kap7-iris.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "colab": {
      "name": "kap7-iris.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DJCordhose/buch-machine-learning-notebooks/blob/master/kap7-iris.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4eRosoGpl8_"
      },
      "source": [
        "# Kapitel 7: Neuronale Netzwerke - Grundlagen"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KYbHMTMjpl8_"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83Ttu0Pnpl9D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "531b3430-ee31-44d1-e2b2-ea083feef198"
      },
      "source": [
        "%matplotlib inline\n",
        "%pylab inline"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJ1DvBlApl9G"
      },
      "source": [
        "import matplotlib.pylab as plt\n",
        "import numpy as np"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0rvxIp4pl9J"
      },
      "source": [
        "colors = 'bwr'#['b','y','r']\n",
        "CMAP = colors#plt.cm.rainbow"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5AGdJTFpl9N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdd4778c-80ec-466a-db4d-de74a642cbd2"
      },
      "source": [
        "import sklearn\n",
        "print(sklearn.__version__)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5bwPx-ipl9P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cb8bea1-c5b8-424c-ec30-a840baf07a25"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ssBE6lbypl9S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3487cec-3e2d-4f7f-9ca9-75a0567e9c7c"
      },
      "source": [
        "import keras\n",
        "print(keras.__version__)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9-yW3Skpl9V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40f7d8ce-819a-42ac-d8ac-f51f13c0ed45"
      },
      "source": [
        "import pandas as pd\n",
        "print(pd.__version__)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.5.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lKGPNR5Vpl9Y"
      },
      "source": [
        "## Iris mit Neuronalen Netzwerken"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5h7LeQWFpl9Y"
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtNqwr6Gpl9a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebdcdf25-b67c-4aa1-b164-69dba1f62434"
      },
      "source": [
        "print(iris.DESCR)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 150 (50 in each of three classes)\n",
            "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
            "    :Attribute Information:\n",
            "        - sepal length in cm\n",
            "        - sepal width in cm\n",
            "        - petal length in cm\n",
            "        - petal width in cm\n",
            "        - class:\n",
            "                - Iris-Setosa\n",
            "                - Iris-Versicolour\n",
            "                - Iris-Virginica\n",
            "                \n",
            "    :Summary Statistics:\n",
            "\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "                    Min  Max   Mean    SD   Class Correlation\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
            "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
            "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
            "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "    :Class Distribution: 33.3% for each of 3 classes.\n",
            "    :Creator: R.A. Fisher\n",
            "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            "    :Date: July, 1988\n",
            "\n",
            "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
            "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
            "Machine Learning Repository, which has two wrong data points.\n",
            "\n",
            "This is perhaps the best known database to be found in the\n",
            "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
            "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
            "data set contains 3 classes of 50 instances each, where each class refers to a\n",
            "type of iris plant.  One class is linearly separable from the other 2; the\n",
            "latter are NOT linearly separable from each other.\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
            "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
            "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
            "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
            "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
            "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
            "     Structure and Classification Rule for Recognition in Partially Exposed\n",
            "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
            "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
            "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
            "     on Information Theory, May 1972, 431-433.\n",
            "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
            "     conceptual clustering system finds 3 classes in the data.\n",
            "   - Many, many more ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEmm7Weipl9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d4e29004-1f10-4853-bd53-72cae106da7d"
      },
      "source": [
        "iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
        "pd.plotting.scatter_matrix(iris_df, c=iris.target, cmap=CMAP, edgecolor='black', figsize=(20, 20))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: xlabel='sepal length (cm)', ylabel='sepal length (cm)'>,\n",
              "        <Axes: xlabel='sepal width (cm)', ylabel='sepal length (cm)'>,\n",
              "        <Axes: xlabel='petal length (cm)', ylabel='sepal length (cm)'>,\n",
              "        <Axes: xlabel='petal width (cm)', ylabel='sepal length (cm)'>],\n",
              "       [<Axes: xlabel='sepal length (cm)', ylabel='sepal width (cm)'>,\n",
              "        <Axes: xlabel='sepal width (cm)', ylabel='sepal width (cm)'>,\n",
              "        <Axes: xlabel='petal length (cm)', ylabel='sepal width (cm)'>,\n",
              "        <Axes: xlabel='petal width (cm)', ylabel='sepal width (cm)'>],\n",
              "       [<Axes: xlabel='sepal length (cm)', ylabel='petal length (cm)'>,\n",
              "        <Axes: xlabel='sepal width (cm)', ylabel='petal length (cm)'>,\n",
              "        <Axes: xlabel='petal length (cm)', ylabel='petal length (cm)'>,\n",
              "        <Axes: xlabel='petal width (cm)', ylabel='petal length (cm)'>],\n",
              "       [<Axes: xlabel='sepal length (cm)', ylabel='petal width (cm)'>,\n",
              "        <Axes: xlabel='sepal width (cm)', ylabel='petal width (cm)'>,\n",
              "        <Axes: xlabel='petal length (cm)', ylabel='petal width (cm)'>,\n",
              "        <Axes: xlabel='petal width (cm)', ylabel='petal width (cm)'>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oa3iYStkpl9f"
      },
      "source": [
        "# plt.savefig('ML_0701.png', bbox_inches='tight')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-exG806pl9g"
      },
      "source": [
        "## Das künstliche Neuron"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PrYfSutGpl9h"
      },
      "source": [
        "w0 = 3\n",
        "w1 = -4\n",
        "w2 = 2\n",
        "\n",
        "def neuron_no_activation(x1, x2):\n",
        "    sum = w0 + x1 * w1 + x2 * w2\n",
        "    return sum"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yhw0DMUWpl9j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90a33662-19f1-4583-bf0d-07e1a66a41a2"
      },
      "source": [
        "iris.data[0]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5.1, 3.5, 1.4, 0.2])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rVqDyYWKpl9l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "147481de-5342-4d9e-bcc1-c8c40c2873fa"
      },
      "source": [
        "neuron_no_activation(5.1, 3.5)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-10.399999999999999"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpcxmzDspl9o"
      },
      "source": [
        "### Activation Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wP80A7hkpl9o"
      },
      "source": [
        "def centerAxis(uses_negative=False):\n",
        "    # http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot\n",
        "    ax = plt.gca()\n",
        "    ax.spines['left'].set_position('center')\n",
        "    if uses_negative:\n",
        "        ax.spines['bottom'].set_position('center')\n",
        "    ax.spines['right'].set_color('none')\n",
        "    ax.spines['top'].set_color('none')\n",
        "    ax.xaxis.set_ticks_position('bottom')\n",
        "    ax.yaxis.set_ticks_position('left')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNq7ZiU0pl9r"
      },
      "source": [
        "#### Step Function: abrupter, nicht stetig differenzierbarer Übergang zwischen 0 und 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZqUtjXQ1pl9r"
      },
      "source": [
        "def np_step(X):\n",
        "    return 0.5 * (np.sign(X) + 1)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIBsOI4epl9t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "9b9b6209-021d-4d3c-9dd9-d4c2485e62b5"
      },
      "source": [
        "x = np.arange(-10,10,0.01)\n",
        "y = np_step(x)\n",
        "\n",
        "centerAxis()\n",
        "plt.plot(x, y, lw=3)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff3d08e6020>]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trtAvxTmpl9w"
      },
      "source": [
        "#### Sigmoid Function: Fließender Übergang zwischen 0 und 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN8Y7fXPpl9x"
      },
      "source": [
        "def np_sigmoid(X):\n",
        "    return 1 / (1 + np.exp(X * -1))"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HyC7OoVnpl9z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "e39d1446-a678-476e-a76e-596129edbef1"
      },
      "source": [
        "x = np.arange(-10,10,0.01)\n",
        "y = np_sigmoid(x)\n",
        "\n",
        "centerAxis()\n",
        "plt.plot(x,y,lw=3)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff3d0b8b5e0>]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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DhgxRTk6OtmzZovvvv1+jR4/2B4NA89bqb3Sk2utfjnA6dO0wbjUE7EYgAALI2LFjtX//fk2bNk2FhYUaPHiwFi5c6B9oWFBQYDkjcN9998nhcOi+++7Tnj171LFjR40ePVoPPvigXbvQKGOMXq51uSCvf5rSE2Js6gjAcQ7T0HlFAGHB7XYrMTFRpaWlrX754MvdJfruM59aanMnnqeL+zJ+ALAbYwgAtJl/fl53MOHI3h1t6gbAyQgEANrE4aoavbXaOpjwuvMYTAgECgIBgDax4Ku9qvBYBxP+iOcWAAGDQACgTfxr5W7L8iV9OzKYEAggBAIArW5XcYU+21Zsqf3g3M42dQOgPgQCAK3ujVV7LMuJ7aJ0aX/uLAACCYEAQKsyxtS5XDB6UCe5IgNz4iQgXBEIALSqlQWHtONghaXG5QIg8BAIALSq/7fSermgR2qcBmcn2dMMgAYRCAC0mspqr/5T60FGPxjaWQ4Hcw8AgYZAAKDVfLBhn9yVNf5lh0MaMyTLxo4ANIRAAKDV/LvW2YFvdU9RVlI7m7oB0BgCAYBWUVZZrQ827LPUxgzJtKkbAKdCIADQKt5fX6SqGp9/OSrCoVFnZ9jYEYDGEAgAtIp/r9lrWb6wd0clxUbb1A2AUyEQAGhxJRUefbxpv6U2ehCXC4BARiAA0OIWri1Ujc/4l12RTuWdlW5jRwBOhUAAoMX9+0vr3QWX9U9TvCvSpm4ANAWBAECL2ldWqaVbD1pqo8/hcgEQ6AgEAFrUf78q1ElXCxQXHaFL+vFkQyDQEQgAtKj/1Lpc8O2z0hUTxZMNgUBHIADQYvaVVWr5zkOW2ne4XAAEBQIBgBbz3tdFMiddLoh3ReqC3qn2NQSgyQgEAFrMwrWFluVL+qVxuQAIEgQCAC2itKK6zt0FVzBVMRA0CAQAWsT764vqTEZ0cd+ONnYEoDkIBABaxMJ11ssFF/bpqDgmIwKCBoEAwBkrr6qp8+wCLhcAwYVAAOCMLdq43/Ko40inQ3n9eXYBEEwIBADOWO3LBbk9U5QYG2VTNwBOB4EAwBmprPbqg/VFltoVA7hcAAQbAgGAM7Jk6wGVe7z+ZYfj6HTFAIILgQDAGXnv632W5WFdOyitfYxN3QA4XQQCAKfNGKMPNlgvF3B2AAhOBAIAp23tHreK3FWW2mXcXQAEJQIBgNP2Xq3BhN1T49SzY7xN3QA4EwQCAKctv1YgyOufZlMnAM4UgQDAadlbekTrvnFbalwuAIIXgQDAaXl/vfXugsR2URrWtYNN3QA4UwQCAKel9uWCi/t2VGQEv1KAYMVPL4BmK6+q0ZKtBy01nl0ABDcCAYBm+2TzAXlqPczoor4dbewIwJkiEABottqXC4Z3T1ZCDA8zAoIZgQBAs/h8Rh9utA4o5HIBEPwIBACaZfXuEh047LHUCARA8CMQAGiWDzdYzw70TotXl5RYm7oB0FIIBACapfblgkuZnRAICQQCAE22r6xSa/dYZye8uA+BAAgFBAIATfbxpgOW5XhXpIZ1Y3ZCIBQQCAA02aJalwvO75WiKGYnBEICP8kAmqTG69Mnm61nCC7uy+UCIFQQCAA0yZrdJSo9Um2pXczshEDIIBAAaJJFG/dblvumt1enxHY2dQOgpREIADRJ7UDA2QEgtBAIAJzS/rIqfbWn1FLjYUZAaCEQADiljzdZzw7ERUdoWNdkm7oB0BoIBABOqfbshOf3SlV0JL8+gFDCTzQQYGbPnq1u3bopJiZGOTk5+vzzzxtdv6SkRJMnT1anTp3kcrnUp08fLViwoMX64XZDIDxE2t0AgBPmzZunKVOmaM6cOcrJydGsWbM0atQobdy4UWlpdf8Iezweffvb31ZaWppef/11ZWVlaefOnUpKSmqxnrjdEAgPBAIggDzxxBO66aabNHHiREnSnDlzNH/+fL3wwgu655576qz/wgsvqLi4WEuWLFFUVJQkqVu3bi3aU+27C/qkxyszidsNgVDDJQMgQHg8Hq1YsUJ5eXn+mtPpVF5enpYuXVrvNm+//bZyc3M1efJkpaena8CAAXrooYfk9Xob/D5VVVVyu92Wj8bUDgSXcLkACEkEAiBAHDhwQF6vV+np6ZZ6enq6CgsL691m27Ztev311+X1erVgwQLdf//9evzxx/X73/++we8zc+ZMJSYm+j+ys7Mb7ukwtxsC4YJAAAQxn8+ntLQ0/fnPf9bQoUM1duxY/eY3v9GcOXMa3Gbq1KkqLS31f+zatavBdRfXGkzI7YZA6GIMARAgUlNTFRERoaKiIku9qKhIGRkZ9W7TqVMnRUVFKSIiwl/r37+/CgsL5fF4FB0dXWcbl8sll8vVpJ5q312Q2zOF2w2BEMVPNhAgoqOjNXToUOXn5/trPp9P+fn5ys3NrXeb888/X1u2bJHP5/PXNm3apE6dOtUbBprDGKNPNlvHD4zszeUCIFQRCIAAMmXKFD333HP661//qvXr1+vWW29VeXm5/66D8ePHa+rUqf71b731VhUXF+v222/Xpk2bNH/+fD300EOaPHnyGfeyed9h7SurstQu6J16xu8LIDBxyQAIIGPHjtX+/fs1bdo0FRYWavDgwVq4cKF/oGFBQYGczhM5Pjs7W++8847uvPNOnXPOOcrKytLtt9+uu++++4x7qT1dcVZSO/VIjTvj9wUQmBzGGGN3EwDs43a7lZiYqNLSUiUkJPjrE174XB+dFArGDsvWIz88x44WAbQBLhkAqKOqxqtl2w9aaiP7cLkACGUEAgB1rNhxSJXVJwYqOhzS+T0JBEAoIxAAqOOTLdbbDQdmJapD3JndtQAgsBEIANRR93ZDzg4AoY5AAMDi4OEqrd1jfb7BBb2YfwAIdQQCABafbrUOJoyNjtC5XZPsaQZAmyEQALD4pNb8A9/qkSJXZEQDawMIFQQCAH7GGC2uNaDwgl6MHwDCAYEAgN/W/Ye1t7TSUruQ+QeAsEAgAOD38Sbr2YGMhBj17BhvUzcA2hKBAIBf7csFI3unyuFw2NQNgLZEIAAgSfLU+PTZttrTFXO7IRAuCAQAJElrdpWowuO11M7vmWJTNwDaGoEAgCRpyVbr5YIBWQlKiXfZ1A2AtkYgACBJWlJrQiJmJwTCC4EAgCTp673W6Yov5PkFQFghEACQJBlz4uuYKKeGdutgXzMA2hyBAEAdOd2ZrhgINwQCIMyZk08NHMPjjoHwQyAAwtz2g+V1aiN7M6AQCDcEAiDMfVbr7oK09i71SWe6YiDcEAiAMFd7/oELmK4YCEsEAiCMVXt9+nx7saV2IZcLgLBEIADC2KqCElV4fJba+b0YUAiEIwIBEMYWb95vWe7fKUEd2zNdMRCOCARAGPt4s3X8ALMTAuGLQACEqdKKan25u8RSu4BAAIQtAgEQppZsPSDfSXMSRUc6dV63ZPsaAmArAgEQpmpfLhjatYNiopiuGAhXBAIgTC3eYh1QOKInZweAcEYgAMLQzoPl2lV8xFLL7cH4ASCcEQiAMFT7coEk9Ulvb0MnAAIFgQAIQ7XnH5Akp5PpioFwRiAAwkyN16cltR5oBAAEAiDMrNldqrLKGrvbABBgCARAmFlca/xAr7Q4mzoBEEgIBECYqX274be4uwCACARAWCmrrNaqghJLbUSvFHuaARBQCARAGPlsW7FqTpqvODrCqWFdO9jYEYBAQSAAwkjt2w3P7Zqk2OhIm7oBEEgIBEAY+WSLdUDhyN4dbeoEQKAhEABhYk/JEW3bX26pjeRxxwCOIRAAYaL25YKk2CidnZloUzcAAg2BAAgTn9Saf+D8XqmKYLpiAMcQCIAw4PMZfVp7/EAvLhcAOIFAAISBdd+4daii2lK7gPEDAE5CIADCwCe1ZifskRqnzh1ibeoGQCAiEABh4JNN1ssFnB0AUBuBAAhxRzxerdh5yFK7gPEDAGohEAAhbtn2g/J4ff7lCKdDuT15fgEAKwIBEOJq3244JDtJ7WOibOoGQKAiEAAhbvFmxg8AODUCARDC9rkrtbGozFLj+QUA6kMgAELYR5ustxu2d0VqUGemKwZQF4EACGG1A8EFvVMVGcGPPYC6+M0AhCivz9QZUHhRHy4XAKgfgQAIMLNnz1a3bt0UExOjnJwcff75503a7pVXXpHD4dCYMWMkSWt2l6j0iHW64gsJBAAaQCAAAsi8efM0ZcoUTZ8+XStXrtSgQYM0atQo7du3r9HtduzYof/93//VyJEj/bWPNlovF/RJj1dmUrtW6RtA8CMQAAHkiSee0E033aSJEyfqrLPO0pw5cxQbG6sXXnihwW28Xq9uuOEGzZgxQz169PDXa48f4HIBgMYQCIAA4fF4tGLFCuXl5flrTqdTeXl5Wrp0aYPbPfDAA0pLS9OkSZP8tUPlHq3ZXWJZ76I+aZKkqqoqud1uywcAEAiAAHHgwAF5vV6lp6db6unp6SosLKx3m8WLF+v555/Xc889Z61vOSBjTizHRDk1rFsHSdLMmTOVmJjo/8jOzm7ZHQEQlAgEQJAqKyvTj3/8Yz333HNKTbXOPlj7ckFujxTFREVIkqZOnarS0lL/x65du9qsZwCBK9LuBgAclZqaqoiICBUVFVnqRUVFysjIqLP+1q1btWPHDo0ePdpf8/mOPsQoq/MPFBGf7K+fPH7A5XLJ5XK1dPsAghyBAAgQ0dHRGjp0qPLz8/23Dvp8PuXn5+u2226rs36/fv301VdfWWr33XefDtS4tPukMCBJF/VNa7W+AYQGAgEQQKZMmaIJEyZo2LBhGj58uGbNmqXy8nJNnDhRkjR+/HhlZWVp5syZiomJ0YABAyzbJyUlqcjZ1VLrkhyrbimxbbYPAIITgQAIIGPHjtX+/fs1bdo0FRYWavDgwVq4cKF/oGFBQYGczsaH/pTFdbYsX9SnoxwOR6v1DCA0OIw5eSwygGB2uKpGg2e8qxrfiR/rv4wfpryz0hvcxu12KzExUaWlpUpISGiLNgEEIO4yAELIki0HLGEgKsKh3J4pNnYEIFgQCIAQUvt2w/O6JSvOxZVBAKdGIABChDFGizYyXTGA00MgAELE5n2HtafkiKXG0w0BNBWBAAgR+eutT0TMTIxRv4z2NnUDINgQCIAQ8eEGayC4tH8atxsCaDICARACSio8Wr6z2FK7tB+zEwJoOgIBEAI+2rRfvlpPNxzRM7XhDQCgFgIBEAI+qHW5YETPVP/TDQGgKQgEQJCr8frqzD/A5QIAzUUgAILcql0lKqmottQIBACai0AABLnatxv2y2ivzKR2NnUDIFgRCIAgV/t2w8v6c3YAQPMRCIAgtvtQhTYWlVlql/Zr+MmGANAQAgEQxGqfHUiOi9bg7CR7mgEQ1AgEQBDLrxUILu7TURFOZicE0HwEAiBIlVfVaMnWg5bapYwfAHCaCARAkPp40355anz+5UinQyN783RDAKeHQAAEqXe/LrIs5/ZMUWK7KJu6ARDsCARAEKr2+pS/3hoILj+LuwsAnD4CARCElm0rlruyxlLLIxAAOAMEAiAIvft1oWV5UOdEdUpkdkIAp49AAAQZY4zeXVfrcsHZGTZ1AyBUEAiAIPPVnlIVuistNcYPADhTBAIgyNQ+O9A9NU690uJt6gZAqCAQAEGm9viBy89Kl8PB7IQAzgyBAAgi2w+Ua1PRYUvt8rO5XADgzBEIgCDy7jrr2YHUeJcGZ3ewqRsAoYRAAASRBWutgSCvfxoPMwLQIggEQJDYVVyhNbtKLLUrB3aypxkAIYdAAASJ/67da1lOio3SiJ4pNnUDINQQCIAgMf+runcXREXwIwygZfDbBAgCuw/VvVxw9TmZ9jQDICQRCIAg8N9aZwe4XACgpREIgCAw/yvr+AEuFwBoafxGAQLc7kMVWs3lAgCtjEAABDguFwBoCwQCIMBxuQBAW+C3ChDAdh4s53IBgDZBIAAC2Furv7EsJ8dFc7kAQKsgEAAByhijN1fvsdS+c04nLhcAaBX8ZgEC1Fd7SrVtf7mlds3gLJu6ARDqCARAgHpzlfVyQZfkWJ3bJcmeZgCEPAIBEIBqvD79+0trILhmcKYcDh51DKB1EAiAALRk60HtL6uy1LhcAKA1EQiAAFR7MOHArET1Sou3qRsA4YBAAASYCk+N3llrnZ3wmsHMPQCgdREIgADz368KVe7x+pedDum7gwgEAFoXgQAIMK8u32VZvqB3R6UlxNjUDYBwQSAAAsiOA+Vatr3YUhs7LNumbgCEEwIBEEBeX7HbstwhNkp5Z6XZ1A2AcEIgAAKE12fqBIIxQ7LkioywqSMA4YRAAASITzbvV6G70lK7diiXCwC0DQIBECBqDyYcmJWoszITbOoGQLghEAABoLjco/e+LrLUfjSss03dAAhHBAIgALy2fJeqvca/7Ip06rtMVQygDREIAJv5fEYvLdtpqVVt+UzpHdorJydHn3/+eYPbPvfccxo5cqQ6dOigDh06KC8vr9H1AaAhBALAZh9t3q9dxUcstZ9e2EsrV67UoEGDNGrUKO3bt6/ebRctWqRx48bpww8/1NKlS5Wdna3LL79ce/bsqXd9AGiIwxhjTr0agNYyae4Xyt9w4g9+gtetNY9eJ4fDIZ/Pp+zsbP3iF7/QPffcc8r38nq96tChg5555hmNHz++Sd/f7XYrMTFRpaWlSkhgECMQriLtbgAIZ7uKK/TBRuu//q/sFSuHwyFJcjqdysvL09KlS5v0fhUVFaqurlZycnKD61RVVamq6sSjld1u92l0DiDUcMkAsNHLnxfo5HN0vqpyXXFWR8s66enpKiwsVFPcfffdyszMVF5eXoPrzJw5U4mJif6P7GzmOgBAIABsU1Xj1bwvrHMPHP4qXzGRjtN6v4cfflivvPKK3njjDcXENPwwpKlTp6q0tNT/sWvXrgbXBRA+uGQA2OStVd+ouNxjqVV8uVBFRVdaakVFRcrIyGj0vR577DE9/PDDev/993XOOec0uq7L5ZLL5Tq9pgGELM4QADYwxugvi7dZauf3StGg7hnKz8/313w+n/Lz85Wbm9vgez366KP63e9+p4ULF2rYsGGt1jOA0MYZAsAGH28+oE1Fhy21Gy/ooX3tp2jChAkaNmyYhg8frlmzZqm8vFwTJ06UJI0fP15ZWVmaOXOmJOmRRx7RtGnT9PLLL6tbt27+sQbx8fGKj49v250CENQIBIAN/vKJ9exAr7R4XdSno5z9xmr//v2aNm2aCgsLNXjwYC1cuFDp6emSpIKCAjmdJ07s/elPf5LH49EPf/hDy/tNnz5dv/3tb1t9PwCEDuYhANrY+r1uXfnUJ5bazO8P1LjhXWzph3kIAEiMIQDa3F8+2W5ZTomL1veG8NwCAPYiEABtaG/pEb29xjqt8I9zuyomKsKmjgDgKAIB0Iae/Wib5amG0ZFO/fhbXW3sCACOIhAAbWRfWaX++XmBpTZ2WLZS4pkTAID9CARAG3nu422qqvH5l6MiHLrl4p42dgQAJxAIgDZw8HCVXvrMenbgB+d2VlZSO5s6AgArAgHQBv6yeLuOVHv9yxFOh35+cS8bOwIAKwIB0MoOHq7S35bssNTGDM5Sl5RYexoCgHoQCIBWNvvDrSr3nDg74HRIky9h7ACAwEIgAFrR7kMVeumznZbamMFZ6tGR5wwACCwEAqAVzXp/szxe650Fd367j40dAUD9CARAK9lUVKZ/rdxtqd2Q01XZyYwdABB4CARAK3nkvxvkO+nRYXHREbrtUu4sABCYCARAK1i0cZ/yN+yz1G4c2UOpzEoIIEARCIAWVu316YH/fG2ppca7dOPI7jZ1BACnRiAAWthfl+zQtv3lltqvr+ir9jFRNnUEAKdGIABa0IHDVXoqf7Oldk7nRP3w3M42dQQATUMgAFrQg/PXq6yyxlKbPvpsOZ0OmzoCgKYhEAAt5ONN+/XGqj2W2pjBmRratYNNHQFA0xEIgBZQ4anRb978ylJrHxOpe6/ub1NHANA8BAKgBTz1/mbtKj5iqd17VX+ltY+xqSMAaB4CAXCGVhYc0nOfbLPUhndP1thh2TZ1BADNRyAAzkB5VY2mzFttmZEwOsKph743kIGEAIIKgQA4A7+fv147DlZYarfn9VavNJ5mCCC4EAiA05S/vkj//LzAUhvatYN+dmEPmzoCgNNHIABOw56SI/rf19ZYarHREXriR4MUGcGPFYDgw28uoJk8NT5N/sdKHaqottSnfecsdU2Js6krADgzBAKgmR5asF6rd5VYaqPOTtfY87irAEDwIhAAzfDmqj2au2SHpdY1JVZ/uHaQHA7uKgAQvAgEQBOt2FmsX7/+paUWHenUH284Vwk8yRBAkCMQAE2wq7hCN/9thTxen6X+u2vO1tmZiTZ1BQAth0AAnEJJhUeT/vqFDpZ7LPUJuV019rwuNnUFAC2LQAA0oryqRj958QttKjpsqV/Up6Pu/85ZNnUFAC2PQAA0oLLaq5v/vrzOHQV90uP19PVDmG8AQEjhNxpQj6oar257eZU+3XLQUs9IiNHzE85jECGAkBNpdwNAoDni8eqWl1boo037LfXkuGi9dONwZSfH2tQZALQeAgFwksNVNZo09wst215sqbd3RepvPx2uXmntbeoMAFoXgQA4Zp+7Ujf+bbm+3F1qqcdFR+iFiedpQBa3FwIIXQQCQNL6vW5NmvuFvimttNQTYiL1t0k5GpydZE9jANBGCAQIe/nri/TLf65SucdrqafERevvk3J0VmaCTZ0BQNshECBs1Xh9euK9Tfrjoq11XuuSHKsXJ56nnh3jbegMANoegQBhqchdqV/+c1WdwYOSNKxrB/15/DAlx0Xb0BkA2INAgLBijNGbq/do+lvr5K6sqfP6NYMz9cgPzlFMVIQN3QGAfQgECBv7yip13xtr9e7XRXVei4pw6N6r+usnI7rxGGMAYYlAgJBX7fXpr0t26Kn3N6usqu5Zgaykdnrm+iEa0qWDDd0BQGAgECCkfbJ5v2b8+2tt2Xe43tevGZypGd89W0mxjBcAEN4IBAhJX+wo1uPvbtRn2+oOGpSO3lL44PcG6IoBndq4MwAITAQChAxjjJZuO6g5H23Tx7WeQ3Cy75+bpd9c1V8p8a427A4AAhuBAEHPU+PTf778Rn/5ZLu+3utucL0BWQma8d2zNbRrcht2BwDBgUCAoLWxsEyvLd+lN1fv0YHDngbX65QYo9sv661rh2UrwskdBABQHwIBgsqekiNauLZQb6/eozW1HkJUW8f2Lk2+uKeuG96FeQUA4BQIBAhoxhhtKjqs/A1FWri2sM6TCOvTLSVWE8/vrh8Ny1a7aIIAADQFgQABp7C0Up9uOaDFxz72l1U1abvh3ZN14wXddVn/dC4NAEAzEQhgq8pqr9Z9U6pVBSXHPg7VeQRxY9Lau/SDoZ31w6GdeRARAJwBAgHaRI3Xp92HjmhDYZk2FpZpY5FbGwrLtONAuXymee+V2C5K3z4rXVcP7KSRvVMVGeFsnaYBIIwQCNAivD6jg4erVOSu0p6SChUUV2jnwaOfC4ortOfQEdU09y//SbKS2univh115YBOyumRrChCAAC0KAIB6lXj9cldWaOSCo9KjlSrtKJahyo8KqmoVsmRah04XKV97koVuatU5K7UgcNVzf6XfmPax0RqRM8UXdC7o0b2SlXXlNiweejQ7Nmz9Yc//EGFhYUaNGiQnn76aQ0fPrzB9V977TXdf//92rFjh3r37q1HHnlEV111VRt2DCAUEAiChM9n5DVGNV4jT41PVV6vPDU+eWp8qj5W83i9qqqn5qnxqarGpwqPVxVVNSr3eFXhqVF5lfVzhcerCo9X7spqldXzaODW4nBIfdPba0iXJA3J7qAhXZLUs2O8nGE4MHDevHmaMmWK5syZo5ycHM2aNUujRo3Sxo0blZaWVmf9JUuWaNy4cZo5c6a+853v6OWXX9aYMWO0cuVKDRgwwIY9ABCsHMaYFvx3XdMt+GqvPtp4dHpZI6PjXRjppK/N0YK/bizrHG/cGOP/+ugmx9Yz1veq+z1OWq+e96pdl6V+0vvV/p7H1vf6jLxG8vp88vpO/FE//tnrO/p1jc/Id3x9n5HP6Ni2J14PFclx0eqb3l59M9qrX8bRz73T2yveRTaVpJycHJ133nl65plnJEk+n0/Z2dn6xS9+oXvuuafO+mPHjlV5ebn+85//+Gvf+ta3NHjwYM2ZM6dJ39PtdisxMVGlpaVKSEhomR0BEHRs+y28eleJ5i3fZde3RyuJcDqU3t6l7ORYdUmOVdeUWGUnx6prSpy6JMcqOY6nCjbE4/FoxYoVmjp1qr/mdDqVl5enpUuX1rvN0qVLNWXKFEtt1KhRevPNNxv8PlVVVaqqOnErp9vd8HTPAMKHbYEg/E4GB692URFKio1SYrsoJcVGKaldtDrERSsjIUbpCS6lJ8SoY/ujn1PiosPyVH9LOHDggLxer9LT0y319PR0bdiwod5tCgsL612/sLCwwe8zc+ZMzZgx48wbBhBSOE8bIqIjnYqOcFo+R0U4FB0ZoehIp1yRTsVFRyg2OlKx0RGKc9X6HB2pdtERinMd/bpDXLSS2kUpoV0U0/6GmKlTp1rOKrjdbmVnZ9vYEYBAYFsgGNYtWR6vT45j5wocjhNnDRwOyeFwnDiL4FDD68mh44PPHceLx74+UXfU+/6q9T4Nvb9Ofq963/9Yv7WWI5wORRz/7HTI6V+WnI4Ttchjdefx9U7a1uk8eho+8lg96qQ/+lHHP0c4wmYEfihLTU1VRESEioqKLPWioiJlZGTUu01GRkaz1pckl8sll4tHPwOwsi0QfPusdH37rPRTrwiEiejoaA0dOlT5+fkaM2aMpKODCvPz83XbbbfVu01ubq7y8/N1xx13+GvvvfeecnNz26BjAKGESwZAAJkyZYomTJigYcOGafjw4Zo1a5bKy8s1ceJESdL48eOVlZWlmTNnSpJuv/12XXTRRXr88cd19dVX65VXXtHy5cv15z//2c7dABCECARAABk7dqz279+vadOmqbCwUIMHD9bChQv9AwcLCgrkdJ6YpXHEiBF6+eWXdd999+nee+9V79699eabbzIHAYBms20eAgCBgXkIAEgSE8IDAAACAQAAIBAAAAARCAAAgAgEAABABAIAACACAQAAEIEAAACIQAAAANTEqYuNMSorK2vtXgDYwO12Wz4DCD3t27c/5VNxmzR18fGpTQEAQPBpytTkTQoErXmGwO12Kzs7W7t27QrJedTZv+AX6vtYWlqqLl26qKCgIGSDf6gfQ/Yv+LX2PjblDEGTLhk4HI5WPwgJCQkhe6Al9i8UhPo+JiYmhvT+SaF/DNm/4GfnPjKoEAAAEAgAAEAABAKXy6Xp06fL5XLZ3UqrYP+CX6jvY6jvnxT6+8j+Bb9A2McmDSoEAAChzfYzBAAAwH4EAgAAQCAAAAAEAgAAoDYIBA8++KBGjBih2NhYJSUl1btOQUGBrr76asXGxiotLU2/+tWvVFNT0+j7FhcX64YbblBCQoKSkpI0adIkHT58uBX2oHkWLVokh8NR78cXX3zR4HYXX3xxnfVvueWWNuy86bp161an14cffrjRbSorKzV58mSlpKQoPj5eP/jBD1RUVNRGHTfdjh07NGnSJHXv3l3t2rVTz549NX36dHk8nka3C/TjN3v2bHXr1k0xMTHKycnR559/3uj6r732mvr166eYmBgNHDhQCxYsaKNOm2/mzJk677zz1L59e6WlpWnMmDHauHFjo9vMnTu3zvGKiYlpo46b57e//W2dXvv169foNsF0/Or7feJwODR58uR61w+GY/fxxx9r9OjRyszMlMPh0Jtvvml53RijadOmqVOnTmrXrp3y8vK0efPmU75vc3+Om6vVA4HH49G1116rW2+9td7XvV6vrr76ank8Hi1ZskR//etfNXfuXE2bNq3R973hhhu0bt06vffee/rPf/6jjz/+WDfffHNr7EKzjBgxQnv37rV83HjjjerevbuGDRvW6LY33XSTZbtHH320jbpuvgceeMDS6y9+8YtG17/zzjv173//W6+99po++ugjffPNN/r+97/fRt023YYNG+Tz+fTss89q3bp1evLJJzVnzhzde++9p9w2UI/fvHnzNGXKFE2fPl0rV67UoEGDNGrUKO3bt6/e9ZcsWaJx48Zp0qRJWrVqlcaMGaMxY8Zo7dq1bdx503z00UeaPHmyPvvsM7333nuqrq7W5ZdfrvLy8ka3S0hIsByvnTt3tlHHzXf22Wdbel28eHGD6wbb8fviiy8s+/bee+9Jkq699toGtwn0Y1deXq5BgwZp9uzZ9b7+6KOP6v/+7/80Z84cLVu2THFxcRo1apQqKysbfM/m/hyfFtNGXnzxRZOYmFinvmDBAuN0Ok1hYaG/9qc//ckkJCSYqqqqet/r66+/NpLMF1984a/997//NQ6Hw+zZs6fFez8THo/HdOzY0TzwwAONrnfRRReZ22+/vW2aOkNdu3Y1Tz75ZJPXLykpMVFRUea1117z19avX28kmaVLl7ZChy3r0UcfNd27d290nUA+fsOHDzeTJ0/2L3u9XpOZmWlmzpxZ7/o/+tGPzNVXX22p5eTkmJ/97Get2mdL2bdvn5FkPvroowbXaej3USCaPn26GTRoUJPXD/bjd/vtt5uePXsan89X7+vBdOyMMUaSeeONN/zLPp/PZGRkmD/84Q/+WklJiXG5XOaf//xng+/T3J/j02H7GIKlS5dq4MCBSk9P99dGjRolt9utdevWNbhNUlKS5V/ceXl5cjqdWrZsWav33Bxvv/22Dh48qIkTJ55y3X/84x9KTU3VgAEDNHXqVFVUVLRBh6fn4YcfVkpKioYMGaI//OEPjV7iWbFihaqrq5WXl+ev9evXT126dNHSpUvbot0zUlpaquTk5FOuF4jHz+PxaMWKFZb/9k6nU3l5eQ3+t1+6dKllfenoz2QwHCvp6PGSdMpjdvjwYXXt2lXZ2dm65pprGvx9Ewg2b96szMxM9ejRQzfccIMKCgoaXDeYj5/H49FLL72kn/70p40+iCeYjl1t27dvV2FhoeUYJSYmKicnp8FjdDo/x6ejSQ83ak2FhYWWMCDJv1xYWNjgNmlpaZZaZGSkkpOTG9zGLs8//7xGjRqlzp07N7re9ddfr65duyozM1Nffvml7r77bm3cuFH/+te/2qjTpvvlL3+pc889V8nJyVqyZImmTp2qvXv36oknnqh3/cLCQkVHR9cZQ5Kenh5wx6u2LVu26Omnn9Zjjz3W6HqBevwOHDggr9db78/Yhg0b6t2moZ/JQD9WkuTz+XTHHXfo/PPP14ABAxpcr2/fvnrhhRd0zjnnqLS0VI899phGjBihdevWnfJnta3l5ORo7ty56tu3r/bu3asZM2Zo5MiRWrt2rdq3b19n/WA+fm+++aZKSkr0k5/8pMF1gunY1ef4cWjOMTqdn+PTcVqB4J577tEjjzzS6Drr168/5cCXYHI6+7x792698847evXVV0/5/iePfxg4cKA6deqkyy67TFu3blXPnj1Pv/Emas7+TZkyxV8755xzFB0drZ/97GeaOXNmwE4tejrHb8+ePbriiit07bXX6qabbmp0W7uPH46aPHmy1q5d2+g1dknKzc1Vbm6uf3nEiBHq37+/nn32Wf3ud79r7Tab5corr/R/fc455ygnJ0ddu3bVq6++qkmTJtnYWct7/vnndeWVVyozM7PBdYLp2AWb0woEd911V6MJTpJ69OjRpPfKyMioM1Ly+OjzjIyMBrepPZCipqZGxcXFDW5zpk5nn1988UWlpKTou9/9brO/X05OjqSj/0Jtiz8oZ3JMc3JyVFNTox07dqhv3751Xs/IyJDH41FJSYnlLEFRUVGrHa/amrt/33zzjS655BKNGDFCf/7zn5v9/dr6+DUkNTVVERERde7oaOy/fUZGRrPWDxS33Xabf4Bxc/+lGBUVpSFDhmjLli2t1F3LSUpKUp8+fRrsNViP386dO/X+++83+6xaMB076cTftaKiInXq1MlfLyoq0uDBg+vd5nR+jk9Li41GOIVTDSosKiry15599lmTkJBgKisr632v44MKly9f7q+98847ATWo0Ofzme7du5u77rrrtLZfvHixkWTWrFnTwp21vJdeesk4nU5TXFxc7+vHBxW+/vrr/tqGDRsCdlDh7t27Te/evc11111nampqTus9Aun4DR8+3Nx2223+Za/Xa7KyshodVPid73zHUsvNzQ3YQWk+n89MnjzZZGZmmk2bNp3We9TU1Ji+ffuaO++8s4W7a3llZWWmQ4cO5qmnnqr39WA7fsdNnz7dZGRkmOrq6mZtF+jHTg0MKnzsscf8tdLS0iYNKmzOz/Fp9dpi79SAnTt3mlWrVpkZM2aY+Ph4s2rVKrNq1SpTVlZmjDl6MAcMGGAuv/xys3r1arNw4ULTsWNHM3XqVP97LFu2zPTt29fs3r3bX7viiivMkCFDzLJly8zixYtN7969zbhx41p7d5rs/fffN5LM+vXr67y2e/du07dvX7Ns2TJjjDFbtmwxDzzwgFm+fLnZvn27eeutt0yPHj3MhRde2NZtn9KSJUvMk08+aVavXm22bt1qXnrpJdOxY0czfvx4/zq1988YY2655RbTpUsX88EHH5jly5eb3Nxck5uba8cuNGr37t2mV69e5rLLLjO7d+82e/fu9X+cvE4wHb9XXnnFuFwuM3fuXPP111+bm2++2SQlJfnv7Pnxj39s7rnnHv/6n376qYmMjDSPPfaYWb9+vZk+fbqJiooyX331lV270Khbb73VJCYmmkWLFlmOV0VFhX+d2vs4Y8YM884775itW7eaFStWmOuuu87ExMSYdevW2bELjbrrrrvMokWLzPbt282nn35q8vLyTGpqqtm3b58xJviPnzFH/7h16dLF3H333XVeC8ZjV1ZW5v9bJ8k88cQTZtWqVWbnzp3GGGMefvhhk5SUZN566y3z5ZdfmmuuucZ0797dHDlyxP8el156qXn66af9y6f6OW4JrR4IJkyYYCTV+fjwww/96+zYscNceeWVpl27diY1NdXcddddlpT44YcfGklm+/bt/trBgwfNuHHjTHx8vElISDATJ070h4xAMG7cODNixIh6X9u+fbvlv0FBQYG58MILTXJysnG5XKZXr17mV7/6lSktLW3DjptmxYoVJicnxyQmJpqYmBjTv39/89BDD1nO5tTeP2OMOXLkiPn5z39uOnToYGJjY833vvc9yx/ZQPHiiy/W+//rySfTgvH4Pf3006ZLly4mOjraDB8+3Hz22Wf+1y666CIzYcIEy/qvvvqq6dOnj4mOjjZnn322mT9/fht33HQNHa8XX3zRv07tfbzjjjv8/z3S09PNVVddZVauXNn2zTfB2LFjTadOnUx0dLTJysoyY8eONVu2bPG/HuzHz5ijZ3glmY0bN9Z5LRiP3fG/WbU/ju+Hz+cz999/v0lPTzcul8tcdtlldfa9a9euZvr06ZZaYz/HLYHHHwMAAJ5lAAAACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAACQ9P8BY45BZSL3yFUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WAsTI6Chpl91"
      },
      "source": [
        "#### Tangens Hyperbolicus Function: Fließender Übergang zwischen -1 und 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WDP-00Lpl92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "4bc033ee-4c09-4a5b-c4ce-9018d5fda98c"
      },
      "source": [
        "x = np.arange(-10,10,0.01)\n",
        "y = np.tanh(x)\n",
        "\n",
        "centerAxis()\n",
        "plt.plot(x,y,lw=3)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff3d084c850>]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWZW6pkrpl95"
      },
      "source": [
        "#### Relu: Einfach zu berechnen, setzt kompletten negativen Wertebereich auf 0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LTGgI99pl96"
      },
      "source": [
        "def np_relu(x):\n",
        "    return np.maximum(0, x)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqtXbFQvpl98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "0fba378e-a6c5-43d2-e35d-6eedff6d7f9e"
      },
      "source": [
        "x = np.arange(-10,10,0.01)\n",
        "y = np_relu(x)\n",
        "\n",
        "centerAxis()\n",
        "plt.plot(x,y,lw=3)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff3cfe65450>]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDcTe_YHpl9-"
      },
      "source": [
        "# https://docs.python.org/3/library/math.html\n",
        "import math as math\n",
        "def sigmoid(x):\n",
        "    return 1 / (1 + math.exp(x * -1))\n",
        "\n",
        "w0 = 3\n",
        "w1 = -4\n",
        "w2 = 2\n",
        "\n",
        "def neuron(x1, x2):\n",
