from __future__ import print_function, division
import scipy
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
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
import matplotlib.pyplot as plt
import sys
import numpy as np
class COGAN():
"""Reference: https://wiseodd.github.io/techblog/2017/02/18/coupled_gan/"""
def __init__(self):
self.img_rows = 28
self.img_cols = 28
self.channels = 1
self.img_shape = (self.img_rows, self.img_cols, self.channels)
self.latent_dim = 100
optimizer = Adam(0.0002, 0.5)
# Build and compile the discriminator
self.d1, self.d2 = self.build_discriminators()
self.d1.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
self.d2.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
# Build the generator
self.g1, self.g2 = self.build_generators()
# The generator takes noise as input and generated imgs
z = Input(shape=(self.latent_dim,))
img1 = self.g1(z)
img2 = self.g2(z)
# For the combined model we will only train the generators
self.d1.trainable = False
self.d2.trainable = False
# The valid takes generated images as input and determines validity
valid1 = self.d1(img1)
valid2 = self.d2(img2)
# The combined model (stacked generators and discriminators)
# Trains generators to fool discriminators
self.combined = Model(z, [valid1, valid2])
self.combined.compile(loss=['binary_crossentropy', 'binary_crossentropy'],
optimizer=optimizer)
def build_generators(self):
# Shared weights between generators
model = Sequential()
model.add(Dense(256, input_dim=self.latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
noise = Input(shape=(self.latent_dim,))
feature_repr = model(noise)
# Generator 1
g1 = Dense(1024)(feature_repr)
g1 = LeakyReLU(alpha=0.2)(g1)
g1 = BatchNormalization(momentum=0.8)(g1)
g1 = Dense(np.prod(self.img_shape), activation='tanh')(g1)
img1 = Reshape(self.img_shape)(g1)
# Generator 2
g2 = Dense(1024)(feature_repr)
g2 = LeakyReLU(alpha=0.2)(g2)
g2 = BatchNormalization(momentum=0.8)(g2)
g2 = Dense(np.prod(self.img_shape), activation='tanh')(g2)
img2 = Reshape(self.img_shape)(g2)
model.summary()
return Model(noise, img1), Model(noise, img2)
def build_discriminators(self):
img1 = Input(shape=self.img_shape)
img2 = Input(shape=self.img_shape)
# Shared discriminator layers
model = Sequential()
model.add(Flatten(input_shape=self.img_shape))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
img1_embedding = model(img1)
img2_embedding = model(img2)
# Discriminator 1
validity1 = Dense(1, activation='sigmoid')(img1_embedding)
# Discriminator 2
validity2 = Dense(1, activation='sigmoid')(img2_embedding)
return Model(img1, validity1), Model(img2, validity2)
def train(self, epochs, batch_size=128, sample_interval=50):
# Load the dataset
(X_train, _), (_, _) = mnist.load_data()
# Rescale -1 to 1
X_train = (X_train.astype(np.float32) - 127.5) / 127.5
X_train = np.expand_dims(X_train, axis=3)
# Images in domain A and B (rotated)
X1 = X_train[:int(X_train.shape[0]/2)]
X2 = X_train[int(X_train.shape[0]/2):]
X2 = scipy.ndimage.interpolation.rotate(X2, 90, axes=(1, 2))
# Adversarial ground truths
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in range(epochs):
# ----------------------
# Train Discriminators
# ----------------------
# Select a random batch of images
idx = np.random.randint(0, X1.shape[0], batch_size)
imgs1 = X1[idx]
imgs2 = X2[idx]
# Sample noise as generator input
noise = np.random.normal(0, 1, (batch_size, 100))
# Generate a batch of new images
gen_imgs1 = self.g1.predict(noise)
gen_imgs2 = self.g2.predict(noise)
# Train the discriminators
d1_loss_real = self.d1.train_on_batch(imgs1, valid)
d2_loss_real = self.d2.train_on_batch(imgs2, valid)
d1_loss_fake = self.d1.train_on_batch(gen_imgs1, fake)
d2_loss_fake = self.d2.train_on_batch(gen_imgs2, fake)
d1_loss = 0.5 * np.add(d1_loss_real, d1_loss_fake)
d2_loss = 0.5 * np.add(d2_loss_real, d2_loss_fake)
# ------------------
# Train Generators
# ------------------
g_loss = self.combined.train_on_batch(noise, [valid, valid])
# Plot the progress
print ("%d [D1 loss: %f, acc.: %.2f%%] [D2 loss: %f, acc.: %.2f%%] [G loss: %f]" \
% (epoch, d1_loss[0], 100*d1_loss[1], d2_loss[0], 100*d2_loss[1], g_loss[0]))
# If at save interval => save generated image samples
if epoch % sample_interval == 0:
self.sample_images(epoch)
def sample_images(self, epoch):
r, c = 4, 4
noise = np.random.normal(0, 1, (r * int(c/2), 100))
gen_imgs1 = self.g1.predict(noise)
gen_imgs2 = self.g2.predict(noise)
gen_imgs = np.concatenate([gen_imgs1, gen_imgs2])
# Rescale images 0 - 1
gen_imgs = 0.5 * gen_imgs + 0.5
fig, axs = plt.subplots(r, c)
cnt = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
axs[i,j].axis('off')
cnt += 1
fig.savefig("images/mnist_%d.png" % epoch)
plt.close()
if __name__ == '__main__':
gan = COGAN()
gan.train(epochs=30000, batch_size=32, sample_interval=200)
