| /external/tensorflow/tensorflow/contrib/linear_optimizer/python/ |
| D | sdca_estimator_test.py | 44 def input_fn(): function
60 classifier.fit(input_fn=input_fn, steps=100)
61 loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
69 def input_fn(): function
82 classifier.fit(input_fn=input_fn, steps=100)
83 loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
89 def input_fn(): function
109 classifier.fit(input_fn=input_fn, steps=50)
110 metrics = classifier.evaluate(input_fn=input_fn, steps=1)
116 def input_fn(): function
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | linear_test.py | 83 def input_fn(): function
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']
107 def input_fn(): function
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']
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| D | svm_test.py | 33 def input_fn(): function
46 svm_classifier.fit(input_fn=input_fn, steps=30)
47 metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
59 def input_fn(): function
72 svm_classifier.fit(input_fn=input_fn, steps=30)
73 metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
90 def input_fn(): function
104 svm_classifier.fit(input_fn=input_fn, steps=30)
105 metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
114 def input_fn(): function
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| D | dnn_linear_combined_test.py | 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)
227 estimator.predict(input_fn=test_data.iris_input_multiclass_fn)
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)
421 scores = classifier.evaluate(input_fn=_input_fn, steps=100)
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| D | debug_test.py | 63 def input_fn(): function
70 return input_fn
97 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
100 input_fn=_input_fn_builder(test_features, None))
116 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
119 input_fn=_input_fn_builder(test_features, None))
133 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
136 input_fn=_input_fn_builder(test_features, None))
155 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
158 input_fn=_input_fn_builder(test_features, None))
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| D | estimators_test.py | 44 def input_fn(): function
70 estimator.fit(input_fn=input_fn, steps=1)
71 prediction = next(estimator.predict(input_fn=input_fn, as_iterable=True))
75 input_fn=input_fn,
85 def input_fn(): function
110 estimator.fit(input_fn=input_fn, steps=1)
111 prediction = next(estimator.predict(input_fn=input_fn, as_iterable=True))
115 input_fn=input_fn,
125 def input_fn(): function
151 estimator_with_fe_fn.fit(input_fn=input_fn, steps=1)
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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)
303 input_fn = test_data.iris_input_logistic_fn
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)
395 classifier.fit(input_fn=_input_fn, steps=50)
397 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
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| D | composable_model_test.py | 122 def input_fn(): function
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']
147 def input_fn(): function
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']
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| D | kmeans_test.py | 72 def input_fn(self, member in KMeansTestBase
163 kmeans.fit(input_fn=self.input_fn(), steps=1)
169 kmeans.fit(input_fn=self.input_fn(), steps=1)
171 input_fn=self.input_fn(batch_size=self.num_points), steps=1)
173 kmeans.fit(input_fn=self.input_fn(), steps=steps)
175 input_fn=self.input_fn(batch_size=self.num_points), steps=1)
194 input_fn=self.input_fn(),
198 input_fn=self.input_fn(batch_size=self.num_points), steps=1)
206 kmeans.predict_cluster_idx(input_fn=self.input_fn(
212 input_fn=lambda: (constant_op.constant(points), None), steps=1)
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| D | dnn.py | 398 def predict(self, x=None, input_fn=None, batch_size=None, outputs=None, argument
425 input_fn=input_fn,
430 input_fn=input_fn,
