| /external/tensorflow/tensorflow/examples/get_started/regression/ |
| custom_regression.py | 59 if mode == tf.estimator.ModeKeys.TRAIN:
60 optimizer = params.get("optimizer", tf.train.AdamOptimizer)
63 loss=average_loss, global_step=tf.train.get_global_step())
87 (train, test) = imports85.dataset()
93 train = train.map(normalize_price)
101 train.shuffle(1000).batch(128)
141 "optimizer": tf.train.AdamOptimizer,
145 # Train the model.
146 model.train(input_fn=input_train, steps=STEPS
[all...] |
| /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)
|
| 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)
|
| /external/tensorflow/tensorflow/python/tpu/ |
| tpu_context.py | 176 This immutable object holds TPUEstimator config, train/eval batch size, and
397 return (mode == model_fn_lib.ModeKeys.TRAIN and
411 (mode != model_fn_lib.ModeKeys.TRAIN and
459 if mode == model_fn_lib.ModeKeys.TRAIN:
634 if mode == model_fn_lib.ModeKeys.TRAIN:
638 'train batch size {} must be divisible by number of replicas {}'
|
| /external/tensorflow/tensorflow/contrib/gan/python/estimator/python/ |
| gan_estimator_impl.py | 26 from tensorflow.contrib.gan.python import train as tfgan_train
63 different train and evaluation behavior.
71 # See TF-GAN's `train.py` for a description of the generator and
88 generator_optimizer=tf.train.AdamOptimizer(0.1, 0.5),
89 discriminator_optimizer=tf.train.AdamOptimizer(0.1, 0.5))
91 # Train estimator.
92 gan_estimator.train(train_input_fn, steps)
128 in (ex TRAIN, EVAL, PREDICT). This is useful for things like batch
147 train ops, and can be used to implement the GAN training scheme.
148 Defaults to `train.get_sequential_train_hooks()`
[all...] |
| stargan_estimator_impl.py | 26 from tensorflow.contrib.gan.python import train as tfgan_train
58 different train and evaluation behavior.
66 # See TFGAN's `train.py` for a description of the generator and
82 generator_optimizer=tf.train.AdamOptimizer(0.1, 0.5),
83 discriminator_optimizer=tf.train.AdamOptimizer(0.1, 0.5))
85 # Train estimator.
86 stargan_estimator.train(train_input_fn, steps)
119 in (ex TRAIN, EVAL, PREDICT). This is useful for things like batch
136 train ops, and can be used to implement the GAN training scheme.
137 Defaults to `train.get_sequential_train_hooks()`
[all...] |
| /external/tensorflow/tensorflow/python/keras/ |
| callbacks.py | 60 mode=ModeKeys.TRAIN):
68 epochs: Number of epoch to train.
73 mode: String. One of ModeKeys.TRAIN, ModeKeys.TEST, or ModeKeys.PREDICT.
87 if mode == ModeKeys.TRAIN:
121 mode=ModeKeys.TRAIN):
129 epochs: Number of epoch to train.
133 mode: String. One of ModeKeys.TRAIN, ModeKeys.TEST, or ModeKeys.PREDICT.
168 if mode in {ModeKeys.TRAIN, ModeKeys.TEST}:
239 """Helper function for on_{train|test|predict}_begin methods."""
240 if mode == ModeKeys.TRAIN
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| state_space_model_test.py | 97 mode=estimator_lib.ModeKeys.TRAIN)
115 mode=estimator_lib.ModeKeys.TRAIN)
187 estimator.train(combined_input_fn, steps=1)
319 mode=estimator_lib.ModeKeys.TRAIN)
573 mode=estimator_lib.ModeKeys.TRAIN)
645 estimator.train(input_fn=input_fn, max_steps=1)
669 estimator.train(input_fn=train_input_fn, max_steps=1)
732 estimator.train(input_fn=train_input_fn, steps=1)
748 mode=estimator_lib.ModeKeys.TRAIN)
|
| /external/tensorflow/tensorflow/python/keras/engine/ |
| training_distributed.py | 72 batch_size, mode=ModeKeys.TRAIN))
205 mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
308 mode = ModeKeys.TRAIN
326 step_fn = _make_step_fn(model, ModeKeys.TRAIN, current_strategy, out_labels)
427 model, ModeKeys.TRAIN)
449 model, ModeKeys.TRAIN)
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| estimator_input_test.py | 111 assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
125 assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
140 assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
|
| state_saving_rnn_estimator_test.py | 244 mode = model_fn_lib.ModeKeys.TRAIN
326 """Helper for testGetRnnModelFn{Train,Eval,Infer}()."""
353 # testGetRnnModelFn{Train,Eval,Infer}() test which fields
356 model_fn_ops = self._getModelFnOpsForMode(model_fn_lib.ModeKeys.TRAIN)
427 # Train a bit to create an exportable checkpoint.
