74 lines
3.3 KiB
C
74 lines
3.3 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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* ╚═════╝ ╚═════╝ ╚═╝ ╚══════╝
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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_HLAYER_CONVOLUTION_H
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#define _GCTL_DNN_HLAYER_CONVOLUTION_H
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#include "hlayer.h"
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namespace gctl
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{
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class convolution : public dnn_hlayer
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{
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public:
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convolution();
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convolution(int p_st, int channels, int in_rows, int in_cols, int filter_rows, int filter_cols,
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int stride_rows, int stride_cols, pad_type_e pl_type, activation_type_e acti_type);
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virtual ~convolution();
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void forward_propagation(const array<double> &all_weights, const matrix<double> &prev_layer_data);
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void backward_propagation(const array<double> &all_weights, const array<double> &all_ders,
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const matrix<double> &prev_layer_data, const matrix<double> &next_layer_data);
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hlayer_type_e get_layer_type() const;
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std::string get_layer_name() const;
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std::string layer_info() const;
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void save_layer_setup(std::ofstream &os) const;
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void load_layer_setup(std::ifstream &is);
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void save_weights2text(const array<double> &all_weights, std::ofstream &os) const;
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private:
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void init_convolution(int p_st, int channels, int in_rows, int in_cols, int filter_rows, int filter_cols, int stride_rows,
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int stride_cols, pad_type_e pl_type, activation_type_e acti_type);
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private:
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int i_rows_, i_cols_, f_rows_, f_cols_, s_rows_, s_cols_;
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int o_rows_, o_cols_;
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int l_pad_, u_pad_;
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int chls_;
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int w_st_; // weight start index
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int b_st_; // bias start index
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array<int> all_ders_count_;
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matrix<int> der_in_count_;
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array<int> p_idx_;
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pad_type_e p_type_;
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};
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}
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#endif // _GCTL_DNN_HLAYER_CONVOLUTION_H
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