How do I realize something similar to keras TimeDistributedDense (https://github.com/fchollet/keras/issues/1029) in pytorch?
Because of pytorch’s dynamic graph, you don’t need
TimeDistributedDense like in Keras. You can just use
Linear. LSTM networks become very straight forward. See the tutorial for pos tagging with LSTM: http://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html#example-an-lstm-for-part-of-speech-tagging
# 24 fc timedistributed num = 24 fc = nn.ModuleList([nn.Linear(8, 1) for i in range(num)]) # forward pass x = np.zeros(64, 24, 8) outs= for i in range(x.shape): outs.append(fc[i](x[:, i, :].unsqueeze(1))) outs=torch.cat(outs, axis=1)
Hi, could you further explain more about it?