| /external/tensorflow/tensorflow/contrib/distribute/python/ |
| D | mirrored_strategy_multigpu_test.py | 66 distribution=[
77 def testMinimizeLoss(self, distribution): argument
79 self._test_minimize_loss_eager(distribution)
81 self._test_minimize_loss_graph(distribution)
83 def testReplicaId(self, distribution): argument
84 self._test_replica_id(distribution)
86 def testNumReplicasInSync(self, distribution): argument
87 self.assertEqual(2, distribution.num_replicas_in_sync)
89 def testCallAndMergeExceptions(self, distribution): argument
90 self._test_call_and_merge_exceptions(distribution)
[all …]
|
| D | one_device_strategy_test.py | 29 distribution=[
37 def testMinimizeLoss(self, distribution): argument
39 self._test_minimize_loss_eager(distribution)
41 self._test_minimize_loss_graph(distribution)
43 def testReplicaId(self, distribution): argument
44 self._test_replica_id(distribution)
46 def testCallAndMergeExceptions(self, distribution): argument
47 self._test_call_and_merge_exceptions(distribution)
49 def testMakeInputFnIteratorWithDataset(self, distribution): argument
57 iterator = distribution.make_input_fn_iterator(input_fn)
[all …]
|
| D | metrics_v1_test.py | 77 distribution=[combinations.default_strategy,
87 return combinations.combine(distribution=[combinations.tpu_strategy_one_step,
96 def _test_metric(self, distribution, dataset_fn, metric_fn, expected_fn): argument
97 with ops.Graph().as_default(), distribution.scope():
98 iterator = distribution.make_input_fn_iterator(lambda _: dataset_fn())
99 if isinstance(distribution, tpu_strategy.TPUStrategy):
101 value, update = distribution.extended.call_for_each_replica(
104 return distribution.group(update)
106 ctx = distribution.extended.experimental_run_steps_on_iterator(
107 step_fn, iterator, iterations=distribution.extended.steps_per_run)
[all …]
|
| D | keras_utils_test.py | 75 def test_callbacks_in_fit(self, distribution): argument
76 with distribution.scope():
80 dataset = keras_test_lib.get_dataset(distribution)
96 if isinstance(distribution, tpu_strategy.TPUStrategy):
99 steps_per_run = distribution.extended.steps_per_run
123 def test_callbacks_in_eval(self, distribution): argument
124 with distribution.scope():
128 dataset = keras_test_lib.get_dataset(distribution)
142 def test_callbacks_in_predict(self, distribution): argument
143 with distribution.scope():
[all …]
|
| D | keras_dnn_correctness_test.py | 32 return combinations.combine(distribution=keras_correctness_test_base.
38 return combinations.combine(distribution=keras_correctness_test_base.
45 def get_model(self, initial_weights=None, distribution=None): argument
46 with keras_correctness_test_base.MaybeDistributionScope(distribution):
78 def test_dnn_correctness(self, distribution, use_numpy, use_validation_data): argument
79 self.run_correctness_test(distribution, use_numpy, use_validation_data)
82 def test_dnn_with_dynamic_learning_rate(self, distribution): argument
83 self.run_dynamic_lr_test(distribution)
89 def get_model(self, distribution=None): argument
90 with distribution.scope():
[all …]
|
| D | minimize_loss_test.py | 55 distribution=[combinations.tpu_strategy],
59 def testTrainNetwork(self, distribution, optimizer_fn, use_callable_loss): argument
60 with distribution.scope():
66 return distribution.group(
67 distribution.extended.call_for_each_replica(
70 iterator = self._get_iterator(distribution, dataset_fn)
73 return distribution.extended.experimental_run_steps_on_iterator(
96 def testTrainNetworkByCallForEachReplica(self, distribution, optimizer_fn, argument
98 with distribution.scope():
102 iterator = self._get_iterator(distribution, dataset_fn)
[all …]
|
| D | keras_correctness_test_base.py | 71 combinations.combine(distribution=all_strategies),
88 combinations.combine(distribution=
100 combinations.combine(distribution=tpu_strategies),
107 def __init__(self, distribution): argument
108 self._distribution = distribution
122 def batch_wrapper(dataset, batch_size, distribution, repeat=None): argument
127 if isinstance(distribution, tpu_strategy.TPUStrategy):
133 def get_batch_size(global_batch_size, distribution): argument
137 distribution and
138 not distributed_training_utils.global_batch_size_supported(distribution))
