + "outputs": [], + "source": [ + "# Analyse des résultats : courbes d'évolution de la fonction de perte, et de l'IoU des boîtes englobantes ainsi que de la précision des classes prédites\n", + "plot_training_analysis(history, metric='loss')\n", + "plot_training_analysis(history, metric='coord_IoU')\n", + "plot_training_analysis(history, metric='classes_accuracy')\n", + "\n", + "# Prédiction des données de test\n", + "y_pred_presence, y_pred_coords, y_pred_classes = model.predict(x_test)\n", + "y_pred = np.zeros(y_test.shape)\n", + "for i in range(y_pred.shape[0]):\n", + " y_pred[i, 0] = y_pred_presence[i]\n", + " y_pred[i, 1:5] = y_pred_coords[i]\n", + " y_pred[i, 5:] = y_pred_classes[i]\n", + "\n", + "# Affichage des résultats sur plusieurs images\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=1, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=2, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=3, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=4, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=5, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=6, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=7, image_size=IMAGE_SIZE)\n", + "print_data_localisation(x_test, y_test, y_pred = y_pred, id=8, image_size=IMAGE_SIZE)\n", + "\n", + "\n", + "class_res, accuracy = global_accuracy(y_test, y_pred, iou_thres=0.4)\n", + "\n", + "print(f\"La précision globale est de {100 * accuracy:.1f}%\")\n", + "\n", + "print()\n", + "print(\"------------------------------------------\")\n", + "print(\"| Classe | Précision | Rappel | F1-score |\")\n", + "print(\"------------------------------------------\")\n", + "for i in range(num_classes):\n", + " print(f\"| {class_labels[i]:7s}| {class_res[i]['Precision']:.2f} | {class_res[i]['Rappel']:.2f} | {class_res[i]['F-score']:.2f} |\")\n", + " print(\"------------------------------------------\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "stQpmnmAt_bf" + }, + "source": [ + "Compte-tenu de la taille réduite de la base de données, les résultats ne sont pas mal du tout ! On observe quelques confusions entre certaines classes mais les prédictions sont souvent intéressantes.\n", + "\n", + "Il devrait cependant subsister un fort surapprentissage à ce stade. Comme nous l'avons vu dans de précédents TPs, vous avez plusieurs possibilités qui s'offrent à vous pour le corriger : \n", + "\n", + "* Augmentation de la base de données. Vous pouvez pour cela vous appuyer sur l'exemple fourni en TP précédent, avec une classe *Sequence* et l'utilisation de la librairie *Albumentation*. Je vous fournis une Sequence qui fonctionne ci-dessous.\n", + "* Utilisation de *transfer learning* : partant d'un réseau entraîné sur ImageNet (qui contient de nombreuses classes d'animaux), vous bénéficieriez de filtres très généraux qui aiderait à limiter le surapprentissage. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3Jp6jwZidJgx" + }, + "source": [ + "# Code fourni pour vous aider à mettre en place l'augmentation de données" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jKbK2AEkUF3M" + }, + "source": [ + "Il y a des problèmes avec les versions d'openCV et d'Albumentation, voici comment les résoudre :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eTq-d6uadJgx" + }, + "outputs": [], + "source": [ + "!pip uninstall opencv-python-headless==4.5.5.62\n", + "!pip install opencv-python-headless==4.1.2.30\n", + "!pip install -q -U albumentations\n", + "!echo \"$(pip freeze | grep albumentations) is successfully installed\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B5WuA43ZULxP" + }, + "source": [ + "Définition des augmentations (uniquement colorimétriques ici)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WS5wxAXvdJgx" + }, + "outputs": [], + "source": [ + "from albumentations import (Compose, RandomBrightness, RandomContrast, RandomGamma, ShiftScaleRotate, RandomSizedBBoxSafeCrop)\n", + "import albumentations as A\n", + "\n", + "AUGMENTATIONS_TRAIN = Compose([\n", + " RandomContrast(limit=0.2, p=0.5),\n", + " RandomGamma(gamma_limit=(80, 120), p=0.5),\n", + " RandomBrightness(limit=0.2, p=0.5)\n", + "], bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sND_uaWPURfE" + }, + "source": [ + "Une sequence qui permet d'implanter l'augmentation de données :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x9GXnEyYdJgy" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.utils import Sequence\n", + "\n", + "class MangeoireSequence(Sequence):\n", + " # Initialisation de la séquence avec différents paramètres\n", + " def __init__(self, x_set, y_set, batch_size,augmentations):\n", + " self.x, self.y = x_set, y_set\n", + " self.classes = ['MESCHA', 'VEREUR', 'ECUROU', 'PIEBAV', 'SITTOR', 'PINARB', 'MESNOI', 'MESNON', 'MESBLE', 'ROUGOR', 'ACCMOU']\n", + " self.batch_size = batch_size\n", + " self.augment = augmentations\n", + " self.indices1 = np.arange(x_set.shape[0]) \n", + " np.random.shuffle(self.indices1) # Les indices permettent d'accéder\n", + " # aux données et sont randomisés à chaque epoch pour varier la composition\n", + " # des batches au cours de l'entraînement\n", + "\n", + " # Fonction calculant le nombre de pas de descente du gradient par epoch\n", + " def __len__(self):\n", + " return int(np.ceil(len(self.x) / float(self.batch_size)))\n", + "\n", + " # Il y a des problèmes d'arrondi dans les conversions de boîtes englobantes \n", + " # internes à la librairie Albumentations\n", + " # Pour les contourner, si les boîtes sont trop proches des bords, on les érode un peu\n", + " def erode_bounding_box(self, box):\n", + " epsilon = 0.01\n", + " \n", + " xmin = max(box[0] - box[2]/2, epsilon)\n", + " ymin = max(box[1] - box[3]/2, epsilon)\n", + " xmax = min(box[0] + box[2]/2, 1-epsilon)\n", + " ymax = min(box[1] + box[3]/2, 1-epsilon)\n", + " \n", + " cx = xmin + (xmax - xmin)/2\n", + " cy = ymin + (ymax - ymin)/2\n", + " width = xmax - xmin\n", + " height = ymax - ymin\n", + " \n", + " return np.array([cx, cy, width, height])\n", + " \n", + " # Application de l'augmentation de données à chaque image du batch et aux\n", + " # boîtes englobantes