不调用torch.nn中函数，自己实现cnn的功能进行mnist手写体识别

import torchvision.transforms
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets
import numpy as np
import matplotlib.pyplot as plt

# 1.导入mnist数据集
train_dataset = datasets.MNIST(root="./data", train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root="./data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
train_data_size = len(train_dataset)
test_data_size = len(test_dataset)
# 2.加载数据集
train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=64, shuffle=True)


# 定义一个学习率
learning_rate = 0.001
epochs = 5
batch_size=1024

# # 测试一下数据集的形状
# for data in test_loader:
#     imgs,lables = data
#     print(imgs)
#     print(imgs.shape)
#     print(lables)
#     print(lables.shape)

# .对x进行标准化，把输入控制在[0,1]
def Normaliza(x):
    # 计算均值
    x_mean = np.mean(x)
    # 计算标准差
    x_std = np.std(x)
    # 最后将输入控制在[0,1]之间
    X = (x - x_mean) / x_std
    return X


# # 定义sigmoid激活函数diff == True 为要求导
# def Sigmoid(x):
#     # sigmoid函数
#     return 1.0 / (1.0 + np.exp(-x))

# 定义一个softmax来计算y_hat
def softmax(x,w,b):
    y_hat = np.dot(x,w) + b

    y_exp = np.exp(y_hat)
    y