def forward(self, input):
H1 = self.hidden_layer_1(input)
H1 = self.relu(H1)
final_inputs = self.output(H1)
optimizer = torch.optim.SGD(self.parameters(), lr =lr,momentum=momentum)
weights = np.load('./w0.npy')
bias = np.load('./b0.npy')
self.hidden_layer_1.weight.data = torch.from_numpy(weights).float()
self.hidden_layer_1.bias.data = torch.from_numpy(bias).float()
for param in self.hidden_layer_1.parameters():
param.requires_grad = False
I am trying to freeze the layer but the loss is still changing after epochs.
Try to do it (freeze the parameters) before passing them to the optimizer.
I tried that as well but training and validation loss still changing
hidden_layer_1 the only layer in your model?
Show a more complete code if possible
Yes. I have included the forward function in the edited post.
But I saw that you call
self.output in this forward function.
self.output does not have a trainable parameters?
Because if your model is still learning, it means that something is changing somewhere. If you only have one layer and you want to freeze it, what is the point of training the model here then?
I was checking if the freezing part is working for the first layer then I would add a second hidden layer which I would not freeze.