# NTM Hyperparameters
Hyperparameters

The following table lists the hyperparameters that you can set for the Amazon SageMaker AI Neural Topic Model (NTM) algorithm.


| Parameter Name | Description | 
| --- | --- | 
|  `feature_dim`  |  The vocabulary size of the dataset. **Required** Valid values: Positive integer (min: 1, max: 1,000,000)  | 
| num\$1topics |  The number of required topics. **Required** Valid values: Positive integer (min: 2, max: 1000)  | 
| batch\$1norm |  Whether to use batch normalization during training. **Optional** Valid values: *true* or *false* Default value: *false*  | 
| clip\$1gradient |  The maximum magnitude for each gradient component. **Optional** Valid values: Float (min: 1e-3) Default value: Infinity  | 
| encoder\$1layers |  The number of layers in the encoder and the output size of each layer. When set to *auto*, the algorithm uses two layers of sizes 3 x `num_topics` and 2 x `num_topics` respectively.  **Optional** Valid values: Comma-separated list of positive integers or *auto* Default value: *auto*  | 
| encoder\$1layers\$1activation |  The activation function to use in the encoder layers. **Optional** Valid values:  [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/ntm_hyperparameters.html) Default value: `sigmoid`  | 
| epochs |  The maximum number of passes over the training data. **Optional** Valid values: Positive integer (min: 1) Default value: 50  | 
| learning\$1rate |  The learning rate for the optimizer. **Optional** Valid values: Float (min: 1e-6, max: 1.0) Default value: 0.001  | 
| mini\$1batch\$1size |  The number of examples in each mini batch. **Optional** Valid values: Positive integer (min: 1, max: 10000) Default value: 256  | 
| num\$1patience\$1epochs |  The number of successive epochs over which early stopping criterion is evaluated. Early stopping is triggered when the change in the loss function drops below the specified `tolerance` within the last `num_patience_epochs` number of epochs. To disable early stopping, set `num_patience_epochs` to a value larger than `epochs`. **Optional** Valid values: Positive integer (min: 1) Default value: 3  | 
| optimizer |  The optimizer to use for training. **Optional** Valid values: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/ntm_hyperparameters.html) Default value: `adadelta`  | 
| rescale\$1gradient |  The rescale factor for gradient. **Optional** Valid values: float (min: 1e-3, max: 1.0) Default value: 1.0  | 
| sub\$1sample |  The fraction of the training data to sample for training per epoch. **Optional** Valid values: Float (min: 0.0, max: 1.0) Default value: 1.0  | 
| tolerance |  The maximum relative change in the loss function. Early stopping is triggered when change in the loss function drops below this value within the last `num_patience_epochs` number of epochs. **Optional** Valid values: Float (min: 1e-6, max: 0.1) Default value: 0.001  | 
| weight\$1decay |   The weight decay coefficient. Adds L2 regularization. **Optional** Valid values: Float (min: 0.0, max: 1.0) Default value: 0.0  |