生成对抗网络csdn代码
时间: 2023-10-26 07:03:13 浏览: 66
生成对抗网络(GAN)是一种机器学习模型,由两个相互竞争的神经网络组成,分别称为生成器(Generator)和判别器(Discriminator)。生成器的目标是生成具有逼真性的样本,而判别器的目标是将生成器生成的样本与真实样本区分开来。
在CSDN上提供了很多GAN的代码。例如,以下是一个简单的GAN代码示例:
生成器的代码:
```python
import tensorflow as tf
def generator(z_dim):
model = tf.keras.Sequential([
tf.keras.layers.Dense(256, input_dim=z_dim, activation='relu'),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(1024, activation='relu'),
tf.keras.layers.Dense(784, activation='tanh')
])
return model
```
判别器的代码:
```python
import tensorflow as tf
def discriminator():
model = tf.keras.Sequential([
tf.keras.layers.Dense(1024, input_dim=784, activation='relu'),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
return model
```
GAN模型的训练过程代码:
```python
import tensorflow as tf
def train_gan(generator, discriminator, z_dim, epochs, batch_size):
# 编译判别器
discriminator.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(0.0002, 0.5), metrics=['accuracy'])
# 生成器和判别器生成的输入
z = tf.keras.layers.Input(shape=(z_dim,))
img = generator(z)
# 判别器不可被训练
discriminator.trainable = False
# 将生成器生成的图像传入判别器中进行判断
valid = discriminator(img)
# 组合生成器和判别器为一个模型
combined = tf.keras.models.Model(inputs=z, outputs=valid)
combined.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(0.0002, 0.5))
# 训练过程
for epoch in range(epochs):
# 训练判别器
idx = np.random.randint(0, X_train.shape[0], batch_size)
real_imgs = X_train[idx]
z = np.random.normal(0, 1, (batch_size, z_dim))
gen_imgs = generator.predict(z)
d_loss_real = discriminator.train_on_batch(real_imgs, np.ones((batch_size, 1)))
d_loss_fake = discriminator.train_on_batch(gen_imgs, np.zeros((batch_size, 1)))
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# 训练生成器
z = np.random.normal(0, 1, (batch_size, z_dim))
g_loss = combined.train_on_batch(z, np.ones((batch_size, 1)))
# 打印损失函数
print(f"Epoch: {epoch}, Discriminator loss: {d_loss[0]}, Generator loss: {g_loss}")
```
以上是一个简单的GAN代码示例,可以通过调整模型结构、训练参数等进行自定义。希望对你有帮助!
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