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v0.0.0-...-e639e21 Latest Latest
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 Go to latest Published: Nov 18, 2024 License: Apache-2.0 Imports: 6 Imported by: 0

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Index

Constants

This section is empty.

Variables

View Source 
var (
	AutoMlImageClassificationInputs_ModelType_name = map[int32]string{
		0: "MODEL_TYPE_UNSPECIFIED",
		1: "CLOUD",
		2: "MOBILE_TF_LOW_LATENCY_1",
		3: "MOBILE_TF_VERSATILE_1",
		4: "MOBILE_TF_HIGH_ACCURACY_1",
	}
	AutoMlImageClassificationInputs_ModelType_value = map[string]int32{
		"MODEL_TYPE_UNSPECIFIED":    0,
		"CLOUD":                     1,
		"MOBILE_TF_LOW_LATENCY_1":   2,
		"MOBILE_TF_VERSATILE_1":     3,
		"MOBILE_TF_HIGH_ACCURACY_1": 4,
	}
)

Enum value maps for AutoMlImageClassificationInputs_ModelType.

View Source 
var (
	AutoMlImageClassificationMetadata_SuccessfulStopReason_name = map[int32]string{
		0: "SUCCESSFUL_STOP_REASON_UNSPECIFIED",
		1: "BUDGET_REACHED",
		2: "MODEL_CONVERGED",
	}
	AutoMlImageClassificationMetadata_SuccessfulStopReason_value = map[string]int32{
		"SUCCESSFUL_STOP_REASON_UNSPECIFIED": 0,
		"BUDGET_REACHED":                     1,
		"MODEL_CONVERGED":                    2,
	}
)

Enum value maps for AutoMlImageClassificationMetadata_SuccessfulStopReason.

View Source 
var (
	AutoMlImageObjectDetectionInputs_ModelType_name = map[int32]string{
		0: "MODEL_TYPE_UNSPECIFIED",
		1: "CLOUD_HIGH_ACCURACY_1",
		2: "CLOUD_LOW_LATENCY_1",
		3: "MOBILE_TF_LOW_LATENCY_1",
		4: "MOBILE_TF_VERSATILE_1",
		5: "MOBILE_TF_HIGH_ACCURACY_1",
	}
	AutoMlImageObjectDetectionInputs_ModelType_value = map[string]int32{
		"MODEL_TYPE_UNSPECIFIED":    0,
		"CLOUD_HIGH_ACCURACY_1":     1,
		"CLOUD_LOW_LATENCY_1":       2,
		"MOBILE_TF_LOW_LATENCY_1":   3,
		"MOBILE_TF_VERSATILE_1":     4,
		"MOBILE_TF_HIGH_ACCURACY_1": 5,
	}
)

Enum value maps for AutoMlImageObjectDetectionInputs_ModelType.

View Source 
var (
	AutoMlImageObjectDetectionMetadata_SuccessfulStopReason_name = map[int32]string{
		0: "SUCCESSFUL_STOP_REASON_UNSPECIFIED",
		1: "BUDGET_REACHED",
		2: "MODEL_CONVERGED",
	}
	AutoMlImageObjectDetectionMetadata_SuccessfulStopReason_value = map[string]int32{
		"SUCCESSFUL_STOP_REASON_UNSPECIFIED": 0,
		"BUDGET_REACHED":                     1,
		"MODEL_CONVERGED":                    2,
	}
)

Enum value maps for AutoMlImageObjectDetectionMetadata_SuccessfulStopReason.

View Source 
var (
	AutoMlImageSegmentationInputs_ModelType_name = map[int32]string{
		0: "MODEL_TYPE_UNSPECIFIED",
		1: "CLOUD_HIGH_ACCURACY_1",
		2: "CLOUD_LOW_ACCURACY_1",
		3: "MOBILE_TF_LOW_LATENCY_1",
	}
	AutoMlImageSegmentationInputs_ModelType_value = map[string]int32{
		"MODEL_TYPE_UNSPECIFIED":  0,
		"CLOUD_HIGH_ACCURACY_1":   1,
		"CLOUD_LOW_ACCURACY_1":    2,
		"MOBILE_TF_LOW_LATENCY_1": 3,
	}
)

Enum value maps for AutoMlImageSegmentationInputs_ModelType.

View Source 
var (
	AutoMlImageSegmentationMetadata_SuccessfulStopReason_name = map[int32]string{
		0: "SUCCESSFUL_STOP_REASON_UNSPECIFIED",
		1: "BUDGET_REACHED",
		2: "MODEL_CONVERGED",
	}
	AutoMlImageSegmentationMetadata_SuccessfulStopReason_value = map[string]int32{
		"SUCCESSFUL_STOP_REASON_UNSPECIFIED": 0,
		"BUDGET_REACHED":                     1,
		"MODEL_CONVERGED":                    2,
	}
)

Enum value maps for AutoMlImageSegmentationMetadata_SuccessfulStopReason.

View Source 
var (
	AutoMlVideoActionRecognitionInputs_ModelType_name = map[int32]string{
		0: "MODEL_TYPE_UNSPECIFIED",
		1: "CLOUD",
		2: "MOBILE_VERSATILE_1",
		3: "MOBILE_JETSON_VERSATILE_1",
		4: "MOBILE_CORAL_VERSATILE_1",
	}
	AutoMlVideoActionRecognitionInputs_ModelType_value = map[string]int32{
		"MODEL_TYPE_UNSPECIFIED":    0,
		"CLOUD":                     1,
		"MOBILE_VERSATILE_1":        2,
		"MOBILE_JETSON_VERSATILE_1": 3,
		"MOBILE_CORAL_VERSATILE_1":  4,
	}
)

Enum value maps for AutoMlVideoActionRecognitionInputs_ModelType.

View Source 
var (
	AutoMlVideoClassificationInputs_ModelType_name = map[int32]string{
		0: "MODEL_TYPE_UNSPECIFIED",
		1: "CLOUD",
		2: "MOBILE_VERSATILE_1",
		3: "MOBILE_JETSON_VERSATILE_1",
	}
	AutoMlVideoClassificationInputs_ModelType_value = map[string]int32{
		"MODEL_TYPE_UNSPECIFIED":    0,
		"CLOUD":                     1,
		"MOBILE_VERSATILE_1":        2,
		"MOBILE_JETSON_VERSATILE_1": 3,
	}
)

Enum value maps for AutoMlVideoClassificationInputs_ModelType.

View Source 
var (
	AutoMlVideoObjectTrackingInputs_ModelType_name = map[int32]string{
		0: "MODEL_TYPE_UNSPECIFIED",
		1: "CLOUD",
		2: "MOBILE_VERSATILE_1",
		3: "MOBILE_CORAL_VERSATILE_1",
		4: "MOBILE_CORAL_LOW_LATENCY_1",
		5: "MOBILE_JETSON_VERSATILE_1",
		6: "MOBILE_JETSON_LOW_LATENCY_1",
	}
	AutoMlVideoObjectTrackingInputs_ModelType_value = map[string]int32{
		"MODEL_TYPE_UNSPECIFIED":      0,
		"CLOUD":                       1,
		"MOBILE_VERSATILE_1":          2,
		"MOBILE_CORAL_VERSATILE_1":    3,
		"MOBILE_CORAL_LOW_LATENCY_1":  4,
		"MOBILE_JETSON_VERSATILE_1":   5,
		"MOBILE_JETSON_LOW_LATENCY_1": 6,
	}
)

Enum value maps for AutoMlVideoObjectTrackingInputs_ModelType.

View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_object_detection_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_segmentation_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_tables_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_text_classification_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_text_extraction_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_text_sentiment_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_time_series_forecasting_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_video_action_recognition_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_video_classification_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_video_object_tracking_proto protoreflect.FileDescriptor
View Source 
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_export_evaluated_data_items_config_proto protoreflect.FileDescriptor

Functions

This section is empty.

Types

type AutoMlForecasting 

type AutoMlForecasting struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlForecastingInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// The metadata information.
	Metadata *AutoMlForecastingMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Forecasting Model.

func (*AutoMlForecasting) Descriptor deprecated 

func (*AutoMlForecasting) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecasting.ProtoReflect.Descriptor instead.

func (*AutoMlForecasting) GetInputs 

func (x *AutoMlForecasting) GetInputs() *AutoMlForecastingInputs

func (*AutoMlForecasting) GetMetadata 

func (x *AutoMlForecasting) GetMetadata() *AutoMlForecastingMetadata

func (*AutoMlForecasting) ProtoMessage 

func (*AutoMlForecasting) ProtoMessage()

func (*AutoMlForecasting) ProtoReflect 

func (x *AutoMlForecasting) ProtoReflect() protoreflect.Message

func (*AutoMlForecasting) Reset 

func (x *AutoMlForecasting) Reset()

func (*AutoMlForecasting) String 

func (x *AutoMlForecasting) String() string

type AutoMlForecastingInputs 

type AutoMlForecastingInputs struct {

	// The name of the column that the model is to predict.
	TargetColumn string `protobuf:"bytes,1,opt,name=target_column,json=targetColumn,proto3" json:"target_column,omitempty"`
	// The name of the column that identifies the time series.
	TimeSeriesIdentifierColumn string `` /* 143-byte string literal not displayed */
	// The name of the column that identifies time order in the time series.
	TimeColumn string `protobuf:"bytes,3,opt,name=time_column,json=timeColumn,proto3" json:"time_column,omitempty"`
	// Each transformation will apply transform function to given input column.
	// And the result will be used for training.
	// When creating transformation for BigQuery Struct column, the column should
	// be flattened using "." as the delimiter.
	Transformations []*AutoMlForecastingInputs_Transformation `protobuf:"bytes,4,rep,name=transformations,proto3" json:"transformations,omitempty"`
	// Objective function the model is optimizing towards. The training process
	// creates a model that optimizes the value of the objective
	// function over the validation set.
	//
	// The supported optimization objectives:
	//   "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
	//   "minimize-mae" - Minimize mean-absolute error (MAE).
	//   "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
	//   "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
	//   "minimize-wape-mae" - Minimize the combination of weighted absolute
	//     percentage error (WAPE) and mean-absolute-error (MAE).
	//   "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
	//     defined in `quantiles`.
	OptimizationObjective string `protobuf:"bytes,5,opt,name=optimization_objective,json=optimizationObjective,proto3" json:"optimization_objective,omitempty"`
	// Required. The train budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour.
	//
	// The training cost of the model will not exceed this budget. The final cost
	// will be attempted to be close to the budget, though may end up being (even)
	// noticeably smaller - at the backend's discretion. This especially may
	// happen when further model training ceases to provide any improvements.
	//
	// If the budget is set to a value known to be insufficient to train a
	// model for the given dataset, the training won't be attempted and
	// will error.
	//
	// The train budget must be between 1,000 and 72,000 milli node hours,
	// inclusive.
	TrainBudgetMilliNodeHours int64 `` /* 143-byte string literal not displayed */
	// Column name that should be used as the weight column.
	// Higher values in this column give more importance to the row
	// during model training. The column must have numeric values between 0 and
	// 10000 inclusively; 0 means the row is ignored for training. If weight
	// column field is not set, then all rows are assumed to have equal weight
	// of 1.
	WeightColumn string `protobuf:"bytes,7,opt,name=weight_column,json=weightColumn,proto3" json:"weight_column,omitempty"`
	// Column names that should be used as static columns.
	// The value of these columns are static per time series.
	StaticColumns []string `protobuf:"bytes,8,rep,name=static_columns,json=staticColumns,proto3" json:"static_columns,omitempty"`
	// Column names that should be used as time variant past only columns.
	// This column contains information for the given entity (identified by the
	// time_series_identifier_column) that is known for the past but not the
	// future (e.g. population of a city in a given year, or weather on a given
	// day).
	TimeVariantPastOnlyColumns []string `` /* 145-byte string literal not displayed */
	// Column names that should be used as time variant past and future columns.
	// This column contains information for the given entity (identified by the
	// key column) that is known for the past and the future
	TimeVariantPastAndFutureColumns []string `` /* 163-byte string literal not displayed */
	// Expected difference in time granularity between rows in the data. If it is
	// not set, the period is inferred from data.
	Period *AutoMlForecastingInputs_Period `protobuf:"bytes,11,opt,name=period,proto3" json:"period,omitempty"`
	// The number of periods offset into the future as the start of the forecast
	// window (the window of future values to predict, relative to the present.),
	// where each period is one unit of granularity as defined by the `period`
	// field above. Default to 0. Inclusive.
	ForecastWindowStart int64 `protobuf:"varint,12,opt,name=forecast_window_start,json=forecastWindowStart,proto3" json:"forecast_window_start,omitempty"`
	// The number of periods offset into the future as the end of the forecast
	// window (the window of future values to predict, relative to the present.),
	// where each period is one unit of granularity as defined by the `period`
	// field above. Inclusive.
	ForecastWindowEnd int64 `protobuf:"varint,13,opt,name=forecast_window_end,json=forecastWindowEnd,proto3" json:"forecast_window_end,omitempty"`
	// The number of periods offset into the past to restrict past sequence, where
	// each period is one unit of granularity as defined by the `period`. Default
	// value 0 means that it lets algorithm to define the value. Inclusive.
	PastHorizon int64 `protobuf:"varint,14,opt,name=past_horizon,json=pastHorizon,proto3" json:"past_horizon,omitempty"`
	// Configuration for exporting test set predictions to a BigQuery table. If
	// this configuration is absent, then the export is not performed.
	ExportEvaluatedDataItemsConfig *ExportEvaluatedDataItemsConfig `` /* 158-byte string literal not displayed */
	// Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
	// 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
	// the value of optimization_objective is minimize-quantile-loss. Represents
	// the percent quantiles to use for that objective. Quantiles must be unique.
	Quantiles []float64 `protobuf:"fixed64,16,rep,packed,name=quantiles,proto3" json:"quantiles,omitempty"`
	// Validation options for the data validation component. The available options
	// are:
	//   "fail-pipeline" - default, will validate against the validation and
	//                     fail the pipeline if it fails.
	//   "ignore-validation" - ignore the results of the validation and continue
	ValidationOptions string `protobuf:"bytes,17,opt,name=validation_options,json=validationOptions,proto3" json:"validation_options,omitempty"`
	// contains filtered or unexported fields
}

