title: ConcatOp : Dimensions of inputs should match
body: <p>I'm developing a deep learning model with tensor flow and python:</p>
  
  <ul>
  <li>First, using CNN layers, get features.</li>
  <li>Second, reshaping the feature map, I want to use LSTM layer.</li>
  </ul>
  
  <p>However, a error with not-matching dimension...</p>
  
  <p>ConcatOp : Dimensions of inputs should match: <code>shape[0] = [71,48]</code> vs. <code>shape[1] = [1200,24]</code></p>
  
  <pre><code>W_conv1 = weight_variable([1,conv_size,1,12])
  b_conv1 = bias_variable([12])
  
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1)+ b_conv1)
  h_pool1 = max_pool_1xn(h_conv1)
  
  W_conv2 = weight_variable([1,conv_size,12,24])
  b_conv2 = bias_variable([24])
  
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  h_pool2 = max_pool_1xn(h_conv2)
  
  W_conv3 = weight_variable([1,conv_size,24,48])
  b_conv3 = bias_variable([48])
  
  h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
  h_pool3 = max_pool_1xn(h_conv3)
  
  
  print(h_pool3.get_shape())
  h3_rnn_input = tf.reshape(h_pool3, [-1,x_size/8,48])
  
  num_layers = 1
  lstm_size = 24
  num_steps = 4
  
  lstm_cell = tf.nn.rnn_cell.LSTMCell(lstm_size, initializer = tf.contrib.layers.xavier_initializer(uniform = False))
  cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell]*num_layers)
  init_state = cell.zero_state(batch_size,tf.float32)
  
  
  cell_outputs = []
  state = init_state
  with tf.variable_scope("RNN") as scope:
  for time_step in range(num_steps):
      if time_step &gt; 0: scope.reuse_variables() 
      cell_output, state = cell(h3_rnn_input[:,time_step,:],state) ***** Error In here...
  </code></pre>