[all …]
|
| D | keras_test.py | 196 def batch_wrapper(dataset, batch_size, distribution, repeat=None): argument
201 if isinstance(distribution, tpu_strategy.TPUStrategy):
214 def get_dataset(distribution): argument
219 dataset = batch_wrapper(dataset, 10, distribution)
223 def get_predict_dataset(distribution): argument
227 dataset = batch_wrapper(dataset, 10, distribution)
278 return combinations.combine(distribution=strategies_minus_tpu,
283 return combinations.combine(distribution=tpu_strategies,
293 distribution=[
341 distribution=[
[all …]
|
| D | keras_backward_compat_test.py | 162 def batch_wrapper(dataset, batch_size, distribution, repeat=None): argument
167 if isinstance(distribution, tpu_strategy.TPUStrategy):
180 def get_dataset(distribution): argument
185 dataset = batch_wrapper(dataset, 10, distribution)
189 def get_predict_dataset(distribution): argument
193 dataset = batch_wrapper(dataset, 10, distribution)
307 distribution=strategies_minus_tpu,
313 distribution=tpu_strategies,
339 combinations.combine(distribution=strategies_minus_tpu),
347 combinations.combine(distribution=tpu_strategies),
[all …]
|
| D | step_fn.py | 28 def __init__(self, distribution): argument
29 self._distribution = distribution
32 def distribution(self): member in Step
53 def __init__(self, dataset_fn, distribution): argument
54 super(StandardInputStep, self).__init__(distribution)
55 self._iterator = distribution.make_input_fn_iterator(lambda _: dataset_fn())
88 def __init__(self, dataset_fn, loss_fn, optimizer, distribution, argument
90 super(StandardSingleLossStep, self).__init__(dataset_fn, distribution)
102 grads_and_vars = self.distribution.extended.call_for_each_replica(
109 self.distribution, grads_and_vars)
[all …]
|
| D | moving_averages_test.py | 33 distribution=[combinations.default_strategy,
43 def testReplicaModeWithoutZeroDebias(self, distribution): argument
55 with distribution.scope(), self.cached_session() as sess:
56 var, assign = distribution.extended.call_for_each_replica(replica_fn)
59 sess.run(distribution.experimental_local_results(assign))
70 def testReplicaMode(self, distribution): argument
81 with distribution.scope(), self.cached_session() as sess:
82 var, assign_op = distribution.extended.call_for_each_replica(replica_fn)
85 sess.run(distribution.experimental_local_results(assign_op))
92 def testCrossDeviceWithoutZeroDebias(self, distribution): argument
[all …]
|
| /external/bcc/tools/ |
| D | runqlat_example.txt | 12 usecs : count distribution
30 The distribution is bimodal, with one mode between 0 and 15 microseconds,
32 spikes in the ASCII distribution (which is merely a visual representation
49 msecs : count distribution
57 msecs : count distribution
65 msecs : count distribution
73 This shows a similar distribution across the three summaries.
82 msecs : count distribution
90 msecs : count distribution
98 msecs : count distribution
[all …]
|
| D | hardirqs_example.txt | 192 The distribution of interrupt run time can be printed as a histogram with the -d
200 usecs : count distribution
217 usecs : count distribution
232 usecs : count distribution
248 usecs : count distribution
264 usecs : count distribution
280 usecs : count distribution
296 usecs : count distribution
312 usecs : count distribution
328 usecs : count distribution
[all …]
|
| D | ext4dist_example.txt | 12 usecs : count distribution
29 usecs : count distribution
37 usecs : count distribution
44 This output shows a bi-modal distribution for read latency, with a faster
71 msecs : count distribution
81 msecs : count distribution
85 msecs : count distribution
91 msecs : count distribution
102 msecs : count distribution
106 msecs : count distribution
[all …]
|
| D | bitesize_example.txt | 4 The aim of this tool is to show I/O distribution for requested block sizes, by process name.
11 Kbytes : count distribution
17 Kbytes : count distribution
28 Kbytes : count distribution
42 Kbytes : count distribution
48 Kbytes : count distribution
60 Kbytes : count distribution
66 Kbytes : count distribution
72 Kbytes : count distribution
80 Kbytes : count distribution
|
| D | cpudist_example.txt | 7 multiple threads), uneven workload distribution, too-granular tasks, and more.
