机器学习_pytorch_高级神经网络结构_AutoEncoder自编码

AutoEncoder自编码

神经网络也能进行非监督学习, 只需要训练数据, 不需要标签数据. 自编码就是这样一种形式. 自编码能自动分类数据, 而且也能嵌套在半监督学习的上面, 用少量的有标签样本和大量的无标签样本学习.

1. 获取训练数据

自编码只用训练集就好了, 而且只需要训练 training data 的 image, 不用训练 labels.

import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision

# 超参数
EPOCH = 10
BATCH_SIZE = 64
LR = 0.005
DOWNLOAD_MNIST = True   # 下过数据的话, 就可以设置成 False
N_TEST_IMG = 5          # 到时候显示 5张图片看效果, 如上图一

# Mnist digits dataset
train_data = torchvision.datasets.MNIST(
    root='./mnist/',
    train=True,                                     # this is training data
    transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to
                                                    # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
    download=DOWNLOAD_MNIST,                        # download it if you don't have it
)

2. AutoEncoder模型

AutoEncoder 形式很简单, 分别是 encoder 和 decoder, 压缩和解压, 压缩后得到压缩的特征值, 再从压缩的特征值解压成原图片.

class AutoEncoder(nn.Module):
    def __init__(self):
        super(AutoEncoder, self).__init__()

        # 压缩
        self.encoder = nn.Sequential(
            nn.Linear(28*28, 128),
            nn.Tanh(),
            nn.Linear(128, 64),
            nn.Tanh(),
            nn.Linear(64, 12),
            nn.Tanh(),
            nn.Linear(12, 3),   # 压缩成3个特征, 进行 3D 图像可视化
        )
        # 解压
        self.decoder = nn.Sequential(
            nn.Linear(3, 12),
            nn.Tanh(),
            nn.Linear(12, 64),
            nn.Tanh(),
            nn.Linear(64, 128),
            nn.Tanh(),
            nn.Linear(128, 28*28),
            nn.Sigmoid(),       # 激励函数让输出值在 (0, 1)
        )

    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return encoded, decoded

autoencoder = AutoEncoder()

3. 训练

optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)
loss_func = nn.MSELoss()

for epoch in range(EPOCH):
    for step, (x, b_label) in enumerate(train_loader):
        b_x = x.view(-1, 28*28)   # batch x, shape (batch, 28*28)
        b_y = x.view(-1, 28*28)   # batch y, shape (batch, 28*28)

        encoded, decoded = autoencoder(b_x)

        loss = loss_func(decoded, b_y)      # mean square error
        optimizer.zero_grad()               # clear gradients for this training step
        loss.backward()                     # backpropagation, compute gradients
        optimizer.step()                    # apply gradients