GAN学习总结三-Pytorch实现利用GAN进行MNIST手写数字生成
前面两篇博客分别介绍了GAN的基本概念和理论推导，理论联系实际，本节从代码的角度理解GAN网络的实现及相关细节,加深自己的理解.
整个实现过程如下:
导入相关库
import torch
from torch import nn
from torch.autograd import Variable
import torchvision.transforms as tfs
from torch.utils.data import DataLoader, sampler
from torchvision.datasets import MNIST
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # 设置画图的尺寸
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
def show_images(images): # 定义画图工具
images = np.reshape(images, [images.shape[0], -1])
sqrtn = int(np.ceil(np.sqrt(images.shape[0])))
sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))
fig = plt.figure(figsize=(sqrtn, sqrtn))
gs = gridspec.GridSpec(sqrtn, sqrtn)
gs.update(wspace=0.05, hspace=0.05)
for i, img in enumerate(images):
ax = plt.subplot(gs[i])
plt.axis('off')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect('equal')
plt.imshow(img.reshape([sqrtimg,sqrtimg]))
return
def preprocess_img(x):
x = tfs.ToTensor()(x)
return (x - 0.5) / 0.5
def deprocess_img(x):
return (x + 1.0) / 2.0
class ChunkSampler(sampler.Sampler): # 定义一个取样的函数
"""Samples elements sequentially from some offset.
Arguments:
num_samples: # of desired datapoints
start: offset where we should start selecting from
"""
def __init__(self, num_samples, start=0):
self.num_samples = num_samples
self.start = start
def __iter__(self):
return iter(range(self.start, self.start + self.num_samples))
def __len__(self):
return self.num_samples
NUM_TRAIN = 50000
NUM_VAL = 5000
NOISE_DIM = 96
batch_size = 128
train_set = MNIST('mnist', train=True, download=True, transform=preprocess_img)
train_data = DataLoader(train_set, batch_size=batch_size, sampler=ChunkSampler(NUM_TRAIN, 0))
val_set = MNIST('mnist', train=True, download=True, transform=preprocess_img)
val_data = DataLoader(val_set, batch_size=batch_size, sampler=ChunkSampler(NUM_VAL, NUM_TRAIN))
imgs = deprocess_img(train_data.__iter__().next()[0].view(batch_size, 784)).numpy().squeeze() # 可视化图片效果
show_images(imgs)
定义卷积判别网络
class build_dc_classifier(nn.Module):
def __init__(self):
super(build_dc_classifier, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(1, 32, 5, 1),
nn.LeakyReLU(0.01),
nn.MaxPool2d(2, 2),
nn.Conv2d(32, 64, 5, 1),
nn.LeakyReLU(0.01),
nn.MaxPool2d(2, 2)
)
self.fc = nn.Sequential(
nn.Linear(1024, 1024),
nn.LeakyReLU(0.01),
nn.Linear(1024, 1)
)
def forward(self, x):
x = self.conv(x)
x = x.view(x.shape[0], -1)
x = self.fc(x)
return x
定义卷积生成网络
class build_dc_generator(nn.Module):
def __init__(self, noise_dim=NOISE_DIM):
super(build_dc_generator, self).__init__()
self.fc = nn.Sequential(
nn.Linear(noise_dim, 1024),
nn.ReLU(True),
nn.BatchNorm1d(1024),
nn.Linear(1024, 7 * 7 * 128),
nn.ReLU(True),
nn.BatchNorm1d(7 * 7 * 128)
)
self.conv = nn.Sequential(
nn.ConvTranspose2d(128, 64, 4, 2, padding=1),
nn.ReLU(True),
nn.BatchNorm2d(64),
nn.ConvTranspose2d(64, 1, 4, 2, padding=1),
nn.Tanh()
)
def forward(self, x):
x = self.fc(x)
x = x.view(x.shape[0], 128, 7, 7) # reshape 通道是 128，大小是 7x7
x = self.conv(x)
return x
定义损失函数
判别网络的损失函数公式为:
生成网络的损失函数公式为:
bce_loss = nn.BCEWithLogitsLoss()
def discriminator_loss(logits_real, logits_fake): # 判别器的 loss
size = logits_real.shape[0]
true_labels = Variable(torch.ones(size, 1)).float().cuda()
false_labels = Variable(torch.zeros(size, 1)).float().cuda()
loss = bce_loss(logits_real, true_labels) + bce_loss(logits_fake, false_labels)
return loss
def generator_loss(logits_fake): # 生成器的 loss
size = logits_fake.shape[0]
true_labels = Variable(torch.ones(size, 1)).float().cuda()
loss = bce_loss(logits_fake, true_labels)
return loss
定义优化器
# 使用 adam 来进行训练，学习率是 3e-4, beta1 是 0.5, beta2 是 0.999
def get_optimizer(net):
optimizer = torch.optim.Adam(net.parameters(), lr=3e-4, betas=(0.5, 0.999))
return optimizer
定义训练函数
def train_dc_gan(D_net, G_net, D_optimizer, G_optimizer, discriminator_loss, generator_loss, show_every=250,
noise_size=96, num_epochs=10):
iter_count = 0
for epoch in range(num_epochs):
for x, _ in train_data:
bs = x.shape[0]
# 判别网络
real_data = Variable(x).cuda() # 真实数据
logits_real = D_net(real_data) # 判别网络得分
sample_noise = (torch.rand(bs, noise_size) - 0.5) / 0.5 # -1 ~ 1 的均匀分布
g_fake_seed = Variable(sample_noise).cuda()
fake_images = G_net(g_fake_seed) # 生成的假的数据
logits_fake = D_net(fake_images) # 判别网络得分
d_total_error = discriminator_loss(logits_real, logits_fake) # 判别器的 loss
D_optimizer.zero_grad()
d_total_error.backward()
D_optimizer.step() # 优化判别网络
# 生成网络
g_fake_seed = Variable(sample_noise).cuda()
fake_images = G_net(g_fake_seed) # 生成的假的数据
gen_logits_fake = D_net(fake_images)
g_error = generator_loss(gen_logits_fake) # 生成网络的 loss
G_optimizer.zero_grad()
g_error.backward()
G_optimizer.step() # 优化生成网络
if (iter_count % show_every == 0):
print('Iter: {}, D: {:.4}, G:{:.4}'.format(iter_count, d_total_error.data[0], g_error.data[0]))
imgs_numpy = deprocess_img(fake_images.data.cpu().numpy())
show_images(imgs_numpy[0:16])
plt.show()
print()
iter_count += 1
开始训练:
D_DC = build_dc_classifier().cuda()
G_DC = build_dc_generator().cuda()
D_DC_optim = get_optimizer(D_DC)
G_DC_optim = get_optimizer(G_DC)
train_dc_gan(D_DC, G_DC, D_DC_optim, G_DC_optim, discriminator_loss, generator_loss, num_epochs=20)
训练过程中生成结果如下,刚开始图像模糊,后面图像越来越清晰:
参考:
https://github.com/L1aoXingyu/code-of-learn-deep-learning-with-pytorch