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public static final class SmoothGradConfig.Builder extends GeneratedMessageV3.Builder<SmoothGradConfig.Builder> implements SmoothGradConfigOrBuilderConfig 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
Inheritance
Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > SmoothGradConfig.BuilderImplements
SmoothGradConfigOrBuilderStatic Methods
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()| Returns | |
|---|---|
| Type | Description |
Descriptor | |
Methods
addRepeatedField(Descriptors.FieldDescriptor field, Object value)
public SmoothGradConfig.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)| Parameters | |
|---|---|
| Name | Description |
field | FieldDescriptor |
value | Object |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
build()
public SmoothGradConfig build()| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig | |
buildPartial()
public SmoothGradConfig buildPartial()| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig | |
clear()
public SmoothGradConfig.Builder clear()| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
clearFeatureNoiseSigma()
public SmoothGradConfig.Builder clearFeatureNoiseSigma()This is similar to 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 will be used for all features.
.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
clearField(Descriptors.FieldDescriptor field)
public SmoothGradConfig.Builder clearField(Descriptors.FieldDescriptor field)| Parameter | |
|---|---|
| Name | Description |
field | FieldDescriptor |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
clearGradientNoiseSigma()
public SmoothGradConfig.Builder clearGradientNoiseSigma()| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
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.
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 instead for each feature.
float noise_sigma = 1;
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | 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 | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | This builder for chaining. |
clearOneof(Descriptors.OneofDescriptor oneof)
public SmoothGradConfig.Builder clearOneof(Descriptors.OneofDescriptor oneof)| Parameter | |
|---|---|
| Name | Description |
oneof | OneofDescriptor |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
clone()
public SmoothGradConfig.Builder clone()| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
getDefaultInstanceForType()
public SmoothGradConfig getDefaultInstanceForType()| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig | |
getDescriptorForType()
public Descriptors.Descriptor getDescriptorForType()| Returns | |
|---|---|
| Type | Description |
Descriptor | |
getFeatureNoiseSigma()
public FeatureNoiseSigma getFeatureNoiseSigma()This is similar to 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 will be used for all features.
.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;
| Returns | |
|---|---|
| Type | Description |
FeatureNoiseSigma | The featureNoiseSigma. |
getFeatureNoiseSigmaBuilder()
public FeatureNoiseSigma.Builder getFeatureNoiseSigmaBuilder()This is similar to 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 will be used for all features.
.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;
| Returns | |
|---|---|
| Type | Description |
FeatureNoiseSigma.Builder | |
getFeatureNoiseSigmaOrBuilder()
public FeatureNoiseSigmaOrBuilder getFeatureNoiseSigmaOrBuilder()This is similar to 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 will be used for all features.
.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;
| Returns | |
|---|---|
| Type | Description |
FeatureNoiseSigmaOrBuilder | |
getGradientNoiseSigmaCase()
public SmoothGradConfig.GradientNoiseSigmaCase getGradientNoiseSigmaCase()| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.GradientNoiseSigmaCase | |
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.
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 instead for each feature.
float noise_sigma = 1;
| Returns | |
|---|---|
| Type | Description |
float | The noiseSigma. |
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;
| Returns | |
|---|---|
| Type | Description |
int | The noisySampleCount. |
hasFeatureNoiseSigma()
public boolean hasFeatureNoiseSigma()This is similar to 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 will be used for all features.
.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;
| Returns | |
|---|---|
| Type | Description |
boolean | Whether the featureNoiseSigma field is set. |
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.
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 instead for each feature.
float noise_sigma = 1;
| Returns | |
|---|---|
| Type | Description |
boolean | Whether the noiseSigma field is set. |
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()| Returns | |
|---|---|
| Type | Description |
FieldAccessorTable | |
isInitialized()
public final boolean isInitialized()| Returns | |
|---|---|
| Type | Description |
boolean | |
mergeFeatureNoiseSigma(FeatureNoiseSigma value)
public SmoothGradConfig.Builder mergeFeatureNoiseSigma(FeatureNoiseSigma value)This is similar to 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 will be used for all features.
.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;
| Parameter | |
|---|---|
| Name | Description |
value | FeatureNoiseSigma |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
mergeFrom(SmoothGradConfig other)
public SmoothGradConfig.Builder mergeFrom(SmoothGradConfig other)| Parameter | |
|---|---|
| Name | Description |
other | SmoothGradConfig |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public SmoothGradConfig.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)| Parameters | |
|---|---|
| Name | Description |
input | CodedInputStream |
extensionRegistry | ExtensionRegistryLite |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
| Exceptions | |
|---|---|
| Type | Description |
IOException | |
mergeFrom(Message other)
public SmoothGradConfig.Builder mergeFrom(Message other)| Parameter | |
|---|---|
| Name | Description |
other | Message |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
mergeUnknownFields(UnknownFieldSet unknownFields)
public final SmoothGradConfig.Builder mergeUnknownFields(UnknownFieldSet unknownFields)| Parameter | |
|---|---|
| Name | Description |
unknownFields | UnknownFieldSet |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
setFeatureNoiseSigma(FeatureNoiseSigma value)
public SmoothGradConfig.Builder setFeatureNoiseSigma(FeatureNoiseSigma value)This is similar to 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 will be used for all features.
.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;
| Parameter | |
|---|---|
| Name | Description |
value | FeatureNoiseSigma |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
setFeatureNoiseSigma(FeatureNoiseSigma.Builder builderForValue)
public SmoothGradConfig.Builder setFeatureNoiseSigma(FeatureNoiseSigma.Builder builderForValue)This is similar to 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 will be used for all features.
.google.cloud.aiplatform.v1beta1.FeatureNoiseSigma feature_noise_sigma = 2;
| Parameter | |
|---|---|
| Name | Description |
builderForValue | FeatureNoiseSigma.Builder |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
setField(Descriptors.FieldDescriptor field, Object value)
public SmoothGradConfig.Builder setField(Descriptors.FieldDescriptor field, Object value)| Parameters | |
|---|---|
| Name | Description |
field | FieldDescriptor |
value | Object |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
setNoiseSigma(float value)
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.
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 instead for each feature.
float noise_sigma = 1;
| Parameter | |
|---|---|
| Name | Description |
value | floatThe noiseSigma to set. |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | This builder for chaining. |
setNoisySampleCount(int value)
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;
| Parameter | |
|---|---|
| Name | Description |
value | intThe noisySampleCount to set. |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | This builder for chaining. |
setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
public SmoothGradConfig.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)| Parameters | |
|---|---|
| Name | Description |
field | FieldDescriptor |
index | int |
value | Object |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |
setUnknownFields(UnknownFieldSet unknownFields)
public final SmoothGradConfig.Builder setUnknownFields(UnknownFieldSet unknownFields)| Parameter | |
|---|---|
| Name | Description |
unknownFields | UnknownFieldSet |
| Returns | |
|---|---|
| Type | Description |
SmoothGradConfig.Builder | |