{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b8f36fba-e6c1-4f65-ae88-35e795d8e89e",
   "metadata": {},
   "source": [
    "Load the _Wine Quality Dataset (Combined)_ data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96634688-0fce-478d-a570-edad08bd37bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(\"../WineQuality.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b0e979-1712-4422-9baa-4076c58828c9",
   "metadata": {},
   "source": [
    "Sample the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "642be8ad-0912-452d-b914-47189e60dd71",
   "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>Unnamed: 0</th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2732</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.047</td>\n",
       "      <td>23.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0.99390</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.65</td>\n",
       "      <td>10.4</td>\n",
       "      <td>6</td>\n",
       "      <td>White Wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2607</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.38</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.031</td>\n",
       "      <td>53.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.99321</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.46</td>\n",
       "      <td>11.7</td>\n",
       "      <td>6</td>\n",
       "      <td>White Wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1653</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.042</td>\n",
       "      <td>35.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.99080</td>\n",
       "      <td>3.76</td>\n",
       "      <td>0.49</td>\n",
       "      <td>11.3</td>\n",
       "      <td>6</td>\n",
       "      <td>White Wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3264</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.29</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.043</td>\n",
       "      <td>33.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.99020</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.31</td>\n",
       "      <td>12.5</td>\n",
       "      <td>6</td>\n",
       "      <td>White Wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4931</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.030</td>\n",
       "      <td>10.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.99050</td>\n",
       "      <td>2.97</td>\n",
       "      <td>0.28</td>\n",
       "      <td>10.8</td>\n",
       "      <td>5</td>\n",
       "      <td>White Wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32480</th>\n",
       "      <td>2838</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.043</td>\n",
       "      <td>46.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.99238</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.3</td>\n",
       "      <td>6</td>\n",
       "      <td>White Wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32481</th>\n",
       "      <td>6414</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.52</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.046</td>\n",
       "      <td>8.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.99660</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>White Wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32482</th>\n",
       "      <td>1126</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.052</td>\n",
       "      <td>36.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.99374</td>\n",
       "      <td>3.80</td>\n",
       "      <td>0.66</td>\n",
       "      <td>10.8</td>\n",
       "      <td>6</td>\n",
       "      <td>White Wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32483</th>\n",
       "      <td>2924</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.33</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.028</td>\n",
       "      <td>21.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.99012</td>\n",
       "      <td>3.36</td>\n",
       "      <td>0.41</td>\n",
       "      <td>13.5</td>\n",
       "      <td>7</td>\n",
       "      <td>White Wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32484</th>\n",
       "      <td>5462</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.46</td>\n",
       "      <td>15.4</td>\n",
       "      <td>0.059</td>\n",
       "      <td>32.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>1.00040</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>White Wine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32485 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  fixed acidity  volatile acidity  citric acid  \\\n",
       "0            2732            7.4             0.170         0.29   \n",
       "1            2607            5.3             0.310         0.38   \n",
       "2            1653            4.7             0.145         0.29   \n",
       "3            3264            6.9             0.260         0.29   \n",
       "4            4931            6.4             0.450         0.07   \n",
       "...           ...            ...               ...          ...   \n",
       "32480        2838            5.0             0.255         0.22   \n",
       "32481        6414            6.6             0.360         0.52   \n",
