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 = 512
learning_rate = 0.002
is_training = True
img_path = glob.glob('/Users/tongli/Desktop/Python/TensorFlow/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))
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.Adam(learning_rate=learning_rate, beta_1=0.5)
for epoch in range(epochs):
batch_z = tf.random.uniform([batch_size, z_dim], minval=-1., maxval=1.)
batch_x = next(db_iter)
# train D
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))
z = tf.random.uniform([100, z_dim])
fake_image = generator(z, training=False)
img_path = os.path.join('images', 'gan-%d.png' % epoch)
save_result(fake_image.numpy(), 10, img_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.')
# 设置超参数
z_dim = 10
epoch = 3000000
batch_size = 512
learning_rate = 0.002
is_training = True
# 数据集加载,每一张图片路径集
img_path = glob.glob(r'D:\PyCharm Projects\CarNum-CNN\data\faces\*.jpg')
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size)
# print(dataset, img_shape)
# sample = next(iter(dataset))
# print(sample)
# 无线采样
dataset = dataset.repeat()
db_iter = iter(dataset)
# 导入生成器模型和判断器模型
genertor = Generator()
genertor.build(input_shape=(None, z_dim))
discriminator = Discriminator()
discriminator.build(input_shape=(None, 64, 64, 3))
# 分别设置两个优化器
g_optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
d_optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
for epoch in range(epoch):
batch_z = tf.random.uniform([batch_size, z_dim], minval=-1., maxval=1.)
batch_x = next(db_iter)
# train D
with tf.GradientTape() as tape:
d_loss = d_loss_fn(genertor, discriminator, batch_z, batch_x,
is_training)
grads = tape.gradient(d_loss, discriminator.trainable_variables)
d_optimizer.apply_gradients(
zip(grads, discriminator.trainable_variables))
# train G
with tf.GradientTape() as tape:
g_loss = g_loss_fn(genertor, discriminator, batch_z, is_training)
grads = tape.gradient(g_loss, genertor.trainable_variables)
g_optimizer.apply_gradients(zip(grads, genertor.trainable_variables))
# 打印
if epoch % 100 == 0:
print(epoch, "d_loss:", float(d_loss), "g_loss:", float(g_loss))
z = tf.random.uniform([100, z_dim])
fake_image = genertor(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')
