The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Migrating from Catalyst

Intro

PyTorch framework for Deep Learning research and development. It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write another regular train loop. Break the cycle - use the Catalyst!

Catalyst with fastai

Specify loaders from catalyst dict:

library(fastai)
library(magrittr)

loaders = loaders()

data = Data_Loaders(loaders['train'], loaders['valid'])$cuda()

nn = nn()
model = nn$Sequential() +
  nn$Flatten() +
  nn$Linear(28L * 28L, 10L)

Output:

Sequential(
  (0): Flatten()
  (1): Linear(in_features=784, out_features=10, bias=True)
)

Fit

metrics = list(accuracy,top_k_accuracy)
learn = Learner(data, model, loss_func = nn$functional$cross_entropy, opt_func = Adam,
                metrics = metrics)

learn %>% fit_one_cycle(1, 0.02)

epoch     train_loss  valid_loss  accuracy  top_k_accuracy  time    
0         0.269411    0.336529    0.910200  0.993700        00:08

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.