snapshot..

modules/deeplearning/cloudheight.py

@@ -215,12 +215,14 @@ class CloudHeightNN:
         self.accuracy_2 = None
         self.accuracy_3 = None
         self.accuracy_4 = None
+        self.accuracy_5 = None
 
         self.num_0 = 0
         self.num_1 = 0
         self.num_2 = 0
         self.num_3 = 0
         self.num_4 = 0
+        self.num_5 = 0
 
         self.learningRateSchedule = None
         self.num_data_samples = None
@@ -628,6 +630,7 @@ class CloudHeightNN:
         self.accuracy_2 = tf.keras.metrics.MeanAbsoluteError(name='acc_2')
         self.accuracy_3 = tf.keras.metrics.MeanAbsoluteError(name='acc_3')
         self.accuracy_4 = tf.keras.metrics.MeanAbsoluteError(name='acc_4')
+        self.accuracy_5 = tf.keras.metrics.MeanAbsoluteError(name='acc_5')
 
     def build_predict(self):
         _, pred = tf.nn.top_k(self.logits)
@@ -695,6 +698,10 @@ class CloudHeightNN:
         self.num_4 += np.sum(m)
         self.accuracy_4(labels[m], pred[m])
 
+        m = np.logical_and(labels >= 0.01, labels < 0.5)
+        self.num_5 += np.sum(m)
+        self.accuracy_5(labels[m], pred[m])
+
     def do_training(self, ckpt_dir=None):
 
         if ckpt_dir is None:
@@ -813,6 +820,7 @@ class CloudHeightNN:
         print('acc_2', self.num_2, self.accuracy_2.result())
         print('acc_3', self.num_3, self.accuracy_3.result())
         print('acc_4', self.num_4, self.accuracy_4.result())
+        print('acc_5', self.num_5, self.accuracy_5.result())
 
     def run(self, matchup_dict, train_dict=None, valid_dict=None):
         self.setup_pipeline(matchup_dict, train_dict=train_dict, valid_test_dict=valid_dict)