问在PyTorch中寻找具有反向传播和梯度下降的参数

EN

# your code as it is
import torch
import numpy as np 

X = np.array([[3.], [4.], [5.]])
X = torch.from_numpy(X)
X.requires_grad = True
W = np.random.randn(3,3)
W = np.triu(W, k=0)
W = torch.from_numpy(W)
W.requires_grad = True

# define parameters for gradient descent
max_iter=100
lr_rate = 1e-3

# we will do gradient descent for max_iter iteration, or convergence till the criteria is met.
i=0
out = compute_out(X,W)
while (i<max_iter) and (torch.abs(out)>0.01):
    loss = (out-0)**2
    W = W - lr_rate*torch.autograd.grad(loss, W)[0]
    i+=1
    print(f"{i}: {out}")
    out = compute_out(X,W)

print(W)