From 1ac0cd59284cdd33ca1bd3bc3f9cbcce82cf91ad Mon Sep 17 00:00:00 2001
From: Hyo-kyun Park <gyrbsdl18@naver.com>
Date: Thu, 14 Mar 2019 10:10:31 +0900
Subject: [PATCH] clear all ouput from .ipynb

---
 demo1_prepare_data.ipynb    | 368 ++-------------------------------
 demo2_building_blocks.ipynb | 330 +++++------------------------
 demo3_using_it.ipynb        | 402 ++----------------------------------
 3 files changed, 90 insertions(+), 1010 deletions(-)

diff --git a/demo1_prepare_data.ipynb b/demo1_prepare_data.ipynb
index 30462f8..94ee3f0 100644
--- a/demo1_prepare_data.ipynb
+++ b/demo1_prepare_data.ipynb
@@ -13,183 +13,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>workclass</th>\n",
-       "      <th>fnlwgt</th>\n",
-       "      <th>education</th>\n",
-       "      <th>education_num</th>\n",
-       "      <th>marital_status</th>\n",
-       "      <th>occupation</th>\n",
-       "      <th>relationship</th>\n",
-       "      <th>race</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>capital_gain</th>\n",
-       "      <th>capital_loss</th>\n",
-       "      <th>hours_per_week</th>\n",
-       "      <th>native_country</th>\n",
-       "      <th>income_bracket</th>\n",
-       "      <th>income_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>39</td>\n",
-       "      <td>State-gov</td>\n",
-       "      <td>77516</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Adm-clerical</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>2174</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>50</td>\n",
-       "      <td>Self-emp-not-inc</td>\n",
-       "      <td>83311</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Exec-managerial</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>38</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>215646</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>9</td>\n",
-       "      <td>Divorced</td>\n",
-       "      <td>Handlers-cleaners</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>53</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>234721</td>\n",
-       "      <td>11th</td>\n",
-       "      <td>7</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Handlers-cleaners</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>338409</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Prof-specialty</td>\n",
-       "      <td>Wife</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>Cuba</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   age         workclass  fnlwgt  education  education_num  \\\n",
-       "0   39         State-gov   77516  Bachelors             13   \n",
-       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
-       "2   38           Private  215646    HS-grad              9   \n",
-       "3   53           Private  234721       11th              7   \n",
-       "4   28           Private  338409  Bachelors             13   \n",
-       "\n",
-       "       marital_status         occupation   relationship   race  gender  \\\n",
-       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
-       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
-       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
-       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
-       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
-       "\n",
-       "   capital_gain  capital_loss  hours_per_week native_country income_bracket  \\\n",
-       "0          2174             0              40  United-States          <=50K   \n",
-       "1             0             0              13  United-States          <=50K   \n",
-       "2             0             0              40  United-States          <=50K   \n",
-       "3             0             0              40  United-States          <=50K   \n",
-       "4             0             0              40           Cuba          <=50K   \n",
