detection model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8k8LUU7qGTkf"
      },
      "source": [
        "# IMPORT PACKAGES"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8w5oNBhy2Yi",
        "outputId": "6a886aa5-8f54-4821-ca3e-67b123f0f026"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.10.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tTmzPbY9RAf7",
        "outputId": "acc889a9-00f2-4162-e6b5-abfec9cb4333"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow==2.8.0\n",
            "  Downloading tensorflow-2.8.0-cp310-cp310-manylinux2010_x86_64.whl (497.6 MB)\n",
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            "  Downloading tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)\n",
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            "  Downloading tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9 MB)\n",
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            "\u001b[?25hCollecting tensorboard-plugin-wit>=1.6.0 (from tensorboard<2.9,>=2.8->tensorflow==2.8.0)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (3.2.2)\n",
            "Installing collected packages: tf-estimator-nightly, tensorboard-plugin-wit, keras, tensorboard-data-server, keras-preprocessing, google-auth-oauthlib, tensorboard, tensorflow\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.12.0\n",
            "    Uninstalling keras-2.12.0:\n",
            "      Successfully uninstalled keras-2.12.0\n",
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            "    Uninstalling tensorboard-data-server-0.7.1:\n",
            "      Successfully uninstalled tensorboard-data-server-0.7.1\n",
            "  Attempting uninstall: google-auth-oauthlib\n",
            "    Found existing installation: google-auth-oauthlib 1.0.0\n",
            "    Uninstalling google-auth-oauthlib-1.0.0:\n",
            "      Successfully uninstalled google-auth-oauthlib-1.0.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.12.3\n",
            "    Uninstalling tensorboard-2.12.3:\n",
            "      Successfully uninstalled tensorboard-2.12.3\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.12.0\n",
            "    Uninstalling tensorflow-2.12.0:\n",
            "      Successfully uninstalled tensorflow-2.12.0\n",
            "Successfully installed google-auth-oauthlib-0.4.6 keras-2.8.0 keras-preprocessing-1.1.2 tensorboard-2.8.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.8.0 tf-estimator-nightly-2.8.0.dev2021122109\n",
            "Requirement already satisfied: keras==2.8.0 in /usr/local/lib/python3.10/dist-packages (2.8.0)\n"
          ]
        }
      ],
      "source": [
        "# Install packages for compatibility with the autograder\n",
        "!pip install tensorflow==2.8.0\n",
        "!pip install keras==2.8.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N8WQRMYPGSvr"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import numpy as np\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2RNGAy8QJQqG"
      },
      "source": [
        "# LOAD DATASET"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6Pnix2iKXR5",
        "outputId": "142fe39d-55f6-4e35-8f37-9fd00f8cff91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/', force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1T0qxwH9DrEp"
      },
      "outputs": [],
      "source": [
        "TRAINING_DIR = \"/content/drive/My Drive/RiceLeafDiseaseImages/train/\"\n",
        "TESTING_DIR = \"/content/drive/My Drive/RiceLeafDiseaseImages/test/\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWN3lkmkJrrD"
      },
      "source": [
        "# IMAGE AUGMENTATION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MR-AWBDMDrJ1"
      },
      "outputs": [],
      "source": [
        "training_datagen = ImageDataGenerator(\n",
        "        rescale=1. / 255,\n",
        "\n",
        "        # image augmentation\n",
        "        rotation_range=90,\n",
        "        horizontal_flip=True,\n",
        "        vertical_flip=True,\n",
        "        fill_mode='nearest',\n",
