FANN C# NeuralNet Fixed

public NeuralNet( NeuralNet  other )

Creates a copy the other NeuralNet.

public void Dispose()

Destructs the entire network.  Must be called manually.

public NeuralNet( NetworkType  netType, uint  numLayers, params  uint[]  args )

Creates a neural network of the desired NetworkType net_type.

Parameters

Example

unt numLayes = 3;
uint numInput = 2;
uint numHidden = 3;
uint numOutput = 1;

NeuralNet net(numLayers, numInput, numHidden, numOutput);

This function appears in FANN >= 2.3.0.

public NeuralNet( NetworkType  netType, ICollection<uint>  layers )

Creates a neural network of the desired NetworkType netType, based on a collection of layers.

Parameters

Example

NeuralNet net(NetworkType.LAYER, new uint[] {2, 3, 1});

This function appears in FANN >= 2.3.0.

public NeuralNet( float  connectionRate, uint  numLayers, params  uint[]  args )

Creates a standard backpropagation neural network, which is sparsely connected, this will default the NetworkType to NetworkType::LAYER

Parameters

This function appears in FANN >= 2.3.0.

public NeuralNet( float  connectionRate, ICollection<uint>  layers )

Creates a standard backpropagation neural network, which is sparsely connected, this will default the NetworkType to NetworkType::LAYER

Parameters

This function appears in FANN >= 2.3.0.

public NeuralNet( string  filename )

Constructs a backpropagation neural network from a configuration file, which have been saved by Save.

See also

Save, SaveToFixed

This function appears in FANN >= 2.3.0.

public int[] Run( int[]  input )

Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer.

Parameters

See also

Test, fann_run

This function appears in FANN >= 1.0.0.

public int[] Run( DataAccessor  input )

Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer.

Parameters

See also

Test, fann_run

This function appears in FANN >= 1.0.0.

public void RandomizeWeights( int  minWeight, int  maxWeight )

Give each connection a random weight between minWeight and maxWeight

From the beginning the weights are random between -0.1 and 0.1.

See also

InitWeights, fann_randomize_weights

This function appears in FANN >= 1.0.0.

public void InitWeights( TrainingData  data )

Initialize the weights using Widrow + Nguyen’s algorithm.

This function behaves similarly to fann_randomize_weights.  It will use the algorithm developed by Derrick Nguyen and Bernard Widrow to set the weights in such a way as to speed up training.  This technique is not always successful, and in some cases can be less efficient than a purely random initialization.

The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second argument, data, which is the training data that will be used to train the network.

See also

RandomizeWeights, TrainingData::ReadTrainFromFile, fann_init_weights

This function appears in FANN >= 1.1.0.

public void PrintConnections()

Will print the connections of the network in a compact matrix, for easy viewing of the internals of the network.

The output from fann_print_connections on a small (2 2 1) network trained on the xor problem

Layer / Neuron 012345
L   1 / N    3 BBa...
L   1 / N    4 BBA...
L   1 / N    5 ......
L   2 / N    6 ...BBA
L   2 / N    7 ......

This network have five real neurons and two bias neurons.  This gives a total of seven neurons named from 0 to 6.  The connections between these neurons can be seen in the matrix.  “.” is a place where there is no connection, while a character tells how strong the connection is on a scale from a-z.  The two real neurons in the hidden layer (neuron 3 and 4 in layer 1) has connection from the three neurons in the previous layer as is visible in the first two lines.  The output neuron (6) has connections form the three neurons in the hidden layer 3 - 5 as is visible in the fourth line.

To simplify the matrix output neurons is not visible as neurons that connections can come from, and input and bias neurons are not visible as neurons that connections can go to.

This function appears in FANN >= 1.2.0.

public bool Save( string  file )

Save the entire network to a configuration file.

The configuration file contains all information about the neural network and enables <NeuralNet(string filename)> to create an exact copy of the neural network and all of the parameters associated with the neural network.

These two parameters (<SetCallback>, ErrorLog) are NOT saved to the file because they cannot safely be ported to a different location.  Also temporary parameters generated during training like MSE is not saved.

Return

The function returns true on success and false on failure.

See also

NeuralNet, SaveToFixed, fann_save

This function appears in FANN >= 1.0.0.

public int SaveToFixed( string  file )

Saves the entire network to a configuration file.  But it is saved in fixed point format no matter which format it is currently in.

This is useful for training a network in floating points, and then later executing it in fixed point.

The function returns the bit position of the fix point, which can be used to find out how accurate the fixed point network will be.  A high value indicates high precision, and a low value indicates low precision.

A negative value indicates very low precision, and a very strong possibility for overflow.  (the actual fix point will be set to 0, since a negative fix point does not make sence).

Generally, a fix point lower than 6 is bad, and should be avoided.  The best way to avoid this, is to have less connections to each neuron, or just less neurons in each layer.

The fixed point use of this network is only intended for use on machines that have no floating point processor, like an iPAQ.  On normal computers the floating point version is actually faster.