func (*AutoMlForecastingInputs) Descriptor deprecated 

func (*AutoMlForecastingInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs) GetExportEvaluatedDataItemsConfig 

func (x *AutoMlForecastingInputs) GetExportEvaluatedDataItemsConfig() *ExportEvaluatedDataItemsConfig

func (*AutoMlForecastingInputs) GetForecastWindowEnd 

func (x *AutoMlForecastingInputs) GetForecastWindowEnd() int64

func (*AutoMlForecastingInputs) GetForecastWindowStart 

func (x *AutoMlForecastingInputs) GetForecastWindowStart() int64

func (*AutoMlForecastingInputs) GetOptimizationObjective 

func (x *AutoMlForecastingInputs) GetOptimizationObjective() string

func (*AutoMlForecastingInputs) GetPastHorizon 

func (x *AutoMlForecastingInputs) GetPastHorizon() int64

func (*AutoMlForecastingInputs) GetPeriod 

func (x *AutoMlForecastingInputs) GetPeriod() *AutoMlForecastingInputs_Period

func (*AutoMlForecastingInputs) GetQuantiles 

func (x *AutoMlForecastingInputs) GetQuantiles() []float64

func (*AutoMlForecastingInputs) GetStaticColumns 

func (x *AutoMlForecastingInputs) GetStaticColumns() []string

func (*AutoMlForecastingInputs) GetTargetColumn 

func (x *AutoMlForecastingInputs) GetTargetColumn() string

func (*AutoMlForecastingInputs) GetTimeColumn 

func (x *AutoMlForecastingInputs) GetTimeColumn() string

func (*AutoMlForecastingInputs) GetTimeSeriesIdentifierColumn 

func (x *AutoMlForecastingInputs) GetTimeSeriesIdentifierColumn() string

func (*AutoMlForecastingInputs) GetTimeVariantPastAndFutureColumns 

func (x *AutoMlForecastingInputs) GetTimeVariantPastAndFutureColumns() []string

func (*AutoMlForecastingInputs) GetTimeVariantPastOnlyColumns 

func (x *AutoMlForecastingInputs) GetTimeVariantPastOnlyColumns() []string

func (*AutoMlForecastingInputs) GetTrainBudgetMilliNodeHours 

func (x *AutoMlForecastingInputs) GetTrainBudgetMilliNodeHours() int64

func (*AutoMlForecastingInputs) GetTransformations 

func (x *AutoMlForecastingInputs) GetTransformations() []*AutoMlForecastingInputs_Transformation

func (*AutoMlForecastingInputs) GetValidationOptions 

func (x *AutoMlForecastingInputs) GetValidationOptions() string

func (*AutoMlForecastingInputs) GetWeightColumn 

func (x *AutoMlForecastingInputs) GetWeightColumn() string

func (*AutoMlForecastingInputs) ProtoMessage 

func (*AutoMlForecastingInputs) ProtoMessage()

func (*AutoMlForecastingInputs) ProtoReflect 

func (x *AutoMlForecastingInputs) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs) Reset 

func (x *AutoMlForecastingInputs) Reset()

func (*AutoMlForecastingInputs) String 

func (x *AutoMlForecastingInputs) String() string

type AutoMlForecastingInputs_Period 

type AutoMlForecastingInputs_Period struct {

	// The time granularity unit of this time period.
	// The supported unit are:
	//  "minute"
	//  "hour"
	//  "day"
	//  "week"
	//  "month"
	//  "year"
	Unit string `protobuf:"bytes,1,opt,name=unit,proto3" json:"unit,omitempty"`
	// The number of units per period, e.g. 3 weeks or 2 months.
	Quantity int64 `protobuf:"varint,2,opt,name=quantity,proto3" json:"quantity,omitempty"`
	// contains filtered or unexported fields
}

A duration of time expressed in time granularity units.

func (*AutoMlForecastingInputs_Period) Descriptor deprecated 

func (*AutoMlForecastingInputs_Period) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs_Period.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Period) GetQuantity 

func (x *AutoMlForecastingInputs_Period) GetQuantity() int64

func (*AutoMlForecastingInputs_Period) GetUnit 

func (x *AutoMlForecastingInputs_Period) GetUnit() string

func (*AutoMlForecastingInputs_Period) ProtoMessage 

func (*AutoMlForecastingInputs_Period) ProtoMessage()

func (*AutoMlForecastingInputs_Period) ProtoReflect 

func (x *AutoMlForecastingInputs_Period) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs_Period) Reset 

func (x *AutoMlForecastingInputs_Period) Reset()

func (*AutoMlForecastingInputs_Period) String 

func (x *AutoMlForecastingInputs_Period) String() string

type AutoMlForecastingInputs_Transformation 

type AutoMlForecastingInputs_Transformation struct {

	// The transformation that the training pipeline will apply to the input
	// columns.
	//
	// Types that are assignable to TransformationDetail:
	//	*AutoMlForecastingInputs_Transformation_Auto
	//	*AutoMlForecastingInputs_Transformation_Numeric
	//	*AutoMlForecastingInputs_Transformation_Categorical
	//	*AutoMlForecastingInputs_Transformation_Timestamp
	//	*AutoMlForecastingInputs_Transformation_Text
	//	*AutoMlForecastingInputs_Transformation_RepeatedNumeric
	//	*AutoMlForecastingInputs_Transformation_RepeatedCategorical
	//	*AutoMlForecastingInputs_Transformation_RepeatedText
	TransformationDetail isAutoMlForecastingInputs_Transformation_TransformationDetail `protobuf_oneof:"transformation_detail"`
	// contains filtered or unexported fields
}

func (*AutoMlForecastingInputs_Transformation) Descriptor deprecated 

func (*AutoMlForecastingInputs_Transformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs_Transformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation) GetAuto 

func (x *AutoMlForecastingInputs_Transformation) GetAuto() *AutoMlForecastingInputs_Transformation_AutoTransformation

func (*AutoMlForecastingInputs_Transformation) GetCategorical 

func (x *AutoMlForecastingInputs_Transformation) GetCategorical() *AutoMlForecastingInputs_Transformation_CategoricalTransformation

func (*AutoMlForecastingInputs_Transformation) GetNumeric 

func (x *AutoMlForecastingInputs_Transformation) GetNumeric() *AutoMlForecastingInputs_Transformation_NumericTransformation

func (*AutoMlForecastingInputs_Transformation) GetRepeatedCategorical 

func (x *AutoMlForecastingInputs_Transformation) GetRepeatedCategorical() *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation

func (*AutoMlForecastingInputs_Transformation) GetRepeatedNumeric 

func (x *AutoMlForecastingInputs_Transformation) GetRepeatedNumeric() *AutoMlForecastingInputs_Transformation_NumericArrayTransformation

func (*AutoMlForecastingInputs_Transformation) GetRepeatedText 

func (x *AutoMlForecastingInputs_Transformation) GetRepeatedText() *AutoMlForecastingInputs_Transformation_TextArrayTransformation

func (*AutoMlForecastingInputs_Transformation) GetText 

func (x *AutoMlForecastingInputs_Transformation) GetText() *AutoMlForecastingInputs_Transformation_TextTransformation

func (*AutoMlForecastingInputs_Transformation) GetTimestamp 

func (x *AutoMlForecastingInputs_Transformation) GetTimestamp() *AutoMlForecastingInputs_Transformation_TimestampTransformation

func (*AutoMlForecastingInputs_Transformation) GetTransformationDetail 

func (m *AutoMlForecastingInputs_Transformation) GetTransformationDetail() isAutoMlForecastingInputs_Transformation_TransformationDetail

func (*AutoMlForecastingInputs_Transformation) ProtoMessage 

func (*AutoMlForecastingInputs_Transformation) ProtoMessage()

func (*AutoMlForecastingInputs_Transformation) ProtoReflect 

func (x *AutoMlForecastingInputs_Transformation) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs_Transformation) Reset 

func (x *AutoMlForecastingInputs_Transformation) Reset()

func (*AutoMlForecastingInputs_Transformation) String 

func (x *AutoMlForecastingInputs_Transformation) String() string

type AutoMlForecastingInputs_Transformation_Auto 

type AutoMlForecastingInputs_Transformation_Auto struct {
	Auto *AutoMlForecastingInputs_Transformation_AutoTransformation `protobuf:"bytes,1,opt,name=auto,proto3,oneof"`
}

type AutoMlForecastingInputs_Transformation_AutoTransformation 

type AutoMlForecastingInputs_Transformation_AutoTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// contains filtered or unexported fields
}

Training pipeline will infer the proper transformation based on the statistic of dataset.

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) Descriptor deprecated 

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs_Transformation_AutoTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) GetColumnName 

func (x *AutoMlForecastingInputs_Transformation_AutoTransformation) GetColumnName() string

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) ProtoMessage 

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) ProtoMessage()

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) ProtoReflect 

func (x *AutoMlForecastingInputs_Transformation_AutoTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) Reset 

func (x *AutoMlForecastingInputs_Transformation_AutoTransformation) Reset()

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) String 

func (x *AutoMlForecastingInputs_Transformation_AutoTransformation) String() string

type AutoMlForecastingInputs_Transformation_Categorical 

type AutoMlForecastingInputs_Transformation_Categorical struct {
	Categorical *AutoMlForecastingInputs_Transformation_CategoricalTransformation `protobuf:"bytes,3,opt,name=categorical,proto3,oneof"`
}

type AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation 

type AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// contains filtered or unexported fields
}

Treats the column as categorical array and performs following transformation functions. * For each element in the array, convert the category name to a dictionary 

lookup index and generate an embedding for each index.
Combine the embedding of all elements into a single embedding using
the mean.

* Empty arrays treated as an embedding of zeroes.

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) Descriptor deprecated 

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) GetColumnName 

func (x *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) GetColumnName() string

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) ProtoMessage 

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) ProtoMessage()

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) ProtoReflect 

func (x *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) Reset 

func (x *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) Reset()

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) String 

func (x *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) String() string

type AutoMlForecastingInputs_Transformation_CategoricalTransformation 

type AutoMlForecastingInputs_Transformation_CategoricalTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// contains filtered or unexported fields
}

Training pipeline will perform following transformation functions. * The categorical string as is--no change to case, punctuation, spelling, 

	tense, and so on.
  - Convert the category name to a dictionary lookup index and generate an
    embedding for each index.
  - Categories that appear less than 5 times in the training dataset are
    treated as the "unknown" category. The "unknown" category gets its own
    special lookup index and resulting embedding.

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) Descriptor deprecated 

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs_Transformation_CategoricalTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) GetColumnName 

func (x *AutoMlForecastingInputs_Transformation_CategoricalTransformation) GetColumnName() string

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) ProtoMessage 

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) ProtoMessage()

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) ProtoReflect 

func (x *AutoMlForecastingInputs_Transformation_CategoricalTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) Reset 

func (x *AutoMlForecastingInputs_Transformation_CategoricalTransformation) Reset()

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) String 

func (x *AutoMlForecastingInputs_Transformation_CategoricalTransformation) String() string

type AutoMlForecastingInputs_Transformation_Numeric 

type AutoMlForecastingInputs_Transformation_Numeric struct {
	Numeric *AutoMlForecastingInputs_Transformation_NumericTransformation `protobuf:"bytes,2,opt,name=numeric,proto3,oneof"`
}

type AutoMlForecastingInputs_Transformation_NumericArrayTransformation 

type AutoMlForecastingInputs_Transformation_NumericArrayTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// If invalid values is allowed, the training pipeline will create a
	// boolean feature that indicated whether the value is valid.
	// Otherwise, the training pipeline will discard the input row from
	// trainining data.
	InvalidValuesAllowed bool `protobuf:"varint,2,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
	// contains filtered or unexported fields
}

Treats the column as numerical array and performs following transformation functions.

  • All transformations for Numerical types applied to the average of the all elements.
  • The average of empty arrays is treated as zero.

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) Descriptor deprecated 

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs_Transformation_NumericArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) GetColumnName 

func (x *AutoMlForecastingInputs_Transformation_NumericArrayTransformation) GetColumnName() string

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) GetInvalidValuesAllowed 

func (x *AutoMlForecastingInputs_Transformation_NumericArrayTransformation) GetInvalidValuesAllowed() bool

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) ProtoMessage 

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) ProtoMessage()

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) ProtoReflect 

func (x *AutoMlForecastingInputs_Transformation_NumericArrayTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) Reset 

func (x *AutoMlForecastingInputs_Transformation_NumericArrayTransformation) Reset()

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) String 

func (x *AutoMlForecastingInputs_Transformation_NumericArrayTransformation) String() string

type AutoMlForecastingInputs_Transformation_NumericTransformation 

type AutoMlForecastingInputs_Transformation_NumericTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// If invalid values is allowed, the training pipeline will create a
	// boolean feature that indicated whether the value is valid.
	// Otherwise, the training pipeline will discard the input row from
	// trainining data.
	InvalidValuesAllowed bool `protobuf:"varint,2,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
	// contains filtered or unexported fields
}

Training pipeline will perform following transformation functions.

  • The value converted to float32.
  • The z_score of the value.
  • log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
  • z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
  • A boolean value that indicates whether the value is valid.

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) Descriptor deprecated 

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs_Transformation_NumericTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) GetColumnName 

func (x *AutoMlForecastingInputs_Transformation_NumericTransformation) GetColumnName() string

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) GetInvalidValuesAllowed 

func (x *AutoMlForecastingInputs_Transformation_NumericTransformation) GetInvalidValuesAllowed() bool

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) ProtoMessage 

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) ProtoMessage()

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) ProtoReflect 

func (x *AutoMlForecastingInputs_Transformation_NumericTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) Reset 

func (x *AutoMlForecastingInputs_Transformation_NumericTransformation) Reset()

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) String 

func (x *AutoMlForecastingInputs_Transformation_NumericTransformation) String() string

type AutoMlForecastingInputs_Transformation_RepeatedCategorical 

type AutoMlForecastingInputs_Transformation_RepeatedCategorical struct {
	RepeatedCategorical *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation `protobuf:"bytes,7,opt,name=repeated_categorical,json=repeatedCategorical,proto3,oneof"`
}

type AutoMlForecastingInputs_Transformation_RepeatedNumeric 

type AutoMlForecastingInputs_Transformation_RepeatedNumeric struct {
	RepeatedNumeric *AutoMlForecastingInputs_Transformation_NumericArrayTransformation `protobuf:"bytes,6,opt,name=repeated_numeric,json=repeatedNumeric,proto3,oneof"`
}

type AutoMlForecastingInputs_Transformation_RepeatedText 

type AutoMlForecastingInputs_Transformation_RepeatedText struct {
	RepeatedText *AutoMlForecastingInputs_Transformation_TextArrayTransformation `protobuf:"bytes,8,opt,name=repeated_text,json=repeatedText,proto3,oneof"`
}

type AutoMlForecastingInputs_Transformation_Text 

type AutoMlForecastingInputs_Transformation_Text struct {
	Text *AutoMlForecastingInputs_Transformation_TextTransformation `protobuf:"bytes,5,opt,name=text,proto3,oneof"`
}

type AutoMlForecastingInputs_Transformation_TextArrayTransformation 

type AutoMlForecastingInputs_Transformation_TextArrayTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// contains filtered or unexported fields
}

Treats the column as text array and performs following transformation functions. * Concatenate all text values in the array into a single text value using 

a space (" ") as a delimiter, and then treat the result as a single
text value. Apply the transformations for Text columns.

