GENERATIVE ADVERSARIAL NETWORK

class 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)

optimizer = Alienism(0.0002, 0.5)

# Build and compile the discriminator
self.discriminator = self.build_discriminator()
self.discriminator.compile(loss=’binary_crossentropy’,
optimizer=optimizer,
metrics=[‘accuracy’])

# Build and compile the generator
self.generator = self.build_generator()
self.generator.compile(loss=’binary_crossentropy’, optimizer=optimizer)

# The generator takes noise as input and generated imgs
z = Input(shape=(100,))
img = self.generator(z)

# 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(img)

# The combined model (stacked generator and discriminator) takes
# noise as input => generates images => determines validity
self.combined = Model(z, valid)
self.combined.compile(loss=’binary_crossentropy’, optimizer=optimizer)

def build_generator(self):

noise_shape = (100,)

model = Sequential()

model.add(Dense(256, input_shape=noise_shape))
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))
model.add(Dense(1024))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(np.prod(self.img_shape), activation=’tanh’))
model.add(Reshape(self.img_shape))

model.summary()

noise = Input(shape=noise_shape)
img = model(noise)

return Model(noise, img)

def build_discriminator(self):

img_shape = (self.img_rows, self.img_cols, self.channels)

model = Sequential()

model.add(Flatten(input_shape=img_shape))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(1, activation=’sigmoid’))
model.summary()

img = Input(shape=img_shape)
validity = model(img)

return Model(img, validity)

def train(self, epochs, batch_size=128, save_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)

half_batch = int(batch_size / 2)

for epoch in range(epochs):

# ———————
# Train Discriminator
# ———————

# Select a random half batch of images
idx = np.random.randint(0, X_train.shape[0], half_batch)
imgs = X_train[idx]

noise = np.random.normal(0, 1, (half_batch, 100))

# Generate a half batch of new images
gen_imgs = self.generator.predict(noise)

# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(imgs, np.ones((half_batch, 1)))
d_loss_fake = self.discriminator.train_on_batch(gen_imgs, np.zeros((half_batch, 1)))
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ———————
# Train Generator
# ———————

noise = np.random.normal(0, 1, (batch_size, 100))

# The generator wants the discriminator to label the generated samples
# as valid (ones)
valid_y = np.array([1] * batch_size)

# Train the generator
g_loss = self.combined.train_on_batch(noise, valid_y)

# Plot the progress
print (“%d [D loss: %f, acc.: %.2f%%] [G loss: %f]” % (epoch, d_loss[0], 100*d_loss[1], g_loss))

# If at save interval => save generated image samples
if epoch % save_interval == 0:
self.save_imgs(epoch)

def save_imgs(self, epoch):
r, c = 5, 5
noise = np.random.normal(0, 1, (r * c, 100))
gen_imgs = self.generator.predict(noise)

# 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(“gan/images/mnist_%d.png” % epoch)
plt.close()
if __name__ == ‘__main__’:
gan = GAN()
gan.train(epochs=30000, batch_size=32, save_interval=200)

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s