diff --git a/models/renes_networks.py b/models/renes_networks.py
index 1f5085e..94f3caf 100644
--- a/models/renes_networks.py
+++ b/models/renes_networks.py
@@ -39,7 +39,7 @@ def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden
 def get_model(cnnDropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
               dense_dim, cnn, model_output="both"):
     ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
-    encoded = TimeDistributed(cnn)(ipt_domains)
+    encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
     ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
     merged = keras.layers.concatenate([encoded, ipt_flows], -1)
     # CNN processing a small slides of flow windows
diff --git a/utils.py b/utils.py
index 636e3a7..ccab16b 100644
--- a/utils.py
+++ b/utils.py
@@ -1,5 +1,7 @@
 import os
+from operator import itemgetter
 
+import joblib
 import numpy as np
 from sklearn.utils import class_weight
 
@@ -20,3 +22,8 @@ def get_custom_class_weights(client, server):
 
 def get_custom_sample_weights(client, server):
     return class_weight.compute_sample_weight("balanced", np.vstack((client, server)).T)
+
+
+def load_ordered_hyperband_results(path):
+    results = joblib.load(path)
+    return sorted(results, itemgetter("loss"))