Remove redundant flatten layers. (tensorflow#4964)

reedwm · Taylor Robie · commit d64bcfe3b56a · 2018-07-31T17:14:04.000-07:00
The output of an embeddding layer is already flattened, so the Flatten layers acted as no-ops.
diff --git a/official/recommendation/neumf_model.py b/official/recommendation/neumf_model.py
@@ -152,16 +152,14 @@ def construct_model(users, items, params):
       input_length=1)
 
   # GMF part
-  # Flatten the embedding vector as latent features in GMF
-  mf_user_latent = tf.keras.layers.Flatten()(mf_embedding_user(user_input))
-  mf_item_latent = tf.keras.layers.Flatten()(mf_embedding_item(item_input))
+  mf_user_latent = mf_embedding_user(user_input)
+  mf_item_latent = mf_embedding_item(item_input)
   # Element-wise multiply
   mf_vector = tf.keras.layers.multiply([mf_user_latent, mf_item_latent])
 
   # MLP part
-  # Flatten the embedding vector as latent features in MLP
-  mlp_user_latent = tf.keras.layers.Flatten()(mlp_embedding_user(user_input))
-  mlp_item_latent = tf.keras.layers.Flatten()(mlp_embedding_item(item_input))
+  mlp_user_latent = mlp_embedding_user(user_input)
+  mlp_item_latent = mlp_embedding_item(item_input)
   # Concatenation of two latent features
   mlp_vector = tf.keras.layers.concatenate([mlp_user_latent, mlp_item_latent])
 
