ML.Neural.Trainers.BackProp

Class BackProp

object --+ | Trainer --+ | BackProp

implement back propagation (algorithm on pp 153-154 of Patterson) I don't *think* that I've made any assumptions about the connectivity of the net (i.e. full connectivity between layers is not required). **NOTE:** this code is currently making the assumption that the activation functions on the nodes in the network are capable of calculating their derivatives using only their values (i.e. a DerivFromVal method should exist). This shouldn't be too hard to change.

Instance Methods

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StepUpdate(self, example, net, resVect=None)
does a BackProp step based upon the example **Arguments** - example: a 2-tuple: 1) a list of variable values values 2) a list of result values (targets) - net: a _Network_ (or something supporting the same API) - resVect: if this is nonzero, then the network is not required to classify the _example_ **Returns** the backprop error from _network_ **before the update** **Note** In case it wasn't blindingly obvious, the weights in _network_ are modified in the course of taking a backprop step.

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TrainOnLine(self, examples, net, maxIts=5000, errTol=0.1, useAvgErr=1, silent=0)
carries out online training of a neural net The definition of online training is that the network is updated after each example is presented.

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__init__(self, speed=0.5, momentum=0.7)
Constructor...

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Inherited from object: __delattr__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __str__

Properties

[hide private]

Inherited from object: __class__

TrainOnLine(self, examples, net, maxIts=5000, errTol=0.1, useAvgErr=1, silent=0)

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carries out online training of a neural net

The definition of online training is that the network is updated after
  each example is presented.

**Arguments**

  - examples: a list of 2-tuple:
     1) a list of variable values values
     2) a list of result values (targets)

  - net: a _Network_ (or something supporting the same API)

  - maxIts: the maximum number of *training epochs* (see below for definition) to be
    run

  - errTol: the tolerance for convergence

  - useAvgErr: if this toggle is nonzero, then the error at each step will be
    divided by the number of training examples for the purposes of checking
    convergence.

  - silent: controls the amount of visual noise produced as this runs.


**Note**

   a *training epoch* is one complete pass through all the training examples

init(self, speed=0.5, momentum=0.7)
(Constructor)

source code

Constructor

**Arguments**

  - speed: the speed parameter for back prop training

  - momentum: the momentum term for back prop training
    *Not currently used*

Overrides: object.__init__

Class BackProp

TrainOnLine(self, examples, net, maxIts=5000, errTol=0.1, useAvgErr=1, silent=0)

__init__(self, speed=0.5, momentum=0.7) (Constructor)

init(self, speed=0.5, momentum=0.7)
(Constructor)