CIFAR10数据集实战-LeNet5神经网络（中）

import torch.nn.functional as F

self.criteon = nn.CrossEntropyLoss()

return logits

device = torch.device('cuda')

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)

from LeNet5 import LeNet5

model = LeNet5().to(device)

import torch.nn as nn
criteon = nn.CrossEntropyLoss()

logits = model(x)
loss = criteon(logits, label)

import torch.optim as optim
optimizer = optim.Adam(model.parameters(), lr=1e-3)

optimizer.zero_grad()
loss.backward()
optimizer.step()

print(model)

print(epoch, loss.item())

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)

    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())


if __name__ == '__main__':
    main()

LeNet5(
  (conv_unit): Sequential(
    (0): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))
    (1): AvgPool2d(kernel_size=2, stride=2, padding=0)
    (2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
    (3): AvgPool2d(kernel_size=2, stride=2, padding=0)
  )
  (fc_unit): Sequential(
    (0): Linear(in_features=400, out_features=120, bias=True)
    (1): ReLU()
    (2): Linear(in_features=120, out_features=84, bias=True)
    (3): ReLU()
    (4): Linear(in_features=84, out_features=10, bias=True)
  )
  (criteon): CrossEntropyLoss()
)
0 1.5274930000305176
1 1.500401496887207
2 1.3024098873138428
3 1.5396997928619385
4 1.002105712890625
5 1.1430208683013916
6 1.1112192869186401
7 1.3169642686843872
8 0.7898904085159302
9 1.1472938060760498
10 1.6127662658691406
11 0.7561601996421814
12 1.2210408449172974
13 0.6686326265335083
14 0.9837068915367126
15 1.5631325244903564
16 1.0786722898483276
17 0.8204431533813477
18 0.8090471625328064
19 1.131771445274353
20 0.8453748226165771
21 0.31413528323173523
22 0.9720386266708374
23 1.2120721340179443
24 1.0121963024139404
25 0.9113800525665283
26 0.712620735168457
27 1.0069215297698975
28 1.1067134141921997
29 1.2128360271453857
30 0.6671864986419678
31 0.8690224289894104
32 1.530097484588623