MLActivationFunctions

2021年12月26日

Library "MLActivationFunctions"
Activation functions for Neural networks.

binary_step(value) Basic threshold output classifier to activate/deactivate neuron.
  Parameters:
    value: float, value to process.
  Returns: float

linear(value) Input is the same as output.
  Parameters:
    value: float, value to process.
  Returns: float

sigmoid(value) Sigmoid or logistic function.
  Parameters:
    value: float, value to process.
  Returns: float

sigmoid_derivative(value) Derivative of sigmoid function.
  Parameters:
    value: float, value to process.
  Returns: float

tanh(value) Hyperbolic tangent function.
  Parameters:
    value: float, value to process.
  Returns: float

tanh_derivative(value) Hyperbolic tangent function derivative.
  Parameters:
    value: float, value to process.
  Returns: float

relu(value) Rectified linear unit (RELU) function.
  Parameters:
    value: float, value to process.
  Returns: float

relu_derivative(value) RELU function derivative.
  Parameters:
    value: float, value to process.
  Returns: float

leaky_relu(value) Leaky RELU function.
  Parameters:
    value: float, value to process.
  Returns: float

leaky_relu_derivative(value) Leaky RELU function derivative.
  Parameters:
    value: float, value to process.
  Returns: float

relu6(value) RELU-6 function.
  Parameters:
    value: float, value to process.
  Returns: float

softmax(value) Softmax function.
  Parameters:
    value: float array, values to process.
  Returns: float

softplus(value) Softplus function.
  Parameters:
    value: float, value to process.
  Returns: float

softsign(value) Softsign function.
  Parameters:
    value: float, value to process.
  Returns: float

elu(value, alpha) Exponential Linear Unit (ELU) function.
  Parameters:
    value: float, value to process.
    alpha: float, default=1.0, predefined constant, controls the value to which an ELU saturates for negative net inputs. .
  Returns: float

selu(value, alpha, scale) Scaled Exponential Linear Unit (SELU) function.
  Parameters:
    value: float, value to process.
    alpha: float, default=1.67326324, predefined constant, controls the value to which an SELU saturates for negative net inputs. .
    scale: float, default=1.05070098, predefined constant.
  Returns: float

exponential(value) Pointer to math.exp() function.
  Parameters:
    value: float, value to process.
  Returns: float

function(name, value, alpha, scale) Activation function.
  Parameters:
    name: string, name of activation function.
    value: float, value to process.
    alpha: float, default=na, if required.
    scale: float, default=na, if required.
  Returns: float

derivative(name, value, alpha, scale) Derivative Activation function.
  Parameters:
    name: string, name of activation function.
    value: float, value to process.
    alpha: float, default=na, if required.
    scale: float, default=na, if required.
  Returns: float

2022年1月6日

リリースノート

v2

Added:
softmax_derivative(value) Softmax derivative function.
  Parameters:
    value: float array, values to process.
  Returns: float