def __init__(self,
output_size,
activation='relu',
attn_activation='leakyrelu',
use_bias=True,
sparse=False,
initializers=None,
partitioners=None,
regularizers=None,
custom_getter=None,
name="graph_attn"):
super(GraphAttentionLayer, self).__init__(output_size,
use_bias=use_bias,
initializers=initializers,
partitioners=partitioners,
regularizers=regularizers,
custom_getter=custom_getter,
name=name)
self._sparse = sparse
self._activ = tfutils.get_tf_activ(activation)
self._attn_activ = tfutils.get_tf_activ(attn_activation)
self.weight_keys = {("w", output_size), ("u", output_size), ("f1", 1),
("f2", 1)}
self.bias_keys = set()
self.weights = {x[0]: None for x in self.weight_keys}
if use_bias:
self.bias_keys = {("b", output_size), ("c", output_size),
("d1", 1), ("d2", 1)}
for x in self.bias_keys:
self.weights[x[0]] = None
self.possible_keys = self.get_possible_initializer_keys(
use_bias=use_bias)
def __init__(self,
output_size,
activation='relu',
use_bias=True,
initializers=None,
partitioners=None,
regularizers=None,
custom_getter=None,
name="graph_skip"):
super().__init__(output_size,
use_bias=use_bias,
initializers=initializers,
partitioners=partitioners,
regularizers=regularizers,
custom_getter=custom_getter,
name=name)
self._activ = tfutils.get_tf_activ(activation)
self._weight = {
"w": None,
"u": None,
}
self._bias = {
"b": None,
"c": None,
}
self.possible_keys = self.get_possible_initializer_keys(
use_bias=use_bias)
def SingleLayerMLP(self, layer_lens):
return lambda: snt.nets.MLP(layer_lens,
activate_final=True,
regularizers=self.regularizers,
initializers=self.initializers,
custom_getter=self.custom_getter,
use_bias=self.use_bias,
activation=tfutils.get_tf_activ(self.arch.
activ))
def __init__(self,
output_size,
activation='relu',
use_bias=True,
initializers=None,
partitioners=None,
regularizers=None,
custom_getter=None,
name="graph_conv"):
super(GraphConvLayer, self).__init__(output_size,
use_bias=use_bias,
initializers=initializers,
partitioners=partitioners,
regularizers=regularizers,
custom_getter=custom_getter,
name=name)
self._activ = tfutils.get_tf_activ(activation)
self._w = None
self._b = None
self.possible_keys = self.get_possible_initializer_keys(
use_bias=use_bias)