title: Keras add data with Sequential layers
body: <p>I have two sequential models in Keras:</p>
  
  <pre><code>def generator_model():
      model = Sequential()
      model.add(LSTM(512, return_sequences=False, input_shape=(3, 100*100)))
      model.add(Dense(100*100))
      model.add(Reshape((1, 100, 100), input_shape=(100*100,)))
      model.add(Activation('sigmoid'))
      model.compile(optimizer='adadelta', loss='categorical_crossentropy');
      return model
  </code></pre>
  
  <p>and</p>
  
  <pre><code>def discriminator_model():
      model = Sequential()
      model.add(LSTM(512, return_sequences=False, input_shape=(4, 100*100)))
      model.add(Dense(100*100))
      model.add(Dense(1))
      model.add(Activation('sigmoid'))
      model.compile(optimizer='adadelta', loss='categorical_crossentropy');
      return model
  </code></pre>
  
  <p>I also have a function to concatenate these models together. I am trying to train a generative adversarial network with both the generator and discriminator as lstm's. This is why I need the function </p>
  
  <pre><code>def generator_containing_discriminator(generator, discriminator):
      model = Sequential()
      model.add(generator)
      discriminator.trainable = False
      model.add(discriminator)
      return model
  </code></pre>
  
  <p>which I use to train the network using</p>
  
  <pre><code>g_loss = discriminator_on_generator.train_on_batch(noise, [1] * BATCH_SIZE)
  discriminator.trainable = True
  print("batch %d g_loss : %f" % (index, g_loss))
  </code></pre>
  
  <p>In order for the last snippet of code for training to work, the generator and discriminator should be able to merge together. But, the output of my generator cannot be input into my discriminator because I need to add some data to the output of the generator before sending it into the discriminator. How can I do this in Keras so that the discriminator can be added onto the generator? Is there any way to add data in a <code>model.add</code> function? I could not find anything in the Keras documentation</p>