Tune a Text Classification - TensorFlow 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 with SageMaker.

Metrics computed by the Text Classification - TensorFlow algorithm

Refer to the following chart to find which metrics are computed by the Text Classification - TensorFlow algorithm.

Tunable Text Classification - TensorFlow hyperparameters

Tune a text classification model with the following hyperparameters. The hyperparameters that have the greatest impact on text classification objective metrics are: batch_size, learning_rate, and optimizer. Tune the optimizer-related hyperparameters, such as momentum, regularizers_l2, beta_1, beta_2, and eps based on the selected optimizer. For example, use beta_1 and beta_2 only when adamw or adam is the optimizer.

For more information about which hyperparameters are used for each optimizer, see Text Classification - TensorFlow Hyperparameters.