| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | training_distributed.py | 141 steps=None, argument
147 steps, batch_size = distributed_training_utils.get_input_params(
148 model._distribution_strategy, first_x_value, steps, batch_size)
149 batch_size = model._validate_or_infer_batch_size(batch_size, steps, x)
157 model, dataset, verbose=verbose, steps=steps, callbacks=callbacks)
164 steps=steps,
172 steps=None, argument
179 steps, batch_size = distributed_training_utils.get_input_params(
180 model._distribution_strategy, first_x_value, steps,
182 batch_size = model._validate_or_infer_batch_size(batch_size, steps, x)
[all …]
|
| /external/python/cpython3/.azure-pipelines/ |
| D | ci.yml | 17 steps:
29 steps:
30 - template: ./docs-steps.yml
47 steps:
48 - template: ./macos-steps.yml
64 steps:
65 - template: ./posix-steps.yml
92 steps:
93 - template: ./posix-steps.yml
121 steps:
[all …]
|
| D | pr.yml | 17 steps:
29 steps:
30 - template: ./docs-steps.yml
45 steps:
46 - template: ./macos-steps.yml
64 steps:
65 - template: ./posix-steps.yml
92 steps:
93 - template: ./posix-steps.yml
121 steps:
[all …]
|
| /external/tensorflow/tensorflow/contrib/distribute/python/ |
| D | keras_test.py | 361 input_fn=get_ds_test_input_fn, steps=1)
362 est_keras.train(input_fn=get_ds_train_input_fn, steps=_TRAIN_SIZE / 16)
364 steps=1)
390 input_fn=get_ds_test_input_fn, steps=1)
391 est_keras.train(input_fn=get_ds_train_input_fn, steps=_TRAIN_SIZE / 16)
393 steps=1)
446 input_fn=eval_input_fn, steps=1)
447 est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16)
448 eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1)
471 est_keras.train(input_fn=get_ds_train_input_fn, steps=_TRAIN_SIZE / 16)
[all …]
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | linear_test.py | 97 classifier.fit(input_fn=input_fn, steps=100)
98 loss1 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
99 classifier.fit(input_fn=input_fn, steps=200)
100 loss2 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
123 classifier.fit(input_fn=input_fn, steps=100)
124 loss1 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
125 classifier.fit(input_fn=input_fn, steps=200)
126 loss2 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
138 classifier.fit(input_fn=test_data.iris_input_multiclass_fn, steps=100)
140 input_fn=test_data.iris_input_multiclass_fn, steps=100)
[all …]
|
| 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)
[all …]
|
| 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)
[all …]
|
| 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)
[all …]
|
| 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)
[all …]
|
| 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 …]
|
| /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()
[all …]
|
| /external/python/google-api-python-client/docs/dyn/ |
| D | games_v1.achievements.html | 79 <p class="firstline">Increments the steps of the achievement with the given ID for the currently au…
90 …<code><a href="#setStepsAtLeast">setStepsAtLeast(achievementId, steps, consistencyToken=None)</a><…
91 … the steps for the currently authenticated player towards unlocking an achievement. If the steps p…
101 …<pre>Increments the steps of the achievement with the given ID for the currently authenticated pla…
105 stepsToIncrement: integer, The number of steps to increment. (required)
113 "currentSteps": 42, # The current steps recorded for this incremental achievement.
114 …Unlocked": True or False, # Whether the current steps for the achievement has reached the number o…
150 "currentSteps": 42, # The current steps for an incremental achievement.
152 …"formattedCurrentStepsString": "A String", # The current steps for an incremental achievement as a…
195 …<code class="details" id="setStepsAtLeast">setStepsAtLeast(achievementId, steps, consistencyToken=…
[all …]
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
| D | estimator_test.py | 159 classifier.fit(input_fn=_train_input_fn, steps=15)
160 classifier.evaluate(input_fn=_eval_input_fn, steps=1)
179 classifier.fit(input_fn=_train_input_fn, steps=15)
206 model.fit(input_fn=_train_input_fn, steps=15)
207 model.evaluate(input_fn=_eval_input_fn, steps=1)
226 classifier.fit(input_fn=_train_input_fn, steps=15)
227 classifier.evaluate(input_fn=_eval_input_fn, steps=1)
246 regressor.fit(input_fn=_train_input_fn, steps=15)
247 regressor.evaluate(input_fn=_eval_input_fn, steps=1)
274 model.fit(input_fn=_ranking_train_input_fn, steps=1000)
[all …]
|
| D | dnn_tree_combined_estimator_test.py | 86 classifier.fit(input_fn=_train_input_fn, steps=5)
108 classifier.fit(input_fn=_train_input_fn, steps=15)
109 classifier.evaluate(input_fn=_eval_input_fn, steps=1)
132 classifier.fit(input_fn=_train_input_fn, steps=15)
133 classifier.evaluate(input_fn=_eval_input_fn, steps=1)
165 # Train for a few steps.
