| /external/tensorflow/tensorflow/contrib/model_pruning/examples/cifar10/ |
| cifar10_input.py | 121 images, label_batch = tf.train.shuffle_batch(
128 images, label_batch = tf.train.batch(
159 filename_queue = tf.train.string_input_producer(filenames)
194 print('Filling queue with %d CIFAR images before starting to train. '
210 eval_data: bool, indicating if one should use the train or eval data set.
232 filename_queue = tf.train.string_input_producer(filenames)
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| /external/tensorflow/tensorflow/examples/learn/ |
| iris_custom_decay_dnn.py | 53 if mode == tf.estimator.ModeKeys.TRAIN:
54 global_step = tf.train.get_global_step()
55 learning_rate = tf.train.exponential_decay(
58 optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)
78 # Train.
81 classifier.train(input_fn=train_input_fn, steps=1000)
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| iris_custom_model.py | 54 if mode == tf.estimator.ModeKeys.TRAIN:
55 optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
56 train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
75 # Train.
78 classifier.train(input_fn=train_input_fn, steps=1000)
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| /external/tensorflow/tensorflow/examples/saved_model/integration_tests/ |
| use_text_rnn_model.py | 38 model.train(tf.constant(sentences))
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| /external/tensorflow/tensorflow/contrib/distribute/python/examples/ |
| keras_model_with_estimator.py | 51 optimizer = tf.train.GradientDescentOptimizer(0.2)
65 # Train and evaluate the model.
66 keras_estimator.train(input_fn=input_fn, steps=10)
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| /external/tensorflow/tensorflow/contrib/model_pruning/python/ |
| learning.py | 31 optimizer = tf.train.MomentumOptimizer(FLAGS.learning_rate, FLAGS.momentum)
46 learning.train(train_op,
61 def train(train_op, function
86 """Wrapper around tf-slim's train function.
139 sync_optimizer: an instance of tf.train.SyncReplicasOptimizer, or a list of
165 total_loss, _ = _slim.learning.train(
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| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| training.py | 36 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)
266 'train',
392 variables_to_train: an optional list of variables to train. If None, it will
476 def train(train_op function
[all...] |
| /external/tensorflow/tensorflow/examples/tutorials/mnist/ |
| mnist_with_summaries.py | 38 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)
145 # Train the model, and also write summaries.
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
[all...] |
| mnist_softmax_xla.py | 57 train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
69 # Train
72 batch_xs, batch_ys = mnist.train.next_batch(100)
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| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| optimizers.py | 37 from tensorflow.python.training import training as train
40 "Adagrad": train.AdagradOptimizer,
41 "Adam": train.AdamOptimizer,
42 "Ftrl": train.FtrlOptimizer,
43 "Momentum": lambda learning_rate: train.MomentumOptimizer(learning_rate, momentum=0.9), # pylint: disable=line-too-long
44 "RMSProp": train.RMSPropOptimizer,
45 "SGD": train.GradientDescentOptimizer,
79 optimizer=lambda lr: tf.train.MomentumOptimizer(lr, momentum=0.5))`.
82 optimizer=lambda: tf.train.MomentumOptimizer(0.5, momentum=0.5))`.
86 optimizer=tf.train.AdagradOptimizer)`
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/examples/l2hmc/ |
| main.py | 38 global_step = tf.train.get_or_create_global_step()
56 learning_rate = tf.train.exponential_decay(
58 optimizer = tf.train.AdamOptimizer(learning_rate)
59 checkpointer = tf.train.Checkpoint(
65 latest_path = tf.train.latest_checkpoint(FLAGS.train_dir)
155 """Train the sampler for one iteration."""
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/gan/ |
| mnist_test.py | 65 step_counter = tf.train.get_or_create_global_step()
71 generator_optimizer = tf.train.AdamOptimizer(0.001)
73 discriminator_optimizer = tf.train.AdamOptimizer(0.001)
91 self._report('train', start, measure_batches, batch_size)
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| mnist.py | 207 dataset: Dataset of images to train on.
274 tf.data.Dataset.from_tensor_slices(data.train.images).shuffle(60000)
281 'generator_optimizer': tf.train.AdamOptimizer(FLAGS.lr),
282 'discriminator_optimizer': tf.train.AdamOptimizer(FLAGS.lr),
283 'step_counter': tf.train.get_or_create_global_step(),
290 latest_cpkt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
293 checkpoint = tf.train.Checkpoint(**model_objects)
|
| /external/tensorflow/tensorflow/contrib/predictor/ |
| testing_common.py | 81 def get_arithmetic_input_fn(core=True, train=False):
90 if train:
96 if train:
|
| /external/tensorflow/tensorflow/contrib/session_bundle/example/ |
| export_half_plus_two.py | 63 save = tf.train.Saver(
69 write_version=tf.train.SaverDef.V2 if use_checkpoint_v2 else
70 tf.train.SaverDef.V1)
|
| /external/tensorflow/tensorflow/examples/how_tos/reading_data/ |
| fully_connected_reader.py | 15 """Train and Eval the MNIST network.
