LeNet-Model(pytorch版本)

训练、验证代码逻辑

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• major_activation_functions
• major_data_ptrprocessing_ways
• major_dataset_repo
• major_loss_functions
• major_models
• major_optimizers
• major_saved_models_repo
• major_service_api

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• major_config
• major_dataset
• major_evalution
• major_test（指标计算）
• major_predict（生成图像）
• major_train
• major_split_dataset

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• All.ipynb

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L e N e t − M o d e l ( p y t o r c h 版 本 ) LeNet-Model(pytorch版本) LeNet−Model(pytorch版本)

import torch.nn as nn
import torch.nn.functional as F
import torch

class LeNet(nn.Module):
    def __init__(self, num_classes,num_linear=44944):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        
        self.fc1 = nn.Linear(num_linear, 120)  # 44944，这里nn.Linear的第一个参数随输入数据大小改变而改变
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, num_classes)

    def forward(self, x):
        out = F.relu(self.conv1(x))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        return out

    def initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.xavier_normal_(m.weight.data)
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight.data, 0, 0.1)
                m.bias.data.zero_()

# 随机生成输入数据
rgb = torch.randn(1, 3, 224, 224)
# 定义网络
# # num_linear的设置是为了，随着输入图片数据大小的改变，使线性层的神经元数量可以匹配成功
# 默认输入图片数据大小为224*224
net = LeNet(num_classes=8,num_linear=44944)
# 前向传播
out = net(rgb)
print('-----'*5)
# 打印输出大小
print(out.shape)
print('-----'*5)