Documentation

Index

Constants

View Source
const (
	TensorTypeFloat = TensorType(iota)
	TensorTypeDouble
	TensorTypeInt32
	TensorTypeUInt32
	TensorTypeInt16
	TensorTypeInt8
	TensorTypeUInt8
	TensorTypeString
	TensorTypeComplex64
	TensorTypeComplex128
	TensorTypeInt64
	TensorTypeUInt64
	TensorTypeBool
)

Variables

This section is empty.

Functions

This section is empty.

Types

type Class

type Class struct {
	Label string
	Score float32
}

    Class struct returned by classify calls to a model

    type Example

    type Example = example.Example

      An Example is a mostly-normalized data format for storing data for training and inference. It contains a key-value store (features); where each key (string) maps to a Feature message (which is oneof packed BytesList, FloatList, or Int64List).

      type Examplifier

      type Examplifier interface {
      	Examples() ([]*Example, error)
      }

        Examplifier interface for types that can be converted to examples

        type Feature

        type Feature = example.Feature

          Feature contains Lists which may hold zero or more values.

          type MapExample

          type MapExample map[string]interface{}

            MapExample map type that implements Examplifier interface

            func (MapExample) Examples

            func (me MapExample) Examples() ([]*Example, error)

              Examples returns examples (one example) from a given map

              type ModelInfo

              type ModelInfo struct {
              	Name    string
              	Version int
              }

                ModelInfo struct contains infomation about the model used for the prediction (name, version, etc.)

                type Predictor

                type Predictor interface {
                	// Predict runs prediction with given input map. Output is filtered with given filter. (nil defaults to all outputs)
                	Predict(ctx context.Context, inputs map[string]interface{}, outputFilter []string) (map[string]Tensor, ModelInfo, error)
                	// Classify runs classify with given features and context
                	Classify(ctx context.Context, examples []*Example, context *Example) ([][]Class, ModelInfo, error)
                	// Regress runs regression with given features and context
                	Regress(ctx context.Context, examples []*Example, context *Example) ([]Regression, ModelInfo, error)
                	// GetModelInfo returns the ModelInfo for the Predictor
                	GetModelInfo(ctx context.Context) (ModelInfo, error)
                }

                  Predictor interface for unified model execution with different backend (embedded go model & tensorflow serving)

                  type Regression

                  type Regression struct {
                  	Value float32
                  }

                    Regression struct returned by regress calls to a model

                    type Tensor

                    type Tensor interface {
                    	Value() interface{}
                    	Shape() []int64
                    	Type() TensorType
                    }

                      Tensor unified interface for Tensors

                      type TensorType

                      type TensorType int

                        TensorType type of the tensor

                        Source Files