        "\n",
        "        # split dataset\n",
        "        validation_split=0.4\n",
        "        )\n",
        "\n",
        "testing_datagen = ImageDataGenerator(rescale=1. / 255)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZAB2b1HDrOP",
        "outputId": "9ca41c2a-c7de-4e1a-9b58-6149933b1e42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 3082 images belonging to 4 classes.\n",
            "Found 2052 images belonging to 4 classes.\n",
            "Found 800 images belonging to 4 classes.\n"
          ]
        }
      ],
      "source": [
        "train_generator=training_datagen.flow_from_directory(\n",
        "        TRAINING_DIR,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=64,\n",
        "        color_mode='rgb',\n",
        "        class_mode='categorical',\n",
        "        shuffle=True,\n",
        "        subset='training'\n",
        "    )\n",
        "\n",
        "val_generator =training_datagen.flow_from_directory(\n",
        "        TRAINING_DIR,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=64,\n",
        "        color_mode='rgb',\n",
        "        class_mode='categorical',\n",
        "        shuffle=True,\n",
        "        subset='validation'\n",
        "    )\n",
        "\n",
        "test_generator = testing_datagen.flow_from_directory(\n",
        "    TESTING_DIR,\n",
        "    target_size=(224, 224),\n",
        "    batch_size=64,\n",
        "    color_mode='rgb',\n",
        "    class_mode='categorical',\n",
        "    shuffle=False,  # Set shuffle to False for evaluation\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wC0I_mpWJ3mv"
      },
      "source": [
        "# MODEL BUILDING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e57Y45f5DrSc"
      },
      "outputs": [],
      "source": [
        "def feature_extractor(inputs):\n",
        "        feature_extractor = tf.keras.applications.resnet.ResNet50(input_shape=(224, 224, 3),\n",
        "                                                                   include_top=False,\n",
        "                                                                   weights='imagenet')(inputs)\n",
        "        return feature_extractor\n",
        "\n",
        "def classifier(inputs):\n",
        "        x = tf.keras.layers.GlobalAveragePooling2D()(inputs)\n",
        "        x = tf.keras.layers.Flatten()(x)\n",
        "        x = tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "        x = tf.keras.layers.Dense(128, activation=\"relu\")(x)\n",
        "        x = tf.keras.layers.Dense(4, kernel_regularizer=tf.keras.regularizers.l2(0.01), activation='linear')(x)\n",
        "\n",
        "        return x\n",
        "\n",
        "def final_model(inputs):\n",
        "        resnet_feature_extractor = feature_extractor(inputs)\n",
        "        classification_output = classifier(resnet_feature_extractor)\n",
        "        return classification_output\n",
        "\n",
        "def define_compile_model():\n",
        "        inputs = tf.keras.layers.Input(shape=(224,224,3))\n",
        "        classification_output = final_model(inputs)\n",
        "        model = tf.keras.Model(inputs=inputs, outputs=classification_output)\n",
        "        model.compile(optimizer='sgd',\n",
        "                      loss='squared_hinge',\n",
        "                      metrics=['accuracy'])\n",
        "        return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zoMXvYmTDrWo",
        "outputId": "bb76bf79-382c-4717-ebf3-4595f6974f3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94773248/94765736 [==============================] - 0s 0us/step\n",
            "94781440/94765736 [==============================] - 0s 0us/step\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " resnet50 (Functional)       (None, 7, 7, 2048)        23587712  \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 2048)             0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 2048)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 2048)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               262272    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4)                 516       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 23,850,500\n",
            "Trainable params: 23,797,380\n",
            "Non-trainable params: 53,120\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = define_compile_model()\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# INISIASI CALLBACK"
      ],
      "metadata": {
        "id": "n4SVHmxVoj0i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MyCallback(tf.keras.callbacks.Callback):\n",
        "    def on_epoch_end(self, epoch, logs={}):\n",
        "        if (logs.get('accuracy') >0.90 and logs.get('val_accuracy')>0.90):\n",
        "            print(\"\\nReached >90% accuracy and 90% validation accuracy, Stop training!\")\n",
        "            self.model.stop_training = True"
      ],
      "metadata": {
        "id": "_3T4BJ5aoz1T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TRAINING MODEL"
      ],
      "metadata": {
        "id": "8FNgdesZos3t"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "onvyB96pDrbO",
        "outputId": "a909ff3c-60c7-4947-9e4b-5bcd7c2e8cf6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "49/49 [==============================] - 803s 16s/step - loss: 0.4711 - accuracy: 0.7836 - val_loss: 1.0202 - val_accuracy: 0.2412\n",
            "Epoch 2/25\n",
            "49/49 [==============================] - 94s 2s/step - loss: 0.1877 - accuracy: 0.9517 - val_loss: 1.7789 - val_accuracy: 0.2710\n",
            "Epoch 3/25\n",
            "49/49 [==============================] - 91s 2s/step - loss: 0.1302 - accuracy: 0.9802 - val_loss: 4.5000 - val_accuracy: 0.2417\n",
            "Epoch 4/25\n",
            "49/49 [==============================] - 94s 2s/step - loss: 0.1027 - accuracy: 0.9890 - val_loss: 13.5874 - val_accuracy: 0.2417\n",
            "Epoch 5/25\n",
            "49/49 [==============================] - 92s 2s/step - loss: 0.0901 - accuracy: 0.9925 - val_loss: 34.1060 - val_accuracy: 0.2417\n",
            "Epoch 6/25\n",
            "49/49 [==============================] - 92s 2s/step - loss: 0.0796 - accuracy: 0.9971 - val_loss: 64.2000 - val_accuracy: 0.2417\n",
            "Epoch 7/25\n",
            "49/49 [==============================] - 102s 2s/step - loss: 0.0743 - accuracy: 0.9990 - val_loss: 93.4064 - val_accuracy: 0.2442\n",
            "Epoch 8/25\n",
            "49/49 [==============================] - 100s 2s/step - loss: 0.0730 - accuracy: 0.9994 - val_loss: 78.2345 - val_accuracy: 0.2451\n",
            "Epoch 9/25\n",
            "49/49 [==============================] - 92s 2s/step - loss: 0.0707 - accuracy: 0.9994 - val_loss: 66.8712 - val_accuracy: 0.2427\n",
            "Epoch 10/25\n",
            "49/49 [==============================] - 92s 2s/step - loss: 0.0681 - accuracy: 0.9990 - val_loss: 54.6661 - val_accuracy: 0.2437\n",
            "Epoch 11/25\n",
            "49/49 [==============================] - 91s 2s/step - loss: 0.0661 - accuracy: 0.9987 - val_loss: 46.1027 - val_accuracy: 0.2563\n",
            "Epoch 12/25\n",
            "49/49 [==============================] - 94s 2s/step - loss: 0.0643 - accuracy: 1.0000 - val_loss: 19.7594 - val_accuracy: 0.2617\n",
            "Epoch 13/25\n",
            "49/49 [==============================] - 95s 2s/step - loss: 0.0620 - accuracy: 0.9994 - val_loss: 9.2736 - val_accuracy: 0.2778\n",
            "Epoch 14/25\n",
            "49/49 [==============================] - 93s 2s/step - loss: 0.0604 - accuracy: 1.0000 - val_loss: 5.5555 - val_accuracy: 0.3299\n",
            "Epoch 15/25\n",
            "49/49 [==============================] - 93s 2s/step - loss: 0.0583 - accuracy: 0.9997 - val_loss: 3.3683 - val_accuracy: 0.3713\n",
            "Epoch 16/25\n",
            "49/49 [==============================] - 95s 2s/step - loss: 0.0573 - accuracy: 1.0000 - val_loss: 2.0645 - val_accuracy: 0.4493\n",
            "Epoch 17/25\n",
            "49/49 [==============================] - 94s 2s/step - loss: 0.0563 - accuracy: 1.0000 - val_loss: 1.1866 - val_accuracy: 0.5926\n",
            "Epoch 18/25\n",
            "49/49 [==============================] - 94s 2s/step - loss: 0.0561 - accuracy: 0.9997 - val_loss: 0.7612 - val_accuracy: 0.7032\n",
            "Epoch 19/25\n",
            "49/49 [==============================] - 93s 2s/step - loss: 0.0534 - accuracy: 0.9997 - val_loss: 0.3716 - val_accuracy: 0.8509\n",
            "Epoch 20/25\n",
            "49/49 [==============================] - 91s 2s/step - loss: 0.0539 - accuracy: 0.9994 - val_loss: 0.4073 - val_accuracy: 0.8431\n",
            "Epoch 21/25\n",
            "49/49 [==============================] - ETA: 0s - loss: 0.0519 - accuracy: 1.0000\n",
            "Reached >90% accuracy and 90% validation accuracy, Stop training!\n",
            "49/49 [==============================] - 96s 2s/step - loss: 0.0519 - accuracy: 1.0000 - val_loss: 0.1400 - val_accuracy: 0.9586\n"
          ]
        }
      ],
      "source": [
        "# TRAIN MODEL\n",
        "EPOCHS = 25\n",
        "CALLBACK = MyCallback()\n",
        "history = model.fit(train_generator,\n",
        "                    epochs=EPOCHS,\n",
        "                    validation_data=val_generator,\n",
        "                    callbacks=[CALLBACK])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EVALUASI KINERJA MODEL"
      ],
      "metadata": {
        "id": "o2Mg9na0secx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZcAYZIeDrfR",
        "outputId": "9cff5bad-4d23-4b01-a9ca-00e31b276e4c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 213s 18s/step - loss: 0.1121 - accuracy: 0.9837\n",
            "Test loss: 0.1121\n",
            "Test accuracy: 0.9837\n"
          ]
        }
      ],
      "source": [
        "# MODEL EVALUATION\n",
        "loss, accuracy = model.evaluate(test_generator)\n",
        "print(f\"Test loss: {loss:.4f}\")\n",
        "print(f\"Test accuracy: {accuracy:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot the loss\n",
        "plt.plot(history.history['loss'], label='train loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.savefig('LossVal_loss.png')\n",
        "plt.show()\n",
        "\n",
        "# Plot the accuracy\n",
        "plt.plot(history.history['accuracy'], label='train acc')\n",
        "plt.plot(history.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.savefig('AccVal_acc.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "0XVulmKVshYD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "2becf57e-4826-4ad9-e170-cd257b826987"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upfxqqK0vTMc"
      },
      "outputs": [],
      "source": [
        "test_steps_per_epoch = np.math.ceil(test_generator.samples / test_generator.batch_size)\n",
        "\n",
        "predictions = model.predict(test_generator, steps=test_steps_per_epoch)\n",
        "y_pred = np.argmax(predictions, axis=1)\n",
        "class_labels = list(test_generator.class_indices.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqfTTryqKnii",
        "outputId": "dcd45e0e-c3bb-4069-9894-086f174b542d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "Bacterialblight     1.0000    0.9450    0.9717       200\n",
            "          Blast     0.9851    0.9950    0.9900       200\n",
            "      Brownspot     0.9950    0.9950    0.9950       200\n",
            "         Tungro     0.9569    1.0000    0.9780       200\n",
            "\n",
            "       accuracy                         0.9838       800\n",
            "      macro avg     0.9843    0.9838    0.9837       800\n",
            "   weighted avg     0.9843    0.9838    0.9837       800\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Classification Report:\")\n",
        "print(classification_report(test_generator.classes, y_pred, target_names=class_labels, digits=4))\n",
        "\n",
        "model.save(\"model_CS.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "cm = confusion_matrix(test_generator.classes, y_pred)\n",
        "print(cm)\n",
        "sns.heatmap(cm, annot=True)"
      ],
      "metadata": {
        "id": "joCmAt9Tta5p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "outputId": "f54d0bb1-6029-4430-90cf-46a81d272291"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[189   2   1   8]\n",
            " [  0 199   0   1]\n",
            " [  0   1 199   0]\n",
            " [  0   0   0 200]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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mxUqS5s1boP7PDHdSVO5n5IhxGjV6mCZNHquAiv5KSU7TnFmf6s24qc4OzbU5abVCVlaWEhMTrY8PHDig7du3y8/PT1WrVpUkZWZmauHChXrrrbcuOT8hIUGbNm1Sq1at5O3trYSEBA0fPlxPPPGEKlQo+nC0yfL3jDk75eXlKT09XZIUEBCgUjZLAO2Vu4f10K7Cu/GTzg4B5xXz7YnroJwnN9NyJSeyEq9+0DU4890Uh/VVpuPQqx903po1a9SqVatL2vv06aM5c+ZIkt5//30NGzZMycnJ8vX1NRy3bds2Pf/889q7d69yc3NVvXp1Pfnkk4qKirJraKPYyYGjkRy4DpID1+Eib0+I5MDVXPfk4Jt3HNZXmc7DHNbXjXJL3yERAIBicfE5B9cb01UBAIABlQMAAGw5aUKiqyA5AADAlpsPK5AcAABgy80rB8w5AAAABlQOAACwxbACAAAwYFgBAADgAioHAADYcvPKAckBAAC23PzW5QwrAAAAAyoHAADYYlgBAAAYuHlywLACAAAwoHIAAIAtboIEAAAM3HxYgeQAAABbLGUEAAC4gMoBAAC2GFYAAAAGbp4cMKwAAAAMqBwAAGCLpYwAAOBilgJWKwAAAFhROQAAwJabT0gkOQAAwJabzzlgWAEAABhQOQAAwJabT0gkOQAAwBZzDgAAgIGbJwfMOQAAAAZUDgAAsOXmP9lMcgAAgC2GFQAAAC4gOQAAwFaBxXGbHdatW6cuXbooJCREJpNJixcvNuzv27evTCaTYevQoYPhmIyMDPXu3Vs+Pj4qX768+vfvr6ysLLviIDkAAMCWpcBxmx2ys7MVGhqqadOmXfaYDh06KDk52bp9+umnhv29e/fW7t27tWLFCi1btkzr1q3TwIED7YqDOQcAALiIjh07qmPHjlc8xmw2Kzg4uNB9e/bs0fLly7V582bdfffdkqR///vf6tSpkyZNmqSQkJAixUHlAAAAWw4cVsjNzVVmZqZhy83NLXZoa9asUWBgoGrXrq1Bgwbp+PHj1n0JCQkqX768NTGQpLZt28rDw0ObNm0q8jVcpnLgFfqEs0PAeWcOr3F2CDivTJWWzg4B52WdPePsEHADWRy4WiEuLk5jx441tMXExCg2Ntbuvjp06KCHH35Y1atX1/79+zVq1Ch17NhRCQkJKlGihFJSUhQYGGg4p2TJkvLz81NKSkqRr+MyyQEAALei6OhoRUVFGdrMZnOx+urZs6f13xs2bKhGjRrpjjvu0Jo1a9SmTZtrivNiJAcAANhy4A8vmc3mYicDV1OjRg0FBAQoMTFRbdq0UXBwsNLS0gzH5OfnKyMj47LzFArDnAMAAGw5abWCvQ4fPqzjx4+rUqVKkqSIiAidPHlSW7dutR6zatUqFRQUKDw8vMj9UjkAAMCWk36yOSsrS4mJidbHBw4c0Pbt2+Xn5yc/Pz+NHTtW3bt3V3BwsPbv36+XX35ZNWvWVPv27SVJdevWVYcOHTRgwADNnDlTeXl5Gjx4sHr27FnklQoSlQMAAFzGli1b1KRJEzVp0kSSFBUVpSZNmmjMmDEqUaKEdu7cqa5du6pWrVrq37+/wsLC9NNPPxmGLebPn686deqoTZs26tSpk5o3b67333/frjioHAAAYMtJv63QsmVLWa7wo0/ff//9Vfvw8/NTfHz8NcVBcgAAgC0nDSu4CoYVAACAAZUDAABsXedVBq6O5AAAAFsMKwAAAFxA5QAAABuO/G2FmxHJAQAAthhWAAAAuIDKAQAAtty8ckByAACALZYyAgAAAzevHDDnAAAAGFA5AADAhsXNKwckBwAA2HLz5IBhBQAAYEDlAAAAW9whEQAAGDCsAAAAcAGVAwAAbLl55YDkAAAAGxaLeycHDCsAAAADKgcAANhiWAEAABiQHAAAgIu5++2TmXMAAAAMqBwAAGDLzSsHJAcAANhy77snM6wAAACMqBwAAGDD3SckkhwAAGDLzZMDhhUAAIABlQMAAGy5+YREkgMAAGy4+5wDhhUAAIAByYGDDHqujxJ/3aiszP3asH6pmt7d2Nkh3RQ++HiBHhswXPe066EWXXpraPR4HUg6fF2vabFYNPXDT9TywScV1uZhPTPsVR08dMS6/0hyqkZPeFftH+2vsDYPq8Njz2jqR/OVl5d3XeO6FfG+cA3Nm4dr0aI5OvjHVuWdPaKuXds7OyTXV+DA7SZEcuAAPXp01aSJMfrX+LfVNLyDduz8Rd9+M18VK/o7OzSXt2X7LvV6qLPi35uk9yf/S3n5+RoYNVqnz+QUu89ps+br1dcnX3b/rPgvNf/LpRozIlLx772lMmVK69kXxyg396wk6UDSYVkKLBozIlKLP56ukUMGaMGS7/TO+/OKHZM74n3hOry8ymrnzl809IVXnR3KTcNSYHHYdjMiOXCA4S8M0IcfxWvuvAXas+c3PR/5ik6fPqOn+/Z0dmgu7723xqlbp7aqWf121alZQ6+PGq7k1GP6ZV+i9ZjMP7M0ZsIU3ffA4wpv30P9XhilvYm/F+t6FotFHy9YooFPPabW9zVT7ZrV9carUUo7nqGVPyVIkpqHh2n8qGG69567dFtIsFo1D1ffng9p5doNDnnO7oL3hev4/vvViol5U0uWLHd2KDcPJ1UO1q1bpy5duigkJEQmk0mLFy+27svLy9PIkSPVsGFDeXl5KSQkRE899ZSOHj1q6KNatWoymUyGbcKECXbFQXJwjUqVKqW77mqklat+srZZLBatXLVezZqFOTGym1NWdrYkydennLUtaswEZZw8qRmTxmrBh++obq079Myw13Qq80+7+z+cnKr0jBOKuKi87V3OS43q1taO3XuvENdp+fh42309d8X7Aiie7OxshYaGatq0aZfsO336tLZt26bRo0dr27Zt+uqrr7Rv3z517dr1kmPHjRun5ORk6zZkyBC74nDKaoXc3Fzl5uYa2iwWi0wmkzPCuSYBAX4qWbKk0lLTDe1pacdUp/YdTorq5lRQUKAJUz5Qk4b1dGeNapKkbTt3a9eeX7Xu6/ny9CwlSXopsr9W/bRRP6z5j3p07WDXNdKPn5Ak+Vcob2j39yuv9IyThZ6TdPio4r9cqhHP97PrWu6M9wVudhYHzhUo7DvPbDbLbDZfcmzHjh3VsWPHQvvx9fXVihUrDG1Tp07VPffco6SkJFWtWtXa7u3treDg4GLH7PDKwaFDh9Sv35U/ROPi4uTr62vYLAX2/xWIW8v4t2co8cBBTYx92dq2L/GATp/J0b0P9FLTdo9YtyPJqTp0JFmStHXHLsO+Dz5eqG9WrDG0LfthdbFiSj2WrmdHxKhdy+Z6xM5EBMBNzIHDCoV958XFxTkkzFOnTslkMql8+fKG9gkTJsjf319NmjTRxIkTlZ+fb1e/Dq8cZGRkaO7cuZo1a9Zlj4mOjlZUVJShrYJ/HUeHckOkp2coPz9fgUEBhvbAwIpKST3mpKhuPq9PnqG1CZs1998TFBx44b/l6TM5quhfQbOnXPpG8i7nJUmqX+dOfTlrirX9ky+WKi39uKKe62tt8/crL0kK8K8gSTp+4qQqBvhZ9x/POKnad1Y39J+Wflz9ho5S4wZ1FPvy4Gt+ju6E9wVwQWHfeYVVDeyVk5OjkSNHqlevXvLx8bG2Dx06VHfddZf8/Py0YcMGRUdHKzk5WW+//XaR+7Y7Ofj666+vuP/3368+UaywcsrNOKQg/TVBZNu2nWrdqrm+/vp7SX89l9atmmv6jNlOjs71WSwWvfHOTK1cl6DZU+JUJcRYBqtb6w6lZ5xQiRIlVLlSUKF9lDabVbVKiPWxr085ZWefNrT9rUqlIAX4VdDGrdtV584akv6aT7Bzzz492u1CKS/1WLr6DR2lerVranz0MHl4MD3HHrwvcLNz5LDC5YYQrkVeXp4effRRWSwWzZgxw7Dv4kSkUaNG8vT01LPPPqu4uLgix2F3ctCtWzeZTCZZLJdfnnGzftEX1+R3P9DsjyZr67ad2rz5vxo6ZIC8vMpoztzPnR2ayxv/9gx9++NaTXnjNXmVLWudE1CuXFmVNpsVcXdjhdavo6Gjxitq0NOqdltlpaVnaF3CZrVpEaEGde6063omk0lPPvqg3p/7uW6vUlmVKwVp6oefKNDfT23ui5D0V2Lw9NBohQQFakRkP504mWk9/+/KA66O94Xr8PIqq5o1L1TGqlerqtDQ+srIOKFDh45e4Uw35sL3J/g7MTh48KBWrVplqBoUJjw8XPn5+frjjz9Uu3btIl3D7uSgUqVKmj59uh588MFC92/fvl1hYe41G3nhwq9VMcBPsWNGKDi4onbs2K3ODzyhtLT0q5/s5j5f/K0k6emh0Yb28dHD1K1TW5lMJs2YGKt33/9Yo+PeUcbJTAX4VVBYaP1LJhUWVb/Hu+vMmRzFTvy3/szK1l0N62nmpHEymz0lSQmbtyvpcLKSDierzcN9Defu+mlZsa7pjnhfuI6wsFCt/PEL6+NJk2IlSfPmLVD/Z4Y7KSoUx9+JwW+//abVq1fL3//q9w3Zvn27PDw8FBgYWOTrmCxXKgEUomvXrmrcuLHGjRtX6P4dO3aoSZMmKiiwL+0q6VnZruNx/Zw5vMbZIeC8MlVaOjsEnOde9VDXl3f2yNUPugbH/nm/w/qquGJtkY/NyspSYuJf93lp0qSJ3n77bbVq1Up+fn6qVKmSHnnkEW3btk3Lli1TUNCFoVY/Pz95enoqISFBmzZtUqtWreTt7a2EhAQNHz5cHTt21Ny5c4sch92Vg5deeknZ59eiF6ZmzZpavbp4M8MBAHAFjpxzYI8tW7aoVatW1sd/zx/o06ePYmNjrfP+GjdubDhv9erVatmypcxmsz777DPFxsYqNzdX1atX1/Dhwy+ZEHk1dlcOrhcqB66DyoHroHLgOqgcuJbrXTlIbeW4ykHQ6qJXDlwFU7ABAICBU+6QCACAS7O4d62I5AAAABvOmnPgKhhWAAAABlQOAACwYSlgWAEAAFyEYQUAAICLUDkAAMCGhdUKAADgYgwrAAAAXITKAQAANlitAAAADFzjV4ech+QAAAAb7l45YM4BAAAwoHIAAIANd68ckBwAAGDD3eccMKwAAAAMqBwAAGCDYQUAAGDg7rdPZlgBAAAYUDkAAMCGu/+2AskBAAA2ChhWAAAAuIDKAQAANtx9QiLJAQAANljKCAAADLhDIgAAwEWoHAAAYINhBQAAYMBSRgAAgItQOQAAwAZLGQEAgAGrFQAAAC5C5QAAABvuPiGR5AAAABvuPueAYQUAAFzEunXr1KVLF4WEhMhkMmnx4sWG/RaLRWPGjFGlSpVUpkwZtW3bVr/99pvhmIyMDPXu3Vs+Pj4qX768+vfvr6ysLLviIDkAAMCGxeK4zR7Z2dkKDQ3VtGnTCt3/5ptvasqUKZo5c6Y2bdokLy8vtW/fXjk5OdZjevfurd27d2vFihVatmyZ1q1bp4EDB9oVh8licY05mSU9Kzs7BJx35vAaZ4eA88pUaensEHCeexeZXU/e2SPXtf8tVbo5rK+G+z9Xbm6uoc1sNstsNl/xPJPJpEWLFqlbt79isVgsCgkJ0YsvvqgRI0ZIkk6dOqWgoCDNmTNHPXv21J49e1SvXj1t3rxZd999tyRp+fLl6tSpkw4fPqyQkJAixcycA1yiLF9ILoNEzXWQqLkXR845iIuL09ixYw1tMTExio2NtaufAwcOKCUlRW3btrW2+fr6Kjw8XAkJCerZs6cSEhJUvnx5a2IgSW3btpWHh4c2bdqkhx56qEjXIjkAAOA6io6OVlRUlKHtalWDwqSkpEiSgoKCDO1BQUHWfSkpKQoMDDTsL1mypPz8/KzHFAXJAQAANhy5lLEoQwiuhgmJAADYsDhwc5Tg4GBJUmpqqqE9NTXVui84OFhpaWmG/fn5+crIyLAeUxQkBwAA3ASqV6+u4OBgrVy50tqWmZmpTZs2KSIiQpIUERGhkydPauvWrdZjVq1apYKCAoWHhxf5WgwrAABgw1l3SMzKylJiYqL18YEDB7R9+3b5+fmpatWqGjZsmMaPH68777xT1atX1+jRoxUSEmJd0VC3bl116NBBAwYM0MyZM5WXl6fBgwerZ8+eRV6pIJEcAABwCWfdIXHLli1q1aqV9fHfExn79OmjOXPm6OWXX1Z2drYGDhyokydPqnnz5lq+fLlKly5tPWf+/PkaPHiw2rRpIw8PD3Xv3l1TpkyxKw7uc4BLsJ7bdZxmKaPLYCmja8m/zvc5+E/wIw7r696ULxzW141C5QAAABsFzg7AyUgOAACwYXHzGiqrFQAAgAGVAwAAbBS4xGw85yE5AADARoGbDyuQHAAAYIM5BwAAABehcgAAgA2WMgIAAAOGFQAAAC5C5QAAABsMKwAAAAN3Tw4YVgAAAAZUDgAAsOHuExJJDgAAsFHg3rkBwwoAAMCIygEAADb4bQUAAGDg5j/KSHIAAIAtljICAABchMoBAAA2CkzMOQAAABdx9zkHDCsAAAADKgcAANhw9wmJJAcAANjgDokAAAAXoXIAAIAN7pAIAAAMWK0AAABwESoHAADYcPcJiSQHAADYYCkjAAAwYM4BAADARUgOHGTQc32U+OtGZWXu14b1S9X07sbODsktNW8erkWL5ujgH1uVd/aIunZt7+yQbioffLxAjw0Yrnva9VCLLr01NHq8DiQdvq7XtFgsmvrhJ2r54JMKa/Ownhn2qg4eOmLdfyQ5VaMnvKv2j/ZXWJuH1eGxZzT1o/nKy8u7rnHdaviMsk+ByXHbzYjkwAF69OiqSRNj9K/xb6tpeAft2PmLvv1mvipW9Hd2aG7Hy6usdu78RUNfeNXZodyUtmzfpV4PdVb8e5P0/uR/KS8/XwOjRuv0mZxi9zlt1ny9+vrky+6fFf+l5n+5VGNGRCr+vbdUpkxpPfviGOXmnpUkHUg6LEuBRWNGRGrxx9M1csgALVjynd55f16xY3I3fEbZr8CB282I5MABhr8wQB9+FK+58xZoz57f9HzkKzp9+oye7tvT2aG5ne+/X62YmDe1ZMlyZ4dyU3rvrXHq1qmtala/XXVq1tDro4YrOfWYftmXaD0m888sjZkwRfc98LjC2/dQvxdGaW/i78W6nsVi0ccLlmjgU4+p9X3NVLtmdb3xapTSjmdo5U8JkqTm4WEaP2qY7r3nLt0WEqxWzcPVt+dDWrl2g0OeszvgM+rmUa1aNZlMpku2yMhISVLLli0v2ffcc885PA6Sg2tUqlQp3XVXI61c9ZO1zWKxaOWq9WrWLMyJkQHXLis7W5Lk61PO2hY1ZoIyTp7UjEljteDDd1S31h16ZthrOpX5p939H05OVXrGCUVcVOL2LuelRnVra8fuvVeI67R8fLztvp474jOqeJxVOdi8ebOSk5Ot24oVKyRJPXr0sB4zYMAAwzFvvvlmsZ/n5bBa4RoFBPipZMmSSktNN7SnpR1Tndp3OCkq4NoVFBRowpQP1KRhPd1Zo5okadvO3dq151et+3q+PD1LSZJeiuyvVT9t1A9r/qMeXTvYdY304yckSf4Vyhva/f3KKz3jZKHnJB0+qvgvl2rE8/3supa74jOqeCxOmitQsWJFw+MJEybojjvu0P33329tK1u2rIKDg69rHHYnB2fOnNHWrVvl5+enevXqGfbl5ORowYIFeuqpp67YR25urnJzcw1tFotFJtNNOnMDuAWNf3uGEg8c1LxpF/4q2Zd4QKfP5OjeB3oZjs3NPatDR5IlSVt37NJzL8Va9+Xl5UsWi35Y+x9rW8yISD3QrpXdMaUeS9ezI2LUrmVzPWJnIgI4S2HfeWazWWaz+YrnnT17Vp988omioqIM34/z58/XJ598ouDgYHXp0kWjR49W2bJlHRqzXcnBr7/+qnbt2ikpKUkmk0nNmzfXZ599pkqVKkmSTp06paeffvqqyUFcXJzGjh1raDN5lJOphI+d4TtfenqG8vPzFRgUYGgPDKyolNRjTooKuDavT56htQmbNfffExQceOH/7dNnclTRv4JmT4m75Bzvcl6SpPp17tSXs6ZY2z/5YqnS0o8r6rm+1jZ/v/KSpAD/CpKk4ydOqmKAn3X/8YyTqn1ndUP/aenH1W/oKDVuUEexLw++5ufoLviMKh5HTiQs7DsvJiZGsbGxVzxv8eLFOnnypPr27Wtte/zxx3X77bcrJCREO3fu1MiRI7Vv3z599dVXDozYzjkHI0eOVIMGDZSWlqZ9+/bJ29tb9957r5KSkuy6aHR0tE6dOmXYTB435/hhXl6etm3bqdatmlvbTCaTWrdqro0btzoxMsB+FotFr0+eoZXrEjTrnddVJcRYuqxb6w6lZ5xQiRIlVLVKiGGrUN5XklTabDa0+/qUk1eZMoY2r/N/5VSpFKQAvwrauHW79RpZ2ae1c88+hdavY21LPZaup4dEq17tmhofPUweHkyXKio+o4rHkXMOCvvOi46OvmoMH330kTp27KiQkBBr28CBA9W+fXs1bNhQvXv31rx587Ro0SLt37/fYc9dsrNysGHDBv34448KCAhQQECAli5dqueff1733XefVq9eLS8vryL1U1g55WYeUpj87gea/dFkbd22U5s3/1dDhwyQl1cZzZn7ubNDczteXmVVs+aFvzirV6uq0ND6ysg4oUOHjjoxspvD+Ldn6Nsf12rKG6/Jq2xZ65yAcuXKqrTZrIi7Gyu0fh0NHTVeUYOeVrXbKistPUPrEjarTYsINahzp13XM5lMevLRB/X+3M91e5XKqlwpSFM//ESB/n5qc1+EpPOJwdBohQQFakRkP504mWk9/+/KA66MzyjnKsoQgq2DBw/qxx9/vGpFIDw8XJKUmJioO+5w3BwSu5KDM2fOqGTJC6eYTCbNmDFDgwcP1v3336/4+HiHBXYzWbjwa1UM8FPsmBEKDq6oHTt2q/MDTygtLf3qJ8OhwsJCtfLHL6yPJ02KlSTNm7dA/Z8Z7qSobh6fL/5WkvT0UONfNeOjh6lbp7Z/vecnxurd9z/W6Lh3lHEyUwF+FRQWWv+SSYVF1e/x7jpzJkexE/+tP7OydVfDepo5aZzMZk9JUsLm7Uo6nKykw8lq83Bfw7m7flpWrGu6Gz6j7Ofs2yfPnj1bgYGB6ty58xWP2759uyRZh/cdxWSxWIr83+Cee+7RkCFD9OSTT16yb/DgwZo/f74yMzN17tw5uwMp6VnZ7nNwfdy8NZxbz+nDa5wdAs4rU6Wls0PARfLPHrn6Qdfg3apPOKyvF5I+sev4goICVa9eXb169dKECROs7fv371d8fLw6deokf39/7dy5U8OHD1eVKlW0du1ah8Ur2Tnn4KGHHtKnn35a6L6pU6eqV69esiPXAADAJTnzDok//vijkpKS1K+fcbmup6enfvzxR7Vr10516tTRiy++qO7du2vp0qXFeYpXZFfl4HqicuA6qBy4DioHroPKgWu53pWDyQ6sHAy3s3LgCrgJEgAANm7W30RwFJIDAABsuERJ3YlYLAwAAAyoHAAAYKPAzSdfkRwAAGDD3eccMKwAAAAMqBwAAGDD3SckkhwAAGCjwM3TA4YVAACAAZUDAABsuPuERJIDAABsuPegAskBAACXcPfKAXMOAACAAZUDAABscIdEAABgwFJGAACAi1A5AADAhnvXDUgOAAC4BKsVAAAALkLlAAAAG+4+IZHkAAAAG+6dGjCsAAAAbFA5AADAhrtPSCQ5AADABnMOAACAgXunBsw5AAAANqgcAABggzkHAADAwOLmAwsMKwAAAAMqBwAA2GBYAQAAGLj7UkaGFQAAgAGVAwAAbLh33YDkAACASzCsAAAAcBGSAwAAbBQ4cLNHbGysTCaTYatTp451f05OjiIjI+Xv769y5cqpe/fuSk1NvZanWiiSAwAAbFgc+I+96tevr+TkZOu2fv16677hw4dr6dKlWrhwodauXaujR4/q4YcfduRTl8ScAwAALuHM+xyULFlSwcHBl7SfOnVKH330keLj49W6dWtJ0uzZs1W3bl1t3LhRzZo1c1gMVA4AALiOcnNzlZmZadhyc3Mve/xvv/2mkJAQ1ahRQ71791ZSUpIkaevWrcrLy1Pbtm2tx9apU0dVq1ZVQkKCQ2OmcoBLuPccXddSpkpLZ4eA884c/cnZIeAGcuRvK8TFxWns2LGGtpiYGMXGxl5ybHh4uObMmaPatWsrOTlZY8eO1X333addu3YpJSVFnp6eKl++vOGcoKAgpaSkOCxeieQAAIBLOHJYITo6WlFRUYY2s9lc6LEdO3a0/nujRo0UHh6u22+/XQsWLFCZMmUcGNWVMawAAMB1ZDab5ePjY9gulxzYKl++vGrVqqXExEQFBwfr7NmzOnnypOGY1NTUQucoXAuSAwAAbBRYLA7brkVWVpb279+vSpUqKSwsTKVKldLKlSut+/ft26ekpCRFRERc61M2YFgBAAAbzpp7NWLECHXp0kW33367jh49qpiYGJUoUUK9evWSr6+v+vfvr6ioKPn5+cnHx0dDhgxRRESEQ1cqSCQHAAC4jMOHD6tXr146fvy4KlasqObNm2vjxo2qWLGiJGny5Mny8PBQ9+7dlZubq/bt22v69OkOj8NksVxjzcNBSnpWdnYIAHBZrFZwLaUCalzX/h+//SGH9RV/cJHD+rpRqBwAAGDDkUsZb0ZMSAQAAAZUDgAAsOHM2ye7ApIDAABsFLj5sALJAQAANphzAAAAcBEqBwAA2GDOAQAAMHCRWwA5DcMKAADAgMoBAAA2WK0AAAAM3H3OAcMKAADAgMoBAAA23P0+ByQHAADYcPc5BwwrAAAAAyoHAADYcPf7HJAcAABgw91XK5AcAABgw90nJDLnAAAAGFA5AADAhruvViA5AADAhrtPSGRYAQAAGFA5AADABsMKAADAgNUKAAAAF6FyAACAjQI3n5BIcgAAgA33Tg0YVgAAADaoHAAAYIPVCgAAwIDkAAAAGHCHRAAAgItQOQAAwAbDCgAAwIA7JMIhBj3XR4m/blRW5n5tWL9UTe9u7OyQ3BavhevgtSi+D+Z9rsf6D9U9bR9Wi849NfSVcTpw8PB1vabFYtHUD+apZdfHFdbqQT3zQrQOHjpi3X8kOVWj4yar/SN9FdbqQXXo8bSmfvix8vLyrmtcuPFIDhygR4+umjQxRv8a/7aahnfQjp2/6Ntv5qtiRX9nh+Z2eC1cB6/Ftdmy/X/q9XAXxb8/We+/84by8vM1cPirOn0mp9h9TvvoE706/q3L7p81f6Hmf/G1xrw0RPEfvKMypUvr2ajXlJt7VpJ04OAhWQosGvPSEC3+ZKZGDn1WCxZ/q3fem1PsmFyVxWJx2GaPuLg4NW3aVN7e3goMDFS3bt20b98+wzEtW7aUyWQybM8995wjn75MFheZklnSs7KzQyi2DeuXavOWHXph2GuSJJPJpD9+36xp02frzYnTnByde+G1cB232mtx5uhPTr1+xomTavFAL82Z9qbubtxQkpT5Z5YmTf1Qq9cn6OzZPNWvU0svDx2oOnfWKLSPaR99oqPJqXr9tRcv2WexWNTqwd7q0/NhPf34I5KkP7OydX+XXhr/apQ6tW1ZaJ+z5n+hBYu/0fKFsx3zRIuoVEDhz9FR7qrU3GF9bUteX+RjO3TooJ49e6pp06bKz8/XqFGjtGvXLv3yyy/y8vKS9FdyUKtWLY0bN856XtmyZeXj4+OwmKkcXKNSpUrprrsaaeWqCx8cFotFK1etV7NmYU6MzP3wWrgOXgvHy8o+LUny9fG2tkW99oYyTpzUjEn/0oJZ/1bd2nfomReidSrzT7v7P3w0RenHTyji7ibWNu9yXmpUr7Z27Np7hbiy5ePtfdn9sM/y5cvVt29f1a9fX6GhoZozZ46SkpK0detWw3Fly5ZVcHCwdXNkYiAVIznYs2ePZs+erb17//qfZe/evRo0aJD69eunVatWFamP3NxcZWZmGjYXKWDYLSDATyVLllRaarqhPS3tmIKDKjopKvfEa+E6eC0cq6CgQBPefU9NGtXTnTWqSZK27dilXXv26e3xo9Sgbi3dfltlvTR4gLzLeemH1fZXOdIzTkiS/P0qGNr9/Soo/fiJQs9JOnxU8V98rUe7dbT7eq7OkcMKhX3n5ebmFimOU6dOSZL8/PwM7fPnz1dAQIAaNGig6OhonT592qHP367VCsuXL9eDDz6ocuXK6fTp01q0aJGeeuophYaGqqCgQO3atdMPP/yg1q1bX7GfuLg4jR071tBm8ignUwnHZj4AcCsY/9Y0Jf7+h+bNmGRt25d4QKfP5OjeTo8Zjs3NPatDR5IlSVu379JzI0Zb9+Xl5UsWi35Yc6HMHfPSED3Q/sqf2YVJPZauZ6NeU7tW9+mRrrdecuDIpYyFfefFxMQoNjb2yjEUFGjYsGG699571aBBA2v7448/rttvv10hISHauXOnRo4cqX379umrr75yWMx2JQfjxo3TSy+9pPHjx+uzzz7T448/rkGDBun111+XJEVHR2vChAlXTQ6io6MVFRVlaKvgX8fO0F1DenqG8vPzFRgUYGgPDKyolNRjTorKPfFauA5eC8d5/a3pWrvhZ82dNlHBgReqLqfPnFFF/wqaPfXNS87xLvfX2HT9unfqyzkX5nd8snCJ0o4dV9Tz/axt/hXKS5ICzlcMjmecUMWAC3+lHs84odp33mHoP+3YcfUb8ooaN6yn2JFDr/1J3uIK+84zm81XPS8yMlK7du3S+vXGOQsDBw60/nvDhg1VqVIltWnTRvv379cdd9xh202x2DWssHv3bvXt21eS9Oijj+rPP//UI488Yt3fu3dv7dy586r9mM1m+fj4GDaTyWRf5C4iLy9P27btVOtWFyavmEwmtW7VXBs3br3CmXA0XgvXwWtx7SwWi15/a7pWrtugWVMmqEpIsGF/3Vo1lZ5xQiVKlFDVKiGGrUJ5X0lSabPZ0O7r4y2vsmUMbV5eZSVJVUKCFeBfQRu3brdeIys7Wzt/2afQBhf+eEs9lq6nh4xUvdo1NX7UcHl43JpT1ywO/Kew77yrJQeDBw/WsmXLtHr1alWpUuWKx4aHh0uSEhMTHfb87b4J0t9f4h4eHipdurR8fX2t+7y9va3jI+5k8rsfaPZHk7V1205t3vxfDR0yQF5eZTRn7ufODs3t8Fq4Dl6LazP+rWn6dsUaTZkwRl5lyyj9eIYkqVw5L5U2mxXRtIlC69fV0FfGKer5fqpWtYrS0o9r3Yaf1abFP9Sgbi27rmcymfTko930/tzPdHuVyqocEqSpH3yswAB/tbnvH5LOJwaDRyokOFAjBj+jEycvfN4H+PtdruubUoGT5sFZLBYNGTJEixYt0po1a1S9evWrnrN9+3ZJUqVKlRwWh13JQbVq1fTbb79ZyxYJCQmqWrWqdX9SUpJDg7tZLFz4tSoG+Cl2zAgFB1fUjh271fmBJ5SWln71k+FQvBaug9fi2ny+6BtJ0tODRxrax4+KUrfO/5TJZNKMSeP07vtzNfqNyco4eUoB/hUUFtrgkkmFRdWvdw+dOZOj2Den6M+sLN3VqL5mvvUvmc2ekqSEn/+rpMNHlXT4qNp0e9Jw7q7/fFesa7oqZ90hMTIyUvHx8VqyZIm8vb2VkpIiSfL19VWZMmW0f/9+xcfHq1OnTvL399fOnTs1fPhwtWjRQo0aNXJYHHbd52DmzJm67bbb1Llz50L3jxo1Smlpafrwww/tDuRmvs8BgFufs+9zAKPrfZ+D+kHhDutrd+qmIh97uSH22bNnq2/fvjp06JCeeOIJ7dq1S9nZ2brtttv00EMP6bXXXnPockZuggQARUBy4Fqud3JQN/Aeh/W1J+1nh/V1o/DDSwAA2OCHlwAAAC5C5QAAABvOWq3gKkgOAACwwbACAADARagcAABgg2EFAABgwLACAADARagcAABgw2IpcHYITkVyAACAjQI3H1YgOQAAwIaL/LKA0zDnAAAAGFA5AADABsMKAADAgGEFAACAi1A5AADABndIBAAABtwhEQAA4CJUDgAAsOHuExJJDgAAsOHuSxkZVgAAAAZUDgAAsMGwAgAAMGApIwAAMHD3ygFzDgAAgAGVAwAAbLj7agWSAwAAbDCsAAAAcBEqBwAA2GC1AgAAMOCHlwAAAC5C5QAAABsMKwAAAANWKwAAAFyEygEAADbcfUIiyQEAADYYVgAAAAYWi8Vhm72mTZumatWqqXTp0goPD9fPP/98HZ7hlZEcAADgIj7//HNFRUUpJiZG27ZtU2hoqNq3b6+0tLQbGofJ4iK1k5KelZ0dAgBc1pmjPzk7BFykVECN69q/I7+Tsv/8Xbm5uYY2s9kss9l8ybHh4eFq2rSppk6dKkkqKCjQbbfdpiFDhuiVV15xWExXZYFD5OTkWGJiYiw5OTnODgUWXg9XwmvhOngtnCMmJsYiybDFxMRcclxubq6lRIkSlkWLFhnan3rqKUvXrl1vTLDnuUzl4GaXmZkpX19fnTp1Sj4+Ps4Ox+3xergOXgvXwWvhHLm5uUWqHBw9elSVK1fWhg0bFBERYW1/+eWXtXbtWm3atOmGxCuxWgEAgOvqckMIrowJiQAAuICAgACVKFFCqamphvbU1FQFBwff0FhIDgAAcAGenp4KCwvTypUrrW0FBQVauXKlYZjhRmBYwUHMZrNiYmJuutLRrYrXw3XwWrgOXgvXFxUVpT59+ujuu+/WPffco3feeUfZ2dl6+umnb2gcTEgEAMCFTJ06VRMnTlRKSooaN26sKVOmKDw8/IbGQHIAAAAMmHMAAAAMSA4AAIAByQEAADAgOQAAAAYkBw7iCj+xCWndunXq0qWLQkJCZDKZtHjxYmeH5Jbi4uLUtGlTeXt7KzAwUN26ddO+ffucHZbbmjFjhho1aiQfHx/5+PgoIiJC3333nbPDggsjOXAAV/mJTUjZ2dkKDQ3VtGnTnB2KW1u7dq0iIyO1ceNGrVixQnl5eWrXrp2ys7OdHZpbqlKliiZMmKCtW7dqy5Ytat26tR588EHt3r3b2aHBRbGU0QFc5ic2YWAymbRo0SJ169bN2aG4vWPHjikwMFBr165VixYtnB0OJPn5+WnixInq37+/s0OBC6JycI3Onj2rrVu3qm3bttY2Dw8PtW3bVgkJCU6MDHAdp06dkvTXFxKc69y5c/rss8+UnZ19w2/Ji5sHt0++Runp6Tp37pyCgoIM7UFBQdq7d6+TogJcR0FBgYYNG6Z7771XDRo0cHY4but///ufIiIilJOTo3LlymnRokWqV6+es8OCiyI5AHBdRUZGateuXVq/fr2zQ3FrtWvX1vbt23Xq1Cl98cUX6tOnj9auXUuCgEKRHFwjV/qJTcDVDB48WMuWLdO6detUpUoVZ4fj1jw9PVWzZk1JUlhYmDZv3qx3331X7733npMjgytizsE1cqWf2ARchcVi0eDBg7Vo0SKtWrVK1atXd3ZIsFFQUKDc3FxnhwEXReXAAVzlJzYhZWVlKTEx0fr4wIED2r59u/z8/FS1alUnRuZeIiMjFR8fryVLlsjb21spKSmSJF9fX5UpU8bJ0bmf6OhodezYUVWrVtWff/6p+Ph4rVmzRt9//72zQ4OLYimjg7jCT2xCWrNmjVq1anVJe58+fTRnzpwbH5CbMplMhbbPnj1bffv2vbHBQP3799fKlSuVnJwsX19fNWrUSCNHjtQ///lPZ4cGF0VyAAAADJhzAAAADEgOAACAAckBAAAwIDkAAAAGJAcAAMCA5AAAABiQHAAAAAOSAwAAYEByAAAADEgOAACAAckBAAAw+H+rNnfgK+idbgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}