rdkit.ML.Neural.ActFuncs module — The RDKit 2016.09.1 documentation

# rdkit.ML.Neural.ActFuncs module

Activation functions for neural network nodes

Activation functions should implement the following API:

> - _Eval(x)_: returns the value of the function at a given point
> - _Deriv(x)_: returns the derivative of the function at a given point

The current Backprop implementation also requires:

> - _DerivFromVal(val)_: returns the derivative of the function when its

>     value is val

In all cases _x_ is a float as is the value returned.

class `rdkit.ML.Neural.ActFuncs.` `ActFunc`

  Bases: `object`

  “virtual base class” for activation functions

class `rdkit.ML.Neural.ActFuncs.` `Sigmoid`(beta=1.0)

  Bases: `rdkit.ML.Neural.ActFuncs.ActFunc`

  the standard sigmoidal function

  `Deriv`(x)
  `DerivFromVal`(val)
  `Eval`(x)

class `rdkit.ML.Neural.ActFuncs.` `TanH`(beta=1.0)

  Bases: `rdkit.ML.Neural.ActFuncs.ActFunc`

  the standard hyperbolic tangent function

  `Deriv`(x)
  `DerivFromVal`(val)
  `Eval`(x)