from __future__ import print_function, division
from keras.datasets import mnist
from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply, GaussianNoise
from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
from keras.layers import Concatenate
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras import losses
from keras.utils import to_categorical
import keras.backend as K
import scipy
import matplotlib.pyplot as plt
import numpy as np
class CCGAN():
def __init__(self):
self.img_rows = 32
self.img_cols = 32
self.channels = 1
self.img_shape = (self.img_rows, self.img_cols, self.channels)
self.mask_height = 10
self.mask_width = 10
self.num_classes = 10
# Number of filters in first layer of generator and discriminator
self.gf = 32
self.df = 32
optimizer = Adam(0.0002, 0.5)
# Build and compile the discriminator
self.discriminator = self.build_discriminator()
self.discriminator.compile(loss=['mse', 'categorical_crossentropy'],
loss_weights=[0.5, 0.5],
optimizer=optimizer,
metrics=['accuracy'])
# Build the generator
self.generator = self.build_generator()
# The generator takes noise as input and generates imgs
masked_img = Input(shape=self.img_shape)
gen_img = self.generator(masked_img)
# For the combined model we will only train the generator
self.discriminator.trainable = False
# The valid takes generated images as input and determines validity
valid, _ = self.discriminator(gen_img)
# The combined model (stacked generator and discriminator)
# Trains the generator to fool the discriminator
self.combined = Model(masked_img , valid)
self.combined.compile(loss=['mse'],
optimizer=optimizer)
def build_generator(self):
"""U-Net Generator"""
def conv2d(layer_input, filters, f_size=4, bn=True):
"""Layers used during downsampling"""
d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
d = LeakyReLU(alpha=0.2)(d)
if bn:
d = BatchNormalization(momentum=0.8)(d)
return d
def deconv2d(layer_input, skip_input, filters, f_size=4, dropout_rate=0):
"""Layers used during upsampling"""
u = UpSampling2D(size=2)(layer_input)
u = Conv2D(filters, kernel_size=f_size, strides=1, padding='same', activation='relu')(u)
if dropout_rate:
u = Dropout(dropout_rate)(u)
u = BatchNormalization(momentum=0.8)(u)
u = Concatenate()([u, skip_input])
return u
img = Input(shape=self.img_shape)
# Downsampling
d1 = conv2d(img, self.gf, bn=False)
d2 = conv2d(d1, self.gf*2)
d3 = conv2d(d2, self.gf*4)
d4 = conv2d(d3, self.gf*8)
# Upsampling
u1 = deconv2d(d4, d3, self.gf*4)
u2 = deconv2d(u1, d2, self.gf*2)
u3 = deconv2d(u2, d1, self.gf)
u4 = UpSampling2D(size=2)(u3)
output_img = Conv2D(self.channels, kernel_size=4, strides=1, padding='same', activation='tanh')(u4)
return Model(img, output_img)
def build_discriminator(self):
img = Input(shape=self.img_shape)
model = Sequential()
model.add(Conv2D(64, kernel_size=4, strides=2, padding='same', input_shape=self.img_shape))
model.add(LeakyReLU(alpha=0.8))
model.add(Conv2D(128, kernel_size=4, strides=2, padding='same'))
model.add(LeakyReLU(alpha=0.2))
model.add(InstanceNormalization())
model.add(Conv2D(256, kernel_size=4, strides=2, padding='same'))
model.add(LeakyReLU(alpha=0.2))
model.add(InstanceNormalization())
model.summary()
img = Input(shape=self.img_shape)
features = model(img)
validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(features)
label = Flatten()(features)
label = Dense(self.num_classes+1, activation="softmax")(label)
return Model(img, [validity, label])
def mask_randomly(self, imgs):
y1 = np.random.randint(0, self.img_rows - self.mask_height, imgs.shape[0])
y2 = y1 + self.mask_height
x1 = np.random.randint(0, self.img_rows - self.mask_width, imgs.shape[0])
x2 = x1 + self.mask_width
masked_imgs = np.empty_like(imgs)
for i, img in enumerate(imgs):
masked_img = img.copy()
_y1, _y2, _x1, _x2 = y1[i], y2[i], x1[i], x2[i],
masked_img[_y1:_y2, _x1:_x2, :] = 0
masked_imgs[i] = masked_img
return masked_imgs
def train(self, epochs, batch_size=128, sample_interval=50):
# Load the dataset
(X_train, y_train), (_, _) = mnist.load_data()
# Rescale MNIST to 32x32
X_train = np.array([scipy.misc.imresize(x, [self.img_rows, self.img_cols]) for x in X_train])
# Rescale -1 to 1
X_train = (X_train.astype(np.float32) - 127.5) / 127.5
X_train = np.expand_dims(X_train, axis=3)
y_train = y_train.reshape(-1, 1)
# Adversarial ground truths
valid = np.ones((batch_size, 4, 4, 1))
fake = np.zeros((batch_size, 4, 4, 1))
for epoch in range(epochs):
# ---------------------
# Train Discriminator
# ---------------------
# Sample half batch of images
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs = X_train[idx]
labels = y_train[idx]
masked_imgs = self.mask_randomly(imgs)
# Generate a half batch of new images
gen_imgs = self.generator.predict(masked_imgs)
# One-hot encoding of labels
labels = to_categorical(labels, num_classes=self.num_classes+1)
fake_labels = to_categorical(np.full((batch_size, 1), self.num_classes), num_classes=self.num_classes+1)
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(imgs, [valid, labels])
d_loss_fake = self.discriminator.train_on_batch(gen_imgs, [fake, fake_labels])
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
# Train the generator
g_loss = self.combined.train_on_batch(masked_imgs, valid)
# Plot the progress
print ("%d [D loss: %f, op_acc: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[4], g_loss))
# If at save interval => save generated image samples
if epoch % sample_interval == 0:
# Select a random half batch of images
idx = np.random.randint(0, X_train.shape[0], 6)
imgs = X_train[idx]
self.sample_images(epoch, imgs)
self.save_model()
def sample_images(self, epoch, imgs):
r, c = 3, 6
masked_imgs = self.mask_randomly(imgs)
gen_imgs = self.generator.predict(masked_imgs)
imgs = (imgs + 1.0) * 0.5
masked_imgs = (masked_imgs + 1.0) * 0.5
gen_imgs = (gen_imgs + 1.0) * 0.5
gen_imgs = np.where(gen_imgs < 0, 0, gen_imgs)
fig, axs = plt.subplots(r, c)
for i in range(c):
axs[0,i].imshow(imgs[i, :, :, 0], cmap='gray')
axs[0,i].axis('off')
axs[1,i].imshow(masked_imgs[i, :, :, 0], cmap='gray')
axs[1,i].axis('off')
axs[2,i].imshow(gen_imgs[i, :, :, 0], cmap='gray')
axs[2,i].axis('off')
fig.savefig("images/%d.png" % epoch)
plt.close()
def save_model(self):
def save(model, model_name):
model_path = "saved_model/%s.json" % model_name
weights_path = "saved_model/%s_weights.hdf5" % model_name
options = {"file_arch": model_path,
"file_weight": weights_path}
json_string = model.to_json()
open(options['file_arch'], 'w').write(json_string)
model.save_weights(options['file_weight'])
save(self.generator, "ccgan_generator")
save(self.discriminator, "ccgan_discriminator")
if __name__ == '__main__':
ccgan = CCGAN()
ccgan.train(epochs=20000, batch_size=32, sample_interval=200)
