wgan-gp网络中，生成器的loss一直增加，判别器的loss一直降低是为什么

这可能是由于生成器的设计不够好，或者训练数据集不够充分，导致生成器无法生成高质量的样本，而判别器则能够更好地区分真实样本和生成样本，从而导致生成器的loss增加，判别器的loss降低。可以尝试调整生成器的架构或者增加训练数据集来解决这个问题。

相关问题

wgan-gp代码

WGAN-GP是一种基于GAN的深度学习模型，它使用了梯度惩罚技术（Gradient Penalty，GP）来解决GAN训练过程中的不稳定性和模式崩溃问题。以下是一个简单的WGAN-GP的PyTorch实现代码：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义生成器和判别器的网络结构
class Generator(nn.Module):
    def __init__(self, latent_dim=100, img_shape=(1, 28, 28)):
        super(Generator, self).__init__()
        self.latent_dim = latent_dim
        self.img_shape = img_shape

        self.model = nn.Sequential(
            nn.Linear(self.latent_dim, 128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(128, 256),
            nn.BatchNorm1d(256, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 512),
            nn.BatchNorm1d(512, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, np.prod(self.img_shape)),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), *self.img_shape)
        return img

class Discriminator(nn.Module):
    def __init__(self, img_shape=(1, 28, 28)):
        super(Discriminator, self).__init__()
        self.img_shape = img_shape

        self.model = nn.Sequential(
            nn.Linear(np.prod(self.img_shape), 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 1),
        )

    def forward(self, img):
        img = img.view(img.size(0), -1)
        validity = self.model(img)
        return validity

# 定义WGAN-GP模型
class WGAN_GP(nn.Module):
    def __init__(self, latent_dim=100, img_shape=(1, 28, 28), lambda_gp=10):
        super(WGAN_GP, self).__init__()
        self.generator = Generator(latent_dim, img_shape)
        self.discriminator = Discriminator(img_shape)
        self.lambda_gp = lambda_gp

    def forward(self, z):
        return self.generator(z)

    def gradient_penalty(self, real_images, fake_images):
        batch_size = real_images.size(0)

        # 随机生成采样权重
        alpha = torch.rand(batch_size, 1, 1, 1).cuda()
        alpha = alpha.expand_as(real_images)

        # 生成采样图像
        interpolated = (alpha * real_images) + ((1 - alpha) * fake_images)
        interpolated.requires_grad_(True)

        # 计算插值图像的判别器输出
        prob_interpolated = self.discriminator(interpolated)

        # 计算梯度
        gradients = torch.autograd.grad(outputs=prob_interpolated, inputs=interpolated,
                                        grad_outputs=torch.ones(prob_interpolated.size()).cuda(),
                                        create_graph=True, retain_graph=True)[0]

        # 计算梯度惩罚项
        gradients = gradients.view(batch_size, -1)
        gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * self.lambda_gp
        return gradient_penalty

# 定义训练函数
def train_wgan_gp(generator, discriminator, dataloader, num_epochs=200, batch_size=64, lr=0.0002, betas=(0.5, 0.999)):
    # 损失函数
    adversarial_loss = torch.nn.MSELoss()

    # 优化器
    optimizer_G = optim.Adam(generator.parameters(), lr=lr, betas=betas)
    optimizer_D = optim.Adam(discriminator.parameters(), lr=lr, betas=betas)

    for epoch in range(num_epochs):
        for i, (imgs, _) in enumerate(dataloader):
            batch_size = imgs.shape[0]

            # 配置设备
            real_imgs = imgs.cuda()

            # 训练判别器
            optimizer_D.zero_grad()

            # 随机生成噪声
            z = torch.randn(batch_size, 100).cuda()

            # 生成假图像
            fake_imgs = generator(z)

            # 计算判别器损失
            loss_D = -torch.mean(discriminator(real_imgs)) + torch.mean(discriminator(fake_imgs))

            # 计算梯度惩罚项
            gp = discriminator.gradient_penalty(real_imgs, fake_imgs)
            loss_D += gp

            # 反向传播和优化
            loss_D.backward()
            optimizer_D.step()

            # 限制判别器的参数范围
            for p in discriminator.parameters():
                p.data.clamp_(-0.01, 0.01)

            # 训练生成器
            optimizer_G.zero_grad()

            # 随机生成噪声
            z = torch.randn(batch_size, 100).cuda()

            # 生成假图像
            fake_imgs = generator(z)

            # 计算生成器损失
            loss_G = -torch.mean(discriminator(fake_imgs))

            # 反向传播和优化
            loss_G.backward()
            optimizer_G.step()

            # 打印损失
            if i % 50 == 0:
                print("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, num_epochs, i, len(dataloader), loss_D.item(), loss_G.item()))

在使用该代码时，需要先准备好数据集并将其转换为PyTorch的DataLoader格式，并调用train_wgan_gp函数进行训练。

WGAN-GP训练mnist数据集

以下是使用WGAN-GP训练mnist数据集的步骤：

1.导入必要的库和模块

import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt

2.加载mnist数据集

(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5  # 将像素值归一化到[-1, 1]之间
BUFFER_SIZE = 60000
BATCH_SIZE = 256
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

3.定义生成器和判别器模型

def make_generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Reshape((7, 7, 256)))
    assert model.output_shape == (None, 7, 7, 256)  # 注意：batch size 没有限制

    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    assert model.output_shape == (None, 7, 7, 128)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    assert model.output_shape == (None, 14, 14, 64)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
    assert model.output_shape == (None, 28, 28, 1)

    return model

def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
                                     input_shape=[28, 28, 1]))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))

    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))

    model.add(layers.Flatten())
    model.add(layers.Dense(1))

    return model

4.定义损失函数和优化器

generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)

def discriminator_loss(real_output, fake_output):
    real_loss = tf.reduce_mean(real_output)
    fake_loss = tf.reduce_mean(fake_output)
    return fake_loss - real_loss

def generator_loss(fake_output):
    return -tf.reduce_mean(fake_output)

5.定义训练函数

@tf.function
def train_step(images):
    noise = tf.random.normal([BATCH_SIZE, 100])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)

        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)

        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)

    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

6.训练模型

EPOCHS = 100
noise_dim = 100
num_examples_to_generate = 16

# 我们将重复使用该种子（因此在动画 GIF 中更容易可视化进度）
seed = tf.random.normal([num_examples_to_generate, noise_dim])

generator = make_generator_model()
discriminator = make_discriminator_model()

for epoch in range(EPOCHS):
    for image_batch in train_dataset:
        train_step(image_batch)

    # 每 15 个 epoch 生成一次图片
    if epoch % 15 == 0:
        generate_and_save_images(generator, epoch + 1, seed)

# 生成最终的图片
generate_and_save_images(generator, EPOCHS, seed)

7.生成图片

def generate_and_save_images(model, epoch, test_input):
    # 注意 training` 设定为 False
    # 因此，所有层都在推理模式下运行（batchnorm）。
    predictions = model(test_input, training=False)

    fig = plt.figure(figsize=(4, 4))
    for i in range(predictions.shape[0]):
        plt.subplot(4, 4, i+1)
        plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
        plt.axis('off')

    plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
    plt.show()