ok

ANN/Python/ANN_training.py

@@ -11,11 +11,9 @@ from matplotlib import pyplot as plt
 
 
 def train_ANN(saveWeighs=False, saveAlgo=False):
-    # dataset = r'features.csv'
     dataset = "build/Extraction/features.csv"
     df = pd.read_csv(dataset, header=0)
     features = df.columns.values[:-2]
-    # print(features)
     Y = df.Style.values
     X = df.values
     classes = np.unique(Y)
@@ -24,7 +22,7 @@ def train_ANN(saveWeighs=False, saveAlgo=False):
         X, Y, test_size=0.33, random_state=42)
     X_test, X_val, Y_test, Y_val = train_test_split(
         X_test, Y_test, test_size=0.33, random_state=42)
-
+    # Save test'set to a csv in order to compute accuracy in cpp
     X_train = np.delete(X_train, [-1, -2], axis=1)
     X_val = np.delete(X_val, [-1, -2], axis=1)
     new_df = pd.DataFrame(X_test)
@@ -67,11 +65,11 @@ def train_ANN(saveWeighs=False, saveAlgo=False):
     plt.gca().set_ylim(0, 2)  # set the vertical range to [0-1]
     plt.show()
 
+    # Save weights to a .h file
     list_weights = []
     for layer in model.layers:
         weights = layer.get_weights()
         list_weights.append(weights)
-        # print(str(layer.name) + ":" + str(weights))
     mean = scaler.mean_
     std = np.sqrt(scaler.var_)
     mean = transformListToStr(mean)
@@ -92,27 +90,6 @@ def train_ANN(saveWeighs=False, saveAlgo=False):
     return model, history
 
 
-def save_model(model, history):
-    # Save model
-    RESULT_PATH = code_folder+'/results'
-
-    # description
-    model_yaml = model.to_json()
-    with open(RESULT_PATH+"/modelANN.json", "w") as yaml_file:
-        yaml_file.write(model_yaml)
-
-    # save model
-    model.save(RESULT_PATH+"/modelANN.h5")
-
-    # save weights of the model
-    model.save_weights(RESULT_PATH+"/modelANN.h5")
-    print("Model saved to disk")
-
-    # Fit history saving
-    with open(RESULT_PATH+'/trainHistoryANN', 'wb') as file_pi:
-        pickle.dump(history.history, file_pi)
-
-
 def transformArrayToStr(array):
     array = array.T
     text = "{"