associées\n", + " def apply_augmentation(self, bx, by):\n", + "\n", + " batch_x = np.zeros((bx.shape[0], IMAGE_SIZE, IMAGE_SIZE, 3))\n", + " batch_y = by\n", + " \n", + " # Pour chaque image du batch\n", + " for i in range(len(bx)):\n", + " bboxes = []\n", + " box = by[i,1:5]\n", + " # Dénormalisation des coordonnées de boites englobantes\n", + " box[0] = (box[0]*y_std[1] + y_mean[1])\n", + " box[1] = (box[1]*y_std[2] + y_mean[2])\n", + " box[2] = (box[2]*y_std[3] + y_mean[3])\n", + " box[3] = (box[3]*y_std[4] + y_mean[4])\n", + " box = self.erode_bounding_box(box)\n", + " bboxes.append(box)\n", + " \n", + " class_labels = []\n", + " class_id = np.argmax(by[i, 5:])\n", + " class_labels.append(self.classes[class_id])\n", + "\n", + " img = bx[i]\n", + "\n", + " # Application de l'augmentation à l'image et aux masques\n", + " transformed = self.augment(image=img.astype('float32'), bboxes=bboxes, class_labels=class_labels)\n", + " batch_x[i] = transformed['image']\n", + " batch_y_transformed = transformed['bboxes']\n", + " \n", + " # Renormalisation des coordonnées de boîte englobante transformée\n", + " batch_y[i, 1] = (batch_y_transformed[0][0] - y_mean[1])/y_std[1]\n", + " batch_y[i, 2] = (batch_y_transformed[0][1] - y_mean[2])/y_std[2]\n", + " batch_y[i, 3] = (batch_y_transformed[0][2] - y_mean[3])/y_std[3]\n", + " batch_y[i, 4] = (batch_y_transformed[0][3] - y_mean[4])/y_std[4]\n", + "\n", + " return batch_x, batch_y\n", + "\n", + " # Fonction appelée à chaque nouveau batch : sélection et augmentation des données\n", + " def __getitem__(self, idx):\n", + " batch_x = self.x[self.indices1[idx * self.batch_size:(idx + 1) * self.batch_size]]\n", + " batch_y = self.y[self.indices1[idx * self.batch_size:(idx + 1) * self.batch_size]]\n", + " batch_x, batch_y = self.apply_augmentation(batch_x, batch_y)\n", + " \n", + " batch_y = np.array(batch_y)\n", + " return np.array(batch_x), [batch_y[:,0], batch_y[:,1:5], batch_y[:,5:]]\n", + "\n", + " # Fonction appelée à la fin d'un epoch ; on randomise les indices d'accès aux données\n", + " def on_epoch_end(self):\n", + " np.random.shuffle(self.indices1)\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BUbm6gTMdJgy" + }, + "outputs": [], + "source": [ + "# Instanciation d'une Sequence\n", + "train_gen = MangeoireSequence(x_train, y_train, 16, augmentations=AUGMENTATIONS_TRAIN)\n", + "\n", + "# Pour tester la séquence, nous sélectionnons les éléments du premier batch et les affichons\n", + "batch_x, batch_y = train_gen.__getitem__(0)\n", + "\n", + "y_batch = np.zeros((batch_y[0].shape[0],1+4+num_classes))\n", + "\n", + "for i in range(batch_y[0].shape[0]):\n", + " y_batch[i, 0] = batch_y[0][i]\n", + " y_batch[i, 1:5] = batch_y[1][i]\n", + " y_batch[i, 5:] = batch_y[2][i]\n", + "\n", + "print_data_localisation(batch_x, y_batch, image_size=IMAGE_SIZE)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "TP6 - Localisation d'objet - Sujet.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3.10.6 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "vscode": { + "interpreter": { + "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/TP7.ipynb b/TP7.ipynb new file mode 100644 index 0000000..bd1a6f9 --- /dev/null +++ b/TP7.ipynb @@ -0,0 +1,1553 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "CVz03XXMKMoz" + }, + "source": [ + "# Détection d'objet : version simplifiée de YOLO\n", + "\n", + "
\n", + "
Pipeline de l'algorithme YOLO ([Redmon 2016])
\n", + "\n", + "Dans ce TP, nous allons tenter d'aller un peu plus loin que le TP précédent en considérant le problème plus complexe de la détection d'objet, c'est-à-dire de la localisation et la classification conjointe de tous les objets dans l'image ; pour cela nous allons implémenter une version simplifiée de YOLO. Cette version est considérée simplifiée car ne reprenant pas l'intégralité des éléments décrite dans l'article de Redmon (par exemple, sur le choix de l'optimiseur). Une des simplifications principales est également que nous ne considérerons **qu'un objet par cellule**.\n", + "\n", + "Pour rappel, l'idée de YOLO est de découper l'image en une grille de cellules et de réaliser une prédiction de plusieurs boîtes englobantes ainsi qu'une classification par cellule. La vidéo de la cellule suivante rappelle les concepts vus en cours sur YOLO et la détection d'objet en général.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "H-8wH0b0jquq" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import IFrame\n", + "IFrame(\"https://video.polymny.studio/?v=012cd29c-db98-458f-80d3-6cc5c1da9be3/\", width=640, height=360)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Nsjid8knt6GZ" + }, + "source": [ + "Récupération des données" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "wvf5aZetUIE0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fatal: le chemin de destination 'wildlife' existe déjà et n'est pas un répertoire vide.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/axelcarlier/wildlife.git" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MZu_3SudL_ll" + }, + "source": [ + "\n", + "## Fonctions utiles" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Hw4t0vv5uAYv" + }, + "source": [ + "Définition des différentes variables utiles pour la suite" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "79AGCFLvMUlw" + }, + "outputs": [], + "source": [ + "IMAGE_SIZE = 64 # Dimension des images en entrée du réseau\n", + "CELL_PER_DIM = 8 # Nombre de cellules en largeur et en hauteur\n", + "BOX_PER_CELL = 1 # Nombre d'objets par cellule\n", + "NB_CLASSES = 4 # Nombre de classes du problème\n", + "PIX_PER_CELL = round(IMAGE_SIZE/CELL_PER_DIM)\n", + "\n", + "CLASS_LABELS = ['buffalo', 'elephant', 'rhino', 'zebra']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ynBA-Pn5RUmY" + }, + "source": [ + "### Chargement des données" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qyJTpIjFTGii" + }, + "source": [ + "On charge les images dans la dimension demandée, dans un tenseur $x$. Pour les labels, on ne les structure pas directement dans le format YOLO, mais on les place dans une liste de liste de listes : la longueur de la liste parente est celle du nombre d'images de la base, celle de la liste intermédiaire est celle du nombre d'objets d'une image donnée, et enfin la liste de plus bas niveau a une longueur 5 et contiendra les coordonnées de boîte englobante et les labels de classe associés. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "gStC1HJXTFe8" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_918015/1607050384.py:40: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.\n", + " img = img.resize((IMAGE_SIZE, IMAGE_SIZE), Image.ANTIALIAS)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "import math\n", + "\n", + "\n", + "import PIL\n", + "from PIL import Image\n", + "import glob, os, sys\n", + "\n", + "for file in glob.glob(\"*.txt\"):\n", + " print(file)\n", + "\n", + "def load_data_detection(ds_path):\n", + " \n", + " y_paths = []\n", + " # Détermination du nombre d'images total\n", + " for c in CLASS_LABELS:\n", + " path = ds_path + c + '/'\n", + " for file in os.listdir(path):\n", + " if file.endswith('.txt'):\n", + " y_paths.append(os.path.join(path, file))\n", + "\n", + " dataset_size = len(y_paths)\n", + " \n", + " # Préparation des structures de données pour x et y\n", + " x = np.zeros((dataset_size, IMAGE_SIZE, IMAGE_SIZE, 3))\n", + " y = []\n", + "\n", + " for i in range(len(y_paths)):\n", + " text_path = y_paths[i]\n", + " img_path = text_path[:-3] + 'jpg'\n", + " \n", + " if not os.path.exists(img_path):\n", + " img_path = text_path[:-3] + 'JPG'\n", + "\n", + " # Lecture de l'image : on va remplir la variable x\n", + " # Lecture de l'image\n", + " img = Image.open(img_path)\n", + " # Mise à l'échelle de l'image\n", + " img = img.resize((IMAGE_SIZE, IMAGE_SIZE), Image.ANTIALIAS)\n", + " # Remplissage de la variable x\n", + " x[i] = np.asarray(img, dtype=np.int32)\n", + "\n", + " # Texte : coordonnées de boîtes englobantes pour remplir y\n", + " boxes = []\n", + " # Texte : coordonnées de boîtes englobantes pour remplir y\n", + " text_file = open(text_path, \"r\")\n", + " # Récupération des lignes du fichier texte\n", + " rows = text_file.read().split('\\n')\n", + " # Parcours de chaque ligne\n", + " for row in rows:\n", + " if row != '':\n", + " # Séparation des différentes informations\n", + " row = list(row.split(' '))\n", + " box = []\n", + " # réorganisation : les 4 coordonnées de boîte englobantes (castées en flottants) d'abord\n", + " for r in row[1:]:\n", + " box.append(float(r))\n", + " \n", + " box_normal = [box[0]-box[2]/2, box[1]-box[3]/2, box[0]+box[2]/2, box[1]+box[3]/2] \n", + "\n", + " # Puis le label de classe (casté en entier) à la suite\n", + " box.append(int(row[0]))\n", + " boxes.append(box)\n", + "\n", + " y.append(boxes)\n", + " return x, y\n", + "\n", + "# Chemin vers la base de données\n", + "ds_path = \"./wildlife/\" \n", + "x, y = load_data_detection(ds_path)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1HQNMBwSDhXp" + }, + "source": [ + "### Affichage des données\n", + "\n", + "Le code ci-dessous vous permettra d'afficher les images ainsi que leurs boîtes englobantes associées. On peut spécifier l'id d'une image en particulier ou, si l'on en spécifie pas, visualiser une image aléatoire." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "gK0Xl-CgDjjd" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.special import softmax\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def print_data_detection(x, y, id=None, classes=CLASS_LABELS, image_size=IMAGE_SIZE):\n", + " if id==None:\n", + " # Tirage aléatoire d'une image dans la base\n", + " num_img = np.random.randint(x.shape[0]) \n", + " else:\n", + " num_img = id\n", + "\n", + " img = x[num_img]\n", + " if np.max(img) > 1:\n", + " img = img/255\n", + " boxes = y[num_img]\n", + "\n", + " colors = [\"blue\", \"yellow\", \"red\", \"orange\"] # Différentes couleurs pour les différentes classes\n", + "\n", + " # Affichage de l'image\n", + " plt.imshow(img)\n", + " for box in boxes:\n", + "\n", + " # Détermination de la classe\n", + " class_id = box[4]\n", + " \n", + " # Détermination des extrema de la boîte englobante\n", + " p_x = [(box[0]-box[2]/2) * IMAGE_SIZE, (box[0]+box[2]/2) * IMAGE_SIZE]\n", + " p_y = [(box[1]-box[3]/2) * IMAGE_SIZE, (box[1]+box[3]/2) * IMAGE_SIZE]\n", + "\n", + " # Affichage de la boîte englobante, dans la bonne couleur\n", + " plt.plot([p_x[0], p_x[0]],p_y,color=colors[class_id])\n", + " plt.plot([p_x[1], p_x[1]],p_y,color=colors[class_id])\n", + " plt.plot(p_x,[p_y[0],p_y[0]],color=colors[class_id])\n", + " plt.plot(p_x,[p_y[1],p_y[1]],color=colors[class_id], label=classes[class_id])\n", + " #plt.title(\"Vérité Terrain : Image {}\".format(num_img, classes[class_id]))\n", + " \n", + " plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", + " plt.axis('off')\n", + " plt.show() \n", + "\n", + "\n", + "print_data_detection(x, y, id=22)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "77EZpBu8F1Y2" + }, + "source": [ + "### Écriture des labels au format YOLO" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JOVhY98p5WDZ" + }, + "source": [ + "Les deux fonctions ci-dessous sont essentielles car elles permettent de convertir les boîtes englobantes dans le format adopté par YOLO (voir la section Modèle un peu plus bas), mais également de faire l'opération inverse afin d'interpréter la sortie du réseau.\n", + "\n", + "Notez que la fonction *get_box_from_yolo* intègre dans les boîtes englobantes une information supplémentaire par rapport aux données chargées initialement : la probabilité de présence associée à la boîte englobante." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "vYbQVxZXGAET" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0.43125, 0.50188, 0.5925, 0.996241, 0]], [[0.565625, 0.499324, 0.51875, 0.639189, 0]]]\n", + "[[[0.43125, 0.50188, 0.5925, 0.996241, 0, 1.0]], [[0.565625, 0.499324, 0.51875, 0.639189, 0, 1.0]]]\n" + ] + } + ], + "source": [ + "from tensorflow.keras.utils import to_categorical\n", + "from scipy.special import expit, softmax\n", + "\n", + "\n", + "def set_box_for_yolo(y, num_classes=NB_CLASSES, image_size=IMAGE_SIZE, cell_size=PIX_PER_CELL):\n", + " nb_cells_per_dim = round(image_size/cell_size)\n", + "\n", + " y_YOLO = np.zeros((len(y), nb_cells_per_dim, nb_cells_per_dim, 1 + 4 + num_classes))\n", + "\n", + " for i in range(len(y)):\n", + " for box in y[i]:\n", + " # Coordonnées du centre de la boîte englobante dans le repère image\n", + " cx, cy = box[0] * image_size, box[1] * image_size\n", + " # Détermination des indices de la cellule dans laquelle tombe le centre\n", + " ind_x, ind_y = int(cx // cell_size), int(cy // cell_size)\n", + " # YOLO : \"The (x, y) coordinates represent the center of the box relative to the bounds of the grid cell.\"\n", + " # On va donc calculer les coordonnées du centre relativement à la cellule dans laquelle il se situe\n", + " y_YOLO[i, ind_x, ind_y, 1] = (cx - ind_x * cell_size) / cell_size\n", + " y_YOLO[i, ind_x, ind_y, 2] = (cy - ind_y * cell_size) / cell_size\n", + " # Largeur et hauteur de boîte\n", + " y_YOLO[i, ind_x, ind_y, 3] = box[2]\n", + " y_YOLO[i, ind_x, ind_y, 4] = box[3]\n", + "\n", + " # Indice de confiance de la boîte englobante pour la cellule correspondante\n", + " y_YOLO[i, ind_x, ind_y, 0] = 1\n", + " # On range les probabilités de classe à la fin du vecteur ([ Présence ; cx ; cy ; w ; h ; CLASSES])\n", + " y_YOLO[i, ind_x, ind_y, 5:] = to_categorical(box[4], num_classes=num_classes)\n", + "\n", + " return y_YOLO\n", + "\n", + "# Si mode = 'pred', il s'agit d'une prédiction du réseau, il faut alors utiliser la fonction sigmoide\n", + "# pour obtenir la présence prédite, et la fonction softmax pour obtenir les probabilités de classe \n", + "def get_box_from_yolo(y_YOLO, mode=None, confidence_threshold=0.5, num_classes=NB_CLASSES, image_size=IMAGE_SIZE, cell_size=PIX_PER_CELL):\n", + " nb_cells_per_dim = round(image_size/cell_size)\n", + "\n", + " y = []\n", + " for i in range(y_YOLO.shape[0]):\n", + " boxes = []\n", + " for ind_x in range(cell_size):\n", + " for ind_y in range(cell_size):\n", + " if mode == 'pred':\n", + " presence = expit(y_YOLO[i, ind_x, ind_y, 0])\n", + " classes_probabilities = softmax(y_YOLO[i, ind_x, ind_y, 5:])\n", + " # coords = expit(y_YOLO[i, ind_x, ind_y, 1:5])\n", + " else:\n", + " presence = y_YOLO[i, ind_x, ind_y, 0]\n", + " classes_probabilities = y_YOLO[i, ind_x, ind_y, 5:]\n", + "\n", + " coords = y_YOLO[i, ind_x, ind_y, 1:5]\n", + " if presence > confidence_threshold:\n", + "\n", + " box = []\n", + " box.append((coords[0] * cell_size + ind_x * cell_size) / image_size)\n", + " box.append((coords[1] * cell_size + ind_y * cell_size) / image_size)\n", + " box.append(coords[2])\n", + " box.append(coords[3])\n", + " box.append(np.argmax(y_YOLO[i, ind_x, ind_y, 5:]))\n", + " box.append(presence)\n", + " boxes.append(box)\n", + "\n", + " y.append(boxes)\n", + "\n", + " return y \n", + "\n", + "# On s'assure de pouvoir passer d'une représentation à l'autre sans altérer les données\n", + "print(y[:2])\n", + "print(get_box_from_yolo(set_box_for_yolo(y[:2])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AVXo7WaCBNWT" + }, + "source": [ + "### Augmentation de données" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vgXVpt6z6jRU" + }, + "source": [ + "Il nous faut une version récente de la librairie Albumentations pour pouvoir profiter des fonctionnalités lies aux boîtes englobantes." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "_anbyrRRBRK9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found existing installation: opencv-python-headless 4.1.2.30\n", + "Uninstalling opencv-python-headless-4.1.2.30:\n", + " Would remove:\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/*\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/opencv_python_headless-4.1.2.30.dist-info/*\n", + " Would not remove (might be manually added):\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/qt/fonts/DejaVuSans-Bold.ttf\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/qt/fonts/DejaVuSans-BoldOblique.ttf\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/qt/fonts/DejaVuSans-ExtraLight.ttf\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/qt/fonts/DejaVuSans-Oblique.ttf\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/qt/fonts/DejaVuSans.ttf\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/qt/fonts/DejaVuSansCondensed-Bold.ttf\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/qt/fonts/DejaVuSansCondensed-BoldOblique.ttf\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/qt/fonts/DejaVuSansCondensed-Oblique.ttf\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/qt/fonts/DejaVuSansCondensed.ttf\n", + " /tmp/deepl/.env/lib/python3.8/site-packages/cv2/qt/plugins/platforms/libqxcb.so\n", + "\u001b[31mERROR: Exception:\n", + "Traceback (most recent call last):\n", + " File \"/tmp/deepl/.env/lib/python3.8/site-packages/pip/_internal/cli/base_command.py\", line 186, in _main\n", + " status = self.run(options, args)\n", + " File \"/tmp/deepl/.env/lib/python3.8/site-packages/pip/_internal/commands/uninstall.py\", line 78, in run\n", + " uninstall_pathset = req.uninstall(\n", + " File \"/tmp/deepl/.env/lib/python3.8/site-packages/pip/_internal/req/req_install.py\", line 687, in uninstall\n", + " uninstalled_pathset.remove(auto_confirm, verbose)\n", + " File \"/tmp/deepl/.env/lib/python3.8/site-packages/pip/_internal/req/req_uninstall.py\", line 388, in remove\n", + " if auto_confirm or self._allowed_to_proceed(verbose):\n", + " File \"/tmp/deepl/.env/lib/python3.8/site-packages/pip/_internal/req/req_uninstall.py\", line 431, in _allowed_to_proceed\n", + " return ask('Proceed (y/n)? ', ('y', 'n')) == 'y'\n", + " File \"/tmp/deepl/.env/lib/python3.8/site-packages/pip/_internal/utils/misc.py\", line 240, in ask\n", + " _check_no_input(message)\n", + " File \"/tmp/deepl/.env/lib/python3.8/site-packages/pip/_internal/utils/misc.py\", line 230, in _check_no_input\n", + " raise Exception(\n", + "Exception: No input was expected ($PIP_NO_INPUT set); question: Proceed (y/n)? \u001b[0m\n", + "Requirement already satisfied: opencv-python-headless==4.1.2.30 in ./.env/lib/python3.8/site-packages (4.1.2.30)\n", + "Requirement already satisfied: numpy>=1.17.3 in ./.env/lib/python3.8/site-packages (from opencv-python-headless==4.1.2.30) (1.22.3)\n", + "albumentations==1.1.0 is successfully installed\n" + ] + } + ], + "source": [ + "!pip uninstall --no-input opencv-python-headless==4.5.5.62\n", + "!pip install --no-input opencv-python-headless==4.1.2.30\n", + "!pip install --no-input -q -U albumentations\n", + "!echo \"$(pip freeze | grep albumentations) is successfully installed\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pXIF0AMf6pqF" + }, + "source": [ + "Dans la cellule ci-dessous, il vous faudra intégrer les augmentations que vous aurez choisi. **Attention, ne faites cette partie que dans un second temps, lorsque vous aurez une première version du réseau qui fonctionnera !