LSTM for time series forecasting - deep-learning

Question

I have created a LSTM model in Pytorch which looks like this:
LSTMNet
Now I want to build another LSTM model (NewLSTMNet) on top of it (LSTMNet) by freezing the fc1 layer. I used:
model.fc1.weight.requires_grad = False
model.fc1.bias.requires_grad = False
and then I changed fc2 layer with a linear layer with input features = 40 and output features = 40.
So far I did:
class NewLSTMNet(nn.Module):
def __init__(self, model, input_size, hidden_size, num_layers):
super(NewLSTMNet, self).__init__()
self.model = model
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.model.fc2 = nn.Linear(40, 40)
# self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.fc3 = nn.Sequential(
# nn.ReLU(),
nn.Linear (40 , 128),
nn.ReLU(),
nn.Linear(128, 40),
nn.ReLU(),
nn.Linear(40,1),
nn.ReLU(),
)
def forward(self,x):
# input = self.model(x)
# h0 = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size))
# c0 = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size))
# _, (h_out,_) = self.lstm(input, (h0,c0))
# h_out = h_out.view(-1, self.hidden_size)
# print(h_out.shape)
# out = self.fc3(out)
out = self.model(x)
out = self.fc3(out)
return out
Now my new LSTM model looks like this:
NewLSTMNet
My training loop looks like this:
for epoch in range(EPOCHS):
model.train()
output = model(X_train)
train_loss = criterion(output, y_train)
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
with torch.no_grad():
model.eval()
output_val = model(X_valid)
valid_loss = criterion(output_val, y_valid)
if valid_loss <= valid_loss_min:
torch.save(model.state_dict(), './state_dict_new.pt')
print(
f'Epoch {epoch + 0:01}: Validation loss decreased ({valid_loss_min:.6f} --> {valid_loss:.6f}). Saving model ...')
valid_loss_min = valid_loss
early_stopping_counter = 0 # reset counter if validation loss decreases
else:
print(f'Epoch {epoch + 0:01}: Validation loss did not decrease')
early_stopping_counter += 1
if early_stopping_counter > early_stopping_patience:
print('Early stopped at epoch :', epoch)
break
print(f'\t Train_Loss: {train_loss:.4f} Val_Loss: {valid_loss:.4f} BEST VAL Loss: {valid_loss_min:.4f}\n')
Now the model is working fine. But I want to create a LSTM layer in the NewLSTMNet model. I already tried to add a LSTM layer but I was expecting a vector in the output but I am getting a matrix in the output of the prediction. So there is shape mismatch!
How should I modify my code? Any help is appreciated. Thanks in advance!