SimplePCNetwork.h

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#pragma once
#include <vector>
#include <memory>
#include <random>
#include <deepity/layers/SimplePCLayer.h>
#include <deepity/utils/MemoryArena.h>
#include <deepity/utils/DeviceMemoryArena.h>
#include <deepity/backend/Backend.h>

 
namespace Deep
{
    class SimplePCNetwork
    {
    public:
        explicit SimplePCNetwork(int batchSize, DeviceType device = DeviceType::DEVICE_CPU) noexcept;
 
        SimplePCNetwork(const SimplePCNetwork &) = delete;
        SimplePCNetwork &operator=(const SimplePCNetwork &) = delete;
 
        ~SimplePCNetwork() = default;

        void AddLayer(int size, int nextSize, float lr, float ir, float lmbda,
                      void (*act)(float *, size_t), void (*dAct)(float *, size_t, bool));

        void AddLayer(int size, int nextSize, float lr, float ir, float lmbda,
                      ActivationType aType, ActivationType dType);

        void RandomizeWeights(std::mt19937 &rng, const char *distribution);

        void RandomizeWeights(std::mt19937 &rng);

        void ResetState() noexcept;

        void Clamp(const std::vector<float> &input);

        float CalculateState(bool needEnergy = true);

        void UpdateState();

        void UpdateWeights();
 
        // NOTE: no UpdatePrecision() -- precision doesn't exist in this class.

        SimplePCLayer *GetTerminalLayer() noexcept { return layers.back().get(); }

        std::vector<std::unique_ptr<SimplePCLayer>> &GetLayers() noexcept { return layers; }

        const std::vector<std::unique_ptr<SimplePCLayer>> &GetLayers() const noexcept { return layers; }

        int GetBatchSize() const noexcept { return batchSize; }

        DeviceType GetDevice() const noexcept { return device; }

        void SetOptimizer(OptimizerType o) noexcept
        {
            for (auto &layer : layers)
                layer->SetOptimizer(o);
        }

        float TrainStep(const std::vector<float> &x, const std::vector<float> &y, int inferenceSteps);

        std::vector<float> Predict(const std::vector<float> &x, int inferenceSteps);

        void ProjectForward() noexcept;

        float TrainStepWithProjection(const std::vector<float> &x, const std::vector<float> &y,
                                      int inferenceSteps, bool computeEnergy = true);

        std::vector<float> PredictWithProjection(const std::vector<float> &x, int inferenceSteps);
        void SetMuCacheThreshold(float threshold) noexcept;

        void Compile();
 
    private:
        std::vector<std::unique_ptr<SimplePCLayer>> layers;

        std::unique_ptr<IComputeBackend> backend;
        DeviceType device;
 
        bool graphCaptured = false;
        int capturedInferenceSteps = -1;

        std::unique_ptr<MemoryArena> cpuArena;
#if defined(DEEPITY_USE_CUDA)
        std::unique_ptr<DeviceMemoryArena> gpuArena;
#endif
 
        int batchSize;
    };
}