    // Copyright (C) 2016  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_DNn_UTILITIES_ABSTRACT_H_
    #ifdef DLIB_DNn_UTILITIES_ABSTRACT_H_

    #include "core_abstract.h"
    #include "../geometry/vector_abstract.h"

    namespace dlib
    {

    // ----------------------------------------------------------------------------------------

        double log1pexp(
            double x
        );
        /*!
            ensures
                - returns log(1+exp(x))
                  (except computes it using a numerically accurate method)
        !*/

    // ----------------------------------------------------------------------------------------

        void randomize_parameters (
            tensor& params,
            unsigned long num_inputs_and_outputs,
            dlib::rand& rnd
        );
        /*!
            ensures
                - This function assigns random values into params based on the given random
                  number generator.  In particular, it uses the parameter initialization method
                  of formula 16 from the paper "Understanding the difficulty of training deep
                  feedforward neural networks" by Xavier Glorot and Yoshua Bengio.
                - It is assumed that the total number of inputs and outputs from the layer is
                  num_inputs_and_outputs.  That is, you should set num_inputs_and_outputs to
                  the sum of the dimensionalities of the vectors going into and out of the
                  layer that uses params as its parameters.
        !*/

    // ----------------------------------------------------------------------------------------
    }

    #endif // DLIB_DNn_UTILITIES_ABSTRACT_H_