神经网络—卷积层

news2024/11/15 18:04:22

1.讲解 Conv2d

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out_channels 参数为2时，会生成两个卷积核，分别与输入进行卷积。得到的两个输出为输出

新生成的卷积核和原来的卷积核不一定相同

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in_channels (int) – Number of channels in the input image
out_channels (int) – Number of channels produced by the convolution
kernel_size (int or tuple) – Size of the convolving kernel
stride (int or tuple, optional) – Stride of the convolution. Default:1
padding (int, tuple or str, optional) – Padding added to all four
sides of the input. Default: 0

2.代码实现

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

dataset=torchvision.datasets.CIFAR10("../data",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)

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for data in dataloader:
    imgs,targets=data
    output=tudui(imgs)
    print(output.shape)

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tudui=Tudui()

writer=SummaryWriter("./logs")
step=0

for data in dataloader:
    imgs,targets=data
    output=tudui(imgs)
    # print(output.shape)
    print(imgs.shape)
    print(output.shape)
    #torch.Size([64, 3, 32, 32])
    writer.add_images("input",imgs,step)
    #torch.Size([64, 6, 30, 30]) -> [xxx,3,30,30]

    output=torch.reshape(output,(-1,3,30,30))
    writer.add_images("output", output, step)

    step=step+1