Mnist手寫數字自編碼 分類實驗

import torch

import torch.nn as nn
import torch.nn.functional as f
import random
import numpy as np
import matplotlib.pyplot as plt
import torchvision
class autoencodenet(nn.module):
def __init__(self):
super(autoencodenet, self).__init__()
# 編碼
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),   # 壓縮成3個特徵, 進行 3d 影象視覺化
)# 解壓
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(),       # 激勵函式讓輸出值在 (0, 1)
)# 分類器
self.classfier = nn.sequential(
nn.linear(3,128),
nn.tanh(),
nn.linear(128,10),
nn.sigmoid(),
)def forward(self, x):
encoded = self.encoder(x)
decoded = self.decoder(encoded)
lable = self.classfier(encoded)
return encoded, decoded,lable
def train():
# 超引數
epoch = 20
batch_size = 64
lr = 0.005
download_mnist = false   # 下過資料的話, 就可以設定成 false
n_test_img = 5          # 到時候顯示 5張看效果, 如上圖一
# mnist digits dataset
train_data = 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 h**e it
)autoencoder = autoencodenet()
# autoencoder = torch.load("autoencoder_115.pkl")
optimizer = torch.optim.adam(autoencoder.parameters(), lr=lr)
# 編碼損失函式
loss_func = nn.mseloss()
# 分類損失函式
loss_func1 = nn.crossentropyloss()
# 資料載入
train_loader = torch.utils.data.dataloader(train_data,batch_size=128,shuffle=true)
losses =
fig,ax=plt.subplots(2,n_test_img)
plt.ion()   # continuously plot
# 會出驗證的五張原圖
testimg = train_data.data[:5].view(-1,28,28).type(torch.floattensor)/255.
for i in range(5):
ax[0][i].imshow(testimg[i])
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 ,lable= autoencoder(b_x)
# 求損失
				
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