p18神经网络卷积层

一般常改的就是前五个参数，后边都是默认的


单单看参数有些模糊，可从相关网页看示范
https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader

dataset = torchvision.datasets.CIFAR10("./dataset", 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.conv1 = Conv2d(in_channels=3, out_channels=6, kernel_size=3,stride=1, padding=0)

    def forward(self, x):
        self.conv1(x)
        return x

tudui = Tudui()
print(tudui)

D:anacondapython.exe C:/Users/ASUS/Desktop/tudui/conv_2d.py
Files already downloaded and verified
Tudui(
  (conv1): Conv2d(3, 6, kernel_size=(3, 3), stride=(1, 1))
)

Process finished with exit code 0

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10("./dataset", 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.conv1 = Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=0)

    def forward(self, x):
        x = self.conv1(x)
        return x

tudui = Tudui()
print(tudui)

writer = SummaryWriter("./p18")
step = 0
for data in dataloader:
    imgs, targets = data
    output = tudui(imgs)
    print(imgs.shape)
    print(output.shape)
# torch.Size([64, 3, 32, 32])
    writer.add_images("input", imgs, step)
# torch.Size([64, 6, 30, 30]),不符合add_images所要求的格式，可使用不太正规的方法进行转变->[xxx, 3, 30, 30]
    output = torch.reshape(output, ([-1, 3, 30, 30]))  # -1 自动进行计算
    writer.add_images("output", output, step)
    step += 1

writer.close()

对于输出图片大小，pytorch官网中的文档给的很详细