Pytorch -＞ 自定义数据集 + 迁移学习 + AE

自定义数据集必须定义的三个函数

class Pokemon(Dataset):
    # 划分训练与测试集
    def __init__(self, training=True):
        if training:
            self.samples = list(range(1,1001))
        else:
            self.samples = list(range(1001,1501))

    def __len__(self):
        return len(self.samples)

    def __getitem__(self, idx):
        return self.samples[idx]

其余见代码文件

主要思路：

1. 定义数据集（引入参数、划分数据集、索引、生成图片与标签）
2. 生成CSV文件（生成images列表、写入CSV、读取CSV）
3. 预处理（设置预处理参数和visdom）
4. 迁移学习

自编码器

 让p尽可能大

class AE(nn.Module):

    def __init__(self):
        super(AE, self).__init__()


        # [b, 784] => [b, 20]
        self.encoder = nn.Sequential(
            nn.Linear(784, 256),
            nn.ReLU(),
            nn.Linear(256, 64),
            nn.ReLU(),
            nn.Linear(64, 20),
            nn.ReLU()
        )
        # [b, 20] => [b, 784]
        self.decoder = nn.Sequential(
            nn.Linear(20, 64),
            nn.ReLU(),
            nn.Linear(64, 256),
            nn.ReLU(),
            nn.Linear(256, 784),
            nn.Sigmoid()
        )


    def forward(self, x):
        """

        :param x: [b, 1, 28, 28]
        :return:
        """
        batchsz = x.size(0)
        # flatten
        x = x.view(batchsz, 784)
        # encoder
        x = self.encoder(x)
        # decoder
        x = self.decoder(x)
        # reshape
        x = x.view(batchsz, 1, 28, 28)

        return x, None

class VAE(nn.Module):

    def __init__(self):
        super(VAE, self).__init__()


        # [b, 784] => [b, 20]
        # u: [b, 10]
        # sigma: [b, 10]
        self.encoder = nn.Sequential(
            nn.Linear(784, 256),
            nn.ReLU(),
            nn.Linear(256, 64),
            nn.ReLU(),
            nn.Linear(64, 20),
            nn.ReLU()
        )
        # [b, 20] => [b, 784]
        self.decoder = nn.Sequential(
            nn.Linear(10, 64),
            nn.ReLU(),
            nn.Linear(64, 256),
            nn.ReLU(),
            nn.Linear(256, 784),
            nn.Sigmoid()
        )

        self.criteon = nn.MSELoss()

    def forward(self, x):
        """

        :param x: [b, 1, 28, 28]
        :return:
        """
        batchsz = x.size(0)
        # flatten
        x = x.view(batchsz, 784)
        # encoder
        # [b, 20], including mean and sigma
        h_ = self.encoder(x)
        # [b, 20] => [b, 10] and [b, 10]
        # μ和σ
        mu, sigma = h_.chunk(2, dim=1)
        # reparametrize trick, epison~N(0, 1)
        h = mu + sigma * torch.randn_like(sigma)

        # decoder
        x_hat = self.decoder(h)
        # reshape
        x_hat = x_hat.view(batchsz, 1, 28, 28)
        # 计算KL函数，1e-8是为了限幅
        kld = 0.5 * torch.sum(
            torch.pow(mu, 2) +
            torch.pow(sigma, 2) -
            torch.log(1e-8 + torch.pow(sigma, 2)) - 1
        ) / (batchsz*28*28)

        return x_hat, kld

 【深度学习】 自编码器（AutoEncoder） - 知乎

import  torch
from    torch.utils.data import DataLoader
from    torch import nn, optim
from    torchvision import transforms, datasets

from    ae import AE
from    vae import VAE

import  visdom

def main():
    mnist_train = datasets.MNIST('mnist', True, transform=transforms.Compose([
        transforms.ToTensor()
    ]), download=True)
    mnist_train = DataLoader(mnist_train, batch_size=32, shuffle=True)


    mnist_test = datasets.MNIST('mnist', False, transform=transforms.Compose([
        transforms.ToTensor()
    ]), download=True)
    mnist_test = DataLoader(mnist_test, batch_size=32, shuffle=True)


    x, _ = iter(mnist_train).next()
    print('x:', x.shape)

    device = torch.device('cuda')
    # model = AE().to(device)
    model = VAE().to(device)
    criteon = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=1e-3)
    print(model)

    viz = visdom.Visdom()

    for epoch in range(1000):


        for batchidx, (x, _) in enumerate(mnist_train):
            # [b, 1, 28, 28]
            x = x.to(device)

            x_hat, kld = model(x)
            loss = criteon(x_hat, x)

            if kld is not None:
                elbo = - loss - 1.0 * kld
                loss = - elbo

            # backprop
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()


        print(epoch, 'loss:', loss.item(), 'kld:', kld.item())

        x, _ = iter(mnist_test).next()
        x = x.to(device)
        with torch.no_grad():
            x_hat, kld = model(x)
        viz.images(x, nrow=8, win='x', opts=dict(title='x'))
        viz.images(x_hat, nrow=8, win='x_hat', opts=dict(title='x_hat'))