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samples=[] #generator examples

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(epochs):
        num_batches=mnist.train.num_examples//batch_size
        for i in range(num_batches):
            batch=mnist.train.next_batch(batch_size)
            batch_images=batch[0].reshape((batch_size,784))
            batch_images=batch_images*2-1

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lr=0.001

#Do this when multiple networks interact with each other
tvars=tf.trainable_variables()  #returns all variables created(the two variable scopes) and makes trainable true
d_vars=[var for var in tvars if 'dis' in var.name]
g_vars=[var for var in tvars if 'gen' in var.name]

D_trainer=tf.train.AdamOptimizer(lr).minimize(D_loss,var_list=d_vars)
G_trainer=tf.train.AdamOptimizer(lr).minimize(G_loss,var_list=g_vars)

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def loss_func(logits_in,labels_in):
    return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_in,labels=labels_in))

D_real_loss=loss_func(D_logits_real,tf.ones_like(D_logits_real)*0.9) #Smoothing for generalization
D_fake_loss=loss_func(D_logits_fake,tf.zeros_like(D_logits_real))
D_loss=D_real_loss+D_fake_loss

G_loss= loss_func(D_logits_fake,tf.ones_like(D_logits_fake))

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tf.reset_default_graph()

real_images=tf.placeholder(tf.float32,shape=[None,784])
z=tf.placeholder(tf.float32,shape=[None,100])

G=generator(z)
D_output_real,D_logits_real=discriminator(real_images)
D_output_fake,D_logits_fake=discriminator(G,reuse=True)

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def generator(z,reuse=None):
    with tf.variable_scope('gen',reuse=reuse):
        hidden1=tf.layers.dense(inputs=z,units=128,activation=tf.nn.leaky_relu)
        hidden2=tf.layers.dense(inputs=hidden1,units=128,activation=tf.nn.leaky_relu)
        output=tf.layers.dense(inputs=hidden2,units=784,activation=tf.nn.tanh)
        
        return output
    
def discriminator(X,reuse=None):
    with tf.variable_scope('dis',reuse=reuse):

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import tensorflow as tf #tensorflow is a machine learning library
import numpy as np #numpy is useful for matrice multiplication
import matplotlib.pyplot as plt #visual tool
from tensorflow.examples.tutorials.mnist import input_data #import the MNIST datatset

mnist=input_data.read_data_sets("MNIST_data") #read in the MNIST images