theano_shared_tensor.py

# -*- coding: utf-8 -*-

from theano import function, config, shared, sandbox
import theano
import theano.tensor as T
import numpy as np

#https://groups.google.com/forum/#!topic/theano-users/hXHj5J6vi5Y
A=shared()
B = tensor.TensorVariable(A, name=A.name)
B = A + increment
B.name = A.name
tensor.grad(B, ...)

 
#https://groups.google.com/forum/#!topic/theano-users/7_XlZZFvCIw
  
t = T.ones((20,200), dtype='float32')
s = T.zeros((1, 200), dtype='float32')
shared_s = shared(numpy.zeros((1, 200), dtype='float32'))

#This works:
(t + s).eval()

#This doesn't work:
(t + shared_s).eval()


#You can specify the broadcasting pattern when building shared_s: 
shared_s = shared(numpy.zeros((1, 200), dtype='float32'), broadcastable=(True, False)) 

#If you don't, it is assumed that the shape happens to be 1 at this time, 
#but might change later on. For instance, this would work: 
shared_s = shared(numpy.zeros((1, 200), dtype='float32')) 
u = t + shared_s 
shared_s.set_value(numpy.zeros((20, 200), dtype='float32')) 
u.eval() 

#because the value when calling eval() is compatible with t.