import tensorflow as tf
tf.enable_eager_execution()

import glob
import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import time

from IPython import display

(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()


train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]

BUFFER_SIZE = 60000
BATCH_SIZE = 256

train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

def make_generator_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())
      
    model.add(tf.keras.layers.Reshape((7, 7, 256)))
    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size
    
    model.add(tf.keras.layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    assert model.output_shape == (None, 7, 7, 128)  
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    assert model.output_shape == (None, 14, 14, 64)    
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
    assert model.output_shape == (None, 28, 28, 1)
  
    return model

def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same'))
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dropout(0.3))
      
    model.add(tf.keras.layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dropout(0.3))
       
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(1))
     
    return model

generator = make_generator_model()
discriminator = make_discriminator_model()

WARNING:tensorflow:From D:\software\Anaconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:642: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.

def generator_loss(generated_output):
    return tf.losses.sigmoid_cross_entropy(tf.ones_like(generated_output), generated_output)

def discriminator_loss(real_output, generated_output):
    # [1,1,...,1] with real output since it is true and we want our generated examples to look like it
    real_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=tf.ones_like(real_output), logits=real_output)

    # [0,0,...,0] with generated images since they are fake
    generated_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=tf.zeros_like(generated_output), logits=generated_output)

    total_loss = real_loss + generated_loss

    return total_loss

generator_optimizer = tf.train.AdamOptimizer(1e-4)
discriminator_optimizer = tf.train.AdamOptimizer(1e-4)

checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
                                 discriminator_optimizer=discriminator_optimizer,
                                 generator=generator,
                                 discriminator=discriminator)

EPOCHS = 50
noise_dim = 100
num_examples_to_generate = 16

# We'll re-use this random vector used to seed the generator so
# it will be easier to see the improvement over time.
random_vector_for_generation = tf.random_normal([num_examples_to_generate,
                                                 noise_dim])

def train_step(images):
   # generating noise from a normal distribution
    noise = tf.random_normal([BATCH_SIZE, noise_dim])
      
    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)
      
        real_output = discriminator(images, training=True)
        generated_output = discriminator(generated_images, training=True)
         
        gen_loss = generator_loss(generated_output)
        disc_loss = discriminator_loss(real_output, generated_output)
        
    gradients_of_generator = gen_tape.gradient(gen_loss, generator.variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.variables)
      
    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.variables))

train_step = tf.contrib.eager.defun(train_step)

WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.

def train(dataset, epochs):  
    for epoch in range(epochs):
        start = time.time()
    
        for images in dataset:
            train_step(images)

        display.clear_output(wait=True)
        generate_and_save_images(generator,
                                 epoch + 1,
                                 random_vector_for_generation)
    
        # saving (checkpoint) the model every 15 epochs
        if (epoch + 1) % 15 == 0:
            checkpoint.save(file_prefix = checkpoint_prefix)
    
        print ('Time taken for epoch {} is {} sec'.format(epoch + 1,
                                                          time.time()-start))
    # generating after the final epoch
    display.clear_output(wait=True)
    generate_and_save_images(generator,
                           epochs,
                           random_vector_for_generation)

def generate_and_save_images(model, epoch, test_input):
    # make sure the training parameter is set to False because we
    # don't want to train the batchnorm layer when doing inference.
    predictions = model(test_input, training=False)

    fig = plt.figure(figsize=(4,4))

    for i in range(predictions.shape[0]):
        plt.subplot(4, 4, i+1)
        plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
        plt.axis('off')

    plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
    plt.show()

%%time
train(train_dataset, EPOCHS)

Time taken for epoch 1 is 460.8921685218811 sec

# restoring the latest checkpoint in checkpoint_dir
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))

# Display a single image using the epoch number
def display_image(epoch_no):
    return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))

display_image(EPOCHS)

with imageio.get_writer('dcgan.gif', mode='I') as writer:
    filenames = glob.glob('image*.png')
    filenames = sorted(filenames)
    last = -1
    for i,filename in enumerate(filenames):
        frame = 2*(i**0.5)
        if round(frame) > round(last):
            last = frame
        else:
            continue
        image = imageio.imread(filename)
        writer.append_data(image)
    image = imageio.imread(filename)
    writer.append_data(image)
    
# this is a hack to display the gif inside the notebook
os.system('cp dcgan.gif dcgan.gif.png')

display.Image(filename="dcgan.gif.png")