裡面的參考範例如下：
from theano import shared
state = shared(0)
inc = T.iscalar('inc')
accumulator = function([inc], state;
updates=[(state, state+inc)])
- 裡面比較特殊的部份是state為shared variable, 0為其初始值。
- 此值可在多個function當中共用, 在程式當中可用state.get_value()的方式取其值，也可用state.set_value(val)的方式來設定其值。
- 另一需說明的部份為function.update([shared-variable, new-expression]), 此函數必須為pair form，也可使用dict的key=value形式。
- 此式的意義即在每次執行時，都將shared-variable.value更換成new-expression所得到的結果。
因此在執行範例後得到的結果如下：
state.get_value() #程式尚未執行，array(0) accumulator(1) #array(0)->array(1) state.get_value() #array(1) accumulator(300) #array(1)->array(301) state.get_value() #array(301) #reset shared variable state.set_value(-1) accumulator(3) state.get_value() #array(-1)->array(2)
如同上述，shard variable可被多個function共用，因此定義另一個decreaser對state做存取：
decrementor = function([inc], state, updates=[(state, state-inc)]) decrementor(2) state.get_value() #array(2)->array(0)
如果要在shared variable放函數時，需改用function.given()，範例如下：
雖然上述的函數相當方便，但文件中未提到是否會有race condition的情形發生。
fn_of_state = state * 2 + inc
# the type (lscalar) must match the shared ariable we
# are replacing with the ``givens`` list
foo = T.lscalar()
skip_shared = function([inc, foo], fn_of_state,
givens=[(state, foo)])
skip_shared(1, 3) # we're using 3 for the state, not state.value
state.get_value() # old state still there, but we didn't use it
#array(0)
http://deeplearning.net/software/theano/tutorial/aliasing.html
在understanding memory aliasing for speed and correctness這一節中，提到了theano有自已管理記憶體的機制(pool)，而theano會管理pool中變數之變動。
- theano的pool中的變數與python的變數位於不同的memory space，因此不會互相衝突
- Theano functions only modify buffers that are in Theano’s memory space.
- Theano's memory space includes the buffers allocated to store shared variables and the temporaries used to evaluate functions.
- Physically, Theano's memory space may be spread across the host, a GPU device(s), and in the future may even include objects on a remote machine.
- The memory allocated for a shared variable buffer is unique: it is never aliased to anothershared variable.
- Theano's managed memory is constant while Theano functions are not running and Theano's library code is not running.
- The default behaviour of a function is to return user-space values for outputs, and to expect user-space values for inputs.
The distinction between Theano-managed memory and user-managed memory can be broken down by some Theano functions (e.g. shared, get_value and the constructors for In and Out) by using aborrow=True flag. This can make those methods faster (by avoiding copy operations) at the expense of risking subtle bugs in the overall program (by aliasing memory).
