wehub-resource-sync
112 lines
4.5 KiB
Python
112 lines
4.5 KiB
Python
"""Tensorflow Module for Graph Isomorphism Network layer"""
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# pylint: disable= no-member, arguments-differ, invalid-name
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import tensorflow as tf
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from tensorflow.keras import layers
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from .... import function as fn
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from ....utils import expand_as_pair
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class GINConv(layers.Layer):
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r"""Graph Isomorphism Network layer from `How Powerful are Graph
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Neural Networks? <https://arxiv.org/pdf/1810.00826.pdf>`__
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.. math::
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h_i^{(l+1)} = f_\Theta \left((1 + \epsilon) h_i^{l} +
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\mathrm{aggregate}\left(\left\{h_j^{l}, j\in\mathcal{N}(i)
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\right\}\right)\right)
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Parameters
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----------
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apply_func : callable activation function/layer or None
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If not None, apply this function to the updated node feature,
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the :math:`f_\Theta` in the formula.
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aggregator_type : str
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Aggregator type to use (``sum``, ``max`` or ``mean``).
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init_eps : float, optional
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Initial :math:`\epsilon` value, default: ``0``.
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learn_eps : bool, optional
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If True, :math:`\epsilon` will be a learnable parameter. Default: ``False``.
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Example
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-------
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>>> import dgl
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>>> import numpy as np
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>>> import tensorflow as tf
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>>> from dgl.nn import GINConv
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>>>
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>>> with tf.device("CPU:0"):
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>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
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>>> feat = tf.ones((6, 10))
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>>> lin = tf.keras.layers.Dense(10)
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>>> conv = GINConv(lin, 'max')
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>>> res = conv(g, feat)
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>>> res
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<tf.Tensor: shape=(6, 10), dtype=float32, numpy=
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array([[-0.1090256 , 1.9050574 , -0.30704725, -1.995831 , -0.36399186,
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1.10414 , 2.4885745 , -0.35387516, 1.3568261 , 1.7267858 ],
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[-0.1090256 , 1.9050574 , -0.30704725, -1.995831 , -0.36399186,
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1.10414 , 2.4885745 , -0.35387516, 1.3568261 , 1.7267858 ],
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[-0.1090256 , 1.9050574 , -0.30704725, -1.995831 , -0.36399186,
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1.10414 , 2.4885745 , -0.35387516, 1.3568261 , 1.7267858 ],
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[-0.1090256 , 1.9050574 , -0.30704725, -1.995831 , -0.36399186,
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1.10414 , 2.4885745 , -0.35387516, 1.3568261 , 1.7267858 ],
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[-0.1090256 , 1.9050574 , -0.30704725, -1.995831 , -0.36399186,
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1.10414 , 2.4885745 , -0.35387516, 1.3568261 , 1.7267858 ],
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[-0.0545128 , 0.9525287 , -0.15352362, -0.9979155 , -0.18199593,
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0.55207 , 1.2442873 , -0.17693758, 0.67841303, 0.8633929 ]],
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dtype=float32)>
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"""
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def __init__(
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self, apply_func, aggregator_type, init_eps=0, learn_eps=False
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):
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super(GINConv, self).__init__()
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self.apply_func = apply_func
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if aggregator_type == "sum":
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self._reducer = fn.sum
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elif aggregator_type == "max":
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self._reducer = fn.max
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elif aggregator_type == "mean":
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self._reducer = fn.mean
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else:
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raise KeyError(
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"Aggregator type {} not recognized.".format(aggregator_type)
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)
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# to specify whether eps is trainable or not.
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self.eps = tf.Variable(
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initial_value=[init_eps], dtype=tf.float32, trainable=learn_eps
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)
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def call(self, graph, feat):
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r"""Compute Graph Isomorphism Network 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 or pair of tf.Tensor
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If a tf.Tensor is given, the input feature of shape :math:`(N, D_{in})` where
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:math:`D_{in}` is size of input feature, :math:`N` is the number of nodes.
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If a pair of tf.Tensor is given, the pair must contain two tensors of shape
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:math:`(N_{in}, D_{in})` and :math:`(N_{out}, D_{in})`.
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If ``apply_func`` is not None, :math:`D_{in}` should
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fit the input dimensionality requirement of ``apply_func``.
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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
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:math:`D_{out}` is the output dimensionality of ``apply_func``.
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If ``apply_func`` is None, :math:`D_{out}` should be the same
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as input dimensionality.
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"""
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with graph.local_scope():
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feat_src, feat_dst = expand_as_pair(feat, graph)
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graph.srcdata["h"] = feat_src
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graph.update_all(fn.copy_u("h", "m"), self._reducer("m", "neigh"))
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rst = (1 + self.eps) * feat_dst + graph.dstdata["neigh"]
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if self.apply_func is not None:
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rst = self.apply_func(rst)
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return rst
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