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Automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many jobs that test a range of hyperparameters on your dataset. You choose the tunable hyperparameters, a range of values for each, and an objective metric. You choose the objective metric from the metrics that the algorithm computes. Automatic model tuning searches the hyperparameters chosen to find the combination of values that result in the model that optimizes the objective metric.

For more information about model tuning, see Automatic Model Tuning.

The image classification algorithm is a supervised algorithm. It reports an accuracy metric that is computed during training. When tuning the model, choose this metric as the objective metric.

Tune an image classification model with the following hyperparameters. The hyperparameters that have the greatest impact on image classification objective metrics are: mini_batch_size, learning_rate, and optimizer. Tune the optimizer-related hyperparameters, such as momentum, weight_decay, beta_1, beta_2, eps, and gamma, based on the selected optimizer. For example, use beta_1 and beta_2 only when adam is the optimizer.

For more information about which hyperparameters are used in each optimizer, see Image Classification Hyperparameters.