16 usecs : count distribution
36 usecs : count distribution
53 A bimodal distribution is now clearly visible. Most of the time, tasks were
64 usecs : count distribution
91 usecs : count distribution
106 usecs : count distribution
117 usecs : count distribution
130 usecs : count distribution
138 usecs : count distribution
[all …]
|
| D | xfsdist_example.txt | 12 usecs : count distribution
28 usecs : count distribution
32 This output shows a bi-modal distribution for read latency, with a faster
59 msecs : count distribution
67 msecs : count distribution
73 msecs : count distribution
82 msecs : count distribution
88 msecs : count distribution
97 msecs : count distribution
103 msecs : count distribution
[all …]
|
| D | runqlen_example.txt | 13 runqlen : count distribution
23 runqlen : count distribution
45 runqlen : count distribution
63 runqlen : count distribution
73 runqlen : count distribution
79 runqlen : count distribution
91 runqlen : count distribution
102 runqlen : count distribution
108 runqlen : count distribution
115 runqlen : count distribution
[all …]
|
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| D | independent.py | 30 from tensorflow.python.ops.distributions import distribution as distribution_lib
108 self, distribution, reinterpreted_batch_ndims=None, argument
130 name = name or "Independent" + distribution.name
131 self._distribution = distribution
135 distribution)
153 distribution._graph_parents), # pylint: disable=protected-access
156 distribution, reinterpreted_batch_ndims, validate_args)
159 def distribution(self): member in Independent
168 batch_shape = self.distribution.batch_shape_tensor()
177 batch_shape = self.distribution.batch_shape
[all …]
|
| D | batch_reshape.py | 30 from tensorflow.python.ops.distributions import distribution as distribution_lib
85 distribution, argument
116 name = name or "BatchReshape" + distribution.name
121 validate_init_args_statically(distribution, self._batch_shape_unexpanded)
123 distribution.batch_shape_tensor(), self._batch_shape_unexpanded,
125 self._distribution = distribution
130 dtype=distribution.dtype,
131 reparameterization_type=distribution.reparameterization_type,
136 … [self._batch_shape_unexpanded] + distribution._graph_parents), # pylint: disable=protected-access
140 def distribution(self): member in BatchReshape
[all …]
|
| D | quantized_distribution.py | 28 from tensorflow.python.ops.distributions import distribution as distributions
250 distribution, argument
286 list(distribution.parameters.values()) +
289 self._dist = distribution
296 tensors=[self.distribution, low, high])
331 def distribution(self): member in QuantizedDistribution
346 return self.distribution.batch_shape_tensor()
349 return self.distribution.batch_shape
352 return self.distribution.event_shape_tensor()
355 return self.distribution.event_shape
[all …]
|
| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.distributions.pbtxt | 5 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
9 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
13 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
17 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
21 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
25 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
29 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
33 mtype: "<class \'tensorflow.python.ops.distributions.distribution.ReparameterizationType\'>"
37 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
41 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
[all …]
|
| /external/grpc-grpc/src/python/grpcio_tests/ |
| D | commands.py | 107 self.distribution.fetch_build_eggs(self.distribution.install_requires)
108 self.distribution.fetch_build_eggs(self.distribution.tests_require)
182 if self.distribution.install_requires:
183 self.distribution.fetch_build_eggs(
184 self.distribution.install_requires)
185 if self.distribution.tests_require:
186 self.distribution.fetch_build_eggs(self.distribution.tests_require)
220 if self.distribution.install_requires:
221 self.distribution.fetch_build_eggs(
222 self.distribution.install_requires)
[all …]
|
| /external/python/cpython3/Lib/distutils/command/ |
| D | bdist_rpm.py | 206 if not self.distribution.has_ext_modules():
215 "%s <%s>" % (self.distribution.get_contact(),
216 self.distribution.get_contact_email()))
279 "%s.spec" % self.distribution.get_name())
290 saved_dist_files = self.distribution.dist_files[:]
297 self.distribution.dist_files = saved_dist_files
369 if self.distribution.has_ext_modules():
379 self.distribution.dist_files.append(
389 self.distribution.dist_files.append(
401 '%define name ' + self.distribution.get_name(),
[all …]
|
| /external/python/cpython2/Lib/distutils/command/ |
| D | bdist_rpm.py | 215 if not self.distribution.has_ext_modules():
226 "%s <%s>" % (self.distribution.get_contact(),
227 self.distribution.get_contact_email()))
293 "%s.spec" % self.distribution.get_name())
304 saved_dist_files = self.distribution.dist_files[:]
311 self.distribution.dist_files = saved_dist_files
383 if self.distribution.has_ext_modules():
393 self.distribution.dist_files.append(
403 self.distribution.dist_files.append(
416 '%define name ' + self.distribution.get_name(),
[all …]
|