Custom Models, Layers, and Loss Functions with TensorFlow/Week 1 - Functional APIs/C1W1_Assignment.ipynb

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    "\n",
    "\n",
    "# Week 1: Multiple Output Models using the Keras Functional API\n",
    "\n",
    "Welcome to the first programming assignment of the course! Your task will be to use the Keras functional API to train a model to predict two outputs. For this lab, you will use the **[Wine Quality Dataset](https://archive.ics.uci.edu/ml/datasets/Wine+Quality)** from the **UCI machine learning repository**. It has separate datasets for red wine and white wine.\n",
    "\n",
    "Normally, the wines are classified into one of the quality ratings specified in the attributes. In this exercise, you will combine the two datasets to predict the wine quality and whether the wine is red or white solely from the attributes. \n",
    "\n",
    "You will model wine quality estimations as a regression problem and wine type detection as a binary classification problem.\n",
    "\n",
    "#### Please complete sections that are marked **(TODO)**"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Imports"
   ]
  },
  {
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   "execution_count": 1,
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   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\n",
    "import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gQMERzWQYpgm"
   },
   "source": [
    "## Load Dataset\n",
    "\n",
    "\n",
    "You will now download the dataset from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php). \n",
    "\n",
    "### Pre-process the white wine dataset (TODO)\n",
    "You will add a new column named `is_red` in your dataframe to indicate if the wine is white or red. \n",
    "- In the white wine dataset, you will fill the column `is_red` with  zeros (0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "locked": false,
     "schema_version": 3,
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   },
   "outputs": [],
   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "# URL of the white wine dataset\n",
    "URL = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv'\n",
    "\n",
    "# load the dataset from the URL\n",
    "white_df = pd.read_csv(URL, sep=\";\")\n",
    "\n",
    "# fill the `is_red` column with zeros.\n",
    "white_df[\"is_red\"] = 0\n",
    "\n",
    "# keep only the first of duplicate items\n",
    "white_df = white_df.drop_duplicates(keep='first')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "defe38d6ec58fd31cd67b89e46c4373f",
     "grade": true,
     "grade_id": "cell-30575e713b55fc51",
     "locked": true,
     "points": 1,
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     "task": false
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "# You can click `File -> Open` in the menu above and open the `utils.py` file \n",
    "# in case you want to inspect the unit tests being used for each graded function.\n",
    "\n",
    "utils.test_white_df(white_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OQHK0ohBQRCk"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.8\n",
      "9.1\n"
     ]
    }
   ],
   "source": [
    "print(white_df.alcohol[0])\n",
    "print(white_df.alcohol[100])\n",
    "\n",
    "# EXPECTED OUTPUT\n",
    "# 8.8\n",
    "# 9.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the red wine dataset (TODO)\n",
    "- In the red wine dataset, you will fill in the column `is_red` with ones (1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "deletable": false,
    "id": "8y3QxKwBed8v",
    "nbgrader": {
     "cell_type": "code",
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     "grade": false,
     "grade_id": "cell-e47a40f306593274",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "# URL of the red wine dataset\n",
    "URL = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'\n",
    "\n",
    "# load the dataset from the URL\n",
    "red_df = pd.read_csv(URL, sep=\";\")\n",
    "\n",
    "# fill the `is_red` column with ones.\n",
    "red_df[\"is_red\"] = 1\n",
    "\n",
    "# keep only the first of duplicate items\n",
    "red_df = red_df.drop_duplicates(keep='first')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "d8e0c91b0fd668b63ba74a8f2f958b59",
     "grade": true,
     "grade_id": "cell-2a75937adcc0c25b",
     "locked": true,
     "points": 1,
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     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "utils.test_red_df(red_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zsB3LUzNQpo_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.4\n",
      "10.2\n"
     ]
    }
   ],
   "source": [
    "print(red_df.alcohol[0])\n",
    "print(red_df.alcohol[100])\n",
    "\n",
    "# EXPECTED OUTPUT\n",
    "# 9.4\n",
    "# 10.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2G8B-NYuM6-f"
   },
   "source": [
    "### Concatenate the datasets\n",
    "\n",
    "Next, concatenate the red and white wine dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YpQrOjJbfN3m"
   },
   "outputs": [],
   "source": [
    "df = pd.concat([red_df, white_df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Se2dTmThQyjb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.4\n",
      "9.5\n"
     ]
    }
   ],
   "source": [
    "print(df.alcohol[0])\n",
    "print(df.alcohol[100])\n",
    "\n",
    "# EXPECTED OUTPUT\n",
    "# 9.4\n",
    "# 9.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wx6y3rPpQv4k"
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   "source": [
    "# NOTE: In a real-world scenario, you should shuffle the data.\n",
    "# YOU ARE NOT going to do that here because we want to test\n",
    "# with deterministic data. But if you want the code to do it,\n",
    "# it's in the commented line below:\n",
    "\n",
    "#df = df.iloc[np.random.permutation(len(df))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-EqIcbg5M_n1"
   },
   "source": [
    "This will chart the quality of the wines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IsvK0-Sgy17C"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['quality'].hist(bins=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nut1rmYLzf-p"
   },
   "source": [
    "### Imbalanced data (TODO)\n",
    "You can see from the plot above that the wine quality dataset is imbalanced. \n",
    "- Since there are very few observations with quality equal to 3, 4, 8 and 9, you can drop these observations from your dataset. \n",
    "- You can do this by removing data belonging to all classes except those > 4 and < 8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "deletable": false,
    "id": "doH9_-gnf3sz",
    "nbgrader": {
     "cell_type": "code",
     "checksum": "d9ba9fc3a3ca02ccc567be33652b80fe",
     "grade": false,
     "grade_id": "cell-6a3e9db696f6827b",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "# get data with wine quality greater than 4 and less than 8\n",
    "df = df[(df['quality'] > 4) & (df['quality'] < 8 )]\n",
    "\n",
    "# reset index and drop the old one\n",
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "281e1d86a4803560ed5892cd7eda4c01",
     "grade": true,
     "grade_id": "cell-aed3da719d4682c7",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "utils.test_df_drop(df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xNR1iAlMRPXO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.4\n",
      "10.9\n"
     ]
    }
   ],
   "source": [
    "print(df.alcohol[0])\n",
    "print(df.alcohol[100])\n",
    "\n",
    "# EXPECTED OUTPUT\n",
    "# 9.4\n",
    "# 10.9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cwhuRpnVRTzG"
   },
   "source": [
    "You can plot again to see the new range of data and quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "857ygzZiLgGg"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['quality'].hist(bins=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n3k0vqSsp84t"
   },
   "source": [
    "### Train Test Split (TODO)\n",
    "\n",
    "Next, you can split the datasets into training, test and validation datasets.\n",
    "- The data frame should be split 80:20 into `train` and `test` sets.\n",
    "- The resulting `train` should then be split 80:20 into `train` and `val` sets.\n",
    "- The `train_test_split` parameter `test_size` takes a float value that ranges between 0. and 1, and represents the proportion of the dataset that is allocated to the test set.  The rest of the data is allocated to the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "deletable": false,
    "id": "PAVIf2-fgRVY",
    "nbgrader": {
     "cell_type": "code",
     "checksum": "7f5738f4fb51d65adc9a8acbdf2b9970",
     "grade": false,
     "grade_id": "cell-91946cadf745206b",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "# Please do not change the random_state parameter. This is needed for grading.\n",
    "\n",
    "# split df into 80:20 train and test sets\n",
    "train, test = train_test_split(df, test_size=.2, random_state = 1)\n",
    "                               \n",
    "# split train into 80:20 train and val sets\n",
    "train, val = train_test_split(train, test_size=.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "deletable": false,
    "editable": false,
    "id": "57h9LcEzRWpk",
    "nbgrader": {
     "cell_type": "code",
     "checksum": "42adbe9e66efac7c7a5f8cd73ac92f22",
     "grade": true,
     "grade_id": "cell-64b8b38cd0b965f6",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "utils.test_data_sizes(train.size, test.size, val.size)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RwTNu4KFqG-K"
   },
   "source": [
    "Here's where you can explore the training stats. You can pop the labels 'is_red' and 'quality' from the data as these will be used as the labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y_afyhhHM6WQ"
   },
   "outputs": [],
   "source": [
    "train_stats = train.describe()\n",
    "train_stats.pop('is_red')\n",
    "train_stats.pop('quality')\n",
    "train_stats = train_stats.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ahvbYm4fNqSt"
   },
   "source": [
    "Explore the training stats!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n_gAtPjZ0otF"
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fixed acidity</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>7.221616</td>\n",
       "      <td>1.325297</td>\n",
       "      <td>3.80000</td>\n",
       "      <td>6.40000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>7.7000</td>\n",
       "      <td>15.60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatile acidity</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>0.338929</td>\n",
       "      <td>0.162476</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.23000</td>\n",
       "      <td>0.29000</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>1.24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citric acid</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>0.321569</td>\n",
       "      <td>0.147970</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.25000</td>\n",
       "      <td>0.31000</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>1.66000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual sugar</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>5.155911</td>\n",
       "      <td>4.639632</td>\n",
       "      <td>0.60000</td>\n",
       "      <td>1.80000</td>\n",
       "      <td>2.80000</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>65.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorides</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>0.056976</td>\n",
       "      <td>0.036802</td>\n",
       "      <td>0.01200</td>\n",
       "      <td>0.03800</td>\n",
       "      <td>0.04700</td>\n",
       "      <td>0.0660</td>\n",
       "      <td>0.61100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>30.388590</td>\n",
       "      <td>17.236784</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>41.0000</td>\n",
       "      <td>131.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>115.062282</td>\n",
       "      <td>56.706617</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>75.00000</td>\n",
       "      <td>117.00000</td>\n",
       "      <td>156.0000</td>\n",
       "      <td>344.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>0.994633</td>\n",
       "      <td>0.003005</td>\n",
       "      <td>0.98711</td>\n",
       "      <td>0.99232</td>\n",
       "      <td>0.99481</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>1.03898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pH</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>3.223201</td>\n",
       "      <td>0.161272</td>\n",
       "      <td>2.72000</td>\n",
       "      <td>3.11000</td>\n",
       "      <td>3.21000</td>\n",
       "      <td>3.3300</td>\n",
       "      <td>4.01000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulphates</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>0.534051</td>\n",
       "      <td>0.149149</td>\n",
       "      <td>0.22000</td>\n",
       "      <td>0.43000</td>\n",
       "      <td>0.51000</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>1.95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>10.504466</td>\n",
       "      <td>1.154654</td>\n",
       "      <td>8.50000</td>\n",
       "      <td>9.50000</td>\n",
       "      <td>10.30000</td>\n",
       "      <td>11.3000</td>\n",
       "      <td>14.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count        mean        std      min       25%  \\\n",
       "fixed acidity         3155.0    7.221616   1.325297  3.80000   6.40000   \n",
       "volatile acidity      3155.0    0.338929   0.162476  0.08000   0.23000   \n",
       "citric acid           3155.0    0.321569   0.147970  0.00000   0.25000   \n",
       "residual sugar        3155.0    5.155911   4.639632  0.60000   1.80000   \n",
       "chlorides             3155.0    0.056976   0.036802  0.01200   0.03800   \n",
       "free sulfur dioxide   3155.0   30.388590  17.236784  1.00000  17.00000   \n",
       "total sulfur dioxide  3155.0  115.062282  56.706617  6.00000  75.00000   \n",
       "density               3155.0    0.994633   0.003005  0.98711   0.99232   \n",
       "pH                    3155.0    3.223201   0.161272  2.72000   3.11000   \n",
       "sulphates             3155.0    0.534051   0.149149  0.22000   0.43000   \n",
       "alcohol               3155.0   10.504466   1.154654  8.50000   9.50000   \n",
       "\n",
       "                            50%       75%        max  \n",
       "fixed acidity           7.00000    7.7000   15.60000  \n",
       "volatile acidity        0.29000    0.4000    1.24000  \n",
       "citric acid             0.31000    0.4000    1.66000  \n",
       "residual sugar          2.80000    7.6500   65.80000  \n",
       "chlorides               0.04700    0.0660    0.61100  \n",
       "free sulfur dioxide    28.00000   41.0000  131.00000  \n",
       "total sulfur dioxide  117.00000  156.0000  344.00000  \n",
       "density                 0.99481    0.9968    1.03898  \n",
       "pH                      3.21000    3.3300    4.01000  \n",
       "sulphates               0.51000    0.6000    1.95000  \n",
       "alcohol                10.30000   11.3000   14.00000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bGPvt9jir_HC"
   },
   "source": [
    "### Get the labels (TODO)\n",
    "\n",
    "The features and labels are currently in the same dataframe.\n",
    "- You will want to store the label columns `is_red` and `quality` separately from the feature columns.  \n",
    "- The following function, `format_output`, gets these two columns from the dataframe (it's given to you).\n",
    "- `format_output` also formats the data into numpy arrays. \n",
    "- Please use the `format_output` and apply it to the `train`, `val` and `test` sets to get dataframes for the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Z_fs14XQqZVP"
   },
   "outputs": [],
   "source": [
    "def format_output(data):\n",
    "    is_red = data.pop('is_red')\n",
    "    is_red = np.array(is_red)\n",
    "    quality = data.pop('quality')\n",
    "    quality = np.array(quality)\n",
    "    return (quality, is_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "deletable": false,
    "id": "8L3ZZe1fQicm",
    "nbgrader": {
     "cell_type": "code",
     "checksum": "7a86809e54895a816434c48dc903f55d",
     "grade": false,
     "grade_id": "cell-5c30fa2c2a354b0f",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "# format the output of the train set\n",
    "train_Y = format_output(train)\n",
    "\n",
    "# format the output of the val set\n",
    "val_Y = format_output(val)\n",
    "    \n",
    "# format the output of the test set\n",
    "test_Y = format_output(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "359cabbafaed14ec9bbc1e57a7b6f32c",
     "grade": true,
     "grade_id": "cell-4977d8befb80f56b",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "utils.test_format_output(df, train_Y, val_Y, test_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that after you get the labels, the `train`, `val` and `test` dataframes no longer contain the label columns, and contain just the feature columns.\n",
    "- This is because you used `.pop` in the `format_output` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>7.5</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.18</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.088</td>\n",
       "      <td>27.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.99915</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.77</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3557</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.29</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.044</td>\n",
       "      <td>59.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.99782</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.40</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3825</th>\n",
       "      <td>8.8</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.024</td>\n",
       "      <td>52.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.99250</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0.49</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>6.4</td>\n",
       "      <td>0.45</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.99573</td>\n",
       "      <td>3.21</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "225             7.5              0.65         0.18             7.0      0.088   \n",
       "3557            6.3              0.27         0.29            12.2      0.044   \n",
       "3825            8.8              0.27         0.25             5.0      0.024   \n",
       "1740            6.4              0.45         0.07             1.1      0.030   \n",
       "1221            7.2              0.53         0.13             2.0      0.058   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "225                  27.0                  94.0  0.99915  3.38       0.77   \n",
       "3557                 59.0                 196.0  0.99782  3.14       0.40   \n",
       "3825                 52.0                  99.0  0.99250  2.87       0.49   \n",
       "1740                 10.0                 131.0  0.99050  2.97       0.28   \n",
       "1221                 18.0                  22.0  0.99573  3.21       0.68   \n",
       "\n",
       "      alcohol  \n",
       "225       9.4  \n",
       "3557      8.8  \n",
       "3825     11.4  \n",
       "1740     10.8  \n",
       "1221      9.9  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hEdbrruAsN1D"
   },
   "source": [
    "### Normalize the data (TODO)\n",
    "\n",
    "Next, you can normalize the data, x, using the formula:\n",
    "$$x_{norm} = \\frac{x - \\mu}{\\sigma}$$\n",
    "- The `norm` function is defined for you.\n",
    "- Please apply the `norm` function to normalize the dataframes that contains the feature columns of `train`, `val` and `test` sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WWiZPAHCLjUs"
   },
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "    return (x - train_stats['mean']) / train_stats['std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "deletable": false,
    "id": "JEaOi2I2Lk69",
    "nbgrader": {
     "cell_type": "code",
     "checksum": "6bc0cdcb563d192f271067aa3373ff32",
     "grade": false,
     "grade_id": "cell-d8416d975c371095",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "# normalize the train set\n",
    "norm_train_X = norm(train)\n",
    "    \n",
    "# normalize the val set\n",
    "norm_val_X = norm(val)\n",
    "    \n",
    "# normalize the test set\n",
    "norm_test_X = norm(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "4f567db45bf40191601780379cc100b8",
     "grade": true,
     "grade_id": "cell-97fad979d157529b",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "utils.test_norm(norm_train_X, norm_val_X, norm_test_X, train, val, test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hzykDwQhsaPO"
   },
   "source": [
    "## Define the Model (TODO)\n",
    "\n",
    "Define the model using the functional API. The base model will be 2 `Dense` layers of 128 neurons each, and have the `'relu'` activation.\n",
    "- Check out the documentation for [tf.keras.layers.Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "deletable": false,
    "id": "Rhcns3oTFkM6",
    "nbgrader": {
     "cell_type": "code",
     "checksum": "74b031247e569526552bf13a034a1c07",
     "grade": false,
     "grade_id": "cell-73fceedad1fe351c",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "def base_model(inputs):\n",
    "    \n",
    "    # connect a Dense layer with 128 neurons and a relu activation\n",
    "    x = Dense(128, activation='relu')(inputs)\n",
    "    \n",
    "    # connect another Dense layer with 128 neurons and a relu activation\n",
    "    x = Dense(128, activation='relu')(x)\n",
    "    return x\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "9255924b3def80f679616e4c851a43e1",
     "grade": true,
     "grade_id": "cell-54f742a133353d75",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "utils.test_base_model(base_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xem_fcVws6Kz"
   },
   "source": [
    "# Define output layers of the model (TODO)\n",
    "\n",
    "You will add output layers to the base model. \n",
    "- The model will need two outputs.\n",
    "\n",
    "One output layer will predict wine quality, which is a numeric value.\n",
    "- Define a `Dense` layer with 1 neuron.\n",
    "- Since this is a regression output, the activation can be left as its default value `None`.\n",
    "\n",
    "The other output layer will predict the wine type, which is either red `1` or not red `0` (white).\n",
    "- Define a `Dense` layer with 1 neuron.\n",
    "- Since there are two possible categories, you can use a sigmoid activation for binary classification.\n",
    "\n",
    "Define the `Model`\n",
    "- Define the `Model` object, and set the following parameters:\n",
    "  - `inputs`: pass in the inputs to the model as a list.\n",
    "  - `outputs`: pass in a list of the outputs that you just defined: wine quality, then wine type.\n",
    "  - **Note**: please list the wine quality before wine type in the outputs, as this will affect the calculated loss if you choose the other order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "deletable": false,
    "id": "n5UGF8PMVLPt",
    "nbgrader": {
     "cell_type": "code",
     "checksum": "76d35b90d20cdcbb22986cd8211057de",
     "grade": false,
     "grade_id": "cell-19e285f482f021fb",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "def final_model(inputs):\n",
    "    \n",
    "    # get the base model\n",
    "    x = base_model(inputs)\n",
    "\n",
    "    # connect the output Dense layer for regression\n",
    "    wine_quality = Dense(units='1', name='wine_quality')(x)\n",
    "\n",
    "    # connect the output Dense layer for classification. this will use a sigmoid activation.\n",
    "    wine_type = Dense(units='1', activation='sigmoid', name='wine_type')(x)\n",
    "\n",
    "    # define the model using the input and output layers\n",
    "    model = Model(inputs=inputs, outputs=[wine_quality, wine_type])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "89cbf89d8ab5e2e59ecf7f63f517520a",
     "grade": true,
     "grade_id": "cell-40d050f855c817d1",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "utils.test_final_model(final_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5R0BMTsltZyu"
   },
   "source": [
    "## Compiling the Model\n",
    "\n",
    "Next, compile the model. When setting the loss parameter of `model.compile`, you're setting the loss for each of the two outputs (wine quality and wine type).\n",
    "\n",
    "To set more than one loss, use a dictionary of key-value pairs.\n",
    "- You can look at the docs for the losses [here](https://www.tensorflow.org/api_docs/python/tf/keras/losses#functions).\n",
    "    - **Note**: For the desired spelling, please look at the \"Functions\" section of the documentation and not the \"classes\" section on that same page.\n",
    "- wine_type: Since you will be performing binary classification on wine type, you should use the binary crossentropy loss function for it.  Please pass this in as a string.  \n",
    "  - **Hint**, this should be all lowercase.  In the documentation, you'll see this under the \"Functions\" section, not the \"Classes\" section.\n",
    "- wine_quality: since this is a regression output, use the mean squared error.  Please pass it in as a string, all lowercase.\n",
    "  - **Hint**: You may notice that there are two aliases for mean squared error.  Please use the shorter name.\n",
    "\n",
    "\n",
    "You will also set the metric for each of the two outputs.  Again, to set metrics for two or more outputs, use a dictionary with key value pairs.\n",
    "- The metrics documentation is linked [here](https://www.tensorflow.org/api_docs/python/tf/keras/metrics).\n",
    "- For the wine type, please set it to accuracy as a string, all lowercase.\n",
    "- For wine quality, please use the root mean squared error.  Instead of a string, you'll set it to an instance of the class [RootMeanSquaredError](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/RootMeanSquaredError), which belongs to the tf.keras.metrics module.\n",
    "\n",
    "**Note**: If you see the error message \n",
    ">Exception: wine quality loss function is incorrect.\n",
    "\n",
    "- Please also check your other losses and metrics, as the error may be caused by the other three key-value pairs and not the wine quality loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "deletable": false,
    "id": "LK11duUbUjmh",
    "nbgrader": {
     "cell_type": "code",
     "checksum": "22f45067ca69eb2ccadb43874dbcc27b",
     "grade": false,
     "grade_id": "cell-81afdc4dcca51d5e",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "inputs = tf.keras.layers.Input(shape=(11,))\n",
    "rms = tf.keras.optimizers.RMSprop(lr=0.0001)\n",
    "model = final_model(inputs)\n",
    "\n",
    "model.compile(optimizer=rms, \n",
    "              loss = {'wine_type' : 'binary_crossentropy',\n",
    "                      'wine_quality' : 'mean_squared_error'\n",
    "                     },\n",
    "              metrics = {'wine_type' : 'accuracy',\n",
    "                         'wine_quality': tf.keras.metrics.RootMeanSquaredError()\n",
    "                       }\n",
    "             )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "88e02238ea5e456ff65e835cc8158054",
     "grade": true,
     "grade_id": "cell-2eeeba02391c4632",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "utils.test_model_compile(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "90MpAMpWuKm-"
   },
   "source": [
    "## Training the Model\n",
    "\n",
    "Fit the model to the training inputs and outputs. \n",
    "- Check the documentation for [model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).\n",
    "- Remember to use the normalized training set as inputs. \n",
    "- For the validation data, please use the normalized validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "deletable": false,
    "id": "_eiZkle4XwiY",
    "nbgrader": {
     "cell_type": "code",
     "checksum": "2ca7664be03bfd6fd3651ae44d17b793",
     "grade": false,
     "grade_id": "cell-0bb56262896f6680",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3155 samples, validate on 789 samples\n",
      "Epoch 1/180\n",
      "3155/3155 [==============================] - 1s 353us/sample - loss: 24.0099 - wine_quality_loss: 23.2688 - wine_type_loss: 0.7131 - wine_quality_root_mean_squared_error: 4.8267 - wine_type_accuracy: 0.3990 - val_loss: 16.4031 - val_wine_quality_loss: 15.7164 - val_wine_type_loss: 0.7000 - val_wine_quality_root_mean_squared_error: 3.9627 - val_wine_type_accuracy: 0.4689\n",
      "Epoch 2/180\n",
      "3155/3155 [==============================] - 0s 98us/sample - loss: 10.8336 - wine_quality_loss: 10.1343 - wine_type_loss: 0.6700 - wine_quality_root_mean_squared_error: 3.1880 - wine_type_accuracy: 0.6019 - val_loss: 6.1004 - val_wine_quality_loss: 5.5118 - val_wine_type_loss: 0.6320 - val_wine_quality_root_mean_squared_error: 2.3384 - val_wine_type_accuracy: 0.7529\n",
      "Epoch 3/180\n",
      "3155/3155 [==============================] - 0s 96us/sample - loss: 4.3055 - wine_quality_loss: 3.7292 - wine_type_loss: 0.5713 - wine_quality_root_mean_squared_error: 1.9324 - wine_type_accuracy: 0.8143 - val_loss: 3.0938 - val_wine_quality_loss: 2.6303 - val_wine_type_loss: 0.5081 - val_wine_quality_root_mean_squared_error: 1.6079 - val_wine_type_accuracy: 0.8150\n",
      "Epoch 4/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 2.8798 - wine_quality_loss: 2.4352 - wine_type_loss: 0.4460 - wine_quality_root_mean_squared_error: 1.5600 - wine_type_accuracy: 0.8311 - val_loss: 2.5117 - val_wine_quality_loss: 2.1437 - val_wine_type_loss: 0.3955 - val_wine_quality_root_mean_squared_error: 1.4546 - val_wine_type_accuracy: 0.8631\n",
      "Epoch 5/180\n",
      "3155/3155 [==============================] - 0s 97us/sample - loss: 2.3432 - wine_quality_loss: 1.9945 - wine_type_loss: 0.3457 - wine_quality_root_mean_squared_error: 1.4133 - wine_type_accuracy: 0.9151 - val_loss: 2.1235 - val_wine_quality_loss: 1.8323 - val_wine_type_loss: 0.3075 - val_wine_quality_root_mean_squared_error: 1.3475 - val_wine_type_accuracy: 0.9404\n",
      "Epoch 6/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 1.9935 - wine_quality_loss: 1.7233 - wine_type_loss: 0.2675 - wine_quality_root_mean_squared_error: 1.3137 - wine_type_accuracy: 0.9616 - val_loss: 1.8395 - val_wine_quality_loss: 1.6095 - val_wine_type_loss: 0.2402 - val_wine_quality_root_mean_squared_error: 1.2645 - val_wine_type_accuracy: 0.9734\n",
      "Epoch 7/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 1.7458 - wine_quality_loss: 1.5422 - wine_type_loss: 0.2067 - wine_quality_root_mean_squared_error: 1.2406 - wine_type_accuracy: 0.9797 - val_loss: 1.6398 - val_wine_quality_loss: 1.4604 - val_wine_type_loss: 0.1853 - val_wine_quality_root_mean_squared_error: 1.2059 - val_wine_type_accuracy: 0.9848\n",
      "Epoch 8/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 1.5689 - wine_quality_loss: 1.4056 - wine_type_loss: 0.1621 - wine_quality_root_mean_squared_error: 1.1861 - wine_type_accuracy: 0.9835 - val_loss: 1.4759 - val_wine_quality_loss: 1.3336 - val_wine_type_loss: 0.1466 - val_wine_quality_root_mean_squared_error: 1.1528 - val_wine_type_accuracy: 0.9873\n",
      "Epoch 9/180\n",
      "3155/3155 [==============================] - 0s 77us/sample - loss: 1.4300 - wine_quality_loss: 1.3006 - wine_type_loss: 0.1289 - wine_quality_root_mean_squared_error: 1.1406 - wine_type_accuracy: 0.9870 - val_loss: 1.3546 - val_wine_quality_loss: 1.2397 - val_wine_type_loss: 0.1180 - val_wine_quality_root_mean_squared_error: 1.1119 - val_wine_type_accuracy: 0.9873\n",
      "Epoch 10/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 1.3216 - wine_quality_loss: 1.2137 - wine_type_loss: 0.1058 - wine_quality_root_mean_squared_error: 1.1026 - wine_type_accuracy: 0.9883 - val_loss: 1.2548 - val_wine_quality_loss: 1.1582 - val_wine_type_loss: 0.0978 - val_wine_quality_root_mean_squared_error: 1.0755 - val_wine_type_accuracy: 0.9886\n",
      "Epoch 11/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 1.2194 - wine_quality_loss: 1.1292 - wine_type_loss: 0.0891 - wine_quality_root_mean_squared_error: 1.0631 - wine_type_accuracy: 0.9899 - val_loss: 1.1561 - val_wine_quality_loss: 1.0733 - val_wine_type_loss: 0.0830 - val_wine_quality_root_mean_squared_error: 1.0358 - val_wine_type_accuracy: 0.9886\n",
      "Epoch 12/180\n",
      "3155/3155 [==============================] - 0s 96us/sample - loss: 1.1359 - wine_quality_loss: 1.0579 - wine_type_loss: 0.0771 - wine_quality_root_mean_squared_error: 1.0290 - wine_type_accuracy: 0.9914 - val_loss: 1.0943 - val_wine_quality_loss: 1.0209 - val_wine_type_loss: 0.0725 - val_wine_quality_root_mean_squared_error: 1.0107 - val_wine_type_accuracy: 0.9911\n",
      "Epoch 13/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 1.0660 - wine_quality_loss: 1.0003 - wine_type_loss: 0.0682 - wine_quality_root_mean_squared_error: 0.9989 - wine_type_accuracy: 0.9911 - val_loss: 1.0093 - val_wine_quality_loss: 0.9440 - val_wine_type_loss: 0.0647 - val_wine_quality_root_mean_squared_error: 0.9717 - val_wine_type_accuracy: 0.9911\n",
      "Epoch 14/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 1.0044 - wine_quality_loss: 0.9424 - wine_type_loss: 0.0614 - wine_quality_root_mean_squared_error: 0.9710 - wine_type_accuracy: 0.9911 - val_loss: 0.9457 - val_wine_quality_loss: 0.8860 - val_wine_type_loss: 0.0586 - val_wine_quality_root_mean_squared_error: 0.9417 - val_wine_type_accuracy: 0.9937\n",
      "Epoch 15/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.9440 - wine_quality_loss: 0.8866 - wine_type_loss: 0.0566 - wine_quality_root_mean_squared_error: 0.9420 - wine_type_accuracy: 0.9914 - val_loss: 0.8911 - val_wine_quality_loss: 0.8356 - val_wine_type_loss: 0.0541 - val_wine_quality_root_mean_squared_error: 0.9147 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 16/180\n",
      "3155/3155 [==============================] - 0s 96us/sample - loss: 0.8883 - wine_quality_loss: 0.8365 - wine_type_loss: 0.0524 - wine_quality_root_mean_squared_error: 0.9142 - wine_type_accuracy: 0.9911 - val_loss: 0.8364 - val_wine_quality_loss: 0.7845 - val_wine_type_loss: 0.0506 - val_wine_quality_root_mean_squared_error: 0.8863 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 17/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.8409 - wine_quality_loss: 0.7902 - wine_type_loss: 0.0493 - wine_quality_root_mean_squared_error: 0.8897 - wine_type_accuracy: 0.9914 - val_loss: 0.7950 - val_wine_quality_loss: 0.7458 - val_wine_type_loss: 0.0476 - val_wine_quality_root_mean_squared_error: 0.8643 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 18/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.7989 - wine_quality_loss: 0.7514 - wine_type_loss: 0.0469 - wine_quality_root_mean_squared_error: 0.8672 - wine_type_accuracy: 0.9918 - val_loss: 0.7571 - val_wine_quality_loss: 0.7100 - val_wine_type_loss: 0.0454 - val_wine_quality_root_mean_squared_error: 0.8434 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 19/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.7593 - wine_quality_loss: 0.7149 - wine_type_loss: 0.0449 - wine_quality_root_mean_squared_error: 0.8453 - wine_type_accuracy: 0.9914 - val_loss: 0.7193 - val_wine_quality_loss: 0.6738 - val_wine_type_loss: 0.0436 - val_wine_quality_root_mean_squared_error: 0.8218 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 20/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.7224 - wine_quality_loss: 0.6898 - wine_type_loss: 0.0429 - wine_quality_root_mean_squared_error: 0.8242 - wine_type_accuracy: 0.9921 - val_loss: 0.6992 - val_wine_quality_loss: 0.6555 - val_wine_type_loss: 0.0421 - val_wine_quality_root_mean_squared_error: 0.8104 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 21/180\n",
      "3155/3155 [==============================] - 0s 79us/sample - loss: 0.6869 - wine_quality_loss: 0.6471 - wine_type_loss: 0.0414 - wine_quality_root_mean_squared_error: 0.8033 - wine_type_accuracy: 0.9921 - val_loss: 0.6590 - val_wine_quality_loss: 0.6164 - val_wine_type_loss: 0.0405 - val_wine_quality_root_mean_squared_error: 0.7862 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 22/180\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.6564 - wine_quality_loss: 0.6247 - wine_type_loss: 0.0403 - wine_quality_root_mean_squared_error: 0.7849 - wine_type_accuracy: 0.9924 - val_loss: 0.6279 - val_wine_quality_loss: 0.5866 - val_wine_type_loss: 0.0395 - val_wine_quality_root_mean_squared_error: 0.7668 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 23/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.6262 - wine_quality_loss: 0.5867 - wine_type_loss: 0.0390 - wine_quality_root_mean_squared_error: 0.7662 - wine_type_accuracy: 0.9924 - val_loss: 0.5931 - val_wine_quality_loss: 0.5526 - val_wine_type_loss: 0.0386 - val_wine_quality_root_mean_squared_error: 0.7444 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 24/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.5992 - wine_quality_loss: 0.5619 - wine_type_loss: 0.0381 - wine_quality_root_mean_squared_error: 0.7490 - wine_type_accuracy: 0.9927 - val_loss: 0.5629 - val_wine_quality_loss: 0.5235 - val_wine_type_loss: 0.0377 - val_wine_quality_root_mean_squared_error: 0.7244 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 25/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.5751 - wine_quality_loss: 0.5371 - wine_type_loss: 0.0373 - wine_quality_root_mean_squared_error: 0.7333 - wine_type_accuracy: 0.9927 - val_loss: 0.5487 - val_wine_quality_loss: 0.5098 - val_wine_type_loss: 0.0370 - val_wine_quality_root_mean_squared_error: 0.7150 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 26/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.5537 - wine_quality_loss: 0.5161 - wine_type_loss: 0.0364 - wine_quality_root_mean_squared_error: 0.7191 - wine_type_accuracy: 0.9933 - val_loss: 0.5341 - val_wine_quality_loss: 0.4957 - val_wine_type_loss: 0.0364 - val_wine_quality_root_mean_squared_error: 0.7052 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 27/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.5327 - wine_quality_loss: 0.4961 - wine_type_loss: 0.0358 - wine_quality_root_mean_squared_error: 0.7049 - wine_type_accuracy: 0.9930 - val_loss: 0.5119 - val_wine_quality_loss: 0.4746 - val_wine_type_loss: 0.0358 - val_wine_quality_root_mean_squared_error: 0.6898 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 28/180\n",
      "3155/3155 [==============================] - 0s 95us/sample - loss: 0.5144 - wine_quality_loss: 0.4794 - wine_type_loss: 0.0351 - wine_quality_root_mean_squared_error: 0.6922 - wine_type_accuracy: 0.9933 - val_loss: 0.4936 - val_wine_quality_loss: 0.4566 - val_wine_type_loss: 0.0353 - val_wine_quality_root_mean_squared_error: 0.6767 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 29/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.4954 - wine_quality_loss: 0.4611 - wine_type_loss: 0.0346 - wine_quality_root_mean_squared_error: 0.6788 - wine_type_accuracy: 0.9933 - val_loss: 0.4811 - val_wine_quality_loss: 0.4444 - val_wine_type_loss: 0.0349 - val_wine_quality_root_mean_squared_error: 0.6677 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 30/180\n",
