snapshot...

modules/deeplearning/icing_cnn.py

@@ -235,7 +235,7 @@ class IcingIntensityNN:
         dataset = tf.data.Dataset.from_tensor_slices(indexes)
         dataset = dataset.batch(PROC_BATCH_SIZE)
         dataset = dataset.map(self.data_function, num_parallel_calls=8)
-        dataset = dataset.shuffle(PROC_BATCH_BUFFER_SIZE)
+        # dataset = dataset.shuffle(PROC_BATCH_BUFFER_SIZE)
         dataset = dataset.prefetch(buffer_size=1)
         self.train_dataset = dataset
 
@@ -247,13 +247,13 @@ class IcingIntensityNN:
         dataset = dataset.map(self.data_function, num_parallel_calls=8)
         self.test_dataset = dataset
 
-    def setup_pipeline(self, filename, trn_idxs=None, tst_idxs=None):
+    def setup_pipeline(self, filename, trn_idxs=None, tst_idxs=None, seed=None):
         self.filename = filename
         self.h5f = h5py.File(filename, 'r')
         if trn_idxs is None and tst_idxs is None:
             time = self.h5f['time']
             num_obs = time.shape[0]
-            trn_idxs, tst_idxs = split_data(num_obs)
+            trn_idxs, tst_idxs = split_data(num_obs, seed=seed)
         self.num_data_samples = trn_idxs.shape[0]
 
         self.get_train_dataset(trn_idxs)