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algorithmHyperparameters allows you to specify exact values for the hyperparameters, whereas algorithmHyperParameterRanges is used during hyperparameter optimization to set the range for the hyperparameters you want to optimize. The example below (taken from this page of the documentation) shows the difference. The hidden_dimension
variable is set to an exact value of 55
using algorithmHyperParameters
. The bptt
variable is set to be an integer value between 20
and 40
using algorithmHyperParameterRanges
under hpoConfig
- the exact value will be determined during hyperparameter optimization. Note that performHPO
must be set to true
to activate the optimization process. The example also shows how to handle a boolean variable for optimization through the recency_mask
variable, and continuous variables are also supported.
{
"performAutoML": false,
"recipeArn": "arn:aws:personalize:::recipe/aws-hrnn",
"performHPO": true,
"solutionConfig": {
"algorithmHyperParameters": {
"hidden_dimension": "55"
},
"hpoConfig": {
"algorithmHyperParameterRanges": {
"categoricalHyperParameterRanges": [
{
"name": "recency_mask",
"values": [ "true", "false" ]
}
],
"integerHyperParameterRanges": [
{
"name": "bptt",
"minValue": 20,
"maxValue": 40
}
]
},
"hpoResourceConfig": {
"maxNumberOfTrainingJobs": "4",
"maxParallelTrainingJobs": "2"
}
}
}
}
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Okay, got it. Thank you for the clarification!