什么是MaxPool2d
是对二维矩阵进行池化层下采样的方法
MaxPool2d的用法
相较于卷积层，多出来的参数为ceil_mode
这个参数代表，如果所剩的部分不够卷积核的大小，要不要进行池化操作
具体代码为
import torch
import torchvision
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriterdataset = torchvision.datasets.CIFAR10(root='./dataset', transform=torchvision.transforms.ToTensor(), train=False, download=False)dataloader = DataLoader(dataset, batch_size=64)class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 = nn.Conv2d(3, 6, 3)self.pool = nn.MaxPool2d(3)def forward(self, x):x = self.conv1(x)x = self.pool(x)return xnet = Net()
writer = SummaryWriter(log_dir='./logs')
i = 0
for data in dataloader:img, target = dataoutput = net(img)# torch.Size([64, 6, 10, 10])# print(output.shape)output = torch.reshape(output, (-1, 3, 10, 10))writer.add_images('output', output, i)i = i+1writer.close()
