---
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$set(echo = TRUE,eval = FALSE,echo = T)
```
## Intro
Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
## Ignite with fastai
```{r}
library(fastai)
library(magrittr)
data = Data_Loaders(get_data_loaders(64, 128))$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
```