mnist数据集进行自编码

"""
自动编码的核心就是各种全连接的组合，它是一种无监督的形式，因为他的标签是自己。
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import numpy as np

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

# Mnist数据集
train_data = torchvision.datasets.MNIST(
    root=‘./mnist/‘,
    train=True,
    transform=torchvision.transforms.ToTensor(),
    download=DOWNLOAD_MNIST,
)

print(train_data.train_data.size())     # (60000, 28, 28)
print(train_data.train_labels.size())   # (60000)

# 显示出一个例子
plt.imshow(train_data.train_data[2].numpy(), cmap=‘gray‘)
plt.title(‘%i‘ % train_data.train_labels[2])
plt.show()

# 将数据集分为多批数据
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

# 搭建自编码网络框架
class AutoEncoder(nn.Module):
    def __init__(self):
        super(AutoEncoder, self).__init__()

        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),
        )
        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(), # 将输出结果压缩到0到1之间，因为train_data的数据在0到1之间
        )

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

autoencoder = AutoEncoder()

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

# initialize figure
f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))
plt.ion()   # 设置为实时打印

# 第一行是原始图片
view_data = Variable(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)
for i in range(N_TEST_IMG):
    a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap=‘gray‘); a[0][i].set_xticks(()); a[0][i].set_yticks(())

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

        encoded, decoded = autoencoder(b_x)

        loss = loss_func(decoded, b_y)
        optimizer.zero_grad()     # 将上一部的梯度清零
        loss.backward()           # 反向传播，计算梯度          
        optimizer.step()          # 优化网络中的各个参数

        if step % 100 == 0:
            print(‘Epoch: ‘, epoch, ‘| train loss: %.4f‘ % loss.data[0])

            # 第二行画出解码后的图片
            _, decoded_data = autoencoder(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()

# 可视化三维图
view_data = Variable(train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.)
encoded_data, _ = autoencoder(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()