📄 Source: GoogleCloudAiplatformV1XraiAttribution.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\Aiplatform;
class GoogleCloudAiplatformV1XraiAttribution extends \Google\Model
{
protected $blurBaselineConfigType = GoogleCloudAiplatformV1BlurBaselineConfig::class;
protected $blurBaselineConfigDataType = '';
protected $smoothGradConfigType = GoogleCloudAiplatformV1SmoothGradConfig::class;
protected $smoothGradConfigDataType = '';
/**
* Required. The number of steps for approximating the path integral. A good
* value to start is 50 and gradually increase until the sum to diff property
* is met within the desired error range. Valid range of its value is [1,
* 100], inclusively.
*
* @var int
*/
public $stepCount;
/**
* Config for XRAI with blur baseline. When enabled, a linear path from the
* maximally blurred image to the input image is created. Using a blurred
* baseline instead of zero (black image) is motivated by the BlurIG approach
* explained here: https://arxiv.org/abs/2004.03383
*
* @param GoogleCloudAiplatformV1BlurBaselineConfig $blurBaselineConfig
*/
public function setBlurBaselineConfig(GoogleCloudAiplatformV1BlurBaselineConfig $blurBaselineConfig)
{
$this->blurBaselineConfig = $blurBaselineConfig;
}
/**
* @return GoogleCloudAiplatformV1BlurBaselineConfig
*/
public function getBlurBaselineConfig()
{
return $this->blurBaselineConfig;
}
/**
* Config for SmoothGrad approximation of gradients. When enabled, the
* gradients are approximated by averaging the gradients from noisy samples in
* the vicinity of the inputs. Adding noise can help improve the computed
* gradients. Refer to this paper for more details:
* https://arxiv.org/pdf/1706.03825.pdf
*
* @param GoogleCloudAiplatformV1SmoothGradConfig $smoothGradConfig
*/
public function setSmoothGradConfig(GoogleCloudAiplatformV1SmoothGradConfig $smoothGradConfig)
{
$this->smoothGradConfig = $smoothGradConfig;
}
/**
* @return GoogleCloudAiplatformV1SmoothGradConfig
*/
public function getSmoothGradConfig()
{
return $this->smoothGradConfig;
}
/**
* Required. The number of steps for approximating the path integral. A good
* value to start is 50 and gradually increase until the sum to diff property
* is met within the desired error range. Valid range of its value is [1,
* 100], inclusively.
*
* @param int $stepCount
*/
public function setStepCount($stepCount)
{
$this->stepCount = $stepCount;
}
/**
* @return int
*/
public function getStepCount()
{
return $this->stepCount;
}
}
// Adding a class alias for backwards compatibility with the previous class name.
class_alias(GoogleCloudAiplatformV1XraiAttribution::class, 'Google_Service_Aiplatform_GoogleCloudAiplatformV1XraiAttribution');
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