dmlc--dgl
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100 行
3.1 KiB
Python
100 行
3.1 KiB
Python
"""tf Module for Simplifying Graph Convolution layer"""
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# pylint: disable= no-member, arguments-differ, invalid-name, W0613
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import tensorflow as tf
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from tensorflow.keras import layers
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import numpy as np
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from .... import function as fn
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class SGConv(layers.Layer):
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r"""Simplifying Graph Convolution layer from paper `Simplifying Graph
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Convolutional Networks <https://arxiv.org/pdf/1902.07153.pdf>`__.
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.. math::
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H^{l+1} = (\hat{D}^{-1/2} \hat{A} \hat{D}^{-1/2})^K H^{l} \Theta^{l}
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Parameters
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----------
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in_feats : int
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Number of input features.
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out_feats : int
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Number of output features.
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k : int
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Number of hops :math:`K`. Defaults:``1``.
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cached : bool
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If True, the module would cache
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.. math::
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(\hat{D}^{-\frac{1}{2}}\hat{A}\hat{D}^{-\frac{1}{2}})^K X\Theta
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at the first forward call. This parameter should only be set to
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``True`` in Transductive Learning setting.
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bias : bool
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If True, adds a learnable bias to the output. Default: ``True``.
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norm : callable activation function/layer or None, optional
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If not None, applies normalization to the updated node features.
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"""
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def __init__(self,
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in_feats,
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out_feats,
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k=1,
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cached=False,
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bias=True,
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norm=None):
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super(SGConv, self).__init__()
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self.fc = layers.Dense(out_feats, use_bias=bias)
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self._cached = cached
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self._cached_h = None
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self._k = k
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self.norm = norm
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def call(self, graph, feat):
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r"""Compute Simplifying Graph Convolution layer.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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feat : tf.Tensor
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The input feature of shape :math:`(N, D_{in})` where :math:`D_{in}`
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is size of input feature, :math:`N` is the number of nodes.
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Returns
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-------
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tf.Tensor
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The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
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is size of output feature.
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Notes
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-----
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If ``cache`` is se to True, ``feat`` and ``graph`` should not change during
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training, or you will get wrong results.
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"""
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graph = graph.local_var()
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if self._cached_h is not None:
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feat = self._cached_h
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else:
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# compute normalization
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degs = tf.clip_by_value(tf.cast(
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graph.in_degrees(), tf.float32), clip_value_min=1, clip_value_max=np.inf)
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norm = tf.pow(degs, -0.5)
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norm = tf.expand_dims(norm, 1)
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# compute (D^-1 A^k D)^k X
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for _ in range(self._k):
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feat = feat * norm
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graph.ndata['h'] = feat
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graph.update_all(fn.copy_u('h', 'm'),
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fn.sum('m', 'h'))
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feat = graph.ndata.pop('h')
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feat = feat * norm
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if self.norm is not None:
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feat = self.norm(feat)
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# cache feature
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if self._cached:
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self._cached_h = feat
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return self.fc(feat)
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