pytorch CNN CIFAR10数据集识别

尝试使用深层结构进行CIFAR10的识别

import torch
import torchvision
import torchvision.transforms as transforms

BATCH_SIZE = 64
EPOCHES = 50
NUM_WORKERS = 4
LEARNING_RATE = 0.005

# 数据转换
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

# 加载训练数据和测试数据
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,
                                          shuffle=True, num_workers=NUM_WORKERS)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE,
                                         shuffle=False, num_workers=NUM_WORKERS)

# 类别标签
classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

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下面定义网络

import torch.nn as nn
import torch.nn.functional as F

# 参考https://www.jianshu.com/p/016a23bc6554
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv4 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
        self