{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple model for tabular data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "from fastai.gen_doc.nbdoc import *\n",
    "from fastai.tabular.models import TabularModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h2 id=\"TabularModel\"><code>class</code> <code>TabularModel</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/tabular/models.py#L6\" class=\"source_link\">[source]</a></h2>\n",
       "\n",
       "> <code>TabularModel</code>(`emb_szs`:`ListSizes`, `n_cont`:`int`, `out_sz`:`int`, `layers`:`Collection`\\[`int`\\], `ps`:`Collection`\\[`float`\\]=`None`, `emb_drop`:`float`=`0.0`, `y_range`:`OptRange`=`None`, `use_bn`:`bool`=`True`) :: [`Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(TabularModel, doc_string=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a model suitable for tabular data. `emb_szs` match each categorical variable size with an embedding size, `n_cont` is the number of continuous variables. The model consists of `Embedding` layers for the categorical variables, followed by a `Dropout` of `emb_drop`, and a `BatchNorm` for the continuous variables. The results are concatenated and followed by blocks of `BatchNorm`, `Dropout`, `Linear` and `ReLU` (the first block skips `BatchNorm` and `Dropout`, the last block skips the `ReLU`).\n",
    "\n",
    "The sizes of the blocks are given in [`layers`](/layers.html#layers) and the probabilities of the `Dropout` in `ps`. The last size is `out_sz`, and we add a last activation that is a sigmoid rescaled to cover `y_range` (if it's not `None`). Lastly, if `use_bn` is set to False, all `BatchNorm` layers are skipped except the one applied to the continuous variables.\n",
    "\n",
    "Generally it's easiest to just create a learner with [`get_tabular_learner`](/tabular.data.html#get_tabular_learner), which will automatically create a [`TabularModel`](/tabular.models.html#TabularModel) for you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Undocumented Methods - Methods moved below this line will intentionally be hidden"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Methods - Please document or move to the undocumented section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"TabularModel.forward\"><code>forward</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/tabular/models.py#L25\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>forward</code>(`x_cat`:`Tensor`, `x_cont`:`Tensor`) → `Tensor`\n",
       "\n",
       "Defines the computation performed at every call. Should be overridden by all subclasses.\n",
       "\n",
       ".. note::\n",
       "    Although the recipe for forward pass needs to be defined within\n",
       "    this function, one should call the :class:`Module` instance afterwards\n",
       "    instead of this since the former takes care of running the\n",
       "    registered hooks while the latter silently ignores them. "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(TabularModel.forward)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "jekyll": {
   "keywords": "fastai",
   "summary": "Model for training tabular/structured data",
   "title": "tabular.models"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}