def _target_model(config, featurizer_state, targets, n_outputs, train=False, reuse=None, **kwargs):
featurizer_state["sequence_features"] = tf.abs(tf.reduce_sum(featurizer_state["sequence_features"], 1))
featurizer_state["features"] = tf.abs(tf.reduce_sum(featurizer_state["features"], 1))
return regressor(
hidden=featurizer_state['features'],
targets=targets,
n_targets=n_outputs,
config=config,
train=train,
reuse=reuse,
**kwargs
)
def _target_model(config,
featurizer_state,
targets,
n_outputs,
train=False,
reuse=None,
**kwargs):
return regressor(hidden=featurizer_state['features'],
targets=targets,
n_targets=n_outputs,
config=config,
train=train,
reuse=reuse,
**kwargs)