📄 Source: GoogleCloudMlV1HyperparameterSpec.php
<?php
/*
* Copyright 2014 Google Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License"); you may not
* use this file except in compliance with the License. You may obtain a copy of
* the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations under
* the License.
*/
namespace Google\Service\CloudMachineLearningEngine;
class GoogleCloudMlV1HyperparameterSpec extends \Google\Collection
{
/**
* The default algorithm used by the hyperparameter tuning service. This is a
* Bayesian optimization algorithm.
*/
public const ALGORITHM_ALGORITHM_UNSPECIFIED = 'ALGORITHM_UNSPECIFIED';
/**
* Simple grid search within the feasible space. To use grid search, all
* parameters must be `INTEGER`, `CATEGORICAL`, or `DISCRETE`.
*/
public const ALGORITHM_GRID_SEARCH = 'GRID_SEARCH';
/**
* Simple random search within the feasible space.
*/
public const ALGORITHM_RANDOM_SEARCH = 'RANDOM_SEARCH';
/**
* Goal Type will default to maximize.
*/
public const GOAL_GOAL_TYPE_UNSPECIFIED = 'GOAL_TYPE_UNSPECIFIED';
/**
* Maximize the goal metric.
*/
public const GOAL_MAXIMIZE = 'MAXIMIZE';
/**
* Minimize the goal metric.
*/
public const GOAL_MINIMIZE = 'MINIMIZE';
protected $collection_key = 'params';
/**
* Optional. The search algorithm specified for the hyperparameter tuning job.
* Uses the default AI Platform hyperparameter tuning algorithm if
* unspecified.
*
* @var string
*/
public $algorithm;
/**
* Optional. Indicates if the hyperparameter tuning job enables auto trial
* early stopping.
*
* @var bool
*/
public $enableTrialEarlyStopping;
/**
* Required. The type of goal to use for tuning. Available types are
* `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`.
*
* @var string
*/
public $goal;
/**
* 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.
*
* @var string
*/
public $hyperparameterMetricTag;
/**
* 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.
*
* @var int
*/
public $maxFailedTrials;
/**
* 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.
*
* @var int
*/
public $maxParallelTrials;
/**
* Optional. How many training trials should be attempted to optimize the
* specified hyperparameters. Defaults to one.
*
* @var int
*/
public $maxTrials;
protected $paramsType = GoogleCloudMlV1ParameterSpec::class;
protected $paramsDataType = 'array';
/**
* 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.
*
* @var string
*/
public $resumePreviousJobId;
/**
* Optional. The search algorithm specified for the hyperparameter tuning job.
* Uses the default AI Platform hyperparameter tuning algorithm if
* unspecified.
*
* Accepted values: ALGORITHM_UNSPECIFIED, GRID_SEARCH, RANDOM_SEARCH
*
* @param self::ALGORITHM_* $algorithm
*/
public function setAlgorithm($algorithm)
{
$this->algorithm = $algorithm;
}
/**
* @return self::ALGORITHM_*
*/
public function getAlgorithm()
{
return $this->algorithm;
}
/**
* Optional. Indicates if the hyperparameter tuning job enables auto trial
* early stopping.
*
* @param bool $enableTrialEarlyStopping
*/
public function setEnableTrialEarlyStopping($enableTrialEarlyStopping)
{
$this->enableTrialEarlyStopping = $enableTrialEarlyStopping;
}
/**
* @return bool
*/
public function getEnableTrialEarlyStopping()
{
return $this->enableTrialEarlyStopping;
}
/**
* Required. The type of goal to use for tuning. Available types are
* `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`.
*
* Accepted values: GOAL_TYPE_UNSPECIFIED, MAXIMIZE, MINIMIZE
*
* @param self::GOAL_* $goal
*/
public function setGoal($goal)
{
$this->goal = $goal;
}
/**
* @return self::GOAL_*
*/
public function getGoal()
{
return $this->goal;
}
/**
* 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.
*
* @param string $hyperparameterMetricTag
*/
public function setHyperparameterMetricTag($hyperparameterMetricTag)
{
$this->hyperparameterMetricTag = $hyperparameterMetricTag;
}
/**
* @return string
*/
public function getHyperparameterMetricTag()
{
return $this->hyperparameterMetricTag;
}
/**
* 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.
*
* @param int $maxFailedTrials
*/
public function setMaxFailedTrials($maxFailedTrials)
{
$this->maxFailedTrials = $maxFailedTrials;
}
/**
* @return int
*/
public function getMaxFailedTrials()
{
return $this->maxFailedTrials;
}
/**
* 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.
*
* @param int $maxParallelTrials
*/
public function setMaxParallelTrials($maxParallelTrials)
{
$this->maxParallelTrials = $maxParallelTrials;
}
/**
* @return int
*/
public function getMaxParallelTrials()
{
return $this->maxParallelTrials;
}
/**
* Optional. How many training trials should be attempted to optimize the
* specified hyperparameters. Defaults to one.
*
* @param int $maxTrials
*/
public function setMaxTrials($maxTrials)
{
$this->maxTrials = $maxTrials;
}
/**
* @return int
*/
public function getMaxTrials()
{
return $this->maxTrials;
}
/**
* Required. The set of parameters to tune.
*
* @param GoogleCloudMlV1ParameterSpec[] $params
*/
public function setParams($params)
{
$this->params = $params;
}
/**
* @return GoogleCloudMlV1ParameterSpec[]
*/
public function getParams()
{
return $this->params;
}
/**
* 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.
*
* @param string $resumePreviousJobId
*/
public function setResumePreviousJobId($resumePreviousJobId)
{
$this->resumePreviousJobId = $resumePreviousJobId;
}
/**
* @return string
*/
public function getResumePreviousJobId()
{
return $this->resumePreviousJobId;
}
}
// Adding a class alias for backwards compatibility with the previous class name.
class_alias(GoogleCloudMlV1HyperparameterSpec::class, 'Google_Service_CloudMachineLearningEngine_GoogleCloudMlV1HyperparameterSpec');
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