       "32482        1126            6.3             0.200         0.24   \n",
       "32483        2924            6.2             0.200         0.33   \n",
       "32484        5462            8.1             0.280         0.46   \n",
       "\n",
       "       residual sugar  chlorides  free sulfur dioxide  total sulfur dioxide  \\\n",
       "0                 1.4      0.047                 23.0                 107.0   \n",
       "1                10.5      0.031                 53.0                 140.0   \n",
       "2                 1.0      0.042                 35.0                  90.0   \n",
       "3                 4.2      0.043                 33.0                 114.0   \n",
       "4                 1.1      0.030                 10.0                 131.0   \n",
       "...               ...        ...                  ...                   ...   \n",
       "32480             2.7      0.043                 46.0                 153.0   \n",
       "32481            11.3      0.046                  8.0                 110.0   \n",
       "32482             1.7      0.052                 36.0                 135.0   \n",
       "32483             5.4      0.028                 21.0                  75.0   \n",
       "32484            15.4      0.059                 32.0                 177.0   \n",
       "\n",
       "       density    pH  sulphates  alcohol  quality        Type  \n",
       "0      0.99390  3.52       0.65     10.4        6  White Wine  \n",
       "1      0.99321  3.34       0.46     11.7        6  White Wine  \n",
       "2      0.99080  3.76       0.49     11.3        6  White Wine  \n",
       "3      0.99020  3.16       0.31     12.5        6  White Wine  \n",
       "4      0.99050  2.97       0.28     10.8        5  White Wine  \n",
       "...        ...   ...        ...      ...      ...         ...  \n",
       "32480  0.99238  3.75       0.76     11.3        6  White Wine  \n",
       "32481  0.99660  3.07       0.46      9.4        5  White Wine  \n",
       "32482  0.99374  3.80       0.66     10.8        6  White Wine  \n",
       "32483  0.99012  3.36       0.41     13.5        7  White Wine  \n",
       "32484  1.00040  3.27       0.58      9.0        4  White Wine  \n",
       "\n",
       "[32485 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09008208-91d4-49e0-806d-71461d5c3fdc",
   "metadata": {},
   "source": [
    "Retain the useful 11 features and isolate quality alone as a label, then split them both into training/test subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1eb05609-e631-4a26-8ce6-d94dd6f25911",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = data.drop(data.columns[0], axis=1).drop([\"quality\",\"Type\"], axis=1)\n",
    "y_data = data.quality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "040a8912",
   "metadata": {},
   "source": [
    "Split the samples into 80/20 train/test subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8dea41a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 25988 samples for training and 6497 samples for training.\n",
      "Total of 32485 records, dataset size is 32485 rows.\n",
      "Training set has a shape of (32485, 11), labels have a shape of (32485,)\n"
     ]
    }
   ],
   "source": [
    "train_size = int(len(x_data) * 0.8)\n",
    "x_train = x_data[:train_size]\n",
    "y_train = y_data[:train_size]\n",
    "x_test = x_data[train_size:]\n",
    "y_test = y_data[train_size:]\n",
    "\n",
    "print(\"Using {} samples for training and {} samples for training.\\n\".format(len(x_train), len(x_test)) +\n",
    "        \"Total of {} records, dataset size is {} rows.\\n\".format(len(x_train) + len(x_test), len(x_data)) +\n",
    "        \"Training set has a shape of {}, labels have a shape of {}\".format(x_data.shape, y_data.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f5ae2f",
   "metadata": {},
   "source": [
    "Create a sequential model definition, one deep RELU intermediate layer with softmax output and 10 possible values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ecd1f4fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import layers\n",
    "\n",
    "classifier_init = keras.Sequential([\n",
    "    layers.Dense(11, activation=\"relu\"),\n",
    "    layers.Dense(44, activation=\"relu\"),\n",
    "    layers.Dense(10, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693762d0",
   "metadata": {},
   "source": [
    "Compile the model with adam optimiser and sparse categorical cross-entropy loss function. Track accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "775ce661",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_init.compile(optimizer=\"adam\",\n",
    "                   loss=\"sparse_categorical_crossentropy\",\n",
    "                   metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de4aee83",
   "metadata": {},
   "source": [
    "Fit the model across 20 epochs with batches of 1000 samples, using a further 80/20 split for training and validation subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "94db127a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.4789 - loss: 1.2030 - val_accuracy: 0.4542 - val_loss: 1.2096\n",
      "Epoch 2/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.4787 - loss: 1.2024 - val_accuracy: 0.4546 - val_loss: 1.2113\n",
