| /external/tensorflow/tensorflow/python/training/ |
| sync_replicas_optimizer_test.py | 29 from tensorflow.python.training import adam
30 from tensorflow.python.training import gradient_descent
31 from tensorflow.python.training import training
61 sync_rep_opt = training.SyncReplicasOptimizer(
75 session = training.MonitoredTrainingSession(
263 opt = training.SyncReplicasOptimizer(
275 opt = training.SyncReplicasOptimizer(
287 opt = training.SyncReplicasOptimizer(
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| server_lib_sparse_job_test.py | 26 from tensorflow.python.training import server_lib
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| /external/tensorflow/tensorflow/contrib/gan/python/estimator/python/ |
| latent_gan_estimator_test.py | 32 from tensorflow.python.training import training
60 # the input training wrapper. Make sure that everything is wired up
87 optimizer = training.AdamOptimizer
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| gan_estimator_test.py | 48 from tensorflow.python.training import input as input_lib
49 from tensorflow.python.training import learning_rate_decay
50 from tensorflow.python.training import sync_replicas_optimizer
51 from tensorflow.python.training import training
52 from tensorflow.python.training import training_util
142 cls._generator_optimizer = training.GradientDescentOptimizer(1.0)
143 cls._discriminator_optimizer = training.GradientDescentOptimizer(1.0)
178 training.GradientDescentOptimizer(learning_rate=1.0),
219 return training.GradientDescentOptimizer(lr
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| stargan_estimator_test.py | 39 from tensorflow.python.training import learning_rate_decay
40 from tensorflow.python.training import training
41 from tensorflow.python.training import training_util
166 cls._generator_optimizer = training.GradientDescentOptimizer(1.0)
167 cls._discriminator_optimizer = training.GradientDescentOptimizer(1.0)
217 return training.GradientDescentOptimizer(lr)
219 gopt = make_opt if lr_decay else training.GradientDescentOptimizer(1.0)
220 dopt = make_opt if lr_decay else training.GradientDescentOptimizer(1.0)
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| /external/tensorflow/tensorflow/contrib/opt/python/training/ |
| moving_average_optimizer.py | 26 from tensorflow.python.training import moving_averages
27 from tensorflow.python.training import optimizer
28 from tensorflow.python.training import saver
29 from tensorflow.python.training.saving import saveable_object_util
38 the variables at save time so that any code outside of the training loop will
51 // Add the training op that optimizes using opt.
56 // Pass it to your training loop.
139 You should use this saver during training. It will save the moving averages
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| elastic_average_optimizer.py | 30 from tensorflow.python.training import optimizer
31 from tensorflow.python.training import saver
32 from tensorflow.python.training import session_run_hook
33 from tensorflow.python.training.saving import saveable_object_util
144 This is an async optimizer. During the training, Each worker will update
181 True: all workers will wait for each other before start training
182 False: worker can start training when its initilization is done,
185 training without being blocked.
353 variables equal to the global center variables before the training begins"""
404 during training. It will save the global_center_variable of the traine
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| elastic_average_optimizer_test.py | 30 from tensorflow.python.training import device_setter
31 from tensorflow.python.training import gradient_descent
32 from tensorflow.python.training import saver
33 from tensorflow.python.training import server_lib
34 from tensorflow.python.training import training
35 from tensorflow.python.training import training_util
37 from tensorflow.contrib.opt.python.training.elastic_average_optimizer import \
134 sess = training.MonitoredTrainingSession(
281 from tensorflow.python.training import device_sette
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| /external/tensorflow/tensorflow/contrib/slim/python/slim/ |
| evaluation.py | 130 from tensorflow.contrib.training.python.training import evaluation
132 from tensorflow.python.training import monitored_session
133 from tensorflow.python.training import saver as tf_saver
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| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/models/ |
| decisions_to_data_then_nn.py | 23 from tensorflow.python.training import adagrad
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| k_feature_decisions_to_data_then_nn.py | 23 from tensorflow.python.training import adagrad
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| nn.py | 22 from tensorflow.python.training import adagrad
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| stochastic_hard_decisions_to_data_then_nn.py | 15 """A hybrid model that samples paths when training."""
