神经网络-卷积层

卷积

输入通道数， 输出通道数，核大小

参数具体含义

直观理解各个参数的网站(gif)

https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md

大概长这样，cyan是青色的意思

channel数（终于理解论文里图片放好多层的原因了）

查看网络结构可以直接print

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.conv1 = nn.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)

注意kernel_size是(3, 3)，而不是3

想自己算出输出图像的大小，可以按照下面公式进行计算

https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html#torch.nn.Conv2d

完整流程

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.conv1 = nn.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()

writer = SummaryWriter('./logs') # ../logs是放在上级目录
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, global_step=step) # 注意是add_images
    # torch.Size([64, 6, 30, 30]) -> [xxx, 3, 30, 30]
    output = torch.reshape(output, (-1, 3, 30, 30))
    writer.add_images('output', output, global_step=step)
    step += 1
writer.close()

至于为什么最后显示的不一样，是因为kernel是随机生成的，并未设置