| /external/tensorflow/tensorflow/python/saved_model/model_utils/ |
| mode_keys.py | 30 * `TRAIN`: training/fitting mode.
35 TRAIN = 'train'
46 * `TRAIN`: training/fitting mode.
51 TRAIN = 'train'
65 return mode in [KerasModeKeys.TRAIN, EstimatorModeKeys.TRAIN]
95 return KerasModeKeys.TRAIN
|
| mode_keys_test.py | 39 _ = mode_map[mode_keys.KerasModeKeys.TRAIN]
41 _ = mode_map[mode_keys.EstimatorModeKeys.TRAIN]
|
| export_utils.py | 41 ModeKeys.TRAIN: [tag_constants.TRAINING],
51 ModeKeys.TRAIN: signature_constants.DEFAULT_TRAIN_SIGNATURE_DEF_KEY,
147 signature_constants.SUPERVISED_TRAIN_METHOD_NAME: 'Train',
|
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| varma_test.py | 47 mode=estimator_lib.ModeKeys.TRAIN)
66 mode=estimator_lib.ModeKeys.TRAIN)
86 mode=estimator_lib.ModeKeys.TRAIN)
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
| estimator_utils.py | 29 model_fn_lib.ModeKeys.TRAIN: contrib_model_fn_lib.ModeKeys.TRAIN,
|
| dnn_tree_combined_estimator.py | 94 labels: Labels used to train on.
95 mode: Mode we are in. (TRAIN/EVAL/INFER)
120 dnn_steps_to_train: Number of steps to train dnn for before switching
207 if dnn_dropout is not None and mode == model_fn.ModeKeys.TRAIN:
298 update_op = gbdt_model.train(loss, predictions_dict, labels)
305 if mode == model_fn.ModeKeys.TRAIN or mode == model_fn.ModeKeys.INFER:
331 if mode != model_fn.ModeKeys.TRAIN:
363 if mode != model_fn.ModeKeys.TRAIN:
455 dnn_steps_to_train: Number of steps to train dnn for before switching
581 dnn_steps_to_train: Number of steps to train dnn for before switchin
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| model_fn.py | 55 * `TRAIN`: training mode.
60 TRAIN = 'train'
66 if key not in (cls.TRAIN, cls.EVAL, cls.INFER):
134 scaffold: A `tf.train.Scaffold` object that can be used to set
150 if mode == ModeKeys.TRAIN:
158 if mode in (ModeKeys.TRAIN, ModeKeys.EVAL):
291 if self.mode == ModeKeys.TRAIN:
292 core_mode = core_model_fn_lib.ModeKeys.TRAIN
|
| head_test.py | 123 mode=model_fn.ModeKeys.TRAIN,
149 mode=model_fn.ModeKeys.TRAIN,
167 mode=model_fn.ModeKeys.TRAIN,
183 mode=model_fn.ModeKeys.TRAIN,
193 mode=model_fn.ModeKeys.TRAIN,
213 mode=model_fn.ModeKeys.TRAIN,
240 mode=model_fn.ModeKeys.TRAIN,
256 mode=model_fn.ModeKeys.TRAIN,
274 mode=model_fn.ModeKeys.TRAIN,
291 mode=model_fn.ModeKeys.TRAIN,
[all...] |
| /external/tensorflow/tensorflow/python/keras/engine/ |
| training_arrays.py | 61 mode=ModeKeys.TRAIN,
66 """Loop function for arrays of data with modes TRAIN/TEST/PREDICT.
97 mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
112 - In TRAIN mode: `History` object.
140 if mode == ModeKeys.TRAIN:
147 learning_phase=(1 if mode == ModeKeys.TRAIN else 0))
240 if mode == ModeKeys.TRAIN:
282 if mode == ModeKeys.TRAIN:
384 model, ModeKeys.TRAIN)
406 if mode == ModeKeys.TRAIN
[all...] |
| training_generator.py | 56 mode=ModeKeys.TRAIN,
60 """Loop function for arrays of data with modes TRAIN/TEST/PREDICT.
102 mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
112 - In TRAIN mode: `History` object.
