def build(self,
hiddens=[32],
activations=['relu'],
dropout=0.5,
l2_norm=0.,
lr=0.01,
use_bias=False):
# if self.kind == "P":
# raise RuntimeError(f"Currently {self.name} only support for tensorflow backend.")
if self.kind == "T":
with tf.device(self.device):
self.model = tfGCN(self.graph.n_attrs,
self.graph.n_classes,
hiddens=hiddens,
activations=activations,
dropout=dropout,
l2_norm=l2_norm,
lr=lr,
use_bias=use_bias,
experimental_run_tf_function=False)
else:
self.model = pyGCN(self.graph.n_attrs,
self.graph.n_classes,
hiddens=hiddens,
activations=activations,
dropout=dropout,
l2_norm=l2_norm,
lr=lr,
use_bias=use_bias).to(self.device)
def build(self,
hiddens=[16],
activations=['relu'],
dropout=0.5,
l2_norm=5e-4,
lr=0.01,
use_bias=False):
if self.kind == "T":
with tf.device(self.device):
self.model = tfGCN(self.graph.n_attrs,
self.graph.n_classes,
hiddens=hiddens,
activations=activations,
dropout=dropout,
l2_norm=l2_norm,
lr=lr,
use_bias=use_bias)
else:
self.model = pyGCN(self.graph.n_attrs,
self.graph.n_classes,
hiddens=hiddens,
activations=activations,
dropout=dropout,
l2_norm=l2_norm,
lr=lr,
use_bias=use_bias).to(self.device)
def build(self,
hiddens=[16],
activations=['relu'],
dropout=0.5,
lr=0.01,
l2_norm=5e-4,
use_bias=False,
p1=1.,
p2=1.,
n_power_iterations=1,
epsilon=0.03,
xi=1e-6):
if self.kind == "T":
with tf.device(self.device):
self.model = tfGCN(self.graph.n_attrs,
self.graph.n_classes,
hiddens=hiddens,
activations=activations,
dropout=dropout,
l2_norm=l2_norm,
lr=lr,
use_bias=use_bias)
self.index_all = tf.range(self.graph.n_nodes, dtype=self.intx)
else:
raise NotImplementedError
self.p1 = p1 # Alpha
self.p2 = p2 # Beta
self.xi = xi # Small constant for finite difference
# Norm length for (virtual) adversarial training
self.epsilon = epsilon
self.n_power_iterations = n_power_iterations # Number of power iterations
