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def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode, image_path, save, show):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    :param image_path: Path to save the image
    """

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def train(epoch_count, batch_size, z_dim, learning_rate_D, learning_rate_G, beta1, get_batches, data_shape, data_image_mode, alpha):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data

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def discriminator(x, is_reuse=False, alpha = 0.2):
    ''' Build the discriminator network.
    
        Arguments
        ---------
        x : Input tensor for the discriminator
        n_units: Number of units in hidden layer
        reuse : Reuse the variables with tf.variable_scope
        alpha : leak parameter for leaky ReLU
        

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def generator(z, output_channel_dim, is_train=True):
    ''' Build the generator network.
    
        Arguments
        ---------
        z : Input tensor for the generator
        output_channel_dim : Shape of the generator output
        n_units : Number of units in hidden layer
        reuse : Reuse the variables with tf.variable_scope
        alpha : leak parameter for leaky ReLU

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# Size input image for discriminator
real_size = (128,128,3)

# Size of latent vector to generator
z_dim = 100
learning_rate_D =  .00005 # Thanks to Alexia Jolicoeur Martineau https://ajolicoeur.wordpress.com/cats/
learning_rate_G = 2e-4 # Thanks to Alexia Jolicoeur Martineau https://ajolicoeur.wordpress.com/cats/
batch_size = 64
epochs = 215
alpha = 0.2

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def model_inputs(real_dim, z_dim):
    """
    Create the model inputs
    :param real_dim: tuple containing width, height and channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate G, learning rate D)
    """
    # inputs_real for Discriminator
    inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name='inputs_real')
    

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def model_loss(input_real, input_z, output_channel_dim, alpha):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # Generator network here
    g_model = generator(input_z, output_channel_dim)   

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def model_optimizers(d_loss, g_loss, lr_D, lr_G, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """    
    # Get the trainable_variables, split into G and D parts

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# Load the data and train the network here
dataset = helper.Dataset(glob(os.path.join(data_resized_dir, '*.jpg')))

with tf.Graph().as_default():
    losses, samples = train(epochs, batch_size, z_dim, learning_rate_D, learning_rate_G, beta1, dataset.get_batches,
dataset.shape, dataset.image_mode, alpha)

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def train(epoch_count, batch_size, z_dim, learning_rate_D, learning_rate_G, beta1, get_batches, data_shape, data_image_mode, alpha):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data