| /external/tensorflow/tensorflow/tools/compatibility/ |
| D | renames_v2.py | 1397 'tf.train.AdadeltaOptimizer':
1398 'tf.compat.v1.train.AdadeltaOptimizer',
1399 'tf.train.AdagradDAOptimizer':
1400 'tf.compat.v1.train.AdagradDAOptimizer',
1401 'tf.train.AdagradOptimizer':
1402 'tf.compat.v1.train.AdagradOptimizer',
1403 'tf.train.AdamOptimizer':
1404 'tf.compat.v1.train.AdamOptimizer',
1405 'tf.train.CheckpointSaverHook':
1407 'tf.train.CheckpointSaverListener':
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| /external/tensorflow/tensorflow/python/training/ |
| D | training.py | 18 See the [Training](https://tensorflow.org/api_guides/python/train) guide.
130 tf_export("train.BytesList")(BytesList)
131 tf_export("train.ClusterDef")(ClusterDef)
132 tf_export("train.Example")(Example)
133 tf_export("train.Feature")(Feature)
134 tf_export("train.Features")(Features)
135 tf_export("train.FeatureList")(FeatureList)
136 tf_export("train.FeatureLists")(FeatureLists)
137 tf_export("train.FloatList")(FloatList)
138 tf_export("train.Int64List")(Int64List)
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| D | server_lib.py | 29 """Creates a `tf.train.ServerDef` protocol buffer.
32 server_or_cluster_def: A `tf.train.ServerDef` or `tf.train.ClusterDef`
33 protocol buffer, or a `tf.train.ClusterSpec` object, describing the server
47 A `tf.train.ServerDef`.
69 "`tf.train.ServerDef` or `tf.train.ClusterSpec`.")
94 @tf_export("distribute.Server", v1=["distribute.Server", "train.Server"])
95 @deprecation.deprecated_endpoints("train.Server")
102 cluster (specified by a `tf.train.ClusterSpec`), and
120 server_or_cluster_def: A `tf.train.ServerDef` or `tf.train.ClusterDef`
121 protocol buffer, or a `tf.train.ClusterSpec` object, describing the
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| D | training_util.py | 36 @tf_export(v1=['train.global_step'])
48 print('global_step: %s' % tf.compat.v1.train.global_step(sess,
67 @tf_export(v1=['train.get_global_step'])
111 ... global_step = tf.compat.v1.train.get_or_create_global_step()
113 ... optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.1)
126 ... print(sess.run(tf.compat.v1.train.get_global_step()))
161 @tf_export(v1=['train.create_global_step'])
202 ... global_step = tf.compat.v1.train.create_global_step()
204 ... optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.1)
255 @tf_export(v1=['train.get_or_create_global_step'])
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| D | moving_averages.py | 32 @tf_export("__internal__.train.assign_moving_average", v1=[])
281 @tf_export("train.ExponentialMovingAverage")
339 ema = tf.train.ExponentialMovingAverage(decay=0.9999)
358 ...train the model by running train_step multiple times...
368 weights and restore them before continuing to train. You can see the
373 `tf.train.Checkpoint`. At evaluation time, create your shadow variables and
374 use `tf.train.Checkpoint` to restore the moving averages into the shadow
377 3. Checkpoint out your moving average variables in your `tf.train.Checkpoint`.
397 ema = tf.train.ExponentialMovingAverage(decay=0.9999)
403 checkpoint = tf.train.Checkpoint(model_weights=[var0, var1],
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| D | checkpoint_utils.py | 46 @tf_export("train.load_checkpoint")
71 @tf_export("train.load_variable")
89 @tf_export("train.list_variables")
101 ckpt = tf.train.Checkpoint(optimizer=optimizer, model=model)
102 manager = tf.train.CheckpointManager(ckpt, ckpt_directory, max_to_keep=3)
104 tf.train.list_variables(manager.latest_checkpoint)
153 @tf_export("train.checkpoints_iterator")
219 @tf_export(v1=["train.init_from_checkpoint"])
224 `tf.compat.v1.train.init_from_checkpoint` is not recommended for restoring
228 `tf.keras.Model.load_weights` or `tf.train.Checkpoint.restore`. These APIs use
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| D | saver.py | 144 # of a V2 checkpoint: e.g. "/fs/train/ckpt-<step>/tmp/worker<i>-<step>".
