opencv/modules/ml/doc/neural_networks.rst

Neural Networks
===============

ML implements feed-forward artificial neural networks, more particularly, multi-layer perceptrons (MLP), the most commonly used type of neural networks. MLP consists of the input layer, output layer, and one or more hidden layers. Each layer of MLP includes one or more neurons that are directionally linked with the neurons from the previous and the next layer. The example below represents a 3-layer perceptron with three inputs, two outputs, and the hidden layer including five neurons:

.. image:: pics/mlp.png

All the neurons in MLP are similar. Each of them has several input links (it takes the output values from several neurons in the previous layer as input) and several output links (it passes the response to several neurons in the next layer). The values retrieved from the previous layer are summed up with certain weights, individual for each neuron, plus the bias term. The sum is transformed using the activation function
:math:`f` that may be also different for different neurons. 

.. image:: pics/neuron_model.png

In other words, given the outputs
:math:`x_j` of the layer
:math:`n` , the outputs
:math:`y_i` of the layer
:math:`n+1` are computed as:

.. math::

    u_i =  \sum _j (w^{n+1}_{i,j}*x_j) + w^{n+1}_{i,bias}

.. math::

    y_i = f(u_i)

Different activation functions may be used. ML implements three standard functions:

*
    Identity function ( ``CvANN_MLP::IDENTITY``     ):
    :math:`f(x)=x`
*
    Symmetrical sigmoid ( ``CvANN_MLP::SIGMOID_SYM``     ):
    :math:`f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}`     ), which is the default choice for MLP. The standard sigmoid with
    :math:`\beta =1, \alpha =1`     is shown below:

    .. image:: pics/sigmoid_bipolar.png

*
    Gaussian function ( ``CvANN_MLP::GAUSSIAN``     ):
    :math:`f(x)=\beta e^{-\alpha x*x}`     , which is not completely supported at the moment.

In ML, all the neurons have the same activation functions, with the same free parameters (
:math:`\alpha, \beta` ) that are specified by user and are not altered by the training algorithms.

So, the whole trained network works as follows: 

#. It takes the feature vector as input. The vector size is equal to the size of the input layer.
#. Values are passed as input to the first hidden layer.
#. Outputs of the hidden layer are computed using the weights and the activation functions.
#. Outputs are passed further downstream until you compute the output layer.

So, to compute the network, you need to know all the
weights
:math:`w^{n+1)}_{i,j}` . The weights are computed by the training
algorithm. The algorithm takes a training set, multiple input vectors
with the corresponding output vectors, and iteratively adjusts the
weights to enable the network to give the desired response to the
provided input vectors.

The larger the network size (the number of hidden layers and their sizes) is,
the more the potential network flexibility is. The error on the
training set could be made arbitrarily small. But at the same time the
learned network also "learns" the noise present in the training set,
so the error on the test set usually starts increasing after the network
size reaches a limit. Besides, the larger networks are trained much
longer than the smaller ones, so it is reasonable to pre-process the data,
using
:ref:`PCA::operator ()` or similar technique, and train a smaller network
on only essential features.

Another feature of MLP's is their inability to handle categorical
data as is. However, there is a workaround. If a certain feature in the
input or output (in case of ``n`` -class classifier for
:math:`n>2` ) layer is categorical and can take
:math:`M>2` different values, it makes sense to represent it as a binary tuple of ``M`` elements, where the ``i`` -th element is 1 if and only if the
feature is equal to the ``i`` -th value out of ``M`` possible. It
increases the size of the input/output layer but speeds up the
training algorithm convergence and at the same time enables "fuzzy" values
of such variables, that is, a tuple of probabilities instead of a fixed value.

ML implements two algorithms for training MLP's. The first algorithm is a classical
random sequential back-propagation algorithm.
The second (default) one is a batch RPROP algorithm.

References:

*
    http://en.wikipedia.org/wiki/Backpropagation
    . Wikipedia article about the back-propagation algorithm.

