बोर्ड पर डिप्लॉय करना और टेस्ट करना

# What "convert the model to a C array" means: the model file is just bytes.
# Here we use a few made-up bytes instead of a real .tflite file.
model_bytes = bytes([0x1C, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4C, 0x33, 0x14, 0x00, 0x20, 0x00])

lines = []
for i in range(0, len(model_bytes), 8):
    chunk = ", ".join(f"0x{b:02x}" for b in model_bytes[i:i + 8])
    lines.append("  " + chunk + ",")
print("alignas(16) const unsigned char g_model_data[] = {")
print("\n".join(lines))
print("};")
print(f"const unsigned int g_model_data_len = {len(model_bytes)};")

#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "model_data.h"   // g_model_data: the C array made from the .tflite file

constexpr int kArenaSize = 20 * 1024;           // working memory; measure and adjust
alignas(16) static uint8_t tensor_arena[kArenaSize];

static tflite::MicroMutableOpResolver<3> resolver;  // only the layer types the model uses
static tflite::MicroInterpreter* interpreter = nullptr;

void setup_model() {
  const tflite::Model* model = tflite::GetModel(g_model_data);
  resolver.AddFullyConnected();
  resolver.AddRelu();
  resolver.AddSoftmax();
  static tflite::MicroInterpreter interp(model, resolver, tensor_arena, kArenaSize);
  interpreter = &interp;
  interpreter->AllocateTensors();                // fails if the arena is too small
}

int classify(const float* features, int n) {
  TfLiteTensor* input = interpreter->input(0);
  for (int i = 0; i < n; i++) {                  // float feature -> int8, same scale as training
    int q = (int)roundf(features[i] / input->params.scale) + input->params.zero_point;
    input->data.int8[i] = (int8_t)(q < -128 ? -128 : (q > 127 ? 127 : q));
  }
  if (interpreter->Invoke() != kTfLiteOk) return -1;
  TfLiteTensor* output = interpreter->output(0);
  int best = 0;
  for (int c = 1; c < output->dims->data[1]; c++)
    if (output->data.int8[c] > output->data.int8[best]) best = c;
  return best;                                   // index of the most likely class
}