#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Fri Aug 10 16:13:29 2018@author: myhaspl """ from mxnet import nd from mxnet import autograd x = nd.array([[1, 2], [3, 4]]) x.attach_grad()#在ndarray里準備存儲梯度 with autograd.record():#定義f(x)y=2*x*x #反向傳播backward() y.backward() #f'(x)=4*x z=x.grad print x print z

[[1. 2.]
[3. 4.]]
<NDArray 2x2 @cpu(0)>

[[ 4. 8.]
[12. 16.]]
<NDArray 2x2 @cpu(0)>

######################

#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Fri Aug 10 16:13:29 2018@author: myhaspl """ from mxnet import nd from mxnet import autograddef f(x): b=xwhile b.norm().asscalar() < 100: #計算歐氏距離(norm)b=b*2#y=ax ,a=2*2*.....*2print bif b.sum().asscalar() >= 0: y = b[0]else:y = b[1]return yx = nd.array([1,4]) x.attach_grad()#在ndarray里準備存儲梯度 with autograd.record():#定義f(x)y=f(x) #反向傳播backward() y.backward() #f'(x)=a,y=ax z=x.grad print "=======" print [z,x,y,y/x]#a=y/x

[2. 8.]
<NDArray 2 @cpu(0)>

[ 4. 16.]
<NDArray 2 @cpu(0)>

[ 8. 32.]
<NDArray 2 @cpu(0)>

[16. 64.]
<NDArray 2 @cpu(0)>

[ 32. 128.]
<NDArray 2 @cpu(0)>

=======
[
[32. 0.]
<NDArray 2 @cpu(0)>,
[1. 4.]
<NDArray 2 @cpu(0)>,
[32.]
<NDArray 1 @cpu(0)>,
[32. 8.]
<NDArray 2 @cpu(0)>]

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