Documentation

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",
	}
	AutoMlImageSegmentationInputs_ModelType_value = map[string]int32{
		"MODEL_TYPE_UNSPECIFIED": 0,
		"CLOUD_HIGH_ACCURACY_1":  1,
		"CLOUD_LOW_ACCURACY_1":   2,
	}
)

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",
	}
	AutoMlVideoActionRecognitionInputs_ModelType_value = map[string]int32{
		"MODEL_TYPE_UNSPECIFIED": 0,
		"CLOUD":                  1,
		"MOBILE_VERSATILE_1":     2,
	}
)

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",
	}
	AutoMlVideoClassificationInputs_ModelType_value = map[string]int32{
		"MODEL_TYPE_UNSPECIFIED": 0,
		"CLOUD":                  1,
		"MOBILE_VERSATILE_1":     2,
	}
)

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_v1beta1_schema_trainingjob_definition_automl_forecasting_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_automl_image_classification_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_automl_image_object_detection_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_automl_image_segmentation_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_automl_tables_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_automl_text_classification_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_automl_text_extraction_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_automl_text_sentiment_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_automl_video_action_recognition_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_automl_video_classification_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_automl_video_object_tracking_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_aiplatform_v1beta1_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

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

Deprecated: Use AutoMlForecasting.ProtoReflect.Descriptor instead.

func (*AutoMlForecasting) GetInputs

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).
	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 */
	// contains filtered or unexported fields
}

func (*AutoMlForecastingInputs) Descriptor

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 (*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 (*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:
	//  "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

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 (*AutoMlForecastingInputs_Period) ProtoMessage

func (*AutoMlForecastingInputs_Period) ProtoMessage()

func (*AutoMlForecastingInputs_Period) ProtoReflect

func (*AutoMlForecastingInputs_Period) Reset

func (x *AutoMlForecastingInputs_Period) Reset()

func (*AutoMlForecastingInputs_Period) 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

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

Deprecated: Use AutoMlForecastingInputs_Transformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation) GetAuto

func (*AutoMlForecastingInputs_Transformation) GetCategorical

func (*AutoMlForecastingInputs_Transformation) GetNumeric

func (*AutoMlForecastingInputs_Transformation) GetRepeatedNumeric

func (*AutoMlForecastingInputs_Transformation) GetRepeatedText

func (*AutoMlForecastingInputs_Transformation) GetText

func (*AutoMlForecastingInputs_Transformation) GetTimestamp

func (*AutoMlForecastingInputs_Transformation) GetTransformationDetail

func (m *AutoMlForecastingInputs_Transformation) GetTransformationDetail() isAutoMlForecastingInputs_Transformation_TransformationDetail

func (*AutoMlForecastingInputs_Transformation) ProtoMessage

func (*AutoMlForecastingInputs_Transformation) ProtoReflect

func (*AutoMlForecastingInputs_Transformation) Reset

func (*AutoMlForecastingInputs_Transformation) 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: Use AutoMlForecastingInputs_Transformation_AutoTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) GetColumnName

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) ProtoMessage

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) ProtoReflect

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) Reset

func (*AutoMlForecastingInputs_Transformation_AutoTransformation) 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: Use AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) GetColumnName

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) ProtoMessage

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) ProtoReflect

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) Reset

func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) 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: Use AutoMlForecastingInputs_Transformation_CategoricalTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) GetColumnName

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) ProtoMessage

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) ProtoReflect

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) Reset

func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) 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: Use AutoMlForecastingInputs_Transformation_NumericArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) GetColumnName

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) GetInvalidValuesAllowed

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) ProtoMessage

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) ProtoReflect

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) Reset

func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) 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: Use AutoMlForecastingInputs_Transformation_NumericTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) GetColumnName

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) GetInvalidValuesAllowed

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) ProtoMessage

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) ProtoReflect

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) Reset

func (*AutoMlForecastingInputs_Transformation_NumericTransformation) 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: Use AutoMlForecastingInputs_Transformation_TextArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) GetColumnName

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) ProtoMessage

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) ProtoReflect

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) Reset

func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) 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: Use AutoMlForecastingInputs_Transformation_TextTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_TextTransformation) GetColumnName

func (*AutoMlForecastingInputs_Transformation_TextTransformation) ProtoMessage

func (*AutoMlForecastingInputs_Transformation_TextTransformation) ProtoReflect

func (*AutoMlForecastingInputs_Transformation_TextTransformation) Reset

func (*AutoMlForecastingInputs_Transformation_TextTransformation) 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: Use AutoMlForecastingInputs_Transformation_TimestampTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) GetColumnName

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) GetInvalidValuesAllowed

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) GetTimeFormat

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) ProtoMessage

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) ProtoReflect

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) Reset

func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) 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

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 (*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

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

Deprecated: Use AutoMlImageClassification.ProtoReflect.Descriptor instead.

func (*AutoMlImageClassification) GetInputs

func (*AutoMlImageClassification) GetMetadata

func (*AutoMlImageClassification) ProtoMessage

func (*AutoMlImageClassification) ProtoMessage()

func (*AutoMlImageClassification) ProtoReflect

func (*AutoMlImageClassification) Reset

func (x *AutoMlImageClassification) Reset()

func (*AutoMlImageClassification) String

func (x *AutoMlImageClassification) String() string

type AutoMlImageClassificationInputs

type AutoMlImageClassificationInputs struct {
	ModelType AutoMlImageClassificationInputs_ModelType `` /* 198-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

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 (*AutoMlImageClassificationInputs) GetMultiLabel

func (x *AutoMlImageClassificationInputs) GetMultiLabel() bool

func (*AutoMlImageClassificationInputs) ProtoMessage

func (*AutoMlImageClassificationInputs) ProtoMessage()

func (*AutoMlImageClassificationInputs) ProtoReflect

func (*AutoMlImageClassificationInputs) Reset

func (*AutoMlImageClassificationInputs) 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) Enum

func (AutoMlImageClassificationInputs_ModelType) EnumDescriptor

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

Deprecated: Use AutoMlImageClassificationInputs_ModelType.Descriptor instead.

func (AutoMlImageClassificationInputs_ModelType) Number

func (AutoMlImageClassificationInputs_ModelType) String

func (AutoMlImageClassificationInputs_ModelType) Type

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 `` /* 246-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlImageClassificationMetadata) Descriptor

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

Deprecated: Use AutoMlImageClassificationMetadata.ProtoReflect.Descriptor instead.

func (*AutoMlImageClassificationMetadata) GetCostMilliNodeHours

func (x *AutoMlImageClassificationMetadata) GetCostMilliNodeHours() int64

func (*AutoMlImageClassificationMetadata) GetSuccessfulStopReason

func (*AutoMlImageClassificationMetadata) ProtoMessage

func (*AutoMlImageClassificationMetadata) ProtoMessage()

func (*AutoMlImageClassificationMetadata) ProtoReflect

func (*AutoMlImageClassificationMetadata) Reset

func (*AutoMlImageClassificationMetadata) 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) Enum

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) EnumDescriptor

Deprecated: Use AutoMlImageClassificationMetadata_SuccessfulStopReason.Descriptor instead.

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Number

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) String

func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Type

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

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

Deprecated: Use AutoMlImageObjectDetection.ProtoReflect.Descriptor instead.

func (*AutoMlImageObjectDetection) GetInputs

func (*AutoMlImageObjectDetection) GetMetadata

func (*AutoMlImageObjectDetection) ProtoMessage

func (*AutoMlImageObjectDetection) ProtoMessage()

func (*AutoMlImageObjectDetection) ProtoReflect

func (*AutoMlImageObjectDetection) Reset

func (x *AutoMlImageObjectDetection) Reset()

func (*AutoMlImageObjectDetection) String

func (x *AutoMlImageObjectDetection) String() string

type AutoMlImageObjectDetectionInputs

type AutoMlImageObjectDetectionInputs struct {
	ModelType AutoMlImageObjectDetectionInputs_ModelType `` /* 199-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

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 (*AutoMlImageObjectDetectionInputs) ProtoMessage

func (*AutoMlImageObjectDetectionInputs) ProtoMessage()

func (*AutoMlImageObjectDetectionInputs) ProtoReflect

func (*AutoMlImageObjectDetectionInputs) Reset

func (*AutoMlImageObjectDetectionInputs) 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) Enum

func (AutoMlImageObjectDetectionInputs_ModelType) EnumDescriptor

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

Deprecated: Use AutoMlImageObjectDetectionInputs_ModelType.Descriptor instead.

func (AutoMlImageObjectDetectionInputs_ModelType) Number

func (AutoMlImageObjectDetectionInputs_ModelType) String

func (AutoMlImageObjectDetectionInputs_ModelType) Type

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 `` /* 247-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlImageObjectDetectionMetadata) Descriptor

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

Deprecated: Use AutoMlImageObjectDetectionMetadata.ProtoReflect.Descriptor instead.

func (*AutoMlImageObjectDetectionMetadata) GetCostMilliNodeHours

func (x *AutoMlImageObjectDetectionMetadata) GetCostMilliNodeHours() int64

func (*AutoMlImageObjectDetectionMetadata) GetSuccessfulStopReason

func (*AutoMlImageObjectDetectionMetadata) ProtoMessage

func (*AutoMlImageObjectDetectionMetadata) ProtoMessage()

func (*AutoMlImageObjectDetectionMetadata) ProtoReflect

func (*AutoMlImageObjectDetectionMetadata) Reset

func (*AutoMlImageObjectDetectionMetadata) 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) Enum

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) EnumDescriptor

Deprecated: Use AutoMlImageObjectDetectionMetadata_SuccessfulStopReason.Descriptor instead.

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Number

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) String

func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Type

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

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

Deprecated: Use AutoMlImageSegmentation.ProtoReflect.Descriptor instead.

func (*AutoMlImageSegmentation) GetInputs

func (*AutoMlImageSegmentation) GetMetadata

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 `` /* 196-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

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 (*AutoMlImageSegmentationInputs) ProtoMessage

func (*AutoMlImageSegmentationInputs) ProtoMessage()

func (*AutoMlImageSegmentationInputs) ProtoReflect

func (*AutoMlImageSegmentationInputs) Reset

func (x *AutoMlImageSegmentationInputs) Reset()

func (*AutoMlImageSegmentationInputs) 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
)

func (AutoMlImageSegmentationInputs_ModelType) Descriptor

func (AutoMlImageSegmentationInputs_ModelType) Enum

func (AutoMlImageSegmentationInputs_ModelType) EnumDescriptor

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

Deprecated: Use AutoMlImageSegmentationInputs_ModelType.Descriptor instead.

func (AutoMlImageSegmentationInputs_ModelType) Number

func (AutoMlImageSegmentationInputs_ModelType) String

func (AutoMlImageSegmentationInputs_ModelType) Type

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 `` /* 244-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlImageSegmentationMetadata) Descriptor

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

Deprecated: Use AutoMlImageSegmentationMetadata.ProtoReflect.Descriptor instead.

func (*AutoMlImageSegmentationMetadata) GetCostMilliNodeHours

func (x *AutoMlImageSegmentationMetadata) GetCostMilliNodeHours() int64

func (*AutoMlImageSegmentationMetadata) GetSuccessfulStopReason

func (*AutoMlImageSegmentationMetadata) ProtoMessage

func (*AutoMlImageSegmentationMetadata) ProtoMessage()

func (*AutoMlImageSegmentationMetadata) ProtoReflect

func (*AutoMlImageSegmentationMetadata) Reset

func (*AutoMlImageSegmentationMetadata) 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) Enum

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) EnumDescriptor

Deprecated: Use AutoMlImageSegmentationMetadata_SuccessfulStopReason.Descriptor instead.

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) Number

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) String

func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) Type

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

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 */
	// contains filtered or unexported fields
}

func (*AutoMlTablesInputs) Descriptor

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

Deprecated: Use AutoMlTablesInputs.ProtoReflect.Descriptor instead.

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

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

Deprecated: Use AutoMlTablesInputs_Transformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation) GetAuto

func (*AutoMlTablesInputs_Transformation) GetCategorical

func (*AutoMlTablesInputs_Transformation) GetNumeric

func (*AutoMlTablesInputs_Transformation) GetRepeatedCategorical

func (*AutoMlTablesInputs_Transformation) GetRepeatedNumeric

func (*AutoMlTablesInputs_Transformation) GetRepeatedText

func (*AutoMlTablesInputs_Transformation) GetText

func (*AutoMlTablesInputs_Transformation) GetTimestamp

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 (*AutoMlTablesInputs_Transformation) Reset

func (*AutoMlTablesInputs_Transformation) 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: Use AutoMlTablesInputs_Transformation_AutoTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_AutoTransformation) GetColumnName

func (*AutoMlTablesInputs_Transformation_AutoTransformation) ProtoMessage

func (*AutoMlTablesInputs_Transformation_AutoTransformation) ProtoReflect

func (*AutoMlTablesInputs_Transformation_AutoTransformation) Reset

func (*AutoMlTablesInputs_Transformation_AutoTransformation) 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: Use AutoMlTablesInputs_Transformation_CategoricalArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) GetColumnName

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) ProtoMessage

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) ProtoReflect

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) Reset

func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) 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: Use AutoMlTablesInputs_Transformation_CategoricalTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) GetColumnName

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) ProtoMessage

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) ProtoReflect

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) Reset

func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) 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: Use AutoMlTablesInputs_Transformation_NumericArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) GetColumnName

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) GetInvalidValuesAllowed

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) ProtoMessage

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) ProtoReflect

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) Reset

func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) 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: Use AutoMlTablesInputs_Transformation_NumericTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_NumericTransformation) GetColumnName

func (*AutoMlTablesInputs_Transformation_NumericTransformation) GetInvalidValuesAllowed

func (*AutoMlTablesInputs_Transformation_NumericTransformation) ProtoMessage

func (*AutoMlTablesInputs_Transformation_NumericTransformation) ProtoReflect

func (*AutoMlTablesInputs_Transformation_NumericTransformation) Reset

func (*AutoMlTablesInputs_Transformation_NumericTransformation) 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: Use AutoMlTablesInputs_Transformation_TextArrayTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) GetColumnName

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) ProtoMessage

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) ProtoReflect

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) Reset

func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) 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: Use AutoMlTablesInputs_Transformation_TextTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_TextTransformation) GetColumnName

func (*AutoMlTablesInputs_Transformation_TextTransformation) ProtoMessage

func (*AutoMlTablesInputs_Transformation_TextTransformation) ProtoReflect

func (*AutoMlTablesInputs_Transformation_TextTransformation) Reset

func (*AutoMlTablesInputs_Transformation_TextTransformation) 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: Use AutoMlTablesInputs_Transformation_TimestampTransformation.ProtoReflect.Descriptor instead.

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) GetColumnName

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) GetInvalidValuesAllowed

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) GetTimeFormat

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) ProtoMessage

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) ProtoReflect

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) Reset

func (*AutoMlTablesInputs_Transformation_TimestampTransformation) 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

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

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

Deprecated: Use AutoMlTextClassification.ProtoReflect.Descriptor instead.

func (*AutoMlTextClassification) GetInputs

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

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 (*AutoMlTextClassificationInputs) Reset

func (x *AutoMlTextClassificationInputs) Reset()

func (*AutoMlTextClassificationInputs) 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

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

Deprecated: Use AutoMlTextExtraction.ProtoReflect.Descriptor instead.

func (*AutoMlTextExtraction) GetInputs

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

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

Deprecated: Use AutoMlTextExtractionInputs.ProtoReflect.Descriptor instead.

func (*AutoMlTextExtractionInputs) ProtoMessage

func (*AutoMlTextExtractionInputs) ProtoMessage()

func (*AutoMlTextExtractionInputs) ProtoReflect

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

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

Deprecated: Use AutoMlTextSentiment.ProtoReflect.Descriptor instead.

func (*AutoMlTextSentiment) GetInputs

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

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 (*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

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

Deprecated: Use AutoMlVideoActionRecognition.ProtoReflect.Descriptor instead.

func (*AutoMlVideoActionRecognition) GetInputs

func (*AutoMlVideoActionRecognition) ProtoMessage

func (*AutoMlVideoActionRecognition) ProtoMessage()

func (*AutoMlVideoActionRecognition) ProtoReflect

func (*AutoMlVideoActionRecognition) Reset

func (x *AutoMlVideoActionRecognition) Reset()

func (*AutoMlVideoActionRecognition) String

type AutoMlVideoActionRecognitionInputs

type AutoMlVideoActionRecognitionInputs struct {
	ModelType AutoMlVideoActionRecognitionInputs_ModelType `` /* 201-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlVideoActionRecognitionInputs) Descriptor

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

Deprecated: Use AutoMlVideoActionRecognitionInputs.ProtoReflect.Descriptor instead.

func (*AutoMlVideoActionRecognitionInputs) GetModelType

func (*AutoMlVideoActionRecognitionInputs) ProtoMessage

func (*AutoMlVideoActionRecognitionInputs) ProtoMessage()

func (*AutoMlVideoActionRecognitionInputs) ProtoReflect

func (*AutoMlVideoActionRecognitionInputs) Reset

func (*AutoMlVideoActionRecognitionInputs) 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
)

func (AutoMlVideoActionRecognitionInputs_ModelType) Descriptor

func (AutoMlVideoActionRecognitionInputs_ModelType) Enum

func (AutoMlVideoActionRecognitionInputs_ModelType) EnumDescriptor

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

Deprecated: Use AutoMlVideoActionRecognitionInputs_ModelType.Descriptor instead.

