NeuralNetworkSpotify.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import NearestNeighbors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing DataSet\n",
    "df = pd.read_csv('/Users/terrenceam22/Downloads/670134-1261707-bundle-archive-2/data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>artists</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>explicit</th>\n",
       "      <th>id</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.995</td>\n",
       "      <td>['Carl Woitschach']</td>\n",
       "      <td>0.708</td>\n",
       "      <td>158648</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.563</td>\n",
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       "      <td>-12.428</td>\n",
       "      <td>1</td>\n",
       "      <td>Singende Bataillone 1. Teil</td>\n",
       "      <td>0</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.0506</td>\n",
       "      <td>118.469</td>\n",
       "      <td>0.7790</td>\n",
       "      <td>1928</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.994</td>\n",
       "      <td>['Robert Schumann', 'Vladimir Horowitz']</td>\n",
       "      <td>0.379</td>\n",
       "      <td>282133</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0</td>\n",
       "      <td>6KuQTIu1KoTTkLXKrwlLPV</td>\n",
       "      <td>0.901</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0763</td>\n",
       "      <td>-28.454</td>\n",
       "      <td>1</td>\n",
       "      <td>Fantasiestücke, Op. 111: Più tosto lento</td>\n",
       "      <td>0</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.0462</td>\n",
       "      <td>83.972</td>\n",
       "      <td>0.0767</td>\n",
       "      <td>1928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.604</td>\n",
       "      <td>['Seweryn Goszczyński']</td>\n",
       "      <td>0.749</td>\n",
       "      <td>104300</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0</td>\n",
       "      <td>6L63VW0PibdM1HDSBoqnoM</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>-19.924</td>\n",
       "      <td>0</td>\n",
       "      <td>Chapter 1.18 - Zamek kaniowski</td>\n",
       "      <td>0</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.9290</td>\n",
       "      <td>107.177</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>1928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.995</td>\n",
       "      <td>['Francisco Canaro']</td>\n",
       "      <td>0.781</td>\n",
       "      <td>180760</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0</td>\n",
       "      <td>6M94FkXd15sOAOQYRnWPN8</td>\n",
       "      <td>0.887</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>-14.734</td>\n",
       "      <td>0</td>\n",
       "      <td>Bebamos Juntos - Instrumental (Remasterizado)</td>\n",
       "      <td>0</td>\n",
       "      <td>1928-09-25</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>108.003</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>1928</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.990</td>\n",
       "      <td>['Frédéric Chopin', 'Vladimir Horowitz']</td>\n",
       "      <td>0.210</td>\n",
       "      <td>687733</td>\n",
       "      <td>0.2040</td>\n",
       "      <td>0</td>\n",
       "      <td>6N6tiFZ9vLTSOIxkj8qKrd</td>\n",
       "      <td>0.908</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0980</td>\n",
       "      <td>-16.829</td>\n",
       "      <td>1</td>\n",
       "      <td>Polonaise-Fantaisie in A-Flat Major, Op. 61</td>\n",
       "      <td>1</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>62.149</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>1928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness                                   artists  danceability  \\\n",
       "0         0.995                       ['Carl Woitschach']         0.708   \n",
       "1         0.994  ['Robert Schumann', 'Vladimir Horowitz']         0.379   \n",
       "2         0.604                   ['Seweryn Goszczyński']         0.749   \n",
       "3         0.995                      ['Francisco Canaro']         0.781   \n",
       "4         0.990  ['Frédéric Chopin', 'Vladimir Horowitz']         0.210   \n",
       "\n",
       "   duration_ms  energy  explicit                      id  instrumentalness  \\\n",
       "0       158648  0.1950         0  6KbQ3uYMLKb5jDxLF7wYDD             0.563   \n",
       "1       282133  0.0135         0  6KuQTIu1KoTTkLXKrwlLPV             0.901   \n",
       "2       104300  0.2200         0  6L63VW0PibdM1HDSBoqnoM             0.000   \n",
       "3       180760  0.1300         0  6M94FkXd15sOAOQYRnWPN8             0.887   \n",
       "4       687733  0.2040         0  6N6tiFZ9vLTSOIxkj8qKrd             0.908   \n",
       "\n",
       "   key  liveness  loudness  mode  \\\n",