262 # <train dir>/myckpt_temp/
267 # <train dir>/
275 # "<train dir>/myckpt" in this case. Save() and Restore() work with the
547 # - Extend the inference graph to a train graph.
638 @tf_export(v1=["train.Saver"])
644 `tf.compat.v1.train.Saver` is not supported for saving and restoring
645 checkpoints in TF2. Please switch to `tf.train.Checkpoint` or
654 You can load a name-based checkpoint written by `tf.compat.v1.train.Saver`
655 using `tf.train.Checkpoint.restore` or `tf.keras.Model.load_weights`. However,
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| /external/skia/infra/bots/ |
| D | infra_tests.py | 28 def python_unit_tests(train): argument
29 if train:
37 def recipe_test(train): argument
40 if train:
41 cmd.append('train')
47 def gen_tasks_test(train): argument
49 if not train:
60 train = False
61 if '--train' in sys.argv:
62 train = True
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | training_ops_test.cc | 110 Graph* train; in BM_SGD() local
111 SGD(params, &init, &train); in BM_SGD()
112 test::Benchmark("cpu", train, GetOptions(), init, nullptr, "", in BM_SGD()
146 Graph* train; in BM_Adagrad() local
147 Adagrad(params, &init, &train); in BM_Adagrad()
148 test::Benchmark("cpu", train, GetOptions(), init, nullptr, "", in BM_Adagrad()
185 Graph* train; in BM_SparseAdagrad() local
186 SparseAdagrad(m, n, &init, &train); in BM_SparseAdagrad()
187 test::Benchmark("cpu", train, GetMultiThreadedOptions(), init, nullptr, "", in BM_SparseAdagrad()
229 Graph* train; in BM_Momentum() local
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| /external/tensorflow/tensorflow/examples/speech_commands/ |
| D | train_test.py | 22 from tensorflow.examples.speech_commands import train
97 'train_dir': os.path.join(self.get_temp_dir(), 'train'),
117 train.FLAGS = self._getDefaultFlags()
118 train.main('')
121 os.path.join(train.FLAGS.train_dir,
122 train.FLAGS.model_architecture + '.pbtxt')))
125 os.path.join(train.FLAGS.train_dir,
126 train.FLAGS.model_architecture + '_labels.txt')))
129 os.path.join(train.FLAGS.train_dir,
130 train.FLAGS.model_architecture + '.ckpt-1.meta')))
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| D | train.py | 17 This is a self-contained example script that will train a very basic audio
19 runs with reasonable defaults to train within a few hours even only using a CPU.
31 bazel run tensorflow/examples/speech_commands:train
44 If you want to train on your own data, you'll need to create .wavs with your
61 the audio in the 'other' folder would be used to train an 'unknown' category.
65 bazel run tensorflow/examples/speech_commands:train -- \
163 with tf.compat.v1.name_scope('train'), tf.control_dependencies(
168 train_step = tf.compat.v1.train.GradientDescentOptimizer(
171 train_step = tf.compat.v1.train.MomentumOptimizer(
187 global_step = tf.compat.v1.train.get_or_create_global_step()
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| /external/tensorflow/tensorflow/python/keras/saving/utils_v1/ |
| D | mode_keys.py | 27 * `TRAIN`: training/fitting mode.
32 TRAIN = 'train' variable in KerasModeKeys
43 * `TRAIN`: training/fitting mode.
48 TRAIN = 'train' variable in EstimatorModeKeys
62 return mode in [KerasModeKeys.TRAIN, EstimatorModeKeys.TRAIN]
92 return KerasModeKeys.TRAIN
|
| /external/tensorflow/tensorflow/python/saved_model/model_utils/ |
| D | mode_keys.py | 27 * `TRAIN`: training/fitting mode.
