add pauls config test (TMP)

main.py

@@ -19,8 +19,8 @@ parser.add_argument("--test", action="store", dest="test_data",
 # parser.add_argument("--h5data", action="store", dest="h5data",
 #                     default="")
 #
-parser.add_argument("--model", action="store", dest="model",
-                    default="model_x")
+parser.add_argument("--models", action="store", dest="models",
+                    default="models/model_x")
 
 # parser.add_argument("--pred", action="store", dest="pred",
 #                     default="")
@@ -80,6 +80,39 @@ args = parser.parse_args()
 # session = tf.Session(config=config)
 
 
+def main_paul_best():
+    char_dict = dataset.get_character_dict()
+    user_flow_df = dataset.get_user_flow_data(args.train_data)
+
+    param = models.pauls_networks.best_config
+    param["vocab_size"] = len(char_dict) + 1
+    print(param)
+
+    print("create training dataset")
+    domain_tr, flow_tr, client_tr, server_tr = dataset.create_dataset_from_flows(
+        user_flow_df, char_dict,
+        max_len=args.domain_length,
+        window_size=args.window)
+    client_tr = np_utils.to_categorical(client_tr, 2)
+    server_tr = np_utils.to_categorical(server_tr, 2)
+
+    embedding, model = models.get_models_by_params(param)
+
+    model.compile(optimizer='adam',
+                  loss='categorical_crossentropy',
+                  metrics=['accuracy'])
+
+    model.fit([domain_tr, flow_tr],
+              [client_tr, server_tr],
+              batch_size=args.batch_size,
+              epochs=args.epochs,
+              shuffle=True,
+              validation_split=0.2)
+
+    embedding.save(args.models + "_embd.h5")
+    model.save(args.models + "_clf.h5")
+
+
 def main_hyperband():
     char_dict = dataset.get_character_dict()
     user_flow_df = dataset.get_user_flow_data(args.train_data)
@@ -137,13 +170,13 @@ def main_train():
     client_tr = np_utils.to_categorical(client_tr, 2)
     server_tr = np_utils.to_categorical(server_tr, 2)
 
-    shared_cnn = network.get_embedding(len(char_dict) + 1, args.embedding, args.domain_length,
+    embedding = network.get_embedding(len(char_dict) + 1, args.embedding, args.domain_length,
                                       args.hidden_char_dims, kernel_size, args.domain_embedding, 0.5)
-    shared_cnn.summary()
+    embedding.summary()
 
     model = network.get_model(cnnDropout, flow_tr.shape[-1], args.domain_embedding,
                               args.window, args.domain_length, filters, kernel_size,
-                              cnnHiddenDims, shared_cnn)
+                              cnnHiddenDims, embedding)
     model.summary()
 
     model.compile(optimizer='adam',
@@ -157,7 +190,8 @@ def main_train():
               shuffle=True,
               validation_split=0.2)
 
-    model.save(args.model)
+    embedding.save(args.models + "_embd.h5")
+    model.save(args.models + "_clf.h5")
 
 
 def main_test():
@@ -206,6 +240,8 @@ def main():
         main_visualization()
     if "score" in args.modes:
         main_score()
+    if "paul" in args.modes:
+        main_paul_best()
 
 
 if __name__ == "__main__":

models/pauls_networks.py

@@ -3,12 +3,19 @@ from keras.engine import Input, Model
 from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, Activation, TimeDistributed
 
 best_config = {
+    "type": "paul",
+    "batch_size": 64,
+    "window_size": 10,
+    "domain_length": 40,
+    "flow_features": 3,
+    #
+    'dropout': 0.5,
     'domain_features': 32,
     'drop_out': 0.5,
     'embedding_size': 64,
     'filter_main': 512,
     'flow_features': 3,
-    'hidden_dims': 32,
+    'dense_main': 32,
     'filter_embedding': 32,
     'hidden_embedding': 32,
     'kernel_embedding': 8,