      "Epoch 3/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.4793 - loss: 1.2022 - val_accuracy: 0.4598 - val_loss: 1.2097\n",
      "Epoch 4/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.4805 - loss: 1.2004 - val_accuracy: 0.4631 - val_loss: 1.2092\n",
      "Epoch 5/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.4829 - loss: 1.1996 - val_accuracy: 0.4609 - val_loss: 1.2101\n",
      "Epoch 6/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.4845 - loss: 1.1990 - val_accuracy: 0.4636 - val_loss: 1.2095\n",
      "Epoch 7/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.4840 - loss: 1.1980 - val_accuracy: 0.4663 - val_loss: 1.2097\n",
      "Epoch 8/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.4872 - loss: 1.1973 - val_accuracy: 0.4634 - val_loss: 1.2094\n",
      "Epoch 9/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.4882 - loss: 1.1960 - val_accuracy: 0.4636 - val_loss: 1.2101\n",
      "Epoch 10/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.4899 - loss: 1.1953 - val_accuracy: 0.4638 - val_loss: 1.2092\n",
      "Epoch 11/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.4900 - loss: 1.1942 - val_accuracy: 0.4681 - val_loss: 1.2135\n",
      "Epoch 12/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.4893 - loss: 1.1945 - val_accuracy: 0.4658 - val_loss: 1.2078\n",
      "Epoch 13/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.4889 - loss: 1.1955 - val_accuracy: 0.4669 - val_loss: 1.2343\n",
      "Epoch 14/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.4864 - loss: 1.2040 - val_accuracy: 0.4659 - val_loss: 1.2230\n",
      "Epoch 15/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.4912 - loss: 1.1936 - val_accuracy: 0.4752 - val_loss: 1.2085\n",
      "Epoch 16/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.4939 - loss: 1.1877 - val_accuracy: 0.4765 - val_loss: 1.2065\n",
      "Epoch 17/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.4974 - loss: 1.1861 - val_accuracy: 0.4767 - val_loss: 1.2060\n",
      "Epoch 18/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.4980 - loss: 1.1850 - val_accuracy: 0.4775 - val_loss: 1.2050\n",
      "Epoch 19/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.4976 - loss: 1.1835 - val_accuracy: 0.4767 - val_loss: 1.2032\n",
      "Epoch 20/20\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.4976 - loss: 1.1819 - val_accuracy: 0.4777 - val_loss: 1.2013\n"
     ]
    }
   ],
   "source": [
    "epochs_init = 20\n",
    "history_init = classifier_init.fit(x_train, y_train,\n",
    "                         epochs=epochs_init, batch_size=1000,\n",
    "                         validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16dca591",
   "metadata": {},
   "source": [
    "The above doesn't seem to be achieving a particularly good accuracy. Let's tweak the model a bit and retrain:\n",
    "\n",
    "* add an explicit input specification\n",
    "* add more capacity to the deep layers\n",
    "* randomize coefficients\n",
    "* tweak gradient descent parameters\n",
    "* add more epochs\n",
    "* decrease batch size a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "af4cf9c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 24ms/step - accuracy: 0.3593 - loss: 17.3410 - val_accuracy: 0.3596 - val_loss: 3.3322\n",
      "Epoch 2/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.3873 - loss: 2.9919 - val_accuracy: 0.3977 - val_loss: 1.9189\n",
      "Epoch 3/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.4113 - loss: 1.7894 - val_accuracy: 0.4211 - val_loss: 1.3407\n",
      "Epoch 4/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4389 - loss: 1.3377 - val_accuracy: 0.4422 - val_loss: 1.2794\n",
      "Epoch 5/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4576 - loss: 1.2830 - val_accuracy: 0.4526 - val_loss: 1.2479\n",
      "Epoch 6/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4629 - loss: 1.2533 - val_accuracy: 0.4617 - val_loss: 1.2289\n",
      "Epoch 7/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.4738 - loss: 1.2359 - val_accuracy: 0.4615 - val_loss: 1.2208\n",
      "Epoch 8/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4770 - loss: 1.2270 - val_accuracy: 0.4645 - val_loss: 1.2155\n",
      "Epoch 9/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4809 - loss: 1.2223 - val_accuracy: 0.4657 - val_loss: 1.2157\n",
      "Epoch 10/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4810 - loss: 1.2186 - val_accuracy: 0.4701 - val_loss: 1.2144\n",
      "Epoch 11/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.4813 - loss: 1.2167 - val_accuracy: 0.4733 - val_loss: 1.2142\n",
      "Epoch 12/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4810 - loss: 1.2151 - val_accuracy: 0.4742 - val_loss: 1.2139\n",
      "Epoch 13/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4813 - loss: 1.2138 - val_accuracy: 0.4724 - val_loss: 1.2130\n",
      "Epoch 14/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4817 - loss: 1.2128 - val_accuracy: 0.4743 - val_loss: 1.2123\n",
      "Epoch 15/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.4822 - loss: 1.2120 - val_accuracy: 0.4716 - val_loss: 1.2119\n",
      "Epoch 16/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.4822 - loss: 1.2115 - val_accuracy: 0.4724 - val_loss: 1.2111\n",