32 TRAIN = 'train' variable in KerasModeKeys
43 * `TRAIN`: training/fitting mode.
48 TRAIN = 'train' variable in EstimatorModeKeys
62 return mode in [KerasModeKeys.TRAIN, EstimatorModeKeys.TRAIN]
92 return KerasModeKeys.TRAIN
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| /external/tensorflow/tensorflow/python/checkpoint/ |
| D | checkpoint.py | 203 log_fn("Detecting that an object or model or tf.train.Checkpoint is being"
207 "https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restore"
230 `tf.train.latest_checkpoint`.
515 tf.train.latest_checkpoint(checkpoint_directory))
524 `tf.train.latest_checkpoint`.
727 "you. As a workaround, consider either using tf.train.Checkpoint to "
893 being restored by a later call to `tf.train.Checkpoint.restore()`.
975 `tf.train.Checkpoint.restore()`.
999 "Restoring a name-based tf.train.Saver checkpoint using the object-based "
1026 checkpoint saved with train.Saver(), and restored with train.Checkpoint():
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| D | checkpoint_management.py | 62 @tf_export(v1=["train.generate_checkpoint_state_proto"])
82 `tf.train.CheckpointManager` for an implementation).
129 instructions=("Use `tf.train.CheckpointManager` to manage checkpoints "
131 @tf_export(v1=["train.update_checkpoint_state"])
158 `tf.train.CheckpointManager` for an implementation).
173 @tf_export("__internal__.train.update_checkpoint_state", v1=[])
203 `tf.train.CheckpointManager` for an implementation).
248 @tf_export("train.get_checkpoint_state")
326 @tf_export("train.latest_checkpoint")
333 using `v1.train.Saver.save`
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| D | checkpoint_options.py | 20 @tf_export("train.CheckpointOptions")
24 Used as the `options` argument to either `tf.train.Checkpoint.save()` or
25 `tf.train.Checkpoint.restore()` methods to adjust how variables are
32 checkpoint = tf.train.Checkpoint(step=step)
33 options = tf.train.CheckpointOptions(experimental_io_device="/job:localhost")
60 so that the model can continue to train while the checkpoing file
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| /external/tensorflow/tensorflow/lite/testing/op_tests/ |
| D | parse_example.py | 31 features["x"] = tf.train.Feature(
32 float_list=tf.train.FloatList(value=list(data)))
35 features["x"] = tf.train.Feature(
36 int64_list=tf.train.Int64List(value=list(data)))
40 features["x"] = tf.train.Feature(
41 bytes_list=tf.train.BytesList(value=[data]*feature_shape[0]))
42 example = tf.train.Example(features=tf.train.Features(feature=features))
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| /external/tensorflow/tensorflow/lite/experimental/mlir/testing/op_tests/ |
| D | parse_example.py | 35 features["x"] = tf.train.Feature(
36 float_list=tf.train.FloatList(value=list(data)))
39 features["x"] = tf.train.Feature(
40 int64_list=tf.train.Int64List(value=list(data)))
44 features["x"] = tf.train.Feature(
45 bytes_list=tf.train.BytesList(value=[data]*feature_shape[0]))
46 example = tf.train.Example(features=tf.train.Features(feature=features))
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| /external/tensorflow/tensorflow/python/data/experimental/kernel_tests/ |
| D | checkpoint_input_pipeline_hook_test.py | 51 mode='train',
78 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
80 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
93 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
95 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
106 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
108 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
111 est.train(_input_fn, steps=2)
123 est.train(
|
| /external/zstd/programs/ |
| D | zstd.1 | 92 \fB\-\-train FILES\fR
421 …eatly improves efficiency on small files and messages\. It\'s possible to train \fBzstd\fR with a …
424 \fB\-\-train FILEs\fR
425 …et dictionary size (for example, ~10 MB for a 100 KB dictionary)\. \fB\-\-train\fR can be combined…
431 …train\fR supports multithreading if \fBzstd\fR is compiled with threading support (default)\. Addi…
464 \fB\-\-train\-cover[=k#,d=#,steps=#,split=#,shrink[=#]]\fR
474 \fBzstd \-\-train\-cover FILEs\fR
477 \fBzstd \-\-train\-cover=k=50,d=8 FILEs\fR
480 \fBzstd \-\-train\-cover=d=8,steps=500 FILEs\fR
483 \fBzstd \-\-train\-cover=k=50 FILEs\fR
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| D | zstd.1.md | 97 * `--train FILES`:
514 It's possible to train `zstd` with a set of samples,
520 * `--train FILEs`:
525 `--train` can be combined with `-r` to indicate a directory rather than listing all the files,
533 `--train` supports multithreading if `zstd` is compiled with threading support (default).
