google_api_machine_learning v0.3.0 GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1ParameterSpec View Source
Represents a single hyperparameter to optimize.
Attributes
- categoricalValues ([String.t]): Required if type is `CATEGORICAL`. The list of possible categories. Defaults to:
null
. - discreteValues ([float()]): Required if type is `DISCRETE`. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values. Defaults to:
null
. - maxValue (float()): Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is `INTEGER`. Defaults to:
null
. - minValue (float()): Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is INTEGER. Defaults to:
null
. - parameterName (String.t): Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate". Defaults to:
null
. scaleType (String.t): Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., `UNIT_LINEAR_SCALE`). Defaults to:
null
.- Enum - one of [NONE, UNIT_LINEAR_SCALE, UNIT_LOG_SCALE, UNIT_REVERSE_LOG_SCALE]
type (String.t): Required. The type of the parameter. Defaults to:
null
.- Enum - one of [PARAMETER_TYPE_UNSPECIFIED, DOUBLE, INTEGER, CATEGORICAL, DISCRETE]
Link to this section Summary
Functions
Unwrap a decoded JSON object into its complex fields
Link to this section Types
Link to this section Functions
Link to this function
decode(value, options) View Source
Unwrap a decoded JSON object into its complex fields.