Bausager
642bba1198
I fixed the activation+loss function, you can't select it directly, it automaticly uses it if it can. I also fixed one-hot generator. Still haven't tested the activation + loss nor any other backward function. I'll do that when I get to the optimizers which is next.
151 lines
5.3 KiB
C++
151 lines
5.3 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.cpp
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* Revision: 0.1.0
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* Date: 28-07-2026
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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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//---------------------------------------------------------------------------------------------------------------------------
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// INCLUDE DESCRIPTION
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//---------------------------------------------------------------------------------------------------------------------------
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#include <neural_network/layer/layer_dense.hpp>
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#include <config/omp.hpp>
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#include <math/matmul.hpp>
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#include <math/mul.hpp>
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#include <math/add.hpp>
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#include <random/uniform.hpp>
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#include <math/transpose.hpp>
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#include <math/sum.hpp>
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//---------------------------------------------------------------------------------------------------------------------------
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// PRIVATE CONSTANTS
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//---------------------------------------------------------------------------------------------------------------------------
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/**
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* @brief Minimum number of element operations before using the OpenMP-enabled loop.
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*
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* Small vectors and matrices are kept serial because the overhead of starting
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* worker threads can be larger than the work itself.
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*/
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static const panic::types::uint_t layer_dense_omp_min_size = 500;
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//---------------------------------------------------------------------------------------------------------------------------
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// INPLEMENTATION
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//---------------------------------------------------------------------------------------------------------------------------
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namespace panic{
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namespace neural_network{
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//--------------------------------------------------------------------------------------------------------------------------
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// Constructor Name : panic::neural_network::layer_dense
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//
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// Description:
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// Creates an empty layer.
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//--------------------------------------------------------------------------------------------------------------------------
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layer_dense::layer_dense() {
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weights.resize(0,0);
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biases.resize(0);
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dweights.resize(0,0);
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dbiases.resize(0);
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}
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//--------------------------------------------------------------------------------------------------------------------------
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// Constructor Name : panic::neural_network::layer_dense
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//
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// Description:
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// Creates an empty layer with neurons.
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//--------------------------------------------------------------------------------------------------------------------------
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layer_dense::layer_dense(panic::types::uint_t input_size, panic::types::uint_t neurons) {
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weights.resize(input_size, neurons);
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panic::random::uniform(weights);
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panic::math::mul(weights, static_cast<panic::types::real_t>(0.01), weights);
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biases.resize(neurons);
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biases.fill(0);
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}
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//--------------------------------------------------------------------------------------------------------------------------
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// Function Name : panic::neural_network::layer_dense.forward
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//
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// Description:
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// Calculated the forward pass:
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// outputs = inputs * weights + biases
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//--------------------------------------------------------------------------------------------------------------------------
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bool layer_dense::forward(const panic::tensor::real_matrix& input_data){
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inputs = input_data;
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if (inputs.cols() != weights.rows()){
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return false;
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}
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if (!outputs.resize(inputs.rows(), weights.cols())){
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return false;
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}
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if (!panic::math::matmul(inputs, weights, outputs)){
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return false;
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}
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if (!panic::math::add_rowwise(outputs, biases, outputs)){
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return false;
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}
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return true;
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}
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//--------------------------------------------------------------------------------------------------------------------------
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// Function Name : panic::neural_network::layer_dense.backward
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//
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// Description:
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// Calculated the backward pass:
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// ??
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//--------------------------------------------------------------------------------------------------------------------------
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bool layer_dense::backward(const panic::tensor::real_matrix& dvalues){
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// Gradients on parameters
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dweights = panic::math::matmul(panic::math::transpose(inputs), dvalues);
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dbiases = panic::math::sum_colwise(dvalues);
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// Gradients on values
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dinputs = panic::math::matmul(dvalues, panic::math::transpose(weights));
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return true;
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}
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} // namespace tensor
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} // namespace panic
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