def normalize_mnist(
X_train: np.ndarray, X_test: np.ndarray
) -> Output(X_train_normed=np.ndarray, X_test_normed=np.ndarray):
"""Normalize the values for all the images so they are between 0 and 1"""
X_train_normed = X_train / 255.0
X_test_normed = X_test / 255.0
return X_train_normed, X_test_normed
def importer() -> Output(X_train=np.ndarray,
y_train=np.ndarray,
X_test=np.ndarray,
y_test=np.ndarray):
"""Download the MNIST data store it as numpy arrays."""
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
return X_train, y_train, X_test, y_test
def importer_mnist() -> Output(
X_train=np.ndarray, y_train=np.ndarray, X_test=np.ndarray, y_test=np.ndarray
):
"""Download the MNIST data store it as an artifact"""
(X_train, y_train), (
X_test,
y_test,
) = tf.keras.datasets.mnist.load_data()
return X_train, y_train, X_test, y_test
def importer() -> Output(train_df=pd.DataFrame, test_df=pd.DataFrame):
"""Download the MNIST data store it as numpy arrays."""
(X_train, y_train), (
X_test,
y_test,
) = tf.keras.datasets.boston_housing.load_data()
train_df = convert_np_to_pandas(X_train, y_train)
test_df = convert_np_to_pandas(X_test, y_test)
return train_df, test_df
def dynamic_importer(
config: ImporterConfig,
) -> Output(X_train=np.ndarray,
y_train=np.ndarray,
X_test=np.ndarray,
y_test=np.ndarray):
"""Downloads the latest data from a mock API."""
X_train, y_train = get_X_y_from_api(n_days=config.n_days, is_train=True)
X_test, y_test = get_X_y_from_api(n_days=config.n_days, is_train=False)
return X_train, y_train, X_test, y_test
def some_step(shared_name: int) -> Output(shared_name=int):
return shared_name
def some_step() -> Output(some_output=int, some_other_output=str):
pass
def some_step() -> Output(some_output=int):
pass
