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Generative Adversarial Networks for Natural Language Processing





In [18]:



    


import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os

State of art weight Initialization strategy





In [33]:



    


def xavier_init(n_inputs, n_outputs, uniform=True):
  """Set the parameter initialization using the method described.
  This method is designed to keep the scale of the gradients roughly the same
  in all layers.
  Xavier Glorot and Yoshua Bengio (2010):
           Understanding the difficulty of training deep feedforward neural
           networks. International conference on artificial intelligence and
           statistics.
  Args:
    n_inputs: The number of input nodes into each output.
    n_outputs: The number of output nodes for each input.
    uniform: If true use a uniform distribution, otherwise use a normal.
  Returns:
    An initializer.
  """
  if uniform:
    # 6 was used in the paper.
    init_range = tf.sqrt(6.0 / (n_inputs + n_outputs))
    return tf.random_uniform_initializer(-init_range, init_range)
  else:
    # 3 gives us approximately the same limits as above since this repicks
    # values greater than 2 standard deviations from the mean.
    stddev = tf.sqrt(3.0 / (n_inputs + n_outputs))
    return tf.truncated_normal_initializer(stddev=stddev)



    











In [34]:



    


'''A recent paper by He, Rang, Zhen and Sun they build on Glorot & Bengio and suggest using 2/size_of_input_neuron
''' 
def xavier_init(size):
    in_dim = size[0]
#     xavier_stddev = 1. / in_dim
#     xavier_stddev = 2. / in_dim
    xavier_stddev = 1. / tf.sqrt(in_dim / 2.)
    return tf.random_normal(shape=size, stddev=xavier_stddev)

Discriminator





In [22]:



    


X = tf.placeholder(tf.float32, shape=[None, 784])

D_W1 = tf.Variable(xavier_init([784, 128]))
D_b1 = tf.Variable(tf.zeros(shape=[128]))

D_W2 = tf.Variable(xavier_init([128, 1]))
D_b2 = tf.Variable(tf.zeros(shape=[1]))

theta_D = [D_W1, D_W2, D_b1, D_b2]



    











In [ ]:



    


def discriminator(x):
    D_h1 = tf.nn.relu(tf.matmul(x, D_W1) + D_b1)
    D_logit = tf.matmul(D_h1, D_W2) + D_b2
    D_prob = tf.nn.sigmoid(D_logit)

    return D_prob, D_logit

Generator





In [23]:



    


Z = tf.placeholder(tf.float32, shape=[None, 100])

G_W1 = tf.Variable(xavier_init([100, 128]))
G_b1 = tf.Variable(tf.zeros(shape=[128]))

G_W2 = tf.Variable(xavier_init([128, 784]))
G_b2 = tf.Variable(tf.zeros(shape=[784]))

theta_G = [G_W1, G_W2, G_b1, G_b2]



    











In [24]:



    


def sample_Z(m, n):
    return np.random.uniform(-1., 1., size=[m, n])



    











In [25]:



    


def generator(z):
    G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1)
    G_log_prob = tf.matmul(G_h1, G_W2) + G_b2
    G_prob = tf.nn.sigmoid(G_log_prob)

    return G_prob



    











In [27]:



    


def plot(samples):
    fig = plt.figure(figsize=(4, 4))
    gs = gridspec.GridSpec(4, 4)
    gs.update(wspace=0.05, hspace=0.05)

    for i, sample in enumerate(samples):
        ax = plt.subplot(gs[i])
        plt.axis('off')
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_aspect('equal')
        plt.imshow(sample.reshape(28, 28), cmap='Greys_r')

    return fig



    











In [28]:



    


G_sample = generator(Z)
D_real, D_logit_real = discriminator(X)
D_fake, D_logit_fake = discriminator(G_sample)



    











In [29]:



    


D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real, labels=tf.ones_like(D_logit_real)))
D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))
D_loss = D_loss_real + D_loss_fake
G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))



    











In [30]:



    


D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D)
G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G)



    











In [31]:



    


minibatch_size = 128
Z_dim = 100

mnist = input_data.read_data_sets('../../MNIST_data', one_hot=True)

sess = tf.Session()
sess.run(tf.global_variables_initializer())

if not os.path.exists('out/'):
    os.makedirs('out/')

i = 0

for it in range(1000000):
    if it % 1000 == 0:
        samples = sess.run(G_sample, feed_dict={Z: sample_Z(16, Z_dim)})

        fig = plot(samples)
        plt.savefig('out/{}.png'.format(str(i).zfill(3)), bbox_inches='tight')
        i += 1
        plt.close(fig)

    X_mb, _ = mnist.train.next_batch(minibatch_size)

    _, D_loss_curr = sess.run([D_solver, D_loss], feed_dict={X: X_mb, Z: sample_Z(minibatch_size, Z_dim)})
    _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict={Z: sample_Z(minibatch_size, Z_dim)})

    if it % 1000 == 0:
        print('Iter: {}'.format(it))
        print('D loss: {:.4}'. format(D_loss_curr))
        print('G_loss: {:.4}'.format(G_loss_curr))
        print()



    


















    






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Extracting ../../MNIST_data\train-images-idx3-ubyte.gz
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Extracting ../../MNIST_data\t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting ../../MNIST_data\t10k-labels-idx1-ubyte.gz
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G_loss: 3.775

