54_pytorch GAN(生成对抗网络)、Gan代码示例、WGAN代码示例

1.54.GAN(生成对抗网络)

1.54.1.什么是GAN

2014 年，Ian Goodfellow 和他在蒙特利尔大学的同事发表了一篇震撼学界的论文。没错，我说的就是《Generative Adversarial Nets》，这标志着生成对抗网络（GAN）的诞生，而这是通过对计算图和博弈论的创新性结合。他们的研究展示，给定充分的建模能力，两个博弈模型能够通过简单的反向传播（backpropagation）来协同训练。

这两个模型的角色定位十分鲜明。给定真实数据集R，G是生成器（generator）,它的任务是生成能以假乱真的假数据；而D是判别器（discriminator）,它从真实数据集或者G那里获取数据，然后做出判别真假的标记。lan Goodfellow的比喻是，G就像一个赝品作坊，想要让做出来的东西尽可能接近真品，蒙混过关。而D就是文物鉴定专家，要能区分出真品和高仿（但在这个例子中，造假者G看不到原始数据，而只有D的鉴定结果—前者是在盲干）。

理想情况下，D和G都会随着不断训练，做的越来越好----直到G基本上成了一个”赝品制造大师”，而D因无法正确区分两种数据分布输给G。

一、GAN（Generative Adversarial Nets）
神经网络有很多种，常见的有如下几种：

1.普通的前向传播网络
2.用于分析图像的卷积神经网络。
3.用于分析语音或文字等序列信息的RNN神经网络。

以上三种网络都有一个共同点，就是通过数据和结果相关联，来实现自己网络的功能

还有一种比较特殊，可以理解为用来造数据的GAN网络 (生成对抗网络)

Generator根据随机数随机生成有意义的数据，Discriminator用来学习哪些数据是真实的，哪些数据是生成的然后反向传递给Generator，以此来生成更多有价值的数据。所以生成对抗网络就是两个网络，一个生成，一个对抗，对抗的结果是为了让生成网络达到预期的功能。

通过自己的学习过程理解，我认为G网络的目的就是输入随机数，但是可以根据随机数产生数据，产生的数据好不好由D网络说的算，D网络对于现有的数据进行学习和总结，然后指导G网络产生类似于现有的数据，D网络扮演了指导的作用。

