WINE.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "WINE.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPv+t+v4DwMnmKtsZIl/Ou0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SUKI-O/utilities/blob/master/WINE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "anjxHLBAIZIG"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXf9L5z_IXeF"
      },
      "source": [
        "import tensorflow as tf\r\n",
        "from tensorflow.keras.models import Model\r\n",
        "from tensorflow.keras.layers import Dense, Input\r\n",
        "\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import pandas as pd\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "import itertools"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6LutFVPI0Sw"
      },
      "source": [
        "import numpy as np\r\n",
        "import tensorflow as tf\r\n",
        "from tensorflow.keras.models import Model\r\n",
        "from tensorflow.keras.layers import Dense, Input\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "\r\n",
        "def test_loop(test_cases):\r\n",
        "    \r\n",
        "    success = 0\r\n",
        "    fails = 0\r\n",
        "    \r\n",
        "    for test_case in test_cases:\r\n",
        "        try:\r\n",
        "            assert test_case[\"result\"] == test_case[\"expected\"]\r\n",
        "            success += 1\r\n",
        "    \r\n",
        "        except:\r\n",
        "            fails += 1\r\n",
        "            print(f'{test_case[\"name\"]}: {test_case[\"error_message\"]}\\nExpected: {test_case[\"expected\"]}\\nResult: {test_case[\"result\"]}\\nPlease open utils.py if you want to see the unit test here.\\n')\r\n",
        "\r\n",
        "    if fails == 0:\r\n",
        "        print(\"\\033[92m All public tests passed\")\r\n",
        "\r\n",
        "    else:\r\n",
        "        print('\\033[92m', success,\" Tests passed\")\r\n",
        "        print('\\033[91m', fails, \" Tests failed\")\r\n",
        "        raise Exception(test_case[\"error_message\"])\r\n",
        "        \r\n",
        "def test_white_df(white_df):\r\n",
        "    \r\n",
        "    test_cases = [\r\n",
        "        {\r\n",
        "            \"name\": \"type_check\",\r\n",
        "            \"result\": type(white_df.is_red[0]),\r\n",
        "            \"expected\": np.int64,\r\n",
        "            \"error_message\": f'white_df.is_red has an incorrect type.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"output_check\",\r\n",
        "            \"result\": white_df.is_red[0],\r\n",
        "            \"expected\": 0,\r\n",
        "            \"error_message\": \"white_df.is_red is not set correctly\"\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"len_check\",\r\n",
        "            \"result\": len(white_df),\r\n",
        "            \"expected\": 3961,\r\n",
        "            \"error_message\": \"Number of rows is incorrect. Please drop duplicates.\"\r\n",
        "        }\r\n",
        "    ]\r\n",
        "    \r\n",
        "    test_loop(test_cases)\r\n",
        "    \r\n",
        "def test_red_df(red_df):\r\n",
        "    \r\n",
        "    test_cases = [\r\n",
        "        {\r\n",
        "            \"name\": \"type_check\",\r\n",
        "            \"result\": type(red_df.is_red[0]),\r\n",
        "            \"expected\": np.int64,\r\n",
        "            \"error_message\": f'red_df.is_red has an incorrect type.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"output_check\",\r\n",
        "            \"result\": red_df.is_red[0],\r\n",
        "            \"expected\": 1,\r\n",
        "            \"error_message\": \"red_df.is_red is not set correctly\"\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"len_check\",\r\n",
        "            \"result\": len(red_df),\r\n",
        "            \"expected\": 1359,\r\n",
        "            \"error_message\": \"Number of rows is incorrect. Please drop duplicates.\"\r\n",
        "        }\r\n",
        "    ]\r\n",
        "    \r\n",
        "    test_loop(test_cases)\r\n",
        "    \r\n",
        "def test_df_drop(df):\r\n",
        "    \r\n",
        "    test_cases = [\r\n",
        "        {\r\n",
        "            \"name\": \"df.alcohol[0]_check\",\r\n",
        "            \"result\": df.alcohol[0],\r\n",
        "            \"expected\": 9.4,\r\n",
        "            \"error_message\": f'Value is not as expected. Please check quality interval.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"df.alcohol[100]_check\",\r\n",
        "            \"result\": df.alcohol[100],\r\n",
        "            \"expected\": 10.9,\r\n",
        "            \"error_message\": f'Value is not as expected. Please check quality interval.'\r\n",
        "        }\r\n",
        "    ]\r\n",
        "    \r\n",
        "    test_loop(test_cases)\r\n",
        "\r\n",
        "def test_data_sizes(train_size, test_size, val_size):\r\n",
        "    \r\n",
        "    test_cases = [\r\n",
        "        {\r\n",
        "            \"name\": \"train_test_size_check\",\r\n",
        "            \"result\": train_size > test_size,\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'train.size is too small. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"train_val_size_check\",\r\n",
        "            \"result\": train_size > val_size,\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'train.size is too small. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"test_val_size_check\",\r\n",
        "            \"result\": test_size > val_size,\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'test.size is too small. Please check implementation.'\r\n",
        "        }\r\n",
        "    ]\r\n",
        "    \r\n",
        "    test_loop(test_cases)\r\n",
        "\r\n",
        "def test_format_output(df, train_Y, val_Y, test_Y):\r\n",
        "    \r\n",
        "    train, test = train_test_split(df, test_size=0.2, random_state=1)\r\n",
        "    train, val = train_test_split(train, test_size=0.2, random_state=1)\r\n",
        "    \r\n",
        "    test_cases = [\r\n",
        "        {\r\n",
        "            \"name\": \"train_Y[0]_check\",\r\n",
        "            \"result\": np.all(train_Y[0] == np.array(train.quality)),\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'train_Y[0] is not equal to train.quality. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"train_Y[1]_check\",\r\n",
        "            \"result\": np.all(train_Y[1] == np.array(train.is_red)),\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'train_Y[1] is not equal to train.is_red. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"val_Y[0]_check\",\r\n",
        "            \"result\": np.all(val_Y[0] == np.array(val.quality)),\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'train_Y[0] is not equal to val.quality. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"val_Y[1]_check\",\r\n",
        "            \"result\": np.all(val_Y[1] == np.array(val.is_red)),\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'train_Y[1] is not equal to val.is_red. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"test_Y[0]_check\",\r\n",
        "            \"result\": np.all(test_Y[0] == np.array(test.quality)),\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'test_Y[0] is not equal to test.quality. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"test_Y[1]_check\",\r\n",
        "            \"result\": np.all(test_Y[1] == np.array(test.is_red)),\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'test_Y[1] is not equal to test.is_red. Please check implementation.'\r\n",
        "        }\r\n",
        "    ]\r\n",
        "    \r\n",
        "    test_loop(test_cases)\r\n",
        "\r\n",
        "def test_norm(norm_train_X, norm_val_X, norm_test_X, train, val, test):\r\n",
        "    \r\n",
        "    from pandas.core.frame import DataFrame\r\n",
        "    \r\n",
        "    test_cases = [\r\n",
        "        {\r\n",
        "            \"name\": \"norm_train_X_type_check\",\r\n",
        "            \"result\": type(norm_train_X),\r\n",
        "            \"expected\": DataFrame,\r\n",
        "            \"error_message\": f'norm_train_X has an incorrect type.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"norm_val_X_type_check\",\r\n",
        "            \"result\": type(norm_val_X),\r\n",
        "            \"expected\": DataFrame,\r\n",
        "            \"error_message\": f'norm_val_X has an incorrect type.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"norm_test_X_type_check\",\r\n",
        "            \"result\": type(norm_test_X),\r\n",
        "            \"expected\": DataFrame,\r\n",
        "            \"error_message\": f'norm_test_X has an incorrect type.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"norm_train_X_length_check\",\r\n",
        "            \"result\": len(norm_train_X),\r\n",
        "            \"expected\": len(train),\r\n",
        "            \"error_message\": f'norm_train_X has an incorrect length.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"norm_val_X_length_check\",\r\n",
        "            \"result\": len(norm_val_X),\r\n",
        "            \"expected\": len(val),\r\n",
        "            \"error_message\": f'norm_val_X has an incorrect length.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"norm_test_X_length_check\",\r\n",
        "            \"result\": len(norm_test_X),\r\n",
        "            \"expected\": len(test),\r\n",
        "            \"error_message\": f'norm_test_X has an incorrect length.'\r\n",
        "        },\r\n",
        "    ]\r\n",
        "    \r\n",
        "    test_loop(test_cases)\r\n",
        "\r\n",
        "def test_base_model(base_model):\r\n",
        "    \r\n",
        "    test_inputs = tf.keras.layers.Input(shape=(11,))\r\n",
        "    test_output = base_model(test_inputs)\r\n",
        "    test_model = Model(inputs=test_inputs, outputs=test_output)\r\n",
        "\r\n",
        "    test_cases = [\r\n",
        "        {\r\n",
        "            \"name\": \"return_type_check\",\r\n",
        "            \"result\": type(test_output),\r\n",
        "            \"expected\": tf.Tensor,\r\n",
        "            \"error_message\": 'Return type is incorrect. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"return_shape_check\",\r\n",
        "            \"result\": str(test_output.shape),\r\n",
        "            \"expected\": '(None, 128)',\r\n",
        "            \"error_message\": 'Return shape is incorrect. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"tensor_dtype_check\",\r\n",
        "            \"result\": str(test_output.dtype),\r\n",
        "            \"expected\": \"<dtype: 'float32'>\",\r\n",
        "            \"error_message\": 'model dtype is incorrect. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"base_model_num_layers_check\",\r\n",
        "            \"result\": len(test_model.layers),\r\n",
        "            \"expected\": 3,\r\n",
        "            \"error_message\": 'There are too many layers. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"base_model_layer1_check\",\r\n",
        "            \"result\": type(test_model.layers[-2]),\r\n",
        "            \"expected\": Dense,\r\n",
        "            \"error_message\": 'First layer type is incorrect. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"base_model_layer2_check\",\r\n",
        "            \"result\": type(test_model.layers[-1]),\r\n",
        "            \"expected\": Dense,\r\n",
        "            \"error_message\": 'Output layer type is incorrect. Please check implementation.'\r\n",
        "        },\r\n",
        "    ]\r\n",
        "    \r\n",
        "    test_loop(test_cases)\r\n",
        "\r\n",
        "def test_final_model(final_model):\r\n",
        "    \r\n",
        "    test_inputs = tf.keras.layers.Input(shape=(11,))\r\n",
        "    test_output = final_model(test_inputs)\r\n",
        "\r\n",
        "    test_cases = [\r\n",
        "        {\r\n",
        "            \"name\": \"return_type_check\",\r\n",
        "            \"result\": type(test_output),\r\n",
        "            \"expected\": tf.keras.Model,\r\n",
        "            \"error_message\": 'Return type is incorrect. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"layer_3_activation_check\",\r\n",
        "            \"result\": test_output.layers[4].activation,\r\n",
        "            \"expected\": tf.keras.activations.sigmoid,\r\n",
        "            \"error_message\": 'wine_quality layer has an incorrect activation. Please check implementation.'\r\n",
        "        },\r\n",
        "    ]\r\n",
        "    \r\n",
        "    test_loop(test_cases)\r\n",
        "    \r\n",
