One thing that is confusing to me is what the "size" of a cell represents. I think I have a high level understanding of images like . I believe this is showing that the output from the last step in the encoder is the input to the first step in the encoder. In this case each box is the GRU or LSTM cell at a different time-step in the sequence.

2016年8月1日 · Following code of Tensorflow's GRUCell unit shows typical operations to get a updated hidden state, when previous hidden state is provided along with current input in the sequence. def __call__(...

2019年5月2日 · A GRU layer does have an internal hidden state, just like an LSTM cell. However, the LSTM's hidden state is split into two parts: a long term state ( hidden_c ) and a short term state ( hidden_h ). The GRU layer is a simplified variant of the LSTM layer, and it only uses one hidden state ( hidden_h ), this is why you are not getting hidden_c ...

2024年4月2日 · In pytorch the RNN, GRU, and LSTM modules use low level optimized cuda kernels that use techniques like operator fusing to get performance gains. This also makes the kernels inflexible. If you want to implement a custom GRU, you either need to rewrite the C++/cuda kernels, or implement the GRUCell at the pytorch level and take the performance hit

The key difference between a GRU and an LSTM is that a GRU has two gates (reset and update gates) whereas an LSTM has three gates (namely input, output and forget gates). Why do we make use of GRU when we clearly have more control on the network through the LSTM model (as we have three gates)? In which scenario GRU is preferred over LSTM?

2018年6月8日 · If you would use a Dropout() after a RNN/LSTM/GRU with return_sequences=True and before a new RNN/LSTM/etc. layer - that would be the same as setting the dropout of the next layer. Correct? Correct? – Maverick Meerkat

2020年9月18日 · The easiest way I see is to write your own GRU implementation by extending the existing one to return not just the hidden state but each gate that you want to save. Then, you would call a tf.function where the entire graph is executed after each epoch again , but with the gates as the output (similar to this .)

2021年8月21日 · So I want to understand exactly how the outputs and hidden state of a GRU cell are calculated. I obtained the pre-trained model from here and the GRU layer has been defined as nn.GRU(96, 96, bias=True). I looked at the the PyTorch Documentation and confirmed the dimensions of the weights and bias as: weight_ih_l0: (288, 96) weight_hh_l0: (288, 96)

2017年7月10日 · I have gone through the online tutorials and trying to apply it on a real-time problem using gated-recurrent unit (GRU). I have tried all the possibilities which I know to improve the classification. 1) Started adding stacked RNN(GRU) layers 2) Increasing hidden units per RNN layer 3) Added "sigmoid" and "RelU" activation functions for hidden ...

2019年2月28日 · If I want to change the compute rules in a RNN cell (e.g. GRU cell), what should I do? I do not want to implement it via for or while loop considering the issue of efficiency. I have viewed the source code of pytorch, but it seems that the major components of rnn cells are implement in c code which I cannot find and modify.