Class NeuralNetwork

constructor

• new NeuralNetwork(
      graph: ComputationalGraph,
      optimizer?:
          | "sgd"
          | "adam"
          | "momentum"
          | "adagrad"
          | "rmsprop"
          | "adadelta"
          | "rmspropgraves"
          | "smorms3"
          | "adamax"
          | "nadam"
          | "santae"
          | "santasss"
          | "amsgrad"
          | "adabound"
          | "amsbound"
          | "adabelief",
  ): NeuralNetwork

  Parameters

  • graph: ComputationalGraph

    Graph of a network
  • Optionaloptimizer:
        | "sgd"
        | "adam"
        | "momentum"
        | "adagrad"
        | "rmsprop"
        | "adadelta"
        | "rmspropgraves"
        | "smorms3"
        | "adamax"
        | "nadam"
        | "santae"
        | "santasss"
        | "amsgrad"
        | "adabound"
        | "amsbound"
        | "adabelief"

    Optimizer of the network

  Returns NeuralNetwork

_graph

_opt

_opt_managers

_optimizer

_getDatasize

• _getDatasize(x: any): number

  Parameters

  • x: any

  Returns number

calc

• calc(
      x:
          | Tensor<number>
          | Matrix<number>
          | (number | number[])[][]
          | {
              [key: string]:
                  | Tensor<number>
                  | Matrix<number>
                  | (number | number[])[][];
          },
      t?: Matrix,
      out?: string[],
      options?: object,
  ): Matrix<number> | { [key: string]: Matrix<number> }

  Returns calculated values.

  Parameters

  • x:
        | Tensor<number>
        | Matrix<number>
        | (number | number[])[][]
        | {
            [key: string]:
                | Tensor<number>
                | Matrix<number>
                | (number | number[])[][];
        }

    Input value
  • Optionalt: Matrix

    Supervised value
  • Optionalout: string[]

    Name of node from which to get output
  • Optionaloptions: object

    Option

  Returns Matrix<number> | { [key: string]: Matrix<number> }

  Calculated values

copy

• copy(): NeuralNetwork

  Returns a copy of this.

  Returns NeuralNetwork

  Copied network

fit

• fit(
      x:
          | Tensor<number>
          | Matrix<number>
          | (number | number[])[][]
          | {
              [key: string]:
                  | Tensor<number>
                  | Matrix<number>
                  | (number | number[])[][];
          },
      t: Matrix<number> | number[][],
      epoch?: number,
      learning_rate?: number,
      batch_size?: number,
      options?: object,
  ): number[]

  Fit model.

  Parameters

  • x:
        | Tensor<number>
        | Matrix<number>
        | (number | number[])[][]
        | {
            [key: string]:
                | Tensor<number>
                | Matrix<number>
                | (number | number[])[][];
        }

    Training data
  • t: Matrix<number> | number[][]

    Target values
  • Optionalepoch: number

    Iteration count
  • Optionallearning_rate: number

    Learning rate
  • Optionalbatch_size: number

    Batch size
  • Optionaloptions: object

    Option

  Returns number[]

  Loss value

grad

• grad(e?: Matrix): Matrix

  Returns gradient values.

  Parameters

  • Optionale: Matrix

    Input of gradient

  Returns Matrix

  Output of gradient

predict

• predict(
      x:
          | Tensor<number>
          | Matrix<number>
          | (number | number[])[][]
          | {
              [key: string]:
                  | Tensor<number>
                  | Matrix<number>
                  | (number | number[])[][];
          },
  ): number[][]

  Returns predicted values.

  Parameters

  • x:
        | Tensor<number>
        | Matrix<number>
        | (number | number[])[][]
        | {
            [key: string]:
                | Tensor<number>
                | Matrix<number>
                | (number | number[])[][];
        }

    Sample data

  Returns number[][]

  Predicted values

toObject

• toObject(): LayerObject[]

  Returns object representation.

  Returns LayerObject[]

  Object represented this neuralnetwork

update

• update(learning_rate: number): void

  Update model parameters.

  Parameters

  • learning_rate: number

    Learning rate

  Returns void

StaticfromObject

• fromObject(
      layers: LayerObject[],
      loss?: string,
      optimizer?:
          | "sgd"
          | "adam"
          | "momentum"
          | "adagrad"
          | "rmsprop"
          | "adadelta"
          | "rmspropgraves"
          | "smorms3"
          | "adamax"
          | "nadam",
  ): NeuralNetwork

  Returns neuralnetwork.

  Parameters

  • layers: LayerObject[]

    Network layers
  • Optionalloss: string

    Loss name
  • Optionaloptimizer:
        | "sgd"
        | "adam"
        | "momentum"
        | "adagrad"
        | "rmsprop"
        | "adadelta"
        | "rmspropgraves"
        | "smorms3"
        | "adamax"
        | "nadam"

    Optimizer of the network

  Returns NeuralNetwork

  Created Neuralnetwork

StaticfromONNX

• fromONNX(
      buffer: ArrayBuffer | Uint8Array<ArrayBufferLike> | File,
  ): Promise<NeuralNetwork>

  Load onnx model.

  Parameters

  • buffer: ArrayBuffer | Uint8Array<ArrayBufferLike> | File

    File

  Returns Promise<NeuralNetwork>

  Loaded NeuralNetwork