Searched defs:train (Results 1 - 25 of 37) sorted by relevance

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/external/tensorflow/tensorflow/contrib/eager/python/examples/linear_regression/
H A Dlinear_regression_graph_test.py53 optimization_step = tf.train.GradientDescentOptimizer(
59 def train(num_epochs): function in function:GraphLinearRegressionBenchmark.benchmarkGraphLinearRegression
69 train(1)
72 train(num_epochs)
/external/tensorflow/tensorflow/contrib/model_pruning/python/
H A Dlearning.py31 optimizer = tf.train.MomentumOptimizer(FLAGS.learning_rate, FLAGS.momentum)
43 learning.train(train_op,
58 def train(train_op, function
83 """Wrapper around tf-slim's train function.
136 sync_optimizer: an instance of tf.train.SyncReplicasOptimizer, or a list of
162 total_loss, _ = _slim.learning.train(
/external/tensorflow/tensorflow/contrib/gan/
H A D__init__.py33 from tensorflow.contrib.gan.python import train namespace
37 from tensorflow.contrib.gan.python.train import *
48 _allowed_symbols += train.__all__
/external/tensorflow/tensorflow/examples/tutorials/mnist/
H A Dmnist_with_summaries.py38 def train(): function
127 with tf.name_scope('train'):
128 train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
141 train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
149 def feed_dict(train):
151 if train or FLAGS.fake_data:
152 xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
164 else: # Record train set summaries, and train
186 train()
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/external/tensorflow/tensorflow/contrib/model_pruning/
H A D__init__.py27 from tensorflow.contrib.model_pruning.python.learning import train namespace
42 'MaskedBasicLSTMCell', 'MaskedLSTMCell', 'train', 'apply_mask',
/external/tensorflow/tensorflow/contrib/model_pruning/examples/cifar10/
H A Dcifar10_train.py15 """A binary to train pruned CIFAR-10 using a single GPU.
33 data set, compile the program and train the model.
55 def train(): function
72 train_op = cifar10.train(loss, global_step)
90 class _LoggerHook(tf.train.SessionRunHook):
99 return tf.train.SessionRunArgs(loss) # Asks for loss value.
114 with tf.train.MonitoredTrainingSession(
116 hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
117 tf.train.NanTensorHook(loss),
132 train()
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H A Dcifar10_pruning.py30 train_op = train(loss, global_step)
153 eval_data: bool, indicating if one should use the train or eval data set.
309 loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
324 def train(total_loss, global_step): function
342 lr = tf.train.exponential_decay(
355 opt = tf.train.GradientDescentOptimizer(lr)
371 variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,
376 train_op = tf.no_op(name='train')
/external/tensorflow/tensorflow/contrib/training/
H A D__init__.py65 from tensorflow.contrib.training.python.training.training import train namespace
78 'multiply_gradients', 'train']
/external/tensorflow/tensorflow/contrib/framework/python/framework/
H A Dcheckpoint_utils.py31 from tensorflow.python.training import training as train namespace
63 return train.NewCheckpointReader(filename)
/external/tensorflow/tensorflow/contrib/learn/python/learn/
H A D__init__.py41 from tensorflow.contrib.learn.python.learn.graph_actions import train namespace
H A Dexperiment.py119 """Experiment is a class containing all information needed to train a model.
176 means train forever.
200 Perform this many (integer) number of train steps for each
246 "Please call `TPUEstimator` train/evaluate directly. \n"
329 def train(self, delay_secs=None): member in class:Experiment
333 `delay_secs` seconds. If `self._train_steps` is `None`, train forever.
346 # to train. We might as well start as soon as we can.
503 # Exit if we have already reached number of steps to train.
658 self.train(delay_secs=0)
689 1. The procedure will have train an
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H A Dgraph_actions.py61 'graph_actions.py will be deleted. Use tf.train.* utilities instead. '
123 def train(graph, function
158 graph: A graph to train. It is expected that this graph is not in use
180 arg to tf.train.Saver constructor.
190 max_steps: Number of total steps for which to train model. If `None`,
191 train forever. Two calls fit(steps=100) means 200 training iterations.
251 """See train."""
348 'Global step was not incremented by train op at step %s'
496 graph: A `Graph` to train. It is expected that this graph is not in use
/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/
H A Destimators.py38 from tensorflow.python.training import training as train namespace
53 from tf.train.Optimizer. Defaults to Adam with step size 0.02.
62 optimizer = train.AdamOptimizer(0.02)
188 NORMAL_LIKELIHOOD_LOSS, we train the covariance term as well. For
203 from tf.train.Optimizer. Defaults to Adagrad with step size 0.1.
210 optimizer = train.AdagradOptimizer(0.1)
360 from tf.train.Optimizer. Defaults to Adam with step size 0.02.
