torch教程[1]用numpy实现三层全连接神经网络

import numpy as np
N,D_in,H,D_out=64,1000,100,10
x=np.random.randn(D_in,H)
x=np.random.randn(N,D_in)
y=np.random.randn(N,D_out)
w1=np.random.randn(D_in,H)
w2=np.random.randn(H,D_out)
learning_rate=1e-6
for t in range(500):
    h=x.dot(w1)
    h_relu=np.maximum(h,0)
    y_pred=h_relu.dot(w2)
    #compute and print loss
    loss=np.square(y_pred-y).sum()
    print(t,loss)
    
    grad_y_pred=2.0*(y_pred-y)
    grad_w2=h_relu.T.dot(grad_y_pred)
    grad_h_relu=grad_y_pred.dot(w2.T)
    grad_h=grad_h_relu.copy()
    grad_h[h<0]=0
    grad_w1=x.T.dot(grad_h)
    
    w1-=learning_rate*grad_w1
    w2-=learning_rate*grad_w2