       "0   10    0.1510   -12.428     1   \n",
       "1    8    0.0763   -28.454     1   \n",
       "2    5    0.1190   -19.924     0   \n",
       "3    1    0.1110   -14.734     0   \n",
       "4   11    0.0980   -16.829     1   \n",
       "\n",
       "                                            name  popularity release_date  \\\n",
       "0                    Singende Bataillone 1. Teil           0         1928   \n",
       "1       Fantasiestücke, Op. 111: Più tosto lento           0         1928   \n",
       "2                 Chapter 1.18 - Zamek kaniowski           0         1928   \n",
       "3  Bebamos Juntos - Instrumental (Remasterizado)           0   1928-09-25   \n",
       "4    Polonaise-Fantaisie in A-Flat Major, Op. 61           1         1928   \n",
       "\n",
       "   speechiness    tempo  valence  year  \n",
       "0       0.0506  118.469   0.7790  1928  \n",
       "1       0.0462   83.972   0.0767  1928  \n",
       "2       0.9290  107.177   0.8800  1928  \n",
       "3       0.0926  108.003   0.7200  1928  \n",
       "4       0.0424   62.149   0.0693  1928  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['acousticness', 'artists', 'danceability', 'duration_ms', 'energy',\n",
       "       'explicit', 'id', 'instrumentalness', 'key', 'liveness', 'loudness',\n",
       "       'mode', 'name', 'popularity', 'release_date', 'speechiness', 'tempo',\n",
       "       'valence', 'year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>explicit</th>\n",
       "      <th>id</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.9950</td>\n",
       "      <td>0.708</td>\n",
       "      <td>158648</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1928</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.9940</td>\n",
       "      <td>0.379</td>\n",
       "      <td>282133</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0</td>\n",
       "      <td>6KuQTIu1KoTTkLXKrwlLPV</td>\n",
       "      <td>0.901000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0763</td>\n",
       "      <td>-28.454</td>\n",
       "      <td>1</td>\n",
       "      <td>Fantasiestücke, Op. 111: Più tosto lento</td>\n",
       "      <td>0</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.0462</td>\n",
       "      <td>83.972</td>\n",
       "      <td>0.0767</td>\n",
       "      <td>1928</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6040</td>\n",
       "      <td>0.749</td>\n",
       "      <td>104300</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0</td>\n",
       "      <td>6L63VW0PibdM1HDSBoqnoM</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>-19.924</td>\n",
       "      <td>0</td>\n",
       "      <td>Chapter 1.18 - Zamek kaniowski</td>\n",
       "      <td>0</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.9290</td>\n",
       "      <td>107.177</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>1928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.9950</td>\n",
       "      <td>0.781</td>\n",
       "      <td>180760</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0</td>\n",
       "      <td>6M94FkXd15sOAOQYRnWPN8</td>\n",
       "      <td>0.887000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>-14.734</td>\n",
       "      <td>0</td>\n",
       "      <td>Bebamos Juntos - Instrumental (Remasterizado)</td>\n",
       "      <td>0</td>\n",
       "      <td>1928-09-25</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>108.003</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>1928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.9900</td>\n",
       "      <td>0.210</td>\n",
       "      <td>687733</td>\n",
       "      <td>0.2040</td>\n",
       "      <td>0</td>\n",
       "      <td>6N6tiFZ9vLTSOIxkj8qKrd</td>\n",
       "      <td>0.908000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0980</td>\n",
       "      <td>-16.829</td>\n",
       "      <td>1</td>\n",
       "      <td>Polonaise-Fantaisie in A-Flat Major, Op. 61</td>\n",
       "      <td>1</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>62.149</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>1928</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>169904</th>\n",
       "      <td>0.1730</td>\n",
       "      <td>0.875</td>\n",
       "      <td>163800</td>\n",
       "      <td>0.4430</td>\n",
       "      <td>1</td>\n",
       "      <td>4KppkflX7I3vJQk7urOJaS</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0891</td>\n",
       "      <td>-7.461</td>\n",
       "      <td>1</td>\n",
       "      <td>Skechers (feat. Tyga) - Remix</td>\n",
       "      <td>75</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>100.012</td>\n",
       "      <td>0.3060</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169905</th>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.719</td>\n",
