minor...

modules/deeplearning/icing_cnn.py

@@ -150,6 +150,8 @@ class IcingIntensityNN:
         self.test_recall = None
         self.test_precision = None
         self.test_confusion_matrix = None
+        self.test_f1score = None
+        self.test_mcc = None
         self.test_true_pos = None
         self.test_true_neg = None
         self.test_false_pos = None
@@ -483,6 +485,8 @@ class IcingIntensityNN:
             self.test_auc = tf.keras.metrics.AUC(name='test_auc')
             self.test_recall = tf.keras.metrics.Recall(name='test_recall')
             self.test_precision = tf.keras.metrics.Precision(name='test_precision')
+            self.test_f1score = tfa.metrics.F1Score(2, threshold=0.5, name='test_f1score')
+            self.test_mcc = tfa.metrics.MatthewsCorrelationCoefficient(2, name='test_mcc')
             self.test_true_neg = tf.keras.metrics.TrueNegatives(name='test_true_neg')
             self.test_true_pos = tf.keras.metrics.TruePositives(name='test_true_pos')
             self.test_false_neg = tf.keras.metrics.FalseNegatives(name='test_false_neg')
@@ -542,6 +546,8 @@ class IcingIntensityNN:
             self.test_true_pos(labels, pred)
             self.test_false_neg(labels, pred)
             self.test_false_pos(labels, pred)
+            self.test_f1score(labels, pred)
+            self.test_mcc(labels, pred)
 
     def predict(self, mini_batch):
         inputs = [mini_batch[0]]
@@ -562,6 +568,22 @@ class IcingIntensityNN:
         self.test_false_neg(labels, pred)
         self.test_false_pos(labels, pred)
 
+    def reset_test_metrics(self):
+        self.test_loss.reset_states()
+        self.test_accuracy.reset_states()
+        self.test_auc.reset_states()
+        self.test_recall.reset_states()
+        self.test_precision.reset_states()
+        self.test_f1score.reset_states()
+        self.test_mcc.reset_states()
+        self.test_true_neg.reset_states()
+        self.test_true_pos.reset_states()
+        self.test_false_neg.reset_states()
+        self.test_false_pos.reset_states()
+
+    def do_metrics(self):
+        pass
+
     def do_training(self, ckpt_dir=None):
 
         if ckpt_dir is None:
@@ -602,16 +624,7 @@ class IcingIntensityNN:
                             tf.summary.scalar('num_train_steps', step, step=step)
                             tf.summary.scalar('num_epochs', epoch, step=step)
 
-                        self.test_loss.reset_states()
-                        self.test_accuracy.reset_states()
-                        self.test_auc.reset_states()
-                        self.test_recall.reset_states()
-                        self.test_precision.reset_states()
-                        self.test_true_neg.reset_states()
-                        self.test_true_pos.reset_states()
-                        self.test_false_neg.reset_states()
-                        self.test_false_pos.reset_states()
-
+                        self.reset_test_metrics()
                         for data0_tst, label_tst in self.test_dataset:
                             tst_ds = tf.data.Dataset.from_tensor_slices((data0_tst, label_tst))
                             tst_ds = tst_ds.batch(BATCH_SIZE)
@@ -653,16 +666,7 @@ class IcingIntensityNN:
             print('End of Epoch: ', epoch+1, 'elapsed time: ', (t1-t0))
             total_time += (t1-t0)
 
-            self.test_loss.reset_states()
-            self.test_accuracy.reset_states()
-            self.test_auc.reset_states()
-            self.test_recall.reset_states()
-            self.test_precision.reset_states()
-            self.test_true_neg.reset_states()
-            self.test_true_pos.reset_states()
-            self.test_false_neg.reset_states()
-            self.test_false_pos.reset_states()
-
+            self.reset_test_metrics()
             for data0, label in self.test_dataset:
                 ds = tf.data.Dataset.from_tensor_slices((data0, label))
                 ds = ds.batch(BATCH_SIZE)
@@ -681,7 +685,7 @@ class IcingIntensityNN:
             mcc = ((tp*tn) - (fp*fn))/np.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))
 
             print('loss, acc, recall, precision, auc, f1, mcc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),
-                  recall, precsn, self.test_auc.result().numpy(), f1, mcc)
+                  recall, precsn, self.test_auc.result().numpy(), f1, self.test_f1score.result().numpy(), mcc, self.test_mcc.result().numpy())
             print('--------------------------------------------------')
             ckpt_manager.save()