ACGAN的Python代码
ACGAN是一种生成对抗网络(GAN)的变种,其中“AC”代表辅助分类器(auxiliary classifier),它通过将类别标签作为输入来帮助生成器学习生成特定类别的图像。以下是一个简单的ACGAN的Python代码实现:
```python
导入必要的库
import numpy as np import tensorflow as tf from tensorflow.keras.datasets import mnist from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply from tensorflow.keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D from tensorflow.keras.layers import MaxPooling2D, Conv2DTranspose, UpSampling2D, Conv2D from tensorflow.keras.models import Sequential, Model from tensorflow.keras.optimizers import Adam
定义超参数
img_rows = 28 img_cols = 28 channels = 1 img_shape = (img_rows, img_cols, channels) num_classes = 10 latent_dim = 100
构建生成器
def build_generator():
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2DTranspose(128, kernel_size=3, strides=2, padding="same"))
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(Conv2DTranspose(1, kernel_size=3, strides=2, padding="same"))
model.add(Activation("tanh"))
noise = Input(shape=(latent_dim,))
label = Input(shape=(1,), dtype="int32")
label_embedding = Flatten()(Embedding(num_classes, latent_dim)(label))
model_input = multiply([noise, label_embedding])
img = model(model_input)
return Model([noise, label], img)
构建辅助分类器
def build_discriminator():
model = Sequential()
model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=img_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(32, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Flatten())
img = Input(shape=img_shape)
features = model(img)
validity = Dense(1, activation="sigmoid")(features)
label = Dense(num_classes+1, activation="softmax")(features)
return Model(img, [validity, label])
编译辅助分类器
optimizer = Adam(0.0002, 0.5) discriminator = build_discriminator() discriminator.compile(loss=['binary_crossentropy', 'sparse_categorical_crossentropy'], loss_weights=[0.5, 0.5], optimizer=optimizer)
构建组合模型
generator = build_generator() noise = Input(shape=(latent_dim,)) label = Input(shape=(1,)) img = generator([noise, label]) discriminator.trainable = False validity, _ = discriminator(img) combined = Model([noise, label], validity) combined.compile(loss=['binary_crossentropy'], optimizer=optimizer)
训练ACGAN模型
epochs = 20000 batch_size = 32
(X_train, y_train), (_, _) = mnist.load_data() X_train = X_train / 127.5 - 1. X_train = np.expand_dims(X_train, axis=3) y_train = y_train.reshape(-1, 1)
half_batch = int(batch_size / 2)
for epoch in range(epochs):
# 训练辅助分类器
idx = np.random.randint(0, X_train.shape, half_batch)
imgs, labels = X_train[idx], y_train[idx]
noise = np.random.normal(0, 1, (half_batch, latent_dim))
gen_labels = np.random.randint(0, num_classes, half_batch).reshape(-1, 1)
gen_imgs = generator.predict([noise, gen_labels])
d_loss_real = discriminator.train_on_batch(imgs, [np.ones((half_batch, 1)), labels])
d_loss_fake = discriminator.train_on_batch(gen_imgs, [np.zeros((half_batch, 1)), gen_labels])
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# 训练生成器
noise = np.random.normal(0, 1, (batch_size, latent_dim))
valid_y = np.array( * batch_size).reshape(-1, 1)
# 增加噪声,使得生成的图像具有不同的类别标签
sampled_labels = np.random.randint(0, num_classes, batch_size).reshape(-1, 1)
g_loss = combined.train_on_batch([noise, sampled_labels], valid_y)
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