title: ValueError: When feeding symbolic tensors to a model, we expect the tensors to have a static batch size
body: <p>I am new to <code>Keras</code> and I was trying to build a <code>text-classification CNN</code> model using <code>Python 3.6</code> when I encountered this error :</p>
  
  <pre><code>Traceback (most recent call last):
    File "model.py", line 94, in &lt;module&gt;
      model.fit([x1, x2], y_label, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[checkpoint], validation_split=0.2)  # starts training
    File "/../../anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 955, in fit
      batch_size=batch_size)
    File "/../../anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 754, in _standardize_user_data
      exception_prefix='input')
    File "/../../anaconda3/lib/python3.6/site-packages/keras/engine/training_utils.py", line 90, in standardize_input_data
      data = [standardize_single_array(x) for x in data]
    File "/../../anaconda3/lib/python3.6/site-packages/keras/engine/training_utils.py", line 90, in &lt;listcomp&gt;
      data = [standardize_single_array(x) for x in data]
    File "/../../anaconda3/lib/python3.6/site-packages/keras/engine/training_utils.py", line 23, in standardize_single_array
      'Got tensor with shape: %s' % str(shape))
  ValueError: When feeding symbolic tensors to a model, we expect thetensors to have a static batch size. Got tensor with shape: (None, 50, 100)
  </code></pre>
  
  <p>My code for the model is here :</p>
  
  <pre><code>print("\nCreating Model...")
  x1 = Input(shape=(seq_len1, 100), name='x1')
  x2 = Input(shape=(seq_len2, 100), name='x2')
  x1_r = Reshape((seq_len1, embedding_dim, 1))(x1)
  x2_r = Reshape((seq_len2, embedding_dim, 1))(x2)
  
  conv_0 = Conv2D(num_filters, kernel_size=(filter_sizes[0], 1), padding='valid', kernel_initializer='normal', activation='relu')
  .
  # Conv layers with different filter sizes
  .    
  maxpool = MaxPool2D(pool_size=(2, 1), strides=(1,1), padding='valid')
  
  output1 = conv_0(x1_r)
  output1 = maxpool(output1)
  output1 = conv_1(output1)
  output1 = maxpool(output1)
  output1 = conv_2(output1)
  output1 = maxpool(output1)
  .
  # Same for output2
  .
  concatenated_tensor = Concatenate(axis=1)([output1, output2])
  flatten = Flatten()(concatenated_tensor)
  .
  # Dense layers
  . 
  # this creates a model that includes
  model = Model(inputs=[x1, x2], outputs=[output])
  .    
  .
  model.fit([x1, x2], y_label, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[checkpoint], validation_split=0.2)  # starts training
  </code></pre>
  
  <p>I encounter this error in the <code>model.fit</code> line. Here seq_len1 = 50 and seq_len2 = 120. Please help me resolve this.</p>