def batch_size(self, batch_size: int) -> None: if not isinstance(batch_size, int): raise e.TypeError("`batch_size` should be a integer") if batch_size <= 0: raise e.ValueError("`batch_size` should be greater than 0") self._batch_size = batch_size
def margin(self, margin: float) -> None: if not isinstance(margin, float): raise e.TypeError("`margin` should be a float") if margin <= 0: raise e.ValueError("`margin` should be greater than 0") self._margin = margin
def test_type_error(): new_exception = exception.TypeError("error") try: raise new_exception except exception.TypeError: pass
def normalize(self, normalize: Tuple[int, int]) -> None: if not (isinstance(normalize, tuple) or normalize is None): raise e.TypeError("`normalize` should be a tuple or None") self._normalize = normalize
def input_shape(self, input_shape: Tuple[int, ...]) -> None: if not (isinstance(input_shape, tuple) or input_shape is None): raise e.TypeError("`input_shape` should be a tuple or None") self._input_shape = input_shape
def shuffle(self, shuffle: bool) -> None: if not isinstance(shuffle, bool): raise e.TypeError("`shuffle` should be a boolean") self._shuffle = shuffle
def batches(self, batches: tf.data.Dataset) -> None: if not isinstance(batches, tf.data.Dataset): raise e.TypeError("`batches` should be a tf.data.Dataset") self._batches = batches
def n_pairs(self, n_pairs: int) -> None: if not isinstance(n_pairs, int): raise e.TypeError("`n_pairs` should be a integer") self._n_pairs = n_pairs
def soft(self, soft: bool) -> None: if not isinstance(soft, bool): raise e.TypeError("`soft` should be a boolean") self._soft = soft
def B(self, B: Base) -> None: if not isinstance(B, Base): raise e.TypeError("`B` should be a child from Base class") self._B = B