AI News, Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters

Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters

It is hence a good method for meta-optimizing a neural network which is itself an optimisation problem: tuning a neural network uses gradient descent methods, and tuning the hyperparameters needs to be done differently since gradient descent can’t apply.

Therefore, Hyperopt can be useful not only for tuning hyperparameters such as the learning rate, but also to tune more fancy parameters in a flexible way, such as changing the number of layers of certain types, or the number of neurons in a layer, or even the type of layer to use at a certain place in the network given an array of choices, each with nested tunable hyperparameters.

parameter is defined with a certain uniformrange or else a probability distribution, such as: There is also a few quantized versions of those functions, which rounds the generated values at each step of “q”: It is also possible to use a “choice” which can lead to hyperparameter nesting: Visualisations of the parameters for probability distributions can be found below.

That’s because we want to observe changes in the learning rate according to changing it with multiplications rather than additions, e.g.: when adjusting the learning rate, we’ll want to try to divide it or multiply it by 2 rather than adding and substracting a finite value.

In [6]: Here are the space and results of the 3 first trials (out of a total of 1000): What interests us most is the ‘result’ key of each trial(here, we show 7): Note that the optimization could be parallelized by using MongoDB and storing the trials’ state here.

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