AutoEncoder（自编码/非监督学习）数字分类

import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm

# 超参数
EPOCH = 10
BATCH_SIZE = 64
LR = 0.005
DOWNLOAD_MNIST = False
N_TEST_IMG = 5

# 加载训练数据集
train_data = torchvision.datasets.MNIST(
    './mnist/',
    train=True,
    transform=torchvision.transforms.ToTensor(),
    download=DOWNLOAD_MNIST
)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)


# 构造 AutoEncoder 自编码
class AutoEncoder(nn.Module):
    def __init__(self):
        super(AutoEncoder, self).__init__()
        # 压缩（encoder)
        # Sequential 一个有序的容器，神经网络模块将按照在传入构造器的顺序依次被添加到计算图中执行，同时以神经网络模块为元素的有序字典也可以作为传入参数。
        self.encoder = nn.Sequential(
            nn.Linear(28 * 28, 128),
            nn.Tanh(),  # Tanh激励函数
            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
        )
        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


f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))
plt.ion()  # continuously plot
# original data (first row) for viewing
view_data = train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.
# 定义模型对象
auto_encoder = AutoEncoder()
# 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.
optimizer = torch.optim.Adam(auto_encoder.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 = auto_encoder(b_x)
        loss = loss_func(decoded, b_y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if step % 100 == 0:
            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy())
            _, decoded_data = auto_encoder(view_data)
            for i in range(N_TEST_IMG):
                a[1][i].clear()
                a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray')
                a[1][i].set_xticks(());
                a[1][i].set_yticks(())
            plt.draw();
            plt.pause(0.05)
plt.ioff()
plt.show()

# visualize in 3D plot
view_data = train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.
encoded_data, _ = auto_encoder(view_data)
fig = plt.figure(2); ax = Axes3D(fig)
X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()
values = train_data.train_labels[:200].numpy()
for x, y, z, s in zip(X, Y, Z, values):
    c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c)
ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())
plt.show()