add modelinfo

regression/fuel.py

@@ -1,3 +1,10 @@
+#
+# LAA model
+#
+
+
+import json
+
 import pathlib
 
 import matplotlib.pyplot as plt
@@ -55,19 +62,26 @@
 train_stats = train_stats.transpose()
 train_stats
 print("train_stats:")
+print (type(train_stats))
+print(train_stats.to_numpy())
+
+
+train_stats.to_json('ModelInfo.txt')
+
 
-print(train_stats)
 
 train_labels = train_dataset.pop('MPG')
 test_labels = test_dataset.pop('MPG')
 
 
 def norm(x):
   return (x - train_stats['mean']) / train_stats['std']
+
 normed_train_data = norm(train_dataset)
 normed_test_data = norm(test_dataset)
 
 print("length key:"+str(len(train_dataset.keys())))
+
 def build_model():
   model = keras.Sequential([
     layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),

regression/testfroze.py

@@ -1,4 +1,7 @@
 
+#
+# model mpg
+#
 import tensorflow as tf
 from tensorflow import keras
 import numpy as np

service/examples/mpg/main.cpp

@@ -1,5 +1,7 @@
 #include "../../include/Model.h"
 #include "../../include/Tensor.h"
+#include "../../include/ModelInfos.h"
+
 #include <algorithm>
 #include <iterator>
 
@@ -8,10 +10,14 @@ using namespace std;
 
 int main() {
 
+
     // Create model
     Model m("../frozen_graph.pb");
     //m.restore("../checkpoint/train.ckpt");
 
+    ModelInfos<float> mInfos("../ModelInfo.txt");
+    mInfos.show();
+
     // Create Tensors
     Tensor input(m, "x");
     Tensor prediction(m, "Identity");