Copyright	(c) Lars Brünjes, 2016
License	MIT
Maintainer	brunjlar@gmail.com
Stability	experimental
Portability	portable
Safe Haskell	None
Language	Haskell2010
Extensions	ScopedTypeVariables DataKinds TypeOperators ExplicitNamespaces ExplicitForAll

Numeric.Neural.Layer

Description

This modules defines special "layer" components and convenience functions for the creation of such layers.

Synopsis

Documentation

type Layer i o = Component (Vector i Analytic) (Vector o Analytic) Source

A Layer i o is a component that maps a vector of length i to a vector of length j.

linearLayer :: forall i o. (KnownNat i, KnownNat o) => Layer i o Source

Creates a linear Layer, i.e. a layer that multiplies the input with a weight matrix and adds a bias to get the output.

layer :: (KnownNat i, KnownNat o) => (Analytic -> Analytic) -> Layer i o Source

Creates a Layer as a combination of a linear layer and a non-linear activation function.

tanhLayer :: (KnownNat i, KnownNat o) => Layer i o Source

This is simply layer, specialized to tanh-activation. Output values are all in the interval [0,1].

logisticLayer :: (KnownNat i, KnownNat o) => Layer i o Source

This is simply layer, specialized to the logistic function as activation. Output values are all in the interval [-1,1].

softmax :: (Floating a, Functor f, Foldable f) => f a -> f a Source

The softmax function normalizes a vector, so that all entries are in [0,1] with sum 1. This means the output entries can be interpreted as probabilities.