Source code for dgl.nn.tensorflow.conv.ginconv

"""Tensorflow Module for Graph Isomorphism Network layer"""
# pylint: disable= no-member, arguments-differ, invalid-name
import tensorflow as tf
from tensorflow.keras import layers

from .... import function as fn
from ....utils import expand_as_pair


[docs]class GINConv(layers.Layer): r""" Description ----------- Graph Isomorphism Network layer from paper `How Powerful are Graph Neural Networks? <https://arxiv.org/pdf/1810.00826.pdf>`__. .. math:: h_i^{(l+1)} = f_\Theta \left((1 + \epsilon) h_i^{l} + \mathrm{aggregate}\left(\left\{h_j^{l}, j\in\mathcal{N}(i) \right\}\right)\right) Parameters ---------- apply_func : callable activation function/layer or None If not None, apply this function to the updated node feature, the :math:`f_\Theta` in the formula. aggregator_type : str Aggregator type to use (``sum``, ``max`` or ``mean``). init_eps : float, optional Initial :math:`\epsilon` value, default: ``0``. learn_eps : bool, optional If True, :math:`\epsilon` will be a learnable parameter. Default: ``False``. Example ------- >>> import dgl >>> import numpy as np >>> import tensorflow as tf >>> from dgl.nn import GINConv >>> >>> with tf.device("CPU:0"): >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> feat = tf.ones((6, 10)) >>> lin = tf.keras.layers.Dense(10) >>> conv = GINConv(lin, 'max') >>> res = conv(g, feat) >>> res <tf.Tensor: shape=(6, 10), dtype=float32, numpy= array([[-0.1090256 , 1.9050574 , -0.30704725, -1.995831 , -0.36399186, 1.10414 , 2.4885745 , -0.35387516, 1.3568261 , 1.7267858 ], [-0.1090256 , 1.9050574 , -0.30704725, -1.995831 , -0.36399186, 1.10414 , 2.4885745 , -0.35387516, 1.3568261 , 1.7267858 ], [-0.1090256 , 1.9050574 , -0.30704725, -1.995831 , -0.36399186, 1.10414 , 2.4885745 , -0.35387516, 1.3568261 , 1.7267858 ], [-0.1090256 , 1.9050574 , -0.30704725, -1.995831 , -0.36399186, 1.10414 , 2.4885745 , -0.35387516, 1.3568261 , 1.7267858 ], [-0.1090256 , 1.9050574 , -0.30704725, -1.995831 , -0.36399186, 1.10414 , 2.4885745 , -0.35387516, 1.3568261 , 1.7267858 ], [-0.0545128 , 0.9525287 , -0.15352362, -0.9979155 , -0.18199593, 0.55207 , 1.2442873 , -0.17693758, 0.67841303, 0.8633929 ]], dtype=float32)> """ def __init__(self, apply_func, aggregator_type, init_eps=0, learn_eps=False): super(GINConv, self).__init__() self.apply_func = apply_func if aggregator_type == 'sum': self._reducer = fn.sum elif aggregator_type == 'max': self._reducer = fn.max elif aggregator_type == 'mean': self._reducer = fn.mean else: raise KeyError('Aggregator type {} not recognized.'.format(aggregator_type)) # to specify whether eps is trainable or not. self.eps = tf.Variable(initial_value=[init_eps], dtype=tf.float32, trainable=learn_eps) def call(self, graph, feat): r""" Description ----------- Compute Graph Isomorphism Network layer. Parameters ---------- graph : DGLGraph The graph. feat : tf.Tensor or pair of tf.Tensor If a tf.Tensor is given, the input feature of shape :math:`(N, D_{in})` where :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes. If a pair of tf.Tensor is given, the pair must contain two tensors of shape :math:`(N_{in}, D_{in})` and :math:`(N_{out}, D_{in})`. If ``apply_func`` is not None, :math:`D_{in}` should fit the input dimensionality requirement of ``apply_func``. Returns ------- tf.Tensor The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is the output dimensionality of ``apply_func``. If ``apply_func`` is None, :math:`D_{out}` should be the same as input dimensionality. """ with graph.local_scope(): feat_src, feat_dst = expand_as_pair(feat, graph) graph.srcdata['h'] = feat_src graph.update_all(fn.copy_u('h', 'm'), self._reducer('m', 'neigh')) rst = (1 + self.eps) * feat_dst + graph.dstdata['neigh'] if self.apply_func is not None: rst = self.apply_func(rst) return rst