修改训练集

eda/esmm/Model_pipline/train.py

@@ -51,13 +51,6 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
             "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),
             "search_tag2_list": tf.VarLenFeature(tf.int64),
             "search_tag3_list": tf.VarLenFeature(tf.int64),
             "uid": tf.VarLenFeature(tf.string),
@@ -122,13 +115,6 @@ def model_fn(features, labels, mode, params):
     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']
     search_tag2_list = features['search_tag2_list']
     search_tag3_list = features['search_tag3_list']
     uid = features['uid']
@@ -146,20 +132,12 @@ def model_fn(features, labels, mode, params):
         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")
         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,search_tag2,search_tag3], axis=1)
+        x_concat = tf.concat([tf.reshape(embedding_id, shape=[-1, common_dims]), app_id, level2, level3,search_tag2,search_tag3], axis=1)
 
         uid = tf.sparse.to_dense(uid,default_value="")
         city = tf.sparse.to_dense(city,default_value="")