Apple_Products_Image_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Apple Products Image Classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXF9ypKLfgDw"
      },
      "source": [
        "# Apple Products Image Classification Notebook\n",
        "\n",
        "In this notebook, we're making an image classifier which can classify three major Apple products: Macbook, iPad, and iPhone. Sometimes, when presented in image, they look similar to one another, with their signature black bezels, clear screen, as well as sleek design. Some iPads also come with Magic Keyboard, and it starts to look like a Macbook.\n",
        "\n",
        "Can we use deep learning to classify these product images? What model architecture should we use? How should we train our models? It will all be answered in this notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XaI1tKDMgIqI"
      },
      "source": [
        "## Part 1: Import Data\n",
        "\n",
        "First, we'll import the Python packages and our raw dataset. The dataset is scraped by selecting top images from Google Images Search by using a Chrome [plug-in](https://chrome.google.com/webstore/detail/download-all-images/ifipmflagepipjokmbdecpmjbibjnakm).\n",
        "\n",
        "The dataset can be loaded in various ways. In this notebook, the dataset is stored in a zip file which is stored in my Google Drive.\n",
        "\n",
        "The same dataset (in zip format) is available in https://www.kaggle.com/radvian/apple-products-image-dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_miq2QYRycb3"
      },
      "source": [
        "# import Python libraries\n",
        "\n",
        "import os\n",
        "import zipfile\n",
        "import random\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from shutil import copyfile\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import string\n",
        "import random\n",
        "from random import randint\n",
        "import warnings\n",
        "import PIL\n",
        "from PIL import Image\n",
        "warnings.simplefilter(\"ignore\", UserWarning)\n",
        "\n",
        "# import dataset which is mounted in Google Drive\n",
        "\n",
        "local_zip = '/content/drive/MyDrive/Colab Notebooks/Apple Products/Apple Products.zip'\n",
        "zip_ref   = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEfmjumSzsoL",
        "outputId": "66d33afd-df79-42a0-96c8-076efb842353"
      },
      "source": [
        "print('Number of MacBook images: ',len(os.listdir('/tmp/Apple Products/Macbook/')))\n",
        "print('Number of iPad images: ',len(os.listdir('/tmp/Apple Products/iPad/')))\n",
        "print('Number of iPhone images: ',len(os.listdir('/tmp/Apple Products/iPhone/')))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of MacBook images:  403\n",
            "Number of iPad images:  555\n",
            "Number of iPhone images:  556\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pkmi80WTz8BX"
      },
      "source": [
        "# making directories to store training and testing data\n",
        "\n",
        "try:\n",
        "  os.mkdir('/tmp/Apple')\n",
        "  os.mkdir('/tmp/Apple/training')\n",
        "  os.mkdir('/tmp/Apple/testing')\n",
        "  os.mkdir('/tmp/Apple/training/iPad')\n",
        "  os.mkdir('/tmp/Apple/training/iPhone')\n",
        "  os.mkdir('/tmp/Apple/training/Macbook')\n",
        "  os.mkdir('/tmp/Apple/testing/iPad')\n",
        "  os.mkdir('/tmp/Apple/testing/iPhone')\n",
        "  os.mkdir('/tmp/Apple/testing/Macbook')\n",
        "except OSError:\n",
        "  pass"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNd3ri-K0dg1"
      },
      "source": [
        "# splitting the data into training and testing (validation) set\n",
        "\n",
        "def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):\n",
        "    files = []\n",
        "    for filename in os.listdir(SOURCE):\n",
        "        file = SOURCE + filename\n",
        "        if os.path.getsize(file) > 0:\n",
        "            files.append(filename)\n",
        "        else:\n",
        "            print(filename + \" is zero length, so ignoring.\")\n",
        "\n",
        "    training_length = int(len(files) * SPLIT_SIZE)\n",
        "    testing_length = int(len(files) - training_length)\n",
        "    shuffled_set = random.sample(files, len(files))\n",
        "    training_set = shuffled_set[0:training_length]\n",
        "    testing_set = shuffled_set[training_length:]\n",
        "\n",
        "    for filename in training_set:\n",
        "        this_file = SOURCE + filename\n",
        "        destination = TRAINING + filename\n",
        "        copyfile(this_file, destination)\n",
        "\n",
        "    for filename in testing_set:\n",
        "        this_file = SOURCE + filename\n",
        "        destination = TESTING + filename\n",
        "        copyfile(this_file, destination)\n",
        "\n",
        "\n",
        "ipad_source_path = \"/tmp/Apple Products/iPad/\"\n",
        "train_ipad_path = \"/tmp/Apple/training/iPad/\"\n",
        "test_ipad_path = \"/tmp/Apple/testing/iPad/\"\n",
        "\n",
        "iphone_source_path = \"/tmp/Apple Products/iPhone/\"\n",
        "train_iphone_path = \"/tmp/Apple/training/iPhone/\"\n",
        "test_iphone_path = \"/tmp/Apple/testing/iPhone/\"\n",
        "\n",
        "macbook_source_path = \"/tmp/Apple Products/Macbook/\"\n",
        "train_macbook_path = \"/tmp/Apple/training/Macbook/\"\n",
        "test_macbook_path = \"/tmp/Apple/testing/Macbook/\"\n",
        "\n",
        "split_size = .9\n",
        "split_data(ipad_source_path, train_ipad_path, test_ipad_path, split_size)\n",
        "split_data(iphone_source_path, train_iphone_path, test_iphone_path, split_size)\n",
        "split_data(macbook_source_path, train_macbook_path, test_macbook_path, split_size)\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5ivKVhHmDCi"
      },
      "source": [
        "Now, to get a sense of what our images look like, we can run this function many times to see examples of images from each class. Each run will randomize the image chosen. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkPzOY-SUhQn"
      },
      "source": [
        "def load_random_images():\n",
        "  from IPython.display import Image \n",
        "\n",
        "  macbook_path = '/tmp/Apple/training/Macbook/'\n",
        "  ipad_path = '/tmp/Apple/training/iPad/'\n",
        "  iphone_path = '/tmp/Apple/training/iPhone/'\n",
        "\n",
        "  macbook = random.choice(os.listdir(macbook_path))\n",
        "  ipad = random.choice(os.listdir(ipad_path))\n",
        "  iphone = random.choice(os.listdir(iphone_path))\n",
        "\n",
        "  print('Macbook:')\n",
        "  macbook_img = Image(filename = macbook_path+macbook)\n",
        "  display(macbook_img)\n",
        "\n",
        "  print('iPad:')\n",
        "  ipad_img = Image(filename = ipad_path+ipad)\n",
        "  display(ipad_img)\n",
        "\n",
        "  print('iPhone:')\n",
        "  iphone_img = Image(filename = iphone_path+iphone)\n",
        "  display(iphone_img)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "CRsEJX41mTS_",
        "outputId": "ec015d8a-bd08-4aae-f5cf-be2b2c13458f"
      },
      "source": [
        "# Run this cell as much as you want to see random images from each class!\n",
        "load_random_images()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Macbook:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "iPad:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "iPhone:\n"
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      "metadata": {
        "id": "MJIfiwqoilXh"
      },
      "source": [
        "## Part 2: Preparation for Training our Models\n",
        "\n",
        "After splitting the images into training and validation set, we're going to use Keras ```ImageDataGenerator``` to preprocess our images. \n",
        "\n",
        "There are three usages of ```ImageDataGenerator```. First, this is to create 'data generators' which can label and load training data into our model.\n",
        "\n",
        "By using ```ImageDataGenerator```, the labeling part of our training and validation images are done automatically. It will give the same label to images within the same folder. Since we have 3 folders in our training and testing set ('Macbook', 'iPad', and 'iPhone'), the data will be labeled categorically as '0', '1', and '2' respectively.\n",
        "\n",
        "Second, this is to augment our images. In short, this is done to make our model more robust and still can identify images even if the image have noise or is flipped upside down or is not 'straightened'. \n",
        "\n",
        "If we only feed 'ideal' images (for example, official renders, official photos, photos which are taken by photographers using high-end cameras), our model won't be able to recognize 'amateur-ish' images (for example, images taken hurriedly by phone camera on a badly-lit room).\n",
        "\n",
        "Third, we can rescale the images (which comes in various sizes) so it can be fed into the model. Our neural network cannot receive inputs which have different shapes, so we need to make sure all images are rescaled and resized into a particular shape.\n",
        "\n",
        "You can change the augmentation parameters as well as the resizing metrics to see if your combination can improve the model accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k3ZUw7T41eQX",
        "outputId": "07361e8f-08b1-4413-89bf-34a6516dd095"
      },
      "source": [
        "TRAINING_DIR = '/tmp/Apple/training/'\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   rotation_range = 30,\n",
        "                                   width_shift_range = 0.1,\n",
        "                                   height_shift_range = 0.2,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True,\n",
        "                                   fill_mode = 'nearest') \n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(TRAINING_DIR, \n",
        "                                                    batch_size = 128,\n",
        "                                                    class_mode = 'categorical',\n",
        "                                                    target_size = (150,150))\n",
        "\n",
        "VALIDATION_DIR = \"/tmp/Apple/testing\"\n",
        "validation_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,\n",
        "                                                              batch_size=128,\n",
        "                                                              class_mode='categorical',\n",
        "                                                              target_size=(150, 150))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1360 images belonging to 3 classes.\n",
            "Found 153 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acMyRWtlnT9-"
      },
      "source": [
        "Next, we'll prepare functions to plot our training results. There are two metrics which will be shown: accuracy and loss. A good training result will have:\n",
        "- an increase of accuracy per epoch passed for both training and validation set\n",
        "- a decrease of loss per epoch passed for both training and validation set\n",
        "- does not overfit: training accuracy does not surpass validation accuracy by a lot\n",
        "- does not underfit: training accuracy does not trail behind validation accuracy by a lot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0xWs1gwQXx2"
      },
      "source": [
        "import matplotlib.image  as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn-whitegrid')\n",
        "\n",
        "def plot_acc(history):\n",
        "  acc = history.history['accuracy']\n",
        "  val_acc = history.history['val_accuracy']\n",
        "  epochs = range(len(acc))\n",
        "  plt.figure(figsize=(5,5))\n",
        "  acc_plot, = plt.plot(epochs, acc, 'r')\n",
        "  val_acc_plot, = plt.plot(epochs, val_acc, 'b')\n",
        "  plt.title('Training and Validation Accuracy')\n",
        "  plt.legend([acc_plot, val_acc_plot], ['Training Accuracy', 'Validation Accuracy'])\n",
        "\n",
        "\n",
        "def plot_loss(history):\n",
        "  loss = history.history['loss']\n",
        "  val_loss = history.history['val_loss']\n",
        "  epochs = range(len(loss))\n",
        "  plt.figure(figsize=(5,5))\n",
        "  loss_plot, = plt.plot(epochs, loss, 'r')\n",
        "  val_loss_plot, = plt.plot(epochs, val_loss, 'b')\n",
        "  plt.title('Training and Validation Loss')\n",
        "  plt.legend([loss_plot, val_loss_plot], ['Training Loss', 'Validation Loss'])\n",
        "\n",
        "def plot_history(history):\n",
        "  plot_acc(history)\n",
        "  plot_loss(history)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cU4KaI-poWvq"
      },
      "source": [
        "## Part 3: Training a Basic Model with Convolutional Neural Network\n",
        "\n",
        "We'll first train a basic CNN model in an attempt to classify images into its respectie classes. Convolutional Neural Networks are a class of deep neural networks which are fully connected (each neuron in a layer is connected to all neurons in the next layers).\n",
        "\n",
        "CNN is mostly used in image classification task. Because of its hierarchial structure (multiple layers stuck on top of each other) and interconnectedness, CNN can use each pixel in an image as well as its adjacent pixels and process them - extracting their features using convolution, then applying a Dense layer at the end to make prediction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xVqaf7eW2A7S",
        "outputId": "0896258e-c098-416e-d04e-22a97f9156da"
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "model_simple = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv2D(16, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "model_simple.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "history_simple = model_simple.fit(train_generator,\n",
        "                              epochs=20,\n",
        "                              verbose=1,\n",
        "                              validation_data=validation_generator)\n",
        "\n",
        "plot_history(history_simple)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 1.1457 - accuracy: 0.3801 - val_loss: 1.0927 - val_accuracy: 0.4052\n",
            "Epoch 2/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 1.0825 - accuracy: 0.3890 - val_loss: 1.0900 - val_accuracy: 0.3660\n",
            "Epoch 3/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 1.0793 - accuracy: 0.4022 - val_loss: 1.0858 - val_accuracy: 0.4248\n",
            "Epoch 4/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 1.0703 - accuracy: 0.3956 - val_loss: 1.0799 - val_accuracy: 0.3660\n",
            "Epoch 5/20\n",
            "11/11 [==============================] - 17s 2s/step - loss: 1.0634 - accuracy: 0.3787 - val_loss: 1.0669 - val_accuracy: 0.4052\n",
            "Epoch 6/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 1.0556 - accuracy: 0.3978 - val_loss: 1.0464 - val_accuracy: 0.4379\n",
            "Epoch 7/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 1.0409 - accuracy: 0.4331 - val_loss: 1.0380 - val_accuracy: 0.4967\n",
            "Epoch 8/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 1.0189 - accuracy: 0.4537 - val_loss: 1.0060 - val_accuracy: 0.5098\n",
            "Epoch 9/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 1.0031 - accuracy: 0.4890 - val_loss: 0.9976 - val_accuracy: 0.4837\n",
            "Epoch 10/20\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.9644 - accuracy: 0.5206 - val_loss: 0.9205 - val_accuracy: 0.5490\n",
            "Epoch 11/20\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.9480 - accuracy: 0.5559 - val_loss: 0.8860 - val_accuracy: 0.5686\n",
            "Epoch 12/20\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.9290 - accuracy: 0.5434 - val_loss: 0.8372 - val_accuracy: 0.6078\n",
            "Epoch 13/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 0.9061 - accuracy: 0.5713 - val_loss: 0.9001 - val_accuracy: 0.5229\n",
            "Epoch 14/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 0.9060 - accuracy: 0.5537 - val_loss: 0.8110 - val_accuracy: 0.6275\n",
            "Epoch 15/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 0.8843 - accuracy: 0.5684 - val_loss: 0.8260 - val_accuracy: 0.6013\n",
            "Epoch 16/20\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.8658 - accuracy: 0.5978 - val_loss: 0.7725 - val_accuracy: 0.6536\n",
            "Epoch 17/20\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.8630 - accuracy: 0.5831 - val_loss: 0.8309 - val_accuracy: 0.6144\n",
            "Epoch 18/20\n",
            "11/11 [==============================] - 18s 2s/step - loss: 0.8569 - accuracy: 0.5949 - val_loss: 0.7839 - val_accuracy: 0.6405\n",
            "Epoch 19/20\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.8142 - accuracy: 0.6331 - val_loss: 0.7384 - val_accuracy: 0.6601\n",
            "Epoch 20/20\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.8061 - accuracy: 0.6243 - val_loss: 0.7694 - val_accuracy: 0.6536\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FSW6Ua-UqK51"
      },
      "source": [
        "The model above should be adequate if our image classification task is to classify mnist or Fashion-Mnist data. However, it seems that our model's accuracy still can be improved further. We only get about 60% accuracy on our 20th epoch."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7OonK--3qcll"
      },
      "source": [
        "## Part 4: Transfer Learning using InceptionV3\n",
        "\n",
        "We can make a more complicated CNN model by taking an existing model, and tweaking it just a little bit more to fit our problem. This process is known as transfer learning.\n",
        "\n",
        "The main idea is this: instead of teaching a model how to classify images from the beginning, why not take a model which has been proven to be able to classify images, and then tune it a little bit to match our specific case, which is classifying apple products.\n",
        "\n",
        "Keras has a few pre-trained models. In this notebook, we'll use the InceptionV3 model. The InceptionV3 model has once been trained on the ImageNet database, a large set of data containing multiple objects. You can read more about ImageNet [here](https://en.wikipedia.org/wiki/ImageNet). \n",
        "\n",
        "Now, we're loading the weights that the model has after being fitted to the ImageNet database."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ACjkUGd-7w03",
        "outputId": "48a16671-6c01-43e1-8787-b439531f3747"
      },
      "source": [
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "tf.keras.backend.clear_session()\n",
        "\n",
        "pre_trained_model = InceptionV3(input_shape = (150, 150, 3), \n",
        "                                include_top = False, \n",
        "                                weights = 'imagenet')\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQOhRQ1YPdAV",
        "outputId": "430c18ed-002a-4d45-8a02-94c4345ede32"
      },
      "source": [
        "# Run this cell if you want to see how deep is our InceptionV3 model.\n",
        "\n",
        "pre_trained_model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"inception_v3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 150, 150, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 74, 74, 32)   864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 74, 74, 32)   96          conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, 74, 74, 32)   0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 72, 72, 32)   9216        activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 72, 72, 32)   96          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 72, 72, 32)   0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 72, 72, 64)   18432       activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 72, 72, 64)   192         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 72, 72, 64)   0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 35, 35, 64)   0           activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 35, 35, 80)   5120        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 35, 35, 80)   240         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 35, 35, 80)   0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 33, 33, 192)  138240      activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 33, 33, 192)  576         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 33, 33, 192)  0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 192)  0           activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 16, 16, 64)   192         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 16, 16, 64)   0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 16, 16, 48)   9216        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 16, 16, 96)   55296       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 16, 16, 48)   144         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 16, 16, 96)   288         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 16, 16, 48)   0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 16, 16, 96)   0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, 16, 16, 192)  0           max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 16, 16, 64)   76800       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 16, 16, 96)   82944       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 16, 16, 32)   6144        average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 16, 16, 64)   192         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 16, 16, 64)   192         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 16, 16, 96)   288         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 16, 16, 32)   96          conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 16, 16, 64)   0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 16, 16, 64)   0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 16, 16, 96)   0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 16, 16, 32)   0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, 16, 16, 256)  0           activation_5[0][0]               \n",
            "                                                                 activation_7[0][0]               \n",
            "                                                                 activation_10[0][0]              \n",
            "                                                                 activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 16, 16, 64)   192         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 16, 16, 64)   0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 16, 16, 48)   12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 16, 16, 96)   55296       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 16, 16, 48)   144         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 16, 16, 96)   288         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 16, 16, 48)   0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 16, 16, 96)   0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 16, 16, 256)  0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 16, 16, 64)   76800       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 16, 16, 96)   82944       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 16, 16, 64)   16384       average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 16, 16, 64)   192         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 16, 16, 64)   192         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 16, 16, 96)   288         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 16, 16, 64)   192         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 16, 16, 64)   0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 16, 16, 64)   0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 16, 16, 96)   0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 16, 16, 64)   0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, 16, 16, 288)  0           activation_12[0][0]              \n",
            "                                                                 activation_14[0][0]              \n",
            "                                                                 activation_17[0][0]              \n",
            "                                                                 activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 16, 16, 64)   192         conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 16, 16, 64)   0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 16, 16, 48)   13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 16, 16, 96)   55296       activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 16, 16, 48)   144         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 16, 16, 96)   288         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 16, 16, 48)   0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 16, 16, 96)   0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_2 (AveragePoo (None, 16, 16, 288)  0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 16, 16, 64)   76800       activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 16, 16, 96)   82944       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 16, 16, 64)   18432       average_pooling2d_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 16, 16, 64)   192         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 16, 16, 64)   192         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 16, 16, 96)   288         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 16, 16, 64)   192         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 16, 16, 64)   0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 16, 16, 64)   0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 16, 16, 96)   0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 16, 16, 64)   0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, 16, 16, 288)  0           activation_19[0][0]              \n",