*
    Y. LeCun, L. Bottou, G.B. Orr and K.-R. Muller, *Efficient backprop*, in Neural Networks---Tricks of the Trade, Springer Lecture Notes in Computer Sciences 1524, pp.5-50, 1998.

*
    M. Riedmiller and H. Braun, *A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm*, Proc. ICNN, San Francisco (1993).

.. index:: CvANN_MLP_TrainParams

.. _CvANN_MLP_TrainParams:

CvANN_MLP_TrainParams
---------------------
.. c:type:: CvANN_MLP_TrainParams

Parameters of the MLP training algorithm ::

    struct CvANN_MLP_TrainParams
    {
        CvANN_MLP_TrainParams();
        CvANN_MLP_TrainParams( CvTermCriteria term_crit, int train_method,
                               double param1, double param2=0 );
        ~CvANN_MLP_TrainParams();

        enum { BACKPROP=0, RPROP=1 };

        CvTermCriteria term_crit;
        int train_method;

        // back-propagation parameters
        double bp_dw_scale, bp_moment_scale;

        // rprop parameters
        double rp_dw0, rp_dw_plus, rp_dw_minus, rp_dw_min, rp_dw_max;
    };



The structure has a default constructor that initializes parameters for the ``RPROP`` algorithm. There is also a more advanced constructor to customize the parameters and/or choose the back-propagation algorithm. Finally, the individual parameters can be adjusted after the structure is created.

.. index:: CvANN_MLP

.. _CvANN_MLP:

CvANN_MLP
---------
.. c:type:: CvANN_MLP

MLP model ::

    class CvANN_MLP : public CvStatModel
    {
    public:
        CvANN_MLP();
        CvANN_MLP( const Mat& _layer_sizes,
                   int _activ_func=SIGMOID_SYM,
                   double _f_param1=0, double _f_param2=0 );

        virtual ~CvANN_MLP();

        virtual void create( const Mat& _layer_sizes,
                             int _activ_func=SIGMOID_SYM,
                             double _f_param1=0, double _f_param2=0 );

        virtual int train( const Mat& _inputs, const Mat& _outputs,
                           const Mat& _sample_weights,
                           const Mat& _sample_idx=Mat(),
                           CvANN_MLP_TrainParams _params = CvANN_MLP_TrainParams(),
                           int flags=0 );
        virtual float predict( const Mat& _inputs,
                               Mat& _outputs ) const;

        virtual void clear();

        // possible activation functions
        enum { IDENTITY = 0, SIGMOID_SYM = 1, GAUSSIAN = 2 };

        // available training flags
        enum { UPDATE_WEIGHTS = 1, NO_INPUT_SCALE = 2, NO_OUTPUT_SCALE = 4 };

        virtual void read( CvFileStorage* fs, CvFileNode* node );
        virtual void write( CvFileStorage* storage, const char* name );

        int get_layer_count() { return layer_sizes ? layer_sizes->cols : 0; }
        const Mat& get_layer_sizes() { return layer_sizes; }

    protected:

        virtual bool prepare_to_train( const Mat& _inputs, const Mat& _outputs,
                const Mat& _sample_weights, const Mat& _sample_idx,
                CvANN_MLP_TrainParams _params,
                CvVectors* _ivecs, CvVectors* _ovecs, double** _sw, int _flags );

        // sequential random backpropagation
        virtual int train_backprop( CvVectors _ivecs, CvVectors _ovecs,
                                                    const double* _sw );

        // RPROP algorithm
        virtual int train_rprop( CvVectors _ivecs, CvVectors _ovecs,
                                                 const double* _sw );

        virtual void calc_activ_func( Mat& xf, const double* bias ) const;
        virtual void calc_activ_func_deriv( Mat& xf, Mat& deriv,
                                                 const double* bias ) const;
        virtual void set_activ_func( int _activ_func=SIGMOID_SYM,
                                     double _f_param1=0, double _f_param2=0 );
        virtual void init_weights();
        virtual void scale_input( const Mat& _src, Mat& _dst ) const;
        virtual void scale_output( const Mat& _src, Mat& _dst ) const;
        virtual void calc_input_scale( const CvVectors* vecs, int flags );
        virtual void calc_output_scale( const CvVectors* vecs, int flags );

        virtual void write_params( CvFileStorage* fs );
        virtual void read_params( CvFileStorage* fs, CvFileNode* node );

