ECG_refact.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwDZRUv3Nnac",
        "outputId": "960a842b-686b-446a-882e-8f3512800de8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.18.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 20.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (21.3)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow_addons) (3.0.9)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.18.0\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow_addons\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "from sklearn.model_selection import train_test_split\n",
        "import sklearn\n",
        "import itertools\n",
        "from tensorflow_addons.optimizers import CyclicalLearningRate\n",
        "import matplotlib as mpl"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mitbih_test = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/9063/mitbih_test.csv\", header=None)\n",
        "mitbih_train = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/9063/mitbih_train.csv\", header=None)\n",
        "\n",
        "mitbih_train[187]=mitbih_train[187].astype(int)\n",
        "distributation=mitbih_train[187].value_counts()\n",
        "print(distributation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R2RhpixFN-eX",
        "outputId": "922c0608-5c80-4a4d-858e-6fbce0c6672c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    72471\n",
            "4     6431\n",
            "2     5788\n",
            "1     2223\n",
            "3      641\n",
            "Name: 187, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mitbih_train_labels = mitbih_train.iloc[:,-1].replace({0: 'N', 1: 'S', 2: 'V', 3: 'F', 4: 'Q'})\n",
        "plt.hist(mitbih_train_labels)\n",
        "plt.title(\"Distribution of MIT-BIH dataset\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "nHSDlzh9OlW7",
        "outputId": "a86a65bb-346d-41c6-ca80-651340431bab"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of MIT-BIH dataset')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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6rvl/Aj5eVS8C7gWOa/XjgHtb/eOtHUn2BRYCLwbmA59uATMN+BSwANgXeGNrK0maIH2FQZJZwOuAz7T5AK8GLmhNlgFHt+mj2jxt+WGt/VHAeVX1m6q6CRgADm6Pgaq6saoeA85rbSVJE6TfI4N/Ad4N/K7NPx+4r6o2tPm1wMw2PRO4FaAtv7+1f6I+bJ2R6pKkCTJqGCQ5Erirqq6cgPGMNpYlSdYkWTM4ODjZw5Gk3xv9HBm8DPizJDfTOYXzauATwM5Jprc2s4B1bXodsCdAW74TcE93fdg6I9WfpqrOqqq5VTV3xowZfQxdktSPUcOgqt5bVbOqajadC8Dfqao3AZcBb2jNFgMXtekVbZ62/DtVVa2+sN1ttDcwB7gCWA3MaXcnbde2sWJcXp0kqS/TR28yovcA5yX5CPBj4OxWPxs4N8kAsJ7OlztVdW2S84HrgA3ACVX1OECSE4GVwDRgaVVduxnjkiSN0ZjCoKq+C3y3Td9I506g4W1+DfzFCOufBpzWo34xcPFYxiJJGj/+BrIkyTCQJBkGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRJ9hEGSZye5IslPklyb5MOtvneSHyYZSPKlJNu1+rPa/EBbPrurr/e2+s+THN5Vn99qA0lOGv+XKUnamH6ODH4DvLqq9gcOAOYnORT4J+DjVfUi4F7guNb+OODeVv94a0eSfYGFwIuB+cCnk0xLMg34FLAA2Bd4Y2srSZogo4ZBdTzUZp/ZHgW8Grig1ZcBR7fpo9o8bflhSdLq51XVb6rqJmAAOLg9Bqrqxqp6DDivtZUkTZC+rhm0PfirgLuAVcAvgfuqakNrshaY2aZnArcCtOX3A8/vrg9bZ6R6r3EsSbImyZrBwcF+hi5J6kNfYVBVj1fVAcAsOnvyf7RFRzXyOM6qqrlVNXfGjBmTMQRJ+r00pruJquo+4DLgpcDOSaa3RbOAdW16HbAnQFu+E3BPd33YOiPVJUkTpJ+7iWYk2blNbw+8FrieTii8oTVbDFzUple0edry71RVtfrCdrfR3sAc4ApgNTCn3Z20HZ2LzCvG48VJkvozffQm7AEsa3f9PAM4v6q+keQ64LwkHwF+DJzd2p8NnJtkAFhP58udqro2yfnAdcAG4ISqehwgyYnASmAasLSqrh23VyhJGtWoYVBVVwMv6VG/kc71g+H1XwN/MUJfpwGn9ahfDFzcx3glSVuAv4EsSTIMJEmGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJIk+wiDJnkkuS3JdkmuT/F2r75pkVZIb2vMurZ4kZyQZSHJ1kgO7+lrc2t+QZHFX/aAk17R1zkiSLfFiJUm99XNksAF4Z1XtCxwKnJBkX+Ak4NKqmgNc2uYBFgBz2mMJcCZ0wgM4GTgEOBg4eShAWpu3dq03f/NfmiSpX6OGQVXdXlU/atMPAtcDM4GjgGWt2TLg6DZ9FLC8Oi4Hdk6yB3A4sKqq1lfVvcAqYH5btmNVXV5VBSzv6kuSNAHGdM0gyWzgJcAPgd2r6va26A5g9zY9E7i1a7W1rbax+toedUnSBOk7DJLsAHwFeEdVPdC9rO3R1ziPrdcYliRZk2TN4ODglt6cJE0ZfYVBkmfSCYLPV9VXW/nOdoqH9nxXq68D9uxafVarbaw+q0f9aarqrKqaW1VzZ8yY0c/QJUl96OduogBnA9dX1ce6Fq0Ahu4IWgxc1FVf1O4qOhS4v51OWgnMS7JLu3A8D1jZlj2Q5NC2rUVdfUmSJsD0Ptq8DHgzcE2Sq1rtfcDpwPlJjgNuAY5pyy4GjgAGgEeAYwGqan2SU4HVrd0pVbW+TR8PnANsD1zSHpKkCTJqGFTV94GR7vs/rEf7Ak4Yoa+lwNIe9TXAfqONRZK0ZfgbyJIkw0CSZBhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSfQRBkmWJrkryU+7arsmWZXkhva8S6snyRlJBpJcneTArnUWt/Y3JFncVT8oyTVtnTOSZLxfpCRp4/o5MjgHmD+sdhJwaVXNAS5t8wALgDntsQQ4EzrhAZwMHAIcDJw8FCCtzVu71hu+LUnSFjZqGFTVvwHrh5WPApa16WXA0V315dVxObBzkj2Aw4FVVbW+qu4FVgHz27Idq+ryqipgeVdfkqQJsqnXDHavqtvb9B3A7m16JnBrV7u1rbax+toe9Z6SLEmyJsmawcHBTRy6JGm4zb6A3PboaxzG0s+2zqqquVU1d8aMGROxSUmaEjY1DO5sp3hoz3e1+jpgz652s1ptY/VZPeqSpAm0qWGwAhi6I2gxcFFXfVG7q+hQ4P52OmklMC/JLu3C8TxgZVv2QJJD211Ei7r6kiRNkOmjNUjyReBVwG5J1tK5K+h04PwkxwG3AMe05hcDRwADwCPAsQBVtT7JqcDq1u6Uqhq6KH08nTuWtgcuaQ9J0gQaNQyq6o0jLDqsR9sCThihn6XA0h71NcB+o41DkrTl+BvIkiTDQJJkGEiSMAwkSfRxAVmS9HSzT/rmpGz35tNft0X69chAkuSRgTTeft/2GDU1eGQgSTIMJEmGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiT8DeQpw9+KlbQxHhlIkgwDSZJhIEliKwqDJPOT/DzJQJKTJns8kjSVbBUXkJNMAz4FvBZYC6xOsqKqrpvckUnqhzcobPu2liODg4GBqrqxqh4DzgOOmuQxSdKUkaqa7DGQ5A3A/Kr66zb/ZuCQqjpxWLslwJI2uw/w803c5G7A3Zu47lTk+zU2vl9j4/s1Npvzfu1VVTN6LdgqThP1q6rOAs7a3H6SrKmqueMwpCnB92tsfL/GxvdrbLbU+7W1nCZaB+zZNT+r1SRJE2BrCYPVwJwkeyfZDlgIrJjkMUnSlLFVnCaqqg1JTgRWAtOApVV17Rbc5GafappifL/GxvdrbHy/xmaLvF9bxQVkSdLk2lpOE0mSJpFhIEmaOmGQpJJ8tGv+XUk+NIlD2uoleX+Sa5NcneSqJIdM9pi2VkkuS3L4sNo7kpw5WWPa2iV5vP1cDT1mT/aYtmZJZiW5KMkNSW5M8skkzxqv/qdMGAC/Af5Lkt0meyDbgiQvBY4EDqyq/wC8Brh1cke1Vfsinbvgui1sdfX2aFUd0PW4ebIHtLVKEuCrwNeqag4wB9ge+Ofx2sZUCoMNdK7C//1kD2QbsQdwd1X9BqCq7q6q2yZ5TFuzC4DXtVujaXu5LwC+N4lj0u+PVwO/rqrPAlTV43S+yxYl2WE8NjCVwgA6fwzvTUl2muyBbAO+DeyZ5BdJPp3klZM9oK1ZVa0HrgAWtNJC4Pzydr2N2b7rFNGFkz2YrdyLgSu7C1X1AHAz8KLx2MCUCoP25i0H3j7ZY9naVdVDwEF0/hbUIPClJG+Z1EFt/bpPFXmKaHTdp4leP9mDmeqmVBg0/wIcBzx3sgeytauqx6vqu1V1MnAi8OeTPaat3EXAYUkOBJ5TVVeOtoLUp+vo7Jw9IcmOwB+w6X+w8ymmXBi0w/nz6QSCRpBknyRzukoHALdM1ni2Be1o6jJgKR4VaHxdCjwnySJ44v+A+Sjwyap6dDw2MOXCoPkonT8Dq5HtACxLcl2Sq4F9gQ9N7pC2CV8E9scw0Dhq155eD7whyQ3APcDvquq08dqGf45CkrYxSf4jnR2O11fVj8alT8NAkjRVTxNJkroYBpIkw0CSZBhIkjAMJEkYBpIk4P8DTBKBOLToUgQAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(mitbih_train.iloc[0,:])\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('signal')\n",
        "plt.title(\"MIT-BIH Dataset example\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "ish3xbp0OpL3",
        "outputId": "64499011-4b63-4add-f3f3-5bafa828179f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'MIT-BIH Dataset example')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ptbdb_abnormal = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/9063/ptbdb_abnormal.csv\", header=None)\n",
        "ptbdb_normal = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/9063/ptbdb_normal.csv\", header=None)\n",
        "ptbdb = pd.concat([ptbdb_abnormal, ptbdb_normal], axis=0, ignore_index=True)\n",
        "\n",
        "ptbdb[187]=ptbdb[187].astype(int)\n",
        "distributation=ptbdb[187].value_counts()\n",
        "print(distributation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5iKeMHFOsvn",
        "outputId": "621e4342-706e-42db-9247-bc92fd44c4f6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    10506\n",
            "0     4046\n",
            "Name: 187, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ptbdb_labels = ptbdb.iloc[:,-1].replace({0:'Normal',1:'Abnormal'})\n",
        "plt.hist(ptbdb_labels)\n",
        "plt.title(\"Distribution of the PTB Database\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "0_cmay2aPVFK",
        "outputId": "11427e98-6497-40fe-88fe-6c86cef072f8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of the PTB Database')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(ptbdb.iloc[1,:-1])\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('signal')\n",
        "plt.title(\"PTB ECG Database example\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "lbSrqPRfPZFu",
        "outputId": "fac25c7e-7126-4536-f4d6-64b50fbb5afc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'PTB ECG Database example')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Copied from https://github.com/avanwyk/tensorflow-projects/blob/master/lr-finder/lr_finder.py\n",
        "# Apache License 2.0\n",
        "\n",
        "from tensorflow.keras.callbacks import Callback\n",
        "\n",
        "class LRFinder(Callback):\n",
        "    \"\"\"`Callback` that exponentially adjusts the learning rate after each training batch between `start_lr` and\n",
        "    `end_lr` for a maximum number of batches: `max_step`. The loss and learning rate are recorded at each step allowing\n",
        "    visually finding a good learning rate as per https://sgugger.github.io/how-do-you-find-a-good-learning-rate.html via\n",
        "    the `plot` method.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, start_lr: float = 1e-7, end_lr: float = 10, max_steps: int = 100, smoothing=0.9):\n",
        "        super(LRFinder, self).__init__()\n",
        "        self.start_lr, self.end_lr = start_lr, end_lr\n",
        "        self.max_steps = max_steps\n",
        "        self.smoothing = smoothing\n",
        "        self.step, self.best_loss, self.avg_loss, self.lr = 0, 0, 0, 0\n",
        "        self.lrs, self.losses = [], []\n",
        "\n",
        "    def on_train_begin(self, logs=None):\n",
        "        self.step, self.best_loss, self.avg_loss, self.lr = 0, 0, 0, 0\n",
        "        self.lrs, self.losses = [], []\n",
        "\n",
        "    def on_train_batch_begin(self, batch, logs=None):\n",
        "        self.lr = self.exp_annealing(self.step)\n",
        "        tf.keras.backend.set_value(self.model.optimizer.lr, self.lr)\n",
        "\n",
        "    def on_train_batch_end(self, batch, logs=None):\n",
        "        logs = logs or {}\n",
        "        loss = logs.get('loss')\n",
        "        step = self.step\n",
        "        if loss:\n",
        "            self.avg_loss = self.smoothing * self.avg_loss + (1 - self.smoothing) * loss\n",
        "            smooth_loss = self.avg_loss / (1 - self.smoothing ** (self.step + 1))\n",
        "            self.losses.append(smooth_loss)\n",
        "            self.lrs.append(self.lr)\n",
        "\n",
        "            if step == 0 or loss < self.best_loss:\n",
        "                self.best_loss = loss\n",
        "\n",
        "            if smooth_loss > 4 * self.best_loss or tf.math.is_nan(smooth_loss):\n",
        "                self.model.stop_training = True\n",
        "\n",
        "        if step == self.max_steps:\n",
        "            self.model.stop_training = True\n",
        "\n",
        "        self.step += 1\n",
        "\n",
        "    def exp_annealing(self, step):\n",
        "        return self.start_lr * (self.end_lr / self.start_lr) ** (step * 1. / self.max_steps)\n",
        "\n",
        "    def plot(self):\n",
        "        fig, ax = plt.subplots(1, 1)\n",
        "        ax.set_ylabel('Loss')\n",
        "        ax.set_xlabel('Learning Rate (log scale)')\n",
        "        ax.set_xscale('log')\n",
        "        ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0e'))\n",
        "        ax.plot(self.lrs, self.losses)"
      ],
      "metadata": {
        "id": "eBQejYvlPbAD"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def pretty_plot(history, field):\n",
        "  def plot(data, val_data, best_index, best_value, title):\n",
        "    plt.plot(range(1, len(data)+1), data, label='train')\n",
        "    plt.plot(range(1, len(data)+1), val_data, label='validation')\n",
        "    if not best_index is None:\n",
        "      plt.axvline(x=best_index+1, linestyle=':', c=\"#777777\")\n",
        "    if not best_value is None:\n",
        "      plt.axhline(y=best_value, linestyle=':', c=\"#777777\")\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel(field)\n",
        "    plt.xticks(range(0, len(data), 20))\n",
        "    plt.title(title)\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "  data = history.history[field]\n",
        "  val_data = history.history['val_' + field]\n",
        "  tail = int(0.15 * len(data))\n",
        "  g = lambda x: np.argmin(x)\n",
        "  best_index = g(val_data)\n",
        "  best_value = val_data[best_index]\n",
        "\n",
        "  plot(data, val_data, best_index, best_value, \"{} over epochs (best {:06.4f})\".format(field, best_value))\n",
        "  plot(data[-tail:], val_data[-tail:], None, best_value, \"{} over last {} epochs\".format(field, tail))"
      ],
      "metadata": {
        "id": "D6TI2oybPelE"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "HSaNDmFFPhNi"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_resnet_model(categories=2):\n",
        "  def residual_block(X, kernels, stride):\n",
        "    out = keras.layers.Conv1D(kernels, stride, padding='same')(X)\n",
        "    out = keras.layers.BatchNormalization()(out)\n",
        "    out = keras.layers.ReLU()(out)\n",
        "    out = keras.layers.Conv1D(kernels, stride, padding='same')(out)\n",
        "    out = keras.layers.BatchNormalization()(out)\n",
        "    out = keras.layers.add([X, out])\n",
        "    out = keras.layers.ReLU()(out)\n",
        "    return out\n",
        "  \n",
        "  kernels = 32\n",
        "  stride = 5\n",
        "\n",
        "  inputs = keras.layers.Input([187,1])\n",
        "  X = keras.layers.Conv1D(kernels, stride)(inputs)\n",
        "  X = keras.layers.BatchNormalization()(X)\n",
        "  X = keras.layers.ReLU()(X)\n",
        "  X = keras.layers.MaxPool1D(5, 2)(X)\n",
        "    \n",
        "  X = residual_block(X, kernels, stride)\n",
        "  X = residual_block(X, kernels, stride)\n",
        "  X = residual_block(X, kernels, stride)\n",
        "  X = residual_block(X, kernels, stride)\n",
        "  X = residual_block(X, kernels, stride)\n",
        "  X = residual_block(X, kernels, stride)\n",
        "  X = residual_block(X, kernels, stride)\n",
        "  X = residual_block(X, kernels, stride)\n",
        "\n",
        "  X = keras.layers.AveragePooling1D(5, 2)(X)\n",
        "  X = keras.layers.Flatten()(X)\n",
        "  X = keras.layers.Dense(32, activation='relu')(X)\n",
        "  X = keras.layers.Dense(32, activation='relu')(X)\n",
        "  output = (keras.layers.Dense(1, activation='sigmoid')(X) if categories == 2 else keras.layers.Dense(5, activation='softmax')(X))\n",
        "\n",
        "  model = keras.Model(inputs=inputs, outputs=output)\n",
        "  return model"
      ],
      "metadata": {
        "id": "a7QjBr5JPkPR"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_ptbdb_train, X_ptbdb_val, y_ptbdb_train, y_ptbdb_val = train_test_split(ptbdb.iloc[:,:-1].values, ptbdb.iloc[:,-1].values, test_size=0.2, random_state=18)\n",
        "X_ptbdb_val, X_ptbdb_test, y_ptbdb_val, y_ptbdb_test  = train_test_split(X_ptbdb_val, y_ptbdb_val, test_size=0.5, random_state=18)\n",
        "\n",
        "X_ptbdbl_train = np.expand_dims(X_ptbdb_train, -1)\n",
        "y_ptbdbl_train = np.expand_dims(y_ptbdb_train, -1)\n",
        "\n",
        "X_ptbdbl_val = np.expand_dims(X_ptbdb_val, -1)\n",
        "y_ptbdbl_val = np.expand_dims(y_ptbdb_val, -1)\n",
        "\n",
        "X_ptbdbl_test = np.expand_dims(X_ptbdb_test, -1)\n",
        "y_ptbdbl_test = np.expand_dims(y_ptbdb_test, -1)"
      ],
      "metadata": {
        "id": "oo3gKPSQPnK9"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = keras.optimizers.Adam(lr=0.001)\n",
        "model = get_resnet_model() \n",
        "model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "lr_finder = LRFinder(start_lr=1e-7, end_lr= 1e-03, max_steps=100, smoothing=0.6)\n",
        "model.fit(X_ptbdbl_train, y_ptbdbl_train, batch_size=256, epochs=5, callbacks=[lr_finder], verbose=False)\n",
        "lr_finder.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ES6Cc_rQQvXz",
        "outputId": "dffbbb35-d4ce-4eb8-a4a3-0176d5101b6c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "N = X_ptbdbl_train.shape[0]\n",
        "batch_size = 128\n",
        "iterations = N/batch_size\n",
        "step_size= 2 * iterations\n",
        "\n",
        "lr_schedule = CyclicalLearningRate(1e-6, 1e-3, step_size=step_size, scale_fn=lambda x: 1 / (2.0 ** (x - 1)))\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)\n",
        "\n",
        "save_best_weights = ModelCheckpoint(filepath=\"ptbd_weights.hdf5\", verbose=0, save_best_only=True)\n",
        "\n",
        "resnet_model = get_resnet_model() \n",
        "resnet_model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "history = resnet_model.fit(X_ptbdbl_train, y_ptbdbl_train, validation_data=(X_ptbdbl_val, y_ptbdbl_val), \n",
        "                           shuffle=True, batch_size=batch_size, epochs=75, callbacks=[save_best_weights])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DCctIs4Q2Uy",
        "outputId": "19624d3a-3935-4d8a-bc0e-048b90386008"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/75\n",
            "91/91 [==============================] - 8s 30ms/step - loss: 0.3725 - accuracy: 0.8221 - val_loss: 0.6992 - val_accuracy: 0.5086\n",
            "Epoch 2/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.1482 - accuracy: 0.9424 - val_loss: 0.8850 - val_accuracy: 0.7395\n",
            "Epoch 3/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0630 - accuracy: 0.9770 - val_loss: 0.7845 - val_accuracy: 0.5794\n",
            "Epoch 4/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0272 - accuracy: 0.9917 - val_loss: 1.1105 - val_accuracy: 0.5863\n",
            "Epoch 5/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0184 - accuracy: 0.9948 - val_loss: 1.1335 - val_accuracy: 0.6749\n",
            "Epoch 6/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0240 - accuracy: 0.9918 - val_loss: 0.8302 - val_accuracy: 0.7491\n",
            "Epoch 7/75\n",
            "91/91 [==============================] - 2s 24ms/step - loss: 0.0255 - accuracy: 0.9910 - val_loss: 0.3704 - val_accuracy: 0.8536\n",
            "Epoch 8/75\n",
            "91/91 [==============================] - 2s 22ms/step - loss: 0.0079 - accuracy: 0.9980 - val_loss: 0.0972 - val_accuracy: 0.9615\n",
            "Epoch 9/75\n",
            "91/91 [==============================] - 2s 22ms/step - loss: 0.0054 - accuracy: 0.9992 - val_loss: 0.0212 - val_accuracy: 0.9931\n",
            "Epoch 10/75\n",
            "91/91 [==============================] - 2s 22ms/step - loss: 0.0054 - accuracy: 0.9991 - val_loss: 0.0166 - val_accuracy: 0.9952\n",
            "Epoch 11/75\n",
            "91/91 [==============================] - 2s 22ms/step - loss: 0.0042 - accuracy: 0.9993 - val_loss: 0.0156 - val_accuracy: 0.9966\n",
            "Epoch 12/75\n",
            "91/91 [==============================] - 2s 23ms/step - loss: 0.0027 - accuracy: 0.9997 - val_loss: 0.0108 - val_accuracy: 0.9966\n",
            "Epoch 13/75\n",
            "91/91 [==============================] - 2s 22ms/step - loss: 0.0023 - accuracy: 0.9996 - val_loss: 0.0103 - val_accuracy: 0.9979\n",
            "Epoch 14/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0021 - accuracy: 0.9998 - val_loss: 0.0141 - val_accuracy: 0.9945\n",
            "Epoch 15/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 0.0130 - val_accuracy: 0.9966\n",
            "Epoch 16/75\n",
            "91/91 [==============================] - 2s 21ms/step - loss: 0.0016 - accuracy: 0.9999 - val_loss: 0.0114 - val_accuracy: 0.9973\n",
            "Epoch 17/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 18/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.0121 - val_accuracy: 0.9959\n",
            "Epoch 19/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0014 - accuracy: 0.9998 - val_loss: 0.0135 - val_accuracy: 0.9952\n",
            "Epoch 20/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.0116 - val_accuracy: 0.9973\n",
            "Epoch 21/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0114 - val_accuracy: 0.9973\n",
            "Epoch 22/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 23/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.0119 - val_accuracy: 0.9973\n",
            "Epoch 24/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0119 - val_accuracy: 0.9973\n",
            "Epoch 25/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0117 - val_accuracy: 0.9973\n",
            "Epoch 26/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 9.5086e-04 - accuracy: 1.0000 - val_loss: 0.0114 - val_accuracy: 0.9973\n",
            "Epoch 27/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 9.2690e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 28/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.6472e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 29/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 9.0158e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 30/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 9.1093e-04 - accuracy: 0.9999 - val_loss: 0.0111 - val_accuracy: 0.9973\n",
