How can I extract the weight and bias of Linear layers in PyTorch?

user_13184183 0
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In model.state_dict(), model.parameters() and model.named_parameters() weights and biases of nn.Linear() modules are contained separately, e.q. 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)
torch pytorch python
3 Answers

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'])
user_90676150

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'])
user_42282750

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]])
user_81180300
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