实例介绍
【实例简介】
【实例截图】
【实例截图】
【核心代码】
from gae.layers import GraphConvolution, GraphConvolutionSparse, InnerProductDecoder #import tensorflow as tf import tensorflow.compat.v1 as tf flags =tf.compat.v1.flags #flags = tf.app.flags FLAGS = flags.FLAGS class Model(object): def __init__(self, **kwargs): allowed_kwargs = {'name', 'logging'} for kwarg in kwargs.keys(): assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' kwarg for kwarg in kwargs.keys(): assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' kwarg name = kwargs.get('name') if not name: name = self.__class__.__name__.lower() self.name = name logging = kwargs.get('logging', False) self.logging = logging self.vars = {} def _build(self): raise NotImplementedError def build(self): """ Wrapper for _build() """ with tf.variable_scope(self.name): self._build() variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name) self.vars = {var.name: var for var in variables} def fit(self): pass def predict(self): pass class GCNModelAE(Model): def __init__(self, placeholders, num_features, features_nonzero, **kwargs): super(GCNModelAE, self).__init__(**kwargs) self.inputs = placeholders['features'] self.input_dim = num_features self.features_nonzero = features_nonzero self.adj = placeholders['adj'] self.dropout = placeholders['dropout'] self.build() def _build(self): self.hidden1 = GraphConvolutionSparse(input_dim=self.input_dim, output_dim=FLAGS.hidden1, adj=self.adj, features_nonzero=self.features_nonzero, act=tf.nn.relu, dropout=self.dropout, logging=self.logging)(self.inputs) self.embeddings = GraphConvolution(input_dim=FLAGS.hidden1, output_dim=FLAGS.hidden2, adj=self.adj, act=lambda x: x, dropout=self.dropout, logging=self.logging)(self.hidden1) self.z_mean = self.embeddings self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2, act=lambda x: x, logging=self.logging)(self.embeddings) class GCNModelVAE(Model): def __init__(self, placeholders, num_features, num_nodes, features_nonzero, **kwargs): super(GCNModelVAE, self).__init__(**kwargs) self.inputs = placeholders['features'] self.input_dim = num_features self.features_nonzero = features_nonzero self.n_samples = num_nodes self.adj = placeholders['adj'] self.dropout = placeholders['dropout'] self.build() def _build(self): self.hidden1 = GraphConvolutionSparse(input_dim=self.input_dim, output_dim=FLAGS.hidden1, adj=self.adj, features_nonzero=self.features_nonzero, act=tf.nn.relu, dropout=self.dropout, logging=self.logging)(self.inputs) self.z_mean = GraphConvolution(input_dim=FLAGS.hidden1, output_dim=FLAGS.hidden2, adj=self.adj, act=lambda x: x, dropout=self.dropout, logging=self.logging)(self.hidden1) self.z_log_std = GraphConvolution(input_dim=FLAGS.hidden1, output_dim=FLAGS.hidden2, adj=self.adj, act=lambda x: x, dropout=self.dropout, logging=self.logging)(self.hidden1) self.z = self.z_mean tf.random_normal([self.n_samples, FLAGS.hidden2]) * tf.exp(self.z_log_std) self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2, act=lambda x: x, logging=self.logging)(self.z)
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