/external/tensorflow/tensorflow/contrib/slim/python/slim/ |
H A D | evaluation.py | 128 from tensorflow.python.training import saver as tf_saver 198 saver = None 200 saver = tf_saver.Saver(variables_to_restore) 206 init_op=initial_op, init_feed_dict=initial_op_feed_dict, saver=saver), 283 saver = None 285 saver = tf_saver.Saver(variables_to_restore) 292 init_fn=init_fn, saver=saver),
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/external/tensorflow/tensorflow/contrib/data/python/ops/ |
H A D | iterator_ops.py | 22 from tensorflow.python.training import saver namespace 42 saver = tf.train.Saver() 47 saver.save() 63 class _Saveable(saver.BaseSaverBuilder.SaveableObject): 69 saver.BaseSaverBuilder.SaveSpec(serialized_iterator, "",
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/external/tensorflow/tensorflow/contrib/signal/python/kernel_tests/ |
H A D | test_util.py | 23 from tensorflow.python.training import saver namespace 45 metagraph = saver.export_meta_graph(graph_def=graph.as_graph_def())
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/external/tensorflow/tensorflow/contrib/data/python/kernel_tests/ |
H A D | serialization_integration_test.py | 26 from tensorflow.python.training import saver as saver_lib 48 saver = saver_lib.Saver() 49 return init_ops, get_next_ops, saver 60 init_ops, get_next_ops, saver = self._build_graph(num_pipelines, 68 saver.save(sess, self._ckpt_path()) 71 init_ops, get_next_ops, saver = self._build_graph(num_pipelines, 74 saver.restore(sess, self._ckpt_path())
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H A D | dataset_serialization_test_base.py | 35 from tensorflow.python.training import saver as saver_lib 250 saver = self._import_meta_graph() 255 self._restore(saver, sess) 314 _, get_next_op, saver = self._build_graph( 318 self._restore(saver, sess) 375 get_next_op, saver = self._build_empty_graph( 379 self._restore(saver, sess) 409 init_op, get_next_op, saver = self._build_graph( 417 self._save(sess, saver) 487 init_before_restore: Whether init should be called before saver [all...] |
/external/tensorflow/tensorflow/contrib/eager/python/ |
H A D | saver_test.py | 22 from tensorflow.contrib.eager.python import saver as _saver 53 saver = _saver.Saver([v1]) 54 saver.save(ckpt_prefix) 58 saver.restore(ckpt_prefix) 71 saver = _saver.Saver([v1_first_graph, v1_second_graph]) 74 saver.save(ckpt_prefix) 81 saver = _saver.Saver([v1, v1]) 83 saver.save(ckpt_prefix) 96 saver = _saver.Saver({'ckpt/v1': v1, 'ckpt/v2': v2}) 97 saver [all...] |
H A D | tfe.py | 82 from tensorflow.contrib.eager.python.saver import get_optimizer_variables 83 from tensorflow.contrib.eager.python.saver import restore_variables_on_create 84 from tensorflow.contrib.eager.python.saver import Saver
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/external/tensorflow/tensorflow/contrib/learn/python/learn/utils/ |
H A D | export.py | 34 from tensorflow.python.training import saver as tf_saver 49 """Lazy init and return saver.""" 50 saver = _get_first_op_from_collection(ops.GraphKeys.SAVERS) 51 if saver is not None: 52 if saver: 53 saver = saver[0] 55 saver = None 56 if saver is None and variables.global_variables(): 57 saver [all...] |
/external/tensorflow/tensorflow/contrib/boosted_trees/python/ops/ |
H A D | model_ops.py | 32 from tensorflow.python.training import saver namespace 39 class TreeEnsembleVariableSavable(saver.BaseSaverBuilder.SaveableObject): 56 saver.BaseSaverBuilder.SaveSpec(stamp_token, slice_spec, 58 saver.BaseSaverBuilder.SaveSpec(ensemble_config, slice_spec,
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H A D | stats_accumulator_ops.py | 28 from tensorflow.python.training import saver namespace 34 class StatsAccumulator(saver.BaseSaverBuilder.SaveableObject): 95 saver.BaseSaverBuilder.SaveSpec(stamp_token, slice_spec, 97 saver.BaseSaverBuilder.SaveSpec(num_updates, slice_spec, 99 saver.BaseSaverBuilder.SaveSpec(partition_ids, slice_spec, 101 saver.BaseSaverBuilder.SaveSpec(feature_ids, slice_spec, 103 saver.BaseSaverBuilder.SaveSpec(gradients, slice_spec, 105 saver.BaseSaverBuilder.SaveSpec(hessians, slice_spec,
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/external/tensorflow/tensorflow/contrib/model_pruning/python/ |
H A D | learning.py | 78 saver=None, 133 saver: Saver to save checkpoints. If None, a default one will be created 182 saver=saver,
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/external/tensorflow/tensorflow/contrib/rnn/python/tools/ |
H A D | checkpoint_convert_test.py | 29 from tensorflow.python.training import saver as saver_lib 62 saver = saver_lib.Saver() 64 saver.save(sess, self._old_ckpt_path) 77 saver = saver_lib.Saver() 79 saver.save(sess, self._old_ckpt_path) 92 saver = saver_lib.Saver() 94 saver.save(sess, self._old_ckpt_path)
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/external/tensorflow/tensorflow/python/training/ |