最后就可以实现，对于输入的任意分布的随机数据，都可以产生和原数据相似的数据用于其他的用途，以上是我对GAN网络更朴素的理解

1.54.2.How to train

1.54.3.Gan代码示例

# -*- coding: UTF-8 -*-import randomimport numpy as np
import torch
import visdom
from matplotlib import pyplot as plt
from torch import nn, optim, autogradh_dim = 400
batchsz = 512
viz = visdom.Visdom()class Generator(nn.Module):def __init__(self):super(Generator, self).__init__()self.net = nn.Sequential(nn.Linear(2, h_dim),nn.ReLU(True),nn.Linear(h_dim, h_dim),nn.ReLU(True),nn.Linear(h_dim, h_dim),nn.ReLU(True),nn.Linear(h_dim, 2),)def forward(self, z):output = self.net(z)return outputclass Discriminator(nn.Module):def __init__(self):super(Discriminator, self).__init__()self.net = nn.Sequential(nn.Linear(2, h_dim),nn.ReLU(True),nn.Linear(h_dim, h_dim),nn.ReLU(True),nn.Linear(h_dim, h_dim),nn.ReLU(True),nn.Linear(h_dim, 1),nn.Sigmoid())def forward(self, x):output = self.net(x)return output.view(-1)def data_generator():scale = 2.centers = [(1, 0),(-1, 0),(0, 1),(0, -1),(1. / np.sqrt(2), 1. / np.sqrt(2)),(1. / np.sqrt(2), -1. / np.sqrt(2)),(-1. / np.sqrt(2), 1. / np.sqrt(2)),(-1. / np.sqrt(2), -1. / np.sqrt(2))]centers = [(scale * x, scale * y) for x, y in centers]while True:dataset = []for i in range(batchsz):point = np.random.randn(2) * .02center = random.choice(centers)point[0] += center[0]point[1] += center[1]dataset.append(point)dataset = np.array(dataset, dtype='float32')dataset /= 1.414  # stdevyield dataset# for i in range(100000//25):#     for x in range(-2, 3):#         for y in range(-2, 3):#             point = np.random.randn(2).astype(np.float32) * 0.05#             point[0] += 2 * x#             point[1] += 2 * y#             dataset.append(point)## dataset = np.array(dataset)# print('dataset:', dataset.shape)# viz.scatter(dataset, win='dataset', opts=dict(title='dataset', webgl=True))## while True:#     np.random.shuffle(dataset)##     for i in range(len(dataset)//batchsz):#         yield dataset[i*batchsz : (i+1)*batchsz]def generate_image(D, G, xr, epoch):"""Generates and saves a plot of the true distribution, the generator, and thecritic."""N_POINTS = 128RANGE = 3plt.clf()points = np.zeros((N_POINTS, N_POINTS, 2), dtype='float32')points[:, :, 0] = np.linspace(-RANGE, RANGE, N_POINTS)[:, None]points[:, :, 1] = np.linspace(-RANGE, RANGE, N_POINTS)[None, :]points = points.reshape((-1, 2))# (16384, 2)# print('p:', points.shape)# draw contourwith torch.no_grad():points = torch.Tensor(points).cuda()  # [16384, 2]disc_map = D(points).cpu().numpy()  # [16384]x = y = np.linspace(-RANGE, RANGE, N_POINTS)cs = plt.contour(x, y, disc_map.reshape((len(x), len(y))).transpose())plt.clabel(cs, inline=1, fontsize=10)# plt.colorbar()# draw sampleswith torch.no_grad():z = torch.randn(batchsz, 2).cuda()  # [b, 2]samples = G(z).cpu().numpy()  # [b, 2]plt.scatter(xr[:, 0], xr[:, 1], c='orange', marker='.')plt.scatter(samples[:, 0], samples[:, 1], c='green', marker='+')viz.matplot(plt, win='contour', opts=dict(title='p(x):%d' % epoch))def weights_init(m):if isinstance(m, nn.Linear):# m.weight.data.normal_(0.0, 0.02)nn.init.kaiming_normal_(m.weight)m.bias.data.fill_(0)def gradient_penalty(D, xr, xf):""":param D::param xr::param xf::return:"""LAMBDA = 0.3# only constrait for Discriminatorxf = xf.detach()xr = xr.detach()# [b, 1] => [b, 2]alpha = torch.rand(batchsz, 1).cuda()alpha = alpha.expand_as(xr)interpolates = alpha * xr + ((1 - alpha) * xf)interpolates.requires_grad_()disc_interpolates = D(interpolates)gradients = autograd.grad(outputs=disc_interpolates, inputs=interpolates,grad_outputs=torch.ones_like(disc_interpolates),create_graph=True, retain_graph=True, only_inputs=True)[0]gp = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * LAMBDAreturn gpdef main():torch.manual_seed(23)np.random.seed(23)G = Generator().cuda()D = Discriminator().cuda()G.apply(weights_init)D.apply(weights_init)optim_G = optim.Adam(G.parameters(), lr=1e-3, betas=(0.5, 0.9))optim_D = optim.Adam(D.parameters(), lr=1e-3, betas=(0.5, 0.9))data_iter = data_generator()print('batch:', next(data_iter).shape)viz.line([[0, 0]], [0], win='loss', opts=dict(title='loss',legend=['D', 'G']))for epoch in range(50000):# 1. train discriminator for k stepsfor _ in range(5):x = next(data_iter)xr = torch.from_numpy(x).cuda()# [b]predr = (D(xr))# max log(lossr)lossr = - (predr.mean())# [b, 2]z = torch.randn(batchsz, 2).cuda()# stop gradient on G# [b, 2]xf = G(z).detach()# [b]predf = (D(xf))# min predflossf = (predf.mean())# gradient penaltygp = gradient_penalty(D, xr, xf)loss_D = lossr + lossf + gpoptim_D.zero_grad()loss_D.backward()# for p in D.parameters():#     print(p.grad.norm())optim_D.step()# 2. train Generatorz = torch.randn(batchsz, 2).cuda()xf = G(z)predf = (D(xf))# max predfloss_G = - (predf.mean())optim_G.zero_grad()loss_G.backward()optim_G.step()if epoch % 100 == 0:viz.line([[loss_D.item(), loss_G.item()]], [epoch], win='loss', update='append')generate_image(D, G, xr, epoch)print(loss_D.item(), loss_G.item())if __name__ == '__main__':main()

