def inference_graph(self, data):
with ops.device(self.device_assigner):
routes = gen_training_ops.routing_function(
data,
self.tree_parameters,
self.tree_thresholds,
max_nodes=self.params.num_nodes)
leaf_routes = array_ops.slice(
routes, [0, self.params.num_nodes - self.params.num_leaves - 1],
[-1, self.params.num_leaves])
return leaf_routes
def inference_graph(self, data):
with ops.device(self.device_assigner):
routing_probabilities = gen_training_ops.routing_function(
data,
self.tree_parameters,
self.tree_thresholds,
max_nodes=self.params.num_nodes)
output = array_ops.slice(
routing_probabilities,
[0, self.params.num_nodes - self.params.num_leaves - 1],
[-1, self.params.num_leaves])
return output
def testRoutingFunction(self):
with self.cached_session():
route_tensor = gen_training_ops.routing_function(
self.input_data, self.tree_weights, self.tree_thresholds, max_nodes=3)
route_tensor_shape = route_tensor.get_shape()
self.assertEquals(len(route_tensor_shape), 2)
self.assertEquals(route_tensor_shape[0], 4)
self.assertEquals(route_tensor_shape[1], 3)
routes = route_tensor.eval()
# Point 1
# Node 1 is a decision node => probability = 1.0
self.assertAlmostEquals(1.0, routes[0, 0])
# Probability left output = 1.0 / (1.0 + exp(1.0)) = 0.26894142
self.assertAlmostEquals(0.26894142, routes[0, 1])
# Probability right = 1 - 0.2689414 = 0.73105858
self.assertAlmostEquals(0.73105858, routes[0, 2])