If I use the input as following, it seems the h and c of LSTM should be like
input_d = nd.random.uniform(shape=(a,b,c)) layer = gluon.rnn.LSTMCell(hidden_size=100) h0 = nd.random.uniform(shape=(a,c)) c0 = nd.random.uniform(shape=(a,c)) layer.initialize() output, h1 = layer(input_d,[h0,c0])
but generally, we use input layout as (batch_size, seq_length, feature_dim) and I did not discover the
layout attribute in
LSTMCell, according to the above, the h and c are correspond to the batch_size, but its shape should correspond to the seq_length instead, right?
So my question is, is this a problem about the layout or I have some misunderstanding of the concept of LSTM?