In [1]:

    

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import tensorflow.contrib.slim as slim
import os
import scipy.misc
import scipy
from tensorflow.examples.tutorials.mnist import input_data


    

In [2]:

    

mnist = input_data.read_data_sets("mnist/", one_hot = False)


    

    




Extracting mnist/train-images-idx3-ubyte.gz
Extracting mnist/train-labels-idx1-ubyte.gz
Extracting mnist/t10k-images-idx3-ubyte.gz
Extracting mnist/t10k-labels-idx1-ubyte.gz

In [3]:

    

# leaky relu activation, which is needed for discriminator network
def lrelu(x, leak = 0.2, name = "lrelu"):
    with tf.variable_scope(name):
        f1 = 0.5 * (1 + leak)
        f2 = 0.5 * (1 - leak)
        return f1 * x + f2 * abs(x)


    

In [4]:

    

#The below functions are taken from carpdem20's implementation https://github.com/carpedm20/DCGAN-tensorflow
#They allow for saving sample images from the generator to follow progress
def save_images(images, size, image_path):
    return imsave(inverse_transform(images), size, image_path)

def imsave(images, size, path):
    return scipy.misc.imsave(path, merge(images, size))

def inverse_transform(images):
    return (images+1.)/2.

def merge(images, size):
    h, w = images.shape[1], images.shape[2]
    img = np.zeros((h * size[0], w * size[1]))

    for idx, image in enumerate(images):
        i = idx % size[1]
        j = idx // size[1]
        img[j*h:j*h+h, i*w:i*w+w] = image

    return img


    

In [5]:

    

'''
The generator takes a vector of random numbers and transforms it into 32 * 32 image. 
Each layer in the network involves a strided transpose convolution, batch normalization, and rectified nonlinearity.
'''
def generator(z):
    zP = slim.fully_connected(z, 4*4*256, normalizer_fn = slim.batch_norm,
                              activation_fn = tf.nn.relu, 
                              scope = 'g_project', 
                              weights_initializer = initializer)
    zCon = tf.reshape(zP, [-1, 4, 4, 256])
    gen1 = slim.convolution2d_transpose(zCon, 
                                       num_outputs = 64,
                                       kernel_size = [5, 5],
                                       stride = [2, 2],
                                       padding = "SAME",
                                       normalizer_fn = slim.batch_norm,
                                       activation_fn = tf.nn.relu,
                                       scope = 'g_conv1',
                                       weights_initializer = initializer)
    
    gen2 = slim.convolution2d_transpose(gen1,
                                       num_outputs = 32,
                                       kernel_size = [5, 5],
                                       stride = [2, 2],
                                       padding = "SAME",
                                       normalizer_fn = slim.batch_norm,
                                       activation_fn = tf.nn.relu,
                                       scope = 'g_conv2',
                                       weights_initializer = initializer)
    
    gen3 = slim.convolution2d_transpose(gen2,
                                       num_outputs = 16,
                                       kernel_size = [5, 5],
                                       stride = [2, 2],
                                       padding = "SAME",
                                       normalizer_fn = slim.batch_norm,
                                       activation_fn = tf.nn.relu,
                                       scope = 'g_conv3',
                                       weights_initializer = initializer)
    
    g_out = slim.convolution2d_transpose(gen3,
                                        num_outputs = 1,
                                        kernel_size = [32, 32],
                                        padding = "SAME",
                                        biases_initializer = None,
                                        activation_fn = tf.nn.tanh,
                                        scope = 'g_out',
                                        weights_initializer = initializer)
    
    return g_out


    

In [6]:

    

'''
The disrciminator network takes as input a 32 * 32 image 
and transforms it into a single valued probability of being generated from real-world data.
'''
def discriminator(bottom, reuse=False):
    
    dis1 = slim.convolution2d(bottom,16,[4,4],
                              stride=[2,2],
                              padding="SAME",
                              biases_initializer=None,
                              activation_fn=lrelu,
                              reuse=reuse,
                              scope='d_conv1',
                              weights_initializer=initializer)
    
    dis2 = slim.convolution2d(dis1,32,[4,4],
                              stride=[2,2],
                              padding="SAME",
                              normalizer_fn=slim.batch_norm,
                              activation_fn=lrelu,
                              reuse=reuse,
                              scope='d_conv2', 
                              weights_initializer=initializer)
    
    dis3 = slim.convolution2d(dis2,64,[4,4],
                              stride=[2,2],
                              padding="SAME",
                              normalizer_fn=slim.batch_norm,
                              activation_fn=lrelu,
                              reuse=reuse,
                              scope='d_conv3',
                              weights_initializer=initializer)
    
    d_out = slim.fully_connected(slim.flatten(dis3),1,
                                 activation_fn=tf.nn.sigmoid,
                                 reuse=reuse,
                                 scope='d_out', 
                                 weights_initializer=initializer)
    
    return d_out


    

In [7]:

    

# Connecting them together
tf.reset_default_graph()

z_size = 100 #Size of z vector used for generator.

#This initializaer is used to initialize all the weights of the network.
initializer = tf.truncated_normal_initializer(stddev=0.02)

#These two placeholders are used for input into the generator and discriminator, respectively.
z_in = tf.placeholder(shape=[None,z_size],dtype=tf.float32) #Random vector
real_in = tf.placeholder(shape=[None,32,32,1],dtype=tf.float32) #Real images

Gz = generator(z_in) #Generates images from random z vectors
Dx = discriminator(real_in) #Produces probabilities for real images
Dg = discriminator(Gz, reuse=True) #Produces probabilities for generator images

#These functions together define the optimization objective of the GAN.
d_loss = -tf.reduce_mean(tf.log(Dx) + tf.log(1.-Dg)) #This optimizes the discriminator.
g_loss = -tf.reduce_mean(tf.log(Dg)) #This optimizes the generator.

tvars = tf.trainable_variables()

#The below code is responsible for applying gradient descent to update the GAN.
trainerD = tf.train.AdamOptimizer(learning_rate=0.0002,beta1=0.5)
trainerG = tf.train.AdamOptimizer(learning_rate=0.0002,beta1=0.5)
d_grads = trainerD.compute_gradients(d_loss,tvars[9:]) #Only update the weights for the discriminator network.
g_grads = trainerG.compute_gradients(g_loss,tvars[0:9]) #Only update the weights for the generator network.

update_D = trainerD.apply_gradients(d_grads)
update_G = trainerG.apply_gradients(g_grads)


    

In [8]:

    

# Now that we have fully defined our network, it's time to train it
batch_size = 128 #Size of image batch to apply at each iteration.
iterations = 500000 #Total number of iterations to use.
sample_directory = 'figs' #Directory to save sample images from generator in.
model_directory = 'models' #Directory to save trained model to.

init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:  
    sess.run(init)
    for i in range(iterations):
        zs = np.random.uniform(-1.0,1.0,size=[batch_size,z_size]).astype(np.float32) #Generate a random z batch
        xs,_ = mnist.train.next_batch(batch_size) #Draw a sample batch from MNIST dataset.
        xs = (np.reshape(xs,[batch_size,28,28,1]) - 0.5) * 2.0 #Transform it to be between -1 and 1
        xs = np.lib.pad(xs, ((0,0),(2,2),(2,2),(0,0)),'constant', constant_values=(-1, -1)) #Pad the images so the are 32x32
        _,dLoss = sess.run([update_D,d_loss],feed_dict={z_in:zs,real_in:xs}) #Update the discriminator
        _,gLoss = sess.run([update_G,g_loss],feed_dict={z_in:zs}) #Update the generator, twice for good measure.
        _,gLoss = sess.run([update_G,g_loss],feed_dict={z_in:zs})
        if i % 10 == 0:
            print("Gen Loss: " + str(gLoss) + " Disc Loss: " + str(dLoss))
            z2 = np.random.uniform(-1.0,1.0,size=[batch_size,z_size]).astype(np.float32) #Generate another z batch
            newZ = sess.run(Gz,feed_dict={z_in:z2}) #Use new z to get sample images from generator.
            if not os.path.exists(sample_directory):
                os.makedirs(sample_directory)
            #Save sample generator images for viewing training progress.
            save_images(np.reshape(newZ[0:36],[36,32,32]),[6,6],sample_directory+'/fig'+str(i)+'.png')
        if i % 1000 == 0 and i != 0:
            if not os.path.exists(model_directory):
                os.makedirs(model_directory)
            saver.save(sess,model_directory+'/model-'+str(i)+'.cptk')
            print("Saved Model")


    

    




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---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-8-5c80d9fba777> in <module>()
     14         xs = (np.reshape(xs,[batch_size,28,28,1]) - 0.5) * 2.0 #Transform it to be between -1 and 1
     15         xs = np.lib.pad(xs, ((0,0),(2,2),(2,2),(0,0)),'constant', constant_values=(-1, -1)) #Pad the images so the are 32x32
---> 16         _,dLoss = sess.run([update_D,d_loss],feed_dict={z_in:zs,real_in:xs}) #Update the discriminator
     17         _,gLoss = sess.run([update_G,g_loss],feed_dict={z_in:zs}) #Update the generator, twice for good measure.
     18         _,gLoss = sess.run([update_G,g_loss],feed_dict={z_in:zs})

C:\Users\shuhua\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
    764     try:
    765       result = self._run(None, fetches, feed_dict, options_ptr,
--> 766                          run_metadata_ptr)
    767       if run_metadata:
    768         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

C:\Users\shuhua\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    962     if final_fetches or final_targets:
    963       results = self._do_run(handle, final_targets, final_fetches,
--> 964                              feed_dict_string, options, run_metadata)
    965     else:
    966       results = []

C:\Users\shuhua\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1012     if handle is None:
   1013       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1014                            target_list, options, run_metadata)
   1015     else:
   1016       return self._do_call(_prun_fn, self._session, handle, feed_dict,

C:\Users\shuhua\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
   1019   def _do_call(self, fn, *args):
   1020     try:
-> 1021       return fn(*args)
   1022     except errors.OpError as e:
   1023       message = compat.as_text(e.message)

C:\Users\shuhua\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1001         return tf_session.TF_Run(session, options,
   1002                                  feed_dict, fetch_list, target_list,
-> 1003                                  status, run_metadata)
   1004 
   1005     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

In [10]:

    

'''
Using a trained network.
Once we have a trained model saved, we may want to use it to generate new images. 
and explore the representation it has learned
''' 
sample_directory = './figs' #Directory to save sample images from generator in.
model_directory = './models' #Directory to load trained model from.
batch_size_sample = 36

init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:  
    sess.run(init)
    #Reload the model.
    print('Loading Model...')
    ckpt = tf.train.get_checkpoint_state(model_directory)
    saver.restore(sess,ckpt.model_checkpoint_path)
    
    zs = np.random.uniform(-1.0,1.0,size=[batch_size_sample,z_size]).astype(np.float32) #Generate a random z batch
    newZ = sess.run(Gz,feed_dict={z_in:z2}) #Use new z to get sample images from generator.
    if not os.path.exists(sample_directory):
        os.makedirs(sample_directory)
    save_images(np.reshape(newZ[0:batch_size_sample],[36,32,32]),[6,6],sample_directory+'/fig_generated.png')


    

    




Loading Model...

In [11]:

    

'''
https://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39
'''


    

    
Out[11]:





'\nhttps://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39\n'