神经网络-最大池化Maxpool

参数

https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html#torch.nn.MaxPool2d

最大池化

import torch
from torch import nn

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) # torch.Size([1, 1, 5, 5])

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

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

用图片验证

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

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

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.maxpool1 = nn.MaxPool2d(kernel_size=3, ceil_mode=True)
    def forward(self, input):
        output = self.maxpool1(input)
        return output
        
tudui = Tudui()
step = 0
writer = SummaryWriter('./logs_maxpool')
for data in dataloader:
    imgs, targets = data
    writer.add_images('input', imgs, step)
    output = tudui(imgs)
    writer.add_images('output', output, step)
    step += 1
writer.close()

最大池化结果

池化目的：减少数据量，加快训练速度（看yolo说还是卷积效果好，最后只有卷积层了）