      "3155/3155 [==============================] - 0s 79us/sample - loss: 0.4812 - wine_quality_loss: 0.4468 - wine_type_loss: 0.0344 - wine_quality_root_mean_squared_error: 0.6687 - wine_type_accuracy: 0.9937 - val_loss: 0.4752 - val_wine_quality_loss: 0.4389 - val_wine_type_loss: 0.0346 - val_wine_quality_root_mean_squared_error: 0.6635 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 31/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.4667 - wine_quality_loss: 0.4323 - wine_type_loss: 0.0336 - wine_quality_root_mean_squared_error: 0.6580 - wine_type_accuracy: 0.9933 - val_loss: 0.4541 - val_wine_quality_loss: 0.4181 - val_wine_type_loss: 0.0340 - val_wine_quality_root_mean_squared_error: 0.6478 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 32/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.4535 - wine_quality_loss: 0.4203 - wine_type_loss: 0.0330 - wine_quality_root_mean_squared_error: 0.6484 - wine_type_accuracy: 0.9933 - val_loss: 0.4365 - val_wine_quality_loss: 0.4013 - val_wine_type_loss: 0.0337 - val_wine_quality_root_mean_squared_error: 0.6343 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 33/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.4412 - wine_quality_loss: 0.4077 - wine_type_loss: 0.0327 - wine_quality_root_mean_squared_error: 0.6390 - wine_type_accuracy: 0.9940 - val_loss: 0.4272 - val_wine_quality_loss: 0.3922 - val_wine_type_loss: 0.0333 - val_wine_quality_root_mean_squared_error: 0.6273 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 34/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.4312 - wine_quality_loss: 0.3990 - wine_type_loss: 0.0322 - wine_quality_root_mean_squared_error: 0.6315 - wine_type_accuracy: 0.9940 - val_loss: 0.4206 - val_wine_quality_loss: 0.3864 - val_wine_type_loss: 0.0329 - val_wine_quality_root_mean_squared_error: 0.6224 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 35/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.4222 - wine_quality_loss: 0.3900 - wine_type_loss: 0.0318 - wine_quality_root_mean_squared_error: 0.6247 - wine_type_accuracy: 0.9940 - val_loss: 0.4074 - val_wine_quality_loss: 0.3730 - val_wine_type_loss: 0.0327 - val_wine_quality_root_mean_squared_error: 0.6118 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 36/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.4141 - wine_quality_loss: 0.3825 - wine_type_loss: 0.0316 - wine_quality_root_mean_squared_error: 0.6184 - wine_type_accuracy: 0.9943 - val_loss: 0.4127 - val_wine_quality_loss: 0.3786 - val_wine_type_loss: 0.0324 - val_wine_quality_root_mean_squared_error: 0.6164 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 37/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.4070 - wine_quality_loss: 0.3760 - wine_type_loss: 0.0312 - wine_quality_root_mean_squared_error: 0.6129 - wine_type_accuracy: 0.9946 - val_loss: 0.3944 - val_wine_quality_loss: 0.3606 - val_wine_type_loss: 0.0321 - val_wine_quality_root_mean_squared_error: 0.6016 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 38/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.4008 - wine_quality_loss: 0.3695 - wine_type_loss: 0.0309 - wine_quality_root_mean_squared_error: 0.6081 - wine_type_accuracy: 0.9943 - val_loss: 0.3914 - val_wine_quality_loss: 0.3579 - val_wine_type_loss: 0.0319 - val_wine_quality_root_mean_squared_error: 0.5993 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 39/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3938 - wine_quality_loss: 0.3628 - wine_type_loss: 0.0306 - wine_quality_root_mean_squared_error: 0.6026 - wine_type_accuracy: 0.9949 - val_loss: 0.3890 - val_wine_quality_loss: 0.3555 - val_wine_type_loss: 0.0318 - val_wine_quality_root_mean_squared_error: 0.5973 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 40/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.3883 - wine_quality_loss: 0.3579 - wine_type_loss: 0.0303 - wine_quality_root_mean_squared_error: 0.5982 - wine_type_accuracy: 0.9949 - val_loss: 0.3804 - val_wine_quality_loss: 0.3471 - val_wine_type_loss: 0.0317 - val_wine_quality_root_mean_squared_error: 0.5902 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 41/180\n",
      "3155/3155 [==============================] - 0s 78us/sample - loss: 0.3829 - wine_quality_loss: 0.3529 - wine_type_loss: 0.0317 - wine_quality_root_mean_squared_error: 0.5939 - wine_type_accuracy: 0.9949 - val_loss: 0.3813 - val_wine_quality_loss: 0.3481 - val_wine_type_loss: 0.0315 - val_wine_quality_root_mean_squared_error: 0.5911 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 42/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3784 - wine_quality_loss: 0.3481 - wine_type_loss: 0.0297 - wine_quality_root_mean_squared_error: 0.5904 - wine_type_accuracy: 0.9949 - val_loss: 0.3792 - val_wine_quality_loss: 0.3466 - val_wine_type_loss: 0.0312 - val_wine_quality_root_mean_squared_error: 0.5896 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 43/180\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3751 - wine_quality_loss: 0.3449 - wine_type_loss: 0.0295 - wine_quality_root_mean_squared_error: 0.5877 - wine_type_accuracy: 0.9949 - val_loss: 0.3731 - val_wine_quality_loss: 0.3407 - val_wine_type_loss: 0.0311 - val_wine_quality_root_mean_squared_error: 0.5845 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 44/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3720 - wine_quality_loss: 0.3423 - wine_type_loss: 0.0293 - wine_quality_root_mean_squared_error: 0.5853 - wine_type_accuracy: 0.9949 - val_loss: 0.3719 - val_wine_quality_loss: 0.3396 - val_wine_type_loss: 0.0310 - val_wine_quality_root_mean_squared_error: 0.5836 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 45/180\n",
      "3155/3155 [==============================] - 0s 96us/sample - loss: 0.3666 - wine_quality_loss: 0.3371 - wine_type_loss: 0.0291 - wine_quality_root_mean_squared_error: 0.5809 - wine_type_accuracy: 0.9949 - val_loss: 0.3686 - val_wine_quality_loss: 0.3364 - val_wine_type_loss: 0.0307 - val_wine_quality_root_mean_squared_error: 0.5809 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 46/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3643 - wine_quality_loss: 0.3349 - wine_type_loss: 0.0288 - wine_quality_root_mean_squared_error: 0.5791 - wine_type_accuracy: 0.9952 - val_loss: 0.3642 - val_wine_quality_loss: 0.3321 - val_wine_type_loss: 0.0306 - val_wine_quality_root_mean_squared_error: 0.5773 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 47/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3616 - wine_quality_loss: 0.3325 - wine_type_loss: 0.0287 - wine_quality_root_mean_squared_error: 0.5769 - wine_type_accuracy: 0.9952 - val_loss: 0.3637 - val_wine_quality_loss: 0.3319 - val_wine_type_loss: 0.0304 - val_wine_quality_root_mean_squared_error: 0.5770 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 48/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3592 - wine_quality_loss: 0.3301 - wine_type_loss: 0.0284 - wine_quality_root_mean_squared_error: 0.5751 - wine_type_accuracy: 0.9952 - val_loss: 0.3635 - val_wine_quality_loss: 0.3319 - val_wine_type_loss: 0.0303 - val_wine_quality_root_mean_squared_error: 0.5770 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 49/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3563 - wine_quality_loss: 0.3282 - wine_type_loss: 0.0286 - wine_quality_root_mean_squared_error: 0.5728 - wine_type_accuracy: 0.9952 - val_loss: 0.3654 - val_wine_quality_loss: 0.3335 - val_wine_type_loss: 0.0304 - val_wine_quality_root_mean_squared_error: 0.5784 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 50/180\n",
      "3155/3155 [==============================] - 0s 77us/sample - loss: 0.3542 - wine_quality_loss: 0.3264 - wine_type_loss: 0.0280 - wine_quality_root_mean_squared_error: 0.5711 - wine_type_accuracy: 0.9952 - val_loss: 0.3627 - val_wine_quality_loss: 0.3309 - val_wine_type_loss: 0.0301 - val_wine_quality_root_mean_squared_error: 0.5763 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 51/180\n",
      "3155/3155 [==============================] - 0s 95us/sample - loss: 0.3514 - wine_quality_loss: 0.3236 - wine_type_loss: 0.0278 - wine_quality_root_mean_squared_error: 0.5688 - wine_type_accuracy: 0.9952 - val_loss: 0.3766 - val_wine_quality_loss: 0.3447 - val_wine_type_loss: 0.0300 - val_wine_quality_root_mean_squared_error: 0.5884 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 52/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3505 - wine_quality_loss: 0.3223 - wine_type_loss: 0.0276 - wine_quality_root_mean_squared_error: 0.5682 - wine_type_accuracy: 0.9952 - val_loss: 0.3587 - val_wine_quality_loss: 0.3275 - val_wine_type_loss: 0.0299 - val_wine_quality_root_mean_squared_error: 0.5731 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 53/180\n",
      "3155/3155 [==============================] - 0s 96us/sample - loss: 0.3488 - wine_quality_loss: 0.3211 - wine_type_loss: 0.0274 - wine_quality_root_mean_squared_error: 0.5669 - wine_type_accuracy: 0.9952 - val_loss: 0.3563 - val_wine_quality_loss: 0.3251 - val_wine_type_loss: 0.0298 - val_wine_quality_root_mean_squared_error: 0.5711 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 54/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.3459 - wine_quality_loss: 0.3190 - wine_type_loss: 0.0272 - wine_quality_root_mean_squared_error: 0.5645 - wine_type_accuracy: 0.9952 - val_loss: 0.3526 - val_wine_quality_loss: 0.3215 - val_wine_type_loss: 0.0296 - val_wine_quality_root_mean_squared_error: 0.5680 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 55/180\n",
      "3155/3155 [==============================] - 0s 96us/sample - loss: 0.3445 - wine_quality_loss: 0.3182 - wine_type_loss: 0.0271 - wine_quality_root_mean_squared_error: 0.5634 - wine_type_accuracy: 0.9952 - val_loss: 0.3565 - val_wine_quality_loss: 0.3258 - val_wine_type_loss: 0.0295 - val_wine_quality_root_mean_squared_error: 0.5715 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 56/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3419 - wine_quality_loss: 0.3162 - wine_type_loss: 0.0268 - wine_quality_root_mean_squared_error: 0.5613 - wine_type_accuracy: 0.9952 - val_loss: 0.3525 - val_wine_quality_loss: 0.3219 - val_wine_type_loss: 0.0295 - val_wine_quality_root_mean_squared_error: 0.5681 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 57/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3415 - wine_quality_loss: 0.3150 - wine_type_loss: 0.0266 - wine_quality_root_mean_squared_error: 0.5610 - wine_type_accuracy: 0.9952 - val_loss: 0.3548 - val_wine_quality_loss: 0.3243 - val_wine_type_loss: 0.0294 - val_wine_quality_root_mean_squared_error: 0.5702 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 58/180\n",
      "3155/3155 [==============================] - 0s 95us/sample - loss: 0.3405 - wine_quality_loss: 0.3137 - wine_type_loss: 0.0264 - wine_quality_root_mean_squared_error: 0.5603 - wine_type_accuracy: 0.9952 - val_loss: 0.3523 - val_wine_quality_loss: 0.3216 - val_wine_type_loss: 0.0292 - val_wine_quality_root_mean_squared_error: 0.5681 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 59/180\n",
      "3155/3155 [==============================] - 0s 95us/sample - loss: 0.3388 - wine_quality_loss: 0.3119 - wine_type_loss: 0.0263 - wine_quality_root_mean_squared_error: 0.5589 - wine_type_accuracy: 0.9952 - val_loss: 0.3490 - val_wine_quality_loss: 0.3185 - val_wine_type_loss: 0.0292 - val_wine_quality_root_mean_squared_error: 0.5652 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 60/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3364 - wine_quality_loss: 0.3104 - wine_type_loss: 0.0261 - wine_quality_root_mean_squared_error: 0.5570 - wine_type_accuracy: 0.9952 - val_loss: 0.3477 - val_wine_quality_loss: 0.3173 - val_wine_type_loss: 0.0292 - val_wine_quality_root_mean_squared_error: 0.5640 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 61/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.3349 - wine_quality_loss: 0.3093 - wine_type_loss: 0.0260 - wine_quality_root_mean_squared_error: 0.5558 - wine_type_accuracy: 0.9952 - val_loss: 0.3507 - val_wine_quality_loss: 0.3203 - val_wine_type_loss: 0.0291 - val_wine_quality_root_mean_squared_error: 0.5668 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 62/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3340 - wine_quality_loss: 0.3090 - wine_type_loss: 0.0258 - wine_quality_root_mean_squared_error: 0.5551 - wine_type_accuracy: 0.9956 - val_loss: 0.3475 - val_wine_quality_loss: 0.3172 - val_wine_type_loss: 0.0290 - val_wine_quality_root_mean_squared_error: 0.5640 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 63/180\n",
      "3155/3155 [==============================] - 0s 76us/sample - loss: 0.3321 - wine_quality_loss: 0.3065 - wine_type_loss: 0.0256 - wine_quality_root_mean_squared_error: 0.5535 - wine_type_accuracy: 0.9952 - val_loss: 0.3448 - val_wine_quality_loss: 0.3148 - val_wine_type_loss: 0.0287 - val_wine_quality_root_mean_squared_error: 0.5619 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 64/180\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.3316 - wine_quality_loss: 0.3058 - wine_type_loss: 0.0255 - wine_quality_root_mean_squared_error: 0.5532 - wine_type_accuracy: 0.9952 - val_loss: 0.3474 - val_wine_quality_loss: 0.3175 - val_wine_type_loss: 0.0288 - val_wine_quality_root_mean_squared_error: 0.5642 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 65/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.3298 - wine_quality_loss: 0.3050 - wine_type_loss: 0.0252 - wine_quality_root_mean_squared_error: 0.5518 - wine_type_accuracy: 0.9952 - val_loss: 0.3536 - val_wine_quality_loss: 0.3239 - val_wine_type_loss: 0.0286 - val_wine_quality_root_mean_squared_error: 0.5697 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 66/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.3303 - wine_quality_loss: 0.3052 - wine_type_loss: 0.0251 - wine_quality_root_mean_squared_error: 0.5524 - wine_type_accuracy: 0.9952 - val_loss: 0.3500 - val_wine_quality_loss: 0.3200 - val_wine_type_loss: 0.0286 - val_wine_quality_root_mean_squared_error: 0.5666 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 67/180\n",
      "3155/3155 [==============================] - 0s 96us/sample - loss: 0.3289 - wine_quality_loss: 0.3036 - wine_type_loss: 0.0249 - wine_quality_root_mean_squared_error: 0.5513 - wine_type_accuracy: 0.9956 - val_loss: 0.3501 - val_wine_quality_loss: 0.3202 - val_wine_type_loss: 0.0285 - val_wine_quality_root_mean_squared_error: 0.5668 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 68/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3266 - wine_quality_loss: 0.3019 - wine_type_loss: 0.0248 - wine_quality_root_mean_squared_error: 0.5492 - wine_type_accuracy: 0.9956 - val_loss: 0.3477 - val_wine_quality_loss: 0.3180 - val_wine_type_loss: 0.0286 - val_wine_quality_root_mean_squared_error: 0.5646 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 69/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.3264 - wine_quality_loss: 0.3017 - wine_type_loss: 0.0248 - wine_quality_root_mean_squared_error: 0.5492 - wine_type_accuracy: 0.9952 - val_loss: 0.3436 - val_wine_quality_loss: 0.3140 - val_wine_type_loss: 0.0284 - val_wine_quality_root_mean_squared_error: 0.5611 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 70/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.3262 - wine_quality_loss: 0.3012 - wine_type_loss: 0.0259 - wine_quality_root_mean_squared_error: 0.5492 - wine_type_accuracy: 0.9956 - val_loss: 0.3459 - val_wine_quality_loss: 0.3164 - val_wine_type_loss: 0.0282 - val_wine_quality_root_mean_squared_error: 0.5633 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 71/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3241 - wine_quality_loss: 0.2993 - wine_type_loss: 0.0244 - wine_quality_root_mean_squared_error: 0.5474 - wine_type_accuracy: 0.9956 - val_loss: 0.3501 - val_wine_quality_loss: 0.3209 - val_wine_type_loss: 0.0282 - val_wine_quality_root_mean_squared_error: 0.5671 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 72/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3244 - wine_quality_loss: 0.3006 - wine_type_loss: 0.0243 - wine_quality_root_mean_squared_error: 0.5478 - wine_type_accuracy: 0.9956 - val_loss: 0.3423 - val_wine_quality_loss: 0.3129 - val_wine_type_loss: 0.0282 - val_wine_quality_root_mean_squared_error: 0.5601 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 73/180\n",
      "3155/3155 [==============================] - 0s 89us/sample - loss: 0.3227 - wine_quality_loss: 0.2993 - wine_type_loss: 0.0241 - wine_quality_root_mean_squared_error: 0.5464 - wine_type_accuracy: 0.9956 - val_loss: 0.3477 - val_wine_quality_loss: 0.3187 - val_wine_type_loss: 0.0281 - val_wine_quality_root_mean_squared_error: 0.5650 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 74/180\n",
      "3155/3155 [==============================] - 0s 78us/sample - loss: 0.3225 - wine_quality_loss: 0.2981 - wine_type_loss: 0.0240 - wine_quality_root_mean_squared_error: 0.5462 - wine_type_accuracy: 0.9956 - val_loss: 0.3414 - val_wine_quality_loss: 0.3122 - val_wine_type_loss: 0.0280 - val_wine_quality_root_mean_squared_error: 0.5595 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 75/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3209 - wine_quality_loss: 0.2970 - wine_type_loss: 0.0253 - wine_quality_root_mean_squared_error: 0.5449 - wine_type_accuracy: 0.9956 - val_loss: 0.3441 - val_wine_quality_loss: 0.3152 - val_wine_type_loss: 0.0278 - val_wine_quality_root_mean_squared_error: 0.5620 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 76/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3206 - wine_quality_loss: 0.2966 - wine_type_loss: 0.0236 - wine_quality_root_mean_squared_error: 0.5448 - wine_type_accuracy: 0.9956 - val_loss: 0.3445 - val_wine_quality_loss: 0.3153 - val_wine_type_loss: 0.0278 - val_wine_quality_root_mean_squared_error: 0.5624 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 77/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3191 - wine_quality_loss: 0.2949 - wine_type_loss: 0.0236 - wine_quality_root_mean_squared_error: 0.5436 - wine_type_accuracy: 0.9956 - val_loss: 0.3500 - val_wine_quality_loss: 0.3210 - val_wine_type_loss: 0.0278 - val_wine_quality_root_mean_squared_error: 0.5672 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 78/180\n",
      "3155/3155 [==============================] - 0s 89us/sample - loss: 0.3191 - wine_quality_loss: 0.2959 - wine_type_loss: 0.0234 - wine_quality_root_mean_squared_error: 0.5437 - wine_type_accuracy: 0.9956 - val_loss: 0.3411 - val_wine_quality_loss: 0.3121 - val_wine_type_loss: 0.0278 - val_wine_quality_root_mean_squared_error: 0.5595 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 79/180\n",
      "3155/3155 [==============================] - 0s 79us/sample - loss: 0.3180 - wine_quality_loss: 0.2948 - wine_type_loss: 0.0233 - wine_quality_root_mean_squared_error: 0.5428 - wine_type_accuracy: 0.9956 - val_loss: 0.3436 - val_wine_quality_loss: 0.3147 - val_wine_type_loss: 0.0277 - val_wine_quality_root_mean_squared_error: 0.5618 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 80/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3166 - wine_quality_loss: 0.2931 - wine_type_loss: 0.0231 - wine_quality_root_mean_squared_error: 0.5416 - wine_type_accuracy: 0.9956 - val_loss: 0.3437 - val_wine_quality_loss: 0.3147 - val_wine_type_loss: 0.0277 - val_wine_quality_root_mean_squared_error: 0.5618 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 81/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.3175 - wine_quality_loss: 0.2947 - wine_type_loss: 0.0230 - wine_quality_root_mean_squared_error: 0.5426 - wine_type_accuracy: 0.9956 - val_loss: 0.3442 - val_wine_quality_loss: 0.3152 - val_wine_type_loss: 0.0276 - val_wine_quality_root_mean_squared_error: 0.5623 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 82/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3146 - wine_quality_loss: 0.2919 - wine_type_loss: 0.0230 - wine_quality_root_mean_squared_error: 0.5400 - wine_type_accuracy: 0.9956 - val_loss: 0.3453 - val_wine_quality_loss: 0.3171 - val_wine_type_loss: 0.0275 - val_wine_quality_root_mean_squared_error: 0.5634 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 83/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3147 - wine_quality_loss: 0.2916 - wine_type_loss: 0.0227 - wine_quality_root_mean_squared_error: 0.5402 - wine_type_accuracy: 0.9956 - val_loss: 0.3416 - val_wine_quality_loss: 0.3129 - val_wine_type_loss: 0.0274 - val_wine_quality_root_mean_squared_error: 0.5602 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 84/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3139 - wine_quality_loss: 0.2912 - wine_type_loss: 0.0228 - wine_quality_root_mean_squared_error: 0.5396 - wine_type_accuracy: 0.9956 - val_loss: 0.3417 - val_wine_quality_loss: 0.3131 - val_wine_type_loss: 0.0274 - val_wine_quality_root_mean_squared_error: 0.5603 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 85/180\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.3133 - wine_quality_loss: 0.2913 - wine_type_loss: 0.0225 - wine_quality_root_mean_squared_error: 0.5392 - wine_type_accuracy: 0.9956 - val_loss: 0.3428 - val_wine_quality_loss: 0.3143 - val_wine_type_loss: 0.0273 - val_wine_quality_root_mean_squared_error: 0.5613 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 86/180\n",
      "3155/3155 [==============================] - 0s 78us/sample - loss: 0.3124 - wine_quality_loss: 0.2899 - wine_type_loss: 0.0224 - wine_quality_root_mean_squared_error: 0.5385 - wine_type_accuracy: 0.9956 - val_loss: 0.3369 - val_wine_quality_loss: 0.3086 - val_wine_type_loss: 0.0274 - val_wine_quality_root_mean_squared_error: 0.5561 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 87/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3123 - wine_quality_loss: 0.2900 - wine_type_loss: 0.0222 - wine_quality_root_mean_squared_error: 0.5385 - wine_type_accuracy: 0.9959 - val_loss: 0.3393 - val_wine_quality_loss: 0.3109 - val_wine_type_loss: 0.0272 - val_wine_quality_root_mean_squared_error: 0.5583 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 88/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.3107 - wine_quality_loss: 0.2884 - wine_type_loss: 0.0222 - wine_quality_root_mean_squared_error: 0.5371 - wine_type_accuracy: 0.9956 - val_loss: 0.3380 - val_wine_quality_loss: 0.3096 - val_wine_type_loss: 0.0272 - val_wine_quality_root_mean_squared_error: 0.5572 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 89/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.3096 - wine_quality_loss: 0.2880 - wine_type_loss: 0.0220 - wine_quality_root_mean_squared_error: 0.5362 - wine_type_accuracy: 0.9959 - val_loss: 0.3421 - val_wine_quality_loss: 0.3140 - val_wine_type_loss: 0.0271 - val_wine_quality_root_mean_squared_error: 0.5609 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 90/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.3098 - wine_quality_loss: 0.2874 - wine_type_loss: 0.0219 - wine_quality_root_mean_squared_error: 0.5366 - wine_type_accuracy: 0.9956 - val_loss: 0.3362 - val_wine_quality_loss: 0.3080 - val_wine_type_loss: 0.0270 - val_wine_quality_root_mean_squared_error: 0.5558 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 91/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3093 - wine_quality_loss: 0.2875 - wine_type_loss: 0.0217 - wine_quality_root_mean_squared_error: 0.5362 - wine_type_accuracy: 0.9956 - val_loss: 0.3444 - val_wine_quality_loss: 0.3161 - val_wine_type_loss: 0.0271 - val_wine_quality_root_mean_squared_error: 0.5630 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 92/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3086 - wine_quality_loss: 0.2864 - wine_type_loss: 0.0217 - wine_quality_root_mean_squared_error: 0.5356 - wine_type_accuracy: 0.9956 - val_loss: 0.3396 - val_wine_quality_loss: 0.3116 - val_wine_type_loss: 0.0271 - val_wine_quality_root_mean_squared_error: 0.5587 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 93/180\n",
      "3155/3155 [==============================] - 0s 75us/sample - loss: 0.3075 - wine_quality_loss: 0.2859 - wine_type_loss: 0.0215 - wine_quality_root_mean_squared_error: 0.5348 - wine_type_accuracy: 0.9959 - val_loss: 0.3405 - val_wine_quality_loss: 0.3127 - val_wine_type_loss: 0.0270 - val_wine_quality_root_mean_squared_error: 0.5597 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 94/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3069 - wine_quality_loss: 0.2851 - wine_type_loss: 0.0214 - wine_quality_root_mean_squared_error: 0.5342 - wine_type_accuracy: 0.9959 - val_loss: 0.3513 - val_wine_quality_loss: 0.3233 - val_wine_type_loss: 0.0270 - val_wine_quality_root_mean_squared_error: 0.5692 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 95/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.3067 - wine_quality_loss: 0.2855 - wine_type_loss: 0.0213 - wine_quality_root_mean_squared_error: 0.5342 - wine_type_accuracy: 0.9959 - val_loss: 0.3439 - val_wine_quality_loss: 0.3160 - val_wine_type_loss: 0.0268 - val_wine_quality_root_mean_squared_error: 0.5629 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 96/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3062 - wine_quality_loss: 0.2852 - wine_type_loss: 0.0212 - wine_quality_root_mean_squared_error: 0.5338 - wine_type_accuracy: 0.9959 - val_loss: 0.3411 - val_wine_quality_loss: 0.3132 - val_wine_type_loss: 0.0267 - val_wine_quality_root_mean_squared_error: 0.5604 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 97/180\n",
      "3155/3155 [==============================] - 0s 89us/sample - loss: 0.3061 - wine_quality_loss: 0.2845 - wine_type_loss: 0.0213 - wine_quality_root_mean_squared_error: 0.5339 - wine_type_accuracy: 0.9959 - val_loss: 0.3432 - val_wine_quality_loss: 0.3156 - val_wine_type_loss: 0.0266 - val_wine_quality_root_mean_squared_error: 0.5623 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 98/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3049 - wine_quality_loss: 0.2838 - wine_type_loss: 0.0209 - wine_quality_root_mean_squared_error: 0.5328 - wine_type_accuracy: 0.9959 - val_loss: 0.3373 - val_wine_quality_loss: 0.3098 - val_wine_type_loss: 0.0266 - val_wine_quality_root_mean_squared_error: 0.5571 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 99/180\n",
      "3155/3155 [==============================] - 0s 76us/sample - loss: 0.3041 - wine_quality_loss: 0.2835 - wine_type_loss: 0.0208 - wine_quality_root_mean_squared_error: 0.5321 - wine_type_accuracy: 0.9959 - val_loss: 0.3433 - val_wine_quality_loss: 0.3159 - val_wine_type_loss: 0.0266 - val_wine_quality_root_mean_squared_error: 0.5624 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 100/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3046 - wine_quality_loss: 0.2835 - wine_type_loss: 0.0207 - wine_quality_root_mean_squared_error: 0.5327 - wine_type_accuracy: 0.9959 - val_loss: 0.3391 - val_wine_quality_loss: 0.3113 - val_wine_type_loss: 0.0267 - val_wine_quality_root_mean_squared_error: 0.5586 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 101/180\n",
