final_model_tl_vgg16_dataset6000_sidangready.ipynb

{
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  "metadata": {
    "colab": {
      "name": "final_model_tl_vgg16_dataset6000_sidangready.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMApw2pVOFL5Oi9m2axrkW9",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/WiryawanCEO/IndonesianBanknotes-ImageClassificationModel/blob/main/final_model_tl_vgg16_dataset6000_sidangready.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5AfJ0aAmXNQi"
      },
      "source": [
        "Import library dan depedencies yang akan digunakan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ai4TMwb4WrVh"
      },
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import sys\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import itertools\n",
        "import math\n",
        "\n",
        "from google.colab import drive\n",
        "from pathlib import Path\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vT7-nU2NXUTN"
      },
      "source": [
        "Buat koneksi dengan Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQK4YVAXXMOD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "35ed4b3f-b304-485a-c7c8-c44862ad1b76"
      },
      "source": [
        "drive.mount('/content/drive')\n",
        "base = Path('/content/drive/My Drive/')\n",
        "sys.path.append(str(base))\n",
        "\n",
        "zip_path = base/'Dataset73.zip'\n",
        "!cp '{zip_path}' .\n",
        "\n",
        "!unzip -q Dataset73.zip\n",
        "!rm Dataset73.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jtuv8OOqX12h"
      },
      "source": [
        "Definisikan direktori dataset di Google Drive, serta setup datagenerator dataset training & validasi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kb0uI0scXj9D"
      },
      "source": [
        "train_dir = 'Dataset73/train'\n",
        "validation_dir = 'Dataset73/valid'\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qmkOWVqGYaoh"
      },
      "source": [
        "Masukan dataset kedalam generator berdasarkan batch, kemudian resize ke ukuran 224x224 dengan mode kelas berdasarkan kategorikal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dDiZuytYNiE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "55a33cac-5276-475e-fb3f-6984fb83e696"
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=100,\n",
        "        class_mode='categorical')\n",
        "\n",
        "validation_generator = val_datagen.flow_from_directory(\n",
        "        validation_dir,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=100,\n",
        "        shuffle=False,\n",
        "        class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 4200 images belonging to 6 classes.\n",
            "Found 1800 images belonging to 6 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mjw8QCGBa60H"
      },
      "source": [
        "Buat file labels.txt untuk referensi label kelas di implementasi nantinya sekaligus digunakan pada tahap evaluasi dengan confusion matrix dan classification report"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "97WY4RaBaita",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d6ac55bd-d1db-4bea-ba92-3423e0588277"
      },
      "source": [
        "print (train_generator.class_indices)\n",
        "\n",
        "labels = '\\n'.join(sorted(train_generator.class_indices.keys()))\n",
        "\n",
        "with open('labels.txt', 'w') as f:\n",
        "  f.write(labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'100rb': 0, '10rb': 1, '20rb': 2, '2rb': 3, '50rb': 4, '5rb': 5}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MyFRAzeWZlJf"
      },
      "source": [
        "Import model pre-trained VGG16 dari keras.applications(), tentukan input shapenya menjadi 224x224, instruksikan untuk tidak menggunakan lapisan akhir model, dan pilih wightsnya ImageNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xYvELFhZF-k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "13eb7e5d-7af6-423c-d983-65d696b8a385"
      },
      "source": [
        "IMG_SHAPE = (224, 224, 3)\n",
        "\n",
        "base_model = tf.keras.applications.VGG16(input_shape=IMG_SHAPE,\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2f5jOxaZnwI"
      },
      "source": [
        "Freeze lapisan-lapisan yang ada di model pre-trained VGG16"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pm2YEJ4ZPZu"
