Exponential learning rate decay

One of the formulas suggested in the videos for the learning rate decay is
alpha_t = 0.95^t alpha_0 .
In this case the sum_{t=0}^infinity alpha_t is finite. Isn’t it a problem? Doesn’t it prevent the gradient flow from reaching its destination?

Hi, @psv.

It could, depending on the decay rate and the number of epochs you train your model (which is obviously finite).

If it does, just tweak your hyperparameters :slight_smile:

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Ditto Ramon’s response.
In Adam optimization, the main hyperparameter you would be tuning is the alpha, and it changes from one set of data to the another. As far as I know, there is no one-fits-all type of solution :stuck_out_tongue: and they can vary in order of magnitude!

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Couldn’t agree more. Thanks, @suki :slight_smile: