WGAN,WGAN-GP

原理

GAN有多种解释，这里我总结一下：

原始论文解读

https://zhuanlan.zhihu.com/p/25071913

（苏神专场）
互怼的艺术：从零直达WGAN-GP

https://spaces.ac.cn/archives/4439

从Wasserstein距离、对偶理论到WGAN

https://spaces.ac.cn/archives/6280

动力学角度

https://spaces.ac.cn/archives/6583

能量视角下的GAN模型

https://kexue.fm/archives/6316

https://kexue.fm/archives/6331

https://kexue.fm/archives/6612

几何角度

A Geometric View of Optimal Transportation and Generative Model ，https://arxiv.org/abs/1710.05488

我之前尝试着看懂这篇论文，发现需要懂最优传输理论。

然后我就找了一些最优传输的资料（感兴趣的可以在公众号后台回复CHSH获取）：

（Computational Optimal Transport 这本书华东师范大学的王祥丰老师正在翻译。https://zhuanlan.zhihu.com/p/499401130）

又发现没有学过测度论很难读懂。（限制人学习自由的永远是数学，划线以下是另一个境界：
在这里插入图片描述

）

不过，我找到了一篇不用测度论解析的论文(工科生狂喜)：

https://sci-hub.st/10.1109/msp.2017.2695801

看完能大概知道最优传输是干什么的，以及这个理论的奠基者蒙日（Monge）和康托罗维奇（Kantorovich）做了什么。

Pytorch实现：生成正态分布数据

理论很难，实现倒是不难。

WGAN

在这里插入图片描述
图片来源：[1]

import numpy as np
from matplotlib import pyplot as plt
import matplotlib
import torch
import torch.nn as nn
import torch.optim as optim

torch.manual_seed(1)
np.random.seed(1)
matplotlib.rcParams['font.family'] = 'SimHei'
matplotlib.rcParams['axes.unicode_minus'] = False
plt.rcParams['figure.dpi'] = 150


class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(16, 128),
            nn.LeakyReLU(),
            nn.Dropout(p=0.3),
            nn.Linear(128, 256),
            nn.LeakyReLU(),
            nn.Dropout(p=0.3),
            nn.Linear(256, 512)
        )

    def forward(self, inputs):
        return self.model(inputs)


class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(512, 256),
            nn.Tanh(),
            nn.Linear(256, 128),
            nn.Tanh(),
            nn.Linear(128, 1),
            # nn.Sigmoid()  # 去掉
        )

    def forward(self, inputs):
        return self.model(inputs)


def normal_pdf(x, mu, sigma):
    '''# 正态分布,概率密度函数'''
    pdf = np.exp(-((x - mu) ** 2) / (2 * sigma ** 2)) / (sigma * np.sqrt(2 * np.pi))
    return pdf


def draw(G, epoch, g_input_size):
    '''画目标分布和生成分布'''
    plt.clf()
    # 画出目标分布
    x = np.arange(-3, 9, 0.2)
    y = normal_pdf(x, 3, 1)
    plt.plot(x, y, 'r', linewidth=2)

    # 画出生成的分布
    test_data = torch.rand(1, g_input_size)
    data = G(test_data).detach().numpy()
    mean = data.mean()
    std = data.std()
    x = np.arange(np.floor(data.min()) - 5, np.ceil(data.max()) + 5, 0.2)
    y = normal_pdf(x, mean, std)
    plt.plot(x, y, 'orange', linewidth=2)
    plt.hist(data.flatten(), bins=20, color='y', alpha=0.5, rwidth=0.9, density=True)

    # 坐标图设置
    plt.legend(['目标分布', '生成分布'])
    plt.show()
    plt.pause(0.1)


def train():
    # 用于记录生成器生成的数据的均值和方差
    G_mean = []
    G_std = []

    # 目标分布的均值和方差
    data_mean = 3
    data_std = 1

    feature_num = 512
    batch_size = 64
    g_input_size = 16
    epochs = 1001
    d_epoch = 1  # 判别器的训练轮数