        "def test_model_compile(model):\r\n",
        "\r\n",
        "    from tensorflow.python.keras.metrics import MeanMetricWrapper\r\n",
        "\r\n",
        "    test_cases = [\r\n",
        "        {\r\n",
        "            \"name\": \"metrics_0_check\",\r\n",
        "            \"result\": type(model.metrics[0]),\r\n",
        "            \"expected\": tf.keras.metrics.RootMeanSquaredError,\r\n",
        "            \"error_message\": 'wine quality metrics is incorrect. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"metrics_1_check\",\r\n",
        "            \"result\": (model.metrics[1].name == 'wine_type_accuracy') or \r\n",
        "                      (model.metrics[1].name == 'wine_type_binary_accuracy'),\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'wine type metrics: {model.metrics[1].name} is incorrect. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"wine_type_loss_check\",\r\n",
        "            \"result\": (model.loss['wine_type'] == 'binary_crossentropy') or \r\n",
        "                      (model.loss['wine_type'].name == 'binary_crossentropy') or \r\n",
        "                      (str(model.loss['wine_type']).split()[1] == 'binary_crossentropy'),\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'wine type loss: {model.loss[\"wine_type\"]} is incorrect. Please check implementation.'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"wine_quality_loss_check\",\r\n",
        "            \"result\": (model.loss['wine_quality'] in ['mse', 'mean_squared_error']) or \r\n",
        "                      (str(model.loss['wine_quality']).split()[1] == 'mean_squared_error') or \r\n",
        "                      (model.loss['wine_quality'].name == 'mean_squared_error'),\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": f'wine quality loss: {model.loss[\"wine_type\"]} is incorrect. Please check implementation.'\r\n",
        "        },\r\n",
        "    ]\r\n",
        "    \r\n",
        "    test_loop(test_cases)\r\n",
        "    \r\n",
        "def test_history(history):\r\n",
        "\r\n",
        "    vars_history = vars(history)\r\n",
        "    \r\n",
        "    test_cases = [\r\n",
        "        {\r\n",
        "            \"name\": \"type_check\",\r\n",
        "            \"result\": type(history),\r\n",
        "            \"expected\": tf.keras.callbacks.History,\r\n",
        "            \"error_message\": 'history type is incorrect. Please check model.fit().'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"params_samples_check\",\r\n",
        "            \"result\": vars_history['params']['samples'],\r\n",
        "            \"expected\": 3155,\r\n",
        "            \"error_message\": 'Training samples is incorrect. Please check arguments to model.fit().'\r\n",
        "        },\r\n",
        "        {\r\n",
        "            \"name\": \"params_do_validation_check\",\r\n",
        "            \"result\": vars_history['params']['do_validation'],\r\n",
        "            \"expected\": True,\r\n",
        "            \"error_message\": 'No validation data is present. Please check arguments to model.fit().'\r\n",
        "        },\r\n",
        "    ]\r\n",
        "    \r\n",
        "    test_loop(test_cases)\r\n",
        "    "
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGO6QJqII6k-"
      },
      "source": [
        "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\r\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.\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "# URL of the white wine dataset\r\n",
        "URL = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv'\r\n",
        "\r\n",
        "# load the dataset from the URL\r\n",
        "white_df = pd.read_csv(URL, sep=\";\")\r\n",
        "\r\n",
        "# fill the `is_red` column with zeros.\r\n",
        "white_df[\"is_red\"] = np.zeros(white_df.shape[0])\r\n",
        "\r\n",
        "# keep only the first of duplicate items\r\n",
        "white_df = white_df.drop_duplicates(keep='first')\r\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcpEsdICQxsb"
      },
      "source": [
        "# URL of the red wine dataset\r\n",
        "URL = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'\r\n",
        "\r\n",
        "# load the dataset from the URL\r\n",
        "red_df = pd.read_csv(URL, sep=\";\")\r\n",
        "\r\n",
        "# fill the `is_red` column with ones.\r\n",
        "red_df[\"is_red\"] = np.repeat(1, len(red_df))\r\n",
        "\r\n",
        "# keep only the first of duplicate items\r\n",
        "red_df = red_df.drop_duplicates(keep='first')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFg_xLSAQ2Is"
      },
      "source": [
        "df = pd.concat([red_df, white_df], ignore_index=True)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snVf9VbGZ320"
      },
      "source": [
        "# get data with wine quality greater than 4 and less than 8\r\n",
        "df = df[(df['quality'] > 4) & (df['quality'] < 8)]\r\n",
        "\r\n",
        "# reset index and drop the old one\r\n",
        "df = df.reset_index(drop=True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YX3jZRqxZ7M-",
        "outputId": "d85ba191-62d6-408e-f8f5-8ce6864bdc71"
      },
      "source": [
        "df['quality'].hist(bins=20);"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxAfjQW6aZfj"
      },
      "source": [
        "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\r\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.\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "# Please do not change the random_state parameter. This is needed for grading.\r\n",
        "\r\n",
        "# split df into 80:20 train and test sets\r\n",
        "train, test = train_test_split(df, test_size=0.2, random_state = 1)\r\n",
        "                               \r\n",
        "# split train into 80:20 train and val sets\r\n",
        "train, val = train_test_split(train, test_size=0.2, random_state = 1)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "CesDm0vvMOPu",
        "outputId": "2b13ffa0-322e-4fb3-a4e8-19b4605a531d"
      },
      "source": [
        "train_stats = train.describe()\r\n",
        "train_stats.pop('is_red')\r\n",
        "train_stats.pop('quality')\r\n",
        "train_stats = train_stats.transpose()\r\n",
        "train_stats"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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  ...       75%        max\n",
              "fixed acidity         3155.0    7.221616  ...    7.7000   15.60000\n",
              "volatile acidity      3155.0    0.338929  ...    0.4000    1.24000\n",
              "citric acid           3155.0    0.321569  ...    0.4000    1.66000\n",
              "residual sugar        3155.0    5.155911  ...    7.6500   65.80000\n",
              "chlorides             3155.0    0.056976  ...    0.0660    0.61100\n",
              "free sulfur dioxide   3155.0   30.388590  ...   41.0000  131.00000\n",
              "total sulfur dioxide  3155.0  115.062282  ...  156.0000  344.00000\n",
              "density               3155.0    0.994633  ...    0.9968    1.03898\n",
              "pH                    3155.0    3.223201  ...    3.3300    4.01000\n",
              "sulphates             3155.0    0.534051  ...    0.6000    1.95000\n",
              "alcohol               3155.0   10.504466  ...   11.3000   14.00000\n",
              "\n",
              "[11 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_XxRarplMSrm"
      },
      "source": [
        "def format_output(data):\r\n",
        "    is_red = data.pop('is_red')\r\n",
        "    is_red = np.array(is_red)\r\n",
        "    quality = data.pop('quality')\r\n",
        "    quality = np.array(quality)\r\n",
        "    return (quality, is_red)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QmT3mLiNMaKR"
      },
      "source": [
        "# format the output of the train set\r\n",
        "train_Y = format_output(train)\r\n",
        "\r\n",
        "# format the output of the val set\r\n",
        "val_Y = format_output(val)\r\n",
        "    \r\n",
        "# format the output of the test set\r\n",
        "test_Y = format_output(test)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "J11mRnuLMdLX",
        "outputId": "2f037ddc-7f98-4bb1-fd19-ca6c416691f1"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "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  ...    pH  sulphates  alcohol\n",
              "225             7.5              0.65         0.18  ...  3.38       0.77      9.4\n",
              "3557            6.3              0.27         0.29  ...  3.14       0.40      8.8\n",
              "3825            8.8              0.27         0.25  ...  2.87       0.49     11.4\n",
              "1740            6.4              0.45         0.07  ...  2.97       0.28     10.8\n",
              "1221            7.2              0.53         0.13  ...  3.21       0.68      9.9\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chzSZ4yfMgEV"
      },
      "source": [
        "def norm(x):\r\n",
        "    return (x - train_stats['mean']) / train_stats['std']"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dDt0w6GTMjv0"
      },
      "source": [
        "# normalize the train set\r\n",
        "norm_train_X = norm(train)\r\n",
        "    \r\n",
        "# normalize the val set\r\n",
        "norm_val_X = norm(val)\r\n",
        "    \r\n",
        "# normalize the test set\r\n",
        "norm_test_X = norm(test)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rCDcGO9MmBO"
      },
      "source": [
        "\r\n",
        "def base_model(inputs):\r\n",
        "    \r\n",
        "    # connect a Dense layer with 128 neurons and a relu activation\r\n",
        "    x = Dense(128, activation=\"relu\")(inputs)\r\n",
        "    \r\n",
        "    # connect another Dense layer with 128 neurons and a relu activation\r\n",
        "    x = Dense(128, activation=\"relu\")(x)\r\n",
        "    return x"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_uifBZgMoLv"
      },
      "source": [
        "def final_model(inputs):\r\n",
        "    \r\n",
        "    # get the base model\r\n",
        "    x = base_model(inputs)\r\n",
        "\r\n",
        "    # connect the output Dense layer for regression\r\n",
        "    wine_quality = Dense(units='1', name='wine_quality')(x)\r\n",
        "\r\n",
        "    # connect the output Dense layer for classification. this will use a sigmoid activation.\r\n",
        "    wine_type = Dense(units='1', activation='sigmoid', name='wine_type')(x)\r\n",
        "\r\n",
        "    # define the model using the input and output layers\r\n",
        "    model = Model(inputs=inputs, outputs=[wine_quality, wine_type])\r\n",
        "\r\n",
        "    return model"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UrFpTSiMqjl"
      },
      "source": [
        "\r\n",
        "inputs = tf.keras.layers.Input(shape=(11,))\r\n",
        "rms = tf.keras.optimizers.RMSprop(lr=0.0001)\r\n",
        "model = final_model(inputs)\r\n",
        "\r\n",
        "model.compile(optimizer=rms, \r\n",
        "              loss = {'wine_type' : 'binary_crossentropy',\r\n",
        "                      'wine_quality' : 'mse'\r\n",
        "                     },\r\n",
        "              metrics = {'wine_type' : 'accuracy',\r\n",
        "                         'wine_quality': tf.keras.metrics.RootMeanSquaredError()\r\n",
        "                       }\r\n",
        "             )\r\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-uRER52Msn7",
        "outputId": "9da735dd-ca92-478b-f340-ea1a203c6a39"
      },
      "source": [
        "\r\n",
        "history = model.fit(norm_train_X, train_Y,\r\n",
        "                    epochs = 180, validation_data=(norm_test_X, test_Y))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/180\n",
            "99/99 [==============================] - 2s 6ms/step - loss: 30.9232 - wine_quality_loss: 30.1982 - wine_type_loss: 0.7249 - wine_quality_root_mean_squared_error: 5.4911 - wine_type_accuracy: 0.3561 - val_loss: 20.0624 - val_wine_quality_loss: 19.3281 - val_wine_type_loss: 0.7343 - val_wine_quality_root_mean_squared_error: 4.3964 - val_wine_type_accuracy: 0.3445\n",
            "Epoch 2/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 16.4996 - wine_quality_loss: 15.7598 - wine_type_loss: 0.7397 - wine_quality_root_mean_squared_error: 3.9635 - wine_type_accuracy: 0.3325 - val_loss: 8.5413 - val_wine_quality_loss: 7.8277 - val_wine_type_loss: 0.7136 - val_wine_quality_root_mean_squared_error: 2.7978 - val_wine_type_accuracy: 0.4732\n",
