如何使用PyTorch优化一个边缘检测器

import torch
import torch.nn as nn

X = 编程客栈torch.tensor([[10,10,10,0,0,0],[10,10,10,0,0,0],[10,10,10,0,0,0],[10,10,10,0,0,0],[10,10,10,0,0,0],[10,10,10,0,0,0]], dtype=float)
Y = torch.tensor([[0,30,30,0],[0,30,30,0],[0,30,30,0],[0,30,30,0]], dtype=float)

conv2d = nn.Conv2d(1,1,kerUNgWaLnel_size=(3,3), bias=False, dtype=float)

X = X.reshape((1,1,6,6))
Y = Y.reshape((1,1,4,4))
lr = 0.0005

optim = torch.optim.RMSprop(conv2d.parameters(), lr=lr)
loss_fn = torch.nn.MSELoss()
for i in range(4000):
    Y_pred = conv2d(X)
    loss = loss_fn(Y_pred, Y)
    # 更新参数
    if 0: # 手动更新
        conv2d.zero_grad()
        loss.backward()
        conv2d.weight.data[:] -= lr * conv2d.weight.grad
    if 10: # 使用优化器更新
        opt编程im.zero_grad()
        loss.backward()
        optim.step()
    if(i + 1) % 100 == 0:
        print(f'epoch {i+1}, loss {loss.sum():.4f}')

# 打印训练的参数
print(conv2d.weight.data.reshape(3,3))

输出：

epoch 100, loss 331.4604

epoch 200, loss 284.8803

epoch 300, loss 248.8032

epoch 400, loss 218.8007

epoch 500, loss 193.1186

epoch 600, loss 170.4061

epoch 700, los编程客栈s 149.4530

epoch 800, loss 129.7580

epoch 900, loss 111.4134

epoch 1000, loss 94.5393

epoch 1100, loss 79.1782

epoch 1200, loss 65.3312

epoch 1300, loss 52.9822

epoch 1400, loss 42.1062

epoch 1500, loss 32.6718

epoch 1600, loss 24.6388

epoch 1700, loss 17.9555

epoch 1800, loss 12.5522

epoch 1900, loss 8.3332

epoch 2000, loss 5.1700

epoch 2100, loss 2.9096

epoch 2200, loss 1.4077

epoch 2300, loss 0.5341

epoch 2400, loss 0.1348

epoch 2500, loss 0.0166

epoch 2600, loss 0.0006

epoch 2700, loss 0.0000

epoch 2800, loss 0.0001

epoch 2900, loss 0.0001

epoch 3000, loss 0.0001

epoch 3100, loss 0.0001

epoch 3200, loss 0.0002

epoch 3300, loss 0.0002

epoch 3400, loss 0.0002

epoch 3500, loss 0.0002

epoch 3600编程客栈, loss 0.0002

epoch 3700, loss 0.0002

epoch 3800, loss 0.0002

epoch 3900, loss 0.0002

epoch 4000, loss 0.0002

tensor([[ 1.3123, -0.0050, -1.0276],

        [ 0.8334,  0.0677, -0.8868],

        [ 0.8551, -0.0619, -1.0849]], dtype=torch.float64)

由训练出的结果可以看出卷积核参数与实际的卷积核挺接近了。

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