生成对抗网络 (GAN)。通过学习图像训练数据集的隐分布（图像的“隐空间”），GAN 可以生成看起来极为真实的新图像。
一个 GAN 由两部分组成：一个“生成器”模型（可将隐空间中的点映射到图像空间中的点）和一个“判别器”模型，后者是一个可以区分真实图像（来自训练数据集）与虚假图像（生成器网络的输出）之间差异的分类器。
GAN 训练循环如下所示：
1.训练判别器
- 在隐空间中对一批随机点采样
- 通过“生成器”模型将这些点转换为虚假图像
- 获取一批真实图像，并将它们与生成的图像组合
- 训练“判别器”模型以对生成的图像与真实图像进行分类
2.训练生成器
- 在隐空间中对随机点采样
- 通过“生成器”网络将这些点转换为虚假图像
- 获取一批真实图像，并将它们与生成的图像组合
- 训练“生成器”模型以“欺骗”判别器，并将虚假图像分类为真实图像
定义判别器
discriminator = keras.Sequential(
[
keras.Input(shape=(28, 28, 1)),
layers.Conv2D(64, (3, 3), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2D(128, (3, 3), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.GlobalMaxPooling2D(),
layers.Dense(1),
],
name="discriminator",
)
discriminator.summary()
定义生成器
latent_dim = 128
generator = keras.Sequential(
[
keras.Input(shape=(latent_dim,)),
layers.Dense(7 * 7 * 128),
layers.LeakyReLU(alpha=0.2),
layers.Reshape((7, 7, 128)),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2D(1, (7, 7), padding="same", activation="sigmoid"),
],
name="generator",
)
单步训练
d_optimizer = keras.optimizers.Adam(learning_rate=0.0003)
g_optimizer = keras.optimizers.Adam(learning_rate=0.0004)
loss_fn = keras.losses.BinaryCrossentropy(from_logits=True)
@tf.function
def train_step(real_images):
random_latent_vectors = tf.random.normal(shape=(batch_size, latent_dim))
generated_images = generator(random_latent_vectors)
combined_images = tf.concat([generated_images, real_images], axis=0)
labels = tf.concat(
[tf.ones((batch_size, 1)), tf.zeros((real_images.shape[0], 1))], axis=0
)
labels += 0.05 * tf.random.uniform(labels.shape)
with tf.GradientTape() as tape:
predictions = discriminator(combined_images)
d_loss = loss_fn(labels, predictions)
grads = tape.gradient(d_loss, discriminator.trainable_weights)
d_optimizer.apply_gradients(zip(grads, discriminator.trainable_weights))
random_latent_vectors = tf.random.normal(shape=(batch_size, latent_dim))
misleading_labels = tf.zeros((batch_size, 1))
with tf.GradientTape() as tape:
predictions = discriminator(generator(random_latent_vectors))
g_loss = loss_fn(misleading_labels, predictions)
grads = tape.gradient(g_loss, generator.trainable_weights)
g_optimizer.apply_gradients(zip(grads, generator.trainable_weights))
return d_loss, g_loss, generated_images
完整代码
"""
* Created with PyCharm
* 作者: 阿光
* 日期: 2022/1/3
* 时间: 20:49
* 描述:
"""
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
discriminator = keras.Sequential(
[
keras.Input(shape=(28, 28, 1)),
layers.Conv2D(64, (3, 3), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2D(128, (3, 3), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.GlobalMaxPooling2D(),
layers.Dense(1),
],
name="discriminator",
)
discriminator.summary()
latent_dim = 128
generator = keras.Sequential(
[
keras.Input(shape=(latent_dim,)),
layers.Dense(7 * 7 * 128),
layers.LeakyReLU(alpha=0.2),
layers.Reshape((7, 7, 128)),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2D(1, (7, 7), padding="same", activation="sigmoid"),
],
name="generator",
)
d_optimizer = keras.optimizers.Adam(learning_rate=0.0003)
g_optimizer = keras.optimizers.Adam(learning_rate=0.0004)
loss_fn = keras.losses.BinaryCrossentropy(from_logits=True)
@tf.function
def train_step(real_images):
random_latent_vectors = tf.random.normal(shape=(batch_size, latent_dim))
generated_images = generator(random_latent_vectors)
combined_images = tf.concat([generated_images, real_images], axis=0)
labels = tf.concat(
[tf.ones((batch_size, 1)), tf.zeros((real_images.shape[0], 1))], axis=0
)
labels += 0.05 * tf.random.uniform(labels.shape)
with tf.GradientTape() as tape:
predictions = discriminator(combined_images)
d_loss = loss_fn(labels, predictions)
grads = tape.gradient(d_loss, discriminator.trainable_weights)
d_optimizer.apply_gradients(zip(grads, discriminator.trainable_weights))
random_latent_vectors = tf.random.normal(shape=(batch_size, latent_dim))
misleading_labels = tf.zeros((batch_size, 1))
with tf.GradientTape() as tape:
predictions = discriminator(generator(random_latent_vectors))
g_loss = loss_fn(misleading_labels, predictions)
grads = tape.gradient(g_loss, generator.trainable_weights)
g_optimizer.apply_gradients(zip(grads, generator.trainable_weights))
return d_loss, g_loss, generated_images
import os
batch_size = 64
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
all_digits = np.concatenate([x_train, x_test])
all_digits = all_digits.astype("float32") / 255.0
all_digits = np.reshape(all_digits, (-1, 28, 28, 1))
dataset = tf.data.Dataset.from_tensor_slices(all_digits)
dataset = dataset.shuffle(buffer_size=1024).batch(batch_size)
epochs = 1
save_dir = "./"
for epoch in range(epochs):
print("\nStart epoch", epoch)
for step, real_images in enumerate(dataset):
d_loss, g_loss, generated_images = train_step(real_images)
if step % 200 == 0:
print("discriminator loss at step %d: %.2f" % (step, d_loss))
print("adversarial loss at step %d: %.2f" % (step, g_loss))
img = tf.keras.preprocessing.image.array_to_img(
generated_images[0] * 255.0, scale=False
)
img.save(os.path.join(save_dir, "generated_img" + str(step) + ".png"))
if step > 10:
break