* Empty arrays treated as an empty text.

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) Descriptor deprecated 

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs_Transformation_TextArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) GetColumnName 

func (x *AutoMlForecastingInputs_Transformation_TextArrayTransformation) GetColumnName() string

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) ProtoMessage 

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) ProtoMessage()

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) ProtoReflect 

func (x *AutoMlForecastingInputs_Transformation_TextArrayTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) Reset 

func (x *AutoMlForecastingInputs_Transformation_TextArrayTransformation) Reset()

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) String 

func (x *AutoMlForecastingInputs_Transformation_TextArrayTransformation) String() string

type AutoMlForecastingInputs_Transformation_TextTransformation 

type AutoMlForecastingInputs_Transformation_TextTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// contains filtered or unexported fields
}

Training pipeline will perform following transformation functions. * The text as is--no change to case, punctuation, spelling, tense, and so 

on.

* Tokenize text to words. Convert each words to a dictionary lookup index 

and generate an embedding for each index. Combine the embedding of all
elements into a single embedding using the mean.

* Tokenization is based on unicode script boundaries. * Missing values get their own lookup index and resulting embedding. * Stop-words receive no special treatment and are not removed.

func (*AutoMlForecastingInputs_Transformation_TextTransformation) Descriptor deprecated 

func (*AutoMlForecastingInputs_Transformation_TextTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs_Transformation_TextTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_TextTransformation) GetColumnName 

func (x *AutoMlForecastingInputs_Transformation_TextTransformation) GetColumnName() string

func (*AutoMlForecastingInputs_Transformation_TextTransformation) ProtoMessage 

func (*AutoMlForecastingInputs_Transformation_TextTransformation) ProtoMessage()

func (*AutoMlForecastingInputs_Transformation_TextTransformation) ProtoReflect 

func (x *AutoMlForecastingInputs_Transformation_TextTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs_Transformation_TextTransformation) Reset 

func (x *AutoMlForecastingInputs_Transformation_TextTransformation) Reset()

func (*AutoMlForecastingInputs_Transformation_TextTransformation) String 

func (x *AutoMlForecastingInputs_Transformation_TextTransformation) String() string

type AutoMlForecastingInputs_Transformation_Timestamp 

type AutoMlForecastingInputs_Transformation_Timestamp struct {
	Timestamp *AutoMlForecastingInputs_Transformation_TimestampTransformation `protobuf:"bytes,4,opt,name=timestamp,proto3,oneof"`
}

type AutoMlForecastingInputs_Transformation_TimestampTransformation 

type AutoMlForecastingInputs_Transformation_TimestampTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// The format in which that time field is expressed. The time_format must
	// either be one of:
	// * `unix-seconds`
	// * `unix-milliseconds`
	// * `unix-microseconds`
	// * `unix-nanoseconds`
	// (for respectively number of seconds, milliseconds, microseconds and
	// nanoseconds since start of the Unix epoch);
	// or be written in `strftime` syntax. If time_format is not set, then the
	// default format is RFC 3339 `date-time` format, where
	// `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z)
	TimeFormat string `protobuf:"bytes,2,opt,name=time_format,json=timeFormat,proto3" json:"time_format,omitempty"`
	// If invalid values is allowed, the training pipeline will create a
	// boolean feature that indicated whether the value is valid.
	// Otherwise, the training pipeline will discard the input row from
	// trainining data.
	InvalidValuesAllowed bool `protobuf:"varint,3,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
	// contains filtered or unexported fields
}

Training pipeline will perform following transformation functions.

  • Apply the transformation functions for Numerical columns.
  • Determine the year, month, day,and weekday. Treat each value from the
  • timestamp as a Categorical column.
  • Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) Descriptor deprecated 

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingInputs_Transformation_TimestampTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) GetColumnName 

func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) GetColumnName() string

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) GetInvalidValuesAllowed 

func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) GetInvalidValuesAllowed() bool

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) GetTimeFormat 

func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) GetTimeFormat() string

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) ProtoMessage 

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) ProtoMessage()

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) ProtoReflect 

func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) Reset 

func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) Reset()

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) String 

func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) String() string

type AutoMlForecastingMetadata 

type AutoMlForecastingMetadata struct {

	// Output only. The actual training cost of the model, expressed in milli
	// node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
	// to not exceed the train budget.
	TrainCostMilliNodeHours int64 `` /* 137-byte string literal not displayed */
	// contains filtered or unexported fields
}

Model metadata specific to AutoML Forecasting.

func (*AutoMlForecastingMetadata) Descriptor deprecated 

func (*AutoMlForecastingMetadata) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlForecastingMetadata.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingMetadata) GetTrainCostMilliNodeHours 

func (x *AutoMlForecastingMetadata) GetTrainCostMilliNodeHours() int64

func (*AutoMlForecastingMetadata) ProtoMessage 

func (*AutoMlForecastingMetadata) ProtoMessage()

func (*AutoMlForecastingMetadata) ProtoReflect 

func (x *AutoMlForecastingMetadata) ProtoReflect() protoreflect.Message

func (*AutoMlForecastingMetadata) Reset 

func (x *AutoMlForecastingMetadata) Reset()

func (*AutoMlForecastingMetadata) String 

func (x *AutoMlForecastingMetadata) String() string

type AutoMlImageClassification 

type AutoMlImageClassification struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlImageClassificationInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// The metadata information.
	Metadata *AutoMlImageClassificationMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Image Classification Model.

func (*AutoMlImageClassification) Descriptor deprecated 

func (*AutoMlImageClassification) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlImageClassification.ProtoReflect.Descriptor instead.

func (*AutoMlImageClassification) GetInputs 

func (x *AutoMlImageClassification) GetInputs() *AutoMlImageClassificationInputs

func (*AutoMlImageClassification) GetMetadata 

func (x *AutoMlImageClassification) GetMetadata() *AutoMlImageClassificationMetadata

func (*AutoMlImageClassification) ProtoMessage 

func (*AutoMlImageClassification) ProtoMessage()

func (*AutoMlImageClassification) ProtoReflect 

func (x *AutoMlImageClassification) ProtoReflect() protoreflect.Message

func (*AutoMlImageClassification) Reset 

func (x *AutoMlImageClassification) Reset()

func (*AutoMlImageClassification) String 

func (x *AutoMlImageClassification) String() string

type AutoMlImageClassificationInputs 

type AutoMlImageClassificationInputs struct {
	ModelType AutoMlImageClassificationInputs_ModelType `` /* 193-byte string literal not displayed */
	// The ID of the `base` model. If it is specified, the new model will be
	// trained based on the `base` model. Otherwise, the new model will be
	// trained from scratch. The `base` model must be in the same
	// Project and Location as the new Model to train, and have the same
	// modelType.
	BaseModelId string `protobuf:"bytes,2,opt,name=base_model_id,json=baseModelId,proto3" json:"base_model_id,omitempty"`
	// The training budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour. The actual
	// metadata.costMilliNodeHours will be equal or less than this value.
	// If further model training ceases to provide any improvements, it will
	// stop without using the full budget and the metadata.successfulStopReason
	// will be `model-converged`.
	// Note, node_hour  = actual_hour * number_of_nodes_involved.
	// For modelType `cloud`(default), the budget must be between 8,000
	// and 800,000 milli node hours, inclusive. The default value is 192,000
	// which represents one day in wall time, considering 8 nodes are used.
	// For model types `mobile-tf-low-latency-1`, `mobile-tf-versatile-1`,
	// `mobile-tf-high-accuracy-1`, the training budget must be between
	// 1,000 and 100,000 milli node hours, inclusive.
	// The default value is 24,000 which represents one day in wall time on a
	// single node that is used.
	BudgetMilliNodeHours int64 `` /* 126-byte string literal not displayed */
	// Use the entire training budget. This disables the early stopping feature.
	// When false the early stopping feature is enabled, which means that
	// AutoML Image Classification might stop training before the entire
	// training budget has been used.
	DisableEarlyStopping bool `protobuf:"varint,4,opt,name=disable_early_stopping,json=disableEarlyStopping,proto3" json:"disable_early_stopping,omitempty"`
	// If false, a single-label (multi-class) Model will be trained (i.e.
	// assuming that for each image just up to one annotation may be
	// applicable). If true, a multi-label Model will be trained (i.e.
	// assuming that for each image multiple annotations may be applicable).
	MultiLabel bool `protobuf:"varint,5,opt,name=multi_label,json=multiLabel,proto3" json:"multi_label,omitempty"`
	// contains filtered or unexported fields
}

func (*AutoMlImageClassificationInputs) Descriptor deprecated 

func (*AutoMlImageClassificationInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlImageClassificationInputs.ProtoReflect.Descriptor instead.

func (*AutoMlImageClassificationInputs) GetBaseModelId 

func (x *AutoMlImageClassificationInputs) GetBaseModelId() string

func (*AutoMlImageClassificationInputs) GetBudgetMilliNodeHours 

func (x *AutoMlImageClassificationInputs) GetBudgetMilliNodeHours() int64

func (*AutoMlImageClassificationInputs) GetDisableEarlyStopping 

func (x *AutoMlImageClassificationInputs) GetDisableEarlyStopping() bool

func (*AutoMlImageClassificationInputs) GetModelType 

func (x *AutoMlImageClassificationInputs) GetModelType() AutoMlImageClassificationInputs_ModelType

func (*AutoMlImageClassificationInputs) GetMultiLabel 

func (x *AutoMlImageClassificationInputs) GetMultiLabel() bool

func (*AutoMlImageClassificationInputs) ProtoMessage 

func (*AutoMlImageClassificationInputs) ProtoMessage()

func (*AutoMlImageClassificationInputs) ProtoReflect 

func (x *AutoMlImageClassificationInputs) ProtoReflect() protoreflect.Message

func (*AutoMlImageClassificationInputs) Reset 

func (x *AutoMlImageClassificationInputs) Reset()

func (*AutoMlImageClassificationInputs) String 

func (x *AutoMlImageClassificationInputs) String() string

type AutoMlImageClassificationInputs_ModelType 

type AutoMlImageClassificationInputs_ModelType int32
const (
	// Should not be set.
	AutoMlImageClassificationInputs_MODEL_TYPE_UNSPECIFIED AutoMlImageClassificationInputs_ModelType = 0
	// A Model best tailored to be used within Google Cloud, and which cannot
	// be exported.
	// Default.
	AutoMlImageClassificationInputs_CLOUD AutoMlImageClassificationInputs_ModelType = 1
	// A model that, in addition to being available within Google
	// Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
	// or Core ML model and used on a mobile or edge device afterwards.
	// Expected to have low latency, but may have lower prediction
	// quality than other mobile models.
	AutoMlImageClassificationInputs_MOBILE_TF_LOW_LATENCY_1 AutoMlImageClassificationInputs_ModelType = 2
	// A model that, in addition to being available within Google
	// Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
	// or Core ML model and used on a mobile or edge device with afterwards.
	AutoMlImageClassificationInputs_MOBILE_TF_VERSATILE_1 AutoMlImageClassificationInputs_ModelType = 3
	// A model that, in addition to being available within Google
	// Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
	// or Core ML model and used on a mobile or edge device afterwards.
	// Expected to have a higher latency, but should also have a higher
	// prediction quality than other mobile models.
	AutoMlImageClassificationInputs_MOBILE_TF_HIGH_ACCURACY_1 AutoMlImageClassificationInputs_ModelType = 4
)

func (AutoMlImageClassificationInputs_ModelType) Descriptor 

func (AutoMlImageClassificationInputs_ModelType) Descriptor() protoreflect.EnumDescriptor

func (AutoMlImageClassificationInputs_ModelType) Enum 

func (x AutoMlImageClassificationInputs_ModelType) Enum() *AutoMlImageClassificationInputs_ModelType

func (AutoMlImageClassificationInputs_ModelType) EnumDescriptor deprecated 

func (AutoMlImageClassificationInputs_ModelType) EnumDescriptor() ([]byte, []int)

Deprecated: Use AutoMlImageClassificationInputs_ModelType.Descriptor instead.

func (AutoMlImageClassificationInputs_ModelType) Number 

func (x AutoMlImageClassificationInputs_ModelType) Number() protoreflect.EnumNumber

func (AutoMlImageClassificationInputs_ModelType) String 

func (x AutoMlImageClassificationInputs_ModelType) String() string

func (AutoMlImageClassificationInputs_ModelType) Type 

func (AutoMlImageClassificationInputs_ModelType) Type() protoreflect.EnumType

type AutoMlImageClassificationMetadata 

type AutoMlImageClassificationMetadata struct {

	// The actual training cost of creating this model, expressed in
	// milli node hours, i.e. 1,000 value in this field means 1 node hour.
	// Guaranteed to not exceed inputs.budgetMilliNodeHours.
	CostMilliNodeHours int64 `protobuf:"varint,1,opt,name=cost_milli_node_hours,json=costMilliNodeHours,proto3" json:"cost_milli_node_hours,omitempty"`
	// For successful job completions, this is the reason why the job has
	// finished.
	SuccessfulStopReason AutoMlImageClassificationMetadata_SuccessfulStopReason `` /* 241-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlImageClassificationMetadata) Descriptor deprecated 

func (*AutoMlImageClassificationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlImageClassificationMetadata.ProtoReflect.Descriptor instead.

