|
| | train_cnn_continue.parser = argparse.ArgumentParser(description='Run CNN training on patches with a few different hyperparameter sets.') |
| |
| | train_cnn_continue.help |
| |
| | train_cnn_continue.default |
| |
| | train_cnn_continue.args = parser.parse_args() |
| |
| | train_cnn_continue.config = read_config(args.config) |
| | configuration ############################# More...
|
| |
| | train_cnn_continue.cfg_name = args.model |
| |
| | train_cnn_continue.out_name = args.output |
| |
| | train_cnn_continue.CNN_INPUT_DIR = config['training_on_patches']['input_dir'] |
| |
| | train_cnn_continue.PATCH_SIZE_W |
| |
| | train_cnn_continue.PATCH_SIZE_D |
| |
| | train_cnn_continue.img_rows |
| |
| | train_cnn_continue.img_cols |
| |
| | train_cnn_continue.batch_size = config['training_on_patches']['batch_size'] |
| |
| | train_cnn_continue.nb_epoch = config['training_on_patches']['nb_epoch'] |
| |
| | train_cnn_continue.nb_classes = config['training_on_patches']['nb_classes'] |
| |
| | train_cnn_continue.model = load_model(cfg_name) |
| | CNN commpilation ###########################. More...
|
| |
| | train_cnn_continue.sgd = SGD(lr=0.005, decay=1e-5, momentum=0.9, nesterov=True) |
| |
| | train_cnn_continue.optimizer |
| |
| | train_cnn_continue.loss |
| |
| | train_cnn_continue.loss_weights |
| |
| | train_cnn_continue.n_training = count_events(CNN_INPUT_DIR, 'training') |
| | read data sets ############################ More...
|
| |
| | train_cnn_continue.X_train = np.zeros((n_training, PATCH_SIZE_W, PATCH_SIZE_D, 1), dtype=np.float32) |
| |
| | train_cnn_continue.EmTrkNone_train = np.zeros((n_training, 3), dtype=np.int32) |
| |
| | train_cnn_continue.Michel_train = np.zeros((n_training, 1), dtype=np.int32) |
| |
| int | train_cnn_continue.ntot = 0 |
| |
| list | train_cnn_continue.subdirs = [f for f in os.listdir(CNN_INPUT_DIR) if 'training' in f] |
| |
| list | train_cnn_continue.filesX = [f for f in os.listdir(CNN_INPUT_DIR + '/' + dirname) if '_x.npy' in f] |
| |
| | train_cnn_continue.fnameY = fnameX.replace('_x.npy', '_y.npy') |
| |
| | train_cnn_continue.dataX = np.load(CNN_INPUT_DIR + '/' + dirname + '/' + fnameX) |
| |
| | train_cnn_continue.dataY = np.load(CNN_INPUT_DIR + '/' + dirname + '/' + fnameY) |
| |
| | train_cnn_continue.n = dataY.shape[0] |
| |
| | train_cnn_continue.n_testing = count_events(CNN_INPUT_DIR, 'testing') |
| |
| | train_cnn_continue.X_test = np.zeros((n_testing, PATCH_SIZE_W, PATCH_SIZE_D, 1), dtype=np.float32) |
| |
| | train_cnn_continue.EmTrkNone_test = np.zeros((n_testing, 3), dtype=np.int32) |
| |
| | train_cnn_continue.Michel_test = np.zeros((n_testing, 1), dtype=np.int32) |
| |
| | train_cnn_continue.h |
| | training ############################### More...
|
| |
| | train_cnn_continue.score |
| |
1.8.11
