def main():
tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
z_dim = 100
epochs = 3000000
batch_size = 512
learning_rate = 0.005
training = True
img_path = glob.glob(r'.\faces\*.jpg')
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size)
print(dataset, img_shape)
sample_picture = next(iter(dataset))
print(sample_picture.shape, tf.reduce_max(sample_picture).numpy(), tf.reduce_min(sample_picture).numpy())
dataset = dataset.repeat()
ds_iter = iter(dataset)
generator = Generator()
generator.build(input_shape=(None, z_dim))
discriminator = Discriminator()
discriminator.build(input_shape=(None, 64, 64, 3))
g_optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
d_optimizer = tf.optimizers.RMSprop(learning_rate=learning_rate)
for epoch in range(epochs):
batch_z = tf.random.uniform([batch_size, z_dim], minval=-1., maxval=1.)
batch_r = next(ds_iter)
# discriminator training
with tf.GradientTape() as tape:
d_loss = d_loss_func(generator, discriminator, batch_z, batch_r, training)
grads = tape.gradient(d_loss, discriminator.trainable_variables)
d_optimizer.apply_gradients(zip(grads, discriminator.trainable_variables))
with tf.GradientTape() as tape:
g_loss = g_loss_func(generator, discriminator, batch_z, training)
grads = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(zip(grads, generator.trainable_variables))
if epoch % 100 == 0:
print('Current epoch:', epoch, 'd_loss:', d_loss, 'g_loss:', g_loss)
z = tf.random.uniform([100, z_dim])
g_imgs = generator(z, training=False)
save_path = os.path.join('images', 'gan-%d.png' % epoch)
save_result(g_imgs.numpy(), 10, save_path, color_mode='P')
def main():
tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
# hyper parameters
z_dim = 100
epochs = 3000000
batch_size = 128
learning_rate = 0.0002
is_training = True
# for validation purpose
assets_dir = './images'
if not os.path.isdir(assets_dir):
os.makedirs(assets_dir)
val_block_size = 10
val_size = val_block_size * val_block_size
# load mnist data
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
x_train = x_train.astype(np.float32) / 255.
db = tf.data.Dataset.from_tensor_slices(x_train).shuffle(
batch_size * 4).batch(batch_size).repeat()
db_iter = iter(db)
inputs_shape = [-1, 28, 28, 1]
# create generator & discriminator
generator = Generator()
generator.build(input_shape=(batch_size, z_dim))
generator.summary()
discriminator = Discriminator()
discriminator.build(input_shape=(batch_size, 28, 28, 1))
discriminator.summary()
# prepare optimizer
d_optimizer = keras.optimizers.Adam(learning_rate=learning_rate,
beta_1=0.5)
g_optimizer = keras.optimizers.Adam(learning_rate=learning_rate,
beta_1=0.5)
for epoch in range(epochs):
# no need labels
batch_x = next(db_iter)
# rescale images to origin neighborhood
batch_x = tf.reshape(batch_x, shape=inputs_shape)
# -1 -1
batch_x = batch_x * 2.0 - 1.0
batch_z = tf.random.uniform(shape=[batch_size, z_dim],
minval=-1.,
maxval=1.)
with tf.GradientTape() as tape:
d_loss = d_loss_fn(generator, discriminator, batch_z, batch_x,
is_training)
grads = tape.gradient(d_loss, discriminator.trainable_variables)
d_optimizer.apply_gradients(
zip(grads, discriminator.trainable_variables))
with tf.GradientTape() as tape:
g_loss = g_loss_fn(generator, discriminator, batch_z, is_training)
grads = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(zip(grads, generator.trainable_variables))
if epoch % 100 == 0:
print(epoch, 'd loss', float(d_loss), 'g loss:', float(g_loss))
# validation results at every epoch
val_z = np.random.uniform(-1, 1, size=(val_size, z_dim))
fake_image = generator(val_z, training=False)
image_fn = os.path.join(
r'C:\Users\Wojtek\Documents\Projects\DeepLearningPlayground\DCGAN\images',
'gan-val-{:03d}.png'.format(epoch + 1))
save_result(fake_image.numpy(),
val_block_size,
image_fn,
color_mode='L')