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include/lr.cuh

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+#ifndef LR_CUH
+#define	LR_CUH
+
+#include <string>
+#include <vector>
+#include <iostream>
+#include <helper_cuda.h>
+#include <assert.h>
+#include <nvmatrix.cuh>
+#include <matrix.h>
+#include <util.cuh>
+#include <Python.h>
+
+/*
+ * The maximum learning rate is _baseRate.
+ * The minimum learning rate is _baseRate / _tgtFactor.
+ *
+ * These classes define annealing schedules that interpolate between these
+ * two extrema.
+ */
+class LearningRateSchedule {
+protected:
+	double _baseRate, _noiseStdev, _randnSpare;
+	bool _haveRandnSpare;
+	virtual double _getRate(double progress);
+	double randn();
+	double rand() const;
+	double abs(double x) const;
+public:
+	LearningRateSchedule(double base);
+	LearningRateSchedule(double base, double noiseStdev);
+	double getRate(double progress);
+	double getBaseRate() const;
+	virtual ~LearningRateSchedule();
+
+	static LearningRateSchedule& make(PyObject* lrsDict, double base);
+};
+
+class LinearLRS : public LearningRateSchedule {
+protected:
+	double _finalRate;
+public:
+	LinearLRS(double base, double tgtFactor, double noiseStdev);
+	virtual double _getRate(double progress);
+};
+
+class ExpLRS : public LearningRateSchedule {
+protected:
+	double _pow;
+public:
+	ExpLRS(double baseRate, double tgtFactor, double noiseStdev);
+	virtual double _getRate(double progress);
+};
+
+class TanhLRS : public LearningRateSchedule {
+protected:
+	double _alpha, _beta;
+public:
+	TanhLRS(double baseRate, double tgtFactor, double noiseStdev);
+	virtual double _getRate(double progress);
+};
+
+class DiscreteExpLRS : public LearningRateSchedule {
+protected:
+	std::vector<double> _rates;
+public:
+	DiscreteExpLRS(double baseRate, double tgtFactor, double noiseStdev, int numSteps);
+	virtual double _getRate(double progress);
+};
+
+class JumpyDiscreteExpLRS : public DiscreteExpLRS {
+public:
+	JumpyDiscreteExpLRS(double baseRate, double tgtFactor, double noiseStdev, int numSteps);
+	virtual double _getRate(double progress);
+};
+
+#endif	/* LR_CUH */