5_DL_Transfer_learning_solutions.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/donnadamus/mlinapplabs/blob/main/5_DL_Transfer_learning_solutions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lab 5: Transfer learning.\n",
        "\n",
        "Thanks to their capability to learn generalizable descriptors directly from images, deep Convolutional Neural Networks (CNNs) seem the ideal solution to most pattern recognition problems. On the other hand, to learn the image representation, CNNs need huge sets of annotated samples that are unfeasible in many every-day scenarios. This is the case, for example, of Computer-Aided Diagnosis (CAD) systems for digital pathology, where additional challenges are\n",
        "posed by the high variability of the cancerous tissue characteristics.\n",
        "In this notebooks you are going to implement 3 different training paradigms leveraging CNNs:\n",
        "\n",
        "\n",
        "1.   Training from scratch\n",
        "2.   Features extractor followed by SVM\n",
        "3.   Fine tuning\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "mmqHr4v5kHvG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import and settings"
      ],
      "metadata": {
        "id": "n4HXvwQ2lDdD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "92gtriLKjmGp"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import cv2\n",
        "import glob\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from sklearn.svm import SVC\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import RandomizedSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/frpnz/dataset-BIOSTEC2018.git\n",
        "ROOTDIR_DATASET = \"/content/dataset-BIOSTEC2018/crc_images\"\n",
        "IMAGE_SIZE = 32\n",
        "NUM_CLASSES = 3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_7WS06FlJ1x",
        "outputId": "ed4ebd98-193f-4d7e-f6ef-b1c6553dcc36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'dataset-BIOSTEC2018'...\n",
            "remote: Enumerating objects: 13518, done.\u001b[K\n",
            "remote: Counting objects: 100% (6/6), done.\u001b[K\n",
            "remote: Compressing objects: 100% (5/5), done.\u001b[K\n",
            "remote: Total 13518 (delta 1), reused 6 (delta 1), pack-reused 13512\u001b[K\n",
            "Receiving objects: 100% (13518/13518), 1.87 GiB | 13.41 MiB/s, done.\n",
            "Resolving deltas: 100% (1/1), done.\n",
            "Updating files: 100% (13501/13501), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Utils"
      ],
      "metadata": {
        "id": "Q-5zba5A7vYF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def seaborn_cm(cm, ax, tick_labels, fontsize=14, title=None, sum_actual=\"over_columns\",\n",
        "               xrotation=0, yrotation=0):\n",
        "    \"\"\"\n",
        "    Function to plot a confusion matrix\n",
        "    \"\"\"\n",
        "    from matplotlib import cm as plt_cmap\n",
        "    group_counts = [\"{:0.0f}\".format(value) for value in cm.flatten()]\n",
        "    if sum_actual == \"over_columns\":\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "    elif sum_actual == \"over_rows\":\n",
        "        cm = cm.astype('float') / cm.sum(axis=0)[:, np.newaxis]\n",
        "    else:\n",
        "        print(\"sum_actual must be over_columns or over_rows\")\n",
        "        exit()\n",
        "    cm = np.nan_to_num(cm)\n",
        "    mean_acc = np.mean(np.diag(cm)[cm.sum(axis=1) != 0])\n",
        "    std_acc = np.std(np.diag(cm))\n",
        "    group_percentages = [\"{:0.0f}\".format(value*100) for value in cm.flatten()]\n",
        "    cm_labels = [f\"{c}\\n{p}%\" for c, p in zip(group_counts, group_percentages)]\n",
        "    cm_labels = np.asarray(cm_labels).reshape(len(tick_labels), len(tick_labels))\n",
        "    sns.heatmap(cm,\n",
        "                ax=ax,\n",
        "                annot=cm_labels,\n",
        "                fmt='',\n",
        "                cbar=False,\n",
        "                cmap=plt_cmap.Greys,\n",
        "                linewidths=1, linecolor='black',\n",
        "                annot_kws={\"fontsize\": fontsize},\n",
        "                xticklabels=tick_labels,\n",
        "                yticklabels=tick_labels)\n",
        "    ax.set_yticklabels(ax.get_yticklabels(), size=fontsize, rotation=yrotation)\n",
        "    ax.set_xticklabels(ax.get_xticklabels(), size=fontsize, rotation=xrotation)\n",
        "    if title:\n",
        "        title = f\"{title}\\nMean accuracy {mean_acc * 100:.1f} +- {std_acc * 100:.1f}\"\n",
        "    else:\n",
        "        title = f\"Mean accuracy {mean_acc * 100:.1f} +- {std_acc * 100:.1f}\"\n",
        "    ax.set_title(title)\n",
        "    if sum_actual == \"over_columns\":\n",
        "        ax.set_ylabel(\"Actual\")\n",
        "        ax.set_xlabel(\"Predicted\")\n",
        "    else:\n",
        "        ax.set_ylabel(\"Predicted\")\n",
        "        ax.set_xlabel(\"Actual\")\n",
        "    ax.axis(\"off\")"
      ],
      "metadata": {
        "id": "Ac09MprC7tZH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assignment #1: create two tensorflow dataset for train and test.\n",
        "Leverage the API [tf.keras.utils.image_dataset_from_directory](https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory)."
      ],
      "metadata": {
        "id": "EaVggb99lX_M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    os.path.join(ROOTDIR_DATASET, \"train\"),\n",
        "    labels='inferred',\n",
        "    label_mode='categorical',\n",
        "    class_names=None,\n",
        "    color_mode='rgb',\n",
        "    batch_size=32,\n",
        "    image_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    shuffle=True,\n",
        "    seed=None,\n",
        "    validation_split=None,\n",
        "    subset=None,\n",
        "    interpolation='bilinear',\n",
        "    follow_links=False,\n",
        "    crop_to_aspect_ratio=False,\n",
        ")\n",
        "\n",
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    os.path.join(ROOTDIR_DATASET, \"test\"),\n",
        "    labels='inferred',\n",
        "    label_mode='categorical',\n",
        "    class_names=None,\n",
        "    color_mode='rgb',\n",
        "    batch_size=32,\n",
        "    image_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    shuffle=True,\n",
        "    seed=None,\n",
        "    validation_split=None,\n",
        "    subset=None,\n",
        "    interpolation='bilinear',\n",
        "    follow_links=False,\n",
        "    crop_to_aspect_ratio=False,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEs2BYx0lVSw",
        "outputId": "50d7d14b-40a8-496b-d52c-249da285788c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 9000 files belonging to 3 classes.\n",
            "Found 4500 files belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "j = 0\n",
        "fig, ax = plt.subplots(4, 4, figsize=(7, 7))\n",
        "ax = ax.ravel()\n",
        "for batch in train_ds.take(1):\n",
        "    images = batch[0]\n",
        "    labels = batch[1]\n",
        "    for image, label in zip(images, labels):\n",
        "        if j >= 16:\n",
        "            break\n",
        "        image = image.numpy().astype(np.uint8)\n",
        "        ax[j].imshow(image)\n",
        "        ax[j].axis(\"off\")\n",
        "        j += 1"
      ],
      "metadata": {
        "id": "YB2ikJrSm6uI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "outputId": "393aba54-7ecb-4a55-b173-a343b7ec0ca1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assignment #2: dataset preparation\n",
        "1.   Normalize images in the range [0, 1]\n",
        "2.   Prepare the input pipeline via tf.data.Dataset methods\n",
        "\n"
      ],
      "metadata": {
        "id": "UxiwYMwymcmO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def process_data(image, label):\n",
        "    return tf.cast(image, tf.float32) / 255., label\n",
        "\n",
        "train_ds = train_ds.map(\n",
        "    process_data, num_parallel_calls=tf.data.AUTOTUNE).cache().shuffle(100).prefetch(tf.data.AUTOTUNE)\n",
        "test_ds = test_ds.map(\n",
        "    process_data, num_parallel_calls=tf.data.AUTOTUNE).cache().prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "D3bbAtr1mtAt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assignment #3: train a VGG16 from scratch for 50 epochs"
      ],
      "metadata": {
        "id": "k4dX98Iqo63L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "Z6ql8zfHpF-U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vgg16 = tf.keras.applications.VGG16(\n",
        "    include_top=True,\n",
        "    weights=None,\n",
        "    input_tensor=None,\n",
        "    input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3),\n",
        "    pooling=None,\n",
        "    classes=NUM_CLASSES,\n",
        "    classifier_activation=\"softmax\",\n",
        ")"
      ],
      "metadata": {
        "id": "-tcO6iSXo-jQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vgg16.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sOWIaOpfpYb7",
        "outputId": "3362d876-5fc5-4ba1-ccbd-6615b65dbb2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 32, 32, 64)        1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 32, 32, 64)        36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 16, 16, 64)        0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 16, 16, 128)       73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 16, 16, 128)       147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 8, 8, 128)         0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 8, 8, 256)         295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 8, 8, 256)         590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 8, 8, 256)         590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 4, 4, 256)         0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 4, 4, 512)         1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 2, 2, 512)         0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 2, 2, 512)         2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 2, 2, 512)         2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 2, 2, 512)         2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 1, 1, 512)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 512)               0         \n",
            "                                                                 \n",
            " fc1 (Dense)                 (None, 4096)              2101248   \n",
            "                                                                 \n",
            " fc2 (Dense)                 (None, 4096)              16781312  \n",
            "                                                                 \n",
            " predictions (Dense)         (None, 3)                 12291     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 33609539 (128.21 MB)\n",
            "Trainable params: 33609539 (128.21 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def scheduler(epoch, lr):\n",
        "    if epoch < 45:\n",
        "        return lr\n",
        "    else:\n",
        "        return lr * tf.math.exp(-0.1)"
      ],
      "metadata": {
        "id": "TTY2xJcB158N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)\n",
        "vgg16.compile(optimizer=optimizer, loss=\"categorical_crossentropy\", metrics=\"accuracy\")\n",
        "callback = tf.keras.callbacks.LearningRateScheduler(scheduler)\n",
        "history = vgg16.fit(train_ds, epochs=50, callbacks=[callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aS_qiNOMpTPp",
        "outputId": "a88d7950-ed76-4acb-b20b-15c996646709"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "282/282 [==============================] - 43s 94ms/step - loss: 1.0985 - accuracy: 0.3426 - lr: 0.0100\n",
            "Epoch 2/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 1.0981 - accuracy: 0.3549 - lr: 0.0100\n",
            "Epoch 3/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 1.0972 - accuracy: 0.3702 - lr: 0.0100\n",
            "Epoch 4/50\n",
            "282/282 [==============================] - 8s 30ms/step - loss: 1.0942 - accuracy: 0.4854 - lr: 0.0100\n",
            "Epoch 5/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 1.0761 - accuracy: 0.6028 - lr: 0.0100\n",
            "Epoch 6/50\n",
            "282/282 [==============================] - 9s 30ms/step - loss: 0.9844 - accuracy: 0.5704 - lr: 0.0100\n",
            "Epoch 7/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.7812 - accuracy: 0.6411 - lr: 0.0100\n",
            "Epoch 8/50\n",
            "282/282 [==============================] - 9s 30ms/step - loss: 0.6858 - accuracy: 0.6876 - lr: 0.0100\n",
            "Epoch 9/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.6041 - accuracy: 0.7193 - lr: 0.0100\n",
            "Epoch 10/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.5274 - accuracy: 0.7732 - lr: 0.0100\n",