Iter: 920000
D loss: 0.1442
G_loss: 3.736

Iter: 921000
D loss: 0.135
G_loss: 4.064

Iter: 922000
D loss: 0.1318
G_loss: 3.544

Iter: 923000
D loss: 0.1541
G_loss: 3.847

Iter: 924000
D loss: 0.1433
G_loss: 3.869

Iter: 925000
D loss: 0.261
G_loss: 3.489

Iter: 926000
D loss: 0.2182
G_loss: 3.768

Iter: 927000
D loss: 0.2016
G_loss: 3.607

Iter: 928000
D loss: 0.1853
G_loss: 3.767

Iter: 929000
D loss: 0.3451
G_loss: 3.7

Iter: 930000
D loss: 0.1733
G_loss: 3.903

Iter: 931000
D loss: 0.3005
G_loss: 3.953

Iter: 932000
D loss: 0.1109
G_loss: 3.862

Iter: 933000
D loss: 0.1432
G_loss: 3.777

Iter: 934000
D loss: 0.1024
G_loss: 3.496

Iter: 935000
D loss: 0.1932
G_loss: 3.787

Iter: 936000
D loss: 0.1985
G_loss: 3.553

Iter: 937000
D loss: 0.2199
G_loss: 3.708

Iter: 938000
D loss: 0.2405
G_loss: 4.064

Iter: 939000
D loss: 0.1208
G_loss: 3.501

Iter: 940000
D loss: 0.1933
G_loss: 3.712

Iter: 941000
D loss: 0.1117
G_loss: 3.932

Iter: 942000
D loss: 0.116
G_loss: 4.042

Iter: 943000
D loss: 0.1803
G_loss: 4.188

Iter: 944000
D loss: 0.1432
G_loss: 3.43

Iter: 945000
D loss: 0.2393
G_loss: 3.707

Iter: 946000
D loss: 0.09669
G_loss: 4.261

Iter: 947000
D loss: 0.2052
G_loss: 3.949

Iter: 948000
D loss: 0.1569
G_loss: 3.998

Iter: 949000
D loss: 0.2604
G_loss: 3.422

Iter: 950000
D loss: 0.127
G_loss: 3.559

Iter: 951000
D loss: 0.0865
G_loss: 3.712

Iter: 952000
D loss: 0.1853
G_loss: 4.052

Iter: 953000
D loss: 0.1568
G_loss: 4.267

Iter: 954000
D loss: 0.2473
G_loss: 3.848

Iter: 955000
D loss: 0.1935
G_loss: 3.398

Iter: 956000
D loss: 0.1153
G_loss: 4.242

Iter: 957000
D loss: 0.2894
G_loss: 3.541

Iter: 958000
D loss: 0.2526
G_loss: 3.722

Iter: 959000
D loss: 0.1921
G_loss: 3.725

Iter: 960000
D loss: 0.1468
G_loss: 4.32

Iter: 961000
D loss: 0.2058
G_loss: 3.469

Iter: 962000
D loss: 0.2457
G_loss: 3.207

Iter: 963000
D loss: 0.1368
G_loss: 3.243

Iter: 964000
D loss: 0.1626
G_loss: 3.524

Iter: 965000
D loss: 0.2024
G_loss: 3.664

Iter: 966000
D loss: 0.2339
G_loss: 3.703

Iter: 967000
D loss: 0.09176
G_loss: 3.814

Iter: 968000
D loss: 0.1933
G_loss: 3.449

Iter: 969000
D loss: 0.1257
G_loss: 3.649

Iter: 970000
D loss: 0.1197
G_loss: 3.889

Iter: 971000
D loss: 0.09248
G_loss: 4.303

Iter: 972000
D loss: 0.1157
G_loss: 4.018

Iter: 973000
D loss: 0.2459
G_loss: 3.745

Iter: 974000
D loss: 0.1363
G_loss: 3.794

Iter: 975000
D loss: 0.1774
G_loss: 3.961

Iter: 976000
D loss: 0.1306
G_loss: 3.614

Iter: 977000
D loss: 0.1732
G_loss: 3.274

Iter: 978000
D loss: 0.2852
G_loss: 3.889

Iter: 979000
D loss: 0.1105
G_loss: 3.735

Iter: 980000
D loss: 0.274
G_loss: 4.031

Iter: 981000
D loss: 0.1704
G_loss: 3.536

Iter: 982000
D loss: 0.2157
G_loss: 3.391

Iter: 983000
D loss: 0.249
G_loss: 3.563

Iter: 984000
D loss: 0.263
G_loss: 3.959

Iter: 985000
D loss: 0.1704
G_loss: 3.901

Iter: 986000
D loss: 0.2113
G_loss: 4.029

Iter: 987000
D loss: 0.1187
G_loss: 3.559

Iter: 988000
D loss: 0.1795
G_loss: 3.779

Iter: 989000
D loss: 0.2453
G_loss: 3.877

Iter: 990000
D loss: 0.1927
G_loss: 3.835

Iter: 991000
D loss: 0.2889
G_loss: 3.649

Iter: 992000
D loss: 0.2511
G_loss: 3.736

Iter: 993000
D loss: 0.1963
G_loss: 3.518

Iter: 994000
D loss: 0.1559
G_loss: 4.24

Iter: 995000
D loss: 0.1713
G_loss: 3.647

Iter: 996000
D loss: 0.1735
G_loss: 3.445

Iter: 997000
D loss: 0.2216
G_loss: 3.759

Iter: 998000
D loss: 0.1491
G_loss: 4.028

Iter: 999000
D loss: 0.1784
G_loss: 4.136

After checking images in out dir we see that the GAN mode collapsed

Fix for that is to let discriminator see ground truth in mini batches

Here's a YouTube of I made from all 1000 images at 100ms delay 





In [1]:



    


from IPython.display import HTML

# Youtube
HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/ktxhiKhWoEE?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')



    


















    
Out[1]:























In [ ]:

Content source: nikbearbrown/Deep_Learning

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