大佬教程收集整理的这篇文章主要介绍了实验 5 指南，大佬教程大佬觉得挺不错的，现在分享给大家，也给大家做个参考。

手写数字识别

复制代码后依次运行下面命令

python3 lenet5.py
python3 Train.py
python3 test.py

lenet5.py

import torch.nn as nn

class LeNet5(nn.ModulE):
	def __init__(self):
		super(LeNet5, self).__init__()
		self.conv1 = nn.Sequential(	
			nn.Conv2d(				# (1, 28, 28)
				in_chAnnels=1,
				out_chAnnels=6,
				kernel_size=5,
				Stride=1,
				padding=2
			),						# (6, 28, 28)
			nn.ReLU(),
			nn.MaxPool2d(kernel_size=2, Stride=2),	# (6, 14, 14)	
		)
		self.conv2 = nn.Sequential(
			nn.Conv2d(6, 16, 5, 1, 0),
			nn.ReLU(),				# (16, 10, 10)
			nn.MaxPool2d(2, 2)		# (16, 5, 5)
		)
		self.LinNet = nn.Sequential(
			nn.Linear(16 * 5 * 5, 120),
			nn.ReLU(),
			nn.Linear(120, 84),
			nn.ReLU(),
			nn.Linear(84, 10)
		)

	def forWARD(self, X):
		x = self.conv1(X)
		x = self.conv2(X)
		x = x.view(x.size(0), -1)
		out = self.LinNet(X)
		return out

if __name__ == "__main__":
	myNet = LeNet5()
	print(myNet)

Train.py

from lenet5 import LeNet5
import time
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST

transform = transforms.Compose([
	transforms.ToTensor(),
	transforms.Normalize((0.1307, ), (0.3081, ))
])

Train_set = MNIST(root='./MNIST',
                  Train=True,
                  download=True,
                  transform=transform)

Train_loader = DataLoader(
    Train_set,
    batch_size = 100,
    shuffle = True
)

net = LeNet5().cuda()
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)
loss_func = nn.CrossEntropyLoss()

start_time = time.time()

epochs = 10
epoch_loss = []
for epoch in range(epochs):
	running_loss = 0.0
	for i, (inputs, labels) in enumerate(Train_loader):
		inputs = torch.tensor(inputs).type(torch.FloatTensor).cuda()
		labels = torch.tensor(labels).type(torch.LongTensor).cuda()

		out = net(inputs)
		loss = loss_func(out, labels)
		optimizer.zero_grad()
		loss.BACkWARD()
		optimizer.step()

		running_loss += loss.item()
		avr_loss = running_loss / (i+1)
	epoch_loss.append(avr_loss)
	print('epoch %d, loss: %.3f' % (epoch, avr_loss))

end_time = time.time()
print('Finished Training, time used: %.3f' % (end_time - start_timE))

plt.figure(figsize=(8, 5), dpi=150)
plt.plot(epoch_loss, c='r')
plt.savefig('./document/figure/loss.pdf')
plt.show()
torch.save(net, 'net.pkl')

test.py

import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST

transform = transforms.Compose([
	transforms.ToTensor(),
	transforms.Normalize((0.1307, ), (0.3081, ))
])

test_set = MNIST(root='./MNIST',
                Train=false,
                download=True,
                transform=transform)

test_loader = DataLoader(
    test_set,
    batch_size = 100,
    shuffle = True
)

net = torch.load('net.pkl')

correct = 0
for i, (inputs, labels) in enumerate(test_loader):
	inputs = torch.tensor(inputs).type(torch.FloatTensor).cuda()
	labels = torch.tensor(labels).type(torch.LongTensor).cuda()

	out = net(inputs)
	y_pred = out.argmax(axis=1)

	correct += (y_pred == labels).sum()

	print('Average accuracy of %d tests: %.1f' % ((i+1), correct / (i+1)))

大佬总结

以上是大佬教程为你收集整理的实验 5 指南全部内容，希望文章能够帮你解决实验 5 指南所遇到的程序开发问题。

如果觉得大佬教程网站内容还不错，欢迎将大佬教程推荐给程序员好友。

本图文内容来源于网友网络收集整理提供，作为学习参考使用，版权属于原作者。
如您有任何意见或建议可联系处理。小编QQ：384754419，请注明来意。