429 model_fn_lib.ModeKeys.TRAIN, seed=1234),
|
| dynamic_rnn_estimator.py | 487 if mode == model_fn.ModeKeys.TRAIN
501 loss = None # Created below for modes TRAIN and EVAL.
525 if mode == model_fn.ModeKeys.TRAIN:
|
| state_saving_rnn_estimator.py | 471 if mode == model_fn.ModeKeys.TRAIN
501 loss = None # Created below for modes TRAIN and EVAL.
518 if mode == model_fn.ModeKeys.TRAIN:
|
| composable_model_test.py | 50 if mode == model_fn_lib.ModeKeys.TRAIN:
|
| estimator_test.py | 130 assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
144 assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
159 assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
245 assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
252 if mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL):
345 self.assertEqual(model_fn.ModeKeys.TRAIN, mode)
375 self.assertEqual(model_fn.ModeKeys.TRAIN, mode)
402 self.assertEqual(model_fn.ModeKeys.TRAIN, mode)
[all...] |
| /external/tensorflow/tensorflow/contrib/tensor_forest/client/ |
| random_forest.py | 221 if labels is not None and mode == model_fn_lib.ModeKeys.TRAIN:
333 """An estimator that can train and evaluate a random forest.
522 mode == model_fn_lib.ModeKeys.TRAIN):
528 if mode == model_fn_lib.ModeKeys.TRAIN:
544 """An estimator that can train a forest for a multi-headed problems.
550 be used to train a single model that predicts all outputs. This class can
551 be used to train separate forests for each output.
604 """A CORE estimator that can train and evaluate a random forest.
625 estimator.train(input_fn=input_fn_train)
|
| /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...] |
| main_estimator_tpu.py | 15 """Cloud TPU Estimator workflow with RevNet train on ImageNet."""
107 mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT'
131 if mode == tf.estimator.ModeKeys.TRAIN:
132 global_step = tf.train.get_or_create_global_step()
133 learning_rate = tf.train.piecewise_constant(
135 optimizer = tf.train.MomentumOptimizer(learning_rate,
262 else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval'
274 if FLAGS.mode == "train":
275 revnet_classifier.train(
282 # Train for up to steps_per_eval number of steps
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| state_management.py | 58 mode: The tf.estimator.ModeKeys mode to use (TRAIN or EVAL).
81 if mode == estimator_lib.ModeKeys.TRAIN:
|
| /external/tensorflow/tensorflow/contrib/compiler/ |
| xla.py | 580 if mode == model_fn_lib.ModeKeys.TRAIN:
609 raise NotImplementedError('%s is not implemented, only TRAIN and EVAL are'
619 model_fn_lib.ModeKeys.TRAIN, params)
765 est.train(...)
776 est.train(...)
|
| /external/tensorflow/tensorflow/contrib/tpu/python/tpu/ |
| keras_support.py | 33 model.compile(optimizer=tf.train.AdamOptimizer(), ...)
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/training/functions/ |
| gbdt_batch.py | 275 mode: Train/Eval/Infer
281 if mode == learn.ModeKeys.TRAIN:
328 output_leaf_index_modes: A list of modes from (TRAIN, EVAL, INFER) which
455 mode in (learn.ModeKeys.TRAIN, learn.ModeKeys.EVAL,
468 mode: learn.ModeKeys.TRAIN or EVAL or INFER.
505 apply_averaging=mode != learn.ModeKeys.TRAIN,
523 apply_averaging=mode != learn.ModeKeys.TRAIN,
545 mode: Mode the graph is running in (train|predict|eval).
1179 def train(self, loss, predictions_dict, labels, gradients=None, member in class:GradientBoostedDecisionTreeModel
[all...] |
| /external/tensorflow/tensorflow/python/saved_model/model_utils/ |
| export_test.py | 216 "train": export_output.TrainOutput(loss=output_1),
233 "train": signature_def_utils.supervised_train_signature_def(
254 ret = _build_export_output(KerasModeKeys.TRAIN)
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
| model.py | 48 labels: Labels used to train on.
49 mode: Mode we are in. (TRAIN/EVAL/INFER)
137 update_op = gbdt_model.train(loss, predictions_dict, labels)
206 labels: Labels used to train on.
207 mode: Mode we are in. (TRAIN/EVAL/INFER)
274 if mode == learn.ModeKeys.TRAIN or mode == learn.ModeKeys.EVAL:
336 # Logits for train and eval.
355 update_op = gbdt_model_main.train(loss, predictions_dict, labels)
|
| /external/tensorflow/tensorflow/python/compiler/tensorrt/test/ |
| quantization_mnist_test.py | 162 """Train or evaluate the model.
165 is_training: whether to train or evaluate the model. In training mode,
172 num_epochs: how many epochs to train. Ignored if is_training is False.
238 elif mode == ModeKeys.TRAIN:
251 estimator.train(_TrainInputFn)
|