    # 初始化网络
    D = Discriminator()
    G = Generator()

    # 初始化优化器
    d_learning_rate = 0.01
    g_learning_rate = 0.001
    # loss_func = nn.BCELoss()

    optimiser_D = optim.RMSprop(D.parameters(), lr=d_learning_rate)
    optimiser_G = optim.RMSprop(G.parameters(), lr=g_learning_rate)

    clip_value = 0.01
    plt.ion()
    for epoch in range(epochs):
        G.train()
        # 1 训练判别器d_steps次
        for _ in range(d_epoch):
            # 1.1 真实数据real_data输入D,得到d_real
            real_data = torch.tensor(np.random.normal(data_mean, data_std, (batch_size, feature_num)),
                                     dtype=torch.float)
            d_real = D(real_data)
            # 1.2 生成数据的输出fake_data输入D,得到d_fake
            g_input = torch.rand(batch_size, g_input_size)
            fake_data = G(g_input).detach()  # detach：只更新判别器的参数
            d_fake = D(fake_data)

            # 1.3 计算损失值,最大化EM距离
            d_loss = -(d_real.mean() - d_fake.mean())

            # 1.4 反向传播，优化
            optimiser_D.zero_grad()
            d_loss.backward()
            optimiser_D.step()

            # 1.5 截断
            for p in D.parameters():
                p.data.clamp_(-clip_value, clip_value)

        # 2 训练生成器
        # 2.1 G输入g_input，输出fake_data。fake_data输入D，得到d_g_fake
        g_input = torch.rand(batch_size, g_input_size)
        fake_data = G(g_input)
        d_g_fake = D(fake_data)

        # 2.2 计算损失值,最小化EM距离
        g_loss = -d_g_fake.mean()

        # 2.3 反向传播，优化
        optimiser_G.zero_grad()
        g_loss.backward()
        optimiser_G.step()

        # 2.4 记录生成器输出的均值和方差
        G_mean.append(fake_data.mean().item())
        G_std.append(fake_data.std().item())

        if epoch % 10 == 0:
            print("Epoch: {}, 生成数据的均值: {}, 生成数据的标准差: {}".format(epoch, G_mean[-1], G_std[-1]))
            print('-' * 10)
            G.eval()
            draw(G, epoch, g_input_size)

    plt.ioff()
    plt.show()
    plt.plot(G_mean)
    plt.title('均值')
    plt.savefig('wgan_mean')
    plt.show()

    plt.plot(G_std)
    plt.title('标准差')
    plt.savefig('wgan_std')
    plt.show()


if __name__ == '__main__':
    train()

WGAN-GP

在这里插入图片描述
图片来源[2]

import numpy as np
from matplotlib import pyplot as plt
import matplotlib
import torch
import torch.nn as nn
import torch.optim as optim

torch.manual_seed(1)
np.random.seed(1)
matplotlib.rcParams['font.family'] = 'SimHei'
matplotlib.rcParams['axes.unicode_minus'] = False
plt.rcParams['figure.dpi'] = 150


class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(16, 128),
            nn.LeakyReLU(),
            nn.Dropout(p=0.3),
            nn.Linear(128, 256),
            nn.LeakyReLU(),
            nn.Dropout(p=0.3),
            nn.Linear(256, 512)
        )

    def forward(self, inputs):
        return self.model(inputs)


class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(512, 256),
            nn.Tanh(),
            nn.Linear(256, 128),
            nn.Tanh(),
            nn.Linear(128, 1),
        )

    def forward(self, inputs):
        return self.model(inputs)


def cal_gradient_penalty(D, real, fake):
    # 每一个样本对应一个sigma。样本个数为64，特征数为512：[64,512]
    sigma = torch.rand(real.size(0), 1)  # [64,1]
    sigma = sigma.expand(real.size())  # [64, 512]
    # 按公式计算x_hat
    x_hat = sigma * real + (torch.tensor(1.) - sigma) * fake
    x_hat.requires_grad = True
    # 为得到梯度先计算y
    d_x_hat = D(x_hat)

