Pytorch手写数字识别LeNet模型

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已于 2022-10-08 10:45:19 修改

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于 2022-01-19 15:36:07 首次发布

LeNet网络

>>Github链接
在这里插入图片描述
LeNet网络过卷积层时候保持分辨率不变，过池化层时候分辨率变小。实现如下

from PIL import Image
import cv2
import matplotlib.pyplot as plt
import torchvision
from torchvision import transforms
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import numpy as np
import tqdm as tqdm

class LeNet(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.sequential = nn.Sequential(nn.Conv2d(1,6,kernel_size=5,padding=2),nn.Sigmoid(),
                                        nn.AvgPool2d(kernel_size=2,stride=2),
                                        nn.Conv2d(6,16,kernel_size=5),nn.Sigmoid(),
                                        nn.AvgPool2d(kernel_size=2,stride=2),
                                        nn.Flatten(),
                                        nn.Linear(16*25,120),nn.Sigmoid(),
                                        nn.Linear(120,84),nn.Sigmoid(),
                                        nn.Linear(84,10))
        
    
    def forward(self,x):
        return self.sequential(x)

class