机器学习周报（9.16-9.22）-Pytorch学习（四）

摘要

通过学习CNN模型的训练及验证套路，对模型训练以及模型验证套路有了基本认识，并趁热打铁使用CNN模型实现mnist手写数字识别的实操

Abstract

By learning the training and verification routines of CNN model, I have a basic understanding of the training and verification routines of the model, and use the CNN model to realize the practical operation of mnist handwritten digit recognition

1 完整模型训练套路及模型验证套路

完整模型训练套路(以CIFAR10数据集为例)

1.1 模型及训练代码

model.py

import torch
import torch.nn as nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential

# model
class model(nn.Module):
    def __init__(self):
        super(model, self).__init__()
        self.m = Sequential(
            Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, 1, 2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, 1, 2),
            MaxPool2d(2),
            Flatten(),
            Linear(64*4*4, 64),
            Linear(64, 10)
        )

    def forward(self,x):
        return self.m(x)

# 在该模块中测试model
if __name__ == '__main__':
    m = model()
    input = torch.ones([64, 3, 32, 32])
    output = m(input)
    print(output.shape)

补充：

• argmax()使用

import torch

output = torch.tensor([[0.1, 0.5],
                       [0.2, 0.4]])

# dim = 1 数组横向比较中较大的下标
print(output.argmax(dim=1))  # tensor([1, 1])
# dim = 0 数组纵向比较中较大的下标
print(output.argmax(dim=0))  # tensor([1, 0])

train,py

import torch
import torchvision
from torch.utils.data import DataLoader
from torch.nn import CrossEntropyLoss
from torch.utils.tensorboard import SummaryWriter
# 引入网络模型
from model import *


# 训练数据集
train_data = torchvision.datasets.CIFAR10("dataset2", train=True, transform=torchvision.transforms.ToTensor())
# 测试数据集
test_data = torchvision.datasets.CIFAR10("dataset2", train=False, transform=torchvision.transforms.ToTensor())

# 数据长度
train_data_size = len(train_data)
test_data_size =len(test_data)
print("训练集数据长度为：{}".format(train_data_size))  # 50000
print("测试集数据长度为：{}".format(test_data_size))   # 10000

# 利用DataLoader来加载数据集
train_Dataloader = DataLoader(train_data, batch_size=64)
test_Dataloader = DataLoader(test_data, batch_size=64)

# 创建网络模型
mm = model()

# 损失函数
loss_fn = CrossEntropyLoss()

# 优化器
# 学习率：learing_rate = 0.01
# 1e-2 = 1×(10)^（-2)=1/100 = 0.01
learning_rate = 1e-2
optimizer = torch.optim