func (AutoMlVideoActionRecognitionInputs_ModelType) Number

func (AutoMlVideoActionRecognitionInputs_ModelType) String

func (AutoMlVideoActionRecognitionInputs_ModelType) Type

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

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

Deprecated: Use AutoMlVideoClassification.ProtoReflect.Descriptor instead.

func (*AutoMlVideoClassification) GetInputs

func (*AutoMlVideoClassification) ProtoMessage

func (*AutoMlVideoClassification) ProtoMessage()

func (*AutoMlVideoClassification) ProtoReflect

func (*AutoMlVideoClassification) Reset

func (x *AutoMlVideoClassification) Reset()

func (*AutoMlVideoClassification) String

func (x *AutoMlVideoClassification) String() string

type AutoMlVideoClassificationInputs

type AutoMlVideoClassificationInputs struct {
	ModelType AutoMlVideoClassificationInputs_ModelType `` /* 198-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlVideoClassificationInputs) Descriptor

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

Deprecated: Use AutoMlVideoClassificationInputs.ProtoReflect.Descriptor instead.

func (*AutoMlVideoClassificationInputs) GetModelType

func (*AutoMlVideoClassificationInputs) ProtoMessage

func (*AutoMlVideoClassificationInputs) ProtoMessage()

func (*AutoMlVideoClassificationInputs) ProtoReflect

func (*AutoMlVideoClassificationInputs) Reset

func (*AutoMlVideoClassificationInputs) 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
)

func (AutoMlVideoClassificationInputs_ModelType) Descriptor

func (AutoMlVideoClassificationInputs_ModelType) Enum

func (AutoMlVideoClassificationInputs_ModelType) EnumDescriptor

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

Deprecated: Use AutoMlVideoClassificationInputs_ModelType.Descriptor instead.

func (AutoMlVideoClassificationInputs_ModelType) Number

func (AutoMlVideoClassificationInputs_ModelType) String

func (AutoMlVideoClassificationInputs_ModelType) Type

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

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

Deprecated: Use AutoMlVideoObjectTracking.ProtoReflect.Descriptor instead.

func (*AutoMlVideoObjectTracking) GetInputs

func (*AutoMlVideoObjectTracking) ProtoMessage

func (*AutoMlVideoObjectTracking) ProtoMessage()

func (*AutoMlVideoObjectTracking) ProtoReflect

func (*AutoMlVideoObjectTracking) Reset

func (x *AutoMlVideoObjectTracking) Reset()

func (*AutoMlVideoObjectTracking) String

func (x *AutoMlVideoObjectTracking) String() string

type AutoMlVideoObjectTrackingInputs

type AutoMlVideoObjectTrackingInputs struct {
	ModelType AutoMlVideoObjectTrackingInputs_ModelType `` /* 198-byte string literal not displayed */
	// contains filtered or unexported fields
}

func (*AutoMlVideoObjectTrackingInputs) Descriptor

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

Deprecated: Use AutoMlVideoObjectTrackingInputs.ProtoReflect.Descriptor instead.

func (*AutoMlVideoObjectTrackingInputs) GetModelType

func (*AutoMlVideoObjectTrackingInputs) ProtoMessage

func (*AutoMlVideoObjectTrackingInputs) ProtoMessage()

func (*AutoMlVideoObjectTrackingInputs) ProtoReflect

func (*AutoMlVideoObjectTrackingInputs) Reset

func (*AutoMlVideoObjectTrackingInputs) 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) Enum

func (AutoMlVideoObjectTrackingInputs_ModelType) EnumDescriptor

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

Deprecated: Use AutoMlVideoObjectTrackingInputs_ModelType.Descriptor instead.

func (AutoMlVideoObjectTrackingInputs_ModelType) Number

func (AutoMlVideoObjectTrackingInputs_ModelType) String

func (AutoMlVideoObjectTrackingInputs_ModelType) Type

type ExportEvaluatedDataItemsConfig

type ExportEvaluatedDataItemsConfig struct {

	// URI of desired destination BigQuery 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 will be overwritten. Otherwise, if the export destination
	// already exists, then the export operation will not trigger and a failure
	// response is returned.
	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

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 (*ExportEvaluatedDataItemsConfig) Reset

func (x *ExportEvaluatedDataItemsConfig) Reset()

func (*ExportEvaluatedDataItemsConfig) String