        "    sum = w0 + x1 * w1 + x2 * w2\n",
        "    return sigmoid(sum)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jQTVmi6pl-A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55c5b1b4-0c97-4a24-fe07-4b8e57edc983"
      },
      "source": [
        "neuron(5.1, 3.5)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.043155690056538e-05"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Ci-Qwh3pl-C"
      },
      "source": [
        "# Version that takes as many values as you like\n",
        "weights_with_bias = np.array([3, -4, 2])\n",
        "def np_neuron(X):\n",
        "    inputs_with_1_for_bias = np.concatenate((np.array([1]), X))\n",
        "    return np_sigmoid(np.sum(inputs_with_1_for_bias*weights_with_bias));"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kIgFmAhpl-F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e78bf42-e8c7-4571-d335-8e44ed167649"
      },
      "source": [
        "np_neuron(np.array([5.1, 3.5]))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.043155690056538e-05"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcynLtuepl-H"
      },
      "source": [
        "## Unser erste Neuronales Netz mit Keras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uv0NsPfhpl-H"
      },
      "source": [
        "from keras.layers import Input\n",
        "inputs = Input(shape=(4, ))"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8id5zq2Bpl-K"
      },
      "source": [
        "from keras.layers import Dense\n",
        "fc = Dense(3)(inputs)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiFhyYi3pl-L"
      },
      "source": [
        "from keras.models import Model\n",
        "model = Model(inputs=inputs, outputs=fc)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wKyUqPAupl-N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "070c9969-ad69-4e06-d23b-02e352b64a3d"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 4)]               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 3)                 15        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 15\n",
            "Trainable params: 15\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhrAh8GApl-P"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WcVU5Rgpl-R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5698d0e-be78-4d2a-ca1c-e3f6ad376c73"
      },
      "source": [
        "model.predict(np.array([[ 5.1,  3.5,  1.4,  0.2]]))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 384ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-4.934084 ,  0.543613 , -1.8231183]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1OwT7TWpl-T"
      },
      "source": [
        "inputs = Input(shape=(4, ))\n",
        "fc = Dense(3)(inputs)\n",
        "predictions = Dense(3, activation='softmax')(fc)\n",
        "model = Model(inputs=inputs, outputs=predictions)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTxUA8jOpl-X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e3faf60-b0be-4357-ec0a-c65a53dc091a"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 4)]               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 15        \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 3)                 12        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 27\n",
            "Trainable params: 27\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGszqEMDpl-a"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MI_BdRKbpl-c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8346a09b-d724-4721-ae9e-184dfd9ca1c7"
      },
      "source": [
        "model.predict(np.array([[ 5.1,  3.5,  1.4,  0.2]]))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 134ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.32990545, 0.13918178, 0.53091276]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "sdoL_Z7bpl-e"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQ1oRcd-pl-e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4f74a0a-d58d-413f-c205-378506025419"
      },
      "source": [
        "X = np.array(iris.data)\n",
        "y = np.array(iris.target)\n",
        "X.shape, y.shape"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((150, 4), (150,))"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4LEAqRlpl-h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b248fed7-acb9-423b-cba2-58c379269faa"
      },
      "source": [
        "y[100]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2gMaXFGpl-m"
      },
      "source": [
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "num_categories = 3\n",
        "\n",
        "y = to_categorical(y, num_categories)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GdCZmogpl-o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c87b7bfe-021a-426e-881a-eea047061a82"
      },
      "source": [
        "y[100]"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 1.], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJL9v7tQpl-r"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42, stratify=y)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2W10jFK_pl-t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20b12ea6-0d0c-41c4-968b-26245bf3d53e"
      },
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((120, 4), (30, 4), (120, 3), (30, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNNf0b4kpl-w"
      },
      "source": [
        "# !rm -r tf_log\n",
        "\n",
        "# https://keras.io/callbacks/#tensorboard\n",
        "# tb_callback = keras.callbacks.TensorBoard(log_dir='./tf_log')\n",
        "# To start tensorboard\n",
        "# tensorboard --logdir=/mnt/c/Users/olive/Development/ml/tf_log\n",
        "# open http://localhost:6006"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Px5unIxQpl-0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d582e87c-f2cb-4477-e18c-8734203ab132"
      },
      "source": [
        "# %time model.fit(X_train, y_train, epochs=500, validation_split=0.3, callbacks=[tb_callback])\n",
        "%time model.fit(X_train, y_train, epochs=500, validation_split=0.3)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "3/3 [==============================] - 2s 249ms/step - loss: 1.0869 - accuracy: 0.3690 - val_loss: 1.1778 - val_accuracy: 0.3333\n",
            "Epoch 2/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 1.0466 - accuracy: 0.3810 - val_loss: 1.1360 - val_accuracy: 0.3611\n",
            "Epoch 3/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 1.0098 - accuracy: 0.4167 - val_loss: 1.0969 - val_accuracy: 0.3611\n",
            "Epoch 4/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.9763 - accuracy: 0.4286 - val_loss: 1.0604 - val_accuracy: 0.3611\n",
            "Epoch 5/500\n",
            "3/3 [==============================] - 0s 60ms/step - loss: 0.9463 - accuracy: 0.5000 - val_loss: 1.0267 - val_accuracy: 0.4444\n",
            "Epoch 6/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.9175 - accuracy: 0.5238 - val_loss: 0.9962 - val_accuracy: 0.4444\n",
            "Epoch 7/500\n",
            "3/3 [==============================] - 0s 42ms/step - loss: 0.8929 - accuracy: 0.5833 - val_loss: 0.9685 - val_accuracy: 0.5000\n",
            "Epoch 8/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.8702 - accuracy: 0.6071 - val_loss: 0.9435 - val_accuracy: 0.5000\n",
            "Epoch 9/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.8497 - accuracy: 0.6548 - val_loss: 0.9216 - val_accuracy: 0.5000\n",
            "Epoch 10/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.8321 - accuracy: 0.6667 - val_loss: 0.9021 - val_accuracy: 0.5278\n",
            "Epoch 11/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.8163 - accuracy: 0.6786 - val_loss: 0.8848 - val_accuracy: 0.6111\n",
            "Epoch 12/500\n",
            "3/3 [==============================] - 0s 46ms/step - loss: 0.8016 - accuracy: 0.6786 - val_loss: 0.8698 - val_accuracy: 0.5833\n",
            "Epoch 13/500\n",
            "3/3 [==============================] - 0s 45ms/step - loss: 0.7896 - accuracy: 0.6548 - val_loss: 0.8564 - val_accuracy: 0.6111\n",
            "Epoch 14/500\n",
            "3/3 [==============================] - 0s 87ms/step - loss: 0.7809 - accuracy: 0.6548 - val_loss: 0.8441 - val_accuracy: 0.5833\n",
            "Epoch 15/500\n",
            "3/3 [==============================] - 0s 100ms/step - loss: 0.7717 - accuracy: 0.6429 - val_loss: 0.8336 - val_accuracy: 0.5833\n",
            "Epoch 16/500\n",
            "3/3 [==============================] - 0s 116ms/step - loss: 0.7633 - accuracy: 0.6310 - val_loss: 0.8248 - val_accuracy: 0.5833\n",
            "Epoch 17/500\n",
            "3/3 [==============================] - 0s 61ms/step - loss: 0.7565 - accuracy: 0.6190 - val_loss: 0.8173 - val_accuracy: 0.5833\n",
            "Epoch 18/500\n",
            "3/3 [==============================] - 0s 66ms/step - loss: 0.7513 - accuracy: 0.5952 - val_loss: 0.8105 - val_accuracy: 0.5833\n",
            "Epoch 19/500\n",
            "3/3 [==============================] - 0s 95ms/step - loss: 0.7449 - accuracy: 0.6071 - val_loss: 0.8047 - val_accuracy: 0.5833\n",
            "Epoch 20/500\n",
            "3/3 [==============================] - 0s 66ms/step - loss: 0.7404 - accuracy: 0.5833 - val_loss: 0.7993 - val_accuracy: 0.5833\n",
            "Epoch 21/500\n",
            "3/3 [==============================] - 0s 79ms/step - loss: 0.7367 - accuracy: 0.5833 - val_loss: 0.7943 - val_accuracy: 0.5833\n",
            "Epoch 22/500\n",
            "3/3 [==============================] - 0s 80ms/step - loss: 0.7327 - accuracy: 0.5833 - val_loss: 0.7898 - val_accuracy: 0.5556\n",
            "Epoch 23/500\n",
            "3/3 [==============================] - 0s 102ms/step - loss: 0.7289 - accuracy: 0.5833 - val_loss: 0.7858 - val_accuracy: 0.5556\n",
            "Epoch 24/500\n",
            "3/3 [==============================] - 0s 118ms/step - loss: 0.7258 - accuracy: 0.5833 - val_loss: 0.7818 - val_accuracy: 0.5556\n",
            "Epoch 25/500\n",
            "3/3 [==============================] - 0s 65ms/step - loss: 0.7227 - accuracy: 0.5595 - val_loss: 0.7781 - val_accuracy: 0.5278\n",
            "Epoch 26/500\n",
            "3/3 [==============================] - 0s 57ms/step - loss: 0.7198 - accuracy: 0.5476 - val_loss: 0.7749 - val_accuracy: 0.5278\n",
            "Epoch 27/500\n",
            "3/3 [==============================] - 0s 96ms/step - loss: 0.7172 - accuracy: 0.5595 - val_loss: 0.7718 - val_accuracy: 0.5278\n",
            "Epoch 28/500\n",
            "3/3 [==============================] - 0s 72ms/step - loss: 0.7143 - accuracy: 0.5595 - val_loss: 0.7692 - val_accuracy: 0.5278\n",
            "Epoch 29/500\n",
            "3/3 [==============================] - 0s 73ms/step - loss: 0.7114 - accuracy: 0.5595 - val_loss: 0.7664 - val_accuracy: 0.5000\n",
            "Epoch 30/500\n",
            "3/3 [==============================] - 0s 106ms/step - loss: 0.7091 - accuracy: 0.5595 - val_loss: 0.7634 - val_accuracy: 0.5000\n",
            "Epoch 31/500\n",
            "3/3 [==============================] - 0s 105ms/step - loss: 0.7063 - accuracy: 0.5595 - val_loss: 0.7608 - val_accuracy: 0.5000\n",
            "Epoch 32/500\n",
            "3/3 [==============================] - 0s 42ms/step - loss: 0.7037 - accuracy: 0.5595 - val_loss: 0.7584 - val_accuracy: 0.5000\n",
            "Epoch 33/500\n",
            "3/3 [==============================] - 0s 56ms/step - loss: 0.7011 - accuracy: 0.5595 - val_loss: 0.7557 - val_accuracy: 0.5000\n",
            "Epoch 34/500\n",
            "3/3 [==============================] - 0s 84ms/step - loss: 0.6986 - accuracy: 0.5595 - val_loss: 0.7531 - val_accuracy: 0.5000\n",
            "Epoch 35/500\n",
            "3/3 [==============================] - 0s 58ms/step - loss: 0.6962 - accuracy: 0.5595 - val_loss: 0.7503 - val_accuracy: 0.5000\n",
            "Epoch 36/500\n",
            "3/3 [==============================] - 0s 76ms/step - loss: 0.6939 - accuracy: 0.5595 - val_loss: 0.7476 - val_accuracy: 0.5000\n",
            "Epoch 37/500\n",
            "3/3 [==============================] - 0s 51ms/step - loss: 0.6913 - accuracy: 0.5595 - val_loss: 0.7453 - val_accuracy: 0.5000\n",
            "Epoch 38/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.6889 - accuracy: 0.5595 - val_loss: 0.7431 - val_accuracy: 0.5000\n",
            "Epoch 39/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.6867 - accuracy: 0.5595 - val_loss: 0.7410 - val_accuracy: 0.5000\n",
            "Epoch 40/500\n",
            "3/3 [==============================] - 0s 45ms/step - loss: 0.6841 - accuracy: 0.5714 - val_loss: 0.7388 - val_accuracy: 0.5000\n",
            "Epoch 41/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.6818 - accuracy: 0.5595 - val_loss: 0.7365 - val_accuracy: 0.5000\n",
            "Epoch 42/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.6797 - accuracy: 0.5714 - val_loss: 0.7339 - val_accuracy: 0.5000\n",
            "Epoch 43/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.6775 - accuracy: 0.5476 - val_loss: 0.7319 - val_accuracy: 0.5000\n",
            "Epoch 44/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.6749 - accuracy: 0.5595 - val_loss: 0.7298 - val_accuracy: 0.5000\n",
            "Epoch 45/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.6727 - accuracy: 0.5595 - val_loss: 0.7275 - val_accuracy: 0.5000\n",
            "Epoch 46/500\n",
            "3/3 [==============================] - 0s 56ms/step - loss: 0.6705 - accuracy: 0.5595 - val_loss: 0.7254 - val_accuracy: 0.5000\n",
            "Epoch 47/500\n",
            "3/3 [==============================] - 0s 43ms/step - loss: 0.6682 - accuracy: 0.5595 - val_loss: 0.7235 - val_accuracy: 0.5278\n",
