def load_mnist(shuffle_data=True, randseed='default'): train_path = get_file("mnist_train.libsvm", origin=remote_data_dir() + "/mnist_train.libsvm", cache_subdir="demo") test_path = get_file("mnist_test.libsvm", origin=remote_data_dir() + "/mnist_test.libsvm", cache_subdir="demo") x_train, y_train = load_svmlight_file(train_path, n_features=784) x_test, y_test = load_svmlight_file(test_path, n_features=784) x_train = x_train.toarray() / 255.0 x_test = x_test.toarray() / 255.0 if shuffle_data: shuffle(x_train, y_train, randseed=randseed) return (x_train, y_train), (x_test, y_test)
def load_cifar10(shuffle_data=True, randseed='default'): train_path = get_file("cifar10_5k_train.pkl", origin=remote_data_dir() + "/cifar10_5k_train.pkl", cache_subdir="demo") test_path = get_file("cifar10_1k_test.pkl", origin=remote_data_dir() + "/cifar10_1k_test.pkl", cache_subdir="demo") tmp = pickle.load(open(train_path, "rb")) x_train, y_train = tmp['data'] / 255.0, tmp['labels'] tmp = pickle.load(open(test_path, "rb")) x_test, y_test = tmp['data'] / 255.0, tmp['labels'] if shuffle_data: shuffle(x_train, y_train, randseed=randseed) return (x_train, y_train), (x_test, y_test)
def load_housing(shuffle_data=True, randseed='default'): train_path = get_file("housing_scale_train.libsvm", origin=remote_data_dir() + "/housing_scale_train.libsvm", cache_subdir="demo") test_path = get_file("housing_scale_test.libsvm", origin=remote_data_dir() + "/housing_scale_test.libsvm", cache_subdir="demo") x_train, y_train = load_svmlight_file(train_path, n_features=13) x_test, y_test = load_svmlight_file(test_path, n_features=13) x_train = x_train.toarray() x_test = x_test.toarray() if shuffle_data: shuffle(x_train, y_train, randseed=randseed) return (x_train, y_train), (x_test, y_test)