def __init__(self, params, tree_num, training, tree_config='', tree_stat=''):
if (not hasattr(params, 'params_proto') or
not isinstance(params.params_proto, _params_proto.TensorForestParams)):
params.params_proto = build_params_proto(params)
params.serialized_params_proto = params.params_proto.SerializeToString()
self.stats = None
if training:
# TODO(gilberth): Manually shard this to be able to fit it on
# multiple machines.
self.stats = stats_ops.fertile_stats_variable(
params, tree_stat, self.get_tree_name('stats', tree_num))
self.tree = model_ops.tree_variable(params, tree_config, self.stats,
self.get_tree_name('tree', tree_num))
def __init__(self, params, tree_num, training):
if (not hasattr(params, 'params_proto') or not isinstance(
params.params_proto, _params_proto.TensorForestParams)):
params.params_proto = build_params_proto(params)
params.serialized_params_proto = params.params_proto.SerializeToString(
)
self.stats = None
if training:
# TODO(gilberth): Manually shard this to be able to fit it on
# multiple machines.
self.stats = stats_ops.fertile_stats_variable(
params, '', self.get_tree_name('stats', tree_num))
self.tree = model_ops.tree_variable(
params, '', self.stats, self.get_tree_name('tree', tree_num))