# Tune a Text Classification - TensorFlow model
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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 with SageMaker AI](automatic-model-tuning.md).

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


| Metric Name | Description | Optimization Direction | Regex Pattern | 
| --- | --- | --- | --- | 
| validation:accuracy | The ratio of the number of correct predictions to the total number of predictions made. | Maximize | `val_accuracy=([0-9\\.]+)` | 

## Tunable Text Classification - TensorFlow hyperparameters
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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](text-classification-tensorflow-Hyperparameter.md).


| Parameter Name | Parameter Type | Recommended Ranges | 
| --- | --- | --- | 
| batch\$1size | IntegerParameterRanges | MinValue: 4, MaxValue: 128 | 
| beta\$11 | ContinuousParameterRanges | MinValue: 1e-6, MaxValue: 0.999 | 
| beta\$12 | ContinuousParameterRanges | MinValue: 1e-6, MaxValue: 0.999 | 
| eps | ContinuousParameterRanges | MinValue: 1e-8, MaxValue: 1.0 | 
| learning\$1rate | ContinuousParameterRanges | MinValue: 1e-6, MaxValue: 0.5 | 
| momentum | ContinuousParameterRanges | MinValue: 0.0, MaxValue: 0.999 | 
| optimizer | CategoricalParameterRanges | ['adamw', 'adam', 'sgd', 'rmsprop', 'nesterov', 'adagrad', 'adadelta'] | 
| regularizers\$1l2 | ContinuousParameterRanges | MinValue: 0.0, MaxValue: 0.999 | 
| train\$1only\$1on\$1top\$1layer | CategoricalParameterRanges | ['True', 'False'] |