{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from fastai import *\n",
    "from fastai.vision import *\n",
    "from fastai.vision.models.wrn import wrn_22\n",
    "from fastai.docs import *\n",
    "from fastai.docs import CIFAR_PATH\n",
    "\n",
    "torch.backends.cudnn.benchmark = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "untar_data(CIFAR_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_tfms = ([pad(padding=4), crop(size=32, row_pct=(0,1), col_pct=(0,1)), flip_lr(p=0.5)], [])\n",
    "data = image_data_from_folder(CIFAR_PATH, valid='test', ds_tfms=ds_tfms, tfms=cifar_norm, bs=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 00:40\n",
      "epoch  train loss  valid loss  accuracy\n",
      "0      1.295876    1.038308    0.631300  (00:40)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn = Learner(data, wrn_22(), metrics=accuracy).to_fp16()\n",
    "learn.fit_one_cycle(30, 3e-3, wd=0.4, div_factor=10, pct_start=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With mixup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(data, wrn_22(), metrics=accuracy).to_fp16().mixup()\n",
    "learn.fit_one_cycle(24, 3e-3, wd=0.2, div_factor=10, pct_start=0.5)"
   ]
  }
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