            "Epoch 48/500\n",
            "3/3 [==============================] - 0s 68ms/step - loss: 0.6661 - accuracy: 0.5595 - val_loss: 0.7214 - val_accuracy: 0.5278\n",
            "Epoch 49/500\n",
            "3/3 [==============================] - 0s 88ms/step - loss: 0.6639 - accuracy: 0.5595 - val_loss: 0.7193 - val_accuracy: 0.5278\n",
            "Epoch 50/500\n",
            "3/3 [==============================] - 0s 93ms/step - loss: 0.6618 - accuracy: 0.5595 - val_loss: 0.7173 - val_accuracy: 0.5278\n",
            "Epoch 51/500\n",
            "3/3 [==============================] - 0s 92ms/step - loss: 0.6597 - accuracy: 0.5595 - val_loss: 0.7154 - val_accuracy: 0.5278\n",
            "Epoch 52/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.6576 - accuracy: 0.5595 - val_loss: 0.7133 - val_accuracy: 0.5278\n",
            "Epoch 53/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.6557 - accuracy: 0.5595 - val_loss: 0.7111 - val_accuracy: 0.5278\n",
            "Epoch 54/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.6535 - accuracy: 0.5595 - val_loss: 0.7092 - val_accuracy: 0.5556\n",
            "Epoch 55/500\n",
            "3/3 [==============================] - 0s 45ms/step - loss: 0.6515 - accuracy: 0.5595 - val_loss: 0.7073 - val_accuracy: 0.5556\n",
            "Epoch 56/500\n",
            "3/3 [==============================] - 0s 69ms/step - loss: 0.6495 - accuracy: 0.5595 - val_loss: 0.7051 - val_accuracy: 0.5556\n",
            "Epoch 57/500\n",
            "3/3 [==============================] - 0s 48ms/step - loss: 0.6476 - accuracy: 0.5714 - val_loss: 0.7033 - val_accuracy: 0.5556\n",
            "Epoch 58/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.6455 - accuracy: 0.5714 - val_loss: 0.7012 - val_accuracy: 0.5556\n",
            "Epoch 59/500\n",
            "3/3 [==============================] - 0s 91ms/step - loss: 0.6435 - accuracy: 0.5714 - val_loss: 0.6989 - val_accuracy: 0.5556\n",
            "Epoch 60/500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.6415 - accuracy: 0.5714 - val_loss: 0.6965 - val_accuracy: 0.5556\n",
            "Epoch 61/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.6395 - accuracy: 0.5595 - val_loss: 0.6942 - val_accuracy: 0.5278\n",
            "Epoch 62/500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.6379 - accuracy: 0.5595 - val_loss: 0.6917 - val_accuracy: 0.5000\n",
            "Epoch 63/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6359 - accuracy: 0.5595 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
            "Epoch 64/500\n",
            "3/3 [==============================] - 0s 42ms/step - loss: 0.6339 - accuracy: 0.5714 - val_loss: 0.6875 - val_accuracy: 0.5000\n",
            "Epoch 65/500\n",
            "3/3 [==============================] - 0s 57ms/step - loss: 0.6323 - accuracy: 0.5714 - val_loss: 0.6852 - val_accuracy: 0.5000\n",
            "Epoch 66/500\n",
            "3/3 [==============================] - 0s 87ms/step - loss: 0.6303 - accuracy: 0.5714 - val_loss: 0.6833 - val_accuracy: 0.5000\n",
            "Epoch 67/500\n",
            "3/3 [==============================] - 0s 42ms/step - loss: 0.6285 - accuracy: 0.5714 - val_loss: 0.6816 - val_accuracy: 0.5000\n",
            "Epoch 68/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.6268 - accuracy: 0.5714 - val_loss: 0.6798 - val_accuracy: 0.5000\n",
            "Epoch 69/500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.6249 - accuracy: 0.5714 - val_loss: 0.6781 - val_accuracy: 0.5000\n",
            "Epoch 70/500\n",
            "3/3 [==============================] - 0s 50ms/step - loss: 0.6231 - accuracy: 0.5714 - val_loss: 0.6764 - val_accuracy: 0.5000\n",
            "Epoch 71/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.6214 - accuracy: 0.5595 - val_loss: 0.6749 - val_accuracy: 0.5556\n",
            "Epoch 72/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.6197 - accuracy: 0.5595 - val_loss: 0.6733 - val_accuracy: 0.5556\n",
            "Epoch 73/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.6182 - accuracy: 0.5952 - val_loss: 0.6717 - val_accuracy: 0.5556\n",
            "Epoch 74/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.6163 - accuracy: 0.5952 - val_loss: 0.6698 - val_accuracy: 0.5556\n",
            "Epoch 75/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.6146 - accuracy: 0.5714 - val_loss: 0.6677 - val_accuracy: 0.5556\n",
            "Epoch 76/500\n",
            "3/3 [==============================] - 0s 48ms/step - loss: 0.6130 - accuracy: 0.5714 - val_loss: 0.6658 - val_accuracy: 0.5556\n",
            "Epoch 77/500\n",
            "3/3 [==============================] - 0s 86ms/step - loss: 0.6114 - accuracy: 0.5595 - val_loss: 0.6637 - val_accuracy: 0.5000\n",
            "Epoch 78/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.6097 - accuracy: 0.5714 - val_loss: 0.6618 - val_accuracy: 0.5000\n",
            "Epoch 79/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.6081 - accuracy: 0.5714 - val_loss: 0.6602 - val_accuracy: 0.5278\n",
            "Epoch 80/500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.6068 - accuracy: 0.5714 - val_loss: 0.6587 - val_accuracy: 0.5556\n",
            "Epoch 81/500\n",
            "3/3 [==============================] - 0s 47ms/step - loss: 0.6049 - accuracy: 0.5714 - val_loss: 0.6568 - val_accuracy: 0.5278\n",
            "Epoch 82/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.6034 - accuracy: 0.5714 - val_loss: 0.6550 - val_accuracy: 0.5278\n",
            "Epoch 83/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.6019 - accuracy: 0.5714 - val_loss: 0.6531 - val_accuracy: 0.5000\n",
            "Epoch 84/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.6003 - accuracy: 0.5833 - val_loss: 0.6515 - val_accuracy: 0.5278\n",
            "Epoch 85/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.5989 - accuracy: 0.5833 - val_loss: 0.6497 - val_accuracy: 0.5000\n",
            "Epoch 86/500\n",
            "3/3 [==============================] - 0s 88ms/step - loss: 0.5977 - accuracy: 0.5833 - val_loss: 0.6483 - val_accuracy: 0.5278\n",
            "Epoch 87/500\n",
            "3/3 [==============================] - 0s 85ms/step - loss: 0.5960 - accuracy: 0.5833 - val_loss: 0.6466 - val_accuracy: 0.5278\n",
            "Epoch 88/500\n",
            "3/3 [==============================] - 0s 60ms/step - loss: 0.5943 - accuracy: 0.5833 - val_loss: 0.6449 - val_accuracy: 0.5278\n",
            "Epoch 89/500\n",
            "3/3 [==============================] - 0s 46ms/step - loss: 0.5929 - accuracy: 0.5833 - val_loss: 0.6433 - val_accuracy: 0.5278\n",
            "Epoch 90/500\n",
            "3/3 [==============================] - 0s 47ms/step - loss: 0.5917 - accuracy: 0.5833 - val_loss: 0.6413 - val_accuracy: 0.5278\n",
            "Epoch 91/500\n",
            "3/3 [==============================] - 0s 66ms/step - loss: 0.5902 - accuracy: 0.5833 - val_loss: 0.6398 - val_accuracy: 0.5278\n",
            "Epoch 92/500\n",
            "3/3 [==============================] - 0s 43ms/step - loss: 0.5887 - accuracy: 0.5833 - val_loss: 0.6381 - val_accuracy: 0.5278\n",
            "Epoch 93/500\n",
            "3/3 [==============================] - 0s 66ms/step - loss: 0.5875 - accuracy: 0.5833 - val_loss: 0.6367 - val_accuracy: 0.5278\n",
            "Epoch 94/500\n",
            "3/3 [==============================] - 0s 43ms/step - loss: 0.5860 - accuracy: 0.5833 - val_loss: 0.6350 - val_accuracy: 0.5278\n",
            "Epoch 95/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.5846 - accuracy: 0.5833 - val_loss: 0.6335 - val_accuracy: 0.5278\n",
            "Epoch 96/500\n",
            "3/3 [==============================] - 0s 61ms/step - loss: 0.5832 - accuracy: 0.5833 - val_loss: 0.6322 - val_accuracy: 0.5556\n",
            "Epoch 97/500\n",
            "3/3 [==============================] - 0s 80ms/step - loss: 0.5818 - accuracy: 0.5833 - val_loss: 0.6308 - val_accuracy: 0.5556\n",
            "Epoch 98/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5804 - accuracy: 0.5833 - val_loss: 0.6295 - val_accuracy: 0.5556\n",
            "Epoch 99/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5791 - accuracy: 0.6071 - val_loss: 0.6283 - val_accuracy: 0.5278\n",
            "Epoch 100/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.5778 - accuracy: 0.6071 - val_loss: 0.6272 - val_accuracy: 0.5556\n",
            "Epoch 101/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5765 - accuracy: 0.6190 - val_loss: 0.6257 - val_accuracy: 0.5556\n",
            "Epoch 102/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5751 - accuracy: 0.6190 - val_loss: 0.6245 - val_accuracy: 0.5556\n",
            "Epoch 103/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5738 - accuracy: 0.6190 - val_loss: 0.6232 - val_accuracy: 0.5556\n",
            "Epoch 104/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5725 - accuracy: 0.6190 - val_loss: 0.6220 - val_accuracy: 0.5833\n",
            "Epoch 105/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.5716 - accuracy: 0.6190 - val_loss: 0.6212 - val_accuracy: 0.5833\n",
            "Epoch 106/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5700 - accuracy: 0.6190 - val_loss: 0.6199 - val_accuracy: 0.5833\n",
            "Epoch 107/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.5689 - accuracy: 0.6190 - val_loss: 0.6185 - val_accuracy: 0.5833\n",
            "Epoch 108/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5677 - accuracy: 0.6190 - val_loss: 0.6168 - val_accuracy: 0.5833\n",
            "Epoch 109/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5663 - accuracy: 0.6190 - val_loss: 0.6153 - val_accuracy: 0.5833\n",
            "Epoch 110/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5651 - accuracy: 0.6190 - val_loss: 0.6138 - val_accuracy: 0.5833\n",
            "Epoch 111/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.5640 - accuracy: 0.6190 - val_loss: 0.6125 - val_accuracy: 0.5833\n",
            "Epoch 112/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.5627 - accuracy: 0.6190 - val_loss: 0.6109 - val_accuracy: 0.5833\n",
            "Epoch 113/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5615 - accuracy: 0.6310 - val_loss: 0.6093 - val_accuracy: 0.6111\n",
            "Epoch 114/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5606 - accuracy: 0.6190 - val_loss: 0.6077 - val_accuracy: 0.6389\n",
            "Epoch 115/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5591 - accuracy: 0.6310 - val_loss: 0.6066 - val_accuracy: 0.6111\n",
            "Epoch 116/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 0.5579 - accuracy: 0.6310 - val_loss: 0.6055 - val_accuracy: 0.6111\n",
            "Epoch 117/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.5568 - accuracy: 0.6310 - val_loss: 0.6045 - val_accuracy: 0.6111\n",
            "Epoch 118/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.5557 - accuracy: 0.6190 - val_loss: 0.6033 - val_accuracy: 0.6111\n",
            "Epoch 119/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.5545 - accuracy: 0.6190 - val_loss: 0.6021 - val_accuracy: 0.6111\n",
            "Epoch 120/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.5534 - accuracy: 0.6190 - val_loss: 0.6008 - val_accuracy: 0.6111\n",
            "Epoch 121/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5523 - accuracy: 0.6190 - val_loss: 0.5995 - val_accuracy: 0.6111\n",
            "Epoch 122/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.5512 - accuracy: 0.6190 - val_loss: 0.5983 - val_accuracy: 0.6111\n",
            "Epoch 123/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.5500 - accuracy: 0.6190 - val_loss: 0.5968 - val_accuracy: 0.6111\n",
            "Epoch 124/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5489 - accuracy: 0.6190 - val_loss: 0.5954 - val_accuracy: 0.6111\n",
            "Epoch 125/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5479 - accuracy: 0.6310 - val_loss: 0.5942 - val_accuracy: 0.6111\n",
            "Epoch 126/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.5468 - accuracy: 0.6310 - val_loss: 0.5927 - val_accuracy: 0.6389\n",
            "Epoch 127/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5457 - accuracy: 0.6310 - val_loss: 0.5915 - val_accuracy: 0.6389\n",
            "Epoch 128/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5448 - accuracy: 0.6310 - val_loss: 0.5901 - val_accuracy: 0.6389\n",
            "Epoch 129/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5435 - accuracy: 0.6310 - val_loss: 0.5891 - val_accuracy: 0.6389\n",
            "Epoch 130/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5424 - accuracy: 0.6310 - val_loss: 0.5880 - val_accuracy: 0.6389\n",
            "Epoch 131/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5414 - accuracy: 0.6310 - val_loss: 0.5871 - val_accuracy: 0.6111\n",
            "Epoch 132/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5404 - accuracy: 0.6190 - val_loss: 0.5860 - val_accuracy: 0.6111\n",
            "Epoch 133/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5394 - accuracy: 0.6190 - val_loss: 0.5850 - val_accuracy: 0.6111\n",
            "Epoch 134/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5385 - accuracy: 0.6190 - val_loss: 0.5839 - val_accuracy: 0.6111\n",
            "Epoch 135/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.5375 - accuracy: 0.6190 - val_loss: 0.5824 - val_accuracy: 0.6111\n",
            "Epoch 136/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.5364 - accuracy: 0.6190 - val_loss: 0.5813 - val_accuracy: 0.6111\n",
            "Epoch 137/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.5352 - accuracy: 0.6190 - val_loss: 0.5799 - val_accuracy: 0.6389\n",
            "Epoch 138/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.5342 - accuracy: 0.6310 - val_loss: 0.5782 - val_accuracy: 0.6389\n",
            "Epoch 139/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.5334 - accuracy: 0.6310 - val_loss: 0.5766 - val_accuracy: 0.6667\n",
            "Epoch 140/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.5326 - accuracy: 0.6310 - val_loss: 0.5751 - val_accuracy: 0.6667\n",
            "Epoch 141/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.5313 - accuracy: 0.6310 - val_loss: 0.5742 - val_accuracy: 0.6667\n",
            "Epoch 142/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.5302 - accuracy: 0.6310 - val_loss: 0.5732 - val_accuracy: 0.6667\n",
            "Epoch 143/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5293 - accuracy: 0.6310 - val_loss: 0.5721 - val_accuracy: 0.6667\n",
            "Epoch 144/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.5283 - accuracy: 0.6310 - val_loss: 0.5710 - val_accuracy: 0.6667\n",
            "Epoch 145/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.5274 - accuracy: 0.6310 - val_loss: 0.5698 - val_accuracy: 0.6667\n",
            "Epoch 146/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.5265 - accuracy: 0.6310 - val_loss: 0.5688 - val_accuracy: 0.6667\n",
            "Epoch 147/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.5255 - accuracy: 0.6310 - val_loss: 0.5677 - val_accuracy: 0.6667\n",
            "Epoch 148/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.5244 - accuracy: 0.6310 - val_loss: 0.5663 - val_accuracy: 0.6667\n",
            "Epoch 149/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.5237 - accuracy: 0.6310 - val_loss: 0.5647 - val_accuracy: 0.6667\n",
            "Epoch 150/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.5230 - accuracy: 0.6310 - val_loss: 0.5632 - val_accuracy: 0.6667\n",
            "Epoch 151/500\n",
            "3/3 [==============================] - 0s 46ms/step - loss: 0.5218 - accuracy: 0.6310 - val_loss: 0.5621 - val_accuracy: 0.6667\n",
            "Epoch 152/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.5209 - accuracy: 0.6310 - val_loss: 0.5610 - val_accuracy: 0.6667\n",
            "Epoch 153/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.5200 - accuracy: 0.6310 - val_loss: 0.5600 - val_accuracy: 0.6667\n",
            "Epoch 154/500\n",
            "3/3 [==============================] - 0s 46ms/step - loss: 0.5191 - accuracy: 0.6310 - val_loss: 0.5591 - val_accuracy: 0.6667\n",
            "Epoch 155/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.5182 - accuracy: 0.6310 - val_loss: 0.5583 - val_accuracy: 0.6667\n",
            "Epoch 156/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.5172 - accuracy: 0.6310 - val_loss: 0.5573 - val_accuracy: 0.6667\n",
            "Epoch 157/500\n",
            "3/3 [==============================] - 0s 44ms/step - loss: 0.5162 - accuracy: 0.6310 - val_loss: 0.5563 - val_accuracy: 0.6667\n",
            "Epoch 158/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.5153 - accuracy: 0.6310 - val_loss: 0.5553 - val_accuracy: 0.6667\n",
            "Epoch 159/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.5144 - accuracy: 0.6310 - val_loss: 0.5541 - val_accuracy: 0.6667\n",
            "Epoch 160/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.5136 - accuracy: 0.6310 - val_loss: 0.5528 - val_accuracy: 0.6667\n",
            "Epoch 161/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5126 - accuracy: 0.6310 - val_loss: 0.5518 - val_accuracy: 0.6667\n",
            "Epoch 162/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5117 - accuracy: 0.6310 - val_loss: 0.5509 - val_accuracy: 0.6667\n",
            "Epoch 163/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.5110 - accuracy: 0.6310 - val_loss: 0.5501 - val_accuracy: 0.6667\n",
            "Epoch 164/500\n",
            "3/3 [==============================] - 0s 45ms/step - loss: 0.5100 - accuracy: 0.6310 - val_loss: 0.5490 - val_accuracy: 0.6667\n",
            "Epoch 165/500\n",
            "3/3 [==============================] - 0s 46ms/step - loss: 0.5092 - accuracy: 0.6310 - val_loss: 0.5479 - val_accuracy: 0.6667\n",
            "Epoch 166/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.5083 - accuracy: 0.6310 - val_loss: 0.5471 - val_accuracy: 0.6667\n",
            "Epoch 167/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5074 - accuracy: 0.6429 - val_loss: 0.5461 - val_accuracy: 0.6667\n",
            "Epoch 168/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.5066 - accuracy: 0.6429 - val_loss: 0.5452 - val_accuracy: 0.6667\n",
            "Epoch 169/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.5057 - accuracy: 0.6429 - val_loss: 0.5443 - val_accuracy: 0.6667\n",
            "Epoch 170/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.5049 - accuracy: 0.6429 - val_loss: 0.5432 - val_accuracy: 0.6667\n",
            "Epoch 171/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.5040 - accuracy: 0.6429 - val_loss: 0.5422 - val_accuracy: 0.6667\n",
            "Epoch 172/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5032 - accuracy: 0.6429 - val_loss: 0.5412 - val_accuracy: 0.6667\n",
            "Epoch 173/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.5024 - accuracy: 0.6429 - val_loss: 0.5400 - val_accuracy: 0.6667\n",
            "Epoch 174/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5015 - accuracy: 0.6429 - val_loss: 0.5390 - val_accuracy: 0.6667\n",
            "Epoch 175/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.5007 - accuracy: 0.6429 - val_loss: 0.5380 - val_accuracy: 0.6667\n",
            "Epoch 176/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.5001 - accuracy: 0.6548 - val_loss: 0.5374 - val_accuracy: 0.6667\n",
            "Epoch 177/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4992 - accuracy: 0.6667 - val_loss: 0.5365 - val_accuracy: 0.6667\n",
            "Epoch 178/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4982 - accuracy: 0.6667 - val_loss: 0.5352 - val_accuracy: 0.6667\n",
            "Epoch 179/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4975 - accuracy: 0.6429 - val_loss: 0.5339 - val_accuracy: 0.6667\n",
            "Epoch 180/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4965 - accuracy: 0.6429 - val_loss: 0.5328 - val_accuracy: 0.6667\n",
            "Epoch 181/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4957 - accuracy: 0.6429 - val_loss: 0.5318 - val_accuracy: 0.6667\n",
            "Epoch 182/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4950 - accuracy: 0.6429 - val_loss: 0.5308 - val_accuracy: 0.6667\n",
            "Epoch 183/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.4942 - accuracy: 0.6667 - val_loss: 0.5302 - val_accuracy: 0.6667\n",
            "Epoch 184/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4934 - accuracy: 0.6667 - val_loss: 0.5294 - val_accuracy: 0.6667\n",
            "Epoch 185/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4926 - accuracy: 0.6667 - val_loss: 0.5283 - val_accuracy: 0.6667\n",
            "Epoch 186/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4917 - accuracy: 0.6667 - val_loss: 0.5273 - val_accuracy: 0.6667\n",
            "Epoch 187/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4915 - accuracy: 0.6667 - val_loss: 0.5265 - val_accuracy: 0.6667\n",
            "Epoch 188/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.4902 - accuracy: 0.6667 - val_loss: 0.5250 - val_accuracy: 0.6667\n",
            "Epoch 189/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4893 - accuracy: 0.6667 - val_loss: 0.5239 - val_accuracy: 0.6667\n",
            "Epoch 190/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4885 - accuracy: 0.6667 - val_loss: 0.5226 - val_accuracy: 0.6667\n",
            "Epoch 191/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4877 - accuracy: 0.6667 - val_loss: 0.5214 - val_accuracy: 0.6667\n",
            "Epoch 192/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4870 - accuracy: 0.6548 - val_loss: 0.5203 - val_accuracy: 0.7222\n",
            "Epoch 193/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4862 - accuracy: 0.6429 - val_loss: 0.5191 - val_accuracy: 0.7222\n",
            "Epoch 194/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.4854 - accuracy: 0.6429 - val_loss: 0.5180 - val_accuracy: 0.7222\n",
            "Epoch 195/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4847 - accuracy: 0.6429 - val_loss: 0.5170 - val_accuracy: 0.7222\n",
            "Epoch 196/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4839 - accuracy: 0.6548 - val_loss: 0.5163 - val_accuracy: 0.7222\n",
            "Epoch 197/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4830 - accuracy: 0.6548 - val_loss: 0.5154 - val_accuracy: 0.7222\n",
            "Epoch 198/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4823 - accuracy: 0.6667 - val_loss: 0.5147 - val_accuracy: 0.7222\n",
            "Epoch 199/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.4815 - accuracy: 0.6667 - val_loss: 0.5138 - val_accuracy: 0.7222\n",
            "Epoch 200/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4808 - accuracy: 0.6667 - val_loss: 0.5128 - val_accuracy: 0.7222\n",
            "Epoch 201/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4800 - accuracy: 0.6667 - val_loss: 0.5118 - val_accuracy: 0.7222\n",
            "Epoch 202/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4792 - accuracy: 0.6667 - val_loss: 0.5107 - val_accuracy: 0.7222\n",
            "Epoch 203/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4784 - accuracy: 0.6667 - val_loss: 0.5095 - val_accuracy: 0.7222\n",
            "Epoch 204/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4778 - accuracy: 0.6667 - val_loss: 0.5084 - val_accuracy: 0.7222\n",
            "Epoch 205/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4770 - accuracy: 0.6667 - val_loss: 0.5076 - val_accuracy: 0.7222\n",
            "Epoch 206/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4762 - accuracy: 0.6786 - val_loss: 0.5066 - val_accuracy: 0.7222\n",
            "Epoch 207/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4755 - accuracy: 0.6905 - val_loss: 0.5058 - val_accuracy: 0.7222\n",
            "Epoch 208/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4748 - accuracy: 0.7024 - val_loss: 0.5049 - val_accuracy: 0.7222\n",
            "Epoch 209/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4740 - accuracy: 0.6905 - val_loss: 0.5042 - val_accuracy: 0.7222\n",
            "Epoch 210/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.4733 - accuracy: 0.6905 - val_loss: 0.5035 - val_accuracy: 0.7222\n",
            "Epoch 211/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4725 - accuracy: 0.7024 - val_loss: 0.5025 - val_accuracy: 0.7222\n",
            "Epoch 212/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4717 - accuracy: 0.7143 - val_loss: 0.5017 - val_accuracy: 0.7222\n",
            "Epoch 213/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4709 - accuracy: 0.7143 - val_loss: 0.5010 - val_accuracy: 0.7222\n",
            "Epoch 214/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4702 - accuracy: 0.7024 - val_loss: 0.5003 - val_accuracy: 0.7222\n",
            "Epoch 215/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4696 - accuracy: 0.6905 - val_loss: 0.4998 - val_accuracy: 0.7500\n",
            "Epoch 216/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4688 - accuracy: 0.7024 - val_loss: 0.4990 - val_accuracy: 0.7500\n",
            "Epoch 217/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4682 - accuracy: 0.7024 - val_loss: 0.4981 - val_accuracy: 0.7500\n",
            "Epoch 218/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4674 - accuracy: 0.7024 - val_loss: 0.4973 - val_accuracy: 0.7500\n",
            "Epoch 219/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4667 - accuracy: 0.7143 - val_loss: 0.4963 - val_accuracy: 0.7500\n",
            "Epoch 220/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.4659 - accuracy: 0.7143 - val_loss: 0.4953 - val_accuracy: 0.7500\n",
            "Epoch 221/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.4652 - accuracy: 0.7143 - val_loss: 0.4945 - val_accuracy: 0.7500\n",
            "Epoch 222/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.4646 - accuracy: 0.7143 - val_loss: 0.4932 - val_accuracy: 0.7500\n",
            "Epoch 223/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.4638 - accuracy: 0.7262 - val_loss: 0.4921 - val_accuracy: 0.7500\n",
            "Epoch 224/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4631 - accuracy: 0.7381 - val_loss: 0.4911 - val_accuracy: 0.7500\n",
            "Epoch 225/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4622 - accuracy: 0.7381 - val_loss: 0.4902 - val_accuracy: 0.7500\n",
            "Epoch 226/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4615 - accuracy: 0.7381 - val_loss: 0.4895 - val_accuracy: 0.7500\n",
            "Epoch 227/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4609 - accuracy: 0.7381 - val_loss: 0.4884 - val_accuracy: 0.7500\n",
            "Epoch 228/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4602 - accuracy: 0.7381 - val_loss: 0.4874 - val_accuracy: 0.7500\n",
            "Epoch 229/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4597 - accuracy: 0.7500 - val_loss: 0.4863 - val_accuracy: 0.7500\n",
            "Epoch 230/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4588 - accuracy: 0.7500 - val_loss: 0.4857 - val_accuracy: 0.7500\n",
