Neuralnetwork

Constructors

  • 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

Properties

_opt:
    | SGDOptimizer
    | MomentumOptimizer
    | AdaGradOptimizer
    | RMSPropOptimizer
    | AdaDeltaOptimizer
    | AdamOptimizer
    | RMSPropGravesOptimizer
    | SMORMS3Optimizer
    | AdaMaxOptimizer
    | NadamOptimizer
    | SantaEOptimizer
    | SantaSSSOptimizer
    | AMSGradOptimizer
    | AdaBoundOptimizer
    | AMSBoundOptimizer
    | AdaBeliefOptimizer
_opt_managers: { lr: any; delta(key: any, value: any): any }[]
_optimizer:
    | "sgd"
    | "adam"
    | "momentum"
    | "adagrad"
    | "rmsprop"
    | "adadelta"
    | "rmspropgraves"
    | "smorms3"
    | "adamax"
    | "nadam"
    | "santae"
    | "santasss"
    | "amsgrad"
    | "adabound"
    | "amsbound"
    | "adabelief"

Methods

  • Parameters

    • x: any

    Returns any

  • Returns calculated values.

    Parameters

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

      Input value

    • Optionalt: Matrix

      Supervised value

    • Optionalout: string[]

      Name of node from which to get output

    • Optionaloptions: object

      Option

    Returns Matrix | { [key: string]: Matrix }

    Calculated values

  • Fit model.

    Parameters

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

      Training data

    • t: Matrix | number[][]

      Target values

    • Optionalepoch: number

      Iteration count

    • Optionallearning_rate: number

      Learning rate

    • Optionalbatch_size: number

      Batch size

    • Optionaloptions: object

      Option

    Returns number[]

    Loss value

  • Returns gradient values.

    Parameters

    • Optionale: Matrix

      Input of gradient

    Returns Matrix

    Output of gradient

  • Returns predicted values.

    Parameters

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

      Sample data

    Returns number[][]

    Predicted values

  • Returns object representation.

    Returns LayerObject[]

    Object represented this neuralnetwork

  • Update model parameters.

    Parameters

    • learning_rate: number

      Learning rate

    Returns void

  • 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

  • Load onnx model.

    Parameters

    • buffer: Uint8Array | ArrayBuffer | File

      File

    Returns Promise<NeuralNetwork>

    Loaded NeuralNetwork