def _save_outputs(self) -> None:
noise = np.random.normal(
0, 1,
(self._outputs_rows * self._outputs_columns, self._latent_dim))
generated_samples = self._generator.predict(noise)
plot_save_samples(generated_samples, self._outputs_rows,
self._outputs_columns, self._resolution,
self._channels, self._outputs_dir, self._epoch)
def _save_outputs(self) -> None:
noise = np.random.normal(
0, 1,
(self._outputs_rows * self._outputs_columns, self._latent_dim))
random_classes = np.random.randint(
0, self._classes_n, self._outputs_rows * self._outputs_columns)
generated_samples = self._generator.predict(
[noise, to_categorical(random_classes, self._classes_n)])
plot_save_samples(generated_samples, self._outputs_rows,
self._outputs_columns, self._resolution,
self._channels, self._outputs_dir, self._epoch,
np.array(self._classes)[random_classes])