MaxPool2d 的使用(池化层)(附代码)

MaxPool2d 的使用

此处我们仍然使用官网自带的数据集进行训练,最后将其可视化

加载数据集和可视化部分在此处不在介绍,若需要了解:

加载数据集:torch.utils.data中的DataLoader数据加载器(附代码)_硕大的蛋的博客-CSDN博客

tensorboard可视化工具:Tensorboard 可视化工具的使用-史上最简单(附代码)_硕大的蛋的博客-CSDN博客

1. 第一步

   导入相应的模块和包

   import torch.nn as nn
   from torch.nn import MaxPool2d
   import torchvision
   from torch.utils.data import DataLoader
   from tensorboardX import SummaryWriter
   

2. 第二步

   加载数据

   dataset = torchvision.datasets.CIFAR10('../BigData',
                                          train=False,
                                          transform=torchvision.transforms.ToTensor(),
                                          download=True)
   dataloader = DataLoader(dataset,
                           batch_size=64,
                           shuffle=True)
   
   

3. 第三步

   创建神经网络

   class Gsw(nn.Module):
       def __init__(self):
           super(Gsw, self).__init__()
           self.maxpool1 = MaxPool2d(kernel_size=3, ceil_mode=False)
   
       def forward(self, x):
           out = self.maxpool1(x)
           return out
   

4. 第四步

   训练并将其可视化

   gsw = Gsw()
   writer = SummaryWriter('LOGS/012log')
   
   for step, data in enumerate(dataloader):
       imgs, targets = data
       writer.add_images('input', imgs, step)
       output = gsw(imgs)
       writer.add_images('output', output, step)
   
   

完整代码

# 开发时间: 2021/11/22 16:26

import torch.nn as nn
from torch.nn import MaxPool2d
import torchvision
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter

dataset = torchvision.datasets.CIFAR10('../BigData',
                                      train=False,
                                      transform=torchvision.transforms.ToTensor(),
                                      download=True)
dataloader = DataLoader(dataset,
                       batch_size=64,
                       shuffle=True)


class Gsw(nn.Module):
   def __init__(self):
       super(Gsw, self).__init__()
       self.maxpool1 = MaxPool2d(kernel_size=3, ceil_mode=False)

   def forward(self, x):
       out = self.maxpool1(x)
       return out


gsw = Gsw()
writer = SummaryWriter('LOGS/012log')

for step, data in enumerate(dataloader):
   imgs, targets = data
   writer.add_images('input', imgs, step)
   output = gsw(imgs)
   writer.add_images('output', output, step)