CTC gradient seems to be constant.

[mpezeshki]

Hi @sherjilozair ,

Could you please run file rnnctc.py in my forked repo:
https://github.com/mohammadpz/ctc/blob/master/examples/rnnctc.py

Variable gradsx2 should be twice larger than grads. But it's not the case
Am I missing something?

[sherjilozair]

@mohammadpz, this is a known bug in my wrapper.

If you take a look at https://github.com/sherjilozair/ctc/blob/master/python/ctc.py#L91, you will notice that I completely ignore the output_grads, because of which, the cost works only when it's the last node. Any further operations and compositions are completely ignored.

I think the solution should be easy. I'll try to fix it and push it up. You are free to send in a PR with your solution as well.

[mpezeshki]

Oh, I see. You know, I want to divide the ctc cost by the batch size (which is variable). What's the quickest solution comes to your mind?
Thanks!

[mpezeshki]

Can I simply compute output_grads and multiply it by gradients = CPUCTCGrad()(*inputs)?
output_grads is also T.grad(last_output, ctc_cost).

[sherjilozair]

The quickest way would be to compute the grads, and then divide it by the batch size. That should work.

[mpezeshki]

I just replaced gradients = CPUCTCGrad()(*inputs) with gradients = output_grads * CPUCTCGrad()(*inputs) and the gradient is correct. Is there anything wrong with this?

[sherjilozair]

Isn't output_grads a list (of one Theano tensor) ?

[mpezeshki]

It is actually. In my case, I presume since it's just a division by batch size, the output_grads is [Elemwise{second}.0].

[sherjilozair]

Cool. I'll test this with some other examples, and push it in. Thanks!

[mpezeshki]

I'm not 100% sure, though!
Anyway, thanks for your nice job!

[noammor]

I'm trying to define a loss as the sum of two CTC losses. Do you have an insight as to how to complete the CTC theano.Op implementation? I'll try what's presented here and if it works I'll push it to my fork of the repository, and I'll very much appreciate if you have any more information or guidance since I'm not a Theano expert. Thanks!

I think that this would be correct:
gradients = output_grads[0].dimshuffle('x', 0, 'x') * CPUCTCGrad()(*inputs)