>>>  * Shouldn't the backward step for computing delta_h be:
>>>    delta_h[:] = np.dot(delta_o, weights_output.T) * hidden.doutput(x_hidden)
>>>    where hidden.doutput is a derivation of the activation function for
>>> hidden layer?
>>
>> Offhand that sounds right. You can use Theano as a sanity check for your
>> implementation.
>
> Thank you David and Andreas for answering my questions. I will look at Theano.

Alternatively, you can just check it numerically. Scipy already comes
with an implementation [1] for scalar-to-scalar mappings, which you
can use with a double for loop for vector-to-vector functions. It is
much more straightforward to add this to unit tests than theano
(obiviously, because of no additional dependency) and less hassle than
to write out results of derivatives by hand.

[1] 
http://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.derivative.html




> David
>
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-- 
Dipl. Inf. Justin Bayer
Lehrstuhl für Robotik und Echtzeitsysteme, Technische Universität München
http://www6.in.tum.de/Main/Bayerj

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threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
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