feat: matrice de confusion
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Laureηt
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src/notebook_test.ipynb
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src/notebook_test.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import sqlite3\n",
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"import PIL.Image\n",
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"import glob\n",
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"import os\n",
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"\n",
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"import tensorflow as tf\n",
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"import tensorflow_addons as tfa\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import InputLayer, Dense, Flatten, Conv2D, MaxPooling2D\n",
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"from tensorflow.keras import optimizers\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"IMAGE_SIZE = (400, 150, 3)\n",
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"RESIZED_SIZE = (100, 50, 3)\n",
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"RESIZED_SIZE_PIL = (RESIZED_SIZE[1], RESIZED_SIZE[0], RESIZED_SIZE[2])\n",
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"DATASET_PATH = \"./data/\"\n",
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"DATASET_PATH = os.path.abspath(DATASET_PATH)\n",
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"\n",
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"print(DATASET_PATH)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
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"BATCH_SIZE = 32\n",
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"SHUFFLE_SIZE = 64\n",
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"LIMIT = 1000\n",
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"\n",
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"\n",
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"def customGenerator():\n",
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" data = (\n",
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" sqlite3.connect(f\"{DATASET_PATH}/index.db\")\n",
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" .execute(f\"SELECT uuid, model from data LIMIT {LIMIT}\")\n",
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" .fetchall()\n",
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" )\n",
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"\n",
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" for uuid, model in data:\n",
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" img = tf.io.read_file(f\"{DATASET_PATH}/{uuid}.jpg\")\n",
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" img = tf.image.decode_jpeg(img, channels=IMAGE_SIZE[2])\n",
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" img = tf.image.convert_image_dtype(img, tf.float32)\n",
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" img = tf.image.resize(img, RESIZED_SIZE[:-1])\n",
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"\n",
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" label = tf.convert_to_tensor(model, dtype=tf.uint8)\n",
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"\n",
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" yield img, label\n",
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"\n",
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"\n",
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"def cutout(image, label):\n",
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" img = tfa.image.random_cutout(image, (6, 6), constant_values=1)\n",
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" return (img, label)\n",
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"\n",
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"\n",
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"def rotate(image, label):\n",
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" img = tfa.image.rotate(image, tf.constant(np.pi))\n",
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" return (img, label)\n",
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"\n",
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"\n",
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"def set_shapes(image, label):\n",
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" image.set_shape(RESIZED_SIZE)\n",
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" label.set_shape([])\n",
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" return image, label\n",
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"\n",
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"\n",
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"dataset = tf.data.Dataset.from_generator(generator=customGenerator, output_types=(tf.float32, tf.uint8))\n",
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"\n",
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"(dataset_length,) = sqlite3.connect(f\"{DATASET_PATH}/index.db\").execute(\"SELECT count(uuid) from data\").fetchone()\n",
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"dataset_length = min(dataset_length, LIMIT)\n",
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"\n",
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"print(f\"dataset_length = {dataset_length}\")\n",
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"print(f\"batch size = {BATCH_SIZE}\")\n",
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"print(f\"number of batchs = {dataset_length // BATCH_SIZE}\")\n",
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"\n",
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"print()\n",
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"\n",
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"train_size = int(0.8 * dataset_length / BATCH_SIZE)\n",
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"print(f\"train_size = {train_size}\")\n",
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"print(f\"validation_size = {dataset_length - train_size}\")\n",
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"\n",
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"dataset = (\n",
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" dataset.shuffle(SHUFFLE_SIZE)\n",
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" .map(set_shapes)\n",
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" .batch(BATCH_SIZE)\n",
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" # .map(cutout)\n",
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" .prefetch(AUTOTUNE)\n",
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")\n",
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"\n",
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"dataset_train = dataset.take(train_size)\n",
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"dataset_validate = dataset.skip(train_size)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = Sequential(\n",
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" [\n",
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" InputLayer(input_shape=RESIZED_SIZE),\n",
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" Conv2D(32, 3, activation=\"relu\"),\n",
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" MaxPooling2D(pool_size=(2, 2)),\n",
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" Conv2D(64, 3, activation=\"relu\"),\n",
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" MaxPooling2D(pool_size=(2, 2)),\n",
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" Conv2D(92, 3, activation=\"relu\"),\n",
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" MaxPooling2D(pool_size=(2, 2)),\n",
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" Flatten(),\n",
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" Dense(250, activation=\"relu\"),\n",
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" Dense(4, activation=\"softmax\"),\n",
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" ]\n",
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")\n",
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"\n",
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"model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"adam = optimizers.Adam(learning_rate=7e-6)\n",
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"model.compile(optimizer=adam, loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
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"history = model.fit(dataset_train, validation_data=dataset_validate, epochs=5, batch_size=BATCH_SIZE)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_training_analysis():\n",
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" acc = history.history[\"accuracy\"]\n",
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" val_acc = history.history[\"val_accuracy\"]\n",
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" loss = history.history[\"loss\"]\n",
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" val_loss = history.history[\"val_loss\"]\n",
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"\n",
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" epochs = range(len(loss))\n",
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"\n",
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" plt.plot(epochs, acc, \"b\", linestyle=\"--\", label=\"Training acc\")\n",
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" plt.plot(epochs, val_acc, \"g\", label=\"Validation acc\")\n",
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" plt.title(\"Training and validation accuracy\")\n",
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" plt.legend()\n",
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"\n",
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" plt.figure()\n",
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"\n",
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" plt.plot(epochs, loss, \"b\", linestyle=\"--\", label=\"Training loss\")\n",
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" plt.plot(epochs, val_loss, \"g\", label=\"Validation loss\")\n",
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" plt.title(\"Training and validation loss\")\n",
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" plt.legend()\n",
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"\n",
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" plt.show()\n",
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"\n",
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"\n",
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"plot_training_analysis()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Save the weights\n",
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"# model.save('models/rot_25e')"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "e55666fbbf217aa3df372b978577f47b6009e2f78e2ec76a584f49cd54a1e62c"
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},
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"kernelspec": {
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"display_name": ".env",
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"language": "python",
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"name": ".env"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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