pytorch实现VAE

一、VAE的具体结构

二、VAE的pytorch实现

1加载并规范化MNIST

 import相关类：

from __future__ import print_functionimport argparseimport torchimport torch.utils.dataimport torch.nn as nnimport torch.optim as optimfrom torch.autograd import Variablefrom torchvision import datasets, transforms

 设置参数：

parser = argparse.ArgumentParser(description='PyTorch MNIST Example')parser.add_argument('--batch-size', type=int, default=128, metavar='N',                    help='input batch size for training (default: 128)')parser.add_argument('--epochs', type=int, default=10, metavar='N',                    help='number of epochs to train (default: 10)')parser.add_argument('--no-cuda', action='store_true', default=False,                    help='enables CUDA training')parser.add_argument('--seed', type=int, default=1, metavar='S',                    help='random seed (default: 1)')parser.add_argument('--log-interval', type=int, default=10, metavar='N',                    help='how many batches to wait before logging training status')args = parser.parse_args()args.cuda = not args.no_cuda and torch.cuda.is_available()print(args)#Sets the seed for generating random numbers. And returns a torch._C.Generator object.torch.manual_seed(args.seed)if args.cuda:    torch.cuda.manual_seed(args.seed)    

 

输出结果：

Namespace(batch_size=128, cuda=True, epochs=10, log_interval=10, no_cuda=False, seed=1)

下载数据集到./data/目录下：

kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}trainset = datasets.MNIST('../data', train=True, download=True,transform=transforms.ToTensor())train_loader = torch.utils.data.DataLoader(    trainset,    batch_size=args.batch_size, shuffle=True, **kwargs)testset= datasets.MNIST('../data', train=False, transform=transforms.ToTensor())test_loader = torch.utils.data.DataLoader(    testset,    batch_size=args.batch_size, shuffle=True, **kwargs)image, label = trainset[0]  print(len(trainset))print(image.size())image, label = testset[0]  print(len(testset))print(image.size())

输出结果：

60000torch.Size([1, 28, 28])10000torch.Size([1, 28, 28])

2定义VAE

首先我们介绍x.view方法：

x = torch.randn(4, 4)y = x.view(16)z = x.view(-1, 16)  # the size -1 is inferred from other dimensionsprint(x)
print(y)
print(z)

输出结果：

 1.6154  1.1792  0.6450  1.2078-0.4741  1.2145  0.8381  2.3532 0.2070 -0.9054  0.9262  0.6758 1.2613  0.5196 -1.7125 -0.0519[torch.FloatTensor of size 4x4] 1.6154 1.1792 0.6450 1.2078-0.4741 1.2145 0.8381 2.3532 0.2070-0.9054 0.9262 0.6758 1.2613 0.5196-1.7125-0.0519[torch.FloatTensor of size 16]Columns 0 to 9  1.6154  1.1792  0.6450  1.2078 -0.4741  1.2145  0.8381  2.3532  0.2070 -0.9054Columns 10 to 15  0.9262  0.6758  1.2613  0.5196 -1.7125 -0.0519[torch.FloatTensor of size 1x16]

然后建立VAE模型

class VAE(nn.Module):    def __init__(self):        super(VAE, self).__init__()        self.fc1 = nn.Linear(784, 400)        self.fc21 = nn.Linear(400, 20)        self.fc22 = nn.Linear(400, 20)        self.fc3 = nn.Linear(20, 400)        self.fc4 = nn.Linear(400, 784)        self.relu = nn.ReLU()        self.sigmoid = nn.Sigmoid()    def encode(self, x):        h1 = self.relu(self.fc1(x))        return self.fc21(h1), self.fc22(h1)    def reparametrize(self, mu, logvar):        std = logvar.mul(0.5).exp_()        eps = Variable(std.data.new(std.size()).normal_())        return eps.mul(std).add_(mu)    def decode(self, z):        h3 = self.relu(self.fc3(z))        return self.sigmoid(self.fc4(h3))    def forward(self, x):        mu, logvar = self.encode(x.view(-1, 784))        z = self.reparametrize(mu, logvar)        return self.decode(z), mu, logvarmodel = VAE()if args.cuda:    model.cuda()

3.定义一个损失函数

reconstruction_function = nn.BCELoss()reconstruction_function.size_average = Falsedef loss_function(recon_x, x, mu, logvar):    BCE = reconstruction_function(recon_x, x.view(-1, 784))    # see Appendix B from VAE paper:    # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014    # https://arxiv.org/abs/1312.6114    # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)    KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)    KLD = torch.sum(KLD_element).mul_(-0.5)    return BCE + KLDoptimizer = optim.Adam(model.parameters(), lr=1e-3)

4.在训练数据上训练神经网络

我们只需要对数据迭代器进行循环，并将输入反馈到网络并进行优化。

for epoch in range(1, args.epochs + 1):    train(epoch)    test(epoch)

其中 

def train(epoch):    model.train()    train_loss = 0    for batch_idx, (data, _) in enumerate(train_loader):        data = Variable(data)        if args.cuda:            data = data.cuda()        optimizer.zero_grad()        recon_batch, mu, logvar = model(data)        loss = loss_function(recon_batch, data, mu, logvar)        loss.backward()        train_loss += loss.data[0]        optimizer.step()        if batch_idx % args.log_interval == 0:            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(                epoch, batch_idx * len(data), len(train_loader.dataset),                100. * batch_idx / len(train_loader),                loss.data[0] / len(data)))                print('====> Epoch: {} Average loss: {:.4f}'.format(          epoch, train_loss / len(train_loader.dataset)))def test(epoch):    model.eval()    test_loss = 0    for data, _ in test_loader:        if args.cuda:            data = data.cuda()        data = Variable(data, volatile=True)        recon_batch, mu, logvar = model(data)        test_loss += loss_function(recon_batch, data, mu, logvar).data[0]    test_loss /= len(test_loader.dataset)    print('====> Test set loss: {:.4f}'.format(test_loss))