train tractate

requirements.txt

-tensorflow-gpu==1.15.2
+tensorflow==1.15.2
 keras==2.3.1
 pandas==1.0.5
 scikit-learn==0.23.1

src/train_tractate.py

@@ -60,8 +60,7 @@ def main():
     estimator_config = tf.estimator.RunConfig(session_config=session_config)
 
     model = tf.estimator.Estimator(model_fn=esmm_model_fn, params=params, model_dir=model_path, config=estimator_config)
-    # TODO 50000
-    train_spec = tf.estimator.TrainSpec(input_fn=lambda: esmm_input_fn(train_df, shuffle=True), max_steps=15000)
+    train_spec = tf.estimator.TrainSpec(input_fn=lambda: esmm_input_fn(train_df, shuffle=True), max_steps=50000)
     eval_spec = tf.estimator.EvalSpec(input_fn=lambda: esmm_input_fn(val_df, shuffle=False))
     res = tf.estimator.train_and_evaluate(model, train_spec, eval_spec)
     print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
@@ -76,9 +75,8 @@ def main():
     model_export_path = str(Path("/data/files/models/tractate/").expanduser())
     save_path = model_export(model, all_features, model_export_path)
     print("save to: " + save_path)
-    # TODO save model
-    # set_essm_model_save_path("tractate", save_path)
-    # record_esmm_auc_to_db("tractate", ctr_auc, ctcvr_auc, total_time, save_path)
+    set_essm_model_save_path("tractate", save_path)
+    record_esmm_auc_to_db("tractate", ctr_auc, ctcvr_auc, total_time, save_path)
     print("============================================================")
 
     # save_path = get_essm_model_save_path("tractate")