            "Epoch 31/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.9843e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 32/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.4325e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 33/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 34/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.3498e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 35/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 9.1019e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 36/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 9.4341e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 37/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.6549e-04 - accuracy: 0.9999 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 38/75\n",
            "91/91 [==============================] - 2s 22ms/step - loss: 8.8509e-04 - accuracy: 1.0000 - val_loss: 0.0114 - val_accuracy: 0.9973\n",
            "Epoch 39/75\n",
            "91/91 [==============================] - 2s 22ms/step - loss: 8.4697e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 40/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 9.1285e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 41/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.2691e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 42/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.1803e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 43/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.5881e-04 - accuracy: 1.0000 - val_loss: 0.0114 - val_accuracy: 0.9973\n",
            "Epoch 44/75\n",
            "91/91 [==============================] - 2s 21ms/step - loss: 7.8617e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 45/75\n",
            "91/91 [==============================] - 2s 21ms/step - loss: 8.7519e-04 - accuracy: 1.0000 - val_loss: 0.0114 - val_accuracy: 0.9973\n",
            "Epoch 46/75\n",
            "91/91 [==============================] - 2s 21ms/step - loss: 7.6647e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 47/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.7347e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 48/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.0914e-04 - accuracy: 1.0000 - val_loss: 0.0111 - val_accuracy: 0.9973\n",
            "Epoch 49/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.7113e-04 - accuracy: 1.0000 - val_loss: 0.0111 - val_accuracy: 0.9973\n",
            "Epoch 50/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.8663e-04 - accuracy: 1.0000 - val_loss: 0.0110 - val_accuracy: 0.9973\n",
            "Epoch 51/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 6.9908e-04 - accuracy: 1.0000 - val_loss: 0.0111 - val_accuracy: 0.9973\n",
            "Epoch 52/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 9.7107e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 53/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.6086e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 54/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.6227e-04 - accuracy: 1.0000 - val_loss: 0.0111 - val_accuracy: 0.9973\n",
            "Epoch 55/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0111 - val_accuracy: 0.9973\n",
            "Epoch 56/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.5713e-04 - accuracy: 1.0000 - val_loss: 0.0111 - val_accuracy: 0.9973\n",
            "Epoch 57/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 9.2443e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 58/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.8049e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 59/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.7401e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 60/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.1874e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 61/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.5551e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 62/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.7671e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 63/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.2433e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 64/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 6.8398e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 65/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.7871e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 66/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.1392e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 67/75\n",
            "91/91 [==============================] - 2s 21ms/step - loss: 7.5589e-04 - accuracy: 1.0000 - val_loss: 0.0111 - val_accuracy: 0.9973\n",
            "Epoch 68/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.6231e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 69/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 8.8718e-04 - accuracy: 0.9999 - val_loss: 0.0114 - val_accuracy: 0.9973\n",
            "Epoch 70/75\n",
            "91/91 [==============================] - 2s 20ms/step - loss: 7.3261e-04 - accuracy: 1.0000 - val_loss: 0.0114 - val_accuracy: 0.9973\n",
            "Epoch 71/75\n",
            "91/91 [==============================] - 2s 21ms/step - loss: 7.6021e-04 - accuracy: 0.9999 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 72/75\n",
            "91/91 [==============================] - 2s 21ms/step - loss: 7.2452e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9973\n",
            "Epoch 73/75\n",
            "91/91 [==============================] - 2s 21ms/step - loss: 7.5425e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 74/75\n",
            "91/91 [==============================] - 2s 21ms/step - loss: 7.2467e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
            "Epoch 75/75\n",
            "91/91 [==============================] - 2s 21ms/step - loss: 7.0793e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9973\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretty_plot(history, 'loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "Z3atJqAMQ9dY",
        "outputId": "bcacf20b-eb84-40b0-b35d-ad8c3582b169"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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uOiZNmsRnPvOZE+YauvPOO5k1axZnnnkm3/3udwFnIruqqio++clP8slPfhJwprU+csR53snPfvYzpkyZwpQpU7jnnnu6yps0aRJf/OIXOfPMM7n00ktjNqeRjRFEE22eoZAEdw2VlpYmrGxjPPfnu+HgO7HNc9hZcNmPu928ePFivvrVr/KlL30JgMcff5xnnnmGr3zlK+Tn53PkyBHmzJnDlVde2e3zgH/1q1+RnZ3N9u3b2bx5MzNmzOja9qMf/YiioiICgQAXXXQRmzdv5itf+Qo/+9nPePHFFykpKTkhr40bN/LQQw/x+uuvo6qcc845XHDBBRQWFno23bW1CKKJNs9QSHaxc0VRMBjfOrn27NnDnj17ElK2Mclo+vTpHD58mKqqKjZt2kRhYSHDhg3jn//5n5k6dSoXX3wx+/fv59ChQ93m8dJLL3WdkKdOncrUqVO7tj3++OPMmDGD6dOns3XrVrZt29ZjfV555RU+85nPkJOTQ25uLosWLeLll18GvJvu2rMWgYg8CHwaOKyqU6JsF+DnwEKgGbhFVd/0qj6npGueoSiBYFARaNDpPoo2huCx5557DrC5hkyS6uGbu5euvfZali9fzsGDB1m8eDG///3vqa6uZuPGjaSnpzN27Nio00/35v333+c///M/eeONNygsLOSWW275UPmEeDXdtZctgt8CPT1c9zJggvtzO/ArD+tyalp6aRFAwgaMr7nmmm6vVDDGfDiLFy/m0UcfZfny5Vx77bXU19czZMgQ0tPTefHFF3u9Uu/888/vmrhuy5YtbN68GYCGhgZycnIoKCjg0KFDJ0xg19301/PmzePJJ5+kubmZpqYm/vSnPzFv3rwYvtuTedYiUNWXRGRsD0muAn6nqgqsF5HBIjJcVQ94Vac+622MABL2pLLIR+QZY/rvzDPPpLGxkREjRjB8+HBuuOEGrrjiCs466yxmzZrFGWec0eP+d955J7feeiuTJk1i0qRJzJw5E4Bp06Yxffp0zjjjDEaNGsXcuccfdHX77bezYMECysrKePHFF7vWz5gxg1tuuYXZs2cDcNtttzF9+nRPn3omznnYo8ydQPB0N11DTwM/VtVX3OXngW+qao8X386aNUt7uz63357/AbzyM/jXGkiJaDTtfxN+80m4/lGYeJm39Ygi9Ji8CRMm9JLSmIFh+/btTJo0KdHVSCrRjqmIbFTVWdHSD4irhkTkdpzuI0aPHu19gS3uzWSRQQASPt/QCy+8AFggMMbETiIDwX4g/LmLI911J1HV+4H7wWkReF6zligzj4YkOBAsXrw4IeUaY5JXIi8ffQr4P+KYA9R/JMYHwG0RdHNFUEYOpGYmLBAMHjw4YXcfGuMVL7uo/ebDHEsvLx/9AzAfKBGRfcB3gXQAVf01sAbn0tFdOJeP3upVXU5Z81HIGx59W4InntuxYwcAEydOTEj5xsRaVlYWNTU1FBcXd3vDlukbVaWmpoasrKxT2s/Lq4au72W7Al/yqvx+aamDoWd2vz2B8w2tW7cOsEBgksfIkSPZt28f1dXVia5KUsjKymLkyJGntM+AGCyOu57GCCCh8w0tWbIkIeUa45X09HTGjRuX6Gr4mk0xEamzHdqPdT9GAAkNBHl5eeTl5SWkbGNMcrJAEKnrZrIeBmQT2DW0bdu2XucqMcaYU2FdQ5FCdwz3NI9QdrETMIIBSEmNT71cocmnJk+eHNdyjTHJywJBpK4WQU9jBMWAOoPKOfGd8iEWU84aY0w4CwSRuqag7qVFAM44QZwDQU5OTlzLM8YkPxsjiNSnFoEbJBIwYLxlyxa2bNkS93KNMcnLWgSR+jpGAAkJBH/7298Auh6RZ4wx/WWBIFJLLaSkQ0Zu92kSGAhuvvnmuJdpjEluFggiNbs3k/V0q/ugxHUNneqt48YY0xsbI4jUUtv7IygzsiFtUEICwaZNm9i0aVPcyzXGJC9rEUQKPYugN6F7CeJs/fr1gPPkI2OMiQULBJFaamHwmN7TJWiaiVtv/ehM0mqMSQ7+7hpqO/nB0V1jBL1J0FTUGRkZZGRkxL1cY0zy8m8g2P0C/Md4qN174vqWWsj+6AaCt956i7feeivu5Rpjkpd/A8GBTRBoh/dfOr6uowU6Wz7SLYINGzawYcOGuJdrjEle/h0jqK1wfn+wHmb8H+d1113FvVw1BE4gaK2HQAekpntTxyhuu+22uJVljPEH/7YI6j5wfn/w2vF1XfMM9aVF4AaLOF85lJqaSmpqfGc8NcYkNx8HArdFcHQPNB5yXodO6r3dRwAJu7u4vLyc8vLyuJZpjElu/gwEwSDUVcLoTzjLlc61+V3zDJ1KiyDOgWDjxo1s3LgxrmUaY5KbP8cIjh2CQBtMvhKq3nTGCSZfdepjBBD3J5XdcccdcS3PGJP8/NkiCI0PFH8MRsw6Pk5wSmMEiZt4zhhjYsmngcAdHxg8BkbPgQOboe2Y0yJIy3LmEupNgiaes8tHjTGx5vNAMApGnwsagP3lzhhBX1oDAOlZzlTVce4a2rx5M5s3b45rmcaY5ObPMYLaCsgdCumDYNTHAXHGCVrq+jY+EDKoMO6Xj9p9BMaYWPNnIKj7AAaPdl5nFcDQKc44QWd731sE4LQI2qPMV2SMMQOIp11DIrJARHaIyC4RuTvK9tEi8qKIvCUim0VkoZf16VJXceIMo6PnQOUb0FTdt3mGQjJznbGFOHrttdd47bXXek9ojDF95FkgEJFU4F7gMmAycL2ITI5I9m3gcVWdDlwH/NKr+nQJBqB+3/EWATiBoKMJanZ+iBZBfAPB9u3b2b59e1zLNMYkNy+7hmYDu1R1D4CIPApcBWwLS6NAvvu6AKjysD6OhioIdkJheIvg3OOvT2WMIDMXGg/Grm598PnPfz6u5Rljkp+XXUMjgMqw5X3uunDfA24UkX3AGuDvo2UkIreLSLmIlFdXV/evVqF7CMJbBAUjji+fUosgL+4tAmOMibVEXz56PfBbVR0JLAQeFpGT6qSq96vqLFWdVVpa2r8Sw+8hCBdqFfRlnqGQzFxoa+hffU7RK6+8wiuvvBLXMo0xyc3LQLAfGBW2PNJdF+4LwOMAqvoakAWUeFgnt0UgUDDyxPWj5zi/T6VFkJnnDBarxqx6vdm9eze7d++OW3nGmOTn5RjBG8AEERmHEwCuA5ZEpPkAuAj4rYhMwgkE/ez76UVtBeQNh7TME9efvgBG/gHKpvc9r4xc52a0zlbnnoQ4uPnmm+NSjjHGPzwLBKraKSJfBp4BUoEHVXWriHwfKFfVp4CvA78RkX/AGTi+RdXjr9d1H5w4UBySXwa3PXdqeWXmOb/bjsUtEBhjTKx5ekOZqq7BGQQOX/edsNfbgLle1uEkdRUw5hOxySsj1/nd3gj0c+yij156yXm05vnnnx+X8owxyc9fdxYHOqBh/8kDxR9WphsI4nhTWUVFRdzKMsb4g78CQf0+0OCJl472R1eLIH6B4KabbopbWcYYf0j05aPxFbqHINoYwYcRPkZgjDEDlM8CQegeghi3COJ4L8G6detYt25d3MozxiQ/f3UN1X0Akgr5I3tP2xehFkEcu4aqqryfhcMY4y/+CgS1FZA/AlJj9LYTMFi8ZEnkrRjGGNM/Pusa6uYegg8rAYPFxhgTaz4LBBWxGx8ASEmF9Gxoi9/DaZ5//nmef/75uJVnjEl+/uka6myDxgOxu4cgJM7PJOj37KvGGBPBP4Ggzp0RO5YtAoj7U8quu+66uJVljPEH/3QNhS4djeUYASTkKWXGGBNL/gsEMW8R5MV1jODZZ5/l2WefjVt5xpjk55+uofQcKJvhTEEdS5l5zuMv46S+vj5uZRlj/ME/gWDaYucn1uLcNXTttdfGrSxjjD/4p2vIK3EeLDbGmFizQNBfcW4RrF27lrVr18atPGNM8vNP15BXMvOgoxmCAecGM481Nzd7XoYxxl8sEPRX+DQTWQWeF7do0SLPyzDG+It1DfVXAiaeM8aYWLJA0F9dzySIz70Eq1evZvXq1XEpyxjjD9Y11F+Z+c7vOA0Yd3R0xKUcY4x/WCDor8z4tgiuvvrquJRjjPEP6xrqL3smgTFmgLNA0F9xHixetWoVq1atiktZxhh/sEDQXxnxf26xMcbEko0R9FecxwiuuOKKuJRjjPEPT1sEIrJARHaIyC4RububNJ8TkW0islVElnlZH0+kZYGkWovAGDNgedYiEJFU4F7gEmAf8IaIPKWq28LSTAC+BcxV1VoRGeJVfTwj4k48F58WwZNPPgnY1UPGmNjxskUwG9ilqntUtR14FLgqIs0XgXtVtRZAVQ97WB/vZObHbbA4PT2d9PT0uJRljPGHPgUCEblLRPLF8b8i8qaIXNrLbiOAyrDlfe66cKcDp4vI30RkvYgs6Kb820WkXETKP5IPb8/Ihfb4tAguv/xyLr/88riUZYzxh762CD6vqg3ApUAhcBPw4xiUnwZMAOYD1wO/EZHBkYlU9X5VnaWqs0pLS2NQbIzZMwmMMQNYXwOBuL8XAg+r6tawdd3ZD4wKWx7prgu3D3hKVTtU9X3gPZzAMLDE8ZkEK1asYMWKFXEpyxjjD30NBBtF5FmcQPCMiOQBwV72eQOYICLjRCQDuA54KiLNkzitAUSkBKeraE8f6/TREccWQXZ2NtnZ2XEpyxjjD329augLwNnAHlVtFpEi4NaedlDVThH5MvAMkAo8qKpbReT7QLmqPuVuu1REtgEB4B9VtebDvpmEyciLW4tgwYKowyjGGPOh9TUQnAu8rapNInIjMAP4eW87qeoaYE3Euu+EvVbga+7PwBXHy0eNMSbW+to19CugWUSmAV8HdgO/86xWA02GGwhUPS/qiSee4IknnvC8HGOMf/Q1EHS6396vAv5HVe8F8ryr1gCTmQcagM5Wz4sqKCigoMD7R2IaY/yjr11DjSLyLZzLRueJSApgdzWFZLoxse0YpA/ytKhLL+3t9g1jjDk1fW0RLAbacO4nOIhzKehPPavVQNP1TAIbJzDGDDx9CgTuyf/3QIGIfBpoVVUbIwiJ4zMJHn30UR599FHPyzHG+Edfp5j4HLABuBb4HPC6iFzjZcUGlDg+pay0tJSP5N3VxpgBq69jBP8CfDw0KZyIlAJ/AZZ7VbEBJXyMwGMXXXSR52UYY/ylr2MEKREzg9acwr7Jz8YIjDEDWF9bBGtF5BngD+7yYiJuFPO1OD6lbNky59k9S5Ys8bwsY4w/9CkQqOo/ishngbnuqvtV9U/eVWuAiWPXUFlZmedlGGP8pc9PKFPVPwJ/9LAuA1ccB4vnz5/veRnGGH/pMRCISCMQbd4EwZkqKN+TWg00KamQnm3zDRljBqQeA4Gq2jQSfRWnZxI8/PDDANx0002el2WM8QfPHl7vO3F6JsGYMWM8L8MY4y8WCGIlTi2C888/3/MyjDH+YvcCxEpmnj232BgzIFkgiJWMXGhr8LyYpUuXsnTpUs/LMcb4h3UNxUpmHtTs9LyY0047zfMyjDH+YoEgVuI0WHzeeed5XoYxxl+sayhW4jRYbIwxsWaBIFYy86CjGYIBT4t58MEHefDBBz0twxjjL9Y1FCvh00xkefdM4UmTJnmWtzHGnywQxEr4U8o8DATnnnuuZ3kbY/zJuoZiJY4TzxljTCxZIIiVrqmovZ147oEHHuCBBx7wtAxjjL9Y11CsxCkQTJ061dP8jTH+42mLQEQWiMgOEdklInf3kO6zIqIiMsvL+ngqTl1Ds2fPZvbs2Z6WYYzxF88CgYikAvcClwGTgetFZHKUdHnAXcDrXtUlLsIHi40xZgDxskUwG9ilqntUtR14FLgqSrofAD8BWj2si/cy3K4hj1sE9913H/fdd5+nZRhj/MXLQDACqAxb3ueu6yIiM4BRqrq6p4xE5HYRKReR8urq6tjXNBbi9AD7mTNnMnPmTE/LMMb4S8IGi0UkBfgZcEtvaVX1fuB+gFmzZkV7dGbipWWBpHreIpg1a+AOoxhjPpq8bBHsB0aFLY9014XkAVOAdSKyF5gDPDVgB4xF4jLxXCAQIBDwdhoLY4y/eBkI3gAmiMg4EckArgOeCm1U1XpVLVHVsao6FlgPXKmq5R7WyXzm7i8AABi/SURBVFsZeXYfgTFmwPEsEKhqJ/Bl4BlgO/C4qm4Vke+LyJVeldudpzdXcf396wkEPexZysyDdm8DgV0+aoyJNU/HCFR1DbAmYt13ukk738u61Da189qeGmqOtTEkP8ubQjLzoNXbp5RNnz7d0/yNMf7jmykmhron/4MNHl6lmlMCzTXe5Q+0t7fT3t7uaRnGGH/xXyCo9zIQlEKTt5e3PvTQQzz00EOelmGM8RffzDU0rMAJBIca27wrJKcUmo5AMAgp3sTYOXPmeJKvMca/fBMISnIzSU0RDnndItAAtNZBdpEnRUybNs2TfI0x/uWbrqHUFKE0N9P7MQLwtHuotbWV1taBPRuHMeajxTeBAGBoQRaHPA0Epc5vDwPB0qVLWbp0qWf5G2P8xzddQwDD8jPZU93kXQFxCARz5871LG9jjD/5LBBk8epuDy/vDAWCY94FgilTpniWtzHGn3zXNdTY2klze6c3BWQXAeJpi6CpqYmmJg9bNcYY3/FVIBjm9b0EKamQXexpIHjkkUd45JFHPMvfGOM/vusaAufu4vGlud4U4vFNZfPmzfMsb2OMP/kqEITmGDrc4OVNZSXOTWUemTz5pKd9GmNMv/ira6ggDvMN5Q7xtEXQ2NhIY6O3M5waY/zFV4EgNzON3My0OMw35F2LYNmyZSxbtsyz/I0x/uOrriGAofmZHt9UVgJt9dDZBmmZMc9+/vz5Mc/TGONvvgsEwwqyPJ5mInRT2REoGBHz7CdOnBjzPI0x/uarriFwpqP2fOI58GycoK6ujrq6Ok/yNsb4k+8CwbD8LA43thH06pGV4S0CDzz22GM89thjnuRtjPEnX3YNdQaVI01tDMnz4JGVXTOQHo593sCFF17oSb7GGP/yXSAIPansUL1XgcDbrqEJEyZ4kq8xxr982TUEeHflUEYupGV5FghqamqoqfH2ucjGGH/xXSDw/CH2Ip7eS7B8+XKWL1/uSd7GGH/yXddQSW4GKeJhiwDcaSa8aRFccsklnuRrjPEv3wWCtNQUSvMyvb+7+NghT7IeP368J/kaY/zLd11D4IwTeHtT2RDPuoaqq6uprvZuLiNjjP/4MhAMzff62cVu15DG/l6FFStWsGLFipjna4zxL08DgYgsEJEdIrJLRO6Osv1rIrJNRDaLyPMiMsbL+oQMK8jyvmso0A5tDTHPesGCBSxYsCDm+Rpj/MuzQCAiqcC9wGXAZOB6EYmcTP8tYJaqTgWWA//hVX3CDc3PoqG1k5b2gDcFeHh38ZgxYxgzJi7x0hjjE162CGYDu1R1j6q2A48CV4UnUNUXVbXZXVwPjPSwPl2GeX0Jaeju4mOxv7v44MGDHDx4MOb5GmP8y8tAMAKoDFve567rzheAP0fbICK3i0i5iJTHYqC06wE1XnUPeXh38cqVK1m5cmXM8zXG+NdH4vJREbkRmAVcEG27qt4P3A8wa9asfo/Ahm4qO9w48ALBwoULY56nMcbfvAwE+4FRYcsj3XUnEJGLgX8BLlBVDx8mfNzQfOeBMZ61CLKLnd8ejBGMGjWq90TGGHMKvOwaegOYICLjRCQDuA54KjyBiEwH7gOuVFVvpuuMIi8rnZyMVO/GCNIyIGuwJy2CqqoqqqqqYp6vMca/PAsEqtoJfBl4BtgOPK6qW0Xk+yJypZvsp0Au8ISIvC0iT3WTXcwNLfD6XoJSTwLBqlWrWLVqVczzNcb4l6djBKq6BlgTse47Ya8v9rL8ngzLj8O9BB50DV1xxRUxz9MY42++vLMYnEBwqMHDIYlcb1oEZWVllJWVxTxfY4x/+TYQhLqGPH1kpQeBoLKyksrKyt4TGmNMH/k2EAzLdx5ZWdPU7k0BOaXQchQCnTHNds2aNaxZs6b3hMYY00cfifsIEmFcSQ4A2w80UJpXGvsCQncXN9dA3tCYZXvVVVf1nsgYY06Bb1sEHx9bREZaCi+959GUzl03lcX2qthhw4YxbNiwmOZpjPE33waCQRmpzB5bxEs7vQ4Esc2/oqKCioqKmOZpjPE33wYCgPNPL+G9Q8c4UN8S+8w9moF07dq1rF27NqZ5GmP8zeeBwDlZv/yeB08TC40RxLhFsGjRIhYtWhTTPI0x/ubrQDBxaB5D8jK96R7KGgwpaTEPBKWlpZSWejC4bYzxLV8HAhFh3oRSXtl1hECs7ycQ8eRegj179rBnz56Y5mmM8TdfBwJwxgnqmjt4Z3997DPPKYn5GMFzzz3Hc889F9M8jTH+5tv7CELmTShFBF56r5qzRw2ObeY5pTF/Stk111wT0/yMMcb3LYKinAzOGlHgzf0EJRPh8DZob4pZlsXFxRQXF8csP2OM8X0gADh/QilvVdbR0NoR24wnLoDOVtj9Ysyy3LlzJzt37oxZfsYYY4EA5zLSQFB5dVeMLyMdMxeyCmBH7OYGeuGFF3jhhRdilp8xxvh+jABg+ujB5Gam8dLOIyyYMjx2Gaemw4RPwY4/O5PPpfb/cC9evDgGFTPGmOOsRQCkp6Zw7mnFvPReNaoxvoz0jIXOLKSVr8cku8GDBzN4cIwHtY0xvmaBwHX+6aXsq21h1+Fjsc34YxdDaga8uzom2e3YsYMdO3bEJC9jjAELBF0unTyU7IxUfrB6e2xbBZl5MO4C2LEaYpDvunXrWLduXf/rZYwxLgsErqH5WXxzwRm89F41T2zcF9vMz7gcavfC4e39zmrJkiUsWbKk/3UyxhiXBYIwN80Zw+yxRfzg6W0caojhg+0nXub8jkH3UF5eHnl5ef3OxxhjQiwQhElJEX5yzVTaO4P8y5/eiV0XUd4wGDHL6R7qp23btrFt27YYVMoYYxwWCCKMK8nhG5dO5C/bD/PUpqrYZXzG5VD1FtTv71c2L7/8Mi+//HKMKmWMMRYIovr8eeM4e9RgvvvUVrb0YTK6QFCpa27ng5pmWjsC0ROdcbnzu583l914443ceOON/crDGGPC2Q1lUaSmCD+9Ziqf+eWrfPq/X+GMYXlcM3MkV55dRmt7kDf2HqW84ijle2s5UN/KsbbOrn3LCrL47yXTmTmm6MRMS06HotNg8+Mw5hPOPEQf4gaznJyc/r49Y4w5gcT8BiqPzZo1S8vLy+NSVm1TO09vrmL5m/vZVFl3wra8rDRmjilkXEkO+VnpFAxKJys9lV//dTf761r4xqUTueP88aSkyPGdXrkH/vJd53VaFgydAkPOgMx8SB8E6dnOlBTDznJ+Mk4+6W/ZsgWAKVOmOHcr13/gXJaakeP8pOdASj8aeh2tEGiDjLz+5WOM+UgRkY2qOivaNk9bBCKyAPg5kAo8oKo/jtieCfwOmAnUAItVdW9PeVZXV1NeXs6sWbMIBAI88MADzJ49m+nTp9Pe3s5DDz3EnDlzmDZtGq2trSxdupS5c+cyZcoUmpqaeOSRR5g3bx6TJ0+msbGRZcuWMX/+fCZOnEhdXR2PPfYYF154IRMmTCDY2kjz5mf4r8suIZg7lafWv8uxHa8x/6KLOe/sSRw+fIiVK1eycOFCRo0aRVVVFXV5O9heNIGfrH2Xjdt2cjbv89nPXM3w4cOpGHk1a0tg0dlFlB57lz27d/HcG61ck/U8xYFD7OwczgudM1mc8V0Gp7SwI+dc1rVNYslkyMtKZ1ttCsu3BhmS1caUl9az5VAHf2ufxM0Za8mSdjYFTmN955ncmvsSGdl5vNU5gQ3NI7lt/AFSU4Ty2jw21uZxx7j90NnKhppcNjcO5rai16G1gdeOjWB750g+n7EaJJVXUs5hd3AkN4+ugpRUXqopoaJlEDeNOQQpqaw7PJiq1kyWjDsKgU6eP5BLdWsq1w3ZBRrk2aMjqO/M4NohewBh7dHRNAfTWDS0EiSV1UdG0KEpXF1WDZLKqgMlgHDFiKOA8OT+ItJFubysFoAV+4rJTguyoKwBgCc+KKIgI8ClZccA4dH3CyjN6uSiYY0ALHu/kLJBHcwf7sz++vCeQsbkdHD+UGd56e5CTstr47whzQA8uKuQSfktnFvi3FT4wK5SphY2M7u4CUS4b2cpM4uamFXURCCoPLB7CLOLGpleeIz2ADy0dxhzihqYVthCazCFpe8PYW7pMaYMbqGpM4VH3i9m3pBGJhe00tiRwrK9xcwf2sjE/Fbq2lN5rKKIC4c1MCG3lZrWFJbvK+aSoXWMz22lujWdFftLWDC8jjHZrRxsTWPl/mIWDj/KqOx2qloyWFVVzBUjjlI2qJPKlkzWVA3mqlH1DBsUoKIpg7X781g0poHSrAB7GtN5riqXa8Y2UJwZYGdjBi9U5bB4fBODM4PsqE9j3YFsloyvJy89yLbadF4+lMONp9WSkxpkS20Wf6vO5ubTaslKVTYdzWL9kWxu/dhRMlLgraOD2HAkm9tOr3U+e0ey2HgkizvOqANVNlRnsbk2i9sm1IAqr1Vns70+i8+fdgRUeaU6j93HMrl5fA0ALx3Oo6Ipg5vGO5+NdQdzqWpJZ8lYZ//nD+VT3ZrGdWOc9M8eLKC+PY1rRzvLaw8MprkzhUWjnM/S6qrBdKhw9UjnC96qfc7d+leMqnc+e5WDSU9RLh/ZCCKsqMgnOzXIgrJ60CBPVBRSkN7JpcNqQYM8+sEQSjPbuWiIk/+yyqGUZbUxf4iT38MVQxmT08b5QxoBYen7pZyW18p5pc5n7cHdJUwqaOXc0iZAeGBXCVMLW5hd6ly9eN+OImYWtzDr0msJjL3gQ533euJZIBCRVOBe4BJgH/CGiDylquGXvHwBqFXVj4nIdcBPgI/kZDofG5LHTeeOZcWBNxlTnHPiN/0wqSnC3ZedwTmVnfz3qvUEA3U8cu/fyC4sYUxGE8V1Af7tvdFo5hRS0w6TmbmVn477Arn5BWQeq6K14m02TPgk4zt30XiwAq2rp3P73whwDG0fylCdwmcH7aA1dySt2RMIVGdSN+c/yQw201R5lM4PjtF4+jVkBxsIHA5As0JzDRCE1iAEMqH5CKQNclolaVlQNgOy8qE6B44KnPtDaD4Ku+rgaAA0CJ0dTkshmObsr0FoFWjPhiO7nG6uzrFAjpOXpEJjNkia81wGVWjIhM4Up0wNOj9BhdYG0AB05AAKDVXO7/ZMkCA0uAPs7VlOPY6+72xvTYPONji8x1luOxMCzXCkAhBomwzaCNVuq6ltCmg9SKWbfipQi/PxBNqmwbEaSDng7N+Z6zxYSA44+3fkuM+XOAykQGeBM31I6hHQVAgUQWsdNByBQAp05kPTYdAaCKRBp7t/8CgE0t3lQ9B5FDozoTMbGg9CZ4O7nOf87YL10JHlLDcegs5GaM+BgLs9cAxas536Nh6CtmPQmgMdmc6xSj8GbQXQfhpU74D0FmgbfHw5rRlaC6F9PBzcBKlt0FoCbePh4DuQ2gGtpdA+Bg69C2md0DIE2kc6+6cEoHkItI+Aw+5y01Bn+eA7zmevuQzay+DAJudYN42A9mFw+F3naX7HRkBHiXO/DQItZdBR4CyrQvNIaM9zPmsoNI+Bjlzn/YlAy2joHAT1lU7+reJ81kPLbSnOMa/7wP3siPM3O/q+u3288zuUf8sE57N32J3pt3kipHbCkb0gKdCeDsF2aHQ/K4F8pzXd7AQeOguho9n9vKjzWWhthLr9znJHDjTVQqp7QUpHDjTVgLjb2wdBwyEIutvbZ0DDAef/0gOedQ2JyLnA91T1U+7ytwBU9d/D0jzjpnlNRNKAg0Cp9lCpeHYN9dfOQ428uOMwB+vbONjQwoH6VuqbO0BAcB6VGQgqTW2dHGvrpLm9m4HmfkoRJ0CliHT9DqqiCopGveFZBAQhRZx6doU9CeUpiLi/AQWCqgSDbn5umakipKQ4+ZxUBuKWEyrz5EQizk9IqK59+diG9gv9VnV+gqoEVU94fykpTn1Cx6O3/CPz7k60+obeU+j9d7dPZHmhpBp6LzgJw4+jiKCqXX+Prv3dvaMd71D6UJ6h4xL6G6tCwD1mwaCTPsX9HIWOX2QewaBTRuhzliJ07ZOa4qQP6vHt4ccklOfJxyX6HyVa2uPvm6433NP7PiF9+ItuPgca5bg5u0X5n+m2Yj2UIScnA7jr4tO5clpZTzl3X2SCuoZGAJVhy/uAc7pLo6qdIlIPFAMnzActIrcDtwOMHj3aq/rG3ISheUwY2vebvwJBpam9k4aWDhpaOmlo7aChpYPOoNIZVALBIIf2vgcKg0d+jKD7D5oqQlqqkJbi/JN1BpTmjgCt7QGa2wN0BILOP3JQCQSdf8CU0D9e6AMb+YkL+ycNPc459GEP/eMc364nnBRCJ4+ghso7OdhEBqHI/4Wuf66w7ZH/pD39qyknZqzQFbhS5Pi+QfeEpKHgEHY8op2ow//puztJHK9D5IlIut5T6EQSNT0nntxCJ5xQ+eEn/fAgrHo8j/CTkXaTT3iZ4XmGH5dgUJGwLxKh4BD+tw/qyXmEH2snv+NfFAKhwBDx2dOuv0M3x5GTA2/k+4jcL3SMNXwlxwOe9HCcevqCEqp25HE7HoSd1z0FtJ7+3ifUOUxhdnrUuvTXgLhqSFXvB+4Hp0WQ4Op4JjVFyM9KJz8rHQqjp7lvg3NT2nWLLoxjzYwxyczLQLAfGBW2PNJdFy3NPrdrqABn0Nh049Zbb010FYwxScbL6wPfACaIyDgRyQCuA56KSPMUcLP7+hrghZ7GBwxkZGSQkZGR6GoYY5KIZy0Ct8//y8AzOJePPqiqW0Xk+0C5qj4F/C/wsIjsAo7iBAvTg7feeguA6dOnJ7gmxphk4ekYgaquAdZErPtO2OtW4Fov65BsNmzYAFggMMbEzoAYLDbH3XbbbYmugjEmyVggGGBSU1MTXQVjTJKxyWQGmPLycgbKDXXGmIHBAsEAs3HjRjZu3JjoahhjksiAm31URKqBij4mLyHiLmXzodhxjB07lrFhx/HUjVHV0mgbBlwgOBUiUt7d3Bqm7+w4xo4dy9iw4xhb1jVkjDE+Z4HAGGN8LtkDwf2JrkCSsOMYO3YsY8OOYwwl9RiBMcaY3iV7i8AYY0wvLBAYY4zPJW0gEJEFIrJDRHaJyN2Jrs9AISKjRORFEdkmIltF5C53fZGIPCciO93f3Tw6x4QTkVQReUtEnnaXx4nI6+7n8jF3inbTAxEZLCLLReRdEdkuIufa5zG2kjIQiEgqcC9wGTAZuF5EJie2VgNGJ/B1VZ0MzAG+5B67u4HnVXUC8Ly7bHp3F7A9bPknwH+p6seAWuALCanVwPJzYK2qngFMwzme9nmMoaQMBMBsYJeq7lHVduBR4KoE12lAUNUDqvqm+7oR559uBM7xW+omWwpcnZgaDhwiMhK4HHjAXRbgQmC5m8SOYy9EpAA4H+fZJahqu6rWYZ/HmErWQDACqAxb3ueuM6dARMYC04HXgaGqesDddBAYmqBqDST3AP8EBN3lYqBOVTvdZftc9m4cUA085HaxPSAiOdjnMaaSNRCYfhKRXOCPwFdVtSF8m/s4UbvuuAci8mngsKraDIH9kwbMAH6lqtOBJiK6gezz2H/JGgj2A6PClke660wfiEg6ThD4vaqucFcfEpHh7vbhwOFE1W+AmAtcKSJ7cbomL8Tp6x4sIqHngNjnsnf7gH2q+rq7vBwnMNjnMYaSNRC8AUxwr9DIwHkW8lMJrtOA4PZj/y+wXVV/FrbpKeBm9/XNwMp4120gUdVvqepIVR2L8/l7QVVvAF4ErnGT2XHshaoeBCpFZKK76iJgG/Z5jKmkvbNYRBbi9NGmAg+q6o8SXKUBQUTOA14G3uF43/Y/44wTPA6MxpkG/HOqejQhlRxgRGQ+8A1V/bSIjMdpIRQBbwE3qmpbIuv3USciZ+MMuGcAe4Bbcb7E2ucxRpI2EBhjjOmbZO0aMsYY00cWCIwxxucsEBhjjM9ZIDDGGJ+zQGCMMT5ngcCYCCISEJG3w35iNqGZiIwVkS2xys+YWEjrPYkxvtOiqmcnuhLGxIu1CIzpIxHZKyL/ISLviMgGEfmYu36siLwgIptF5HkRGe2uHyoifxKRTe7PJ9ysUkXkN+7zHp4VkUEJe1PGYIHAmGgGRXQNLQ7bVq+qZwH/g3PnOsB/A0tVdSrwe+AX7vpfAH9V1Wk48+NsdddPAO5V1TOBOuCzHr8fY3pkdxYbE0FEjqlqbpT1e4ELVXWPOzHfQVUtFpEjwHBV7XDXH1DVEhGpBkaGTyHhTu39nPtAFUTkm0C6qv7Q+3dmTHTWIjDm1Gg3r09F+NxCAWysziSYBQJjTs3isN+vua9fxZlhFOAGnEn7wHmE4p3Q9ezignhV0phTYd9EjDnZIBF5O2x5raqGLiEtFJHNON/qr3fX/T3OE7T+EedpWre66+8C7heRL+B8878TOIAxHzE2RmBMH7ljBLNU9Uii62JMLFnXkDHG+Jy1CIwxxuesRWCMMT5ngcAYY3zOAoExxvicBQJjjPE5CwTGGONz/x8Wpb38R2q2TAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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DgUozOx/4lLuvAP4S+AUwCPh1mERE5CDRX46LiMh+9JfjIiLSZxQcIiISi4JDRERiUXCIiEgsCg4REYlFwSEiIrEoOEREJBYFh4iIxKLgEBGRWBQcIiISi4JDRERiUXCIiEgsCg4REYlFwSEiIrEoOEREJBYFh4iIxKLgEBGRWBQcIiISi4JDRERiUXCIiEgsCg4REYlFwSEiIrEoOEREJBYFh4iIxKLgEBGRWBQcIiISi4JDRERiKWtwmNksM1tpZqvNbE6J9iozuyu0v2hm9QVt14T6lWb26YL6vzaz5Wa2zMzuNLPqcm6DiIh0VrbgMLMkcCNwFjARuMTMJhZ1uwLY5u7HAj8GfhiWnQjMBiYBs4CbzCxpZmOB/wE0uPtkIBn6iYjIQVLOI44ZwGp3f8vdW4EFwHlFfc4DbgvzC4GZZmahfoG773P3t4HVYX0AFcAgM6sAaoCNZdwGEREpUs7gGAusKyivD3Ul+7h7O7ADGNHVsu6+AfgR8J/AO8AOd3+8LKMXEZGSBtTFcTOrIzoaGQ+MAWrN7Atd9L3SzBrNrLGpqelgDlNE5LBWzuDYABxTUB4X6kr2CaeehgFbuln2k8Db7t7k7m3AfcDHSt25u9/s7g3u3jBq1Kg+2BwREYHyBsfLwHFmNt7MKokuYi8q6rMI+FKYvwB40t091M8On7oaDxwHvER0iuoUM6sJ10JmAq+XcRtERKRIRblW7O7tZvZ14DGiTz/NdfflZnYt0Ojui4BbgXlmthrYSviEVOh3N7ACaAe+5u4Z4EUzWwj8LtQvBW4u1zaIiMj+LHqDf3hraGjwxsbG/h6GiMiAYWZL3L2hVNuAujguIiL9T8EhIiKxKDhERCQWBYeIiMSi4BARkVgUHCIiEouCQ0REYlFwiIhILAoOERGJRcEhIiKxKDhERCQWBYeIiMSi4BARkVgUHCIiEkvZfo9DRKQc2traWL9+PS0tLf09lMNCdXU148aNI5VK9XoZBYeIDCjr169nyJAh1NfXE/0QqBwod2fLli2sX7+e8ePH93o5naoSkQGlpaWFESNGKDT6gJkxYsSI2EdvCg4RGXAUGn3nQB5LBYeISAzbt2/npptuir3c2Wefzfbt28swooNPwSEiEkNXwdHe3t7tco888ghHHHFEuYZ1UOniuIhIDHPmzOHNN99k6tSppFIpqqurqaur44033uAPf/gD559/PuvWraOlpYWrr76aK6+8EoD6+noaGxvZtWsXZ511Fqeddhq//e1vGTt2LA8++CCDBg3q5y3rPQWHiAxY//jQclZs3Nmn65w4Zijf/eykLtt/8IMfsGzZMl555RUWL17MZz7zGZYtW5b/VNLcuXMZPnw4e/fu5cMf/jCf//znGTFiRKd1rFq1ijvvvJOf/exnXHTRRdx777184Qtf6NPtKCcFh4jI+zBjxoxOH2W94YYbuP/++wFYt24dq1at2i84xo8fz9SpUwE4+eSTWbNmzUEbb19QcIjIgNXdkcHBUltbm59fvHgxv/nNb3j++eepqanhjDPOKPlR16qqqvx8Mplk7969B2WsfaVXF8fN7GozG2qRW83sd2b2qXIPTkTkUDNkyBCam5tLtu3YsYO6ujpqamp44403eOGFFw7y6A6O3h5x/Dd3/zcz+zRQB3wRmAc8XraRiYgcgkaMGMGpp57K5MmTGTRoEEcddVS+bdasWfz0pz9lwoQJHH/88Zxyyin9ONLy6W1w5P5C5GxgnrsvN/0Fjoh8QM2fP79kfVVVFb/+9a9LtuWuY4wcOZJly5bl67/1rW/1+fjKrbd/x7HEzB4nCo7HzGwIkC3fsERE5FDV2+C4ApgDfNjd9wAp4PKeFjKzWWa20sxWm9mcEu1VZnZXaH/RzOoL2q4J9SvDKbJc/RFmttDM3jCz183so73cBhER6QO9DY6PAivdfbuZfQH4DrCjuwXMLAncCJwFTAQuMbOJRd2uALa5+7HAj4EfhmUnArOBScAs4KawPoB/Ax519xOAk4DXe7kNIiLSB3obHP8O7DGzk4BvAm8Cv+xhmRnAand/y91bgQXAeUV9zgNuC/MLgZnh2sl5wAJ33+fubwOrgRlmNgz4OHArgLu3uvvh8eUvIiIDRG+Do93dnWiH/hN3vxEY0sMyY4F1BeX1oa5kH3dvJzqKGdHNsuOBJuDnZrbUzG4xs1pKMLMrzazRzBqbmpp6s40iItILvQ2OZjO7huhjuA+bWYLoOsfBVgFMB/7d3acBu4muvezH3W929wZ3bxg1atTBHKOIyGGtt8FxMbCP6O853gXGAf/awzIbgGMKyuNCXck+ZlYBDAO2dLPsemC9u78Y6hcSBYmIyCFp8ODBAGzcuJELLrigZJ8zzjiDxsbGbtdz/fXXs2fPnny5P7+mvVfBEcLiDmCYmZ0DtLh7T9c4XgaOM7PxZlZJdLF7UVGfRcCXwvwFwJPhlNgiYHb41NV44DjgpTCOdWZ2fFhmJrCiN9sgItKfxowZw8KFCw94+eLg6M+vae/tV45cBLwEXAhcBLxoZqWjMwjXLL4OPEb0yae7wx8OXmtm54ZutwIjzGw18DeE007uvhy4mygUHgW+5u6ZsMxfAXeY2WvAVOB/93ZjRUTerzlz5nDjjTfmy9/73vf4/ve/z8yZM5k+fTonnngiDz744H7LrVmzhsmTJwOwd+9eZs+ezYQJE/jc5z7X6buqrrrqKhoaGpg0aRLf/e53geiLEzdu3MiZZ57JmWeeCURf075582YArrvuOiZPnszkyZO5/vrr8/c3YcIEvvKVrzBp0iQ+9alP9dl3Yln0Br+HTmavAv/V3TeF8ijgN+5+Up+MoswaGhq8p8NAERkYXn/9dSZMmBAVfj0H3v19397B0SfCWT/osnnp0qV84xvf4OmnnwZg4sSJPPbYYwwbNoyhQ4eyefNmTjnlFFatWoWZMXjwYHbt2sWaNWs455xzWLZsGddddx3Lli1j7ty5vPbaa0yfPp0XXniBhoYGtm7dyvDhw8lkMsycOZMbbriBKVOm5H/PY+TIkUDH73usXbuWyy67jBdeeAF35yMf+Qi33347dXV1HHvssTQ2NjJ16lQuuugizj333JJf397pMQ3MbIm7N5R6DHp7jSORC41gS4xlRUQOG9OmTWPTpk1s3LiRV199lbq6Oo4++mi+/e1vM2XKFD75yU+yYcMG3nvvvS7X8cwzz+R34FOmTGHKlCn5trvvvpvp06czbdo0li9fzooV3Z+Nf+655/jc5z5HbW0tgwcP5s/+7M949tlngfJ9fXtvv6vqUTN7DLgzlC8GHumTEYiIHKhujgzK6cILL2ThwoW8++67XHzxxdxxxx00NTWxZMkSUqkU9fX1Jb9OvSdvv/02P/rRj3j55Zepq6vjsssuO6D15JTr69t7e3H8b4GbgSlhutnd/65PRiAiMsBcfPHFLFiwgIULF3LhhReyY8cOjjzySFKpFE899RRr167tdvmPf/zj+S9KXLZsGa+99hoAO3fupLa2lmHDhvHee+91+sLErr7O/fTTT+eBBx5gz5497N69m/vvv5/TTz+9D7d2f73+ISd3vxe4t4xjEREZECZNmkRzczNjx45l9OjRXHrppXz2s5/lxBNPpKGhgRNOOKHb5a+66iouv/xyJkyYwIQJEzj55JMBOOmkk5g2bRonnHACxxxzDKeeemp+mSuvvJJZs2YxZswYnnrqqXz99OnTueyyy5gxYwYAX/7yl5k2bVpZf1Ww24vjZtYMlOpggLv70HINrC/p4rjI4aPUhVx5f+JeHO/2iMPde/paERER+YDRJ6NERCQWBYeIiMSi4BCRAac3f7gsvXMgj6WCQ0QGlOrqarZs2aLw6APuzpYtW6iuro61XK8/jisicigYN24c69evR7+z0zeqq6sZN25crGUUHCIyoKRSKcaPH9/fw/hA06kqERGJRcEhIiKxKDhERCQWBYeIiMSi4BARkVgUHCIiEouCQ0REYlFwiIhILAoOERGJRcEhIiKxKDhERCQWBYeIiMSi4BARkVgUHCIiEouCQ0REYlFwiIhILGUNDjObZWYrzWy1mc0p0V5lZneF9hfNrL6g7ZpQv9LMPl20XNLMlprZr8o5fhER2V/ZgsPMksCNwFnAROASM5tY1O0KYJu7Hwv8GPhhWHYiMBuYBMwCbgrry7kaeL1cYxcRka6V84hjBrDa3d9y91ZgAXBeUZ/zgNvC/EJgpplZqF/g7vvc/W1gdVgfZjYO+AxwSxnHLiIiXShncIwF1hWU14e6kn3cvR3YAYzoYdnrgf8JZLu7czO70swazaxRP2ovItJ3BtTFcTM7B9jk7kt66uvuN7t7g7s3jBo16iCMTkTkg6GcwbEBOKagPC7UlexjZhXAMGBLN8ueCpxrZmuITn39qZndXo7Bi4hIaeUMjpeB48xsvJlVEl3sXlTUZxHwpTB/AfCku3uonx0+dTUeOA54yd2vcfdx7l4f1veku3+hjNsgIiJFKsq1YndvN7OvA48BSWCuuy83s2uBRndfBNwKzDOz1cBWojAg9LsbWAG0A19z90y5xioiIr1n0Rv8w1tDQ4M3Njb29zBERAYMM1vi7g2l2gbUxXEREel/Cg4REYlFwSEiIrEoOEREJBYFh4iIxKLgEBGRWBQcIiISi4JDRERiUXCIiEgsCg4REYlFwSEiIrEoOEREJBYFh4iIxKLgEBGRWBQcIiISi4JDRERiUXCIiEgsCg4REYlFwSEiIrEoOEREJBYFh4iIxKLgEBGRWBQcIiISi4JDRERiUXCIiEgsCg4REYlFwSEiIrEoOEREJJayBoeZzTKzlWa22szmlGivMrO7QvuLZlZf0HZNqF9pZp8OdceY2VNmtsLMlpvZ1eUcv4iI7K9swWFmSeBG4CxgInCJmU0s6nYFsM3djwV+DPwwLDsRmA1MAmYBN4X1tQPfdPeJwCnA10qsU0REyqicRxwzgNXu/pa7twILgPOK+pwH3BbmFwIzzcxC/QJ33+fubwOrgRnu/o67/w7A3ZuB14GxZdwGEREpUs7gGAusKyivZ/+dfL6Pu7cDO4ARvVk2nNaaBrxY6s7N7EozazSzxqampgPeCBER6WxAXhw3s8HAvcA33H1nqT7ufrO7N7h7w6hRow7uAEVEDmPlDI4NwDEF5XGhrmQfM6sAhgFbulvWzFJEoXGHu99XlpGLiEiXyhkcLwPHmdl4M6skuti9qKjPIuBLYf4C4El391A/O3zqajxwHPBSuP5xK/C6u19XxrGLiEgXKsq1YndvN7OvA48BSWCuuy83s2uBRndfRBQC88xsNbCVKFwI/e4GVhB9kupr7p4xs9OALwK/N7NXwl19290fKdd2iIhIZxa9wT+8NTQ0eGNjY38PQ0RkwDCzJe7eUKptQF4cFxGR/qPgEBGRWBQcIiISi4JDRERiUXCIiEgsCg4REYlFwSEiIrEoOEREJBYFh4iIxKLgEBGRWBQcIiISi4JDRERiUXCIiEgsZftadZFDnbuzs6Wd5pY2jqippLYySfSTLyLSHQWHHBYyWWf7nla27Wlj+55Wtu5uZfueNrbtaWXrnla2747mtxX02banjUy242cFBqWSjBpSFU2Dqzrmh1QxcnDhfCVVFcl+3FqR/qXg6MaazbuprEgwKJVkUGWSqoqE3pEeBK3t2fyOPQqAaH7bnla27S6YLyjvbGmjq5+WqUwmOKImxfDaSo6oSXHckYOpq62kriZFXU0lg6sq2LG3jabmfTTt2kdT8z7ebNrFi29vYduetpLrHDYoVTJgist1NZUkE3rOyOFFwdGNs294lj2tmU51uRAZlEpSnUoUzCeL2qL5mnDbVfugUF9dmcjPVyQ7Lj1ls05rJktrJktbe+7Wac1kaG2P2toyWVpDW2t7R7mj3ver31fcL5Oltd1py2TJuuMOTrgtnAcoKrt7uM21F5YL1xH1zSleb0tbhu172ti1r73L/5NBqWQ+AOpqKhlXV0NdTYojaioZXpMKgRBNubCoeR+noFrbs2zZHYVJp2lXx/yr67ezaec+9rZl9ls+mTBG1FbuFyy5I5hBqSTtWSeTddqzWdozufnO5bZslkzG832Ly537Opkeyu5OMt1+ZxoAAASUSURBVGGkkglSyei2IpGgssKoSCQ61yeNynAb1Re2JahMhmUqEqQSxct09M3Vu0dHiG2ZbBh/lrbc2DLROHPbH9U57aFv/rZwPlNUzmY71bVlnUzGybpTkTSSiWic+fmk5R+LioRFUzIR6qJtq8jdhuUqOvUt6pMM9QXrTiaMhBmJhJE0I5GAhIX5UM73MSNhUflQfaOq4OjGDz4/hb2t7extzbC3LcvetkxUbsuwtzVLS1smzGdobmmnqXlfvry3LUNLW4a2TPxfWMw9WXMvrL6UMKisiF7EVeE2V64ML/JEwjDALHcLhhH+YQkwElF9aMs9vzsv07kcehS0dSxrBlUVybDTT3FEbSXDc/M1lfmwqE4d3FNElRUJRg8bxOhhg3rsu3tfez5UNheFS65+5bvNNDXve1//r7kdUSrc5ndyCSNZsINLFu4cQ7kqlaAmkSBhHTvvlrYsu1raaQ076LZMtCPPPf/a2rO0FezcDzUJo9OOPJXf6Sfyj0EqET1fc6HclukIpVyothUE1aHCrCNgLIRJ4XynMDJIhLqoDUbUVnH3Vz/a5+NScHTj3JPGvO91RC/MECStIXxCuBQGTy5ocvNtmWzHDr0i2ql32sHn64zKZJJU0roOhILlddqkfGqrKqitqqB+ZG23/bJZZ8feNjY176O1PVuwg492cMnwjrW4nKvrz3eh2XC0kzsKaA1HCJ3CJuOhPtRlo6Pl3A4bKNi5F76TL/Uuv2C+VH0i2nH2JXcn63QcEXUKlc5HRPsd7WQK6zvqMtnoiCfrTiZLfj6bdTIePa5ZdzLhaD8TjiQ91GVDn2g9FKwrtx5Cv4J1ujOkqjy7eAVHmeUO0YdUp/p7KHKISCQsOqVWW9nfQ4ktkTCqEknKtD86JJgZSYNkQh+A6Ir+jkNERGJRcIiISCwKDhERiUXBISIisSg4REQkFgWHiIjEouAQEZFYFBwiIhKLFX530OHKzJqAtf09DhGRAeRD7j6qVMMHIjhERKTv6FSViIjEouAQEZFYFBwiIhKLgkOkD5hZxsxeKZjm9OG6681sWV+tT+T9Ooy/HFnkoNrr7lP7exAiB4OOOETKyMzWmNm/mNnvzewlMzs21Neb2ZNm9pqZPWFmfxTqjzKz+83s1TB9LKwqaWY/M7PlZva4mfX8k4QiZaLgEOkbg4pOVV1c0LbD3U8EfgJcH+r+H3Cbu08B7gBuCPU3AE+7+0nAdGB5qD8OuNHdJwHbgc+XeXtEuqS/4xDpA2a2y90Hl6hfA/ypu79lZingXXcfYWabgdHu3hbq33H3keGPVce5+76CddQD/+Hux4Xy3wEpd/9++bdMZH864hApP+9iPo59BfMZdH1S+pGCQ6T8Li64fT7M/xaYHeYvBZ4N808AVwGYWdLMhh2sQYr0lt61iPSNQWb2SkH5UXfPfSS3zsxeIzpquCTU/RXwczP7W6AJuDzUXw3cbGZXEB1ZXAW8U/bRi8SgaxwiZRSucTS4++b+HotIX9GpKhERiUVHHCIiEouOOEREJBYFh4iIxKLgEBGRWBQcIiISi4JDRERi+f9CR9hSEK7ktgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretty_plot(history, 'accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "fkjU38WBQ94O",
        "outputId": "313a2764-477d-4ad3-b7b0-7185eb36d77c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resnet_model.load_weights('ptbd_weights.hdf5')\n",
        "resnet_model.evaluate(X_ptbdbl_test, y_ptbdbl_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FiKMiPzDQ_tg",
        "outputId": "3bc21a57-4c9e-47b0-aae0-699b978ee5bb"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "46/46 [==============================] - 0s 7ms/step - loss: 0.0171 - accuracy: 0.9952\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.017123641446232796, 0.995192289352417]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = (resnet_model.predict(X_ptbdbl_test) > 0.5).astype(\"int32\")\n",
        "\n",
        "cnf_matrix = sklearn.metrics.confusion_matrix(y_ptbdb_test, y_pred)\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=['Normal', 'Abnormal'],\n",
        "                      title='Confusion matrix, without normalization')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "ltuC_Sz_RnUc",
        "outputId": "63b75d5b-cc66-4d51-c1fb-862f8bf2ddc8"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "46/46 [==============================] - 1s 4ms/step\n",
            "Confusion matrix, without normalization\n",
            "[[ 422    6]\n",
            " [   1 1027]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ResNet MIT-BIH"
      ],
      "metadata": {
        "id": "30S1xdUWTXxG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_mitbih_train, X_mitbih_val, y_mitbih_train, y_mitbih_val = train_test_split(mitbih_train.iloc[:,:-1].values, mitbih_train.iloc[:,-1].values, test_size=0.1, random_state=42)\n",
        "X_mitbih_test = mitbih_test.iloc[:,:-1].values\n",
        "y_mitbih_test = mitbih_test.iloc[:,-1].values\n",
        "\n",
        "X_mitbihl_train = np.expand_dims(X_mitbih_train, -1)\n",
        "y_mitbihl_train = np.expand_dims(y_mitbih_train, -1)\n",
        "X_mitbihl_val = np.expand_dims(X_mitbih_val, -1)\n",
        "y_mitbihl_val = np.expand_dims(y_mitbih_val, -1)\n",
        "X_mitbihl_test = np.expand_dims(X_mitbih_test, -1)\n",
        "y_mitbihl_test = np.expand_dims(y_mitbih_test, -1)"
      ],
      "metadata": {
        "id": "FHEK9cg3TnIn"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = keras.optimizers.Adam(lr=0.001)\n",
        "model = get_resnet_model(5) \n",
        "model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "lr_finder = LRFinder(start_lr=1e-6, end_lr= 1e-01, max_steps=45, smoothing=0.6)\n",
        "model.fit(X_mitbihl_train, y_mitbihl_train, batch_size=256, epochs=5, callbacks=[lr_finder], verbose=False)\n",
        "lr_finder.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "6tUA-bqrT1Y_",
        "outputId": "4d60a19a-0053-42d7-b9c1-071d8b825588"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m_resnet_model = get_resnet_model(5)\n",
        "lr_schedule = CyclicalLearningRate(1e-5, 1e-3, step_size=step_size, scale_fn=lambda x: 1 / (2.0 ** (x - 1)))\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)\n",
        "save_weights = ModelCheckpoint(filepath=\"mit_weights.hdf5\", verbose=0, save_best_only=True)\n",
        "\n",
        "m_resnet_model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "history = m_resnet_model.fit(X_mitbihl_train, y_mitbihl_train, validation_data=(X_mitbihl_val, y_mitbihl_val), \n",
        "                             shuffle=True, batch_size=128, epochs=50, callbacks=[save_weights])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_BJ1irGUAUt",
        "outputId": "617761ed-f371-473b-c693-c4254f10d7f0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "616/616 [==============================] - 18s 22ms/step - loss: 0.2003 - accuracy: 0.9404 - val_loss: 0.2228 - val_accuracy: 0.9427\n",
            "Epoch 2/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0755 - accuracy: 0.9789 - val_loss: 0.0707 - val_accuracy: 0.9792\n",
            "Epoch 3/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0599 - accuracy: 0.9832 - val_loss: 0.0612 - val_accuracy: 0.9818\n",
            "Epoch 4/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0528 - accuracy: 0.9853 - val_loss: 0.0598 - val_accuracy: 0.9824\n",
            "Epoch 5/50\n",
            "616/616 [==============================] - 13s 21ms/step - loss: 0.0489 - accuracy: 0.9862 - val_loss: 0.0582 - val_accuracy: 0.9828\n",
            "Epoch 6/50\n",
            "616/616 [==============================] - 13s 21ms/step - loss: 0.0469 - accuracy: 0.9864 - val_loss: 0.0572 - val_accuracy: 0.9828\n",
            "Epoch 7/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0455 - accuracy: 0.9868 - val_loss: 0.0569 - val_accuracy: 0.9832\n",
            "Epoch 8/50\n",
            "616/616 [==============================] - 13s 21ms/step - loss: 0.0441 - accuracy: 0.9873 - val_loss: 0.0565 - val_accuracy: 0.9826\n",
            "Epoch 9/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0429 - accuracy: 0.9874 - val_loss: 0.0554 - val_accuracy: 0.9834\n",
            "Epoch 10/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0417 - accuracy: 0.9879 - val_loss: 0.0552 - val_accuracy: 0.9833\n",
            "Epoch 11/50\n",
            "616/616 [==============================] - 13s 20ms/step - loss: 0.0409 - accuracy: 0.9879 - val_loss: 0.0546 - val_accuracy: 0.9836\n",
            "Epoch 12/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0397 - accuracy: 0.9883 - val_loss: 0.0549 - val_accuracy: 0.9840\n",
            "Epoch 13/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0383 - accuracy: 0.9887 - val_loss: 0.0536 - val_accuracy: 0.9840\n",
            "Epoch 14/50\n",
            "616/616 [==============================] - 13s 20ms/step - loss: 0.0376 - accuracy: 0.9888 - val_loss: 0.0534 - val_accuracy: 0.9837\n",
            "Epoch 15/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0364 - accuracy: 0.9891 - val_loss: 0.0535 - val_accuracy: 0.9837\n",
            "Epoch 16/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0357 - accuracy: 0.9893 - val_loss: 0.0531 - val_accuracy: 0.9839\n",
            "Epoch 17/50\n",
            "616/616 [==============================] - 13s 21ms/step - loss: 0.0345 - accuracy: 0.9893 - val_loss: 0.0529 - val_accuracy: 0.9841\n",
            "Epoch 18/50\n",
            "616/616 [==============================] - 13s 20ms/step - loss: 0.0333 - accuracy: 0.9900 - val_loss: 0.0519 - val_accuracy: 0.9838\n",
            "Epoch 19/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0330 - accuracy: 0.9902 - val_loss: 0.0525 - val_accuracy: 0.9839\n",
            "Epoch 20/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0322 - accuracy: 0.9903 - val_loss: 0.0532 - val_accuracy: 0.9848\n",
            "Epoch 21/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0310 - accuracy: 0.9905 - val_loss: 0.0513 - val_accuracy: 0.9840\n",
            "Epoch 22/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0302 - accuracy: 0.9910 - val_loss: 0.0520 - val_accuracy: 0.9848\n",
            "Epoch 23/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0296 - accuracy: 0.9911 - val_loss: 0.0521 - val_accuracy: 0.9846\n",
            "Epoch 24/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0287 - accuracy: 0.9912 - val_loss: 0.0525 - val_accuracy: 0.9841\n",
            "Epoch 25/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0283 - accuracy: 0.9912 - val_loss: 0.0517 - val_accuracy: 0.9848\n",
            "Epoch 26/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0278 - accuracy: 0.9917 - val_loss: 0.0510 - val_accuracy: 0.9844\n",
            "Epoch 27/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0268 - accuracy: 0.9916 - val_loss: 0.0504 - val_accuracy: 0.9845\n",
            "Epoch 28/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0263 - accuracy: 0.9920 - val_loss: 0.0520 - val_accuracy: 0.9841\n",
            "Epoch 29/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0253 - accuracy: 0.9924 - val_loss: 0.0511 - val_accuracy: 0.9845\n",
            "Epoch 30/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0249 - accuracy: 0.9928 - val_loss: 0.0508 - val_accuracy: 0.9844\n",
            "Epoch 31/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0240 - accuracy: 0.9930 - val_loss: 0.0509 - val_accuracy: 0.9840\n",
            "Epoch 32/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0236 - accuracy: 0.9929 - val_loss: 0.0511 - val_accuracy: 0.9846\n",
            "Epoch 33/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0227 - accuracy: 0.9932 - val_loss: 0.0507 - val_accuracy: 0.9846\n",
            "Epoch 34/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0228 - accuracy: 0.9932 - val_loss: 0.0518 - val_accuracy: 0.9848\n",
            "Epoch 35/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0216 - accuracy: 0.9936 - val_loss: 0.0514 - val_accuracy: 0.9839\n",
            "Epoch 36/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0211 - accuracy: 0.9936 - val_loss: 0.0520 - val_accuracy: 0.9849\n",
            "Epoch 37/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0206 - accuracy: 0.9937 - val_loss: 0.0517 - val_accuracy: 0.9849\n",
            "Epoch 38/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0200 - accuracy: 0.9941 - val_loss: 0.0509 - val_accuracy: 0.9848\n",
            "Epoch 39/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0196 - accuracy: 0.9944 - val_loss: 0.0537 - val_accuracy: 0.9848\n",
            "Epoch 40/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0194 - accuracy: 0.9942 - val_loss: 0.0519 - val_accuracy: 0.9849\n",
            "Epoch 41/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0184 - accuracy: 0.9948 - val_loss: 0.0518 - val_accuracy: 0.9848\n",
            "Epoch 42/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0182 - accuracy: 0.9945 - val_loss: 0.0531 - val_accuracy: 0.9845\n",
            "Epoch 43/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0176 - accuracy: 0.9947 - val_loss: 0.0537 - val_accuracy: 0.9854\n",
            "Epoch 44/50\n",
            "616/616 [==============================] - 12s 20ms/step - loss: 0.0169 - accuracy: 0.9951 - val_loss: 0.0521 - val_accuracy: 0.9846\n",
            "Epoch 45/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0169 - accuracy: 0.9951 - val_loss: 0.0527 - val_accuracy: 0.9841\n",
            "Epoch 46/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0161 - accuracy: 0.9953 - val_loss: 0.0548 - val_accuracy: 0.9849\n",
            "Epoch 47/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0157 - accuracy: 0.9957 - val_loss: 0.0531 - val_accuracy: 0.9844\n",
            "Epoch 48/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0154 - accuracy: 0.9954 - val_loss: 0.0536 - val_accuracy: 0.9846\n",
            "Epoch 49/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0151 - accuracy: 0.9956 - val_loss: 0.0532 - val_accuracy: 0.9844\n",
            "Epoch 50/50\n",
            "616/616 [==============================] - 12s 19ms/step - loss: 0.0148 - accuracy: 0.9957 - val_loss: 0.0549 - val_accuracy: 0.9849\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretty_plot(history, 'loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "WCHq_pX9UWKn",
        "outputId": "4a4173aa-9cf3-479c-b34c-681404ba1cd2"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretty_plot(history, 'accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "4wAf3rg2UcSK",
        "outputId": "c7b0a49a-f54d-4448-deeb-4a59c440aa33"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m_resnet_model.load_weights('mit_weights.hdf5')\n",
        "X_mitbih_test = m_resnet_model.evaluate(X_mitbihl_test, y_mitbihl_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E83CciGKUkVd",
        "outputId": "01c75b46-51b0-430f-98f1-cab21f9a0fa1"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "685/685 [==============================] - 4s 5ms/step - loss: 0.0609 - accuracy: 0.9835\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = tf.argmax(m_resnet_model.predict(X_mitbihl_test), axis=-1)\n",
        "cnf_matrix = sklearn.metrics.confusion_matrix(y_mitbih_test, y_pred)\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],\n",
        "                      title='Confusion matrix, without normalization')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "OhH1JQDwUomy",
        "outputId": "a5cb0516-4148-4a07-a641-40da434c04ec"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "685/685 [==============================] - 3s 4ms/step\n",
            "Confusion matrix, without normalization\n",
            "[[18031    45    23     8    11]\n",
            " [  126   420    10     0     0]\n",
            " [   44     4  1377    18     5]\n",
            " [   24     0    18   120     0]\n",
            " [   19     1     5     0  1583]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m_resnet_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zY_O5GMnWz6F",
        "outputId": "2ffaa7b9-acea-4164-dedb-1c93ca82f194"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_5\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_8 (InputLayer)           [(None, 187, 1)]     0           []                               \n",
            "                                                                                                  \n",
            " conv1d_101 (Conv1D)            (None, 183, 32)      192         ['input_8[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_101 (Batch  (None, 183, 32)     128         ['conv1d_101[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_99 (ReLU)                (None, 183, 32)      0           ['batch_normalization_101[0][0]']\n",
            "                                                                                                  \n",
            " max_pooling1d_7 (MaxPooling1D)  (None, 90, 32)      0           ['re_lu_99[0][0]']               \n",
            "                                                                                                  \n",
            " conv1d_102 (Conv1D)            (None, 90, 32)       5152        ['max_pooling1d_7[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_102 (Batch  (None, 90, 32)      128         ['conv1d_102[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_100 (ReLU)               (None, 90, 32)       0           ['batch_normalization_102[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_103 (Conv1D)            (None, 90, 32)       5152        ['re_lu_100[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_103 (Batch  (None, 90, 32)      128         ['conv1d_103[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_47 (Add)                   (None, 90, 32)       0           ['max_pooling1d_7[0][0]',        \n",
            "                                                                  'batch_normalization_103[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_101 (ReLU)               (None, 90, 32)       0           ['add_47[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_104 (Conv1D)            (None, 90, 32)       5152        ['re_lu_101[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_104 (Batch  (None, 90, 32)      128         ['conv1d_104[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_102 (ReLU)               (None, 90, 32)       0           ['batch_normalization_104[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_105 (Conv1D)            (None, 90, 32)       5152        ['re_lu_102[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_105 (Batch  (None, 90, 32)      128         ['conv1d_105[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_48 (Add)                   (None, 90, 32)       0           ['re_lu_101[0][0]',              \n",
            "                                                                  'batch_normalization_105[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_103 (ReLU)               (None, 90, 32)       0           ['add_48[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_106 (Conv1D)            (None, 90, 32)       5152        ['re_lu_103[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_106 (Batch  (None, 90, 32)      128         ['conv1d_106[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_104 (ReLU)               (None, 90, 32)       0           ['batch_normalization_106[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_107 (Conv1D)            (None, 90, 32)       5152        ['re_lu_104[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_107 (Batch  (None, 90, 32)      128         ['conv1d_107[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_49 (Add)                   (None, 90, 32)       0           ['re_lu_103[0][0]',              \n",
            "                                                                  'batch_normalization_107[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_105 (ReLU)               (None, 90, 32)       0           ['add_49[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_108 (Conv1D)            (None, 90, 32)       5152        ['re_lu_105[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_108 (Batch  (None, 90, 32)      128         ['conv1d_108[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_106 (ReLU)               (None, 90, 32)       0           ['batch_normalization_108[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_109 (Conv1D)            (None, 90, 32)       5152        ['re_lu_106[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_109 (Batch  (None, 90, 32)      128         ['conv1d_109[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_50 (Add)                   (None, 90, 32)       0           ['re_lu_105[0][0]',              \n",
            "                                                                  'batch_normalization_109[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_107 (ReLU)               (None, 90, 32)       0           ['add_50[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_110 (Conv1D)            (None, 90, 32)       5152        ['re_lu_107[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_110 (Batch  (None, 90, 32)      128         ['conv1d_110[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_108 (ReLU)               (None, 90, 32)       0           ['batch_normalization_110[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_111 (Conv1D)            (None, 90, 32)       5152        ['re_lu_108[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_111 (Batch  (None, 90, 32)      128         ['conv1d_111[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_51 (Add)                   (None, 90, 32)       0           ['re_lu_107[0][0]',              \n",
            "                                                                  'batch_normalization_111[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_109 (ReLU)               (None, 90, 32)       0           ['add_51[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_112 (Conv1D)            (None, 90, 32)       5152        ['re_lu_109[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_112 (Batch  (None, 90, 32)      128         ['conv1d_112[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_110 (ReLU)               (None, 90, 32)       0           ['batch_normalization_112[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_113 (Conv1D)            (None, 90, 32)       5152        ['re_lu_110[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_113 (Batch  (None, 90, 32)      128         ['conv1d_113[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_52 (Add)                   (None, 90, 32)       0           ['re_lu_109[0][0]',              \n",
            "                                                                  'batch_normalization_113[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_111 (ReLU)               (None, 90, 32)       0           ['add_52[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_114 (Conv1D)            (None, 90, 32)       5152        ['re_lu_111[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_114 (Batch  (None, 90, 32)      128         ['conv1d_114[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_112 (ReLU)               (None, 90, 32)       0           ['batch_normalization_114[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_115 (Conv1D)            (None, 90, 32)       5152        ['re_lu_112[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_115 (Batch  (None, 90, 32)      128         ['conv1d_115[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_53 (Add)                   (None, 90, 32)       0           ['re_lu_111[0][0]',              \n",
            "                                                                  'batch_normalization_115[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_113 (ReLU)               (None, 90, 32)       0           ['add_53[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_116 (Conv1D)            (None, 90, 32)       5152        ['re_lu_113[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_116 (Batch  (None, 90, 32)      128         ['conv1d_116[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_114 (ReLU)               (None, 90, 32)       0           ['batch_normalization_116[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_117 (Conv1D)            (None, 90, 32)       5152        ['re_lu_114[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_117 (Batch  (None, 90, 32)      128         ['conv1d_117[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_54 (Add)                   (None, 90, 32)       0           ['re_lu_113[0][0]',              \n",
            "                                                                  'batch_normalization_117[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_115 (ReLU)               (None, 90, 32)       0           ['add_54[0][0]']                 \n",
            "                                                                                                  \n",
            " average_pooling1d_5 (AveragePo  (None, 43, 32)      0           ['re_lu_115[0][0]']              \n",
            " oling1D)                                                                                         \n",
            "                                                                                                  \n",
            " flatten_5 (Flatten)            (None, 1376)         0           ['average_pooling1d_5[0][0]']    \n",
            "                                                                                                  \n",
            " dense_15 (Dense)               (None, 32)           44064       ['flatten_5[0][0]']              \n",
            "                                                                                                  \n",
            " dense_16 (Dense)               (None, 32)           1056        ['dense_15[0][0]']               \n",
            "                                                                                                  \n",
            " dense_17 (Dense)               (None, 5)            165         ['dense_16[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 130,085\n",
            "Trainable params: 128,997\n",
            "Non-trainable params: 1,088\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resnet_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nw5KSsPotZBh",
        "outputId": "d7708fb7-cfe7-4cde-dd18-86144974695c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_6 (InputLayer)           [(None, 187, 1)]     0           []                               \n",
            "                                                                                                  \n",
            " conv1d_67 (Conv1D)             (None, 183, 32)      192         ['input_6[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_67 (BatchN  (None, 183, 32)     128         ['conv1d_67[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_65 (ReLU)                (None, 183, 32)      0           ['batch_normalization_67[0][0]'] \n",
            "                                                                                                  \n",
            " max_pooling1d_5 (MaxPooling1D)  (None, 90, 32)      0           ['re_lu_65[0][0]']               \n",
            "                                                                                                  \n",
            " conv1d_68 (Conv1D)             (None, 90, 32)       5152        ['max_pooling1d_5[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_68 (BatchN  (None, 90, 32)      128         ['conv1d_68[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_66 (ReLU)                (None, 90, 32)       0           ['batch_normalization_68[0][0]'] \n",
            "                                                                                                  \n",
            " conv1d_69 (Conv1D)             (None, 90, 32)       5152        ['re_lu_66[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_69 (BatchN  (None, 90, 32)      128         ['conv1d_69[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " add_31 (Add)                   (None, 90, 32)       0           ['max_pooling1d_5[0][0]',        \n",
            "                                                                  'batch_normalization_69[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu_67 (ReLU)                (None, 90, 32)       0           ['add_31[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_70 (Conv1D)             (None, 90, 32)       5152        ['re_lu_67[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_70 (BatchN  (None, 90, 32)      128         ['conv1d_70[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_68 (ReLU)                (None, 90, 32)       0           ['batch_normalization_70[0][0]'] \n",
            "                                                                                                  \n",
            " conv1d_71 (Conv1D)             (None, 90, 32)       5152        ['re_lu_68[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_71 (BatchN  (None, 90, 32)      128         ['conv1d_71[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " add_32 (Add)                   (None, 90, 32)       0           ['re_lu_67[0][0]',               \n",
            "                                                                  'batch_normalization_71[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu_69 (ReLU)                (None, 90, 32)       0           ['add_32[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_72 (Conv1D)             (None, 90, 32)       5152        ['re_lu_69[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_72 (BatchN  (None, 90, 32)      128         ['conv1d_72[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_70 (ReLU)                (None, 90, 32)       0           ['batch_normalization_72[0][0]'] \n",
            "                                                                                                  \n",
            " conv1d_73 (Conv1D)             (None, 90, 32)       5152        ['re_lu_70[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_73 (BatchN  (None, 90, 32)      128         ['conv1d_73[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " add_33 (Add)                   (None, 90, 32)       0           ['re_lu_69[0][0]',               \n",
            "                                                                  'batch_normalization_73[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu_71 (ReLU)                (None, 90, 32)       0           ['add_33[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_74 (Conv1D)             (None, 90, 32)       5152        ['re_lu_71[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_74 (BatchN  (None, 90, 32)      128         ['conv1d_74[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_72 (ReLU)                (None, 90, 32)       0           ['batch_normalization_74[0][0]'] \n",
            "                                                                                                  \n",
            " conv1d_75 (Conv1D)             (None, 90, 32)       5152        ['re_lu_72[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_75 (BatchN  (None, 90, 32)      128         ['conv1d_75[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " add_34 (Add)                   (None, 90, 32)       0           ['re_lu_71[0][0]',               \n",
            "                                                                  'batch_normalization_75[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu_73 (ReLU)                (None, 90, 32)       0           ['add_34[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_76 (Conv1D)             (None, 90, 32)       5152        ['re_lu_73[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_76 (BatchN  (None, 90, 32)      128         ['conv1d_76[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_74 (ReLU)                (None, 90, 32)       0           ['batch_normalization_76[0][0]'] \n",
            "                                                                                                  \n",
            " conv1d_77 (Conv1D)             (None, 90, 32)       5152        ['re_lu_74[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_77 (BatchN  (None, 90, 32)      128         ['conv1d_77[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " add_35 (Add)                   (None, 90, 32)       0           ['re_lu_73[0][0]',               \n",
            "                                                                  'batch_normalization_77[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu_75 (ReLU)                (None, 90, 32)       0           ['add_35[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_78 (Conv1D)             (None, 90, 32)       5152        ['re_lu_75[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_78 (BatchN  (None, 90, 32)      128         ['conv1d_78[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_76 (ReLU)                (None, 90, 32)       0           ['batch_normalization_78[0][0]'] \n",
            "                                                                                                  \n",
            " conv1d_79 (Conv1D)             (None, 90, 32)       5152        ['re_lu_76[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_79 (BatchN  (None, 90, 32)      128         ['conv1d_79[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " add_36 (Add)                   (None, 90, 32)       0           ['re_lu_75[0][0]',               \n",
            "                                                                  'batch_normalization_79[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu_77 (ReLU)                (None, 90, 32)       0           ['add_36[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_80 (Conv1D)             (None, 90, 32)       5152        ['re_lu_77[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_80 (BatchN  (None, 90, 32)      128         ['conv1d_80[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_78 (ReLU)                (None, 90, 32)       0           ['batch_normalization_80[0][0]'] \n",
            "                                                                                                  \n",
            " conv1d_81 (Conv1D)             (None, 90, 32)       5152        ['re_lu_78[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_81 (BatchN  (None, 90, 32)      128         ['conv1d_81[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " add_37 (Add)                   (None, 90, 32)       0           ['re_lu_77[0][0]',               \n",
            "                                                                  'batch_normalization_81[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu_79 (ReLU)                (None, 90, 32)       0           ['add_37[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_82 (Conv1D)             (None, 90, 32)       5152        ['re_lu_79[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_82 (BatchN  (None, 90, 32)      128         ['conv1d_82[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_80 (ReLU)                (None, 90, 32)       0           ['batch_normalization_82[0][0]'] \n",
            "                                                                                                  \n",
            " conv1d_83 (Conv1D)             (None, 90, 32)       5152        ['re_lu_80[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_83 (BatchN  (None, 90, 32)      128         ['conv1d_83[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " add_38 (Add)                   (None, 90, 32)       0           ['re_lu_79[0][0]',               \n",
            "                                                                  'batch_normalization_83[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu_81 (ReLU)                (None, 90, 32)       0           ['add_38[0][0]']                 \n",
            "                                                                                                  \n",
            " average_pooling1d_3 (AveragePo  (None, 43, 32)      0           ['re_lu_81[0][0]']               \n",
            " oling1D)                                                                                         \n",
            "                                                                                                  \n",
            " flatten_3 (Flatten)            (None, 1376)         0           ['average_pooling1d_3[0][0]']    \n",
            "                                                                                                  \n",
            " dense_9 (Dense)                (None, 32)           44064       ['flatten_3[0][0]']              \n",
            "                                                                                                  \n",
            " dense_10 (Dense)               (None, 32)           1056        ['dense_9[0][0]']                \n",
            "                                                                                                  \n",
            " dense_11 (Dense)               (None, 1)            33          ['dense_10[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 129,953\n",
            "Trainable params: 128,865\n",
            "Non-trainable params: 1,088\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_transfer_model(model):\n",
        "  model.trainable = False\n",
        "  model_input = model.inputs\n",
        "  model_output = model.layers[-4].output\n",
        "\n",
        "  X = keras.layers.Dense(64, activation='relu')(model_output)\n",
        "  X = keras.layers.Dense(32, activation='relu')(X)\n",
        "  out = keras.layers.Dense(1, activation='sigmoid')(X)\n",
        "\n",
        "  transfer_model = keras.Model(inputs=model_input, outputs=out)\n",
        "  return transfer_model\n",
        "\n",
        "get_transfer_model(m_resnet_model).summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kccmX1HbW_gV",
        "outputId": "874fd9b1-30ed-4b1f-c942-3622aed3ca31"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_6\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_8 (InputLayer)           [(None, 187, 1)]     0           []                               \n",
            "                                                                                                  \n",
            " conv1d_101 (Conv1D)            (None, 183, 32)      192         ['input_8[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_101 (Batch  (None, 183, 32)     128         ['conv1d_101[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_99 (ReLU)                (None, 183, 32)      0           ['batch_normalization_101[0][0]']\n",
            "                                                                                                  \n",
            " max_pooling1d_7 (MaxPooling1D)  (None, 90, 32)      0           ['re_lu_99[0][0]']               \n",
            "                                                                                                  \n",
            " conv1d_102 (Conv1D)            (None, 90, 32)       5152        ['max_pooling1d_7[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_102 (Batch  (None, 90, 32)      128         ['conv1d_102[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_100 (ReLU)               (None, 90, 32)       0           ['batch_normalization_102[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_103 (Conv1D)            (None, 90, 32)       5152        ['re_lu_100[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_103 (Batch  (None, 90, 32)      128         ['conv1d_103[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_47 (Add)                   (None, 90, 32)       0           ['max_pooling1d_7[0][0]',        \n",
            "                                                                  'batch_normalization_103[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_101 (ReLU)               (None, 90, 32)       0           ['add_47[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_104 (Conv1D)            (None, 90, 32)       5152        ['re_lu_101[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_104 (Batch  (None, 90, 32)      128         ['conv1d_104[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_102 (ReLU)               (None, 90, 32)       0           ['batch_normalization_104[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_105 (Conv1D)            (None, 90, 32)       5152        ['re_lu_102[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_105 (Batch  (None, 90, 32)      128         ['conv1d_105[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_48 (Add)                   (None, 90, 32)       0           ['re_lu_101[0][0]',              \n",
            "                                                                  'batch_normalization_105[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_103 (ReLU)               (None, 90, 32)       0           ['add_48[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_106 (Conv1D)            (None, 90, 32)       5152        ['re_lu_103[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_106 (Batch  (None, 90, 32)      128         ['conv1d_106[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_104 (ReLU)               (None, 90, 32)       0           ['batch_normalization_106[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_107 (Conv1D)            (None, 90, 32)       5152        ['re_lu_104[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_107 (Batch  (None, 90, 32)      128         ['conv1d_107[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_49 (Add)                   (None, 90, 32)       0           ['re_lu_103[0][0]',              \n",
            "                                                                  'batch_normalization_107[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_105 (ReLU)               (None, 90, 32)       0           ['add_49[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_108 (Conv1D)            (None, 90, 32)       5152        ['re_lu_105[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_108 (Batch  (None, 90, 32)      128         ['conv1d_108[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_106 (ReLU)               (None, 90, 32)       0           ['batch_normalization_108[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_109 (Conv1D)            (None, 90, 32)       5152        ['re_lu_106[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_109 (Batch  (None, 90, 32)      128         ['conv1d_109[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_50 (Add)                   (None, 90, 32)       0           ['re_lu_105[0][0]',              \n",
            "                                                                  'batch_normalization_109[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_107 (ReLU)               (None, 90, 32)       0           ['add_50[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_110 (Conv1D)            (None, 90, 32)       5152        ['re_lu_107[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_110 (Batch  (None, 90, 32)      128         ['conv1d_110[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_108 (ReLU)               (None, 90, 32)       0           ['batch_normalization_110[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_111 (Conv1D)            (None, 90, 32)       5152        ['re_lu_108[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_111 (Batch  (None, 90, 32)      128         ['conv1d_111[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_51 (Add)                   (None, 90, 32)       0           ['re_lu_107[0][0]',              \n",
            "                                                                  'batch_normalization_111[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_109 (ReLU)               (None, 90, 32)       0           ['add_51[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_112 (Conv1D)            (None, 90, 32)       5152        ['re_lu_109[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_112 (Batch  (None, 90, 32)      128         ['conv1d_112[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_110 (ReLU)               (None, 90, 32)       0           ['batch_normalization_112[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_113 (Conv1D)            (None, 90, 32)       5152        ['re_lu_110[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_113 (Batch  (None, 90, 32)      128         ['conv1d_113[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_52 (Add)                   (None, 90, 32)       0           ['re_lu_109[0][0]',              \n",
            "                                                                  'batch_normalization_113[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_111 (ReLU)               (None, 90, 32)       0           ['add_52[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_114 (Conv1D)            (None, 90, 32)       5152        ['re_lu_111[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_114 (Batch  (None, 90, 32)      128         ['conv1d_114[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_112 (ReLU)               (None, 90, 32)       0           ['batch_normalization_114[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_115 (Conv1D)            (None, 90, 32)       5152        ['re_lu_112[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_115 (Batch  (None, 90, 32)      128         ['conv1d_115[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_53 (Add)                   (None, 90, 32)       0           ['re_lu_111[0][0]',              \n",
            "                                                                  'batch_normalization_115[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_113 (ReLU)               (None, 90, 32)       0           ['add_53[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_116 (Conv1D)            (None, 90, 32)       5152        ['re_lu_113[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_116 (Batch  (None, 90, 32)      128         ['conv1d_116[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_114 (ReLU)               (None, 90, 32)       0           ['batch_normalization_116[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_117 (Conv1D)            (None, 90, 32)       5152        ['re_lu_114[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_117 (Batch  (None, 90, 32)      128         ['conv1d_117[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_54 (Add)                   (None, 90, 32)       0           ['re_lu_113[0][0]',              \n",
            "                                                                  'batch_normalization_117[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_115 (ReLU)               (None, 90, 32)       0           ['add_54[0][0]']                 \n",
            "                                                                                                  \n",
            " average_pooling1d_5 (AveragePo  (None, 43, 32)      0           ['re_lu_115[0][0]']              \n",
            " oling1D)                                                                                         \n",
            "                                                                                                  \n",
            " flatten_5 (Flatten)            (None, 1376)         0           ['average_pooling1d_5[0][0]']    \n",
            "                                                                                                  \n",
            " dense_18 (Dense)               (None, 64)           88128       ['flatten_5[0][0]']              \n",
            "                                                                                                  \n",
            " dense_19 (Dense)               (None, 32)           2080        ['dense_18[0][0]']               \n",
            "                                                                                                  \n",
            " dense_20 (Dense)               (None, 1)            33          ['dense_19[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 175,041\n",
            "Trainable params: 90,241\n",
            "Non-trainable params: 84,800\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = keras.optimizers.Adam(lr=0.001)\n",
        "transfer_model = get_transfer_model(m_resnet_model)\n",
        "\n",
        "save_best_weights = ModelCheckpoint(filepath=\"t_weights.hdf5\", verbose=0, save_best_only=True)\n",
        "\n",
        "transfer_model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "history = transfer_model.fit(X_ptbdbl_train, y_ptbdbl_train, validation_data=(X_ptbdbl_val, y_ptbdbl_val), \n",
        "                             shuffle=True, batch_size=128, epochs=100, callbacks=[save_best_weights])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0IjA_wp8C1tb",
        "outputId": "5a845095-dc7f-4c43-880d-8613335cf4ab"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "91/91 [==============================] - 3s 15ms/step - loss: 0.3194 - accuracy: 0.8620 - val_loss: 0.2027 - val_accuracy: 0.9113\n",
            "Epoch 2/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.1673 - accuracy: 0.9314 - val_loss: 0.1534 - val_accuracy: 0.9409\n",
            "Epoch 3/100\n",
            "91/91 [==============================] - 1s 8ms/step - loss: 0.1219 - accuracy: 0.9546 - val_loss: 0.1095 - val_accuracy: 0.9608\n",
            "Epoch 4/100\n",
            "91/91 [==============================] - 1s 10ms/step - loss: 0.0964 - accuracy: 0.9656 - val_loss: 0.0826 - val_accuracy: 0.9698\n",
            "Epoch 5/100\n",
            "91/91 [==============================] - 1s 11ms/step - loss: 0.0739 - accuracy: 0.9740 - val_loss: 0.0534 - val_accuracy: 0.9849\n",
            "Epoch 6/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.0646 - accuracy: 0.9779 - val_loss: 0.0586 - val_accuracy: 0.9814\n",
            "Epoch 7/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 0.0595 - accuracy: 0.9796 - val_loss: 0.0604 - val_accuracy: 0.9753\n",
            "Epoch 8/100\n",
            "91/91 [==============================] - 1s 8ms/step - loss: 0.0428 - accuracy: 0.9860 - val_loss: 0.0549 - val_accuracy: 0.9787\n",
            "Epoch 9/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.0367 - accuracy: 0.9872 - val_loss: 0.0343 - val_accuracy: 0.9897\n",
            "Epoch 10/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.0411 - accuracy: 0.9845 - val_loss: 0.0336 - val_accuracy: 0.9904\n",
            "Epoch 11/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.0307 - accuracy: 0.9902 - val_loss: 0.0319 - val_accuracy: 0.9897\n",
            "Epoch 12/100\n",
            "91/91 [==============================] - 1s 8ms/step - loss: 0.0290 - accuracy: 0.9906 - val_loss: 0.0817 - val_accuracy: 0.9649\n",
            "Epoch 13/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.0254 - accuracy: 0.9910 - val_loss: 0.0275 - val_accuracy: 0.9924\n",
            "Epoch 14/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.0181 - accuracy: 0.9939 - val_loss: 0.0246 - val_accuracy: 0.9931\n",
            "Epoch 15/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 0.0185 - accuracy: 0.9941 - val_loss: 0.0267 - val_accuracy: 0.9918\n",
            "Epoch 16/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 0.0141 - accuracy: 0.9954 - val_loss: 0.0298 - val_accuracy: 0.9897\n",
            "Epoch 17/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 0.0106 - accuracy: 0.9972 - val_loss: 0.0266 - val_accuracy: 0.9911\n",
            "Epoch 18/100\n",
            "91/91 [==============================] - 1s 8ms/step - loss: 0.0104 - accuracy: 0.9971 - val_loss: 0.0657 - val_accuracy: 0.9759\n",
            "Epoch 19/100\n",
            "91/91 [==============================] - 1s 8ms/step - loss: 0.0172 - accuracy: 0.9942 - val_loss: 0.0262 - val_accuracy: 0.9924\n",
            "Epoch 20/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 0.0116 - accuracy: 0.9964 - val_loss: 0.0284 - val_accuracy: 0.9904\n",
            "Epoch 21/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.0087 - accuracy: 0.9979 - val_loss: 0.0245 - val_accuracy: 0.9897\n",
            "Epoch 22/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.0077 - accuracy: 0.9977 - val_loss: 0.0471 - val_accuracy: 0.9869\n",
            "Epoch 23/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.0059 - accuracy: 0.9986 - val_loss: 0.0259 - val_accuracy: 0.9918\n",
            "Epoch 24/100\n",
            "91/91 [==============================] - 1s 10ms/step - loss: 0.0043 - accuracy: 0.9994 - val_loss: 0.0199 - val_accuracy: 0.9945\n",
            "Epoch 25/100\n",
            "91/91 [==============================] - 1s 9ms/step - loss: 0.0032 - accuracy: 0.9994 - val_loss: 0.0171 - val_accuracy: 0.9945\n",
            "Epoch 26/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0190 - val_accuracy: 0.9938\n",
            "Epoch 27/100\n",
            "91/91 [==============================] - 1s 8ms/step - loss: 0.0015 - accuracy: 0.9999 - val_loss: 0.0185 - val_accuracy: 0.9945\n",
            "Epoch 28/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0242 - val_accuracy: 0.9931\n",
            "Epoch 29/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 8.8041e-04 - accuracy: 1.0000 - val_loss: 0.0201 - val_accuracy: 0.9945\n",
            "Epoch 30/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 7.1259e-04 - accuracy: 1.0000 - val_loss: 0.0215 - val_accuracy: 0.9931\n",
            "Epoch 31/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 6.4858e-04 - accuracy: 1.0000 - val_loss: 0.0193 - val_accuracy: 0.9945\n",
            "Epoch 32/100\n",
            "91/91 [==============================] - 1s 8ms/step - loss: 5.0696e-04 - accuracy: 1.0000 - val_loss: 0.0182 - val_accuracy: 0.9945\n",
            "Epoch 33/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 5.4610e-04 - accuracy: 1.0000 - val_loss: 0.0204 - val_accuracy: 0.9938\n",
            "Epoch 34/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 4.6524e-04 - accuracy: 1.0000 - val_loss: 0.0184 - val_accuracy: 0.9945\n",
            "Epoch 35/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 3.8673e-04 - accuracy: 1.0000 - val_loss: 0.0187 - val_accuracy: 0.9945\n",
            "Epoch 36/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 3.4780e-04 - accuracy: 1.0000 - val_loss: 0.0189 - val_accuracy: 0.9945\n",
            "Epoch 37/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 3.4085e-04 - accuracy: 1.0000 - val_loss: 0.0196 - val_accuracy: 0.9938\n",
            "Epoch 38/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 3.0766e-04 - accuracy: 1.0000 - val_loss: 0.0204 - val_accuracy: 0.9931\n",
            "Epoch 39/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.9489e-04 - accuracy: 1.0000 - val_loss: 0.0188 - val_accuracy: 0.9945\n",
            "Epoch 40/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.7195e-04 - accuracy: 1.0000 - val_loss: 0.0202 - val_accuracy: 0.9938\n",
            "Epoch 41/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.3946e-04 - accuracy: 1.0000 - val_loss: 0.0192 - val_accuracy: 0.9952\n",
            "Epoch 42/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.4197e-04 - accuracy: 1.0000 - val_loss: 0.0193 - val_accuracy: 0.9945\n",
            "Epoch 43/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.1637e-04 - accuracy: 1.0000 - val_loss: 0.0192 - val_accuracy: 0.9945\n",
            "Epoch 44/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.0901e-04 - accuracy: 1.0000 - val_loss: 0.0203 - val_accuracy: 0.9938\n",
            "Epoch 45/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.8954e-04 - accuracy: 1.0000 - val_loss: 0.0201 - val_accuracy: 0.9945\n",
            "Epoch 46/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.8640e-04 - accuracy: 1.0000 - val_loss: 0.0194 - val_accuracy: 0.9952\n",
            "Epoch 47/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.6848e-04 - accuracy: 1.0000 - val_loss: 0.0190 - val_accuracy: 0.9952\n",
            "Epoch 48/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.5527e-04 - accuracy: 1.0000 - val_loss: 0.0192 - val_accuracy: 0.9952\n",
            "Epoch 49/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.3785e-04 - accuracy: 1.0000 - val_loss: 0.0191 - val_accuracy: 0.9952\n",
            "Epoch 50/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.3203e-04 - accuracy: 1.0000 - val_loss: 0.0196 - val_accuracy: 0.9945\n",
            "Epoch 51/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.2549e-04 - accuracy: 1.0000 - val_loss: 0.0183 - val_accuracy: 0.9952\n",
