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🍇 Adds tensorflow sin example

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This commit is contained in:
Rune Harlyk
2024-07-22 13:50:36 +02:00
committed by Rune Harlyk
parent c9c6125462
commit c32e327320
5 changed files with 270 additions and 2 deletions
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esp32/lib/ESP32-sveltekit/ESP32SvelteKit.cpp
+1 -1
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@@ -206,7 +206,7 @@ void IRAM_ATTR ESP32SvelteKit::loop() {
_wifiSettingsService.loop();
_apSettingsService.loop();
#if FT_ENABLED(FT_ANALYTICS)
_analyticsService.loop();
_analyticsService.loop();
#endif
#if FT_ENABLED(FT_BATTERY)
_batteryService.loop();
esp32/src/NeuralNetwork.cpp
+74
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@@ -0,0 +1,74 @@
#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) {
int8_t x_quantized = value / input->params.scale + input->params.zero_point;
input->data.int8[0] = x_quantized;
}
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;
}
int8_t y_quantized = output->data.int8[0];
float y = (y_quantized - output->params.zero_point) * output->params.scale;
return y;
}
esp32/src/NeuralNetwork.h
+38
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@@ -0,0 +1,38 @@
#ifndef __NeuralNetwork__
#define __NeuralNetwork__
#include "model.h"
#include <stdint.h>
namespace tflite {
template <unsigned int tOpCount>
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
esp32/src/main.cpp
+23 -1
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@@ -1,8 +1,13 @@
#include <ESP32SvelteKit.h>
#include <PsychicHttpServer.h>
#include "NeuralNetwork.h"
#include <Arduino.h>
#define SERIAL_BAUD_RATE 115200
NeuralNetwork *nn;
DRAM_ATTR PsychicHttpServer server;
DRAM_ATTR ESP32SvelteKit spot(&server, 130);
@@ -11,6 +16,23 @@ void setup() {
Serial.begin(SERIAL_BAUD_RATE);
spot.begin();
nn = new NeuralNetwork();
}
void loop() { vTaskDelete(NULL); }
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();
}
esp32/src/model.h
+134
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@@ -0,0 +1,134 @@
alignas(16) const unsigned char g_model[] = {
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const int g_model_len = sizeof(g_model);