            "                                                                 activation_21[0][0]              \n",
            "                                                                 activation_24[0][0]              \n",
            "                                                                 activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 16, 16, 64)   18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 16, 16, 64)   192         conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 16, 16, 64)   0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 16, 16, 96)   55296       activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 16, 16, 96)   288         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 16, 16, 96)   0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 7, 7, 384)    995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 7, 7, 96)     82944       activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 7, 7, 384)    1152        conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 7, 7, 96)     288         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 7, 7, 384)    0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 7, 7, 96)     0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 288)    0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, 7, 7, 768)    0           activation_26[0][0]              \n",
            "                                                                 activation_29[0][0]              \n",
            "                                                                 max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 7, 7, 128)    384         conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 7, 7, 128)    0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 7, 7, 128)    114688      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 7, 7, 128)    384         conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 7, 7, 128)    0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 7, 7, 128)    114688      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 7, 7, 128)    384         conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 7, 7, 128)    384         conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 7, 7, 128)    0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 7, 7, 128)    0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 7, 7, 128)    114688      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 7, 7, 128)    114688      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 7, 7, 128)    384         conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 7, 7, 128)    384         conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 7, 7, 128)    0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 7, 7, 128)    0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_3 (AveragePoo (None, 7, 7, 768)    0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 7, 7, 192)    147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 7, 7, 192)    172032      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 7, 7, 192)    172032      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 7, 7, 192)    576         conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 7, 7, 192)    576         conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 7, 7, 192)    576         conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 7, 7, 192)    576         conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 7, 7, 192)    0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 7, 7, 192)    0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 7, 7, 192)    0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 7, 7, 192)    0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, 7, 7, 768)    0           activation_30[0][0]              \n",
            "                                                                 activation_33[0][0]              \n",
            "                                                                 activation_38[0][0]              \n",
            "                                                                 activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 7, 7, 160)    480         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 7, 7, 160)    0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 7, 7, 160)    179200      activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 7, 7, 160)    480         conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 7, 7, 160)    0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 7, 7, 160)    179200      activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 7, 7, 160)    480         conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 7, 7, 160)    480         conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 7, 7, 160)    0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 7, 7, 160)    0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 7, 7, 160)    179200      activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 7, 7, 160)    179200      activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 7, 7, 160)    480         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 7, 7, 160)    480         conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 7, 7, 160)    0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 7, 7, 160)    0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_4 (AveragePoo (None, 7, 7, 768)    0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 7, 7, 192)    147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 7, 7, 192)    215040      activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 7, 7, 192)    215040      activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 7, 7, 192)    576         conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 7, 7, 192)    576         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 7, 7, 192)    576         conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 7, 7, 192)    576         conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 7, 7, 192)    0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 7, 7, 192)    0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 7, 7, 192)    0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 7, 7, 192)    0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, 7, 7, 768)    0           activation_40[0][0]              \n",
            "                                                                 activation_43[0][0]              \n",
            "                                                                 activation_48[0][0]              \n",
