<reponame>ketyi/dgl<gh_stars>1000+
"""TAGCN using DGL nn package

References:
- Topology Adaptive Graph Convolutional Networks
- Paper: https://arxiv.org/abs/1710.10370
"""
import mxnet as mx
from mxnet import gluon
import dgl
from dgl.nn.mxnet import TAGConv

class TAGCN(gluon.Block):
    def __init__(self,
                 g,
                 in_feats,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 dropout):
        super(TAGCN, self).__init__()
        self.g = g
        self.layers = gluon.nn.Sequential()
        # input layer
        self.layers.add(TAGConv(in_feats, n_hidden, activation=activation))
        # hidden layers
        for i in range(n_layers - 1):
            self.layers.add(TAGConv(n_hidden, n_hidden, activation=activation))
        # output layer
        self.layers.add(TAGConv(n_hidden, n_classes)) #activation=None
        self.dropout = gluon.nn.Dropout(rate=dropout)

    def forward(self, features):
        h = features
        for i, layer in enumerate(self.layers):
            if i != 0:
                h = self.dropout(h)
            h = layer(self.g, h)
        return h

"""TAGCN using DGL nn package

References:
- Topology Adaptive Graph Convolutional Networks
- Paper: https://arxiv.org/abs/1710.10370
"""
import mxnet as mx
from mxnet import gluon
import dgl
from dgl.nn.mxnet import TAGConv

class TAGCN(gluon.Block):
    def __init__(self,
                 g,
                 in_feats,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 dropout):
        super(TAGCN, self).__init__()
        self.g = g
        self.layers = gluon.nn.Sequential()
        # input layer
        self.layers.add(TAGConv(in_feats, n_hidden, activation=activation))
        # hidden layers
        for i in range(n_layers - 1):
            self.layers.add(TAGConv(n_hidden, n_hidden, activation=activation))
        # output layer
        self.layers.add(TAGConv(n_hidden, n_classes)) #activation=None
        self.dropout = gluon.nn.Dropout(rate=dropout)

    def forward(self, features):
        h = features
        for i, layer in enumerate(self.layers):
            if i != 0:
                h = self.dropout(h)
            h = layer(self.g, h)
        return h
TAGCN using DGL nn package  References: - Topology Adaptive Graph Convolutional Networks - Paper: https://arxiv.org/abs/1710.10370 # input layer # hidden layers # output layer #activation=None