{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computer vision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cnn_learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cut': -2,\n",
       " 'split': <function fastai2.vision.learner._resnet_split(m)>,\n",
       " 'stats': ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_meta[resnet50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): AdaptiveConcatPool2d(\n",
       "    (ap): AdaptiveAvgPool2d(output_size=1)\n",
       "    (mp): AdaptiveMaxPool2d(output_size=1)\n",
       "  )\n",
       "  (1): full: False\n",
       "  (2): BatchNorm1d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (3): Dropout(p=0.25, inplace=False)\n",
       "  (4): Linear(in_features=20, out_features=512, bias=False)\n",
       "  (5): ReLU(inplace=True)\n",
       "  (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (7): Dropout(p=0.5, inplace=False)\n",
       "  (8): Linear(in_features=512, out_features=2, bias=False)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "create_head(20,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### unet_learner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Natural language processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabular"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrapping up architectures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
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 "nbformat": 4,
 "nbformat_minor": 2
}