-       "\n",
-       "   income_label  \n",
-       "0             0  \n",
-       "1             0  \n",
-       "2             0  \n",
-       "3             0  \n",
-       "4             0  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from __future__ import print_function\n",
     "import pandas as pd\n",
@@ -218,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,25 +124,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       Bachelors-Adm-clerical\n",
-       "1    Bachelors-Exec-managerial\n",
-       "2    HS-grad-Handlers-cleaners\n",
-       "3       11th-Handlers-cleaners\n",
-       "4     Bachelors-Prof-specialty\n",
-       "Name: education_occupation, dtype: object"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_tmp['education_occupation'].head()"
    ]
@@ -339,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -393,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -450,176 +260,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train_dataset(wide=array([[46, 50,  0, ...,  0,  0,  0],\n",
-      "       [32, 45,  1, ...,  0,  0,  0],\n",
-      "       [30, 30,  0, ...,  0,  0,  0],\n",
-      "       ...,\n",
-      "       [40, 40,  0, ...,  0,  0,  0],\n",
-      "       [45, 37,  1, ...,  0,  0,  0],\n",
-      "       [40, 45,  1, ...,  0,  0,  0]]), deep=array([[ 3.        ,  1.        ,  6.        , ...,  0.        ,\n",
-      "         0.53655844,  0.77292975],\n",
-      "       [ 0.        ,  0.        ,  2.        , ...,  0.        ,\n",
-      "        -0.48456647,  0.36942139],\n",
-      "       [ 1.        ,  4.        ,  2.        , ...,  0.        ,\n",
-      "        -0.63044146, -0.84110367],\n",
-      "       ...,\n",
-      "       [ 1.        ,  0.        ,  2.        , ...,  0.        ,\n",
-      "         0.09893348, -0.03408696],\n",
-      "       [ 0.        ,  1.        ,  2.        , ...,  0.        ,\n",
-      "         0.46362095, -0.27619198],\n",
-      "       [ 0.        ,  1.        ,  2.        , ...,  0.        ,\n",
-      "         0.09893348,  0.36942139]]), labels=array([1, 0, 0, ..., 0, 0, 0]))\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(wd_dataset['train_dataset'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('education', 16, 10), ('relationship', 6, 8), ('native_country', 42, 12), ('workclass', 9, 10), ('occupation', 15, 10)]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(wd_dataset['embeddings_input'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'education': 0, 'relationship': 1, 'workclass': 2, 'occupation': 3, 'native_country': 4, 'age': 5, 'hours_per_week': 6}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(wd_dataset['deep_column_idx'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'education': {'Bachelors': 0,\n",
-       "  'HS-grad': 1,\n",
-       "  '11th': 2,\n",
-       "  'Masters': 3,\n",
-       "  '9th': 4,\n",
-       "  'Some-college': 5,\n",
-       "  'Assoc-acdm': 6,\n",
-       "  'Assoc-voc': 7,\n",
-       "  '7th-8th': 8,\n",
-       "  'Doctorate': 9,\n",
-       "  'Prof-school': 10,\n",
-       "  '5th-6th': 11,\n",
-       "  '10th': 12,\n",
-       "  '1st-4th': 13,\n",
-       "  'Preschool': 14,\n",
-       "  '12th': 15},\n",
-       " 'relationship': {'Not-in-family': 0,\n",
-       "  'Husband': 1,\n",
-       "  'Wife': 2,\n",
-       "  'Own-child': 3,\n",
-       "  'Unmarried': 4,\n",
-       "  'Other-relative': 5},\n",
-       " 'native_country': {'United-States': 0,\n",
-       "  'Cuba': 1,\n",
-       "  'Jamaica': 2,\n",
-       "  'India': 3,\n",
-       "  '?': 4,\n",
-       "  'Mexico': 5,\n",
-       "  'South': 6,\n",
-       "  'Puerto-Rico': 7,\n",
-       "  'Honduras': 8,\n",
-       "  'England': 9,\n",
-       "  'Canada': 10,\n",
-       "  'Germany': 11,\n",
-       "  'Iran': 12,\n",
-       "  'Philippines': 13,\n",
-       "  'Italy': 14,\n",
-       "  'Poland': 15,\n",
-       "  'Columbia': 16,\n",
-       "  'Cambodia': 17,\n",
-       "  'Thailand': 18,\n",
-       "  'Ecuador': 19,\n",
-       "  'Laos': 20,\n",
-       "  'Taiwan': 21,\n",
-       "  'Haiti': 22,\n",
-       "  'Portugal': 23,\n",
-       "  'Dominican-Republic': 24,\n",
-       "  'El-Salvador': 25,\n",
-       "  'France': 26,\n",
-       "  'Guatemala': 27,\n",
-       "  'China': 28,\n",
-       "  'Japan': 29,\n",
-       "  'Yugoslavia': 30,\n",
-       "  'Peru': 31,\n",
-       "  'Outlying-US(Guam-USVI-etc)': 32,\n",
-       "  'Scotland': 33,\n",
-       "  'Trinadad&Tobago': 34,\n",
-       "  'Greece': 35,\n",
-       "  'Nicaragua': 36,\n",
-       "  'Vietnam': 37,\n",
-       "  'Hong': 38,\n",
-       "  'Ireland': 39,\n",
-       "  'Hungary': 40,\n",
-       "  'Holand-Netherlands': 41},\n",
-       " 'workclass': {'State-gov': 0,\n",
-       "  'Self-emp-not-inc': 1,\n",
-       "  'Private': 2,\n",
-       "  'Federal-gov': 3,\n",
-       "  'Local-gov': 4,\n",
-       "  '?': 5,\n",
-       "  'Self-emp-inc': 6,\n",
-       "  'Without-pay': 7,\n",
-       "  'Never-worked': 8},\n",
-       " 'occupation': {'Adm-clerical': 0,\n",
-       "  'Exec-managerial': 1,\n",
-       "  'Handlers-cleaners': 2,\n",
-       "  'Prof-specialty': 3,\n",
-       "  'Other-service': 4,\n",
-       "  'Sales': 5,\n",
-       "  'Craft-repair': 6,\n",
-       "  'Transport-moving': 7,\n",
-       "  'Farming-fishing': 8,\n",
-       "  'Machine-op-inspct': 9,\n",
-       "  'Tech-support': 10,\n",
-       "  '?': 11,\n",
-       "  'Protective-serv': 12,\n",
-       "  'Armed-Forces': 13,\n",
-       "  'Priv-house-serv': 14}}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "wd_dataset['encoding_dict']"
    ]
@@ -633,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/demo2_building_blocks.ipynb b/demo2_building_blocks.ipynb
index 75315a9..eccb950 100644
--- a/demo2_building_blocks.ipynb
+++ b/demo2_building_blocks.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,38 +53,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "train_dataset(wide=array([[46, 50,  0, ...,  0,  0,  0],\n",
-       "       [32, 45,  1, ...,  0,  0,  0],\n",
-       "       [30, 30,  0, ...,  0,  0,  0],\n",
-       "       ...,\n",
-       "       [40, 40,  0, ...,  0,  0,  0],\n",
-       "       [45, 37,  1, ...,  0,  0,  0],\n",
-       "       [40, 45,  1, ...,  0,  0,  0]]), deep=array([[ 3.        ,  1.        ,  6.        , ...,  0.        ,\n",
-       "         0.53655844,  0.77292975],\n",
-       "       [ 0.        ,  0.        ,  2.        , ...,  0.        ,\n",
-       "        -0.48456647,  0.36942139],\n",
-       "       [ 1.        ,  4.        ,  2.        , ...,  0.        ,\n",
-       "        -0.63044146, -0.84110367],\n",
-       "       ...,\n",
-       "       [ 1.        ,  0.        ,  2.        , ...,  0.        ,\n",
-       "         0.09893348, -0.03408696],\n",
-       "       [ 0.        ,  1.        ,  2.        , ...,  0.        ,\n",
-       "         0.46362095, -0.27619198],\n",
-       "       [ 0.        ,  1.        ,  2.        , ...,  0.        ,\n",
-       "         0.09893348,  0.36942139]]), labels=array([1, 0, 0, ..., 0, 0, 0]))"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "wd_dataset['train_dataset']"
    ]
@@ -100,26 +71,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {
-      "image/png": {
-       "height": 500,
-       "width": 1000
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from IPython.display import Image\n",
     "PATH = \"./image04.png\"\n",
@@ -154,17 +108,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Linear(in_features=798, out_features=1, bias=True)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
@@ -185,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,19 +181,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wide(\n",
-      "  (linear): Linear(in_features=798, out_features=1, bias=True)\n",
-      ")\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(wide_model)"
    ]
@@ -263,46 +199,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(34189, 1)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "wd_dataset['train_dataset'].labels.reshape(-1, 1).shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1, 46, 50, ...,  0,  0,  0],\n",
-       "       [ 0, 32, 45, ...,  0,  0,  0],\n",
-       "       [ 0, 30, 30, ...,  0,  0,  0],\n",
-       "       ...,\n",
-       "       [ 0, 40, 40, ...,  0,  0,  0],\n",
-       "       [ 0, 45, 37, ...,  0,  0,  0],\n",
-       "       [ 0, 40, 45, ...,  0,  0,  0]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train_dataset = np.hstack([wd_dataset['train_dataset'].labels.reshape(-1, 1), wd_dataset['train_dataset'].wide])\n",
     "train_dataset"
@@ -319,36 +227,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "26682.0 34189\n",
-      "Epoch 1 of 10, Loss: 0.227, accuracy: 0.7804\n",
-      "28058.0 34189\n",
-      "Epoch 2 of 10, Loss: 0.374, accuracy: 0.8207\n",
-      "28261.0 34189\n",
-      "Epoch 3 of 10, Loss: 0.473, accuracy: 0.8266\n",
-      "28373.0 34189\n",
-      "Epoch 4 of 10, Loss: 0.17, accuracy: 0.8299\n",
-      "28475.0 34189\n",
-      "Epoch 5 of 10, Loss: 0.344, accuracy: 0.8329\n",
-      "28499.0 34189\n",
-      "Epoch 6 of 10, Loss: 0.573, accuracy: 0.8336\n",
-      "28530.0 34189\n",
-      "Epoch 7 of 10, Loss: 0.328, accuracy: 0.8345\n",
-      "28544.0 34189\n",
-      "Epoch 8 of 10, Loss: 0.301, accuracy: 0.8349\n",
-      "28568.0 34189\n",
-      "Epoch 9 of 10, Loss: 0.21, accuracy: 0.8356\n",
-      "28587.0 34189\n",
-      "Epoch 10 of 10, Loss: 0.248, accuracy: 0.8361\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "optimizer = torch.optim.Adam(wide_model.parameters())\n",
     "batch_size = 64\n",
@@ -395,18 +276,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('education', 16, 10), ('occupation', 15, 10), ('relationship', 6, 8), ('native_country', 42, 10), ('workclass', 9, 10)]\n",
-      "{'education': 0, 'relationship': 1, 'workclass': 2, 'occupation': 3, 'native_country': 4, 'age': 5, 'hours_per_week': 6}\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(wd_dataset['embeddings_input'])\n",
     "print(wd_dataset['deep_column_idx'])"
@@ -423,17 +295,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Embedding(16, 10)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "col_name, unique_vals, n_emb = wd_dataset['embeddings_input'][0]\n",
     "emb_layer = nn.Embedding(unique_vals, n_emb)\n",
@@ -449,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -518,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -530,26 +394,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Deep(\n",
-      "  (emb_layer_education): Embedding(16, 10)\n",
-      "  (emb_layer_occupation): Embedding(15, 10)\n",
-      "  (emb_layer_relationship): Embedding(6, 8)\n",
-      "  (emb_layer_native_country): Embedding(42, 10)\n",
-      "  (emb_layer_workclass): Embedding(9, 10)\n",
-      "  (linear_1): Linear(in_features=50, out_features=100, bias=True)\n",
-      "  (linear_2): Linear(in_features=100, out_features=50, bias=True)\n",
-      "  (output): Linear(in_features=50, out_features=1, bias=True)\n",
-      ")\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(deep_model)"
    ]
@@ -570,32 +417,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.        ,  3.        ,  1.        , ...,  0.        ,\n",
-       "         0.53655844,  0.77292975],\n",
-       "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
-       "        -0.48456647,  0.36942139],\n",
-       "       [ 0.        ,  1.        ,  4.        , ...,  0.        ,\n",
-       "        -0.63044146, -0.84110367],\n",
-       "       ...,\n",
