diff --git a/esp32/lib/ESP32-sveltekit/NeuralNetwork.cpp b/esp32/lib/ESP32-sveltekit/NeuralNetwork.cpp deleted file mode 100644 index 20a553c..0000000 --- a/esp32/lib/ESP32-sveltekit/NeuralNetwork.cpp +++ /dev/null @@ -1,67 +0,0 @@ -#include "NeuralNetwork.h" - -#include "tensorflow/lite/micro/all_ops_resolver.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" -#include "tensorflow/lite/micro/micro_interpreter.h" -#include "tensorflow/lite/schema/schema_generated.h" -#include "tensorflow/lite/version.h" - -NeuralNetwork::NeuralNetwork(const void *model_data, const int kArenaSize) : _kArenaSize(kArenaSize) { - error_reporter = new tflite::MicroErrorReporter(); - - model = tflite::GetModel(model_data); - if (model->version() != TFLITE_SCHEMA_VERSION) { - TF_LITE_REPORT_ERROR(error_reporter, "Model provided is schema version %d not equal to supported version %d.", - model->version(), TFLITE_SCHEMA_VERSION); - return; - } - // This pulls in the operators implementations we need - resolver = new tflite::MicroMutableOpResolver<10>(); - resolver->AddFullyConnected(); - resolver->AddMul(); - resolver->AddAdd(); - resolver->AddLogistic(); - resolver->AddReshape(); - resolver->AddQuantize(); - resolver->AddDequantize(); - - tensor_arena = (uint8_t *)malloc(_kArenaSize); - if (!tensor_arena) { - TF_LITE_REPORT_ERROR(error_reporter, "Could not allocate arena"); - return; - } - - // Build an interpreter to run the model with. - interpreter = new tflite::MicroInterpreter(model, *resolver, tensor_arena, _kArenaSize, error_reporter); - - // Allocate memory from the tensor_arena for the model's tensors. - TfLiteStatus allocate_status = interpreter->AllocateTensors(); - if (allocate_status != kTfLiteOk) { - TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed"); - return; - } - - size_t used_bytes = interpreter->arena_used_bytes(); - TF_LITE_REPORT_ERROR(error_reporter, "Used bytes %d\n", used_bytes); - - // Obtain pointers to the model's input and output tensors. - input = interpreter->input(0); - output = interpreter->output(0); -} - -void NeuralNetwork::setInput(float value) { input->data.f[0] = value; } - -float NeuralNetwork::predict(float value) { - setInput(value); - return predict(); -} - -float NeuralNetwork::predict() { - TfLiteStatus invoke_status = interpreter->Invoke(); - if (invoke_status != kTfLiteOk) { - TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed!"); - return -1; - } - - return output->data.f[0]; -} \ No newline at end of file diff --git a/esp32/lib/ESP32-sveltekit/NeuralNetwork.h b/esp32/lib/ESP32-sveltekit/NeuralNetwork.h deleted file mode 100644 index 891e7f8..0000000 --- a/esp32/lib/ESP32-sveltekit/NeuralNetwork.h +++ /dev/null @@ -1,38 +0,0 @@ -#ifndef __NeuralNetwork__ -#define __NeuralNetwork__ - -#include "model.h" -#include - -namespace tflite { -template -class MicroMutableOpResolver; -class ErrorReporter; -class Model; -class MicroInterpreter; -} // namespace tflite - -struct TfLiteTensor; - -class NeuralNetwork { - private: - tflite::MicroMutableOpResolver<10> *resolver; - tflite::ErrorReporter *error_reporter; - tflite::MicroInterpreter *interpreter; - const tflite::Model *model; - uint8_t *tensor_arena; - TfLiteTensor *input; - TfLiteTensor *output; - const int _kArenaSize = 20000; - - void setInput(float value); - - float predict(); - - public: - NeuralNetwork(const void *model_data = g_model, const int kArenaSize = 20000); - - float predict(float value); -}; - -#endif \ No newline at end of file diff --git a/esp32/src/main.cpp b/esp32/src/main.cpp index a5aaea4..eb0bbb5 100644 --- a/esp32/src/main.cpp +++ b/esp32/src/main.cpp @@ -1,13 +1,8 @@ #include #include -#include "NeuralNetwork.h" -#include - #define SERIAL_BAUD_RATE 115200 -NeuralNetwork *nn; - DRAM_ATTR PsychicHttpServer server; DRAM_ATTR ESP32SvelteKit spot(&server, 130); @@ -16,23 +11,6 @@ void setup() { Serial.begin(SERIAL_BAUD_RATE); spot.begin(); - - nn = new NeuralNetwork(); } -void run_nn() { - float x = random(6); - x = max(min(x, 6.28f), 0.0f); - - float y_pred = nn->predict(x); - float y = sin(x); - - float error = abs(y_pred - y); - - ESP_LOGI("Run nn", "Predicted sin:%.2f,\t actual:%.2f,\t error%.2f", y_pred, y, error); -} - -void loop() { - EXECUTE_EVERY_N_MS(1000, TIME_IT(run_nn())); - spot.loop(); -} +void loop() { vTaskDelete(NULL); } \ No newline at end of file diff --git a/esp32/src/model.h b/esp32/src/model.h deleted file mode 100644 index 025c34a..0000000 --- a/esp32/src/model.h +++ /dev/null @@ -1,162 +0,0 @@ -alignas(16) const unsigned char g_model[] = { - 0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00, - 0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, - 0x08, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, - 0x80, 0x00, 0x00, 0x00, 0xd8, 0x00, 0x00, 0x00, 0x3c, 0x02, 0x00, 0x00, - 0x4c, 0x02, 0x00, 0x00, 0xf0, 0x06, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, - 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x76, 0xfd, 0xff, 0xff, - 0x0c, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, - 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