Hi, is it possible to adapt the code example for A3TGCN (Attention Temporal Graph Convolutional Cell) to support a multi-variate-output? Do you know what changes I must do?
In the example, A3TGCN2() returns a tensor of size (batch_size, nodes, out_channels); after, with the use of a linear layer, the output is formed by 12 values (12 timesteps).
This is ok for predicting an output sequence of one variable, but I need to output 12 timesteps of 2 variables simultaneously. (Batch_size, nodes, features, timesteps).
The current implementation of A3TGCN supports a multi-variable output?
Thanks in advance.
# Making the model
class TemporalGNN(torch.nn.Module):
def __init__(self, node_features, periods, batch_size):
super(TemporalGNN, self).__init__()
# Attention Temporal Graph Convolutional Cell
self.tgnn = A3TGCN2(in_channels=node_features, out_channels=32, periods=periods,batch_size=batch_size) # node_features=2, periods=12
# Equals single-shot prediction
self.linear = torch.nn.Linear(32, periods)
def forward(self, x, edge_index):
"""
x = Node features for T time steps
edge_index = Graph edge indices
"""
h = self.tgnn(x, edge_index) # x [b, 207, 2, 12] returns h [b, 207, 12]
print("tgn output shape",h.shape)
h = F.relu(h)
h = self.linear(h)
return h
Hi @victorsergio,
I don't know if your thread is still. I kind of needed the same thing. To do so, I used a decoder-like linear layers after the tgnn layer. Hope this will help.
Best.
Hi, is it possible to adapt the code example for A3TGCN (Attention Temporal Graph Convolutional Cell) to support a multi-variate-output? Do you know what changes I must do?
In the example, A3TGCN2() returns a tensor of size (batch_size, nodes, out_channels); after, with the use of a linear layer, the output is formed by 12 values (12 timesteps).
This is ok for predicting an output sequence of one variable, but I need to output 12 timesteps of 2 variables simultaneously. (Batch_size, nodes, features, timesteps).
The current implementation of A3TGCN supports a multi-variable output?
Thanks in advance.