TreeConv

class paddle.fluid.dygraph.TreeConv(feature_size, output_size, num_filters=1, max_depth=2, act='tanh', param_attr=None, bias_attr=None, name=None, dtype='float32')[source]

This interface is used to construct a callable object of the TreeConv class. For more details, refer to code examples. Tree-Based Convolution is a kind of convolution based on tree structure. Tree-Based Convolution is a part of Tree-Based Convolution Neural Network(TBCNN), which is used to classify tree structures, such as Abstract Syntax Tree. Tree-Based Convolution proposed a kind of data structure called continuous binary tree, which regards multiway tree as binary tree. The paper of Tree-Based Convolution Operator is here: tree-based convolution .

Parameters
  • feature_size (int) – last dimension of nodes_vector.

  • output_size (int) – output feature width.

  • num_filters (int, optional) – number of filters, Default: 1.

  • max_depth (int, optional) – max depth of filters, Default: 2.

  • act (str, optional) – activation function, Default: tanh.

  • param_attr (ParamAttr, optional) – the parameter attribute for the filters, Default: None.

  • bias_attr (ParamAttr, optional) – the parameter attribute for the bias of this layer, Default: None.

  • name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name .

  • dtype (str, optional) – Data type, it can be “float32” or “float64”. Default: “float32”.

Attribute:

weight (Parameter): the learnable weights of filters of this layer.

bias (Parameter or None): the learnable bias of this layer.

Returns

None

Examples

import paddle.fluid as fluid
import numpy

with fluid.dygraph.guard():
    nodes_vector = numpy.random.random((1, 10, 5)).astype('float32')
    edge_set = numpy.random.random((1, 9, 2)).astype('int32')
    treeConv = fluid.dygraph.nn.TreeConv(
      feature_size=5, output_size=6, num_filters=1, max_depth=2)
    ret = treeConv(fluid.dygraph.base.to_variable(nodes_vector), fluid.dygraph.base.to_variable(edge_set))
forward(nodes_vector, edge_set)

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments