4.pytorch学习：pool2d——2d池化

目录

自建一个tensor理解池化

加入图像数据集

---

自建一个tensor理解池化

最大池化

nn.MaxPool2d

import torch
from torch import nn

input = torch.randn(1, 3, 5, 5)


class ZiDingYi(nn.Module):
    def __init__(self):
        super(ZiDingYi, self).__init__()
        self.maxpool1 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))

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


zidingyi = ZiDingYi()
print(zidingyi)
output = zidingyi(input)
print(input)
print(output)

结果：

最大池化操作，池化区域大小为3×3，步长为2

ZiDingYi(
  (maxpool1): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
)
tensor([[[[ 1.5797,  0.1586, -1.6101, -1.2335, -0.0374],
          [-0.3541,  0.5332, -0.4232, -0.9439, -1.1685],
          [-1.5927, -1.4457,  1.2221,  0.2481,  0.1824],
          [-0.2517,  0.0480, -0.9640,  0.1537, -0.8519],
          [-1.5356, -0.9889, -0.6552,  0.0652,  0.1927]],

         [[-0.7988,  1.1190,  0.6354, -0.2665,  2.0248],
          [ 0.1559, -0.0030,  0.7831,  0.5012,  0.23