title: Generalize TensorflowGANS from grayscale to RGB
body: <p>I am writing a tensorflow based GAN script.
  It was for grayscale images but I cannot make the generator works for RGB
  which in my code is represented by 
  dim =3
  I can imagine I do have to update also the other layers adding the third dimension, but how do I do it at the Dense layer?</p>
  
  <pre><code>model = tf.keras.Sequential()
  model.add(layers.Dense( (IMG_SIZE//4) * (IMG_SIZE//4) *256, use_bias=False, input_shape=(100,)))
  model.add(layers.BatchNormalization())
  model.add(layers.LeakyReLU())
  
  model.add(layers.Reshape((IMG_SIZE//4, IMG_SIZE//4, 256)))
  assert model.output_shape == (None, IMG_SIZE//4, IMG_SIZE//4, 256) # Note: None is the batch size
  
  model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
  assert model.output_shape == (None, IMG_SIZE//4, IMG_SIZE//4, 128)
  model.add(layers.BatchNormalization())
  model.add(layers.LeakyReLU())
  
  model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
  assert model.output_shape == (None, IMG_SIZE//2, IMG_SIZE//2, 64)
  model.add(layers.BatchNormalization())
  model.add(layers.LeakyReLU())
  
  model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
  assert model.output_shape == (None, IMG_SIZE, IMG_SIZE, dim)
  
  return model
  </code></pre>