How to access the network weights while using PyTorch 'nn.Sequential'?

Question

I'm building a neural network and I don't know how to access the model weights for each layer.

I've tried

model.input_size.weight

Code:

input_size = 784
hidden_sizes = [128, 64]
output_size = 10

# Build a feed-forward network
model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
                      nn.ReLU(),
                      nn.Linear(hidden_sizes[0], hidden_sizes[1]),
                      nn.ReLU(),
                      nn.Linear(hidden_sizes[1], output_size),
                      nn.Softmax(dim=1))

I expected to get the weights but I got

'Sequential' object has no attribute 'input_size'

Answer 1

If you print out the model usingprint(model), you would get

Sequential(
  (0): Linear(in_features=784, out_features=128, bias=True)
  (1): ReLU()
  (2): Linear(in_features=128, out_features=64, bias=True)
  (3): ReLU()
  (4): Linear(in_features=64, out_features=10, bias=True)
  (5): Softmax(dim=1) )

Now you have access to all indices of layers so you can get the weights of (let's say) second linear layer by model[4].weight.

Answer 2

As per the official pytorch discussion forum here, you can access weights of a specific module in nn.Sequential() using

model.layer[0].weight # for accessing weights of first layer wrapped in nn.Sequential()

Answer 3

I've tried many ways, and it seems that the only way is by naming each layer by passing OrderedDict

from collections import OrderedDict
model = nn.Sequential(OrderedDict([
                  ('fc1', nn.Linear(input_size, hidden_sizes[0])),
                  ('relu1', nn.ReLU()),
                  ('fc2', nn.Linear(hidden_sizes[0], hidden_sizes[1])),
                  ('relu2', nn.ReLU()),
                  ('output', nn.Linear(hidden_sizes[1], output_size)),
                  ('softmax', nn.Softmax(dim=1))]))

So to access the weights of each layer, we need to call it by its own unique layer name.

For example to access weights of layer 1 model.fc1.weight

Parameter containing:
tensor([[-7.3584e-03, -2.3753e-02, -2.2565e-02,  ...,  2.1965e-02,
      1.0699e-02, -2.8968e-02],
    [ 2.2930e-02, -2.4317e-02,  2.9939e-02,  ...,  1.1536e-02,
      1.9830e-02, -1.4294e-02],
    [ 3.0891e-02,  2.5781e-02, -2.5248e-02,  ..., -1.5813e-02,
      6.1708e-03, -1.8673e-02],
    ...,
    [-1.2596e-03, -1.2320e-05,  1.9106e-02,  ...,  2.1987e-02,
     -3.3817e-02, -9.4880e-03],
    [ 1.4234e-02,  2.1246e-02, -1.0369e-02,  ..., -1.2366e-02,
     -4.7024e-04, -2.5259e-02],
    [ 7.5356e-03,  3.4400e-02, -1.0673e-02,  ...,  2.8880e-02,
     -1.0365e-02, -1.2916e-02]], requires_grad=True)

Answer 4

Let's say you define the model as a class. Then you can call model.parameters().

`# Build a feed-forward network
 class FFN(nn.Module):
     def __init__(self):
         super().__init__()
         self.layer1 = nn.Linear(input_size, hidden_sizes[0])
         self.layer2 = nn.Linear(hidden_sizes[0], hidden_sizes[1])
         self.layer3 = nn.Linear(hidden_sizes[1], output_size)
         self.relu = nn.ReLU()
         self.softmax = nn.Softmax(dim=1)
     def forward(self, x):
         x = self.relu(self.layer1(x))
         x = self.relu(self.layer2(x))
         x = self.softmax(self.layer3(x))
         return x

model = FFN()
print(model.parameters())`

Which will print <generator object Module.parameters at 0x7f99886d0d58>, so you can pass that to an optimizer right away!

But, if you want to access particular weights or look at them manually, you can just convert to a list: print(list(model.parameters())). Which will spit out a giant list of weights.

But, let's say you only want the last layer, then you can do: print(list(model.parameters())[-1]), which will print: tensor([-0.0347, -0.0289, -0.0652, -0.1233, 0.1093, 0.1187, -0.0407, 0.0885, -0.0045, -0.1238], requires_grad=True)