tdm_child¶
- paddle.fluid.contrib.layers.nn. tdm_child ( x, node_nums, child_nums, param_attr=None, dtype='int32' ) [source]
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- Tdm Child
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- According to the input node_id on the given tree, return the corresponding child node_id and
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whether child is a leaf node by leaf_mask value.
Given: tree[[0], [1, 2], [3, 4], [5, 6]] # A binary tree with seven nodes x = [[2], [3]] node_nums = 7 child_nums = 2 we get: child = [[5, 6], [0, 0]] leaf_mask = [[1, 1], [0, 0]]- Parameters
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x (Variable) – Variable contained the node_id information, dtype support int32/int64.
node_nums (int) – Number of total nodes.
child_nums (int) – Maximum number of child nodes per node.
param_attr (ParamAttr) – To specify the tdm-tree-info parameter property. Default: None, which means the default weight parameter property is used. See usage for details in: ref: api_fluid_ParamAttr, should has shape(node_nums, 3 + child_nums), dtype support int32/int64. The dimension[1] of tdm-tree-info contains the following: 1. Item_id(int, shape(1)), if node is a leaf node, give its item_id corresponding to node_id, else give 0. 2. Layer_id(int, shape(1)), indicates which layer the node is on. 3. Parent_id(int, shape(1)), node’s parent node. 4. Child_id(int, shape(child_nums)), all child node’s node_id of this node should be given. If the number of child nodes is insufficient, padding 0 until child nums equal to child_nums
dtype (str) – The data type of output child and leaf_mask, support int32/int64.
- Returns
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- A tuple including input node’s child(Variable) and leaf_mask(Variable).
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If child is a leaf node, leaf_mask equal ot 1, otherwise equal to 0.
- Return type
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tuple
Examples
import paddle.fluid as fluid import numpy as np x = fluid.data(name=”x”, shape=[None, 1], dtype=”int32”, lod_level=1) tree_info = [[0,0,0,1,2],
[0,1,0,3,4],[0,1,0,5,6], [0,2,1,0,0],[1,2,1,0,0],[2,2,2,0,0],[3,2,2,0,0]]
tree_info_np = np.array(tree_info) tree_info_np = np.reshape(tree_info_np, (7,5)) node_nums = 7 child_nums = 2 child, leaf_mask = fluid.contrib.layers.tdm_child(x, node_nums, child_nums,
- param_attr=fluid.ParamAttr(
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- initializer=fluid.initializer.NumpyArrayInitializer(
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tree_info_np)))
place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) xx = np.array([[2],[3]]).reshape((2,1)).astype(“int32”) child_res, leaf_mask_res = exe.run(feed={“x”:xx}, fetch_list=[child, leaf_mask])