            "Epoch 11/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.4913 - accuracy: 0.7954 - lr: 0.0100\n",
            "Epoch 12/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.4597 - accuracy: 0.8111 - lr: 0.0100\n",
            "Epoch 13/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.4392 - accuracy: 0.8246 - lr: 0.0100\n",
            "Epoch 14/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.4076 - accuracy: 0.8359 - lr: 0.0100\n",
            "Epoch 15/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.3978 - accuracy: 0.8433 - lr: 0.0100\n",
            "Epoch 16/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.3785 - accuracy: 0.8483 - lr: 0.0100\n",
            "Epoch 17/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.3607 - accuracy: 0.8618 - lr: 0.0100\n",
            "Epoch 18/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.3409 - accuracy: 0.8710 - lr: 0.0100\n",
            "Epoch 19/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.3476 - accuracy: 0.8677 - lr: 0.0100\n",
            "Epoch 20/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.3354 - accuracy: 0.8739 - lr: 0.0100\n",
            "Epoch 21/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.3187 - accuracy: 0.8829 - lr: 0.0100\n",
            "Epoch 22/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.2964 - accuracy: 0.8949 - lr: 0.0100\n",
            "Epoch 23/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.3245 - accuracy: 0.8847 - lr: 0.0100\n",
            "Epoch 24/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.2991 - accuracy: 0.8939 - lr: 0.0100\n",
            "Epoch 25/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.2755 - accuracy: 0.9028 - lr: 0.0100\n",
            "Epoch 26/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.2656 - accuracy: 0.9078 - lr: 0.0100\n",
            "Epoch 27/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.2678 - accuracy: 0.9068 - lr: 0.0100\n",
            "Epoch 28/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.2499 - accuracy: 0.9138 - lr: 0.0100\n",
            "Epoch 29/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.2463 - accuracy: 0.9149 - lr: 0.0100\n",
            "Epoch 30/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.2330 - accuracy: 0.9190 - lr: 0.0100\n",
            "Epoch 31/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.2037 - accuracy: 0.9316 - lr: 0.0100\n",
            "Epoch 32/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.2343 - accuracy: 0.9218 - lr: 0.0100\n",
            "Epoch 33/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.1887 - accuracy: 0.9330 - lr: 0.0100\n",
            "Epoch 34/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.1700 - accuracy: 0.9404 - lr: 0.0100\n",
            "Epoch 35/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.1507 - accuracy: 0.9497 - lr: 0.0100\n",
            "Epoch 36/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.1478 - accuracy: 0.9480 - lr: 0.0100\n",
            "Epoch 37/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.1326 - accuracy: 0.9537 - lr: 0.0100\n",
            "Epoch 38/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.1765 - accuracy: 0.9372 - lr: 0.0100\n",
            "Epoch 39/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.1237 - accuracy: 0.9570 - lr: 0.0100\n",
            "Epoch 40/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.1202 - accuracy: 0.9590 - lr: 0.0100\n",
            "Epoch 41/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.1174 - accuracy: 0.9574 - lr: 0.0100\n",
            "Epoch 42/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.1093 - accuracy: 0.9627 - lr: 0.0100\n",
            "Epoch 43/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.1104 - accuracy: 0.9623 - lr: 0.0100\n",
            "Epoch 44/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.1040 - accuracy: 0.9647 - lr: 0.0100\n",
            "Epoch 45/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.1006 - accuracy: 0.9642 - lr: 0.0100\n",
            "Epoch 46/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.0815 - accuracy: 0.9720 - lr: 0.0090\n",
            "Epoch 47/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.0771 - accuracy: 0.9734 - lr: 0.0082\n",
            "Epoch 48/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.0949 - accuracy: 0.9679 - lr: 0.0074\n",
            "Epoch 49/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.0609 - accuracy: 0.9780 - lr: 0.0067\n",
            "Epoch 50/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.0583 - accuracy: 0.9807 - lr: 0.0061\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on test set: {:.2f}\".format(vgg16.evaluate(test_ds)[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7xzNbihwxn9B",
        "outputId": "c7b605f1-26ff-4675-8421-e254ce223867"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "141/141 [==============================] - 16s 100ms/step - loss: 0.2425 - accuracy: 0.9171\n",
            "Accuracy on test set: 0.92\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot train loss and accuracy among epochs (see [History](tf.keras.callbacks.History) object)"
      ],
      "metadata": {
        "id": "Ej9Rpfq85_k-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use(\"Solarize_Light2\")\n",
        "fig, ax = plt.subplots(2, 1)\n",
        "ax[0].plot([e for e in range(history.params['epochs'])], history.history['accuracy'])\n",
        "ax[0].set_title(\"Accuracy\");\n",
        "ax[1].plot([e for e in range(history.params['epochs'])], history.history['loss'])\n",
        "ax[1].set_title(\"Loss\");"
      ],
      "metadata": {
        "id": "zRPGo2F15z5l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "dabffa9d-60bb-4963-8f38-6a7f7def55eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assignment #4: extract features from VGG16 pre-trained on ImageNet and then train and test a downstream SVM.\n",
        "\n",
        "Pipeline:\n",
        "1. Features extraction from the CNN\n",
        "2. PCA\n",
        "3. SVM training and test\n",
        "\n",
        "N.B. Do not shuffle train and test in order to extract labels in the correct order.\n",
        "\n",
        "\n",
        "So, to clarify, the pre-trained VGG16 model is used as a \"feature transformer\" or \"feature mapper\" to map your biological images into a space of features that were learned during its training on imagenet."
      ],
      "metadata": {
        "id": "CobA0F6o8CsW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    os.path.join(ROOTDIR_DATASET, \"train\"),\n",
        "    labels='inferred',\n",
        "    label_mode='categorical',\n",
        "    class_names=None,\n",
        "    color_mode='rgb',\n",
        "    batch_size=32,\n",
        "    image_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    shuffle=False,\n",
        "    seed=None,\n",
        "    validation_split=None,\n",
        "    subset=None,\n",
        "    interpolation='bilinear',\n",
        "    follow_links=False,\n",
        "    crop_to_aspect_ratio=False,\n",
        ")\n",
        "\n",
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    os.path.join(ROOTDIR_DATASET, \"test\"),\n",
        "    labels='inferred',\n",
        "    label_mode='categorical',\n",
        "    class_names=None,\n",
        "    color_mode='rgb',\n",
        "    batch_size=32,\n",
        "    image_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    shuffle=False,\n",
        "    seed=None,\n",
        "    validation_split=None,\n",
        "    subset=None,\n",
        "    interpolation='bilinear',\n",
        "    follow_links=False,\n",
        "    crop_to_aspect_ratio=False,\n",
        ")"
      ],
      "metadata": {
        "id": "l_jlVXTrEesA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cf35cdc-6da0-41c0-ce73-5ab71716fb7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 9000 files belonging to 3 classes.\n",
            "Found 4500 files belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def process_data(image, label):\n",
        "    image = tf.cast(image, tf.float32)\n",
        "    return  tf.keras.applications.vgg16.preprocess_input(image), label\n",
        "\n",
        "train_ds = train_ds.map(\n",
        "    process_data, num_parallel_calls=tf.data.AUTOTUNE).cache().prefetch(tf.data.AUTOTUNE)\n",
        "test_ds = test_ds.map(\n",
        "    process_data, num_parallel_calls=tf.data.AUTOTUNE).cache().prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "f-L8ohLUFUfv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vgg16 = tf.keras.applications.VGG16(\n",
        "    include_top=False,\n",
        "    weights=\"imagenet\",\n",
        "    input_tensor=None,\n",
        "    input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3),\n",
        "    pooling=\"avg\",\n",
        "    classes=NUM_CLASSES,\n",
        "    classifier_activation=\"softmax\",\n",
        ")"
      ],
      "metadata": {
        "id": "p2bCgBIr8MVo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8feff6f3-ef50-4269-a05f-a7c7d2fe12f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58889256/58889256 [==============================] - 4s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "In transfer learning, we often remove the top layer(s) of a pre-trained neural network because these layers are typically specialized in recognizing high-level features that are specific to the original task the network was trained on. By removing these layers, we retain the lower layers, which capture more generic features like edges, textures, and shapes. These lower layers are often valuable for recognizing similar features in a new task.\n",
        "\n",
        "After removing the top layers, we replace them with a new classifier, such as a Support Vector Machine (SVM), to adapt the pre-trained model to the new task. SVMs are a popular choice for this purpose because they are effective for classification tasks, even when dealing with high-dimensional data. SVMs can learn decision boundaries that separate different classes in the feature space defined by the output of the pre-trained network's lower layers.\n",
        "\n",
        "By combining the generic feature representations learned by the pre-trained network with a new classifier trained specifically for the new task, transfer learning allows us to leverage knowledge from the original task and adapt it to a related task with potentially less labeled data and computational resources. This approach often leads to faster convergence and better performance compared to training a model from scratch.\n",
        "\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "id": "0Fw1gQSGnxG_",
        "outputId": "5586f870-49c3-4cc9-b9b9-175c9354c332"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nIn transfer learning, we often remove the top layer(s) of a pre-trained neural network because these layers are typically specialized in recognizing high-level features that are specific to the original task the network was trained on. By removing these layers, we retain the lower layers, which capture more generic features like edges, textures, and shapes. These lower layers are often valuable for recognizing similar features in a new task.\\n\\nAfter removing the top layers, we replace them with a new classifier, such as a Support Vector Machine (SVM), to adapt the pre-trained model to the new task. SVMs are a popular choice for this purpose because they are effective for classification tasks, even when dealing with high-dimensional data. SVMs can learn decision boundaries that separate different classes in the feature space defined by the output of the pre-trained network's lower layers.\\n\\nBy combining the generic feature representations learned by the pre-trained network with a new classifier trained specifically for the new task, transfer learning allows us to leverage knowledge from the original task and adapt it to a related task with potentially less labeled data and computational resources. This approach often leads to faster convergence and better performance compared to training a model from scratch.\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_train = vgg16.predict(train_ds)\n",
        "features_test = vgg16.predict(test_ds)"
      ],
      "metadata": {
        "id": "McrDzC3T82WE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92996995-237d-4c4f-b6a9-55a36cd24a86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "282/282 [==============================] - 29s 103ms/step\n",
            "141/141 [==============================] - 15s 104ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels_train = np.concatenate([np.argmax(y, axis=1) for _, y in train_ds], axis=0)\n",
        "labels_test = np.concatenate([np.argmax(y, axis=1) for _, y in test_ds], axis=0)"
      ],
      "metadata": {
        "id": "3YOTrznlFxGX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features_train.shape\n",
        "# The shape of this vector corresponds to the number of filters in the last convolutional layer of VGG16, which is 512."
