ConcatOp : Dimensions of inputs should match
<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>