-       "       [ 0.        ,  1.        ,  0.        , ...,  0.        ,\n",
-       "         0.09893348, -0.03408696],\n",
-       "       [ 0.        ,  0.        ,  1.        , ...,  0.        ,\n",
-       "         0.46362095, -0.27619198],\n",
-       "       [ 0.        ,  0.        ,  1.        , ...,  0.        ,\n",
-       "         0.09893348,  0.36942139]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train_dataset = np.hstack([wd_dataset['train_dataset'].labels.reshape(-1, 1), wd_dataset['train_dataset'].deep])\n",
     "train_dataset"
@@ -603,26 +427,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1 of 10, Loss: 0.433, accuracy: 0.8252\n",
-      "Epoch 2 of 10, Loss: 0.487, accuracy: 0.8395\n",
-      "Epoch 3 of 10, Loss: 0.235, accuracy: 0.8427\n",
-      "Epoch 4 of 10, Loss: 0.706, accuracy: 0.8426\n",
-      "Epoch 5 of 10, Loss: 0.425, accuracy: 0.8443\n",
-      "Epoch 6 of 10, Loss: 0.495, accuracy: 0.8456\n",
-      "Epoch 7 of 10, Loss: 0.291, accuracy: 0.8463\n",
-      "Epoch 8 of 10, Loss: 0.181, accuracy: 0.8455\n",
-      "Epoch 9 of 10, Loss: 0.362, accuracy: 0.8474\n",
-      "Epoch 10 of 10, Loss: 0.158, accuracy: 0.8468\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "optimizer = torch.optim.Adam(deep_model.parameters())\n",
     "batch_size = 64\n",
@@ -675,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -731,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -740,29 +547,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "WideDeep(\n",
-       "  (emb_layer_education): Embedding(16, 10)\n",
-       "  (emb_layer_occupation): Embedding(15, 10)\n",
-       "  (emb_layer_relationship): Embedding(6, 8)\n",
-       "  (emb_layer_native_country): Embedding(42, 10)\n",
-       "  (emb_layer_workclass): Embedding(9, 10)\n",
-       "  (linear_1): Linear(in_features=50, out_features=100, bias=True)\n",
-       "  (linear_2): Linear(in_features=100, out_features=50, bias=True)\n",
-       "  (output): Linear(in_features=848, out_features=1, bias=True)\n",
-       ")"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "wide_deep_model"
    ]
@@ -778,7 +565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -823,26 +610,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1 of 10, Loss: 0.144, accuracy: 0.8245\n",
-      "Epoch 2 of 10, Loss: 0.316, accuracy: 0.8384\n",
-      "Epoch 3 of 10, Loss: 0.262, accuracy: 0.8416\n",
-      "Epoch 4 of 10, Loss: 0.219, accuracy: 0.8421\n",
-      "Epoch 5 of 10, Loss: 0.311, accuracy: 0.8433\n",
-      "Epoch 6 of 10, Loss: 0.303, accuracy: 0.8439\n",
-      "Epoch 7 of 10, Loss: 0.478, accuracy: 0.844\n",
-      "Epoch 8 of 10, Loss: 0.366, accuracy: 0.8451\n",
-      "Epoch 9 of 10, Loss: 0.356, accuracy: 0.8477\n",
-      "Epoch 10 of 10, Loss: 0.544, accuracy: 0.8459\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "optimizer = torch.optim.Adam(wide_deep_model.parameters())\n",
     "\n",
diff --git a/demo3_using_it.ipynb b/demo3_using_it.ipynb
index bed5166..2918b57 100644
--- a/demo3_using_it.ipynb
+++ b/demo3_using_it.ipynb
@@ -22,183 +22,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>workclass</th>\n",
-       "      <th>fnlwgt</th>\n",
-       "      <th>education</th>\n",
-       "      <th>education_num</th>\n",
-       "      <th>marital_status</th>\n",
-       "      <th>occupation</th>\n",
-       "      <th>relationship</th>\n",
-       "      <th>race</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>capital_gain</th>\n",
-       "      <th>capital_loss</th>\n",
-       "      <th>hours_per_week</th>\n",
-       "      <th>native_country</th>\n",
-       "      <th>income_bracket</th>\n",
-       "      <th>income_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>39</td>\n",
-       "      <td>State-gov</td>\n",
-       "      <td>77516</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Adm-clerical</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>2174</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>50</td>\n",
