通过Sequential类来实现LeNet模型

#import
import sys
sys.path.append("/home/kesci/input")
import d2lzh1981 as d2l
import torch
import torch.nn as nn
import torch.optim as optim
import time

#net
class Flatten(torch.nn.Module):  #展平操作
def forward(self, x):
return x.view(x.shape[0], -1)

class Reshape(torch.nn.Module): #将图像大小重定型
def forward(self, x):
return x.view(-1,1,28,28)      #(B x C x H x W)

net = torch.nn.Sequential(     #Lelet
Reshape(),
nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, padding=2), #b*1*28*28  =>b*6*28*28
nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2),                              #b*6*28*28  =>b*6*14*14
nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5),           #b*6*14*14  =>b*16*10*10
nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2),                              #b*16*10*10  => b*16*5*5
Flatten(),                                                          #b*16*5*5   => b*400
nn.Linear(in_features=16*5*5, out_features=120),
nn.Sigmoid(),
nn.Linear(120, 84),
nn.Sigmoid(),
nn.Linear(84, 10)
)

构造一个高和宽均为28的单通道数据样本，并逐层进行前向计算来查看每个层的输出形状

#print
X = torch.randn(size=(1,1,28,28), dtype = torch.float32)
for layer in net:
X = layer(X)
print(layer.__class__.__name__,'output shape: \t',X.shape)

获取数据和训练模型

# 数据
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(
batch_size=batch_size, root='/home/kesci/input/FashionMNIST2065')
print(len(train_iter))

#数据展示
import matplotlib.pyplot as plt
def show_fashion_mnist(images, labels):
d2l.use_svg_display()
# 这里的_表示我们忽略（不使用）的变量
_, figs = plt.subplots(1, len(images), figsize=(12, 12))
for f, img, lbl in zip(figs, images, labels):
f.imshow(img.view((28, 28)).numpy())
f.set_title(lbl)
f.axes.get_xaxis().set_visible(False)
f.axes.get_yaxis().set_visible(False)
plt.show()

for Xdata,ylabel in train_iter:
break
X, y = [], []
for i in range(10):
print(Xdata[i].shape,ylabel[i].numpy())
X.append(Xdata[i]) # 将第i个feature加到X中
y.append(ylabel[i].numpy()) # 将第i个label加到y中
show_fashion_mnist(X, y)

没有GPU

# This function has been saved in the d2l package for future use
#use GPU
def try_gpu():
"""If GPU is available, return torch.device as cuda:0; else return torch.device as cpu."""
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
return device

device = try_gpu()
device

#计算准确率
'''
(1). net.train()
启用 BatchNormalization 和 Dropout，将BatchNormalization和Dropout置为True
(2). net.eval()
不启用 BatchNormalization 和 Dropout，将BatchNormalization和Dropout置为False
'''

def evaluate_accuracy(data_iter, net,device=torch.device('cpu')):
"""Evaluate accuracy of a model on the given data set."""
acc_sum,n = torch.tensor([0],dtype=torch.float32,device=device),0
for X,y in data_iter:
# If device is the GPU, copy the data to the GPU.
X,y = X.to(device),y.to(device)
net.eval()
with torch.no_grad():
y = y.long()
acc_sum += torch.sum((torch.argmax(net(X), dim=1) == y))  #[[0.2 ,0.4 ,0.5 ,0.6 ,0.8] ,[ 0.1,0.2 ,0.4 ,0.3 ,0.1]] => [ 4 , 2 ]
n += y.shape[0]
return acc_sum.item()/n

#训练函数
def train_ch5(net, train_iter, test_iter,criterion, num_epochs, batch_size, device,lr=None):
"""Train and evaluate a model with CPU or GPU."""
print('training on', device)
net.to(device)
optimizer = optim.SGD(net.parameters(), lr=lr)
for epoch in range(num_epochs):
train_l_sum = torch.tensor([0.0],dtype=torch.float32,device=device)
train_acc_sum = torch.tensor([0.0],dtype=torch.float32,device=device)
n, start = 0, time.time()
for X, y in train_iter:
net.train()

optimizer.zero_grad()
X,y = X.to(device),y.to(device)
y_hat = net(X)
loss = criterion(y_hat, y)
loss.backward()
optimizer.step()

with torch.no_grad():
y = y.long()
train_l_sum += loss.float()
train_acc_sum += (torch.sum((torch.argmax(y_hat, dim=1) == y))).float()
n += y.shape[0]
test_acc = evaluate_accuracy(test_iter, net,device)
print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, '
'time %.1f sec'
% (epoch + 1, train_l_sum/n, train_acc_sum/n, test_acc,
time.time() - start))

# 训练
lr, num_epochs = 0.9, 10

def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
torch.nn.init.xavier_uniform_(m.weight)

net.apply(init_weights)
net = net.to(device)

criterion = nn.CrossEntropyLoss()   #交叉熵描述了两个概率分布之间的距离，交叉熵越小说明两者之间越接近
train_ch5(net, train_iter, test_iter, criterion,num_epochs, batch_size,device, lr)

# test
for testdata,testlabe in test_iter:
testdata,testlabe = testdata.to(device),testlabe.to(device)
break
print(testdata.shape,testlabe.shape)
net.eval()
y_pre = net(testdata)
print(torch.argmax(y_pre,dim=1)[:10])
print(testlabe[:10])

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