snapshot...

modules/deeplearning/cloud_opd_srcnn_viirs.py

@@ -487,23 +487,11 @@ class SRCNN:
         self.initial_learning_rate = initial_learning_rate
 
     def build_evaluation(self):
+        self.train_accuracy = tf.keras.metrics.MeanAbsoluteError(name='train_accuracy')
+        self.test_accuracy = tf.keras.metrics.MeanAbsoluteError(name='test_accuracy')
         self.train_loss = tf.keras.metrics.Mean(name='train_loss')
         self.test_loss = tf.keras.metrics.Mean(name='test_loss')
 
-        if NumClasses == 2:
-            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_recall = tf.keras.metrics.Recall(name='test_recall')
-            self.test_precision = tf.keras.metrics.Precision(name='test_precision')
-            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')
-            self.test_false_pos = tf.keras.metrics.FalsePositives(name='test_false_pos')
-        else:
-            self.train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
-            self.test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
-
     @tf.function(input_signature=[tf.TensorSpec(None, tf.float32), tf.TensorSpec(None, tf.float32)])
     def train_step(self, inputs, labels):
         with tf.GradientTape() as tape: