com.google.cloud.aiplatform.v1beta1

Class SmoothGradConfig.Builder

java.lang.Object

com.google.protobuf.AbstractMessageLite.Builder

com.google.protobuf.AbstractMessage.Builder<BuilderT>

com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

com.google.cloud.aiplatform.v1beta1.SmoothGradConfig.Builder

All Implemented Interfaces:

SmoothGradConfigOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable

Enclosing class:

SmoothGradConfig

public static final class SmoothGradConfig.Builder
extends com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>
implements SmoothGradConfigOrBuilder

 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
 

Protobuf type google.cloud.aiplatform.v1beta1.SmoothGradConfig

Method Summary

Modifier and Type	Method and Description
SmoothGradConfig.Builder	addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
SmoothGradConfig	build()
SmoothGradConfig	buildPartial()
SmoothGradConfig.Builder	clear()
SmoothGradConfig.Builder	clearFeatureNoiseSigma() This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but provides additional flexibility.
SmoothGradConfig.Builder	clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
SmoothGradConfig.Builder	clearGradientNoiseSigma()
SmoothGradConfig.Builder	clearNoiseSigma() This is a single float value and will be used to add noise to all the features.
SmoothGradConfig.Builder	clearNoisySampleCount() The number of gradient samples to use for approximation.
SmoothGradConfig.Builder	clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
SmoothGradConfig.Builder	clone()
SmoothGradConfig	getDefaultInstanceForType()
static com.google.protobuf.Descriptors.Descriptor	getDescriptor()
com.google.protobuf.Descriptors.Descriptor	getDescriptorForType()
FeatureNoiseSigma	getFeatureNoiseSigma() This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but provides additional flexibility.
FeatureNoiseSigma.Builder	getFeatureNoiseSigmaBuilder() This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but provides additional flexibility.
FeatureNoiseSigmaOrBuilder	getFeatureNoiseSigmaOrBuilder() This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but provides additional flexibility.
SmoothGradConfig.GradientNoiseSigmaCase	getGradientNoiseSigmaCase()
float	getNoiseSigma() This is a single float value and will be used to add noise to all the features.
int	getNoisySampleCount() The number of gradient samples to use for approximation.
boolean	hasFeatureNoiseSigma() This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but provides additional flexibility.
boolean	hasNoiseSigma() This is a single float value and will be used to add noise to all the features.
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable	internalGetFieldAccessorTable()
boolean	isInitialized()
SmoothGradConfig.Builder	mergeFeatureNoiseSigma(FeatureNoiseSigma value) This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but provides additional flexibility.
SmoothGradConfig.Builder	mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
SmoothGradConfig.Builder	mergeFrom(com.google.protobuf.Message other)
SmoothGradConfig.Builder	mergeFrom(SmoothGradConfig other)
SmoothGradConfig.Builder	mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
SmoothGradConfig.Builder	setFeatureNoiseSigma(FeatureNoiseSigma.Builder builderForValue) This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but provides additional flexibility.
SmoothGradConfig.Builder	setFeatureNoiseSigma(FeatureNoiseSigma value) This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but provides additional flexibility.
SmoothGradConfig.Builder	setField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
SmoothGradConfig.Builder	setNoiseSigma(float value) This is a single float value and will be used to add noise to all the features.
SmoothGradConfig.Builder	setNoisySampleCount(int value) The number of gradient samples to use for approximation.
SmoothGradConfig.Builder	setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value)
SmoothGradConfig.Builder	setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)

Methods inherited from class com.google.protobuf.GeneratedMessageV3.Builder

getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, internalGetMutableMapField, internalGetMutableMapFieldReflection, isClean, markClean, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setUnknownFieldSetBuilder, setUnknownFieldsProto3

Methods inherited from class com.google.protobuf.AbstractMessage.Builder

findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toString

Methods inherited from class com.google.protobuf.AbstractMessageLite.Builder

addAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, newUninitializedMessageException

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.Message.Builder

mergeDelimitedFrom, mergeDelimitedFrom

Method Detail

getDescriptor

public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

internalGetFieldAccessorTable

protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Specified by:

internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

clear

public SmoothGradConfig.Builder clear()

Specified by:

clear in interface com.google.protobuf.Message.Builder

Specified by:

clear in interface com.google.protobuf.MessageLite.Builder

Overrides:

clear in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

getDescriptorForType

public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()

Specified by:

getDescriptorForType in interface com.google.protobuf.Message.Builder

Specified by:

getDescriptorForType in interface com.google.protobuf.MessageOrBuilder

Overrides:

getDescriptorForType in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

getDefaultInstanceForType

public SmoothGradConfig getDefaultInstanceForType()

Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder

Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

build

public SmoothGradConfig build()

Specified by:

build in interface com.google.protobuf.Message.Builder

Specified by:

build in interface com.google.protobuf.MessageLite.Builder

buildPartial

public SmoothGradConfig buildPartial()

Specified by:

buildPartial in interface com.google.protobuf.Message.Builder

Specified by:

buildPartial in interface com.google.protobuf.MessageLite.Builder

clone

public SmoothGradConfig.Builder clone()

Specified by:

clone in interface com.google.protobuf.Message.Builder

Specified by:

clone in interface com.google.protobuf.MessageLite.Builder

Overrides:

clone in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

setField

public SmoothGradConfig.Builder setField(com.google.protobuf.Descriptors.FieldDescriptor field,
                                         Object value)

Specified by:

setField in interface com.google.protobuf.Message.Builder

Overrides:

setField in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

clearField

public SmoothGradConfig.Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field)

Specified by:

clearField in interface com.google.protobuf.Message.Builder

Overrides:

clearField in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

clearOneof

public SmoothGradConfig.Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)

Specified by:

clearOneof in interface com.google.protobuf.Message.Builder

Overrides:

clearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

setRepeatedField

public SmoothGradConfig.Builder setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                 int index,
                                                 Object value)

Specified by:

setRepeatedField in interface com.google.protobuf.Message.Builder

Overrides:

setRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

addRepeatedField

public SmoothGradConfig.Builder addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                 Object value)

Specified by:

addRepeatedField in interface com.google.protobuf.Message.Builder

Overrides:

addRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

mergeFrom

public SmoothGradConfig.Builder mergeFrom(com.google.protobuf.Message other)

Specified by:

mergeFrom in interface com.google.protobuf.Message.Builder

Overrides:

mergeFrom in class com.google.protobuf.AbstractMessage.Builder<SmoothGradConfig.Builder>

mergeFrom

public SmoothGradConfig.Builder mergeFrom(SmoothGradConfig other)

isInitialized

public final boolean isInitialized()

Specified by:

isInitialized in interface com.google.protobuf.MessageLiteOrBuilder

Overrides:

isInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

mergeFrom

public SmoothGradConfig.Builder mergeFrom(com.google.protobuf.CodedInputStream input,
                                          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                   throws IOException

Specified by:

mergeFrom in interface com.google.protobuf.Message.Builder

Specified by:

mergeFrom in interface com.google.protobuf.MessageLite.Builder

Overrides:

mergeFrom in class com.google.protobuf.AbstractMessage.Builder<SmoothGradConfig.Builder>

Throws:

IOException

getGradientNoiseSigmaCase

public SmoothGradConfig.GradientNoiseSigmaCase getGradientNoiseSigmaCase()

Specified by:

getGradientNoiseSigmaCase in interface SmoothGradConfigOrBuilder

clearGradientNoiseSigma

public SmoothGradConfig.Builder clearGradientNoiseSigma()

hasNoiseSigma

public boolean hasNoiseSigma()

 This is a single float value and will be used to add noise to all the
 features. Use this field when all features are normalized to have the
 same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where
 features are normalized to have 0-mean and 1-variance. Learn more about
 [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization).

 For best results the recommended value is about 10% - 20% of the standard
 deviation of the input feature. Refer to section 3.2 of the SmoothGrad
 paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1.

 If the distribution is different per feature, set
 [feature_noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.feature_noise_sigma]
 instead for each feature.
 

float noise_sigma = 1;

Specified by:

hasNoiseSigma in interface SmoothGradConfigOrBuilder

Returns:

Whether the noiseSigma field is set.

getNoiseSigma

public float getNoiseSigma()

 This is a single float value and will be used to add noise to all the
 features. Use this field when all features are normalized to have the
 same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where
 features are normalized to have 0-mean and 1-variance. Learn more about
 [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization).

 For best results the recommended value is about 10% - 20% of the standard
 deviation of the input feature. Refer to section 3.2 of the SmoothGrad
 paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1.

 If the distribution is different per feature, set
 [feature_noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.feature_noise_sigma]
 instead for each feature.
 

float noise_sigma = 1;

Specified by:

getNoiseSigma in interface SmoothGradConfigOrBuilder

Returns:

The noiseSigma.

setNoiseSigma

public SmoothGradConfig.Builder setNoiseSigma(float value)

 This is a single float value and will be used to add noise to all the
 features. Use this field when all features are normalized to have the
 same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where
 features are normalized to have 0-mean and 1-variance. Learn more about
 [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization).

 For best results the recommended value is about 10% - 20% of the standard
 deviation of the input feature. Refer to section 3.2 of the SmoothGrad
 paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1.

 If the distribution is different per feature, set
 [feature_noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.feature_noise_sigma]
 instead for each feature.
 

float noise_sigma = 1;

Parameters:

value - The noiseSigma to set.

Returns:

This builder for chaining.

clearNoiseSigma

public SmoothGradConfig.Builder clearNoiseSigma()

 This is a single float value and will be used to add noise to all the
 features. Use this field when all features are normalized to have the
 same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where
 features are normalized to have 0-mean and 1-variance. Learn more about
 [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization).

 For best results the recommended value is about 10% - 20% of the standard
 deviation of the input feature. Refer to section 3.2 of the SmoothGrad
 paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1.

 If the distribution is different per feature, set
 [feature_noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.feature_noise_sigma]
 instead for each feature.
 

float noise_sigma = 1;

Returns:

This builder for chaining.

hasFeatureNoiseSigma

public boolean hasFeatureNoiseSigma()

 This is similar to
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma],
 but provides additional flexibility. A separate noise sigma can be
 provided for each feature, which is useful if their distributions are
 different. No noise is added to features that are not set. If this field
 is unset,
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma]
 will be used for all features.
 

.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;

Specified by:

hasFeatureNoiseSigma in interface SmoothGradConfigOrBuilder

Returns:

Whether the featureNoiseSigma field is set.

getFeatureNoiseSigma

public FeatureNoiseSigma getFeatureNoiseSigma()

 This is similar to
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma],
 but provides additional flexibility. A separate noise sigma can be
 provided for each feature, which is useful if their distributions are
 different. No noise is added to features that are not set. If this field
 is unset,
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma]
 will be used for all features.
 

.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;

Specified by:

getFeatureNoiseSigma in interface SmoothGradConfigOrBuilder

Returns:

The featureNoiseSigma.

setFeatureNoiseSigma

public SmoothGradConfig.Builder setFeatureNoiseSigma(FeatureNoiseSigma value)

 This is similar to
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma],
 but provides additional flexibility. A separate noise sigma can be
 provided for each feature, which is useful if their distributions are
 different. No noise is added to features that are not set. If this field
 is unset,
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma]
 will be used for all features.
 

.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;

setFeatureNoiseSigma

public SmoothGradConfig.Builder setFeatureNoiseSigma(FeatureNoiseSigma.Builder builderForValue)

 This is similar to
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma],
 but provides additional flexibility. A separate noise sigma can be
 provided for each feature, which is useful if their distributions are
 different. No noise is added to features that are not set. If this field
 is unset,
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma]
 will be used for all features.
 

.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;

mergeFeatureNoiseSigma

public SmoothGradConfig.Builder mergeFeatureNoiseSigma(FeatureNoiseSigma value)

 This is similar to
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma],
 but provides additional flexibility. A separate noise sigma can be
 provided for each feature, which is useful if their distributions are
 different. No noise is added to features that are not set. If this field
 is unset,
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma]
 will be used for all features.
 

.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;

clearFeatureNoiseSigma

public SmoothGradConfig.Builder clearFeatureNoiseSigma()

 This is similar to
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma],
 but provides additional flexibility. A separate noise sigma can be
 provided for each feature, which is useful if their distributions are
 different. No noise is added to features that are not set. If this field
 is unset,
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma]
 will be used for all features.
 

.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;

getFeatureNoiseSigmaBuilder

public FeatureNoiseSigma.Builder getFeatureNoiseSigmaBuilder()

 This is similar to
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma],
 but provides additional flexibility. A separate noise sigma can be
 provided for each feature, which is useful if their distributions are
 different. No noise is added to features that are not set. If this field
 is unset,
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma]
 will be used for all features.
 

.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;

getFeatureNoiseSigmaOrBuilder

public FeatureNoiseSigmaOrBuilder getFeatureNoiseSigmaOrBuilder()

 This is similar to
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma],
 but provides additional flexibility. A separate noise sigma can be
 provided for each feature, which is useful if their distributions are
 different. No noise is added to features that are not set. If this field
 is unset,
 [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma]
 will be used for all features.
 

.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;

Specified by:

getFeatureNoiseSigmaOrBuilder in interface SmoothGradConfigOrBuilder

getNoisySampleCount

public int getNoisySampleCount()

 The number of gradient samples to use for
 approximation. The higher this number, the more accurate the gradient
 is, but the runtime complexity increases by this factor as well.
 Valid range of its value is [1, 50]. Defaults to 3.
 

int32 noisy_sample_count = 3;

Specified by:

getNoisySampleCount in interface SmoothGradConfigOrBuilder

Returns:

The noisySampleCount.

setNoisySampleCount

public SmoothGradConfig.Builder setNoisySampleCount(int value)

 The number of gradient samples to use for
 approximation. The higher this number, the more accurate the gradient
 is, but the runtime complexity increases by this factor as well.
 Valid range of its value is [1, 50]. Defaults to 3.
 

int32 noisy_sample_count = 3;

Parameters:

value - The noisySampleCount to set.

Returns:

This builder for chaining.

clearNoisySampleCount

public SmoothGradConfig.Builder clearNoisySampleCount()

 The number of gradient samples to use for
 approximation. The higher this number, the more accurate the gradient
 is, but the runtime complexity increases by this factor as well.
 Valid range of its value is [1, 50]. Defaults to 3.
 

int32 noisy_sample_count = 3;

Returns:

This builder for chaining.

setUnknownFields

public final SmoothGradConfig.Builder setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)

Specified by:

setUnknownFields in interface com.google.protobuf.Message.Builder

Overrides:

setUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>

mergeUnknownFields

public final SmoothGradConfig.Builder mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)

Specified by:

mergeUnknownFields in interface com.google.protobuf.Message.Builder

Overrides:

mergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<SmoothGradConfig.Builder>