| 330 | return (proba > 0.5).astype('int32') |
| 331 | |
| 332 | def get_config(self): |
| 333 | layer_configs = [] |
| 334 | for layer in self.layers: |
| 335 | layer_configs.append({ |
| 336 | 'class_name': layer.__class__.__name__, |
| 337 | 'config': layer.get_config() |
| 338 | }) |
| 339 | # When constructed using an `InputLayer` the first non-input layer may not |
| 340 | # have the shape information to reconstruct `Sequential` as a graph network. |
| 341 | if (self._is_graph_network and layer_configs and |
| 342 | 'batch_input_shape' not in layer_configs[0]['config'] and |
| 343 | isinstance(self._layers[0], input_layer.InputLayer)): |
| 344 | batch_input_shape = self._layers[0]._batch_input_shape |
| 345 | layer_configs[0]['config']['batch_input_shape'] = batch_input_shape |
| 346 | |
| 347 | config = { |
| 348 | 'name': self.name, |
| 349 | 'layers': copy.deepcopy(layer_configs) |
| 350 | } |
| 351 | if self._build_input_shape: |
| 352 | config['build_input_shape'] = self._build_input_shape |
| 353 | return config |
| 354 | |
| 355 | @classmethod |
| 356 | def from_config(cls, config, custom_objects=None): |