166 est.train(input_fn=_train_input_fn, steps=1000)
167 # 10 steps for dnn, 3 for 1 tree of depth 3 + 1 after the tree finished
169 res = est.evaluate(input_fn=_eval_input_fn, steps=1)
196 # Train for a few steps.
[all …]
|
| /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:
[all …]
|
| /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:
[all …]
|
| /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()
[all …]
|
| /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 …
|
| /external/tensorflow/tensorflow/compiler/tests/ |
| D | ftrl_test.py | 42 def equivAdagradTest_FtrlPart(self, steps, dtype): argument
56 # Run Ftrl for a few steps
57 for _ in range(steps):
62 def equivAdagradTest_AdagradPart(self, steps, dtype): argument
71 # Run Adagrad for a few steps
72 for _ in range(steps):
77 def equivGradientDescentTest_FtrlPart(self, steps, dtype): argument
91 # Run Ftrl for a few steps
92 for _ in range(steps):
97 def equivGradientDescentTest_GradientDescentPart(self, steps, dtype): argument
[all …]
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/client/ |
| D | random_forest_test.py | 75 classifier.fit(input_fn=input_fn, steps=100)
76 res = classifier.evaluate(input_fn=input_fn, steps=10)
100 regressor.fit(input_fn=input_fn, steps=100)
101 res = regressor.evaluate(input_fn=input_fn, steps=10)
133 classifier.fit(input_fn=input_fn, steps=100)
162 classifier.fit(input_fn=input_fn, steps=100)
186 est.train(input_fn=input_fn, steps=100)
187 res = est.evaluate(input_fn=input_fn, steps=1)
215 regressor.train(input_fn=input_fn, steps=100)
216 res = regressor.evaluate(input_fn=input_fn, steps=10)
[all …]
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/ |
| D | trainable.py | 43 steps=None, argument
70 steps: Number of steps for which to train model. If `None`, train forever.
71 'steps' works incrementally. If you call two times fit(steps=10) then
72 training occurs in total 20 steps. If you don't want to have incremental
79 max_steps: Number of total steps for which to train model. If `None`,
80 train forever. If set, `steps` must be `None`.
82 Two calls to `fit(steps=100)` means 200 training
85 all 100 steps.
|
| D | experiment.py | 176 train_steps: Perform this many steps of training. `None`, the default,
179 is raised), or for `eval_steps` steps, if specified.
185 eval in steps. If `None`, runs evaluation only at the end of training.
191 number of steps between evaluations. Of course, evaluation does not
202 Perform this many (integer) number of train steps for each
211 `min_eval_frequency` will be ignored, and the number of steps between
333 Train the estimator for `self._train_steps` steps, after waiting for
369 # Wait 5500 global steps for the second worker. Each worker waits more
370 # then previous one but with a diminishing number of steps.
395 `self._eval_steps` steps, or if it's `None`, then run until input is
[all …]
|
| /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}}
|
| /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()
[all …]
|
| /external/skia/src/core/ |
| D | SkConvertPixels.cpp | 19 const SkColorSpaceXformSteps& steps) { in rect_memcpy() argument
25 && steps.flags.mask() != 0b00000) { in rect_memcpy()
36 const SkColorSpaceXformSteps& steps) { in swizzle_or_premul() argument
42 steps.flags.linearize || in swizzle_or_premul()
43 steps.flags.gamut_transform || in swizzle_or_premul()
44 steps.flags.unpremul || in swizzle_or_premul()
45 steps.flags.encode) { in swizzle_or_premul()
53 if (steps.flags.premul) { in swizzle_or_premul()
166 const SkColorSpaceXformSteps& steps) { in convert_with_pipeline() argument
173 steps.apply(&pipeline, srcInfo.colorType()); in convert_with_pipeline()
[all …]
|