snapshot...

modules/deeplearning/icing.py

@@ -489,8 +489,8 @@ class IcingIntensityNN:
             proc_batch_cnt = 0
             n_samples = 0
 
-            for abi, temp, lbfp in self.train_dataset:
-                trn_ds = tf.data.Dataset.from_tensor_slices((abi, temp, lbfp))
+            for data0, data1, label in self.train_dataset:
+                trn_ds = tf.data.Dataset.from_tensor_slices((data0, data1, label))
                 trn_ds = trn_ds.batch(BATCH_SIZE)
                 for mini_batch in trn_ds:
                     if self.learningRateSchedule is not None:
@@ -506,8 +506,8 @@ class IcingIntensityNN:
                         self.test_loss.reset_states()
                         self.test_accuracy.reset_states()
 
-                        for abi_tst, temp_tst, lbfp_tst in self.test_dataset:
-                            tst_ds = tf.data.Dataset.from_tensor_slices((abi_tst, temp_tst, lbfp_tst))
+                        for data0_tst, data1_tst, label_tst in self.test_dataset:
+                            tst_ds = tf.data.Dataset.from_tensor_slices((data0_tst, data1_tst, label_tst))
                             tst_ds = tst_ds.batch(BATCH_SIZE)
                             for mini_batch_test in tst_ds:
                                 self.test_step(mini_batch_test)
@@ -524,7 +524,7 @@ class IcingIntensityNN:
                     print('train loss: ', loss.numpy())
 
                 proc_batch_cnt += 1
-                n_samples += abi.shape[0]
+                n_samples += data0.shape[0]
                 print('proc_batch_cnt: ', proc_batch_cnt, n_samples)
 
             t1 = datetime.datetime.now().timestamp()
@@ -533,8 +533,8 @@ class IcingIntensityNN:
 
             self.test_loss.reset_states()
             self.test_accuracy.reset_states()
-            for abi, temp, lbfp in self.test_dataset:
-                ds = tf.data.Dataset.from_tensor_slices((abi, temp, lbfp))
+            for data0, data1, label in self.test_dataset:
+                ds = tf.data.Dataset.from_tensor_slices((data0, data1, label))
                 ds = ds.batch(BATCH_SIZE)
                 for mini_batch in ds:
                     self.test_step(mini_batch)