change test file

tensnsorflow/train_multi.py

@@ -50,7 +50,16 @@ 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),
-            "level2_ids": tf.VarLenFeature(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)
         }
         parsed = tf.parse_single_example(record, features)
         y = parsed.pop('y')
@@ -95,8 +104,16 @@ def model_fn(features, labels, mode, params):
     Feat_Emb = tf.get_variable(name='embeddings', shape=[feature_size, embedding_size], initializer=tf.glorot_normal_initializer())
 
     feat_ids = features['ids']
-    level2_list = features['level2_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']
 
     if FLAGS.task_type != "infer":
         y = labels['y']
@@ -105,11 +122,20 @@ def model_fn(features, labels, mode, params):
     #------build f(x)------
     with tf.variable_scope("Shared-Embedding-layer"):
         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]),level2], axis=1)
+        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)
 
     with tf.name_scope("CVR_Task"):
         if mode == tf.estimator.ModeKeys.TRAIN:
@@ -289,9 +315,6 @@ def main(_):
     print('ctr_task_wgt ', FLAGS.ctr_task_wgt)
 
     #------init Envs------
-    path = "hdfs://172.16.32.4:8020/strategy/esmm/"
-
-    # tr_files = ["hdfs:///strategy/va.tfrecord"]
     tr_files = [path+"tr/part-r-00000"]
     va_files = [path+"va/part-r-00000"]
     te_files = ["%s/part-r-00000" % FLAGS.hdfs_dir]
@@ -355,5 +378,6 @@ if __name__ == "__main__":
     # w
     a = "export CLASSPATH='$(hadoop classpath --glob)'"
     os.system(a)
+    path = "hdfs://172.16.32.4:8020/strategy/esmm/"
     tf.logging.set_verbosity(tf.logging.INFO)
     tf.app.run()
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