      "3155/3155 [==============================] - 0s 95us/sample - loss: 0.3024 - wine_quality_loss: 0.2817 - wine_type_loss: 0.0207 - wine_quality_root_mean_squared_error: 0.5307 - wine_type_accuracy: 0.9959 - val_loss: 0.3378 - val_wine_quality_loss: 0.3104 - val_wine_type_loss: 0.0265 - val_wine_quality_root_mean_squared_error: 0.5577 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 102/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3019 - wine_quality_loss: 0.2817 - wine_type_loss: 0.0205 - wine_quality_root_mean_squared_error: 0.5304 - wine_type_accuracy: 0.9959 - val_loss: 0.3385 - val_wine_quality_loss: 0.3114 - val_wine_type_loss: 0.0263 - val_wine_quality_root_mean_squared_error: 0.5585 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 103/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.3012 - wine_quality_loss: 0.2810 - wine_type_loss: 0.0204 - wine_quality_root_mean_squared_error: 0.5299 - wine_type_accuracy: 0.9959 - val_loss: 0.3378 - val_wine_quality_loss: 0.3106 - val_wine_type_loss: 0.0264 - val_wine_quality_root_mean_squared_error: 0.5578 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 104/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3015 - wine_quality_loss: 0.2811 - wine_type_loss: 0.0203 - wine_quality_root_mean_squared_error: 0.5302 - wine_type_accuracy: 0.9959 - val_loss: 0.3384 - val_wine_quality_loss: 0.3109 - val_wine_type_loss: 0.0263 - val_wine_quality_root_mean_squared_error: 0.5584 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 105/180\n",
      "3155/3155 [==============================] - 0s 95us/sample - loss: 0.3011 - wine_quality_loss: 0.2813 - wine_type_loss: 0.0202 - wine_quality_root_mean_squared_error: 0.5299 - wine_type_accuracy: 0.9962 - val_loss: 0.3366 - val_wine_quality_loss: 0.3092 - val_wine_type_loss: 0.0262 - val_wine_quality_root_mean_squared_error: 0.5568 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 106/180\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.3000 - wine_quality_loss: 0.2797 - wine_type_loss: 0.0202 - wine_quality_root_mean_squared_error: 0.5290 - wine_type_accuracy: 0.9959 - val_loss: 0.3385 - val_wine_quality_loss: 0.3113 - val_wine_type_loss: 0.0262 - val_wine_quality_root_mean_squared_error: 0.5585 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 107/180\n",
      "3155/3155 [==============================] - 0s 76us/sample - loss: 0.2991 - wine_quality_loss: 0.2791 - wine_type_loss: 0.0200 - wine_quality_root_mean_squared_error: 0.5282 - wine_type_accuracy: 0.9962 - val_loss: 0.3423 - val_wine_quality_loss: 0.3148 - val_wine_type_loss: 0.0261 - val_wine_quality_root_mean_squared_error: 0.5620 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 108/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.2984 - wine_quality_loss: 0.2778 - wine_type_loss: 0.0199 - wine_quality_root_mean_squared_error: 0.5277 - wine_type_accuracy: 0.9962 - val_loss: 0.3393 - val_wine_quality_loss: 0.3121 - val_wine_type_loss: 0.0260 - val_wine_quality_root_mean_squared_error: 0.5594 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 109/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.2977 - wine_quality_loss: 0.2791 - wine_type_loss: 0.0198 - wine_quality_root_mean_squared_error: 0.5271 - wine_type_accuracy: 0.9962 - val_loss: 0.3445 - val_wine_quality_loss: 0.3174 - val_wine_type_loss: 0.0261 - val_wine_quality_root_mean_squared_error: 0.5640 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 110/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2976 - wine_quality_loss: 0.2777 - wine_type_loss: 0.0197 - wine_quality_root_mean_squared_error: 0.5270 - wine_type_accuracy: 0.9962 - val_loss: 0.3406 - val_wine_quality_loss: 0.3136 - val_wine_type_loss: 0.0260 - val_wine_quality_root_mean_squared_error: 0.5606 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 111/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.2975 - wine_quality_loss: 0.2778 - wine_type_loss: 0.0196 - wine_quality_root_mean_squared_error: 0.5271 - wine_type_accuracy: 0.9962 - val_loss: 0.3335 - val_wine_quality_loss: 0.3064 - val_wine_type_loss: 0.0261 - val_wine_quality_root_mean_squared_error: 0.5542 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 112/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2969 - wine_quality_loss: 0.2773 - wine_type_loss: 0.0196 - wine_quality_root_mean_squared_error: 0.5266 - wine_type_accuracy: 0.9962 - val_loss: 0.3366 - val_wine_quality_loss: 0.3098 - val_wine_type_loss: 0.0260 - val_wine_quality_root_mean_squared_error: 0.5570 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 113/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2966 - wine_quality_loss: 0.2768 - wine_type_loss: 0.0194 - wine_quality_root_mean_squared_error: 0.5264 - wine_type_accuracy: 0.9962 - val_loss: 0.3341 - val_wine_quality_loss: 0.3071 - val_wine_type_loss: 0.0259 - val_wine_quality_root_mean_squared_error: 0.5548 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 114/180\n",
      "3155/3155 [==============================] - 0s 78us/sample - loss: 0.2964 - wine_quality_loss: 0.2770 - wine_type_loss: 0.0193 - wine_quality_root_mean_squared_error: 0.5264 - wine_type_accuracy: 0.9962 - val_loss: 0.3370 - val_wine_quality_loss: 0.3103 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5576 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 115/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2966 - wine_quality_loss: 0.2774 - wine_type_loss: 0.0192 - wine_quality_root_mean_squared_error: 0.5266 - wine_type_accuracy: 0.9962 - val_loss: 0.3343 - val_wine_quality_loss: 0.3077 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5551 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 116/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2950 - wine_quality_loss: 0.2763 - wine_type_loss: 0.0191 - wine_quality_root_mean_squared_error: 0.5251 - wine_type_accuracy: 0.9962 - val_loss: 0.3540 - val_wine_quality_loss: 0.3275 - val_wine_type_loss: 0.0259 - val_wine_quality_root_mean_squared_error: 0.5725 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 117/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2950 - wine_quality_loss: 0.2756 - wine_type_loss: 0.0190 - wine_quality_root_mean_squared_error: 0.5252 - wine_type_accuracy: 0.9962 - val_loss: 0.3353 - val_wine_quality_loss: 0.3086 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5560 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 118/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2946 - wine_quality_loss: 0.2755 - wine_type_loss: 0.0191 - wine_quality_root_mean_squared_error: 0.5250 - wine_type_accuracy: 0.9962 - val_loss: 0.3381 - val_wine_quality_loss: 0.3113 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5585 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 119/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2936 - wine_quality_loss: 0.2747 - wine_type_loss: 0.0188 - wine_quality_root_mean_squared_error: 0.5241 - wine_type_accuracy: 0.9962 - val_loss: 0.3347 - val_wine_quality_loss: 0.3079 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5555 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 120/180\n",
      "3155/3155 [==============================] - 0s 90us/sample - loss: 0.2921 - wine_quality_loss: 0.2733 - wine_type_loss: 0.0188 - wine_quality_root_mean_squared_error: 0.5227 - wine_type_accuracy: 0.9962 - val_loss: 0.3357 - val_wine_quality_loss: 0.3091 - val_wine_type_loss: 0.0257 - val_wine_quality_root_mean_squared_error: 0.5565 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 121/180\n",
      "3155/3155 [==============================] - 0s 79us/sample - loss: 0.2926 - wine_quality_loss: 0.2735 - wine_type_loss: 0.0187 - wine_quality_root_mean_squared_error: 0.5233 - wine_type_accuracy: 0.9962 - val_loss: 0.3392 - val_wine_quality_loss: 0.3126 - val_wine_type_loss: 0.0256 - val_wine_quality_root_mean_squared_error: 0.5597 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 122/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2916 - wine_quality_loss: 0.2731 - wine_type_loss: 0.0186 - wine_quality_root_mean_squared_error: 0.5223 - wine_type_accuracy: 0.9962 - val_loss: 0.3331 - val_wine_quality_loss: 0.3063 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5540 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 123/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2919 - wine_quality_loss: 0.2729 - wine_type_loss: 0.0186 - wine_quality_root_mean_squared_error: 0.5227 - wine_type_accuracy: 0.9962 - val_loss: 0.3409 - val_wine_quality_loss: 0.3144 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5610 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 124/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2907 - wine_quality_loss: 0.2722 - wine_type_loss: 0.0188 - wine_quality_root_mean_squared_error: 0.5217 - wine_type_accuracy: 0.9962 - val_loss: 0.3433 - val_wine_quality_loss: 0.3161 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5632 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 125/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.2901 - wine_quality_loss: 0.2721 - wine_type_loss: 0.0184 - wine_quality_root_mean_squared_error: 0.5212 - wine_type_accuracy: 0.9962 - val_loss: 0.3375 - val_wine_quality_loss: 0.3108 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5579 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 126/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.2884 - wine_quality_loss: 0.2699 - wine_type_loss: 0.0183 - wine_quality_root_mean_squared_error: 0.5196 - wine_type_accuracy: 0.9962 - val_loss: 0.3481 - val_wine_quality_loss: 0.3213 - val_wine_type_loss: 0.0257 - val_wine_quality_root_mean_squared_error: 0.5676 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 127/180\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3155/3155 [==============================] - 0s 77us/sample - loss: 0.2878 - wine_quality_loss: 0.2693 - wine_type_loss: 0.0184 - wine_quality_root_mean_squared_error: 0.5191 - wine_type_accuracy: 0.9962 - val_loss: 0.3344 - val_wine_quality_loss: 0.3082 - val_wine_type_loss: 0.0257 - val_wine_quality_root_mean_squared_error: 0.5553 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 128/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2903 - wine_quality_loss: 0.2721 - wine_type_loss: 0.0181 - wine_quality_root_mean_squared_error: 0.5217 - wine_type_accuracy: 0.9962 - val_loss: 0.3386 - val_wine_quality_loss: 0.3119 - val_wine_type_loss: 0.0257 - val_wine_quality_root_mean_squared_error: 0.5590 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 129/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2877 - wine_quality_loss: 0.2693 - wine_type_loss: 0.0181 - wine_quality_root_mean_squared_error: 0.5192 - wine_type_accuracy: 0.9962 - val_loss: 0.3418 - val_wine_quality_loss: 0.3151 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5619 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 130/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2885 - wine_quality_loss: 0.2703 - wine_type_loss: 0.0180 - wine_quality_root_mean_squared_error: 0.5200 - wine_type_accuracy: 0.9962 - val_loss: 0.3391 - val_wine_quality_loss: 0.3124 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5594 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 131/180\n",
      "3155/3155 [==============================] - 0s 89us/sample - loss: 0.2877 - wine_quality_loss: 0.2698 - wine_type_loss: 0.0180 - wine_quality_root_mean_squared_error: 0.5193 - wine_type_accuracy: 0.9962 - val_loss: 0.3378 - val_wine_quality_loss: 0.3113 - val_wine_type_loss: 0.0257 - val_wine_quality_root_mean_squared_error: 0.5584 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 132/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2880 - wine_quality_loss: 0.2703 - wine_type_loss: 0.0178 - wine_quality_root_mean_squared_error: 0.5198 - wine_type_accuracy: 0.9962 - val_loss: 0.3438 - val_wine_quality_loss: 0.3168 - val_wine_type_loss: 0.0256 - val_wine_quality_root_mean_squared_error: 0.5637 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 133/180\n",
      "3155/3155 [==============================] - 0s 79us/sample - loss: 0.2876 - wine_quality_loss: 0.2694 - wine_type_loss: 0.0177 - wine_quality_root_mean_squared_error: 0.5194 - wine_type_accuracy: 0.9962 - val_loss: 0.3339 - val_wine_quality_loss: 0.3075 - val_wine_type_loss: 0.0256 - val_wine_quality_root_mean_squared_error: 0.5550 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 134/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2861 - wine_quality_loss: 0.2693 - wine_type_loss: 0.0176 - wine_quality_root_mean_squared_error: 0.5181 - wine_type_accuracy: 0.9962 - val_loss: 0.3407 - val_wine_quality_loss: 0.3140 - val_wine_type_loss: 0.0257 - val_wine_quality_root_mean_squared_error: 0.5610 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 135/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2876 - wine_quality_loss: 0.2704 - wine_type_loss: 0.0177 - wine_quality_root_mean_squared_error: 0.5195 - wine_type_accuracy: 0.9962 - val_loss: 0.3354 - val_wine_quality_loss: 0.3090 - val_wine_type_loss: 0.0256 - val_wine_quality_root_mean_squared_error: 0.5563 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 136/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.2852 - wine_quality_loss: 0.2681 - wine_type_loss: 0.0178 - wine_quality_root_mean_squared_error: 0.5173 - wine_type_accuracy: 0.9962 - val_loss: 0.3345 - val_wine_quality_loss: 0.3079 - val_wine_type_loss: 0.0254 - val_wine_quality_root_mean_squared_error: 0.5556 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 137/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.2848 - wine_quality_loss: 0.2676 - wine_type_loss: 0.0174 - wine_quality_root_mean_squared_error: 0.5171 - wine_type_accuracy: 0.9962 - val_loss: 0.3361 - val_wine_quality_loss: 0.3096 - val_wine_type_loss: 0.0254 - val_wine_quality_root_mean_squared_error: 0.5571 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 138/180\n",
