def __init__(self, adj_mx, **model_kwargs):
nn.Module.__init__(self)
Seq2SeqAttrs.__init__(self, adj_mx, **model_kwargs)
self.input_dim = int(model_kwargs.get('input_dim', 1))
self.seq_len = int(model_kwargs.get('seq_len')) # for the encoder
self.dcgru_layers = nn.ModuleList(
[DCGRUCell(self.rnn_units, adj_mx, self.max_diffusion_step, self.num_nodes,
filter_type=self.filter_type) for _ in range(self.num_rnn_layers)])
def __init__(self, adj_mx, **model_kwargs):
# super().__init__(is_training, adj_mx, **model_kwargs)
nn.Module.__init__(self)
Seq2SeqAttrs.__init__(self, adj_mx, **model_kwargs)
self.output_dim = int(model_kwargs.get('output_dim', 1))
self.horizon = int(model_kwargs.get('horizon', 1)) # for the decoder
self.projection_layer = nn.Linear(self.rnn_units, self.output_dim)
self.dcgru_layers = nn.ModuleList(
[DCGRUCell(self.rnn_units, adj_mx, self.max_diffusion_step, self.num_nodes,
filter_type=self.filter_type) for _ in range(self.num_rnn_layers)])