wehub-resource-sync
110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
"""Tensorflow Module for DenseChebConv"""
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# pylint: disable= no-member, arguments-differ, invalid-name
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras import layers
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class DenseChebConv(layers.Layer):
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r"""Chebyshev Spectral Graph Convolution layer from `Convolutional
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Neural Networks on Graphs with Fast Localized Spectral Filtering
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<https://arxiv.org/pdf/1606.09375.pdf>`__
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We recommend to use this module when applying ChebConv on dense graphs.
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Parameters
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----------
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in_feats: int
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Dimension of input features :math:`h_i^{(l)}`.
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out_feats: int
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Dimension of output features :math:`h_i^{(l+1)}`.
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k : int
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Chebyshev filter size.
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activation : function, optional
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Activation function, default is ReLu.
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bias : bool, optional
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If True, adds a learnable bias to the output. Default: ``True``.
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See also
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--------
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`ChebConv <https://docs.dgl.ai/api/python/nn.tensorflow.html#chebconv>`__
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"""
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def __init__(self, in_feats, out_feats, k, bias=True):
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super(DenseChebConv, self).__init__()
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self._in_feats = in_feats
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self._out_feats = out_feats
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self._k = k
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# keras initializer assume last two dims as fan_in and fan_out
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xinit = tf.keras.initializers.glorot_normal()
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self.W = tf.Variable(
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initial_value=xinit(
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shape=(k, in_feats, out_feats), dtype="float32"
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),
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trainable=True,
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)
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if bias:
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zeroinit = tf.keras.initializers.zeros()
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self.bias = tf.Variable(
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initial_value=zeroinit(shape=(out_feats), dtype="float32"),
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trainable=True,
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)
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else:
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self.bias = None
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def call(self, adj, feat, lambda_max=None):
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r"""Compute (Dense) Chebyshev Spectral Graph Convolution layer.
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Parameters
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----------
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adj : tf.Tensor
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The adjacency matrix of the graph to apply Graph Convolution on,
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should be of shape :math:`(N, N)`, where a row represents the destination
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and a column represents the source.
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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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lambda_max : float or None, optional
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A float value indicates the largest eigenvalue of given graph.
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Default: None.
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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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"""
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A = adj
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num_nodes = A.shape[0]
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in_degree = 1 / tf.sqrt(
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tf.clip_by_value(
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tf.reduce_sum(A, 1), clip_value_min=1, clip_value_max=np.inf
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)
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)
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D_invsqrt = tf.linalg.diag(in_degree)
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I = tf.eye(num_nodes)
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L = I - D_invsqrt @ A @ D_invsqrt
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if lambda_max is None:
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lambda_ = tf.linalg.eig(L)[0][:, 0]
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lambda_max = tf.reduce_max(lambda_)
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L_hat = 2 * L / lambda_max - I
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Z = [tf.eye(num_nodes)]
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for i in range(1, self._k):
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if i == 1:
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Z.append(L_hat)
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else:
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Z.append(2 * L_hat @ Z[-1] - Z[-2])
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Zs = tf.stack(Z, 0) # (k, n, n)
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Zh = Zs @ tf.expand_dims(feat, axis=0) @ self.W
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Zh = tf.reduce_sum(Zh, 0)
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if self.bias is not None:
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Zh = Zh + self.bias
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return Zh
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