def __init__(self, mlp_layers, N, f_dict):
super(RecurrentGraphEmbeddingRDS, self).__init__(f_dict)
self.N = N
model_fn = gn.mlp_fn(mlp_layers)
self.component = 'RDS'
self.gnn1 = gn.DeepSetPlus(
gn.DS_NodeModel(self.fx, self.fu, model_fn, self.fx),
gn.DS_GlobalModel(self.fx, self.fu, model_fn, self.fu))
self.gnn2 = gn.DeepSetPlus(
gn.DS_NodeModel(self.fx, 2 * self.fu, model_fn, self.fx),
gn.DS_GlobalModel(self.fx, 2 * self.fu, model_fn, self.fu))
self.mlp = model_fn(self.fu, self.fout)
def __init__(self, mlp_layers, N, f_dict):
super(RecurrentGraphEmbeddingRDSv2, self).__init__(f_dict)
model_fn = gn.mlp_fn(mlp_layers)
self.N = N
self.component = 'RDS'
self.proj = torch.nn.Linear(self.fx, self.h)
self.gnn1 = gn.DeepSetPlus(
gn.DS_NodeModel(self.h, self.h, model_fn, self.h),
gn.DS_GlobalModel(self.h, self.h, model_fn, self.h))
self.gnn2 = gn.DeepSetPlus(
gn.DS_NodeModel(self.h, 2 * self.h, model_fn, self.h),
gn.DS_GlobalModel(self.h, 2 * self.h, model_fn, self.h))
self.mlp = model_fn(self.h, self.fout)
def __init__(self, mlp_layers, N, f_dict):
super(AlternatingDoubleRDS, self).__init__(f_dict)
model_fn = gn.mlp_fn(mlp_layers)
self.N = N
self.component = 'RDS'
# f_e, f_x, f_u, f_out = self.get_features(f_dict)
self.gnn1 = gn.DeepSetPlus(
gn.DS_NodeModel(self.fx, 2 * self.fu, model_fn, self.fx),
gn.DS_GlobalModel(self.fx, 2 * self.fu, model_fn, self.fu))
self.gnn2 = gn.DeepSetPlus(
gn.DS_NodeModel(self.fx, 2 * self.fu, model_fn, self.fx),
gn.DS_GlobalModel(self.fx, 2 * self.fu, model_fn, self.fu))
self.mlp = model_fn(2 * self.fu, self.fout)
def __init__(self, mlp_layers, N, f_dict):
super(DeepSetPlus_A, self).__init__(f_dict)
self.N = N # we allow multiple rounds
mlp_fn = gn.mlp_fn(mlp_layers)
self.deepset = gn.DeepSetPlus(
gn.DS_NodeModel(self.fx, self.fu, mlp_fn, self.fx),
gn.DS_GlobalModel_A(self.fx, self.fu, self.h, mlp_fn, self.fu))
self.mlp = mlp_fn(self.fu, self.fout)