            "Epoch 231/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4579 - accuracy: 0.7500 - val_loss: 0.4849 - val_accuracy: 0.7500\n",
            "Epoch 232/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.4574 - accuracy: 0.7381 - val_loss: 0.4840 - val_accuracy: 0.7500\n",
            "Epoch 233/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4566 - accuracy: 0.7500 - val_loss: 0.4829 - val_accuracy: 0.7500\n",
            "Epoch 234/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4558 - accuracy: 0.7500 - val_loss: 0.4820 - val_accuracy: 0.7500\n",
            "Epoch 235/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4551 - accuracy: 0.7500 - val_loss: 0.4810 - val_accuracy: 0.7500\n",
            "Epoch 236/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4544 - accuracy: 0.7500 - val_loss: 0.4801 - val_accuracy: 0.7500\n",
            "Epoch 237/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4538 - accuracy: 0.7500 - val_loss: 0.4788 - val_accuracy: 0.7500\n",
            "Epoch 238/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.4530 - accuracy: 0.7500 - val_loss: 0.4779 - val_accuracy: 0.7778\n",
            "Epoch 239/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4522 - accuracy: 0.7500 - val_loss: 0.4768 - val_accuracy: 0.7778\n",
            "Epoch 240/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4516 - accuracy: 0.7500 - val_loss: 0.4757 - val_accuracy: 0.7778\n",
            "Epoch 241/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4509 - accuracy: 0.7500 - val_loss: 0.4748 - val_accuracy: 0.7778\n",
            "Epoch 242/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4506 - accuracy: 0.7500 - val_loss: 0.4743 - val_accuracy: 0.7778\n",
            "Epoch 243/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4495 - accuracy: 0.7500 - val_loss: 0.4733 - val_accuracy: 0.7778\n",
            "Epoch 244/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4493 - accuracy: 0.7500 - val_loss: 0.4720 - val_accuracy: 0.7778\n",
            "Epoch 245/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4480 - accuracy: 0.7500 - val_loss: 0.4711 - val_accuracy: 0.7778\n",
            "Epoch 246/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4478 - accuracy: 0.7500 - val_loss: 0.4704 - val_accuracy: 0.7778\n",
            "Epoch 247/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4468 - accuracy: 0.7500 - val_loss: 0.4697 - val_accuracy: 0.7778\n",
            "Epoch 248/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.4459 - accuracy: 0.7500 - val_loss: 0.4686 - val_accuracy: 0.7778\n",
            "Epoch 249/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4455 - accuracy: 0.7500 - val_loss: 0.4674 - val_accuracy: 0.7778\n",
            "Epoch 250/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4446 - accuracy: 0.7619 - val_loss: 0.4664 - val_accuracy: 0.8056\n",
            "Epoch 251/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4439 - accuracy: 0.7619 - val_loss: 0.4658 - val_accuracy: 0.7778\n",
            "Epoch 252/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4431 - accuracy: 0.7500 - val_loss: 0.4649 - val_accuracy: 0.7778\n",
            "Epoch 253/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4425 - accuracy: 0.7500 - val_loss: 0.4640 - val_accuracy: 0.8056\n",
            "Epoch 254/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4419 - accuracy: 0.7500 - val_loss: 0.4632 - val_accuracy: 0.8056\n",
            "Epoch 255/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4412 - accuracy: 0.7500 - val_loss: 0.4621 - val_accuracy: 0.8056\n",
            "Epoch 256/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.4404 - accuracy: 0.7738 - val_loss: 0.4613 - val_accuracy: 0.8056\n",
            "Epoch 257/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4397 - accuracy: 0.7738 - val_loss: 0.4604 - val_accuracy: 0.8056\n",
            "Epoch 258/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4390 - accuracy: 0.7738 - val_loss: 0.4596 - val_accuracy: 0.8056\n",
            "Epoch 259/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.4384 - accuracy: 0.7738 - val_loss: 0.4585 - val_accuracy: 0.8056\n",
            "Epoch 260/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.4377 - accuracy: 0.7738 - val_loss: 0.4577 - val_accuracy: 0.8333\n",
            "Epoch 261/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4370 - accuracy: 0.7738 - val_loss: 0.4568 - val_accuracy: 0.8333\n",
            "Epoch 262/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4363 - accuracy: 0.7738 - val_loss: 0.4559 - val_accuracy: 0.8333\n",
            "Epoch 263/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.4356 - accuracy: 0.7738 - val_loss: 0.4552 - val_accuracy: 0.8333\n",
            "Epoch 264/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.4349 - accuracy: 0.7738 - val_loss: 0.4544 - val_accuracy: 0.8333\n",
            "Epoch 265/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4342 - accuracy: 0.7738 - val_loss: 0.4535 - val_accuracy: 0.8333\n",
            "Epoch 266/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.4337 - accuracy: 0.7738 - val_loss: 0.4528 - val_accuracy: 0.8333\n",
            "Epoch 267/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.4329 - accuracy: 0.7738 - val_loss: 0.4520 - val_accuracy: 0.8333\n",
            "Epoch 268/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4324 - accuracy: 0.7738 - val_loss: 0.4512 - val_accuracy: 0.8333\n",
            "Epoch 269/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4317 - accuracy: 0.7738 - val_loss: 0.4499 - val_accuracy: 0.8333\n",
            "Epoch 270/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4308 - accuracy: 0.7976 - val_loss: 0.4489 - val_accuracy: 0.8333\n",
            "Epoch 271/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4302 - accuracy: 0.8095 - val_loss: 0.4479 - val_accuracy: 0.8333\n",
            "Epoch 272/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4296 - accuracy: 0.8095 - val_loss: 0.4472 - val_accuracy: 0.8333\n",
            "Epoch 273/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4287 - accuracy: 0.8095 - val_loss: 0.4462 - val_accuracy: 0.8333\n",
            "Epoch 274/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4283 - accuracy: 0.8095 - val_loss: 0.4453 - val_accuracy: 0.8333\n",
            "Epoch 275/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.4274 - accuracy: 0.8333 - val_loss: 0.4443 - val_accuracy: 0.8611\n",
            "Epoch 276/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4268 - accuracy: 0.8333 - val_loss: 0.4433 - val_accuracy: 0.8611\n",
            "Epoch 277/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.4261 - accuracy: 0.8452 - val_loss: 0.4425 - val_accuracy: 0.8611\n",
            "Epoch 278/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4253 - accuracy: 0.8452 - val_loss: 0.4416 - val_accuracy: 0.8611\n",
            "Epoch 279/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4248 - accuracy: 0.8452 - val_loss: 0.4405 - val_accuracy: 0.8611\n",
            "Epoch 280/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4240 - accuracy: 0.8452 - val_loss: 0.4397 - val_accuracy: 0.8611\n",
            "Epoch 281/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.4233 - accuracy: 0.8452 - val_loss: 0.4388 - val_accuracy: 0.8611\n",
            "Epoch 282/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.4226 - accuracy: 0.8452 - val_loss: 0.4381 - val_accuracy: 0.8611\n",
            "Epoch 283/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.4219 - accuracy: 0.8452 - val_loss: 0.4375 - val_accuracy: 0.8611\n",
            "Epoch 284/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4213 - accuracy: 0.8452 - val_loss: 0.4369 - val_accuracy: 0.8611\n",
            "Epoch 285/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4205 - accuracy: 0.8452 - val_loss: 0.4361 - val_accuracy: 0.8611\n",
            "Epoch 286/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4198 - accuracy: 0.8571 - val_loss: 0.4352 - val_accuracy: 0.8611\n",
            "Epoch 287/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4192 - accuracy: 0.8571 - val_loss: 0.4342 - val_accuracy: 0.8611\n",
            "Epoch 288/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4185 - accuracy: 0.8571 - val_loss: 0.4333 - val_accuracy: 0.8611\n",
            "Epoch 289/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4179 - accuracy: 0.8690 - val_loss: 0.4326 - val_accuracy: 0.8611\n",
            "Epoch 290/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4173 - accuracy: 0.8810 - val_loss: 0.4318 - val_accuracy: 0.8611\n",
            "Epoch 291/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.4165 - accuracy: 0.8810 - val_loss: 0.4311 - val_accuracy: 0.8611\n",
            "Epoch 292/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4158 - accuracy: 0.8810 - val_loss: 0.4303 - val_accuracy: 0.8611\n",
            "Epoch 293/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4151 - accuracy: 0.8810 - val_loss: 0.4294 - val_accuracy: 0.8611\n",
            "Epoch 294/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4146 - accuracy: 0.8810 - val_loss: 0.4286 - val_accuracy: 0.8611\n",
            "Epoch 295/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4137 - accuracy: 0.8810 - val_loss: 0.4274 - val_accuracy: 0.8611\n",
            "Epoch 296/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4129 - accuracy: 0.8929 - val_loss: 0.4263 - val_accuracy: 0.8611\n",
            "Epoch 297/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4123 - accuracy: 0.8810 - val_loss: 0.4251 - val_accuracy: 0.8611\n",
            "Epoch 298/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4115 - accuracy: 0.8810 - val_loss: 0.4241 - val_accuracy: 0.8611\n",
            "Epoch 299/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4109 - accuracy: 0.8810 - val_loss: 0.4232 - val_accuracy: 0.8611\n",
            "Epoch 300/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4102 - accuracy: 0.8810 - val_loss: 0.4222 - val_accuracy: 0.8611\n",
            "Epoch 301/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4095 - accuracy: 0.8810 - val_loss: 0.4214 - val_accuracy: 0.8611\n",
            "Epoch 302/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4088 - accuracy: 0.8810 - val_loss: 0.4204 - val_accuracy: 0.8611\n",
            "Epoch 303/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4081 - accuracy: 0.8810 - val_loss: 0.4194 - val_accuracy: 0.8611\n",
            "Epoch 304/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4073 - accuracy: 0.8810 - val_loss: 0.4184 - val_accuracy: 0.8611\n",
            "Epoch 305/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4067 - accuracy: 0.8810 - val_loss: 0.4175 - val_accuracy: 0.8611\n",
            "Epoch 306/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.4064 - accuracy: 0.8810 - val_loss: 0.4162 - val_accuracy: 0.8611\n",
            "Epoch 307/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4053 - accuracy: 0.8690 - val_loss: 0.4153 - val_accuracy: 0.8611\n",
            "Epoch 308/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4046 - accuracy: 0.8810 - val_loss: 0.4144 - val_accuracy: 0.8611\n",
            "Epoch 309/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4041 - accuracy: 0.8810 - val_loss: 0.4136 - val_accuracy: 0.8611\n",
            "Epoch 310/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.4031 - accuracy: 0.8810 - val_loss: 0.4128 - val_accuracy: 0.8611\n",
            "Epoch 311/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.4025 - accuracy: 0.8810 - val_loss: 0.4121 - val_accuracy: 0.8611\n",
            "Epoch 312/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.4018 - accuracy: 0.8810 - val_loss: 0.4112 - val_accuracy: 0.8611\n",
            "Epoch 313/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4012 - accuracy: 0.8810 - val_loss: 0.4104 - val_accuracy: 0.8611\n",
            "Epoch 314/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4003 - accuracy: 0.8810 - val_loss: 0.4092 - val_accuracy: 0.8611\n",
            "Epoch 315/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.3996 - accuracy: 0.8810 - val_loss: 0.4082 - val_accuracy: 0.8611\n",
            "Epoch 316/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.3990 - accuracy: 0.8810 - val_loss: 0.4072 - val_accuracy: 0.8611\n",
            "Epoch 317/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.3982 - accuracy: 0.8810 - val_loss: 0.4062 - val_accuracy: 0.8611\n",
            "Epoch 318/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.3976 - accuracy: 0.8810 - val_loss: 0.4053 - val_accuracy: 0.8611\n",
            "Epoch 319/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.3970 - accuracy: 0.8810 - val_loss: 0.4043 - val_accuracy: 0.8611\n",
            "Epoch 320/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3962 - accuracy: 0.8810 - val_loss: 0.4037 - val_accuracy: 0.8611\n",
            "Epoch 321/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3956 - accuracy: 0.8810 - val_loss: 0.4030 - val_accuracy: 0.8611\n",
            "Epoch 322/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3947 - accuracy: 0.8810 - val_loss: 0.4020 - val_accuracy: 0.8611\n",
            "Epoch 323/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.3940 - accuracy: 0.8810 - val_loss: 0.4012 - val_accuracy: 0.8611\n",
            "Epoch 324/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.3935 - accuracy: 0.8810 - val_loss: 0.4001 - val_accuracy: 0.8611\n",
            "Epoch 325/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3927 - accuracy: 0.8810 - val_loss: 0.3992 - val_accuracy: 0.8611\n",
