API Reference - ActivationLayers

Functions

Constructors 


ActivationLayers.FastSigmoid{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

FastBinaryStep 


ActivationLayers.FastBinaryStep{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

FastRectifiedLinearUnit 


ActivationLayers.FastRectifiedLinearUnit{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

FastLeakyRectifiedLinearUnit 


ActivationLayers.FastLeakyRectifiedLinearUnit{tensor: tensor, negativeSlopeFactor: number}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

  • negativeSlopeFactor: The value to be multiplied with negative input values. [Default: 0.01]

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

FastExponentLinearUnit 


ActivationLayers.FastExponentLinearUnit{tensor: tensor, negativeSlopeFactor: number}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

  • negativeSlopeFactor: The value to be multiplied with negative input values. [Default: 0.01]

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

FastSigmoidLinearUnit 


ActivationLayers.FastSigmoidLinearUnit{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

FastGaussian 


ActivationLayers.FastGaussian{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

FastMish 


ActivationLayers.FastMish{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

FastTanh 


ActivationLayers.FastTanh{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

FastSoftmax 


ActivationLayers.FastSoftmax{tensor: tensor, dimension: number}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

  • dimension: The dimension at which the exponent values are summed. [Default: 1]

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

Sigmoid 


ActivationLayers.Sigmoid{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

BinaryStep 


ActivationLayers.BinaryStep{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

RectifiedLinearUnit 


ActivationLayers.RectifiedLinearUnit{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

LeakyRectifiedLinearUnit 


ActivationLayers.LeakyRectifiedLinearUnit{tensor: tensor, negativeSlopeFactor: number}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

  • negativeSlopeFactor: The value to be multiplied with negative input values. [Default: 0.01]

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

ExponentLinearUnit 


ActivationLayers.ExponentLinearUnit{tensor: tensor, negativeSlopeFactor: number}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

  • negativeSlopeFactor: The value to be multiplied with negative input values. [Default: 0.01]

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

SigmoidLinearUnit 


ActivationLayers.SigmoidLinearUnit{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

Gaussian 


ActivationLayers.Gaussian{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

Mish 


ActivationLayers.Mish{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

Tanh 


ActivationLayers.Tanh{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.

Softmax 


ActivationLayers.Softmax{tensor: tensor, dimension: number}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be transformed.

  • dimension: The dimension at which the exponent values are summed. [Default: 1]

Returns:

  • AutomaticDifferentiationTensor: The automatic differentiation tensor that is created as a result of calling this function.