I trained a model and than continued to train the same model with an additional layer, where I needed to add parameters to the optimizer with this line

Note here that the speaker_encoder.stl is the new added layer.

than I added a new loss to the existing model and wanted to continue training this model with loaded optimizer. But it throws an error
ValueError: loaded state dict has a different number of parameter groups

I don’t know which code raises the error exactly, as it seems you might be trying to load an optimizer state_dict after manipulating the parameters?
If so, could you try to restore the optimizer before adding new parameter groups?

I am training some model and save at 100k step to continue.

I need to add additional parameters to the previous model and train more 100k steps.
For that I am adding parameters of the model after loading the model and optimizer. Then, I add model.speaker_encoder.stl this layer to the model and scheduled_optim._optimizer.add_param_group({‘params’: model.speaker_encoder.stl.parameters()})
these params to the optimizer. It worked fine. I saved the model and optimizer at step 200k.

Now I want to load model at 200k step and use additional loss for the training. When I am doing this it says.
ValueError: loaded state dict has a different number of parameter groups

def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
#x = self.fc4(x)
return x

net = Net()

Then I load the model and optimizer and also add lines
net.fc4 = nn.Linear(10, 10)
optimizer.add_param_group(
{‘params’: [net.fc4.weight]})

Then I train the this model a few steps and save the state of model and optimizer.
After that I want to continue the model training.
So I am loading the new model with fc4 linear layer already in the model. The model loaded succesfully.
But the optimizer throws an error

[ValueError: loaded state dict has a different number of parameter groups]