GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_ParameterSpec (google_api_machine_learning v0.27.0) View Source

Represents a single hyperparameter to optimize.

Attributes

  • categoricalValues (type: list(String.t), default: nil) - Required if type is CATEGORICAL. The list of possible categories.
  • discreteValues (type: list(float()), default: nil) - 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.
  • maxValue (type: float(), default: nil) - 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.
  • minValue (type: float(), default: nil) - 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.
  • parameterName (type: String.t, default: nil) - Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
  • scaleType (type: String.t, default: nil) - 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).
  • type (type: String.t, default: nil) - Required. The type of the parameter.

Link to this section Summary

Functions

Unwrap a decoded JSON object into its complex fields.

Link to this section Types

Specs

t() :: %GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_ParameterSpec{
  categoricalValues: [String.t()] | nil,
  discreteValues: [float()] | nil,
  maxValue: float() | nil,
  minValue: float() | nil,
  parameterName: String.t() | nil,
  scaleType: String.t() | nil,
  type: String.t() | nil
}

Link to this section Functions

Specs

decode(struct(), keyword()) :: struct()

Unwrap a decoded JSON object into its complex fields.