       "      <td>167468</td>\n",
       "      <td>0.3850</td>\n",
       "      <td>0</td>\n",
       "      <td>1ehhGlTvjtHo2e4xJFB0SZ</td>\n",
       "      <td>0.031300</td>\n",
       "      <td>8</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>-10.907</td>\n",
       "      <td>1</td>\n",
       "      <td>Sweeter (feat. Terrace Martin)</td>\n",
       "      <td>64</td>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>0.0403</td>\n",
       "      <td>128.000</td>\n",
       "      <td>0.2700</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169906</th>\n",
       "      <td>0.5380</td>\n",
       "      <td>0.514</td>\n",
       "      <td>180700</td>\n",
       "      <td>0.5390</td>\n",
       "      <td>0</td>\n",
       "      <td>52eycxprLhK3lPcRLbQiVk</td>\n",
       "      <td>0.002330</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>-9.332</td>\n",
       "      <td>1</td>\n",
       "      <td>How Would I Know</td>\n",
       "      <td>70</td>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>0.1050</td>\n",
       "      <td>123.700</td>\n",
       "      <td>0.1530</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169907</th>\n",
       "      <td>0.0714</td>\n",
       "      <td>0.646</td>\n",
       "      <td>167308</td>\n",
       "      <td>0.7610</td>\n",
       "      <td>0</td>\n",
       "      <td>3wYOGJYD31sLRmBgCvWxa4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2220</td>\n",
       "      <td>-2.557</td>\n",
       "      <td>1</td>\n",
       "      <td>I Found You</td>\n",
       "      <td>70</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>129.916</td>\n",
       "      <td>0.4720</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169908</th>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.512</td>\n",
       "      <td>214787</td>\n",
       "      <td>0.4280</td>\n",
       "      <td>0</td>\n",
       "      <td>60RFlt48hm0l4Fu0JoccOl</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1050</td>\n",
       "      <td>-7.387</td>\n",
       "      <td>1</td>\n",
       "      <td>More Hearts Than Mine</td>\n",
       "      <td>65</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>0.0271</td>\n",
       "      <td>80.588</td>\n",
       "      <td>0.3660</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169909 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        acousticness  danceability  duration_ms  energy  explicit  \\\n",
       "0             0.9950         0.708       158648  0.1950         0   \n",
       "1             0.9940         0.379       282133  0.0135         0   \n",
       "2             0.6040         0.749       104300  0.2200         0   \n",
       "3             0.9950         0.781       180760  0.1300         0   \n",
       "4             0.9900         0.210       687733  0.2040         0   \n",
       "...              ...           ...          ...     ...       ...   \n",
       "169904        0.1730         0.875       163800  0.4430         1   \n",
       "169905        0.0167         0.719       167468  0.3850         0   \n",
       "169906        0.5380         0.514       180700  0.5390         0   \n",
       "169907        0.0714         0.646       167308  0.7610         0   \n",
       "169908        0.1090         0.512       214787  0.4280         0   \n",
       "\n",
       "                            id  instrumentalness  key  liveness  loudness  \\\n",
       "0       6KbQ3uYMLKb5jDxLF7wYDD          0.563000   10    0.1510   -12.428   \n",
       "1       6KuQTIu1KoTTkLXKrwlLPV          0.901000    8    0.0763   -28.454   \n",
       "2       6L63VW0PibdM1HDSBoqnoM          0.000000    5    0.1190   -19.924   \n",
       "3       6M94FkXd15sOAOQYRnWPN8          0.887000    1    0.1110   -14.734   \n",
       "4       6N6tiFZ9vLTSOIxkj8qKrd          0.908000   11    0.0980   -16.829   \n",
       "...                        ...               ...  ...       ...       ...   \n",
       "169904  4KppkflX7I3vJQk7urOJaS          0.000032    1    0.0891    -7.461   \n",
       "169905  1ehhGlTvjtHo2e4xJFB0SZ          0.031300    8    0.1110   -10.907   \n",
       "169906  52eycxprLhK3lPcRLbQiVk          0.002330    7    0.1080    -9.332   \n",
       "169907  3wYOGJYD31sLRmBgCvWxa4          0.000000    1    0.2220    -2.557   \n",
       "169908  60RFlt48hm0l4Fu0JoccOl          0.000000    0    0.1050    -7.387   \n",
       "\n",
       "        mode                                           name  popularity  \\\n",
       "0          1                    Singende Bataillone 1. Teil           0   \n",
       "1          1       Fantasiestücke, Op. 111: Più tosto lento           0   \n",
       "2          0                 Chapter 1.18 - Zamek kaniowski           0   \n",