      },
      "source": [
        "base_model.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bitI-YTaZ_bY"
      },
      "source": [
        "Buat dan gabungkan lapisan fully connected yang dibuat dengan model pre-trained yang sudah di bekukan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neBaDX5IZPGu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "b0adc264-4d8b-41e2-b6ee-42f3070b6373"
      },
      "source": [
        "model_fine = tf.keras.models.Sequential()\n",
        "model_fine.add(base_model)\n",
        "model_fine.add(tf.keras.layers.Flatten())\n",
        "model_fine.add(tf.keras.layers.Dense(6, activation='softmax'))\n",
        "\n",
        "model_fine.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg16 (Model)                (None, 7, 7, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 6)                 150534    \n",
            "=================================================================\n",
            "Total params: 14,865,222\n",
            "Trainable params: 150,534\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwQgTgMIbUN8"
      },
      "source": [
        "Konfigurasi parameter pelatihan dan mulai proses training-nya"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2jS5pP9aOje",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "outputId": "3c6f5190-babe-4bfc-b8ae-84220acf4eae"
      },
      "source": [
        "model_fine.compile(loss='categorical_crossentropy',\n",
        "              optimizer=Adam(lr=0.0001),\n",
        "              metrics=['categorical_accuracy'])\n",
        "\n",
        "history = model_fine.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=42,  # 4200 images = batch_size * steps\n",
        "      epochs=20,\n",
        "      validation_data=validation_generator,\n",
        "      validation_steps=18,  # 1800 images = batch_size * steps\n",
        "      verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 1.7102 - categorical_accuracy: 0.2840 - val_loss: 1.5567 - val_categorical_accuracy: 0.4411\n",
            "Epoch 2/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 1.4305 - categorical_accuracy: 0.5164 - val_loss: 1.4067 - val_categorical_accuracy: 0.4872\n",
            "Epoch 3/20\n",
            "42/42 [==============================] - 66s 2s/step - loss: 1.2360 - categorical_accuracy: 0.6467 - val_loss: 1.2355 - val_categorical_accuracy: 0.6256\n",
            "Epoch 4/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 1.0945 - categorical_accuracy: 0.7279 - val_loss: 1.1204 - val_categorical_accuracy: 0.6911\n",
            "Epoch 5/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.9760 - categorical_accuracy: 0.7879 - val_loss: 1.0345 - val_categorical_accuracy: 0.7250\n",
            "Epoch 6/20\n",
            "42/42 [==============================] - 66s 2s/step - loss: 0.8788 - categorical_accuracy: 0.8331 - val_loss: 0.9647 - val_categorical_accuracy: 0.7478\n",
            "Epoch 7/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.7974 - categorical_accuracy: 0.8621 - val_loss: 0.8967 - val_categorical_accuracy: 0.7828\n",
            "Epoch 8/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.7322 - categorical_accuracy: 0.8793 - val_loss: 0.8513 - val_categorical_accuracy: 0.7939\n",
            "Epoch 9/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.6767 - categorical_accuracy: 0.8960 - val_loss: 0.7992 - val_categorical_accuracy: 0.8133\n",
            "Epoch 10/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.6262 - categorical_accuracy: 0.9086 - val_loss: 0.7645 - val_categorical_accuracy: 0.8178\n",
            "Epoch 11/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.5847 - categorical_accuracy: 0.9190 - val_loss: 0.7356 - val_categorical_accuracy: 0.8144\n",
            "Epoch 12/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.5420 - categorical_accuracy: 0.9317 - val_loss: 0.6918 - val_categorical_accuracy: 0.8361\n",
            "Epoch 13/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.5067 - categorical_accuracy: 0.9421 - val_loss: 0.6634 - val_categorical_accuracy: 0.8517\n",
            "Epoch 14/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.4803 - categorical_accuracy: 0.9417 - val_loss: 0.6408 - val_categorical_accuracy: 0.8489\n",
