Merge pull request #1 from mollerhoj/fix-main

Fix main script

main.py

@@ -4,6 +4,7 @@
 from data_utils import Data
 from models.char_cnn_zhang import CharCNNZhang
 from models.char_cnn_kim import CharCNNKim
+from models.char_tcn import CharTCN
 
 tf.flags.DEFINE_string("model", "char_cnn_zhang", "Specifies which model to use: char_cnn_zhang or char_cnn_kim")
 FLAGS = tf.flags.FLAGS
@@ -13,52 +14,52 @@
     # Load configurations
     config = json.load(open("config.json"))
     # Load training data
-    training_data = Data(data_source=config["data"]["data_source"],
+    training_data = Data(data_source=config["data"]["training_data_source"],
                          alphabet=config["data"]["alphabet"],
                          input_size=config["data"]["input_size"],
-                         num_of_classes=config["data"]["no_of_classes"])
+                         num_of_classes=config["data"]["num_of_classes"])
     training_data.load_data()
     training_inputs, training_labels = training_data.get_all_data()
     # Load validation data
-    validation_data = Data(data_source=config["data"]["data_source"],
+    validation_data = Data(data_source=config["data"]["validation_data_source"],
                            alphabet=config["data"]["alphabet"],
                            input_size=config["data"]["input_size"],
-                           num_of_classes=config["data"]["no_of_classes"])
+                           num_of_classes=config["data"]["num_of_classes"])
     validation_data.load_data()
     validation_inputs, validation_labels = validation_data.get_all_data()
 
     # Load model configurations and build model
     if FLAGS.model == "kim":
         model = CharCNNKim(input_size=config["data"]["input_size"],
                            alphabet_size=config["data"]["alphabet_size"],
-                           embedding_size=config["model"]["embedding_size"],
-                           conv_layers=config["model"]["conv_layers"],
-                           fully_connected_layers=config["model"]["fully_connected_layers"],
+                           embedding_size=config["char_cnn_kim"]["embedding_size"],
+                           conv_layers=config["char_cnn_kim"]["conv_layers"],
+                           fully_connected_layers=config["char_cnn_kim"]["fully_connected_layers"],
                            num_of_classes=config["data"]["num_of_classes"],
-                           dropout_p=config["model"]["dropout_p"],
-                           optimizer=config["model"]["optimizer"],
-                           loss=config["model"]["loss"])
+                           dropout_p=config["char_cnn_kim"]["dropout_p"],
+                           optimizer=config["char_cnn_kim"]["optimizer"],
+                           loss=config["char_cnn_kim"]["loss"])
     elif FLAGS.model == 'tcn':
-      model = CharTCN(input_size=config["data"]["input_size"],
-                      alphabet_size=config["data"]["alphabet_size"],
-                      embedding_size=config["model"]["embedding_size"],
-                      conv_layers=config["model"]["conv_layers"],
-                      fully_connected_layers=config["model"]["fully_connected_layers"],
-                      num_of_classes=config["data"]["num_of_classes"],
-                      dropout_p=config["model"]["dropout_p"],
-                      optimizer=config["model"]["optimizer"],
-                      loss=config["model"]["loss"])
+        model = CharTCN(input_size=config["data"]["input_size"],
+                        alphabet_size=config["data"]["alphabet_size"],
+                        embedding_size=config["char_tcn"]["embedding_size"],
+                        conv_layers=config["char_tcn"]["conv_layers"],
+                        fully_connected_layers=config["char_tcn"]["fully_connected_layers"],
+                        num_of_classes=config["data"]["num_of_classes"],
+                        dropout_p=config["char_tcn"]["dropout_p"],
+                        optimizer=config["char_tcn"]["optimizer"],
+                        loss=config["char_tcn"]["loss"])
     else:
         model = CharCNNZhang(input_size=config["data"]["input_size"],
                              alphabet_size=config["data"]["alphabet_size"],
-                             embedding_size=config["model"]["embedding_size"],
-                             conv_layers=config["model"]["conv_layers"],
-                             fully_connected_layers=config["model"]["fully_connected_layers"],
+                             embedding_size=config["char_cnn_zhang"]["embedding_size"],
+                             conv_layers=config["char_cnn_zhang"]["conv_layers"],
+                             fully_connected_layers=config["char_cnn_zhang"]["fully_connected_layers"],
                              num_of_classes=config["data"]["num_of_classes"],
-                             threshold=config["model"]["threshold"],
-                             dropout_p=config["model"]["dropout_p"],
-                             optimizer=config["model"]["optimizer"],
-                             loss=config["model"]["loss"])
+                             threshold=config["char_cnn_zhang"]["threshold"],
+                             dropout_p=config["char_cnn_zhang"]["dropout_p"],
+                             optimizer=config["char_cnn_zhang"]["optimizer"],
+                             loss=config["char_cnn_zhang"]["loss"])
     # Train model
     model.train(training_inputs=training_inputs,
                 training_labels=training_labels,