            "Epoch 52/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.1609e-04 - accuracy: 1.0000 - val_loss: 0.0192 - val_accuracy: 0.9952\n",
            "Epoch 53/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.0501e-04 - accuracy: 1.0000 - val_loss: 0.0203 - val_accuracy: 0.9952\n",
            "Epoch 54/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.0475e-04 - accuracy: 1.0000 - val_loss: 0.0210 - val_accuracy: 0.9945\n",
            "Epoch 55/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 9.5058e-05 - accuracy: 1.0000 - val_loss: 0.0201 - val_accuracy: 0.9952\n",
            "Epoch 56/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 8.8158e-05 - accuracy: 1.0000 - val_loss: 0.0201 - val_accuracy: 0.9952\n",
            "Epoch 57/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 8.2430e-05 - accuracy: 1.0000 - val_loss: 0.0199 - val_accuracy: 0.9952\n",
            "Epoch 58/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 7.9057e-05 - accuracy: 1.0000 - val_loss: 0.0212 - val_accuracy: 0.9952\n",
            "Epoch 59/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 7.4469e-05 - accuracy: 1.0000 - val_loss: 0.0197 - val_accuracy: 0.9952\n",
            "Epoch 60/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 7.1303e-05 - accuracy: 1.0000 - val_loss: 0.0197 - val_accuracy: 0.9952\n",
            "Epoch 61/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 6.6909e-05 - accuracy: 1.0000 - val_loss: 0.0199 - val_accuracy: 0.9952\n",
            "Epoch 62/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 6.0654e-05 - accuracy: 1.0000 - val_loss: 0.0210 - val_accuracy: 0.9952\n",
            "Epoch 63/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 5.7317e-05 - accuracy: 1.0000 - val_loss: 0.0193 - val_accuracy: 0.9952\n",
            "Epoch 64/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 5.5594e-05 - accuracy: 1.0000 - val_loss: 0.0226 - val_accuracy: 0.9945\n",
            "Epoch 65/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 5.1789e-05 - accuracy: 1.0000 - val_loss: 0.0206 - val_accuracy: 0.9952\n",
            "Epoch 66/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 5.0256e-05 - accuracy: 1.0000 - val_loss: 0.0202 - val_accuracy: 0.9952\n",
            "Epoch 67/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 4.7791e-05 - accuracy: 1.0000 - val_loss: 0.0220 - val_accuracy: 0.9945\n",
            "Epoch 68/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 4.3877e-05 - accuracy: 1.0000 - val_loss: 0.0213 - val_accuracy: 0.9952\n",
            "Epoch 69/100\n",
            "91/91 [==============================] - 1s 8ms/step - loss: 4.1359e-05 - accuracy: 1.0000 - val_loss: 0.0209 - val_accuracy: 0.9952\n",
            "Epoch 70/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 3.8609e-05 - accuracy: 1.0000 - val_loss: 0.0212 - val_accuracy: 0.9952\n",
            "Epoch 71/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 3.6683e-05 - accuracy: 1.0000 - val_loss: 0.0210 - val_accuracy: 0.9952\n",
            "Epoch 72/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 3.5593e-05 - accuracy: 1.0000 - val_loss: 0.0216 - val_accuracy: 0.9952\n",
            "Epoch 73/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 3.0774e-05 - accuracy: 1.0000 - val_loss: 0.0213 - val_accuracy: 0.9952\n",
            "Epoch 74/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.9816e-05 - accuracy: 1.0000 - val_loss: 0.0210 - val_accuracy: 0.9952\n",
            "Epoch 75/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.7568e-05 - accuracy: 1.0000 - val_loss: 0.0231 - val_accuracy: 0.9952\n",
            "Epoch 76/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.7283e-05 - accuracy: 1.0000 - val_loss: 0.0226 - val_accuracy: 0.9952\n",
            "Epoch 77/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.5853e-05 - accuracy: 1.0000 - val_loss: 0.0218 - val_accuracy: 0.9952\n",
            "Epoch 78/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.4607e-05 - accuracy: 1.0000 - val_loss: 0.0222 - val_accuracy: 0.9952\n",
            "Epoch 79/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.2784e-05 - accuracy: 1.0000 - val_loss: 0.0228 - val_accuracy: 0.9952\n",
            "Epoch 80/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 2.2049e-05 - accuracy: 1.0000 - val_loss: 0.0223 - val_accuracy: 0.9952\n",
            "Epoch 81/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.9509e-05 - accuracy: 1.0000 - val_loss: 0.0222 - val_accuracy: 0.9952\n",
            "Epoch 82/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.8421e-05 - accuracy: 1.0000 - val_loss: 0.0222 - val_accuracy: 0.9952\n",
            "Epoch 83/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.7422e-05 - accuracy: 1.0000 - val_loss: 0.0224 - val_accuracy: 0.9952\n",
            "Epoch 84/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.6149e-05 - accuracy: 1.0000 - val_loss: 0.0225 - val_accuracy: 0.9952\n",
            "Epoch 85/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.6386e-05 - accuracy: 1.0000 - val_loss: 0.0230 - val_accuracy: 0.9952\n",
            "Epoch 86/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.5152e-05 - accuracy: 1.0000 - val_loss: 0.0231 - val_accuracy: 0.9952\n",
            "Epoch 87/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.3944e-05 - accuracy: 1.0000 - val_loss: 0.0222 - val_accuracy: 0.9952\n",
            "Epoch 88/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.3305e-05 - accuracy: 1.0000 - val_loss: 0.0234 - val_accuracy: 0.9952\n",
            "Epoch 89/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.3622e-05 - accuracy: 1.0000 - val_loss: 0.0241 - val_accuracy: 0.9952\n",
            "Epoch 90/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.1936e-05 - accuracy: 1.0000 - val_loss: 0.0233 - val_accuracy: 0.9952\n",
            "Epoch 91/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.0909e-05 - accuracy: 1.0000 - val_loss: 0.0234 - val_accuracy: 0.9952\n",
            "Epoch 92/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 1.1133e-05 - accuracy: 1.0000 - val_loss: 0.0233 - val_accuracy: 0.9952\n",
            "Epoch 93/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 9.9835e-06 - accuracy: 1.0000 - val_loss: 0.0234 - val_accuracy: 0.9952\n",
            "Epoch 94/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 9.3075e-06 - accuracy: 1.0000 - val_loss: 0.0241 - val_accuracy: 0.9952\n",
            "Epoch 95/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 9.2126e-06 - accuracy: 1.0000 - val_loss: 0.0227 - val_accuracy: 0.9952\n",
            "Epoch 96/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 8.5758e-06 - accuracy: 1.0000 - val_loss: 0.0236 - val_accuracy: 0.9952\n",
            "Epoch 97/100\n",
            "91/91 [==============================] - 1s 8ms/step - loss: 7.9739e-06 - accuracy: 1.0000 - val_loss: 0.0232 - val_accuracy: 0.9952\n",
            "Epoch 98/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 7.6139e-06 - accuracy: 1.0000 - val_loss: 0.0228 - val_accuracy: 0.9952\n",
            "Epoch 99/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 7.2656e-06 - accuracy: 1.0000 - val_loss: 0.0234 - val_accuracy: 0.9952\n",
            "Epoch 100/100\n",
            "91/91 [==============================] - 1s 7ms/step - loss: 6.7050e-06 - accuracy: 1.0000 - val_loss: 0.0238 - val_accuracy: 0.9952\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretty_plot(history, 'loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "AgTQ4hNeIQx3",
        "outputId": "e34f5a7f-9827-4dc9-beb0-750f859090fe"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretty_plot(history, 'accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "GVYllRfvIVQ3",
        "outputId": "52aeca18-568d-4c6e-e491-df200cbe793e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transfer_model.load_weights('t_weights.hdf5')\n",
        "transfer_model.evaluate(X_ptbdbl_test, y_ptbdb_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9mqVTdyIaxP",
        "outputId": "89679559-5835-4d99-b0e4-1ff51a304877"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "46/46 [==============================] - 1s 6ms/step - loss: 0.0614 - accuracy: 0.9837\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.061368659138679504, 0.9836956262588501]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = (transfer_model.predict(X_ptbdbl_test) >= 0.5).astype(\"int32\")\n",
        "\n",
        "cnf_matrix = sklearn.metrics.confusion_matrix(y_ptbdb_test, y_pred)\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=['Normal', 'Abnormal'],\n",
        "                      title='Confusion matrix, without normalization')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "0OCmWIHQIpMZ",
        "outputId": "8014935e-4f12-47d9-c38b-d130385824f2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "46/46 [==============================] - 1s 4ms/step\n",
            "Confusion matrix, without normalization\n",
            "[[ 416   12]\n",
            " [  11 1017]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def get_transfer_model2(model):\n",
        "  model.trainable = False\n",
        "  model_input = model.inputs\n",
        "  model_output = model.layers[-4].output\n",
        "\n",
        "  X = keras.layers.Dense(64, activation='relu')(model_output)\n",
        "  X = keras.layers.Dense(32, activation='relu')(X)\n",
        "  out = keras.layers.Dense(5, activation='sigmoid')(X)\n",
        "\n",
        "  transfer_model = keras.Model(inputs=model_input, outputs=out)\n",
        "  return transfer_model\n",
        "\n",
        "get_transfer_model2(m_resnet_model).summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wMUCC46VJGYd",
        "outputId": "ff07ad35-4601-4691-fe8a-49e4f644d498"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_8\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_8 (InputLayer)           [(None, 187, 1)]     0           []                               \n",
            "                                                                                                  \n",
            " conv1d_101 (Conv1D)            (None, 183, 32)      192         ['input_8[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_101 (Batch  (None, 183, 32)     128         ['conv1d_101[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_99 (ReLU)                (None, 183, 32)      0           ['batch_normalization_101[0][0]']\n",
            "                                                                                                  \n",
            " max_pooling1d_7 (MaxPooling1D)  (None, 90, 32)      0           ['re_lu_99[0][0]']               \n",
            "                                                                                                  \n",
            " conv1d_102 (Conv1D)            (None, 90, 32)       5152        ['max_pooling1d_7[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_102 (Batch  (None, 90, 32)      128         ['conv1d_102[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_100 (ReLU)               (None, 90, 32)       0           ['batch_normalization_102[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_103 (Conv1D)            (None, 90, 32)       5152        ['re_lu_100[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_103 (Batch  (None, 90, 32)      128         ['conv1d_103[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_47 (Add)                   (None, 90, 32)       0           ['max_pooling1d_7[0][0]',        \n",
            "                                                                  'batch_normalization_103[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_101 (ReLU)               (None, 90, 32)       0           ['add_47[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_104 (Conv1D)            (None, 90, 32)       5152        ['re_lu_101[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_104 (Batch  (None, 90, 32)      128         ['conv1d_104[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_102 (ReLU)               (None, 90, 32)       0           ['batch_normalization_104[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_105 (Conv1D)            (None, 90, 32)       5152        ['re_lu_102[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_105 (Batch  (None, 90, 32)      128         ['conv1d_105[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_48 (Add)                   (None, 90, 32)       0           ['re_lu_101[0][0]',              \n",
            "                                                                  'batch_normalization_105[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_103 (ReLU)               (None, 90, 32)       0           ['add_48[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_106 (Conv1D)            (None, 90, 32)       5152        ['re_lu_103[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_106 (Batch  (None, 90, 32)      128         ['conv1d_106[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_104 (ReLU)               (None, 90, 32)       0           ['batch_normalization_106[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_107 (Conv1D)            (None, 90, 32)       5152        ['re_lu_104[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_107 (Batch  (None, 90, 32)      128         ['conv1d_107[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_49 (Add)                   (None, 90, 32)       0           ['re_lu_103[0][0]',              \n",
            "                                                                  'batch_normalization_107[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_105 (ReLU)               (None, 90, 32)       0           ['add_49[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_108 (Conv1D)            (None, 90, 32)       5152        ['re_lu_105[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_108 (Batch  (None, 90, 32)      128         ['conv1d_108[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_106 (ReLU)               (None, 90, 32)       0           ['batch_normalization_108[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_109 (Conv1D)            (None, 90, 32)       5152        ['re_lu_106[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_109 (Batch  (None, 90, 32)      128         ['conv1d_109[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_50 (Add)                   (None, 90, 32)       0           ['re_lu_105[0][0]',              \n",
            "                                                                  'batch_normalization_109[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_107 (ReLU)               (None, 90, 32)       0           ['add_50[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_110 (Conv1D)            (None, 90, 32)       5152        ['re_lu_107[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_110 (Batch  (None, 90, 32)      128         ['conv1d_110[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_108 (ReLU)               (None, 90, 32)       0           ['batch_normalization_110[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_111 (Conv1D)            (None, 90, 32)       5152        ['re_lu_108[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_111 (Batch  (None, 90, 32)      128         ['conv1d_111[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_51 (Add)                   (None, 90, 32)       0           ['re_lu_107[0][0]',              \n",
            "                                                                  'batch_normalization_111[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_109 (ReLU)               (None, 90, 32)       0           ['add_51[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_112 (Conv1D)            (None, 90, 32)       5152        ['re_lu_109[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_112 (Batch  (None, 90, 32)      128         ['conv1d_112[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_110 (ReLU)               (None, 90, 32)       0           ['batch_normalization_112[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_113 (Conv1D)            (None, 90, 32)       5152        ['re_lu_110[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_113 (Batch  (None, 90, 32)      128         ['conv1d_113[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_52 (Add)                   (None, 90, 32)       0           ['re_lu_109[0][0]',              \n",
            "                                                                  'batch_normalization_113[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_111 (ReLU)               (None, 90, 32)       0           ['add_52[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_114 (Conv1D)            (None, 90, 32)       5152        ['re_lu_111[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_114 (Batch  (None, 90, 32)      128         ['conv1d_114[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_112 (ReLU)               (None, 90, 32)       0           ['batch_normalization_114[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_115 (Conv1D)            (None, 90, 32)       5152        ['re_lu_112[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_115 (Batch  (None, 90, 32)      128         ['conv1d_115[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_53 (Add)                   (None, 90, 32)       0           ['re_lu_111[0][0]',              \n",
            "                                                                  'batch_normalization_115[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_113 (ReLU)               (None, 90, 32)       0           ['add_53[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_116 (Conv1D)            (None, 90, 32)       5152        ['re_lu_113[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_116 (Batch  (None, 90, 32)      128         ['conv1d_116[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " re_lu_114 (ReLU)               (None, 90, 32)       0           ['batch_normalization_116[0][0]']\n",
            "                                                                                                  \n",
            " conv1d_117 (Conv1D)            (None, 90, 32)       5152        ['re_lu_114[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_117 (Batch  (None, 90, 32)      128         ['conv1d_117[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_54 (Add)                   (None, 90, 32)       0           ['re_lu_113[0][0]',              \n",
            "                                                                  'batch_normalization_117[0][0]']\n",
            "                                                                                                  \n",
            " re_lu_115 (ReLU)               (None, 90, 32)       0           ['add_54[0][0]']                 \n",
            "                                                                                                  \n",
            " average_pooling1d_5 (AveragePo  (None, 43, 32)      0           ['re_lu_115[0][0]']              \n",
            " oling1D)                                                                                         \n",
            "                                                                                                  \n",
            " flatten_5 (Flatten)            (None, 1376)         0           ['average_pooling1d_5[0][0]']    \n",
            "                                                                                                  \n",
            " dense_24 (Dense)               (None, 64)           88128       ['flatten_5[0][0]']              \n",
            "                                                                                                  \n",
            " dense_25 (Dense)               (None, 32)           2080        ['dense_24[0][0]']               \n",
            "                                                                                                  \n",
            " dense_26 (Dense)               (None, 5)            165         ['dense_25[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 175,173\n",
            "Trainable params: 90,373\n",
            "Non-trainable params: 84,800\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = keras.optimizers.Adam(lr=0.001)\n",
        "transfer_model = get_transfer_model2(resnet_model)\n",
        "\n",
        "save_best_weights = ModelCheckpoint(filepath=\"t2_weights.hdf5\", verbose=0, save_best_only=True)\n",
        "\n",
