Activation

TimeSeriesClassificationActivationFunctions Library "TimeSeriesClassificationActivationFunctions" Provides some activation functions useful in time series classification. ___ reference: github.com method scale(dist, weights) Activate values by a normalized scale. Namespace types: map Parameters: dist (map) : Source distribution map. weights (map) : Weights distribution map. Returns: Normalized distribution map. method softmax(dist, weights) Activate values with a softmax algorithm. Namespace types: map Parameters: dist (map) : Source distribution map. weights (map) : Weights distribution map. Returns: Normalized distribution map. method argmax(dist, weights) Activate values with a argmax algorithm. Namespace types: map Parameters: dist (map) : Source distribution map. weights (map) : Weights distribution map. Returns: first key of argmax value of the transformed distribution.

Library

by RicardoSantos

MLActivationFunctions 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

Library

by RicardoSantos

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