H A D | session_manager.py | 27 from tensorflow.python.training import saver as saver_mod 67 sess = sm.prepare_session(master, init_op, saver, checkpoint_dir) 74 and `saver` as an argument. 152 saver=None, 163 saver: A `Saver` object used to restore a model. 185 # If either saver or checkpoint_* is not specified, cannot restore. Just 187 if not saver or not (checkpoint_dir or checkpoint_filename_with_path): 191 saver.restore(sess, checkpoint_filename_with_path) 207 saver.restore(sess, ckpt.model_checkpoint_path) 208 saver [all...] |
H A D | session_manager_test.py | 33 from tensorflow.python.training import saver as saver_lib 84 saver = saver_lib.Saver({"v": v}) 88 saver=saver, 93 saver.save(sess, checkpoint_filename) 104 saver = saver_lib.Saver({"v": v}) 111 saver=saver, 122 saver=saver, [all...] |
H A D | quantize_training_test.py | 31 from tensorflow.python.training import saver as saver_module 66 saver = saver_module.Saver({'b': b}) 81 saver.save(sess, save_path) 88 saver.restore(sess, save_path)
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H A D | training.py | 168 from tensorflow.python.training.saver import Saver 169 from tensorflow.python.training.saver import checkpoint_exists 170 from tensorflow.python.training.saver import generate_checkpoint_state_proto 171 from tensorflow.python.training.saver import get_checkpoint_mtimes 172 from tensorflow.python.training.saver import get_checkpoint_state 173 from tensorflow.python.training.saver import latest_checkpoint 174 from tensorflow.python.training.saver import update_checkpoint_state 175 from tensorflow.python.training.saver import export_meta_graph 176 from tensorflow.python.training.saver import import_meta_graph 215 "generate_checkpoint_state_proto", # Used internally by saver [all...] |
H A D | saver_large_partitioned_variable_test.py | 15 """Tests for tensorflow.python.training.saver.py.""" 30 from tensorflow.python.training import saver namespace 50 save = saver.Saver(partitioned_var)
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H A D | saver_large_variable_test.py | 15 """Tests for tensorflow.python.training.saver.py.""" 31 from tensorflow.python.training import saver namespace 49 save = saver.Saver(
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/external/tensorflow/tensorflow/contrib/predictor/ |
H A D | contrib_estimator_predictor.py | 26 from tensorflow.python.training import saver namespace 58 checkpoint_path = saver.latest_checkpoint(estimator.model_dir)
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/external/tensorflow/tensorflow/contrib/opt/python/training/ |
H A D | moving_average_optimizer.py | 28 from tensorflow.python.training import saver namespace 53 // Then create your saver like this: 54 saver = opt.swapping_saver() 59 saver=saver) 62 Note that for evaluation, the normal saver should be used instead of 110 """Create a saver swapping moving averages and variables. 112 You should use this saver during training. It will save the moving averages 114 evaluations or inference you should use a regular saver and it will 124 name: The name of the saver [all...] |
/external/tensorflow/tensorflow/contrib/tensor_forest/python/ops/ |
H A D | model_ops.py | 34 from tensorflow.python.training import saver namespace 49 class TreeVariableSavable(saver.BaseSaverBuilder.SaveableObject): 67 specs = [saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name),]
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H A D | stats_ops.py | 31 from tensorflow.python.training import saver namespace 46 class FertileStatsVariableSavable(saver.BaseSaverBuilder.SaveableObject): 64 specs = [saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name),]
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/external/tensorflow/tensorflow/contrib/learn/python/learn/ |
H A D | graph_actions.py | 47 from tensorflow.python.training import saver as tf_saver 95 def _restore_from_checkpoint(session, graph, checkpoint_path, saver=None): 97 saver = saver or _make_saver(graph) 98 if saver: 99 saver.restore(session, checkpoint_path) 302 saver=_make_saver(graph, keep_checkpoint_max), 383 supervisor.saver.save(session, ckpt_path, global_step=last_step) 415 """Lazy init and return saver.""" 416 saver [all...] |
/external/tensorflow/tensorflow/contrib/model_pruning/examples/cifar10/ |
H A D | cifar10_eval.py | 50 def eval_once(saver, summary_writer, top_k_op, summary_op): 54 saver: Saver. 63 saver.restore(sess, ckpt.model_checkpoint_path) 122 saver = tf.train.Saver(variables_to_restore) 130 eval_once(saver, summary_writer, top_k_op, summary_op)
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/external/tensorflow/tensorflow/python/saved_model/ |
H A D | loader_impl.py | 33 from tensorflow.python.training import saver as tf_saver 218 # Build a saver by importing the meta graph def to load. 219 saver = tf_saver.import_meta_graph(meta_graph_def_to_load, **saver_kwargs) 221 if saver: 228 # Restore the variables using the built saver in the provided session. 229 saver.restore(sess, variables_path)
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