dumbnet 0.1.0

a [no_std] neural network library
Documentation 
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//! Activations for use in [layers](crate::layers)
//!
//! The Enums in this module are empty and therefore not initializable. They are only used as type
//! paramenters.
use core::fmt::Debug;
use serde::{de::DeserializeOwned, Serialize};

pub trait Activation: Debug + Serialize + DeserializeOwned + Clone {
	/// Input is the summed and weighted inputs of this neuron
	///
	/// Output is the output of the neuron
	fn activate(inputs: f32) -> f32;

	/// Input is the previous activation, as in the result of this traits activate() function
	///
	/// Output is the derivate of this activation function at that point.
	/// The derivate may be slightly adapted to better suit the needs of gradient descent
	fn derivate(activation: f32) -> f32;
}

/// A smooth sigmoid between -1 and 1.
///
/// 1/ 1 + (e^input)
#[derive(Debug, Serialize, Deserialize, Clone)]
pub enum Sigmoid {}

impl Activation for Sigmoid {
	#[inline(always)]
	fn activate(input: f32) -> f32 {
		// clamp to make the .exp() in sigmoid not go crazy
		let clamped = input.max(-20.).min(20.);
		let res = 1. / (1. + (-clamped).exp());
		debug_assert!(!res.is_nan());
		res
	}
	#[inline(always)]
	fn derivate(activation: f32) -> f32 {
		debug_assert!(!activation.is_nan());
		let res = activation * (1. - activation);
		debug_assert!(!res.is_nan());
		res
	}
}

/// Returns max(0, input)
#[derive(Debug, Serialize, Deserialize, Clone)]
pub enum ReLu {}

impl Activation for ReLu {
	#[inline(always)]
	fn activate(input: f32) -> f32 { input.max(0.) }
	#[inline(always)]
	fn derivate(activation: f32) -> f32 { if activation > 0. { 1. } else { 0.01 } }
}

#[doc(hidden)]
#[derive(Debug, Serialize, Deserialize, Clone)]
pub enum SoftMax {}

impl Activation for SoftMax {
	#[inline(always)]
	fn activate(_input: f32) -> f32 { panic!() }
	#[inline(always)]
	fn derivate(_activation: f32) -> f32 { panic!() }
}