See also

NeuralNet, Save, fann_save_to_fixed

This function appears in FANN >= 1.0.0.

public int[] Test( int[]  input, int[]  desiredOutput )

Test with a set of inputs, and a set of desired outputs.  This operation updates the mean square error, but does not change the network in any way.

See also

TestData, <Train>, fann_test

This function appears in FANN >= 1.0.0.

public float TestData( TrainingData  data )

Test a set of training data and calculates the MSE for the training data.

This function updates the MSE and the bit fail values.

See also

Test, MSE, BitFail, fann_test_data

This function appears in FANN >= 1.2.0.

public void ResetMSE()

Resets the mean square error from the network.

This function also resets the number of bits that fail.

See also

MSE, BitFailLimit, fann_reset_MSE

This function appears in FANN >= 1.1.0

public void PrintParameters()

Prints all of the parameters and options of the neural network

See also

fann_print_parameters

This function appears in FANN >= 1.2.0.

public ActivationFunction GetActivationFunction( int  layer, int  neuron )

Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.

It is not possible to get activation functions for the neurons in the input layer.

Information about the individual activation functions is available at ActivationFunction.

Returns

The activation function for the neuron or -1 if the neuron is not defined in the neural network.

See also

SetActivationFunctionLayer, ActivationFunctionHidden, ActivationFunctionOutput, SetActivationSteepness, SetActivationFunction, fann_get_activation_function

This function appears in FANN >= 2.1.0

public void SetActivationFunction( ActivationFunction  function, int  layer, int  neuron )

Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.

It is not possible to set activation functions for the neurons in the input layer.

When choosing an activation function it is important to note that the activation functions have different range.  FANN::SIGMOID is e.g. in the 0 - 1 range while FANN::SIGMOID_SYMMETRIC is in the -1 - 1 range and FANN::LINEAR is unbounded.

Information about the individual activation functions is available at ActivationFunction.

The default activation function is FANN::SIGMOID_STEPWISE.

See also

SetActivationFunctionLayer, ActivationFunctionHidden, ActivationFunctionOutput, SetActivationSteepness, GetActivationFunction, fann_get_activation_function

This function appears in FANN >= 2.0.0.

public void SetActivationFunctionLayer( ActivationFunction  function, int  layer )

Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.

It is not possible to set activation functions for the neurons in the input layer.

See also

SetActivationFunction, ActivationFunctionHidden, ActivationFunctionOutput, SetActivationSteepnessLayer, fann_set_activation_function_layer

This function appears in FANN >= 2.0.0.

public int GetActivationSteepness( int  layer, int  neuron )

Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.

It is not possible to get activation steepness for the neurons in the input layer.

The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum.  A high value for the activation function will also give a more aggressive training.

When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g.  1.0).

The default activation steepness is 0.5.

Returns

The activation steepness for the neuron or -1 if the neuron is not defined in the neural network.

See also

SetActivationSteepnessLayer, ActivationSteepnessHidden, ActivationSteepnessOutput, SetActivationFunction, SetActivationSteepness, fann_get_activation_steepness

This function appears in FANN >= 2.1.0

public void SetActivationSteepness( int  steepness, int  layer, int  neuron )

Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.

It is not possible to set activation steepness for the neurons in the input layer.

The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum.  A high value for the activation function will also give a more aggressive training.

When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g.  1.0).

The default activation steepness is 0.5.

See also

SetActivationSteepnessLayer, ActivationSteepnessHidden, ActivationSteepnessOutput, SetActivationFunction, GetActivationSteepness, fann_set_activation_steepness

This function appears in FANN >= 2.0.0.

public void SetActivationSteepnessLayer( int  steepness, int  layer )

Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.

It is not possible to set activation steepness for the neurons in the input layer.

See also

SetActivationSteepness, ActivationSteepnessHidden, ActivationSteepnessOutput, SetActivationFunctionLayer, fann_set_activation_steepness_layer

This function appears in FANN >= 2.0.0.

public void SetWeight( uint  fromNeuron, uint  toNeuron, int  weight )

Set a connection in the network.

Only the weights can be changed.  The connection/weight is ignored if it does not already exist in the network.

See also

fann_set_weight

This function appears in FANN >= 2.1.0

public void ResetErrno()

Resets the last error number.

This function appears in FANN >= 1.1.0.

public void ResetErrstr()

Resets the last error string.

This function appears in FANN >= 1.1.0.

public void PrintError()

Prints the last error to Console.Error.

This function appears in FANN >= 1.1.0.

public void DisableSeedRand()

Disables the automatic random generator seeding that happens in FANN.

Per default FANN will always seed the random generator when creating a new network, unless FANN_NO_SEED is defined during compilation of the library.  This method can disable this at runtime.

This function appears in FANN >= 2.3.0

public void EnableSeedRand()

Enables the automatic random generator seeding that happens in FANN.

Per default FANN will always seed the random generator when creating a new network, unless FANN_NO_SEED is defined during compilation of the library.  This method can disable this at runtime.

This function appears in FANN >= 2.3.0