CIFAR10数据集实战-LeNet5神经网络（下）

用户6719124

发布于 2020-01-02 14:21:28

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发布于 2020-01-02 14:21:28

文章被收录于专栏：python pytorch AI机器学习实践 python pytorch AI机器学习实践python pytorch AI机器学习实践

下面开始加入test部分

先写入test部分代码

for x, label in cifar_test:
    x, label = x.to(device), label.to(device)

    logits = model(x)
    pred = logits.armax(dim=1)
    # 用argmax选出可能性最大的值的索引

为进行比对

定义正确率

写入对比

total_correct += torch.eq(pred, label).float().sum().item()
# torch.eq函数用于对比，同时要转为numpy数据
total_num += x.size(0)

再定义正确率并输出

acc = total_correct / total_num
print('acc:', acc)

可以加入模式切换

Model.train()和model.eval()

最终main.py文件为

import torch
from torchvision import datasets
# 引入pytorch、datasets工具包
from torchvision import transforms
# 引入数据变换工具包
from torch.utils.data import DataLoader
# 多线程数据读取
from LeNet5 import LeNet5
import torch.nn as nn

import torch.optim as optim
def main():

    batchsz=32
    # 这个batch_size数值不宜太大也不宜过小

    cifar_train = datasets.CIFAR10('cifar', train=True, transform=transforms.Compose([
        transforms.Resize((32, 32)),
        # .Compose相当于一个数据转换的集合
        # 进行数据转换，首先将图片统一为32*32
        transforms.ToTensor()
        # 将数据转化到Tensor中

    ]), download=True)
    # 直接在datasets中导入CIFAR10数据集，放在"cifar"文件夹中

    cifar_train = DataLoader(cifar_train, batch_size=batchsz, shuffle=True)
    # 按照其要求，这里的参数需要有batch_size,
    # 在该部分代码前面定义batch_size
    # 再使数据加载的随机化



    cifar_test = datasets.CIFAR10('cifar', train=False, transform=transforms.Compose([
        transforms.Resize((32, 32)),
        transforms.ToTensor()
    ]), download=True)

    cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)


    x, label = iter(cifar_train).next()
    # 通过.iter方法输出一个数据进行查看
    # print('s.shape:', x.shape, 'label.shape:', label.shape)
    # 输出shape进行查看




    device = torch.device('cuda')
    model = LeNet5().to(device)
    criteon = nn.CrossEntropyLoss().to(device)
    optimizer = optim.Adam(model.parameters(), lr=1e-3)
    print(model)

    model.train()
    for epoch in range(1000):

        for batchidx, (x, label) in enumerate(cifar_train):
            # batchidx代表了有多少个batch，
            x, label = x.to(device), label.to(device)

            logits = model(x)
            loss = criteon(logits, label)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()


        # print(epoch, loss.item())

        model.eval()
        total_correct = 0
        total_num = 0

        for x, label in cifar_test:
            x, label = x.to(device), label.to(device)

            logits = model(x)
            pred = logits.argmax(dim=1)
            # 用argmax选出可能性最大的值的索引
            # 进行比对
            total_correct += torch.eq(pred, label).float().sum().item()
            # torch.eq函数用于对比，同时要转为numpy数据
            total_num += x.size(0)
        acc = total_correct / total_num
        print('acc:', acc)

输出为