{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from fastai import *\n",
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dogs and cats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resnet 34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/ubuntu/.fastai/data/dogscats')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = untar_data(URLs.DOGS)\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = ImageDataBunch.from_folder(path, ds_tfms=get_transforms(), size=224).normalize(imagenet_stats)\n",
    "img,label = data.valid_ds[-1]\n",
    "img.show(title=data.classes[label])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, max=1), HTML(value='0.00% [0/1 00:00<00:00]'))), HTML(value…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 03:24\n",
      "epoch  train loss  valid loss  accuracy\n",
      "0      0.051807    0.019401    0.993000  (03:24)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn = create_cnn(data, models.resnet34, metrics=accuracy)\n",
    "learn.fit_one_cycle(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, max=6), HTML(value='0.00% [0/6 00:00<00:00]'))), HTML(value…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 06:40\n",
      "epoch  train loss  valid loss  accuracy\n",
      "0      0.045248    0.031617    0.986000  (01:05)\n",
      "1      0.031750    0.021102    0.992000  (01:06)\n",
      "2      0.016185    0.021817    0.993000  (01:06)\n",
      "3      0.015322    0.023137    0.991500  (01:07)\n",
      "4      0.009295    0.021500    0.994000  (01:07)\n",
      "5      0.008227    0.020382    0.994000  (01:07)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(6, slice(1e-5,3e-4), pct_start=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=16), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=16), HTML(value='0.00% [0/16 00:00<00:00]')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, max=8), HTML(value='0.00% [0/8 00:00<00:00]'))), HTML(value…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 00:33\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor(0.9965)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(*learn.TTA())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rn50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, max=6), HTML(value='0.00% [0/6 00:00<00:00]'))), HTML(value…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 09:24\n",
      "epoch  train loss  valid loss  accuracy\n",
      "0      0.054099    0.024692    0.992500  (01:36)\n",
      "1      0.039004    0.025797    0.991500  (01:33)\n",
      "2      0.040528    0.021088    0.992000  (01:33)\n",
      "3      0.026295    0.022694    0.993000  (01:33)\n",
      "4      0.019064    0.016771    0.994000  (01:33)\n",
      "5      0.016541    0.015919    0.995500  (01:33)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn = create_cnn(data, models.resnet50, metrics=accuracy)\n",
    "learn.fit_one_cycle(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, max=6), HTML(value='0.00% [0/6 00:00<00:00]'))), HTML(value…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 12:42\n",
      "epoch  train loss  valid loss  accuracy\n",
      "0      0.031718    0.019076    0.991000  (02:07)\n",
      "1      0.031773    0.025698    0.991000  (02:07)\n",
      "2      0.014313    0.032038    0.990500  (02:06)\n",
      "3      0.009425    0.024969    0.991500  (02:06)\n",
      "4      0.006091    0.016411    0.995500  (02:06)\n",
      "5      0.004848    0.016143    0.995500  (02:07)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(6, slice(1e-5,3e-4), pct_start=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=16), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=16), HTML(value='0.00% [0/16 00:00<00:00]')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, max=8), HTML(value='0.00% [0/8 00:00<00:00]'))), HTML(value…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 00:45\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor(0.9965)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(*learn.TTA())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}