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## org.encog.neural.networks.training.propagation.back
Class Backpropagation

    java.lang.Object
      org.encog.ml.train.BasicTraining
          org.encog.neural.networks.training.propagation.Propagation
              org.encog.neural.networks.training.propagation.back.Backpropagation

All Implemented Interfaces:
  MLTrain, LearningRate, Momentum, Train, MultiThreadable

    public class Backpropagation

extends Propagation
implements Momentum, LearningRate

This class implements a backpropagation training algorithm for feed forward neural networks. It is used in the same manner as any other training class that implements the Train interface. Backpropagation is a common neural network training algorithm. It works by analyzing the error of the output of the neural network. Each neuron in the output layer's contribution, according to weight, to this error is determined. These weights are then adjusted to minimize this error. This process continues working its way backwards through the layers of the neural network. This implementation of the backpropagation algorithm uses both momentum and a learning rate. The learning rate specifies the degree to which the weight matrixes will be modified through each iteration. The momentum specifies how much the previous learning iteration affects the current. To use no momentum at all specify zero. One primary problem with backpropagation is that the magnitude of the partial derivative is often detrimental to the training of the neural network. The other propagation methods of Manhatten and Resilient address this issue in different ways. In general, it is suggested that you use the resilient propagation technique for most Encog training tasks over back propagation.

Field Summary
`static String` | `LAST_DELTA` The resume key for backpropagation.

Constructor Summary
`Backpropagation(ContainsFlat network, MLDataSet training)` Create a class to train using backpropagation.
`Backpropagation(ContainsFlat network, MLDataSet training, double learnRate, double momentum)`

Method Summary
`boolean` | `canContinue()`
`double[]` | `getLastDelta()`
`double` | `getLearningRate()`
`double` | `getMomentum()`
`boolean` | `isValidResume(TrainingContinuation state)` Determine if the specified continuation object is valid to resume with.
`TrainingContinuation` | `pause()` Pause the training.
`void` | `resume(TrainingContinuation state)` Resume training.
`void` | `setLearningRate(double rate)` Set the learning rate, this is value is essentially a percent.
`void` | `setMomentum(double m)` Set the momentum for training.

Methods inherited from class org.encog.neural.networks.training.propagation.Propagation
`finishTraining, fixFlatSpot, getCurrentFlatNetwork, getFlatTraining, getMethod, getThreadCount, iteration, iteration, setErrorFunction, setFlatTraining, setThreadCount`

Methods inherited from class org.encog.ml.train.BasicTraining
`addStrategy, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, postIteration, preIteration, setError, setIteration, setTraining`

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Methods inherited from interface org.encog.ml.train.MLTrain
`addStrategy, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, setError, setIteration`

Field Detail

### LAST_DELTA

    public static final String LAST_DELTA

  The resume key for backpropagation.

  See Also:
    Constant Field Values

Constructor Detail

### Backpropagation

    public Backpropagation(ContainsFlat network,
                           MLDataSet training)

  Create a class to train using backpropagation. Use auto learn rate and momentum. Use the CPU to train.

  Parameters:
    `network` - The network that is to be trained.
    `training` - The training data to be used for backpropagation.

### Backpropagation

    public Backpropagation(ContainsFlat network,
                           MLDataSet training,
                           double learnRate,
                           double momentum)

Parameters:
  `network` - The network that is to be trained
  `training` - The training set
  `learnRate` - The rate at which the weight matrix will be adjusted based on learning.
  `momentum` - The influence that previous iteration's training deltas will have on the current iteration.

Method Detail

### canContinue

    public final boolean canContinue()

  Specified by:
    `canContinue` in interface `MLTrain`
  Returns:
    True if the training can be paused, and later continued.

### getLastDelta

    public final double[] getLastDelta()

  Returns:
    Ther last delta values.

### getLearningRate

    public final double getLearningRate()

  Specified by:
    `getLearningRate` in interface `LearningRate`
  Returns:
    The learning rate, this is value is essentially a percent. It is the degree to which the gradients are applied to the weight matrix to allow learning.

### getMomentum

    public final double getMomentum()

  Specified by:
    `getMomentum` in interface `Momentum`
  Returns:
    The momentum for training. This is the degree to which changes from which the previous training iteration will affect this training iteration. This can be useful to overcome local minima.

### isValidResume

    public final boolean isValidResume(TrainingContinuation state)

  Determine if the specified continuation object is valid to resume with.

  Parameters:
    `state` - The continuation object to check.
  Returns:
    True if the specified continuation object is valid for this training method and network.

### pause

    public final TrainingContinuation pause()

  Pause the training.

  Specified by:
    `pause` in interface `MLTrain`
  Returns:
    A training continuation object to continue with.

### resume

    public final void resume(TrainingContinuation state)

  Resume training.

  Specified by:
    `resume` in interface `MLTrain`
  Parameters:
    `state` - The training state to return to.

### setLearningRate

    public final void setLearningRate(double rate)

  Set the learning rate, this is value is essentially a percent. It is the degree to which the gradients are applied to the weight matrix to allow learning.

  Specified by:
    `setLearningRate` in interface `LearningRate`
  Parameters:
    `rate` - The learning rate.

### setMomentum

    public final void setMomentum(double m)

  Set the momentum for training. This is the degree to which changes from which the previous training iteration will affect this training iteration. This can be useful to overcome local minima.

  Specified by:
    `setMomentum` in interface `Momentum`
  Parameters:
    `m` - The momentum.

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