-       "      <td>Self-emp-not-inc</td>\n",
-       "      <td>83311</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Exec-managerial</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>38</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>215646</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>9</td>\n",
-       "      <td>Divorced</td>\n",
-       "      <td>Handlers-cleaners</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>53</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>234721</td>\n",
-       "      <td>11th</td>\n",
-       "      <td>7</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Handlers-cleaners</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>338409</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Prof-specialty</td>\n",
-       "      <td>Wife</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>Cuba</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   age         workclass  fnlwgt  education  education_num  \\\n",
-       "0   39         State-gov   77516  Bachelors             13   \n",
-       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
-       "2   38           Private  215646    HS-grad              9   \n",
-       "3   53           Private  234721       11th              7   \n",
-       "4   28           Private  338409  Bachelors             13   \n",
-       "\n",
-       "       marital_status         occupation   relationship   race  gender  \\\n",
-       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
-       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
-       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
-       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
-       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
-       "\n",
-       "   capital_gain  capital_loss  hours_per_week native_country income_bracket  \\\n",
-       "0          2174             0              40  United-States          <=50K   \n",
-       "1             0             0              13  United-States          <=50K   \n",
-       "2             0             0              40  United-States          <=50K   \n",
-       "3             0             0              40  United-States          <=50K   \n",
-       "4             0             0              40           Cuba          <=50K   \n",
-       "\n",
-       "   income_label  \n",
-       "0             0  \n",
-       "1             0  \n",
-       "2             0  \n",
-       "3             0  \n",
-       "4             0  "
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from __future__ import print_function\n",
     "import pandas as pd\n",
@@ -225,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,26 +127,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WideDeep(\n",
-      "  (emb_layer_native_country): Embedding(42, 10)\n",
-      "  (emb_layer_relationship): Embedding(6, 8)\n",
-      "  (emb_layer_occupation): Embedding(15, 10)\n",
-      "  (emb_layer_education): Embedding(16, 10)\n",
-      "  (emb_layer_workclass): Embedding(9, 10)\n",
-      "  (linear_1): Linear(in_features=50, out_features=100, bias=True)\n",
-      "  (linear_2): Linear(in_features=100, out_features=50, bias=True)\n",
-      "  (output): Linear(in_features=848, out_features=1, bias=True)\n",
-      ")\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(model)"
    ]
@@ -334,27 +143,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1 of 10, Loss: 0.136, accuracy: 0.8246\n",
-      "Epoch 2 of 10, Loss: 0.106, accuracy: 0.8392\n",
-      "Epoch 3 of 10, Loss: 0.513, accuracy: 0.8421\n",
-      "Epoch 4 of 10, Loss: 0.345, accuracy: 0.8414\n",
-      "Epoch 5 of 10, Loss: 0.29, accuracy: 0.843\n",
-      "Epoch 6 of 10, Loss: 0.227, accuracy: 0.8443\n",
