能不能给我写一段条件生成对抗网络的示例代码
时间: 2023-04-06 15:00:25 浏览: 56
当然可以,以下是一个简单的条件生成对抗网络的示例代码:
```
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate
from tensorflow.keras.layers import BatchNormalization, Activation, ZeroPadding2D, UpSampling2D, Conv2D
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam
import numpy as np
# 定义生成器
def build_generator(z_dim, img_shape, label_dim):
model = Sequential()
model.add(Dense(256, input_dim=z_dim+label_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(1024))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(np.prod(img_shape), activation='tanh'))
model.add(Reshape(img_shape))
z = Input(shape=(z_dim,))
label = Input(shape=(label_dim,))
img = model(Concatenate()([z, label]))
return Model([z, label], img)
# 定义判别器
def build_discriminator(img_shape, label_dim):
model = Sequential()
model.add(Flatten(input_shape=img_shape))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(1, activation='sigmoid'))
img = Input(shape=img_shape)
label = Input(shape=(label_dim,))
flat_img = Flatten()(img)
validity = model(Concatenate()([flat_img, label]))
return Model([img, label], validity)
# 定义CGAN模型
def build_cgan(generator, discriminator):
z_dim = generator.input_shape[0][1]
label_dim = generator.input_shape[1][1]
discriminator.trainable = False
z = Input(shape=(z_dim,))
label = Input(shape=(label_dim,))
img = generator([z, label])
validity = discriminator([img, label])
return Model([z, label], validity)
# 训练CGAN模型
def train_cgan(generator, discriminator, cgan, x_train, y_train, epochs, batch_size):
z_dim = generator.input_shape[0][1]
label_dim = generator.input_shape[1][1]
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in range(epochs):
idx = np.random.randint(0, x_train.shape[0], batch_size)
imgs, labels = x_train[idx], y_train[idx]
z = np.random.normal(0, 1, (batch_size, z_dim))
gen_imgs = generator.predict([z, labels])
d_loss_real = discriminator.train_on_batch([imgs, labels], valid)
d_loss_fake = discriminator.train_on_batch([gen_imgs, labels], fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
z = np.random.normal(0, 1, (batch_size, z_dim))
g_loss = cgan.train_on_batch([z, labels], valid)
print("%d [D loss: %f] [G loss: %f]" % (epoch, d_loss, g_loss))
```
这是一个简单的CGAN模型,可以用于生成带标签的图像。其中,生成器和判别器都是基于全连接层的简单模型,可以根据需要进行修改。训练过程中,我们先训练判别器,再训练生成器,以此交替进行。
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)