            "Epoch 3/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 6.5985 - wine_quality_loss: 5.9128 - wine_type_loss: 0.6857 - wine_quality_root_mean_squared_error: 2.4246 - wine_type_accuracy: 0.5563 - val_loss: 3.5883 - val_wine_quality_loss: 2.9978 - val_wine_type_loss: 0.5905 - val_wine_quality_root_mean_squared_error: 1.7314 - val_wine_type_accuracy: 0.7619\n",
            "Epoch 4/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 3.0747 - wine_quality_loss: 2.5332 - wine_type_loss: 0.5415 - wine_quality_root_mean_squared_error: 1.5909 - wine_type_accuracy: 0.7992 - val_loss: 2.6392 - val_wine_quality_loss: 2.2017 - val_wine_type_loss: 0.4374 - val_wine_quality_root_mean_squared_error: 1.4838 - val_wine_type_accuracy: 0.8085\n",
            "Epoch 5/180\n",
            "99/99 [==============================] - 0s 4ms/step - loss: 2.4335 - wine_quality_loss: 2.0407 - wine_type_loss: 0.3927 - wine_quality_root_mean_squared_error: 1.4271 - wine_type_accuracy: 0.8346 - val_loss: 2.2256 - val_wine_quality_loss: 1.8965 - val_wine_type_loss: 0.3291 - val_wine_quality_root_mean_squared_error: 1.3771 - val_wine_type_accuracy: 0.9017\n",
            "Epoch 6/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 2.0450 - wine_quality_loss: 1.7442 - wine_type_loss: 0.3008 - wine_quality_root_mean_squared_error: 1.3193 - wine_type_accuracy: 0.9281 - val_loss: 1.9361 - val_wine_quality_loss: 1.6861 - val_wine_type_loss: 0.2500 - val_wine_quality_root_mean_squared_error: 1.2985 - val_wine_type_accuracy: 0.9726\n",
            "Epoch 7/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 1.7552 - wine_quality_loss: 1.5209 - wine_type_loss: 0.2343 - wine_quality_root_mean_squared_error: 1.2325 - wine_type_accuracy: 0.9723 - val_loss: 1.7383 - val_wine_quality_loss: 1.5461 - val_wine_type_loss: 0.1922 - val_wine_quality_root_mean_squared_error: 1.2434 - val_wine_type_accuracy: 0.9878\n",
            "Epoch 8/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 1.6615 - wine_quality_loss: 1.4847 - wine_type_loss: 0.1768 - wine_quality_root_mean_squared_error: 1.2182 - wine_type_accuracy: 0.9862 - val_loss: 1.5793 - val_wine_quality_loss: 1.4291 - val_wine_type_loss: 0.1502 - val_wine_quality_root_mean_squared_error: 1.1955 - val_wine_type_accuracy: 0.9868\n",
            "Epoch 9/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 1.5946 - wine_quality_loss: 1.4613 - wine_type_loss: 0.1333 - wine_quality_root_mean_squared_error: 1.2076 - wine_type_accuracy: 0.9878 - val_loss: 1.4532 - val_wine_quality_loss: 1.3320 - val_wine_type_loss: 0.1212 - val_wine_quality_root_mean_squared_error: 1.1541 - val_wine_type_accuracy: 0.9889\n",
            "Epoch 10/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 1.3405 - wine_quality_loss: 1.2273 - wine_type_loss: 0.1132 - wine_quality_root_mean_squared_error: 1.1074 - wine_type_accuracy: 0.9920 - val_loss: 1.3466 - val_wine_quality_loss: 1.2464 - val_wine_type_loss: 0.1002 - val_wine_quality_root_mean_squared_error: 1.1164 - val_wine_type_accuracy: 0.9919\n",
            "Epoch 11/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 1.2350 - wine_quality_loss: 1.1454 - wine_type_loss: 0.0896 - wine_quality_root_mean_squared_error: 1.0700 - wine_type_accuracy: 0.9919 - val_loss: 1.2402 - val_wine_quality_loss: 1.1549 - val_wine_type_loss: 0.0853 - val_wine_quality_root_mean_squared_error: 1.0747 - val_wine_type_accuracy: 0.9909\n",
            "Epoch 12/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 1.1008 - wine_quality_loss: 1.0237 - wine_type_loss: 0.0771 - wine_quality_root_mean_squared_error: 1.0112 - wine_type_accuracy: 0.9912 - val_loss: 1.1597 - val_wine_quality_loss: 1.0848 - val_wine_type_loss: 0.0749 - val_wine_quality_root_mean_squared_error: 1.0416 - val_wine_type_accuracy: 0.9899\n",
            "Epoch 13/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 1.1187 - wine_quality_loss: 1.0432 - wine_type_loss: 0.0755 - wine_quality_root_mean_squared_error: 1.0202 - wine_type_accuracy: 0.9905 - val_loss: 1.0898 - val_wine_quality_loss: 1.0227 - val_wine_type_loss: 0.0672 - val_wine_quality_root_mean_squared_error: 1.0113 - val_wine_type_accuracy: 0.9909\n",
            "Epoch 14/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.9977 - wine_quality_loss: 0.9377 - wine_type_loss: 0.0601 - wine_quality_root_mean_squared_error: 0.9681 - wine_type_accuracy: 0.9901 - val_loss: 1.0263 - val_wine_quality_loss: 0.9650 - val_wine_type_loss: 0.0613 - val_wine_quality_root_mean_squared_error: 0.9823 - val_wine_type_accuracy: 0.9919\n",
            "Epoch 15/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.9441 - wine_quality_loss: 0.8812 - wine_type_loss: 0.0629 - wine_quality_root_mean_squared_error: 0.9384 - wine_type_accuracy: 0.9907 - val_loss: 0.9639 - val_wine_quality_loss: 0.9071 - val_wine_type_loss: 0.0568 - val_wine_quality_root_mean_squared_error: 0.9524 - val_wine_type_accuracy: 0.9919\n",
            "Epoch 16/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.8751 - wine_quality_loss: 0.8215 - wine_type_loss: 0.0535 - wine_quality_root_mean_squared_error: 0.9055 - wine_type_accuracy: 0.9925 - val_loss: 0.9047 - val_wine_quality_loss: 0.8514 - val_wine_type_loss: 0.0533 - val_wine_quality_root_mean_squared_error: 0.9227 - val_wine_type_accuracy: 0.9919\n",
            "Epoch 17/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.8713 - wine_quality_loss: 0.8144 - wine_type_loss: 0.0569 - wine_quality_root_mean_squared_error: 0.9020 - wine_type_accuracy: 0.9910 - val_loss: 0.8572 - val_wine_quality_loss: 0.8064 - val_wine_type_loss: 0.0507 - val_wine_quality_root_mean_squared_error: 0.8980 - val_wine_type_accuracy: 0.9919\n",
            "Epoch 18/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.7916 - wine_quality_loss: 0.7326 - wine_type_loss: 0.0589 - wine_quality_root_mean_squared_error: 0.8558 - wine_type_accuracy: 0.9931 - val_loss: 0.8145 - val_wine_quality_loss: 0.7661 - val_wine_type_loss: 0.0483 - val_wine_quality_root_mean_squared_error: 0.8753 - val_wine_type_accuracy: 0.9919\n",
            "Epoch 19/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.8907 - wine_quality_loss: 0.8457 - wine_type_loss: 0.0450 - wine_quality_root_mean_squared_error: 0.9102 - wine_type_accuracy: 0.9917 - val_loss: 0.7884 - val_wine_quality_loss: 0.7420 - val_wine_type_loss: 0.0464 - val_wine_quality_root_mean_squared_error: 0.8614 - val_wine_type_accuracy: 0.9919\n",
            "Epoch 20/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.6960 - wine_quality_loss: 0.6533 - wine_type_loss: 0.0427 - wine_quality_root_mean_squared_error: 0.8078 - wine_type_accuracy: 0.9923 - val_loss: 0.7438 - val_wine_quality_loss: 0.6989 - val_wine_type_loss: 0.0449 - val_wine_quality_root_mean_squared_error: 0.8360 - val_wine_type_accuracy: 0.9919\n",
            "Epoch 21/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.7009 - wine_quality_loss: 0.6526 - wine_type_loss: 0.0483 - wine_quality_root_mean_squared_error: 0.8074 - wine_type_accuracy: 0.9930 - val_loss: 0.7090 - val_wine_quality_loss: 0.6654 - val_wine_type_loss: 0.0437 - val_wine_quality_root_mean_squared_error: 0.8157 - val_wine_type_accuracy: 0.9919\n",
            "Epoch 22/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.6609 - wine_quality_loss: 0.6169 - wine_type_loss: 0.0441 - wine_quality_root_mean_squared_error: 0.7852 - wine_type_accuracy: 0.9919 - val_loss: 0.6811 - val_wine_quality_loss: 0.6385 - val_wine_type_loss: 0.0426 - val_wine_quality_root_mean_squared_error: 0.7991 - val_wine_type_accuracy: 0.9929\n",
            "Epoch 23/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.6044 - wine_quality_loss: 0.5643 - wine_type_loss: 0.0401 - wine_quality_root_mean_squared_error: 0.7509 - wine_type_accuracy: 0.9934 - val_loss: 0.6483 - val_wine_quality_loss: 0.6067 - val_wine_type_loss: 0.0416 - val_wine_quality_root_mean_squared_error: 0.7789 - val_wine_type_accuracy: 0.9929\n",
            "Epoch 24/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.5821 - wine_quality_loss: 0.5481 - wine_type_loss: 0.0341 - wine_quality_root_mean_squared_error: 0.7401 - wine_type_accuracy: 0.9951 - val_loss: 0.6367 - val_wine_quality_loss: 0.5959 - val_wine_type_loss: 0.0409 - val_wine_quality_root_mean_squared_error: 0.7719 - val_wine_type_accuracy: 0.9929\n",
            "Epoch 25/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.5759 - wine_quality_loss: 0.5321 - wine_type_loss: 0.0439 - wine_quality_root_mean_squared_error: 0.7294 - wine_type_accuracy: 0.9909 - val_loss: 0.6025 - val_wine_quality_loss: 0.5624 - val_wine_type_loss: 0.0401 - val_wine_quality_root_mean_squared_error: 0.7499 - val_wine_type_accuracy: 0.9929\n",
            "Epoch 26/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.5802 - wine_quality_loss: 0.5420 - wine_type_loss: 0.0382 - wine_quality_root_mean_squared_error: 0.7359 - wine_type_accuracy: 0.9934 - val_loss: 0.5862 - val_wine_quality_loss: 0.5467 - val_wine_type_loss: 0.0395 - val_wine_quality_root_mean_squared_error: 0.7394 - val_wine_type_accuracy: 0.9929\n",
            "Epoch 27/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.5461 - wine_quality_loss: 0.5097 - wine_type_loss: 0.0364 - wine_quality_root_mean_squared_error: 0.7137 - wine_type_accuracy: 0.9931 - val_loss: 0.5560 - val_wine_quality_loss: 0.5171 - val_wine_type_loss: 0.0389 - val_wine_quality_root_mean_squared_error: 0.7191 - val_wine_type_accuracy: 0.9939\n",
            "Epoch 28/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.5270 - wine_quality_loss: 0.4910 - wine_type_loss: 0.0359 - wine_quality_root_mean_squared_error: 0.7006 - wine_type_accuracy: 0.9952 - val_loss: 0.5342 - val_wine_quality_loss: 0.4958 - val_wine_type_loss: 0.0384 - val_wine_quality_root_mean_squared_error: 0.7041 - val_wine_type_accuracy: 0.9939\n",
            "Epoch 29/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.4900 - wine_quality_loss: 0.4530 - wine_type_loss: 0.0371 - wine_quality_root_mean_squared_error: 0.6728 - wine_type_accuracy: 0.9937 - val_loss: 0.5200 - val_wine_quality_loss: 0.4820 - val_wine_type_loss: 0.0379 - val_wine_quality_root_mean_squared_error: 0.6943 - val_wine_type_accuracy: 0.9939\n",
            "Epoch 30/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.4767 - wine_quality_loss: 0.4386 - wine_type_loss: 0.0381 - wine_quality_root_mean_squared_error: 0.6621 - wine_type_accuracy: 0.9946 - val_loss: 0.5115 - val_wine_quality_loss: 0.4739 - val_wine_type_loss: 0.0376 - val_wine_quality_root_mean_squared_error: 0.6884 - val_wine_type_accuracy: 0.9939\n",
            "Epoch 31/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.4659 - wine_quality_loss: 0.4313 - wine_type_loss: 0.0346 - wine_quality_root_mean_squared_error: 0.6565 - wine_type_accuracy: 0.9928 - val_loss: 0.4951 - val_wine_quality_loss: 0.4578 - val_wine_type_loss: 0.0373 - val_wine_quality_root_mean_squared_error: 0.6766 - val_wine_type_accuracy: 0.9939\n",
            "Epoch 32/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.4598 - wine_quality_loss: 0.4218 - wine_type_loss: 0.0380 - wine_quality_root_mean_squared_error: 0.6491 - wine_type_accuracy: 0.9945 - val_loss: 0.4821 - val_wine_quality_loss: 0.4452 - val_wine_type_loss: 0.0369 - val_wine_quality_root_mean_squared_error: 0.6672 - val_wine_type_accuracy: 0.9939\n",
            "Epoch 33/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.4368 - wine_quality_loss: 0.3971 - wine_type_loss: 0.0396 - wine_quality_root_mean_squared_error: 0.6300 - wine_type_accuracy: 0.9937 - val_loss: 0.4725 - val_wine_quality_loss: 0.4358 - val_wine_type_loss: 0.0366 - val_wine_quality_root_mean_squared_error: 0.6602 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 34/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.4454 - wine_quality_loss: 0.4095 - wine_type_loss: 0.0360 - wine_quality_root_mean_squared_error: 0.6398 - wine_type_accuracy: 0.9945 - val_loss: 0.4663 - val_wine_quality_loss: 0.4299 - val_wine_type_loss: 0.0364 - val_wine_quality_root_mean_squared_error: 0.6557 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 35/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.4329 - wine_quality_loss: 0.4022 - wine_type_loss: 0.0307 - wine_quality_root_mean_squared_error: 0.6341 - wine_type_accuracy: 0.9941 - val_loss: 0.4604 - val_wine_quality_loss: 0.4243 - val_wine_type_loss: 0.0361 - val_wine_quality_root_mean_squared_error: 0.6514 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 36/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.4227 - wine_quality_loss: 0.3801 - wine_type_loss: 0.0426 - wine_quality_root_mean_squared_error: 0.6165 - wine_type_accuracy: 0.9927 - val_loss: 0.4443 - val_wine_quality_loss: 0.4084 - val_wine_type_loss: 0.0359 - val_wine_quality_root_mean_squared_error: 0.6391 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 37/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.4383 - wine_quality_loss: 0.3965 - wine_type_loss: 0.0417 - wine_quality_root_mean_squared_error: 0.6295 - wine_type_accuracy: 0.9931 - val_loss: 0.4392 - val_wine_quality_loss: 0.4036 - val_wine_type_loss: 0.0356 - val_wine_quality_root_mean_squared_error: 0.6353 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 38/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3961 - wine_quality_loss: 0.3725 - wine_type_loss: 0.0236 - wine_quality_root_mean_squared_error: 0.6103 - wine_type_accuracy: 0.9960 - val_loss: 0.4347 - val_wine_quality_loss: 0.3993 - val_wine_type_loss: 0.0354 - val_wine_quality_root_mean_squared_error: 0.6319 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 39/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.4131 - wine_quality_loss: 0.3853 - wine_type_loss: 0.0278 - wine_quality_root_mean_squared_error: 0.6206 - wine_type_accuracy: 0.9937 - val_loss: 0.4314 - val_wine_quality_loss: 0.3963 - val_wine_type_loss: 0.0351 - val_wine_quality_root_mean_squared_error: 0.6296 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 40/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3820 - wine_quality_loss: 0.3497 - wine_type_loss: 0.0323 - wine_quality_root_mean_squared_error: 0.5912 - wine_type_accuracy: 0.9929 - val_loss: 0.4264 - val_wine_quality_loss: 0.3915 - val_wine_type_loss: 0.0349 - val_wine_quality_root_mean_squared_error: 0.6257 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 41/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3636 - wine_quality_loss: 0.3396 - wine_type_loss: 0.0240 - wine_quality_root_mean_squared_error: 0.5825 - wine_type_accuracy: 0.9942 - val_loss: 0.4201 - val_wine_quality_loss: 0.3854 - val_wine_type_loss: 0.0347 - val_wine_quality_root_mean_squared_error: 0.6208 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 42/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3991 - wine_quality_loss: 0.3681 - wine_type_loss: 0.0310 - wine_quality_root_mean_squared_error: 0.6067 - wine_type_accuracy: 0.9945 - val_loss: 0.4157 - val_wine_quality_loss: 0.3812 - val_wine_type_loss: 0.0345 - val_wine_quality_root_mean_squared_error: 0.6174 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 43/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3701 - wine_quality_loss: 0.3426 - wine_type_loss: 0.0276 - wine_quality_root_mean_squared_error: 0.5852 - wine_type_accuracy: 0.9962 - val_loss: 0.4209 - val_wine_quality_loss: 0.3866 - val_wine_type_loss: 0.0343 - val_wine_quality_root_mean_squared_error: 0.6218 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 44/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3697 - wine_quality_loss: 0.3494 - wine_type_loss: 0.0203 - wine_quality_root_mean_squared_error: 0.5910 - wine_type_accuracy: 0.9966 - val_loss: 0.4117 - val_wine_quality_loss: 0.3776 - val_wine_type_loss: 0.0341 - val_wine_quality_root_mean_squared_error: 0.6145 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 45/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3833 - wine_quality_loss: 0.3381 - wine_type_loss: 0.0452 - wine_quality_root_mean_squared_error: 0.5814 - wine_type_accuracy: 0.9949 - val_loss: 0.4147 - val_wine_quality_loss: 0.3807 - val_wine_type_loss: 0.0340 - val_wine_quality_root_mean_squared_error: 0.6170 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 46/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3736 - wine_quality_loss: 0.3334 - wine_type_loss: 0.0403 - wine_quality_root_mean_squared_error: 0.5773 - wine_type_accuracy: 0.9927 - val_loss: 0.4067 - val_wine_quality_loss: 0.3730 - val_wine_type_loss: 0.0338 - val_wine_quality_root_mean_squared_error: 0.6107 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 47/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3642 - wine_quality_loss: 0.3315 - wine_type_loss: 0.0327 - wine_quality_root_mean_squared_error: 0.5757 - wine_type_accuracy: 0.9944 - val_loss: 0.4037 - val_wine_quality_loss: 0.3701 - val_wine_type_loss: 0.0336 - val_wine_quality_root_mean_squared_error: 0.6084 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 48/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3445 - wine_quality_loss: 0.3200 - wine_type_loss: 0.0245 - wine_quality_root_mean_squared_error: 0.5654 - wine_type_accuracy: 0.9949 - val_loss: 0.4087 - val_wine_quality_loss: 0.3754 - val_wine_type_loss: 0.0334 - val_wine_quality_root_mean_squared_error: 0.6127 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 49/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3630 - wine_quality_loss: 0.3289 - wine_type_loss: 0.0342 - wine_quality_root_mean_squared_error: 0.5732 - wine_type_accuracy: 0.9922 - val_loss: 0.3990 - val_wine_quality_loss: 0.3658 - val_wine_type_loss: 0.0332 - val_wine_quality_root_mean_squared_error: 0.6049 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 50/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3438 - wine_quality_loss: 0.3255 - wine_type_loss: 0.0184 - wine_quality_root_mean_squared_error: 0.5703 - wine_type_accuracy: 0.9969 - val_loss: 0.3993 - val_wine_quality_loss: 0.3663 - val_wine_type_loss: 0.0331 - val_wine_quality_root_mean_squared_error: 0.6052 - val_wine_type_accuracy: 0.9959\n",
            "Epoch 51/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3652 - wine_quality_loss: 0.3322 - wine_type_loss: 0.0330 - wine_quality_root_mean_squared_error: 0.5763 - wine_type_accuracy: 0.9933 - val_loss: 0.3997 - val_wine_quality_loss: 0.3668 - val_wine_type_loss: 0.0329 - val_wine_quality_root_mean_squared_error: 0.6057 - val_wine_type_accuracy: 0.9959\n",
            "Epoch 52/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3523 - wine_quality_loss: 0.3253 - wine_type_loss: 0.0269 - wine_quality_root_mean_squared_error: 0.5703 - wine_type_accuracy: 0.9947 - val_loss: 0.3935 - val_wine_quality_loss: 0.3608 - val_wine_type_loss: 0.0328 - val_wine_quality_root_mean_squared_error: 0.6006 - val_wine_type_accuracy: 0.9959\n",
            "Epoch 53/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3540 - wine_quality_loss: 0.3292 - wine_type_loss: 0.0249 - wine_quality_root_mean_squared_error: 0.5736 - wine_type_accuracy: 0.9957 - val_loss: 0.3982 - val_wine_quality_loss: 0.3656 - val_wine_type_loss: 0.0326 - val_wine_quality_root_mean_squared_error: 0.6046 - val_wine_type_accuracy: 0.9959\n",
            "Epoch 54/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3639 - wine_quality_loss: 0.3343 - wine_type_loss: 0.0296 - wine_quality_root_mean_squared_error: 0.5779 - wine_type_accuracy: 0.9955 - val_loss: 0.3923 - val_wine_quality_loss: 0.3598 - val_wine_type_loss: 0.0325 - val_wine_quality_root_mean_squared_error: 0.5998 - val_wine_type_accuracy: 0.9959\n",
            "Epoch 55/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3581 - wine_quality_loss: 0.3176 - wine_type_loss: 0.0405 - wine_quality_root_mean_squared_error: 0.5634 - wine_type_accuracy: 0.9925 - val_loss: 0.3864 - val_wine_quality_loss: 0.3541 - val_wine_type_loss: 0.0323 - val_wine_quality_root_mean_squared_error: 0.5951 - val_wine_type_accuracy: 0.9959\n",
            "Epoch 56/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3527 - wine_quality_loss: 0.3240 - wine_type_loss: 0.0287 - wine_quality_root_mean_squared_error: 0.5691 - wine_type_accuracy: 0.9938 - val_loss: 0.3886 - val_wine_quality_loss: 0.3564 - val_wine_type_loss: 0.0322 - val_wine_quality_root_mean_squared_error: 0.5970 - val_wine_type_accuracy: 0.9959\n",
            "Epoch 57/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3472 - wine_quality_loss: 0.3232 - wine_type_loss: 0.0240 - wine_quality_root_mean_squared_error: 0.5684 - wine_type_accuracy: 0.9949 - val_loss: 0.3905 - val_wine_quality_loss: 0.3584 - val_wine_type_loss: 0.0321 - val_wine_quality_root_mean_squared_error: 0.5987 - val_wine_type_accuracy: 0.9949\n",
            "Epoch 58/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3296 - wine_quality_loss: 0.3092 - wine_type_loss: 0.0204 - wine_quality_root_mean_squared_error: 0.5559 - wine_type_accuracy: 0.9961 - val_loss: 0.3845 - val_wine_quality_loss: 0.3526 - val_wine_type_loss: 0.0319 - val_wine_quality_root_mean_squared_error: 0.5938 - val_wine_type_accuracy: 0.9959\n",
            "Epoch 59/180\n",
            "99/99 [==============================] - 0s 4ms/step - loss: 0.3331 - wine_quality_loss: 0.2999 - wine_type_loss: 0.0332 - wine_quality_root_mean_squared_error: 0.5475 - wine_type_accuracy: 0.9926 - val_loss: 0.3890 - val_wine_quality_loss: 0.3573 - val_wine_type_loss: 0.0318 - val_wine_quality_root_mean_squared_error: 0.5977 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 60/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3340 - wine_quality_loss: 0.3093 - wine_type_loss: 0.0247 - wine_quality_root_mean_squared_error: 0.5558 - wine_type_accuracy: 0.9955 - val_loss: 0.3842 - val_wine_quality_loss: 0.3526 - val_wine_type_loss: 0.0316 - val_wine_quality_root_mean_squared_error: 0.5938 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 61/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3494 - wine_quality_loss: 0.3215 - wine_type_loss: 0.0279 - wine_quality_root_mean_squared_error: 0.5669 - wine_type_accuracy: 0.9940 - val_loss: 0.3818 - val_wine_quality_loss: 0.3504 - val_wine_type_loss: 0.0315 - val_wine_quality_root_mean_squared_error: 0.5919 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 62/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3254 - wine_quality_loss: 0.3023 - wine_type_loss: 0.0231 - wine_quality_root_mean_squared_error: 0.5496 - wine_type_accuracy: 0.9948 - val_loss: 0.3821 - val_wine_quality_loss: 0.3508 - val_wine_type_loss: 0.0313 - val_wine_quality_root_mean_squared_error: 0.5923 - val_wine_type_accuracy: 0.9959\n",
            "Epoch 63/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3417 - wine_quality_loss: 0.3049 - wine_type_loss: 0.0368 - wine_quality_root_mean_squared_error: 0.5519 - wine_type_accuracy: 0.9953 - val_loss: 0.3860 - val_wine_quality_loss: 0.3548 - val_wine_type_loss: 0.0312 - val_wine_quality_root_mean_squared_error: 0.5956 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 64/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3311 - wine_quality_loss: 0.3002 - wine_type_loss: 0.0309 - wine_quality_root_mean_squared_error: 0.5476 - wine_type_accuracy: 0.9952 - val_loss: 0.3829 - val_wine_quality_loss: 0.3518 - val_wine_type_loss: 0.0311 - val_wine_quality_root_mean_squared_error: 0.5931 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 65/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3323 - wine_quality_loss: 0.3129 - wine_type_loss: 0.0194 - wine_quality_root_mean_squared_error: 0.5593 - wine_type_accuracy: 0.9951 - val_loss: 0.3820 - val_wine_quality_loss: 0.3510 - val_wine_type_loss: 0.0310 - val_wine_quality_root_mean_squared_error: 0.5924 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 66/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3445 - wine_quality_loss: 0.3150 - wine_type_loss: 0.0295 - wine_quality_root_mean_squared_error: 0.5610 - wine_type_accuracy: 0.9938 - val_loss: 0.3771 - val_wine_quality_loss: 0.3463 - val_wine_type_loss: 0.0308 - val_wine_quality_root_mean_squared_error: 0.5885 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 67/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3380 - wine_quality_loss: 0.3092 - wine_type_loss: 0.0288 - wine_quality_root_mean_squared_error: 0.5559 - wine_type_accuracy: 0.9938 - val_loss: 0.3819 - val_wine_quality_loss: 0.3512 - val_wine_type_loss: 0.0307 - val_wine_quality_root_mean_squared_error: 0.5926 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 68/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3304 - wine_quality_loss: 0.3080 - wine_type_loss: 0.0225 - wine_quality_root_mean_squared_error: 0.5548 - wine_type_accuracy: 0.9949 - val_loss: 0.3863 - val_wine_quality_loss: 0.3557 - val_wine_type_loss: 0.0306 - val_wine_quality_root_mean_squared_error: 0.5964 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 69/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3155 - wine_quality_loss: 0.2927 - wine_type_loss: 0.0228 - wine_quality_root_mean_squared_error: 0.5407 - wine_type_accuracy: 0.9963 - val_loss: 0.3804 - val_wine_quality_loss: 0.3499 - val_wine_type_loss: 0.0304 - val_wine_quality_root_mean_squared_error: 0.5916 - val_wine_type_accuracy: 0.9959\n",
