def load_dataset(data_path): train_path = os.path.join(data_path, "train.npz") test_path = os.path.join(data_path, "test.npz") dictionary_path = os.path.join(data_path, "word_index.pkl") # Load the data toxic = ToxicData(train_path, test_path, dictionary_path) train_ids, train_dataset = toxic.load_train(mode="sup") return train_dataset
# And create the generators testgen = DatasetGenerator(test_data, batch_size=args.test_batch_size, shuffle=False) # Initialize the model model = load_model(args.model) model.load_state(MODEL_FILE) # Get the predictions predictions = model.predict_generator(testgen, testgen.steps_per_epoch, verbose=1) ToxicData.save_submission(SUBMISSION_FILE, ids, predictions) if __name__ == "__main__": # Create the paths for the data train_path = os.path.join(args.data, "train.npz") test_path = os.path.join(args.data, "test.npz") dictionary_path = os.path.join(args.data, "word_index.pkl") # Load the data toxic = ToxicData(train_path, test_path, dictionary_path) if args.train: train(toxic) if args.test: test(toxic)
ToxicData.save_submission(submission_file, ids, predictions) if __name__ == "__main__": # Create the paths for the data train_path = os.path.join(args.data, "train.npz") test_path = os.path.join(args.data, "test.npz") dictionary_path = os.path.join(args.data, "word_index.pkl") if args.use_augmented: augmented_path = os.path.join(args.data, "train_*.npz") else: augmented_path = "" # Load the data toxic = ToxicData(train_path, test_path, dictionary_path, augmented_path=augmented_path, original_prob=args.original_prob, fixed_len=args.fixed_len) model = None if args.train: if args.kfold: model = kfold(toxic) else: model = train(toxic) if args.test: test(toxic, model=model)