**\n", + "\n", + "Aidez-vous de [cette page](https://albumentations.ai/docs/getting_started/transforms_and_targets/) pour déterminer des augmentations qui peuvent fonctionner." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "2-z8AmRpCdLK" + }, + "outputs": [], + "source": [ + "from albumentations import (Compose, HorizontalFlip)\n", + "import albumentations as A\n", + "\n", + "AUGMENTATIONS_TRAIN = Compose([\n", + " #### A COMPLETER, MAIS SEULEMENT LORSQUE VOUS AVEZ UN RESEAU QUI (SUR-)APPREND !\n", + " # HorizontalFlip(p=0.5), \n", + " # ...\n", + "], bbox_params=A.BboxParams(format='yolo'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q2oSuKEV7DyU" + }, + "source": [ + "L'objet Sequence défini ci-dessous nous permettra la mise en batch de nos données. On est obligé d'avoir recours à cette solution (plutôt qu'un ImageDataGenerator comme lors du TP3) car ici les augmentations à appliquer altèrent également les labels, ce qui n'est pas supporté par un ImageDataGenerator." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "yALbgasfBYOI" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.utils import Sequence\n", + "\n", + "class YOLOSequence(Sequence):\n", + " # Initialisation de la séquence avec différents paramètres\n", + " def __init__(self, x_set, y_set, batch_size,augmentations):\n", + " self.x, self.y = x_set, y_set\n", + " self.batch_size = batch_size\n", + " self.augment = augmentations\n", + " self.indices1 = np.arange(x_set.shape[0], dtype='int') \n", + " np.random.shuffle(self.indices1) # Les indices permettent d'accéder\n", + " # aux données et sont randomisés à chaque epoch pour varier la composition\n", + " # des batches au cours de l'entraînement\n", + "\n", + " # Fonction calculant le nombre de pas de descente du gradient par epoch\n", + " def __len__(self):\n", + " return int(np.ceil(len(self.x) / float(self.batch_size)))\n", + " \n", + "\n", + " # Il y a des problèmes d'arrondi dans les conversions de boîtes englobantes \n", + " # internes à la librairie Albumentations\n", + " # Pour les contourner, si les boîtes sont trop proches des bords, on les érode un peu\n", + " def erode_bounding_box(self, box, epsilon = 0.02):\n", + " eroded_box = []\n", + " \n", + " xmin = max(box[0] - box[2]/2, epsilon)\n", + " ymin = max(box[1] - box[3]/2, epsilon)\n", + " xmax = min(box[0] + box[2]/2, 1-epsilon)\n", + " ymax = min(box[1] + box[3]/2, 1-epsilon)\n", + " \n", + " cx = xmin + (xmax - xmin)/2\n", + " cy = ymin + (ymax - ymin)/2\n", + " width = xmax - xmin\n", + " height = ymax - ymin\n", + " \n", + " eroded_box = [cx, cy, width, height, box[4]]\n", + " return eroded_box\n", + "\n", + " # Application de l'augmentation de données à chaque image du batch et aux\n", + " # boîtes englobantes associées\n", + " def apply_augmentation(self, bx, by):\n", + "\n", + " batch_x = np.zeros((bx.shape[0], IMAGE_SIZE, IMAGE_SIZE, 3))\n", + " batch_y = []\n", + "\n", + " # Pour chaque image du batch\n", + " for i in range(len(bx)):\n", + " boxes = []\n", + " # Erosion des boîtes englobantes\n", + " for box in by[i]:\n", + " boxes.append(self.erode_bounding_box(box))\n", + "\n", + " # Application de l'augmentation à l'image et aux boîtes englobantes\n", + " transformed = self.augment(image=bx[i].astype('float32'), bboxes=boxes)\n", + " batch_x[i] = transformed['image']\n", + " batch_y_transformed = transformed['bboxes']\n", + " batch_y.append(batch_y_transformed)\n", + "\n", + " return batch_x, batch_y\n", + "\n", + " # Fonction appelée à chaque nouveau batch : sélection et augmentation des données\n", + " def __getitem__(self, idx):\n", + " # Sélection des indices des données du prochain batch\n", + " batch_indices = self.indices1[idx * self.batch_size:(idx + 1) * self.batch_size]\n", + " # Récupération des données puis des labels du batch\n", + " batch_x = self.x[batch_indices]\n", + " batch_boxes = [self.y[item] for item in list(batch_indices)]\n", + " # Application de l'augmentation de données\n", + " batch_x_aug, batch_boxes_aug = self.apply_augmentation(batch_x, batch_boxes)\n", + " \n", + " # Préparation des données pour le réseau :\n", + " # Normalisation des entrées\n", + " batch_x_aug = batch_x_aug/255\n", + " # Passage des sorties au format YOLO\n", + " batch_y_YOLO = set_box_for_yolo(batch_boxes_aug)\n", + " \n", + " return np.array(batch_x_aug), batch_y_YOLO\n", + "\n", + " # Fonction appelée à la fin d'un epoch ; on randomise les indices d'accès aux données\n", + " def on_epoch_end(self):\n", + " np.random.shuffle(self.indices1)\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "FekjKk5rDcrY" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Instanciation d'une Sequence\n", + "train_gen = YOLOSequence(x[:1], y[:1], 1, augmentations=AUGMENTATIONS_TRAIN)\n", + "\n", + "# Pour tester la séquence, nous sélectionnons les éléments du premier batch et les affichons\n", + "batch_x, batch_y = train_gen.__getitem__(0)\n", + "\n", + "print_data_detection(batch_x, get_box_from_yolo(batch_y))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FDkeWyl0TCJ1" + }, + "source": [ + "## Implémentation de YOLO" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tT7sNJTC120-" + }, + "source": [ + "### Modèle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zrEtJvKAwsB2" + }, + "source": [ + "\n", + "
\n", + "
Illustration de la couche de sortie de YOLO.
