Hi @ptrblck, I’ve changed net.forward(inputs) to net(inputs) , unfortunately there’s still no change. To debug further, I removed ALL dropouts and also implemented the same model in Keras (no dropouts as well) where accuracy is ~80% but in PyTorch it’s still ~65%, I think this indicates an error in my training procedure in PyTorch. However, in Keras the label is in categorical format but in Pytorch the label is an integer index itself.
Could it be a loss function issue due to different label format? because I’ve implemented everything else exactly the same in both frameworks: utility functions, model architecture, learning rate, optimizer, batch size, number of epochs, data shuffling. I’ll re-check everything especially my model architecture and forward function, till then any suggestions will be extremely helpful.