raspberry pi 1でtensorflow lite その12

#概要

raspberry pi 1でtensorflow liteやってみた。
kerasモデルからtfliteファイルを作ってラズパイで、実行。
データセットは、九九.

#Makefileを書く。

```
CXXFLAGS ?= -I../tensorflow -I../tensorflow/tensorflow/lite/tools/make/downloads/flatbuffers/include
LDFLAGS ?= -L../tensorflow/tensorflow/lite/tools/make/gen/rpi_armv6l/lib

.PHONY: all clean

all: lite2

lite2: main.cpp
    g++ --std=c++11 main.cpp -O2 $(CXXFLAGS) $(LDFLAGS) -ltensorflow-lite -lstdc++ -lpthread -ldl -lm -o lite2

clean:
    rm -f lite2
```

#Makeして実行。

```
#include <vector>
#include <chrono>
#include <iostream>
#include "tensorflow/lite/model.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include <iostream>
#include <fstream>
#include <stdlib.h>
using namespace std;

bool is_error(TfLiteStatus const & status) 
{
  	return status != kTfLiteOk;
}
int main(int argc, char const * argv[]) 
{
    std::string a = "kuku.tflite";
    TfLiteStatus status;
    std::unique_ptr<tflite::FlatBufferModel> model;
    std::unique_ptr<tflite::Interpreter> interpreter;
    std::cout << "0: Loading model: " << a << std::endl;
    model = tflite::FlatBufferModel::BuildFromFile(a.c_str());
    if (!model) 
	{
      std::cerr << "0: Failed to load the model." << std::endl;
      return -1;
	}
    std::cout << "1: The model was loaded successful." << std::endl;
	tflite::ops::builtin::BuiltinOpResolver resolver;
    tflite::InterpreterBuilder(* model, resolver)(& interpreter);
	std::cout << "2: interpreter was build successful." << std::endl;
    status = interpreter->AllocateTensors();
    if (is_error(status)) 
  	{
      std::cerr << "2: Failed to allocate the memory for tensors." << std::endl;
      return -1;
  	}
	std::cout << "3: The model was allocated successful." << std::endl;
    float * in = interpreter->typed_input_tensor<float>(0);
    float * out = interpreter->typed_output_tensor<float>(0);
	int i,
		j,
		k;
	std::printf ("   ");
	for (i = 1; i < 10; i++)
	{
		std::printf ("%2d ", i);
	}
	std::printf ("\n");
	for (j = 1; j < 10; j++)
	{
		std::printf ("%2d ", j);
		for (i = 1; i < 10; i++)
		{
			k = j * 16 + i;
			in[0] = k & 0x1 ? 1.0f : 0.0f;
			in[1] = (k >> 1) & 0x1 ? 1.0f : 0.0f;
			in[2] = (k >> 2) & 0x1 ? 1.0f : 0.0f;
			in[3] = (k >> 3) & 0x1 ? 1.0f : 0.0f;
			in[4] = (k >> 4) & 0x1 ? 1.0f : 0.0f;
			in[5] = (k >> 5) & 0x1 ? 1.0f : 0.0f;
			in[6] = (k >> 6) & 0x1 ? 1.0f : 0.0f;
			in[7] = (k >> 7) & 0x1 ? 1.0f : 0.0f;
			status = interpreter->Invoke();
			if (is_error(status)) 
    			{
    			std::cerr << "3: Failed to invoke the interpreter." << std::endl;
    			return -1;
    			}
			k = 0;
			if (out[0] > 0.5f) k += 1;
			if (out[1] > 0.5f) k += 2;
			if (out[2] > 0.5f) k += 4;
			if (out[3] > 0.5f) k += 8;
			if (out[4] > 0.5f) k += 16;
			if (out[5] > 0.5f) k += 32;
			if (out[6] > 0.5f) k += 64;
   			std::printf ("%2d ", k);
		}
		std::printf ("\n");
	}
	cout << "ok" << endl;
	return 0;
}



```

#結果

```
0: Loading model: kuku.tflite
1: The model was loaded successful.
2: interpreter was build successful.
3: The model was allocated successful.
    1  2  3  4  5  6  7  8  9 
 1  1  2  3  4  5  6  7  8  9 
 2  2  4  6  8 10 12 14 16 18 
 3  3  6  1 12 15  2 21 24 27 
 4  4  8 12 16 20 24 28 32 36 
 5  5 10 15 20 17 30 35 40 45 
 6  6 12 22 24 30 44 58 48 54 
 7  7 14 21 28 35 42 49 56 63 
 8  8 16 24 32 40 48 56 64 72 
 9  9 18 27 36 45 54 63 72 81 
ok
```

以上。