Struct googapis::google::cloud::ml::v1::HyperparameterSpec[][src]

pub struct HyperparameterSpec {
    pub goal: i32,
    pub params: Vec<ParameterSpec>,
    pub max_trials: i32,
    pub max_parallel_trials: i32,
    pub hyperparameter_metric_tag: String,
}
Expand description

Represents a set of hyperparameters to optimize.

Fields

goal: i32

Required. The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE.

Defaults to MAXIMIZE.

params: Vec<ParameterSpec>

Required. The set of parameters to tune.

max_trials: i32

Optional. How many training trials should be attempted to optimize the specified hyperparameters.

Defaults to one.

max_parallel_trials: i32

Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization.

Each trial will use the same scale tier and machine types.

Defaults to one.

hyperparameter_metric_tag: String

Optional. The Tensorflow summary tag name to use for optimizing trials. For current versions of Tensorflow, this tag name should exactly match what is shown in Tensorboard, including all scopes. For versions of Tensorflow prior to 0.12, this should be only the tag passed to tf.Summary. By default, “training/hptuning/metric” will be used.

Implementations

Returns the enum value of goal, or the default if the field is set to an invalid enum value.

Sets goal to the provided enum value.

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