            "                                                                 activation_49[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, 7, 7, 160)    480         conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, 7, 7, 160)    0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 7, 7, 160)    179200      activation_54[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, 7, 7, 160)    480         conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_55 (Activation)      (None, 7, 7, 160)    0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 7, 7, 160)    179200      activation_55[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 7, 7, 160)    480         conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, 7, 7, 160)    480         conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, 7, 7, 160)    0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_56 (Activation)      (None, 7, 7, 160)    0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 7, 7, 160)    179200      activation_51[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 7, 7, 160)    179200      activation_56[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 7, 7, 160)    480         conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, 7, 7, 160)    480         conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, 7, 7, 160)    0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_57 (Activation)      (None, 7, 7, 160)    0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_5 (AveragePoo (None, 7, 7, 768)    0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 7, 7, 192)    147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 7, 7, 192)    215040      activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, 7, 7, 192)    215040      activation_57[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 7, 7, 192)    576         conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 7, 7, 192)    576         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, 7, 7, 192)    576         conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, 7, 7, 192)    576         conv2d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, 7, 7, 192)    0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, 7, 7, 192)    0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_58 (Activation)      (None, 7, 7, 192)    0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_59 (Activation)      (None, 7, 7, 192)    0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, 7, 7, 768)    0           activation_50[0][0]              \n",
            "                                                                 activation_53[0][0]              \n",
            "                                                                 activation_58[0][0]              \n",
            "                                                                 activation_59[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_64 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_64 (BatchNo (None, 7, 7, 192)    576         conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_64 (Activation)      (None, 7, 7, 192)    0           batch_normalization_64[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, 7, 7, 192)    258048      activation_64[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_65 (BatchNo (None, 7, 7, 192)    576         conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_65 (Activation)      (None, 7, 7, 192)    0           batch_normalization_65[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, 7, 7, 192)    258048      activation_65[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, 7, 7, 192)    576         conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_66 (BatchNo (None, 7, 7, 192)    576         conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_61 (Activation)      (None, 7, 7, 192)    0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_66 (Activation)      (None, 7, 7, 192)    0           batch_normalization_66[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, 7, 7, 192)    258048      activation_61[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, 7, 7, 192)    258048      activation_66[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, 7, 7, 192)    576         conv2d_62[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_67 (BatchNo (None, 7, 7, 192)    576         conv2d_67[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_62 (Activation)      (None, 7, 7, 192)    0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_67 (Activation)      (None, 7, 7, 192)    0           batch_normalization_67[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_6 (AveragePoo (None, 7, 7, 768)    0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, 7, 7, 192)    258048      activation_62[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, 7, 7, 192)    258048      activation_67[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_6[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, 7, 7, 192)    576         conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_63 (BatchNo (None, 7, 7, 192)    576         conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_68 (BatchNo (None, 7, 7, 192)    576         conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_69 (BatchNo (None, 7, 7, 192)    576         conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_60 (Activation)      (None, 7, 7, 192)    0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_63 (Activation)      (None, 7, 7, 192)    0           batch_normalization_63[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_68 (Activation)      (None, 7, 7, 192)    0           batch_normalization_68[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_69 (Activation)      (None, 7, 7, 192)    0           batch_normalization_69[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, 7, 7, 768)    0           activation_60[0][0]              \n",
            "                                                                 activation_63[0][0]              \n",
            "                                                                 activation_68[0][0]              \n",
            "                                                                 activation_69[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_72 (Conv2D)              (None, 7, 7, 192)    147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_72 (BatchNo (None, 7, 7, 192)    576         conv2d_72[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_72 (Activation)      (None, 7, 7, 192)    0           batch_normalization_72[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_73 (Conv2D)              (None, 7, 7, 192)    258048      activation_72[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_73 (BatchNo (None, 7, 7, 192)    576         conv2d_73[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_73 (Activation)      (None, 7, 7, 192)    0           batch_normalization_73[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, 7, 7, 192)    147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_74 (Conv2D)              (None, 7, 7, 192)    258048      activation_73[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_70 (BatchNo (None, 7, 7, 192)    576         conv2d_70[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_74 (BatchNo (None, 7, 7, 192)    576         conv2d_74[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_70 (Activation)      (None, 7, 7, 192)    0           batch_normalization_70[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_74 (Activation)      (None, 7, 7, 192)    0           batch_normalization_74[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_71 (Conv2D)              (None, 3, 3, 320)    552960      activation_70[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_75 (Conv2D)              (None, 3, 3, 192)    331776      activation_74[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_71 (BatchNo (None, 3, 3, 320)    960         conv2d_71[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_75 (BatchNo (None, 3, 3, 192)    576         conv2d_75[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_71 (Activation)      (None, 3, 3, 320)    0           batch_normalization_71[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_75 (Activation)      (None, 3, 3, 192)    0           batch_normalization_75[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 3, 3, 768)    0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed8 (Concatenate)            (None, 3, 3, 1280)   0           activation_71[0][0]              \n",
