def __init__(self, input_dims, output_dim, gate_layer=None, combination_type='concat',
dropout=0., bias=False, act=tf.nn.relu, name=None, **kwargs):
super(ConcatAggregator, self).__init__(**kwargs)
assert combination_type in ('concat', 'add', 'elem-mul'), \
'Unknown combination type: ' + combination_type
self.combination_type = combination_type
self.dropout = dropout
self.bias = bias
self.act = act
self.input_dims = input_dims
self.output_dim = 2 * output_dim if combination_type == 'concat' else output_dim
if name is not None:
name = '/' + name
else:
name = ''
if gate_layer is None:
self.gate_layer = Gate(input_dims, name=(self.name + name + '_gate'))
else:
self.gate_layer = gate_layer
with tf.variable_scope(self.name + name + '_vars'):
self.vars['self_weights'] = glorot((input_dims[0], output_dim),
name='self_weights')
self.vars['neigh_weights'] = glorot((input_dims[1] + input_dims[2], output_dim),
name='neigh_weights')
if self.bias:
self.vars['bias'] = zeros([self.output_dim], name='bias')
if self.logging:
self._log_vars()
def __init__(self, input_dims, output_dim, gate_layer=None,
dropout=0., bias=False, act=tf.nn.relu, name=None, **kwargs):
super(RandWalkAggregator, self).__init__(**kwargs)
self.dropout = dropout
self.bias = bias
self.act = act
self.input_dims = input_dims
self.output_dim = output_dim
if name is not None:
name = '/' + name
else:
name = ''
if gate_layer is None:
self.gate_layer = Gate(input_dims, name=(self.name + name + '_gate'))
else:
self.gate_layer = gate_layer
with tf.variable_scope(self.name + name + '_vars'):
self.vars['self_weights'] = glorot((input_dims[0], input_dims[2]),
name='self_weights')
self.vars['neigh_weights'] = glorot((input_dims[2], output_dim),
name='neigh_weights')
self.vars['link_weights'] = glorot((input_dims[3], input_dims[2]),
name='link_weights')
if self.bias:
self.vars['bias'] = zeros([self.output_dim], name='bias')
if self.logging:
self._log_vars()
def __init__(self, input_dim, output_dim, dropout=0.,
act=None, placeholders=None, bias=True, featureless=False,
sparse_inputs=False, **kwargs):
super(Dense, self).__init__(**kwargs)
self.dropout = dropout
self.act = act
self.featureless = featureless
self.bias = bias
self.input_dim = input_dim
self.output_dim = output_dim
# helper variable for sparse dropout
self.sparse_inputs = sparse_inputs
if sparse_inputs:
self.num_features_nonzero = placeholders['num_features_nonzero']
with tf.variable_scope(self.name + '_vars'):
self.vars['weights'] = tf.get_variable('weights', shape=(input_dim, output_dim),
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer(),
regularizer=tf.contrib.layers.l2_regularizer(FLAGS.weight_decay))
if self.bias:
self.vars['bias'] = zeros([output_dim], name='bias')
if self.logging:
self._log_vars()
def __init__(self, dims, bias=False, act=tf.nn.sigmoid, name=None, **kwargs):
super(Gate, self).__init__(**kwargs)
self.bias = bias
self.act = act
if name is not None:
name = '/' + name
else:
name = ''
with tf.variable_scope(self.name + name + '_vars'):
self.vars['self_weights'] = glorot((dims[0],), name='self_weights')
self.vars['neigh_weights'] = glorot((dims[1],), name='neigh_weights')
self.vars['link_weights'] = glorot((dims[2],), name='link_weights')
if self.bias:
self.vars['bias'] = zeros(1, name='bias')
if self.logging:
self._log_vars()
self.input_dims = dims
self.output_dim = None