API
输入：[ batch_size, channels, height_1, width_1 ]
Conv2d输入参数：[ channels, output, height_2, width_2 ]
输出：[ batch_size,output, height_3, width_3 ]
实例：
def torch_practice(): x = torch.randn(2,1,16,4) conv = torch.nn.Conv2d(1, 32, (2,2)) res = conv(x) print(res.shape) if __name__ == ‘__main__‘: torch_practice()
输出：torch.Size([2, 32, 15, 3])
batch大小不变：2
输出通道加厚：32。由卷积核的通道数决定
卷积结果:[15,3]。计算公式，n-m+1, 16-2+1=15
原文：https://www.cnblogs.com/demo-deng/p/12376866.html