            "                                                                 activation_75[0][0]              \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_80 (Conv2D)              (None, 3, 3, 448)    573440      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_80 (BatchNo (None, 3, 3, 448)    1344        conv2d_80[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_80 (Activation)      (None, 3, 3, 448)    0           batch_normalization_80[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_77 (Conv2D)              (None, 3, 3, 384)    491520      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_81 (Conv2D)              (None, 3, 3, 384)    1548288     activation_80[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_77 (BatchNo (None, 3, 3, 384)    1152        conv2d_77[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_81 (BatchNo (None, 3, 3, 384)    1152        conv2d_81[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_77 (Activation)      (None, 3, 3, 384)    0           batch_normalization_77[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_81 (Activation)      (None, 3, 3, 384)    0           batch_normalization_81[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_78 (Conv2D)              (None, 3, 3, 384)    442368      activation_77[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_79 (Conv2D)              (None, 3, 3, 384)    442368      activation_77[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_82 (Conv2D)              (None, 3, 3, 384)    442368      activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_83 (Conv2D)              (None, 3, 3, 384)    442368      activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_7 (AveragePoo (None, 3, 3, 1280)   0           mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_76 (Conv2D)              (None, 3, 3, 320)    409600      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_78 (BatchNo (None, 3, 3, 384)    1152        conv2d_78[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_79 (BatchNo (None, 3, 3, 384)    1152        conv2d_79[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_82 (BatchNo (None, 3, 3, 384)    1152        conv2d_82[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_83 (BatchNo (None, 3, 3, 384)    1152        conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_84 (Conv2D)              (None, 3, 3, 192)    245760      average_pooling2d_7[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_76 (BatchNo (None, 3, 3, 320)    960         conv2d_76[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_78 (Activation)      (None, 3, 3, 384)    0           batch_normalization_78[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_79 (Activation)      (None, 3, 3, 384)    0           batch_normalization_79[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_82 (Activation)      (None, 3, 3, 384)    0           batch_normalization_82[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_83 (Activation)      (None, 3, 3, 384)    0           batch_normalization_83[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_84 (BatchNo (None, 3, 3, 192)    576         conv2d_84[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_76 (Activation)      (None, 3, 3, 320)    0           batch_normalization_76[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_0 (Concatenate)          (None, 3, 3, 768)    0           activation_78[0][0]              \n",
            "                                                                 activation_79[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 3, 3, 768)    0           activation_82[0][0]              \n",
            "                                                                 activation_83[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_84 (Activation)      (None, 3, 3, 192)    0           batch_normalization_84[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9 (Concatenate)            (None, 3, 3, 2048)   0           activation_76[0][0]              \n",
            "                                                                 mixed9_0[0][0]                   \n",
            "                                                                 concatenate[0][0]                \n",
            "                                                                 activation_84[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_89 (Conv2D)              (None, 3, 3, 448)    917504      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_89 (BatchNo (None, 3, 3, 448)    1344        conv2d_89[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_89 (Activation)      (None, 3, 3, 448)    0           batch_normalization_89[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_86 (Conv2D)              (None, 3, 3, 384)    786432      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_90 (Conv2D)              (None, 3, 3, 384)    1548288     activation_89[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_86 (BatchNo (None, 3, 3, 384)    1152        conv2d_86[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_90 (BatchNo (None, 3, 3, 384)    1152        conv2d_90[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_86 (Activation)      (None, 3, 3, 384)    0           batch_normalization_86[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_90 (Activation)      (None, 3, 3, 384)    0           batch_normalization_90[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_87 (Conv2D)              (None, 3, 3, 384)    442368      activation_86[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_88 (Conv2D)              (None, 3, 3, 384)    442368      activation_86[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_91 (Conv2D)              (None, 3, 3, 384)    442368      activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_92 (Conv2D)              (None, 3, 3, 384)    442368      activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_8 (AveragePoo (None, 3, 3, 2048)   0           mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_85 (Conv2D)              (None, 3, 3, 320)    655360      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_87 (BatchNo (None, 3, 3, 384)    1152        conv2d_87[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_88 (BatchNo (None, 3, 3, 384)    1152        conv2d_88[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_91 (BatchNo (None, 3, 3, 384)    1152        conv2d_91[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_92 (BatchNo (None, 3, 3, 384)    1152        conv2d_92[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_93 (Conv2D)              (None, 3, 3, 192)    393216      average_pooling2d_8[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_85 (BatchNo (None, 3, 3, 320)    960         conv2d_85[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_87 (Activation)      (None, 3, 3, 384)    0           batch_normalization_87[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_88 (Activation)      (None, 3, 3, 384)    0           batch_normalization_88[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_91 (Activation)      (None, 3, 3, 384)    0           batch_normalization_91[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_92 (Activation)      (None, 3, 3, 384)    0           batch_normalization_92[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_93 (BatchNo (None, 3, 3, 192)    576         conv2d_93[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_85 (Activation)      (None, 3, 3, 320)    0           batch_normalization_85[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_1 (Concatenate)          (None, 3, 3, 768)    0           activation_87[0][0]              \n",
            "                                                                 activation_88[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 3, 3, 768)    0           activation_91[0][0]              \n",
            "                                                                 activation_92[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_93 (Activation)      (None, 3, 3, 192)    0           batch_normalization_93[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed10 (Concatenate)           (None, 3, 3, 2048)   0           activation_85[0][0]              \n",
            "                                                                 mixed9_1[0][0]                   \n",
            "                                                                 concatenate_1[0][0]              \n",
            "                                                                 activation_93[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 21,802,784\n",
            "Trainable params: 21,768,352\n",
            "Non-trainable params: 34,432\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KOn-nln3s1WV"
      },
      "source": [
        "The Inception_V3 comes with a lot of stacked layers. Since we have loaded them with weights, then how are we going to tweak it to our case here?\n",
        "\n",
        "The answer is, we cut off the model at a certain layer, then add a few untrained layers of our own. These untrained layers that we define is true to their name - they are untrained.\n",
        "\n",
        "When we fit our training data into this new model, the new untrained layers at the end of the model are the ones getting 'trained'. However, because the images first pass through numerous stacked layers at the top, we get the benefit of the complex convolutions happening within the model prior to training our layers.\n",