        Mat& layer_sizes;
        Mat& wbuf;
        Mat& sample_weights;
        double** weights;
        double f_param1, f_param2;
        double min_val, max_val, min_val1, max_val1;
        int activ_func;
        int max_count, max_buf_sz;
        CvANN_MLP_TrainParams params;
        CvRNG rng;
    };
    


Unlike many other models in ML that are constructed and trained at once, in the MLP model these steps are separated. First, a network with the specified topology is created using the non-default constructor or the method ``create`` . All the weights are set to zeros. Then, the network is trained using a set of input and output vectors. The training procedure can be repeated more than once, that is, the weights can be adjusted based on the new training data.

.. index:: CvANN_MLP::create

.. _CvANN_MLP::create:

CvANN_MLP::create
-----------------
.. cpp:function:: void CvANN_MLP::create(  const Mat& _layer_sizes,                          int _activ_func=SIGMOID_SYM,                          double _f_param1=0,  double _f_param2=0 )

    Constructs MLP with the specified topology.

    :param _layer_sizes: Integer vector specifying the number of neurons in each layer including the input and output layers.

    :param _activ_func: Parameter specifying the activation function for each neuron: one of  ``CvANN_MLP::IDENTITY`` ,  ``CvANN_MLP::SIGMOID_SYM`` , and  ``CvANN_MLP::GAUSSIAN`` .

    :param _f_param1,_f_param2: Free parameters of the activation function,  :math:`\alpha`  and  :math:`\beta` , respectively. See the formulas in the introduction section.

The method creates an MLP network with the specified topology and assigns the same activation function to all the neurons.

.. index:: CvANN_MLP::train

.. _CvANN_MLP::train:

CvANN_MLP::train
----------------
.. cpp:function:: int CvANN_MLP::train(  const Mat& _inputs,  const Mat& _outputs,                        const Mat& _sample_weights,  const Mat& _sample_idx=Mat(),                        CvANN_MLP_TrainParams _params = CvANN_MLP_TrainParams(),                        int flags=0 )

    Trains/updates MLP.

    :param _inputs: Floating-point matrix of input vectors, one vector per row.

    :param _outputs: Floating-point matrix of the corresponding output vectors, one vector per row.

    :param _sample_weights: (RPROP only) Optional floating-point vector of weights for each sample. Some samples may be more important than others for training. You may want to raise the weight of certain classes to find the right balance between hit-rate and false-alarm rate, and so on.

    :param _sample_idx: Optional integer vector indicating the samples (rows of  ``_inputs``  and  ``_outputs`` ) that are taken into account.

    :param _params: Training parameters. See the ``CvANN_MLP_TrainParams``  description.

    :param _flags: Various parameters to control the training algorithm. A combination of the following parameters is possible:

            * **UPDATE_WEIGHTS = 1** Algorithm updates the network weights, rather than computes them from scratch (in the latter case the weights are initialized using the  Nguyen-Widrow  algorithm).

            * **NO_INPUT_SCALE** Algorithm does not normalize the input vectors. If this flag is not set, the training algorithm normalizes each input feature independently, shifting its mean value to 0 and making the standard deviation =1. If the network is assumed to be updated frequently, the new training data could be much different from original one. In this case, you should take care of proper normalization.

            * **NO_OUTPUT_SCALE** Algorithm does not normalize the output vectors. If the flag is not set, the training algorithm normalizes each output feature independently, by transforming it to the certain range depending on the used activation function.

This method applies the specified training algorithm to computing/adjusting the network weights. It returns the number of done iterations.