Generate output predictions for the input samples. See :meth:`keras.models.Model.predict` for more details. :param x: input data :param batch_size: number of samples per gradient update :return: numpy array(s) of predictions
(
self,
x: typing.Dict[str, np.ndarray],
batch_size=128
)
| 381 | return val |
| 382 | |
| 383 | def predict( |
| 384 | self, |
| 385 | x: typing.Dict[str, np.ndarray], |
| 386 | batch_size=128 |
| 387 | ) -> np.ndarray: |
| 388 | """ |
| 389 | Generate output predictions for the input samples. |
| 390 | |
| 391 | See :meth:`keras.models.Model.predict` for more details. |
| 392 | |
| 393 | :param x: input data |
| 394 | :param batch_size: number of samples per gradient update |
| 395 | :return: numpy array(s) of predictions |
| 396 | """ |
| 397 | return self._backend.predict(x=x, batch_size=batch_size) |
| 398 | |
| 399 | def save(self, dirpath: typing.Union[str, Path]): |
| 400 | """ |
no outgoing calls