func (*AutoMlImageClassificationMetadata) GetCostMilliNodeHours 

func (x *AutoMlImageClassificationMetadata) GetCostMilliNodeHours() int64

func (*AutoMlImageClassificationMetadata) GetSuccessfulStopReason 

func (x *AutoMlImageClassificationMetadata) GetSuccessfulStopReason() AutoMlImageClassificationMetadata_SuccessfulStopReason

func (*AutoMlImageClassificationMetadata) ProtoMessage 

func (*AutoMlImageClassificationMetadata) ProtoMessage()

func (*AutoMlImageClassificationMetadata) ProtoReflect 

func (x *AutoMlImageClassificationMetadata) ProtoReflect() protoreflect.Message

func (*AutoMlImageClassificationMetadata) Reset 

func (x *AutoMlImageClassificationMetadata) Reset()

func (*AutoMlImageClassificationMetadata) String 

func (x *AutoMlImageClassificationMetadata) String() string

type AutoMlImageClassificationMetadata_SuccessfulStopReason 

type AutoMlImageClassificationMetadata_SuccessfulStopReason int32
const (
	// Should not be set.
	AutoMlImageClassificationMetadata_SUCCESSFUL_STOP_REASON_UNSPECIFIED AutoMlImageClassificationMetadata_SuccessfulStopReason = 0
	// The inputs.budgetMilliNodeHours had been reached.
	AutoMlImageClassificationMetadata_BUDGET_REACHED AutoMlImageClassificationMetadata_SuccessfulStopReason = 1
	// Further training of the Model ceased to increase its quality, since it
	// already has converged.
	AutoMlImageClassificationMetadata_MODEL_CONVERGED AutoMlImageClassificationMetadata_SuccessfulStopReason = 2
)

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Descriptor 

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Descriptor() protoreflect.EnumDescriptor

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Enum 

func (x AutoMlImageClassificationMetadata_SuccessfulStopReason) Enum() *AutoMlImageClassificationMetadata_SuccessfulStopReason

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) EnumDescriptor deprecated 

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) EnumDescriptor() ([]byte, []int)

Deprecated: Use AutoMlImageClassificationMetadata_SuccessfulStopReason.Descriptor instead.

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Number 

func (x AutoMlImageClassificationMetadata_SuccessfulStopReason) Number() protoreflect.EnumNumber

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) String 

func (x AutoMlImageClassificationMetadata_SuccessfulStopReason) String() string

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Type 

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Type() protoreflect.EnumType

type AutoMlImageObjectDetection 

type AutoMlImageObjectDetection struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlImageObjectDetectionInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// The metadata information
	Metadata *AutoMlImageObjectDetectionMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Image Object Detection Model.

func (*AutoMlImageObjectDetection) Descriptor deprecated 

func (*AutoMlImageObjectDetection) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlImageObjectDetection.ProtoReflect.Descriptor instead.

func (*AutoMlImageObjectDetection) GetInputs 

func (x *AutoMlImageObjectDetection) GetInputs() *AutoMlImageObjectDetectionInputs

func (*AutoMlImageObjectDetection) GetMetadata 

func (x *AutoMlImageObjectDetection) GetMetadata() *AutoMlImageObjectDetectionMetadata

func (*AutoMlImageObjectDetection) ProtoMessage 

func (*AutoMlImageObjectDetection) ProtoMessage()

func (*AutoMlImageObjectDetection) ProtoReflect 

func (x *AutoMlImageObjectDetection) ProtoReflect() protoreflect.Message

func (*AutoMlImageObjectDetection) Reset 

func (x *AutoMlImageObjectDetection) Reset()

func (*AutoMlImageObjectDetection) String 

func (x *AutoMlImageObjectDetection) String() string

type AutoMlImageObjectDetectionInputs 

type AutoMlImageObjectDetectionInputs struct {
	ModelType AutoMlImageObjectDetectionInputs_ModelType `` /* 194-byte string literal not displayed */
	// The training budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour. The actual
	// metadata.costMilliNodeHours will be equal or less than this value.
	// If further model training ceases to provide any improvements, it will
	// stop without using the full budget and the metadata.successfulStopReason
	// will be `model-converged`.
	// Note, node_hour  = actual_hour * number_of_nodes_involved.
	// For modelType `cloud`(default), the budget must be between 20,000
	// and 900,000 milli node hours, inclusive. The default value is 216,000
	// which represents one day in wall time, considering 9 nodes are used.
	// For model types `mobile-tf-low-latency-1`, `mobile-tf-versatile-1`,
	// `mobile-tf-high-accuracy-1`
	// the training budget must be between 1,000 and 100,000 milli node hours,
	// inclusive. The default value is 24,000 which represents one day in
	// wall time on a single node that is used.
	BudgetMilliNodeHours int64 `` /* 126-byte string literal not displayed */
	// Use the entire training budget. This disables the early stopping feature.
	// When false the early stopping feature is enabled, which means that AutoML
	// Image Object Detection might stop training before the entire training
	// budget has been used.
	DisableEarlyStopping bool `protobuf:"varint,3,opt,name=disable_early_stopping,json=disableEarlyStopping,proto3" json:"disable_early_stopping,omitempty"`
	// contains filtered or unexported fields
}

func (*AutoMlImageObjectDetectionInputs) Descriptor deprecated 

func (*AutoMlImageObjectDetectionInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlImageObjectDetectionInputs.ProtoReflect.Descriptor instead.

func (*AutoMlImageObjectDetectionInputs) GetBudgetMilliNodeHours 

func (x *AutoMlImageObjectDetectionInputs) GetBudgetMilliNodeHours() int64

func (*AutoMlImageObjectDetectionInputs) GetDisableEarlyStopping 

func (x *AutoMlImageObjectDetectionInputs) GetDisableEarlyStopping() bool

func (*AutoMlImageObjectDetectionInputs) GetModelType 

func (x *AutoMlImageObjectDetectionInputs) GetModelType() AutoMlImageObjectDetectionInputs_ModelType

func (*AutoMlImageObjectDetectionInputs) ProtoMessage 

func (*AutoMlImageObjectDetectionInputs) ProtoMessage()

func (*AutoMlImageObjectDetectionInputs) ProtoReflect 

func (x *AutoMlImageObjectDetectionInputs) ProtoReflect() protoreflect.Message

func (*AutoMlImageObjectDetectionInputs) Reset 

func (x *AutoMlImageObjectDetectionInputs) Reset()

func (*AutoMlImageObjectDetectionInputs) String 

func (x *AutoMlImageObjectDetectionInputs) String() string

type AutoMlImageObjectDetectionInputs_ModelType 

type AutoMlImageObjectDetectionInputs_ModelType int32
const (
	// Should not be set.
	AutoMlImageObjectDetectionInputs_MODEL_TYPE_UNSPECIFIED AutoMlImageObjectDetectionInputs_ModelType = 0
	// A model best tailored to be used within Google Cloud, and which cannot
	// be exported. Expected to have a higher latency, but should also have a
	// higher prediction quality than other cloud models.
	AutoMlImageObjectDetectionInputs_CLOUD_HIGH_ACCURACY_1 AutoMlImageObjectDetectionInputs_ModelType = 1
	// A model best tailored to be used within Google Cloud, and which cannot
	// be exported. Expected to have a low latency, but may have lower
	// prediction quality than other cloud models.
	AutoMlImageObjectDetectionInputs_CLOUD_LOW_LATENCY_1 AutoMlImageObjectDetectionInputs_ModelType = 2
	// A model that, in addition to being available within Google
	// Cloud can also be exported (see ModelService.ExportModel) and
	// used on a mobile or edge device with TensorFlow afterwards.
	// Expected to have low latency, but may have lower prediction
	// quality than other mobile models.
	AutoMlImageObjectDetectionInputs_MOBILE_TF_LOW_LATENCY_1 AutoMlImageObjectDetectionInputs_ModelType = 3
	// A model that, in addition to being available within Google
	// Cloud can also be exported (see ModelService.ExportModel) and
	// used on a mobile or edge device with TensorFlow afterwards.
	AutoMlImageObjectDetectionInputs_MOBILE_TF_VERSATILE_1 AutoMlImageObjectDetectionInputs_ModelType = 4
	// A model that, in addition to being available within Google
	// Cloud, can also be exported (see ModelService.ExportModel) and
	// used on a mobile or edge device with TensorFlow afterwards.
	// Expected to have a higher latency, but should also have a higher
	// prediction quality than other mobile models.
	AutoMlImageObjectDetectionInputs_MOBILE_TF_HIGH_ACCURACY_1 AutoMlImageObjectDetectionInputs_ModelType = 5
)

func (AutoMlImageObjectDetectionInputs_ModelType) Descriptor 

func (AutoMlImageObjectDetectionInputs_ModelType) Descriptor() protoreflect.EnumDescriptor

func (AutoMlImageObjectDetectionInputs_ModelType) Enum 

func (x AutoMlImageObjectDetectionInputs_ModelType) Enum() *AutoMlImageObjectDetectionInputs_ModelType

func (AutoMlImageObjectDetectionInputs_ModelType) EnumDescriptor deprecated 

func (AutoMlImageObjectDetectionInputs_ModelType) EnumDescriptor() ([]byte, []int)

Deprecated: Use AutoMlImageObjectDetectionInputs_ModelType.Descriptor instead.

func (AutoMlImageObjectDetectionInputs_ModelType) Number 

func (x AutoMlImageObjectDetectionInputs_ModelType) Number() protoreflect.EnumNumber

func (AutoMlImageObjectDetectionInputs_ModelType) String 

func (x AutoMlImageObjectDetectionInputs_ModelType) String() string

func (AutoMlImageObjectDetectionInputs_ModelType) Type 

func (AutoMlImageObjectDetectionInputs_ModelType) Type() protoreflect.EnumType

type AutoMlImageObjectDetectionMetadata 

type AutoMlImageObjectDetectionMetadata struct {

	// The actual training cost of creating this model, expressed in
	// milli node hours, i.e. 1,000 value in this field means 1 node hour.
	// Guaranteed to not exceed inputs.budgetMilliNodeHours.
	CostMilliNodeHours int64 `protobuf:"varint,1,opt,name=cost_milli_node_hours,json=costMilliNodeHours,proto3" json:"cost_milli_node_hours,omitempty"`
	// For successful job completions, this is the reason why the job has
	// finished.
	SuccessfulStopReason AutoMlImageObjectDetectionMetadata_SuccessfulStopReason `` /* 242-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlImageObjectDetectionMetadata) Descriptor deprecated 

func (*AutoMlImageObjectDetectionMetadata) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlImageObjectDetectionMetadata.ProtoReflect.Descriptor instead.

func (*AutoMlImageObjectDetectionMetadata) GetCostMilliNodeHours 

func (x *AutoMlImageObjectDetectionMetadata) GetCostMilliNodeHours() int64

func (*AutoMlImageObjectDetectionMetadata) GetSuccessfulStopReason 

func (x *AutoMlImageObjectDetectionMetadata) GetSuccessfulStopReason() AutoMlImageObjectDetectionMetadata_SuccessfulStopReason

func (*AutoMlImageObjectDetectionMetadata) ProtoMessage 

func (*AutoMlImageObjectDetectionMetadata) ProtoMessage()

func (*AutoMlImageObjectDetectionMetadata) ProtoReflect 

func (x *AutoMlImageObjectDetectionMetadata) ProtoReflect() protoreflect.Message

func (*AutoMlImageObjectDetectionMetadata) Reset 

func (x *AutoMlImageObjectDetectionMetadata) Reset()

func (*AutoMlImageObjectDetectionMetadata) String 

func (x *AutoMlImageObjectDetectionMetadata) String() string

type AutoMlImageObjectDetectionMetadata_SuccessfulStopReason 

type AutoMlImageObjectDetectionMetadata_SuccessfulStopReason int32
const (
	// Should not be set.
	AutoMlImageObjectDetectionMetadata_SUCCESSFUL_STOP_REASON_UNSPECIFIED AutoMlImageObjectDetectionMetadata_SuccessfulStopReason = 0
	// The inputs.budgetMilliNodeHours had been reached.
	AutoMlImageObjectDetectionMetadata_BUDGET_REACHED AutoMlImageObjectDetectionMetadata_SuccessfulStopReason = 1
	// Further training of the Model ceased to increase its quality, since it
	// already has converged.
	AutoMlImageObjectDetectionMetadata_MODEL_CONVERGED AutoMlImageObjectDetectionMetadata_SuccessfulStopReason = 2
)

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Descriptor 

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Descriptor() protoreflect.EnumDescriptor

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Enum 

func (x AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Enum() *AutoMlImageObjectDetectionMetadata_SuccessfulStopReason

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) EnumDescriptor deprecated 

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) EnumDescriptor() ([]byte, []int)

Deprecated: Use AutoMlImageObjectDetectionMetadata_SuccessfulStopReason.Descriptor instead.

