diff --git a/REUNION/Bgru.py b/REUNION/Bgru.py
index 1f8a908868c8f483858b91f2d77fbda0bd1750f2..039e53550de5ba750ecab1f548391df48420eb08 100644
--- a/REUNION/Bgru.py
+++ b/REUNION/Bgru.py
@@ -35,34 +35,30 @@ def Bgru(x, nunits, nlayer, timesteps, nclasses, dropout):
 #def getPrediction(x_rnn, x_rnn_b, x_cnn, nunits, nlayer, n_classes, choice):
 def getPrediction(x, nunits, nlayer, n_classes, dropout, is_training):
 	n_timetamps = 34
-	prediction = None
-	features = None
 	features = Bgru( x, nunits, nlayer, n_timetamps, n_classes, dropout )
 	# Trainable parameters
-	print "output",features.get_shape()
+	print "output ",features.get_shape()
 	attention_size = int(nunits)
-	print "units",nunits
+	print "units ",nunits
 	print "att size",attention_size
 	W_omega = tf.Variable(tf.random_normal([nunits*2, attention_size], stddev=0.1))
-	print "womega",W_omega.get_shape()
+	print "womega ",W_omega.get_shape()
 	b_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
 	u_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
-
 	# Applying fully connected layer with non-linear activation to each of the B*T timestamps;
 	#  the shape of `v` is (B,T,D)*(D,A)=(B,T,A), where A=attention_size
 	v = tf.tanh(tf.tensordot(features, W_omega, axes=1) + b_omega)
-
 	# For each of the timestamps its vector of size A from `v` is reduced with `u` vector
 	vu = tf.tensordot(v, u_omega, axes=1)   # (B,T) shape
-
 	alphas = tf.nn.softmax(vu)              # (B,T) shape also
 	# Output of (Bi-)RNN is reduced with attention vector; the result has (B,D) shape
 	features = tf.reduce_sum(features * tf.expand_dims(alphas, -1), 1)
-	print "output",features.get_shape()
+	print "output", features.get_shape()
 	features = tf.reshape(features, [-1, nunits*2])
-	print "output",features.get_shape()
+	print "output ",features.get_shape()
 	prediction = tf.layers.dense( features, n_classes, activation=None, name='prediction')
-	print "prediction",prediction.get_shape()
+	print "prediction ",prediction.get_shape()
+
 	return prediction
 
 def getBatch(X, Y, i, batch_size):
diff --git a/REUNION/RFCts.py b/REUNION/RFCts.py
index 9625e2a483e19b77dc357916037f7e00c28f1aeb..1bc9b3012e0ba3dc05bd927afc9446d9e9a2da5a 100644
--- a/REUNION/RFCts.py
+++ b/REUNION/RFCts.py
@@ -44,7 +44,7 @@ predC = clf.predict(test_x)
 
 KAPPA = cohen_kappa_score( test_y, predC )
 accuracy = accuracy_score( test_y, predC )
-fscore = f1_score( test_y, predC, average='micro' )
+fscore = f1_score( test_y, predC, average='weighted' )
 var_prec, var_rec, var_fsc, _ = precision_recall_fscore_support( test_y, predC )
 
 
diff --git a/REUNION/RFCvhsr.py b/REUNION/RFCvhsr.py
index f7c24f6e3cae570a80b368527bf604aed0ee9e68..6fce37377f391e8dd1c2e6b7bd555ebf0885f5b1 100644
--- a/REUNION/RFCvhsr.py
+++ b/REUNION/RFCvhsr.py
@@ -63,7 +63,7 @@ predC = clf.predict(test_x)
 
 KAPPA = cohen_kappa_score( test_y, predC )
 accuracy = accuracy_score( test_y, predC )
-fscore = f1_score( test_y, predC, average='micro' )
+fscore = f1_score( test_y, predC, average='weighted' )
 var_prec, var_rec, var_fsc, _ = precision_recall_fscore_support( test_y, predC )