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View Code? Open in Web Editor NEWtorch implementation of a convolutional LSTM
torch implementation of a convolutional LSTM
I've been trying to use your convlstm into a real use case and for that I'm still suffering a bit with the tuning. I wanted to properly define a learning rate first, and this can be leading me to some issues. I'm always having Inf values for the loss after a while.
This way, I wanted to use luz package to define a learning rate, which has been simply impossible, because I think the returning value of the forward method of the convlstm is a list.
Do you mind having a look and perhaps coming up with any ideas?
input_dim = 1
hidden_dims = c(64, 1)
kernel_sizes = c(3, 3)
n_layers = 2
model <- convlstm %>%
setup(
loss = nn_mse_loss(),
optimizer = torch::optim_adam,
) %>%
set_hparams(n_layers = n_layers,
input_dim = input_dim,
hidden_dims = hidden_dims,
kernel_sizes = kernel_sizes)
rates_and_losses <- model %>% lr_finder(
train_dl,
# start_lr = 1e-4,
# end_lr = 0.5,
verbose = TRUE
)
The error I get is always
Error in input$size() : attempt to apply non-function
My train_dl is a bit complex to reproduce, but you'll find the same error using your dummy dataset from the blogpost https://blogs.rstudio.com/ai/posts/2020-12-17-torch-convlstm/
Thanks a lot on advance!
Huriel Reichel
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