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138 lines
4.8 KiB
Markdown
138 lines
4.8 KiB
Markdown
# 4.4 – AutoEncoder (自编码/非监督学习)
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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.
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这次我们还用 MNIST 手写数字数据来压缩再解压图片.
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然后用压缩的特征进行非监督分类.
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## 训练数据
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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.
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```py
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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import torch.utils.data as Data
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import torchvision
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# 超参数
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EPOCH = 10
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BATCH_SIZE = 64
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LR = 0.005
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DOWNLOAD_MNIST = True # 下过数据的话, 就可以设置成 False
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N_TEST_IMG = 5 # 到时候显示 5张图片看效果, 如上图一
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# Mnist digits dataset
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train_data = torchvision.datasets.MNIST(
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root=\'./mnist/\',
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train=True, # this is training data
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transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to
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# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
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download=DOWNLOAD_MNIST, # download it if you don\'t have it
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)
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```
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这就是一张我们要训练的手写数字 4.
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## AutoEncoder
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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.
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```py
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class AutoEncoder(nn.Module):
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def __init__(self):
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super(AutoEncoder, self).__init__()
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# 压缩
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self.encoder = nn.Sequential(
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nn.Linear(28*28, 128),
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nn.Tanh(),
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nn.Linear(128, 64),
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nn.Tanh(),
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nn.Linear(64, 12),
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nn.Tanh(),
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nn.Linear(12, 3), # 压缩成3个特征, 进行 3D 图像可视化
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)
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# 解压
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self.decoder = nn.Sequential(
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nn.Linear(3, 12),
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nn.Tanh(),
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nn.Linear(12, 64),
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nn.Tanh(),
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nn.Linear(64, 128),
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nn.Tanh(),
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nn.Linear(128, 28*28),
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nn.Sigmoid(), # 激励函数让输出值在 (0, 1)
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)
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def forward(self, x):
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encoded = self.encoder(x)
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decoded = self.decoder(encoded)
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return encoded, decoded
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autoencoder = AutoEncoder()
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```
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#### 训练
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训练, 并可视化训练的过程. 我们可以有效的利用 encoder 和 decoder 来做很多事, 比如这里我们用 decoder 的信息输出看和原图片的对比, 还能用 encoder 来看经过压缩后, 神经网络对原图片的理解. encoder 能将不同图片数据大概的分离开来. 这样就是一个无监督学习的过程.
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```py
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optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)
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loss_func = nn.MSELoss()
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for epoch in range(EPOCH):
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for step, (x, y) in enumerate(train_loader):
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b_x = Variable(x.view(-1, 28*28)) # batch x, shape (batch, 28*28)
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b_y = Variable(x.view(-1, 28*28)) # batch y, shape (batch, 28*28)
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b_label = Variable(y) # batch label
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encoded, decoded = autoencoder(b_x)
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loss = loss_func(decoded, b_y) # mean square error
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optimizer.zero_grad() # clear gradients for this training step
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loss.backward() # backpropagation, compute gradients
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optimizer.step() # apply gradients
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```
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## 画3D图
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3D 的可视化图挺有趣的, 还能挪动观看, 更加直观, 好理解.
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```py
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# 要观看的数据
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view_data = Variable(train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.)
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encoded_data, _ = autoencoder(view_data) # 提取压缩的特征值
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fig = plt.figure(2)
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ax = Axes3D(fig) # 3D 图
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# x, y, z 的数据值
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X = encoded_data.data[:, 0].numpy()
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Y = encoded_data.data[:, 1].numpy()
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Z = encoded_data.data[:, 2].numpy()
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values = train_data.train_labels[:200].numpy() # 标签值
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for x, y, z, s in zip(X, Y, Z, values):
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c = cm.rainbow(int(255*s/9)) # 上色
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ax.text(x, y, z, s, backgroundcolor=c) # 标位子
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ax.set_xlim(X.min(), X.max())
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ax.set_ylim(Y.min(), Y.max())
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ax.set_zlim(Z.min(), Z.max())
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plt.show()
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```
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所以这也就是在我 [github 代码](https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/404_autoencoder.py) 中的每一步的意义啦.
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文章来源：[莫烦](https://morvanzhou.github.io/) |