18 to a TFRecords file containing tf.train.Example protocol buffers.
43 TRAIN_FILE = 'train.tfrecords'
84 def inputs(train, batch_size, num_epochs):
88 train: Selects between the training (True) and validation (False) data.
91 train forever.
107 if train else VALIDATION_FILE)
134 """Train MNIST for a number of steps."""
140 train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs)
148 # Add to the Graph operations that train the model
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/datasets/ |
| mnist.py | 246 train = fake()
249 return base.Datasets(train=train, validation=validation, test=test)
254 TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
255 TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
290 train = DataSet(train_images, train_labels, **options)
294 return base.Datasets(train=train, validation=validation, test=test)
|
| /external/tensorflow/tensorflow/examples/tf2_showcase/ |
| mnist.py | 54 name='train_epochs', default=10, help='Number of epochs to train')
140 def train(model, optimizer, dataset, step_counter, log_interval=None, function
197 optimizer = tf.train.MomentumOptimizer(
201 train_dir = os.path.join(flags_obj.model_dir, 'summaries', 'train')
211 step_counter = tf.train.get_or_create_global_step()
212 checkpoint = tf.train.Checkpoint(
215 checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
217 # Train and evaluate for a set number of epochs.
221 train(model, optimizer, train_ds, step_counter,
|
| /external/tensorflow/tensorflow/lite/experimental/examples/lstm/ |
| bidirectional_sequence_lstm_test.py | 30 # Number of steps to train model.
116 opt = tf.train.AdamOptimizer(
123 batch_x, batch_y = self.mnist.train.next_batch(
141 saver = tf.train.Saver()
146 b1, _ = self.mnist.train.next_batch(batch_size=1)
197 saver = tf.train.Saver()
215 saver = tf.train.Saver()
|
| unidirectional_sequence_lstm_test.py | 29 # Number of steps to train model.
107 opt = tf.train.AdamOptimizer(
114 batch_x, batch_y = self.mnist.train.next_batch(
130 saver = tf.train.Saver()
135 b1, _ = self.mnist.train.next_batch(batch_size=1)
182 saver = tf.train.Saver()
200 saver = tf.train.Saver()
|
| unidirectional_sequence_rnn_test.py | 32 # Number of steps to train model.
103 opt = tf.train.AdamOptimizer(
109 batch_x, batch_y = self.mnist.train.next_batch(
122 saver: saver created by tf.train.Saver()
142 saver = tf.train.Saver()
147 b1, _ = self.mnist.train.next_batch(batch_size=1)
190 saver = tf.train.Saver()
208 saver = tf.train.Saver()
|
| bidirectional_sequence_rnn_test.py | 34 # Number of steps to train model.
135 opt = tf.train.AdamOptimizer(
142 batch_x, batch_y = self.mnist.train.next_batch(
165 saver = tf.train.Saver()
170 b1, _ = self.mnist.train.next_batch(batch_size=1)
218 saver = tf.train.Saver()
239 saver = tf.train.Saver()
262 saver = tf.train.Saver()
288 saver = tf.train.Saver()
|
| /external/tensorflow/tensorflow/examples/get_started/regression/ |
| imports85.py | 75 """Load the imports85 data as a (train,test) pair of `Dataset`.
84 A (train,test) pair of `Datasets`
141 train = (base_dataset
152 return train, test
196 # Split the data into train/test subsets.
|
| /external/tensorflow/tensorflow/python/ops/ |
| batch_norm_benchmark.py | 68 def build_graph(device, input_shape, axes, num_layers, mode, scale, train):
78 train: if true, also run backprop.
98 if train:
111 if train:
127 train, num_iters):
137 train: if true, also run backprop.
146 train)
154 print("%s shape:%d/%d #layers:%d mode:%s scale:%r train:%r - %f secs" %
155 (device, len(input_shape), len(axes), num_layers, mode, scale, train,
161 "train_{train}")
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/examples/revnet/ |
| main_estimator.py | 15 """Estimator workflow with RevNet train on CIFAR-10."""
36 mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT'
50 if mode == tf.estimator.ModeKeys.TRAIN:
51 global_step = tf.train.get_or_create_global_step()
52 learning_rate = tf.train.piecewise_constant(
54 optimizer = tf.train.MomentumOptimizer(
98 split: One of `train`, `validation`, `train_all`, and `test`
106 if split == "train_all" or split == "train":
166 # Train and evaluate estimator
167 revnet_estimator.train(input_fn=train_input_fn
[all...] |
