fix build

examples/SimilarityLearning/mnist-embeddings.py

@@ -243,11 +243,12 @@ class SiameseModel(EmbeddingModel):
 
     def build_graph(self, x, y, label):
         # embed them
+        single_input = x
         x, y = self.embed([x, y])
 
         # tag the embedding of 'input' with name 'emb', just for inference later on
         with tf.variable_scope(tf.get_variable_scope(), reuse=True):
-            tf.identity(self.embed(x), name="emb")
+            tf.identity(self.embed(single_input), name="emb")
 
         # compute the actual loss
         cost, pos_dist, neg_dist = contrastive_loss(x, y, label, 5., extra=True, scope="loss")
@@ -260,10 +261,11 @@ class SiameseModel(EmbeddingModel):
 
 class CosineModel(SiameseModel):
     def build_graph(self, x, y, label):
+        single_input = x
         x, y = self.embed([x, y])
 
         with tf.variable_scope(tf.get_variable_scope(), reuse=True):
-            tf.identity(self.embed(x), name="emb")
+            tf.identity(self.embed(single_input), name="emb")
 
         cost = siamese_cosine_loss(x, y, label, scope="loss")
         cost = tf.identity(cost, name="cost")
@@ -287,10 +289,11 @@ class TripletModel(EmbeddingModel):
         return triplet_loss(a, p, n, 5., extra=True, scope="loss")
 
     def build_graph(self, a, p, n):
+        single_input = a
         a, p, n = self.embed([a, p, n])
 
         with tf.variable_scope(tf.get_variable_scope(), reuse=True):
-            tf.identity(self.embed(a), name="emb")
+            tf.identity(self.embed(single_input), name="emb")
 
         cost, pos_dist, neg_dist = self.loss(a, p, n)
 
@@ -318,10 +321,7 @@ class CenterModel(EmbeddingModel):
     def build_graph(self, x, label):
         # embed them
         x = self.embed(x)
-
-        # tag the embedding of 'input' with name 'emb', just for inference later on
-        with tf.variable_scope(tf.get_variable_scope(), reuse=True):
-            tf.identity(self.embed(x), name="emb")
+        x = tf.identity(x, name='emb')
 
         # compute the embedding loss
         emb_cost = center_loss(x, label, 10, 0.01)