            "Epoch 326/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3918 - accuracy: 0.8810 - val_loss: 0.3986 - val_accuracy: 0.8611\n",
            "Epoch 327/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.3911 - accuracy: 0.8929 - val_loss: 0.3980 - val_accuracy: 0.8611\n",
            "Epoch 328/500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.3907 - accuracy: 0.8929 - val_loss: 0.3975 - val_accuracy: 0.8611\n",
            "Epoch 329/500\n",
            "3/3 [==============================] - 0s 44ms/step - loss: 0.3900 - accuracy: 0.8929 - val_loss: 0.3969 - val_accuracy: 0.8611\n",
            "Epoch 330/500\n",
            "3/3 [==============================] - 0s 42ms/step - loss: 0.3892 - accuracy: 0.8929 - val_loss: 0.3960 - val_accuracy: 0.8611\n",
            "Epoch 331/500\n",
            "3/3 [==============================] - 0s 42ms/step - loss: 0.3885 - accuracy: 0.8929 - val_loss: 0.3949 - val_accuracy: 0.8611\n",
            "Epoch 332/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.3877 - accuracy: 0.8929 - val_loss: 0.3938 - val_accuracy: 0.8611\n",
            "Epoch 333/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.3871 - accuracy: 0.8929 - val_loss: 0.3925 - val_accuracy: 0.8611\n",
            "Epoch 334/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3863 - accuracy: 0.8929 - val_loss: 0.3916 - val_accuracy: 0.8611\n",
            "Epoch 335/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.3857 - accuracy: 0.8929 - val_loss: 0.3903 - val_accuracy: 0.8611\n",
            "Epoch 336/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3851 - accuracy: 0.9048 - val_loss: 0.3892 - val_accuracy: 0.8611\n",
            "Epoch 337/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3840 - accuracy: 0.9167 - val_loss: 0.3884 - val_accuracy: 0.8611\n",
            "Epoch 338/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.3835 - accuracy: 0.9167 - val_loss: 0.3878 - val_accuracy: 0.8611\n",
            "Epoch 339/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.3828 - accuracy: 0.9048 - val_loss: 0.3869 - val_accuracy: 0.8611\n",
            "Epoch 340/500\n",
            "3/3 [==============================] - 0s 44ms/step - loss: 0.3819 - accuracy: 0.9048 - val_loss: 0.3860 - val_accuracy: 0.8611\n",
            "Epoch 341/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.3813 - accuracy: 0.9167 - val_loss: 0.3849 - val_accuracy: 0.8611\n",
            "Epoch 342/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3806 - accuracy: 0.9167 - val_loss: 0.3838 - val_accuracy: 0.8889\n",
            "Epoch 343/500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.3798 - accuracy: 0.9167 - val_loss: 0.3827 - val_accuracy: 0.8889\n",
            "Epoch 344/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3791 - accuracy: 0.9048 - val_loss: 0.3817 - val_accuracy: 0.8889\n",
            "Epoch 345/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3784 - accuracy: 0.9048 - val_loss: 0.3806 - val_accuracy: 0.8889\n",
            "Epoch 346/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3778 - accuracy: 0.9048 - val_loss: 0.3796 - val_accuracy: 0.8889\n",
            "Epoch 347/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.3771 - accuracy: 0.9167 - val_loss: 0.3788 - val_accuracy: 0.8889\n",
            "Epoch 348/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.3763 - accuracy: 0.9048 - val_loss: 0.3779 - val_accuracy: 0.8889\n",
            "Epoch 349/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.3756 - accuracy: 0.9048 - val_loss: 0.3770 - val_accuracy: 0.8889\n",
            "Epoch 350/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.3749 - accuracy: 0.9167 - val_loss: 0.3761 - val_accuracy: 0.8889\n",
            "Epoch 351/500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.3746 - accuracy: 0.9286 - val_loss: 0.3755 - val_accuracy: 0.8889\n",
            "Epoch 352/500\n",
            "3/3 [==============================] - 0s 50ms/step - loss: 0.3735 - accuracy: 0.9167 - val_loss: 0.3747 - val_accuracy: 0.8889\n",
            "Epoch 353/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.3727 - accuracy: 0.9167 - val_loss: 0.3737 - val_accuracy: 0.8889\n",
            "Epoch 354/500\n",
            "3/3 [==============================] - 0s 45ms/step - loss: 0.3720 - accuracy: 0.9167 - val_loss: 0.3727 - val_accuracy: 0.8889\n",
            "Epoch 355/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.3712 - accuracy: 0.9167 - val_loss: 0.3716 - val_accuracy: 0.8889\n",
            "Epoch 356/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.3711 - accuracy: 0.9167 - val_loss: 0.3702 - val_accuracy: 0.8611\n",
            "Epoch 357/500\n",
            "3/3 [==============================] - 0s 44ms/step - loss: 0.3700 - accuracy: 0.9286 - val_loss: 0.3691 - val_accuracy: 0.8611\n",
            "Epoch 358/500\n",
            "3/3 [==============================] - 0s 45ms/step - loss: 0.3693 - accuracy: 0.9286 - val_loss: 0.3682 - val_accuracy: 0.8611\n",
            "Epoch 359/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.3686 - accuracy: 0.9286 - val_loss: 0.3672 - val_accuracy: 0.8611\n",
            "Epoch 360/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3680 - accuracy: 0.9286 - val_loss: 0.3663 - val_accuracy: 0.8611\n",
            "Epoch 361/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.3670 - accuracy: 0.9286 - val_loss: 0.3654 - val_accuracy: 0.8611\n",
            "Epoch 362/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.3667 - accuracy: 0.9286 - val_loss: 0.3648 - val_accuracy: 0.8889\n",
            "Epoch 363/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.3655 - accuracy: 0.9286 - val_loss: 0.3638 - val_accuracy: 0.8889\n",
            "Epoch 364/500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.3649 - accuracy: 0.9286 - val_loss: 0.3630 - val_accuracy: 0.8889\n",
            "Epoch 365/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.3639 - accuracy: 0.9405 - val_loss: 0.3623 - val_accuracy: 0.9167\n",
            "Epoch 366/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3637 - accuracy: 0.9286 - val_loss: 0.3616 - val_accuracy: 0.9167\n",
            "Epoch 367/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3625 - accuracy: 0.9286 - val_loss: 0.3607 - val_accuracy: 0.9167\n",
            "Epoch 368/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.3618 - accuracy: 0.9286 - val_loss: 0.3597 - val_accuracy: 0.9167\n",
            "Epoch 369/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3612 - accuracy: 0.9405 - val_loss: 0.3585 - val_accuracy: 0.9167\n",
            "Epoch 370/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3604 - accuracy: 0.9405 - val_loss: 0.3576 - val_accuracy: 0.9167\n",
            "Epoch 371/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3596 - accuracy: 0.9405 - val_loss: 0.3567 - val_accuracy: 0.9167\n",
            "Epoch 372/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3590 - accuracy: 0.9405 - val_loss: 0.3557 - val_accuracy: 0.9167\n",
            "Epoch 373/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3588 - accuracy: 0.9405 - val_loss: 0.3550 - val_accuracy: 0.9167\n",
            "Epoch 374/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3574 - accuracy: 0.9405 - val_loss: 0.3540 - val_accuracy: 0.9167\n",
            "Epoch 375/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3569 - accuracy: 0.9405 - val_loss: 0.3528 - val_accuracy: 0.9167\n",
            "Epoch 376/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3563 - accuracy: 0.9405 - val_loss: 0.3519 - val_accuracy: 0.9167\n",
            "Epoch 377/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.3554 - accuracy: 0.9405 - val_loss: 0.3509 - val_accuracy: 0.9167\n",
            "Epoch 378/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3546 - accuracy: 0.9405 - val_loss: 0.3501 - val_accuracy: 0.9167\n",
            "Epoch 379/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.3540 - accuracy: 0.9405 - val_loss: 0.3493 - val_accuracy: 0.9167\n",
            "Epoch 380/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3531 - accuracy: 0.9405 - val_loss: 0.3483 - val_accuracy: 0.9167\n",
            "Epoch 381/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3526 - accuracy: 0.9405 - val_loss: 0.3474 - val_accuracy: 0.9167\n",
            "Epoch 382/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3517 - accuracy: 0.9405 - val_loss: 0.3467 - val_accuracy: 0.9167\n",
            "Epoch 383/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3509 - accuracy: 0.9405 - val_loss: 0.3460 - val_accuracy: 0.9167\n",
            "Epoch 384/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3502 - accuracy: 0.9405 - val_loss: 0.3452 - val_accuracy: 0.9167\n",
            "Epoch 385/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.3498 - accuracy: 0.9405 - val_loss: 0.3445 - val_accuracy: 0.9167\n",
            "Epoch 386/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3488 - accuracy: 0.9405 - val_loss: 0.3434 - val_accuracy: 0.9167\n",
            "Epoch 387/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3480 - accuracy: 0.9405 - val_loss: 0.3424 - val_accuracy: 0.9167\n",
            "Epoch 388/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3473 - accuracy: 0.9405 - val_loss: 0.3414 - val_accuracy: 0.9167\n",
            "Epoch 389/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.3466 - accuracy: 0.9405 - val_loss: 0.3403 - val_accuracy: 0.9167\n",
            "Epoch 390/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3458 - accuracy: 0.9405 - val_loss: 0.3393 - val_accuracy: 0.9167\n",
            "Epoch 391/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.3450 - accuracy: 0.9405 - val_loss: 0.3382 - val_accuracy: 0.9167\n",
            "Epoch 392/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3445 - accuracy: 0.9405 - val_loss: 0.3371 - val_accuracy: 0.9167\n",
            "Epoch 393/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3436 - accuracy: 0.9405 - val_loss: 0.3362 - val_accuracy: 0.9167\n",
            "Epoch 394/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.3429 - accuracy: 0.9405 - val_loss: 0.3354 - val_accuracy: 0.9167\n",
            "Epoch 395/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3422 - accuracy: 0.9405 - val_loss: 0.3346 - val_accuracy: 0.9167\n",
            "Epoch 396/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3414 - accuracy: 0.9405 - val_loss: 0.3336 - val_accuracy: 0.9167\n",
            "Epoch 397/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3408 - accuracy: 0.9405 - val_loss: 0.3324 - val_accuracy: 0.9167\n",
            "Epoch 398/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.3401 - accuracy: 0.9405 - val_loss: 0.3314 - val_accuracy: 0.9444\n",
            "Epoch 399/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3393 - accuracy: 0.9405 - val_loss: 0.3305 - val_accuracy: 0.9444\n",
            "Epoch 400/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.3384 - accuracy: 0.9405 - val_loss: 0.3295 - val_accuracy: 0.9444\n",
            "Epoch 401/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.3377 - accuracy: 0.9405 - val_loss: 0.3286 - val_accuracy: 0.9444\n",
            "Epoch 402/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3370 - accuracy: 0.9405 - val_loss: 0.3277 - val_accuracy: 0.9444\n",
            "Epoch 403/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3362 - accuracy: 0.9405 - val_loss: 0.3268 - val_accuracy: 0.9444\n",
            "Epoch 404/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3356 - accuracy: 0.9524 - val_loss: 0.3258 - val_accuracy: 0.9444\n",
            "Epoch 405/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3348 - accuracy: 0.9524 - val_loss: 0.3248 - val_accuracy: 0.9444\n",
            "Epoch 406/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3341 - accuracy: 0.9524 - val_loss: 0.3239 - val_accuracy: 0.9444\n",
            "Epoch 407/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3337 - accuracy: 0.9524 - val_loss: 0.3231 - val_accuracy: 0.9444\n",
            "Epoch 408/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3326 - accuracy: 0.9524 - val_loss: 0.3222 - val_accuracy: 0.9444\n",
            "Epoch 409/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3319 - accuracy: 0.9524 - val_loss: 0.3213 - val_accuracy: 0.9444\n",
            "Epoch 410/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3311 - accuracy: 0.9524 - val_loss: 0.3202 - val_accuracy: 0.9444\n",
            "Epoch 411/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3304 - accuracy: 0.9524 - val_loss: 0.3193 - val_accuracy: 0.9722\n",
            "Epoch 412/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.3298 - accuracy: 0.9524 - val_loss: 0.3183 - val_accuracy: 0.9444\n",
            "Epoch 413/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3289 - accuracy: 0.9524 - val_loss: 0.3174 - val_accuracy: 0.9722\n",
            "Epoch 414/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3282 - accuracy: 0.9524 - val_loss: 0.3165 - val_accuracy: 0.9722\n",
            "Epoch 415/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3275 - accuracy: 0.9524 - val_loss: 0.3156 - val_accuracy: 0.9722\n",
            "Epoch 416/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3266 - accuracy: 0.9524 - val_loss: 0.3148 - val_accuracy: 0.9722\n",
            "Epoch 417/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3259 - accuracy: 0.9524 - val_loss: 0.3140 - val_accuracy: 0.9444\n",
            "Epoch 418/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3252 - accuracy: 0.9524 - val_loss: 0.3134 - val_accuracy: 0.9444\n",