            "Epoch 70/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3227 - wine_quality_loss: 0.3030 - wine_type_loss: 0.0198 - wine_quality_root_mean_squared_error: 0.5501 - wine_type_accuracy: 0.9968 - val_loss: 0.3792 - val_wine_quality_loss: 0.3489 - val_wine_type_loss: 0.0304 - val_wine_quality_root_mean_squared_error: 0.5907 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 71/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3277 - wine_quality_loss: 0.2982 - wine_type_loss: 0.0294 - wine_quality_root_mean_squared_error: 0.5460 - wine_type_accuracy: 0.9947 - val_loss: 0.3785 - val_wine_quality_loss: 0.3482 - val_wine_type_loss: 0.0302 - val_wine_quality_root_mean_squared_error: 0.5901 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 72/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3386 - wine_quality_loss: 0.3188 - wine_type_loss: 0.0197 - wine_quality_root_mean_squared_error: 0.5644 - wine_type_accuracy: 0.9958 - val_loss: 0.3797 - val_wine_quality_loss: 0.3496 - val_wine_type_loss: 0.0301 - val_wine_quality_root_mean_squared_error: 0.5913 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 73/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3193 - wine_quality_loss: 0.2956 - wine_type_loss: 0.0236 - wine_quality_root_mean_squared_error: 0.5435 - wine_type_accuracy: 0.9956 - val_loss: 0.3795 - val_wine_quality_loss: 0.3494 - val_wine_type_loss: 0.0300 - val_wine_quality_root_mean_squared_error: 0.5911 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 74/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3361 - wine_quality_loss: 0.3064 - wine_type_loss: 0.0296 - wine_quality_root_mean_squared_error: 0.5535 - wine_type_accuracy: 0.9953 - val_loss: 0.3807 - val_wine_quality_loss: 0.3508 - val_wine_type_loss: 0.0299 - val_wine_quality_root_mean_squared_error: 0.5923 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 75/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3376 - wine_quality_loss: 0.3083 - wine_type_loss: 0.0294 - wine_quality_root_mean_squared_error: 0.5551 - wine_type_accuracy: 0.9950 - val_loss: 0.3817 - val_wine_quality_loss: 0.3519 - val_wine_type_loss: 0.0298 - val_wine_quality_root_mean_squared_error: 0.5932 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 76/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3123 - wine_quality_loss: 0.2889 - wine_type_loss: 0.0234 - wine_quality_root_mean_squared_error: 0.5372 - wine_type_accuracy: 0.9954 - val_loss: 0.3787 - val_wine_quality_loss: 0.3490 - val_wine_type_loss: 0.0297 - val_wine_quality_root_mean_squared_error: 0.5908 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 77/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3095 - wine_quality_loss: 0.2926 - wine_type_loss: 0.0169 - wine_quality_root_mean_squared_error: 0.5409 - wine_type_accuracy: 0.9957 - val_loss: 0.3766 - val_wine_quality_loss: 0.3470 - val_wine_type_loss: 0.0296 - val_wine_quality_root_mean_squared_error: 0.5891 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 78/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3317 - wine_quality_loss: 0.3059 - wine_type_loss: 0.0257 - wine_quality_root_mean_squared_error: 0.5529 - wine_type_accuracy: 0.9950 - val_loss: 0.3801 - val_wine_quality_loss: 0.3507 - val_wine_type_loss: 0.0294 - val_wine_quality_root_mean_squared_error: 0.5922 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 79/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3210 - wine_quality_loss: 0.3013 - wine_type_loss: 0.0197 - wine_quality_root_mean_squared_error: 0.5488 - wine_type_accuracy: 0.9959 - val_loss: 0.3754 - val_wine_quality_loss: 0.3461 - val_wine_type_loss: 0.0293 - val_wine_quality_root_mean_squared_error: 0.5883 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 80/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3156 - wine_quality_loss: 0.2922 - wine_type_loss: 0.0234 - wine_quality_root_mean_squared_error: 0.5403 - wine_type_accuracy: 0.9964 - val_loss: 0.3780 - val_wine_quality_loss: 0.3487 - val_wine_type_loss: 0.0292 - val_wine_quality_root_mean_squared_error: 0.5905 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 81/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3158 - wine_quality_loss: 0.2906 - wine_type_loss: 0.0252 - wine_quality_root_mean_squared_error: 0.5390 - wine_type_accuracy: 0.9949 - val_loss: 0.3775 - val_wine_quality_loss: 0.3484 - val_wine_type_loss: 0.0291 - val_wine_quality_root_mean_squared_error: 0.5903 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 82/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3175 - wine_quality_loss: 0.3040 - wine_type_loss: 0.0135 - wine_quality_root_mean_squared_error: 0.5512 - wine_type_accuracy: 0.9983 - val_loss: 0.3835 - val_wine_quality_loss: 0.3545 - val_wine_type_loss: 0.0291 - val_wine_quality_root_mean_squared_error: 0.5954 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 83/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3092 - wine_quality_loss: 0.2902 - wine_type_loss: 0.0190 - wine_quality_root_mean_squared_error: 0.5386 - wine_type_accuracy: 0.9965 - val_loss: 0.3722 - val_wine_quality_loss: 0.3432 - val_wine_type_loss: 0.0290 - val_wine_quality_root_mean_squared_error: 0.5858 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 84/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3216 - wine_quality_loss: 0.2987 - wine_type_loss: 0.0229 - wine_quality_root_mean_squared_error: 0.5465 - wine_type_accuracy: 0.9960 - val_loss: 0.3769 - val_wine_quality_loss: 0.3481 - val_wine_type_loss: 0.0288 - val_wine_quality_root_mean_squared_error: 0.5900 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 85/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3045 - wine_quality_loss: 0.2874 - wine_type_loss: 0.0171 - wine_quality_root_mean_squared_error: 0.5360 - wine_type_accuracy: 0.9974 - val_loss: 0.3742 - val_wine_quality_loss: 0.3455 - val_wine_type_loss: 0.0288 - val_wine_quality_root_mean_squared_error: 0.5878 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 86/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3033 - wine_quality_loss: 0.2806 - wine_type_loss: 0.0227 - wine_quality_root_mean_squared_error: 0.5296 - wine_type_accuracy: 0.9953 - val_loss: 0.3761 - val_wine_quality_loss: 0.3475 - val_wine_type_loss: 0.0286 - val_wine_quality_root_mean_squared_error: 0.5895 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 87/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3206 - wine_quality_loss: 0.2943 - wine_type_loss: 0.0263 - wine_quality_root_mean_squared_error: 0.5424 - wine_type_accuracy: 0.9938 - val_loss: 0.3737 - val_wine_quality_loss: 0.3452 - val_wine_type_loss: 0.0285 - val_wine_quality_root_mean_squared_error: 0.5875 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 88/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3288 - wine_quality_loss: 0.2948 - wine_type_loss: 0.0341 - wine_quality_root_mean_squared_error: 0.5429 - wine_type_accuracy: 0.9939 - val_loss: 0.3771 - val_wine_quality_loss: 0.3487 - val_wine_type_loss: 0.0284 - val_wine_quality_root_mean_squared_error: 0.5905 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 89/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3158 - wine_quality_loss: 0.2941 - wine_type_loss: 0.0217 - wine_quality_root_mean_squared_error: 0.5422 - wine_type_accuracy: 0.9961 - val_loss: 0.3839 - val_wine_quality_loss: 0.3556 - val_wine_type_loss: 0.0283 - val_wine_quality_root_mean_squared_error: 0.5964 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 90/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3237 - wine_quality_loss: 0.2907 - wine_type_loss: 0.0330 - wine_quality_root_mean_squared_error: 0.5392 - wine_type_accuracy: 0.9945 - val_loss: 0.3751 - val_wine_quality_loss: 0.3468 - val_wine_type_loss: 0.0283 - val_wine_quality_root_mean_squared_error: 0.5889 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 91/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3132 - wine_quality_loss: 0.2888 - wine_type_loss: 0.0244 - wine_quality_root_mean_squared_error: 0.5374 - wine_type_accuracy: 0.9965 - val_loss: 0.3761 - val_wine_quality_loss: 0.3480 - val_wine_type_loss: 0.0282 - val_wine_quality_root_mean_squared_error: 0.5899 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 92/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3152 - wine_quality_loss: 0.2930 - wine_type_loss: 0.0222 - wine_quality_root_mean_squared_error: 0.5412 - wine_type_accuracy: 0.9956 - val_loss: 0.3786 - val_wine_quality_loss: 0.3505 - val_wine_type_loss: 0.0281 - val_wine_quality_root_mean_squared_error: 0.5920 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 93/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3094 - wine_quality_loss: 0.2950 - wine_type_loss: 0.0144 - wine_quality_root_mean_squared_error: 0.5431 - wine_type_accuracy: 0.9972 - val_loss: 0.3752 - val_wine_quality_loss: 0.3472 - val_wine_type_loss: 0.0280 - val_wine_quality_root_mean_squared_error: 0.5892 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 94/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3039 - wine_quality_loss: 0.2784 - wine_type_loss: 0.0255 - wine_quality_root_mean_squared_error: 0.5275 - wine_type_accuracy: 0.9965 - val_loss: 0.3766 - val_wine_quality_loss: 0.3487 - val_wine_type_loss: 0.0279 - val_wine_quality_root_mean_squared_error: 0.5905 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 95/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3046 - wine_quality_loss: 0.2834 - wine_type_loss: 0.0212 - wine_quality_root_mean_squared_error: 0.5323 - wine_type_accuracy: 0.9957 - val_loss: 0.3694 - val_wine_quality_loss: 0.3415 - val_wine_type_loss: 0.0279 - val_wine_quality_root_mean_squared_error: 0.5844 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 96/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3146 - wine_quality_loss: 0.2918 - wine_type_loss: 0.0228 - wine_quality_root_mean_squared_error: 0.5399 - wine_type_accuracy: 0.9969 - val_loss: 0.3780 - val_wine_quality_loss: 0.3503 - val_wine_type_loss: 0.0277 - val_wine_quality_root_mean_squared_error: 0.5919 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 97/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3363 - wine_quality_loss: 0.3033 - wine_type_loss: 0.0330 - wine_quality_root_mean_squared_error: 0.5504 - wine_type_accuracy: 0.9933 - val_loss: 0.3715 - val_wine_quality_loss: 0.3438 - val_wine_type_loss: 0.0277 - val_wine_quality_root_mean_squared_error: 0.5863 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 98/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3087 - wine_quality_loss: 0.2818 - wine_type_loss: 0.0269 - wine_quality_root_mean_squared_error: 0.5308 - wine_type_accuracy: 0.9958 - val_loss: 0.3703 - val_wine_quality_loss: 0.3427 - val_wine_type_loss: 0.0275 - val_wine_quality_root_mean_squared_error: 0.5854 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 99/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2984 - wine_quality_loss: 0.2831 - wine_type_loss: 0.0153 - wine_quality_root_mean_squared_error: 0.5319 - wine_type_accuracy: 0.9975 - val_loss: 0.3773 - val_wine_quality_loss: 0.3499 - val_wine_type_loss: 0.0275 - val_wine_quality_root_mean_squared_error: 0.5915 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 100/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3026 - wine_quality_loss: 0.2789 - wine_type_loss: 0.0237 - wine_quality_root_mean_squared_error: 0.5280 - wine_type_accuracy: 0.9958 - val_loss: 0.3691 - val_wine_quality_loss: 0.3417 - val_wine_type_loss: 0.0274 - val_wine_quality_root_mean_squared_error: 0.5846 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 101/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3208 - wine_quality_loss: 0.2951 - wine_type_loss: 0.0257 - wine_quality_root_mean_squared_error: 0.5431 - wine_type_accuracy: 0.9951 - val_loss: 0.3759 - val_wine_quality_loss: 0.3485 - val_wine_type_loss: 0.0274 - val_wine_quality_root_mean_squared_error: 0.5904 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 102/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3007 - wine_quality_loss: 0.2868 - wine_type_loss: 0.0140 - wine_quality_root_mean_squared_error: 0.5355 - wine_type_accuracy: 0.9986 - val_loss: 0.3740 - val_wine_quality_loss: 0.3467 - val_wine_type_loss: 0.0273 - val_wine_quality_root_mean_squared_error: 0.5888 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 103/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3077 - wine_quality_loss: 0.2880 - wine_type_loss: 0.0197 - wine_quality_root_mean_squared_error: 0.5366 - wine_type_accuracy: 0.9969 - val_loss: 0.3699 - val_wine_quality_loss: 0.3428 - val_wine_type_loss: 0.0272 - val_wine_quality_root_mean_squared_error: 0.5855 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 104/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3051 - wine_quality_loss: 0.2872 - wine_type_loss: 0.0179 - wine_quality_root_mean_squared_error: 0.5358 - wine_type_accuracy: 0.9964 - val_loss: 0.3681 - val_wine_quality_loss: 0.3411 - val_wine_type_loss: 0.0271 - val_wine_quality_root_mean_squared_error: 0.5840 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 105/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3103 - wine_quality_loss: 0.2931 - wine_type_loss: 0.0172 - wine_quality_root_mean_squared_error: 0.5413 - wine_type_accuracy: 0.9971 - val_loss: 0.3715 - val_wine_quality_loss: 0.3444 - val_wine_type_loss: 0.0271 - val_wine_quality_root_mean_squared_error: 0.5869 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 106/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3149 - wine_quality_loss: 0.2919 - wine_type_loss: 