        "\n",
        "This is the essence of transfer learning. We use the trained layers to 'extract features' from the images in a way that we can't do with our simple CNNs. \n",
        "\n",
        "It's as if we hire an expert to do the initial workings of our job - and then when the expert finishes doing the 'difficult' tasks, we take over the project and finishes it with our own work. \n",
        "\n",
        "Welp, sounds a bit lazy and opportunistic if we put it that way. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWq6xBNBszbB",
        "outputId": "626d9eed-7969-4f2b-f715-59f40aabbd91"
      },
      "source": [
        "for layer in pre_trained_model.layers:\n",
        "  layer.trainable = False\n",
        "  \n",
        "# pre_trained_model.summary()\n",
        "\n",
        "# cut off at the layer named 'mixed7'\n",
        "last_layer = pre_trained_model.get_layer('mixed7')\n",
        "\n",
        "# know its output shape\n",
        "print('last layer output shape: ', last_layer.output_shape)\n",
        "last_output = last_layer.output\n",
        "\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "\n",
        "# Feed the last cut-off layer to our own layers\n",
        "# Flatten the output layer to 1 dimension\n",
        "x = tf.keras.layers.Flatten()(last_output)\n",
        "# Add a dropout rate of 0.4\n",
        "x = tf.keras.layers.Dropout(0.4)(x) \n",
        "# Add a fully connected layer with 512 hidden units and ReLU activation\n",
        "x = tf.keras.layers.Dense(256, activation='relu')(x)\n",
        "# Add a dropout rate of 0.3\n",
        "x = tf.keras.layers.Dropout(0.3)(x)\n",
        "# Add a fully connected layer with 512 hidden units and ReLU activation\n",
        "x = tf.keras.layers.Dense(256, activation='relu')(x)\n",
        "# Add a final dropout layer\n",
        "x = tf.keras.layers.Dropout(0.2)(x)                  \n",
        "# Add a final softmax layer for classification\n",
        "x = tf.keras.layers.Dense  (3, activation='softmax')(x)           \n",
        "\n",
        "model_inception = tf.keras.Model(pre_trained_model.input, x) "
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "last layer output shape:  (None, 7, 7, 768)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhwbNlOaRl-3"
      },
      "source": [
        "One thing we can add to our training process is 'callback'. We can use specific callbacks to modify our training process to our liking.\n",
        "\n",
        "There are two callbacks that we are going to use:\n",
        "- Reduce Learning Rate on Plateau. If the training loss does not get any lower after a few epochs (which means it hit a plateau), we will reduce the learning rate.\n",
        "\n",
        "- Early Stopping. If we have reduced our learning rate to our minimum amount, and the model still isn't improving during the training process, we can end the training process early. Usually I use this callback on trainings that reach thousands of epochs (for image classification) or NLP / Time Series problems. However, for demonstration purposes, we'll try to use it here."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqilF4iMBhVI"
      },
      "source": [
        "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,\n",
        "                              patience=5, min_lr=1.5e-5)\n",
        "\n",
        "early_stop = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0,\n",
        "    patience=12,\n",
        "    verbose=0,\n",
        "    mode=\"auto\",\n",
        "    baseline=None,\n",
        "    restore_best_weights=True,\n",
        ")\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8fJ1e5cZ7-DK",
        "outputId": "262cea41-520f-4d2d-9fcc-a415209e5d87"
      },
      "source": [
        "model_inception.compile(optimizer = 'adam', \n",
        "              loss = 'categorical_crossentropy', \n",
        "              metrics = ['accuracy'])\n",
        "history_inception = model_inception.fit(train_generator,\n",
        "                              epochs=100,\n",
        "                              callbacks = [reduce_lr, early_stop],\n",
        "                              verbose=1,\n",
        "                              validation_data=validation_generator)\n",
        "plot_history(history_inception)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "11/11 [==============================] - 20s 2s/step - loss: 1.4373 - accuracy: 0.4706 - val_loss: 0.7935 - val_accuracy: 0.6536\n",
            "Epoch 2/100\n",
            "11/11 [==============================] - 18s 2s/step - loss: 0.7886 - accuracy: 0.6596 - val_loss: 0.5985 - val_accuracy: 0.6993\n",
            "Epoch 3/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.6037 - accuracy: 0.7441 - val_loss: 0.4122 - val_accuracy: 0.8627\n",
            "Epoch 4/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.4958 - accuracy: 0.7971 - val_loss: 0.3690 - val_accuracy: 0.8693\n",
            "Epoch 5/100\n",
            "11/11 [==============================] - 18s 2s/step - loss: 0.4295 - accuracy: 0.8243 - val_loss: 0.3580 - val_accuracy: 0.8627\n",
            "Epoch 6/100\n",
            "11/11 [==============================] - 18s 2s/step - loss: 0.3900 - accuracy: 0.8478 - val_loss: 0.3305 - val_accuracy: 0.8758\n",
            "Epoch 7/100\n",
            "11/11 [==============================] - 18s 2s/step - loss: 0.3220 - accuracy: 0.8757 - val_loss: 0.3062 - val_accuracy: 0.8824\n",
            "Epoch 8/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.3051 - accuracy: 0.8831 - val_loss: 0.3271 - val_accuracy: 0.8889\n",
            "Epoch 9/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.3262 - accuracy: 0.8750 - val_loss: 0.3259 - val_accuracy: 0.8824\n",
            "Epoch 10/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.3093 - accuracy: 0.8787 - val_loss: 0.3525 - val_accuracy: 0.8562\n",
            "Epoch 11/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.3181 - accuracy: 0.8765 - val_loss: 0.3308 - val_accuracy: 0.8889\n",
            "Epoch 12/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.2880 - accuracy: 0.8926 - val_loss: 0.3190 - val_accuracy: 0.8824\n",
            "Epoch 13/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.2455 - accuracy: 0.9066 - val_loss: 0.3307 - val_accuracy: 0.8824\n",
            "Epoch 14/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.2322 - accuracy: 0.9118 - val_loss: 0.3390 - val_accuracy: 0.8824\n",
            "Epoch 15/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.2417 - accuracy: 0.8978 - val_loss: 0.3228 - val_accuracy: 0.9020\n",
            "Epoch 16/100\n",
            "11/11 [==============================] - 17s 2s/step - loss: 0.2093 - accuracy: 0.9176 - val_loss: 0.3242 - val_accuracy: 0.8824\n",
            "Epoch 17/100\n",
            "11/11 [==============================] - 16s 1s/step - loss: 0.2301 - accuracy: 0.9088 - val_loss: 0.3158 - val_accuracy: 0.8824\n",
            "Epoch 18/100\n",
            "11/11 [==============================] - 16s 1s/step - loss: 0.2089 - accuracy: 0.9162 - val_loss: 0.3146 - val_accuracy: 0.8824\n",
            "Epoch 19/100\n",
            "11/11 [==============================] - 16s 1s/step - loss: 0.1983 - accuracy: 0.9279 - val_loss: 0.3168 - val_accuracy: 0.8889\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ofy_XgXs8BB"
      },
      "source": [
        "The training result speaks for itself. Using transfer learning as well as callbacks, we achieved a 92% accuracy for our training set and 89% for our validation set. This result does not show that our model overfits nor underfit. \n",
        "\n",
        "Also, the callbacks stopped our training below 30 epochs. If we don't use callbacks, we will be training until 100 epochs, and wasting more than 70 epochs. The callbacks also automated adjustment of learning rate for us. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvD9q9d3tLg5"
      },
      "source": [
        "## Part 4: Evaluation\n",
        "\n",
        "In this last part, we're going to get ourselves a full classification report score (not only an overall accuracy score). We'll see if the high accuracy is obtained in all 3 classes. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NR4k_Ykvdj-e",
        "outputId": "cc62ab26-0cba-47c2-cb81-07cad0f423fe"
      },
      "source": [
        "num_of_test_samples = 159\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "model = model_inception\n",
