try #1 to fix CTL

official/recommendation/ncf_keras_main.py

@@ -68,7 +68,7 @@ def call(self, inputs):
     return inputs[0]
 
 
-  def _get_train_and_eval_data(producer, params):
+def _get_train_and_eval_data(producer, params):
   """Returns the datasets for training and evalutating."""
 
   def preprocess_train_input(features, labels):
@@ -313,8 +313,7 @@ def step_fn(inputs):
         """Computes loss and applied gradient per replica."""
         features, labels = inputs
         with tf.GradientTape() as tape:
-          softmax_logits = keras_model([features[movielens.USER_COLUMN],
-                                        features[movielens.ITEM_COLUMN]])
+          softmax_logits = keras_model(features)
           loss = loss_object(labels, softmax_logits,
                              sample_weight=features[rconst.VALID_POINT_MASK])
           loss *= (1.0 / (batch_size*strategy.num_replicas_in_sync))
@@ -336,8 +335,7 @@ def eval_step():
       def step_fn(inputs):
         """Computes eval metrics per replica."""
         features, _ = inputs
-        softmax_logits = keras_model([features[movielens.USER_COLUMN],
-                                      features[movielens.ITEM_COLUMN]])
+        softmax_logits = keras_model(features)
         logits = tf.slice(softmax_logits, [0, 0, 1], [-1, -1, -1])
         dup_mask = features[rconst.DUPLICATE_MASK]
         in_top_k, _, metric_weights, _ = neumf_model.compute_top_k_and_ndcg(
@@ -412,7 +410,7 @@ def step_fn(inputs):
       train_history = history.history
       train_loss = train_history["loss"][-1]
 
-  stats = build_stats(train_loss, eval_results, time_callback)
+  stats = build_stats(train_loss, eval_results, None) #, time_callback)
   return stats