       "3          0  Bebamos Juntos - Instrumental (Remasterizado)           0   \n",
       "4          1    Polonaise-Fantaisie in A-Flat Major, Op. 61           1   \n",
       "...      ...                                            ...         ...   \n",
       "169904     1                  Skechers (feat. Tyga) - Remix          75   \n",
       "169905     1                 Sweeter (feat. Terrace Martin)          64   \n",
       "169906     1                               How Would I Know          70   \n",
       "169907     1                                    I Found You          70   \n",
       "169908     1                          More Hearts Than Mine          65   \n",
       "\n",
       "       release_date  speechiness    tempo  valence  year  \n",
       "0              1928       0.0506  118.469   0.7790  1928  \n",
       "1              1928       0.0462   83.972   0.0767  1928  \n",
       "2              1928       0.9290  107.177   0.8800  1928  \n",
       "3        1928-09-25       0.0926  108.003   0.7200  1928  \n",
       "4              1928       0.0424   62.149   0.0693  1928  \n",
       "...             ...          ...      ...      ...   ...  \n",
       "169904   2020-05-15       0.1430  100.012   0.3060  2020  \n",
       "169905   2020-06-08       0.0403  128.000   0.2700  2020  \n",
       "169906   2020-05-29       0.1050  123.700   0.1530  2020  \n",
       "169907   2020-02-28       0.0385  129.916   0.4720  2020  \n",
       "169908   2020-03-27       0.0271   80.588   0.3660  2020  \n",
       "\n",
       "[169909 rows x 18 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Holding Test Set\n",
    "test = df[['artists']]\n",
    "df.drop(columns=['artists'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, val = train_test_split(df, test_size=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['danceability', 'energy', 'loudness', 'speechiness']\n"
     ]
    }
   ],
   "source": [
    "target = 'artists'\n",
    "# Features\n",
    "features = ['danceability', 'energy', 'loudness', 'speechiness']\n",
    "\n",
    "X_train = train[features]\n",
    "y_train = train[target]\n",
    "X_val = val[features]\n",
    "y_val = val[target]\n",
    "X_test = train[features]\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>artists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['Carl Woitschach']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['Robert Schumann', 'Vladimir Horowitz']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['Seweryn Goszczyński']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['Francisco Canaro']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['Frédéric Chopin', 'Vladimir Horowitz']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169904</th>\n",
       "      <td>['DripReport', 'Tyga']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169905</th>\n",
       "      <td>['Leon Bridges', 'Terrace Martin']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169906</th>\n",
       "      <td>['Kygo', 'Oh Wonder']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169907</th>\n",
       "      <td>['Cash Cash', 'Andy Grammer']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169908</th>\n",
       "      <td>['Ingrid Andress']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169909 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         artists\n",
       "0                            ['Carl Woitschach']\n",
       "1       ['Robert Schumann', 'Vladimir Horowitz']\n",
       "2                        ['Seweryn Goszczyński']\n",
       "3                           ['Francisco Canaro']\n",
       "4       ['Frédéric Chopin', 'Vladimir Horowitz']\n",
       "...                                          ...\n",
       "169904                    ['DripReport', 'Tyga']\n",
       "169905        ['Leon Bridges', 'Terrace Martin']\n",
       "169906                     ['Kygo', 'Oh Wonder']\n",
       "169907             ['Cash Cash', 'Andy Grammer']\n",
       "169908                        ['Ingrid Andress']\n",
       "\n",
       "[169909 rows x 1 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import category_encoders as ce\n",
    "\n",
    "encoder = ce.OneHotEncoder(use_cat_names=True)\n",
    "X_train = encoder.fit_transform(X_train)\n",
    "X_test = encoder.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((84954, 4), (84954,))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/sklearn/utils/validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "# X_train = X_train.astype('float32')\n",
    "# y_train = y_train.astype('float32')\n",
    "# y_train = encoder.fit_transform(y_train)\n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "y_train = le.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_val = le.fit_transform(y_val)\n",