534 Additional advanced parameters can be specified with `--train-fastcover`.
535 The legacy dictionary builder can be accessed with `--train-legacy`.
536 The slower cover dictionary builder can be accessed with `--train-cover`.
537 Default `--train` is equivalent to `--train-fastcover=d=8,steps=4`.
586 * `--train-cover[=k#,d=#,steps=#,split=#,shrink[=#]]`:
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| /external/tensorflow/tensorflow/python/ops/ |
| D | batch_norm_benchmark.py | 64 def build_graph(device, input_shape, axes, num_layers, mode, scale, train): argument
74 train: if true, also run backprop.
94 if train:
107 if train:
123 train, num_iters): argument
133 train: if true, also run backprop.
142 train)
150 print("%s shape:%d/%d #layers:%d mode:%s scale:%r train:%r - %f secs" %
151 (device, len(input_shape), len(axes), num_layers, mode, scale, train,
157 "train_{train}")
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| /external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
| D | legacy_learning_rate_decay.py | 28 @tf_export(v1=["train.exponential_decay"])
59 learning_rate = tf.compat.v1.train.exponential_decay(starter_learning_rate,
64 tf.compat.v1.train.GradientDescentOptimizer(learning_rate)
104 @tf_export(v1=["train.piecewise_constant_decay", "train.piecewise_constant"])
115 learning_rate = tf.compat.v1.train.piecewise_constant(global_step, boundaries,
182 @tf_export(v1=["train.polynomial_decay"])
229 learning_rate = tf.compat.v1.train.polynomial_decay(starter_learning_rate,
235 tf.compat.v1.train.GradientDescentOptimizer(learning_rate)
283 @tf_export(v1=["train.natural_exp_decay"])
320 learning_rate = tf.compat.v1.train.natural_exp_decay(learning_rate,
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| /external/zstd/tests/ |
| D | playTests.sh | 1100 zstd --train -B2K tmpCorpusHighCompress -o tmpDictHighCompress
1101 zstd --train -B2K tmpCorpusLowCompress -o tmpDictLowCompress
1110 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c -o tmpDict
1135 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c "$PRGDIR"/*.h -o tmpDictC
1138 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c --dictID=1 -o tmpDict1
1141 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c --dictID -o 1 tmpDict1 && die "wrong order : --dictID mus…
1143 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c -o tmpDict2 --maxdict=4K -v
1145 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c -o tmpDict3 --maxdict=1K -v
1147 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c -o tmpDict3 --maxdict -v 4K && die "wrong order : --maxdi…
1168 zstd --train-legacy -q tmp && die "Dictionary training should fail : not enough input source"
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| /external/sdv/vsomeip/implementation/endpoints/include/ |
| D | server_endpoint_impl.hpp | 56 const std::shared_ptr<train>& _train);
69 const std::shared_ptr<train>& _train,
92 const std::shared_ptr<train>& _train,
95 std::shared_ptr<train> find_or_create_train_unlocked(const endpoint_type& _target);
107 std::map<endpoint_type, std::shared_ptr<train>> trains_;
124 void wait_until_debounce_time_reached(const std::shared_ptr<train>& _train) const;
|