title: Siamese network showing ValueError
body: <p>I'm using Siamese network for 2000 features with different domains. I want to train on similar pairs and test on dissimilar pair of features. I'm encountering value error when I try to fit the model.</p>
  <pre><code>def get_siamese_conv_unit(input):
      encoder = models.Sequential(name='encoder')
      encoder.add(layer=layers.Dense(units=64, activation=activations.relu))
      encoder.add(layers.Dropout(0.1))
      encoder.add(layer=layers.Dense(units= 32, activation=activations.relu))
      encoder.add(layers.Flatten())
      encoder.summary()
      return encoder
  def get_classifier_model(input_shape):
      left_input = Input(input_shape)
      right_input = Input(input_shape)
      model = get_siamese_conv_unit(input_shape)
      encoded_l = model(left_input)
      encoded_r = model(right_input)
      L1_layer = Lambda(lambda tensors:k.backend.abs(tensors[0] - tensors[1]))
      L1_distance = L1_layer([encoded_l, encoded_r])
      prediction = Dense(1,activation='sigmoid',bias_initializer=initialize_bias)(L1_distance)
      siamese_net = Model(inputs=[left_input,right_input],outputs=prediction)
      return siamese_net
  
   # After optimization
  model.fit([left_input, right_input] ,target , epochs=100, verbose=1,validation_data=[test1, test2])
  </code></pre>
  <p>I get the following error</p>
  <pre><code>Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. 
  Expected to see 2 array(s), for inputs ['input_17', 'input_18'] 
  but instead got the following list of 1 arrays
  </code></pre>
  <p>The type of left_input ,right_input and target are all arrays</p>