        "transfer_model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "history = transfer_model.fit(X_mitbihl_train, y_mitbihl_train, validation_data=(X_mitbihl_val, y_mitbihl_val), \n",
        "                             shuffle=True, batch_size=128, epochs=100, callbacks=[save_best_weights])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePfPUUGUJSCW",
        "outputId": "94f33e67-cf9e-45a0-c087-5d84029eda02"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "616/616 [==============================] - 7s 8ms/step - loss: 0.1795 - accuracy: 0.9519 - val_loss: 0.1468 - val_accuracy: 0.9601\n",
            "Epoch 2/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.1073 - accuracy: 0.9696 - val_loss: 0.1094 - val_accuracy: 0.9711\n",
            "Epoch 3/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0920 - accuracy: 0.9739 - val_loss: 0.1110 - val_accuracy: 0.9698\n",
            "Epoch 4/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0821 - accuracy: 0.9762 - val_loss: 0.0892 - val_accuracy: 0.9762\n",
            "Epoch 5/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0748 - accuracy: 0.9782 - val_loss: 0.1030 - val_accuracy: 0.9729\n",
            "Epoch 6/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0694 - accuracy: 0.9793 - val_loss: 0.0866 - val_accuracy: 0.9760\n",
            "Epoch 7/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0660 - accuracy: 0.9804 - val_loss: 0.0878 - val_accuracy: 0.9785\n",
            "Epoch 8/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0622 - accuracy: 0.9814 - val_loss: 0.0924 - val_accuracy: 0.9749\n",
            "Epoch 9/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0621 - accuracy: 0.9811 - val_loss: 0.0871 - val_accuracy: 0.9766\n",
            "Epoch 10/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0581 - accuracy: 0.9818 - val_loss: 0.0724 - val_accuracy: 0.9797\n",
            "Epoch 11/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0563 - accuracy: 0.9825 - val_loss: 0.0977 - val_accuracy: 0.9746\n",
            "Epoch 12/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0534 - accuracy: 0.9834 - val_loss: 0.0855 - val_accuracy: 0.9770\n",
            "Epoch 13/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0504 - accuracy: 0.9839 - val_loss: 0.0807 - val_accuracy: 0.9786\n",
            "Epoch 14/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0472 - accuracy: 0.9851 - val_loss: 0.0836 - val_accuracy: 0.9800\n",
            "Epoch 15/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0469 - accuracy: 0.9854 - val_loss: 0.0780 - val_accuracy: 0.9783\n",
            "Epoch 16/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0450 - accuracy: 0.9853 - val_loss: 0.0888 - val_accuracy: 0.9783\n",
            "Epoch 17/100\n",
            "616/616 [==============================] - 5s 7ms/step - loss: 0.0445 - accuracy: 0.9856 - val_loss: 0.0872 - val_accuracy: 0.9760\n",
            "Epoch 18/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0431 - accuracy: 0.9858 - val_loss: 0.0792 - val_accuracy: 0.9796\n",
            "Epoch 19/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0405 - accuracy: 0.9870 - val_loss: 0.0799 - val_accuracy: 0.9788\n",
            "Epoch 20/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0395 - accuracy: 0.9869 - val_loss: 0.0874 - val_accuracy: 0.9798\n",
            "Epoch 21/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0382 - accuracy: 0.9876 - val_loss: 0.0799 - val_accuracy: 0.9790\n",
            "Epoch 22/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0357 - accuracy: 0.9880 - val_loss: 0.0848 - val_accuracy: 0.9776\n",
            "Epoch 23/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0370 - accuracy: 0.9872 - val_loss: 0.0850 - val_accuracy: 0.9802\n",
            "Epoch 24/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0352 - accuracy: 0.9882 - val_loss: 0.0801 - val_accuracy: 0.9809\n",
            "Epoch 25/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0330 - accuracy: 0.9889 - val_loss: 0.0820 - val_accuracy: 0.9806\n",
            "Epoch 26/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0313 - accuracy: 0.9896 - val_loss: 0.0814 - val_accuracy: 0.9788\n",
            "Epoch 27/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0314 - accuracy: 0.9893 - val_loss: 0.0800 - val_accuracy: 0.9814\n",
            "Epoch 28/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0326 - accuracy: 0.9892 - val_loss: 0.0864 - val_accuracy: 0.9784\n",
            "Epoch 29/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0303 - accuracy: 0.9897 - val_loss: 0.0920 - val_accuracy: 0.9769\n",
            "Epoch 30/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0284 - accuracy: 0.9904 - val_loss: 0.0901 - val_accuracy: 0.9793\n",
            "Epoch 31/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0290 - accuracy: 0.9901 - val_loss: 0.0914 - val_accuracy: 0.9804\n",
            "Epoch 32/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0282 - accuracy: 0.9900 - val_loss: 0.0868 - val_accuracy: 0.9808\n",
            "Epoch 33/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0270 - accuracy: 0.9907 - val_loss: 0.0963 - val_accuracy: 0.9812\n",
            "Epoch 34/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0261 - accuracy: 0.9912 - val_loss: 0.0967 - val_accuracy: 0.9807\n",
            "Epoch 35/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0266 - accuracy: 0.9911 - val_loss: 0.0969 - val_accuracy: 0.9807\n",
            "Epoch 36/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0275 - accuracy: 0.9905 - val_loss: 0.0969 - val_accuracy: 0.9808\n",
            "Epoch 37/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0243 - accuracy: 0.9916 - val_loss: 0.1020 - val_accuracy: 0.9790\n",
            "Epoch 38/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0238 - accuracy: 0.9917 - val_loss: 0.1018 - val_accuracy: 0.9813\n",
            "Epoch 39/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0234 - accuracy: 0.9919 - val_loss: 0.0929 - val_accuracy: 0.9785\n",
            "Epoch 40/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0233 - accuracy: 0.9918 - val_loss: 0.0870 - val_accuracy: 0.9818\n",
            "Epoch 41/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0217 - accuracy: 0.9924 - val_loss: 0.0993 - val_accuracy: 0.9822\n",
            "Epoch 42/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0223 - accuracy: 0.9925 - val_loss: 0.1073 - val_accuracy: 0.9801\n",
            "Epoch 43/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0226 - accuracy: 0.9922 - val_loss: 0.0933 - val_accuracy: 0.9823\n",
            "Epoch 44/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0197 - accuracy: 0.9930 - val_loss: 0.1018 - val_accuracy: 0.9788\n",
            "Epoch 45/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0213 - accuracy: 0.9929 - val_loss: 0.1107 - val_accuracy: 0.9801\n",
            "Epoch 46/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0208 - accuracy: 0.9929 - val_loss: 0.1031 - val_accuracy: 0.9793\n",
            "Epoch 47/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0217 - accuracy: 0.9924 - val_loss: 0.0992 - val_accuracy: 0.9812\n",
            "Epoch 48/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0196 - accuracy: 0.9930 - val_loss: 0.0979 - val_accuracy: 0.9821\n",
            "Epoch 49/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0208 - accuracy: 0.9926 - val_loss: 0.1187 - val_accuracy: 0.9798\n",
            "Epoch 50/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0187 - accuracy: 0.9932 - val_loss: 0.1041 - val_accuracy: 0.9794\n",
            "Epoch 51/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0197 - accuracy: 0.9932 - val_loss: 0.0985 - val_accuracy: 0.9815\n",
            "Epoch 52/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0169 - accuracy: 0.9940 - val_loss: 0.0961 - val_accuracy: 0.9824\n",
            "Epoch 53/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0202 - accuracy: 0.9929 - val_loss: 0.1228 - val_accuracy: 0.9809\n",
            "Epoch 54/100\n",
            "616/616 [==============================] - 5s 7ms/step - loss: 0.0184 - accuracy: 0.9933 - val_loss: 0.1118 - val_accuracy: 0.9808\n",
            "Epoch 55/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0174 - accuracy: 0.9937 - val_loss: 0.1023 - val_accuracy: 0.9822\n",
            "Epoch 56/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0177 - accuracy: 0.9936 - val_loss: 0.1029 - val_accuracy: 0.9807\n",
            "Epoch 57/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0144 - accuracy: 0.9949 - val_loss: 0.1133 - val_accuracy: 0.9825\n",
            "Epoch 58/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0189 - accuracy: 0.9932 - val_loss: 0.1029 - val_accuracy: 0.9820\n",
            "Epoch 59/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0167 - accuracy: 0.9942 - val_loss: 0.1230 - val_accuracy: 0.9798\n",
            "Epoch 60/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0174 - accuracy: 0.9938 - val_loss: 0.1014 - val_accuracy: 0.9808\n",
            "Epoch 61/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0155 - accuracy: 0.9944 - val_loss: 0.1110 - val_accuracy: 0.9832\n",
            "Epoch 62/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0150 - accuracy: 0.9947 - val_loss: 0.1190 - val_accuracy: 0.9813\n",
            "Epoch 63/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0155 - accuracy: 0.9945 - val_loss: 0.1173 - val_accuracy: 0.9810\n",
            "Epoch 64/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0145 - accuracy: 0.9948 - val_loss: 0.1153 - val_accuracy: 0.9823\n",
            "Epoch 65/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0174 - accuracy: 0.9938 - val_loss: 0.1401 - val_accuracy: 0.9806\n",
            "Epoch 66/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0147 - accuracy: 0.9946 - val_loss: 0.1079 - val_accuracy: 0.9808\n",
            "Epoch 67/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0168 - accuracy: 0.9943 - val_loss: 0.1380 - val_accuracy: 0.9800\n",
            "Epoch 68/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0137 - accuracy: 0.9952 - val_loss: 0.1177 - val_accuracy: 0.9824\n",
            "Epoch 69/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0148 - accuracy: 0.9950 - val_loss: 0.1094 - val_accuracy: 0.9837\n",
            "Epoch 70/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0103 - accuracy: 0.9963 - val_loss: 0.1163 - val_accuracy: 0.9818\n",
            "Epoch 71/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0180 - accuracy: 0.9937 - val_loss: 0.1221 - val_accuracy: 0.9817\n",
            "Epoch 72/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0131 - accuracy: 0.9953 - val_loss: 0.1130 - val_accuracy: 0.9823\n",
            "Epoch 73/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0152 - accuracy: 0.9949 - val_loss: 0.1381 - val_accuracy: 0.9797\n",
            "Epoch 74/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.1247 - val_accuracy: 0.9826\n",
            "Epoch 75/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0126 - accuracy: 0.9954 - val_loss: 0.1292 - val_accuracy: 0.9809\n",
            "Epoch 76/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0123 - accuracy: 0.9955 - val_loss: 0.1269 - val_accuracy: 0.9810\n",
            "Epoch 77/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0118 - accuracy: 0.9957 - val_loss: 0.1231 - val_accuracy: 0.9824\n",
            "Epoch 78/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0133 - accuracy: 0.9955 - val_loss: 0.1220 - val_accuracy: 0.9807\n",
            "Epoch 79/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0138 - accuracy: 0.9955 - val_loss: 0.1370 - val_accuracy: 0.9817\n",
            "Epoch 80/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0143 - accuracy: 0.9953 - val_loss: 0.1264 - val_accuracy: 0.9820\n",
            "Epoch 81/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0109 - accuracy: 0.9960 - val_loss: 0.1475 - val_accuracy: 0.9808\n",
            "Epoch 82/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0138 - accuracy: 0.9952 - val_loss: 0.1388 - val_accuracy: 0.9817\n",
            "Epoch 83/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0106 - accuracy: 0.9961 - val_loss: 0.1444 - val_accuracy: 0.9807\n",
            "Epoch 84/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0138 - accuracy: 0.9950 - val_loss: 0.1198 - val_accuracy: 0.9826\n",
            "Epoch 85/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0131 - accuracy: 0.9954 - val_loss: 0.1289 - val_accuracy: 0.9817\n",
            "Epoch 86/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0119 - accuracy: 0.9960 - val_loss: 0.1257 - val_accuracy: 0.9817\n",
            "Epoch 87/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.1340 - val_accuracy: 0.9810\n",
            "Epoch 88/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0111 - accuracy: 0.9963 - val_loss: 0.1349 - val_accuracy: 0.9820\n",
            "Epoch 89/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0125 - accuracy: 0.9956 - val_loss: 0.1278 - val_accuracy: 0.9820\n",
            "Epoch 90/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0130 - accuracy: 0.9955 - val_loss: 0.1398 - val_accuracy: 0.9825\n",
            "Epoch 91/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0115 - accuracy: 0.9961 - val_loss: 0.1439 - val_accuracy: 0.9804\n",
            "Epoch 92/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0131 - accuracy: 0.9954 - val_loss: 0.1417 - val_accuracy: 0.9802\n",
            "Epoch 93/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0104 - accuracy: 0.9965 - val_loss: 0.1424 - val_accuracy: 0.9809\n",
            "Epoch 94/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0115 - accuracy: 0.9959 - val_loss: 0.1433 - val_accuracy: 0.9797\n",
            "Epoch 95/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0098 - accuracy: 0.9968 - val_loss: 0.1430 - val_accuracy: 0.9805\n",
            "Epoch 96/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0121 - accuracy: 0.9959 - val_loss: 0.1378 - val_accuracy: 0.9808\n",
            "Epoch 97/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0101 - accuracy: 0.9963 - val_loss: 0.1379 - val_accuracy: 0.9806\n",
            "Epoch 98/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0126 - accuracy: 0.9960 - val_loss: 0.1517 - val_accuracy: 0.9798\n",
            "Epoch 99/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0114 - accuracy: 0.9957 - val_loss: 0.1498 - val_accuracy: 0.9813\n",
            "Epoch 100/100\n",
            "616/616 [==============================] - 4s 7ms/step - loss: 0.0105 - accuracy: 0.9961 - val_loss: 0.1586 - val_accuracy: 0.9800\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretty_plot(history, 'loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "ezIggq61LF4P",
        "outputId": "24567763-8d53-4625-afd2-4a2f4a393be9"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretty_plot(history, 'accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "z1WwGWnqLMTP",
        "outputId": "6ac66e3f-73a2-4409-bb11-eea02f8d2a5a"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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X8NpEx6djjOHTO8X99MYvUbz2y24CfH1oHBrEzw8PLqTYYhLSaVY3uNg1cnLz8l1TRTmZlsWcrUeZs/UIdUICeGBoW9o3rnVaz7goxph11treHstUQSiKUh5W7D1Bq7AahNcJJiYhnaEvL+bGvs35+5gu9HthAX1b1+OtiRfl13/+x+2cTM/miZHtaVgrqNj5Dp1MZ/x/V3IkKYOagX40Dg2iVVgNhnVoyGUdGxJWM5DV+xP4fnMsK/fGk5aZw6nsXNIyc8izUCPAl5BAPzKycpl+T386h4ey+1gK172zAh8DyRk5/Pv6bozv3ZyNMYmMfWc5t1/SmqdGdeSrtTG8Mm8XaZm5vH5jD67oXLa7+VRWLot2HWdjTCIbDp4kO9fy6oTuRDSomV/n1Xm7ePfXvXx8e18GtK1fMQ++glEFUQaxsbEAhIeHl1FTUaqejOxc/H198PWpeJeCt3yx+iBPztwCQJ9WdTEYNh1KZMkTQ2lUO4hHv9rEwp3HWPeXEfj4GPbGpTL8lV8BqBXkxxMjOzCxb4v8e4hLyWT8f1eQkJbF53f1P62Aa3pWDiv2xLNw13E2xSTy16s75bttABbsOMbkT9YytkdTXpnQPb/H/tS3W/hi9UHaNKhJ1PFUeresy3NjutAp3HPwubykZ+UQElB9vfmlKYjqK/VZ5Pvvvwc0BqFUf9KzcrjslV+5oktj/ja682kfb60lN88WcmGkZubw9doYth5O5rkxnakRWHqzsP7gSf723TYGRdanb6t6zN4US9TxVO4ZHEGj2mIZDIqszzfrD7EtNpmuzUKZsmw/AX4+fDa5H6//spunZ23lf0v2MqRdQy5pE8abC/dwLDmTaZP7nvZonJAAPy7r1IjLOjXyWD68YyOWPD6U8DqF3TlPXNGB+duPkZmTx7s3X8TILo0rxY9fnZVDWZy7klcgo0ePrmoRFMUrPl5xgNikDL5cE8MjI9rl+8/LIjUzh5nrD/Hpymj2n0ijY5Pa9GheB18fwzfrDpGSmQNAoL8PL4ztmn9cUno2U1ceoHfLuvSLCCM+LZP7pq2jcWgQ/7mpJ3VCAnhweCQxCek0CS1wG7ncKUui4mhWN5hv1h/i2h7h9GlVj2l39uOHzUeYteEw36w/xKerovH3NXxwax96tSw8KqiiaF4vpNi+0BB/Fj42hEA/H/xL8Plf6KiCQF1LyrlB0qls3vt1H20b1mTP8VS+3XCY313cqtRj8vIs7/66l3cW7SEtK5euTUO5fUArtsUmM3P9ITJz8hjVrQm3D2jNnC1HeG/JPkZ0asTQ9g1Jz8rh9o9Xs/6gLMnboFYgtQL9SD6Vw8zf96VOSED+dYo2wA1qBdKpSW2WRZ0AICM7jzsHSs4zYwyju4czuns4WTl5rD94kmB/X7o3P/tL/tYsw1q60NGng45iUs4NPly6j6RT2Xw2uR9PfbuFT1dGM6l/S4wx5OZZ7vpkLSdSM3n8ivYMimxAamYOj3y5kXnbj3F5p0bcN6QNPZrXyXej5OZZMrJz811KHRrXYvGuOP44YzM/PDiQR7/exMaYRN64sQf+vj58vymW5XtO8O/x3UqcJObOoHb1mbJsP3viUhkUWd/jaJsAPx/6u8ULlOqFKgjgp59+AjQGoZwdluyO459zdvLqDd3p0Ni7gGhCWhYfLtvPVV0b06VpKDf3b8kTMzbz2/4E+keE8d9f97Jw53Hq1wxg0ocyEexIUgb7T6Tx9NWduGNAq2L+dV8fUyjeEOTvy6s3dOfat5dz2au/kpyRw0vXd2NMj6YAXNW1yWnd56C2DXjv133EpWTy7+u7ndaxSvVAFQQwZsyYqhZBuUDYFpvEfdPWkZaVyyNfbmLW/QMI8Cvu/446lsL7S/cR6OdLZKOabIxJ5FR2Lo+MaAfA6G7hPP/jDqatiibI35fX5u9mVLcmvDqhO9NWHeQ/C6PwccbkX9LG++GVncNDefiydvz75138ZVRHJvQuv1Xdu1VdAv18aFEvhEvbNSj3eZSqQ4e5KspZIjbxFGPfWY6PMdw/tC1/mbWVPwxryyOXt8+vE5+ayWu/7OaL1TEE+fngY0x+APm6i5ry6oQe+XX/8cN2Pl5xgMahQeTlWeY8NJjQEAlap2XmkJNnCQ32LojtjrWWw4mnaFa3eGD3dPlhcyxN6wTTs0XdMz6XUjnoMNcyiI6OBjRpn1Kx/LYvns9+O0itID8a1ApkzpajpGfm8vV9F9OhcW02HEzk7cV7Gd6xEa3q12DKsv1MWbaf9OxcbunXgocua0fdEH+OJWdyID6NrkWGf97cvyUfLNtPbOIpvrirf75yAMocqloaxpgKUQ5AsVQWyrmFKghg7ty5gMYgFEnn/OTMLRxMSKNtw1pENqxJ/VqB+PsY/Hx96NikVrHGc9W+eLYcSuKmfi3yR8XM3hTLY19tIiTQFwOcTM8m2N+X93/XOz/u8NfRnVi+5wT3TltHWmYOyRk5XNG5EY9f0YG2DQtm4zYODaJxaPGZx63r1+DBYW1pVDuo0MQwRako1MUE+avJNWigftILnb99t5WpK6Pp3rwO0fFpJKYXXuAl2N+X58Z0Zrzjm/905QGe+X47uXmWejUC+P2QNuTmWV6cs5O+rerx/u96ExriT3ZuHrl5liD/wknmlkbFccfHa7i0XUMeviyyUlI2K0ppaKoNRXHYF5fK8ZRMcvMsedbSrVmdfD/912tjeHzGZu4a1JqnRnXCWsuJ1CySTmWRnWs5lZ3LS3N3smpfAtdd1JQaAX58uiqa4R0aMnlQBG8v2sOyPTLu/8oujXnthh7FFIInMnNyCfQru56iVAaqIMpg3759AERERFSkSEo149OVB3j6u22F9gX7+3Jtz3D6R4Tx+IzN9G5Zl0/u6FtiNs3cPMubC6J4c2EU1sLdgyP448gO+TmFVuw5wZ64VG7u17JKcyUpireogiiD9957D9AYxPnMR8v38+z32xneoSF3DmyNr48hO9fy/aZYvtt0mIzsPJrWCeb7BwdSr0ZAmedbeyCBk+nZjCgh/4+inCuogiiD+Ph4AMLCNNB3LnMk6RS/bD/GvO3H2BiTSIfGtbg4Ioxca3l70V6u6NyI/9x0UbF5B0np2fywJZb+EWG0cUvVrCgXAqoglPOSQyfTeXNBFHuOp3IgPp2EtCxARvf0bVWPnUeT2XI4iTwLo7o24XUnZYSiKAXoPIgyiIqKAiAyMrKKJVG8JS/P8tD0jWyLTaJH8zpc0bkRbRrUZEj7BrRpUDM/rURyRjbRJ9LpFF5bYwKKcpqoggAWLlwIqIKoLlhrmbf9GCv3xhPo70OQny/tGtXiqq4F+fo/XRXNuuiTvDK+O+N6NSvxXLWD/OnaTIeOKkp5UAUB3HDDDVUtguIQm3iKv363jV92HCPY35dca8nKyQNgYr8WPHtNZ44lZ/DS3J0MiqzPdRc1rWKJFeX8RRUEUKfO2c9DrxRn1obDPPXtFnKt5amrOnL7gFb4+fqQm2d5Zd4u3lm8l5iEdAAs8MLYrpWyApiiKEKlRuyMMSONMbuMMXuMMX/yUN7SGLPAGLPZGLPYGNPMrexfxpitzqtSu/i7du1i165dlXmJC55TWbnk5OaVWP7TliP831cb6dw0lHkPX8pdgyPy5yL4+hieGNmBl8Z1Y+XeeJZGneDxK9p7XCVMUZSKo9IsCGOML/A2MAI4BKwxxsy21m53q/Yy8Im1dqoxZhjwIjDJGDMKuAjoAQQCi40xc6y1yZUh6+LFiwFo37596RWVcrE9NplbPvwNPx/DdRc1Y3zvZoWGky7fc4KHp2+kV4u6TL29L8EBnmcVT+jTnJZhISzfG1/mSmqKopw5leli6gvssdbuAzDGTAfGAO4KohPwiPN5ETDLbf8Sa20OkGOM2QyMBL6qDEEnTpxYGadVEOVw8werCPL3pXN4bd5fuo///rqXyIY16dmiDpENa/H6L7tpXb8GH97ap0Tl4KJfRJgmplOUs0RlKoimQIzb9iGgX5E6m4DrgDeAsUAtY0yYs/9vxphXgBBgKIUVCwDGmLuBuwFatGhRbkFr1Sq+FKJy5myPTWbiB6sI9vdl+t39aRlWg+MpGXy3IZaV++KZv/0YX609RLO6wXxyZ99C6aoVRal6qjpI/RjwljHmNmAJcBjItdbOM8b0AVYAccBKILfowdba/wH/A5koV14htm8X3dOpU6fynuKCIupYCs98v42ru4UzvlezYnmLrLXMWHeI537YTs1Av3zlANCwVhB3DY7grsERWGuJSThFg1qBZVoOiqKcfSpTQRwG3NcrbObsy8daG4tYEBhjagLjrLWJTtnzwPNO2efA7soSdOnSpYAqCG+ITTzF76asJi4lk+V74vlw2X4eu7w93ZqFEuTvS/KpbP46extLdsfRp1VdXp3Qo8RgsjGGFmEaaFaU6kplKog1QKQxpjWiGG4ECjn7jTH1gQRrbR7wJDDF2e8L1LHWxhtjugHdgHmVJegtt9xSWac+r0hMz+LWKatJzchh9gMDOZiQzktzd3LvtHWF6oUE+PLsNZ2Z1L8lPjp7WVHOWSpNQVhrc4wxDwA/A77AFGvtNmPMc8Baa+1sYAjwojHGIi6m+53D/YGlzhj3ZOAWJ2BdKdSoUaOyTn3ekJ6Vw51T1xIdn87UO/rSKbw2ncJrM7xjQxbtPE5CWhYZ2bnk5Fmu6NxYh6AqynmAJusDtm7dCkCXLl0qUqTzhriUTCZPXcOWw0n856aLGNWtSVWLpChKBaHJ+spg+fLlgCoIT+yNS+W2jyTm8N6k3rr+gaJcQKiCAG699daqFqFasj02mZveX4W/r2H63RfTo7mmJFGUCwlNjg8EBQURFBRU1WJUCTm5eby1MIp7Pl1LWmZBmCcjO5eHv9xAoJ8P3/5+gCoHRbkAUQsC2LRpEwDdu3evYknOLjEJ6Tz85UbWRZ8E4KHpG3hvUm98fQyv/bKb3cdS+ej2PhpwVpQLFFUQwKpVq4ALS0HM3XqEx77ejAHeuLEHyaeyefq7bbz40w6u7NqY95fs46a+zRnavmFVi6ooShWhCgK4/fbbq1qEs8pXa2L408zNdGtWh//c1DPfQtgbl8YHy/bz9bpDhNcJ5qlROnFQUS5kVEEAAQEBVS1CpWGtxVryJ6x9tHw/z36/nUGR9XlvUi9CAgp+An8Z1ZED8Wks3hXHu7dcRM1A/XkoyoWMtgDAhg0bAOjZs2cVS1KxWGu5c+paluyOI6xmAHWCA9h1LIUrOjfizZt6EuhXOP+Rn68P703qRUxCOm0bagJDRbnQUQUBrF69Gjj/FMTsTbEs3Hmc0d3DCfH3JS41kyEdGvD45e2LJdhzEejnq8pBURRAFQQAkydPrmoRKpzkjGz+8eMOujcL5fUbeuCrOZEURTlNVEEAvr7nX6rpV+ft5kRqJlNu7aPKQVGUcqET5YC1a9dS3jxO1ZGth5P4ZOUBJvVvSddmoVUtjqIo5yhqQQDr1km66t69PearOiew1rLlcBLfb4rl2w2x1KsRyKOX6xrbiqKUH1UQwD333FPVIpSbo0kZfLP+EDPWHWL/iTT8fQ2XtmvI/UPbEBqsS3gqilJ+VEGcA6RkZLN4Vxzztx9jSVQceXmWujUCqBHgx86jyeRZ6Nu6HvdeGsHIzk10bWdFUSoEVRAUDHPt27dvFUtSnAMn0rjmrWUkZ+QQViOA4R0aUTPQl5Pp2SSdymZohzaM79WcVvV10SNFUSoWVRDA5s2bgeqpIJ7/aQe5eZbpd/enT6t6OiJJUZSzhioIqu88iKVR4lb648gO9I8Iq2pxFEW5wNBhrtWU7Nw8nvt+Oy3DQrhjYKuqFkdRlAsQVRDAypUrWblyZVWLUYjPVkUTdTyVp67qWCxnkqIoytlAFQSwY8cOduzYUdVi5LP7WAqv/RLFwLb1dQ1oRVGqDI1BAHfccUdViwDIZLfPfjvI33/YTq0gP565pjPGaFBaqeZYC2s/hDbDoV7rqpZGqUDUgqgmpGflcO+0dfxl1lb6RYQx56HBtG1Ys6rFUpSy2TUHfnwUFj1f1ZIoFYxaEMCyZcsAGDhwYJVc31rLU99uZd72Yzx1VUfuHNg6f4EfRTljjmyGxGjoOLriz52bDa1DE+8AACAASURBVPOfls87vodTiRBcp+Kvo1QJakEAe/fuZe/evVV2/S/XxPDthsM8PLwddw2OUOWgVCzznoIZd0jjXdGs+xji98CQJyEnA7Z+U/HXUKoMVRDArbfeyq233lol195xJJm/zd7GwLb1eWBY2yqRQTmPyUiG6BWQmwXbv6vgcyfB4heh1SC49I/QqCtsmFax16hsrK1qCao1qiCqkJSMbO7/bD2hwf68fqMu6nNBcSYN084f4fB67+ruWwx5OeAfApu/Kv81PbHsNUiPh8v/DsZAz5shdj0c23Z655n/N/ho1NlvrPPy4MPLYc4fz/xcJ6Jg89fnncJRBQEsWbKEJUuWnNVrnsrK5c6pa4lOSOfNm3pSv2bgWb2+UoTt30HC/rNzrZ0/wmtdTr8hBUg5Cl9OkoZt/Sdl14/6GQJD4ZI/QPQySIzxXC81DtZ8KI2mN6TFw8p3oNuNEO4s1dt1Avj4w4bPPB+zex7Ebii+f/t3ItuhNSVfb+MX8EoHSE/wTj5viFkFh1bDb/+F9Z+W/zy75sD/hsLMyRC3q+LkqwaoggCio6OJjo4+a9fLyM7l7k/XsuZAAq9O6K5pNM4GKcfg56fgX61g19ziZV/dCktfKd+5j22TAK03JOyDb++F5EOlXy/1OLzSETZ9WXj/pulgc6VRnv0g/PQE5OZ4Poe1EDUf2g6DHjfJvi1fe6475wn48RHYt8i7+9jzC+RmQr+7C/bVCIMOV8Hm6ZCTVbA/NwfmPgmfj4fvHyp8npSjcNJRzGs+9Hyt7AxY8BykHIGNn3snnzds/lIsq5YD4afH4OgW747Ly5VXbg4sfRW+uAnqtgQM7JhdcfJVA1RBAJMmTWLSpEln5VpZOXnc/9l6lkad4KVx3RjTo+lZue4FS14ezPsLvNENVr0DOZmw7qPCdXb+AFg4sun0z59yDD4dC1/fLv7+0sg+BV/9DoyP9La3fQvxJQyOWP4GpMTC4hcKFIC14uNv3h9unwP974fV70kQ2hNHNkHqMYi8HOq2ghYXS6NY1A0Sswa2zZTPa6d4d99R86BGA2jSs/D+npPE7bToeTiwXBTiZ9fLs6/XRkZUuVsBB1fJe9Ne8jw8WQjrp8qzqNlY5Csqf1q8WFPTrocXm8NeD0qu6DE5mXK9jqNh/McQXFe+m1OJ8pvJyyt+jLUynPe5evL6exgseBa6XAd3zofmfVVBKOUnKyePB79Yz4Kdx/nHtV0Y37t5VYt0/hO9DFb8B9pfBQ+shd53wJ4FcOpkQR3Xn/r4Dmk4vCUvF765E9LiIC9betWlMecJ6aVe9z/x2/v4i2xFST4Caz6AsLZw8gBsnyX7D62B+CjoeQv4+sHIF6DLOGn0PVkRUfPlve0Iee82AeJ2Fu4pWysKpkZD6HOXuEuSY0u/j9wcude2I8CnSBPSZhiEXwTLX4ePr4I3e8KBZXDNW3Dtu4CF/W7u3IOrwC8YRr0qFklRCyErXSytlgNhxLOQsBf2/1pQvnchvNpBrKn4KAioCT/8n1gdLhL2ixxr3ToGUfMkyN5tAtRsANd/BCej4V8t4bm68vpkDGSmFByz/hP5XrqMg6FPyeu692HchxAQIsrm6Jaz56o8C6iCABYvXszixYsr9Jz74lL5ak0MqZnyx3Uph5+3HeOZ0Z24pX/LCr1elWGt+JwzU6taEs8cluVkGfUKhLWBzmOlMd/5o+xPT4D9SyEsUvYf3+79uRe9AAeWwtWvQ3A9aVxLYvc8aWAGPQrtroBajaHHRNj4mbhZ3Fn2mswvmPgV1G8Hy153nvOn4hLpfG1B3c5jRdlFLyt+zah50ljXbCDbna4VpbTZzW21YzbE/AbDnoKL7xf3VVmxjcNrISMR2l1evMzHF+5aCP+3DW75Bkb+Cyb/AhdNgqYXQUAtCZy7OLgSmvWG8B7QvJ9YCO5xkLVTxAoa+meRP7hugZVz6iTMuh/qtoZ7lsAfNsLYd8Vltew1qZOdIZbByf0w908STAZ5BjUaQushst3yYpF3yJ/ldcmDEL1crMOMJLF8fnocIoaKUrj0CXl1myABeiiYZ+Ktu/EcQBUEEBsbS2xsGb2m0yA5I5vbPlrDE99s5uIXFvD3H7Zz/+cFyuG2AedROoLoFfDd7898eGNenjSiUfPLNxIkZrUEfpOPFN5/eB3Ui4CQerLdtBfUaSHuBYDdc6VRHPpn2Y7d6N31oubD0pelN9/rVmg3UgLCudme62/8DELqS+Pj4pIHZYTRqncK9iUdFhdYz5tFoQ14GI5tkUDu1pmiEAJrFdRvM1yURtFGKS1eLI52VxTsC6kn22unwHf3w+6fZQRRw07iGqrXWs63bmrJcQ2Q44yvNJaeMAZCm0Hby6D/vdL4A/j6Q6uBBQoiMwWObhbXF0DvO8VCOOBYGJmp0tBHDIFWA8A/CHrcLMo95SjM+ZMoj+vegybd5boRQ6DL9XJc/F5RCkc3w+g3wC8IZt0HaSfkHrpeL5ZY/rMcCkP+KK/L/yGup9gN8Mm1omRCwmDcB6IEPVG3FTTudnYUxPSbRTlWMqoggIkTJzJx4sQKOZe1lj/P3MLhxFO8NK4bwzo2ZOqKA8zffg4qh7QTEjgsbeSI689+sJzZcPPypLF+b5AEMT+7HqZPLN7Ql8XGzyEpBvbML7z/8HpRCi6MkUZ232K5r+2zoXYz6Z0GhXofh1jwnPTur3pZtttfKT1Nl0/dncxUaZA6jSncIIW1keuumQJbZsDRrfDrv0RBDn5c6nQdD7WbSoOelSoKyZ2AEGmId/xQuOe9dwFgIXJE4fpXPA8drpb7/nyC9KxH/L2g0etzp/j7o34u+d6j5kujXp4Z0xFD5JonD4gCs3nQor+UdRojFsLcP8O0cfBWb0g/Ia4cF73vEKX6zWQJhg9+vGAUVf49vgB+gdL7X/cRDPw/6HWbWJGH1si5c7Ok918aHUfDhE/FbZQUIwqjRv0yjrlGRkZ5+v1mpcloLHf3V3k4+JvEzTZO8/x7q0A01UYF89XaGH7YfITHr2jPhD7NmdCnOU9e2ZHDien0almvqsXznpSjMHU0nNgN856GvpPh4gcL3BUu8hXEKmnYTie54JHNMPsBaZTDImHse+LPX/gPeLufNFYBIVI3I0mGEMbtAt8AuHcp+AdLWV6eWAIg7qKLfiefk49A8uHCCgKg83USBN70hfiwe98hvvQm3eGIFxbEkU3SK73q5QIZ2gwD30BxM7UeVLj+7rmQc0qCmUUZ/LjERL65s2Bf7zvFygHwCxBLY+6fxBJy9bbd6TRGXEWH1kCLfvI9bPnacxC5bisY977EWvYtlk5A5GUF5ZFXQK1wGfqZkSyWy6E18t1EXibxiWNb4LJny35OnogYIu/7fpXvxvhAsz6yzz8I+v9eev8+vjIBr+1wCf66CGsj59i3WHrrgx8rfo1ajWDY0zDncYldDP2L7O8yTnr322eJcm/So2x5O1wFt/8k1k6LfmXX73QNLPqHNOB97yrYn5kCn02AgytEOQ59svixmSmw+n3plNUOl3N1vMYZIeXGstfEpekXKCPzJv9yev+706BSFYQxZiTwBuALfGCt/WeR8pbAFKABkADcYq095JS9BIxCrJz5wEPWVs4slAULFgAwfPjwMzpP1LGU/FnR913aJn9/49AgGocGndG5zypJh0U5pByVhiFqPix/U3q69y6Rhgqk0T68Dmo1kSGIJw94l80zJxN+fUkCmcH1xKfbZVxBL7b9VTIcctmrBcf4Bsifun47sRJ2/ADdxkvZkQ1y/cDaEhNwKapYZzJZUQXRpLvcw6IXJDDq8h036Q6/vSduIl//kuXf8Jkog67XF+wLrAkRl8KuH6WX7v6H3fatjMDx1Lg36gSP7ZZ0FXE7IfGg9Hbdueh30nD0v89zQxB5uTyfHbOlEdv6jcQfLnu2eBDZhV9gYfeTC18/cZktflGCybWbiUvr61uloXTNY/B0rDc0aC/PYt9isQ4ad4Wg2gXllz4hSrO0Bm/AwzI6aux/S/6e+twJATXE9eey2oyRYPiRTdD3bu8b1eansRRxg/byG90xu0BBZCSLZXxorcw2X/GmfMe1m0h5Xp78F1a8KXGV1pfK+7y/yOviB8TlZQwc2w6754irMtSxLLfNlP9PJVBpCsIY4wu8DYwADgFrjDGzrbXuUcCXgU+stVONMcOAF4FJxphLgAFAN6feMuBSYHFlyBoXF3fG58jNszz69SZqBPjx6g3dz918SslHZPRJWjxM+lYanO43wqBH4L+DxD89wuk9Hlgu/vuBj0hv7eCqshVEThZ8PEp6pd0nSmMaUsSyCmsDt/1Q2J9vfKWxy8uDN3tIwNalIHbNkZ7ogIdg4d/F91y/rSgvHz9phNwxRqyIpS9LL9vl4mjSQ1wPx3dAk254JDtDApwdrxZ3iDvtr5SGOW4nNOwo+zKSRcH2vr1k37V/EDTuIi9PBNSAP5QyczqotvSqd8yWxuTHR6FZX7E8ykP/38s1W14iQe6Uo/DBZdIDrtsSQptDgw7lO7crThA1T3I3uay9onVKo81QeGhz6fV8fCWOU5QaYfCQl3Gm8tJxtMyPeO9SeU5xO2SuzPiP5Lf4Vl8ZBjzmLam/4BmxaCOvkJQlzZwOTcI+GcG18i2xKC6+XxSJfw1RPkGhsOpd+OUZcRv6Vfxk28qMQfQF9lhr91lrs4DpwJgidToBC53Pi9zKLRAEBACBgD9wrLIEvfHGG7nxxhvP6BwfrzjA5kNJPDumMw1rVaC1YK34pzOSKu6cpfHbfyHpEPxuVmGTulFn6TVu+qIggLlvsQxRvGiS/Fi9iUMsfbnAZTH23eLKwR1f/4KXqyfs4yN++P2/yrBEEAXR4mLx50NBkPPwOpHb5QZyx+Xu6TCqoOF2uRxccQhr4evbYNbvZUgrwK6fZARP0VgASG/VVcfFrjlipXT24F6qSDpeI9bHtHFioY39b8kKqSyCaotyadpLGuHaTeDmryE7XUY8RV5+Zi6NiCFwKkHO51LOp0t1Xiel372iZEPqiUWbcAAmfCKuwHoR0O8eGdRxdKsMm13+hrgVJ35ZoBxA6o7+jyicn5+CFW9JW9DrNjm3j69YFokHYfX/KuVWKlNBNAXc5/Ufcva5swlw/XPGArWMMWHW2pWIwjjivH621hZb8s0Yc7cxZq0xZm1FWAHl5dDJdF6Zt4thHRoyqmuTij35zh/EP7301bLrlkXcLoguoxHft1iGGzbrXbys5y0yasQ13n/fYull+gfL5K2yFETsBljyMnS7QayS8tL9JsCIsjoZDce2ilsqrI24u/YvFUvj8Ibi7iUXDTvBmLcLgsEgf8iAWgVxiH2LxT208TP5g4J8Dm0uboCi1A6XgOn27woCkdtmipvG5WevLNpfJVbU8W1i4YW1KfuY06FRJ7hhmriHut1wZueKcHt2zcupIKozNRvKHJVJ38Ij2+FP0dIRcTHoUelQzbhDhs62GwlXvuRZ6fn4iAu2WW+Zr2J8xJJw0WaozEfZMqNS8kBV9Simx4BLjTEbEBfSYSDXGNMW6Ag0Q5TKMGPMoKIHW2v/Z63tba3t3aBBg6LFXjNv3jzmzZtXrmOttfz1O8mp89wYL1aA2/eruGqy0ss+eV4eLHpRPm+Y5v0krpjV0jtxJz1B4gofjyp5Qld6gvSeI4Z4Lo+8XFwyG6dJsPLELvmBgowjP7Fbgp6eyM6QFBM1G8KV//LuPkqiTnORccNnBfMZ2l8pf7DWg6XXFr8HMpNKVhDGiMILbVawz8dHXEtHNsmfbdHz0rj3uQt+e1eC9XsWyPyFknrn3SfK8W90l/kLexbIvIWSYgEVRY0waYTajhB5K4OIS+HRnd4Fa0ujdjjUby8B89oV3KGqjhRtE0LqSazlxC4JtF8/pfDotqL4B8NN06FRF7E+Qov0s699B+6cVylWVWUGqQ8D7lOFmzn78rHWxuJYEMaYmsA4a22iMeYuYJW1NtUpmwNcDCytDEGTksrvvvlxyxEW7jzO01d3olndkLIP2PatjICJj5KgaGlsnyU9wh43S891x/eFA6Ml8c2dcCoJ7vy5wBf+0+OSAqFehOQdumNucd/8/l8BW7KC8PWXnv+qd8XKgIK6rgBszG+Fe0suFj0vvvmbvynuuy8PPW9xLKtXpLFx9ZhbDZIYwUYnYVxJCqIkmnSXGbe754or7OrXxU+eckSCiCAKoiT63gUNO8Dif8Evf5N9nkYvVQY3TDv9kWSnS0Wde/TrJc8ZuRDoc5fEDDqOkXhPWdSoD/d6mAwJ0umqJCqzW7MGiDTGtDbGBAA3AoUSlRhj6htjXDI8iYxoAjiIWBZ+xhh/xLoo5mKqKMaPH8/48eNP+7jE9Cyemb2Nrk1Due2SVt4dFLNa3hP2lV4vLxcW/1OCXKPflN6WN3ly0hPEJ5mZJLlpko/AtlmwdYYEwG6dLebtZxNktJI7+xaLiyX8opLP3+MWGYe+6EWZ+NWws+wP7ykjezy5mTJTJbbR/abCQyrPhA5Xy32knxDrwYVriOnaKZJ2oX670ztvkx4yJPWH/5Ohpj1uFmth3AfiVuo0Rr6LknBZMbf/CLf9JKNmSnueFU119s270/KSwq6mCw2/AOgzufiw8dIw5qx/v5WmIKy1OcADwM9I4/6VtXabMeY5Y8w1TrUhwC5jzG6gEeBa1HYGsBfYgsQpNllrq9389Rd+2sHJ9Gz+Oa6rd2s5ZCQVpHIoS0Fs/UZM0CFPOkMPb5ep/8d3ln6cy38+4u8yVO6z8ZKlM7ynTBiqHS4Bx6xUmH5TQfAVREG0HlS6uduwAzTtDdlp8gd3uU78AiWVgqeJO/uXyOig7jeVLvvp4B8kk8igsMVStxWEtoDMZLnn0w3Uuqy6lCOiUP0CnOsFi3IdP9X7c7UaIMMtz5VGW1GK4JWCMMbMNMaMcuvte4W19idrbTtrbRtr7fPOvr9aa2c7n2dYayOdOpOttZnO/lxr7T3W2o7W2k7W2kdO98ZOh7lz5zJ37tyyK7qxfM8Jvlp7iLsHR9A5PNS7gw6tRQZoUXpCr9wcsR4adZHRKeAkaAso24pwpYq4aBJMmCoKKTMVrnUbM96oM1z9mvjKXSknEpzZrRFDyr4P1wieonVb9JfrF42vRP0slomneQBnwuDHYeQ/RWG503qwvDctR8+9fqQMI6wXIWsdFEUbe+UCwtsG/x1gIhBljPmnMaZ9Jcp01klPTyc93YugsUNGdi5//nYLrcJCeGh4ZEHByrclEFsSMatlFELDTqUriB2zJSfNkD8V9NBr1Bf3xqbpMmW/JI5sgjotxc8fOUKGzt34mfT83el8ncix+EVRSK4MmRFDSrt1oftNcNkzxSfntLhYEt65EuRBwZoEbYYU9MYrilqNZfJY0QCwy81UHteOj6+MT7/ug9ItKUW5APDqH2Ct/QX4xRgTCtzkfI4B3gemWWvP6WjTddedXhDxzQVRRMen8/ld/Qjyd3NhrPlA/P+j3/A8aSXmN/HZN+5SOOVxUVa9Kxkq2xcJ9va+Q1IovNLRabyMjIPu4ea6ObKxIDkaFM/F48LHR9xXX02S+MS+xTJE1BufvX+QuKuK0ryfWDnbvi1opI9tk5QKQ/5U9nkrik5jxEXkmpdwupytoLKiVHO8dhkZY8KA24DJwAYkhcZFSBqMC4bMnFymrYrm6m5NuKSNW+KuhP0SV8jL8ZwyOi9XetbN+0rjn3xYFpApyqF1kuzLU8+4xcXSc+82XhLO+QcXXvzm1ElxE5U1OspFh6tlJNPif8rw24ghZ+ZCCa4jo5w2fiYrooHMmIWCNQnOBv7BosD8z6H0JopSDfE2BvEtMsQ0BBhtrb3GWvultfZBoGZlCng2+PHHH/nxxx+9qrt09wmSM3IY16tZ4YK9Cwo+H9lc/MC4nRI4bd6vIJeRayawO7+9KzmFPA2lNEYavlGvyKvnJHFbpcYVvq43SchAFNDQpyS75qkE79xLZXHJQzJfY9W7sh01T8Z6Xwjj3RXlPMNbC+JNJ1j8orW2UB5ba62HKbfnFtnZ2WRne+cl+2FzLHVC/BnYtkja3z0LZfRMQC3Pa9vG/CbvzfsW5CsqOpIpOVbcMz0nFc75XxLtrwRsQWpmV4oIby0IEDeMy1cfMcT740qifltx8az5QBRgzG/lT+ymKEqV4q2C6GSMyU/+boypa4z5fSXJdNa59tprufbaa8usl5Gdy/ztx7iyS2P8fd0eXU6WBHnbDheXzVEPFkTMapmFXLeVmwVRJFC95gPJj+++EHxpNO4qaR92Orl/jmyUmb9l5ax3xxgJyl71sgR9K4KBD4u1NON2uZ9IDyuPKYpS7fFWQdxlrU10bVhrTwKVNJ+/+rJo53HSsnIZ3S28cMGh1TKvoO1wSdVwdGvh+QUgPenm/aRBDq4LgaGFLYisdJnB2/6q0idiuWOMWBF7F0o848imwgFqb2nUuXDu+jMlvKesNnZ4naTzPt3ZzIqiVAu8VRC+xi3JkJPKu4LHLFYd33//Pd9/X/Y8vO83x1K/ZiD9IsIKF+xZIOmoWw8Wf3t2WuHGPzVOtl155Y0RN5P7UNfdcyUO0O+e0xO+/ZUy83fHD5J/6HTcS5XJIGfqStvLyp9VVFGUKsVbBTEX+NIYM9wYMxz4wtl3wZCamcPCnce5qmvj4rOm9y4Q6yAotCC/kfvSlYec9BrN3ZKc1YsorESi5oll0XLA6QnWcqDEPZb8W7a9DVBXNq0GyWzuQZU6x1FRlErE25lAfwTuAe5ztucDH1SKRFXA6NGjy6yzYMcxMrLzGN29iHspNU6UwTBnWcMGHcDHX+IQrsR6u3+WdRPcG+96rWVCXG62WB9R88vX2/YLkPxGrhnR1cWCMAYG/KGqpVAU5QzwdqJcHvCu87ogCVn4NPfUaEivFlcVLti3SN7bOMuV+gVIBlXXkNOMZMnV3nVc4XH59SJkzkRSDKSflKRzkeUc7dN+lCiIWk1kPV5FUZQKwCsFYYyJRJYD7YSs9AaAtTaikuQ6q8yaNQugxJFMaZk59E2aS8caLYsvJbp3IYSEFbYOmnSDXXMlzcTmLyUm0fuOwsfVdRvq6krB0baca2JHXiZWSHWxHhRFOS/wNgbxEWI95ABDgU+AaZUl1NnG398ff/+SF6lfvv0AoSaN8Iw9xRftiV4hcQP3Wc+Nu4tFkHJEkus16VF8JI9rqGvCfok/NOtT+vKbpRFcVybOeUp/oSiKUk68VRDB1toFgLHWRltrnwE8rApzbjJq1ChGjSr5dtZtkdQZPnlZsryli5SjkBhdfF1d14L3q/8naTeKWg8gcw78gmX4a+yGknMmeUvv28u/vq+iKIoHvFUQmU6q7yhjzAPGmLGcByk2vCEnN4/9+6MKdhxeX/A5xsPoJJA03RhZZDywtudV4FxDXbeJe0snkymKUt3wVkE8hORh+gPQC7gFuLWyhDrbzJw5k5kzZ3osW3PgJLUyncRzPn6FU1nH/CarqDXuVvigwJqyBGZetiSvK2lJwbqtpU7NxsXPoSiKUsWUGaR2JsXdYK19DEgFbq90qc4yISElryU9f/sxmvqelI1Wg4ooiNWyKI2ndQ4ad5OJa71KeVyunEyRI3QhGkVRqh1lKghrba4xZuDZEKaqGDnS87oB1lrm7zjKs3XSIaeeBKMX/UOWDvUNlNxH/e/zeCz975O0F406lXxhV6Bak9kpilIN8Xai3AZjzGzgayB/OTNrrWe/zHnCzqMpxCScol2LFDBNC5awjN0gAebcrOLxBxfN+xak1iiJDlfLMNezuVaCoiiKl3irIIKAeGCY2z4LnBcK4uuvvwZg/PjxhfbP334MY6AR8VA7XJLQgbiZfJxhsc3KUAKlUasRXPF8+Y9XFEWpRLydSX3exR3cCQ0N9bj/lx3H6NG8Dv6pR6B5b5mnUK9NwUimehFQs8FZlFRRFOXs4e1M6o8Qi6EQ1loPA/zPPS6/vPgQ04zsXLYeTuIPlzaHVSegdlMpaNpL1pO2eeWf+awoinIO4K2L6Qe3z0HAWCC24sWpPkTHp5NnoXOtdNlR20nS17QXbPlKPpcVY1AURTmH8dbF9I37tjHmC2BZpUhUBUyfPh2AG2+8MX/fnuOpALQNSpYd7grCRUkBakVRlPMAby2IokQCDStSkKqkQYPicYQ9x1MxBpr6OHMgXC6mxl1lwpx/iKT2VhRFOU/xNgaRQuEYxFFkjYjzguHDi8cS9sSl0rROMAFpTpqN2k3k3T9IRi4F19GV0hRFOa/x1sVUq7IFqW7sOZ5K24Y1ITlW1o8OdHsEN30h6bkVRVHOY7xq5YwxY40xoW7bdYwxnhdPOAf5/PPP+fzzz/O3c/Ms++JSadugJiQfLog/uAiuA0G1z7KUiqIoZxdvu8F/s9YmuTastYnA3ypHpLNPeHg44eEFSuDwyVNk5uQVWBBFFYSiKMoFgLdBak+KpLwB7mrHkCFDCm3viUsBKFAQjTpXgVSKoihVi7cWxFpjzKvGmDbO61VgXZlHnaPsPS7pptqGBULqMbUgFEW5IPFWQTwIZAFfAtOBDOD+yhLqbPPpp5/y6aef5m/vOZ5K/ZoB1Mk7CVhVEIqiXJB4O4opDfhTJctSZbRs2bJgI+kQ+44nEdHAcS9BwRwIRVGUCwhvRzHNN8bUcduua4z5ufLEOrsMHjyYwYMHQ24O9u3+jDv+Hyf+cFgqqAWhKMoFiLcupvrOyCUArLUnOY9mUueTmYzJSuF6O5/ewcfcLAhVEIqiXHh4qyDyjDEtXBvGmFZ4yO5aFGPMSGPMLmPMHmNMMReVMaalMWaBMWazMWaxMaaZs3+oMWaj2yujMuddTJ06lalTp8IpSavhZ/K49OBboiD8QyCoThlnUBRFOf/wdqjqU8AyY8yvgAEGAXeXdoCzlvXbwAjgELDGGDPbWrvdrdrLwCfWeFe+YQAAFp9JREFU2qnGmGHAi8Aka+0ioIdznnrAHmCe97d1erRp00Y+ZMhUj9V57ekbuwjS9or1oOtFK4pyAeKVBWGtnQv0BnYBXwCPAqfKOKwvsMdau89am4WMfhpTpE4nYKHzeZGHcoDrgTnW2nRvZC0PAwcOZODAgfkK4n9cjw1tBkkH1b2kKMoFi7dB6snAAkQxPAZ8CjxTxmFNgRi37UPOPnc2Adc5n8cCtYwxYUXq3IgoJU9y3W2MWWuMWRsXF1fWbZRNhoRZguuFY4Y/I/t0BJOiKBco3sYgHgL6ANHW2qFATyCx9EO84jHgUmPMBuBS4DCQ6yo0xjQBugIeR0xZa/9nre1tre3tKWW3t0yZMoUpU6bkWxD16zeELuOg123QyZNRoyiKcv7jbQwiw1qbYYzBGBNord1pjGlfxjGHgeZu282cfflYa2NxLAhjTE1gnPtoKWAC8K21NttLOctFx44dAchN/w1foH6DhuDjA6PfqMzLKoqiVGu8VRCHnHkQs4D5xpiTQHQZx6wBIo0xrRHFcCMw0b2CMaY+kGCtzQOeBKYUOcdNzv5K5eKLLwYg8+e5WOtDYPAFl91cURSlGN7OpB7rfHzGGLMICAXmlnFMjjHmAcQ95AtMsdZuM8Y8B6y11s4GhgAvGmMssAS39B3OUNrmwK+nc0NnQm56ImmEEBx43uQhVBRFKTen3RJaa71usK21PwE/Fdn3V7fPM4AZJRx7gOJB7Urhgw8+AOCmkESSbA2C/XWlOEVRFO0qA926dQPAbvmBZFRBKIqigCoIAPr27QtA2ppEkm0IQQGqIBRFUXRhZTdMZjJJakEoiqIAakEA8N577wFwW1YyybYFzVVBKIqiqIIA6NWrFwB+PyVJDEJdTIqiKKogAHr37g3ZGfBDFsk6iklRFAXQGAQAubm55KYlAJBMiFoQiqIoqAUBOPMgsk9xD6gFoSiK4qAKAmeYa/xeWAZJ1CBIFYSiKIq6mAB69uxJz5ahAKT71MTXRxcIUhRFUQUBZGVlkZUqMYgsP03UpyiKAupiAuCjjz6ClCPcA2T7q4JQFEUBtSAA6N+/P/2b+QOQG1C7iqVRFEWpHqiCALp37073ehlkmQB8A4KrWhxFUZRqgbqYgIyMDEhNJMPU1DkQiqIoDqoggKlTp0KcH1cZnQOhKIriQl1MwIABAxhQJ44UnQOhKIqSjyoIoEuXLnQJOESKJupTFEXJRxUEkJaWRlpaGok2hGB/fSSKoiigMQgApk2bBvFdCfVLJSRAH4miKAqoBQHAoEGDGOSznpN5wRqDUBRFcVAFAXRq05xOPvtJyA3RUUyKoigO6k8BUk7Egg0mmRo0CFCdqSiKAqogAPj8258gawTJRi0IRVEUF9pdBoZ0a8UQvw0kE6IxCEVRFAe1IID2DQPBN4akHJ0HoSiK4kIVBJAYHwe2BsnUIEQVhKIoCqAKAoAvV+yDrOEkW3UxKYqiuNAYBDCstR/D/NaRggapFUVRXKgFAUTWTCcnMJHcbF+NQSiKojioggDiE5PJ8GkEoBaEoiiKgyoIYEaUPzlZfQFVEIqiKC5UQQAj6sUSn5YBCRCkLiZFURRAFQQAET6xEFQXUAtCURTFhY5iAuJScziSWxd/X4O/rz4SRVEUqGQFYYwZaYzZZYzZY8z/t3fvwVFVeQLHvz9DQxJgSSZBIAQNKitBQYIZVhdx0J3ZAVbwMbjoulNKjUUVhYtauru4WzUrlta4o+UoOz6uIi5jyTIQAWVABsVgZOXVIQZDeEQxQAiBGEkIj5gQfvvHvbnbxA6PmE6H7t+niqLPfXR+J7crvz7n3HuOzA6z/3IRWSsi20RknYhkhuy7TETWiMgOESkVkaxIxbm0NpvPavvaMxDGGBMiYglCRBKAl4EJwDDgXhEZ1uqw54E/qOoI4CngNyH7/gA8p6rZwGjgcEQCPd3M+IT/ZXDKaeteMsaYEJFsQYwGvlTVParaCCwCbm91zDDgY+91fst+L5F0U9UPAVT1mKqeONsPq66uJhgMAtDc3IzjOBQVFQHQ2NiI4zgUFxcD0NDQgOM4lJSUwHdHSZc6dtX2IIMaAOrr63Ech127dgFQW1uL4ziUlZUBUFNTg+M47Nmzx//ZjuOwd+9eAKqqqnAch/379wNQWVmJ4zhUVlYCsH//fhzHoaqqCoC9e/fiOA7V1dUA7NmzB8dxqKlx4ykrK8NxHGprawHYtWsXjuNQX18PQGlpKY7jcPz4cQBKSkpwHIeGhgYAiouLcRyHxsZGAIqKinAch+bmZgCCwSCO4/i/y82bNzNv3jy/vGHDBubPn++X169fz4IFC/xyQUEBb7/9tl9et24dCxcu9Mtr165l0aJFfnnNmjUsWbLEL69evZqlS5f65ZUrV7J8+XK/vGLFClasWOGXly9fzsqVK/3y0qVLWb16tV9esmQJa9as8cuLFi1i7dq1fnnhwoWsW7fOL7/99tsUFBT45QULFrB+/Xq/PH/+fDZs2OCX582bx+bNm/2y4zjt++zhLnfrOA6lpaWAffbss9f5n72ziWSCGAjsDylXeNtCFQN3ea/vBHqLSBrwl0CtiCwVkSIRec5rkZxBRKaLSFBEgk1NTe2LUi7h8LAHOaGJdLfxB2OM8YmqRuaNRaYA41X1Qa/8S+CvVPWhkGMygN8Dg4EC4BfAtcBPgTeBHGAf8Edglaq+2dbPy83N1ZZMeqEcx2FnVT1fp9/I8plj2vUexhhzMRKRQlXNDbcvkl+ZDwCDQsqZ3jafqlaq6l2qmgP8u7etFre18bnXPXUKWA6MilSgEydO5JuUYTaTqzHGhIhkgtgCDBGRwSLSHbgHeD/0ABFJF5GWGJ4A5oecmyIifb3yrUBppAIdNGgQRy7pbYPUxhgTImIJwvvm/xDwZ2AHsFhVt4vIUyIy2TtsHLBLRHYD/YBnvHObgceBtSLyBSDAG5GKtbKyEk4csaeojTEmRESfpFbVVcCqVtt+HfI6D8hr49wPgRGRjK/FihUryDxaS1Lg8s74ccYYc1Gw23aASZMmsSMwxLqYjDEmhCUIICMjg8PNybYWhDHGhLDJ+oB9+/aR1FRLYuCKaIdijPE0NTVRUVHhP3RnfpjExEQyMzMJBALnfY4lCGDlqg8Yybckd/9xtEMxxngqKiro3bs3WVlZiEi0w7moqSo1NTVUVFQwePDg8z7PupiAW38+kUKutDEIY7qQhoYG0tLSLDl0ABEhLS3tgltjliCAXqnp1NHTEoQxXYwlh47Tnt+lJQigvLycNI7acxDGGBPCEgSw6dN8rqPcWhDGGF9tbS2vvPLKBZ83ceJEf/bbi50lCGDk2J+xhassQRhjfG0liFOnTp31vFWrVpGSkhKpsDqV3cUEdEtOoZ5kkrpbvjSmK5qzYjullUc79D2HZfwF/zHpmjb3z549m6+++oqRI0cSCARITEwkNTWVnTt3snv3bu644w72799PQ0MDDz/8MNOnTwcgKyuLYDDIsWPHmDBhAjfddBOfffYZAwcO5L333iMpKalD6xFJ9hcRqKzYS19qSQpYvjTGuJ599lmuvPJKPv/8c5577jm2bt3KSy+9xO7duwF3MZ/CwkKCwSBz5871F1kKVVZWxsyZM9m+fTspKSm8++67nV2NH8T+IgJfFW1gOMfsSWpjuqizfdPvLKNHjz7jGYK5c+eybNkywF2pr6ysjLS0tDPOGTx4MCNHjgTg+uuvp7y8vNPi7QjWggAGjhrHJmwuJmNM23r27Om/XrduHR999BEbNmyguLiYnJycsM8Y9OjRw3+dkJBwzvGLrsYSBHA60IvjJFmCMMb4evfu7a+93VpdXR2pqakkJyezc+dONm7c2MnRdQ7rYgKOVO2jH0dItEFqY4wnLS2NMWPGcO2115KUlES/fv38fePHj+e1114jOzubq6++mhtuuCGKkUaOJQig9ssiruEk3RMsQRhj/t/ChQvDbu/RowcffPBB2H0t4wzp6emUlJT42x9//PEOjy/S7C8iEBgyhuJAtj3Wb4wxISxBAN9dkoj06BXtMIwxpkuxLiag4ZsKBhB+MMoYY+KVJQhADpZyWVNztMMwxpguxRIE8O2AH1N7ojHaYRhjTJdiCQI4fjpAIPH8l+Ezxph4YIPUQLejlaQ0Ho52GMaYi1ivXu6NLpWVlUyZMiXsMePGjSMYDJ71fV588UVOnDjhl6M5fbglCCDl6Ff0PvJltMMwxsSAjIwM8vLy2n1+6wQRzenDrYsJKO05gpzLUqMdhjGmLR/MhqovOvY9+w+HCc+2uXv27NkMGjSImTNnAvDkk0/SrVs38vPzOXLkCE1NTTz99NPcfvvtZ5xXXl7ObbfdRklJCSdPnmTatGkUFxczdOhQTp486R83Y8YMtmzZwsmTJ5kyZQpz5sxh7ty5VFZWcsstt5Cenk5+fr4/fXh6ejovvPAC8+fPB+DBBx/kkUceoby8PGLTilsLAqg/1Y2kpORoh2GM6UKmTp3K4sWL/fLixYu5//77WbZsGVu3biU/P5/HHnsMVW3zPV599VWSk5PZsWMHc+bMobCw0N/3zDPPEAwG2bZtG5988gnbtm1j1qxZZGRkkJ+fT35+/hnvVVhYyFtvvcWmTZvYuHEjb7zxBkVFRUDkphW3FgTQp+EggbomYHi0QzHGhHOWb/qRkpOTw+HDh6msrKS6uprU1FT69+/Po48+SkFBAZdccgkHDhzg0KFD9O/fP+x7FBQUMGvWLABGjBjBiBEj/H2LFy/m9ddf59SpUxw8eJDS0tIz9re2fv167rzzTn9W2bvuuotPP/2UyZMnR2xa8bhPEKrK5acqaK6qASZEOxxjTBdy9913k5eXR1VVFVOnTuWdd96hurqawsJCAoEAWVlZYaf5Ppevv/6a559/ni1btpCamsoDDzzQrvdp0Xpa8dCurB8i7ruYGptP8ynDuHTU30Y7FGNMFzN16lQWLVpEXl4ed999N3V1dVx66aUEAgHy8/PZu3fvWc+/+eab/Qn/SkpK2LZtGwBHjx6lZ8+e9OnTh0OHDp0x8V9b04yPHTuW5cuXc+LECY4fP86yZcsYO3ZsB9b2++K+BdHQeJomutEz+eJZJ9YY0zmuueYa6uvrGThwIAMGDOC+++5j0qRJDB8+nNzcXIYOHXrW82fMmMG0adPIzs4mOzub66+/HoDrrruOnJwchg4dyqBBgxgzZox/zvTp0xk/frw/FtFi1KhRPPDAA4wePRpwB6lzcnIiukqdnG2A5WKSm5ur57q/OJy6k038+q2V3HhFGveMj2w2Nsacvx07dpCdnR3tMGJKuN+piBSqam644+O+i6lPUoBhCYeo21sa7VCMMaZLifsuJoBp06ZFOwRjjOlyLEEA3bt3j3YIxpgwVNUW8uog7RlOiPsuJoCioiL/gRNjTNeQmJhITU1Nu/6wmTOpKjU1NSQmJl7QeRFtQYjIeOAlIAGYp6rPttp/OTAf6At8C/yjqlZ4+5qBlmfr96nq5EjFuXnzZsB9MMYY0zVkZmZSUVFBdXV1tEOJCYmJiWRmZl7QORG7i0lEEoDdwM+ACmALcK+qloYcswT4k6ouEJFbgWmq+ktv3zFVPe91QNt7FxNAc7O7WFBCQkK7zjfGmItVtO5iGg18qap7VLURWATc3uqYYcDH3uv8MPs7RUJCgiUHY4xpJZIJYiCwP6Rc4W0LVQzc5b2+E+gtImleOVFEgiKyUUTuCPcDRGS6d0zwhzRDg8HgOedoN8aYeBPtQerHgZ+ISBHwE+AA0LI49OVes+cfgBdF5MrWJ6vq66qaq6q5ffv2bXcQhYWFZ8yyaIwxJrJjEDcCT6rqz73yEwCq+ps2ju8F7FTV742iiMh/445VtLkKh4hUA2efGOX70oFvLvCci1081hnis97xWGeI33q31+WqGvYbdiTvYtoCDBGRwbgtg3twWwM+EUkHvlXV08ATuHc0ISKpwAlV/c47Zgzw27P9sLYqeDYiEmxrcCZWxWOdIT7rHY91hvitdyRErItJVU8BDwF/BnYAi1V1u4g8JSItt6yOA3aJyG6gH/CMtz0bCIpIMe7g9bOhdz8ZY4yJvJiZrK894vGbRjzWGeKz3vFYZ4jfekdCtAepo+31aAcQBfFYZ4jPesdjnSF+693h4roFYYwxpm3x3oIwxhjTBksQxhhjworLBCEi40Vkl4h8KSKzox1PpIjIIBHJF5FSEdkuIg97238kIh+KSJn3f2q0Y+1oIpIgIkUi8ievPFhENnnX/I8iElNzvItIiojkichOEdkhIjfGyXV+1Ptsl4jI/4hIYqxf684UdwnCm0TwZWAC7lxQ94rIsOhGFTGngMdUdRhwAzDTq+tsYK2qDgHWeuVY8zDu7dUt/hP4napeBRwBfhWVqCLnJWC1qg4FrsOte0xfZxEZCMwCclX1WtxZo+8h9q91p4m7BMH5TSIYE1T1oKpu9V7X4/7RGIhb3wXeYQuAsHNdXaxEJBP4O2CeVxbgVqDlSfyYqrOI9AFuBt4EUNVGVa0lxq+zpxuQJCLdgGTgIDF8rTtbPCaI85lEMOaISBaQA2wC+qnqQW9XFe5DirHkReBfgNNeOQ2o9R7ehNi75oOBauAtr1ttnoj0JMavs6oeAJ4H9uEmhjqgkNi+1p0qHhNE3PHmuXoXeERVj4buU/c+55i511lEbgMOq2o8zb7YDRgFvKqqOcBxWnUnxdp1Bn9KnttxE2QG0BMYH9WgYkw8JogDwKCQcqa3LSaJSAA3Obyjqku9zYdEZIC3fwBwOFrxRcAYYLKIlON2H96K2z+f4nVDQOxd8wqgQlU3eeU83IQRy9cZ4KfA16parapNwFLc6x/L17pTxWOC8CcR9O5uuAd4P8oxRYTX9/4msENVXwjZ9T5wv/f6fuC9zo4tUlT1CVXNVNUs3Gv7sarehzun1xTvsFircxWwX0Su9jb9DVBKDF9nzz7gBhFJ9j7rLfWO2Wvd2eLySWoRmYjbT50AzFfVZ85xykVJRG4CPsVd27ulP/7fcMchFgOX4U6R/veq+m1UgowgERkHPK6qt4nIFbgtih8BRbjrn38Xzfg6koiMxB2U7w7sAabhfgGM6essInOAqbh37BUBD+KOOcTste5McZkgjDHGnFs8djEZY4w5D5YgjDHGhGUJwhhjTFiWIIwxxoRlCcIYY0xYliCMuQAi0iwin4f867AJ8EQkS0RKOur9jPmhup37EGNMiJOqOjLaQRjTGawFYUwHEJFyEfmtiHwhIptF5Cpve5aIfCwi20RkrYhc5m3vJyLLRKTY+/fX3lsliMgb3hoHa0QkKWqVMnHPEoQxFyapVRfT1JB9dao6HPg97pP6AP8FLFDVEcA7wFxv+1zgE1W9DnfepO3e9iHAy6p6DVAL/CLC9TGmTfYktTEXQESOqWqvMNvLgVtVdY83QWKVqqaJyDfAAFVt8rYfVNV0EakGMkOngPCmZP/QW+AHEflXIKCqT0e+ZsZ8n7UgjOk42sbrCxE6Z1AzNk5oosgShDEdZ2rI/xu815/hzioLcB/u5IngLgE6A/z1s/t0VpDGnC/7dmLMhUkSkc9DyqtVteVW11QR2YbbCrjX2/ZPuCu9/TPuqm/TvO0PA6+LyK9wWwozcFdFM6bLsDEIYzqANwaRq6rfRDsWYzqKdTEZY4wJy1oQxhhjwrIWhDHGmLAsQRhjjAnLEoQxxpiwLEEYY4wJyxKEMcaYsP4PXPTB3LjvDdIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transfer_model.load_weights('t2_weights.hdf5')\n",
        "X_mitbih_test = transfer_model.evaluate(X_mitbihl_test, y_mitbihl_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hE97trZMLR6x",
        "outputId": "32b11041-d21c-489a-f1c4-c35ca33187a7"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "685/685 [==============================] - 4s 5ms/step - loss: 0.0787 - accuracy: 0.9792\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = tf.argmax(m_resnet_model.predict(X_mitbihl_test), axis=-1)\n",
        "cnf_matrix = sklearn.metrics.confusion_matrix(y_mitbih_test, y_pred)\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],\n",
        "                      title='Confusion matrix, without normalization')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "r6eMQEHCLbJi",
        "outputId": "9899e3fe-258a-4b70-bbd4-2d66da5b68ca"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "685/685 [==============================] - 3s 5ms/step\n",
            "Confusion matrix, without normalization\n",
            "[[18031    45    23     8    11]\n",
            " [  126   420    10     0     0]\n",
            " [   44     4  1377    18     5]\n",
            " [   24     0    18   120     0]\n",
            " [   19     1     5     0  1583]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}