def prediction_input_fn():
return ({
PredictionFeatures.TIMES:
training.limit_epochs(predict_times, num_epochs=1),
PredictionFeatures.STATE_TUPLE:
(state_times, state_values, state_exogenous)
}, {})
def prediction_input_fn():
return ({
PredictionFeatures.TIMES: training.limit_epochs(
predict_times, num_epochs=1),
PredictionFeatures.STATE_TUPLE: (state_times,
state_values,
state_exogenous)
}, {})
def _predict_input_fn():
"""An input_fn for predict()."""
# Prevents infinite iteration with a constant output in an Estimator's
# predict().
limited_features = {}
for key, values in features.items():
limited_values = nest.map_structure(
lambda value: training.limit_epochs(value, num_epochs=1), values)
limited_features[key] = limited_values
return (limited_features, None)
def _predict_input_fn():
"""An input_fn for predict()."""
# Prevents infinite iteration with a constant output in an Estimator's
# predict().
limited_features = {}
for key, values in features.items():
limited_values = nest.map_structure(
lambda value: training.limit_epochs(value, num_epochs=1), values)
limited_features[key] = limited_values
return (limited_features, None)