| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | linear_test.py | 96 classifier.fit(input_fn=input_fn, steps=100)
97 loss1 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
98 classifier.fit(input_fn=input_fn, steps=200)
99 loss2 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
122 classifier.fit(input_fn=input_fn, steps=100)
123 loss1 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
124 classifier.fit(input_fn=input_fn, steps=200)
125 loss2 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
137 classifier.fit(input_fn=test_data.iris_input_multiclass_fn, steps=100)
139 input_fn=test_data.iris_input_multiclass_fn, steps=100)
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| D | debug_test.py | 97 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
116 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
133 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
155 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
178 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
199 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
225 classifier.fit(input_fn=input_fn, steps=5)
226 scores = classifier.evaluate(input_fn=input_fn, steps=1)
242 classifier.fit(input_fn=_input_fn, steps=5)
243 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
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| D | dnn_linear_combined_test.py | 96 """Counts the number of training steps."""
106 def steps(self): member in _StepCounterHook
221 estimator.fit(input_fn=test_data.iris_input_multiclass_fn, steps=10)
224 estimator.evaluate(input_fn=test_data.iris_input_multiclass_fn, steps=10)
277 classifier.fit(input_fn=_input_fn, steps=2)
297 input_fn=test_data.iris_input_multiclass_fn, steps=100,
348 classifier.fit(input_fn=_input_fn_float_label, steps=50)
370 classifier.fit(input_fn=test_data.iris_input_logistic_fn, steps=100)
372 input_fn=test_data.iris_input_logistic_fn, steps=100)
420 classifier.fit(input_fn=_input_fn, steps=100)
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| D | dnn_test.py | 212 dnn_estimator.fit(input_fn=_input_fn_train, steps=5)
213 scores = dnn_estimator.evaluate(input_fn=_input_fn_eval, steps=1)
287 classifier.fit(input_fn=_input_fn_float_label, steps=50)
304 classifier.fit(input_fn=input_fn, steps=5)
305 scores = classifier.evaluate(input_fn=input_fn, steps=1)
327 classifier.fit(input_fn=_input_fn, steps=5)
328 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
342 classifier.fit(x=train_x, y=train_y, steps=5)
343 scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
395 classifier.fit(input_fn=_input_fn, steps=50)
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| D | estimator_test.py | 205 est.fit(input_fn=_input_fn, steps=20)
268 est.fit(input_fn=_input_fn, steps=1)
353 est.fit(input_fn=_make_input_fn(features, labels), steps=1)
388 est.fit(input_fn=_make_input_fn(features, labels), steps=1)
412 est.fit(input_fn=boston_input_fn, steps=1)
426 est.fit(input_fn=boston_input_fn, steps=1)
443 est.fit(input_fn=boston_input_fn, steps=1)
445 est.evaluate(input_fn=boston_eval_fn, steps=1)
459 est.fit(input_fn=boston_input_fn, steps=1)
461 est.evaluate(input_fn=boston_eval_fn, steps=1)
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| D | svm_test.py | 46 svm_classifier.fit(input_fn=input_fn, steps=30)
47 metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
72 svm_classifier.fit(input_fn=input_fn, steps=30)
73 metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
104 svm_classifier.fit(input_fn=input_fn, steps=30)
105 metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
127 svm_classifier.fit(input_fn=input_fn, steps=30)
128 metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
155 svm_classifier.fit(input_fn=input_fn, steps=30)
156 metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
[all …]
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| D | composable_model_test.py | 137 classifier.fit(input_fn=input_fn, steps=1000)
138 loss1 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
