输入列表用python实现CGAN生成列表

首先，需要确定生成列表的具体规则，例如列表中元素的数据类型、元素数量等等。假设我们要生成一个包含10个随机整数的列表，可以使用以下代码实现CGAN生成列表：

import numpy as np
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, concatenate
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.models import Sequential, Model
from keras.optimizers import Adam

# 定义生成器
def build_generator():
    noise_shape = (100,)
    model = Sequential()

    model.add(Dense(256, input_shape=noise_shape))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(512))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(1024))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(10, activation='tanh'))
    model.add(Reshape((1, 10)))

    noise = Input(shape=noise_shape)
    img = model(noise)

    return Model(noise, img)

# 定义判别器
def build_discriminator():
    model = Sequential()

    model.add(Flatten(input_shape=(1, 10)))
    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=(1, 10))
    validity = model(img)

    return Model(img, validity)

# 定义CGAN
def build_cgan(generator, discriminator):
    noise = Input(shape=(100,))
    label = Input(shape=(1, 10))
    img = generator(noise)

    discriminator.trainable = False
    validity = discriminator(img)

    concatenated = concatenate([noise, label], axis=1)
    combined = discriminator(concatenated)

    cgan = Model([noise, label], [validity, combined])
    cgan.compile(loss=['binary_crossentropy', 'binary_crossentropy'], optimizer=Adam(0.0002, 0.5))

    return cgan

# 生成随机整数列表
def generate_random_list(generator):
    noise = np.random.normal(0, 1, (1, 100))
    label = np.random.uniform(-1, 1, (1, 1, 10))
    generated_list = generator.predict([noise, label])
    generated_list = np.squeeze(generated_list)

    return generated_list

# 训练CGAN
def train(generator, discriminator, cgan, epochs, batch_size):
    X_train = np.random.uniform(-1, 1, (10000, 1, 10))
    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)
        real_lists = X_train[idx]
        noise = np.random.normal(0, 1, (batch_size, 100))
        fake_lists = generator.predict([noise, real_lists])

        d_loss_real = discriminator.train_on_batch(real_lists, valid)
        d_loss_fake = discriminator.train_on_batch(fake_lists, fake)
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

        noise = np.random.normal(0, 1, (batch_size, 100))
        g_loss = cgan.train_on_batch([noise, real_lists], [valid, fake])

        if epoch % 100 == 0:
            random_list = generate_random_list(generator)
            print("Epoch %d: D_loss = %f, G_loss = %f, generated_list = %s" % (epoch, d_loss, g_loss[0], random_list))

# 构建CGAN模型
generator = build_generator()
discriminator = build_discriminator()
cgan = build_cgan(generator, discriminator)

# 训练CGAN模型
train(generator, discriminator, cgan, epochs=5000, batch_size=128)

在训练过程中，我们每100个epoch生成一次随机整数列表，并输出当前的D_loss、G_loss以及生成的列表。最终，训练完成后，我们可以通过调用`generate_random_list`函数来生成任意数量的随机整数列表。