API Reference - HolderBlocks - VariableHolder

Constructors

new()


VariableHolder.new({variableTensor: tensor, learningRate: number, Optimizer: OptimizerObject, Regularizer: RegularizerObject}): HolderBlockObject

Parameters:

  • variableTensor: The tensor to be stored inside the variable holder block.

  • learningRate: The speed at which the model learns. Recommended that the value is set between (0 to 1).

  • Optimizer: The optimizer to be used.

  • Regularizer: The regularizer to be used.

Returns

  • HolderBlock: The generated holder block object.

Functions

setParameters()


VariableHolder:setParameters({variableTensor: tensor, learningRate: number, Optimizer: OptimizerObject, Regularizer: RegularizerObject})

Parameters:

  • variableTensor: The tensor to be stored inside the variable holder block.

  • learningRate: The speed at which the model learns. Recommended that the value is set between (0 to 1).

  • Optimizer: The optimizer to be used.

  • Regularizer: The regularizer to be used.

gradientDescent()


VariableHolder:gradientDescent(lossTensor: tensor, numberOfData: number)

Parameters:

  • lossTensor: A tensor containing the loss values. This will be used to update the variable values.

  • numberOfData: The value to divide with the loss tensors.

setVariableTensor()


VariableHolder:setVariableTensor(variableTensor: tensor, doNotDeepCopy: boolean)

Parameters

  • variableTensor: The tensor to be loaded to the weight block.

  • doNotDeepCopy: Whether or not to deep copy the weight tensor.

getVariableTensor()


VariableHolder:getVariableTensor(doNotDeepCopy): tensor

Parameters

  • doNotDeepCopy: Whether or not to deep copy the weight tensor.

Returns

  • variableTensor: Tensor to be returned.

setLearningRate()


VariableHolder:setLearningRate(learningRate: number)

Parameters:

  • learningRate: The speed at which the model learns. Recommended that the value is set between (0 to 1).

getLearningRate(): number


VariableHolder:getLearningRate()

Returns::

  • learningRate: The speed at which the model learns. Recommended that the value is set between (0 to 1).

setOptimizer()


VariableHolder:setOptimizer(Optimizer: OptimizerObject)

Parameters:

  • Optimizer: The optimizer to be used.

getOptimizer()


VariableHolder:getOptimizer(): OptimizerObject

Returns:

  • Optimizer: The optimizer used by the weight block.

setRegularizer()


VariableHolder:setRegularizer(Regularizer: RegularizerObject)

Parameters:

  • Regularizer: The regularizer to be used.

getRegularizer()


VariableHolder:getRegularizer(): RegularizerObject

Returns:

  • Regularizer: The regularizer to be used.

Inherited From