0.0230 - wine_quality_root_mean_squared_error: 0.5402 - wine_type_accuracy: 0.9959 - val_loss: 0.3682 - val_wine_quality_loss: 0.3412 - val_wine_type_loss: 0.0270 - val_wine_quality_root_mean_squared_error: 0.5841 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 107/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3046 - wine_quality_loss: 0.2827 - wine_type_loss: 0.0218 - wine_quality_root_mean_squared_error: 0.5317 - wine_type_accuracy: 0.9961 - val_loss: 0.3673 - val_wine_quality_loss: 0.3403 - val_wine_type_loss: 0.0269 - val_wine_quality_root_mean_squared_error: 0.5834 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 108/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3096 - wine_quality_loss: 0.2858 - wine_type_loss: 0.0238 - wine_quality_root_mean_squared_error: 0.5345 - wine_type_accuracy: 0.9964 - val_loss: 0.3697 - val_wine_quality_loss: 0.3429 - val_wine_type_loss: 0.0268 - val_wine_quality_root_mean_squared_error: 0.5856 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 109/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3023 - wine_quality_loss: 0.2802 - wine_type_loss: 0.0221 - wine_quality_root_mean_squared_error: 0.5294 - wine_type_accuracy: 0.9959 - val_loss: 0.3656 - val_wine_quality_loss: 0.3389 - val_wine_type_loss: 0.0267 - val_wine_quality_root_mean_squared_error: 0.5821 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 110/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2955 - wine_quality_loss: 0.2691 - wine_type_loss: 0.0265 - wine_quality_root_mean_squared_error: 0.5187 - wine_type_accuracy: 0.9963 - val_loss: 0.3706 - val_wine_quality_loss: 0.3440 - val_wine_type_loss: 0.0267 - val_wine_quality_root_mean_squared_error: 0.5865 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 111/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3152 - wine_quality_loss: 0.2864 - wine_type_loss: 0.0288 - wine_quality_root_mean_squared_error: 0.5351 - wine_type_accuracy: 0.9954 - val_loss: 0.3689 - val_wine_quality_loss: 0.3423 - val_wine_type_loss: 0.0266 - val_wine_quality_root_mean_squared_error: 0.5851 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 112/180\n",
            "99/99 [==============================] - 0s 4ms/step - loss: 0.2985 - wine_quality_loss: 0.2788 - wine_type_loss: 0.0197 - wine_quality_root_mean_squared_error: 0.5279 - wine_type_accuracy: 0.9956 - val_loss: 0.3676 - val_wine_quality_loss: 0.3410 - val_wine_type_loss: 0.0266 - val_wine_quality_root_mean_squared_error: 0.5840 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 113/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2964 - wine_quality_loss: 0.2763 - wine_type_loss: 0.0201 - wine_quality_root_mean_squared_error: 0.5256 - wine_type_accuracy: 0.9965 - val_loss: 0.3691 - val_wine_quality_loss: 0.3426 - val_wine_type_loss: 0.0264 - val_wine_quality_root_mean_squared_error: 0.5853 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 114/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2823 - wine_quality_loss: 0.2683 - wine_type_loss: 0.0140 - wine_quality_root_mean_squared_error: 0.5179 - wine_type_accuracy: 0.9982 - val_loss: 0.3726 - val_wine_quality_loss: 0.3462 - val_wine_type_loss: 0.0264 - val_wine_quality_root_mean_squared_error: 0.5884 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 115/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2988 - wine_quality_loss: 0.2775 - wine_type_loss: 0.0213 - wine_quality_root_mean_squared_error: 0.5267 - wine_type_accuracy: 0.9955 - val_loss: 0.3696 - val_wine_quality_loss: 0.3433 - val_wine_type_loss: 0.0263 - val_wine_quality_root_mean_squared_error: 0.5859 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 116/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.2806 - wine_quality_loss: 0.2680 - wine_type_loss: 0.0126 - wine_quality_root_mean_squared_error: 0.5176 - wine_type_accuracy: 0.9975 - val_loss: 0.3709 - val_wine_quality_loss: 0.3447 - val_wine_type_loss: 0.0262 - val_wine_quality_root_mean_squared_error: 0.5871 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 117/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2932 - wine_quality_loss: 0.2717 - wine_type_loss: 0.0215 - wine_quality_root_mean_squared_error: 0.5212 - wine_type_accuracy: 0.9975 - val_loss: 0.3783 - val_wine_quality_loss: 0.3521 - val_wine_type_loss: 0.0262 - val_wine_quality_root_mean_squared_error: 0.5934 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 118/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3080 - wine_quality_loss: 0.2838 - wine_type_loss: 0.0242 - wine_quality_root_mean_squared_error: 0.5327 - wine_type_accuracy: 0.9958 - val_loss: 0.3750 - val_wine_quality_loss: 0.3489 - val_wine_type_loss: 0.0261 - val_wine_quality_root_mean_squared_error: 0.5907 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 119/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3012 - wine_quality_loss: 0.2796 - wine_type_loss: 0.0216 - wine_quality_root_mean_squared_error: 0.5287 - wine_type_accuracy: 0.9954 - val_loss: 0.3716 - val_wine_quality_loss: 0.3455 - val_wine_type_loss: 0.0261 - val_wine_quality_root_mean_squared_error: 0.5878 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 120/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3055 - wine_quality_loss: 0.2741 - wine_type_loss: 0.0314 - wine_quality_root_mean_squared_error: 0.5235 - wine_type_accuracy: 0.9954 - val_loss: 0.3704 - val_wine_quality_loss: 0.3444 - val_wine_type_loss: 0.0260 - val_wine_quality_root_mean_squared_error: 0.5869 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 121/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3039 - wine_quality_loss: 0.2806 - wine_type_loss: 0.0232 - wine_quality_root_mean_squared_error: 0.5297 - wine_type_accuracy: 0.9953 - val_loss: 0.3656 - val_wine_quality_loss: 0.3396 - val_wine_type_loss: 0.0259 - val_wine_quality_root_mean_squared_error: 0.5828 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 122/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2918 - wine_quality_loss: 0.2752 - wine_type_loss: 0.0167 - wine_quality_root_mean_squared_error: 0.5245 - wine_type_accuracy: 0.9965 - val_loss: 0.3720 - val_wine_quality_loss: 0.3461 - val_wine_type_loss: 0.0259 - val_wine_quality_root_mean_squared_error: 0.5883 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 123/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3052 - wine_quality_loss: 0.2782 - wine_type_loss: 0.0270 - wine_quality_root_mean_squared_error: 0.5274 - wine_type_accuracy: 0.9961 - val_loss: 0.3788 - val_wine_quality_loss: 0.3530 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5941 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 124/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.3027 - wine_quality_loss: 0.2740 - wine_type_loss: 0.0287 - wine_quality_root_mean_squared_error: 0.5233 - wine_type_accuracy: 0.9943 - val_loss: 0.3710 - val_wine_quality_loss: 0.3452 - val_wine_type_loss: 0.0258 - val_wine_quality_root_mean_squared_error: 0.5875 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 125/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2968 - wine_quality_loss: 0.2779 - wine_type_loss: 0.0189 - wine_quality_root_mean_squared_error: 0.5271 - wine_type_accuracy: 0.9971 - val_loss: 0.3676 - val_wine_quality_loss: 0.3419 - val_wine_type_loss: 0.0257 - val_wine_quality_root_mean_squared_error: 0.5847 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 126/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2902 - wine_quality_loss: 0.2689 - wine_type_loss: 0.0214 - wine_quality_root_mean_squared_error: 0.5184 - wine_type_accuracy: 0.9954 - val_loss: 0.3703 - val_wine_quality_loss: 0.3447 - val_wine_type_loss: 0.0256 - val_wine_quality_root_mean_squared_error: 0.5871 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 127/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3168 - wine_quality_loss: 0.2921 - wine_type_loss: 0.0247 - wine_quality_root_mean_squared_error: 0.5402 - wine_type_accuracy: 0.9959 - val_loss: 0.3669 - val_wine_quality_loss: 0.3413 - val_wine_type_loss: 0.0255 - val_wine_quality_root_mean_squared_error: 0.5842 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 128/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2935 - wine_quality_loss: 0.2759 - wine_type_loss: 0.0176 - wine_quality_root_mean_squared_error: 0.5252 - wine_type_accuracy: 0.9967 - val_loss: 0.3700 - val_wine_quality_loss: 0.3445 - val_wine_type_loss: 0.0255 - val_wine_quality_root_mean_squared_error: 0.5870 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 129/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.3026 - wine_quality_loss: 0.2733 - wine_type_loss: 0.0293 - wine_quality_root_mean_squared_error: 0.5228 - wine_type_accuracy: 0.9948 - val_loss: 0.3693 - val_wine_quality_loss: 0.3439 - val_wine_type_loss: 0.0255 - val_wine_quality_root_mean_squared_error: 0.5864 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 130/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2971 - wine_quality_loss: 0.2772 - wine_type_loss: 0.0199 - wine_quality_root_mean_squared_error: 0.5264 - wine_type_accuracy: 0.9964 - val_loss: 0.3671 - val_wine_quality_loss: 0.3418 - val_wine_type_loss: 0.0254 - val_wine_quality_root_mean_squared_error: 0.5846 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 131/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2859 - wine_quality_loss: 0.2705 - wine_type_loss: 0.0154 - wine_quality_root_mean_squared_error: 0.5200 - wine_type_accuracy: 0.9973 - val_loss: 0.3771 - val_wine_quality_loss: 0.3517 - val_wine_type_loss: 0.0253 - val_wine_quality_root_mean_squared_error: 0.5931 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 132/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2913 - wine_quality_loss: 0.2759 - wine_type_loss: 0.0154 - wine_quality_root_mean_squared_error: 0.5252 - wine_type_accuracy: 0.9969 - val_loss: 0.3682 - val_wine_quality_loss: 0.3429 - val_wine_type_loss: 0.0253 - val_wine_quality_root_mean_squared_error: 0.5856 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 133/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.2871 - wine_quality_loss: 0.2681 - wine_type_loss: 0.0190 - wine_quality_root_mean_squared_error: 0.5176 - wine_type_accuracy: 0.9969 - val_loss: 0.3744 - val_wine_quality_loss: 0.3492 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5909 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 134/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2826 - wine_quality_loss: 0.2655 - wine_type_loss: 0.0171 - wine_quality_root_mean_squared_error: 0.5152 - wine_type_accuracy: 0.9976 - val_loss: 0.3642 - val_wine_quality_loss: 0.3390 - val_wine_type_loss: 0.0252 - val_wine_quality_root_mean_squared_error: 0.5823 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 135/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2866 - wine_quality_loss: 0.2663 - wine_type_loss: 0.0204 - wine_quality_root_mean_squared_error: 0.5159 - wine_type_accuracy: 0.9956 - val_loss: 0.3725 - val_wine_quality_loss: 0.3474 - val_wine_type_loss: 0.0251 - val_wine_quality_root_mean_squared_error: 0.5894 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 136/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2947 - wine_quality_loss: 0.2789 - wine_type_loss: 0.0158 - wine_quality_root_mean_squared_error: 0.5279 - wine_type_accuracy: 0.9973 - val_loss: 0.3707 - val_wine_quality_loss: 0.3457 - val_wine_type_loss: 0.0250 - val_wine_quality_root_mean_squared_error: 0.5880 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 137/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2960 - wine_quality_loss: 0.2731 - wine_type_loss: 0.0229 - wine_quality_root_mean_squared_error: 0.5226 - wine_type_accuracy: 0.9951 - val_loss: 0.3674 - val_wine_quality_loss: 0.3425 - val_wine_type_loss: 0.0250 - val_wine_quality_root_mean_squared_error: 0.5852 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 138/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2880 - wine_quality_loss: 0.2678 - wine_type_loss: 0.0202 - wine_quality_root_mean_squared_error: 0.5174 - wine_type_accuracy: 0.9966 - val_loss: 0.3686 - val_wine_quality_loss: 0.3438 - val_wine_type_loss: 0.0249 - val_wine_quality_root_mean_squared_error: 0.5863 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 139/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2819 - wine_quality_loss: 0.2629 - wine_type_loss: 0.0190 - wine_quality_root_mean_squared_error: 0.5125 - wine_type_accuracy: 0.9964 - val_loss: 0.3682 - val_wine_quality_loss: 0.3433 - val_wine_type_loss: 0.0249 - val_wine_quality_root_mean_squared_error: 0.5859 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 140/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.2790 - wine_quality_loss: 0.2600 - wine_type_loss: 0.0190 - wine_quality_root_mean_squared_error: 0.5093 - wine_type_accuracy: 0.9966 - val_loss: 0.3646 - val_wine_quality_loss: 0.3397 - val_wine_type_loss: 0.0248 - val_wine_quality_root_mean_squared_error: 0.5829 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 141/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2716 - wine_quality_loss: 0.2573 - wine_type_loss: 0.0143 - wine_quality_root_mean_squared_error: 0.5069 - wine_type_accuracy: 0.9980 - val_loss: 0.3648 - val_wine_quality_loss: 0.3401 - val_wine_type_loss: 0.0248 - val_wine_quality_root_mean_squared_error: 0.5832 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 142/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2844 - wine_quality_loss: 0.2684 - wine_type_loss: 0.0160 - wine_quality_root_mean_squared_error: 0.5180 - wine_type_accuracy: 0.9973 - val_loss: 0.3659 - val_wine_quality_loss: 0.3412 - val_wine_type_loss: 0.0247 - val_wine_quality_root_mean_squared_error: 0.5841 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 143/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2718 - wine_quality_loss: 0.2523 - wine_type_loss: 0.0195 - wine_quality_root_mean_squared_error: 0.5021 - wine_type_accuracy: 0.9969 - val_loss: 0.3682 - val_wine_quality_loss: 0.3436 - val_wine_type_loss: 0.0246 - val_wine_quality_root_mean_squared_error: 0.5862 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 144/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.2837 - wine_quality_loss: 0.2597 - wine_type_loss: 0.0241 - wine_quality_root_mean_squared_error: 0.5094 - wine_type_accuracy: 0.9952 - val_loss: 0.3678 - val_wine_quality_loss: 0.3432 - val_wine_type_loss: 0.0246 - val_wine_quality_root_mean_squared_error: 0.5858 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 145/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2861 - wine_quality_loss: 0.2640 - wine_type_loss: 0.0222 - wine_quality_root_mean_squared_error: 0.5137 - wine_type_accuracy: 0.9965 - val_loss: 0.3638 - val_wine_quality_loss: 0.3392 - val_wine_type_loss: 0.0246 - val_wine_quality_root_mean_squared_error: 0.5824 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 146/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2820 - wine_quality_loss: 0.2669 - wine_type_loss: 0.0151 - wine_quality_root_mean_squared_error: 0.5166 - wine_type_accuracy: 0.9974 - val_loss: 0.3668 - val_wine_quality_loss: 0.3422 - val_wine_type_loss: 0.0246 - val_wine_quality_root_mean_squared_error: 0.5850 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 147/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2892 - wine_quality_loss: 0.2712 - wine_type_loss: 0.0179 - wine_quality_root_mean_squared_error: 0.5207 - wine_type_accuracy: 0.9964 - val_loss: 0.3741 - val_wine_quality_loss: 0.3496 - val_wine_type_loss: 0.0245 - val_wine_quality_root_mean_squared_error: 0.5913 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 148/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2872 - wine_quality_loss: 0.2668 - wine_type_loss: 0.0204 - wine_quality_root_mean_squared_error: 0.5165 - wine_type_accuracy: 0.9972 - val_loss: 0.3653 - val_wine_quality_loss: 0.3409 - val_wine_type_loss: 0.0244 - val_wine_quality_root_mean_squared_error: 0.5839 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 149/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2884 - wine_quality_loss: 0.2600 - wine_type_loss: 0.0284 - wine_quality_root_mean_squared_error: 0.5099 - wine_type_accuracy: 0.9966 - val_loss: 0.3674 - val_wine_quality_loss: 0.3431 - val_wine_type_loss: 0.0244 - val_wine_quality_root_mean_squared_error: 0.5857 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 150/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2748 - wine_quality_loss: 0.2615 - wine_type_loss: 0.0133 - wine_quality_root_mean_squared_error: 0.5113 - wine_type_accuracy: 0.9971 - val_loss: 0.3775 - val_wine_quality_loss: 0.3532 - val_wine_type_loss: 0.0243 - val_wine_quality_root_mean_squared_error: 0.5943 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 151/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2887 - wine_quality_loss: 0.2659 - wine_type_loss: 0.0228 - wine_quality_root_mean_squared_error: 0.5154 - wine_type_accuracy: 0.9971 - val_loss: 0.3673 - val_wine_quality_loss: 0.3430 - val_wine_type_loss: 0.0243 - val_wine_quality_root_mean_squared_error: 0.5857 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 152/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2894 - wine_quality_loss: 0.2738 - wine_type_loss: 0.0156 - wine_quality_root_mean_squared_error: 0.5233 - wine_type_accuracy: 0.9969 - val_loss: 0.3709 - val_wine_quality_loss: 0.3467 - val_wine_type_loss: 0.0243 - val_wine_quality_root_mean_squared_error: 0.5888 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 153/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2813 - wine_quality_loss: 0.2694 - wine_type_loss: 0.0119 - wine_quality_root_mean_squared_error: 0.5189 - wine_type_accuracy: 0.9978 - val_loss: 0.3675 - val_wine_quality_loss: 0.3433 - val_wine_type_loss: 0.0243 - val_wine_quality_root_mean_squared_error: 0.5859 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 154/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2815 - wine_quality_loss: 0.2609 - wine_type_loss: 0.0206 - wine_quality_root_mean_squared_error: 0.5108 - wine_type_accuracy: 0.9960 - val_loss: 0.3651 - val_wine_quality_loss: 0.3409 - val_wine_type_loss: 0.0242 - val_wine_quality_root_mean_squared_error: 0.5839 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 155/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2826 - wine_quality_loss: 0.2631 - wine_type_loss: 0.0195 - wine_quality_root_mean_squared_error: 0.5129 - wine_type_accuracy: 0.9969 - val_loss: 0.3710 - val_wine_quality_loss: 0.3469 - val_wine_type_loss: 0.0241 - val_wine_quality_root_mean_squared_error: 0.5890 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 156/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2701 - wine_quality_loss: 0.2585 - wine_type_loss: 0.0116 - wine_quality_root_mean_squared_error: 0.5084 - wine_type_accuracy: 0.9981 - val_loss: 0.3696 - val_wine_quality_loss: 0.3455 - val_wine_type_loss: 0.0241 - val_wine_quality_root_mean_squared_error: 0.5878 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 157/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2765 - wine_quality_loss: 0.2625 - wine_type_loss: 0.0140 - wine_quality_root_mean_squared_error: 0.5123 - wine_type_accuracy: 0.9972 - val_loss: 0.3818 - val_wine_quality_loss: 0.3578 - val_wine_type_loss: 0.0240 - val_wine_quality_root_mean_squared_error: 0.5982 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 158/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2757 - wine_quality_loss: 0.2583 - wine_type_loss: 0.0174 - wine_quality_root_mean_squared_error: 0.5082 - wine_type_accuracy: 0.9975 - val_loss: 0.3663 - val_wine_quality_loss: 0.3424 - val_wine_type_loss: 0.0239 - val_wine_quality_root_mean_squared_error: 0.5851 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 159/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2722 - wine_quality_loss: 0.2601 - wine_type_loss: 0.0120 - wine_quality_root_mean_squared_error: 0.5099 - wine_type_accuracy: 0.9979 - val_loss: 0.3639 - val_wine_quality_loss: 0.3399 - val_wine_type_loss: 0.0239 - val_wine_quality_root_mean_squared_error: 0.5830 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 160/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2931 - wine_quality_loss: 0.2750 - wine_type_loss: 0.0181 - wine_quality_root_mean_squared_error: 0.5243 - wine_type_accuracy: 0.9962 - val_loss: 0.3614 - val_wine_quality_loss: 0.3375 - val_wine_type_loss: 0.0239 - val_wine_quality_root_mean_squared_error: 0.5810 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 161/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2870 - wine_quality_loss: 0.2654 - wine_type_loss: 0.0216 - wine_quality_root_mean_squared_error: 0.5150 - wine_type_accuracy: 0.9955 - val_loss: 0.3659 - val_wine_quality_loss: 0.3421 - val_wine_type_loss: 0.0239 - val_wine_quality_root_mean_squared_error: 0.5849 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 162/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2813 - wine_quality_loss: 0.2643 - wine_type_loss: 0.0169 - wine_quality_root_mean_squared_error: 0.5141 - wine_type_accuracy: 0.9971 - val_loss: 0.3660 - val_wine_quality_loss: 0.3422 - val_wine_type_loss: 0.0238 - val_wine_quality_root_mean_squared_error: 0.5850 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 163/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2721 - wine_quality_loss: 0.2566 - wine_type_loss: 0.0154 - wine_quality_root_mean_squared_error: 0.5064 - wine_type_accuracy: 0.9971 - val_loss: 0.3727 - val_wine_quality_loss: 0.3489 - val_wine_type_loss: 0.0238 - val_wine_quality_root_mean_squared_error: 0.5906 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 164/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2787 - wine_quality_loss: 0.2596 - wine_type_loss: 0.0191 - wine_quality_root_mean_squared_error: 0.5095 - wine_type_accuracy: 0.9960 - val_loss: 0.3714 - val_wine_quality_loss: 0.3476 - val_wine_type_loss: 0.0238 - val_wine_quality_root_mean_squared_error: 0.5896 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 165/180\n",
            "99/99 [==============================] - 0s 4ms/step - loss: 0.2830 - wine_quality_loss: 0.2625 - wine_type_loss: 0.0205 - wine_quality_root_mean_squared_error: 0.5123 - wine_type_accuracy: 0.9963 - val_loss: 0.3664 - val_wine_quality_loss: 0.3427 - val_wine_type_loss: 0.0237 - val_wine_quality_root_mean_squared_error: 0.5854 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 166/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2843 - wine_quality_loss: 0.2679 - wine_type_loss: 0.0164 - wine_quality_root_mean_squared_error: 0.5174 - wine_type_accuracy: 0.9970 - val_loss: 0.3646 - val_wine_quality_loss: 0.3410 - val_wine_type_loss: 0.0236 - val_wine_quality_root_mean_squared_error: 0.5839 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 167/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2806 - wine_quality_loss: 0.2618 - wine_type_loss: 0.0188 - wine_quality_root_mean_squared_error: 0.5116 - wine_type_accuracy: 0.9963 - val_loss: 0.3724 - val_wine_quality_loss: 0.3488 - val_wine_type_loss: 0.0236 - val_wine_quality_root_mean_squared_error: 0.5906 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 168/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2857 - wine_quality_loss: 0.2640 - wine_type_loss: 0.0216 - wine_quality_root_mean_squared_error: 0.5138 - wine_type_accuracy: 0.9964 - val_loss: 0.3683 - val_wine_quality_loss: 0.3447 - val_wine_type_loss: 0.0235 - val_wine_quality_root_mean_squared_error: 0.5871 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 169/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2760 - wine_quality_loss: 0.2561 - wine_type_loss: 0.0199 - wine_quality_root_mean_squared_error: 0.5060 - wine_type_accuracy: 0.9957 - val_loss: 0.3654 - val_wine_quality_loss: 0.3419 - val_wine_type_loss: 0.0235 - val_wine_quality_root_mean_squared_error: 0.5847 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 170/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2599 - wine_quality_loss: 0.2420 - wine_type_loss: 0.0179 - wine_quality_root_mean_squared_error: 0.4917 - wine_type_accuracy: 0.9967 - val_loss: 0.3654 - val_wine_quality_loss: 0.3419 - val_wine_type_loss: 0.0235 - val_wine_quality_root_mean_squared_error: 0.5847 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 171/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2621 - wine_quality_loss: 0.2493 - wine_type_loss: 0.0128 - wine_quality_root_mean_squared_error: 0.4991 - wine_type_accuracy: 0.9978 - val_loss: 0.3676 - val_wine_quality_loss: 0.3441 - val_wine_type_loss: 0.0235 - val_wine_quality_root_mean_squared_error: 0.5866 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 172/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2767 - wine_quality_loss: 0.2549 - wine_type_loss: 0.0218 - wine_quality_root_mean_squared_error: 0.5046 - wine_type_accuracy: 0.9946 - val_loss: 0.3658 - val_wine_quality_loss: 0.3424 - val_wine_type_loss: 0.0234 - val_wine_quality_root_mean_squared_error: 0.5851 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 173/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2685 - wine_quality_loss: 0.2538 - wine_type_loss: 0.0148 - wine_quality_root_mean_squared_error: 0.5037 - wine_type_accuracy: 0.9977 - val_loss: 0.3647 - val_wine_quality_loss: 0.3413 - val_wine_type_loss: 0.0234 - val_wine_quality_root_mean_squared_error: 0.5842 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 174/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2736 - wine_quality_loss: 0.2611 - wine_type_loss: 0.0124 - wine_quality_root_mean_squared_error: 0.5109 - wine_type_accuracy: 0.9975 - val_loss: 0.3718 - val_wine_quality_loss: 0.3485 - val_wine_type_loss: 0.0233 - val_wine_quality_root_mean_squared_error: 0.5903 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 175/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2685 - wine_quality_loss: 0.2498 - wine_type_loss: 0.0187 - wine_quality_root_mean_squared_error: 0.4997 - wine_type_accuracy: 0.9961 - val_loss: 0.3683 - val_wine_quality_loss: 0.3451 - val_wine_type_loss: 0.0232 - val_wine_quality_root_mean_squared_error: 0.5875 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 176/180\n",
