def inference_graph(self, data):
with ops.device(self.device_assigner):
path_probability, path = gen_training_ops.hard_routing_function(
data,
self.tree_parameters,
self.tree_thresholds,
max_nodes=self.params.num_nodes,
tree_depth=self.params.hybrid_tree_depth)
output = array_ops.slice(
gen_training_ops.unpack_path(path, path_probability),
[0, self.params.num_nodes - self.params.num_leaves - 1],
[-1, self.params.num_leaves])
return output
def inference_graph(self, data):
with ops.device(self.device_assigner):
path_probability, path = gen_training_ops.hard_routing_function(
data,
self.tree_parameters,
self.tree_thresholds,
max_nodes=self.params.num_nodes,
tree_depth=self.params.hybrid_tree_depth)
output = array_ops.slice(
gen_training_ops.unpack_path(path, path_probability),
[0, self.params.num_nodes - self.params.num_leaves - 1],
[-1, self.params.num_leaves])
return output
def soft_inference_graph(self, data):
with ops.device(self.device_assigner):
path_probability, path = (
gen_training_ops.stochastic_hard_routing_function(
data,
self.tree_parameters,
self.tree_thresholds,
tree_depth=self.params.hybrid_tree_depth,
random_seed=self.params.base_random_seed))
output = array_ops.slice(
gen_training_ops.unpack_path(path, path_probability),
[0, self.params.num_nodes - self.params.num_leaves - 1],
[-1, self.params.num_leaves])
return output
def soft_inference_graph(self, data):
with ops.device(self.device_assigner):
path_probability, path = (
gen_training_ops.stochastic_hard_routing_function(
data,
self.tree_parameters,
self.tree_thresholds,
tree_depth=self.params.hybrid_tree_depth,
random_seed=self.params.base_random_seed))
output = array_ops.slice(
gen_training_ops.unpack_path(path, path_probability),
[0, self.params.num_nodes - self.params.num_leaves - 1],
[-1, self.params.num_leaves])
return output