      "3155/3155 [==============================] - 0s 95us/sample - loss: 0.2854 - wine_quality_loss: 0.2678 - wine_type_loss: 0.0173 - wine_quality_root_mean_squared_error: 0.5177 - wine_type_accuracy: 0.9962 - val_loss: 0.3351 - val_wine_quality_loss: 0.3089 - val_wine_type_loss: 0.0254 - val_wine_quality_root_mean_squared_error: 0.5562 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 139/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2843 - wine_quality_loss: 0.2678 - wine_type_loss: 0.0172 - wine_quality_root_mean_squared_error: 0.5167 - wine_type_accuracy: 0.9965 - val_loss: 0.3400 - val_wine_quality_loss: 0.3140 - val_wine_type_loss: 0.0254 - val_wine_quality_root_mean_squared_error: 0.5606 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 140/180\n",
      "3155/3155 [==============================] - 0s 75us/sample - loss: 0.2835 - wine_quality_loss: 0.2657 - wine_type_loss: 0.0172 - wine_quality_root_mean_squared_error: 0.5160 - wine_type_accuracy: 0.9965 - val_loss: 0.3362 - val_wine_quality_loss: 0.3100 - val_wine_type_loss: 0.0255 - val_wine_quality_root_mean_squared_error: 0.5572 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 141/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.2837 - wine_quality_loss: 0.2661 - wine_type_loss: 0.0172 - wine_quality_root_mean_squared_error: 0.5162 - wine_type_accuracy: 0.9962 - val_loss: 0.3355 - val_wine_quality_loss: 0.3095 - val_wine_type_loss: 0.0254 - val_wine_quality_root_mean_squared_error: 0.5566 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 142/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2821 - wine_quality_loss: 0.2651 - wine_type_loss: 0.0171 - wine_quality_root_mean_squared_error: 0.5148 - wine_type_accuracy: 0.9965 - val_loss: 0.3465 - val_wine_quality_loss: 0.3201 - val_wine_type_loss: 0.0253 - val_wine_quality_root_mean_squared_error: 0.5664 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 143/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2812 - wine_quality_loss: 0.2645 - wine_type_loss: 0.0170 - wine_quality_root_mean_squared_error: 0.5140 - wine_type_accuracy: 0.9962 - val_loss: 0.3361 - val_wine_quality_loss: 0.3100 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5573 - val_wine_type_accuracy: 0.9937\n",
      "Epoch 144/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.2831 - wine_quality_loss: 0.2663 - wine_type_loss: 0.0169 - wine_quality_root_mean_squared_error: 0.5158 - wine_type_accuracy: 0.9965 - val_loss: 0.3379 - val_wine_quality_loss: 0.3118 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5588 - val_wine_type_accuracy: 0.9937\n",
      "Epoch 145/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2808 - wine_quality_loss: 0.2644 - wine_type_loss: 0.0168 - wine_quality_root_mean_squared_error: 0.5138 - wine_type_accuracy: 0.9965 - val_loss: 0.3418 - val_wine_quality_loss: 0.3157 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5624 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 146/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2812 - wine_quality_loss: 0.2640 - wine_type_loss: 0.0167 - wine_quality_root_mean_squared_error: 0.5142 - wine_type_accuracy: 0.9965 - val_loss: 0.3384 - val_wine_quality_loss: 0.3125 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5594 - val_wine_type_accuracy: 0.9937\n",
      "Epoch 147/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2803 - wine_quality_loss: 0.2637 - wine_type_loss: 0.0167 - wine_quality_root_mean_squared_error: 0.5133 - wine_type_accuracy: 0.9965 - val_loss: 0.3318 - val_wine_quality_loss: 0.3059 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5534 - val_wine_type_accuracy: 0.9937\n",
      "Epoch 148/180\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3155/3155 [==============================] - 0s 77us/sample - loss: 0.2801 - wine_quality_loss: 0.2638 - wine_type_loss: 0.0166 - wine_quality_root_mean_squared_error: 0.5133 - wine_type_accuracy: 0.9965 - val_loss: 0.3448 - val_wine_quality_loss: 0.3192 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5650 - val_wine_type_accuracy: 0.9937\n",
      "Epoch 149/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2794 - wine_quality_loss: 0.2622 - wine_type_loss: 0.0166 - wine_quality_root_mean_squared_error: 0.5126 - wine_type_accuracy: 0.9965 - val_loss: 0.3391 - val_wine_quality_loss: 0.3128 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5600 - val_wine_type_accuracy: 0.9937\n",
      "Epoch 150/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.2790 - wine_quality_loss: 0.2622 - wine_type_loss: 0.0164 - wine_quality_root_mean_squared_error: 0.5123 - wine_type_accuracy: 0.9965 - val_loss: 0.3379 - val_wine_quality_loss: 0.3118 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5588 - val_wine_type_accuracy: 0.9937\n",
      "Epoch 151/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.2784 - wine_quality_loss: 0.2624 - wine_type_loss: 0.0164 - wine_quality_root_mean_squared_error: 0.5118 - wine_type_accuracy: 0.9965 - val_loss: 0.3402 - val_wine_quality_loss: 0.3142 - val_wine_type_loss: 0.0253 - val_wine_quality_root_mean_squared_error: 0.5608 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 152/180\n",
      "3155/3155 [==============================] - 0s 95us/sample - loss: 0.2790 - wine_quality_loss: 0.2620 - wine_type_loss: 0.0163 - wine_quality_root_mean_squared_error: 0.5124 - wine_type_accuracy: 0.9965 - val_loss: 0.3429 - val_wine_quality_loss: 0.3167 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5634 - val_wine_type_accuracy: 0.9937\n",
      "Epoch 153/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2783 - wine_quality_loss: 0.2619 - wine_type_loss: 0.0162 - wine_quality_root_mean_squared_error: 0.5119 - wine_type_accuracy: 0.9965 - val_loss: 0.3511 - val_wine_quality_loss: 0.3250 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5706 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 154/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2790 - wine_quality_loss: 0.2631 - wine_type_loss: 0.0162 - wine_quality_root_mean_squared_error: 0.5126 - wine_type_accuracy: 0.9965 - val_loss: 0.3408 - val_wine_quality_loss: 0.3145 - val_wine_type_loss: 0.0253 - val_wine_quality_root_mean_squared_error: 0.5615 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 155/180\n",
      "3155/3155 [==============================] - 0s 89us/sample - loss: 0.2774 - wine_quality_loss: 0.2618 - wine_type_loss: 0.0162 - wine_quality_root_mean_squared_error: 0.5110 - wine_type_accuracy: 0.9965 - val_loss: 0.3368 - val_wine_quality_loss: 0.3108 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5579 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 156/180\n",
      "3155/3155 [==============================] - 0s 78us/sample - loss: 0.2769 - wine_quality_loss: 0.2603 - wine_type_loss: 0.0161 - wine_quality_root_mean_squared_error: 0.5106 - wine_type_accuracy: 0.9965 - val_loss: 0.3394 - val_wine_quality_loss: 0.3138 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5603 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 157/180\n",
      "3155/3155 [==============================] - 0s 95us/sample - loss: 0.2778 - wine_quality_loss: 0.2616 - wine_type_loss: 0.0160 - wine_quality_root_mean_squared_error: 0.5116 - wine_type_accuracy: 0.9965 - val_loss: 0.3418 - val_wine_quality_loss: 0.3158 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5625 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 158/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2767 - wine_quality_loss: 0.2607 - wine_type_loss: 0.0159 - wine_quality_root_mean_squared_error: 0.5106 - wine_type_accuracy: 0.9965 - val_loss: 0.3365 - val_wine_quality_loss: 0.3105 - val_wine_type_loss: 0.0250 - val_wine_quality_root_mean_squared_error: 0.5578 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 159/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.2766 - wine_quality_loss: 0.2605 - wine_type_loss: 0.0159 - wine_quality_root_mean_squared_error: 0.5105 - wine_type_accuracy: 0.9965 - val_loss: 0.3362 - val_wine_quality_loss: 0.3104 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5575 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 160/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2770 - wine_quality_loss: 0.2612 - wine_type_loss: 0.0158 - wine_quality_root_mean_squared_error: 0.5110 - wine_type_accuracy: 0.9965 - val_loss: 0.3345 - val_wine_quality_loss: 0.3085 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5559 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 161/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2750 - wine_quality_loss: 0.2590 - wine_type_loss: 0.0171 - wine_quality_root_mean_squared_error: 0.5090 - wine_type_accuracy: 0.9965 - val_loss: 0.3367 - val_wine_quality_loss: 0.3110 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5580 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 162/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.2751 - wine_quality_loss: 0.2594 - wine_type_loss: 0.0157 - wine_quality_root_mean_squared_error: 0.5092 - wine_type_accuracy: 0.9965 - val_loss: 0.3444 - val_wine_quality_loss: 0.3186 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5648 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 163/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2742 - wine_quality_loss: 0.2582 - wine_type_loss: 0.0173 - wine_quality_root_mean_squared_error: 0.5083 - wine_type_accuracy: 0.9965 - val_loss: 0.3495 - val_wine_quality_loss: 0.3234 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5692 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 164/180\n",
      "3155/3155 [==============================] - 0s 74us/sample - loss: 0.2734 - wine_quality_loss: 0.2577 - wine_type_loss: 0.0156 - wine_quality_root_mean_squared_error: 0.5077 - wine_type_accuracy: 0.9965 - val_loss: 0.3437 - val_wine_quality_loss: 0.3175 - val_wine_type_loss: 0.0249 - val_wine_quality_root_mean_squared_error: 0.5643 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 165/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2736 - wine_quality_loss: 0.2582 - wine_type_loss: 0.0155 - wine_quality_root_mean_squared_error: 0.5079 - wine_type_accuracy: 0.9965 - val_loss: 0.3373 - val_wine_quality_loss: 0.3117 - val_wine_type_loss: 0.0250 - val_wine_quality_root_mean_squared_error: 0.5586 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 166/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2727 - wine_quality_loss: 0.2568 - wine_type_loss: 0.0155 - wine_quality_root_mean_squared_error: 0.5071 - wine_type_accuracy: 0.9965 - val_loss: 0.3342 - val_wine_quality_loss: 0.3084 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5557 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 167/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.2732 - wine_quality_loss: 0.2582 - wine_type_loss: 0.0154 - wine_quality_root_mean_squared_error: 0.5076 - wine_type_accuracy: 0.9968 - val_loss: 0.3340 - val_wine_quality_loss: 0.3081 - val_wine_type_loss: 0.0250 - val_wine_quality_root_mean_squared_error: 0.5556 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 168/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.2707 - wine_quality_loss: 0.2552 - wine_type_loss: 0.0154 - wine_quality_root_mean_squared_error: 0.5053 - wine_type_accuracy: 0.9965 - val_loss: 0.3386 - val_wine_quality_loss: 0.3128 - val_wine_type_loss: 0.0250 - val_wine_quality_root_mean_squared_error: 0.5597 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 169/180\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2720 - wine_quality_loss: 0.2567 - wine_type_loss: 0.0153 - wine_quality_root_mean_squared_error: 0.5066 - wine_type_accuracy: 0.9965 - val_loss: 0.3344 - val_wine_quality_loss: 0.3086 - val_wine_type_loss: 0.0250 - val_wine_quality_root_mean_squared_error: 0.5559 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 170/180\n",
      "3155/3155 [==============================] - 0s 94us/sample - loss: 0.2713 - wine_quality_loss: 0.2562 - wine_type_loss: 0.0153 - wine_quality_root_mean_squared_error: 0.5060 - wine_type_accuracy: 0.9968 - val_loss: 0.3458 - val_wine_quality_loss: 0.3201 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5661 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 171/180\n",
      "3155/3155 [==============================] - 0s 90us/sample - loss: 0.2713 - wine_quality_loss: 0.2561 - wine_type_loss: 0.0152 - wine_quality_root_mean_squared_error: 0.5061 - wine_type_accuracy: 0.9965 - val_loss: 0.3423 - val_wine_quality_loss: 0.3162 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5629 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 172/180\n",