-      "Epoch 7 of 10, Loss: 0.426, accuracy: 0.845\n",
-      "Epoch 8 of 10, Loss: 0.183, accuracy: 0.8454\n",
-      "Epoch 9 of 10, Loss: 0.322, accuracy: 0.8461\n",
-      "Epoch 10 of 10, Loss: 0.246, accuracy: 0.8469\n",
-      "0.8382583771241384\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_dataset = wd_dataset['train_dataset']\n",
     "test_dataset  = wd_dataset['test_dataset']\n",
@@ -376,67 +167,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Bachelors': array([-1.1927266 ,  0.13337217,  0.751513  , -0.3854133 , -1.512503  ,\n",
-       "         0.43075648,  0.03185017,  0.2740599 , -1.3502986 , -0.51524764],\n",
-       "       dtype=float32),\n",
-       " 'HS-grad': array([ 0.01510752, -0.41036212, -1.2737428 , -0.03190449,  0.30465913,\n",
-       "        -0.4891645 , -0.35087353,  1.7667191 ,  0.90333945, -0.42637545],\n",
-       "       dtype=float32),\n",
-       " '11th': array([-1.3361819 , -1.0304003 , -0.7671982 ,  1.1118906 ,  0.6290409 ,\n",
-       "         0.09973534, -0.41261104, -0.79101914,  1.2672484 ,  0.7189385 ],\n",
-       "       dtype=float32),\n",
-       " 'Masters': array([ 0.5837133 , -1.3451334 ,  0.9863935 ,  0.35932744, -0.13541682,\n",
-       "         0.34770364, -0.8982047 ,  0.4550249 , -1.326133  , -0.08214497],\n",
-       "       dtype=float32),\n",
-       " '9th': array([ 0.00944321, -0.2883264 ,  1.1186845 ,  0.16699162,  0.20891678,\n",
-       "        -2.222243  ,  0.90257394, -2.499814  ,  0.32215422, -0.02830464],\n",
-       "       dtype=float32),\n",
-       " 'Some-college': array([ 0.11737815, -0.9354352 , -1.6950701 , -0.3879866 , -0.34800476,\n",
-       "         0.65498114, -1.0632497 ,  1.2390918 , -1.3980893 , -1.5068939 ],\n",
-       "       dtype=float32),\n",
-       " 'Assoc-acdm': array([-0.3248521 ,  0.67525595, -0.7607256 , -1.688361  , -0.01024881,\n",
-       "        -0.17185631, -1.5726321 , -0.33589116, -0.6568722 , -0.83356154],\n",
-       "       dtype=float32),\n",
-       " 'Assoc-voc': array([ 0.11711311,  1.6658193 ,  0.2525636 , -1.7053522 ,  0.11374688,\n",
-       "         0.69635576,  0.39209226,  0.55386406,  1.4460421 , -0.4076955 ],\n",
-       "       dtype=float32),\n",
-       " '7th-8th': array([ 0.8109543 , -0.9696295 , -1.1880634 , -2.673678  ,  1.387889  ,\n",
-       "         0.03207216,  0.28635803,  0.32005164, -0.14126171, -0.12705447],\n",
-       "       dtype=float32),\n",
-       " 'Doctorate': array([ 2.5456786 ,  0.9495662 , -0.65327275,  0.63417935, -1.4665067 ,\n",
-       "        -1.0520831 , -0.8822009 ,  1.7168643 ,  1.3397688 ,  1.0705113 ],\n",
-       "       dtype=float32),\n",
-       " 'Prof-school': array([-0.3236308 ,  0.36975744,  0.79298687, -0.24033554,  0.8012961 ,\n",
-       "        -0.38213903,  0.20259416, -0.30737472, -2.190927  ,  0.47054496],\n",
-       "       dtype=float32),\n",
-       " '5th-6th': array([-0.71498626, -1.3042029 ,  0.04956457, -0.20074964,  0.85997975,\n",
-       "         2.4887364 ,  0.9329344 , -0.33221987, -0.37141427,  1.9041626 ],\n",
-       "       dtype=float32),\n",
-       " '10th': array([-0.5197668 , -1.2800047 ,  1.5472891 , -1.141539  ,  0.00724531,\n",
-       "         1.3354197 ,  1.4840577 ,  0.9995618 , -0.03808165, -1.1237134 ],\n",
-       "       dtype=float32),\n",
-       " '1st-4th': array([ 1.1701114 , -1.0981313 , -1.5367142 ,  0.16519445, -0.0972092 ,\n",
-       "        -0.3711076 ,  0.9954778 , -0.94091356, -0.75837976, -1.9332327 ],\n",
-       "       dtype=float32),\n",
-       " 'Preschool': array([-0.7313262 , -0.56184304,  0.30143896,  0.8417214 ,  2.0694172 ,\n",
-       "        -1.2695692 ,  1.461705  , -0.6897159 ,  1.6769298 ,  0.55851436],\n",
-       "       dtype=float32),\n",
-       " '12th': array([-1.2723062 , -0.27862272, -0.3878713 , -0.9044023 , -0.00804312,\n",
-       "        -1.1498355 ,  1.0327121 ,  0.29477796,  0.2951289 ,  0.96019965],\n",
