写一个利用自己真实数据的生成对抗代码
时间: 2024-04-19 14:27:15 浏览: 9
生成对抗网络(GAN)是一种常用的深度学习模,用于生成具有与真实相似特征的新数据。在编利用真实数据的生成对抗代码之前,我们需要准备好数据集。假设我们要生成手写数字图片,可以使用MNIST数据集作为真实数据。
以下是一个基本的生成对抗网络代码示例:
```python
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
# 定义生成器模型
def build_generator():
model = tf.keras.Sequential()
model.add(layers.Dense(256, input_shape=(100,), activation='relu'))
model.add(layers.Dense(784, activation='sigmoid'))
model.add(layers.Reshape((28, 28, 1)))
return model
# 定义判别器模型
def build_discriminator():
model = tf.keras.Sequential()
model.add(layers.Flatten(input_shape=(28, 28, 1)))
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
return model
# 定义生成对抗网络模型
def build_gan(generator, discriminator):
discriminator.trainable = False
model = tf.keras.Sequential()
model.add(generator)
model.add(discriminator)
return model
# 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
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 = cross_entropy(tf.ones_like(fake_output), fake_output)
disc_loss_real = cross_entropy(tf.ones_like(real_output), real_output)
disc_loss_fake = cross_entropy(tf.zeros_like(fake_output), fake_output)
disc_loss = disc_loss_real + disc_loss_fake
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))
# 加载数据集
(X_train, _), (_, _) = tf.keras.datasets.mnist.load_data()
X_train = np.expand_dims(X_train, axis=-1)
X_train = X_train.astype('float32') / 255.0
BUFFER_SIZE = X_train.shape[0]
BATCH_SIZE = 256
# 创建数据集
train_dataset = tf.data.Dataset.from_tensor_slices(X_train).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
# 创建生成器、判别器和生成对抗网络
generator = build_generator()
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)
# 定义训练循环
def train(dataset, epochs):
for epoch in range(epochs):
for image_batch in dataset:
train_step(image_batch)
# 开始训练
EPOCHS = 50
train(train_dataset, EPOCHS)
```
上述代码中,我们首先定义了生成器模型、判别器模型和生成对抗网络模型。然后定义了损失函数和优化器。接下来,我们通过`train_step`函数来定义每个训练步骤的操作。最后,我们加载数据集,创建生成器、判别器和生成对抗网络,然后通过`train`函数进行训练。
请注意,上述代码只是一个简单的示例,实际应用中可能需要根据具体的数据集和任务进行适当的调整和优化。