Bausager
189d605bfa
added the dense layer with a forward functions. I also made the add functions for most cases.
112 lines
4.2 KiB
C++
112 lines
4.2 KiB
C++
/**++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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*
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* PANIC
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* Portable Algorithms and Numerics In C++
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*
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* Scientific computing from scratch, with feeling.
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*
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* Copyright (c) 2026 Michelle Bausager
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*
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* This file is part of PANIC.
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*
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* PANIC is free software licensed under the GNU General Public License v3.0 or later.
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* You may redistribute and/or modify it under the terms of the GPL.
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*
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* PANIC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
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* without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
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* See the LICENSE file for the full license text.
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*
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* SPDX-License-Identifier: GPL-3.0-or-later
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*
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*++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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*
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* Project Name: PANIC
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* Module Name: neural_network
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* File Name: layer_dense.hpp
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* Revision: 0.1.0
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* Date: 23-06-2026
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* Author: Michelle Bausager
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*
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* Description:
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* Defines the dense layers used in neural network
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*
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*++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++*/
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#pragma once
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//---------------------------------------------------------------------------------------------------------------------------
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// INCLUDE DESCRIPTION
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//---------------------------------------------------------------------------------------------------------------------------
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#include <config/types.hpp> // panic::uint_t, panic::int_t, and panic::real_t
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#include <neural_network/layer/layer.hpp> // for base layer struct
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#include <tensor/vector.hpp>
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#include <tensor/matrix.hpp>
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//---------------------------------------------------------------------------------------------------------------------------
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// DEFINE DESCRIPTION
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//---------------------------------------------------------------------------------------------------------------------------
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//---------------------------------------------------------------------------------------------------------------------------
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// TYPE DESCRIPTION
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//---------------------------------------------------------------------------------------------------------------------------
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//--------------------------------------------------------------------------------------------------------------------------
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// Function Name : panic::neural_network::layer_dense
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//
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// Description:
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// Dense/fully-connected neural network layer.
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//
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// Inputs:
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// Samples x input size const panic::tensor::real_matrix&
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//
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// Weight shape:
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// input_size x neuron_count
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//
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// Bias shape:
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// 1 x neuron_count
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//
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// Output shape:
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// samples x neuron_count panic::tensor::real_matrix
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//
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// Notes:
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// forward(input) calculates:
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//
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// outputs = inputs * weights + biases
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//--------------------------------------------------------------------------------------------------------------------------
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namespace panic{
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namespace neural_network{
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struct layer_dense : public layer{
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panic::tensor::real_matrix weights;
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panic::tensor::real_vector biases;
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panic::tensor::real_matrix outputs;
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// Empthy contructor
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layer_dense();
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// Contructor with layer size
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layer_dense(panic::types::uint_t input_size, panic::types::uint_t neurons);
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// Decontructor (All internal variables has decontructors, so we can just use default)
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~layer_dense() = default;
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// Forward function for forward pass
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bool forward(const panic::tensor::real_matrix& inputs);
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// Backward pass
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bool backward(const panic::tensor::real_matrix& dinpus);
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};
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} // namespace tensor
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} // namespace panic
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//---------------------------------------------------------------------------------------------------------------------------
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// VARIABLE DESCRIPTION
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//---------------------------------------------------------------------------------------------------------------------------
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//---------------------------------------------------------------------------------------------------------------------------
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// FUNCTION PROTOTYPE
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//---------------------------------------------------------------------------------------------------------------------------
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