-       "       dtype=float32)}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.get_embeddings('education')"
    ]
@@ -457,28 +190,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WideDeep(\n",
-      "  (emb_layer_native_country): Embedding(42, 10)\n",
-      "  (emb_layer_relationship): Embedding(6, 10)\n",
-      "  (emb_layer_occupation): Embedding(15, 10)\n",
-      "  (emb_layer_education): Embedding(16, 10)\n",
-      "  (emb_layer_workclass): Embedding(9, 10)\n",
-      "  (linear_1): Linear(in_features=51, out_features=100, bias=True)\n",
-      "  (linear_1_drop): Dropout(p=0.5)\n",
-      "  (linear_2): Linear(in_features=100, out_features=50, bias=True)\n",
-      "  (linear_2_drop): Dropout(p=0.2)\n",
-      "  (output): Linear(in_features=847, out_features=3, bias=True)\n",
-      ")\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Let's define age groups\n",
     "age_groups = [0, 25, 50, 90]\n",
@@ -513,34 +227,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1 of 10, Loss: 1.131, accuracy: 0.6735\n",
-      "Epoch 2 of 10, Loss: 0.846, accuracy: 0.6843\n",
-      "Epoch 3 of 10, Loss: 0.902, accuracy: 0.686\n",
-      "Epoch 4 of 10, Loss: 0.806, accuracy: 0.691\n",
-      "Epoch 5 of 10, Loss: 1.015, accuracy: 0.6931\n",
-      "Epoch 6 of 10, Loss: 0.77, accuracy: 0.694\n",
-      "Epoch 7 of 10, Loss: 0.868, accuracy: 0.6962\n",
-      "Epoch 8 of 10, Loss: 0.808, accuracy: 0.6973\n",
-      "Epoch 9 of 10, Loss: 0.977, accuracy: 0.6972\n",
-      "Epoch 10 of 10, Loss: 0.851, accuracy: 0.6968\n",
-      "\n",
-      " [[9.9808323e-01 1.9167198e-03 1.2708337e-07]\n",
-      " [1.8705309e-12 1.0000000e+00 1.0048575e-09]\n",
-      " [2.1682714e-08 9.9999905e-01 9.1604261e-07]\n",
-      " ...\n",
-      " [1.0082698e-03 9.6010476e-01 3.8887005e-02]\n",
-      " [2.4448596e-07 9.9994826e-01 5.1442345e-05]\n",
-      " [6.8863249e-01 3.0600473e-01 5.3628702e-03]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_dataset = wd_dataset['train_dataset']\n",
     "model.fit(dataset=train_dataset, n_epochs=10, batch_size=64)\n",
@@ -553,20 +242,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " 0.7318796365288384\n",
-      "\n",
-      " 0.7006756295639118\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.metrics import f1_score, accuracy_score\n",
     "\n",
@@ -586,28 +264,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WideDeep(\n",
-      "  (emb_layer_native_country): Embedding(42, 10)\n",
-      "  (emb_layer_relationship): Embedding(6, 8)\n",
-      "  (emb_layer_occupation): Embedding(15, 10)\n",
-      "  (emb_layer_education): Embedding(16, 10)\n",
-      "  (emb_layer_workclass): Embedding(9, 10)\n",
-      "  (linear_1): Linear(in_features=49, out_features=100, bias=True)\n",
-      "  (linear_1_drop): Dropout(p=0.5)\n",
-      "  (linear_2): Linear(in_features=100, out_features=50, bias=True)\n",
-      "  (linear_2_drop): Dropout(p=0.2)\n",
-      "  (output): Linear(in_features=847, out_features=1, bias=True)\n",
-      ")\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Set the experiment\n",
     "wide_cols = ['hours_per_week','education', 'relationship','workclass',\n",
@@ -636,28 +295,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1 of 10, Loss: 78.643\n",
-      "Epoch 2 of 10, Loss: 144.536\n",
-      "Epoch 3 of 10, Loss: 53.688\n",
-      "Epoch 4 of 10, Loss: 177.116\n",
-      "Epoch 5 of 10, Loss: 198.454\n",
-      "Epoch 6 of 10, Loss: 90.156\n",
-      "Epoch 7 of 10, Loss: 44.655\n",
-      "Epoch 8 of 10, Loss: 205.163\n",
-      "Epoch 9 of 10, Loss: 246.263\n",
-      "Epoch 10 of 10, Loss: 102.745\n",
-      "\n",
-      " RMSE: 11.113094884527147\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_dataset = wd_dataset['train_dataset']\n",
     "model.fit(dataset=train_dataset, n_epochs=10, batch_size=64)\n",
-- 
GitLab