# Text Classification - TensorFlow Hyperparameters
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Hyperparameters are parameters that are set before a machine learning model begins learning. The following hyperparameters are supported by the Amazon SageMaker AI built-in Object Detection - TensorFlow algorithm. See [Tune a Text Classification - TensorFlow model](text-classification-tensorflow-tuning.md) for information on hyperparameter tuning. 


| Parameter Name | Description | 
| --- | --- | 
| batch\_size | The batch size for training. For training on instances with multiple GPUs, this batch size is used across the GPUs. <br />Valid values: positive integer.<br />Default value: `32`. | 
| beta\_1 | The beta1 for the `"adam"` and `"adamw"` optimizers. Represents the exponential decay rate for the first moment estimates. Ignored for other optimizers.<br />Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.9`. | 
| beta\_2 | The beta2 for the `"adam"` and `"adamw"` optimizers. Represents the exponential decay rate for the second moment estimates. Ignored for other optimizers.<br />Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.999`. | 
| dropout\_rate | The dropout rate for the dropout layer in the top classification layer. Used only when `reinitialize_top_layer` is set to `"True"`.<br />Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.2` | 
| early\_stopping | Set to `"True"` to use early stopping logic during training. If `"False"`, early stopping is not used.<br />Valid values: string, either: (`"True"` or `"False"`).<br />Default value: `"False"`. | 
| early\_stopping\_min\_delta | The minimum change needed to qualify as an improvement. An absolute change less than the value of early\_stopping\_min\_delta does not qualify as improvement. Used only when early\_stopping is set to "True".Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.0`. | 
| early\_stopping\_patience | The number of epochs to continue training with no improvement. Used only when `early_stopping` is set to `"True"`.<br />Valid values: positive integer.<br />Default value: `5`. | 
| epochs | The number of training epochs.<br />Valid values: positive integer.<br />Default value: `10`. | 
| epsilon | The epsilon for `"adam"`, `"rmsprop"`, `"adadelta"`, and `"adagrad"` optimizers. Usually set to a small value to avoid division by 0. Ignored for other optimizers.<br />Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `1e-7`. | 
| initial\_accumulator\_value | The starting value for the accumulators, or the per-parameter momentum values, for the `"adagrad"` optimizer. Ignored for other optimizers.<br />Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.0001`. | 
| learning\_rate | The optimizer learning rate. Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.001`. | 
| momentum | The momentum for the `"sgd"` and `"nesterov"` optimizers. Ignored for other optimizers.<br />Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.9`. | 
| optimizer | The optimizer type. For more information, see [Optimizers](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) in the TensorFlow documentation.<br />Valid values: string, any of the following: (`"adamw"`, `"adam"`, `"sgd"`, `"nesterov"`, `"rmsprop"`,` "adagrad"` , `"adadelta"`).<br />Default value: `"adam"`. | 
| regularizers\_l2 | The L2 regularization factor for the dense layer in the classification layer. Used only when `reinitialize_top_layer` is set to `"True"`.<br />Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.0001`. | 
| reinitialize\_top\_layer | If set to `"Auto"`, the top classification layer parameters are re-initialized during fine-tuning. For incremental training, top classification layer parameters are not re-initialized unless set to `"True"`.<br />Valid values: string, any of the following: (`"Auto"`, `"True"` or `"False"`).<br />Default value: `"Auto"`. | 
| rho | The discounting factor for the gradient of the `"adadelta"` and `"rmsprop"` optimizers. Ignored for other optimizers. <br />Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.95`. | 
| train\_only\_on\_top\_layer | If `"True"`, only the top classification layer parameters are fine-tuned. If `"False"`, all model parameters are fine-tuned.<br />Valid values: string, either: (`"True"` or `"False"`).<br />Default value: `"False"`. | 
| validation\_split\_ratio | The fraction of training data to randomly split to create validation data. Only used if validation data is not provided through the `validation` channel.<br />Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.2`. | 
| warmup\_steps\_fraction | The fraction of the total number of gradient update steps, where the learning rate increases from 0 to the initial learning rate as a warm up. Only used with the `adamw` optimizer.<br />Valid values: float, range: [`0.0`, `1.0`].<br />Default value: `0.1`. |