            "99/99 [==============================] - 0s 2ms/step - loss: 0.2745 - wine_quality_loss: 0.2642 - wine_type_loss: 0.0103 - wine_quality_root_mean_squared_error: 0.5139 - wine_type_accuracy: 0.9980 - val_loss: 0.3651 - val_wine_quality_loss: 0.3419 - val_wine_type_loss: 0.0232 - val_wine_quality_root_mean_squared_error: 0.5847 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 177/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2793 - wine_quality_loss: 0.2578 - wine_type_loss: 0.0215 - wine_quality_root_mean_squared_error: 0.5076 - wine_type_accuracy: 0.9962 - val_loss: 0.3647 - val_wine_quality_loss: 0.3415 - val_wine_type_loss: 0.0232 - val_wine_quality_root_mean_squared_error: 0.5844 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 178/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2727 - wine_quality_loss: 0.2604 - wine_type_loss: 0.0122 - wine_quality_root_mean_squared_error: 0.5099 - wine_type_accuracy: 0.9972 - val_loss: 0.3691 - val_wine_quality_loss: 0.3459 - val_wine_type_loss: 0.0232 - val_wine_quality_root_mean_squared_error: 0.5882 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 179/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2673 - wine_quality_loss: 0.2545 - wine_type_loss: 0.0128 - wine_quality_root_mean_squared_error: 0.5044 - wine_type_accuracy: 0.9967 - val_loss: 0.3652 - val_wine_quality_loss: 0.3421 - val_wine_type_loss: 0.0232 - val_wine_quality_root_mean_squared_error: 0.5849 - val_wine_type_accuracy: 0.9970\n",
            "Epoch 180/180\n",
            "99/99 [==============================] - 0s 3ms/step - loss: 0.2612 - wine_quality_loss: 0.2492 - wine_type_loss: 0.0120 - wine_quality_root_mean_squared_error: 0.4990 - wine_type_accuracy: 0.9977 - val_loss: 0.3700 - val_wine_quality_loss: 0.3469 - val_wine_type_loss: 0.0231 - val_wine_quality_root_mean_squared_error: 0.5890 - val_wine_type_accuracy: 0.9970\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z2JeMqsnM4rH",
        "outputId": "cd1d9407-e093-4190-d254-bb4e7ffbcd13"
      },
      "source": [
        "# Gather the training metrics\r\n",
        "loss, wine_quality_loss, wine_type_loss, wine_quality_rmse, wine_type_accuracy = model.evaluate(x=norm_val_X, y=val_Y)\r\n",
        "\r\n",
        "print()\r\n",
        "print(f'loss: {loss}')\r\n",
        "print(f'wine_quality_loss: {wine_quality_loss}')\r\n",
        "print(f'wine_type_loss: {wine_type_loss}')\r\n",
        "print(f'wine_quality_rmse: {wine_quality_rmse}')\r\n",
        "print(f'wine_type_accuracy: {wine_type_accuracy}')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25/25 [==============================] - 0s 1ms/step - loss: 0.3381 - wine_quality_loss: 0.3132 - wine_type_loss: 0.0249 - wine_quality_root_mean_squared_error: 0.5596 - wine_type_accuracy: 0.9962\n",
            "\n",
            "loss: 0.3380642831325531\n",
            "wine_quality_loss: 0.3131885528564453\n",
            "wine_type_loss: 0.024875735864043236\n",
            "wine_quality_rmse: 0.5596325397491455\n",
            "wine_type_accuracy: 0.9961977005004883\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "psgZObZoM9B4"
      },
      "source": [
        "predictions = model.predict(norm_test_X)\r\n",
        "quality_pred = predictions[0]\r\n",
        "type_pred = predictions[1]"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "947MRCLyM-wl"
      },
      "source": [
        "def plot_metrics(metric_name, title, ylim=5):\r\n",
        "    plt.title(title)\r\n",
        "    plt.ylim(0,ylim)\r\n",
        "    plt.plot(history.history[metric_name],color='blue',label=metric_name)\r\n",
        "    plt.plot(history.history['val_' + metric_name],color='green',label='val_' + metric_name)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuozBruSM_aP"
      },
      "source": [
        "def plot_confusion_matrix(y_true, y_pred, title='', labels=[0,1]):\r\n",
        "    cm = confusion_matrix(y_true, y_pred)\r\n",
        "    fig = plt.figure()\r\n",
        "    ax = fig.add_subplot(111)\r\n",
        "    cax = ax.matshow(cm)\r\n",
        "    plt.title('Confusion matrix of the classifier')\r\n",
        "    fig.colorbar(cax)\r\n",
        "    ax.set_xticklabels([''] + labels)\r\n",
        "    ax.set_yticklabels([''] + labels)\r\n",
        "    plt.xlabel('Predicted')\r\n",
        "    plt.ylabel('True')\r\n",
        "    fmt = 'd'\r\n",
        "    thresh = cm.max() / 2.\r\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\r\n",
        "          plt.text(j, i, format(cm[i, j], fmt),\r\n",
        "                  horizontalalignment=\"center\",\r\n",
        "                  color=\"black\" if cm[i, j] > thresh else \"white\")\r\n",
        "    plt.show()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQYL49d2NAtY"
      },
      "source": [
        "def plot_diff(y_true, y_pred, title = '' ):\r\n",
        "    plt.scatter(y_true, y_pred)\r\n",
        "    plt.title(title)\r\n",
        "    plt.xlabel('True Values')\r\n",
        "    plt.ylabel('Predictions')\r\n",
        "    plt.axis('equal')\r\n",
        "    plt.axis('square')\r\n",
        "    plt.plot([-100, 100], [-100, 100])\r\n",
        "    return plt"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "ZRCiQwIxNEo6",
        "outputId": "0899803a-fda5-433f-a20a-25f41e07040f"
      },
      "source": [
        "plot_metrics('wine_quality_root_mean_squared_error', 'RMSE', ylim=2)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "Vdrn_hyuNFK3",
        "outputId": "c3d04ace-a458-4506-e2a0-6681c9ab10fd"
      },
      "source": [
        "plot_metrics('wine_type_loss', 'Wine Type Loss', ylim=0.2)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "vl9fTV6zNG5E",
        "outputId": "8ecce652-d1ee-4416-8915-58954e5f78bb"
      },
      "source": [
        "plot_confusion_matrix(test_Y[1], np.round(type_pred), title='Wine Type', labels = [0, 1])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9F/xx4kq+8oX3B7ifXryUTh3FMf+WpZ9zwVJOHtOLHt39f4vVpobTRNb3dkkbQsW6qJI6ApcBB5FdVPu4pPER8VyljllNf75vJbt9dFMG9F/7kV57yxv86d6VTLp1EFL2y35s2jvcftcKzvjREpa9UU+HDtBlU3HiN3pVq+pmOdVOF7WSY3B7ADMjYhaApJvJblhXyAB38x9WcOSXeq7Zvvu+lVxw2evcf8dWdOu29svwlzu3WrN+zgVL6NG9g4Ob1Rw/k6Hpm9MVzsq36rn3wbf48iHd16T9538v5s2VweeOnMfwz7zCCacvaqYEay/OvPFkLvnbTxg8bEtueuXXjPjGAdWuUruTzaJ2LGuptqrPoqYb4I0BGDKo6tVple7dOvDac9u9L+3FR7Zu8XVnnda3UlWyVvrpMZdUuwrtXi2d6FvJFlxZN6eLiCsiYveI2L1/3+pHfDNrWX16dGBLS7VVssn0ODBU0rZkge1I4OgKHs/MNgBfbA9ExGpJJwETgY7ANRHxbKWOZ2YbjmdRgYiYQHYXTjMriAix2gHOzIpqo++imlkxeQzOzArNAc7MCsnnwZlZobXVeXCS/inpaUlPSZqa0vpImiTppfSzd0qXpEvTzTumSxreUvkOcGaWSwSsru9Q1lKmT0fErhHR8PjAM4DJETEUmJy2AQ4GhqZlDHB5SwU7wJlZbhW+XdJIYGxaHwscVpJ+XWSmAL0kDWyuIAc4M8ulYQyujQJcAPdIeiJdlw4wICLmp/UFwIC0nvsGHp5kMLPcovzWWb+GsbXkioi4omT7kxExT9IWwCRJz7//OBGSorX1dIAzs9xyXEi/uGRs7QMiYl76uUjSOLL7SC6UNDAi5qcuaMO9xsq6gUcpd1HNLJeIthmDk9RdUs+GdeCzwDPAeGBUyjYKuDOtjweOS7OpewHLS7qyjXILzsxyEnVt80jAAcC4dDv/TsBNEXG3pMeBWyWNBmYDR6T8E4BDgJnAW8DxLR3AAc7McssxBtdMGTEL2KWR9CXAgY2kB3BinmM4wJlZLr4W1cyKK7JxuFrgAGdmubWH25GXwwHOzHKJtptkqDgHODPLzV1UMyustphF3RAc4MwslwgHODMrMJ8mYmaF5TE4MyukQNR7FtXMiqpGGnAOcGaWkycZzKzQaqQJ5wBnZrm5BWdmhRRAfb0DnJkVUQBuwZlZUfk8ODMrLgc4MysmeZLBzArMLTgzK6SA8CyqmRVXbQS42rhi1szalyhzKYOkjpKelHRX2t5W0qOSZkq6RdImKX3TtD0z7d+mpbId4MwsvzYMcMDJwIyS7fOBiyJiB+B1YHRKHw28ntIvSvma5QBnZvk0nOhbztICSVsBnweuStsCDgBuS1nGAoel9ZFpm7T/wJS/SQ5wZpZbRHkL0E/S1JJlzDpFXQycDtSn7b7AsohYnbbnAoPS+iBgTnb8WA0sT/mb5EkGM8uv/FnUxRGxe2M7JB0KLIqIJyTt31ZVK9VigEtNwGOA7SLiXElDgA9FxGOVqJCZtX9qm/Pg9gG+KOkQoAuwGXAJ0EtSp9RK2wqYl/LPAwYDcyV1AjYHljR3gHK6qL8CPgEclbbfBC7L+UbMrCjKnWBoIQhGxH9FxFYRsQ1wJHBfRBwD3A98JWUbBdyZ1senbdL++yKavyq2nAC3Z0ScCLyTKvU6sEkZrzOzQipzgqH1l3P9ADhF0kyyMbarU/rVQN+UfgpwRksFlTMGt0pSR1I8ltSftQOCZrYxauNLtSLiAeCBtD4L2KORPO8Ah+cpt5wW3KXAOGALST8BHgZ+mucgZlYw9WUuVdZiCy4ibpT0BHAg2fUZh0XEjBZeZmZFVaQbXqZZ07eAP5amRcQrlayYmbVfbTSLWnHljMH9iSxmi2wqd1vgBeAjFayXmbVnRQlwEfHR0m1Jw4H/qFiNzMzaSO4rGSJimqQ9K1GZF6d343Nb7lqJoq1CFp20d7WrYDmsvmVKm5RTmC6qpFNKNjsAw4FXK1YjM2vfgjyXalVVOS24niXrq8nG5G6vTHXMrCYUoQWXTvDtGRGnbaD6mFkNqPkuasPFrpL22ZAVMrMaUOsBDniMbLztKUnjgd8DKxt2RsQdFa6bmbVXBQhwDbqQ3ZLkANaeDxeAA5zZRkhRgC4q2bWnpwDPsDawNaiRt2dmFVGAWdSOQA8afz6YA5zZRqwILbj5EXHuBquJmdWOAgS42miDmtmGVZAxuAM3WC3MrLbUeoCLiKUbsiJmVjvUDm5mWQ4/F9XMCsvPRTWz/Gq9i2pm1qgammRwF9XM8muD56JK6iLpMUl/l/SspHNS+raSHpU0U9ItkjZJ6Zum7Zlp/zYtVdMBzszya4MAB7wLHBARuwC7AiMk7QWcD1wUETsArwOjU/7RwOsp/aKUr1kOcGaWi8hmUctZmhOZFWmzc1qC7Lr321L6WOCwtD4ybZP2Hyip2fN1HeDMLJ9Ye8F9SwvQT9LUkmVMaVGSOkp6ClgETAL+ASyLiNUpy1xgUFofBMwBSPuXA32bq6onGcwsv/InGRZHxO5NFhNRB+wqqRfZA+Z3XP/KreUWnJnl1zZjcGuLi1gG3A98AuglqaHxtRUwL63PAwZDdkNeYHOyW7k1yQHOzHLL0UVtugypf2q5IakrcBAwgyzQfSVlGwXcmdbHp23S/vsiotmjuItqZvm1zXlwA4Gx6dkvHYBbI+IuSc8BN0v6MfAkcHXKfzVwvaSZwFLgyJYO4ABnZvlE21yLGhHTgd0aSZ8F7NFI+jvA4XmO4QBnZvnVyJUMDnBmllutXKrlAGdm+TnAmVkh5TwFpJoc4MwsF+EuqpkVmAOcmRWXA5yZFZYDnJkVUg3d0dcBzszyc4Azs6KqlccGOsCZWW7uoppZMflEXzMrNAc4MysiX8lgZoWm+tqIcA5wZpaPx+DMrMjcRTWz4nKAM7OicgvOzIqrRgKcn4tqZvmkp2qVszRH0mBJ90t6TtKzkk5O6X0kTZL0UvrZO6VL0qWSZkqaLml4S1V1C65C+m/Vl9PHnkTvAb2ICCZceS/jLp1Q7WoZMKBXD35y7Aj69OwGAbf97Wlu+suTABy13658dd9dqK8PHnz2ZS4e/xAA3zjo43xpr52pr6/n/Nsf4G/Pz67mW6iqNjwPbjVwakRMk9QTeELSJODrwOSIOE/SGcAZwA+Ag4GhadkTuDz9bJIDXIXUra7jN6ddx8wnX6Zrjy78aur5PDFpOq/MmFvtqm306uqDC8Y9yPNzF9Ft087c/P1jmPLCbPr27Mb+H92ew8+/gVWr6+jToysA232oDyOGD+PL//86ttisO7856d/44o+upb75h6oXWxu894iYD8xP629KmgEMAkYC+6dsY4EHyALcSOC69DT7KZJ6SRqYymmUu6gVsnTBMmY++TIAb694h1dmzKPfoD5VrpUBLH5jJc/PXQTAW++uYtbCpWyxeQ8O/+QuXDPpcVatrgNg6Yq3Adj/o9tz97QXWLW6jnlL32DOa8vYeesPVa3+7YGivAXoJ2lqyTKm0fKkbcgeAv0oMKAkaC0ABqT1QcCckpfNTWlNcgtuAxiwdX922G1bnn/0pWpXxdaxZZ/N2HFQf56evYDvjdyX4dsP4juH7s27q+u48A8P8uwrCxmweQ+m/3NtI2HhshVs0atHFWtdZflO9F0cEbs3l0FSD+B24LsR8YaktYeKCKn1HeKKteAkXSNpkaRnKnWMWtClexd+eNtpXP693/LWm29XuzpWousmnfn56EP52R1/YeU779GpQwc277YpX7vwZi76w4P87PjPV7uK7VZbTDIASOpMFtxujIg7UvJCSQPT/oHAopQ+Dxhc8vKtUlqTKtlFvRYYUcHy272OnTpy1m2nct9ND/HwuMeqXR0r0alDBy4cfSgTpj7P5OkzAVi4fMWa9WdeWUh9BL17dGXh8hUM6N1zzWsH9OrBomUrqlLv9qKNZlEFXA3MiIgLS3aNB0al9VHAnSXpx6XZ1L2A5c2Nv0EFA1xEPAgsrVT5teDUq07glefncftFd1W7KraOs48+iFkLl3L9/dPWpN0//R98fGjWQNi6fy86d+zI6yve5i9Pz2LE8GF07tSRQX02Y0j/3jwze0G1ql59QTbJUM7SvH2AY4EDJD2VlkOA84CDJL0EfCZtA0wAZgEzgSuB/2jpAFUfg0uDjmMAutCtyrVpOx/ZZ0cOOu5TzJo+m19P+xkA1/z3TTz25yerXDPbbbst+cIeO/HivNe45fRjAPjFXX9l3JRnOPfoz3L7Gceyqq6O/3fDRAD+sWAJ9zz5IuPOPI66unp++vv7Nu4ZVNrmNJGIeJjsrJPGHNhI/gBOzHMMRQV/UWlm5K6I2Lmc/JupT+ypD7wva8cWnbR3tatgObx0y4W8tXBOU0GlLD16D45dP31yWXn/Ou77T7Q0yVBJVW/BmVlt8Q0vzay4ImrmhpeVPE3kd8AjwDBJcyWNrtSxzGwDizKXKqtYCy4ijqpU2WZWXe6imlkxBVAjXVQHODPLrzbimwOcmeXnLqqZFVatzKI6wJlZPu1khrQcDnBmlkt2om9tRDgHODPLr4xbIbUHDnBmlptbcGZWTB6DM7Piqp1rUR3gzCw/d1HNrJCivOcttAcOcGaWn1twZlZYtRHfHODMLD/V10Yf1QHOzPIJauZE30o+F9XMCkgEivKWFstq5AHxkvpImiTppfSzd0qXpEslzZQ0XdLwlsp3gDOz/NrmuajQ+APizwAmR8RQYHLaBjgYGJqWMcDlLRXuAGdm+bVRgGviAfEjgbFpfSxwWEn6dZGZAvSSNLC58j0GZ2b55BuD6ydpasn2FRFxRQuvGRAR89P6AmBAWh8EzCnJNzelzacJDnBmlluOWdTF6/Pg54gIqfX3D3YX1cxyKrN72vqTgRc2dD3Tz0UpfR4wuCTfVimtSQ5wZpZPUOkANx4YldZHAXeWpB+XZlP3ApaXdGUb5S6qmeXXRufBpQfE7082VjcXOAs4D7g1PSx+NnBEyj4BOASYCbwFHN9S+Q5wZpZbW93wspkHxB/YSN4ATsxTvgOcmeXni+3NrJAioK42rtVygDOz/NyCM7PCcoAzs0IKwM9kMLNiCgiPwZlZEQWeZDCzAvMYnJkVlgOcmRXTel1nukE5wJlZPgH4oTNmVlhuwZlZMflSLTMrqoDweXBmVli+ksHMCstjcGZWSBGeRTWzAnMLzsyKKYi6umpXoiwOcGaWj2+XZGaF5tNEzKyIAgi34MyskMI3vDSzAquVSQZFO5rulfQa2ZOsi6YfsLjalbBcivo72zoi+q9PAZLuJvt8yrE4Ikasz/HWR7sKcEUlaWpE7F7telj5/Dsrhg7VroCZWaU4wJlZYTnAbRhXVLsClpt/ZwXgALcBRERV/1gk1Ul6StIzkn4vqdt6lHWtpK+k9ask7dRM3v0l7d2KY/xTUrmD2BVR7d+ZtQ0HuI3D2xGxa0TsDLwHfLt0p6RWnS4UEd+MiOeaybI/kDvAmbUVB7iNz0PADql19ZCk8cBzkjpK+pmkxyVNl/QtAGV+KekFSfcCWzQUJOkBSbun9RGSpkn6u6TJkrYhC6TfS63HfSX1l3R7OsbjkvZJr+0r6R5Jz0q6CtCG/UisqHyi70YktdQOBu5OScOBnSPiZUljgOUR8XFJmwJ/lXQPsBswDNgJGAA8B1yzTrn9gSuB/VJZfSJiqaRfAysi4oKU7ybgooh4WNIQYCLwL8BZwMMRca6kzwOjK/pB2EbDAW7j0FXSU2n9IeBqsq7jYxHxckr/LPCvDeNrwObAUGA/4HcRUQe8Kum+RsrfC3iwoayIWNpEPT4D7CStaaBtJqlHOsaX02v/JOn1Vr5Ps/dxgNs4vB0Ru5YmpCCzsjQJ+E5ETFwn3yFtWI8OwF4R8U4jdTFrcx6DswYTgRMkdQaQ9GFJ3YEHga+mMbqBwKcbee0UYD9J26bX9knpbwI9S/LdA3ynYUNSQ9B9EDg6pR0M9G6zd2UbNQc4a3AV2fjaNEnPAL8ha+GPA15K+64DHln3hRHxGjAGuEPS34Fb0q4/Al9qmGQA/hPYPU1iPMfa2dxzyALks2Rd1Vcq9B5tI+NrUc2ssNyCM7PCcoAzs8JygMXQO80AAAAdSURBVDOzwnKAM7PCcoAzs8JygDOzwnKAM7PC+j+ULPeaAP1CEwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "xWhjbXfbNJpr",
        "outputId": "fce08f88-ecac-4449-dff2-c721b842829e"
      },
      "source": [
        "scatter_plot = plot_diff(test_Y[0], quality_pred, title='Type')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MSMStR0NLwc"
      },
      "source": [
        ""
      ],
      "execution_count": 29,
      "outputs": []
    }
  ]
}