MultiRNN And Static_rnn Error: Dimensions Must Be Equal, But Are 256 And 129

I want to build an LSTM network with 3 Layers. Here's the code: num_layers=3 time_steps=10 num_units=128 n_input=1 learning_rate=0.001 n_classes=1 ... x=tf.placeholder('float',[No

Solution 1:

You should not reuse the same cell for the first and deeper layers, because their inputs are different, hence kernel matrices are different. Try this:

# Extra function is for readability. No problem to inline it.
def make_cell(lstm_size):
  return tf.nn.rnn_cell.BasicLSTMCell(lstm_size, state_is_tuple=True)

network = rnn_cell.MultiRNNCell([make_cell(num_units) for _ in range(num_layers)], 
                                state_is_tuple=True)