    "y_val = y_val.astype('float32')\n",
    "X_val = X_val.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, LSTM, Conv2D, Input\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.constraints import max_norm\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "# opt=SGD(learning_rate=.01)\n",
    "\n",
    "\n",
    "model = Sequential([Dense(32, input_dim=4, activation='selu'),\n",
    "                    Dense(32, activation='relu', kernel_constraint=max_norm(2,)),\n",
    "                    Dense(32, activation='relu'),\n",
    "                    Dense(24, activation='relu'),\n",
    "                    Dense(20, activation='relu'),\n",
    "                    Dense(1, activation='sigmoid')\n",
    "                    \n",
    "])\n",
    "\n",
    "model.compile(optimizer='nadam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_41 (Dense)             (None, 32)                160       \n",
      "_________________________________________________________________\n",
      "dense_42 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_43 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_44 (Dense)             (None, 24)                792       \n",
      "_________________________________________________________________\n",
      "dense_45 (Dense)             (None, 20)                500       \n",
      "_________________________________________________________________\n",
      "dense_46 (Dense)             (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 3,585\n",
      "Trainable params: 3,585\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "2655/2655 [==============================] - 5s 2ms/step - loss: 0.0013 - accuracy: 1.1771e-05 - val_loss: 0.0013 - val_accuracy: 2.3542e-05\n",
      "Epoch 2/10\n",
      "2655/2655 [==============================] - 5s 2ms/step - loss: 0.0013 - accuracy: 1.1771e-05 - val_loss: 0.0013 - val_accuracy: 2.3542e-05\n",
      "Epoch 3/10\n",
      "2655/2655 [==============================] - 6s 2ms/step - loss: 0.0013 - accuracy: 1.1771e-05 - val_loss: 0.0013 - val_accuracy: 2.3542e-05\n",
      "Epoch 4/10\n",
      "   1/2655 [..............................] - ETA: 0s - loss: 0.0012 - accuracy: 0.0000e+00"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-149-e72ee82282ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    846\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    847\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    849\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1659\u001b[0m       \u001b[0;31m`\u001b[0m\u001b[0margs\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1660\u001b[0m     \"\"\"\n\u001b[0;32m-> 1661\u001b[0;31m     return self._call_flat(\n\u001b[0m\u001b[1;32m   1662\u001b[0m         (t for t in nest.flatten((args, kwargs), expand_composites=True)\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1743\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1745\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1746\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    591\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    594\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     57\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>84954 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        danceability  energy  loudness  speechiness\n",
       "14199          0.367  0.7760    -6.878       0.1050\n",
       "38940          0.541  0.9100    -6.289       0.1160\n",
       "110884         0.569  0.5400   -11.022       0.0365\n",
       "47045          0.299  0.6070    -6.808       0.0432\n",
       "156273         0.628  0.7830    -7.799       0.0323\n",
       "...              ...     ...       ...          ...\n",
       "41216          0.560  0.0855   -18.577       0.1350\n",
       "159498         0.867  0.3840    -8.334       0.2680\n",
       "35186          0.433  0.1380   -15.738       0.0402\n",
       "37609          0.570  0.1260   -17.349       0.2180\n",
       "122754         0.641  0.7010   -14.348       0.0346\n",
       "\n",
