Project Repository: https://github.com/soumik12345/Adventures-with-GANS

import os, warnings
warnings.filterwarnings('ignore')

import numpy as np
import cv2, glob
from random import shuffle, randint
from tqdm import tqdm
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Reshape, BatchNormalization
from tensorflow.keras.layers import Activation, Conv2DTranspose, Conv2D, LeakyReLU
from tensorflow.keras.optimizers import Adam
from keras.utils.vis_utils import model_to_dot
from IPython.display import SVG

Using TensorFlow backend.

image_files = glob.glob('../input/data/data/*.png')
shuffle(image_files)

x = []
for file in tqdm(image_files):
    image = cv2.imread(file)
    image = image / 127.5
    image = image - 1
    x.append(image)
x = np.array(x)
x.shape

100%|██████████| 21551/21551 [00:47<00:00, 453.27it/s]

(21551, 64, 64, 3)

fig, axes = plt.subplots(nrows = 4, ncols = 4, figsize = (16, 16))
plt.setp(axes.flat, xticks = [], yticks = [])
for i, ax in enumerate(axes.flat):
    index = randint(0, 10000)
    ax.imshow(x[index], cmap = 'gray')
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

def build_discriminator(image_shape, learning_rate, beta_1):
    discriminator = Sequential([
        Conv2D(
            filters = 64,
            kernel_size = (5, 5),
            strides = (2, 2),
            padding = 'same',
            data_format = 'channels_last',
            kernel_initializer = 'glorot_uniform',
            input_shape = (image_shape)
        ),
        LeakyReLU(0.2),
        
        Conv2D(
            filters = 128,
            kernel_size = (5, 5),
            strides = (2, 2),
            padding = 'same',
            data_format = 'channels_last',
            kernel_initializer = 'glorot_uniform',
        ),
        BatchNormalization(momentum = 0.5),
        LeakyReLU(0.2),
        
        Conv2D(
            filters = 256,
            kernel_size = (5, 5),
            strides = (2, 2),
            padding = 'same',
            data_format = 'channels_last',
            kernel_initializer = 'glorot_uniform',
        ),
        BatchNormalization(momentum = 0.5),
        LeakyReLU(0.2),
        
        Conv2D(
            filters = 512,
            kernel_size = (5, 5),
            strides = (2, 2),
            padding = 'same',
            data_format = 'channels_last',
            kernel_initializer = 'glorot_uniform',
        ),
        BatchNormalization(momentum = 0.5),
        LeakyReLU(0.2),
        
        Flatten(),
        Dense(1),
        Activation('sigmoid')
    ], name = 'Discriminator')
    
    discriminator.compile(
        loss = 'binary_crossentropy',
        optimizer = Adam(
            lr = learning_rate,
            beta_1 = beta_1
        ),
        metrics = None
    )
    
    return discriminator

def build_generator(input_shape, learning_rate, beta_1):
    generator = Sequential([
        Dense(
            input_shape,
            kernel_initializer = 'glorot_uniform',
            input_shape = (1, 1, 100)
        ),
        Reshape(target_shape = (4, 4, 512)),
        BatchNormalization(momentum = 0.5),
        Activation('relu'),
        
        Conv2DTranspose(
            filters = 256,
            kernel_size = (5, 5),
            strides = (2, 2),
            padding = 'same',
            data_format = 'channels_last',
            kernel_initializer = 'glorot_uniform'
        ),
        BatchNormalization(momentum = 0.5),
        Activation('relu'),
        
        Conv2DTranspose(
            filters = 128,
            kernel_size = (5, 5),
            strides = (2, 2),
            padding = 'same',
            data_format = 'channels_last',
            kernel_initializer = 'glorot_uniform'
        ),
        BatchNormalization(momentum = 0.5),
        Activation('relu'),
        
        Conv2DTranspose(
            filters = 64,
            kernel_size = (5, 5),
            strides = (2, 2),
            padding = 'same',
            data_format = 'channels_last',
            kernel_initializer = 'glorot_uniform'
        ),
        BatchNormalization(momentum = 0.5),
        Activation('relu'),
        
        Conv2DTranspose(
            filters = 3,
            kernel_size = (5, 5),
            strides = (2, 2),
            padding = 'same',
            data_format = 'channels_last',
            kernel_initializer = 'glorot_uniform'
        ),
        Activation('tanh'),
    ], name = 'Generator')
    
    generator.compile(
        loss = 'binary_crossentropy',
        optimizer = Adam(
            lr = learning_rate,
            beta_1 = beta_1
        ),
        metrics = None
    )
    
    return generator

def build_gan(generator, discriminator, learning_rate, beta_1):
    gan = Sequential([
        generator,
        discriminator
    ], name = 'GAN')
    gan.compile(
        loss = 'binary_crossentropy',
        optimizer = Adam(
            lr = learning_rate,
            beta_1 = beta_1
        ),
        metrics = None
    )
    return gan

discriminator = build_discriminator((64, 64, 3), 0.0002, 0.5)
discriminator.summary()

WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 32, 32, 64)        4864      
_________________________________________________________________
leaky_re_lu (LeakyReLU)      (None, 32, 32, 64)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 16, 16, 128)       204928    
_________________________________________________________________
batch_normalization_v1 (Batc (None, 16, 16, 128)       512       
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU)    (None, 16, 16, 128)       0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 8, 8, 256)         819456    
_________________________________________________________________
batch_normalization_v1_1 (Ba (None, 8, 8, 256)         1024      
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU)    (None, 8, 8, 256)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 4, 4, 512)         3277312   
_________________________________________________________________
batch_normalization_v1_2 (Ba (None, 4, 4, 512)         2048      
_________________________________________________________________
leaky_re_lu_3 (LeakyReLU)    (None, 4, 4, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 8192)              0         
_________________________________________________________________
dense (Dense)                (None, 1)                 8193      
_________________________________________________________________
activation (Activation)      (None, 1)                 0         
=================================================================
Total params: 4,318,337
Trainable params: 4,316,545
Non-trainable params: 1,792
_________________________________________________________________

