NTM Hyperparameters
The following table lists the hyperparameters that you can set for the Amazon SageMaker AI Neural Topic Model (NTM) algorithm.
| Parameter Name | Description |
|---|---|
|
|
The vocabulary size of the dataset. Required Valid values: Positive integer (min: 1, max: 1,000,000) |
num_topics |
The number of required topics. Required Valid values: Positive integer (min: 2, max: 1000) |
batch_norm |
Whether to use batch normalization during training. Optional Valid values: true or false Default value: false |
clip_gradient |
The maximum magnitude for each gradient component. Optional Valid values: Float (min: 1e-3) Default value: Infinity |
encoder_layers |
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 Optional Valid values: Comma-separated list of positive integers or auto Default value: auto |
encoder_layers_activation |
The activation function to use in the encoder layers. Optional Valid values:
Default value: |
epochs |
The maximum number of passes over the training data. Optional Valid values: Positive integer (min: 1) Default value: 50 |
learning_rate |
The learning rate for the optimizer. Optional Valid values: Float (min: 1e-6, max: 1.0) Default value: 0.001 |
mini_batch_size |
The number of examples in each mini batch. Optional Valid values: Positive integer (min: 1, max: 10000) Default value: 256 |
num_patience_epochs |
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
Optional Valid values: Positive integer (min: 1) Default value: 3 |
optimizer |
The optimizer to use for training. Optional Valid values:
Default value: |
rescale_gradient |
The rescale factor for gradient. Optional Valid values: float (min: 1e-3, max: 1.0) Default value: 1.0 |
sub_sample |
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 Optional Valid values: Float (min: 1e-6, max: 0.1) Default value: 0.001 |
weight_decay |
The weight decay coefficient. Adds L2 regularization. Optional Valid values: Float (min: 0.0, max: 1.0) Default value: 0.0 |
