from keras.datasets import mnist
from keras.layers import Dense, Dropout, Input
from keras.models import Model,Sequential
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import Adam
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from google.colab import drive

# Load the dataset
def load_data():(x_train, y_train), (_, _) = mnist.load_data()x_train = (x_train.astype(np.float32) - 127.5)/127.5# Convert shape from (60000, 28, 28) to (60000, 784)x_train = x_train.reshape(60000, 784)return (x_train, y_train)X_train, y_train = load_data()
print(X_train.shape, y_train.shape)

def build_generator():model = Sequential()model.add(Dense(units=256, input_dim=100))model.add(LeakyReLU(alpha=0.2))model.add(Dense(units=512))model.add(LeakyReLU(alpha=0.2))model.add(Dense(units=1024))model.add(LeakyReLU(alpha=0.2))model.add(Dense(units=784, activation='tanh'))model.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))return modelgenerator = build_generator()
generator.summary()

def build_discriminator():model = Sequential()model.add(Dense(units=1024 ,input_dim=784))model.add(LeakyReLU(alpha=0.2))model.add(Dropout(0.3))model.add(Dense(units=512))model.add(LeakyReLU(alpha=0.2))model.add(Dropout(0.3))model.add(Dense(units=256))model.add(LeakyReLU(alpha=0.2))model.add(Dropout(0.3))model.add(Dense(units=1, activation='sigmoid'))model.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))return modeldiscriminator = build_discriminator()
discriminator.summary()

def build_GAN(discriminator, generator):discriminator.trainable=FalseGAN_input = Input(shape=(100,))x = generator(GAN_input)GAN_output= discriminator(x)GAN = Model(inputs=GAN_input, outputs=GAN_output)GAN.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))return GANGAN = build_GAN(discriminator, generator)
GAN.summary()

def draw_images(generator, epoch, examples=25, dim=(5,5), figsize=(10,10)):noise= np.random.normal(loc=0, scale=1, size=[examples, 100])generated_images = generator.predict(noise)generated_images = generated_images.reshape(25,28,28)plt.figure(figsize=figsize)for i in range(generated_images.shape[0]):plt.subplot(dim[0], dim[1], i+1)plt.imshow(generated_images[i], interpolation='nearest', cmap='Greys')plt.axis('off')plt.tight_layout()plt.savefig('Generated_images %d.png' %epoch)

def train_GAN(epochs=1, batch_size=128):#Loading the dataX_train, y_train = load_data()# Creating GANgenerator= build_generator()discriminator= build_discriminator()GAN = build_GAN(discriminator, generator)for i in range(1, epochs+1):print("Epoch %d" %i)for _ in tqdm(range(batch_size)):# Generate fake images from random noisetnoise= np.random.normal(0,1, (batch_size, 100))fake_images = generator.predict(noise)# Select a random batch of real images from MNISTreal_images = X_train[np.random.randint(0, X_train.shape[0], batch_size)]# Labels for fake and real images           label_fake = np.zeros(batch_size)label_real = np.ones(batch_size) # Concatenate fake and real images X = np.concatenate([fake_images, real_images])y = np.concatenate([label_fake, label_real])# Train the discriminatordiscriminator.trainable=Truediscriminator.train_on_batch(X, y)# Train the generator/chained GAN model (with frozen weights in discriminator) discriminator.trainable=FalseGAN.train_on_batch(noise, label_real)# Draw generated images every 15 epoches     if i == 1 or i % 10 == 0:draw_images(generator, i)
train_GAN(epochs=400, batch_size=128)

  #Loading the dataX_train, y_train = load_data()

  # Creating GANgenerator= build_generator()discriminator= build_discriminator()GAN = build_GAN(discriminator, generator)

 for i in range(1, epochs+1):print("Epoch %d" %i)for _ in tqdm(range(batch_size)):

  # Generate fake images from random noisetnoise= np.random.normal(0,1, (batch_size, 100))fake_images = generator.predict(noise)

 # Select a random batch of real images from MNISTreal_images = X_train[np.random.randint(0, X_train.shape[0], batch_size)]# Labels for fake and real images           label_fake = np.zeros(batch_size)label_real = np.ones(batch_size) # Concatenate fake and real images X = np.concatenate([fake_images, real_images])y = np.concatenate([label_fake, label_real])

# Train the discriminator
discriminator.trainable=True
discriminator.train_on_batch(X, y)

 # Train the generator/chained GAN model (with frozen weights in discriminator) discriminator.trainable=FalseGAN.train_on_batch(noise, label_real)