title: How to use fit_generator with multiple inputs
body: <p>Is it possible to have two fit_generator?</p>
  
  <p>I'm creating a model with two inputs,
  The model configuration is shown below.</p>
  
  <p><a href="https://i.stack.imgur.com/FDY0W.png" rel="noreferrer"><img src="https://i.stack.imgur.com/FDY0W.png" alt="enter image description here"></a></p>
  
  <p>Label Y uses the same labeling for X1 and X2 data.</p>
  
  <p>The following error will continue to occur.</p>
  
  <blockquote>
    <p><em>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), but instead got the following list of 1 arrays:
    [array([[[[0.75686276, 0.75686276, 0.75686276],
             [0.75686276, 0.75686276, 0.75686276],
             [0.75686276, 0.75686276, 0.75686276],
             ...,
             [0.65882355, 0.65882355, 0.65882355...</em></p>
  </blockquote>
  
  <p>My code looks like this:</p>
  
  
  
  <pre class="lang-python prettyprint-override"><code>def generator_two_img(X1, X2, Y,batch_size):
      generator = ImageDataGenerator(rotation_range=15,
                                     width_shift_range=0.2,
                                     height_shift_range=0.2,
                                     shear_range=0.2,
                                     zoom_range=0.2,
                                     horizontal_flip=True,
                                     fill_mode='nearest')
  
      genX1 = generator.flow(X1, Y, batch_size=batch_size)
      genX2 = generator.flow(X2, Y, batch_size=batch_size)
  
      while True:
          X1 = genX1.__next__()
          X2 = genX2.__next__()
          yield [X1, X2], Y
    """
        .................................
    """
  hist = model.fit_generator(generator_two_img(x_train, x_train_landmark, 
                  y_train, batch_size),
                  steps_per_epoch=len(x_train) // batch_size, epochs=nb_epoch,
                  callbacks = callbacks,
                  validation_data=(x_validation, y_validation),
                  validation_steps=x_validation.shape[0] // batch_size, 
                  `enter code here`verbose=1)
  </code></pre>