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// Copyright (C) 2007 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_MLP_KERNEl_C_
#define DLIB_MLP_KERNEl_C_
#include "mlp_kernel_abstract.h"
#include "../algs.h"
#include "../assert.h"
namespace dlib
{
template <
typename mlp_base // is an implementation of mlp_kernel_abstract.h
>
class mlp_kernel_c : public mlp_base
{
long verify_constructor_args (
long nodes_in_input_layer,
long nodes_in_first_hidden_layer,
long nodes_in_second_hidden_layer,
long nodes_in_output_layer
)
{
// make sure requires clause is not broken
DLIB_CASSERT(nodes_in_input_layer > 0 &&
nodes_in_first_hidden_layer > 0 &&
nodes_in_second_hidden_layer >= 0 &&
nodes_in_output_layer > 0,
"\tconst mlp::constructor()"
<< "\n\tinvalid constructor arguments"
<< "\n\tnodes_in_input_layer: " << nodes_in_input_layer
<< "\n\tnodes_in_first_hidden_layer: " << nodes_in_first_hidden_layer
<< "\n\tnodes_in_second_hidden_layer: " << nodes_in_second_hidden_layer
<< "\n\tnodes_in_output_layer: " << nodes_in_output_layer
);
return nodes_in_input_layer;
}
public:
mlp_kernel_c (
long nodes_in_input_layer,
long nodes_in_first_hidden_layer,
long nodes_in_second_hidden_layer = 0,
long nodes_in_output_layer = 1,
double alpha = 0.1,
double momentum = 0.8
) : mlp_base( verify_constructor_args(
nodes_in_input_layer,
nodes_in_input_layer,
nodes_in_second_hidden_layer,
nodes_in_output_layer),
nodes_in_first_hidden_layer,
nodes_in_second_hidden_layer,
nodes_in_output_layer,
alpha,
momentum)
{
}
template <typename EXP>
const matrix<double> operator() (
const matrix_exp<EXP>& in
) const
{
// make sure requires clause is not broken
DLIB_CASSERT(in.nr() == this->input_layer_nodes() &&
in.nc() == 1,
"\tconst matrix<double> mlp::operator()(matrix_exp)"
<< "\n\tthe input matrix dimensions are not correct"
<< "\n\tin.nr(): " << in.nr()
<< "\n\tin.nc(): " << in.nc()
<< "\n\tinput_layer_nodes(): " << this->input_layer_nodes()
<< "\n\tthis: " << this
);
return mlp_base::operator()(in);
}
template <typename EXP1, typename EXP2>
void train (
const matrix_exp<EXP1>& example_in,
const matrix_exp<EXP2>& example_out
)
{
// make sure requires clause is not broken
DLIB_CASSERT(example_in.nr() == this->input_layer_nodes() &&
example_in.nc() == 1 &&
example_out.nr() == this->output_layer_nodes() &&
example_out.nc() == 1 &&
max(example_out) <= 1.0 && min(example_out) >= 0.0,
"\tvoid mlp::train(matrix_exp, matrix_exp)"
<< "\n\tthe training example dimensions are not correct"
<< "\n\texample_in.nr(): " << example_in.nr()
<< "\n\texample_in.nc(): " << example_in.nc()
<< "\n\texample_out.nr(): " << example_out.nr()
<< "\n\texample_out.nc(): " << example_out.nc()
<< "\n\tmax(example_out): " << max(example_out)
<< "\n\tmin(example_out): " << min(example_out)
<< "\n\tinput_layer_nodes(): " << this->input_layer_nodes()
<< "\n\toutput_layer_nodes(): " << this->output_layer_nodes()
<< "\n\tthis: " << this
);
mlp_base::train(example_in,example_out);
}
template <typename EXP>
void train (
const matrix_exp<EXP>& example_in,
double example_out
)
{
// make sure requires clause is not broken
DLIB_CASSERT(example_in.nr() == this->input_layer_nodes() &&
example_in.nc() == 1 &&
this->output_layer_nodes() == 1 &&
example_out <= 1.0 && example_out >= 0.0,
"\tvoid mlp::train(matrix_exp, double)"
<< "\n\tthe training example dimensions are not correct"
<< "\n\texample_in.nr(): " << example_in.nr()
<< "\n\texample_in.nc(): " << example_in.nc()
<< "\n\texample_out: " << example_out
<< "\n\tinput_layer_nodes(): " << this->input_layer_nodes()
<< "\n\toutput_layer_nodes(): " << this->output_layer_nodes()
<< "\n\tthis: " << this
);
mlp_base::train(example_in,example_out);
}
};
template <
typename mlp_base
>
inline void swap (
mlp_kernel_c<mlp_base>& a,
mlp_kernel_c<mlp_base>& b
) { a.swap(b); }
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_MLP_KERNEl_C_
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