Amazon SageMaker
Developer Guide

Tuning an Image Classification Model

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.

Metrics Computed by the Image Classification Algorithm

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.

Metric Name Description Optimization Direction
validation:accuracy

The ratio of the number of correct predictions to the total number of predictions made.

Maximize

Tunable Hyperparameters

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 Hyperparameters.

Parameter Name Parameter Type Recommended Ranges
mini_batch_size

IntegerParameterRanges

MinValue: 8, MaxValue: 512

learning_rate

ContinuousParameterRanges

MinValue: 1e-6, MaxValue: 0.5

optimizer

CategoricalParameterRanges

['sgd', ‘adam’, ‘rmsprop’, 'nag']

momentum

ContinuousParameterRanges

MinValue: 0.0, MaxValue: 0.999

weight_decay

ContinuousParameterRanges

MinValue: 0.0, MaxValue: 0.999

beta_1

ContinuousParameterRanges

MinValue: 1e-6, MaxValue: 0.999

beta_2

ContinuousParameterRanges

MinValue: 1e-6, MaxValue: 0.999

eps

ContinuousParameterRanges

MinValue: 1e-8, MaxValue: 1.0

gamma

ContinuousParameterRanges

MinValue: 1e-8, MaxValue: 0.999