Theano broadcasting is hardcoded in the Theano graph. You will need to
build 2 nodes and probably compile 2 Theano function for that.
If you would like to have a partially working version that don't hardcode
in the graph the number of dimensions, you could have the inputs/outputs of
the nodes that changes be a Generic() variable instead of a TensorVariable.
But you won't have any operation working on it.
Fred
On Fri, Apr 28, 2017 at 7:10 PM Brad Buran <bbu...@gmail.com> wrote:
> Given the following function where:
>
> - y is a ND array
> - g has one less dimension than y
> - x is a 1D array
> - the result is the same number of dimensions as g
>
> How do I write a Theano operation that wraps this function? I can get it
> to work for the case where y is a 1D array and g is a scalar (see below).
> However, when y is a 2D array, I get the following error. I understand why
> I'm getting the error, but I'm not clear how to write a more suitably
> polymorphic function that can handle broadcasting. Any pointers would be
> appreciated.
>
> TypeError: We expected inputs of types '[TensorType(float64, vector),
> TensorType(float64, scalar)]' but got types '[TensorType(float64, matrix),
> TensorType(float64, vector)]'
>
>
> class Threshold(theano.Op):
>
> __props__ = ('threshold', 'lower', 'upper', 'step')
>
> itypes = [theano.tensor.dvector, theano.tensor.dscalar]
> otypes = [theano.tensor.dscalar]
>
> def __init__(self, threshold, lower, upper, step):
> self.threshold = threshold
> self.lower = lower
> self.upper = upper
> self.step = step
> self.x = np.arange(lower, upper, step)
> super().__init__()
>
> def perform(self, node, inputs, output_storage):
> y = inputs[0]
> g = inputs[1]
> z = output_storage[0]
>
> y = norm.ppf(y)-norm.ppf(g)
> fx = lambda y: np.interp([1], y, x)[0]
> z[0] = np.apply_along_axis(fx, 0, y)
>
>
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>
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