/external/tensorflow/tensorflow/contrib/learn/python/learn/ |
D | graph_actions.py | 147 max_steps=None): argument 232 max_steps) 256 max_steps): argument 258 if (steps is not None) and (max_steps is not None): 296 if max_steps is None: 297 max_steps = (start_step + steps) if steps else None 300 monitor.begin(max_steps=max_steps) 326 if max_steps is None else str(max_steps)) 331 (max_steps is None) or (last_step < max_steps)): 358 is_last_step = (max_steps is not None) and (last_step >= max_steps) [all …]
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D | graph_actions_test.py | 86 def begin(self, max_steps=None): argument 88 return super(_BaseMonitorWrapper, self).begin(max_steps) 301 max_steps=1) 327 max_steps=1) 342 max_steps=3) 366 max_steps=3) 388 max_steps=1) 560 max_steps=10) 573 max_steps=15)
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D | experiment.py | 387 max_steps=self._train_steps, 868 max_steps=None, argument 881 max_steps=max_steps, 886 input_fn=input_fn, steps=steps, max_steps=max_steps, monitors=hooks)
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D | monitors.py | 105 def begin(self, max_steps=None): argument 118 self._max_steps = max_steps 888 def begin(self, max_steps=None): argument 889 super(GraphDump, self).begin(max_steps=max_steps) 1132 def begin(self, max_steps=None): argument 1133 super(CheckpointSaver, self).begin(max_steps) 1270 m.begin(max_steps=None)
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D | monitors_test.py | 108 max_steps = num_epochs * num_steps_per_epoch - 1 110 max_steps = None 111 monitor.begin(max_steps=max_steps) 396 monitor.begin(max_steps=100) 481 monitor.begin(max_steps=100) 513 monitor.begin(max_steps=100) 760 def begin(self, max_steps): argument
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D | trainable.py | 46 max_steps=None): argument
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/external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
D | nonlinear_test.py | 46 classifier.fit(iris.data, iris.target, max_steps=200) 103 classifier.fit(iris.data, iris.target, max_steps=200) 115 classifier.fit(iris.data, iris.target, max_steps=200) 127 classifier.fit(iris.data, iris.target, max_steps=200)
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D | kmeans_test.py | 228 max_steps = 1 229 kmeans.fit(input_fn=self.input_fn(), max_steps=max_steps) 347 max_steps = 10 * self.num_points // self.batch_size 348 self.kmeans.fit(input_fn=self.input_fn(), max_steps=max_steps) 363 max_steps = 10 * self.num_points // self.batch_size 364 self.kmeans.fit(input_fn=self.input_fn(), max_steps=max_steps)
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D | estimator.py | 496 max_steps=None): argument 504 if (steps is not None) and (max_steps is not None): 508 SKCompat(self).fit(x, y, batch_size, steps, max_steps, monitors) 511 if max_steps is not None: 514 if max_steps <= start_step: 521 if steps is not None or max_steps is not None: 522 hooks.append(basic_session_run_hooks.StopAtStepHook(steps, max_steps)) 1506 def fit(self, x, y, batch_size=128, steps=None, max_steps=None, argument 1525 max_steps=max_steps,
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/external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
D | kmeans_test.py | 222 max_steps = 1 223 kmeans.train(input_fn=self.input_fn(), max_steps=max_steps) 379 max_steps = 10 * self.num_points // self.batch_size 380 self.kmeans.train(input_fn=self.input_fn(), max_steps=max_steps) 396 max_steps = 10 * self.num_points // self.batch_size 397 self.kmeans.train(input_fn=self.input_fn(), max_steps=max_steps)
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/external/tensorflow/tensorflow/contrib/autograph/examples/notebooks/ |
D | ag_vs_eager_mnist_speed_test.ipynb | 99 "max_steps = 2\n" 130 "max_steps = 500\n" 358 " while i \u003c hp.max_steps:\n", 424 " max_steps=max_steps,\n", 516 " if i \u003e hp.max_steps:\n", 586 " max_steps=max_steps,\n",
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/external/tensorflow/tensorflow/examples/tutorials/mnist/ |
D | BUILD | 100 "--max_steps=10", 119 "--max_steps=10",
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D | fully_connected_feed.py | 163 for step in xrange(FLAGS.max_steps): 192 if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
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/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
D | structural_ensemble_test.py | 79 estimator.train(input_fn=train_input_fn, max_steps=1) 81 estimator.train(input_fn=train_input_fn, max_steps=3)
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/external/tensorflow/tensorflow/tools/api/golden/v2/ |
D | tensorflow.estimator.-train-spec.pbtxt | 15 name: "max_steps"
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D | tensorflow.estimator.-estimator.pbtxt | 59 …argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\']…
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/external/tensorflow/tensorflow/tools/api/golden/v1/ |
D | tensorflow.estimator.-train-spec.pbtxt | 15 name: "max_steps"
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/external/tensorflow/tensorflow/contrib/factorization/examples/ |
D | mnist.py | 230 for step in xrange(FLAGS.max_steps): 253 if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
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/external/tensorflow/tensorflow/python/debug/examples/ |
D | examples_test.sh | 80 cat << EOF | ${DEBUG_MNIST_BIN} --debug --max_steps=1 --fake_data --ui_type=readline
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/external/tensorflow/tensorflow/python/ops/parallel_for/ |
D | gradients_test.py | 86 def dynamic_lstm_model_fn(batch_size, state_size, max_steps): argument 90 np.random.rand(batch_size, max_steps, state_size), dtype=dtypes.float32) 92 np.random.randint(0, size=[batch_size], high=max_steps + 1), 120 def create_dynamic_lstm_batch_jacobian(batch_size, state_size, max_steps): argument 122 max_steps)
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D | control_flow_ops_test.py | 861 def dynamic_lstm_input_fn(batch_size, state_size, max_steps): argument 865 np.random.rand(batch_size, max_steps, state_size), dtype=dtypes.float32) 866 sequence_length = np.random.randint(0, size=[batch_size], high=max_steps + 1) 871 def create_dynamic_lstm(cell_fn, batch_size, state_size, max_steps): argument 875 max_steps) 877 dtypes.float32, size=max_steps, element_shape=[batch_size, state_size]) 903 return t < max_steps 908 tensor_array_ops.TensorArray(dtypes.float32, max_steps)
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/external/tensorflow/tensorflow/contrib/tpu/ |
D | README.md | 41 estimator.train(input_fn=input_fn, max_steps=FLAGS.train_steps)
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/external/tensorflow/tensorflow/contrib/eager/python/examples/revnet/ |
D | main_estimator_tpu.py | 277 max_steps=revnet_config.max_train_iter) 287 input_fn=imagenet_train.input_fn, max_steps=next_checkpoint)
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/external/tensorflow/tensorflow/python/distribute/ |
D | estimator_training.py | 251 max_steps=train_spec.max_steps,
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/external/tensorflow/tensorflow/contrib/metrics/ |
D | README.md | 31 for step_num in range(max_steps):
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