Lines Matching refs:features
48 def define_loss(self, model, features, mode):
53 features: A dictionary with the following key/value pairs:
57 features] Tensor with values for each observation.
64 if feature_keys.State.STATE_TUPLE in features:
67 return model.define_loss(features, mode)
74 def _define_loss_with_saved_state(self, model, features, mode):
77 def define_loss(self, model, features, mode):
79 if feature_keys.FilteringFeatures.STATE_TUPLE in features:
86 start_state = features[feature_keys.FilteringFeatures.STATE_TUPLE]
87 del features[feature_keys.FilteringFeatures.STATE_TUPLE]
89 features=features, mode=mode, state=start_state)
93 model=model, features=features, mode=mode)
105 def _define_loss_with_saved_state(self, model, features, mode):
106 return model.define_loss(features, mode)
155 def _define_loss_with_saved_state(self, model, features, mode):
160 features: Dictionary with Tensor values defining the data to be
166 size x num features] Tensor with values for each observation,
181 features=features,
187 batch_predictions["observed"] = features[
193 prediction_times=features[feature_keys.TrainEvalFeatures.TIMES])
229 def _update_cached_states(self, model, features, mode):
231 times = features[feature_keys.TrainEvalFeatures.TIMES]
235 features=features,
