20-学习卷积核

import torch
import torch.nn as nnX = torch.rand((6, 8))
Y = torch.rand((6, 7))# （批量⼤⼩、通道、⾼度、宽度）
X = X.reshape((1, 1, 6, 8))
Y = Y.reshape((1, 1, 6, 7))
lr = 3e-2# 构造卷积层   PyTorch 会使用默认的初始化方法，例如 Xavier 初始化或 Kaim 初始化，来随机初始化卷积核的权重
conv2d = nn.Conv2d(1, 1, kernel_size=(1, 2), bias=False) # 卷积核的形状是(1, 1, 1, 2)
print(conv2d.weight) # 卷积核权重for i in range(10):y_pred = conv2d(X)loss = (y_pred - Y) ** 2conv2d.zero_grad()loss.sum().backward()conv2d.weight.data[:] -= lr*conv2d.weight.gradprint('epoch{}   loss{}'.format(i+1, loss.sum()))