      ],
      "metadata": {
        "id": "TM3ekLxO9Jax",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b86300d-0af2-47b2-ab3d-8eda3b8c98b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9000, 512)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA()\n",
        "features_train_pca = pca.fit_transform(features_train)\n",
        "features_test_pca = pca.transform(features_test)"
      ],
      "metadata": {
        "id": "MYhg3EEs9yMq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features_train_pca.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMcbxGLirnOc",
        "outputId": "97ab1e0d-eee7-4af6-9df0-a979ce021eba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9000, 512)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explained variance"
      ],
      "metadata": {
        "id": "m2GaAyasIs8N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(13, 8))\n",
        "xi = np.arange(1, features_train_pca.shape[1] + 1, step=1)\n",
        "y = pca.explained_variance_ratio_.cumsum()\n",
        "\n",
        "ax.plot(xi, y, marker='o', linestyle='--', color='b')\n",
        "\n",
        "ax.set_xlabel('Number of Components')\n",
        "ax.set_xticks(np.arange(0, features_train_pca.shape[1] + 1, step=25))\n",
        "ax.set_xticklabels(np.arange(0, features_train_pca.shape[1] + 1, step=25), rotation=60)\n",
        "ax.set_ylabel('Cumulative variance (%)')\n",
        "ax.set_title('The number of components needed to explain variance')\n",
        "\n",
        "ax.axhline(y=0.95, color='r', linestyle='-')\n",
        "ax.text(0.5, 0.85, '95% cut-off threshold', color = 'red', fontsize=16)\n",
        "ax.grid(axis='x',\n",
        "        alpha=0.75,\n",
        "        linestyle='--')"
      ],
      "metadata": {
        "id": "H5hqu55xIYhl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "outputId": "b590b248-6de3-4fdd-bf4a-a03123021c87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "clf = SVC()\n",
        "\n",
        "random_grid_svm = {\"kernel\": [\"rbf\"],\n",
        "                    \"gamma\": [\"scale\", \"auto\", 0.001, 0.01, 0.1],\n",
        "                    \"class_weight\": [None, \"balanced\"],\n",
        "                    \"C\": [0.01, 0.1, 1]}\n",
        "search = RandomizedSearchCV(estimator=SVC(),\n",
        "                            param_distributions=random_grid_svm,\n",
        "                            scoring=\"balanced_accuracy\",\n",
        "                            n_iter=2,\n",
        "                            cv=2,\n",
        "                            verbose=1,\n",
        "                            random_state=42,\n",
        "                            n_jobs=-1)\n",
        "search.fit(features_train_pca[:, :100], labels_train)\n",
        "clf = search.best_estimator_"
      ],
      "metadata": {
        "id": "Zzjm-cWs9VWF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6dea024f-0126-401e-9d8e-af1432bec4d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/joblib/externals/loky/backend/fork_exec.py:38: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  pid = os.fork()\n",
            "/usr/local/lib/python3.10/dist-packages/joblib/externals/loky/backend/fork_exec.py:38: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  pid = os.fork()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_test = clf.predict(features_test_pca[:, :100])"
      ],
      "metadata": {
        "id": "HpScpY2Z-pBT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "cm = confusion_matrix(labels_test, preds_test, labels=[0, 1, 2])\n",
        "seaborn_cm(cm, ax, [0, 1, 2])"
      ],
      "metadata": {
        "id": "UL8_Sd7o-tvN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "1ac2dfab-93d9-4fba-c320-a9335edb9298"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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TdQsotrUArKysaN6kPhcuerBn32FTGAgPD2f5yg1R+7WKfQ2Btq2b4Lr7wFvdYkoonh7HYi2PXiwsPNj3jcf46/zlWEND5Ypl38miPl06tmL+L0vZ9MeOeIUBjys3cPe4Rrp0aejee0iM7XfvRV0N/HLwKFKmdKB2zSp8M/TtBgPLh6MwkIh07tiScRNnsHXbboB43ceNvpxaruznzJ7xQ7xfa+36rQDMnz2JRg1qxdh+/XrM0fsJJbotK5fOi/WD6VosMwmir0xc+f+VGd8k+t6o3937BAQ8jdfVAWtrKwACA5/Fut3n5u1Yy+Njy7bdPH/+giaN6jD5h+9ibI/r7589WxauXL2Bx5Ub5M2TK96v1693Z6b//Au/Ll7B8GEDmPf/4y36veVVgZuvTFNNkSL2S9PRc/0fPX5iKrt/39805TSFQ+xrAaRMEXO/j9noEYMZPWJwgh0/erzP/QcP32o/f/9HHDh0PM7t585fAiBHjqz/vHHy3mkAYSKSPVsWGjWoRdq0qSlT6jNKl3J64z7R07y2/LmLFy/if2k++g03R/aY/+AvXb7CufOX432sf+t/bYk5mGnnrv34+8e8d1utcnmsra0543aBs24X3vgamTJloFiRgkRERJjGY7xJ9CC4uNZc2Lbjn68N//g1fY6MjGTlms2x7he90twil9/f6vWyZ8tC44a1ueN3j9Hjf+Lo8dM4Zs5Is8Z13+o4rw4MPHHKLdY6x09GLRr06qyAtGlTk+z/1yeIa7/o8lw5s79Vm4xq776oxZ/y5Y19gOrfFS9WiPBg3zj/i34vOLB7PeHBvgk2/kEShsJAIrN+1a/c9z3Pkf2xfxj8nVPxwjRrUg/fW3do3qYX3j4xL18+exbMilUbuff/lwEB8v//vfZ5C5eYXe7287tHlx6DCAsL+5c9ib8C+aPaMme+i1n5las36Dcw5rdmiLof36dn1Ejq1h36cPGSh9n26NHqAQFPTWUjv/sKgBFjpsQYpQ9RUwtfnT1R6vPipEjhwGX3qyz7fb1Z3bUbtjJ7nsvfDxFv0X//9Ru34ef3v8GW4eHhjB43Lc41HgYN7ImDgz1b/tzFyDFTCA0NNdt+/75/nA+aGdi/GwA/TpsLQK/uHWJd3e51smfLQskSxaLa8vWYGOfb8pUbWLMuasGktq2bmMqtra1p+P/z4UePm8b5C+5m++3Zd5if5yyK2u9vtxFu3/ajYPEqFCxeJcbCXInZ76s3cubs+Rjl0QttjRw3FYAesUwZrVmvDQWLV2Hj5u0J3k75b9BtAmHxwp8ICHjKDtd9FChWhWJFCpAzZzYiI8HHx5e/LrgTEhLCJbd9ZMyYHoDhX3/Bzl0HWOTyO/sPHsWpeGGePg3i4OHj5M6VnSaN6rxxQN+7MnL4IFq1782ocdNYu2ErBQvk48GDhxw6cpKK5UvhmDljrB+OP074Di/vm2z5cxdOpWtTuqQTOXNmw//hIy5fvsrtO3e54X7UdEugaeO6/DBmGCPHTqVVu97k/zQvRYsU4PnzF9zw9OGy+1WcF/5kmhGRPHlyxowYzOBhY+nS4ysW/roMR8dMeFy5xmX3a3z/zUB+mPzzP+pzw/o1KeFUhDNuF8hfrDKVKpTBzi45J0+d447fPYYN6ceUn+bF2C97tiysWbGAVu37MHHKbJx/W0WZ0p9hZWXFzZu3cPvrEm1bN471nnTF8qVxKlYYt78uYmVlFeu6A/GxaME0qtdtjbvHNQo5VaVMqc9ImzYNHleucenyVQDat21G+zZNzfab/uMozrqdx9PrJp+Xq0uZUp/h6JgRL6+bnP7/D71qVcoz5KveZvuFhoWZrs6ExhJS+3/5nWlRrZcvo2ZjhIeHU65yI1OdenWqMWL4V