What is Hyperparameter optimization in neural networks in R

This recipe explains what is Hyperparameter optimization in neural networks

Recipe Objective - What is Hyperparameter optimization in neural networks?

Hyperparameter optimization is defined as the technique of choosing a set of optimal hyperparameters manually for the learning algorithm. Hyperparameter optimization is also popularly known as hyperparameter tuning. The hyperparameter is a parameter whose value is majorly used to control a learning process. Also, Parameters are different from the hyperparameters that is parameters are learned automatically without the user interference whereas hyperparameters are manually set to help guide learning process. The Hyperparameter optimization finds the tuple of different hyperparameters that yields the optimal model which particularly minimizes the predefined loss function on a given independent data. In it, the objective function takes the tuple of hyperparameters as its input and returns the associated loss as an output. Cross-validation is popularly used to estimate the generalization performance of hyperparameter optimization.

This recipe explains what is Hyperparameter optimization, how it is beneficial for neural network models and how it can be executed.

Explanation of Hyperparameter Optimization.

The Hyperparameter optimization or tuning technique involves defining the search space that is it can be thought of geometrically as the n-dimensional volume where each hyperparameter represents the different dimension and scale of the dimension are values that hyperparameter may take such as real valued, integer-valued or the categorical. A point in Search space is a vector with the specific value for each hyperparameter value. The goal of the hyperparameter optimization technique is to find the vector that results in best performance of model after learning or training giving maximum accuracy or minimum error.

Grid search, a hyperparameter optimization method in which a neural network model is build for each possible combination of all of the hyperparameter values provided which evaluates each neural network model and selects the architecture which further produces the best results in terms of accuracy and loss. it involves the exhaustive sampling of hyperparameter space and can be somewhat inefficient.

Random search, a hyperparameter optimization method in which discrete set of values are no longer provided to explore for each hyperparameter although a statistical distribution is provided for each hyperparameter from which many values may be randomly sampled. Different hyper-parameters are important on the different data sets. On most datasets, random search method works best under assumption that not all hyperparameters are equally important.

Bayesian optimization, a hyperparameter optimization method is defined as a sequential model-based optimization (SMBO) algorithm that allows for one to use results of the previous iteration to improve the sampling method of the next experiment. Bayesian optimzation method better than Grid search optimization method and Random search optimization method as both the methods works under isolation and the information from one experiment will not be used to improve the second experiment. In bayesian optimization, a model is constructed with hyperparameters "?" that is after training is scored as "v" according to evaluation metric. Previously evaluated hyperparameter values are used to compute the posterior expectation of the hyperparameter space. Then choosing the optimal hyperparameter values according to the posterior expectation as the model candidate and the process is iteratively repeated until converging to the optimum. Also, the Gaussian process to model the prior probability of model scores across the hyperparameter space.

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