23 from tensorflow.python.training import adagrad
28 """A hybrid model that samples paths when training."""
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| stochastic_soft_decisions_to_data_then_nn.py | 15 """A hybrid model that samples paths when training."""
23 from tensorflow.python.training import adagrad
28 """A hybrid model that samples paths when training."""
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| /external/tensorflow/tensorflow/python/data/experimental/ops/ |
| iterator_ops.py | 23 from tensorflow.python.training import basic_session_run_hooks
24 from tensorflow.python.training import checkpoint_management
25 from tensorflow.python.training import saver as saver_lib
26 from tensorflow.python.training import session_run_hook
51 ... Perform training ...
91 training is resumed the input pipeline continues from where it left off.
93 number of training steps per eval are small compared to the dataset
94 size or if the training pipeline is pre-empted.
101 Example of checkpointing the training pipeline:
120 2. If the input pipeline is shared between training and validation, restorin
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| /external/tensorflow/tensorflow/python/grappler/ |
| graph_placer.py | 30 from tensorflow.python.training import training
88 session_creator = training.ChiefSessionCreator()
89 with training.MonitoredSession(session_creator=session_creator) as sess:
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| time_distributed_learning_phase_test.py | 36 x, training=True)
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| /external/tensorflow/tensorflow/python/kernel_tests/signal/ |
| test_util.py | 23 from tensorflow.python.training import saver
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| /external/tensorflow/tensorflow/python/training/tracking/ |
| python_state.py | 25 from tensorflow.python.training.tracking import base
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| tracking_test.py | 25 from tensorflow.python.keras.engine import training
28 from tensorflow.python.training.tracking import base
29 from tensorflow.python.training.tracking import data_structures
30 from tensorflow.python.training.tracking import tracking
31 from tensorflow.python.training.tracking import util
63 class NoDependencyModel(training.Model):
164 a = training.Model()
165 b = training.Model()
167 c = training.Model()
188 a = training.Model(
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| /external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_ptb/ |
| rnn_ptb.py | 58 def call(self, input_seq, training):
69 if training:
133 def call(self, input_seq, training):
138 training: Is this a training call.
143 if training:
145 y = self.rnn(y, training=training)[0]
155 def loss_fn(model, inputs, targets, training):
157 outputs = model(inputs, training=training
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| /external/tensorflow/tensorflow/contrib/checkpoint/python/ |
| visualize.py | 21 from tensorflow.python.training.tracking import base as trackable
22 from tensorflow.python.training.tracking import util as trackable_utils
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| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| evaluation.py | 58 tf.contrib.training.StopAfterNEvalsHook(num_evals),
59 tf.contrib.training.SummaryAtEndHook(logdir),
98 tf.contrib.training.evaluate_repeatedly(
102 tf.contrib.training.StopAfterNEvalsHook(num_evals),
103 tf.contrib.training.SummaryAtEndHook(logdir),
126 tf.contrib.training.evaluate_repeatedly(
129 tf.contrib.training.SummaryAtEndHook(logdir),
144 from tensorflow.python.training import basic_session_run_hooks
145 from tensorflow.python.training import checkpoint_management
146 from tensorflow.python.training import evaluatio
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| /external/tensorflow/tensorflow/python/ops/parallel_for/ |
| gradients_test.py | 33 from tensorflow.python.keras.engine import training as keras_training
243 def __call__(self, inputs, training):
248 training: A boolean. Set to True to add operations required only when
249 training the classifier.
261 y = self.dropout(y, training=training)
265 def create_mnist_autobatch(batch_size, data_format, training):
268 manual = model(images, training=training)
272 return model(image, training=training
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| sequential.py | 27 from tensorflow.python.keras.engine import training
31 from tensorflow.python.training.tracking import base as trackable
38 class Sequential(training.Model):
63 # training and evaluation methods).
73 # to a training/evaluation method (since it isn't yet built):
238 def call(self, inputs, training=None, mask=None): # pylint: disable=redefined-outer-name
242 return super(Sequential, self).call(inputs, training=training, mask=mask)
253 if 'training' in argspec:
254 kwargs['training'] = trainin
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