给我写一个可以实现对抗神经网络GAN的代码
时间: 2024-05-10 08:17:37 浏览: 9
GAN(Generative Adversarial Networks)是一种生成模型,由两个神经网络组成:生成器和判别器。生成器用于生成新的数据样本,而判别器则用于判定生成器生成的样本是否真实。两个神经网络不断进行对抗训练,使得生成器逐渐生成更加逼真的样本。
以下是一个简单的GAN代码实现:
```python
import tensorflow as tf
from tensorflow.keras.layers import Dense, Input, LeakyReLU
from tensorflow.keras import Model
# 定义生成器
def build_generator(input_shape, output_shape):
inputs = Input(shape=input_shape)
x = Dense(64, activation=LeakyReLU(alpha=0.2))(inputs)
x = Dense(128, activation=LeakyReLU(alpha=0.2))(x)
x = Dense(256, activation=LeakyReLU(alpha=0.2))(x)
outputs = Dense(output_shape, activation='sigmoid')(x)
generator = Model(inputs=inputs, outputs=outputs)
return generator
# 定义判别器
def build_discriminator(input_shape):
inputs = Input(shape=input_shape)
x = Dense(256, activation=LeakyReLU(alpha=0.2))(inputs)
x = Dense(128, activation=LeakyReLU(alpha=0.2))(x)
x = Dense(64, activation=LeakyReLU(alpha=0.2))(x)
outputs = Dense(1, activation='sigmoid')(x)
discriminator = Model(inputs=inputs, outputs=outputs)
return discriminator
# 定义GAN模型
def build_gan(generator, discriminator):
discriminator.trainable = False
inputs = Input(shape=generator.input_shape[1:])
generated_data = generator(inputs)
outputs = discriminator(generated_data)
gan = Model(inputs=inputs, outputs=outputs)
return gan
# 定义损失函数和优化器
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)
# 定义训练步骤
@tf.function
def train_step(real_data, generator, discriminator):
# 生成器生成假数据
noise = tf.random.normal([real_data.shape[0], 100])
generated_data = generator(noise)
# 训练判别器
with tf.GradientTape() as tape:
real_output = discriminator(real_data)
fake_output = discriminator(generated_data)
real_loss = cross_entropy(tf.ones_like(real_output), real_output)
fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
discriminator_loss = real_loss + fake_loss
grads = tape.gradient(discriminator_loss, discriminator.trainable_variables)
discriminator_optimizer.apply_gradients(zip(grads, discriminator.trainable_variables))
# 训练生成器
with tf.GradientTape() as tape:
fake_output = discriminator(generated_data)
generator_loss = cross_entropy(tf.ones_like(fake_output), fake_output)
grads = tape.gradient(generator_loss, generator.trainable_variables)
generator_optimizer.apply_gradients(zip(grads, generator.trainable_variables))
# 加载数据集
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 784).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)
# 创建生成器和判别器
generator = build_generator((100,), 784)
discriminator = build_discriminator((784,))
# 创建GAN模型
gan = build_gan(generator, discriminator)
# 训练GAN模型
EPOCHS = 100
for epoch in range(EPOCHS):
for real_data in train_dataset:
train_step(real_data, generator, discriminator)
if epoch % 10 == 0:
print('Epoch {}: done.'.format(epoch))
```
上述代码中,我们通过 `build_generator` 和 `build_discriminator` 分别定义了生成器和判别器。然后,我们通过 `build_gan` 将生成器和判别器组合成一个GAN模型。
在训练过程中,我们需要定义 `train_step` 函数,并在其中完成生成器和判别器的训练。具体来说,我们首先使用生成器生成假数据,然后训练判别器来区分真实数据和假数据。接着,我们使用生成器的输出来训练生成器,使得生成器逐渐生成更加逼真的假数据。
最后,我们使用 `train_dataset` 中的真实数据来训练GAN模型。在每个epoch结束后,我们打印一条日志。
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