/+ovx/Krj0H6dh1IFmzZKZI4QKkSpWChw8fc+XqDdPgxn69O8c6K+CGpw8+N28R8DQwxjZJnHRlQHBwsGfHlhUs/2021atW4KbvHTb9sZN9B47w/MUL2rVuwvpVv5In9//mcJcu5cTJw1tpWL8mz54Fs+XPXXh6+TCgb1eO7Nsc5/3ghNCsSV32ua6letUK+N29z5Y/d3H/gT+jvx/En5uWYmUVe+a1trZm4xrnqH5Xq8DV656s2/AnFy54kDtXdqZM/J5M/x9+og0f9gWH922iTavGBAYGsXHzDo4cO4VV0qR8Pbgv1aqUN6v/5YAeuPw6g8+KF8Htr4vs2nOQjBnSs2PLijcuBvU6SZMmZe/OtQz/egBZHDOxd/9hDhw6TvFihTiyb9NrHzxTq0Zlzp/ezcD+3UmVKgU7d+1nh+s+ngQ8pUPbZvTuEffc85r/v6x186b1yJQpwz9qe+FC+Tl/ajfDhvQj3ye5OXXmLzZv2cn9Bw+pVaMyK5fNY6nzzzFGq2fJkpmzx3cyfvTXfFa8MBcuebBh03Y8vW9SuWIZ5s+ezI4tK0yDWOPL3eMaJ065ceKUm+l+N2AqO3HKjRtePv+orx9Sj67t+KJfNzJlTI/buYus2/Anh4+eJFkyazp1aMGB3euZPeMHzQoQQI8wFpF4Cg8P55NCFfC5eYvD+za99cp1IvLfpSsDIhIvvzivwOfmLcqWLqEgIJLIaMyAiMTpytUbTJuxgLv3HrBz134sLS2ZMmnEh26WiLxjCgMiEie/u/dZvGQV1tbWFCqYj1HfDaJcmc8/dLNE5B3TmAERERGD05gBERERg1MYEBERMbh4jxlIYpvtzZVERETkPyU+D8bSlQERERGDe+vZBJkyZXpzJZEEdvfuXdPPwcHBH7AlIv9ja2tr+lnnpfwXvHpOvo6uDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSX90A34r2revDmlS5emaNGi5M+fn2TJkvHll1+yZs0as3pJkyaldu3a1KpVCycnJxwdHYmMjOTq1ausXr2a5cuXExERYbbPzJkzad269Wtff8qUKcyYMSPO7Y0aNWLhwoUA9OnTh82bN//Dnkpi8Oeff3L27Fnc3d25du0aoaGhjB07lsaNG8eo6+HhgaurK+7u7nh4ePD48WNKlCiBs7PzG1/j999/58aNG1hZWVG8eHH69etHgQIFEqpb8hF7H+dkaGgoq1atYvv27Xh7ewOQOXNmSpQowXfffZcQ3Uq0FAbi8M0335AtWzYePnzI/fv3yZYtW6z1cubMyaJFiwgKCuLw4cPs3LmTFClSULNmTX788UeqV69O586dzfbZsWMHvr6+sR6vb9++2NnZsW/fvjjblj59eiZNmsSzZ8+ws7P7552URGPOnDn4+fmROnVq0qVLh5+fX5x19+3bx+LFi7GysiJHjhw8fvz4jcf/9ddfmTt3LpkzZ6Zly5Y8e/aMnTt30rlzZxYuXIiTk9O77I4kAgl9Tj59+pR+/fpx8eJFihUrRosWLQC4ffs2rq6uCgNvSWEgDkOGDMHLy4tbt24xYMAAvv/++1jrBQUF8e2337JmzRqeP39uKh8zZgwbNmygVq1aNGjQgK1bt5q27dixgx07dsQ4VtGiRRk6dCiXL1/m3LlzcbZt6tSpBAUFsXr1avr27fvPOymJxujRo8mePTuOjo4sXryYWbNmxVm3Zs2aVKlShbx58xIQEECNGjVee2wfHx8WLlxIjhw5WL58OQ4ODgC0atWKTp06MX78eNatW4elpe46yv8k5DkZffxLly4xceJE6tWrZ7YtLCzsX7ffaPSvNw6HDh3i1q1bb6x39+5dlixZYhYEAJ4/f266jF+2bNl4vWbbtm0BWLlyZZx1WrVqRe3atRkyZAjPnj2L13El8StTpgyOjo7xqps3b14KFCiAlZVVvOpv3ryZsLAwevToYQoCAPnz56dOnTp4enri5ub2j9otiVdCnpPnz59n37591K9fP0YQgKjbt/J2FAYSUGhoKADh4eFvrGtjY0PTpk158eIF69ati7WOo6Mj48aNY9myZRw+fPidtlUkLqdPnwZiD7XlypUD4MyZM++1TWJsO3fuBKKuKDx+/JhNmzbh7OzMn3/+yZMnTz5s4z5Sik8JKPqb/v79+99Yt0GDBqRMmZKNGzfGeTJPnz6doKAgxo4d+w5bKfJ6vr6+2Nraki5duhjbsmfPDsDNmzffd7PEwNzd3YGo8+77778nKCjItM3W1pbRo0dTu3btD9W8j5KuDCSQDh06UL16dQ4dOsTevXvfWD86OPz++++xbu/UqROVK1dm6NChuj0g71VgYCD29vaxbosewPrqm7FIQnv06BEQNTOrSpUqbN26lYMHDzJhwgQsLS35/vvvuXr16gdu5cdFYSAB1KhRgwkTJuDr68uAAQPeWD9nzpyUKVMGHx+fWC//Z8+enVGjRvH777/H6yqDiEhiFj1dO2/evIwfP56sWbOSIkUK6tevz8CBAwkLC3vt2CuJSWHgHatWrRq//vorDx48oEWLFty/f/+N+7Rt2xZLS0tWrVoV6/bp06cTEBDAmDFj3nFrRd7MwcEhzm/+0Vep4rpyIJIQos+3ypUrY2FhYbatcuXKAFy6dOm9t+tjpjDwDlWvXh1nZ2cePXpEixYt4nUf1dLSkpYtWxIWFhZnGChcuDCOjo5cvXoVPz8/039Dhw4FYMGCBfj5+dGzZ8932h8RgGzZshEcHIy/v3+MbdHnePTYAZH3IWfOnABms1uipUiRAoCXL1++zyZ99DSA8B2pXr06ixYt4smTJ7Ro0cK0GlZ89sucOTO7du3i7t27sdZZu3YtyZMnj1FetGhRihQpwuHDh/Hx8cHDw+PfdEEkVp9//jnnz5/n2LFjNGzY0Gzb0aNHAShRosSHaJoYVKlSpdi2bRuenp4xtt24cQMg3tMaJYrCwDtQrVo1Fi1aREBAAC1atMDLyyve+7Zr1w54/doCI0eOjLV8yJAhFClShOXLl2s5YkkwjRs3ZunSpSxatIgqVaqYvo15eHiwY8cOcufOrRUI5b2qUaMGP//8M9u2baNdu3Z88sknQNR07vnz5wNQq1atD9nEj47CQBzatWtHqVKlAExrr7dv3940r/rkyZP8/vvv5M2bF2dnZ2xsbNi+fTtNmjSJcSxfX98YzzQASJcuHdWrV+f+/fu4uromXGck0duwYYNp4Z/r168DsHHjRtMaAU5OTjRr1gwALy8vFi9eDPzvUqq3t7dZ6Bw/frzp5xw5ctC7d2/mzp1Lq1atqFGjhmk5YogKq1p9UP4uIc9Je3t7Ro4cyddff03Hjh2pUaMGKVKk4MSJE9y4cYMKFSrQqFGjhO9kIqIwEIdSpUrFeJhQqVKlTAEBoqYBpk+fHhsbGwCaNm0a67GOHj0aaxho1aoVVlZWrFmzJl4LE4nExc3NjS1btpiVnTt3zmxZ6+g3Xn9//xh1Hz58aFb26hsvQM+ePXF0dGTFihWsXbuWpEmT4uTkRP/+/fWgIolVQp+T0VdkFy1axIEDB3jx4gXZs2fnyy+/pGPHjiRJkuQd9yhxswgP9o2MT8UktlEP6smUKVOCNkgkPl4dXxEcHPwBWyLyP7a2tqafdV7Kf4GtrS3hwbE/GO9VurYnIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoD8VSvXj3WrVvHuXPn8PT05NChQ0yZMiXGA1qGDBli9jChv/+XNWtWs/qWlpYMHDiQ48ePc+XKFVatWkW+fPlibUOHDh3w9PQkR44cCdZP+bht3ryZ4sWLv/a/Xr16merfuHGD3r17U6FCBRo0aICzs3OsC2C9ePGChg0bMm7cuPfZHUkkIiMj2bNnDz169KBGjRqUKVOGxo0bM378eG7dumVWV+fkh6EVCONh9OjR9OnTh7t377Jjxw4CAwMpVKgQ7du3p0mTJjRs2JArV66Y7bN69Wp8fWMu9PD06VOz33v16sXw4cPZvXs3np6eNG/enJUrV1KxYkWzRUsyZMjAiBEjmDZtGj4+PgnTUfnoffrpp/Tu3TvWbbt37+bGjRuULVsWiHr8cJ8+fQgLC6NJkyZ4eXkxe/ZsrK2t6dixo9m+8+fP58WLFwwaNCjB+yCJz/Tp01m2bBnp06enatWq2NnZcfXqVTZs2MCOHTtYsmQJefPm1Tn5ASkMvEH69Onp2bMnvr6+VK9encDAQNO2Xr16MXbsWHr37s3gwYPN9lu9ejXHjh174/Hbt2/PgQMHTCf6tm3b2LRpEzVr1jR7+NCkSZO4efMmCxcufEc9k8Qof/785M+fP0Z5aGgoq1evJmnSpKY12w8ePMiDBw9wcXExPWioV69erF+/3uyN18PDgxUrVvDjjz/G+shYkdfx9/dnxYoVZM6cmTVr1pidQ8uWLeOnn35i2bJljB07VufkB6TbBG+QLVs2kiRJwqlTp8yCAMCuXbsASJs27T8+fpYsWTh//rzp97/++stUHq1evXrUqlWLIUOG6BkG8o/s3buXJ0+eULFiRdP5Gr2kc8GCBU31ChYsaLbUc3h4OGPGjKFSpUpUr179/TZaEoU7d+4QERFB8eLFY3xwV6pUCYDHjx8DOic/JF0ZeAMvLy9evnxJyZIlsbe3JygoyLStZs2aABw+fDjGfmXKlOGzzz4jIiICLy8vDh48GOta5bdv36ZIkSKm36N/vn37NgAODg5MmDCBX375hQsXLrzTvolxbNy4ETB/mFb0c0Y8PDwoVqwYAO7u7mbPH1m6dCm3bt1i1qxZ77G1kphkz54dKysrzp07R1BQEPb29qZtBw8eBDA9AE7n5IejMPAGjx8/ZuLEiYwePZpDhw6xc+dO05iB8uXL4+LiYnr05quGDRtm9vuTJ08YNWoUa9euNSv//fffGTVqFEuXLjWNGfDz8zNddRg1ahQvXrxg6tSpCddJSdTu3LnDyZMnyZgxI+XLlzeVV6xYkXTp0jF48GDq1q2Lt7c3J06cYMiQIUDUo7cXLlzI0KFDyZAhw4dqvnzkUqVKxcCBA5k+fTpNmzalSpUqpjEDJ0+epFWrVrRp0wbQOfkh6amF8dSwYUN++ukns8tcJ06cYMKECZw6dcpUVrduXVKkSMHRo0e5f/8+6dOnp2bNmnz99dekTJmSrl274urqaqofPZugbdu2pEqVinPnzjFy5EiuXr1K2bJlWbduHW3atOHo0aOMGDGC1q1bkzx5cvbv38/QoUN5+PDhe/07/FfoqYXxN3/+fBYuXEjPnj3p37+/2bbr168zZcoULl26RKpUqWjatCldu3YlSZIk9O7dm9DQUJydnbl48SKTJk3iypUrZMiQgb59++p58bHQUwvjtnPnTsaNG8ezZ89MZU5OTgwcONA0PgB0Tr5r8X1qocJAPAwaNIivvvqKqVOnsn79egICAihcuDBjx46lUKFC9OjRw+wDPjYVKlRg9erVeHh4xOs+V7Jkydi9ezdnzpzhq6++4osvvmDw4MGMHj0aPz8/Jk6cyJUrV+jQocO76uZHRWEgfiIiIqhfvz53795l69atZmNRXmfTpk1MnDiRNWvWkD59eurXr8+nn35K165dOXjwIL///jvLli0zu8UlCgNxWbhwIb/++iv9+vWjfv36ODg4cOXKFaZOncrVq1eZNm0aVapUee0xdE7+M3qE8TtSsWJFhg0bhouLC3PmzMHPz4/g4GBOnjxJp06dCAsLY/To0W88zuHDh/H29qZgwYJm98ziMnjwYFKmTMmYMWMA6NGjB+vWrWPp0qXs2rWLSZMmUb16dfLkyfNvuyiJ2PHjx/Hz86NUqVLxDgIPHz5k+vTp9OzZk5w5c7Jt2zYCAgIYN24cZcqUYdiwYWTPnp0VK1YkcOslMTh+/Djz58+nTZs2dOvWjYwZM2Jra4uTkxOzZs0iadKk/PTTT689hs7JhKcw8AbVqlUD4MiRIzG2PXjwgOvXr5M7d26zbwRxefToEQDJkyd/bb0CBQrQt29fRo4cyZMnT3BwcCBDhgxcvHjRVCf657x588a7L2I8sQ0cfJPJkyeTMWNGunTpAoC3tzepUqUiY8aMpjqffvopXl5e77StkjhFv3eWLFkyxrZ06dKRM2dOfH19X3slRedkwtMAwjewsrIC4p4+mCZNGsLDwwkLC3vtcZInT86nn37Ks2fPTKEgNpaWlvz000/s37/fbJ0BiLp1EM3a2hqIWtlLJDZPnjxh//79pEyZ0hRq3+TAgQPs2bOHJUuWmM59iFqn4FWhoaFYWFi80/ZK4hR97kRPH/y7x48fY2lpSdKksX8c6Zx8P3Rl4A2iBwf27t07xhzZTp06kSVLFs6cOUNISAh2dnbkzp07xjFsbGyYNm0aDg4O/PHHH69dK6Bnz5588sknfPvtt6aywMBA7t69a/aGHj3u4Nq1a/+qf5J4bd26ldDQUOrVq2cKj68TFBTExIkTad26tdl911y5chEUFISbmxsQtXLh2bNnyZUrV4K1XRKP4sWLA1ELDP19rZa1a9dy7949ihYtGus5qnPy/dEAwjewtLRk3bp1lC1blgcPHuDq6kpAQABFihShYsWKPH/+nGbNmnHu3DmyZs3KiRMnOHfuHNeuXTPNJqhYsSJZsmTh8uXLtGjRIs6EnDVrVvbv38/EiRNjTFfs378/I0aMYPfu3fj5+dGmTRv27dtH586d38ef4T9HAwjfrEWLFly/fp21a9fyySefvLH+hAkTOHz4MOvXr48xEK5+/fpYWFhQp04dTp8+zbVr11i2bBmFCxdOyC58dDSAMKbw8HB69erFmTNnSJMmDZUrV8bBwQEPDw9OnjyJjY0Nv/76a6wD/3RO/nuaTfAOWVtb06tXLxo1akSePHmwsrLiwYMHHD16lFmzZpm+ndvb2zN8+HCcnJzIli0bKVOm5MWLF1y7do2tW7fi4uLCixcv4nydlStXYm9vT6NGjWJc/re0tOS7776jTZs22NrasnfvXoYNG/baWw6JmcLA6124cIGOHTtSuHBhli9f/sb6bm5udOvWjdmzZ1OhQoUY2y9evMjkyZNN07iiR4WLOYWB2IWEhLB8+XJcXV3x9vYmLCyMNGnSULJkSbp37x7rFVWdk++GwoAkagoD8l+kMCD/NZpaKCIiIvGiMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIgZnER7sGxmfiklssyV0W0REROQdCw/2fWMdXRkQERExOIUBERERg0v6tjs8f/48Idoh8laSJ0/+oZsg8lqRkfG6AyuSoCwsLOJVT1cGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgkn7oBnxstm7dytmzZ3F3d+fatWuEhoYybtw4GjduHGv9oKAg5s+fz549e/D39yd9+vTUrFmTPn36YGtra1b3/PnzrFmzhsuXL/PgwQNevHhBxowZKVCgAF26dKFQoULvo4vyH9W+fXsqVqxIiRIlKFKkCMmSJaNLly4sWbIkRt127drRokULihYtSoYMGbCwsMDHxwdXV1emTZvGnTt3YuyTKlUqvv/+e5o0aUK2bNl4+vQpBw4cYPTo0Vy+fNmsbpo0aWjevDkNGjSgcOHCZMmShcDAQE6dOsXMmTNxdXVNsL+DJF7VqlXj9u3bsW4rVaoUy5Yte88tMg6Fgbc0d+5c7ty5Q+rUqUmfPn2sb6rRgoOD6datG1euXKFs2bLUqVMHDw8PlixZwunTp3FxcSFZsmSm+m5ubhw/fpwiRYpQqlQpbGxsuH37NgcOHGDXrl2MHz+ehg0bvo9uyn/QDz/8QM6cOXnw4AF+fn7kzJkzzrpt2rThk08+4fjx4/j5+WFhYUHx4sX58ssv6dKlCxUqVDD7gE+TJg3Hjh0jX758HD16lM2bN5M5c2aaN29O3bp1qVatGidPnjTVb9myJQsWLOD27dvs2bOH27dvkzVrVlP9r7/+mmnTpiXkn0MSKQcHBzp37hyjPEuWLB+gNcZhER7sGxmfiklsswHw/PnzBG3Qf93x48fJnj07jo6OODs7M2vWrDivDMybN4+FCxfStWtXvvrqK1P5zJkzcXFxYeDAgXTv3t1U/vLlS7NwEO3atWu0b98eOzs79u7di4WFRYL07WOSPHnyD92E96569epcu3aNmzdv8s033zB58uQ4rwwkS5aMly9fxijv1q0bzs7OrF27llatWpnKZ8+ezYABA/jpp58YOnSoqbxMmTIcOnSIK1euUKRIESIjo94uqlatip2dHX/++aepDCBfvnycOHECW1tbcubMiZ+f37v8E3xUXv27SPxUq1YNgL17937gliQeFhYWhAf7vrGexgy8pTJlyuDo6PjGepGRkWzYsAFbW1t69epltq1Xr17Y2tqyYcMGs/LYggDAJ598Qq5cuXj06BFBQUH/vPHyUduzZw83b96MV93YggDA2rVrAcibN69ZeePGjQkPD2f06NFm5cePH2fLli0UKlSIypUrm8r37dvH1q1bY3zgXb16ldWrV2NtbU25cuXi1VYR+fAUBhKIj48PDx48oHjx4jHGBtja2lK8eHFu3brF3bt333gsX19fvL29yZQpEw4ODgnVZDGA+vXrA3Dx4kWz8kyZMuHv78+zZ89i7OPl5QX871vbm4SGhgIQFhb2b5oqBhUSEsKGDRtYsGABy5cv56+//vrQTTIEjRlIINHf4LJnzx7r9uzZs3P06FF8fHzIlCmT2bYLFy5w+PBhwsLCuHPnDgcOHABgxIgRCdtoSXRatmxJwYIFsbW1pVChQtSuXRtPT09GjRplVs/f358MGTJgZ2cXIxDkypULiLoF8CYODg60aNGC58+fc+jQoXfXETGMBw8eMHz4cLOyIkWKMH369DjfT+XfUxhIINGX8+P6Jm9vb29W71UXL15kwYIFpt/Tpk3LDz/8oMuu8tZatWpFixYtTL+fOnWKNm3a4O3tbVZv+/btdOvWjdGjRzNs2DBTealSpWjQoAEQNdvgTRYsWECmTJkYOXIkjx49eid9EONo1qwZJUqUIF++fNja2uLt7Y2LiwubN2+mS5cu/PHHH6b3Tnm3dJvgP6ht27b89ddfnDhxgrVr11K+fHn69+8f60Axkddp2bIlFhYWpEqViqpVqxIaGsqZM2eoWrWqWb1Ro0Zx584dvv76aw4dOsTUqVNZvnw5Bw8eNM06iIiIeO1rTZw4kXbt2rF9+3YmTpyYYH2SxGvAgAGULVuWtGnTkjx5cgoUKMCUKVNo3Lgxt2/fNo15kXdPYSCBRKfXwMDAWLdHXxF4Xcq1sbEhX758jB8/nvLlyzNz5kyuXbv27hsriV5AQAD79++nTp06PH/+nKVLl5I06f8uDN6+fZuSJUuyaNEicuXKxcCBAylTpgyjRo0yfbDfv38/zuOPGzeO4cOHs2fPHpo1a/bG4CDyNlq3bg3A2bNnP3BLEi+FgQQSfW8rrtHf0eU5cuSI1/HKli1LREQEbm5u76aBYkiBgYEcP36crFmzxphRcOfOHXr27EnWrFlJliwZefPmZcqUKRQoUACA06dPx3rMcePGMXLkSPbt20fDhg158eJFgvdDjCV16tRA1NotkjAUBhJIjhw5SJ8+PefOnYtxAgcHB3Pu3DmyZMkSY/BgXKK/lb36bU7kn4ieGhs96v91LC0tadOmDaGhoaxfvz7G9uggsH//furXr2/4dUgkYZw/fx7QwkMJSWEggVhYWNCsWTOCg4P55ZdfzLb98ssvBAcH07x5c7PyS5cuxXosDw8P1q1bR9KkSSldunSCtVkSB3t7+zhH/nft2pXSpUtz9epVbty4YSpPmjQpNjY2ZnUtLCyYNm0a+fPnZ/bs2TEWEBo7diwjR47k4MGDCgLyr924cSPWc+jGjRum1Sy1AmvC0QqEb2nDhg2mS/XXrl3D3d2d4sWLm24LODk50axZMyDqCkCXLl1MyxEXKFAAd3d3jh07RqFChVi8eLHZG3DdunVJkiQJBQoUIHPmzISGhuLt7c3x48eJjIxk2LBhtGvX7v13+j/IiCsQdu/enQoVKgBRU61KlCjB4cOHuX79OgCHDx/G2dmZHDly4OnpyenTp/Hw8OD27dukTp2akiVLUqJECQICAqhTpw7Hjx83HTtLlixcunQJV1dXvLy8sLa2pnbt2hQoUICtW7fSvHlzQkJCTPU7d+7Mb7/9RmhoKD///HOss2L2799vmhZrRFqB8O3Mnj0bFxcXSpYsiaOjI8mTJ8fb25uDBw8SGhpK7969GTx48Idu5kcnvisQKgy8pZEjR/LHH3/Eub1Ro0aMHz/e9HtgYCALFixg9+7dMR5UZGdnZ7bvunXrOHjwIFevXuXx48dERESQLl06nJycaNOmDUWLFk2wfn1sjBgGXFxc6NKlS5zbf/vtN7p27YqtrS3Dhg2jSpUq5MuXj7Rp0xISEoK3tzeurq5Mnz49xsNg7O3tmTt3LuXLl8fR0ZHQ0FAuXrzI4sWLWbx4cYwPttGjRzNmzJjXtnfMmDGMHTv2n3b3o6cw8HZOnjzJ77//jru7O/7+/rx48YLUqVNTtGhR2rVrZwrC8nYUBiRRM2IYkI+LwoD8F+jZBCIiIhIvCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgP/wubNmylWrNhr/+vZs6ep/o0bN+jVqxfly5enfv36ODs7Ex4eHuO4L168oEGDBowbN+59dkc+MhYWFvTv358zZ87w7NkzAgICOHDgQKxLtrZr144NGzZw/fp1nj59SmBgIBcvXmT69OmmZxW8KlmyZPz000/cvHkTf39/1q9fH+e68BMmTMDPz49UqVK96y5KInL+/Hl69uzJ559/TvHixWnVqhXbtm2LUe/MmTO0bt0aJycn6tatG+dji/39/SlVqhQLFixI6KYbghYd+hc8PDzYt29frNt27drFjRs3+Oqrr+jatSvPnj2jcePGhIWFUb9+fby8vDhy5AhDhgyhU6dOZvvOmDGDP//8k40bN+Lg4PA+uvLR0aJDsHbtWlq0aMH169fZvn07yZIlo3HjxmTMmJEBAwYwd+5cU90//viDTz75hDNnzuDn54eFhQXFixenatWqBAQEUKFCBS5fvmyqP3v2bPr168eaNWvw9/ena9euXLlyhZIlS5o9nrhIkSKcOXOG9u3b61nzf6NFh/7n+PHj9OjRA2tra+rXr4+dnR2urq7cvn2bb775hm7dugFRT86sV68eadOmpVatWri5ueHm5sbs2bOpVauW2TG//PJLvLy82LBhgx7g9hpagfADCg0NpUaNGgQFBeHq6kratGnZvn073377Lb/99htOTk4A9OzZk/v377N582bTvu7u7nTo0IEff/yRGjVqfKgu/OcZPQw0b96cdevWcfjwYWrWrGl6bHDatGk5ffo0mTJlIn/+/Pj4+ABR3/RfvnwZ4zjdunXD2dmZtWvX0qpVKyDqzePZs2csX76cXr16