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They just need to be broadcastable. For instance, if the mask has shape
(b, t)
and the output has shape(b, t, c)
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Actually documentation states that mask should be of same shape. May be we need to add here that same shape or broadcastable shape.
Also the first dimension of shape i.e
batch_size
should not be considered in shape. Because if we passinput_shape
as (5, 4, 1) and mask as (5,4) we get error as "Incompatible shapes: [5,4,1] vs. [5,4]
". Do we need to mention this explicitly in docs?Also the compute_output_shape method simply returns input_shape. Suppose if I pass
Noticed output shape is different from
compute_output_shape
method as themask
is broadcastable here.If I change mask shape to (1,5) instead of (5,1) then all shapes are same.
Attached gist for reference in case.
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Hi @fchollet Any update on this PR? Please. Thank you!