add zh nbs

viv1729 · Nov 19, 2019 · 094697b · 094697b
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diff --git a/zh-nbs/00_notebook_tutorial.ipynb b/zh-nbs/00_notebook_tutorial.ipynb
diff --git a/zh-nbs/Lesson1_pets.ipynb b/zh-nbs/Lesson1_pets.ipynb
diff --git a/zh-nbs/Lesson2_download.ipynb b/zh-nbs/Lesson2_download.ipynb
diff --git a/zh-nbs/Lesson2_sgd.ipynb b/zh-nbs/Lesson2_sgd.ipynb
diff --git a/zh-nbs/Lesson3_camvid.ipynb b/zh-nbs/Lesson3_camvid.ipynb
diff --git a/zh-nbs/Lesson3_camvid_tiramisu.ipynb b/zh-nbs/Lesson3_camvid_tiramisu.ipynb
diff --git a/zh-nbs/Lesson3_head_pose.ipynb b/zh-nbs/Lesson3_head_pose.ipynb
diff --git a/zh-nbs/Lesson3_imdb.ipynb b/zh-nbs/Lesson3_imdb.ipynb
diff --git a/zh-nbs/Lesson3_planet.ipynb b/zh-nbs/Lesson3_planet.ipynb
diff --git a/zh-nbs/Lesson4_collab.ipynb b/zh-nbs/Lesson4_collab.ipynb
diff --git a/zh-nbs/Lesson4_tabular.ipynb b/zh-nbs/Lesson4_tabular.ipynb
@@ -0,0 +1,368 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical Deep Learning for Coders, v3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lesson4_tabular"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tabular models\n",
+    "# Tabular(表格)模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from fastai.tabular import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tabular data should be in a Pandas `DataFrame`.\n",
+    "\n",
+    "Tabular数据是Pandas里的`DataFrame`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "path = untar_data(URLs.ADULT_SAMPLE)\n",
+    "df = pd.read_csv(path/'adult.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dep_var = 'salary'\n",
+    "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']\n",
+    "cont_names = ['age', 'fnlwgt', 'education-num']\n",
+    "procs = [FillMissing, Categorify, Normalize]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "test = TabularList.from_df(df.iloc[800:1000].copy(), path=path, cat_names=cat_names, cont_names=cont_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = (TabularList.from_df(df, path=path, cat_names=cat_names, cont_names=cont_names, procs=procs)\n",
+    "                           .split_by_idx(list(range(800,1000)))\n",
+    "                           .label_from_df(cols=dep_var)\n",
+    "                           .add_test(test)\n",
+    "                           .databunch())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <tr>\n",
+       "    <th>workclass</th>\n",
+       "    <th>education</th>\n",
+       "    <th>marital-status</th>\n",
+       "    <th>occupation</th>\n",
+       "    <th>relationship</th>\n",
+       "    <th>race</th>\n",
+       "    <th>education-num_na</th>\n",
+       "    <th>age</th>\n",
+       "    <th>fnlwgt</th>\n",
+       "    <th>education-num</th>\n",
+       "    <th>target</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th> Private</th>\n",
+       "    <th> HS-grad</th>\n",
+       "    <th> Never-married</th>\n",
+       "    <th> Sales</th>\n",
+       "    <th> Not-in-family</th>\n",
+       "    <th> White</th>\n",
+       "    <th>False</th>\n",
+       "    <th>-1.2158</th>\n",
+       "    <th>1.1004</th>\n",
+       "    <th>-0.4224</th>\n",
+       "    <th><50k</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th> ?</th>\n",
+       "    <th> HS-grad</th>\n",
+       "    <th> Widowed</th>\n",
+       "    <th> ?</th>\n",
+       "    <th> Not-in-family</th>\n",
+       "    <th> White</th>\n",
+       "    <th>False</th>\n",
+       "    <th>1.8627</th>\n",
+       "    <th>0.0976</th>\n",
+       "    <th>-0.4224</th>\n",
+       "    <th><50k</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th> Self-emp-not-inc</th>\n",
+       "    <th> HS-grad</th>\n",
+       "    <th> Never-married</th>\n",
+       "    <th> Craft-repair</th>\n",
+       "    <th> Own-child</th>\n",
+       "    <th> Black</th>\n",
+       "    <th>False</th>\n",
+       "    <th>0.0303</th>\n",
+       "    <th>0.2092</th>\n",
+       "    <th>-0.4224</th>\n",
