anaconda+pytorch实现简单的两层神经网络

以下代码已经测试通过，可以实现功能

import torch
from torch.autograd import Variable
N,D_in,H,D_out = 64,1000,100,10
x = Variable(torch.randn(N,D_in),requires_grad=False)
y = Variable(torch.randn(N,D_out),requires_grad=False)
w1 = Variable(torch.randn(D_in,H),requires_grad=True)
w2 = Variable(torch.randn(H,D_out),requires_grad=True)

learning_rate = 1e-6
for t in range(500):
    y_pred = x.mm(w1).clamp(min=0).mm(w2)
    loss = ((y_pred - y).pow(2).sum())
    # loss_function = loss.MSELoss()
    # w1.grad = True
    # w2.grad = True
    # print("#########")
    # print(w1.grad)
    # print ("########")
    if (w1.grad is  not None): w1.grad.data.zero_()
    if (w2.grad is  not None): w2.grad.data.zero_()
    loss.backward()

    w1.data -= learning_rate * w1.grad.data
    w2.data -= learning_rate * w2.grad.data
    print("1#############")
    print(w1.data)
    print("2#############")
    print(w2.data)
    print("3#############")
    print (y_pred)
    print("4#############")
    print (y)