\n", + "\n", + "Le modèle que je vous propose ci-dessous n'est qu'une possibilité parmi beaucoup d'autres. \n", + "A vous de compléter la dernière couche pour avoir une sortie de la bonne dimension.\n", + "\n", + "**Remarque importante** : comme le tenseur de sortie de YOLO est un peu complexe à manipuler, on choisit ici de regrouper l'ensemble des prédictions dans une seule et même sortie, ce qui nous oblige à utiliser la même fonction d'activation pour toutes nos sorties. On utilisera donc l'activation **linéaire** pour toutes ces sorties. On appliquera les fonctions sigmoïde et softmax pour les sorties \"présence\" et \"probablités de classe\" directement dans la fonction de coût !" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "o3VZsE7EmRPC" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Reshape, Dropout, Input\n", + "from tensorflow.keras.models import Model, Sequential\n", + "from tensorflow.keras import regularizers\n", + "\n", + "\n", + "def create_model_YOLO(input_shape=(64, 64, 3)):\n", + " input_layer = Input(shape=input_shape)\n", + "\n", + " conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(input_layer)\n", + " conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\n", + " pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n", + " \n", + " conv2 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\n", + " conv2 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\n", + " pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n", + " \n", + " conv3 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)\n", + " conv3 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)\n", + " pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n", + "\n", + " conv4 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)\n", + "\n", + " output = Conv2D(NB_CLASSES + 5 * BOX_PER_CELL, 1, activation = 'linear')(conv4)\n", + "\n", + " model = Model(input_layer, output)\n", + "\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "qGi3Yz8Tm4li" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_6\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_8 (InputLayer) [(None, 64, 64, 3)] 0 \n", + " \n", + " conv2d_59 (Conv2D) (None, 64, 64, 32) 896 \n", + " \n", + " conv2d_60 (Conv2D) (None, 64, 64, 32) 9248 \n", + " \n", + " max_pooling2d_21 (MaxPoolin (None, 32, 32, 32) 0 \n", + " g2D) \n", + " \n", + " conv2d_61 (Conv2D) (None, 32, 32, 64) 18496 \n", + " \n", + " conv2d_62 (Conv2D) (None, 32, 32, 64) 36928 \n", + " \n", + " max_pooling2d_22 (MaxPoolin (None, 16, 16, 64) 0 \n", + " g2D) \n", + " \n", + " conv2d_63 (Conv2D) (None, 16, 16, 128) 73856 \n", + " \n", + " conv2d_64 (Conv2D) (None, 16, 16, 128) 147584 \n", + " \n", + " max_pooling2d_23 (MaxPoolin (None, 8, 8, 128) 0 \n", + " g2D) \n", + " \n", + " conv2d_65 (Conv2D) (None, 8, 8, 1024) 1180672 \n", + " \n", + " conv2d_66 (Conv2D) (None, 8, 8, 9) 9225 \n", + " \n", + "=================================================================\n", + "Total params: 1,476,905\n", + "Trainable params: 1,476,905\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = create_model_YOLO()\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z7QasIJE2wiG" + }, + "source": [ + "### Fonction de coût" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yuCP3Ju8Uo9e" + }, + "source": [ + "
\n", + "
Détail de la fonction de perte définie dans l'article YOLO v1
\n", + "\n", + "Nous arrivons maintenant à la partie délicate de l'implémentation de YOLO : la définition de la fonction de coût à utiliser.\n", + "\n", + "Comme nous l'avons vu dans le TP4, lorsque l'on écrit une fonction de coût personnalisée en Keras, il est nécessaire d'utiliser uniquement les fonctions présentes sur la page suivante : \n", + "https://keras.rstudio.com/articles/backend.html\n", + "\n", + "En effet cette fonction de coût qui sera appelée pendant l'entraînement traitera des tenseurs, et non des tableau *numpy*. On doit donc utiliser la librairie Tensorflow qui permet de manipuler les tenseurs.\n", + "\n", + "Une partie essentielle de la fonction est déjà écrite : celle qui permet de séparer les données des cellules dites \"vide\" (la vérité terrain ne contient pas de boîte englobante) des \"non vides\".\n", + "\n", + "Le détail de la fonction de coût est indiqué ci-dessus : dans l'article $\\lambda_{\\text{coord}} = 5$ et $\\lambda_{\\text{noobj}} = 0.5$. Les $x_i$, $y_i$, $w_i$, $h_i$ correspondent aux coordonnées d'une boîte englobante, $C_i$ correspond à la probabilité de présence d'un objet dans la cellule, et les $p_i(c)$ sont les probabilités de classe.\n", + "\n", + "A vous de compléter l'expression des sous-fonctions de la fonction de coût (les fonctions *K.sum*, *K.square*, *K.sigmoid* et *K.softmax* devraient vous suffire !). **N'oubliez pas d'appliquer une sigmoïde aux présences ($C_i$) et une softmax aux probabilités de classe $p_i$) !!**\n", + "\n", + "**NB : cette implémentation de la fonction de coût est très simplifiée et prend en compte le fait qu'il n'y a qu'une seule boîte englobante par cellule.**" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "uS91oePKnE_K" + }, + "outputs": [], + "source": [ + "from keras import backend as K\n", + "\n", + "# Définition de la fonction de perte YOLO\n", + "def YOLOss(lambda_coord, lambda_noobj, batch_size):\n", + "\n", + " # Partie \"verte\" : sous-partie concernant l'indice de confiance et les \n", + " # probabilités de classe dans le cas où une boîte est présente dans la cellule\n", + " def box_loss(y_true, y_pred):\n", + " loss = 0\n", + " loss += K.sum(K.square(y_true[:,0] - K.sigmoid(y_pred[:,0])))\n", + " loss += K.sum(K.sum(K.square(y_true[:, 5:9] - K.softmax(y_pred[:, 5:9]))))\n", + " return loss\n", + "\n", + " # Partie \"bleue\" : sous-partie concernant les coordonnées de boîte englobante \n", + " # dans le cas où une boîte est présente dans la cellule\n", + " def coord_loss(y_true, y_pred):\n", + " loss = 0\n", + " loss += K.sum(K.square(y_true[:,1] - y_pred[:,1]))\n", + " loss += K.sum(K.square(y_true[:,2] - y_pred[:,2]))\n", + "\n", + " loss += K.sum(K.square(K.sqrt(y_true[:,3]) - K.sqrt(y_pred[:,3])))\n", + " loss += K.sum(K.square(K.sqrt(y_true[:,4]) - K.sqrt(y_pred[:,4])))\n", + " return loss\n", + "\n", + "\n", + " # Partie \"rouge\" : sous-partie concernant l'indice de confiance \n", + " # dans le cas où aucune boîte n'est présente dans la cellule\n", + " def nobox_loss(y_true, y_pred):\n", + " loss = 0\n", + " loss += K.sum(K.square(y_true[:,0] - K.sigmoid(y_pred[:,0])))\n", + " return loss\n", + "\n", + "\n", + " def YOLO_loss(y_true, y_pred):\n", + "\n", + " # On commence par reshape les tenseurs de bs x S x S x (5B+C) à (bsxSxS) x (5B+C)\n", + " y_true = K.reshape(y_true, shape=(-1, 9))\n", + " y_pred = K.reshape(y_pred, shape=(-1, 9))\n", + "\n", + " # On cherche (dans les labels y_true) les indices des cellules pour lesquelles au moins la première boîte englobante est présente\n", + " not_empty = K.greater_equal(y_true[:, 0], 1) \n", + " indices = K.arange(0, K.shape(y_true)[0])\n", + " indices_notempty_cells = indices[not_empty]\n", + "\n", + " empty = K.less_equal(y_true[:, 0], 0)\n", + " indices_empty_cells = indices[empty]\n", + "\n", + " # On sépare les cellules de y_true et y_pred avec ou sans boîte englobante\n", + " y_true_notempty = K.gather(y_true, indices_notempty_cells)\n", + " y_pred_notempty = K.gather(y_pred, indices_notempty_cells)\n", + "\n", + " y_true_empty = K.gather(y_true, indices_empty_cells)\n", + " y_pred_empty = K.gather(y_pred, indices_empty_cells)\n", + "\n", + " return (box_loss(y_true_notempty, y_pred_notempty) + lambda_coord*coord_loss(y_true_notempty, y_pred_notempty) + lambda_noobj*nobox_loss(y_true_empty, y_pred_empty))/batch_size\n", + "\n", + " \n", + " # Return a function\n", + " return YOLO_loss" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "urf1W1nl22EP" + }, + "source": [ + "### Apprentissage" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J9nL1qrv8o1b" + }, + "source": [ + "Comme d'habitude, on sépare nos données en plusieurs ensembles (ici apprentissage et validation suffiront)." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "wsW6jpYsvIK7" + }, + "outputs": [], + "source": [ + "# Séparation des données en ensemble de validation et d'apprentissage\n", + "indices = np.arange(x.shape[0], dtype='int') \n", + "np.random.seed(1)\n", + "np.random.shuffle(indices)\n", + "\n", + "x_train = x[indices[:1355]]\n", + "y_train = [y[i] for i in indices[:1355]]\n", + "\n", + "x_val = x[indices[1355:]]\n", + "y_val = [y[i] for i in indices[1355:]]\n", + "\n", + "y_val_YOLO = set_box_for_yolo(y_val)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_42Z4gjN8xK2" + }, + "source": [ + "Prenez le temps de tester votre modèle et votre fonction de coût, ainsi que vos réglages d'hyperparamètres, en sur-apprenant sur une image d'abord, puis sur un batch d'images. Entraînez votre réseau et visualisez ses prédictions sur les données d'entraînement, puis de validation, pour obtenir une intuition sur les valeurs de *loss* permettant d'obtenir des résultats \"acceptables\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "91QkqtUCZc78" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.callbacks import ModelCheckpoint\n", + "import tensorflow as tf\n", + "\n", + "\n", + "batch_size=16\n", + "model = create_model_YOLO()\n", + "learning_rate = 1e-4\n", + "opt = Adam(learning_rate=learning_rate) \n", + "\n", + "# Instanciation de la séquence pour préparer les données et, plus tard, \n", + "train_gen = YOLOSequence(x_train, y_train, batch_size, augmentations=AUGMENTATIONS_TRAIN)\n", + "\n", + "# Comme l'entraînement est instable, on déclenche une sauvegarde du modèle à chaque fois que\n", + "# la perte de validation atteint un nouveau minimum\n", + "model_saver = ModelCheckpoint('tmp/best_weights', monitor='val_loss', verbose=1, save_weights_only=True, save_best_only=True, mode='min')\n", + "\n", + "loss=[YOLOss(5, 0.5, batch_size)]\n", + "\n", + "model.compile(loss=loss,\n", + " optimizer=opt)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.9844\n", + "Epoch 1: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.9844 - val_loss: 5.2965\n", + "Epoch 2/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.9115\n", + "Epoch 2: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.9115 - val_loss: 5.4211\n", + "Epoch 3/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.8580\n", + "Epoch 3: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.8580 - val_loss: 5.2131\n", + "Epoch 4/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.7808\n", + "Epoch 4: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.7808 - val_loss: 5.4535\n", + "Epoch 5/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.7321\n", + "Epoch 5: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.7321 - val_loss: 5.2900\n", + "Epoch 6/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.6989\n", + "Epoch 6: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.6989 - val_loss: 5.3179\n", + "Epoch 7/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.6898\n", + "Epoch 7: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.6898 - val_loss: 5.3656\n", + "Epoch 8/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.7137\n", + "Epoch 8: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.7137 - val_loss: 5.4410\n", + "Epoch 9/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.6198\n", + "Epoch 9: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.6198 - val_loss: 5.3616\n", + "Epoch 10/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.5681\n", + "Epoch 10: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.5681 - val_loss: 5.4706\n", + "Epoch 11/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.5454\n", + "Epoch 11: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.5454 - val_loss: 5.4999\n", + "Epoch 12/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.5339\n", + "Epoch 12: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.5339 - val_loss: 5.5265\n", + "Epoch 13/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.5280\n", + "Epoch 13: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.5280 - val_loss: 5.3426\n", + "Epoch 14/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.5328\n", + "Epoch 14: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.5328 - val_loss: 5.5101\n", + "Epoch 15/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.4303\n", + "Epoch 15: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.4303 - val_loss: 5.4970\n", + "Epoch 16/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.3850\n", + "Epoch 16: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.3850 - val_loss: 5.4678\n", + "Epoch 17/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.3286\n", + "Epoch 17: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.3286 - val_loss: 5.4427\n", + "Epoch 18/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.3441\n", + "Epoch 18: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.3441 - val_loss: 5.4016\n", + "Epoch 19/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.3138\n", + "Epoch 19: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.3138 - val_loss: 5.3618\n", + "Epoch 20/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.2716\n", + "Epoch 20: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.2716 - val_loss: 5.5261\n", + "Epoch 21/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.2530\n", + "Epoch 21: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.2530 - val_loss: 5.5041\n", + "Epoch 22/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.1285\n", + "Epoch 22: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.1285 - val_loss: 5.5205\n", + "Epoch 23/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.1394\n", + "Epoch 23: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.1394 - val_loss: 5.5057\n", + "Epoch 24/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.0966\n", + "Epoch 24: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.0966 - val_loss: 5.6128\n", + "Epoch 25/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.0823\n", + "Epoch 25: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.0823 - val_loss: 5.5188\n", + "Epoch 26/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.0245\n", + "Epoch 26: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.0245 - val_loss: 5.5571\n", + "Epoch 27/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.0234\n", + "Epoch 27: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.0234 - val_loss: 5.6479\n", + "Epoch 28/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.0620\n", + "Epoch 28: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.0620 - val_loss: 5.6678\n", + "Epoch 29/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.0227\n", + "Epoch 29: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.0227 - val_loss: 5.5731\n", + "Epoch 30/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.0236\n", + "Epoch 30: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.0236 - val_loss: 5.5618\n", + "Epoch 31/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.9298\n", + "Epoch 31: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.9298 - val_loss: 5.5772\n", + "Epoch 32/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.8803\n", + "Epoch 32: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.8803 - val_loss: 