139 classifier.fit(input_fn=input_fn, steps=2000)
140 loss2 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
163 classifier.fit(input_fn=input_fn, steps=1000)
164 loss1 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
165 classifier.fit(input_fn=input_fn, steps=2000)
166 loss2 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
178 classifier.fit(input_fn=_iris_input_fn, steps=1000)
179 classifier.evaluate(input_fn=_iris_input_fn, steps=100)
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/ode/ |
| D | ContinuousOutputModel.java | 38 * stores a copy of all steps information in a sorted collection for
64 * output model handles the steps of all integration phases, the user
78 * is large, if the integration interval is long or if the steps are
107 /** Steps table. */
108 private List<StepInterpolator> steps; field in ContinuousOutputModel
114 steps = new ArrayList<StepInterpolator>(); in ContinuousOutputModel()
129 if (model.steps.size() == 0) { in append()
133 if (steps.size() == 0) { in append()
149 final StepInterpolator lastInterpolator = steps.get(index); in append()
161 for (StepInterpolator interpolator : model.steps) { in append()
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| /external/python/cpython3/Lib/ |
| D | pipes.py | 8 conversion involves several steps (e.g. piping it through compress or
9 uuencode). Some of the conversion steps may require that their input
15 more conversion steps together. It will take care of creating and
22 different conversion steps and store them in a dictionary, for
92 return '<Template instance, steps=%r>' % (self.steps,)
96 self.steps = []
102 t.steps = self.steps[:]
118 if self.steps and self.steps[-1][1] == SINK:
124 self.steps.append((cmd, kind))
134 if self.steps and self.steps[0][1] == SOURCE:
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| /external/python/cpython2/Lib/ |
| D | pipes.py | 8 conversion involves several steps (e.g. piping it through compress or
9 uuencode). Some of the conversion steps may require that their input
15 more conversion steps together. It will take care of creating and
22 different conversion steps and store them in a dictionary, for
90 return '<Template instance, steps=%r>' % (self.steps,)
94 self.steps = []
100 t.steps = self.steps[:]
119 if self.steps and self.steps[-1][1] == SINK:
128 self.steps.append((cmd, kind))
141 if self.steps and self.steps[0][1] == SOURCE:
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| /external/tensorflow/tensorflow/python/estimator/ |
| D | estimator_test.py | 155 def _convert_train_steps_to_hooks(self, steps, max_steps): argument
158 def _convert_eval_steps_to_hooks(self, steps): argument
420 input_fn=_make_input_fn(expected_features, expected_labels), steps=1)
448 est.train(InputFn(), steps=1)
472 est.train(_input_fn, steps=1)
495 est.train(input_fn=_input_fn, steps=1)
513 est.train(input_fn=_input_fn_with_labels, steps=1)
525 est.train(input_fn=_input_fn, steps=1)
552 input_fn=_make_input_fn(expected_features, expected_labels), steps=1)
586 input_fn=_make_input_fn(expected_features, expected_labels), steps=1)
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| D | estimator.py | 289 steps=None, argument
310 steps: Number of steps for which to train model. If `None`, train forever
312 `StopIteration` exception. 'steps' works incrementally. If you call two
313 times train(steps=10) then training occurs in total 20 steps. If
315 before 20 steps. If you don't want to have incremental behavior please
317 max_steps: Number of total steps for which to train model. If `None`,
319 or `StopIteration` exception. If set, `steps` must be `None`. If
321 before `max_steps` steps.
322 Two calls to `train(steps=100)` means 200 training
325 all 100 steps.
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| /external/droiddriver/src/io/appium/droiddriver/actions/ |
| D | SwipeAction.java | 70 * not behave as expected, you can change steps with {@link #setScrollSteps}.
86 * expected, you can change steps with {@link #setFlingSteps}.