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Number 

func (x AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Number() protoreflect.EnumNumber

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) String 

func (x AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) String() string

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Type 

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Type() protoreflect.EnumType

type AutoMlImageSegmentation 

type AutoMlImageSegmentation struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlImageSegmentationInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// The metadata information.
	Metadata *AutoMlImageSegmentationMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Image Segmentation Model.

func (*AutoMlImageSegmentation) Descriptor deprecated 

func (*AutoMlImageSegmentation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlImageSegmentation.ProtoReflect.Descriptor instead.

func (*AutoMlImageSegmentation) GetInputs 

func (x *AutoMlImageSegmentation) GetInputs() *AutoMlImageSegmentationInputs

func (*AutoMlImageSegmentation) GetMetadata 

func (x *AutoMlImageSegmentation) GetMetadata() *AutoMlImageSegmentationMetadata

func (*AutoMlImageSegmentation) ProtoMessage 

func (*AutoMlImageSegmentation) ProtoMessage()

func (*AutoMlImageSegmentation) ProtoReflect 

func (x *AutoMlImageSegmentation) ProtoReflect() protoreflect.Message

func (*AutoMlImageSegmentation) Reset 

func (x *AutoMlImageSegmentation) Reset()

func (*AutoMlImageSegmentation) String 

func (x *AutoMlImageSegmentation) String() string

type AutoMlImageSegmentationInputs 

type AutoMlImageSegmentationInputs struct {
	ModelType AutoMlImageSegmentationInputs_ModelType `` /* 191-byte string literal not displayed */
	// The training budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour. The actual
	// metadata.costMilliNodeHours will be equal or less than this value.
	// If further model training ceases to provide any improvements, it will
	// stop without using the full budget and the metadata.successfulStopReason
	// will be `model-converged`.
	// Note, node_hour  = actual_hour * number_of_nodes_involved. Or
	// actaul_wall_clock_hours = train_budget_milli_node_hours /
	//
	//	(number_of_nodes_involved * 1000)
	//
	// For modelType `cloud-high-accuracy-1`(default), the budget must be between
	// 20,000 and 2,000,000 milli node hours, inclusive. The default value is
	// 192,000 which represents one day in wall time
	// (1000 milli * 24 hours * 8 nodes).
	BudgetMilliNodeHours int64 `` /* 126-byte string literal not displayed */
	// The ID of the `base` model. If it is specified, the new model will be
	// trained based on the `base` model. Otherwise, the new model will be
	// trained from scratch. The `base` model must be in the same
	// Project and Location as the new Model to train, and have the same
	// modelType.
	BaseModelId string `protobuf:"bytes,3,opt,name=base_model_id,json=baseModelId,proto3" json:"base_model_id,omitempty"`
	// contains filtered or unexported fields
}

func (*AutoMlImageSegmentationInputs) Descriptor deprecated 

func (*AutoMlImageSegmentationInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlImageSegmentationInputs.ProtoReflect.Descriptor instead.

func (*AutoMlImageSegmentationInputs) GetBaseModelId 

func (x *AutoMlImageSegmentationInputs) GetBaseModelId() string

func (*AutoMlImageSegmentationInputs) GetBudgetMilliNodeHours 

func (x *AutoMlImageSegmentationInputs) GetBudgetMilliNodeHours() int64

func (*AutoMlImageSegmentationInputs) GetModelType 

func (x *AutoMlImageSegmentationInputs) GetModelType() AutoMlImageSegmentationInputs_ModelType

func (*AutoMlImageSegmentationInputs) ProtoMessage 

func (*AutoMlImageSegmentationInputs) ProtoMessage()

func (*AutoMlImageSegmentationInputs) ProtoReflect 

func (x *AutoMlImageSegmentationInputs) ProtoReflect() protoreflect.Message

func (*AutoMlImageSegmentationInputs) Reset 

func (x *AutoMlImageSegmentationInputs) Reset()

func (*AutoMlImageSegmentationInputs) String 

func (x *AutoMlImageSegmentationInputs) String() string

type AutoMlImageSegmentationInputs_ModelType 

type AutoMlImageSegmentationInputs_ModelType int32
const (
	// Should not be set.
	AutoMlImageSegmentationInputs_MODEL_TYPE_UNSPECIFIED AutoMlImageSegmentationInputs_ModelType = 0
	// A model to be used via prediction calls to uCAIP API. Expected
	// to have a higher latency, but should also have a higher prediction
	// quality than other models.
	AutoMlImageSegmentationInputs_CLOUD_HIGH_ACCURACY_1 AutoMlImageSegmentationInputs_ModelType = 1
	// A model to be used via prediction calls to uCAIP API. Expected
	// to have a lower latency but relatively lower prediction quality.
	AutoMlImageSegmentationInputs_CLOUD_LOW_ACCURACY_1 AutoMlImageSegmentationInputs_ModelType = 2
	// A model that, in addition to being available within Google
	// Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
	// model and used on a mobile or edge device afterwards.
	// Expected to have low latency, but may have lower prediction
	// quality than other mobile models.
	AutoMlImageSegmentationInputs_MOBILE_TF_LOW_LATENCY_1 AutoMlImageSegmentationInputs_ModelType = 3
)

func (AutoMlImageSegmentationInputs_ModelType) Descriptor 

func (AutoMlImageSegmentationInputs_ModelType) Descriptor() protoreflect.EnumDescriptor

func (AutoMlImageSegmentationInputs_ModelType) Enum 

func (x AutoMlImageSegmentationInputs_ModelType) Enum() *AutoMlImageSegmentationInputs_ModelType

func (AutoMlImageSegmentationInputs_ModelType) EnumDescriptor deprecated 

func (AutoMlImageSegmentationInputs_ModelType) EnumDescriptor() ([]byte, []int)

Deprecated: Use AutoMlImageSegmentationInputs_ModelType.Descriptor instead.

func (AutoMlImageSegmentationInputs_ModelType) Number 

func (x AutoMlImageSegmentationInputs_ModelType) Number() protoreflect.EnumNumber

func (AutoMlImageSegmentationInputs_ModelType) String 

func (x AutoMlImageSegmentationInputs_ModelType) String() string

func (AutoMlImageSegmentationInputs_ModelType) Type 

func (AutoMlImageSegmentationInputs_ModelType) Type() protoreflect.EnumType

type AutoMlImageSegmentationMetadata 

type AutoMlImageSegmentationMetadata struct {

	// The actual training cost of creating this model, expressed in
	// milli node hours, i.e. 1,000 value in this field means 1 node hour.
	// Guaranteed to not exceed inputs.budgetMilliNodeHours.
	CostMilliNodeHours int64 `protobuf:"varint,1,opt,name=cost_milli_node_hours,json=costMilliNodeHours,proto3" json:"cost_milli_node_hours,omitempty"`
	// For successful job completions, this is the reason why the job has
	// finished.
	SuccessfulStopReason AutoMlImageSegmentationMetadata_SuccessfulStopReason `` /* 239-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlImageSegmentationMetadata) Descriptor deprecated 

func (*AutoMlImageSegmentationMetadata) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlImageSegmentationMetadata.ProtoReflect.Descriptor instead.

func (*AutoMlImageSegmentationMetadata) GetCostMilliNodeHours 

func (x *AutoMlImageSegmentationMetadata) GetCostMilliNodeHours() int64

func (*AutoMlImageSegmentationMetadata) GetSuccessfulStopReason 

func (x *AutoMlImageSegmentationMetadata) GetSuccessfulStopReason() AutoMlImageSegmentationMetadata_SuccessfulStopReason

func (*AutoMlImageSegmentationMetadata) ProtoMessage 

func (*AutoMlImageSegmentationMetadata) ProtoMessage()

func (*AutoMlImageSegmentationMetadata) ProtoReflect 

func (x *AutoMlImageSegmentationMetadata) ProtoReflect() protoreflect.Message

func (*AutoMlImageSegmentationMetadata) Reset 

func (x *AutoMlImageSegmentationMetadata) Reset()

func (*AutoMlImageSegmentationMetadata) String 

func (x *AutoMlImageSegmentationMetadata) String() string

type AutoMlImageSegmentationMetadata_SuccessfulStopReason 

type AutoMlImageSegmentationMetadata_SuccessfulStopReason int32
const (
	// Should not be set.
	AutoMlImageSegmentationMetadata_SUCCESSFUL_STOP_REASON_UNSPECIFIED AutoMlImageSegmentationMetadata_SuccessfulStopReason = 0
	// The inputs.budgetMilliNodeHours had been reached.
	AutoMlImageSegmentationMetadata_BUDGET_REACHED AutoMlImageSegmentationMetadata_SuccessfulStopReason = 1
	// Further training of the Model ceased to increase its quality, since it
	// already has converged.
	AutoMlImageSegmentationMetadata_MODEL_CONVERGED AutoMlImageSegmentationMetadata_SuccessfulStopReason = 2
)

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) Descriptor 

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) Descriptor() protoreflect.EnumDescriptor

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) Enum 

func (x AutoMlImageSegmentationMetadata_SuccessfulStopReason) Enum() *AutoMlImageSegmentationMetadata_SuccessfulStopReason

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) EnumDescriptor deprecated 

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) EnumDescriptor() ([]byte, []int)

Deprecated: Use AutoMlImageSegmentationMetadata_SuccessfulStopReason.Descriptor instead.

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) Number 

func (x AutoMlImageSegmentationMetadata_SuccessfulStopReason) Number() protoreflect.EnumNumber

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) String 

func (x AutoMlImageSegmentationMetadata_SuccessfulStopReason) String() string

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) Type 

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) Type() protoreflect.EnumType

type AutoMlTables 

type AutoMlTables struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlTablesInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// The metadata information.
	Metadata *AutoMlTablesMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Tables Model.

func (*AutoMlTables) Descriptor deprecated 

func (*AutoMlTables) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTables.ProtoReflect.Descriptor instead.

func (*AutoMlTables) GetInputs 

func (x *AutoMlTables) GetInputs() *AutoMlTablesInputs

func (*AutoMlTables) GetMetadata 

func (x *AutoMlTables) GetMetadata() *AutoMlTablesMetadata

func (*AutoMlTables) ProtoMessage 

func (*AutoMlTables) ProtoMessage()

func (*AutoMlTables) ProtoReflect 

func (x *AutoMlTables) ProtoReflect() protoreflect.Message

func (*AutoMlTables) Reset 

func (x *AutoMlTables) Reset()

func (*AutoMlTables) String 

func (x *AutoMlTables) String() string

type AutoMlTablesInputs 

type AutoMlTablesInputs struct {

	// Additional optimization objective configuration. Required for
	// `maximize-precision-at-recall` and `maximize-recall-at-precision`,
	// otherwise unused.
	//
	// Types that are assignable to AdditionalOptimizationObjectiveConfig:
	//
	//	*AutoMlTablesInputs_OptimizationObjectiveRecallValue
	//	*AutoMlTablesInputs_OptimizationObjectivePrecisionValue
	AdditionalOptimizationObjectiveConfig isAutoMlTablesInputs_AdditionalOptimizationObjectiveConfig `protobuf_oneof:"additional_optimization_objective_config"`
	// The type of prediction the Model is to produce.
	//
	//	"classification" - Predict one out of multiple target values is
	//	                   picked for each row.
	//	"regression" - Predict a value based on its relation to other values.
	//	               This type is available only to columns that contain
	//	               semantically numeric values, i.e. integers or floating
	//	               point number, even if stored as e.g. strings.
	PredictionType string `protobuf:"bytes,1,opt,name=prediction_type,json=predictionType,proto3" json:"prediction_type,omitempty"`
	// The column name of the target column that the model is to predict.
	TargetColumn string `protobuf:"bytes,2,opt,name=target_column,json=targetColumn,proto3" json:"target_column,omitempty"`
	// Each transformation will apply transform function to given input column.
	// And the result will be used for training.
	// When creating transformation for BigQuery Struct column, the column should
	// be flattened using "." as the delimiter.
	Transformations []*AutoMlTablesInputs_Transformation `protobuf:"bytes,3,rep,name=transformations,proto3" json:"transformations,omitempty"`
	// Objective function the model is optimizing towards. The training process
	// creates a model that maximizes/minimizes the value of the objective
	// function over the validation set.
	//
	// The supported optimization objectives depend on the prediction type.
	// If the field is not set, a default objective function is used.
	//
	// classification (binary):
	//
	//	"maximize-au-roc" (default) - Maximize the area under the receiver
	//	                              operating characteristic (ROC) curve.
	//	"minimize-log-loss" - Minimize log loss.
	//	"maximize-au-prc" - Maximize the area under the precision-recall curve.
	//	"maximize-precision-at-recall" - Maximize precision for a specified
	//	                                recall value.
	//	"maximize-recall-at-precision" - Maximize recall for a specified
	//	                                 precision value.
	//
	// classification (multi-class):
	//
	//	"minimize-log-loss" (default) - Minimize log loss.
	//
	// regression:
	//
	//	"minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
	//	"minimize-mae" - Minimize mean-absolute error (MAE).
	//	"minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
	OptimizationObjective string `protobuf:"bytes,4,opt,name=optimization_objective,json=optimizationObjective,proto3" json:"optimization_objective,omitempty"`
	// Required. The train budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour.
	//
	// The training cost of the model will not exceed this budget. The final cost
	// will be attempted to be close to the budget, though may end up being (even)
	// noticeably smaller - at the backend's discretion. This especially may
	// happen when further model training ceases to provide any improvements.
	//
	// If the budget is set to a value known to be insufficient to train a
	// model for the given dataset, the training won't be attempted and
	// will error.
	//
	// The train budget must be between 1,000 and 72,000 milli node hours,
	// inclusive.
	TrainBudgetMilliNodeHours int64 `` /* 143-byte string literal not displayed */
	// Use the entire training budget. This disables the early stopping feature.
	// By default, the early stopping feature is enabled, which means that AutoML
	// Tables might stop training before the entire training budget has been used.
	DisableEarlyStopping bool `protobuf:"varint,8,opt,name=disable_early_stopping,json=disableEarlyStopping,proto3" json:"disable_early_stopping,omitempty"`
	// Column name that should be used as the weight column.
	// Higher values in this column give more importance to the row
	// during model training. The column must have numeric values between 0 and
	// 10000 inclusively; 0 means the row is ignored for training. If weight
	// column field is not set, then all rows are assumed to have equal weight
	// of 1.
	WeightColumnName string `protobuf:"bytes,9,opt,name=weight_column_name,json=weightColumnName,proto3" json:"weight_column_name,omitempty"`
	// Configuration for exporting test set predictions to a BigQuery table. If
	// this configuration is absent, then the export is not performed.
	ExportEvaluatedDataItemsConfig *ExportEvaluatedDataItemsConfig `` /* 158-byte string literal not displayed */
	// Additional experiment flags for the Tables training pipeline.
	AdditionalExperiments []string `protobuf:"bytes,11,rep,name=additional_experiments,json=additionalExperiments,proto3" json:"additional_experiments,omitempty"`
	// contains filtered or unexported fields
}

func (*AutoMlTablesInputs) Descriptor deprecated 

func (*AutoMlTablesInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesInputs.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs) GetAdditionalExperiments 

func (x *AutoMlTablesInputs) GetAdditionalExperiments() []string

func (*AutoMlTablesInputs) GetAdditionalOptimizationObjectiveConfig 

func (m *AutoMlTablesInputs) GetAdditionalOptimizationObjectiveConfig() isAutoMlTablesInputs_AdditionalOptimizationObjectiveConfig

func (*AutoMlTablesInputs) GetDisableEarlyStopping 

func (x *AutoMlTablesInputs) GetDisableEarlyStopping() bool

func (*AutoMlTablesInputs) GetExportEvaluatedDataItemsConfig 

func (x *AutoMlTablesInputs) GetExportEvaluatedDataItemsConfig() *ExportEvaluatedDataItemsConfig

func (*AutoMlTablesInputs) GetOptimizationObjective 

func (x *AutoMlTablesInputs) GetOptimizationObjective() string

func (*AutoMlTablesInputs) GetOptimizationObjectivePrecisionValue 

func (x *AutoMlTablesInputs) GetOptimizationObjectivePrecisionValue() float32

func (*AutoMlTablesInputs) GetOptimizationObjectiveRecallValue 

func (x *AutoMlTablesInputs) GetOptimizationObjectiveRecallValue() float32

func (*AutoMlTablesInputs) GetPredictionType 

func (x *AutoMlTablesInputs) GetPredictionType() string

func (*AutoMlTablesInputs) GetTargetColumn 

func (x *AutoMlTablesInputs) GetTargetColumn() string

func (*AutoMlTablesInputs) GetTrainBudgetMilliNodeHours 

func (x *AutoMlTablesInputs) GetTrainBudgetMilliNodeHours() int64

func (*AutoMlTablesInputs) GetTransformations 

func (x *AutoMlTablesInputs) GetTransformations() []*AutoMlTablesInputs_Transformation

func (*AutoMlTablesInputs) GetWeightColumnName 

func (x *AutoMlTablesInputs) GetWeightColumnName() string

func (*AutoMlTablesInputs) ProtoMessage 

func (*AutoMlTablesInputs) ProtoMessage()

func (*AutoMlTablesInputs) ProtoReflect 

func (x *AutoMlTablesInputs) ProtoReflect() protoreflect.Message

func (*AutoMlTablesInputs) Reset 

func (x *AutoMlTablesInputs) Reset()

func (*AutoMlTablesInputs) String 

func (x *AutoMlTablesInputs) String() string

type AutoMlTablesInputs_OptimizationObjectivePrecisionValue 

type AutoMlTablesInputs_OptimizationObjectivePrecisionValue struct {
	// Required when optimization_objective is "maximize-recall-at-precision".
	// Must be between 0 and 1, inclusive.
	OptimizationObjectivePrecisionValue float32 `protobuf:"fixed32,6,opt,name=optimization_objective_precision_value,json=optimizationObjectivePrecisionValue,proto3,oneof"`
}

type AutoMlTablesInputs_OptimizationObjectiveRecallValue 

type AutoMlTablesInputs_OptimizationObjectiveRecallValue struct {
	// Required when optimization_objective is "maximize-precision-at-recall".
	// Must be between 0 and 1, inclusive.
	OptimizationObjectiveRecallValue float32 `protobuf:"fixed32,5,opt,name=optimization_objective_recall_value,json=optimizationObjectiveRecallValue,proto3,oneof"`
}

type AutoMlTablesInputs_Transformation 

type AutoMlTablesInputs_Transformation struct {

	// The transformation that the training pipeline will apply to the input
	// columns.
	//
	// Types that are assignable to TransformationDetail:
	//
	//	*AutoMlTablesInputs_Transformation_Auto
	//	*AutoMlTablesInputs_Transformation_Numeric
	//	*AutoMlTablesInputs_Transformation_Categorical
	//	*AutoMlTablesInputs_Transformation_Timestamp
	//	*AutoMlTablesInputs_Transformation_Text
	//	*AutoMlTablesInputs_Transformation_RepeatedNumeric
	//	*AutoMlTablesInputs_Transformation_RepeatedCategorical
	//	*AutoMlTablesInputs_Transformation_RepeatedText
	TransformationDetail isAutoMlTablesInputs_Transformation_TransformationDetail `protobuf_oneof:"transformation_detail"`
	// contains filtered or unexported fields
}

func (*AutoMlTablesInputs_Transformation) Descriptor deprecated 

func (*AutoMlTablesInputs_Transformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesInputs_Transformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation) GetAuto 

func (x *AutoMlTablesInputs_Transformation) GetAuto() *AutoMlTablesInputs_Transformation_AutoTransformation

func (*AutoMlTablesInputs_Transformation) GetCategorical 

func (x *AutoMlTablesInputs_Transformation) GetCategorical() *AutoMlTablesInputs_Transformation_CategoricalTransformation

func (*AutoMlTablesInputs_Transformation) GetNumeric 

func (x *AutoMlTablesInputs_Transformation) GetNumeric() *AutoMlTablesInputs_Transformation_NumericTransformation

func (*AutoMlTablesInputs_Transformation) GetRepeatedCategorical 

func (x *AutoMlTablesInputs_Transformation) GetRepeatedCategorical() *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation

func (*AutoMlTablesInputs_Transformation) GetRepeatedNumeric 

func (x *AutoMlTablesInputs_Transformation) GetRepeatedNumeric() *AutoMlTablesInputs_Transformation_NumericArrayTransformation

func (*AutoMlTablesInputs_Transformation) GetRepeatedText 

func (x *AutoMlTablesInputs_Transformation) GetRepeatedText() *AutoMlTablesInputs_Transformation_TextArrayTransformation

func (*AutoMlTablesInputs_Transformation) GetText 

func (x *AutoMlTablesInputs_Transformation) GetText() *AutoMlTablesInputs_Transformation_TextTransformation

func (*AutoMlTablesInputs_Transformation) GetTimestamp 

func (x *AutoMlTablesInputs_Transformation) GetTimestamp() *AutoMlTablesInputs_Transformation_TimestampTransformation

func (*AutoMlTablesInputs_Transformation) GetTransformationDetail 

func (m *AutoMlTablesInputs_Transformation) GetTransformationDetail() isAutoMlTablesInputs_Transformation_TransformationDetail

func (*AutoMlTablesInputs_Transformation) ProtoMessage 

func (*AutoMlTablesInputs_Transformation) ProtoMessage()

func (*AutoMlTablesInputs_Transformation) ProtoReflect 

func (x *AutoMlTablesInputs_Transformation) ProtoReflect() protoreflect.Message

func (*AutoMlTablesInputs_Transformation) Reset 

func (x *AutoMlTablesInputs_Transformation) Reset()

func (*AutoMlTablesInputs_Transformation) String 

func (x *AutoMlTablesInputs_Transformation) String() string

type AutoMlTablesInputs_Transformation_Auto 

type AutoMlTablesInputs_Transformation_Auto struct {
	Auto *AutoMlTablesInputs_Transformation_AutoTransformation `protobuf:"bytes,1,opt,name=auto,proto3,oneof"`
}

type AutoMlTablesInputs_Transformation_AutoTransformation 

type AutoMlTablesInputs_Transformation_AutoTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// contains filtered or unexported fields
}

Training pipeline will infer the proper transformation based on the statistic of dataset.

func (*AutoMlTablesInputs_Transformation_AutoTransformation) Descriptor deprecated 

func (*AutoMlTablesInputs_Transformation_AutoTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesInputs_Transformation_AutoTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_AutoTransformation) GetColumnName 

func (x *AutoMlTablesInputs_Transformation_AutoTransformation) GetColumnName() string

func (*AutoMlTablesInputs_Transformation_AutoTransformation) ProtoMessage 

func (*AutoMlTablesInputs_Transformation_AutoTransformation) ProtoMessage()

func (*AutoMlTablesInputs_Transformation_AutoTransformation) ProtoReflect 

func (x *AutoMlTablesInputs_Transformation_AutoTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlTablesInputs_Transformation_AutoTransformation) Reset 

func (x *AutoMlTablesInputs_Transformation_AutoTransformation) Reset()

func (*AutoMlTablesInputs_Transformation_AutoTransformation) String 

func (x *AutoMlTablesInputs_Transformation_AutoTransformation) String() string

type AutoMlTablesInputs_Transformation_Categorical 

type AutoMlTablesInputs_Transformation_Categorical struct {
	Categorical *AutoMlTablesInputs_Transformation_CategoricalTransformation `protobuf:"bytes,3,opt,name=categorical,proto3,oneof"`
}

type AutoMlTablesInputs_Transformation_CategoricalArrayTransformation 

type AutoMlTablesInputs_Transformation_CategoricalArrayTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// contains filtered or unexported fields
}

Treats the column as categorical array and performs following transformation functions. * For each element in the array, convert the category name to a dictionary 

lookup index and generate an embedding for each index.
Combine the embedding of all elements into a single embedding using
the mean.

* Empty arrays treated as an embedding of zeroes.

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) Descriptor deprecated 

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesInputs_Transformation_CategoricalArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) GetColumnName 

func (x *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) GetColumnName() string

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) ProtoMessage 

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) ProtoMessage()

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) ProtoReflect 

func (x *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) Reset 

func (x *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) Reset()

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) String 

func (x *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) String() string

type AutoMlTablesInputs_Transformation_CategoricalTransformation 

type AutoMlTablesInputs_Transformation_CategoricalTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// contains filtered or unexported fields
}

Training pipeline will perform following transformation functions. * The categorical string as is--no change to case, punctuation, spelling, 

	tense, and so on.
  - Convert the category name to a dictionary lookup index and generate an
    embedding for each index.
  - Categories that appear less than 5 times in the training dataset are
    treated as the "unknown" category. The "unknown" category gets its own
    special lookup index and resulting embedding.

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) Descriptor deprecated 

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesInputs_Transformation_CategoricalTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) GetColumnName 

func (x *AutoMlTablesInputs_Transformation_CategoricalTransformation) GetColumnName() string

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) ProtoMessage 

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) ProtoMessage()

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) ProtoReflect 

func (x *AutoMlTablesInputs_Transformation_CategoricalTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) Reset 

func (x *AutoMlTablesInputs_Transformation_CategoricalTransformation) Reset()

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) String 

func (x *AutoMlTablesInputs_Transformation_CategoricalTransformation) String() string

type AutoMlTablesInputs_Transformation_Numeric 

type AutoMlTablesInputs_Transformation_Numeric struct {
	Numeric *AutoMlTablesInputs_Transformation_NumericTransformation `protobuf:"bytes,2,opt,name=numeric,proto3,oneof"`
}

type AutoMlTablesInputs_Transformation_NumericArrayTransformation 

type AutoMlTablesInputs_Transformation_NumericArrayTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// If invalid values is allowed, the training pipeline will create a
	// boolean feature that indicated whether the value is valid.
	// Otherwise, the training pipeline will discard the input row from
	// trainining data.
	InvalidValuesAllowed bool `protobuf:"varint,2,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
	// contains filtered or unexported fields
}

Treats the column as numerical array and performs following transformation functions.

  • All transformations for Numerical types applied to the average of the all elements.
  • The average of empty arrays is treated as zero.

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) Descriptor deprecated 

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesInputs_Transformation_NumericArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) GetColumnName 

func (x *AutoMlTablesInputs_Transformation_NumericArrayTransformation) GetColumnName() string

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) GetInvalidValuesAllowed 

func (x *AutoMlTablesInputs_Transformation_NumericArrayTransformation) GetInvalidValuesAllowed() bool

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) ProtoMessage 

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) ProtoMessage()

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) ProtoReflect 

func (x *AutoMlTablesInputs_Transformation_NumericArrayTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) Reset 

func (x *AutoMlTablesInputs_Transformation_NumericArrayTransformation) Reset()

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) String 

func (x *AutoMlTablesInputs_Transformation_NumericArrayTransformation) String() string

type AutoMlTablesInputs_Transformation_NumericTransformation 

type AutoMlTablesInputs_Transformation_NumericTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// If invalid values is allowed, the training pipeline will create a
	// boolean feature that indicated whether the value is valid.
	// Otherwise, the training pipeline will discard the input row from
	// trainining data.
	InvalidValuesAllowed bool `protobuf:"varint,2,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
	// contains filtered or unexported fields
}

Training pipeline will perform following transformation functions.

  • The value converted to float32.
  • The z_score of the value.
  • log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
  • z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
  • A boolean value that indicates whether the value is valid.

func (*AutoMlTablesInputs_Transformation_NumericTransformation) Descriptor deprecated 

func (*AutoMlTablesInputs_Transformation_NumericTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesInputs_Transformation_NumericTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_NumericTransformation) GetColumnName 

func (x *AutoMlTablesInputs_Transformation_NumericTransformation) GetColumnName() string

func (*AutoMlTablesInputs_Transformation_NumericTransformation) GetInvalidValuesAllowed 

func (x *AutoMlTablesInputs_Transformation_NumericTransformation) GetInvalidValuesAllowed() bool

func (*AutoMlTablesInputs_Transformation_NumericTransformation) ProtoMessage 

func (*AutoMlTablesInputs_Transformation_NumericTransformation) ProtoMessage()

func (*AutoMlTablesInputs_Transformation_NumericTransformation) ProtoReflect 

func (x *AutoMlTablesInputs_Transformation_NumericTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlTablesInputs_Transformation_NumericTransformation) Reset 

func (x *AutoMlTablesInputs_Transformation_NumericTransformation) Reset()

func (*AutoMlTablesInputs_Transformation_NumericTransformation) String 

func (x *AutoMlTablesInputs_Transformation_NumericTransformation) String() string

type AutoMlTablesInputs_Transformation_RepeatedCategorical 

type AutoMlTablesInputs_Transformation_RepeatedCategorical struct {
	RepeatedCategorical *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation `protobuf:"bytes,7,opt,name=repeated_categorical,json=repeatedCategorical,proto3,oneof"`
}

type AutoMlTablesInputs_Transformation_RepeatedNumeric 

type AutoMlTablesInputs_Transformation_RepeatedNumeric struct {
	RepeatedNumeric *AutoMlTablesInputs_Transformation_NumericArrayTransformation `protobuf:"bytes,6,opt,name=repeated_numeric,json=repeatedNumeric,proto3,oneof"`
}

type AutoMlTablesInputs_Transformation_RepeatedText 

type AutoMlTablesInputs_Transformation_RepeatedText struct {
	RepeatedText *AutoMlTablesInputs_Transformation_TextArrayTransformation `protobuf:"bytes,8,opt,name=repeated_text,json=repeatedText,proto3,oneof"`
}

type AutoMlTablesInputs_Transformation_Text 

type AutoMlTablesInputs_Transformation_Text struct {
	Text *AutoMlTablesInputs_Transformation_TextTransformation `protobuf:"bytes,5,opt,name=text,proto3,oneof"`
}

type AutoMlTablesInputs_Transformation_TextArrayTransformation 

type AutoMlTablesInputs_Transformation_TextArrayTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// contains filtered or unexported fields
}

Treats the column as text array and performs following transformation functions. * Concatenate all text values in the array into a single text value using 

a space (" ") as a delimiter, and then treat the result as a single
text value. Apply the transformations for Text columns.

