"""
自動編碼的核心就是各種全連接的組合，它是一種無監督的形式，因為他的標簽是自己。
"""
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

# 超參數
EPOCH = 10
BATCH_SIZE 
 
= 64
LR = 0.005
DOWNLOAD_MNIST = False
N_TEST_IMG = 5

# Mnist數據集
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)

# 顯示出一個例子
plt.imshow(train_data.train_data[2].numpy(), cmap=‘gray‘)
plt.title(‘%i‘ % train_data.train_labels[2])
plt.show()

# 將數據集分為多批數據
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

# 搭建自編碼網絡框架
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(), # 將輸出結果壓縮到0到1之間，因為train_data的數據在0到1之間
        )

    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()   # 設置為實時打印

# 第一行是原始圖片
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()     # 將上一部的梯度清零
        loss.backward()           # 反向傳播，計算梯度          
        optimizer.step()          # 優化網絡中的各個參數

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

            # 第二行畫出解碼後的圖片
            _, 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()

# 可視化三維圖
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()

mnist數據集進行自編碼