"""自動編碼的核心就是各種全連線的組合，它是一種無監督的形式，因為他的標籤是自己。
"""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
cmimport
numpy as np
#超引數
epoch = 10batch_size = 64lr = 0.005download_mnist =false
n_test_img = 5
#mnist資料集
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)
#顯示出乙個例子
plt.imshow(train_data.train_data[2].numpy(), cmap='
gray')
plt.title('%i
' % train_data.train_labels[2])
plt.show()
#將資料集分為多批資料
train_loader = data.dataloader(dataset=train_data, batch_size=batch_size, shuffle=true)
#搭建自編碼網路框架
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的資料在0到1之間
)
defforward(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()
#設定為實時列印
#第一行是原始
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() #
優化網路中的各個引數
if step % 100 ==0:
print('
epoch:
', epoch, '
| train loss: %.4f
' %loss.data[0])
#第二行畫出解碼後的
_, 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()
#視覺化三維圖
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()
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