H A Dhead_test.py36 from tensorflow.python.training import training as train namespace
96 optimizer=train.GradientDescentOptimizer(0.001)).create_estimator_spec
133 optimizer=train.AdamOptimizer(0.001)).create_estimator_spec
H A Dstate_management_test.py42 from tensorflow.python.training import training as train namespace
304 with train.MonitoredSession() as session:
/external/tensorflow/tensorflow/contrib/training/python/training/
H A Dtraining.py36 optimizer = tf.train.MomentumOptimizer(FLAGS.learning_rate, FLAGS.momentum)
42 tf.contrib.training.train(train_op, my_log_dir)
48 In order to use the `train` function, one needs a train_op: an `Operation` that
144 tf.contrib.training.train(train_op, my_log_dir, scaffold=scaffold)
177 tf.contrib.training.train(train_op, my_log_dir, scaffold=scaffold)
208 tf.contrib.training.train(train_op, my_log_dir, scaffold=scaffold)
240 tf.contrib.training.train(train_op, my_log_dir, scaffold=scaffold)
267 'train',
393 variables_to_train: an optional list of variables to train. If None, it will
477 def train(train_o function
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/external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_ptb/
H A Drnn_ptb.py188 def train(model, optimizer, train_data, sequence_length, clip_ratio): function
225 self.train = self.tokenize(os.path.join(path, "ptb.train.txt"))
298 train_data = _divide_into_batches(corpus.train, FLAGS.batch_size)
305 tf.train.latest_checkpoint(FLAGS.logdir)):
314 optimizer = tf.train.GradientDescentOptimizer(learning_rate)
318 train(model, optimizer, train_data, FLAGS.seq_len, FLAGS.clip)
/external/tensorflow/tensorflow/contrib/layers/python/layers/
H A Doptimizers.py38 from tensorflow.python.training import training as train namespace
41 "Adagrad": train.AdagradOptimizer,
42 "Adam": train.AdamOptimizer,
43 "Ftrl": train.FtrlOptimizer,
44 "Momentum": lambda lr: train.MomentumOptimizer(lr, momentum=0.9),
45 "RMSProp": train.RMSPropOptimizer,
46 "SGD": train.GradientDescentOptimizer,
80 optimizer=lambda lr: tf.train.MomentumOptimizer(lr, momentum=0.5))`.
83 optimizer=lambda: tf.train.MomentumOptimizer(0.5, momentum=0.5))`.
87 optimizer=tf.train
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/external/tensorflow/tensorflow/contrib/slim/python/slim/
H A Dlearning.py22 tf.train.Supervisor and its managed_session in its implementation to ensure the
38 optimizer = tf.train.MomentumOptimizer(FLAGS.learning_rate, FLAGS.momentum)
44 slim.learning.train(train_op, my_log_dir)
50 In order to train, TF-Slim's train loop needs a train_op: an `Operation` that
143 slim.learning.train(train_op, my_log_dir, init_fn=InitAssignFn)
177 slim.learning.train(train_op, my_log_dir, init_fn=InitAssignFn)
210 slim.learning.train(train_op, my_log_dir, init_fn=InitAssignFn)
244 slim.learning.train(train_op, my_log_dir, init_fn=InitAssignFn)
276 'create_train_op', 'train_step', 'train'
531 def train(train_op, function
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/external/tensorflow/tensorflow/python/grappler/
H A Dmemory_optimizer_test.py35 from tensorflow.python.training import training as train namespace
122 optimizer = train.AdamOptimizer(0.001)
125 metagraph = train.export_meta_graph()
190 train.import_meta_graph(metagraph)
243 optimizer = train.AdamOptimizer(0.001)
265 metagraph = train.export_meta_graph()
/external/tensorflow/tensorflow/contrib/boosted_trees/python/training/functions/
H A Dgbdt_batch.py404 mode: Mode the graph is running in (train|predict|eval).
468 def train(self, loss, predictions_dict, labels): member in class:GradientBoostedDecisionTreeModel
/external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/
H A Dlinear.py48 from tensorflow.python.training import training as train namespace
280 return train.FtrlOptimizer(learning_rate=learning_rate)
326 optimizer=tf.train.FtrlOptimizer(
418 optimizer: The optimizer used to train the model. If specified, it should
690 optimizer: An instance of `tf.Optimizer` used to train the model. If
927 optimizer: An instance of `tf.Optimizer` used to train the model. If
/external/tensorflow/tensorflow/core/kernels/
H A Dsdca_ops_test.cc233 Graph* train = nullptr; local
236 20 /* dense features per group */, &init, &train);
238 test::Benchmark("cpu", train, GetSingleThreadedOptions(), init).Run(iters);
244 Graph* train = nullptr; local
247 200000 /* dense features per group */, &init, &train);
249 test::Benchmark("cpu", train, GetSingleThreadedOptions(), init).Run(iters);
255 Graph* train = nullptr; local
258 0 /* dense features per group */, &init, &train);
260 test::Benchmark("cpu", train, GetMultiThreadedOptions(), init).Run(iters);
H A Dtraining_ops_test.cc82 Graph* train; local
83 SGD(params, &init, &train);
84 test::Benchmark("cpu", train, GetOptions(), init).Run(iters);
114 Graph* train; local
115 Adagrad(params, &init, &train);
116 test::Benchmark("cpu", train, GetOptions(), init).Run(iters);
148 Graph* train; local
149 Momentum(params, &init, &train);
150 test::Benchmark("cpu", train, GetOptions(), init).Run(iters);
191 Graph* train; local
231 Graph* train; local
268 Graph* train; local
305 Graph* train; local
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/external/opencv/ml/src/
H A Dmlknearest.cpp65 train( _train_data, _responses, _sample_idx, _is_regression, _max_k, false );
92 bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses, function in class:CvKNearest
99 CV_FUNCNAME( "CvKNearest::train" );
113 CV_CALL( cvPrepareTrainData( "CvKNearest::train", _train_data, CV_ROW_SAMPLE,
320 CV_ERROR( CV_StsError, "The search tree must be constructed first using train method" );

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