Module activate

Expand description

this module provides the Activate trait alongside additional primitives and utilities for activating neurons within a neural network.

Modules§

rho: this module defines structural implementations of various activation functions
utils

Activate: Activate is a higher-kinded trait that provides a mechanism to apply a function over the elements within a container or structure.
Activator: An Activator defines an interface for structural activation functions that can be applied onto various types.
ActivatorGradient: The ActivatorGradient trait extends the Activator trait to include a method for computing the gradient of the activation function.
HeavysideActivation
LinearActivation
ReLUActivation
SigmoidActivation
SoftmaxActivation
SoftmaxAxis: Compute the softmax activation along a specified axis.
TanhActivation

heavyside: Heaviside activation function:
linear: the linear method is essentially a passthrough method often used in simple models or layers where no activation is needed.
linear_derivative: the linear_derivative method always returns 1 as it is a simple, single variable function
relu: the relu activation function:
relu_derivative
sigmoid: the sigmoid activation function:
sigmoid_derivative: the derivative of the sigmoid function
softmax: Softmax function:
softmax_axis: Softmax function along a specific axis:
tanh: Hyperbolic tangent
tanh_derivative: the derivative of the tanh function