机器学习|机器学习_pytorch_高级神经网络结构_AutoEncoder自编码机器学习|深度学习|pytorch

文章目录

- AutoEncoder自编码
- - 1. 获取训练数据
  - 2. AutoEncoder模型
  - 3. 训练

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
【机器学习|机器学习_pytorch_高级神经网络结构_AutoEncoder自编码】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 = https://www.it610.com/article/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, decodedautoencoder = 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