# 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\$1size |  The batch size for training. For training on instances with multiple GPUs, this batch size is used across the GPUs.  Valid values: positive integer. Default value: `32`.  | 
| beta\$11 |  The beta1 for the `"adam"` and `"adamw"` optimizers. Represents the exponential decay rate for the first moment estimates. Ignored for other optimizers. Valid values: float, range: [`0.0`, `1.0`]. Default value: `0.9`.  | 
| beta\$12 |  The beta2 for the `"adam"` and `"adamw"` optimizers. Represents the exponential decay rate for the second moment estimates. Ignored for other optimizers. Valid values: float, range: [`0.0`, `1.0`]. Default value: `0.999`.  | 
| dropout\$1rate | The dropout rate for the dropout layer in the top classification layer. Used only when `reinitialize_top_layer` is set to `"True"`. Valid values: float, range: [`0.0`, `1.0`]. Default value: `0.2` | 
| early\$1stopping |  Set to `"True"` to use early stopping logic during training. If `"False"`, early stopping is not used. Valid values: string, either: (`"True"` or `"False"`). Default value: `"False"`.  | 
| early\$1stopping\$1min\$1delta | The minimum change needed to qualify as an improvement. An absolute change less than the value of early\$1stopping\$1min\$1delta does not qualify as improvement. Used only when early\$1stopping is set to "True".Valid values: float, range: [`0.0`, `1.0`].Default value: `0.0`. | 
| early\$1stopping\$1patience |  The number of epochs to continue training with no improvement. Used only when `early_stopping` is set to `"True"`. Valid values: positive integer. Default value: `5`.  | 
| epochs |  The number of training epochs. Valid values: positive integer. 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. Valid values: float, range: [`0.0`, `1.0`]. Default value: `1e-7`.  | 
| initial\$1accumulator\$1value |  The starting value for the accumulators, or the per-parameter momentum values, for the `"adagrad"` optimizer. Ignored for other optimizers. Valid values: float, range: [`0.0`, `1.0`]. Default value: `0.0001`.  | 
| learning\$1rate | The optimizer learning rate. Valid values: float, range: [`0.0`, `1.0`].Default value: `0.001`. | 
| momentum |  The momentum for the `"sgd"` and `"nesterov"` optimizers. Ignored for other optimizers. Valid values: float, range: [`0.0`, `1.0`]. 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. Valid values: string, any of the following: (`"adamw"`, `"adam"`, `"sgd"`, `"nesterov"`, `"rmsprop"`,` "adagrad"` , `"adadelta"`). Default value: `"adam"`.  | 
| regularizers\$1l2 |  The L2 regularization factor for the dense layer in the classification layer. Used only when `reinitialize_top_layer` is set to `"True"`. Valid values: float, range: [`0.0`, `1.0`]. Default value: `0.0001`.  | 
| reinitialize\$1top\$1layer |  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"`. Valid values: string, any of the following: (`"Auto"`, `"True"` or `"False"`). Default value: `"Auto"`.  | 
| rho |  The discounting factor for the gradient of the `"adadelta"` and `"rmsprop"` optimizers. Ignored for other optimizers.  Valid values: float, range: [`0.0`, `1.0`]. Default value: `0.95`.  | 
| train\$1only\$1on\$1top\$1layer |  If `"True"`, only the top classification layer parameters are fine-tuned. If `"False"`, all model parameters are fine-tuned. Valid values: string, either: (`"True"` or `"False"`). Default value: `"False"`.  | 
| validation\$1split\$1ratio |  The fraction of training data to randomly split to create validation data. Only used if validation data is not provided through the `validation` channel. Valid values: float, range: [`0.0`, `1.0`]. Default value: `0.2`.  | 
| warmup\$1steps\$1fraction |  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. Valid values: float, range: [`0.0`, `1.0`]. Default value: `0.1`.  |