Searched refs:TrainEvalFeatures (Results 1 – 22 of 22) sorted by relevance
/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
D | input_pipeline_test.py | 28 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures 64 times = example.features.feature[TrainEvalFeatures.TIMES] 66 values = example.features.feature[TrainEvalFeatures.VALUES] 77 return {TrainEvalFeatures.TIMES: times, 78 TrainEvalFeatures.VALUES: values} 99 features[TrainEvalFeatures.TIMES].shape) 104 features[TrainEvalFeatures.TIMES][batch_position, 106 features[TrainEvalFeatures.TIMES][batch_position, 109 features[TrainEvalFeatures.VALUES].shape) 110 self.assertEqual("int64", features[TrainEvalFeatures.TIMES].dtype) [all …]
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D | input_pipeline.py | 246 dataset_size = self._features[feature_keys.TrainEvalFeatures.TIMES].shape[1] 428 column_names=(feature_keys.TrainEvalFeatures.TIMES, 429 feature_keys.TrainEvalFeatures.VALUES), 455 if feature_keys.TrainEvalFeatures.TIMES not in column_names: 457 feature_keys.TrainEvalFeatures.TIMES)) 458 if feature_keys.TrainEvalFeatures.VALUES not in column_names: 460 feature_keys.TrainEvalFeatures.VALUES)) 467 if column_name == feature_keys.TrainEvalFeatures.TIMES) != 1: 470 "one is required.".format(feature_keys.TrainEvalFeatures.TIMES)) 484 if column_name == feature_keys.TrainEvalFeatures.TIMES [all …]
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D | head_test.py | 100 feature_keys.TrainEvalFeatures.TIMES: 102 feature_keys.TrainEvalFeatures.VALUES: 153 feature_keys.TrainEvalFeatures.TIMES: [[1, 2, 3], [7, 8, 9]], 154 feature_keys.TrainEvalFeatures.VALUES: 161 target = features[feature_keys.TrainEvalFeatures.VALUES][:, -1, 0] 203 feature_keys.TrainEvalFeatures.TIMES)): 205 features={feature_keys.TrainEvalFeatures.VALUES: [[[1.]]]}, 213 feature_keys.TrainEvalFeatures.VALUES)): 215 features={feature_keys.TrainEvalFeatures.TIMES: [[1]]}, 224 feature_keys.TrainEvalFeatures.TIMES)): [all …]
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D | ar_model_test.py | 30 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures 79 train_data = {TrainEvalFeatures.TIMES: time[0:split], 80 TrainEvalFeatures.VALUES: data[0:split]} 81 test_data = {TrainEvalFeatures.TIMES: time[split:], 82 TrainEvalFeatures.VALUES: data[split:]} 148 train_data_times = train_data[TrainEvalFeatures.TIMES] 149 train_data_values = train_data[TrainEvalFeatures.VALUES] 150 test_data_times = test_data[TrainEvalFeatures.TIMES] 151 test_data_values = test_data[TrainEvalFeatures.VALUES] 226 return ({TrainEvalFeatures.TIMES: [[1]], [all …]
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D | head.py | 179 feature_keys.TrainEvalFeatures.TIMES, 183 if name == feature_keys.TrainEvalFeatures.VALUES: 239 feature_keys.TrainEvalFeatures.TIMES, 240 feature_keys.TrainEvalFeatures.VALUES)) 302 if feature_keys.TrainEvalFeatures.VALUES not in features: 304 feature_keys.TrainEvalFeatures.VALUES)) 324 feature_keys.TrainEvalFeatures.VALUES, 403 if feature_keys.TrainEvalFeatures.TIMES not in features: 405 feature_keys.TrainEvalFeatures.TIMES)) 406 if feature_keys.TrainEvalFeatures.VALUES not in features: [all …]
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D | estimators.py | 168 key=feature_keys.TrainEvalFeatures.TIMES, dtype=dtypes.int64) 170 key=feature_keys.TrainEvalFeatures.VALUES, dtype=values_input_dtype, 179 if key == feature_keys.TrainEvalFeatures.VALUES: 199 features[feature_keys.TrainEvalFeatures.TIMES] = array_ops.squeeze( 200 features[feature_keys.TrainEvalFeatures.TIMES], axis=-1) 201 features[feature_keys.TrainEvalFeatures.VALUES] = math_ops.cast( 202 features[feature_keys.TrainEvalFeatures.VALUES], 207 features[feature_keys.TrainEvalFeatures.TIMES])[0], 238 name=feature_keys.TrainEvalFeatures.TIMES, 241 placeholders[feature_keys.TrainEvalFeatures.TIMES] = time_placeholder [all …]
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D | model.py | 28 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures 202 batch_size=array_ops.shape(features[TrainEvalFeatures.TIMES])[0]) 543 outputs["observed"] = features[TrainEvalFeatures.VALUES] 548 prediction_times=features[TrainEvalFeatures.TIMES]) 567 TrainEvalFeatures.TIMES, 568 TrainEvalFeatures.VALUES]} 609 times = math_ops.cast(features[TrainEvalFeatures.TIMES], dtype=dtypes.int64) 610 values = math_ops.cast(features[TrainEvalFeatures.VALUES], dtype=self.dtype) 616 if key not in [TrainEvalFeatures.TIMES, 617 TrainEvalFeatures.VALUES]})
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D | state_management_test.py | 71 times = features[feature_keys.TrainEvalFeatures.TIMES] 72 values = features[feature_keys.TrainEvalFeatures.VALUES] 107 feature_keys.TrainEvalFeatures.TIMES: times, 108 feature_keys.TrainEvalFeatures.VALUES: values 266 outputs["observed"] = features[feature_keys.TrainEvalFeatures.VALUES] 271 prediction_times=features[feature_keys.TrainEvalFeatures.TIMES]) 282 features[feature_keys.TrainEvalFeatures.VALUES])
