LeNet网络

LeNet网络

导入必要的工具包

import time
import torch
from torch import nn,optim

添加文件路径

import sys
sys.path.append("..")
import d2lzh_pytorch as d2l
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

构造神经网络层

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet,self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(1,6,5),
            nn.Sigmoid(),
            nn.MaxPool2d(2,2),
            nn.Conv2d(6,16,5),
            nn.Sigmoid(),
            nn.MaxPool2d(2,2)
        )
        self.fc = nn.Sequential(
            nn.Linear(16*4*4,120),
            nn.Sigmoid(),
            nn.Linear(120,84),
            nn.Sigmoid(),
            nn.Linear(84,10)
        )
        
    def forward(self,img):
        feature = self.conv(img)
        output = self.fc(feature.view(img.shape[0],-1))
        return output

查看神经网络

net = LeNet()
print(net)

加载数据集

batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)

估计精度函数

def evaluate_accuracy(data_iter, net,
                      device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')):
    acc_sum,n = 0.0,0
    with torch.no_grad():
        for X,y in data_iter:
            if isinstance(net,nn.Module):
                net.eval()
                acc_sum += (net(X.to(device)).argmax(dim = 1) == y.to(device)).float().sum().cpu().item()
                net.train()
            else:
                if('is_training' in net.__code__.co_varnames):
                    acc_sum += (net(X,is_training = False).argmax(dim = 1) == y).float().sum().item()
                else:
                    acc_sum += (net(X).argmax(dim = 1) == y).float().sum().item()
            n += y.shape[0]
    return acc_sum/n

训练函数

def train_ch5(net,train_iter,test_iter,batch_size,optimizer,device,num_epochs):
    net = net.to(device)
    print("training on",device)
    loss = torch.nn.CrossEntropyLoss()
    batch_count = 0
    for epoch in range(num_epochs):
        train_l_sum,train_acc_sum,n,start = 0.0,0.0,0,time.time()
        for X,y in train_iter:
            X = X.to(device)
            y = y.to(device)
            y_hat = net(X)
            l = loss(y_hat,y)
            optimizer.zero_grad()
            l.backward()
            optimizer.step()
            train_l_sum += l.cpu().item()
            train_acc_sum += (y_hat.argmax(dim =1) == y).sum().cpu().item()
            n += y.shape[0]
            batch_count += 1
        test_acc = evaluate_accuracy(test_iter,net)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec'
              % (epoch + 1, train_l_sum / batch_count,
                 train_acc_sum / n, test_acc, time.time() - start))

执行神经网络

lr,num_epochs = 0.001,5
optimizer = torch.optim.Adam(net.parameters(),lr = lr)
train_ch5(net,train_iter,test_iter,batch_size,optimizer,device,num_epochs)

来源：CSDN

作者：jasper-cell

链接：https://blog.csdn.net/weixin_44830961/article/details/103461753