Raise error when inputs and mask don't have same shape in Softmax #19736
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## master #19736 +/- ##
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- Coverage 78.53% 56.30% -22.24%
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- Misses 8086 18402 +10316
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| @@ -50,6 +50,12 @@ def __init__(self, axis=-1, **kwargs): | |||
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| def call(self, inputs, mask=None): | |||
| if mask is not None: | |||
| if mask.shape != inputs.shape: | |||
| raise ValueError( | |||
| "`mask` and `inputs` must have same shape. " | |||
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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) that's fine.
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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.
mask: A boolean mask of the same shape as `inputs`. The mask
specifies 1 to keep and 0 to mask. Defaults to `None`.
Also the first dimension of shape i.e batch_size should not be considered in shape. Because if we pass input_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
x = np.random.rand(7, 1, 5)
mask = np.ones((5, 1))
l_1 = keras.layers.Softmax(axis=-1,trainable = True,dtype='float32',autocast = True)
print(l_1(x).shape) #(7, 1, 5)
print(l_1(x,mask).shape) #(7, 5,1)
print(l_1.compute_output_shape(x.shape)) #(7, 1, 5)
Noticed output shape is different from compute_output_shape method as the mask 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!
Currently keras.layers.Softmax not having check to verify inputs and mask shape same or not. As per documnetation the mask should be A boolean mask of the same shape as inputs.
But Its generating output even though mask shape is not same as that of inputs.
Hence proposing a check to raise exception when inputs and mask don't have same shape.
Fixes #19722 .