       "[84954 rows x 4 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1147 predict_function  *\n        outputs = self.distribute_strategy.run(\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica\n        return fn(*args, **kwargs)\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1122 predict_step  **\n        return self(x, training=False)\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:885 __call__\n        input_spec.assert_input_compatibility(self.input_spec, inputs,\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/input_spec.py:212 assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer sequential_3 is incompatible with the layer: expected axis -1 of input shape to have value 4 but received input with shape [None, 2]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-134-9c464a849bb3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnew_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.367\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m       raise ValueError('{} is not supported in multi-worker mode.'.format(\n\u001b[1;32m     87\u001b[0m           method.__name__))\n\u001b[0;32m---> 88\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m   return tf_decorator.make_decorator(\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1266\u001b[0m           \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1267\u001b[0m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_predict_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1268\u001b[0;31m             \u001b[0mtmp_batch_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1269\u001b[0m             \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1270\u001b[0m             \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    616\u001b[0m       \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m       \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    619\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m         raise ValueError(\"Creating variables on a non-first call to a function\"\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2417\u001b[0m     \u001b[0;34m\"\"\"Calls a graph function specialized to the inputs.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2419\u001b[0;31m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2420\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   2772\u001b[0m           \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_signature\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2773\u001b[0m           and call_context_key in self._function_cache.missed):\n\u001b[0;32m-> 2774\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_define_function_with_shape_relaxation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2776\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_define_function_with_shape_relaxation\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   2703\u001b[0m     self._function_cache.arg_relaxed_shapes[rank_only_cache_key] = (\n\u001b[1;32m   2704\u001b[0m         relaxed_arg_shapes)\n\u001b[0;32m-> 2705\u001b[0;31m     graph_function = self._create_graph_function(\n\u001b[0m\u001b[1;32m   2706\u001b[0m         args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)\n\u001b[1;32m   2707\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marg_relaxed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrank_only_cache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   2655\u001b[0m     \u001b[0marg_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase_arg_names\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmissing_arg_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2656\u001b[0m     graph_function = ConcreteFunction(\n\u001b[0;32m-> 2657\u001b[0;31m         func_graph_module.func_graph_from_py_func(\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2659\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_python_function\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m    979\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m       \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    439\u001b[0m         \u001b[0;31m# __wrapped__ allows AutoGraph to swap in a converted function. We give\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m         \u001b[0;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    442\u001b[0m     \u001b[0mweak_wrapped_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    966\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    969\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1147 predict_function  *\n        outputs = self.distribute_strategy.run(\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica\n        return fn(*args, **kwargs)\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1122 predict_step  **\n        return self(x, training=False)\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:885 __call__\n        input_spec.assert_input_compatibility(self.input_spec, inputs,\n    /Users/terrenceam22/.local/share/virtualenvs/terrenceam22-N_X-jW-K/lib/python3.8/site-packages/tensorflow/python/keras/engine/input_spec.py:212 assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer sequential_3 is incompatible with the layer: expected axis -1 of input shape to have value 4 but received input with shape [None, 2]\n"
     ]
    }
   ],
   "source": [
    "new_input = [[-1, 0.367]]\n",
    "predict = model.predict(new_input)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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