99 private final int steps; field in SwipeAction
108 public SwipeAction(PhysicalDirection direction, int steps) { in SwipeAction() argument
109 this(direction, steps, false, 1000L); in SwipeAction()
115 public SwipeAction(PhysicalDirection direction, int steps, boolean drag, long timeoutMillis) { in SwipeAction() argument
116 this(direction, steps, drag, timeoutMillis, 0.1F, 0.1F, 0.1F, 0.1F); in SwipeAction()
122 * @param steps minimum 2; (steps-1) is the number of {@code ACTION_MOVE} that
131 public SwipeAction(PhysicalDirection direction, int steps, boolean drag, long timeoutMillis, in SwipeAction() argument
135 this.steps = Math.max(2, steps); in SwipeAction()
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| /external/libhevc/decoder/ |
| D | ihevcd_statistics.h | 100 …l_size_hist[16][16]; /* PU Sizes [Width from 4 to 64 in steps of 4] [Height from 4 to 64 in steps …
101 …st[16][16]; /* PU sizes for skip [Width from 4 to 64 in steps of 4] [Height from 4 to 64 in steps …
102 …t[16][16]; /* PU sizes for inter [Width from 4 to 64 in steps of 4] [Height from 4 to 64 in steps …
103 …t[16][16]; /* PU sizes for intra [Width from 4 to 64 in steps of 4] [Height from 4 to 64 in steps …
104 …[16][16]; /* PU sizes for bipred [Width from 4 to 64 in steps of 4] [Height from 4 to 64 in steps …
105 …t[16][16]; /* PU sizes for merge [Width from 4 to 64 in steps of 4] [Height from 4 to 64 in steps …
106 …16][16]; /* PU sizes for Zero MV [Width from 4 to 64 in steps of 4] [Height from 4 to 64 in steps …
107 …l less than +/- 1 full pel units [Width from 4 to 64 in steps of 4] [Height from 4 to 64 in steps …
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | ftrl_test.py | 43 def equivAdagradTest_FtrlPart(self, steps, dtype): argument
57 # Run Ftrl for a few steps
58 for _ in range(steps):
63 def equivAdagradTest_AdagradPart(self, steps, dtype): argument
72 # Run Adagrad for a few steps
73 for _ in range(steps):
78 def equivGradientDescentTest_FtrlPart(self, steps, dtype): argument
92 # Run Ftrl for a few steps
93 for _ in range(steps):
98 def equivGradientDescentTest_GradientDescentPart(self, steps, dtype): argument
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| /external/autotest/server/site_tests/firmware_Cr50CCDServoCap/ |
| D | firmware_Cr50CCDServoCap.py | 50 # A dictionary containing an order of steps to verify and the expected ccd
53 # The keys are a list of strings with the order of steps to run.
221 def run_steps(self, steps): argument
222 """Do each step in steps and then verify the uart state.
225 previous steps to verify the state. This will do all of the steps in
228 @param steps: a comma separated string with the steps to run
230 # The order of steps is separated by ', '. Remove the last step and
231 # run all of the steps before it.
232 separated_steps = steps.rsplit(', ', 1)
243 self.verify_ccdstate(steps)
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| /external/harfbuzz_ng/.circleci/ |
| D | config.yml | 8 steps:
20 steps:
28 steps:
36 steps:
44 steps:
55 steps:
64 steps:
73 steps:
81 steps:
89 steps:
[all …]
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/ |
| D | trainable.py | 34 steps=None, argument
61 steps: Number of steps for which to train model. If `None`, train forever.
62 'steps' works incrementally. If you call two times fit(steps=10) then
63 training occurs in total 20 steps. If you don't want to have incremental
70 max_steps: Number of total steps for which to train model. If `None`,
71 train forever. If set, `steps` must be `None`.
73 Two calls to `fit(steps=100)` means 200 training
76 all 100 steps.
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| D | experiment.py | 175 train_steps: Perform this many steps of training. `None`, the default,
178 is raised), or for `eval_steps` steps, if specified.
184 eval in steps. If `None`, runs evaluation only at the end of training.
190 number of steps between evaluations. Of course, evaluation does not
200 Perform this many (integer) number of train steps for each
209 `min_eval_frequency` will be ignored, and the number of steps between
332 Train the estimator for `self._train_steps` steps, after waiting for
368 # Wait 5500 global steps for the second worker. Each worker waits more
369 # then previous one but with a diminishing number of steps.
