layers

conv_bn_relu

class layers.conv_bn_relu.ConvBNReLU(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)

Bases: torch.nn.modules.module.Module

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

gcn

class layers.gcn.GraphConvolution(in_features, out_features, bias=True)

Bases: torch.nn.modules.module.Module

Simple GCN layer, similar to https://arxiv.org/abs/1609.02907

forward(x, adj)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

reset_parameters()

training: bool