--- title: "Migrating from Ignite" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Migrating from Ignite} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE,eval = FALSE,echo = T) ```
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## Intro Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. ## Ignite with fastai ```{r} library(fastai) library(magrittr) data = Data_Loaders(get_data_loaders(64, 128))$cuda() nn = nn() opt_func = partial(SGD, momentum=0.5) learn = Learner(data, Net(), loss_func = nn$NLLLoss(), opt_func = opt_func, metrics = accuracy) learn %>% fit_one_cycle(1, 0.01) ``` ``` epoch train_loss valid_loss accuracy time 0 1.084753 0.908347 0.826600 00:13 ```