Closed Kian-Soon closed 2 years ago
@Kian-Soon yes you can predict on multiple time-steps.
The output shape of the Attention layer is a 2D tensor with shape (batch_size, units)
.
From there, you can use RepeatVector(n=5)
if you want to output 5 time-steps.
On top of that, use a TimeDistributed(Dense(1))
if each time-step is of output_dim=1
Hi,
Can this be used for predicting output with multiple time-steps? If no, how can the code be changed to accommodate this? Thanks.