      "3155/3155 [==============================] - 0s 77us/sample - loss: 0.2709 - wine_quality_loss: 0.2559 - wine_type_loss: 0.0151 - wine_quality_root_mean_squared_error: 0.5057 - wine_type_accuracy: 0.9965 - val_loss: 0.3377 - val_wine_quality_loss: 0.3116 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5588 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 173/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.2709 - wine_quality_loss: 0.2565 - wine_type_loss: 0.0150 - wine_quality_root_mean_squared_error: 0.5057 - wine_type_accuracy: 0.9968 - val_loss: 0.3388 - val_wine_quality_loss: 0.3127 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5598 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 174/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2704 - wine_quality_loss: 0.2555 - wine_type_loss: 0.0150 - wine_quality_root_mean_squared_error: 0.5053 - wine_type_accuracy: 0.9965 - val_loss: 0.3423 - val_wine_quality_loss: 0.3165 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5628 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 175/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2695 - wine_quality_loss: 0.2545 - wine_type_loss: 0.0150 - wine_quality_root_mean_squared_error: 0.5044 - wine_type_accuracy: 0.9965 - val_loss: 0.3349 - val_wine_quality_loss: 0.3089 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5562 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 176/180\n",
      "3155/3155 [==============================] - 0s 93us/sample - loss: 0.2692 - wine_quality_loss: 0.2541 - wine_type_loss: 0.0149 - wine_quality_root_mean_squared_error: 0.5042 - wine_type_accuracy: 0.9968 - val_loss: 0.3365 - val_wine_quality_loss: 0.3105 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5577 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 177/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.2693 - wine_quality_loss: 0.2540 - wine_type_loss: 0.0148 - wine_quality_root_mean_squared_error: 0.5044 - wine_type_accuracy: 0.9965 - val_loss: 0.3345 - val_wine_quality_loss: 0.3088 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5558 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 178/180\n",
      "3155/3155 [==============================] - 0s 92us/sample - loss: 0.2687 - wine_quality_loss: 0.2540 - wine_type_loss: 0.0148 - wine_quality_root_mean_squared_error: 0.5038 - wine_type_accuracy: 0.9968 - val_loss: 0.3417 - val_wine_quality_loss: 0.3159 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5624 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 179/180\n",
      "3155/3155 [==============================] - 0s 79us/sample - loss: 0.2682 - wine_quality_loss: 0.2533 - wine_type_loss: 0.0147 - wine_quality_root_mean_squared_error: 0.5035 - wine_type_accuracy: 0.9965 - val_loss: 0.3339 - val_wine_quality_loss: 0.3080 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5553 - val_wine_type_accuracy: 0.9949\n",
      "Epoch 180/180\n",
      "3155/3155 [==============================] - 0s 91us/sample - loss: 0.2687 - wine_quality_loss: 0.2542 - wine_type_loss: 0.0147 - wine_quality_root_mean_squared_error: 0.5039 - wine_type_accuracy: 0.9965 - val_loss: 0.3354 - val_wine_quality_loss: 0.3095 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5567 - val_wine_type_accuracy: 0.9949\n"
     ]
    }
   ],
   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "history = model.fit(norm_train_X, train_Y,\n",
    "                    epochs = 180, validation_data=(norm_val_X, val_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "fadad8896eda9c8c2115970724b15508",
     "grade": true,
     "grade_id": "cell-eb4d5b41bef8f0ab",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "utils.test_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CubF2J2gSf6q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "789/789 [==============================] - 0s 61us/sample - loss: 0.3354 - wine_quality_loss: 0.3095 - wine_type_loss: 0.0252 - wine_quality_root_mean_squared_error: 0.5567 - wine_type_accuracy: 0.9949\n",
      "\n",
      "loss: 0.335441492922859\n",
      "wine_quality_loss: 0.3094671964645386\n",
      "wine_type_loss: 0.02524704672396183\n",
      "wine_quality_rmse: 0.5566568374633789\n",
      "wine_type_accuracy: 0.9949302673339844\n"
     ]
    }
   ],
   "source": [
    "# Gather the training metrics\n",
    "loss, wine_quality_loss, wine_type_loss, wine_quality_rmse, wine_type_accuracy = model.evaluate(x=norm_val_X, y=val_Y)\n",
    "\n",
    "print()\n",
    "print(f'loss: {loss}')\n",
    "print(f'wine_quality_loss: {wine_quality_loss}')\n",
    "print(f'wine_type_loss: {wine_type_loss}')\n",
    "print(f'wine_quality_rmse: {wine_quality_rmse}')\n",
    "print(f'wine_type_accuracy: {wine_type_accuracy}')\n",
    "\n",
    "# EXPECTED VALUES\n",
    "# ~ 0.30 - 0.38\n",
    "# ~ 0.30 - 0.38\n",
    "# ~ 0.018 - 0.030\n",
    "# ~ 0.50 - 0.62\n",
    "# ~ 0.97 - 1.0\n",
    "\n",
    "# Example:\n",
    "#0.3657050132751465\n",
    "#0.3463745415210724\n",
    "#0.019330406561493874\n",
    "#0.5885359048843384\n",
    "#0.9974651336669922"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gPtTGAP4usnm"
   },
   "source": [
    "## Analyze the Model Performance\n",
    "\n",
    "Note that the model has two outputs. The output at index 0 is quality and index 1 is wine type\n",
    "\n",
    "So, round the quality predictions to the nearest integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tBq9PEeAaW-Y"
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(norm_test_X)\n",
    "quality_pred = predictions[0]\n",
    "type_pred = predictions[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YLhgTR4xTIxj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.587502]\n"
     ]
    }
   ],
   "source": [
    "print(quality_pred[0])\n",
    "\n",
    "# EXPECTED OUTPUT\n",
    "# 5.6 - 6.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MPi-eYfGTUXi"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00022221]\n",
      "[0.9999976]\n"
     ]
    }
   ],
   "source": [
    "print(type_pred[0])\n",
    "print(type_pred[944])\n",
    "\n",
    "# EXPECTED OUTPUT\n",
    "# A number close to zero\n",
    "# A number close to or equal to 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Kohk-9C6vt_s"
   },
   "source": [
    "### Plot Utilities\n",
    "\n",
    "We define a few utilities to visualize the model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "62gEOFUhn6aQ"
   },
   "outputs": [],
   "source": [
    "def plot_metrics(metric_name, title, ylim=5):\n",
    "    plt.title(title)\n",
    "    plt.ylim(0,ylim)\n",
    "    plt.plot(history.history[metric_name],color='blue',label=metric_name)\n",
    "    plt.plot(history.history['val_' + metric_name],color='green',label='val_' + metric_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6rfgSx7uz5dj"
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, title='', labels=[0,1]):\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the classifier')\n",
    "    fig.colorbar(cax)\n",
    "    ax.set_xticklabels([''] + labels)\n",
    "    ax.set_yticklabels([''] + labels)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    fmt = 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "          plt.text(j, i, format(cm[i, j], fmt),\n",
    "                  horizontalalignment=\"center\",\n",
    "                  color=\"black\" if cm[i, j] > thresh else \"white\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dfVLIqi017Vf"
   },
   "outputs": [],
   "source": [
    "def plot_diff(y_true, y_pred, title = '' ):\n",
    "    plt.scatter(y_true, y_pred)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('True Values')\n",
    "    plt.ylabel('Predictions')\n",
    "    plt.axis('equal')\n",
    "    plt.axis('square')\n",
    "    plt.plot([-100, 100], [-100, 100])\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8sd1jdFbwE0I"
   },
   "source": [
    "### Plots for Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f3MwZ5J1pOfj"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics('wine_quality_root_mean_squared_error', 'RMSE', ylim=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QIAxEezCppnd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics('wine_type_loss', 'Wine Type Loss', ylim=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uYV9AOAMwI9p"
   },
   "source": [
    "### Plots for Confusion Matrix\n",
    "\n",
    "Plot the confusion matrices for wine type. You can see that the model performs well for prediction of wine type from the confusion matrix and the loss metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C3hvTYxIaf3n"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(test_Y[1], np.round(type_pred), title='Wine Type', labels = [0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GW91ym8P2I5y"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FcHwK9VgbOUSsUxrtkPb95Nqa1ZTO1lwA3JlaVdYyh4h1UqMd0v6DpHdS6tEKsCoibk6vLGuFQ8Q6rdEtEoANwAsRcYekOZIOjIgX0irMZsYhYllo9N6/F1MaT2RyDNf5wHBaRdnMOEQsK42uaZcBZ1AaGY2I+BmlU8LWJRwilqVG17ZfRcTLk08kzaKBYQSsMxwi3UnVBhKdpj3PGl3j7pL0N8CApLcANwLfTK8sa5RDpHtFjf9qa7XnWaNr3V9TGhF+M/BBSiObLU+rKGuMQ6S79dXY9KjVnmd1z9pI2g+4PyJ+B7g6/ZKsEQ6R7jdRY9OjVnue1V37ImIvcJ8k31CmSzhE8mGgv/rnUqs9zxrtR3I48FNJ91C6Pw0AEXFuKlVZTQ6R/Bjfvbep9jxrNEiuTLUKa4hDxLpVvfFI9geWAm+gdKD1mojY04nCbCqHiHWzemvjtZRuJ7EZOIfqQy5ayhwi1u3q7dr8dkScCCDpGuCe9Euycg6R/OrfD6odDingsda6WyS7Jx94l6bzHCL5tqfGWd5a7XlWb4vkJEk7k8ei1LN1Z/I4IuKgVKvrYQ6R/Oulnq31hlrs61Qh9iqHSDH0SVU7nxWxZ6vX0C7jECmOC089qqn2PPNa2kUcIpZXXlO7hEOkeFav29ZUe555be0CDpFi8kV71jEOESuCZgZ/bpqkrcALwASwJyKGKqafCfwT8FjStCYi/luaNXUTh4gVRapBkjgrIp6ZZvoPI+LtHaijqzhErEi89mbAIWJFk/YaHMB3JN0r6ZIa85wu6T5Jt0k6odoMki6RtF7S+h07dqRXbQc4RHqHh1psnzMiYlTSYcB3JT0cET8om74BODoiXpT0Nkr3yjmu8k0iYhWwCmBoaCi3h7wdIr3FZ23aJCJGk99PAzcDp1RM3xkRLyaPbwX6Jc1Ns6asOER6z/zBgaba8yy1tVnSAZIOnHwMvBV4oGKe10ml7TxJpyT1PJtWTVlxiPSmZUsW0t83dTemv08sW7Iwo4rSk+auzWuBm5OcmAV8LSJul7QUICJWAucDl0raA4wDF0QUa7vPIdLjKtfmQq3dr0otSCLiUeCkKu0ryx5/Hvh8WjVkzSHS21as3cLuvVOTY/feYMXaLZy3aH5GVaXDa3ZKHCI2OjbeVHueee1OgUPEAI6ocVC1VnueeQ1vM4eITVq2ZCED/VPHBhvo7/PBVpueQ8TKTR4HWbF2C6Nj4xwxOMCyJQsLd3wEHCRt4xCxas5bNL+QwVHJQdIGDhGrZXjjiLdIrD6HiNUyvHGEy9dsZnz3BAAjY+NcvmYzQOHCxGt9CxwiNp0Va7e8EiKTxndPsGLtlowqSo/X/BlyiFg97kdi03KIWCPcj8RqcohYo9yPxKpyiFgz3I/E9uEQsZnolX4k/jY0wCFiNj1/I+pwiJjV52/FNBwiZo3xN6MGh4hZ4/ztqMIhYtYcf0MqOETMmudvSRmHiNnM+JuScIiYzZy/LThEzFrV898Yh4hZ63r6W+MQMWuPnv3mOETM2qcnvz0OEbP26rlvkEPErP166lvkEDFLR898kxwiZunpiW+TQ8QsXYX/RjlEzNJX+G+VQ6Q5gwP9TbWbQQ8EiUOkOVecewL9+2lKW/9+4opzT8ioIsuDVAd/lrQVeAGYAPZExFDFdAGfAd4GvAR8ICI2tLMGh0hzemnkc2ufTowif1ZEPFNj2jnAccnPqcBVye+2cYg0r1dGPrf2yfpb9g7guii5GxiUdHjGNZlZk9LeIgngO5IC+EJErKqYPh/YVvZ8e9L2ZPlMki4BLgFYsGBBetUaAMMbR7xrY01JO0jOiIhRSYcB35X0cET8oGy6qrwm9mkoBdAqgKGhoX2mW/sMbxzh8jWbGd89AcDI2DiXr9kM4DCxmlLdtYmI0eT308DNwCkVs2wHjip7fiQw2szfuP7HW2deoO1jxdotr4TIpPHdE6xYuyWjiiwPUgsSSQdIOnDyMfBW4IGK2W4B3q+S04DnI+JJGjTZ2czaZ3RsvKl2M0h3i+S1wD9Lug+4B/h2RNwuaamkpck8twKPAo8AVwN/0eibl/dYHZhVbQ8J5vRnfSw5f44YHGiq3QxSPEYSEY8CJ1VpX1n2OIDLmn3vym7vxy+/rep8L+3e2+xb97xlSxZOOUYCMNDfx7IlCzOsyrpdJ/qRtNWzu17ep9u7BFHlEKyqb6jYNNwhzWYid0EyOjbO+yq6vVcLkenabXrukGbNyt1BhIP2n+Vu72ZdJnffxgWHHuAQMesyuftGVjvuccic6pe412o3s/bKXZBU84e/W/3ynFrtZtZehQiSOx/e0VS7mbVX7s7aVDNSo9dlrXabni/as2YVIkjcj6R9fNGezUQhdm3cj6R9fNGezUQhgsTaxxft2UwUIkhm91Xfh6nVbrX5oj2biUIEye691fdharVbbcuWLGSgv29Kmy/as3oKcbDVx0jaxxft2UwUIkj6JCaqpEafT9vMiC/as2YVYtfmwlOPaqrdzNqrEFskf3veiQCsXreNiQj6JC489ahX2s0sXYqcHUgYGhqK9evXZ12GWaFJurfyzpjTKcSujZlly0FiZi1zkJhZyxwkZtYyB4mZtSx3Z20k7QAen2aWucAzHSqnFXmo0zW2Tx7qLK/x6IiY1+gLcxck9Uha38xpq6zkoU7X2D55qLOVGr1rY2Ytc5CYWcuKGCSrsi6gQXmo0zW2Tx7qnHGNhTtGYmadV8QtEjPrMAeJmbUst0EiaaukzZI2SdrncmCVfFbSI5Lul7S4S+s8U9LzyfRNkj6eQY2Dkm6S9LCkhySdXjE982XZQI2ZLkdJC8v+9iZJOyX9VcU83bAcG6mz+WUZEbn8AbYCc6eZ/jbgNkDAacC6Lq3zTOBbGS/La4E/Tx7PBga7bVk2UGPmy7Gslj7gKUqdurpqOTZYZ9PLMrdbJA14B3BdlNwNDEryzYArSDoIeDNwDUBEvBwRYxWzZbosG6yxm5wN/DwiKntgd9s6WavOpuU5SAL4jqR7JV1SZfp8YFvZ8+1JW6fVqxPgdEn3SbpN0gmdLA54PbAD+LKkjZK+KOmAinmyXpaN1AjZLsdyFwCrq7RnvRwr1aoTmlyWeQ6SMyJiMXAOcJmkN1dMrzbycxbnuuvVuYHSpuVJwOeA4Q7XNwtYDFwVEYuAXcBHK+bJelk2UmPWyxEASbOBc4Ebq02u0pZJ/4s6dTa9LHMbJBExmvx+GrgZOKVilu1A+ejPRwKjnanuVfXqjIidEfFi8vhWoF/S3A6WuB3YHhHrkuc3UfrSVs6T5bKsW2MXLMdJ5wAbIuIXVaZlvRzL1axzJssyl0Ei6QBJB04+Bt4KPFAx2y3A+5Mj5acBz0fEk91Wp6TXSaX7Zkg6hdJn8mynaoyIp4BtkibvgHU28GDFbJkuy0ZqzHo5lrmQ2rsLma+TZWrWOZNlmddR5F8L3Jz8W2cBX4uI2yUtBYiIlcCtlI6SPwK8BFzUpXWeD1wqaQ8wDlwQyaHzDvoQcEOyufsocFEXLst6NWa+HCXNAd4CfLCsrduWYyN1Nr0s3UXezFqWy10bM+suDhIza5mDxMxa5iAxs5Y5SMysZQ6SHiPp18uu6nxK0kjZ89lteP8rJH2iou1kSQ/Vec1/bvVvW3by2o/EZigingVOhtIXGHgxIj41OV3SrIjY08KfWE3pCtfLy9ouAL7Wwntal/MWiSHpK5I+LelO4O8qtxAkPSDpmOTxeyXdk2zBfEFSX/l7RcQWYEzSqWXN7wa+LuliST9JLgb7RtIxqrKW70saSh7PlbQ1edwnaUXy+vslfTBpP1zSD5J6HpD0e21dONYQB4lNOh74g4j4SK0ZJP0W8O8pXYh4MjABvKfKrKspbYWQdAV/NiJ+BqyJiDclF4M9BPxZE/X9GaUu5W8C3gRcLOlY4E+AtUk9JwGbmnhPaxPv2tikGyNios48ZwNvBH6SdPsfAJ6uMt/XgX+R9BGmXqr+O5L+FhgEfg1Y20R9bwV+V9L5yfODgeOAnwBfktQPDEeEgyQDDhKbtKvs8R6mbq3un/wWcG1ElB//2EdEbEt2SX4f+GNgcljErwDnRcR9kj5AaSSuSuV/e/+ydgEfioh9wicZmuEPgeslrYiI66arz9rPuzZWzVaSy/RVGlf02KT9/wLnSzosmXaopKNrvMdq4B8ojcC1PWk7EHgy2Xqotks0+bffmDw+v6x9LaULyfqTv318cnX10cDTEXE1pRHUMhmbt9c5SKyabwCHStoEXAr8K0BEPAgspzTi2/3Ad4FaQwXeCJxAaTdn0seAdcnrHq7xuk9RCox/oXRT60lfpDR0wAZJDwBfoLRFfSawSdJGSls/n2nqX2pt4at/zaxl3iIxs5Y5SMysZQ4SM2uZg8TMWuYgMbOWOUjMrGUOEjNr2f8HWL9nzlgtNPIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_plot = plot_diff(test_Y[0], quality_pred, title='Type')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "name": "exercise-answer.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}