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02.conv.py
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73 lines (54 loc) · 1.88 KB
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# -*- coding: utf-8 -*-
"""
@author:XuMing([email protected])
@description:
"""
import torch
from torch import nn
def corr2d(X, K): # 本函数已保存在d2lzh_pytorch包中方便以后使用
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
X = torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
Y = torch.tensor([[0, 1], [2, 3]])
Z = corr2d(X, Y)
print(Z)
print(torch.randn(1))
class Conv2d(nn.Module):
def __init__(self, kernel_size):
super().__init__()
self.weight = nn.Parameter(torch.randn(kernel_size))
self.b = nn.Parameter(torch.randn(1))
def forward(self, x):
return corr2d(x, self.weight) + self.b
X = torch.ones(6, 8)
X[:, 2:6] = 0
print(X)
K = torch.tensor([[1, -2]])
Y = corr2d(X, K)
print(Y)
# 5.1.4 通过数据学习核数组
# 最后我们来看一个例子，它使用物体边缘检测中的输入数据X和输出数据Y来学习我们构造的核数组K。
# 我们首先构造一个卷积层，其卷积核将被初始化成随机数组。接下来在每一次迭代中，我们使用平方误差来比较Y和卷积层的输出，
# 然后计算梯度来更新权重。
conv2d = Conv2d(kernel_size=(1, 2))
step = 40
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.b.data -= lr * conv2d.b.grad
# 清理梯度
conv2d.weight.grad.fill_(0)
conv2d.b.grad.fill_(0)
if (i + 1) % 5 == 0:
print(f'Step {i + 1}, loss {l.item():.4f}')
# 可以看到，40次迭代后误差已经降到了一个比较小的值。现在来看一下学习到的卷积核的参数。
print("weight: ", conv2d.weight.data)
print("bias: ", conv2d.b.data)