Struct GoogleCloudMlV1__HyperparameterSpec

pub struct GoogleCloudMlV1__HyperparameterSpec {
    pub algorithm: Option<String>,
    pub enable_trial_early_stopping: Option<bool>,
    pub goal: Option<String>,
    pub hyperparameter_metric_tag: Option<String>,
    pub max_failed_trials: Option<i32>,
    pub max_parallel_trials: Option<i32>,
    pub max_trials: Option<i32>,
    pub params: Option<Vec<GoogleCloudMlV1__ParameterSpec>>,
    pub resume_previous_job_id: Option<String>,
}

Represents a set of hyperparameters to optimize.

This type is not used in any activity, and only used as part of another schema.

Fields

algorithm: Option<String>

Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.

enable_trial_early_stopping: Option<bool>

Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.

goal: Option<String>

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

hyperparameter_metric_tag: Option<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.

max_failed_trials: Option<i32>

Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail.

max_parallel_trials: Option<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.

max_trials: Option<i32>

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

params: Option<Vec<GoogleCloudMlV1__ParameterSpec>>

Required. The set of parameters to tune.

resume_previous_job_id: Option<String>

Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study.

Trait Implementations

impl Clone for GoogleCloudMlV1__HyperparameterSpec

fn clone(&self) -> GoogleCloudMlV1__HyperparameterSpec

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GoogleCloudMlV1__HyperparameterSpec

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for GoogleCloudMlV1__HyperparameterSpec

fn default() -> GoogleCloudMlV1__HyperparameterSpec

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GoogleCloudMlV1__HyperparameterSpec

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for GoogleCloudMlV1__HyperparameterSpec

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Part for GoogleCloudMlV1__HyperparameterSpec