Lines Matching refs:features
74 def _train_ops(self, features):
78 self.model, features, estimator_lib.ModeKeys.TRAIN)
99 def create_loss(self, features, mode, logits, labels):
108 def _evaluate_ops(self, features):
112 self.model, features, estimator_lib.ModeKeys.EVAL)
129 def _predict_ops(self, features):
132 prediction = self.model.predict(features=features)
133 prediction[feature_keys.PredictionResults.TIMES] = features[
138 def _serving_ops(self, features):
141 prediction_outputs = self.model.predict(features=features)
144 self.model, features, estimator_lib.ModeKeys.EVAL)
160 """Casts features to the correct dtype based on their name."""
172 def _gather_state(self, features):
173 """Returns `features` with state packed, indicates if packing was done."""
177 for key, tensor in features.items():
182 return features, False
183 features = features.copy()
185 del features[key]
187 features[feature_keys.State.STATE_TUPLE] = nest.pack_sequence_as(
190 return features, True
192 def create_estimator_spec(self, features, mode, labels=None):
199 "features.".format(feature_keys.TrainEvalFeatures.TIMES,
202 features = {
204 for name, value in features.items()
208 features, update_statistics=(mode == estimator_lib.ModeKeys.TRAIN))
215 features, passed_flat_state = self._gather_state(features)
218 _check_train_eval_features(features, self.model)
220 _check_predict_features(features)
228 return self._train_ops(features)
230 return self._evaluate_ops(features)
232 return self._predict_ops(features)
237 return self._serving_ops(features)
240 def _check_feature_shapes_compatible_with(features,
244 """Checks all features are compatible with the given time-like feature."""
247 for name, value in features.items():
255 ("Features must have shape (batch dimension, window size, ...) "
260 ("Features must have shape (batch dimension, window size, ...) "
271 def _check_predict_features(features):
272 """Raises errors if features are not suitable for prediction."""
273 if feature_keys.PredictionFeatures.TIMES not in features:
276 if feature_keys.PredictionFeatures.STATE_TUPLE not in features:
279 times_feature = features[feature_keys.PredictionFeatures.TIMES]
286 features=features,
294 def _check_train_eval_features(features, model):
295 """Raise errors if features are not suitable for training/evaluation."""
296 if feature_keys.TrainEvalFeatures.TIMES not in features:
299 if feature_keys.TrainEvalFeatures.VALUES not in features:
302 times_feature = features[feature_keys.TrainEvalFeatures.TIMES]
308 values_feature = features[feature_keys.TrainEvalFeatures.VALUES]
319 features=features,
