def main():
tf.random.set_seed(233)
np.random.seed(233)
z_dim = 100
epochs = 3000000
batch_size = 512
learning_rate = 2e-4
# ratios = D steps:G steps
ratios = 2
img_path = glob.glob(os.path.join('faces', '*.jpg'))
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size)
print(dataset, img_shape)
sample = next(iter(dataset))
print(sample.shape,
tf.reduce_max(sample).numpy(),
tf.reduce_min(sample).numpy())
dataset = dataset.repeat()
db_iter = iter(dataset)
generator = Generator()
generator.build(input_shape=(None, z_dim))
# generator.load_weights(os.path.join('checkpoints', 'generator-5000'))
discriminator = Discriminator()
discriminator.build(input_shape=(None, 64, 64, 3))
# discriminator.load_weights(os.path.join('checkpoints', 'discriminator-5000'))
g_optimizer = tf.optimizers.Adam(learning_rate, beta_1=0.5)
d_optimizer = tf.optimizers.Adam(learning_rate, beta_1=0.5)
# a fixed noise for sampling
z_sample = tf.random.normal([100, z_dim])
g_loss_meter = keras.metrics.Mean()
d_loss_meter = keras.metrics.Mean()
gp_meter = keras.metrics.Mean()
for epoch in range(epochs):
# train D
for step in range(ratios):
batch_z = tf.random.normal([batch_size, z_dim])
batch_x = next(db_iter)
with tf.GradientTape() as tape:
d_loss, gp = d_loss_fn(generator, discriminator, batch_z,
batch_x)
d_loss_meter.update_state(d_loss)
gp_meter.update_state(gp)
gradients = tape.gradient(d_loss,
discriminator.trainable_variables)
d_optimizer.apply_gradients(
zip(gradients, discriminator.trainable_variables))
# train G
batch_z = tf.random.normal([batch_size, z_dim])
with tf.GradientTape() as tape:
g_loss = g_loss_fn(generator, discriminator, batch_z)
g_loss_meter.update_state(g_loss)
gradients = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(
zip(gradients, generator.trainable_variables))
if epoch % 100 == 0:
fake_image = generator(z_sample, training=False)
print(epoch, 'd-loss:',
d_loss_meter.result().numpy(), 'g-loss',
g_loss_meter.result().numpy(), 'gp',
gp_meter.result().numpy())
d_loss_meter.reset_states()
g_loss_meter.reset_states()
gp_meter.reset_states()
# save generated image samples
img_path = os.path.join('images_wgan_gp', 'wgan_gp-%d.png' % epoch)
save_result(fake_image.numpy(), 10, img_path, color_mode='P')
if epoch + 1 % 2000 == 0:
generator.save_weights(
os.path.join('checkpoints_gp', 'generator-%d' % epoch))
discriminator.save_weights(
os.path.join('checkpoints_gp', 'discriminator-%d' % epoch))
def main():
tf.random.set_seed(3333)
np.random.seed(3333)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
z_dim = 100 # 隐藏向量z的长度
epochs = 3000000 # 训练步数
batch_size = 64
learning_rate = 0.0002
is_training = True
# 获取数据集路径
img_path = glob.glob(r'C:\Users\jay_n\.keras\datasets\faces\*.jpg') + \
glob.glob(r'C:\Users\jay_n\.keras\datasets\faces\*.png')
print('images num:', len(img_path))
# 构建数据集对象
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size, resize=64)
print(dataset, img_shape)
sample = next(iter(dataset)) # 采样
print(sample.shape, tf.reduce_max(sample).numpy(), tf.reduce_min(sample).numpy())
dataset = dataset.repeat(100)
db_iter = iter(dataset)
generator = Generator()
generator.build(input_shape=(4, z_dim))
discriminator = Discriminator()
discriminator.build(input_shape=(4, 64, 64, 3))