188 backend.set_eager_learning_phase(1 if mode == ModeKeys.TRAIN else 0)
200 if mode == ModeKeys.TRAIN:
232 if mode == ModeKeys.TRAIN:
322 if mode == ModeKeys.TRAIN:
339 if mode == ModeKeys.TRAIN:
345 fit_generator = functools.partial(model_iteration, mode=ModeKeys.TRAIN)
[all...] |
| /external/tensorflow/tensorflow/examples/tutorials/layers/ |
| cnn_mnist.py | 82 inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
99 # Calculate Loss (for both TRAIN and EVAL modes)
102 # Configure the Training Op (for TRAIN mode)
103 if mode == tf.estimator.ModeKeys.TRAIN:
104 optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
107 global_step=tf.train.get_global_step())
121 train_data = mnist.train.images # Returns np.array
122 train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
133 logging_hook = tf.train.LoggingTensorHook(
136 # Train the mode
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| head_test.py | 55 from tensorflow.python.training import training as train
62 for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL,
118 optimizer=train.GradientDescentOptimizer(0.001)).create_estimator_spec
194 optimizer=train.AdamOptimizer(0.001)).create_estimator_spec
201 for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
211 for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
221 for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
235 for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
249 for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]:
263 for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL]
[all...] |
| state_management_test.py | 42 from tensorflow.python.training import training as train
129 model=stub_model, features=features, mode=estimator_lib.ModeKeys.TRAIN)
177 mode=estimator_lib.ModeKeys.TRAIN)
180 mode=estimator_lib.ModeKeys.TRAIN)
223 model=stub_model, features=features, mode=estimator_lib.ModeKeys.TRAIN)
304 with train.MonitoredSession() as session:
|
| head.py | 97 mode = estimator_lib.ModeKeys.TRAIN
248 features, update_statistics=(mode == estimator_lib.ModeKeys.TRAIN))
256 if (mode == estimator_lib.ModeKeys.TRAIN or
267 if mode == estimator_lib.ModeKeys.TRAIN:
|
| /external/tensorflow/tensorflow/contrib/gan/python/estimator/python/ |
| tpu_gan_estimator_impl.py | 22 from tensorflow.contrib.gan.python import train as tfgan_train
50 # See TFGAN's `train.py` for a description of the generator and
67 generator_optimizer=tf.train.AdamOptimizer(0.1, 0.5),
68 discriminator_optimizer=tf.train.AdamOptimizer(0.1, 0.5),
72 # Train estimator.
73 gan_estimator.train(train_input_fn, train_steps)
115 in (ex TRAIN, EVAL, PREDICT). This is useful for things like batch
142 This is ignored for jobs that run on TPU, such as the train job if
144 joint_train: A Python boolean. If `True`, jointly train the generator and
145 the discriminator. If `False`, sequentially train them. See `train.py
[all...] |
| head_test.py | 91 self._test_modes_helper(model_fn_lib.ModeKeys.TRAIN)
|
| head_impl.py | 24 from tensorflow.contrib.gan.python import train as tfgan_train
107 list of hooks. Defaults to `train.get_sequential_train_hooks()`
186 example, this function can come from TFGAN's `train.py` library, or can
194 ValueError: If `train_op_fn` isn't provided in train mode.
233 elif mode == model_fn_lib.ModeKeys.TRAIN:
243 mode=model_fn_lib.ModeKeys.TRAIN,
|
| stargan_estimator_test.py | 64 @parameterized.named_parameters(('train', model_fn_lib.ModeKeys.TRAIN),
169 @parameterized.named_parameters(('train', model_fn_lib.ModeKeys.TRAIN),
186 elif mode == model_fn_lib.ModeKeys.TRAIN:
230 # TRAIN
232 est.train(train_input_fn, steps=num_steps)
|
| tpu_gan_estimator_test.py | 107 ('joint_train', model_fn_lib.ModeKeys.TRAIN, True),
108 ('train_sequential', model_fn_lib.ModeKeys.TRAIN, False),
131 elif mode == model_fn_lib.ModeKeys.TRAIN:
179 # Train.
181 est.train(train_input_fn, steps=num_steps_train)
280 est.train(train_input_fn, steps=1)
295 est_warm.train(train_input_fn, steps=1)
|
| gan_estimator_test.py | 71 ('train', model_fn_lib.ModeKeys.TRAIN),
146 ('train', model_fn_lib.ModeKeys.TRAIN),
164 elif mode == model_fn_lib.ModeKeys.TRAIN:
187 model_fn_lib.ModeKeys.TRAIN,
233 # Train.
235 est.train(train_input_fn, steps=num_steps)
385 est.train(train_input_fn, steps=1)
397 est_warm.train(train_input_fn, steps=1
[all...] |
| /external/tensorflow/tensorflow/contrib/distribute/python/examples/ |
| simple_estimator_example.py | 32 optimizer = tf.train.GradientDescentOptimizer(0.2)
55 assert mode == tf.estimator.ModeKeys.TRAIN
57 global_step = tf.train.get_global_step()
79 estimator.train(input_fn=train_input_fn, steps=steps)
|
| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| wals.py | 160 and mode == model_fn.ModeKeys.TRAIN)
162 mode == model_fn.ModeKeys.TRAIN)
184 # TRAIN mode:
185 if mode == model_fn.ModeKeys.TRAIN:
310 mode=model_fn.ModeKeys.TRAIN,
420 TRAIN:
514 to train the model, where a sweep is defined as a full update of all the
|
| wals_test.py | 95 In TRAIN mode, one only needs to specify the np_matrix and the batch_size.
102 mode: Can be one of model_fn.ModeKeys.{TRAIN, INFER, EVAL}.
262 mode=model_fn.ModeKeys.TRAIN,
273 mode=model_fn.ModeKeys.TRAIN,
284 mode=model_fn.ModeKeys.TRAIN,
327 mode=model_fn.ModeKeys.TRAIN,
|
| /external/tensorflow/tensorflow/python/tpu/ |
| _tpu_estimator_embedding.py | 233 if mode == model_fn_lib.ModeKeys.TRAIN:
238 if mode == model_fn_lib.ModeKeys.TRAIN:
|
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| estimator_test.py | 88 mode=model_fn.ModeKeys.TRAIN,
|
