provide supplementary functions to the main scripts

modify the .gitignore

@@ -3,3 +3,4 @@ first_matrix_connection_GO.csv
 go_level.csv
 go_level.csv
 X_test.npz
 X_test.npz
 X_train.npz
 X_train.npz
+__pycache__/

@@ -6,13 +6,11 @@ FLAGS = tf.app.flags.FLAGS
 class BaseModel():
 class BaseModel():
 
 
     def __init__(self,X,n_input,n_classes,n_hidden_1,n_hidden_2,n_hidden_3,n_hidden_4,n_hidden_5,n_hidden_6,is_training,keep_prob):
     def __init__(self,X,n_input,n_classes,n_hidden_1,n_hidden_2,n_hidden_3,n_hidden_4,n_hidden_5,n_hidden_6,is_training,keep_prob):
+        
+        
+        # Parameters
         self.X = X
         self.X = X
         self.n_input = n_input
         self.n_input = n_input
-        self.is_training = is_training
-
-        #Hyperparameters
-        self.keep_prob = keep_prob # Dropout
-
         self.n_classes=n_classes
         self.n_classes=n_classes
         self.n_hidden_1=n_hidden_1
         self.n_hidden_1=n_hidden_1
         self.n_hidden_2=n_hidden_2
         self.n_hidden_2=n_hidden_2
@@ -21,6 +19,11 @@ class BaseModel():
         self.n_hidden_5=n_hidden_5
         self.n_hidden_5=n_hidden_5
         self.n_hidden_6=n_hidden_6
         self.n_hidden_6=n_hidden_6
 
 
+        # Hyperparameters
+        self.keep_prob = keep_prob # Dropout
+        self.is_training = is_training # BN
+
+
     def store_layer_weights_and_bias(self):
     def store_layer_weights_and_bias(self):
         self.weights = {
         self.weights = {
             'h1_w': tf.get_variable('W1', shape=(self.n_input, self.n_hidden_1), initializer=tf.contrib.layers.variance_scaling_initializer()),
             'h1_w': tf.get_variable('W1', shape=(self.n_input, self.n_hidden_1), initializer=tf.contrib.layers.variance_scaling_initializer()),