无法理解theano扫描适用于毕业生的行为

Question

所以事情是这样的:我必须在下面编写代码，当我应用grad wrt A时，一切都运行得很好，它正确地计算了梯度。但是，如果我使用wrt=i，那么它会给我一个DisconnectedInputError。为什么会这样，我该如何去区分i呢？

def step(i, A):
    return A*i, i

A = T.scalar("A")
outputs, _ = theano.scan(step, sequences=T.arange(2,6), non_sequences=A)
res, i = outputs
grad = T.grad(cost=res[3], wrt=A)
func = theano.function([A],[grad, res, i])

print func(3.0)

Traceback (most recent call last):
  File "test.py", line 17, in <module>
    grad = T.grad(cost=res[3], wrt=i)
  File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line 545, in grad
    handle_disconnected(elem)
  File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line 532, in handle_disconnected
    raise DisconnectedInputError(message)
theano.gradient.DisconnectedInputError: grad method was asked to compute the gradient with respect to a variable that is not part of the computational graph of the cost, or is used only by a non-differentiable operator: for{cpu,scan_fn}.1
Backtrace when the node is created:
  File "test.py", line 15, in <module>
    outputs, _ = theano.scan(step, sequences=T.arange(2,6), non_sequences=A)

Answer 1

为了区分wrt和I，您需要在扫描操作之前声明整个I数组，并使用整个数组来计算它的梯度wrt。

i_array = T.arange(2,6)

def step(i, A):
    return A*i, i

A = T.scalar("A")
A.tag.test_value = 5.0

outputs, _ = theano.scan(step, sequences=i_array, non_sequences=A)
res, i = outputs
grad = T.grad(cost=res[3], wrt=i_array)
func = theano.function([A],[grad, res, i])

print func(3.0)

如果需要i的特定元素，则应在计算i_array的grad后使用索引选择该元素