import torch
import torch.nn.functional as F
input = torch.tensor([[1, 2, 0, 3, 1],
[0, 1, 2, 3, 1],
[1, 2, 1, 0, 0],
[5, 2, 3, 1, 1],
[2, 1, 0, 1, 1]])
kernel = torch.tensor([[1, 2, 1],
[0, 1, 0],
[2, 1, 0]])
input = torch.reshape(input, (1, 1, 5, 5))
kernel = torch.reshape(kernel, (1, 1, 3, 3))
# print(input.shape)
# print(kernel.shape)
output = F.conv2d(input, kernel, stride=1)
print(output)
output2 = F.conv2d(input, kernel, stride=2)
print(output2)
output3 = F.conv2d(input, kernel, stride=1, padding=1)
print(output3)
Previous
![PyTorch_10_神经网络-卷积层](http://pic.huangruimin.tech/The_Wandering_Earth2_20.jpg)
2021-11-10
Next
![PyTorch_8_神经网络的基本骨架-nn_Module的使用](http://pic.huangruimin.tech/The_Wandering_Earth2_27.jpg)
2021-11-08