gan.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#%matplotlib inline

import keras
from keras.layers import Dense, Dropout, Input
from keras.models import Model,Sequential
from keras.datasets import mnist
from tqdm import tqdm
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import adam

def load_data():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = (x_train.astype(np.float32) - 127.5)/127.5

    # convert shape of x_train from (60000, 28, 28) to (60000, 784)
    # 784 columns per row
    x_train = x_train.reshape(60000, 784)
    return (x_train, y_train, x_test, y_test)

(X_train, y_train,X_test, y_test)=load_data()
print(X_train.shape)

def adam_optimizer():
    return adam(lr=0.0002, beta_1=0.5)

def create_generator():
      generator=Sequential()
      generator.add(Dense(units=256,input_dim=100))
      generator.add(LeakyReLU(0.2))

      generator.add(Dense(units=512))
      generator.add(LeakyReLU(0.2))

      generator.add(Dense(units=1024))
      generator.add(LeakyReLU(0.2))

      generator.add(Dense(units=784, activation='tanh'))

      generator.compile(loss='binary_crossentropy', optimizer=adam_optimizer())
      return generator

g=create_generator()
g.summary()


def create_discriminator():
    discriminator=Sequential()
    discriminator.add(Dense(units=1024,input_dim=784))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))

    discriminator.add(Dense(units=512))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))

    discriminator.add(Dense(units=256))
    discriminator.add(LeakyReLU(0.2))

    discriminator.add(Dense(units=1, activation='sigmoid'))

    discriminator.compile(loss='binary_crossentropy', optimizer=adam_optimizer())
    return discriminator

d =create_discriminator()
d.summary()

def create_gan(discriminator, generator):
    discriminator.trainable=False
    gan_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')
    return gan

gan = create_gan(d,g)
gan.summary()