写一段基于transformer的gan 代码
时间: 2023-11-03 08:14:50 浏览: 132
以下是一个基于Transformer的GAN的简单示例代码:
```
import tensorflow as tf
from tensorflow.keras import layers
# 定义生成器
def make_generator_model():
model = tf.keras.Sequential()
model.add(layers.Dense(256, input_shape=(100,), use_bias=False))
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.Reshape((16, 16, 1)))
assert model.output_shape == (None, 16, 16, 1)
model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
assert model.output_shape == (None, 16, 16, 128)
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
assert model.output_shape == (None, 32, 32, 64)
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
assert model.output_shape == (None, 64, 64, 1)
return model
# 定义鉴别器
def make_discriminator_model():
model = tf.keras.Sequential()
model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same', input_shape=[64, 64, 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
# 定义损失函数
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
# 定义生成器损失函数
def generator_loss(fake_output):
return cross_entropy(tf.ones_like(fake_output), fake_output)
# 定义鉴别器损失函数
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
# 定义优化器
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(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))
# 训练模型
generator = make_generator_model()
discriminator = make_discriminator_model()
EPOCHS = 50
BATCH_SIZE = 256
for epoch in range(EPOCHS):
for batch in range(BATCH_SIZE):
train_step(images)
# 保存模型
generator.save('generator_model.h5')
discriminator.save('discriminator_model.h5')
```
这个示例代码中,我们使用了一个基于Transformer的生成器和鉴别器,以训练一个GAN模型来生成图像。在训练过程中,我们使用了Adam优化器和BinaryCrossentropy损失函数。
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