cgan_model.h5怎么生成
时间: 2024-04-30 09:19:17 浏览: 13
生成 cgan_model.h5 的步骤如下:
1. 准备数据集,包括真实图片和对应的标签。
2. 定义生成器和判别器网络结构。生成器用于生成假图片,判别器用于判断真假图片。
3. 定义损失函数和优化器。损失函数可以是二元交叉熵或 Wasserstein 损失等。优化器可以选择 Adam 优化器或其他优化器。
4. 训练模型。使用真实图片和标签训练判别器,使用生成器生成假图片并与标签一起训练判别器。最后,将生成器和判别器合并为一个 cgan_model 模型。
5. 保存 cgan_model 模型为 cgan_model.h5。
下面是一个简单的 Keras 代码示例:
```python
from keras.models import Model
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, Embedding, multiply
from keras.layers import Conv2DTranspose, Conv2D, Lambda
from keras.optimizers import Adam
from keras.datasets import mnist
import keras.backend as K
# 定义生成器
def build_generator(z_dim, img_shape, num_classes):
z = Input(shape=(z_dim,))
label = Input(shape=(1,), dtype='int32')
emb = Flatten()(Embedding(num_classes, z_dim)(label))
x = multiply([z, emb])
x = Dense(256, activation='relu')(x)
x = Reshape((1, 1, 256))(x)
x = Conv2DTranspose(128, 4, strides=1, padding='valid')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(64, 4, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(1, 4, strides=2, padding='same')(x)
img = Activation('tanh')(x)
return Model([z, label], img)
# 定义判别器
def build_discriminator(img_shape, num_classes):
img = Input(shape=img_shape)
label = Input(shape=(1,), dtype='int32')
emb = Flatten()(Embedding(num_classes, np.prod(img_shape))(label))
emb = Reshape(img_shape)(emb)
x = multiply([img, emb])
x = Conv2D(64, 4, strides=2, padding='same')(x)
x = Activation('relu')(x)
x = Conv2D(128, 4, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Flatten()(x)
x = Dense(1, activation='sigmoid')(x)
return Model([img, label], x)
# 定义损失函数和优化器
def wasserstein_loss(y_true, y_pred):
return K.mean(y_true * y_pred)
def build_cgan(generator, discriminator):
z = Input(shape=(z_dim,))
label = Input(shape=(1,), dtype='int32')
img = generator([z, label])
discriminator.trainable = False
validity = discriminator([img, label])
return Model([z, label], validity)
generator = build_generator(z_dim, img_shape, num_classes)
discriminator = build_discriminator(img_shape, num_classes)
cgan = build_cgan(generator, discriminator)
optimizer = Adam(lr=0.0002, beta_1=0.5, beta_2=0.999)
discriminator.compile(loss=wasserstein_loss, optimizer=optimizer, metrics=['accuracy'])
cgan.compile(loss=wasserstein_loss, optimizer=optimizer)
# 训练模型
for epoch in range(num_epochs):
for i, (imgs, labels) in enumerate(data_loader):
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], -np.ones((batch_size, 1)))
d_loss_fake = discriminator.train_on_batch([gen_imgs, labels], np.ones((batch_size, 1)))
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
z = np.random.normal(0, 1, (batch_size, z_dim))
labels = np.random.randint(0, num_classes, batch_size).reshape(-1, 1)
g_loss = cgan.train_on_batch([z, labels], -np.ones((batch_size, 1)))
# 保存模型
if epoch % save_interval == 0:
generator.save_weights('cgan_model.h5')
```
在训练完成后,可以使用以下代码将生成器的权重保存为 cgan_model.h5 文件:
```python
generator.save_weights('cgan_model.h5')
```