            "Epoch 15/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.4460 - categorical_accuracy: 0.9529 - val_loss: 0.6119 - val_categorical_accuracy: 0.8650\n",
            "Epoch 16/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.4203 - categorical_accuracy: 0.9557 - val_loss: 0.5933 - val_categorical_accuracy: 0.8683\n",
            "Epoch 17/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.3970 - categorical_accuracy: 0.9631 - val_loss: 0.5732 - val_categorical_accuracy: 0.8694\n",
            "Epoch 18/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.3768 - categorical_accuracy: 0.9657 - val_loss: 0.5533 - val_categorical_accuracy: 0.8822\n",
            "Epoch 19/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.3583 - categorical_accuracy: 0.9693 - val_loss: 0.5419 - val_categorical_accuracy: 0.8756\n",
            "Epoch 20/20\n",
            "42/42 [==============================] - 65s 2s/step - loss: 0.3409 - categorical_accuracy: 0.9695 - val_loss: 0.5232 - val_categorical_accuracy: 0.8844\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XcGQcp_9cKjM"
      },
      "source": [
        "Evaluasi hasil pelatihan dengan grafik visualisasi proses training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWCl8K0Ib8cE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "2ff0b741-3d27-4f81-be20-0f164f274ea1"
      },
      "source": [
        "# Retrieve a list of accuracy results on training and validation data\n",
        "# sets for each training epoch\n",
        "acc = history.history['categorical_accuracy']\n",
        "val_acc = history.history['val_categorical_accuracy']\n",
        "\n",
        "# Retrieve a list of list results on training and validation data\n",
        "# sets for each training epoch\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "# Get number of epochs\n",
        "epochs = range(len(acc))\n",
        "\n",
        "# Plot training and validation accuracy per epoch\n",
        "plt.plot(epochs, acc, label = 'Training Accuracy')\n",
        "plt.plot(epochs, val_acc, label = 'Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and validation accuracy')\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "# Plot training and validation loss per epoch\n",
        "plt.plot(epochs, loss, label = 'Training Loss')\n",
        "plt.plot(epochs, val_loss, label = 'Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and validation loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training and validation loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BP4Et9txb2eK"
      },
      "source": [
        "Evaluasi akurasi akhir model pada kumpulan dataset validasi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmpV05w8bi2P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "3866115b-7a87-4812-9f73-fce98e23f4fd"
      },
      "source": [
        "validation_generator.reset()\n",
        "STEP_SIZE_VALID=validation_generator.n//validation_generator.batch_size\n",
        "model_fine.evaluate(validation_generator, steps=STEP_SIZE_VALID)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18/18 [==============================] - 18s 1s/step - loss: 0.5232 - categorical_accuracy: 0.8844\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5231653451919556, 0.8844444155693054]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iE4vdNqRedCu"
      },
      "source": [
        "Definisikan plotting confusion matrix terlebih dahulu"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeAg7D2Bdh3N"
      },
      "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",
        "    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",
        "    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",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAtTFzLKej3_"
      },
      "source": [
        "Evaluasi klasifikasi dengan menggunakan confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Npc8dSLJdqg5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 812
        },
        "outputId": "08d63dee-8277-4bb4-8673-0f3acfa38f3e"
      },
      "source": [
        "validation_generator.reset()\n",
        "Y_pred = model_fine.predict(validation_generator, steps=STEP_SIZE_VALID)\n",
        "y_pred = np.argmax(Y_pred, axis=1)\n",
        "target_names = !cat labels.txt\n",
        "\n",
        "cm = confusion_matrix(validation_generator.classes, y_pred)\n",
        "\n",
        "# Compute confusion matrix\n",
