I am using Python 3.8 and PyTorch 1.7 to manually assign and change the weights and biases for a neural network. As an example, I have defined a LeNet-300-100 fully-connected neural network to train on MNIST dataset. The code for class definition is:
class LeNet300(nn.Module):
    def __init__(self):
        super(LeNet300, self).__init__()
        
        # Define layers-
        self.fc1 = nn.Linear(in_features = input_size, out_features = 300)
        self.fc2 = nn.Linear(in_features = 300, out_features = 100)
        self.output = nn.Linear(in_features = 100, out_features = 10)
        
        self.weights_initialization()
    
    
    def forward(self, x):
        out = F.relu(self.fc1(x))
        out = F.relu(self.fc2(out))
        return self.output(out)
    
    
    def weights_initialization(self):
        '''
        When we define all the modules such as the layers in '__init__()'
        method above, these are all stored in 'self.modules()'.
        We go through each module one by one. This is the entire network,
        basically.
        '''
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.xavier_normal_(m.weight)
                nn.init.constant_(m.bias, 0)

To experiment with trying to change the weights for this model-
# Instantiate model-
mask_model = LeNet300()

To assign all of the weights in each of the layers to one (1), I use the code-
with torch.no_grad():
    for layer in mask_model.state_dict():
        mask_model.state_dict()[layer] = nn.parameter.Parameter(torch.ones_like(mask_model.state_dict()[layer]))

# Sanity check-
mask_model.state_dict()['fc1.weight']

This output shows that the weights are not equal to 1.
I also tried the code-
for param in mask_model.parameters():
    # print(param.shape)
    param = nn.parameter.Parameter(torch.ones_like(param))

But this does not work as well.
Help?