mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-09 23:12:02 +00:00
add footnote for PyTorch paper and link to walk
This commit is contained in:
parent
2f9e30bf63
commit
add4f37d97
|
@ -9,7 +9,7 @@ When we say models are implemented in a declarative way in Refiners, what this m
|
|||
|
||||
## A first example
|
||||
|
||||
To give you an idea of how it looks, let us take an example similar to the one from the PyTorch paper:
|
||||
To give you an idea of how it looks, let us take an example similar to the one from the PyTorch paper[^1]:
|
||||
|
||||
```py
|
||||
class BasicModel(nn.Module):
|
||||
|
@ -106,7 +106,7 @@ Did it work? Let's see:
|
|||
|
||||
## Accessing and iterating
|
||||
|
||||
There are also many ways to access or iterate nodes even if they are deep in the tree. Most of them are implemented using a powerful iterator named `walk`. However, most of the time, you can use simpler helpers. For instance, to iterate all the modules in the tree that hold weights (the `Conv2d` and the `Linear`s), we can just do:
|
||||
There are also many ways to access or iterate nodes even if they are deep in the tree. Most of them are implemented using a powerful iterator named [`walk`][refiners.fluxion.layers.Chain.walk]. However, most of the time, you can use simpler helpers. For instance, to iterate all the modules in the tree that hold weights (the `Conv2d` and the `Linear`s), we can just do:
|
||||
|
||||
```py
|
||||
for x in m.layers(fl.WeightedModule):
|
||||
|
@ -120,3 +120,5 @@ Conv2d(in_channels=1, out_channels=128, kernel_size=(3, 3), device=cpu, dtype=fl
|
|||
Linear(in_features=128, out_features=40, device=cpu, dtype=float32)
|
||||
Linear(in_features=40, out_features=10, device=cpu, dtype=float32
|
||||
```
|
||||
|
||||
[^1]: Paszke et al., 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library.
|
||||
|
|
Loading…
Reference in a new issue