        "cnf_matrix = cm\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=target_names, title='Confusion matrix, without normalization')\n",
        "\n",
        "# Plot normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=target_names, normalize=True, title='Normalized confusion matrix')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[277   3   3   0   1  16]\n",
            " [  6 233   9  19   8  25]\n",
            " [  0   4 276   9   4   7]\n",
            " [  4   6   3 260   0  27]\n",
            " [  4   8   2   1 281   4]\n",
            " [  9  11   2  10   3 265]]\n",
            "Normalized confusion matrix\n",
            "[[0.92 0.01 0.01 0.   0.   0.05]\n",
            " [0.02 0.78 0.03 0.06 0.03 0.08]\n",
            " [0.   0.01 0.92 0.03 0.01 0.02]\n",
            " [0.01 0.02 0.01 0.87 0.   0.09]\n",
            " [0.01 0.03 0.01 0.   0.94 0.01]\n",
            " [0.03 0.04 0.01 0.03 0.01 0.88]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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e4ybACbcqdGpKi8hN9ud7gKWOqTUYDNeITzpLkoBhqloNaAIMERHX6PlRqlrH3mYD2Md6AdWxOmzHiUiG9XMnh89MAlYA0SISJyIDgdeBdiKyA2hr7wPMBv4CdgIfYbX7pcc2rBuxBShEGj3MBoMhSPDB8BlV3aeq6+zPp4AtXOpDSIsuwDeqekFVd2H5lUYZ2XCss0RV02uUa5NGXgWGpJG+CFjktr8b8H83pcFguEp8G5jV7ieoC6wCmgEPi0g/YC1WqfEYlpNc6Xaae+drmgR+AI/BYLi+8a6NsIiIrHXbBl95GckHTAWGqupJrNpgBaAOsA9462olmqALBoPBWbwrER7OqNdYRHJgOcGvVPV7AFU94Hb8I2CmvZtR52uamBKhwWBwDtc4wmtoIxRrQOQnwBZVfdstvYRbtm6Aa77ndKCXiOQSkXJYM9ZWZ2TDlAgNBoOzXPvA7mZAX+B3EXEFYHwO6C0idbCG0O0GHgBQ1U0i8i3WbLQkYIiqJmdkwDhCg8HgKNc6w8WeaJHWRWZncM6rwKve2jCO0GAwOIYISBaYRWQcocFgcBAJjjnPHjCO0GAwOIpxhAaDIdtjHKHBYMjemDZCg8GQ3RHTRhg81KlSiiXL3vac0WEiB34daAns/rBXoCUAcOHCxUBLAOCGHIEPGnohKTjuhVMYR2gwGLI9xhEaDIbsjWkjNBgMBlMiNBgM2RzTWWIwGAyYqrHBYMjuiKkaGwwGg3GEBoPBYByhwWDI1giSJdoITah+Lzh+/Dj39u5JvVrVqF+7OqtWrnDETmThPEz/dxtWvN6Z5a914oH20QA8170WS1/tyJJXOjD16dYUD88NQId6USnpP4+4jSaVIxzR5c77Y8dwU4PaNKlfi3HvveO4PReP/et+qpWPpGXjOilpm37/jY5tWnBzk7rce1dXTp086Tc9AGPHjKZxvZo0qV+LAf3u4fz5836xGx+3hx63t6dVk9rcclMdPh7/LgBvvf4y9auVo12LhrRr0ZCF8+f4RU+G2G2EnrZAYxyhFzw9bCht293Kuo2bWbFmPdFVqjpiJyn5Is9/vY6bnp1J+xHzGNS2MtElC/DurM00/7/ZtHx+DvM2xPN015oALNm0PyX9kY9X8s7Axo7ocrF50x988dknLFyygqWr1jFvziz++nOnozZd9OrTj2++n3lZ2hMPP8jzI15l8cr1dLy9K2PfuepFzDLN3vh4xo97l0XLVrMydiPJyclMnfKNX2yHhYXx4itvsGjlb8yY/ysTPh7P9q1bALj/oUdY8OsaFvy6hjbtO/hFjyeMI7wOOHHiBMuX/kr/+wYCkDNnTsLDwx2xdeDEeTb+fQyA0+eT2L73BCUK5+HU+aSUPHlzhaEoAGcuXErPkysMVUdkpbB921bqN2hEnjx5CAsLo1nzlsyY9oOzRm1uataC8EKFLkv7888d3NSsBQA339KGWdP9o8VFclIS586dIykpiXPnzlK8REm/2C1WvAQ1a9cFIF/+/FSqXIX9+zJcpC2gGEd4HfD37l0UiYjgwfsH0KxxfYY8eD9nzpxx3G6pInmpVaYwsTsPA/B8j9r8MborPZuW5b9TN6bk61Q/ilVvdGbysFY88vHK9C7nE6pWq86K5Us5euQIZ8+eZcG8OcTFxTlqMyOiq1RjzqzpAMz4cSrx8f7TUjIykkeGDqNG5bJULhdJgQIFadO2vd/su9jzz27+2Pgbdes3AuCzj8bTtll9nnh4MMePH/O7nrSQEPG4BZqAOEIR+VREDorIH25phUVkgYjssP8vlM65MSLynr+0JiUlsWH9OgYNfpBlq2LJmzcvb//vDUdt5s0VxhePtuDfX8WmlAZf+e43agz9kSnLd3N/u8opeWfFxtH4mZncO3oJz3Wv5aiu6CpVeeyJp+h2ewe6d+lIzVp1CA0NXPSW0eM+ZMJHH9CuZWNOnzpFzhw5/Wb72LFjzJo5nY1b/mTbX3GcPXOGyZO+9Jt9gDOnT3N/v16MeG0k+QsUoN+AwSxfv4X5v66haLHivPT8M37VkxbelAazc4lwAnBbqrRngYWqWglYaO9fhoj4vZc7MjKKyMgoGjay2t+6dOvOhg3rHLMXFip8/mgLpizfzcy1e644PmX5Lu5oWPqK9OXbDlK2aD4K58vlmDaAfjEDWLx8NXMWLCI8PJyKFSs5ai8jKlWuwrfTZrNgySq69bibMuXK+832op9/okzZshSJiCBHjhzc3rWbY51oaZGYmMj9/e+mW89edLy9KwARRYsRGhpKSEgIffoPYEPsGr/pyQjjCNNBVZcAR1MldwE+tz9/DnQFEJHhIjJRRJYBE+3jpURkkV16fNFJrcWKFycyqhTbt28DYPEvP1OlajXH7L07qAnb955k3NytKWnli+VP+dyhXhTb91q9o+WK5ktJr1WmEDnDQjl6+oJj2gAOHTwIwJ49/zBj+o/0uLu3o/Yy1HLI0nLx4kVG/e81+g8c7DfbpUqVZu3qVZw9exZVZfEvPxMd7UwnWmpUlWGPPEDFylV4YMjQlPQD+/elfJ4zcxrRVav7RY8nsoIjDKZxhMVU1fUk9wPF3I5VA5qr6jkRiQEaATWAs8AaEZmlqmvdLyYig4HBYL2018LIUe8wKKYvCQkJlC1Xjvc//PSarpceTSpH0Kt5eTb9c4wlr1g9fi9P+Y17b65ApRIFuHhR2XPkDE98thqAOxqW5u7m5UhKvsi5hGQGjl3qiC53+t3Tk6NHjxKWIwcjR41xrOMoNQ/cdy/Lly7h6JHD1KlSjqee+w9nTp/ms4/eB6DjHV3pfW9/v2gBaNCoMV26daflTQ0ICwujVu06xAy83y+216xcztTJX1G1Wg3atWgIwLMvvMSPU79l8++/ISJElS7DG6PG+kWPJ4KhDdATok53NaZnWKQsMFNVa9j7x1U13O34MVUtJCLDAVXVEXZ6DNBaVfvZ+y8BR1V1dHq26tVvoEuWr3bqT/GaUoMmBVpC8ESoDpKozMEQofq026iAQBJZKFesqjbw5TVzFa+kUX3GeMz319sdfW47MwRTr/EBESkBYP9/0O1Y6m7a1N47MN7cYDBkiGAv8u5hCzTB5AinA666TX9gWgZ529m9zLmx2hKXOS3OYDBcDUJIiOct0ARq+MwkYAUQLSJxIjIQeB3Lwe0A2tr76bEamApsBKambh80GAzBw7V2lohIKRH5RUQ2i8gmEXnMTk9zyJ1YjBGRnSKyUUTqedIYkM4SVU2vq7FNGnmHp9qfgDX8xmAwBDu+qfomAcNUdZ2I5AdiRWQBEIM15O51EXkWa8jdM0AHoJK9NQbet/9Pl2CqGhsMhusMgWuuGqvqPlVdZ38+BWwBIklnyJ2d/oVarATCXf0P6RFMw2cMBsN1iC/bAO3RJnWBVaQ/5C4ScJ+NEGen7SMdjCM0GAzO4X3VuIiIuLf1f6iqH152KZF8WH0DQ1X1pHvboqqqiFz16BHjCA0Gg2NYw2e88oSHMxpHKCI5sJzgV6r6vZ18QERKqOq+VEPu4oFSbqdH2WnpYtoIDQaDg1x70AWxMnwCbFHVt90OpTfkbjrQz+49bgKccKtCp4kpERoMBkfxQRthM6Av8LuIbLDTnsMaYvetPfzub+Au+9hsoCOwE2sa7n2eDBhHaDAYnMMHw2dUdal1pTRJa8idAkMyY8M4QoPB4BiZaCMMKMYRGgwGRwmGKXSeMI7QYDA4ShYoEGYPRyhAWGjgO8jjPrkn0BIo0mxYoCUAsH/J/wItAYDQICit5L/hOv4aiqkaGwyGbI4rDFewYxyhwWBwkOAIs+UJ4wgNBoOjmKqxwWDI3gRJBGpPGEdoMBgcw4wjNBgMBsw4QoPBYDAlQoPBkM0xbYQGgyG7I3gOsxUMBH66RRZg/ry51KoeTfUqFfnfmxktrucsycnJNG1Ujx5db3fUTlSxcOa+/y/WTX6a2MlPM6RXCwAm/rcvK78axsqvhrF12vOs/OrSLJUaFUuw6JNHiZ38NGsmPUWunM7+xr4/dgw3NahNk/q1GPfeO47aygh/PZP02LF9G80a10vZIouGM/bdwN2PtAgNEY9boDElQg8kJycz9NEhzJqzgMioKJo3aUjnzndQtVo1v2sZ9+47RFepyqmTJx21k5SUzLOjp7FhWzz58uRi+RePs3DVdvo+NzElz+tD7+DE6fMAhIaG8OlLfRj44tf8vmMvhQvmITEp2TF9mzf9wReffcLCJSvImTMn3bt05LYOnShfoaJjNtPDX88kPSpVjmbZqnWA9a5GVyjF7Xd09XCWf8kCBcL0S4Qi8q69Nmiamz9FBpI1q1dToUJFypUvT86cOel5dy9mzsho7XlniI+LY+6c2fS/b6DjtvYfOcWGbVZk89NnL7B190FKRhS8LE/3trX5dp71BWzbOJo/du7j9x17ATh64iwXL1718hEe2b5tK/UbNCJPnjyEhYXRrHlLZkz7wTF76eHPZ+INi35ZSLlyFShdpkygpaQgcu3rGvuDjEqEZtF0YO/eeKKiLi1/EBkZxerVq/yu4+knH+eV197g1KlTfrVbukQh6kRHsmbT3ylpzeqW58CR0/y55zAAlcpEoKpMHzOYIoXy8d389bw98RfHNFWtVp2Xh7/A0SNHuCF3bhbMm0Odeukud+EYgXom6TF1ymR63NUr0DKuIBiqvp5I1xGq6ufu+yKSR1XPXqtBESkFfIG19J5irVb1jogUBiYDZYHdwF2qeiyN82OABqr68LVqySrMmTWTiIgI6tarz5LFi/xmN2/unEx6I4an3v6RU2cupKTf1b4uU+avS9kPCw2hae1yNO8/mrPnE5gz7iHWbY1j0ZodjuiKrlKVx554im63dyBP3jzUrFWH0NBQR2ylR6CeSXokJCQwe9YMhr/030BLuYIgKPB5xGNniYjcJCKbga32fm0RGXcNNl2r1lcDmgBDRKQa1ir1C1W1ErDQ3k+txe9tmiVLRhIXd2mJ1Pj4OCIjI/2qYeWKZcyeNYNqlcsR07c3ixf9zMCYvo7aDAsNYdIbMUyeu45pv/yekh4aGkKXW2rx3YINKWnxB46zdP1fHDlxhnMXEpm7fAt1o6Mc1dcvZgCLl69mzoJFhIeHU7FiJUftpSYQzyQjFsybQ+06dSlarJjnzH5EsHuOPfwLNN70Go8GbgWOAKjqb0DLqzWY2VXrRWS4iEwUkWWAq7W+lIgsEpEdIvLi1WrxhgYNG7Jz5w5279pFQkICUyZ/Q6fOdzhp8gpGvPIa2//aw+btu5gwcRI3t2rNJxMmej7xGhj/wt1s232QMV8vviy9daPKbP/7IPEHT6SkLVi5jeoVS5A7Vw5CQ0NoUa8CW3btd1TfoYPWyo179vzDjOk/0uPu3o7aS00gnklGTPn2G3oGYbUYIEQ8b4HGqxKWqu5J1aDpky5BL1etB6gGNFfVc3bVuBFQA2uFqjUiMktVL2vTFJHBwGCAUqVLX7XGsLAwRr3zHrd3upXk5GT6xwygWvXqV329rEDT2uXo06khv+/YmzJE5sWxs5m3fAs929dJ6SRxcfzUOcZ8vZilXzyOqjJv2RbmLtviqMZ+9/Tk6NGjhOXIwchRYwgPD3fUXjBz5swZfvn5J955b3ygpVyJZI0wXGIt+JRBBpHvgLeB94DGwGNYbXTX9PNjr1q/GHhVVb8XkeOqGu52/JiqFhKR4VgLU42w02OA1qraz95/CTiqqqPTs1W/fgNdtirwfT/JDvakeouJUH05wRC53Mke9sxQIHdobEaLrF8NhcpW01te8FxS/mFQA5/bzgzevAUPYi2NFwnsBeqQyaXyUpPRqvX2cfdV6wHOpLpE6jcnON4kg8FwBSKet0DjsWqsqoeBPr4y6MWq9a9z+ar1adHO7mU+h9WWOMBX+gwGg28JhnGCnvCm17i8iMwQkUMiclBEpolI+Wuw6Vq1vrWIbLC3jlgOsJ2I7ADa2vvpsRqrRLkRmJq6fdBgMAQHItfPFLuvgbFAN3u/FzAJq70w01zFqvXDU+1PACZcjW2DweB/Au/mPONNG2EeVZ2oqkn29iVwg9PCDAbD9UFWmGKX0VzjwnY73BwReVZEyopIGRF5GpjtP4kGgyGrIuK5WuxN1VhEPsc4uFgAACAASURBVLWb5v5wSxsuIvGpmthcx/4tIjtFZJuI3Orp+hlVjWOxemNdKh9wO6bAvz2qNxgM2R4fFfgmYA3h+yJV+ihVHXm5PamG1YRXHSgJ/CQilVU13fHPGc01Lne1ig0Gg8GFL6q+qrrEnoDhDV2Ab1T1ArBLRHZiTcJYkd4JXs0sEZEaWLM7UtoGVTW1ZzYYDIbLELyeQldERNxHf3yoqh96cd7DItIPK1rWMDtQSySw0i1PnJ2WLh4doT2XtxWWI5wNdACWcmUR1WAwGK4gxLsS4eGrmFnyPvAyVlPdy8BbXOWYYm96jXtgDWvZr6r3AbWBghmfYjAYDFb7YIiIx+1qUNUDqpqsqheBj7CqvwDxQCm3rFF2Wrp44wjP2YaSRKQA1tS3Uh7OMRgMBsC5KXauKbk23QBXj/J0oJeI5BKRckAlrEkY6eJNG+FaEQnH8rixwGkyaHQ0GAwGd3zRWSIik7Ca6IqISBzwItBKROpgVY13Y49sUdVNIvItsBkr/umQjHqMwbu5xv+yP44XkblAAVXdeHV/jsFgyE4IvplCp6ppBZz8JIP8rwKvenv9dB2hiNTL6JgruKrBYDCkS5BEl/FERiXCtzI4pkBrH2txDCU4YgF6iv3oD44sy+ix+o8bO74ZaAkAHJv7TKAlcPxMQqAlOEowTKHzREYDqm/xpxCDwXB9EvjQt54xC7wbDAbHELL4cp4Gg8HgC7KAHzSO0GAwOIc1TjD4PaE3EapFRO4Vkf/Y+6VFpJGn8wwGgwEgNMTzFmi8kTAOuAlwjeM5hRWx2mAwGDLECrrgzBQ7X+JN1bixqtYTkfUAqnpMRHI6rMtgMFwnBEGBzyPeOMJEEQnFXjJTRCKAi46qMhgM1w1BUODziDeOcAzwA1BURF7FikbzvKOqDAbDdYErVH+w481c469EJBYrFJcAXVV1i+PKgozk5GRa3NSQkiUj+e7HGX63f/z4cR5+6H42b9qEiDDug49p3OQmv2o4f/487dvczIULF0hOSqLrnd15/j8jHLEVFZGfj5/pRNFCeVGFT2dtYOwPsQA81LUeD9xRj+SLytxVf/J/Hy0C4MneTYi5rRbJFy8ybOxCflq7yxFtLubPm8uTTzxGcnIyMQMG8dTTzzpqz8XeuD089q+BHD54EBHhnv4DGfTgwzw04F7+3LkdgJMnjlOgYDjzl2QYdMUvZAE/6FVg1tLAWWCGe5qq/uOksGBj3LvvEF2lKqdOngyI/aeHDaVtu1v5ctIUEhISOHv2rN815MqVi9nzFpIvXz4SExNpe0sL2t/agUaNm/jcVlLyRZ4d/wsbdh4gX+6cLH+/Pwtjd1O0UF46N61Eowc+IyExmYjwPABUKX0jPVtVpd6gTyhxYz5mv3k3NWM+4qJDUyuTk5MZ+ugQZs1ZQGRUFM2bNKRz5zuoWq2aI/bcCQ0L4z8vv0HN2nU5feoUHVrfRMtWbXj/0y9T8rz0/DPkL1DAcS2ecHWWBDvetGPOAmba/y8E/gLmOCkq2IiPi2PunNn0v29gQOyfOHGC5Ut/TbGfM2dOwsPD/a5DRMiXLx8AiYmJJCYmOjZGbP/RM2zYeQCA0+cS2PrPEUoWyc/gO+oy8puVJCRaUZUOHbd+EDo3q8SURVtISEzm7/0n+HPvcRpGl0j3+tfKmtWrqVChIuXKlydnzpz0vLsXM2dMc8yeO8WKl6Bm7boA5Mufn0qVq7B/36W4o6rKjB+/o0v3u/2ixxNOxSP0JR4doarWVNVa9v+V8LAIyvXI008+ziuvvUFISGD6v/7evYsiERE8eP8AmjWuz5AH7+fMmTMB0ZKcnEyThnUpG1WM1m3a0rBRY8dtli5WgDoVi7Fm614qRhaiWY1SLHm3L/Pf6k396OIARN6Yj7iDl0rr8YdOUbJIfsc07d0bT1TUpfjEkZFRxMdnGATZEfb8s5s/Nm6gbv1LQ3tXrVhKRNFilK9Q0e96rkAgVMTjFmgy/c22w2/5/O0XkVIi8ouIbBaRTSLyWDr5JohID1/bT485s2YSERFB3Xr1/WXyCpKSktiwfh2DBj/IslWx5M2bl7f/90ZAtISGhrJyzXq2/7WH2LVr2LTpD88nXQN5b8jBpBe78dS4hZw6m0BYaAiFC9xAy0cm8tyHi/jy+S6O2g9mzpw+zeD+vRn+35GXVYOnTf2WLnfeFUBll3At3uRpCzTetBE+4bYbAtQD9jqgJQlrFap1IpIfiBWRBaq62U2L36cErlyxjNmzZjB/3hzOnz/PqZMnGRjTl08mTPSbhsjIKCIjo1JKX126deftkYFxhC7Cw8NpeXMrFsybS/XqNRyxERYawqTh3Zi8cDPTllqdAPGHT/Hjr9bntdv2cVGVIgVzE3/kNFFFLzmDyIj87D18yhFdACVLRhIXtydlPz4+jsjIDBdK8ymJiYkM7t+Lbj160fH2rinpSUlJzJk5jdk/L/ebFk8Eg6PzhDclwvxuWy6stkKf/wyr6j5XsFdVPQVsASJFZJGIjLaX+nOVEtuKyFoR2S4inX2txZ0Rr7zG9r/2sHn7LiZMnMTNrVr71QkCFCtenMioUmzfvg2Axb/8TJWqzjfKp+bQoUMcP34cgHPnzvHzwp+Ijq7imL3xT3Zg299HGDN1TUrajGU7uLlOaQAqRhYiZ1goh0+cY9bynfRsVZWcOUIpU7wgFSMLsWbbPse0NWjYkJ07d7B71y4SEhKYMvkbOnW+wzF77qgqTz76ABUrV2HwkMsrTr8u+pkKlSpTMjLKL1o84Yo+42kLNBmWsOyB1PlV9Uk/6XHZLQvUBVbZSTldS/2JyASgLFZbZQXgFxGpqKrnU11jMDAYoFTp0v6Q7SgjR73DoJi+JCQkULZcOd7/8FO/a9i/fx+DB8aQnJzMxYsX6d6jJx06OfM71LRGJH3a1eD3vw6ycnwMAC9+uoTP527kgyc7svajASQkJTPozVkAbPn7MFMXb2X9JwNJSr7I0DELHOsxBggLC2PUO+9xe6dbSU5Opn/MAKpVr+6YPXfWrFrO1MlfU6VaDdq3tNoGn3nhJdq0u43pP3xL1yDpJAGyTIRqSS9qsoiEqWqSiKxQVb8NWBORfMBi4FVV/V5EFgEvqupi+/gEYImqfmrvLwEeVdUN6V2zXv0G+uuKNekd9hvBEKE6WIYymAjVlzhy6kKgJQAQVfiG2KtYWzhDSlWpqcM+mu4x3+Mty/vcdmbIqES4Gqs9cIOITAemACldlar6va/FiEgOYCrwVarrp+4iTe1RAu9hDAbDFbg6S4IdbzofbgCOYK1Rolh/mwI+dYRiDUj7BNiiqm97yN5TRD4HygHlgW2+1GIwGHxFcAyP8URGjrCo3WP8B5ccoAsnSmDNgL7A7yLiquY+l07ef7BKrAWAB1O3DxoMhuBAyBpthBk5wlAgH5c7QBc+d4SqujQdW7NT5YvxtW2DweAQQTJO0BMZOcJ9qvqS35QYDIbrkmDpoMuIjBxh8Ks3GAxBzfWwil0bv6kwGAzXLVmgQJj+zBJVPepPIQaD4fpDsJyMp83jdUQ+FZGDIvKHW1phEVkgIjvs/wvZ6SIiY0Rkp4hsFJF6nq6fFZYTMBgMWRV7OU9PmxdMAG5LlfYssNCOirXQ3gfoAFSyt8HA+54ubhyhwWBwDME3YbhUdQmQupbaBfjc/vw50NUt/Qu1WAmEi0iGwSmNIzQYDI4iXmxAETuQimsb7MWli6mqK7LGfqCY/TkS2OOWL85OSxe/h7UyGAzZCy87Sw5fy1xjVVURuerxzcYRGgwGxxBnp9gdEJESqrrPrvoetNPjgVJu+aLstHQxVWODweAoPuosSYvpQH/7c39gmlt6P7v3uAlwwq0KnSamRGgwGBzFF+VBEZkEtMJqS4wDXgReB74VkYHA34BrfYLZQEdgJ9YKnPd5ur5xhH7EwTihXhMaJHWAYIgDCFCo4cOBlsDhVe8GWoJz2MNnrhVV7Z3OoSsmfqgV+HNIZq5vHKHBYHAM1/CZYMc4QoPB4CjB7waNIzQYDA6TBQqExhEaDAbnsOYaB78nNI7QYDA4iGT5eIQGg8FwzWQBP2gcocFgcA5TNTYYDAaBkCAZu5oRWUBicJCcnEzTRvXo0fX2gNgfO2Y0jevVpEn9Wgzodw/nz/t/4b64PXu4rV1r6tWqTv3aNRj77jt+1wDwwKABlC5ZlPp1ajhuK6pYOHM/fJR1U/+P2O/+jyG9WwFQq3Ikiz8fxspvnmXpV0/ToHoZACqXLcaiz4dxfNUohvb1X5D3QL+fGSFe/As0xhF6ybh33yG6StWA2N4bH8/4ce+yaNlqVsZuJDk5malTvvG7jtCwMF57cyTrNm5i0dIVfPD+OLZs3ux3HX37xzBt5ly/2EpKvsizb39Pve6vcnO/kTxwd0uqlC/Oq0O78uqHc2jS63Vefn8mrw61QuEdO3GGYW9MYfQXP/tFn4tAvp8Z4Vrg3dMWaIwj9IL4uDjmzplN//sGBkxDclIS586dIykpiXPnzlK8REm/ayhRogR161pRz/Pnz090lars3ZthUA9HaN6iJYULF/aLrf2HT7JhaxwAp89eYOuu/ZSMCEcVCuS9AYCC+XKz79AJAA4dO03s5n9ITEr2iz4IjvczI7JCidC0EXrB008+ziuvvcGpU6cCYr9kZCSPDB1GjcpluSF3blq3aUebtu0DosXF37t389tv62nYqHFAdfiT0iUKUyc6ijV/7Oapkd8xY+wQXnu8GyEhwi0xbwVMV6DfT09kheEzASkRishuEfldRDaIyFo7Lc2FWNI4N0ZE3vOX1jmzZhIREUHdevX9ZfIKjh07xqyZ09m45U+2/RXH2TNnmDzpy4DpOX36NL3v7sGbI0dRoECBgOnwJ3lz52TSyEE8NXIqp86cZ3DPFjz91vdU6vACT4+cyvsv9gmIrmB4PzPCVI09c4uq1nGLSpveQiwpiIjfS7ArVyxj9qwZVKtcjpi+vVm86GcGxvT1q4ZFP/9EmbJlKRIRQY4cObi9azdWrVzhVw0uEhMTuefuHvTqfQ9du90ZEA3+JiwshEkj72fynLVM+/k3APp0bsyPCzcAMHXB+pTOEn8TDO9nxnhTMQ68JwymNsI0F2IRkeEiMlFElgET7eOlRGSRXXp80UlRI155je1/7WHz9l1MmDiJm1u15pMJEz2f6ENKlSrN2tWrOHv2LKrK4l9+Jjra/w3jqspDgwcRXaUKjw59wu/2A8X4F/uwbdd+xnx5qQNk36ETtKhfCYBWjSqz859DAdEWDO9nhog1oNrTFmgC1UaowHx7jYEPVPVD0l+IBaAa0FxVz4lIDNAIqIEVdHGNiMxS1bX+k+9fGjRqTJdu3Wl5UwPCwsKoVbsOMQPv97uOFcuX8fVXE6lRoyaNG9QFYMTLr3Jbh45+1dHv3t78ungRhw8fpkLZKF74zwhiBjjTUdC0Tnn6dG7M79vjWfmNVUl58b3pDHn5a/73VA/CwkK4cCGJh1+ZBECxG/Oz7KunyZ/3Bi6q8nCfVtTt/iqnzvh/uFMwkFXCcIkVw9DPRkUiVTVeRIoCC4BHgOmqGu6W55iqFhKR4VixFkfY6TFAa1XtZ++/BBxV1dGpbAzGWtOUUqVL19+yY7fzf5gHkoMgMmuO0OB4KX0RrNMXmMCsl8iXKyT2WhZQSouqNevqZz/84jHfTZUK+dx2ZghI1VhV4+3/DwI/YJXwDrjWHk21EAvAmdSX8LCPqn6oqg1UtUGRIhE+024wGDKJl+t5BhK/O0IRySsi+V2fgfbAH6S/EEtatLN7mXNjtSUuc1CywWC4BkJEPG6BJhBthMWAH+yqURjwtarOFZE1pL0QS1qsBqZiLdP35fXcPmgwZHUC7+Y843dHqKp/AbXTSD9C2guxDE+1PwGY4Iw6g8Hgc7KAJzQzSwwGg2NYTYDB7wmNIzQYDM4RJDNHPGEcocFgcBbjCA0GQ/YmOKbQecI4QoPB4ChBMDrGI8YRGgwGxxCMIzQYDAafVY1FZDdwCkgGklS1gYgUBiYDZYHdwF2qeiyz1w6m6DMGg+E6xMfRZzIdvs8bjCM0GAyO4vBU4zTD92UW4wgNBoNziBVpyNMGFBGRtW7b4DSu5grfF+t2PKPwfV5j2ggNBoNjZKKz5LAXYbiau4fvE5Gt7gdVVe0Yp5kmWzhCVWtZxkATgNCPVxIk8QjPJ/pvlbeMOBIEsQBvbPdSoCU4iq/eOPfwfSJyWfg+Vd2XRvg+rzFVY4PB4CheVo09XcMX4fvSJVuUCA0GQ+Dw0ThCX4TvSxfjCA0Gg6P4wg9mNnxfZjGO0GAwOEtwNEtniHGEBoPBMUQIilD8njCO0GAwOErwu0HjCA0Gg9NkAU9oHKHBYHAQE4/QYDBkc4SsEarfDKj2gvfHjuGmBrVpUr8W4957x292H35wEJXKlOCmBpdGDfz4/Xfc1KAWhfPlYP06/65iGrdnD7e1a029WtWpX7sGY9/147144Mp7cezoUbp1vpX6NavQrfOtHD+W6ehLV8358+dp2awxjRvUoUGdGrzy0ouO2YqKKMDc0f1Y9/m/iJ3wEEO6N0459tCdjdjwxRBiJzzEqw+2BaB08YIcnf8cKz9+gJUfP8CYJzo5ps0rzALvWZ/Nm/7gi88+YeGSFSxdtY55c2bx1587/WK79739+O7HWZelVa1WnS++nkLT5i38osGd0LAwXntzJOs2bmLR0hV88P44tmze7BfbvfteeS9GvfUGLVu1Jvb3rbRs1ZpRb73hFy0AuXLlYva8haxau4EVa9azYP48Vq9a6YitpOSLPDt2PvX6j+Pmhz7hgW4NqVKmCC3rlqVzs2gaDRxP/Zj3Gf3N8pRz/oo/RpNBH9Bk0Ac8+vasDK7uPOLFv0BjHKEHtm/bSv0GjciTJw9hYWE0a96SGdN+8IvtZs1bUqhw4cvSoqtUpVLlaL/YT02JEiWoW7ceAPnz5ye6SlX27o33i+207sWcmTPo3acfAL379GP2jOl+0QLWtLF8+fIBkJiYSGJioldTxa6G/UdPs2HHfgBOn0tg69+HKBlRgMFdGjDy66Uk2PO2Dx0/64j9ayVEPG+BxjhCD1StVp0Vy5dy9MgRzp49y4J5c4iLiwu0rIDz9+7d/Pbbeho2auw5s0McPHiA4iVKAFCseHEOHjzgV/vJyck0aViXslHFaN2mrV/uReniBalTqQRrNsdRMepGmtUqw5L3BzL/nf7Ur1IyJV/ZEuGs+Hgw89/pT7NapR3XlS5eBGUNhmGGQddZklY47jTyTABmqup3TuuJrlKVx554im63dyBP3jzUrFWH0NBQp80GNadPn6b33T14c+QoChQoEGg5AF5P3vcloaGhrFyznuPHj9P7rjvZtOkPqlev4Zi9vLlzMOmlu3jq3bmcOptAWGgIhQvkpuVDn9CgSkm+HN6Dqr3GsP/IaSrfNZqjJ89Rt3IJvn31bur1H8epswmOacuYIPB0HgjWEmHqcNwpiIjfnXe/mAEsXr6aOQsWER4eTsWKlfwtIWhITEzknrt70Kv3PXTtdmdAtRQtWoz9+6yYnPv37SMiomhAdISHh9Py5lYsmDfXMRthoSFMeukuJv/0O9N+tcLwxR86yY9LtgCwduteLl5UihTMQ0JiMkdPngNg/fZ9/BV/jEqlbnRMW0a44hEGe4kwWB3hZYjIIhEZLSJrgcfs5LZ2JNvtItLZSfuHDlohzvbs+YcZ03+kx929nTQXtKgqDw0eRHSVKjw69IlAy+G2Tp2Z9NUXAEz66gs6dL7db7YPHTrE8ePHATh37hw/L/yJ6Ogqjtkb/8wdbPv7MGO+vdQhM2PpVm6uWxaAilGFyZkjlMMnzlKkYB5C7Ia3siXCqRhVmF17/dejnpqs0EYYdFVjLoXjVuADVf3QTs/pKiHaVeOyWIEZKwC/iEhFVT3vuogdynswQKlS19ZG0u+enhw9epSwHDkYOWoM4eHh13Q9bxnYvw/Lfl3MkSOHqV6pDM8+/yKFChXmmWGPcfjwIe6+8w5q1qrN1Olz/KJnxfJlfP3VRGrUqEnjBnUBGPHyq9zWoaPjtgf278OyJfa9qGjdi8eHPcN9fXvx5eefUap0aT6b+I3jOlzs37+PwQNjSE5O5uLFi3Tv0ZMOnZz5PW5asxR9bq3N738eYOXHDwDw4kcL+Xz2ej54pgtrP3uIhKRkBv33RwCa1y7DCwNakZh0kYuqPPL2LI6dOp+BBWcJhl5hT4gGRdjkS4hIpHs4buAR4CXgRVVdbOeZACxR1U/t/SXAo6q6Ia1r1q3XQBctW+UX/RkRDLc6V47gqARcSAp8xHCAnKGBvx/BEqH6/JLhsV6Ey88UtevW13mLPQ8rKlEwp89tZ4agKxGmE44b4EzqrB72DQZDEBD85cEgayPMIBx3WvQUkRARqQCUB7b5SabBYPASVxguT1ugCbYSYXrhuNNatPkfYDVQAHjQvX3QYDAEEYH3cx4JKkeYQTjuVqn2Y/wkyWAwXCNZwA8GlyM0GAzXH0FQ8/WIcYQGg8ExhOBoA/REUHWWGAwGQyAwJUKDweAoWaBAaByhwWBwELOKncFgyO4ESQBqjxhHaDAYnCULeELjCA0Gg6NkhaALptfYYDA4ii/CcInIbSKyTUR2pjPT7No0+vqCBoPBcBnXuIqdiIQCY4EOQDWgt4hU86VE4wgNBoOj+GAVu0bATlX9S1UTgG+ALr7UmC3aCDesjz0cnifs72u8TBHgsC/0XCPBoCMYNIDR4WsNZXwhxJ3162Ln5ckpRbzIeoMdgd7Fh25BmSOBPW7H4gCfrpSVLRyhqkZc6zVEZG0gA0cGk45g0GB0BJ+GtFDV2wKtwRtM1dhgMAQ78UApt/0oO81nGEdoMBiCnTVAJREpJyI5gV7AdF8ayBZVYx/xoecsfiEYdASDBjA63AkGDY6gqkki8jAwDwgFPlXVTb60EXSLNxkMBoO/MVVjg8GQ7TGO0GAwZHuMIzQYDNke4wgNWRaRywPdpd7PTph7cW0YR3iVuF607PzCiUhA3x+1e/pcz0AD2PNn7kXWxgyfuXoEUCA/cDIlUUScfgn9YcMLDWGqmmR/rgacB3b5Q5eIlAYuqmqciDwCVBaRfMBTwBF/3xtzL7I+pkSYSUSkiYiUUdWLIvIo8L2IvCUiD4L1S+xkKVFEirn9+ncWkUEiEi0iBZyymYaGaOAF+/ODwBfAj8AIEWnjsO3CwGtANxHpD8QAXwN5gNFAJTufX0rq5l5cH5hxhJlERIYDdwJvA93s/8sDrYGtqvqqg7ajgOHA91iT7IcBG4G8wApgoqrud8q+rUGANsBdwDmgCtARqAh0snW9q6r7nLBt/9DcAtyLVaNZoarj7eOjgBqq2s7XttPTg7kX1wWmROglbm0vw4FxwCvAYlVdDEwB3geiRcTnETzcSAI2AO2BAUAbVe0LjAdKALXdtfoaEQlRi5+A+cAprHmfuVR1G9a0p6aAzyf/p2oOUGAb1g9AExGpAKCqjwPJIlLC1/bT0BPIexESTPfiesA4Qi9w/xKKyFDgN+BL4BkRiVTV08BKoCgQ7oR9ALu0F4/lDMsAfez0+cARt31HivmqetHWMwDLGf8MbAWeFZGCqroTWIdVQva1bdf9bw+8pKpvAv/DmnJ1p4g0F5E7gbLABV/bT0NPIO+Fy3ZbguBeXA+YzhIvcPsSdgNuB35Q1WdFJAGYJyKPATmxImQcddD+fUBXrCrxm0B9EblHVb8GdgPVRSSXqjr28otICyyH21lVz9nRg7titZVOB9oB3X1oryLWj8tG+9qPAHMAVHWViOQBHsCqnm4Beqqqz59BOtr8fS9uB8qr6jsi0hF4giC5F1keVTVbOht2IHH7cyUgFhhr74fax/8HXMRqJK/soJaWWC99LXs/ChgM/AHMBBYDNR2wWwrLyecDcgETgE0uW1i1iuZY1cOvgYo+tN0ZywH+AnwG3ApMBSYBxdzy3YwVdKCow+9DIO9Fe6yaQDt7vzjwA/AVUNzf9+J62wIuIFg3rFDgn9lbM+AG4H6sX9oubvkEeAjrl9qX9hvbL3Uje/9+rKrWCCCnnXYjMBR4DyjjwD3oZDvaD7DCo5eyt4+Bf7u+6LYDuAUo4kPbTe17Xdfe/9D+0ckPzABeAiLc8udx+H0I9L044PYu3Gg740rAXODZVD8Mjt6L63EzvcZpICK1gclYVY/SwBDgVaySSRus6tB4VZ3hkP0OwBjbXjHgb1V9VER6YznlZcAUtcITlQDOquoJH9oXrBLnbKyq6BagP/AoVsk0DOue7AV+VKtzwKeISFOsEvYEez8C+ExVO4tIKaz78yfwpqoe9LV9Nx3BcC+igYW2naXAd0AisBPYB9xk63pNVQO9ZEDWJNCeOBg3rCrYj277HbC+CD2xXvx7sV7IDg7YDsUqcfS19wtgDY35wt6/D3jH/j/UwXsQilUKi+RS88DjwD9