predict add sample id

eda/esmm/Model_pipline/dist_predict.py

@@ -38,6 +38,7 @@ def model_fn(features, labels, mode, params):
     tag5_list = features['tag5_list']
     tag6_list = features['tag6_list']
     tag7_list = features['tag7_list']
+    number = features['number']
 
 
     #------build f(x)------
@@ -58,6 +59,8 @@ def model_fn(features, labels, mode, params):
         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)
 
+        sample_id = tf.sparse.to_dense(number)
+
     with tf.name_scope("CVR_Task"):
         if mode == tf.estimator.ModeKeys.TRAIN:
             train_phase = True
@@ -90,7 +93,7 @@ def model_fn(features, labels, mode, params):
         pcvr = tf.sigmoid(y_cvr)
         pctcvr = pctr*pcvr
 
-    predictions={"pcvr": pcvr, "pctr": pctr, "pctcvr": pctcvr}
+    predictions={"pcvr": pcvr, "pctr": pctr, "pctcvr": pctcvr, "sample_id": sample_id}
     export_outputs = {tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: tf.estimator.export.PredictOutput(predictions)}
     # Provide an estimator spec for `ModeKeys.PREDICT`
     if mode == tf.estimator.ModeKeys.PREDICT:

eda/esmm/Model_pipline/train.py

@@ -318,9 +318,10 @@ def main(_):
     FLAGS.model_dir = FLAGS.model_dir + FLAGS.dt_dir
     #FLAGS.data_dir  = FLAGS.data_dir + FLAGS.dt_dir
 
-    tr_files = ["hdfs://172.16.32.4:8020/strategy/esmm/tr/part-r-00000"]
+    tr_files = ["hdfs://172.16.32.4:8020/strategy/esmm/test_tr/part-r-00000"]
     va_files = ["hdfs://172.16.32.4:8020/strategy/esmm/va/part-r-00000"]
-    te_files = ["%s/part-r-00000" % FLAGS.hdfs_dir]
+    # te_files = ["%s/part-r-00000" % FLAGS.hdfs_dir]
+    te_files = ["hdfs://172.16.32.4:8020/strategy/esmmtest_nearby/part-r-00000"]
 
     if FLAGS.clear_existing_model:
         try: