From e24f596f40c8adeb801d7680c87e6dc40a4675a0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ren=C3=A9=20Knaebel?= <rknaebel@uni-potsdam.de>
Date: Sun, 30 Jul 2017 14:07:39 +0200
Subject: [PATCH] add argument for using the new model architecture

---
 arguments.py |  1 +
 main.py      | 20 ++++++++++----------
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/arguments.py b/arguments.py
index b32432a..6559fab 100644
--- a/arguments.py
+++ b/arguments.py
@@ -63,6 +63,7 @@ parser.add_argument("--domain_embd", action="store", dest="domain_embedding",
 parser.add_argument("--stop_early", action="store_true", dest="stop_early")
 parser.add_argument("--balanced_weights", action="store_true", dest="class_weights")
 parser.add_argument("--gpu", action="store_true", dest="gpu")
+parser.add_argument("--new_model", action="store_true", dest="new_model")
 
 
 
diff --git a/main.py b/main.py
index 37568ed..8ccb6e8 100644
--- a/main.py
+++ b/main.py
@@ -112,18 +112,18 @@ def main_hyperband():
     json.dump(results, open("hyperband.json"))
 
 
-def main_train(param=None, train_new_model=False):
+def main_train(param=None):
+    logger.info(f"Create model path {args.model_path}")
     exists_or_make_path(args.model_path)
+    logger.info(f"Use command line arguments: {args}")
 
     domain_tr, flow_tr, client_tr, server_windows_tr = load_or_generate_h5data(args.train_h5data, args.train_data,
                                                                                args.domain_length, args.window)
 
     if not param:
         param = PARAMS
-
+    logger.info(f"Generator model with params: {param}")
     embedding, model, new_model = models.get_models_by_params(param)
-    embedding.summary()
-    model.summary()
     logger.info("define callbacks")
     callbacks = []
     callbacks.append(ModelCheckpoint(filepath=args.clf_model,
@@ -131,11 +131,11 @@ def main_train(param=None, train_new_model=False):
                                      verbose=False,
                                      save_best_only=True))
     callbacks.append(CSVLogger(args.train_log))
+    logger.info(f"Use early stopping: {args.stop_early}")
     if args.stop_early:
         callbacks.append(EarlyStopping(monitor='val_loss',
                                        patience=5,
                                        verbose=False))
-    logger.info("compile model")
     custom_metrics = models.get_metric_functions()
 
     server_tr = np.max(server_windows_tr, axis=1)
@@ -147,12 +147,14 @@ def main_train(param=None, train_new_model=False):
     else:
         logger.info("class weights: set default")
         custom_class_weights = None
-    logger.info("start training")
 
-    if train_new_model:
+    logger.info(f"select model: {'new' if args.new_model else 'old'}")
+    if args.new_model:
         server_tr = np.expand_dims(server_windows_tr, 2)
         model = new_model
-
+    logger.info("compile and train model")
+    embedding.summary()
+    model.summary()
     model.compile(optimizer='adam',
                   loss='binary_crossentropy',
                   metrics=['accuracy'] + custom_metrics)
@@ -271,8 +273,6 @@ def main():
         main_paul_best()
     if "data" in args.modes:
         main_data()
-    if "train_new" in args.modes:
-        main_train(train_new_model=True)
 
 
 if __name__ == "__main__":