YjqEm8JGdlsMh+wXcPkcB64ESdlpDrN56n5W8POj5ECgZiHth26sN/IW1Xsf9WCXP+4F3sXqmv8KthGy2TN7fQAsIxg2rFPYFVvU0xE7rCGzmUvXkLqCUQ/afcTlCt7RlwNv256FYA2kLOGC7ou1kbsQqFT+d6vizWIExwaq6F/O1hjQ0hWFVBRfa+/2xhi8VdNju7S7nZv84PRfIe4G1lOXDqdJ+cuo9zE5bwAUEy8alNrkG9v5LWIOly7pKXljVIZczCvGx/cpun+/Fao8q7ZZWBKtxvAyQGyjkwD1wdU4sxmp3vAOrN/rfbnnKAh8F6BlNsH8AYrE7jRy05eqcuNXt7/4HeCYY7oVtvztWu3HxQGm4XjYzfIYr2uSKi8hOVX1CRMZjOb8fgF+xeodvgEtjuXxkvzPwrYhMV9Veqvql3S60TESaqeo/qnpYRC4AJVX1b6yZDD7DbpP7H3CPqq4XkQ+xwuOwsQAABjFJREFU1pNtCqy0h4Z8g9UrWldECqnqMV9qyECbYJXKWtj/t1HVHQ7aawpMBG5X1dUiUgSrStoVmCUiiVg99U3x872w9QlW08iTWENkHJ1NlB3I9p0l9hf8K2CWqk605+zOB35X1ftF5AUgGqtEVgrLUfzmQ/t5sYaEfI/1xcqlqr3tYy9jlcrG2fb7AJ1UdZev7LvpSKtzYoKqdhKR8sDzWMEEGgMxqvq7rzV4oTEGWKM+Xq8iDTtpdU4kYQ2VOYU1SPokVtvcAH/fC9sR3gzsV9Wt/rR9vZLtHSGAiDwD7FXViW5py4FlqvqUiBQCamBFFIlzwH5JrC/WDVjT5RLdnGE3rDFj9YHRqvqHr+3bdkKBvKp60v5cAmuYSkdV3WdPHYy38/ishzqTGv0WdcceOfAD1rjBEcAnwCCsTovXVXWPv0uCBufItlPsRKSy22481nS50m5pdwAVRKSaqh5T1V+dcIIAqrpXVU+rNfThASCniEyyD28HZqvqIKecoK0hWVVd4cQEOA4ctZ3gvcBzWD2iAXGCtka//Wrbpf7OWE7vI1W9qKofYnUmRdjZjvtLj8FZsqUjtNvkNojINwCq+iXWr/8ylzO0nVICVm+l31DVI1jO8LyIbAOmAcl+1pCk1vzpPSLyGlbP6Xuq6tN2yWBHVTer6nuufRHpjtVEEW8fN9Wp64Rs11lit8k9jDUEpamITFLV3qr6gh3bYIaIuNrkagGH/K3R7hjZiDV+sZ1TJdH08HfnRLCTRufEgQBLMviYbNlGGAxtch70FQK+BYap6kZ/23fTEYMfOieCHdM5cf2TLR2hOyJyI9asgQRV7S0i1YHT9hCVQOq6QVXPB1hDwJcEMBj8QbZsI3QnGNrk0iLQTtDWYJygIVuQ7R0hpHSMbAQKAt383SZnMBgCi3GEpLTJdQTaB2KgsMFgCCzZvo3QRTC0yRkMhsBgHKHBYMj2mKqxwWDI9hhHaDAYsj3GERoMhmyPcYQGgyHbYxxhNkFEkkVkg4j8ISJT7DVwr/ZaE0Skh/35YxGplkHeVnasw8za2G0HRPUqPVWe05m0NVxEnsysRsP1g3GE2YdzqlpHVWtgRdV50P2giFxVAA47PNjmDLK0wgo4azAELcYRZk9+BSrapbVfRWQ6/9/e2bxqVUVx+HnMEOlDUgyaFSGoSFRompKURGQNxEAEmyVYgQr+A4aOBAMnTQJxEKFEWEEE3kuKXAWja6LSvREOhAZNQvtUR7Ec7HXw9eWWL+KkznpG56xz1l777MGPvfdhrwXT6n3qfnVSvai+De3MsfqB+qP6NfBo15B6Ul2e16+q59QL6nH1cZrg7srZ6AvqQvVoxphU16TvAnVcnVIP0nIi/ivqF+p36bNt6NmBtB/PbNuoT6rH0ueUuvheDGbx36d3abj6Ts781tMKgwM8CyyLiMspJr9HxAp1Di0/4zjwDK1cwVJahb9p4NBQuwtpJS3XZlvzI+Kqre7LXxHxfr53GDgQEacz9+MYsAR4DzgdEXvV14GtI3zOWxljLjCpHs2z4w8AZyNil7o7295OS67xTkRcUlfSSiCsu4thLP5nlBD2h7nq+bw+RUs9vxr4dqAGyivAU93+H+3s9SJaIfMjEfE38LN6Yob2VwETXVsRcfUf+vEysDRzPwI8rD6YMd5I36/UUVLg78y0adDqySwCrtCKbH2S9o+BzzLGauDTgdhzRohR9IASwv5wIyKeHjSkIFwbNAE7ImJs6L3X7mE/ZgGrho8zDojTSKgv0kT1+Yi4rp4kKwzOQGTc34bHoCig9giL2xkD3lXvh1bXJTN6TwCbcw/xMeClGXy/AdaqT6Tv/LT/CTw08N44sKO7UTthmgC2pG098Mgd+joP+DVFcDFtRtoxC+hmtVtoS+4/gMvqpoyhrUBTUZQQFrdxkLb/d079HviQtmr4HLiUzz4Czgw7RsQvwDbaMvQCt5amXwIbu58ltDrRy/NnzDS3/l7voQnpFG2J/NMd+noMmK3+AOyjCXHHNeC5/IZ1wN60vwlszf5NARtGGJOiB1TShaIoek/NCIui6D0lhEVR9J4SwqIoek8JYVEUvaeEsCiK3lNCWBRF7ykhLIqi99wE8HKBVKCkj6IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v2Cr2Th6epsR"
      },
      "source": [
        "Evaluasi klasifikasi dengan laporan klasifikasi (classification report)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4lHYM-HdXBY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "83eec150-b618-4a09-fade-300c1d7d3755"
      },
      "source": [
        "print('Classification Report')\n",
        "target_names = !cat labels.txt\n",
        "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       100rb       0.92      0.92      0.92       300\n",
            "        10rb       0.88      0.78      0.82       300\n",
            "        20rb       0.94      0.92      0.93       300\n",
            "         2rb       0.87      0.87      0.87       300\n",
            "        50rb       0.95      0.94      0.94       300\n",
            "         5rb       0.77      0.88      0.82       300\n",
            "\n",
            "    accuracy                           0.88      1800\n",
            "   macro avg       0.89      0.88      0.88      1800\n",
            "weighted avg       0.89      0.88      0.88      1800\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vMwHBsEye9hR"
      },
      "source": [
        "Simpan model dan konversikan kedalam format TensorFlow Lite"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAhAVd_Me8nO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1ee0ad77-b22d-4e73-b524-4dc2cb6b561f"
      },
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model_fine)\n",
        "tflite_model = converter.convert()\n",
        "open (\"final_tl_vgg16_dataset73.tflite\" , \"wb\") .write(tflite_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59466896"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    }
  ]
}