minor...

modules/deeplearning/icing_cnn.py

@@ -633,17 +633,6 @@ class IcingIntensityNN:
                             for mini_batch_test in tst_ds:
                                 self.test_step(mini_batch_test)
 
-                        # recall = self.test_recall.result()
-                        # precsn = self.test_precision.result()
-                        # f1 = 2 * (precsn * recall) / (precsn + recall)
-                        #
-                        # tn = self.test_true_neg.result()
-                        # tp = self.test_true_pos.result()
-                        # fn = self.test_false_neg.result()
-                        # fp = self.test_false_pos.result()
-                        #
-                        # mcc = ((tp * tn) - (fp * fn)) / np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
-
                         f1, mcc = self.get_metrics()
 
                         with self.writer_valid.as_default():
@@ -677,16 +666,6 @@ class IcingIntensityNN:
                 for mini_batch in ds:
                     self.test_step(mini_batch)
 
-            # recall = self.test_recall.result().numpy()
-            # precsn = self.test_precision.result().numpy()
-            # f1 = 2 * (precsn * recall) / (precsn + recall)
-            #
-            # tn = self.test_true_neg.result().numpy()
-            # tp = self.test_true_pos.result().numpy()
-            # fn = self.test_false_neg.result().numpy()
-            # fp = self.test_false_pos.result().numpy()
-            #
-            # mcc = ((tp*tn)-(fp*fn))/np.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))
             f1, mcc = self.get_metrics()
 
             print('loss, acc, recall, precision, auc, f1, mcc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),