Updated triplet

bob/learn/tensorflow/estimators/Triplet.py

@@ -121,13 +121,12 @@ class Triplet(estimator.Estimator):
                     is_trainable = is_trainable_checkpoint(self.extra_checkpoint)
 
                 # Building one graph
-                prelogits_anchor = self.architecture(features['anchor'], is_trainable=is_trainable)[0]
-                prelogits_positive = self.architecture(features['positive'], reuse=True, is_trainable=is_trainable)[0]
-                prelogits_negative = self.architecture(features['negative'], reuse=True, is_trainable=is_trainable)[0]
+                prelogits_anchor = self.architecture(features['anchor'], is_training_mode = True)[0]
+                prelogits_positive = self.architecture(features['positive'], reuse=True, is_training_mode = True)[0]
+                prelogits_negative = self.architecture(features['negative'], reuse=True, is_training_mode = True)[0]
 
                 if self.extra_checkpoint is not None:
                     tf.contrib.framework.init_from_checkpoint(self.extra_checkpoint["checkpoint_path"],
-                                                              self.extra_checkpoint["scopes"])
 
                 # Compute Loss (for both TRAIN and EVAL modes)
                 self.loss = self.loss_op(prelogits_anchor, prelogits_positive, prelogits_negative)
@@ -141,7 +140,7 @@ class Triplet(estimator.Estimator):
             data = features['data']
 
             # Compute the embeddings
-            prelogits = self.architecture(data)[0]
+            prelogits = self.architecture(data, is_training_mode = False)[0]
             embeddings = tf.nn.l2_normalize(prelogits, 1)
             predictions = {"embeddings": embeddings}