Searched refs:per_replica_values (Results 1 – 7 of 7) sorted by relevance
| /external/tensorflow/tensorflow/python/distribute/ |
| D | cross_device_ops.py | 626 def _group_value_by_device(per_replica_values): argument
644 destinations = per_replica_values[0]._devices # pylint: disable=protected-access
646 for per_replica_value in per_replica_values:
859 def _batch_all_reduce(self, reduce_op, per_replica_values): argument
862 cross_device_utils.split_by_sparsity(per_replica_values))
1181 def _batch_all_reduce(self, reduce_op, per_replica_values, options): argument
1184 cross_device_utils.split_by_sparsity(per_replica_values))
1198 def _do_batch_all_reduce_dense(self, reduce_op, per_replica_values, options): argument
1201 batch_size = len(per_replica_values)
1224 for per_replica in reversed(per_replica_values):
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| D | parameter_server_strategy.py | 402 per_replica_values = []
404 per_replica_values.append(
407 return distribute_utils.regroup(per_replica_values, always_wrap=True)
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| D | collective_all_reduce_strategy.py | 625 per_replica_values = []
632 per_replica_values.append(value_fn(value_context))
633 return distribute_utils.regroup(per_replica_values, always_wrap=True)
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| D | mirrored_strategy.py | 592 per_replica_values = []
594 per_replica_values.append(value_fn(
597 return distribute_utils.regroup(per_replica_values, always_wrap=True)
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| D | input_lib_test.py | 1095 def map_fn(per_replica_values): argument
1098 map(sparse_tensor.is_sparse, per_replica_values.values)) else
1099 math_ops.reduce_sum, (per_replica_values,))
1238 def map_fn(per_replica_values): argument
1241 map(sparse_tensor.is_sparse, per_replica_values.values)) else
1242 math_ops.reduce_sum, (per_replica_values,))
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| D | tpu_strategy.py | 980 per_replica_values = []
982 per_replica_values.append(
985 return distribute_utils.regroup(per_replica_values, always_wrap=True)
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| /external/tensorflow/tensorflow/python/keras/distribute/ |
| D | distributed_training_utils_v1.py | 216 def flatten_per_replica_values(distribution_strategy, per_replica_values): argument
236 return [e for flattened in nest.flatten(per_replica_values)
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