如何在 PyTorch 中提取线性层的权重和偏差？

Question

In model.state_dict() , model.parameters() and model.named_parameters() weights and biases of nn.Linear() modules are contained separately, eq fc1.weight and fc1.bias . Is there a simple pythonic way to get both of them?

Expected example looks similar to this:

layer = model['fc1']
print(layer.weight)
print(layer.bias)

Answer 1

From the full model, no. There isn't. But you can get the state_dict() of that particular Module and then you'd have a single dict with the weight and bias :

import torch

m = torch.nn.Linear(3, 5)  # arbitrary values
l = m.state_dict()

print(l['weight'])
print(l['bias'])

The equivalent in your code would be:

layer = model.fc1.state_dict()
print(layer['weight'])
print(layer['bias'])

Answer 2

To extract the Values from a Layer.

layer = model['fc1']
print(layer.weight.data[0])
print(layer.bias.data[0])

instead of 0 index you can use which neuron values to be extracted.

>> nn.Linear(2,3).weight.data
tensor([[-0.4304,  0.4926],
        [ 0.0541,  0.2832],
        [-0.4530, -0.3752]])

Answer 3

You can recover the named parameters for each linear layer in your model like so:

from torch import nn

for layer in model.children():
    if isinstance(layer, nn.Linear):
        print(layer.state_dict()['weight'])
        print(layer.state_dict()['bias'])