Issues about optimizing other parameters besides learning rate

[nicozorza]

I have emailed Luca Franceschi about some issues with this library, and he asked me to share it.
I've been working on a MLP and wanted to optimize the following parameters, but found some problems:

• Keep probability of a dropout layer: Luca explained to me that this is not posible since it has some non differentiable points.
• Regularization beta: We are using tf.nn.l2_loss, but can't optimize the beta.
• AdamOptimizer: When we tried to use far.AdamOptimizer() for the inner optimizer the code started crashing. Apparently there are some undefined variables: _beta1_power and _beta2_power. I think this is an error in the library.
  Until now we have been able to optimize only the learning rate. It would be great if there could be a list of the things you can and can't do with this library.

Best regards,
Nicolás Zorzano.

[lucfra]

Ciao Nicolas,

I've pushed an update that fixes the problems for AdamOptimizer. In newer versions of TensorFlow the protected variables _beta1_power and _beta2_power changed name; from there the error!

For the regularization parameter, could you please be more specific?
Something like this:

w = .. your variable ..
rho = far.get_hyperparameter('rho', -3.)
l2_loss = tf.exp(rho)*tf.nn.l2_loss(w)

should allow you to optimize rho, (the exp is to ensure positive values of the regularization coefficient).

For dropout, it is not a totally trivial problem, and may be a topic of research. Anyway (I did not mention it in the email) under some assumptions, dropout can approximately be replaced by multiplicative Gaussian noise: see http://proceedings.mlr.press/v28/wang13a.pdf . This might suggest treating the variance of the noise as an hyperparameter, that could be optimized by g.d. with this package.

As soon as I have time, I will add an IPython book with a list of things that you can and cannot do, as you suggest!

Cheers,

Luca