（五）神经网络-卷积层

【声明】来源于b站视频小土堆PyTorch深度学习快速入门教程（绝对通俗易懂！）【小土堆】_哔哩哔哩_bilibili

import torchvision.datasets
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=dataset, batch_size=64)

class Model(nn.Module):

    def __init__(self):
        super(Model,self).__init__()
        self.conv1 = Conv2d(in_channels=3,
                            out_channels=6,
                            kernel_size=3,
                            padding=0,
                            stride=1)
    def forward(self,x):
        x = self.conv1(x)
        return x

MyModel = Model()
for data in dataloader:
    imgs,labels = data
    output = MyModel(imgs)
    print(imgs.shape)
    print(output.shape)