FashionMNIST_CNN with pytorch (accuracy: 97.58%/90.1%)

FashionMNIST_CNN with pytorch (accuracy: 97.58%/90.1%)

接上篇
手工简单复现经典CNN网络测试(LeNet5、AlexNet、VGG16、GoogLeNet、ResNe)
想进行各种CCN经典网络的效果对比，因为个人电脑配置原因失败，不得以科学上网，注册了kaggle进行训练，整体训练分两部分:

1 创建CNN网络训练(网络简单，大概后台运行了5h);

2 使用后台运行产生的模型，进行测试集预测

结果如下：

#1 ----------- first/第一步：kaggle后台训练模型 -------------------------

import torch
import torch.nn as nn
from torch.utils.data import Dataset,DataLoader,TensorDataset
from torch.nn import CrossEntropyLoss
from torch.optim import SGD,Adam

# from torchsummary import summary

from torchvision.datasets import ImageFolder,FashionMNIST
import  torchvision.transforms as transforms


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import joblib

# 读入数据
train_data = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
train_y = torch.tensor(train_data['label'])
train_x = torch.tensor(train_data.iloc[:,1:].values.reshape(60000,1,28,28))/255

# 创建批量数据加载器
dataset = TensorDataset(train_x,train_y)
batch_size= 5000 # 批次大小
dataloader = DataLoader(dataset=dataset,batch_size=batch_size,shuffle=True)

# 创建模型
class Net(nn.Module):
    
    def __init__(self)