| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | training_eager_v1.py | 42 def _eager_metrics_fn(model, outputs, targets, sample_weights=None, masks=None): argument
71 sample_weights=sample_weights,
89 sample_weights=None, argument
134 if sample_weights:
135 sample_weights = [
138 if val is not None else None for val in sample_weights
162 weights = sample_weights[i] if sample_weights else None
228 sample_weights=None, argument
261 sample_weights=sample_weights,
290 sample_weights=None, argument
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| D | training_arrays_v1.py | 49 sample_weights=None, argument
165 ins = _prepare_feed_values(model, inputs, targets, sample_weights, mode)
421 sample_weights=val_sample_weights,
486 def _prepare_feed_values(model, inputs, targets, sample_weights, mode): argument
506 model, inputs, targets, sample_weights, mode)
522 inputs, targets, sample_weights = model._standardize_user_data(
528 sample_weights = list(sample_weights or [])
529 ins = inputs + targets + sample_weights
565 sample_weights = None
571 sample_weights = inputs[len(model._feed_inputs) + len(model._feed_targets):]
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| D | training_utils.py | 65 def handle_partial_sample_weights(outputs, sample_weights, sample_weight_modes, argument
80 any_sample_weight = sample_weights is not None and any(
81 w is not None for w in sample_weights)
83 w is None for w in sample_weights)
89 return sample_weights, any_sample_weight, partial_sample_weight
93 list_to_tuple(sample_weights),
94 list_to_tuple(nest.flatten(sample_weights)))
103 for i, sw in enumerate(sample_weights):
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| D | data_adapter.py | 256 sample_weights=None, argument
264 x, y, sample_weights = _process_tensorlike((x, y, sample_weights))
266 sample_weights, sample_weight_modes)
269 (sample_weights, _, _) = training_utils.handle_partial_sample_weights(
270 y, sample_weights, sample_weight_modes, check_all_flat=True)
272 inputs = pack_x_y_sample_weight(x, y, sample_weights)
584 sample_weights=None, argument
591 x, y, sample_weights = _process_tensorlike((x, y, sample_weights))
593 sample_weights, sample_weight_modes)
596 (sample_weights, _, _) = training_utils.handle_partial_sample_weights(
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| D | training_v1.py | 1068 x, y, sample_weights = self._standardize_user_data(
1081 sample_weights=sample_weights,
1088 ins = x + list(y or []) + list(sample_weights or [])
1093 self._update_sample_weight_modes(sample_weights=sample_weights)
1150 x, y, sample_weights = self._standardize_user_data(
1160 sample_weights=sample_weights,
1167 inputs = x + list(y or []) + list(sample_weights or [])
1169 self._update_sample_weight_modes(sample_weights=sample_weights)
1474 def _update_sample_weight_modes(self, sample_weights=None): argument
1497 if sample_weights and any(s is not None for s in sample_weights):
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| D | training_generator_v1.py | 524 def predict_on_batch(x, y=None, sample_weights=None): # pylint: disable=unused-argument argument
744 x, y, sample_weights = model._standardize_user_data(
761 (x, y, sample_weights, val_x, val_y,
764 x, y, sample_weights, validation_split))
772 model, (x, y, sample_weights),
797 x, y, sample_weights = model._standardize_user_data(
806 model, (x, y, sample_weights),
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| D | training_utils_v1_test.py | 201 sample_weights = np.array([0.5, 1., 1., 0., 2.])
202 weights = training_utils_v1.standardize_weights(y, sample_weights)
203 self.assertAllClose(weights, sample_weights)
214 sample_weights = np.array([0.5, 1., 1., 0., 2.])
216 weights = training_utils_v1.standardize_weights(y, sample_weights,
218 expected = sample_weights * np.array([0.5, 1., 0.5, 0.5, 1.5])
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| D | training_dataset_test.py | 220 sample_weights = np.ones((10), np.float32)
222 (inputs, targets, sample_weights))
243 sample_weights = np.array([0.25, 0.5, 0.75, 1], np.float32)
245 (inputs, targets, sample_weights)).batch(2)
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| D | training_utils_v1.py | 1835 def split_training_and_validation_data(x, y, sample_weights, validation_split): argument
1848 if sample_weights:
1849 sample_weights, val_sample_weights = (
1850 generic_utils.slice_arrays(sample_weights, 0, split_at),
1851 generic_utils.slice_arrays(sample_weights, split_at),
1855 return x, y, sample_weights, val_x, val_y, val_sample_weights
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| D | data_adapter_test.py | 662 self.adapter_cls(self.dataset_input, sample_weights=self.dataset_input)
721 self.generator_input, sample_weights=self.generator_input)
789 self.adapter_cls(self.sequence_input, sample_weights=self.sequence_input)
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| D | training_test.py | 2032 sample_weights = array_ops.constant(
2034 dataset = dataset_ops.Dataset.from_tensor_slices(sample_weights)
2035 sample_weights = dataset_ops.make_one_shot_iterator(dataset).get_next()
2036 sample_weights = training_utils_v1.standardize_sample_weights(
2037 sample_weights, model.output_names)
2040 model._compile_weights_loss_and_weighted_metrics(sample_weights)
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| /external/tensorflow/tensorflow/python/keras/distribute/ |
| D | distributed_training_utils_v1.py | 276 sample_weights=None): argument
315 if sample_weights is not None:
317 sample_weights)
597 sample_weights = None
601 sample_weights = None
603 x, y, sample_weights = next_element
607 model._distribution_strategy, x, y, sample_weights)
608 return x, y, sample_weights
611 def _prepare_feed_values(model, inputs, targets, sample_weights, mode): argument
625 inputs, targets, sample_weights = _get_input_from_iterator(inputs, model)
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| D | distribute_strategy_test.py | 661 sample_weights = np.array([0.25, 0.5, 0.75, 1], np.float32)
667 sample_weight=sample_weights,
1579 sample_weights = np.array([0.25, 0.5, 0.75, 1], np.float32)
1581 (inputs, targets, sample_weights)).batch(2)
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| /external/tensorflow/tensorflow/python/keras/tests/ |
| D | temporal_sample_weights_correctness_test.py | 107 def custom_generator_multi_io_temporal(self, sample_weights=None): argument
126 if sample_weights:
127 sw = nest.map_structure(lambda w: w[start:end], sample_weights)
434 sample_weights=[self.sample_weight_1, self.sample_weight_2]),
448 sample_weights={'output_2': self.sample_weight_2}),
476 sample_weights=[self.sample_weight_1, self.sample_weight_2]),
493 sample_weights={'output_2': self.sample_weight_2}),
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| /external/tensorflow/tensorflow/python/keras/ |
| D | metrics_correctness_test.py | 48 def custom_generator_multi_io(sample_weights=None): argument
61 if sample_weights:
62 sw = nest.map_structure(lambda w: w[start:end], sample_weights)
308 sample_weights=[self.sample_weight_1, self.sample_weight_2]),
317 sample_weights={'output_2': self.sample_weight_2}),
332 sample_weights=[self.sample_weight_1, self.sample_weight_2]),
340 sample_weights={'output_2': self.sample_weight_2}),
700 sample_weights=[self.sample_weight_1, self.sample_weight_2]),
711 sample_weights=[self.sample_weight_1, self.sample_weight_2]),
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