394 `self._eval_steps` steps, or if it's `None`, then run until input is
[all …]
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| /external/libxml2/ |
| D | pattern.c | 102 int nbStep; /* number of steps in the automata */
103 int maxStep; /* allocated number of steps */
104 xmlStreamStepPtr steps; /* the array of steps */ member
172 xmlStepOpPtr steps; /* ops for computation */ member
214 cur->steps = (xmlStepOpPtr) xmlMalloc(cur->maxStep * sizeof(xmlStepOp)); in xmlNewPattern()
215 if (cur->steps == NULL) { in xmlNewPattern()
243 if (comp->steps != NULL) { in xmlFreePattern()
246 op = &comp->steps[i]; in xmlFreePattern()
253 xmlFree(comp->steps); in xmlFreePattern()
353 temp = (xmlStepOpPtr) xmlRealloc(comp->steps, comp->maxStep * 2 * in xmlPatternAdd()
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| /external/tensorflow/tensorflow/contrib/kernel_methods/python/ |
| D | kernel_estimators_test.py | 88 input_fn=_linearly_separable_binary_input_fn, steps=100)
91 input_fn=_linearly_separable_binary_input_fn, steps=1)
119 input_fn=_linearly_inseparable_binary_input_fn, steps=50)
121 input_fn=_linearly_inseparable_binary_input_fn, steps=1)
141 input_fn=_linearly_inseparable_binary_input_fn, steps=50)
143 input_fn=_linearly_inseparable_binary_input_fn, steps=1)
157 input_fn=_linearly_inseparable_binary_input_fn, steps=50)
171 input_fn=_linearly_inseparable_binary_input_fn, steps=50)
211 linear_classifier.fit(input_fn=input_fn, steps=100)
212 linear_metrics = linear_classifier.evaluate(input_fn=input_fn, steps=1)
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| /external/clang/test/SemaCXX/ |
| D | constexpr-steps.cpp | 1 // RUN: %clang_cc1 -std=c++1y -fsyntax-only -verify %s -DMAX=1234 -fconstexpr-steps 1234
2 // RUN: %clang_cc1 -std=c++1y -fsyntax-only -verify %s -DMAX=10 -fconstexpr-steps 10
3 // RUN: %clang -std=c++1y -fsyntax-only -Xclang -verify %s -DMAX=12345 -fconstexpr-steps=12345
5 // This takes a total of n + 4 steps according to our current rules:
11 constexpr bool steps(int n) { in steps() function
16 static_assert(steps((MAX - 4)), ""); // ok
17 static_assert(steps((MAX - 3)), ""); // expected-error {{constant}} expected-note{{call}}
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| /external/tensorflow/tensorflow/contrib/linear_optimizer/python/ |
| D | sdca_estimator_test.py | 52 classifier.fit(input_fn=input_fn, steps=100)
53 loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
74 classifier.fit(input_fn=input_fn, steps=100)
75 loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
101 classifier.fit(input_fn=input_fn, steps=50)
102 metrics = classifier.evaluate(input_fn=input_fn, steps=1)
131 classifier.fit(input_fn=input_fn, steps=50)
132 metrics = classifier.evaluate(input_fn=input_fn, steps=1)
162 classifier.fit(input_fn=input_fn, steps=50)
163 metrics = classifier.evaluate(input_fn=input_fn, steps=1)
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| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| D | estimators_test.py | 63 first_estimator.train(input_fn=train_input_fn, steps=5)
65 input_fn=eval_input_fn, steps=1)["loss"]
66 first_estimator.train(input_fn=train_input_fn, steps=50)
68 input_fn=eval_input_fn, steps=1)["loss"]
71 second_estimator.train(input_fn=train_input_fn, steps=2)
75 input_fn=whole_dataset_input_fn, steps=1)
78 steps=10)
93 steps=10,
113 steps=1,
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| /external/tensorflow/tensorflow/python/profiler/ |
| D | profile_context.py | 52 # Count the session steps.
114 # Trace steps 100~200, profile at [150, 200] and dump profile at 200.
124 # Run train/eval loop for at least few hundred steps. Profiles will be
140 trace_steps: A list of session run steps to trace. If None, use
141 pre-defined steps.
142 dump_steps: A list of steps to dump the profile to `profile_dir`. If None,
143 use pre-defined steps.
170 raise ValueError('Only support tracing up to 100 steps.\n')
191 """Traces and profiles at some session run steps.
197 will be run automatically at these integer steps. Each step is
[all …]
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