Grid search

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

Grid search is a hyperparameter-optimization technique that evaluates configurations formed from a specified Cartesian product of candidate values.

Grid search automates hyperparameter optimization by mapping each parameter to discrete candidate values, forming a grid of combinations, and evaluating its cells methodically. section 5.3 section 5.7

A practical grid-search workflow:

• Choose candidate values for each hyperparameter and represent them as a parameter grid. section 5.3.1
• Use ParameterGrid to construct combinations across any number of dimensions. section 5.3.1
• Evaluate configurations in sequence, then inspect plotted results to identify promising regions for further tuning. section 5.3 section 5.7

A four-parameter grid

A Python configuration varies middle_layer from 8 to 64 by six, learning_rate from 0.035 to 0.35 by 0.03, batch_size from 16 to 20 by four, and epochs from 200 to 225 by 25. The resulting grid contains 90 configurations; successive calls return configurations such as middle_layer=20 and middle_layer=26 with learning_rate=0.065.

Figure 5.9 Comparison of random search and grid search

Compare random sampling with grid traversal across a hyperparameter space; the figure makes systematic coverage and search patterns visible. section 5.3

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes

Explains grid construction, iteration, visualization, and resolution trade-offs, but stops short of evolutionary optimization.

Examples

Uses Python generators, scikit-learn ParameterGrid, a 90-cell neural-network configuration, and plotted search results.

What it assumes

Assumes familiarity with hyperparameters and neural-network training while demonstrating grid construction and iteration from implementation basics.

What you can do after

Apply grid search via Python and scikit-learn ParameterGrid to evaluate discrete hyperparameter combinations and inspect promising regions.

How it connects

Connects hyperparameter optimization with random search, evolutionary computation, dimensionality reduction, and PCA.

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Also explained in

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  Grokking Machine LearningA model-selection technique that evaluates an exhaustive set of specified hyperparameter combinations.
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  Designing Deep Learning Systems: A guide for software engineersA hyperparameter search method that evaluates configurations formed from the Cartesian product of specified candidate values.
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  Bayesian Optimization in ActionGrid search is an optimization technique that evaluates points arranged on a regular grid over the search space.
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  Ensemble Methods for Machine LearningGrid search is a model-selection method that evaluates combinations from a specified set of parameter values to identify a performant configuration.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIGrid search is an exhaustive hyperparameter optimization technique that evaluates every combination of specified hyperparameter values.

Compare how 6 books explain grid search →

Related concepts

• hyperparameteruses · A hyperparameter is a setting chosen outside ordinary parameter learning that controls a model, algorithm, or learning procedure.
• cross-validationuses · Cross-validation is a resampling technique that evaluates a model by repeatedly fitting it on some portions of available data and assessing it on held-out portions.
• random searchcontrasts with · Random search is an optimization technique that explores a search space by evaluating candidate solutions or configurations selected through randomized choices.
• Bayesian optimizationcontrasts with · Bayesian optimization is a sequential method for optimizing an expensive or unknown objective by using a probabilistic model to guide evaluations.
• linear regressionused by · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.

This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.