import torch from torch import nn def corr2d(X, K): h, w = K.shape Y = torch.zeros((X.shape[0] -h+1, X.shape[1] - w + 1)) for i in range(Y.shape[0]): for j in range(Y.shape[1]): Y[i, j] = (X[i:i+h, j:j+w] * K).sum() return Y print('验证一下二维互相关') X = torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) K = torch.tensor([[0, 1], [2, 3]]) print(corr2d(X, K)) print('定义二维卷积') class Conv2D(nn.Module): def __init__(self, kernel_size): super(Conv2D, self).__init__() self.weight = nn.Parameter(torch.randn(kernel_size)) self.bias = nn.Parameter(torch.randn(1)) def forward(self, x): return corr2d(x, self.weight) + self.bias print('定义一张带变化的图像') X = torch.ones(6, 8) X[:, 2:6] = 0 print(X) print('边缘检测,边缘变成非 0') K = torch.tensor([[1, -1]]) Y = corr2d(X, K) print(Y) print('前面的 K 是我们定义的,现在我们学习出 K') conv2d = Conv2D(kernel_size=(1, 2)) step = 20 lr = 0.01 for i in range(step): Y_hat = conv2d(X) l = ((Y_hat - Y) ** 2).sum() l.backward() # 梯度下降 conv2d.weight.data -= lr * conv2d.weight.grad conv2d.bias.data -= lr * conv2d.bias.grad # 梯度清零 conv2d.weight.grad.fill_(0) conv2d.bias.grad.fill_(0) if (i + 1) % 5 == 0: print('Step %d, loss %.3f' % (i+1, l.item())) print('查看学习到的卷积核') print('weight:', conv2d.weight.data) print('bias:', conv2d.bias.data) print('可以看到和我们之前定义的 K 很接近')