如何用python实现用GAN生成列表

GAN（Generative Adversarial Networks）是一种用于生成数据的深度学习模型，可以用于生成各种类型的数据，包括列表。

以下是一个简单的Python实现，用于使用GAN生成列表：

首先，导入必要的库：

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout
from tensorflow.keras.layers import BatchNormalization, Activation, ZeroPadding2D
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam

接下来，定义生成器和鉴别器模型：

def build_generator():

    model = Sequential()

    model.add(Dense(256, input_dim=100))
    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(10, activation='tanh'))

    noise = Input(shape=(100,))
    list = model(noise)

    return Model(noise, list)


def build_discriminator():

    model = Sequential()

    model.add(Dense(512, input_dim=10))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(256))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(1, activation='sigmoid'))

    list = Input(shape=(10,))
    validity = model(list)

    return Model(list, validity)

接下来，编写训练代码：

def train(epochs, batch_size=128, sample_interval=50):

    # 加载数据集
    (x_train, y_train), (_, _) = tf.keras.datasets.mnist.load_data()
    x_train = x_train / 127.5 - 1.
    x_train = np.expand_dims(x_train, axis=3)

    # 构建GAN
    optimizer = Adam(0.0002, 0.5)
    discriminator = build_discriminator()
    discriminator.compile(loss='binary_crossentropy',
                          optimizer=optimizer,
                          metrics=['accuracy'])
    generator = build_generator()
    noise = Input(shape=(100,))
    list = generator(noise)
    discriminator.trainable = False
    validity = discriminator(list)
    combined = Model(noise, validity)
    combined.compile(loss='binary_crossentropy',
                     optimizer=optimizer)

    # 开始训练
    for epoch in range(epochs):

        # 训练鉴别器
        idx = np.random.randint(0, x_train.shape[0], batch_size)
        lists = y_train[idx]
        noise = np.random.normal(0, 1, (batch_size, 100))
        gen_lists = generator.predict(noise)
        d_loss_real = discriminator.train_on_batch(lists, np.ones((batch_size, 1)))
        d_loss_fake = discriminator.train_on_batch(gen_lists, np.zeros((batch_size, 1)))
        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, 100))
        valid_y = np.array([1] * batch_size)
        g_loss = combined.train_on_batch(noise, valid_y)

        # 打印训练指标
        print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))

        # 保存生成的列表
        if epoch % sample_interval == 0:
            save_lists(generator, epoch)
            
def save_lists(generator, epoch):
    r, c = 2, 5
    noise = np.random.normal(0, 1, (r * c, 100))
    gen_lists = generator.predict(noise)
    gen_lists = 0.5 * gen_lists + 0.5
    fig, axs = plt.subplots(r, c)
    cnt = 0
    for i in range(r):
        for j in range(c):
            axs[i,j].imshow(gen_lists[cnt], cmap='gray')
            axs[i,j].axis('off')
            cnt += 1
    fig.savefig("generated_lists/%d.png" % epoch)
    plt.close()

最后，调用train函数开始训练：

train(epochs=20000, batch_size=32, sample_interval=1000)

这是一个简单的示例，可以根据具体需求进行调整和改进。