def load_data(): """Loads CIFAR10 dataset. Returns: Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. """ dirname = 'cifar-10-batches-py' origin = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' path = get_file(dirname, origin=origin, untar=True) num_train_samples = 50000 x_train = np.zeros((num_train_samples, 3, 32, 32), dtype='uint8') y_train = np.zeros((num_train_samples,), dtype='uint8') for i in range(1, 6): fpath = os.path.join(path, 'data_batch_' + str(i)) data, labels = load_batch(fpath) x_train[(i - 1) * 10000:i * 10000, :, :, :] = data y_train[(i - 1) * 10000:i * 10000] = labels fpath = os.path.join(path, 'test_batch') x_test, y_test = load_batch(fpath) y_train = np.reshape(y_train, (len(y_train), 1)) y_test = np.reshape(y_test, (len(y_test), 1)) if K.image_data_format() == 'channels_last': x_train = x_train.transpose(0, 2, 3, 1) x_test = x_test.transpose(0, 2, 3, 1) return (x_train, y_train), (x_test, y_test)
def load_data(): """Loads CIFAR10 dataset. Returns: Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. """ dirname = 'data/' # origin = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' path = dirname # get_file(dirname, origin=origin, untar=True) num_train_samples = 50000 x_train = np.zeros((num_train_samples, 3, 32, 32), dtype='uint8') y_train = np.zeros((num_train_samples,), dtype='uint8') for i in range(1, 6): fpath = os.path.join(path, 'data_batch_' + str(i)) data, labels = load_batch(fpath) x_train[(i - 1) * 10000:i * 10000, :, :, :] = data y_train[(i - 1) * 10000:i * 10000] = labels fpath = os.path.join(path, 'test_batch') x_test, y_test = load_batch(fpath) y_train = np.reshape(y_train, (len(y_train), 1)) y_test = np.reshape(y_test, (len(y_test), 1)) if K.image_data_format() == 'channels_last': x_train = x_train.transpose(0, 2, 3, 1) x_test = x_test.transpose(0, 2, 3, 1) x_test = 1.0 - x_test / 255.0 x_train = 1.0 - x_train / 255.0 return (x_train, y_train), (x_test, y_test)
def load_data(label_mode='fine'): """Loads CIFAR100 dataset. Arguments: label_mode: one of "fine", "coarse". Returns: Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. Raises: ValueError: in case of invalid `label_mode`. """ if label_mode not in ['fine', 'coarse']: raise ValueError('`label_mode` must be one of `"fine"`, `"coarse"`.') dirname = 'cifar-100-python' origin = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz' path = get_file(dirname, origin=origin, untar=True) fpath = os.path.join(path, 'train') x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels') fpath = os.path.join(path, 'test') x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels') y_train = np.reshape(y_train, (len(y_train), 1)) y_test = np.reshape(y_test, (len(y_test), 1)) if K.image_data_format() == 'channels_last': x_train = x_train.transpose(0, 2, 3, 1) x_test = x_test.transpose(0, 2, 3, 1) return (x_train, y_train), (x_test, y_test)
def load_data(label_mode='fine'): """Loads CIFAR100 dataset. Arguments: label_mode: one of "fine", "coarse". Returns: Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. Raises: ValueError: in case of invalid `label_mode`. """ if label_mode not in ['fine', 'coarse']: raise ValueError('label_mode must be one of "fine" "coarse".') dirname = 'cifar-100-python' origin = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz' path = get_file(dirname, origin=origin, untar=True) fpath = os.path.join(path, 'train') x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels') fpath = os.path.join(path, 'test') x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels') y_train = np.reshape(y_train, (len(y_train), 1)) y_test = np.reshape(y_test, (len(y_test), 1)) if K.image_data_format() == 'channels_last': x_train = x_train.transpose(0, 2, 3, 1) x_test = x_test.transpose(0, 2, 3, 1) return (x_train, y_train), (x_test, y_test)
def load_cifar100_data(label_mode='fine'): """loads cifar100 dataset. arguments: label_mode: one of "fine", "coarse". returns: tuple of numpy arrays: `(x_train, y_train), (x_test, y_test)`. raises: valueerror: in case of invalid `label_mode`. """ if label_mode not in ['fine', 'coarse']: raise valueerror('label_mode must be one of "fine" "coarse".') print('load cifar100 dataset') dirname = 'cifar-100-python' origin = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz' path = get_file(dirname, origin=origin, untar=True) fpath = os.path.join(path, 'train') x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels') fpath = os.path.join(path, 'test') x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels') y_train = np.reshape(y_train, (len(y_train), 1)) y_test = np.reshape(y_test, (len(y_test), 1)) if K.image_data_format() == 'channels_last': x_train = x_train.transpose(0, 2, 3, 1) x_test = x_test.transpose(0, 2, 3, 1) y_test = map(one_hot_vec_100, y_test) y_train = map(one_hot_vec_100, y_train) return (x_train, y_train, x_test, y_test)
def load_data(): dirname = 'data/' path = dirname num_train_samples = 50000 x_train = np.zeros((num_train_samples, 3, 32, 32), dtype='uint8') y_train = np.zeros((num_train_samples, ), dtype='uint8') for i in range(1, 6): fpath = os.path.join(path, 'data_batch_' + str(i)) data, labels = load_batch(fpath) x_train[(i - 1) * 10000:i * 10000, :, :, :] = data y_train[(i - 1) * 10000:i * 10000] = labels fpath = os.path.join(path, 'test_batch') x_test, y_test = load_batch(fpath) y_train = np.reshape(y_train, (len(y_train), 1)) y_test = np.reshape(y_test, (len(y_test), 1)) if K.image_data_format() == 'channels_last': x_train = x_train.transpose(0, 2, 3, 1) x_test = x_test.transpose(0, 2, 3, 1) x_test = 1.0 - x_test / 255.0 x_train = 1.0 - x_train / 255.0 return (x_train, y_train), (x_test, y_test) - x_train / 255.0 return (x_train, y_train), (x_test, y_test)