5.5985\n", + "Epoch 33/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.9596\n", + "Epoch 33: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.9596 - val_loss: 5.5050\n", + "Epoch 34/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.9670\n", + "Epoch 34: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.9670 - val_loss: 5.6204\n", + "Epoch 35/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.9812\n", + "Epoch 35: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.9812 - val_loss: 5.4566\n", + "Epoch 36/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.8827\n", + "Epoch 36: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.8827 - val_loss: 5.4542\n", + "Epoch 37/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.7940\n", + "Epoch 37: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.7940 - val_loss: 5.5093\n", + "Epoch 38/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.7499\n", + "Epoch 38: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.7499 - val_loss: 5.5257\n", + "Epoch 39/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.7058\n", + "Epoch 39: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.7058 - val_loss: 5.5092\n", + "Epoch 40/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.7094\n", + "Epoch 40: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.7094 - val_loss: 5.5911\n", + "Epoch 41/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.7509\n", + "Epoch 41: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.7509 - val_loss: 5.6771\n", + "Epoch 42/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.7350\n", + "Epoch 42: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.7350 - val_loss: 5.6715\n", + "Epoch 43/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.7038\n", + "Epoch 43: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.7038 - val_loss: 5.4962\n", + "Epoch 44/50\n", + "85/85 [==============================] - ETA: 0s - loss: 1.1871\n", + "Epoch 44: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 1.1871 - val_loss: 5.5976\n", + "Epoch 45/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.7698\n", + "Epoch 45: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.7698 - val_loss: 5.3620\n", + "Epoch 46/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.8461\n", + "Epoch 46: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.8461 - val_loss: 5.4388\n", + "Epoch 47/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.7162\n", + "Epoch 47: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.7162 - val_loss: 5.3595\n", + "Epoch 48/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.6588\n", + "Epoch 48: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.6588 - val_loss: 5.4414\n", + "Epoch 49/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.6026\n", + "Epoch 49: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.6026 - val_loss: 5.4616\n", + "Epoch 50/50\n", + "85/85 [==============================] - ETA: 0s - loss: 0.5965\n", + "Epoch 50: val_loss did not improve from 5.02758\n", + "85/85 [==============================] - 6s 65ms/step - loss: 0.5965 - val_loss: 5.4642\n" + ] + } + ], + "source": [ + "\n", + "history = model.fit(train_gen,\n", + " epochs=50,\n", + " batch_size=batch_size, \n", + " validation_data=(x_val/255, y_val_YOLO),\n", + " callbacks = [model_saver])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q-coL0Hx24Ei" + }, + "source": [ + "### Test et affichage des résultats" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EIaImTjf1fvF" + }, + "source": [ + "#### Test de la version à la fin de l'entrainement" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lHD3nVgG2-G4" + }, + "source": [ + "**Sur l'ensemble d'apprentissage**" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "id": "ZjPnyo6G1b1W" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y_pred = model.predict(x_train/255)\n", + "y_pred_YOLO = get_box_from_yolo(y_pred, confidence_threshold=0.5, mode='pred')\n", + "print_data_detection(x_train, y_pred_YOLO)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3EmVROimOQml" + }, + "source": [ + "Une fois les prédictions effectuées, vous pouvez pour aller plus rapidement uniquement relancer l'affichae aléatoire d'un seul résultat." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "sPZeZxIRg_M1" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y_pred = model.predict(x_val/255)\n", + "y_pred_YOLO = get_box_from_yolo(y_pred, confidence_threshold=0.5, mode='pred')\n", + "print_data_detection(x_val, y_pred_YOLO)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "id": "gbsnVJ6_0E3e" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print_data_detection(x_val, y_pred_YOLO)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FqMHwVaJ1iyN" + }, + "source": [ + "#### Test de la meilleure version sauvegardée" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F09SWs1n9iD8" + }, + "source": [ + "La cellule ci-dessous vous permettra de charger les poids sauvegardés lorsque la meilleur performance a été atteinte sur l'ensemble de validation pendant l'entraînement." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "id": "RRjn1SLn0JL4" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.load_weights('tmp/best_weights')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g66MczLI3GGL" + }, + "source": [ + "**Sur l'ensemble d'apprentissage**" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "id": "dfytX2yC1AdY" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y_pred = model.predict(x_train/255)\n", + "y_pred_YOLO = get_box_from_yolo(y_pred, confidence_threshold=0.4, mode='pred')\n", + "print_data_detection(x_train, y_pred_YOLO)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "id": "uXr2p3MHx_hF" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print_data_detection(x_train, y_pred_YOLO)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2elJjjts3JtD" + }, + "source": [ + "**Sur l'ensemble de validation**" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "id": "zBiTzmyu0QqH" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y_pred = model.predict(x_val/255)\n", + "y_pred_YOLO = get_box_from_yolo(y_pred, confidence_threshold=0.3, mode='pred')\n", + "print_data_detection(x_val, y_pred_YOLO)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "id": "6YkUFRQ70M3t" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print_data_detection(x_val, y_pred_YOLO)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T1ZZ1P4nOe3F" + }, + "source": [ + "**Si vous êtes rapide** \n", + "\n", + "Une amélioration possible pourrait être d'inclure une étape supplémentaire de suppression des prédictions \"non maximales\" (*non-max suppression*) pour se débarasser des boîtes redondantes, comme illustré sur la figure ci-dessous :\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "Pour cela, référez-vous au cours, il faut procéder de la manière suivante. Une fois les boîtes présentant un indice de confiance inférieur à un seuil (que nous avons fixé à $0.5$ plus tôt dans le TP) éliminées, on sélectionne la boîte englobante d'indice de confiance maximal $b_{max}$. Puis on parcourt l'ensemble des autres prédictions, et on élimine celles associées à la même classe qui présentent une intersection sur union avec $b_{max}$ supérieure à un second seuil (que, soyons honnêtes, on prend également souvent égal à $0.5$).\n", + "\n", + "On procède ainsi jusqu'à avoir visité toutes les boîtes !\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "TP7 - YOLO - Sujet.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": ".env", + "language": "python", + "name": ".env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}