test features

eda/esmm/Model_pipline/train.py

@@ -132,7 +132,7 @@ 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']
+    sample_id = features['number']
 
     if FLAGS.task_type != "infer":
         y = labels['y']
@@ -155,7 +155,6 @@ def model_fn(features, labels, mode, params):
         # 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)
-        number1 = tf.reshape(number,shape=[-1])
 
     with tf.name_scope("CVR_Task"):
         if mode == tf.estimator.ModeKeys.TRAIN:
@@ -200,7 +199,7 @@ def model_fn(features, labels, mode, params):
         pctr = tf.sigmoid(y_ctr)
         pcvr = tf.sigmoid(y_cvr)
         pctcvr = pctr*pcvr
-        sample_id = number1
+
 
     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)}