使用TensorFlow创建生成式对抗网络GAN案例

导入必要的库和模块

以下是使用TensorFlow创建一个生成式对抗网络(GAN)的案例: 首先,我们需要导入必要的库和模块:

import tensorflow as tf
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import numpy as np

然后,我们定义生成器和鉴别器模型。生成器模型将随机噪声作为输入,并输出伪造的图像。鉴别器模型则将图像作为输入,并输出一个0到1之间的概率值,表示输入图像是真实图像的概率。

# 定义生成器模型
def make_generator_model():
 model = tf.keras.Sequential()
 model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))
 model.add(layers.BatchNormalization())
 model.add(layers.LeakyReLU())
 model.add(layers.Reshape((7, 7, 256)))
 assert model.output_shape == (None, 7, 7, 256) 
 model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
 assert model.output_shape == (None, 7, 7, 128)
 model.add(layers.BatchNormalization())
 model.add(layers.LeakyReLU())
 model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
 assert model.output_shape == (None, 14, 14, 64)
 model.add(layers.BatchNormalization())
 model.add(layers.LeakyReLU())
 model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
 assert model.output_shape == (None, 28, 28, 1)
 return model
# 定义鉴别器模型
def make_discriminator_model():
 model = tf.keras.Sequential()
 model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
 input_shape=[28, 28, 1]))
 model.add(layers.LeakyReLU())
 model.add(layers.Dropout(0.3))
 model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
 model.add(layers.LeakyReLU())
 model.add(layers.Dropout(0.3))
 model.add(layers.Flatten())
 model.add(layers.Dense(1))
 return model

接下来,我们定义损失函数和优化器。生成器和鉴别器都有自己的损失函数和优化器。

# 定义鉴别器损失函数
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def discriminator_loss(real_output, fake_output):
 real_loss = cross_entropy(tf.ones_like(real_output), real_output)
 fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
 total_loss = real_loss + fake_loss
 return total_loss
# 定义生成器损失函数
def generator_loss(fake_output):
 return cross_entropy(tf.ones_like(fake_output), fake_output)
# 定义优化器
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)

定义训练循环

在每个epoch中,我们将随机生成一组噪声作为输入,并使用生成器生成伪造图像。然后,我们将真实图像和伪造图像一起传递给鉴别器,计算鉴别器和生成器的损失函数,并使用优化器更新模型参数。

# 定义训练循环
@tf.function
def train_step(images):
 noise = tf.random.normal([BATCH_SIZE, 100])
 with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
 generated_images = generator(noise, training=True)
 real_output = discriminator(images, training=True)
 fake_output = discriminator(generated_images, training=True)
 gen_loss = generator_loss(fake_output)
 disc_loss = discriminator_loss(real_output, fake_output)
 gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
 gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
 generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
 discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

最后定义主函数

加载MNIST数据集并训练模型。

# 加载数据集
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5 # 将像素值归一化到[-1, 1]之间
BUFFER_SIZE = 60000
BATCH_SIZE = 256
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
# 创建生成器和鉴别器模型
generator = make_generator_model()
discriminator = make_discriminator_model()
# 训练模型
EPOCHS = 100
noise_dim = 100
num_examples_to_generate = 16
# 用于可视化生成的图像
seed = tf.random.normal([num_examples_to_generate, noise_dim])
for epoch in range(EPOCHS):
 for image_batch in train_dataset:
 train_step(image_batch)
 # 每个epoch结束后生成一些图像并可视化
 generated_images = generator(seed, training=False)
 fig = plt.figure(figsize=(4, 4))
 for i in range(generated_images.shape[0]):
 plt.subplot(4, 4, i+1)
 plt.imshow(generated_images[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
 plt.axis('off')
 plt.show()

这个案例使用了TensorFlow的高级API,可以帮助我们更快速地创建和训练GAN模型。在实际应用中,可能需要根据不同的数据集和任务进行调整和优化。

作者:italks

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