互相关运算
import torch
from torch import nn
from d2l import torch as d2l
def corr2d(X, K):
"""计算二维互相关运算。"""
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
验证上述二维互相关运算的输出
X = torch.tensor([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]])
K = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
corr2d(X, K)
tensor([[19., 25.], [37., 43.]])
实现二维卷积层
class Conv2D(nn.Module):
def __init__(self, kernel_size):
super().__init__()
self.weight = nn.Parameter(torch.rand(kernel_size))
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, x):
return corr2d(x, self.weight) + self.bias
卷积层的一个简单应用: 检测图像中不同颜色的边缘
X = torch.ones((6, 8))
X[:, 2:6] = 0
X
tensor([[1., 1., 0., 0., 0., 0., 1., 1.], [1., 1., 0., 0., 0., 0., 1., 1.], [1., 1., 0., 0., 0., 0., 1., 1.], [1., 1., 0., 0., 0., 0., 1., 1.], [1., 1., 0., 0., 0., 0., 1., 1.], [1., 1., 0., 0., 0., 0., 1., 1.]])
K = torch.tensor([[1.0, -1.0]])
输出Y
中的1代表从白色到黑色的边缘,-1代表从黑色到白色的边缘
Y = corr2d(X, K)
Y
tensor([[ 0., 1., 0., 0., 0., -1., 0.], [ 0., 1., 0., 0., 0., -1., 0.], [ 0., 1., 0., 0., 0., -1., 0.], [ 0., 1., 0., 0., 0., -1., 0.], [ 0., 1., 0., 0., 0., -1., 0.], [ 0., 1., 0., 0., 0., -1., 0.]])
卷积核K
只可以检测垂直边缘
corr2d(X.t(), K)
tensor([[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]])
学习由X
生成Y
的卷积核
conv2d = nn.Conv2d(1, 1, kernel_size=(1, 2), bias=False)
X = X.reshape((1, 1, 6, 8))
Y = Y.reshape((1, 1, 6, 7))
for i in range(10):
Y_hat = conv2d(X)
l = (Y_hat - Y)**2
conv2d.zero_grad()
l.sum().backward()
conv2d.weight.data[:] -= 3e-2 * conv2d.weight.grad
if (i + 1) % 2 == 0:
print(f'batch {i+1}, loss {l.sum():.3f}')
batch 2, loss 1.650 batch 4, loss 0.284 batch 6, loss 0.050 batch 8, loss 0.010 batch 10, loss 0.002
所学的卷积核的权重张量
conv2d.weight.data.reshape((1, 2))
tensor([[ 0.9905, -0.9963]])