      "Epoch 17/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4823 - loss: 1.2104 - val_accuracy: 0.4724 - val_loss: 1.2103\n",
      "Epoch 18/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4825 - loss: 1.2101 - val_accuracy: 0.4735 - val_loss: 1.2098\n",
      "Epoch 19/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.4835 - loss: 1.2096 - val_accuracy: 0.4747 - val_loss: 1.2101\n",
      "Epoch 20/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.4828 - loss: 1.2089 - val_accuracy: 0.4769 - val_loss: 1.2081\n",
      "Epoch 21/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4845 - loss: 1.2080 - val_accuracy: 0.4780 - val_loss: 1.2078\n",
      "Epoch 22/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4839 - loss: 1.2070 - val_accuracy: 0.4781 - val_loss: 1.2064\n",
      "Epoch 23/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.4846 - loss: 1.2058 - val_accuracy: 0.4789 - val_loss: 1.2049\n",
      "Epoch 24/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.4834 - loss: 1.2048 - val_accuracy: 0.4802 - val_loss: 1.2044\n",
      "Epoch 25/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4856 - loss: 1.2040 - val_accuracy: 0.4825 - val_loss: 1.2030\n",
      "Epoch 26/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4854 - loss: 1.2031 - val_accuracy: 0.4825 - val_loss: 1.2027\n",
      "Epoch 27/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4862 - loss: 1.2027 - val_accuracy: 0.4822 - val_loss: 1.2021\n",
      "Epoch 28/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.4865 - loss: 1.2016 - val_accuracy: 0.4849 - val_loss: 1.2008\n",
      "Epoch 29/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.4871 - loss: 1.2008 - val_accuracy: 0.4865 - val_loss: 1.1995\n",
      "Epoch 30/30\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.4867 - loss: 1.1996 - val_accuracy: 0.4848 - val_loss: 1.1989\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.random.set_seed(42);\n",
    "tf.keras.utils.set_random_seed(42);\n",
    "\n",
    "classifier_new = keras.Sequential([\n",
    "    layers.Input((11,)),\n",
    "    layers.Dense(128, activation=\"relu\"),\n",
    "    layers.Dense(128, activation=\"relu\"),\n",
    "    layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "\n",
    "classifier_new.compile(optimizer=keras.optimizers.Adam(learning_rate=0.0001),\n",
    "                   loss=\"sparse_categorical_crossentropy\",\n",
    "                   metrics=[\"accuracy\"])\n",
    "\n",
    "epochs_new = 30\n",
    "history_new = classifier_new.fit(x_train, y_train,\n",
    "                         epochs=epochs_new, batch_size=500,\n",
    "                         validation_split=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0519464",
   "metadata": {},
   "source": [
    "This looks better, but how can you be sure? Visualise it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c236c890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "val_loss = history_new.history[\"val_loss\"]\n",
    "trn_loss = history_new.history[\"loss\"]\n",
    "val_accuracy = history_new.history[\"val_accuracy\"]\n",
    "trn_accuracy = history_new.history[\"accuracy\"]\n",
    "\n",
    "fig, loss = plt.subplots()\n",
    "loss.plot(range(0, epochs_new), val_loss, \"b-\", label=\"Validation Loss\")\n",
    "loss.plot(range(0, epochs_new), trn_loss, \"r-\", label=\"Training Loss\")\n",
    "loss.set_ylabel(\"Loss\")\n",
    "h1, l1 = loss.get_legend_handles_labels()\n",
    "\n",
    "accr = loss.twinx()\n",
    "accr.plot(range(0, epochs_new), val_accuracy, \"g\", label=\"Validation Accuracy\")\n",
    "accr.plot(range(0, epochs_new), trn_accuracy, \"y\", label=\"Training Accuracy\")\n",
    "accr.set_ylabel(\"Accuracy\")\n",
    "h2, l2 = accr.get_legend_handles_labels()\n",
    "\n",
    "fig.legend(h1 + h2, l1 + l2, loc=(0.5, 0.5))\n",
    "\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.xticks(range(0, epochs_new, 5))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a84be1",
   "metadata": {},
   "source": [
    "Let's compare that with the previous run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d5731bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_loss = history_init.history[\"val_loss\"]\n",
    "trn_loss = history_init.history[\"loss\"]\n",
    "val_accuracy = history_init.history[\"val_accuracy\"]\n",
    "trn_accuracy = history_init.history[\"accuracy\"]\n",
    "\n",
    "fig, loss = plt.subplots()\n",
    "loss.plot(range(0, epochs_init), val_loss, \"b-\", label=\"Validation Loss\")\n",
    "loss.plot(range(0, epochs_init), trn_loss, \"r-\", label=\"Training Loss\")\n",
    "loss.set_ylabel(\"Loss\")\n",
    "h1, l1 = loss.get_legend_handles_labels()\n",
    "\n",
    "accr = loss.twinx()\n",
    "accr.plot(range(0, epochs_init), val_accuracy, \"g\", label=\"Validation Accuracy\")\n",
    "accr.plot(range(0, epochs_init), trn_accuracy, \"y\", label=\"Training Accuracy\")\n",
    "accr.set_ylabel(\"Accuracy\")\n",
    "h2, l2 = accr.get_legend_handles_labels()\n",
    "\n",
    "fig.legend(h1 + h2, l1 + l2, loc=(0.1, 0.7))\n",
    "\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.xticks(range(0, epochs_init, 5))\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf-2",
   "language": "python",
   "name": "tf-2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}