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.discriminator = self.build_discriminator()

self.discriminator.compile(loss='binary_crossentropy',

optimizer=optimizer,

metrics=['accuracy'])

# Build the generator

self.generator = self.build_generator()

# The generator takes noise as input and generates imgs

z = Input(shape=(self.latent_dim,))

img = self.generator(z)

# For the combined model we will only train the generator

self.discriminator.trainable = False

# The discriminator takes generated images as input and determines validity

validity = self.discriminator(img)

# The combined model (stacked generator and discriminator)

# Trains the generator to fool the discriminator

self.combined = Model(z, validity)

self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)

def build_generator(self):

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))

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=(self.latent_dim,))

img = model(noise)

return Model(noise, img)

def build_discriminator(self):

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))

model.add(Dense(1, activation='sigmoid'))

model.summary()

img = Input(shape=self.img_shape)

validity = model(img)

return Model(img, validity)

def train(self, epochs, batch_size=128, sample_interval=50):

# Load the dataset

X_train = pickle.load(open("X.pickle", "rb"))

y_train = pickle.load(open("y.pickle", "rb"))

X_train = np.array(X_train, dtype=np.float32)

# Rescale -1 to 1

X_train = X_train / 127.5 - 1.

X_train = np.expand_dims(X_train, axis=3)

# Adversarial ground truths

valid = np.ones((batch_size, 1))

fake = np.zeros((batch_size, 1))

for epoch in range(epochs):

# ---------------------

# Train Discriminator

# ---------------------

# Select a random batch of images

idx = np.random.randint(0, X_train.shape[0], batch_size)

imgs = X_train[idx]

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

# Generate a batch of new images

gen_imgs = self.generator.predict(noise)

# Train the discriminator

d_loss_real = self.discriminator.train_on_batch(imgs, valid)

d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)

d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

# ---------------------

# Train Generator

# ---------------------

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

# Train the generator (to have the discriminator label samples as valid)

g_loss = self.combined.train_on_batch(noise, valid)

# 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 % sample_interval == 0:

self.sample_images(epoch)

if epoch % 100 == 0:

plt.xlabel('Epoch')

plt.ylabel('Loss')

plt.plot(epoch, d_loss[0], 'g+', linestyle='solid', label='D-Loss')

plt.plot(epoch, g_loss, 'b*', linestyle='solid', label='G-loss')

plt.plot(epoch, d_loss[1], 'y+', linestyle='solid', label='acc')

plt.show()

def sample_images(self, epoch):

r, c = 5, 5

noise = np.random.normal(0, 1, (r * c, self.latent_dim))

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("images/%d.png" % epoch)