    # 计算梯度,autograd.grad返回的是一个元组(梯度值，)
    gradients = torch.autograd.grad(outputs=d_x_hat, inputs=x_hat,
                                    grad_outputs=torch.ones(d_x_hat.size()),
                                    create_graph=True, retain_graph=True, only_inputs=True)[0]
    # 利用梯度计算出gradient penalty
    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
    return gradient_penalty


def normal_pdf(x, mu, sigma):
    '''正态分布的概率密度函数'''
    pdf = np.exp(-((x - mu) ** 2) / (2 * sigma ** 2)) / (sigma * np.sqrt(2 * np.pi))
    return pdf



def draw(G, epoch, g_input_size):
    '''画目标分布和生成分布'''
    plt.clf()
    # 画出目标分布
    x = np.arange(-3, 9, 0.2)
    y = normal_pdf(x, 3, 1)
    plt.plot(x, y, 'r', linewidth=2)

    # 画出生成的分布
    test_data = torch.rand(1, g_input_size)
    data = G(test_data).detach().numpy()
    mean = data.mean()
    std = data.std()
    x = np.arange(np.floor(data.min()) - 5, np.ceil(data.max()) + 5, 0.2)
    y = normal_pdf(x, mean, std)
    plt.plot(x, y, 'orange', linewidth=2)
    plt.hist(data.flatten(), bins=20, color='y', alpha=0.5, rwidth=0.9, density=True)

    # 坐标图设置
    plt.legend(['目标分布', '生成分布'])

    plt.show()
    plt.pause(0.1)


def train():
    G_mean = []
    G_std = []  # 用于记录生成器生成的数据的均值和方差
    data_mean = 3
    data_std = 1  # 目标分布的均值和方差
    batch_size = 64
    g_input_size = 16
    g_output_size = 512
    epochs = 1001
    d_epoch = 1  # 每个epoch判别器的训练轮数

    # 初始化网络
    D = Discriminator()
    G = Generator()

    # 初始化优化器
    d_learning_rate = 0.01
    g_learning_rate = 0.001
    # loss_func = nn.BCELoss()
    optimiser_D = optim.Adam(D.parameters(), lr=d_learning_rate)
    optimiser_G = optim.Adam(G.parameters(), lr=g_learning_rate)

    # clip_value = 0.01
    plt.ion()
    for epoch in range(epochs):
        G.train()
        # 1 训练判别器d_steps次
        for _ in range(d_epoch):
            # 1.1 真实数据real_data输入D,得到d_real
            real_data = torch.tensor(np.random.normal(data_mean, data_std, (batch_size, g_output_size)),
                                     dtype=torch.float)
            d_real = D(real_data)
            # 1.2 生成数据的输出fake_data输入D,得到d_fake
            g_input = torch.rand(batch_size, g_input_size)
            fake_data = G(g_input).detach()  # detach：只更新判别器的参数
            d_fake = D(fake_data)

            # 1.3 计算损失值，最大化EM距离
            d_loss = -(d_real.mean() - d_fake.mean())
            gradient_penalty = cal_gradient_penalty(D, real_data, fake_data)
            d_loss = d_loss + gradient_penalty * 0.5  # lambda=0.5，这个参数对效果影响很大

            # 1.4 反向传播，优化
            optimiser_D.zero_grad()
            d_loss.backward()
            optimiser_D.step()

        # 2 训练生成器
        # 2.1 G输入g_input，输出fake_data。fake_data输入D，得到d_g_fake
        g_input = torch.rand(batch_size, g_input_size)
        fake_data = G(g_input)
        d_g_fake = D(fake_data)

        # 2.2 计算损失值，最小化EM距离
        g_loss = -d_g_fake.mean()

        # 2.3 反向传播，优化
        optimiser_G.zero_grad()
        g_loss.backward()
        optimiser_G.step()

        # 2.4 记录生成器输出的均值和方差
        G_mean.append(fake_data.mean().item())
        G_std.append(fake_data.std().item())

        if epoch % 10 == 0:
            print("Epoch: {}, 生成数据的均值: {}, 生成数据的标准差: {}".format(epoch, G_mean[-1], G_std[-1]))
            print('-' * 10)
            G.eval()
            draw(G, epoch, g_input_size)

    plt.ioff()
    plt.show()
    plt.plot(G_mean)
    plt.title('均值')
    plt.show()

    plt.plot(G_std)
    plt.title('标准差')
    plt.show()


if __name__ == '__main__':
    train()