minor...

modules/deeplearning/icing_cnn.py

@@ -148,7 +148,6 @@ class IcingIntensityNN:
         self.test_loss = None
         self.test_accuracy = None
         self.test_auc = None
-        self.test_f1 = None
         self.test_recall = None
         self.test_precision = None
         self.test_confusion_matrix = None
@@ -463,14 +462,12 @@ class IcingIntensityNN:
             self.train_accuracy = tf.keras.metrics.BinaryAccuracy(name='train_accuracy')
             self.test_accuracy = tf.keras.metrics.BinaryAccuracy(name='test_accuracy')
             self.test_auc = tf.keras.metrics.AUC(name='test_auc')
-            self.test_f1 = tfa.metrics.F1Score(NumClasses, name='test_f1')
             self.test_recall = tf.keras.metrics.Recall(name='test_recall')
             self.test_precision = tf.keras.metrics.Precision(name='test_precision')
         else:
             self.train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
             self.test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
             self.test_auc = tf.keras.metrics.AUC(name='test_auc')
-            self.test_f1 = tfa.metrics.F1Score(NumClasses, name='f1_score')
             self.test_recall = tf.keras.metrics.Recall(name='test_recall')
             self.test_precision = tf.keras.metrics.Precision(name='test_precision')
 
@@ -512,7 +509,6 @@ class IcingIntensityNN:
         self.test_accuracy(labels, pred)
         if NumClasses == 2:
             self.test_auc(labels, pred)
-            #self.test_f1(labels, pred)
             self.test_recall(labels, pred)
             self.test_precision(labels, pred)
 
@@ -528,7 +524,6 @@ class IcingIntensityNN:
         self.test_loss(t_loss)
         self.test_accuracy(labels, pred)
         self.test_auc(labels, pred)
-        #self.test_f1(labels, pred)
         self.test_recall(labels, pred)
         self.test_precision(labels, pred)
 
@@ -574,6 +569,9 @@ class IcingIntensityNN:
 
                         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()
 
                         for data0_tst, label_tst in self.test_dataset:
                             tst_ds = tf.data.Dataset.from_tensor_slices((data0_tst, label_tst))
@@ -585,7 +583,6 @@ class IcingIntensityNN:
                             tf.summary.scalar('loss_val', self.test_loss.result(), step=step)
                             tf.summary.scalar('acc_val', self.test_accuracy.result(), step=step)
                             tf.summary.scalar('auc_val', self.test_auc.result(), step=step)
-                            # tf.summary.scalar('f1_val', self.test_f1.result(), step=step)
                             tf.summary.scalar('recall_val', self.test_recall.result(), step=step)
                             tf.summary.scalar('prec_val', self.test_precision.result(), step=step)
                             tf.summary.scalar('num_train_steps', step, step=step)
@@ -607,7 +604,6 @@ class IcingIntensityNN:
             self.test_loss.reset_states()
             self.test_accuracy.reset_states()
             self.test_auc.reset_states()
-            self.test_f1.reset_states()
             self.test_recall.reset_states()
             self.test_precision.reset_states()
 
@@ -621,8 +617,8 @@ class IcingIntensityNN:
             precsn = self.test_precision.result().numpy()
             f1 = 2 * (precsn * recall) / (precsn + recall)
 
-            print('loss, acc, auc, recall, precision, f1: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),
-                  self.test_auc.result().numpy(), self.test_recall.result().numpy(), self.test_precision.result().numpy(), f1)
+            print('loss, acc, recall, precision, auc, f1: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),
+                  recall, precsn, self.test_auc.result().numpy(), f1)
             print('--------------------------------------------------')
             ckpt_manager.save()