I am writing a custom layer with the DL toolbox and a part of the forward pass of this layer is making draws from a beta distribution where the b parameter is to be optimised as part of the network training. However, I seem to be having difficulty using betarnd (and by extension randg) with a dlarray valued parameter.
Consider the following, which works as expected.
>> betarnd(1, 0.1)

ans =

0.2678
However, if I instead do the following, then it does not work.
>> b = dlarray(0.1)

b =

1×1 dlarray

0.1000

>> betarnd(1, b)
Error using randg
SHAPE must be a full real double or single array.

Error in betarnd (line 34)
g2 = randg(b,sizeOut); % could be Infs or NaNs
Is it not possible to use such functions with parameters to be optimised via automatic differentiation (hence dlarray)?
Many thanksI am writing a custom layer with the DL toolbox and a part of the forward pass of this layer is making draws from a beta distribution where the b parameter is to be optimised as part of the network training. However, I seem to be having difficulty using betarnd (and by extension randg) with a dlarray valued parameter.
Consider the following, which works as expected.
>> betarnd(1, 0.1)

ans =

0.2678
However, if I instead do the following, then it does not work.
>> b = dlarray(0.1)

b =

1×1 dlarray

0.1000

>> betarnd(1, b)
Error using randg
SHAPE must be a full real double or single array.

Error in betarnd (line 34)
g2 = randg(b,sizeOut); % could be Infs or NaNs
Is it not possible to use such functions with parameters to be optimised via automatic differentiation (hence dlarray)?
Many thanks I am writing a custom layer with the DL toolbox and a part of the forward pass of this layer is making draws from a beta distribution where the b parameter is to be optimised as part of the network training. However, I seem to be having difficulty using betarnd (and by extension randg) with a dlarray valued parameter.
Consider the following, which works as expected.
>> betarnd(1, 0.1)

ans =

0.2678
However, if I instead do the following, then it does not work.
>> b = dlarray(0.1)

b =

1×1 dlarray

0.1000

>> betarnd(1, b)
Error using randg
SHAPE must be a full real double or single array.

Error in betarnd (line 34)
g2 = randg(b,sizeOut); % could be Infs or NaNs
Is it not possible to use such functions with parameters to be optimised via automatic differentiation (hence dlarray)?
Many thanks deep learning, statistics, matlab, neural networks, random number generator MATLAB Answers — New Questions

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