update model path

src/train_diary.py

@@ -57,7 +57,7 @@ def main():
     eval_spec = tf.estimator.EvalSpec(input_fn=lambda: esmm_input_fn(val_df, shuffle=False))
     tf.estimator.train_and_evaluate(model, train_spec, eval_spec)
 
-    model_export_path = str(Path("~/data/models/").expanduser())
+    model_export_path = str(Path("~/data/models/diary").expanduser())
     save_path = model_export(model, all_features, model_export_path)
     print("save to: " + save_path)
 

src/train_tractate.py

@@ -25,8 +25,8 @@ def main():
 
     cc_df = click_fe.click_feature_engineering(tractate_click_df, tractate_conversion_df)
     df = tractate_fe.join_features(device_df, tractate_df, cc_df)
-    for i in df.columns:
-        print(i)
+    # for i in df.columns:
+    #     print(i)
     # print(df.dtypes)
 
     train_df, test_df = train_test_split(df, test_size=0.2)
@@ -38,6 +38,20 @@ def main():
     # if os.path.exists(model_path):
     #     shutil.rmtree(model_path)
 
+    session_config = tf.compat.v1.ConfigProto()
+    session_config.gpu_options.allow_growth = True
+    session_config.gpu_options.per_process_gpu_memory_fraction = 0.9
+    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)
+    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))
+    tf.estimator.train_and_evaluate(model, train_spec, eval_spec)
+
+    model_export_path = str(Path("~/data/models/tractate/").expanduser())
+    save_path = model_export(model, all_features, model_export_path)
+    print("save to: " + save_path)
+
     total_time = (time.time() - time_begin) / 60
     print("total cost {:.2f} mins at {}".format(total_time, datetime.now()))