神经网络——最大池化

绿树疯子2024-08-26 18:43

1.Pooling Layers讲解：

最大池化有时也被称为下采样，对应的有上采样。注意ceil_mode参数的使用

2.代码实战：

python 复制代码

import torch
from torch import nn
from torch.nn import MaxPool2d

input=torch.tensor([[1,2,0,3,1],
                    [0,1,2,3,1],
                    [1,2,1,0,0],
                    [5,2,3,1,1],
                    [2,1,0,1,1]],dtype=torch.float32)

input=torch.reshape(input,(-1,1,5,5))
print(input.shape)

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

    def forward(self,input):
        output=self.maxpool1(input)
        return output

tudui=Tudui()
output=tudui(input)
print(output)

最大池化无法在长整型的数据上执行。生成tensor时可以使用dtype参数改变其数据类型，比如从长整型变为浮点型。

最大池化的作用：保留输入数据的特征并减小数据的规模。

python 复制代码

import torch
import torchvision
from torch import nn
from torch.nn import MaxPool2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

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

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

    def forward(self, input):
        output = self.maxpool1(input)
        return output


tudui = Tudui()

writer = SummaryWriter("logs_maxpool")
step = 0

for data in dataloader:
    imgs, targets = data
    writer.add_images("input", imgs, step)
    output = tudui(imgs)
    writer.add_images("output",output, step)
    step = step + 1

writer.close()