SVG(model_to_dot(discriminator, show_shapes = True, show_layer_names = True).create(prog = 'dot', format = 'svg'))

generator = build_generator(np.prod(discriminator.layers[-4].output_shape[1:]), 0.00015, 0.5)
generator.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 1, 1, 8192)        827392    
_________________________________________________________________
reshape (Reshape)            (None, 4, 4, 512)         0         
_________________________________________________________________
batch_normalization_v1_3 (Ba (None, 4, 4, 512)         2048      
_________________________________________________________________
activation_1 (Activation)    (None, 4, 4, 512)         0         
_________________________________________________________________
conv2d_transpose (Conv2DTran (None, 8, 8, 256)         3277056   
_________________________________________________________________
batch_normalization_v1_4 (Ba (None, 8, 8, 256)         1024      
_________________________________________________________________
activation_2 (Activation)    (None, 8, 8, 256)         0         
_________________________________________________________________
conv2d_transpose_1 (Conv2DTr (None, 16, 16, 128)       819328    
_________________________________________________________________
batch_normalization_v1_5 (Ba (None, 16, 16, 128)       512       
_________________________________________________________________
activation_3 (Activation)    (None, 16, 16, 128)       0         
_________________________________________________________________
conv2d_transpose_2 (Conv2DTr (None, 32, 32, 64)        204864    
_________________________________________________________________
batch_normalization_v1_6 (Ba (None, 32, 32, 64)        256       
_________________________________________________________________
activation_4 (Activation)    (None, 32, 32, 64)        0         
_________________________________________________________________
conv2d_transpose_3 (Conv2DTr (None, 64, 64, 3)         4803      
_________________________________________________________________
activation_5 (Activation)    (None, 64, 64, 3)         0         
=================================================================
Total params: 5,137,283
Trainable params: 5,135,363
Non-trainable params: 1,920
_________________________________________________________________

SVG(model_to_dot(generator, show_shapes = True, show_layer_names = True).create(prog = 'dot', format = 'svg'))

discriminator.trainable = False
gan = build_gan(generator, discriminator, 0.00015, 0.5)
gan.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
Generator (Sequential)       (None, 64, 64, 3)         5137283   
_________________________________________________________________
Discriminator (Sequential)   (None, 1)                 4318337   
=================================================================
Total params: 9,455,620
Trainable params: 5,135,363
Non-trainable params: 4,320,257
_________________________________________________________________

SVG(model_to_dot(gan, show_shapes = True, show_layer_names = True).create(prog = 'dot', format = 'svg'))

EPOCHS = 15000
BATCH_SIZE = 32

def plot_images(nrows, ncols, figsize, generator):
    noise = np.random.normal(0, 1, size = (BATCH_SIZE * 2, ) + (1, 1, 100))
    prediction = generator.predict(noise)
    fig, axes = plt.subplots(nrows = nrows, ncols = ncols, figsize = figsize)
    plt.setp(axes.flat, xticks = [], yticks = [])
    for i, ax in enumerate(axes.flat):
        index = randint(0, 63)
        ax.imshow(cv2.cvtColor(prediction[index], cv2.COLOR_BGR2RGB), cmap = 'gray')
    plt.show()

discriminator_loss_history, generator_loss_history = [], []

for epoch in tqdm(range(1, EPOCHS + 1)):
    
    # Select a random batch of images from training data
    index = np.random.randint(0, x.shape[0], BATCH_SIZE)
    batch_images = x[index]
    
    # Adversarial Noise
    noise = np.random.normal(0, 1, size = (BATCH_SIZE, ) + (1, 1, 100))
    
    # Fenerate Fake Images
    generated_images = generator.predict(noise)
    
    # Adding noise to the labels before passing to the discriminator
    real_y = (np.ones(BATCH_SIZE) -  np.random.random_sample(BATCH_SIZE) * 0.2)
    fake_y = np.random.random_sample(BATCH_SIZE) * 0.2
    
    # Training the discriminator
    discriminator.trainable = True
    discriminator_loss = discriminator.train_on_batch(batch_images, real_y)
    discriminator_loss += discriminator.train_on_batch(generated_images, fake_y)
    discriminator.trainable = False
    
    # Adversarial Noise
    noise = np.random.normal(0, 1, size = (BATCH_SIZE * 2,) + (1, 1, 100))
    
    # We try to mislead the discriminator by giving the opposite labels
    fake_y = (np.ones(BATCH_SIZE * 2) - np.random.random_sample(BATCH_SIZE * 2) * 0.2)
    
    # Training the Generator
    generator_loss = gan.train_on_batch(noise, fake_y)
    
    if epoch % 100 == 0:
        discriminator_loss_history.append(discriminator_loss)
        generator_loss_history.append(generator_loss)
        if epoch % 1000 == 0:
            plot_images(2, 8, (16, 4), generator)

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WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.

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Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

 53%|█████▎    | 7999/15000 [09:17<07:52, 14.81it/s]Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

plt.figure(figsize = (20, 8))
plt.plot(generator_loss_history)
plt.title('Generator Loss History')
plt.show()

plt.figure(figsize = (20, 8))
plt.plot(discriminator_loss_history)
plt.title('Discriminator Loss History')
plt.show()

plot_images(4, 4, (16, 16), generator)

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

generator.save('./generator.h5')