如何在Theano中分配/更新张量共享变量的子集?

问题描述

在theano中编译函数时，可以通过指定updates=[(X, new_value)]来更新共享变量(例如X).现在，我仅尝试更新共享变量的子集:

When compiling a function in theano, a shared variable(say X) can be updated by specifying updates=[(X, new_value)].Now I am trying to update only subset of a shared variable:

from theano import tensor as T
from theano import function
import numpy

X = T.shared(numpy.array([0,1,2,3,4]))
Y = T.vector()
f = function([Y], updates=[(X[2:4], Y)] # error occur:
                                        # 'update target must 
                                        # be a SharedVariable'

代码将引发错误更新目标必须是SharedVariable"，我想这意味着更新目标不能是非共享变量.那么，有什么方法可以编译一个函数来udpate共享变量的子集?

The codes will raise a error "update target must be a SharedVariable", I guess that means update targets can't be non-shared variables. So is there any way to compile a function to just udpate subset of shared variables?

推荐答案

使用 set_subtensor 或 inc_subtensor :

from theano import tensor as T
from theano import function, shared
import numpy

X = shared(numpy.array([0,1,2,3,4]))
Y = T.vector()
X_update = (X, T.set_subtensor(X[2:4], Y))
f = function([Y], updates=[X_update])
f([100,10])
print X.get_value() # [0 1 100 10 4]

Theano FAQ中现在有一个与此有关的页面: http://deeplearning.net /software/theano/tutorial/faq_tutorial.html

There's now a page about this in the Theano FAQ: http://deeplearning.net/software/theano/tutorial/faq_tutorial.html

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