# 分别为生成器和判别器创建优化器
g_optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
d_optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
# generator.load_weights('generator.ckpt')
# discriminator.load_weights('discriminator.ckpt')
# print('Loaded ckpt!!')
d_losses, g_losses = [], []
for epoch in range(epochs):
# 1. 训练判别器
for _ in range(1):
# 采样隐藏向量
batch_z = tf.random.normal([batch_size, z_dim])
batch_x = next(db_iter) # 采样真实图片
# 判别器前向计算
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))
# 2. 训练生成器
# 采样隐藏向量
batch_z = tf.random.normal([batch_size, z_dim])
# 生成器前向计算
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))
# 可视化
z = tf.random.normal([100, z_dim])
fake_image = generator(z, training=False)
img_path = os.path.join('gan_images', 'gan-%d.png' % epoch)
save_result(fake_image.numpy(), 10, img_path, color_mode='P')
d_losses.append(float(d_loss))
g_losses.append(float(g_loss))
if epoch % 10000 == 1:
generator.save_weights('generator.ckpt')
discriminator.save_weights('discriminator.ckpt')
def main():
tf.random.set_seed(3333)
np.random.seed(3333)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
z_dim = 100 # 隐藏向量z的长度
epochs = 3000000 # 训练步数
batch_size = 64 # batch size
learning_rate = 0.0002
is_training = True
# 获取数据集路径
# C:\Users\z390\Downloads\anime-faces
# r'C:\Users\z390\Downloads\faces\*.jpg'
# img_path = glob.glob(r'C:\Users\z390\Downloads\anime-faces\*\*.jpg') + \
# glob.glob(r'C:\Users\z390\Downloads\anime-faces\*\*.png')
img_path = glob.glob(
r'/home/ulysses/workspace/AI/Deep-Learning-with-TensorFlow-book/ch13/faces/*.jpg'
)
# img_path.extend(img_path2)
print('images num:', len(img_path))
# 构建数据集对象
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size, resize=64)
print(dataset, img_shape)
sample = next(iter(dataset)) # 采样
print(sample.shape,
tf.reduce_max(sample).numpy(),
tf.reduce_min(sample).numpy())
dataset = dataset.repeat(100) # 重复循环
db_iter = iter(dataset)
generator = Generator() # 创建生成器
generator.build(input_shape=(4, z_dim))
discriminator = Discriminator() # 创建判别器
discriminator.build(input_shape=(4, 64, 64, 3))
# 分别为生成器和判别器创建优化器
g_optimizer = keras.optimizers.Adam(learning_rate=learning_rate,
beta_1=0.5)
d_optimizer = keras.optimizers.Adam(learning_rate=learning_rate,
beta_1=0.5)
if os.path.exists(r'./generator.ckpt.index'):
generator.load_weights('generator.ckpt')
print('Loaded generator chpt!!')
if os.path.exists(r'./discriminator.ckpt.index'):
discriminator.load_weights('discriminator.ckpt')
print('Loaded discriminator chpt!!')
d_losses, g_losses = [], []
for epoch in range(epochs): # 训练epochs次
# 1. 训练判别器
for _ in range(1):
# 采样隐藏向量
batch_z = tf.random.normal([batch_size, z_dim])
batch_x = next(db_iter) # 采样真实图片
# 判别器前向计算
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))
# 2. 训练生成器
# 采样隐藏向量
batch_z = tf.random.normal([batch_size, z_dim])
batch_x = next(db_iter) # 采样真实图片
# 生成器前向计算
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))
# 可视化
z = tf.random.normal([100, z_dim])
fake_image = generator(z, training=False)
img_path = os.path.join('gan_images1', 'gan-%d.png' % epoch)
save_result(fake_image.numpy(), 10, img_path, color_mode='P')
d_losses.append(float(d_loss))
g_losses.append(float(g_loss))
if epoch % 10000 == 0:
# print(d_losses)
# print(g_losses)
generator.save_weights('./check_point/generator.ckpt')
discriminator.save_weights('./check_point/discriminator.ckpt')