Bausager
7fe6be9ba7
Sync public mirror / sync (push) Successful in 27s
Made the backwards for dense layers and ReLU, but I think I soon need to refactor some code to make it all steamlined.
54 lines
1.2 KiB
C++
54 lines
1.2 KiB
C++
#pragma once
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#include "./core/omp_config.h"
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#include "./utils/vector.h"
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#include "./utils/matrix.h"
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#include "./utils/random.h"
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namespace neural_networks{
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template <typename T>
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struct Dense_Layer{
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utils::Matrix<T> _inputs;
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utils::Matrix<T> weights;
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utils::Vector<T> biases;
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utils::Matrix<T> outputs;
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utils::Matrix<T> dweights;
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utils::Vector<T> dbiases;
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utils::Matrix<T> dinputs;
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// Default Constructor
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Dense_Layer() = default;
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// Constructor
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Dense_Layer(const uint64_t n_inputs, const uint64_t n_neurons){
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weights.random(n_inputs, n_neurons, -1, 1);
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biases.resize(n_neurons, T{0});
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}
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void forward(const utils::Matrix<T>& inputs){
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_inputs = inputs;
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outputs = numerics::matadd(numerics::matmul_auto(inputs, weights), biases, "row");
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}
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void backward(const utils::Matrix<T>& dvalues){
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// Gradients on parameters
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dweights = numerics::matmul(numerics::transpose(_inputs), dvalues);
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dbiases = numerics::matsum(dvalues, "row");
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//Gradient on values
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dinputs = numerics::matmul(dvalues, numerics::transpose(weights));
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}
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};
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} // end namespace neural_networks
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