1.54.4.WGAN代码示例

import  torch
from    torch import nn, optim, autograd
import  numpy as np
import  visdom
from    torch.nn import functional as F
from    matplotlib import pyplot as plt
import  randomh_dim = 400
batchsz = 512
viz = visdom.Visdom()class Generator(nn.Module):def __init__(self):super(Generator, self).__init__()self.net = nn.Sequential(nn.Linear(2, h_dim),nn.ReLU(True),nn.Linear(h_dim, h_dim),nn.ReLU(True),nn.Linear(h_dim, h_dim),nn.ReLU(True),nn.Linear(h_dim, 2),)def forward(self, z):output = self.net(z)return outputclass Discriminator(nn.Module):def __init__(self):super(Discriminator, self).__init__()self.net = nn.Sequential(nn.Linear(2, h_dim),nn.ReLU(True),nn.Linear(h_dim, h_dim),nn.ReLU(True),nn.Linear(h_dim, h_dim),nn.ReLU(True),nn.Linear(h_dim, 1),nn.Sigmoid())def forward(self, x):output = self.net(x)return output.view(-1)def data_generator():scale = 2.centers = [(1, 0),(-1, 0),(0, 1),(0, -1),(1. / np.sqrt(2), 1. / np.sqrt(2)),(1. / np.sqrt(2), -1. / np.sqrt(2)),(-1. / np.sqrt(2), 1. / np.sqrt(2)),(-1. / np.sqrt(2), -1. / np.sqrt(2))]centers = [(scale * x, scale * y) for x, y in centers]while True:dataset = []for i in range(batchsz):point = np.random.randn(2) * .02center = random.choice(centers)point[0] += center[0]point[1] += center[1]dataset.append(point)dataset = np.array(dataset, dtype='float32')dataset /= 1.414  # stdevyield dataset# for i in range(100000//25):#     for x in range(-2, 3):#         for y in range(-2, 3):#             point = np.random.randn(2).astype(np.float32) * 0.05#             point[0] += 2 * x#             point[1] += 2 * y#             dataset.append(point)## dataset = np.array(dataset)# print('dataset:', dataset.shape)# viz.scatter(dataset, win='dataset', opts=dict(title='dataset', webgl=True))## while True:#     np.random.shuffle(dataset)##     for i in range(len(dataset)//batchsz):#         yield dataset[i*batchsz : (i+1)*batchsz]def generate_image(D, G, xr, epoch):"""Generates and saves a plot of the true distribution, the generator, and thecritic."""N_POINTS = 128RANGE = 3plt.clf()points = np.zeros((N_POINTS, N_POINTS, 2), dtype='float32')points[:, :, 0] = np.linspace(-RANGE, RANGE, N_POINTS)[:, None]points[:, :, 1] = np.linspace(-RANGE, RANGE, N_POINTS)[None, :]points = points.reshape((-1, 2))# (16384, 2)# print('p:', points.shape)# draw contourwith torch.no_grad():points = torch.Tensor(points).cuda() # [16384, 2]disc_map = D(points).cpu().numpy() # [16384]x = y = np.linspace(-RANGE, RANGE, N_POINTS)cs = plt.contour(x, y, disc_map.reshape((len(x), len(y))).transpose())plt.clabel(cs, inline=1, fontsize=10)# plt.colorbar()# draw sampleswith torch.no_grad():z = torch.randn(batchsz, 2).cuda() # [b, 2]samples = G(z).cpu().numpy() # [b, 2]plt.scatter(xr[:, 0], xr[:, 1], c='orange', marker='.')plt.scatter(samples[:, 0], samples[:, 1], c='green', marker='+')viz.matplot(plt, win='contour', opts=dict(title='p(x):%d'%epoch))def weights_init(m):if isinstance(m, nn.Linear):# m.weight.data.normal_(0.0, 0.02)nn.init.kaiming_normal_(m.weight)m.bias.data.fill_(0)def gradient_penalty(D, xr, xf):""":param D::param xr::param xf::return:"""LAMBDA = 0.3# only constrait for Discriminatorxf = xf.detach()xr = xr.detach()# [b, 1] => [b, 2]alpha = torch.rand(batchsz, 1).cuda()alpha = alpha.expand_as(xr)interpolates = alpha * xr + ((1 - alpha) * xf)interpolates.requires_grad_()disc_interpolates = D(interpolates)gradients = autograd.grad(outputs=disc_interpolates, inputs=interpolates,grad_outputs=torch.ones_like(disc_interpolates),create_graph=True, retain_graph=True, only_inputs=True)[0]gp = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * LAMBDAreturn gpdef main():torch.manual_seed(23)np.random.seed(23)G = Generator().cuda()D = Discriminator().cuda()G.apply(weights_init)D.apply(weights_init)optim_G = optim.Adam(G.parameters(), lr=1e-3, betas=(0.5, 0.9))optim_D = optim.Adam(D.parameters(), lr=1e-3, betas=(0.5, 0.9))data_iter = data_generator()print('batch:', next(data_iter).shape)viz.line([[0,0]], [0], win='loss', opts=dict(title='loss',legend=['D', 'G']))for epoch in range(50000):# 1. train discriminator for k stepsfor _ in range(5):x = next(data_iter)xr = torch.from_numpy(x).cuda()# [b]predr = (D(xr))# max log(lossr)lossr = - (predr.mean())# [b, 2]z = torch.randn(batchsz, 2).cuda()# stop gradient on G# [b, 2]xf = G(z).detach()# [b]predf = (D(xf))# min predflossf = (predf.mean())# gradient penaltygp = gradient_penalty(D, xr, xf)loss_D = lossr + lossf + gpoptim_D.zero_grad()loss_D.backward()# for p in D.parameters():#     print(p.grad.norm())optim_D.step()# 2. train Generatorz = torch.randn(batchsz, 2).cuda()xf = G(z)predf = (D(xf))# max predfloss_G = - (predf.mean())optim_G.zero_grad()loss_G.backward()optim_G.step()if epoch % 100 == 0:viz.line([[loss_D.item(), loss_G.item()]], [epoch], win='loss', update='append')generate_image(D, G, xr, epoch)print(loss_D.item(), loss_G.item())if __name__ == '__main__':main()

1.54.5.参考文章

https://zhuanlan.zhihu.com/p/117529144
https://blog.csdn.net/jizhidexiaoming/article/details/96485095