439 def predict_classes(self, x=None, input_fn=None, batch_size=None, argument
460 input_fn=input_fn,
474 input_fn=None, argument
495 input_fn=input_fn,
506 input_fn=None, argument
520 input_fn=input_fn or default_input_fn,
675 input_fn=None, argument
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| D | svm.py | 164 def predict_classes(self, x=None, input_fn=None, batch_size=None, argument
170 input_fn=input_fn,
181 def predict_proba(self, x=None, input_fn=None, batch_size=None, outputs=None, argument
187 input_fn=input_fn,
198 input_fn=None, default_batch_size=1, argument
204 input_fn=input_fn,
213 input_fn=None, argument
222 input_fn=input_fn or default_input_fn,
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| D | estimator.py | 98 def _verify_input_args(x, y, input_fn, feed_fn, batch_size): argument
100 if input_fn is None:
116 def _get_input_fn(x, y, input_fn, feed_fn, batch_size, shuffle=False, epochs=1): argument
134 _verify_input_args(x, y, input_fn, feed_fn, batch_size)
135 if input_fn is not None:
136 return input_fn, feed_fn
148 def infer_real_valued_columns_from_input_fn(input_fn): argument
164 features, _ = input_fn()
181 input_fn, _ = _get_input_fn(
182 x=x, y=None, input_fn=None, feed_fn=None, batch_size=None)
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| D | linear.py | 510 def predict(self, x=None, input_fn=None, batch_size=None, outputs=None, argument
537 input_fn=input_fn,
542 input_fn=input_fn,
550 def predict_classes(self, x=None, input_fn=None, batch_size=None, argument
571 input_fn=input_fn,
582 def predict_proba(self, x=None, input_fn=None, batch_size=None, argument
602 input_fn=input_fn,
613 input_fn=None, argument
626 input_fn=input_fn or default_input_fn,
781 def predict(self, x=None, input_fn=None, batch_size=None, outputs=None, argument
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| D | dnn_linear_combined.py | 720 def predict(self, x=None, input_fn=None, batch_size=None, outputs=None, argument
747 input_fn=input_fn,
752 input_fn=input_fn,
760 def predict_classes(self, x=None, input_fn=None, batch_size=None, argument
781 input_fn=input_fn,
793 self, x=None, input_fn=None, batch_size=None, as_iterable=True): argument
812 input_fn=input_fn,
823 input_fn=None, argument
835 input_fn=input_fn or default_input_fn,
1036 input_fn=None, argument
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| D | debug.py | 209 def predict_classes(self, input_fn=None, batch_size=None): argument
222 input_fn=input_fn, batch_size=batch_size, outputs=[key])
226 input_fn=None, argument
239 input_fn=input_fn,
324 def predict_scores(self, input_fn=None, batch_size=None): argument
336 input_fn=input_fn, batch_size=batch_size, outputs=[key])
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| /external/tensorflow/tensorflow/contrib/tensor_forest/client/ |
| D | random_forest_test.py | 74 input_fn, predict_input_fn = _get_classification_input_fns()
75 classifier.fit(input_fn=input_fn, steps=100)
76 res = classifier.evaluate(input_fn=input_fn, steps=10)
81 predictions = list(classifier.predict(input_fn=predict_input_fn))
98 input_fn, predict_input_fn = _get_regression_input_fns()
100 regressor.fit(input_fn=input_fn, steps=100)
101 res = regressor.evaluate(input_fn=input_fn, steps=10)
104 predictions = list(regressor.predict(input_fn=predict_input_fn))
123 input_fn = numpy_io.numpy_input_fn(
133 classifier.fit(input_fn=input_fn, steps=100)
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| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| D | gmm_test.py | 38 def input_fn(self, batch_size=None, points=None): member in GMMTest
68 clusterer.fit(input_fn=lambda: (constant_op.constant(self.points), None),
92 gmm.fit(input_fn=self.input_fn(), steps=0)
102 gmm.fit(input_fn=self.input_fn(), steps=0)
111 gmm.fit(input_fn=self.input_fn(), steps=1)
112 score1 = gmm.score(input_fn=self.input_fn(batch_size=self.num_points),
114 gmm.fit(input_fn=self.input_fn(), steps=10)
115 score2 = gmm.score(input_fn=self.input_fn(batch_size=self.num_points),
124 gmm.fit(input_fn=self.input_fn(), steps=60)