            "Epoch 419/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3244 - accuracy: 0.9405 - val_loss: 0.3127 - val_accuracy: 0.9444\n",
            "Epoch 420/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3239 - accuracy: 0.9405 - val_loss: 0.3120 - val_accuracy: 0.9444\n",
            "Epoch 421/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3231 - accuracy: 0.9405 - val_loss: 0.3111 - val_accuracy: 0.9444\n",
            "Epoch 422/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3224 - accuracy: 0.9405 - val_loss: 0.3101 - val_accuracy: 0.9444\n",
            "Epoch 423/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3217 - accuracy: 0.9405 - val_loss: 0.3092 - val_accuracy: 0.9444\n",
            "Epoch 424/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3209 - accuracy: 0.9405 - val_loss: 0.3081 - val_accuracy: 0.9444\n",
            "Epoch 425/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.3201 - accuracy: 0.9405 - val_loss: 0.3072 - val_accuracy: 0.9444\n",
            "Epoch 426/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.3199 - accuracy: 0.9405 - val_loss: 0.3059 - val_accuracy: 0.9722\n",
            "Epoch 427/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3186 - accuracy: 0.9524 - val_loss: 0.3049 - val_accuracy: 0.9722\n",
            "Epoch 428/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.3178 - accuracy: 0.9524 - val_loss: 0.3040 - val_accuracy: 0.9722\n",
            "Epoch 429/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3172 - accuracy: 0.9524 - val_loss: 0.3031 - val_accuracy: 0.9722\n",
            "Epoch 430/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.3163 - accuracy: 0.9524 - val_loss: 0.3020 - val_accuracy: 0.9722\n",
            "Epoch 431/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3157 - accuracy: 0.9643 - val_loss: 0.3009 - val_accuracy: 0.9722\n",
            "Epoch 432/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.3150 - accuracy: 0.9643 - val_loss: 0.3000 - val_accuracy: 0.9722\n",
            "Epoch 433/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.3143 - accuracy: 0.9643 - val_loss: 0.2990 - val_accuracy: 0.9722\n",
            "Epoch 434/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3136 - accuracy: 0.9643 - val_loss: 0.2982 - val_accuracy: 0.9722\n",
            "Epoch 435/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.3129 - accuracy: 0.9643 - val_loss: 0.2974 - val_accuracy: 0.9722\n",
            "Epoch 436/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3120 - accuracy: 0.9643 - val_loss: 0.2964 - val_accuracy: 0.9722\n",
            "Epoch 437/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.3114 - accuracy: 0.9643 - val_loss: 0.2955 - val_accuracy: 0.9722\n",
            "Epoch 438/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3105 - accuracy: 0.9643 - val_loss: 0.2945 - val_accuracy: 0.9722\n",
            "Epoch 439/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3100 - accuracy: 0.9643 - val_loss: 0.2935 - val_accuracy: 0.9722\n",
            "Epoch 440/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3091 - accuracy: 0.9643 - val_loss: 0.2926 - val_accuracy: 0.9722\n",
            "Epoch 441/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.3084 - accuracy: 0.9643 - val_loss: 0.2917 - val_accuracy: 0.9722\n",
            "Epoch 442/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3076 - accuracy: 0.9643 - val_loss: 0.2909 - val_accuracy: 0.9722\n",
            "Epoch 443/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3069 - accuracy: 0.9643 - val_loss: 0.2901 - val_accuracy: 0.9722\n",
            "Epoch 444/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.3063 - accuracy: 0.9643 - val_loss: 0.2893 - val_accuracy: 0.9722\n",
            "Epoch 445/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3057 - accuracy: 0.9643 - val_loss: 0.2882 - val_accuracy: 0.9722\n",
            "Epoch 446/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3048 - accuracy: 0.9643 - val_loss: 0.2874 - val_accuracy: 0.9722\n",
            "Epoch 447/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.3040 - accuracy: 0.9643 - val_loss: 0.2865 - val_accuracy: 0.9722\n",
            "Epoch 448/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.3033 - accuracy: 0.9643 - val_loss: 0.2854 - val_accuracy: 0.9722\n",
            "Epoch 449/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.3025 - accuracy: 0.9643 - val_loss: 0.2845 - val_accuracy: 0.9722\n",
            "Epoch 450/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.3018 - accuracy: 0.9643 - val_loss: 0.2835 - val_accuracy: 0.9722\n",
            "Epoch 451/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3011 - accuracy: 0.9643 - val_loss: 0.2827 - val_accuracy: 0.9722\n",
            "Epoch 452/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.3004 - accuracy: 0.9643 - val_loss: 0.2818 - val_accuracy: 0.9722\n",
            "Epoch 453/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2996 - accuracy: 0.9643 - val_loss: 0.2809 - val_accuracy: 0.9722\n",
            "Epoch 454/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.2990 - accuracy: 0.9643 - val_loss: 0.2799 - val_accuracy: 0.9722\n",
            "Epoch 455/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.2981 - accuracy: 0.9643 - val_loss: 0.2791 - val_accuracy: 0.9722\n",
            "Epoch 456/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2974 - accuracy: 0.9643 - val_loss: 0.2783 - val_accuracy: 0.9722\n",
            "Epoch 457/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.2966 - accuracy: 0.9643 - val_loss: 0.2775 - val_accuracy: 0.9722\n",
            "Epoch 458/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.2959 - accuracy: 0.9643 - val_loss: 0.2767 - val_accuracy: 0.9722\n",
            "Epoch 459/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.2952 - accuracy: 0.9643 - val_loss: 0.2759 - val_accuracy: 0.9722\n",
            "Epoch 460/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.2945 - accuracy: 0.9643 - val_loss: 0.2749 - val_accuracy: 0.9722\n",
            "Epoch 461/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.2938 - accuracy: 0.9643 - val_loss: 0.2739 - val_accuracy: 0.9722\n",
            "Epoch 462/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.2932 - accuracy: 0.9643 - val_loss: 0.2731 - val_accuracy: 0.9722\n",
            "Epoch 463/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.2923 - accuracy: 0.9643 - val_loss: 0.2720 - val_accuracy: 0.9722\n",
            "Epoch 464/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.2916 - accuracy: 0.9643 - val_loss: 0.2710 - val_accuracy: 0.9722\n",
            "Epoch 465/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.2910 - accuracy: 0.9643 - val_loss: 0.2701 - val_accuracy: 0.9722\n",
            "Epoch 466/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2901 - accuracy: 0.9643 - val_loss: 0.2693 - val_accuracy: 0.9722\n",
            "Epoch 467/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.2895 - accuracy: 0.9643 - val_loss: 0.2685 - val_accuracy: 0.9722\n",
            "Epoch 468/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2887 - accuracy: 0.9643 - val_loss: 0.2676 - val_accuracy: 0.9722\n",
            "Epoch 469/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2880 - accuracy: 0.9643 - val_loss: 0.2667 - val_accuracy: 0.9722\n",
            "Epoch 470/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.2873 - accuracy: 0.9643 - val_loss: 0.2658 - val_accuracy: 0.9722\n",
            "Epoch 471/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2866 - accuracy: 0.9643 - val_loss: 0.2649 - val_accuracy: 0.9722\n",
            "Epoch 472/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2858 - accuracy: 0.9643 - val_loss: 0.2641 - val_accuracy: 0.9722\n",
            "Epoch 473/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2852 - accuracy: 0.9643 - val_loss: 0.2632 - val_accuracy: 0.9722\n",
            "Epoch 474/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2847 - accuracy: 0.9643 - val_loss: 0.2622 - val_accuracy: 0.9722\n",
            "Epoch 475/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2839 - accuracy: 0.9643 - val_loss: 0.2615 - val_accuracy: 0.9722\n",
            "Epoch 476/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2830 - accuracy: 0.9643 - val_loss: 0.2606 - val_accuracy: 0.9722\n",
            "Epoch 477/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2825 - accuracy: 0.9643 - val_loss: 0.2596 - val_accuracy: 0.9722\n",
            "Epoch 478/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2816 - accuracy: 0.9643 - val_loss: 0.2588 - val_accuracy: 0.9722\n",
            "Epoch 479/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2808 - accuracy: 0.9643 - val_loss: 0.2579 - val_accuracy: 0.9722\n",
            "Epoch 480/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.2801 - accuracy: 0.9643 - val_loss: 0.2570 - val_accuracy: 0.9722\n",
            "Epoch 481/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2795 - accuracy: 0.9643 - val_loss: 0.2561 - val_accuracy: 0.9722\n",
            "Epoch 482/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.2789 - accuracy: 0.9643 - val_loss: 0.2552 - val_accuracy: 0.9722\n",
            "Epoch 483/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2780 - accuracy: 0.9643 - val_loss: 0.2544 - val_accuracy: 0.9722\n",
            "Epoch 484/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.2776 - accuracy: 0.9643 - val_loss: 0.2536 - val_accuracy: 0.9722\n",
            "Epoch 485/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.2767 - accuracy: 0.9643 - val_loss: 0.2527 - val_accuracy: 0.9722\n",
            "Epoch 486/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.2760 - accuracy: 0.9643 - val_loss: 0.2519 - val_accuracy: 0.9722\n",
            "Epoch 487/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.2753 - accuracy: 0.9643 - val_loss: 0.2511 - val_accuracy: 0.9722\n",
            "Epoch 488/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.2745 - accuracy: 0.9643 - val_loss: 0.2504 - val_accuracy: 0.9722\n",
            "Epoch 489/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.2746 - accuracy: 0.9643 - val_loss: 0.2498 - val_accuracy: 0.9722\n",
            "Epoch 490/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.2734 - accuracy: 0.9643 - val_loss: 0.2488 - val_accuracy: 0.9722\n",
            "Epoch 491/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2727 - accuracy: 0.9643 - val_loss: 0.2479 - val_accuracy: 0.9722\n",
            "Epoch 492/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.2718 - accuracy: 0.9643 - val_loss: 0.2470 - val_accuracy: 0.9722\n",
            "Epoch 493/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.2711 - accuracy: 0.9643 - val_loss: 0.2461 - val_accuracy: 0.9722\n",
            "Epoch 494/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2704 - accuracy: 0.9643 - val_loss: 0.2452 - val_accuracy: 0.9722\n",
            "Epoch 495/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.2697 - accuracy: 0.9643 - val_loss: 0.2443 - val_accuracy: 0.9722\n",
            "Epoch 496/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2691 - accuracy: 0.9643 - val_loss: 0.2435 - val_accuracy: 0.9722\n",
            "Epoch 497/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2684 - accuracy: 0.9643 - val_loss: 0.2426 - val_accuracy: 0.9722\n",
            "Epoch 498/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2688 - accuracy: 0.9643 - val_loss: 0.2418 - val_accuracy: 0.9444\n",
            "Epoch 499/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2672 - accuracy: 0.9643 - val_loss: 0.2410 - val_accuracy: 0.9722\n",
            "Epoch 500/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.2664 - accuracy: 0.9643 - val_loss: 0.2402 - val_accuracy: 0.9722\n",
            "CPU times: user 29.5 s, sys: 1.14 s, total: 30.6 s\n",
            "Wall time: 42.7 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff3cfb35de0>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmsuPaqYpl-2"
      },
      "source": [
        "# Bewertung"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCCbIyrjpl-3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "044dc6d7-10d2-4196-dfd2-2a20b3ce1388"
      },
      "source": [
        "model.predict(np.array([[ 5.1,  3.5,  1.4,  0.2]]))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 25ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.88026619e-01, 1.19650345e-02, 8.30476074e-06]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQaEAtyVpl-4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db1a4c62-1558-49ac-ff30-6afa58d53650"
      },
      "source": [
        "X[0], y[0]"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([5.1, 3.5, 1.4, 0.2]), array([1., 0., 0.], dtype=float32))"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrzGbtchpl-7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dda561c7-b3dd-4567-ba07-a2c1a9d7b583"
      },
      "source": [
        "train_loss, train_accuracy = model.evaluate(X_train, y_train)\n",
        "train_loss, train_accuracy"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2582 - accuracy: 0.9667\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.2582014799118042, 0.9666666388511658)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnXEggalpl-8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb09f990-7d94-4f69-eb3e-ff0146682761"
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
        "test_loss, test_accuracy"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 28ms/step - loss: 0.2390 - accuracy: 0.9333\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.23902304470539093, 0.9333333373069763)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    }
  ]
}