AdChQweWLVtGmTJlOHHiBBD1RMNjx45x9+5dGjdu/D66/VFRGIgSFhZG3bp1uXv3LmvWrDE9EjswMJAWLVpw+/Ztdu7cSZYsWVi4cCEzZ85kz549ODo6Eh4eTr169ciaNSvOzs6mY+7du5cBAwawatUqLdP+BlqB8APau3cvT548oWLFiqRNmxaAu3fvAlCwYEFTvYIFC5rKAcLDwxkzZgyVKlVSEJDXiv7wnThxoikIADx8+JAZM2ZgY2ND165dTeWxBQHA9G0+b968prJ06dKRPHlyzpw5Yyo7ffo0gOmBXBD1zSx//vz069fvHfRIEqvjx49z8+ZNGjRoYAoCAA4ODvTp04fQ0FA2btwIgJ+fH2nSpDHduop+cNurT8wMCgpi7NixdOzYUUHgHVIYSAAbNmwAMD29ECBTpkxA1K2FaB4eHqZygKVLl3L79m2GDx/+nloqH6vo88bLyyvGtuiyatWqvfE49evXB+DixYumMn9/f54/f266ggXw2WefAXDz5k0AcuTIwbhx4xg+fHiMhx6JvOrkyZMAsT5oKLrs1KlTAGTOnJnHjx+bviRFRETg4eFB5syZTftMmzaNJEmS8OWXXyZ00w1FN1resTt37nDy5EkyZsxI+fLlTeUVK1YkXbp0DBo0iLp16+Lj48Px48cZMmQIAL6+vixYsIChQ4eSIUOGD9V8+Uj4+/sDkCtXLrOAGV0GkC9fvhj7tWzZkoIFC2Jra0uhQoWoXbs2np6ejBo1ylQnMjKSxYsX07dvXxwcHHj48CFdunTh7NmzpjfthQsX8tdffzFv3ryE6qIkEt7e3kBUgPy79OnTY2tra7qd1aBBA+bNm0fHjh2pUaMG586dw8vLy/To4jNnzrB69Wp++eUXbG1t31sfjEBXBt6xzZs3ExERQaNGjUiSJImp3N7engULFpAnTx42btyIp6cnX3zxBe3btwdg/PjxFCxYkBYtWnD+/Hnatm3LZ599Rp06dV77yGQxpu3btwPw7bffkixZMlN5mjRp+OqrrwBiHd3fqlUrxowZw7Bhw6hfvz5ubm7UrFnT9IYdbejQocyaNYvKlSvTvn17du3aRaNGjYiIiKBjx45UqVKFnj17Ymdnh4uLC4GBgQQGBuLi4qI3aTETFBQEEOdgaHt7ewIDAwHIkiULzs7OpEqVilWrVvH48WN++OEHatWqRUhICCNHjqRBgwZUrFiRAwcOULduXQoWLEidOnU4cODAe+tTYqQBhO9QREQE9erV4+7du2zdupWsWbPGa7+NGzcyceJE1q5dS/r06alXrx6ffvop3bp14+DBg6xYsYJly5bp/tgrjD6AMEmSJLi6ulKtWjWuXbvGjh07sLKyokmTJty7d49ixYrx/PnzOD+YU6ZMiZOTExMmTKBgwYI0a9Yszpkxr0qXLh3u7u7MmTOHsWPHsmDBAlq1akX//v2JjIxk3rx5rFy5kv79+7/rLn90NIAwSrdu3Thy5Aiurq6xXh2oWLEiwcHBZmNUYvPzzz+zcuVKtm3bxvPnz6lduza1atWiZcuWrFu3DldXV3bu3BnrVFkj0wDCD+D48eP4+flRqlSpeAeBhw8fMn36dHr27EnOnDn5888/CQgIYPz48ZQpU4Zhw4aRPXt2VqxYkcCtl49JeHg4devWZfTo0URERNCrVy+aNWvG5s2badGiBQD379+Pc/+AgAD2799PnTp1eP78OUuXLo3X9Kyff/6Ze/fuMXHiROzt7enWrRtTp05l5cqVrFq1imnTptGjRw/s7OzeWV/l42Zvbw9g+vb/d0FBQW+cQn3t2jV+/fVXhg8fTpo0aVi5ciXJkiVj4sSJlC1blgkTJmBtbc3KlSvfefuNQmHgHYoeEdu0adN47zN58mQyZsxoGvnt4+NDqlSpyJgxo6lO/vz5Yx0oJsYWEhLCuHHjyJ8/PzY2NmTMmJE+ffqYFgeKngHwOoGBgRw/fpysWbOazSiITd26dWndujU9e/YkNDSU3LlzY2Vlxblz50x13NzcsLa2Jk+ePP+qb5J45MyZE8A0LuBVDx48IDg4ONYrBtEiIiIYMWIEZcqUMc2i8fLyIleuXNjY2ABgY2NDrly58PT0fPcdMAiFgXfkyZMn7Nu3j5QpU1K9evV47bN//352797NqFGjsLKyMpWHhoaa1QsJCcHCwuKdtlcSr+hxKKtWrYpX/ejLqn8/715lZ2fH/PnzmT9/PseOHTPb9uqYheifdYlcopUsWRKAw4cPx9gWXRZdJzbLly/n6tWrjBkzxqw8JCQkxu96n/znFAbeka1btxIaGkr9+vWxtrZ+Y/2goCAmTpxImzZtzMYC5MqVi6CgINzc3AB49uwZZ8+eJXfu3AnWdvk4xXZptXnz5nTr1o2TJ0+aprja29vHOrMAoGvXrpQuXZqrV69y48aNOF9r4sSJWFpamk17vXHjBiEhIdSrV89UVq9ePV6+fPnaY4mxlC1blmzZsrF161bc3d1N5YGBgSxYsMA01iU2d+7cYcaMGXz55Zdmt17z5MnD9evXTdNab9++zfXr13VF6l/Q1MJ3JPoWwatrC7zOzz//jKWlJV988YVZeb169ZgzZw6DBw+mbt26nD59msDAQNO3PZFoJ06cwNfXF3d3d168eEGpUqWoWrUqN27coGXLlqZlg9OmTYu7uzunT5/Gw8OD27dvkzp1akqWLEmJEiUICAigc+fOcb5O6dKl6d+/P02aNDGNDIeooOrs7Ezfvn1NAxXbt2/P7NmzCQ4OTtjOy0cjadKk/PDDD/To0YP27dvHuhxxXGOsxowZQ548eWIs2d6mTRtcXFzo3Lkz1apVY+/evSRNmpS2bdu+jy4lSppN8A5cuHCBDh06ULhw4XgN9HNzc6Nr167MmTMn1oU4Ll68yKRJk7hy5QoZMmSgf//+psVhJIrRZxMAjB49mmbNmpEzZ06srKzw8vJi/fr1TJ061Wywlq2tLcOGDaNKlSrky5ePtGnTEhISgre3N66urkyfPj3OhYOSJk3K2bNnuXz5Mm3atImx3dbWllmzZpkGLa5du5YvvvjCbFVEo9KtEnPnz59n1qxZuLm5ERYWRr58+ejatavZlaVXbdmyhW+//Zb169eTP3/+GNsPHjzIlClT8Pb2JmfOnHz77bexvp8anZ5NIImawoD81ykMyH+BphaKiIhIvCgMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYnMKAiIiIwSkMiIiIGJzCgIiIiMEpDIiIiBicwoCIiIjBKQyIiIgYXNK33SF58uQJ0Q4RkUTFwsLiQzdBJN50ZUBERMTgFAZEREQM7q1vEwQFBSVEO0Teir29vennkJCQD9gSkf+xtrY2/ZwuXboP2BKRKP7+/vGqpysDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJwST90Az42f/75J25ubri7u3P9+nVCQ0MZM2YMjRo1ilF327Zt7Nmzh2vXrvHo0SMiIyPJnDkzZcqUoVOnTmTIkMGsvre3N8uWLePy5cvcu3ePZ8+ekS5dOvLkyUP79u0pXbr0++qmJAKbNm1ixIgRr61TunRpnJ2dTb8HBQUxb948du3ahb+/P+nTp6dWrVr069cPW1vbhG6yfIRatGhBmTJlKFasGAUKFCBZsmR88cUXrFq1yqxe0qRJqVOnDnXq1MHJyYksWbIQGRnJlStXWLVqFUuXLiUiIiLG8VOmTMmgQYOoV68ejo6OBAYGcuzYMX788UeuXLliVrdcuXJs3rz5te318vKiVKlS/77jiYzCwFuaN28efn5+pEqVinTp0uHn5xdn3Z07d+Lr60uRIkVIly6d6cRfuXIlW7ZsYfHixeTJk8dU//r16+zdu5ciRYpQtGhR7O3tuX//PgcOHODw4cP069ePHj16vI9uSiKQP39++vbtG+u2Xbt2cf36dcqXL28qCw4OpkuXLnh4eFCuXDnq1auHu7s7v/32G6dPn2bJkiUkS5bsfTVfPhLDhw8ne/bs+Pv7c+/ePbJnzx5rvZw5c+Li4kJQUBAHDx5kx44dpEiRgtq1azN16lRq1KhBhw4dzPZJnTo127dvJ0+ePJw8eZLt27eTMWNGGjRoQLVq1WjWrBlnz5411ff19WXKlCmxvn7lypUpXbo0+/bte3edT0QswoN9I+NTMYltNiDqm4ORnThxgmzZsuHo6IiLiwuzZ8+O88rAy5cvY33z3LRpE+PGjaNGjRpmJ25ISAhWVlZYWFiY1X/w4AFt27bl6dOn7NmzBwcHh3ffsY+Mvb296eeQkJAP2JKPT2hoKFWrViUoKIjdu3eTLl06AObMmcOCBQvo3r07gwYNMtWfMWMGzs7OfPnll/Ts2fNDNfujYG1tbfo5+u+a2FWqVAlPT09u3brFwIEDGTlyZKxXBjJlykTdunVZvXo1wcHBpnJbW1s2bdqEk5MT3bt3548//jBtmzx5Mt27d2fevHmMHj3aVP7555+zZcsWrl+/TqVKlYiMfPPH2IEDByhYsCDVq1fn/Pnz76DnHwd/f3/Cg33fWE9jBt5S6dKlcXR0jFfduL5F1ahRA4hKsa+ytraOEQQA0qdPT7FixQgLC3vtlQiR+NizZw9PnjyhUqVKpg+syMhINmzYgK2tLb179zar37t3b2xtbVm/fv2HaK78xx08eJBbt269sd7du3dxcXExCwIQdUVq/vz5QNRl/lfVqVOH8PDwGN/2T58+zc6dO8mfP3+MfWLj5OREwYIFuXDhgqGCwNtQGPgADh8+DGB2i+B1njx5wsWLF7GxsSFLliwJ2TQxgOgP9ebNm5vKfHx8uH//Pk5OTjHGBtja2uLk5MStW7cURiVBhIWFmf1vtAwZMvDw4UOePXsWY5+bN28CULFixTceP/r2w/Lly/9tUxMtjRl4D1xdXfH09OTFixd4enpy7NgxsmTJEuf9XB8fH7Zv305ERAQPHjzgwIEDBAUF8d1332FnZ/eeWy+JyZ07dzhx4gQZM2akQoUKpnIfHx8AcuTIEet+OXLk4MiRI9y8eZPMmTO/l7aKcbRr1w6A/fv3m5U/evSIdOnSYWdnFyMQRI9NeNOXKltbW5o2bUpwcDDr1q17d41OZBQG3oNdu3axZ88e0+8FCxZk0qRJcX7L9/Hx4ZdffjH9bmtry+jRo6lfv36Ct1USt40bNxIREUGTJk1IkiSJqTx6LNCrYzFeFR1CAwMDE76RYigdO3akRo0aHDx4kN27d5tt27NnD+3atWPo0KGMHTvWVP7ZZ59Rq1YtIGq2wes0atQIBwcH1qxZw9OnT999BxIJhYH3YOrUqUDUG6mHhwdz586lffv2TJs2LdYpLpUqVeLs2bOEhoZy584dNmzYwKhRo7h06RLDhg17382XRCIiIoJNmzZhYWFB06ZNP3RzRKhZsyaTJ0/m5s2b9OvXL8b2yZMnU61aNQYMGEDJkiU5ffo0GTNmpGHDhly5coXChQvHOh3xVe3btwdgxYoVCdKHxEJjBt4jBwcHSpYsyZw5c7CxsWHUqFGEhobGWd/KyoocOXIwaNAgWrRowapVqzhy5Mh7bLEkJseOHcPPz4/SpUuTNWtWs23RVwTimi0UfYlWM1nkXalRowaLFy/mwYMHNGvWjHv37sWo4+fnR82aNVm+fDnZs2enZ8+elChRgh9//JGff/4ZiBotH5c8efJQpkwZPD09OXr0aIL1JTFQGPgA7O3tKVKkCPfv348xoyAuZcuWBaJG0Yr8Exs2bACgWbNmMbZFjxWIHjvwd9Hlcc0hF3kbNWvWxMXFhUePHtG0adM4zzuImoUwaNAgihYtSpYsWShVqhSzZ8/mk08+AeDcuXNx7qurAvGnMPCBPHjwAIhalSsh6ou86smTJ+zdu5eUKVOapra+KkeOHGTIkAE3N7dYp365ubmRNWtWDR6Uf61mzZosXryYJ0+e0LRpU7y8vN76GJaWljRt2pTQ0FC2bNkSa50kSZLQunVrQkNDY6x5IDEpDCSQZ8+e4e3tHeu2TZs2cfHiRbJnz272Tevy5cuxLp5x584dXFxcAMxWjBOJry1bthAaGkqDBg3MFsaJZmFhQbNmzQgODmbhwoVm2xYuXEhwcLDZVESRf6J69eosXryYgIAAmjRpgqen52vrJ02aFBsbG7MyCwsLxo4dyyeffMKiRYtivb0AUKtWLTJkyMDu3bu5f//+O+tDYqWvmW9p48aNpstS165dA6I+3M+cOQNA8eLFadq0KQEBATRv3pyCBQuSM2dOMmTIwNOnT7l06RIeHh7Y29ubjY6FqJXefH19KVy4MJkyZcLS0pJbt25x5MgRQkND6dixI8WLF3+f3ZVEIvoWwes+0Lt168a+fftwdnbG3d2dAgUK4O7uztGjRylcuDAdO3Z8X82Vj0iHDh1Mz00pUKCAqSz6i8uJEydYvnw5efPm5bfffsPGxoZt27bFervK19fX7Ft8+vTpOXz4MPv378fHxwdra2uqVq1Kvnz5cHV15YcffoizXdG3CLS2QPwoDLylc+fOxbgsde7cObP7Vk2bNiV16tT06NGDM2fOcOLECZ48eYKVlRWOjo60b9+eDh06kDFjRrPjtG7dml27duHu7s6xY8cIDQ0lTZo0VKxYkWbNmsVrpS2Rv7tw4QLXrl2jSJEi5MuXL856tra2/Pbbb6YHFZ08eZL06dPTuXNn+vXrF+MbmghErcrapk2bGGWvPlht+fLlZMiQwXQOxRYEAI4cOWIWBp4+fcr27dspVaoUNWvWJCwsDHd3dwYNGsSKFSviXIY4Y8aMVK9eHT8/vxjTFSV2ejaBfJT0bAL5LzLiswnkv03PJhAREZF4URgQERExOIUBERERg1MYEBERMTiFAREREYNTGBARETE4hQERERGDUxj4FyIiIli1ahXt2rWjXLlyVKxYke7du3PgwIEYdT09PenTpw+VKlWiUaNGLF68mPDw8Bj1Xrx4QaNGjRg/fvz76IIkQrVq1aJw4cKx/telSxezunfv3mXgwIGUK1eO6tWrM3XqVF6+fBnjmJGRkXTs2JFevXq9p17Ix6p+/fps3LiRixcv4uPjw7Fjx5g2bZrpYVh/lz17dqZPn87Zs2e5desWly5dYuPGjTRq1MisXrJkyRg3bhznzp3jypUruLi4xPmsjO+//55Lly6RMmXKd96/xEorEP5DkZGRfPPNN+zZs4esWbPSuHFjQkJCOHDgAIMGDWLYsGGmVbmePXtGnz59CA8Pp3Hjxnh5eTFnzhysra3p0KGD2XEXLFjAixcv+Oqrrz5ArySxcHBwiHFuAWTJksX0c3h4OP379+fmzZs0bdqUhw8fsmTJEkJDQ/nuu+/M9lu9ejUeHh5s3LgxwdsuH6+xY8fSr18/7t69y7Zt2wgMDDQtZd2sWTPq1auHh4eHqX7lypVZsmQJAK6urvj4+JAyZUoKFSpEpUqV+OOPP8yO3bVrVzZv3syjR49o06YNy5cvp2bNmkRERJjqFSxYkP79+9O3b18CAgLeX+c/cgoD/9CePXvYs2cPxYsXZ968eaZlNgcMGECHDh2YOXMmlSpVwtHRkUOHDuHv78/ixYtNzxbo3bs3GzZsMHvD9vDw4Pfff2fSpEl6brz8Kw4ODvTv3/+1dS5cuMCVK1eYPHkyDRo0AKKWJN6wYQPDhw/HwsICgHv37jFz5kwGDBhA1qxZE7zt8nHKkCEDvXv35ubNm1SpUoXAwEDTtt69e/PDDz/Qt29fvvzySyAqmC5evJi7d+/SvHlzbt++bXa8JEmSmH62sLCgXbt2rFixgsGDBwNw5swZ5s2bh5OTk+nZMJaWlsyYMYM9e/awefPmhO5yoqLbBP/Q/v37gaiHu7y6Znvq1Klp3749ISEhppPx7t27wP8e4gFR6TW6HKK+pY0bN46KFStSvXr199ADMbro869gwYKmskKFCvHixQsePXpkKvvhhx/IkSNHrFcaRKJly5aNJEmScPLkSbMgALBr1y4A0qZNayr76quvSJEiBUOHDo0RBACz26hp06YlefLk/PXXX6ay6OfBvBpQe/XqxSeffMKwYcPeSZ+MRFcG/qGHDx8C4OjoGGNb9KXYU6dO0bdvXzJlygREffMvVqyY6efocoBly5Zx69Ytfv7554RuuhhASEgImzZt4v79+9jb21O4cGGKFi1qVufV8zJ37twAuLu7Y2NjQ5o0aQDYsWMHhw4dYtWqVWbf1ET+ztPTk5cvX1KqVCns7e3NnmNTs2ZNAA4dOmQqa9SoEQ8fPuTw4cMULVqUcuXKYWlpycWLFzl06JDZQ4gePnzI8+fPKVKkiKks+ny+desWEBVGvv32W8aNG4efn1+C9jUxUhj4h1KlSgXAnTt3TG+k0aJT7s2bNwGoUKEC6dKlY+jQodSpUwcfHx9OnDhhutzl6+vLL7/8wuDBg0mfPv3764QkWv7+/owYMcKsrHDhwkyZMoXs2bMDmJ5iOHbsWM6dO8fjx4/ZsWMHbdu2xcLCgoCAACZNmkTnzp3Jnz//h+iGfEQeP37M+PHjGTduHMeOHWP79u2mMQMVKlRg8eLFLFq0CIAcOXKQJk0a3NzcmDZtGp07dzY71vnz5+nQoYPpQz0yMpKVK1fSpUsX7O3tefz4MW3atOH8+fO4ubkB8NNPP3Hp0iUWL178fjueSOg2wT8U/axuFxcXs9HXT5484ffffwcwXSqzt7dn3rx55M6dm02bNuHl5UX//v1p27YtABMmTKBAgQI0b96cCxcu0L59e0qWLEm9evViPC5Z5E2aNGmCs7MzBw4c4NSpU6xbt46GDRty8eJFevTowbNnz4Coe7Jz586ldOnSbN26lbNnz9KpUyeGDBkCwNSpU7Gzs6Nfv354e3vTrVs3ihcvTqVKlXB2dv6QXZT/qIULF9KrVy/s7Ozo2rUrAwcOpFq1apw9e5b169ebLv1HP9GxSJEiNG/enC+++IK8efPy2WefsXTpUooWLRrjQ3306NH8+uuvlCtXjubNm7N//346dOhAREQErVq1oly5cgwaNAg7Oztmz56Nt7c33t7ezJ49G1tb2/f+t/jY6BHG/1BYWBj9+vXj9OnTZMuWjXLlyhEWFsb+/ftJkyYN165dI1myZBw7duy1x9m0aROTJ09m1apVpE+fnoYNG5IvXz66du3KwYMHWblyJUuWLDG7PCZ6hPE/MXz4cLZs2cLXX38d45vY3504cYIePXrg7OzM559/TpMmTbCysmLw4MFcvHiROXPmMHXqVOrUqfOeWv9xMPojjIcMGcLgwYP58ccfWbt2LU+fPqVw4cKMHz+ewoUL07VrV3bu3EnJkiXZtm0bACNGjGDhwoVmx9m+fTuff/45DRo04MSJE699zbRp03LkyBGcnZ2ZOnUq06ZNo3HjxnzzzTdERkYyZcoUNmzYwDfffJNg/f4v0yOME1jSpEmZM2cOvXv3xtLSkg0bNrB3714qV67M1KlTgajBhK/z8OFDZs6cSY8ePciZMyfbt28nICCAsWPHUrp0ab7++muyZ89uutIg8m+0bNkSwHRZNS4vXrxgzJgxNG3alFKlSnH06FE8PT0ZMWIE5cuXp3fv3pQuXZply5a9j2bLR6JSpUp8++23ODs7M2vWLPz8/Hj27BknTpygQ4cOhIaGMm7cOACePn1q2m/Hjh0xjuXq6gpgmn31OhMmTODBgwfMnDkTOzs72rVrx9y5c9mwYQMbN25k3rx5dOjQATs7u3fT0URKYwb+BWtra3r37k3v3r3Nyk+fPg2Yj9KOzZQpU8iQIYPpW5q3tzepUqUiY8aMpjqffvop3t7e77bhYkjR4fT58+evrTd37lyeP3/O0KFDAfDy8gIwGzdQoEAB1q9fn0AtlY9R9Cyow4cPx9h2//59rl+/TtGiRbGzs8Pb25uwsDCSJk0a61oA0WWvztSKTY0aNWjSpAkNGzYkNDSUfPnyYWVlxcWLF011Lly4gLW1NTlz5uTSpUv/pouJmq4MJIDt27cDULt27TjrHDhwgD179jBy5EisrKxM5aGhoWb1QkJCTPO9Rf6N8+fPA7HPgInm7u7OsmXL+O6770iRIoXZtldvx+jWjPxd9C2SuG6PpE2blvDwcEJDQ3n58iWnTp0Cor7w/F10WfQg7NjY2dkxZcoUfvvtN9Ox/t6WV39+dXaCxKQw8C/ENn5i9+7dbN68mUKFClGtWrU495s8eTKtW7c2GwuQK1cugoKCTPNnnz17hpubG7ly5UqQ9kvi4+npGes3f09PT2bMmAFELRcbm/DwcEaNGkXFihWpVauWqTx6tkz0tLCwsDCOHj0aYxaNGFv0vf0+ffrEWDStc+fOZMmShdOnT5uCpIuLCwBff/212Yd33rx5ad26NYGBgezduzfO1/v++++xtLQ0W7rd29ubkJAQatSoYSqrUaMGL1++1BXWN9Btgn+hU6dOZMqUiVy5cmFtbc2lS5c4ffo0WbNm5ccff4xzXvbs2bOxsLCIsUJc3bp1mTdvHkOHDqV27dqcOXOGwMBA2rVr9z66I4nA9u3bWbp0KSVKlMDR0ZHkyZPj4+PDwYMHCQsLo0ePHnz++eex7rtkyRJ8fX2ZM2eOWXnZsmXJkycP48aN4/z581y5cgUvLy9TuBAB+OOPP+jatSvlypXj+PHj7Ny5k4CAAIoWLUqlSpUIDg5m5MiRpvobN26kQYMGNGrUiP3797Nv3z5SpEhBgwYNsLGxoX///nEuJ1yiRAm6detGp06dTLNjIOoL1IoVK+jatSvJkycHoEWLFixatIjg4OCE/QN85DSb4F9YsGABe/fuxc/Pj7CwMBwdHalevTqdOnUyG+3+qnPnztG9e3dmzZplmp74qkuXLvHjjz9y5coVMmTIQN++falXr15Cd+Wjo9kEsTt16hSrVq3Cw8ODhw8f8uLFC1KlSkXRokVp3bp1rOccRF2ObdasGUOHDjU9U+Pv28eNG8fZs2dJlSoVnTt3fuOMBCMy+mwCa2tr+vTpQ+PGjcmTJw/W1tY8ePCAw4cPM3PmTK5du2ZWP0mSJPTo0YP27duTK1cuQkJCOHPmDDNnzuTo0aOxvkbSpEnZs2cPV69epWfPnjG229raMnHiRBo2bAhEhZThw4fz4sWLd9/hj0B8ZxMoDMhHSWFA/ouMHgbkv0dTC0VERCReFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAxOYUBERMTgFAZEREQMTmFARETE4BQGREREDE5hQERExOAUBkRERAwu6dvuYG9vnxDtEPnHrK2tP3QTRGLw9/f/0E0Qibd4h4HwYN+EbIeIiIh8ILpNICIiYnAKAyIiIganMCAiImJwCgMiIiIGpzAgIiJicAoDIiIiBqcwICIiYnAKAyIiIganMCAiImJw/wc3asxFchi/FAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assignment #5: Fine tune the VGG16 pre-trained on ImageNet."