133 input_fn=self.input_fn(points=points, batch_size=num_points)):
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| D | kmeans_test.py | 72 def input_fn(self, member in KMeansTestBase
163 kmeans.train(input_fn=self.input_fn(), steps=1)
169 kmeans.train(input_fn=self.input_fn(), steps=1)
170 score1 = kmeans.score(input_fn=self.input_fn(batch_size=self.num_points))
172 kmeans.train(input_fn=self.input_fn(), steps=steps)
173 score2 = kmeans.score(input_fn=self.input_fn(batch_size=self.num_points))
192 input_fn=self.input_fn(),
195 score = kmeans.score(input_fn=self.input_fn(batch_size=self.num_points))
201 input_fn = self.input_fn(batch_size=num_points, points=points, num_epochs=1)
203 assignments = list(kmeans.predict_cluster_index(input_fn))
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| D | wals_test.py | 82 def input_fn(self, np_matrix, batch_size, mode, member in WALSMatrixFactorizationTest
260 input_fn = self.input_fn(np_matrix=self.input_matrix,
264 self._model.fit(input_fn=input_fn, steps=self.row_steps)
271 input_fn = self.input_fn(np_matrix=self.input_matrix,
275 self._model.fit(input_fn=input_fn, steps=self.col_steps)
282 input_fn = self.input_fn(np_matrix=self.input_matrix,
288 proj_input_fn = self.input_fn(
295 self._model.fit(input_fn=input_fn, steps=self.row_steps)
305 proj_input_fn = self.input_fn(
312 self._model.fit(input_fn=input_fn, steps=self.col_steps)
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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)
100 logreg_classifier.predict_proba(input_fn=
119 input_fn=_linearly_inseparable_binary_input_fn, steps=50)
121 input_fn=_linearly_inseparable_binary_input_fn, steps=1)
125 input_fn=_linearly_inseparable_binary_input_fn, as_iterable=False)
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)
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| /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)
180 result_iter = classifier.predict(input_fn=_eval_input_fn)
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)
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| 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)
166 est.train(input_fn=_train_input_fn, steps=1000)
169 res = est.evaluate(input_fn=_eval_input_fn, steps=1)
171 est.predict(input_fn=_eval_input_fn)
197 est.train(input_fn=_train_input_fn, steps=1000)
198 res = est.evaluate(input_fn=_eval_input_fn, steps=1)
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/learn_io/ |
| D | pandas_io_test.py | 47 def callInputFnOnce(self, input_fn, session): argument
48 results = input_fn()
70 input_fn = pandas_io.pandas_input_fn(
73 features, target = self.callInputFnOnce(input_fn, session)
88 input_fn = pandas_io.pandas_input_fn(
91 results = input_fn()
117 input_fn = pandas_io.pandas_input_fn(
120 results = input_fn()
151 input_fn = pandas_io.pandas_input_fn(
154 features = self.callInputFnOnce(input_fn, session)
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| /external/tensorflow/tensorflow/contrib/predictor/ |
| D | predictor_factories_test.py | 58 input_fn = testing_common.get_arithmetic_input_fn(core=False)
60 estimator, input_fn, output_alternative_key='sum')
64 input_fn = testing_common.get_arithmetic_input_fn(core=False)
66 estimator, input_fn, output_alternative_key='sum',
71 input_fn = testing_common.get_arithmetic_input_fn(core=True)
73 predictor_factories.from_contrib_estimator(estimator, input_fn)
77 input_fn = testing_common.get_arithmetic_input_fn(core=True)
78 predictor_factories.from_estimator(estimator, input_fn)
82 input_fn = testing_common.get_arithmetic_input_fn(core=True)
84 estimator, input_fn, config=config_pb2.ConfigProto())
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/ |
| D | experiment.py | 386 input_fn=self._train_input_fn,
417 input_fn=self._eval_input_fn,
436 input_fn, argument
535 input_fn=input_fn,
638 input_fn=self._eval_input_fn,
664 input_fn=self._eval_input_fn,
683 input_fn=self._eval_input_fn,
776 input_fn=self._train_input_fn,
785 input_fn=self._eval_input_fn,
831 input_fn=self._train_input_fn,
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