2
$\begingroup$

My transformer is not working on a toy problem.


Toy problem

Input : Sequence of random integer, one-hot-encoded. Example :

[[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
 [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
 [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
 [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
]

Output : 1 if the first random integer is < 5, 0 otherwise. For the given example, the output would be 1.


Problem

Running my Transformer on this toy problem works perfectly. However, running with a bigger size (one-hot encoding on 800 dimension) does not work anymore : the network always output similar results, no matter the input.

Note : I did change the condition of the output to be x < 800, in order to have class balance.

Why my transformer architecture is not working with this variation of the toy problem ?


Another problem

I also tried to use 2 inputs (similarly generated, with size = 10) and generate the output based on these 2 inputs (1 if the first random integer of both input is < 5 or both > 5, 0 otherwise)

$\endgroup$
1
$\begingroup$

I am not sure but maybe the excess zeroes are the culprit. I guess the results you are getting are just the accumulation of the biases. I would suggest converting your one-hot vectors to dense vectors using an Embedding Layer.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.