def build_generator_face(latent_dim=128*7*7, channels=3, face_sequence=True):

  model = Sequential()

  model.add(Dense(128 * 7 * 7, activation="relu", input_shape=(None, latent_dim)))
  model.add(Reshape((7, 7, 128)))
  model.add(UpSampling2D())
  model.add(Conv2D(128, kernel_size=4, padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(Activation("relu"))

  model.add(UpSampling2D())
  model.add(Conv2D(64, kernel_size=4, padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(Activation("relu"))

  if face_sequence == False:
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

  else:
    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    # go from 56 to 35 and continue upsampling

    model.add(Reshape((-1,3), input_shape=(28,56,3)))
    model.add(Lambda(lambda x: x[:2940]))
    model.add(Reshape((28,35,3)))


    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))


  model.add(Conv2D(channels, kernel_size=4, padding="same"))
  model.add(Activation("tanh"))

  model.summary()


build_generator_face()