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D | ar_model.py | 26 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures 515 if key not in [TrainEvalFeatures.TIMES, 516 TrainEvalFeatures.VALUES, 668 times = math_ops.cast(features[TrainEvalFeatures.TIMES], dtypes.int64) 669 values = math_ops.cast(features[TrainEvalFeatures.VALUES], dtype=self.dtype) 687 times_feature=TrainEvalFeatures.TIMES, 756 times = features[TrainEvalFeatures.TIMES] 760 if key not in [TrainEvalFeatures.TIMES, 761 TrainEvalFeatures.VALUES, 784 times.get_shape(), TrainEvalFeatures.TIMES)) [all …]
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D | test_utils.py | 24 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures 64 times = features[TrainEvalFeatures.TIMES] 125 feature_dict[TrainEvalFeatures.VALUES] = math_ops.cast( 126 feature_dict[TrainEvalFeatures.VALUES], generative_model.dtype)
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D | state_management.py | 189 feature_keys.TrainEvalFeatures.VALUES] 194 prediction_times=features[feature_keys.TrainEvalFeatures.TIMES]) 232 times = features[feature_keys.TrainEvalFeatures.TIMES]
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D | estimators_test.py | 61 feature_keys.TrainEvalFeatures.TIMES: times, 62 feature_keys.TrainEvalFeatures.VALUES: values, 248 feature_keys.TrainEvalFeatures.TIMES: times, 249 feature_keys.TrainEvalFeatures.VALUES: values,
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D | feature_keys.py | 46 class TrainEvalFeatures(Times, Values): class
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D | math_utils_test.py | 25 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures 289 features = {TrainEvalFeatures.TIMES: times, 290 TrainEvalFeatures.VALUES: values}
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D | math_utils.py | 29 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures 719 if (TrainEvalFeatures.TIMES in features 720 and TrainEvalFeatures.VALUES in features): 721 times = features[TrainEvalFeatures.TIMES] 722 values = features[TrainEvalFeatures.VALUES]
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/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
D | varma_test.py | 24 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures 44 TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]), 45 TrainEvalFeatures.VALUES: constant_op.constant([[[1.], [2.]]]) 62 TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]), 63 TrainEvalFeatures.VALUES: constant_op.constant( 84 TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]), 85 TrainEvalFeatures.VALUES: constant_op.constant([[[1.], [2.]]])},
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D | state_space_model_test.py | 92 feature_keys.TrainEvalFeatures.TIMES: 94 feature_keys.TrainEvalFeatures.VALUES: 110 feature_keys.TrainEvalFeatures.TIMES: 112 feature_keys.TrainEvalFeatures.VALUES: 132 feature_keys.TrainEvalFeatures.TIMES: times, 133 feature_keys.TrainEvalFeatures.VALUES: values 136 times = features[feature_keys.TrainEvalFeatures.TIMES] 137 values = features[feature_keys.TrainEvalFeatures.VALUES] 140 feature_keys.TrainEvalFeatures.TIMES: times, 141 feature_keys.TrainEvalFeatures.VALUES: values [all …]
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D | structural_ensemble_test.py | 28 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures 47 return {TrainEvalFeatures.TIMES: numpy.reshape(time, [1, -1]), 48 TrainEvalFeatures.VALUES: numpy.reshape( 113 features = {TrainEvalFeatures.TIMES: times, 114 TrainEvalFeatures.VALUES: values, 138 features = {TrainEvalFeatures.TIMES: times, 139 TrainEvalFeatures.VALUES: values}
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D | state_space_model.py | 31 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures 833 return {TrainEvalFeatures.TIMES: times, 834 TrainEvalFeatures.VALUES: observations}
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/external/tensorflow/tensorflow/contrib/timeseries/examples/ |
D | multivariate.py | 54 column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,) 55 + (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5)) 88 tf.contrib.timeseries.TrainEvalFeatures.TIMES: current_prediction[ 90 tf.contrib.timeseries.TrainEvalFeatures.VALUES: next_sample[
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D | known_anomaly.py | 105 column_names=(tf.contrib.timeseries.TrainEvalFeatures.TIMES, 106 tf.contrib.timeseries.TrainEvalFeatures.VALUES,
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D | lstm.py | 210 column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,) 211 + (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5
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