* Empty arrays treated as an empty text.

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) Descriptor deprecated 

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesInputs_Transformation_TextArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) GetColumnName 

func (x *AutoMlTablesInputs_Transformation_TextArrayTransformation) GetColumnName() string

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) ProtoMessage 

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) ProtoMessage()

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) ProtoReflect 

func (x *AutoMlTablesInputs_Transformation_TextArrayTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) Reset 

func (x *AutoMlTablesInputs_Transformation_TextArrayTransformation) Reset()

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) String 

func (x *AutoMlTablesInputs_Transformation_TextArrayTransformation) String() string

type AutoMlTablesInputs_Transformation_TextTransformation 

type AutoMlTablesInputs_Transformation_TextTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// contains filtered or unexported fields
}

Training pipeline will perform following transformation functions. * The text as is--no change to case, punctuation, spelling, tense, and so 

on.

* Tokenize text to words. Convert each words to a dictionary lookup index 

and generate an embedding for each index. Combine the embedding of all
elements into a single embedding using the mean.

* Tokenization is based on unicode script boundaries. * Missing values get their own lookup index and resulting embedding. * Stop-words receive no special treatment and are not removed.

func (*AutoMlTablesInputs_Transformation_TextTransformation) Descriptor deprecated 

func (*AutoMlTablesInputs_Transformation_TextTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesInputs_Transformation_TextTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_TextTransformation) GetColumnName 

func (x *AutoMlTablesInputs_Transformation_TextTransformation) GetColumnName() string

func (*AutoMlTablesInputs_Transformation_TextTransformation) ProtoMessage 

func (*AutoMlTablesInputs_Transformation_TextTransformation) ProtoMessage()

func (*AutoMlTablesInputs_Transformation_TextTransformation) ProtoReflect 

func (x *AutoMlTablesInputs_Transformation_TextTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlTablesInputs_Transformation_TextTransformation) Reset 

func (x *AutoMlTablesInputs_Transformation_TextTransformation) Reset()

func (*AutoMlTablesInputs_Transformation_TextTransformation) String 

func (x *AutoMlTablesInputs_Transformation_TextTransformation) String() string

type AutoMlTablesInputs_Transformation_Timestamp 

type AutoMlTablesInputs_Transformation_Timestamp struct {
	Timestamp *AutoMlTablesInputs_Transformation_TimestampTransformation `protobuf:"bytes,4,opt,name=timestamp,proto3,oneof"`
}

type AutoMlTablesInputs_Transformation_TimestampTransformation 

type AutoMlTablesInputs_Transformation_TimestampTransformation struct {
	ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
	// The format in which that time field is expressed. The time_format must
	// either be one of:
	// * `unix-seconds`
	// * `unix-milliseconds`
	// * `unix-microseconds`
	// * `unix-nanoseconds`
	// (for respectively number of seconds, milliseconds, microseconds and
	// nanoseconds since start of the Unix epoch);
	// or be written in `strftime` syntax. If time_format is not set, then the
	// default format is RFC 3339 `date-time` format, where
	// `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z)
	TimeFormat string `protobuf:"bytes,2,opt,name=time_format,json=timeFormat,proto3" json:"time_format,omitempty"`
	// If invalid values is allowed, the training pipeline will create a
	// boolean feature that indicated whether the value is valid.
	// Otherwise, the training pipeline will discard the input row from
	// trainining data.
	InvalidValuesAllowed bool `protobuf:"varint,3,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
	// contains filtered or unexported fields
}

Training pipeline will perform following transformation functions.

  • Apply the transformation functions for Numerical columns.
  • Determine the year, month, day,and weekday. Treat each value from the
  • timestamp as a Categorical column.
  • Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) Descriptor deprecated 

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesInputs_Transformation_TimestampTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) GetColumnName 

func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) GetColumnName() string

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) GetInvalidValuesAllowed 

func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) GetInvalidValuesAllowed() bool

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) GetTimeFormat 

func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) GetTimeFormat() string

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) ProtoMessage 

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) ProtoMessage()

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) ProtoReflect 

func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) ProtoReflect() protoreflect.Message

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) Reset 

func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) Reset()

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) String 

func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) String() string

type AutoMlTablesMetadata 

type AutoMlTablesMetadata struct {

	// Output only. The actual training cost of the model, expressed in milli
	// node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
	// to not exceed the train budget.
	TrainCostMilliNodeHours int64 `` /* 137-byte string literal not displayed */
	// contains filtered or unexported fields
}

Model metadata specific to AutoML Tables.

func (*AutoMlTablesMetadata) Descriptor deprecated 

func (*AutoMlTablesMetadata) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTablesMetadata.ProtoReflect.Descriptor instead.

func (*AutoMlTablesMetadata) GetTrainCostMilliNodeHours 

func (x *AutoMlTablesMetadata) GetTrainCostMilliNodeHours() int64

func (*AutoMlTablesMetadata) ProtoMessage 

func (*AutoMlTablesMetadata) ProtoMessage()

func (*AutoMlTablesMetadata) ProtoReflect 

func (x *AutoMlTablesMetadata) ProtoReflect() protoreflect.Message

func (*AutoMlTablesMetadata) Reset 

func (x *AutoMlTablesMetadata) Reset()

func (*AutoMlTablesMetadata) String 

func (x *AutoMlTablesMetadata) String() string

type AutoMlTextClassification 

type AutoMlTextClassification struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlTextClassificationInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Text Classification Model.

func (*AutoMlTextClassification) Descriptor deprecated 

func (*AutoMlTextClassification) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTextClassification.ProtoReflect.Descriptor instead.

func (*AutoMlTextClassification) GetInputs 

func (x *AutoMlTextClassification) GetInputs() *AutoMlTextClassificationInputs

func (*AutoMlTextClassification) ProtoMessage 

func (*AutoMlTextClassification) ProtoMessage()

func (*AutoMlTextClassification) ProtoReflect 

func (x *AutoMlTextClassification) ProtoReflect() protoreflect.Message

func (*AutoMlTextClassification) Reset 

func (x *AutoMlTextClassification) Reset()

func (*AutoMlTextClassification) String 

func (x *AutoMlTextClassification) String() string

type AutoMlTextClassificationInputs 

type AutoMlTextClassificationInputs struct {
	MultiLabel bool `protobuf:"varint,1,opt,name=multi_label,json=multiLabel,proto3" json:"multi_label,omitempty"`
	// contains filtered or unexported fields
}

func (*AutoMlTextClassificationInputs) Descriptor deprecated 

func (*AutoMlTextClassificationInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTextClassificationInputs.ProtoReflect.Descriptor instead.

func (*AutoMlTextClassificationInputs) GetMultiLabel 

func (x *AutoMlTextClassificationInputs) GetMultiLabel() bool

func (*AutoMlTextClassificationInputs) ProtoMessage 

func (*AutoMlTextClassificationInputs) ProtoMessage()

func (*AutoMlTextClassificationInputs) ProtoReflect 

func (x *AutoMlTextClassificationInputs) ProtoReflect() protoreflect.Message

func (*AutoMlTextClassificationInputs) Reset 

func (x *AutoMlTextClassificationInputs) Reset()

func (*AutoMlTextClassificationInputs) String 

func (x *AutoMlTextClassificationInputs) String() string

type AutoMlTextExtraction 

type AutoMlTextExtraction struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlTextExtractionInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Text Extraction Model.

func (*AutoMlTextExtraction) Descriptor deprecated 

func (*AutoMlTextExtraction) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTextExtraction.ProtoReflect.Descriptor instead.

func (*AutoMlTextExtraction) GetInputs 

func (x *AutoMlTextExtraction) GetInputs() *AutoMlTextExtractionInputs

func (*AutoMlTextExtraction) ProtoMessage 

func (*AutoMlTextExtraction) ProtoMessage()

func (*AutoMlTextExtraction) ProtoReflect 

func (x *AutoMlTextExtraction) ProtoReflect() protoreflect.Message

func (*AutoMlTextExtraction) Reset 

func (x *AutoMlTextExtraction) Reset()

func (*AutoMlTextExtraction) String 

func (x *AutoMlTextExtraction) String() string

type AutoMlTextExtractionInputs 

type AutoMlTextExtractionInputs struct {
	// contains filtered or unexported fields
}

func (*AutoMlTextExtractionInputs) Descriptor deprecated 

func (*AutoMlTextExtractionInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTextExtractionInputs.ProtoReflect.Descriptor instead.

func (*AutoMlTextExtractionInputs) ProtoMessage 

func (*AutoMlTextExtractionInputs) ProtoMessage()

func (*AutoMlTextExtractionInputs) ProtoReflect 

func (x *AutoMlTextExtractionInputs) ProtoReflect() protoreflect.Message

func (*AutoMlTextExtractionInputs) Reset 

func (x *AutoMlTextExtractionInputs) Reset()

func (*AutoMlTextExtractionInputs) String 

func (x *AutoMlTextExtractionInputs) String() string

type AutoMlTextSentiment 

type AutoMlTextSentiment struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlTextSentimentInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Text Sentiment Model.

func (*AutoMlTextSentiment) Descriptor deprecated 

func (*AutoMlTextSentiment) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTextSentiment.ProtoReflect.Descriptor instead.

func (*AutoMlTextSentiment) GetInputs 

func (x *AutoMlTextSentiment) GetInputs() *AutoMlTextSentimentInputs

func (*AutoMlTextSentiment) ProtoMessage 

func (*AutoMlTextSentiment) ProtoMessage()

func (*AutoMlTextSentiment) ProtoReflect 

func (x *AutoMlTextSentiment) ProtoReflect() protoreflect.Message

func (*AutoMlTextSentiment) Reset 

func (x *AutoMlTextSentiment) Reset()

func (*AutoMlTextSentiment) String 

func (x *AutoMlTextSentiment) String() string

type AutoMlTextSentimentInputs 

type AutoMlTextSentimentInputs struct {

	// A sentiment is expressed as an integer ordinal, where higher value
	// means a more positive sentiment. The range of sentiments that will be used
	// is between 0 and sentimentMax (inclusive on both ends), and all the values
	// in the range must be represented in the dataset before a model can be
	// created.
	// Only the Annotations with this sentimentMax will be used for training.
	// sentimentMax value must be between 1 and 10 (inclusive).
	SentimentMax int32 `protobuf:"varint,1,opt,name=sentiment_max,json=sentimentMax,proto3" json:"sentiment_max,omitempty"`
	// contains filtered or unexported fields
}

func (*AutoMlTextSentimentInputs) Descriptor deprecated 

func (*AutoMlTextSentimentInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlTextSentimentInputs.ProtoReflect.Descriptor instead.

func (*AutoMlTextSentimentInputs) GetSentimentMax 

func (x *AutoMlTextSentimentInputs) GetSentimentMax() int32

func (*AutoMlTextSentimentInputs) ProtoMessage 

func (*AutoMlTextSentimentInputs) ProtoMessage()

func (*AutoMlTextSentimentInputs) ProtoReflect 

func (x *AutoMlTextSentimentInputs) ProtoReflect() protoreflect.Message

func (*AutoMlTextSentimentInputs) Reset 

func (x *AutoMlTextSentimentInputs) Reset()

func (*AutoMlTextSentimentInputs) String 

func (x *AutoMlTextSentimentInputs) String() string

type AutoMlVideoActionRecognition 

type AutoMlVideoActionRecognition struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlVideoActionRecognitionInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Video Action Recognition Model.

func (*AutoMlVideoActionRecognition) Descriptor deprecated 

func (*AutoMlVideoActionRecognition) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlVideoActionRecognition.ProtoReflect.Descriptor instead.

func (*AutoMlVideoActionRecognition) GetInputs 

func (x *AutoMlVideoActionRecognition) GetInputs() *AutoMlVideoActionRecognitionInputs

func (*AutoMlVideoActionRecognition) ProtoMessage 

func (*AutoMlVideoActionRecognition) ProtoMessage()

func (*AutoMlVideoActionRecognition) ProtoReflect 

func (x *AutoMlVideoActionRecognition) ProtoReflect() protoreflect.Message

func (*AutoMlVideoActionRecognition) Reset 

func (x *AutoMlVideoActionRecognition) Reset()

func (*AutoMlVideoActionRecognition) String 

func (x *AutoMlVideoActionRecognition) String() string

type AutoMlVideoActionRecognitionInputs 

type AutoMlVideoActionRecognitionInputs struct {
	ModelType AutoMlVideoActionRecognitionInputs_ModelType `` /* 196-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlVideoActionRecognitionInputs) Descriptor deprecated 

func (*AutoMlVideoActionRecognitionInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlVideoActionRecognitionInputs.ProtoReflect.Descriptor instead.