+       "    <th><50k</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th> Private</th>\n",
+       "    <th> HS-grad</th>\n",
+       "    <th> Married-civ-spouse</th>\n",
+       "    <th> Protective-serv</th>\n",
+       "    <th> Husband</th>\n",
+       "    <th> White</th>\n",
+       "    <th>False</th>\n",
+       "    <th>1.5695</th>\n",
+       "    <th>-0.5938</th>\n",
+       "    <th>-0.4224</th>\n",
+       "    <th><50k</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th> Private</th>\n",
+       "    <th> HS-grad</th>\n",
+       "    <th> Married-civ-spouse</th>\n",
+       "    <th> Handlers-cleaners</th>\n",
+       "    <th> Husband</th>\n",
+       "    <th> White</th>\n",
+       "    <th>False</th>\n",
+       "    <th>-0.9959</th>\n",
+       "    <th>-0.0318</th>\n",
+       "    <th>-0.4224</th>\n",
+       "    <th><50k</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th> Private</th>\n",
+       "    <th> 10th</th>\n",
+       "    <th> Married-civ-spouse</th>\n",
+       "    <th> Farming-fishing</th>\n",
+       "    <th> Wife</th>\n",
+       "    <th> White</th>\n",
+       "    <th>False</th>\n",
+       "    <th>-0.7027</th>\n",
+       "    <th>0.6071</th>\n",
+       "    <th>-1.5958</th>\n",
+       "    <th><50k</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th> Private</th>\n",
+       "    <th> HS-grad</th>\n",
+       "    <th> Married-civ-spouse</th>\n",
+       "    <th> Machine-op-inspct</th>\n",
+       "    <th> Husband</th>\n",
+       "    <th> White</th>\n",
+       "    <th>False</th>\n",
+       "    <th>0.1036</th>\n",
+       "    <th>-0.0968</th>\n",
+       "    <th>-0.4224</th>\n",
+       "    <th><50k</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th> Private</th>\n",
+       "    <th> Some-college</th>\n",
+       "    <th> Married-civ-spouse</th>\n",
+       "    <th> Exec-managerial</th>\n",
+       "    <th> Own-child</th>\n",
+       "    <th> White</th>\n",
+       "    <th>False</th>\n",
+       "    <th>-0.7760</th>\n",
+       "    <th>-0.6653</th>\n",
+       "    <th>-0.0312</th>\n",
+       "    <th>>=50k</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th> State-gov</th>\n",
+       "    <th> Some-college</th>\n",
+       "    <th> Never-married</th>\n",
+       "    <th> Tech-support</th>\n",
+       "    <th> Own-child</th>\n",
+       "    <th> White</th>\n",
+       "    <th>False</th>\n",
+       "    <th>-0.8493</th>\n",
+       "    <th>-1.4959</th>\n",
+       "    <th>-0.0312</th>\n",
+       "    <th><50k</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th> Private</th>\n",
+       "    <th> 11th</th>\n",
+       "    <th> Never-married</th>\n",
+       "    <th> Machine-op-inspct</th>\n",
+       "    <th> Not-in-family</th>\n",
+       "    <th> White</th>\n",
+       "    <th>False</th>\n",
+       "    <th>-1.0692</th>\n",
+       "    <th>-0.9516</th>\n",
+       "    <th>-1.2046</th>\n",
+       "    <th><50k</th>\n",
+       "  </tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.show_batch(rows=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "learn = tabular_learner(data, layers=[200,100], metrics=accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "Total time: 00:03 <p><table style='width:300px; margin-bottom:10px'>\n",
+       "  <tr>\n",
+       "    <th>epoch</th>\n",
+       "    <th>train_loss</th>\n",
+       "    <th>valid_loss</th>\n",
+       "    <th>accuracy</th>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <th>1</th>\n",
+       "    <th>0.354604</th>\n",
+       "    <th>0.378520</th>\n",
+       "    <th>0.820000</th>\n",
+       "  </tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn.fit(1, 1e-2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inference  预测"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "row = df.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Category >=50k, tensor(1), tensor([0.4402, 0.5598]))"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "learn.predict(row)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}