change test file

eda/esmm/Model_pipline/train.py

@@ -63,7 +63,9 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
             "tag4_list": tf.VarLenFeature(tf.int64),
             "tag5_list": tf.VarLenFeature(tf.int64),
             "tag6_list": tf.VarLenFeature(tf.int64),
-            "tag7_list": tf.VarLenFeature(tf.int64)
+            "tag7_list": tf.VarLenFeature(tf.int64),
+            "search_tag2_list": tf.VarLenFeature(tf.int64),
+            "search_tag3_list": tf.VarLenFeature(tf.int64)
         }
         parsed = tf.parse_single_example(record, features)
         y = parsed.pop('y')
@@ -131,6 +133,8 @@ def model_fn(features, labels, mode, params):
     tag5_list = features['tag5_list']
     tag6_list = features['tag6_list']
     tag7_list = features['tag7_list']
+    search_tag2_list = features['search_tag2_list']
+    search_tag3_list = features['search_tag3_list']
 
     if FLAGS.task_type != "infer":
         y = labels['y']
@@ -149,10 +153,13 @@ def model_fn(features, labels, mode, params):
         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")
+        search_tag2 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=search_tag2_list, sp_weights=None, combiner="sum")
+        search_tag3 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=search_tag3_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)
+                              tag2, tag3, tag4, tag5, tag6, tag7,search_tag2,search_tag3], axis=1)
 
     with tf.name_scope("CVR_Task"):
         if mode == tf.estimator.ModeKeys.TRAIN: