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：

# 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)

# 提出相关问题：