merishnaSuwal

merishnaSuwal

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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):
    ''' Building 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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# Define the directory with real image data
data_dir = './data/' # Data
resized_data_dir = "./resized_data" # folder for saving resized data

# Resize images into 128x128
preprocess = True # set to False if no resizing

if preprocess == True:
    # Create resized folder if not exist
    if not os.path.exists(resized_data_dir):

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show_images = 5
# Plot the images from last epoch
data_images = helper.get_batch(glob(os.path.join("./generated_images/epoch_" + str(num_epochs-1) +"/", '*.jpg'))[:show_images], 64, 64, 'RGB')
plt.imshow(helper.images_square_grid(data_images, 'RGB'))

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# Size of latent (noise) vector to generator
z_dim = 100 

# Learning ratess
learning_rate_D =  .00005 
learning_rate_G = 2e-4 

# Batch size
batch_size = 4
# Number of epochs

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def train_gan_model(epoch, batch_size, z_dim, learning_rate_D, learning_rate_G, beta1, get_batches, data_shape, data_image_mode, alpha):
    """
    Train the GAN model.
    
    Arguments:
    ----------
    :param epoch: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate

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def generator_output(sess, n_images, input_z, output_channel_dim, image_mode, image_path):
    """
    Save output from the generator.
    
    Arguments:
    ----------
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor (noise vector)
    :param output_channel_dim: The number of channels in the output image

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def gan_model_optimizers(d_loss, g_loss, disc_lr, gen_lr, beta1):
    """
    Get optimization operations
    
    Arguments:
    ----------
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param disc_lr: Placeholder for Learning Rate for discriminator
    :param gen_lr: Placeholder for Learning Rate for generator