for item in dataset_structure.keys(): X_train, y_train, X_test, y_test = load_ABCDE_datasets(dataset_dir + item) dataset_data.update( {item: { 'train': [X_train, y_train], 'test': [X_test, y_test] }}) # %% DNN Training for dataset in dataset_structure.keys(): data = dataset_data[dataset] structure = dataset_structure[dataset] # loading train and test examples print('Data Preprocessing...') X_train = preprocess_data(structure['preprocessing'], data['train'][0]) X_test = preprocess_data(structure['preprocessing'], data['test'][0]) y_train = data['train'][1] Y_train = to_categorical(y_train) y_test = data['test'][1] Y_test = to_categorical(y_test) # some useful value nb_classes = len(np.unique(y_train)) model_version = structure['model_version'] # reshaping train and test set X_train, X_test, input_shape = reshape(X_train, X_test, *structure['shape'])
for dataset in dataset_structure.keys(): data = {} # Loading datasets print('Loading Dataset...') train, X_train, y_train, X_test, y_test = load_ABCDE_datasets( dataset_dir + dataset) __TRAIN__ = (X_train.copy(), y_train.copy()) __TEST__ = (X_test.copy(), y_test.copy()) data.update({'train': [X_train, y_train], 'test': [X_test, y_test]}) data.update(train) structure = dataset_structure[dataset] # loading train and test examples print('Data Preprocessing...') X_train = preprocess_data(structure['preprocessing'], data['train'][0]) X_test = preprocess_data(structure['preprocessing'], data['test'][0]) y_train = data['train'][1] Y_train = to_categorical(y_train) y_test = data['test'][1] Y_test = to_categorical(y_test) # some useful value nb_classes = len(np.unique(np.concatenate((y_train, y_test)))) model_version = structure['model_version'] # reshaping train and test set X_train, X_test, input_shape = reshape(X_train, X_test, *structure['shape'])