change test file

tensnsorflow/train_multi.py

@@ -50,16 +50,7 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
             "y": tf.FixedLenFeature([], tf.float32),
             "z": tf.FixedLenFeature([], tf.float32),
             "ids": tf.FixedLenFeature([FLAGS.field_size], tf.int64),
-            "app_list": tf.VarLenFeature(tf.int64),
-            "level2_list": tf.VarLenFeature(tf.int64),
-            "level3_list": tf.VarLenFeature(tf.int64),
-            "tag1_list": tf.VarLenFeature(tf.int64),
-            "tag2_list": tf.VarLenFeature(tf.int64),
-            "tag3_list": tf.VarLenFeature(tf.int64),
-            "tag4_list": tf.VarLenFeature(tf.int64),
-            "tag5_list": tf.VarLenFeature(tf.int64),
-            "tag6_list": tf.VarLenFeature(tf.int64),
-            "tag7_list": tf.VarLenFeature(tf.int64)
+            "level2_ids": tf.VarLenFeature(tf.int64)
         }
 
         parsed = tf.parse_single_example(record, features)
@@ -106,15 +97,8 @@ def model_fn(features, labels, mode, params):
 
     feat_ids = features['ids']
     app_list = features['app_list']
-    level2_list = features['level2_list']
-    level3_list = features['level3_list']
-    tag1_list = features['tag1_list']
-    tag2_list = features['tag2_list']
-    tag3_list = features['tag3_list']
-    tag4_list = features['tag4_list']
-    tag5_list = features['tag5_list']
-    tag6_list = features['tag6_list']
-    tag7_list = features['tag7_list']
+    level2_list = features['level2_ids']
+
 
     if FLAGS.task_type != "infer":
         y = labels['y']
@@ -125,18 +109,10 @@ def model_fn(features, labels, mode, params):
         embedding_id = tf.nn.embedding_lookup(Feat_Emb,feat_ids)
         app_id = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=app_list, sp_weights=None, combiner="sum")
         level2 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=level2_list, sp_weights=None, combiner="sum")
-        level3 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=level3_list, sp_weights=None, combiner="sum")
-        tag1 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag1_list, sp_weights=None, combiner="sum")
-        tag2 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag2_list, sp_weights=None, combiner="sum")
-        tag3 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag3_list, sp_weights=None, combiner="sum")
-        tag4 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag4_list, sp_weights=None, combiner="sum")
-        tag5 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag5_list, sp_weights=None, combiner="sum")
-        tag6 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag6_list, sp_weights=None, combiner="sum")
-        tag7 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag7_list, sp_weights=None, combiner="sum")
+
 
         # x_concat = tf.reshape(embedding_id,shape=[-1, common_dims])  # None * (F * K)
-        x_concat = tf.concat([tf.reshape(embedding_id,shape=[-1,common_dims]),app_id,level2,level3,tag1,
-                              tag2,tag3,tag4,tag5,tag6,tag7], axis=1)
+        x_concat = tf.concat([tf.reshape(embedding_id,shape=[-1,common_dims]),app_id,level2], axis=1)
 
     with tf.name_scope("CVR_Task"):
         if mode == tf.estimator.ModeKeys.TRAIN: