# Pytorch1.0入门实战一：LeNet神经网络实现 MNIST手写数字识别

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets,transforms
import torchvision
import cv2

class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(1, 6, 3, 1, 2),
nn.ReLU(),
nn.MaxPool2d(2, 2)
)

self.conv2 = nn.Sequential(
nn.Conv2d(6, 16, 5),
nn.ReLU(),
nn.MaxPool2d(2, 2)
)

self.fc1 = nn.Sequential(
nn.Linear(16 * 5 * 5, 120),
nn.BatchNorm1d(120),
nn.ReLU()
)

self.fc2 = nn.Sequential(
nn.Linear(120, 84),
nn.BatchNorm1d(84),#加快收敛速度的方法（注：批标准化一般放在全连接层后面，激活函数层的前面）
nn.ReLU()
)

self.fc3 = nn.Linear(84, 10)

#         self.sfx = nn.Softmax()

def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
#         print(x.shape)
x = x.view(x.size()[0], -1)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
#         x = self.sfx(x)
return x

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = 64
LR = 0.001
Momentum = 0.9

# 下载数据集
train_dataset = datasets.MNIST(root = './data/',
train=True,
transform = transforms.ToTensor(),
test_dataset =datasets.MNIST(root = './data/',
train=False,
transform=transforms.ToTensor(),
#建立一个数据迭代器
batch_size = batch_size,
shuffle = True)
batch_size = batch_size,
shuffle = False)

#实现单张图片可视化
# img  = torchvision.utils.make_grid(images)
# img = img.numpy().transpose(1,2,0)
# # img.shape
# std = [0.5,0.5,0.5]
# mean = [0.5,0.5,0.5]
# img = img*std +mean
# cv2.imshow('win',img)
# key_pressed = cv2.waitKey(0)

net = LeNet().to(device)
criterion = nn.CrossEntropyLoss()#定义损失函数
optimizer = optim.SGD(net.parameters(),lr=LR,momentum=Momentum)

epoch = 1
if __name__ == '__main__':
for epoch in range(epoch):
sum_loss = 0.0
inputs, labels = data
inputs, labels = Variable(inputs).cuda(), Variable(labels).cuda()
outputs = net(inputs)#将数据传入网络进行前向运算
loss = criterion(outputs, labels)#得到损失函数
loss.backward()#反向传播
optimizer.step()#通过梯度做一步参数更新

# print(loss)
sum_loss += loss.item()
if i % 100 == 99:
print('[%d,%d] loss:%.03f' % (epoch + 1, i + 1, sum_loss / 100))
sum_loss = 0.0

#验证测试集
net.eval()#将模型变换为测试模式
correct = 0
total = 0
images, labels = data_test
images, labels = Variable(images).cuda(), Variable(labels).cuda()
output_test = net(images)
# print("output_test:",output_test.shape)

_, predicted = torch.max(output_test, 1)#此处的predicted获取的是最大值的下标
# print("predicted:",predicted.shape)
total += labels.size(0)
correct += (predicted == labels).sum()
print("correct1: ",correct)
print("Test acc: {0}".format(correct.item() / len(test_dataset)))#.cpu().numpy()

posted @ 2019-03-02 23:51  泽积  阅读(1628)  评论(0编辑  收藏