      ],
      "metadata": {
        "id": "qaYPBB0RSgQa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    os.path.join(ROOTDIR_DATASET, \"train\"),\n",
        "    labels='inferred',\n",
        "    label_mode='categorical',\n",
        "    class_names=None,\n",
        "    color_mode='rgb',\n",
        "    batch_size=32,\n",
        "    image_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    shuffle=True,\n",
        "    seed=None,\n",
        "    validation_split=None,\n",
        "    subset=None,\n",
        "    interpolation='bilinear',\n",
        "    follow_links=False,\n",
        "    crop_to_aspect_ratio=False,\n",
        ")\n",
        "\n",
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    os.path.join(ROOTDIR_DATASET, \"test\"),\n",
        "    labels='inferred',\n",
        "    label_mode='categorical',\n",
        "    class_names=None,\n",
        "    color_mode='rgb',\n",
        "    batch_size=32,\n",
        "    image_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    shuffle=True,\n",
        "    seed=None,\n",
        "    validation_split=None,\n",
        "    subset=None,\n",
        "    interpolation='bilinear',\n",
        "    follow_links=False,\n",
        "    crop_to_aspect_ratio=False,\n",
        ")"
      ],
      "metadata": {
        "id": "bjseah0vSqJ-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9deadbaa-cd91-491f-dfce-e65611e677fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 9000 files belonging to 3 classes.\n",
            "Found 4500 files belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def process_data(image, label):\n",
        "    image = tf.cast(image, tf.float32)\n",
        "    return  tf.keras.applications.vgg16.preprocess_input(image), label\n",
        "\n",
        "train_ds = train_ds.map(\n",
        "    process_data, num_parallel_calls=tf.data.AUTOTUNE).cache().prefetch(tf.data.AUTOTUNE)\n",
        "test_ds = test_ds.map(\n",
        "    process_data, num_parallel_calls=tf.data.AUTOTUNE).cache().prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "hqemLer3THeg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = tf.keras.applications.VGG16(\n",
        "    include_top=False,\n",
        "    weights=\"imagenet\",\n",
        "    input_tensor=None,\n",
        "    input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3),\n",
        "    pooling=None,\n",
        "    classes=NUM_CLASSES,\n",
        "    classifier_activation=\"softmax\",\n",
        ")"
      ],
      "metadata": {
        "id": "jS0YdW-_Thz7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i, layer in enumerate(base_model.layers):\n",
        "    print(f\"layer {i}: {layer.name}\")"
      ],
      "metadata": {
        "id": "xsmk6ERpThsb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8675ae2b-d0e7-4293-cfce-edf38dc14d26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "layer 0: input_3\n",
            "layer 1: block1_conv1\n",
            "layer 2: block1_conv2\n",
            "layer 3: block1_pool\n",
            "layer 4: block2_conv1\n",
            "layer 5: block2_conv2\n",
            "layer 6: block2_pool\n",
            "layer 7: block3_conv1\n",
            "layer 8: block3_conv2\n",
            "layer 9: block3_conv3\n",
            "layer 10: block3_pool\n",
            "layer 11: block4_conv1\n",
            "layer 12: block4_conv2\n",
            "layer 13: block4_conv3\n",
            "layer 14: block4_pool\n",
            "layer 15: block5_conv1\n",
            "layer 16: block5_conv2\n",
            "layer 17: block5_conv3\n",
            "layer 18: block5_pool\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, layer in enumerate(base_model.layers):\n",
        "    if i > 10:\n",
        "        layer.trainable = True\n",
        "    else:\n",
        "        layer.trainable = False\n",
        "\n",
        "# freezing layer weights from 0 to 10"
      ],
      "metadata": {
        "id": "fzI8fWUUUxPK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tf.keras.Input(shape=(IMAGE_SIZE, IMAGE_SIZE, 3))\n",
        "x = base_model(inputs)\n",
        "x = tf. keras.layers.GlobalAveragePooling2D()(x)\n",
        "x = tf.keras.layers.Dense(4096, activation=\"relu\")(x)\n",
        "x = tf.keras.layers.Dense(4096, activation=\"relu\")(x)\n",
        "outputs = tf.keras.layers.Dense(NUM_CLASSES, activation=\"softmax\")(x)\n",
        "model = tf.keras.Model(inputs, outputs)"
      ],
      "metadata": {
        "id": "SNhQRXWET3ef"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def scheduler(epoch, lr):\n",
        "    if epoch < 45:\n",
        "        return lr\n",
        "    else:\n",
        "        return lr * tf.math.exp(-0.1)\n",
        "\n",
        "optimizer = tf.keras.optimizers.SGD(learning_rate=0.001)\n",
        "model.compile(optimizer=optimizer, loss=\"categorical_crossentropy\", metrics=\"accuracy\")\n",
        "callback = tf.keras.callbacks.LearningRateScheduler(scheduler)\n",
        "history = model.fit(train_ds, epochs=50,callbacks=[callback])"
      ],
      "metadata": {
        "id": "D23JpLMpVf0q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a29a1f8-67ad-4112-f86a-53f70ab41290"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "282/282 [==============================] - 33s 108ms/step - loss: 0.7935 - accuracy: 0.7687 - lr: 0.0010\n",
            "Epoch 2/50\n",
            "282/282 [==============================] - 7s 27ms/step - loss: 0.4163 - accuracy: 0.8731 - lr: 0.0010\n",
            "Epoch 3/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 0.2034 - accuracy: 0.9274 - lr: 0.0010\n",
            "Epoch 4/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 0.1328 - accuracy: 0.9512 - lr: 0.0010\n",
            "Epoch 5/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 0.1007 - accuracy: 0.9653 - lr: 0.0010\n",
            "Epoch 6/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 0.0881 - accuracy: 0.9704 - lr: 0.0010\n",
            "Epoch 7/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 0.0637 - accuracy: 0.9786 - lr: 0.0010\n",
            "Epoch 8/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 0.0492 - accuracy: 0.9844 - lr: 0.0010\n",
            "Epoch 9/50\n",
            "282/282 [==============================] - 7s 24ms/step - loss: 0.0725 - accuracy: 0.9824 - lr: 0.0010\n",
            "Epoch 10/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 0.0335 - accuracy: 0.9892 - lr: 0.0010\n",
            "Epoch 11/50\n",
            "282/282 [==============================] - 9s 31ms/step - loss: 0.0220 - accuracy: 0.9929 - lr: 0.0010\n",
            "Epoch 12/50\n",
            "282/282 [==============================] - 9s 32ms/step - loss: 0.0161 - accuracy: 0.9952 - lr: 0.0010\n",
            "Epoch 13/50\n",
            "282/282 [==============================] - 8s 28ms/step - loss: 0.0129 - accuracy: 0.9968 - lr: 0.0010\n",
            "Epoch 14/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 0.0067 - accuracy: 0.9983 - lr: 0.0010\n",
            "Epoch 15/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 0.0039 - accuracy: 0.9994 - lr: 0.0010\n",
            "Epoch 16/50\n",
            "282/282 [==============================] - 8s 29ms/step - loss: 0.0024 - accuracy: 0.9998 - lr: 0.0010\n",
            "Epoch 17/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 0.0017 - accuracy: 0.9999 - lr: 0.0010\n",
            "Epoch 18/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 0.0013 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 19/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 0.0011 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 20/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 7.8897e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 21/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 6.4505e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 22/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 5.4126e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 23/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 4.6299e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 24/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 4.0266e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 25/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 3.5504e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 26/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 3.1705e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 27/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 2.8561e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 28/50\n",
            "282/282 [==============================] - 8s 27ms/step - loss: 2.5892e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 29/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 2.3670e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 30/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 2.1749e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 31/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 2.0091e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 32/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 1.8655e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 33/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 1.7373e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 34/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 1.6238e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 35/50\n",
            "282/282 [==============================] - 7s 27ms/step - loss: 1.5233e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 36/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 1.4327e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 37/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 1.3514e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 38/50\n",
            "282/282 [==============================] - 7s 25ms/step - loss: 1.2781e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 39/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 1.2107e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 40/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 1.1500e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 41/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 1.0944e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 42/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 1.0432e-04 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 43/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 9.9605e-05 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 44/50\n",
            "282/282 [==============================] - 7s 27ms/step - loss: 9.5261e-05 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 45/50\n",
            "282/282 [==============================] - 7s 27ms/step - loss: 9.1250e-05 - accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 46/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 8.7833e-05 - accuracy: 1.0000 - lr: 9.0484e-04\n",
            "Epoch 47/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 8.4872e-05 - accuracy: 1.0000 - lr: 8.1873e-04\n",
            "Epoch 48/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 8.2303e-05 - accuracy: 1.0000 - lr: 7.4082e-04\n",
            "Epoch 49/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 8.0063e-05 - accuracy: 1.0000 - lr: 6.7032e-04\n",
            "Epoch 50/50\n",
            "282/282 [==============================] - 7s 26ms/step - loss: 7.8104e-05 - accuracy: 1.0000 - lr: 6.0653e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on test set: {:.2f}\".format(model.evaluate(test_ds)[1]))"
      ],
      "metadata": {
        "id": "DQkS-q2hV0Ju",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8253bc12-8ca1-4f82-e4df-112d8342421b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "141/141 [==============================] - 14s 94ms/step - loss: 0.5571 - accuracy: 0.9247\n",
            "Accuracy on test set: 0.92\n"
          ]
        }
      ]
    }
  ]
}