title: Multiple Layer RNN Tensorflow
body: <p>I have two layer LSTM network. (config.n_input is 3, config.n_steps is 5)</p>
  
  <p>I think this may be related to the shape of my inputs, but I'm not sure how to fix it, I tried changing the projecting of the LSTM so that they would be the same input size, but that didn't work.</p>
  
  <pre><code>      self.input_data = tf.placeholder(tf.float32, [None, config.n_steps, config.n_input], name='input')
  
         # Tensorflow LSTM cell requires 2x n_hidden length (state &amp; cell)
         self.initial_state = tf.placeholder(tf.float32, [None, 2*config.n_hidden], name='state')
         self.targets = tf.placeholder(tf.float32, [None, config.n_classes], name='target')
  
         _X = tf.transpose(self.input_data, [1, 0, 2])  # permute n_steps and batch_size
         _X = tf.reshape(_X, [-1, config.n_input]) # (n_steps*batch_size, n_input)
  
         input_cell = rnn_cell.LSTMCell(num_units=config.n_hidden, input_size=3, num_proj=300, forget_bias=1.0)
         print(input_cell.output_size)
         inner_cell = rnn_cell.LSTMCell(num_units=config.n_hidden, input_size=300)
         cells = [input_cell, inner_cell]
         cell = rnn.rnn_cell.MultiRNNCell(cells)
  </code></pre>
  
  <p>It returns the following error when attempt to run it.</p>
  
  <pre><code>tensorflow.python.pywrap_tensorflow.StatusNotOK: Invalid argument: Expected size[1] in [0, 0], but got 600
   [[Node: RNN/MultiRNNCell/Cell1/Slice = Slice[Index=DT_INT32, T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](_recv_state_0/_3, RNN/MultiRNNCell/Cell1/Slice/begin, RNN/MultiRNNCell/Cell1/Slice/size)]]
  </code></pre>
  
  <p>any superior explanations of the error message? Or are there any ways to easily fix this?</p>