func (*AutoMlVideoActionRecognitionInputs) GetModelType 

func (x *AutoMlVideoActionRecognitionInputs) GetModelType() AutoMlVideoActionRecognitionInputs_ModelType

func (*AutoMlVideoActionRecognitionInputs) ProtoMessage 

func (*AutoMlVideoActionRecognitionInputs) ProtoMessage()

func (*AutoMlVideoActionRecognitionInputs) ProtoReflect 

func (x *AutoMlVideoActionRecognitionInputs) ProtoReflect() protoreflect.Message

func (*AutoMlVideoActionRecognitionInputs) Reset 

func (x *AutoMlVideoActionRecognitionInputs) Reset()

func (*AutoMlVideoActionRecognitionInputs) String 

func (x *AutoMlVideoActionRecognitionInputs) String() string

type AutoMlVideoActionRecognitionInputs_ModelType 

type AutoMlVideoActionRecognitionInputs_ModelType int32
const (
	// Should not be set.
	AutoMlVideoActionRecognitionInputs_MODEL_TYPE_UNSPECIFIED AutoMlVideoActionRecognitionInputs_ModelType = 0
	// A model best tailored to be used within Google Cloud, and which c annot
	// be exported. Default.
	AutoMlVideoActionRecognitionInputs_CLOUD AutoMlVideoActionRecognitionInputs_ModelType = 1
	// A model that, in addition to being available within Google Cloud, can
	// also be exported (see ModelService.ExportModel) as a TensorFlow or
	// TensorFlow Lite model and used on a mobile or edge device afterwards.
	AutoMlVideoActionRecognitionInputs_MOBILE_VERSATILE_1 AutoMlVideoActionRecognitionInputs_ModelType = 2
	// A model that, in addition to being available within Google Cloud, can
	// also be exported (see ModelService.ExportModel) to a Jetson device
	// afterwards.
	AutoMlVideoActionRecognitionInputs_MOBILE_JETSON_VERSATILE_1 AutoMlVideoActionRecognitionInputs_ModelType = 3
	// A model that, in addition to being available within Google Cloud, can
	// also be exported (see ModelService.ExportModel) as a TensorFlow or
	// TensorFlow Lite model and used on a Coral device afterwards.
	AutoMlVideoActionRecognitionInputs_MOBILE_CORAL_VERSATILE_1 AutoMlVideoActionRecognitionInputs_ModelType = 4
)

func (AutoMlVideoActionRecognitionInputs_ModelType) Descriptor 

func (AutoMlVideoActionRecognitionInputs_ModelType) Descriptor() protoreflect.EnumDescriptor

func (AutoMlVideoActionRecognitionInputs_ModelType) Enum 

func (x AutoMlVideoActionRecognitionInputs_ModelType) Enum() *AutoMlVideoActionRecognitionInputs_ModelType

func (AutoMlVideoActionRecognitionInputs_ModelType) EnumDescriptor deprecated 

func (AutoMlVideoActionRecognitionInputs_ModelType) EnumDescriptor() ([]byte, []int)

Deprecated: Use AutoMlVideoActionRecognitionInputs_ModelType.Descriptor instead.

func (AutoMlVideoActionRecognitionInputs_ModelType) Number 

func (x AutoMlVideoActionRecognitionInputs_ModelType) Number() protoreflect.EnumNumber

func (AutoMlVideoActionRecognitionInputs_ModelType) String 

func (x AutoMlVideoActionRecognitionInputs_ModelType) String() string

func (AutoMlVideoActionRecognitionInputs_ModelType) Type 

func (AutoMlVideoActionRecognitionInputs_ModelType) Type() protoreflect.EnumType

type AutoMlVideoClassification 

type AutoMlVideoClassification struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlVideoClassificationInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Video Classification Model.

func (*AutoMlVideoClassification) Descriptor deprecated 

func (*AutoMlVideoClassification) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlVideoClassification.ProtoReflect.Descriptor instead.

func (*AutoMlVideoClassification) GetInputs 

func (x *AutoMlVideoClassification) GetInputs() *AutoMlVideoClassificationInputs

func (*AutoMlVideoClassification) ProtoMessage 

func (*AutoMlVideoClassification) ProtoMessage()

func (*AutoMlVideoClassification) ProtoReflect 

func (x *AutoMlVideoClassification) ProtoReflect() protoreflect.Message

func (*AutoMlVideoClassification) Reset 

func (x *AutoMlVideoClassification) Reset()

func (*AutoMlVideoClassification) String 

func (x *AutoMlVideoClassification) String() string

type AutoMlVideoClassificationInputs 

type AutoMlVideoClassificationInputs struct {
	ModelType AutoMlVideoClassificationInputs_ModelType `` /* 193-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlVideoClassificationInputs) Descriptor deprecated 

func (*AutoMlVideoClassificationInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlVideoClassificationInputs.ProtoReflect.Descriptor instead.

func (*AutoMlVideoClassificationInputs) GetModelType 

func (x *AutoMlVideoClassificationInputs) GetModelType() AutoMlVideoClassificationInputs_ModelType

func (*AutoMlVideoClassificationInputs) ProtoMessage 

func (*AutoMlVideoClassificationInputs) ProtoMessage()

func (*AutoMlVideoClassificationInputs) ProtoReflect 

func (x *AutoMlVideoClassificationInputs) ProtoReflect() protoreflect.Message

func (*AutoMlVideoClassificationInputs) Reset 

func (x *AutoMlVideoClassificationInputs) Reset()

func (*AutoMlVideoClassificationInputs) String 

func (x *AutoMlVideoClassificationInputs) String() string

type AutoMlVideoClassificationInputs_ModelType 

type AutoMlVideoClassificationInputs_ModelType int32
const (
	// Should not be set.
	AutoMlVideoClassificationInputs_MODEL_TYPE_UNSPECIFIED AutoMlVideoClassificationInputs_ModelType = 0
	// A model best tailored to be used within Google Cloud, and which cannot
	// be exported. Default.
	AutoMlVideoClassificationInputs_CLOUD AutoMlVideoClassificationInputs_ModelType = 1
	// A model that, in addition to being available within Google Cloud, can
	// also be exported (see ModelService.ExportModel) as a TensorFlow or
	// TensorFlow Lite model and used on a mobile or edge device afterwards.
	AutoMlVideoClassificationInputs_MOBILE_VERSATILE_1 AutoMlVideoClassificationInputs_ModelType = 2
	// A model that, in addition to being available within Google Cloud, can
	// also be exported (see ModelService.ExportModel) to a Jetson device
	// afterwards.
	AutoMlVideoClassificationInputs_MOBILE_JETSON_VERSATILE_1 AutoMlVideoClassificationInputs_ModelType = 3
)

func (AutoMlVideoClassificationInputs_ModelType) Descriptor 

func (AutoMlVideoClassificationInputs_ModelType) Descriptor() protoreflect.EnumDescriptor

func (AutoMlVideoClassificationInputs_ModelType) Enum 

func (x AutoMlVideoClassificationInputs_ModelType) Enum() *AutoMlVideoClassificationInputs_ModelType

func (AutoMlVideoClassificationInputs_ModelType) EnumDescriptor deprecated 

func (AutoMlVideoClassificationInputs_ModelType) EnumDescriptor() ([]byte, []int)

Deprecated: Use AutoMlVideoClassificationInputs_ModelType.Descriptor instead.

func (AutoMlVideoClassificationInputs_ModelType) Number 

func (x AutoMlVideoClassificationInputs_ModelType) Number() protoreflect.EnumNumber

func (AutoMlVideoClassificationInputs_ModelType) String 

func (x AutoMlVideoClassificationInputs_ModelType) String() string

func (AutoMlVideoClassificationInputs_ModelType) Type 

func (AutoMlVideoClassificationInputs_ModelType) Type() protoreflect.EnumType

type AutoMlVideoObjectTracking 

type AutoMlVideoObjectTracking struct {

	// The input parameters of this TrainingJob.
	Inputs *AutoMlVideoObjectTrackingInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
	// contains filtered or unexported fields
}

A TrainingJob that trains and uploads an AutoML Video ObjectTracking Model.

func (*AutoMlVideoObjectTracking) Descriptor deprecated 

func (*AutoMlVideoObjectTracking) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlVideoObjectTracking.ProtoReflect.Descriptor instead.

func (*AutoMlVideoObjectTracking) GetInputs 

func (x *AutoMlVideoObjectTracking) GetInputs() *AutoMlVideoObjectTrackingInputs

func (*AutoMlVideoObjectTracking) ProtoMessage 

func (*AutoMlVideoObjectTracking) ProtoMessage()

func (*AutoMlVideoObjectTracking) ProtoReflect 

func (x *AutoMlVideoObjectTracking) ProtoReflect() protoreflect.Message

func (*AutoMlVideoObjectTracking) Reset 

func (x *AutoMlVideoObjectTracking) Reset()

func (*AutoMlVideoObjectTracking) String 

func (x *AutoMlVideoObjectTracking) String() string

type AutoMlVideoObjectTrackingInputs 

type AutoMlVideoObjectTrackingInputs struct {
	ModelType AutoMlVideoObjectTrackingInputs_ModelType `` /* 193-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlVideoObjectTrackingInputs) Descriptor deprecated 

func (*AutoMlVideoObjectTrackingInputs) Descriptor() ([]byte, []int)

Deprecated: Use AutoMlVideoObjectTrackingInputs.ProtoReflect.Descriptor instead.

func (*AutoMlVideoObjectTrackingInputs) GetModelType 

func (x *AutoMlVideoObjectTrackingInputs) GetModelType() AutoMlVideoObjectTrackingInputs_ModelType

func (*AutoMlVideoObjectTrackingInputs) ProtoMessage 

func (*AutoMlVideoObjectTrackingInputs) ProtoMessage()

func (*AutoMlVideoObjectTrackingInputs) ProtoReflect 

func (x *AutoMlVideoObjectTrackingInputs) ProtoReflect() protoreflect.Message

func (*AutoMlVideoObjectTrackingInputs) Reset 

func (x *AutoMlVideoObjectTrackingInputs) Reset()

func (*AutoMlVideoObjectTrackingInputs) String 

func (x *AutoMlVideoObjectTrackingInputs) String() string

type AutoMlVideoObjectTrackingInputs_ModelType 

type AutoMlVideoObjectTrackingInputs_ModelType int32
const (
	// Should not be set.
	AutoMlVideoObjectTrackingInputs_MODEL_TYPE_UNSPECIFIED AutoMlVideoObjectTrackingInputs_ModelType = 0
	// A model best tailored to be used within Google Cloud, and which c annot
	// be exported. Default.
	AutoMlVideoObjectTrackingInputs_CLOUD AutoMlVideoObjectTrackingInputs_ModelType = 1
	// A model that, in addition to being available within Google Cloud, can
	// also be exported (see ModelService.ExportModel) as a TensorFlow or
	// TensorFlow Lite model and used on a mobile or edge device afterwards.
	AutoMlVideoObjectTrackingInputs_MOBILE_VERSATILE_1 AutoMlVideoObjectTrackingInputs_ModelType = 2
	// A versatile model that is meant to be exported (see
	// ModelService.ExportModel) and used on a Google Coral device.
	AutoMlVideoObjectTrackingInputs_MOBILE_CORAL_VERSATILE_1 AutoMlVideoObjectTrackingInputs_ModelType = 3
	// A model that trades off quality for low latency, to be exported (see
	// ModelService.ExportModel) and used on a Google Coral device.
	AutoMlVideoObjectTrackingInputs_MOBILE_CORAL_LOW_LATENCY_1 AutoMlVideoObjectTrackingInputs_ModelType = 4
	// A versatile model that is meant to be exported (see
	// ModelService.ExportModel) and used on an NVIDIA Jetson device.
	AutoMlVideoObjectTrackingInputs_MOBILE_JETSON_VERSATILE_1 AutoMlVideoObjectTrackingInputs_ModelType = 5
	// A model that trades off quality for low latency, to be exported (see
	// ModelService.ExportModel) and used on an NVIDIA Jetson device.
	AutoMlVideoObjectTrackingInputs_MOBILE_JETSON_LOW_LATENCY_1 AutoMlVideoObjectTrackingInputs_ModelType = 6
)

func (AutoMlVideoObjectTrackingInputs_ModelType) Descriptor 

func (AutoMlVideoObjectTrackingInputs_ModelType) Descriptor() protoreflect.EnumDescriptor

func (AutoMlVideoObjectTrackingInputs_ModelType) Enum 

func (x AutoMlVideoObjectTrackingInputs_ModelType) Enum() *AutoMlVideoObjectTrackingInputs_ModelType

func (AutoMlVideoObjectTrackingInputs_ModelType) EnumDescriptor deprecated 

func (AutoMlVideoObjectTrackingInputs_ModelType) EnumDescriptor() ([]byte, []int)

Deprecated: Use AutoMlVideoObjectTrackingInputs_ModelType.Descriptor instead.

func (AutoMlVideoObjectTrackingInputs_ModelType) Number 

func (x AutoMlVideoObjectTrackingInputs_ModelType) Number() protoreflect.EnumNumber

func (AutoMlVideoObjectTrackingInputs_ModelType) String 

func (x AutoMlVideoObjectTrackingInputs_ModelType) String() string

func (AutoMlVideoObjectTrackingInputs_ModelType) Type 

func (AutoMlVideoObjectTrackingInputs_ModelType) Type() protoreflect.EnumType

type ExportEvaluatedDataItemsConfig 

type ExportEvaluatedDataItemsConfig struct {

	// URI of desired destination BigQuery table. Expected format:
	// bq://<project_id>:<dataset_id>:<table>
	//
	// If not specified, then results are exported to the following auto-created
	// BigQuery table:
	// <project_id>:export_evaluated_examples_<model_name>_<yyyy_MM_dd'T'HH_mm_ss_SSS'Z'>.evaluated_examples
	DestinationBigqueryUri string `` /* 129-byte string literal not displayed */
	// If true and an export destination is specified, then the contents of the
	// destination are overwritten. Otherwise, if the export destination already
	// exists, then the export operation fails.
	OverrideExistingTable bool `` /* 127-byte string literal not displayed */
	// contains filtered or unexported fields
}

Configuration for exporting test set predictions to a BigQuery table.

func (*ExportEvaluatedDataItemsConfig) Descriptor deprecated 

func (*ExportEvaluatedDataItemsConfig) Descriptor() ([]byte, []int)

Deprecated: Use ExportEvaluatedDataItemsConfig.ProtoReflect.Descriptor instead.

func (*ExportEvaluatedDataItemsConfig) GetDestinationBigqueryUri 

func (x *ExportEvaluatedDataItemsConfig) GetDestinationBigqueryUri() string

func (*ExportEvaluatedDataItemsConfig) GetOverrideExistingTable 

func (x *ExportEvaluatedDataItemsConfig) GetOverrideExistingTable() bool

func (*ExportEvaluatedDataItemsConfig) ProtoMessage 

func (*ExportEvaluatedDataItemsConfig) ProtoMessage()

func (*ExportEvaluatedDataItemsConfig) ProtoReflect 

func (x *ExportEvaluatedDataItemsConfig) ProtoReflect() protoreflect.Message

func (*ExportEvaluatedDataItemsConfig) Reset 

func (x *ExportEvaluatedDataItemsConfig) Reset()

func (*ExportEvaluatedDataItemsConfig) String 

func (x *ExportEvaluatedDataItemsConfig) String() string

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