89 lines
4.0 KiB
C++
89 lines
4.0 KiB
C++
/********************************************************
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* ██████╗ ██████╗████████╗██╗
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* ██╔════╝ ██╔════╝╚══██╔══╝██║
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* ██║ ███╗██║ ██║ ██║
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* ██║ ██║██║ ██║ ██║
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* ╚██████╔╝╚██████╗ ██║ ███████╗
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* ╚═════╝ ╚═════╝ ╚═╝ ╚══════╝
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* Geophysical Computational Tools & Library (GCTL)
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*
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* Copyright (c) 2022 Yi Zhang (yizhang-geo@zju.edu.cn)
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*
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* GCTL is distributed under a dual licensing scheme. You can redistribute
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* it and/or modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation, either version 2
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* of the License, or (at your option) any later version. You should have
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* received a copy of the GNU Lesser General Public License along with this
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* program. If not, see <http://www.gnu.org/licenses/>.
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*
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* If the terms and conditions of the LGPL v.2. would prevent you from using
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* the GCTL, please consider the option to obtain a commercial license for a
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* fee. These licenses are offered by the GCTL's original author. As a rule,
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* licenses are provided "as-is", unlimited in time for a one time fee. Please
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* send corresponding requests to: yizhang-geo@zju.edu.cn. Please do not forget
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* to include some description of your company and the realm of its activities.
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* Also add information on how to contact you by electronic and paper mail.
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******************************************************/
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#ifndef _GCTL_DNN_OLAYER_H
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#define _GCTL_DNN_OLAYER_H
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#include "gctl/core.h"
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#include "gctl/algorithms.h"
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namespace gctl
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{
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enum olayer_type_e
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{
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RegressionMSE,
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MultiClassEntropy,
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BinaryClassEntropy,
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};
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class dnn_olayer
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{
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public:
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dnn_olayer();
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virtual ~dnn_olayer();
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// The derivative of the input of this layer, which is also the derivative
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// of the output of previous layer
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const matrix<double> &backward_propagation_data();
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// Check the format of target data, e.g. in classification problems the
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// target data should be binary (either 0 or 1)
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virtual void check_target_data(const matrix<double> &target);
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// Another type of target data where each element is a class label
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// This version may not be sensible for regression tasks, so by default
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// we raise an exception
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virtual void check_target_data(const array<int> &target);
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// A combination of the forward stage and the back-propagation stage for the output layer
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// The computed derivative of the input should be stored in this layer, and can be retrieved by
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// the backward_propagation_data() function
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virtual void evaluation(const matrix<double> &prev_layer_data, const matrix<double> &target) = 0;
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// Another type of target data where each element is a class label
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// This version may not be sensible for regression tasks, so by default
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// we raise an exception
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virtual void evaluation(const matrix<double> &prev_layer_data, const array<int> &target);
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// Return the loss function value after the evaluation
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// This function can be assumed to be called after evaluate(), so that it can make use of the
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// intermediate result to save some computation
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virtual double loss_value() const = 0;
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// Return the output layer name. It is used to export the NN model.
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virtual std::string get_output_name() const = 0;
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// Return the output layer type. It is used to export the NN model.
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virtual olayer_type_e get_output_type() const = 0;
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protected:
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matrix<double> der_in_; // Derivative of the input of this layer
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// Note that input of this layer is also the output of previous layer
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};
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}
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#endif // _GCTL_DNN_OLAYER_H
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