45193a2c01
- Remove platform-specific code (ARM_MATH_CM33, XMOS __XS3A__, std::printf) - Add set_output()/set_outputs()/add_example() API for programmatic training - Fix release build crash: side effect inside assert() for loss function init - Replace fake smoke test with real convergence tests (5 tests, all pass) - Rewrite example to demonstrate actual training with real output - Update README, CHANGELOG, and extraction plan to match reality
83 lines
2 KiB
C++
83 lines
2 KiB
C++
#ifndef NISPS_IML_HPP
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#define NISPS_IML_HPP
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#include "mlp.hpp"
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#include "dataset.hpp"
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#include <vector>
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#include <cstddef>
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#include <functional>
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namespace nisps {
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template<typename Float = float>
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class IML {
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public:
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enum class Mode { Inference, Training };
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using LogFn = void(*)(const char*);
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IML(size_t n_inputs, size_t n_outputs,
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std::vector<size_t> hidden_layers = {10, 10, 14},
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size_t max_iterations = 1000,
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Float learning_rate = 1.0f,
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Float convergence_threshold = 0.00001f);
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// Input
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void set_input(size_t index, Float value);
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void set_inputs(const Float* values, size_t count);
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// Output (valid after process())
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const Float* get_outputs() const;
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size_t num_inputs() const { return n_inputs_; }
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size_t num_outputs() const { return n_outputs_; }
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// Set outputs directly (for programmatic training without hardware)
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void set_output(size_t index, Float value);
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void set_outputs(const Float* values, size_t count);
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// Runtime
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void process();
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// Training workflow
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void set_mode(Mode mode);
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Mode get_mode() const { return mode_; }
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void save_example();
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void add_example(const Float* inputs, size_t n_in, const Float* outputs, size_t n_out);
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void clear_dataset();
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void randomise_weights();
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// Optional logging
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void set_logger(LogFn fn) { log_fn_ = fn; }
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private:
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void log(const char* msg) const {
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if (log_fn_) log_fn_(msg);
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}
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void train();
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size_t n_inputs_;
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size_t n_outputs_;
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size_t max_iterations_;
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Float learning_rate_;
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Float convergence_threshold_;
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Mode mode_ = Mode::Inference;
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bool input_updated_ = false;
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bool perform_inference_ = true;
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std::vector<Float> input_state_;
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std::vector<Float> output_state_;
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std::unique_ptr<Dataset> dataset_;
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std::unique_ptr<MLP<Float>> mlp_;
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typename MLP<Float>::mlp_weights stored_weights_;
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bool weights_randomised_ = false;
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LogFn log_fn_ = nullptr;
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};
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} // namespace nisps
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#include "iml_impl.hpp"
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#endif // NISPS_IML_HPP
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