        "validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,\n",
        "                                                              batch_size=159,\n",
        "                                                              class_mode='categorical',\n",
        "                                                              target_size=(150, 150),\n",
        "                                                              shuffle = False)\n",
        "\n",
        "filenames = validation_generator.filenames\n",
        "nb_samples = len(filenames)\n",
        "Y_pred = model.predict_generator(validation_generator, steps = nb_samples)\n",
        "y_pred = np.argmax(Y_pred, axis=1)\n",
        "print('Confusion Matrix')\n",
        "print(confusion_matrix(validation_generator.classes, y_pred))\n",
        "print('Classification Report')\n",
        "target_names = ['Macbook', 'iPad', 'iPhone']\n",
        "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 153 images belonging to 3 classes.\n",
            "WARNING:tensorflow:From <ipython-input-15-a6b9030417b5>:13: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.predict, which supports generators.\n",
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 153 batches). You may need to use the repeat() function when building your dataset.\n",
            "Confusion Matrix\n",
            "[[38  2  1]\n",
            " [ 1 47  8]\n",
            " [ 2  4 50]]\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Macbook       0.93      0.93      0.93        41\n",
            "        iPad       0.89      0.84      0.86        56\n",
            "      iPhone       0.85      0.89      0.87        56\n",
            "\n",
            "    accuracy                           0.88       153\n",
            "   macro avg       0.89      0.89      0.89       153\n",
            "weighted avg       0.88      0.88      0.88       153\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQnzJTvWU6By"
      },
      "source": [
        "Our precision, recall, and f-1 score are high for all three classes. This is good news. We can thus conclude that our model performed really well to classify these three apple products.\n",
        "\n",
        "The last part is to detect which images that our model predicts wrongly. This is just to satisfy our curiosity, as well as analyzing potential weakness of our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rh664gEBty8d"
      },
      "source": [
        "def find_wrong_predict(prediction, actual):\n",
        "  product_dict = {\n",
        "    0: 'Macbook',\n",
        "    1: 'iPad',\n",
        "    2: 'iPhone'\n",
        "  }\n",
        "  wrong_index = list()\n",
        "  wrong_prediction = list()\n",
        "  actual_label = list()\n",
        "  for i in range(len(prediction)):\n",
        "    if prediction[i] == actual[i]:\n",
        "      pass\n",
        "    else:\n",
        "      wrong_index.append(i)\n",
        "      wrong_prediction.append(product_dict[prediction[i]])\n",
        "      actual_label.append(product_dict[actual[i]])\n",
        "\n",
        "    df = pd.DataFrame(list(zip(wrong_index, wrong_prediction, actual_label)),\n",
        "                      columns = ['Index', 'Predicted Label', 'Actual Label'])\n",
        "  \n",
        "  return df"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "l5TWBmhRBZ4l",
        "outputId": "44284388-6aae-40ca-92b7-70a756640569"
      },
      "source": [
        "wrong_classify = find_wrong_predict(y_pred, validation_generator.classes)\n",
        "wrong_classify"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Index</th>\n",
              "      <th>Predicted Label</th>\n",
              "      <th>Actual Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21</td>\n",
              "      <td>iPad</td>\n",
              "      <td>Macbook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23</td>\n",
              "      <td>iPad</td>\n",
              "      <td>Macbook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25</td>\n",
              "      <td>iPhone</td>\n",
              "      <td>Macbook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>54</td>\n",
              "      <td>iPhone</td>\n",
              "      <td>iPad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>iPhone</td>\n",
              "      <td>iPad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>64</td>\n",
              "      <td>iPhone</td>\n",
              "      <td>iPad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>67</td>\n",
              "      <td>iPhone</td>\n",
              "      <td>iPad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>74</td>\n",
              "      <td>iPhone</td>\n",
              "      <td>iPad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>82</td>\n",
              "      <td>iPhone</td>\n",
              "      <td>iPad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>86</td>\n",
              "      <td>Macbook</td>\n",
              "      <td>iPad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>87</td>\n",
              "      <td>iPhone</td>\n",
              "      <td>iPad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>89</td>\n",
              "      <td>iPhone</td>\n",
              "      <td>iPad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>114</td>\n",
              "      <td>Macbook</td>\n",
              "      <td>iPhone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>118</td>\n",
              "      <td>iPad</td>\n",
              "      <td>iPhone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>128</td>\n",
              "      <td>iPad</td>\n",
              "      <td>iPhone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>135</td>\n",
              "      <td>Macbook</td>\n",
              "      <td>iPhone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>146</td>\n",
              "      <td>iPad</td>\n",
              "      <td>iPhone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>150</td>\n",
              "      <td>iPad</td>\n",
              "      <td>iPhone</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Index Predicted Label Actual Label\n",
              "0      21            iPad      Macbook\n",
              "1      23            iPad      Macbook\n",
              "2      25          iPhone      Macbook\n",
              "3      54          iPhone         iPad\n",
              "4      57          iPhone         iPad\n",
              "5      64          iPhone         iPad\n",
              "6      67          iPhone         iPad\n",
              "7      74          iPhone         iPad\n",
              "8      82          iPhone         iPad\n",
              "9      86         Macbook         iPad\n",
              "10     87          iPhone         iPad\n",
              "11     89          iPhone         iPad\n",
              "12    114         Macbook       iPhone\n",
              "13    118            iPad       iPhone\n",
              "14    128            iPad       iPhone\n",
              "15    135         Macbook       iPhone\n",
              "16    146            iPad       iPhone\n",
              "17    150            iPad       iPhone"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPdR_59jC-3i"
      },
      "source": [
        "def show_misclassified(index):\n",
        "  x,y = validation_generator.next()\n",
        "  image = x[index]\n",
        "  print('==================')\n",
        "  plt.figure(figsize=(3,3))\n",
        "  plt.imshow(image)\n",
        "  plt.show()\n",
        "  print('Actual Image :', str(wrong_classify[wrong_classify['Index']==index]['Actual Label'].values)[2:-2])\n",
        "  print('Predicted Label:', str(wrong_classify[wrong_classify['Index']==index]['Predicted Label'].values)[2:-2])\n",
        "  print('==================')"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jM8pk6KiDO-T",
        "outputId": "dbf8ef8d-8e5f-4ce1-b1b0-45018391511a"
      },
      "source": [
        "for index in wrong_classify.Index:\n",
        "  show_misclassified(index)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : Macbook\n",
            "Predicted Label: iPad\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : Macbook\n",
            "Predicted Label: iPad\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : Macbook\n",
            "Predicted Label: iPhone\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPad\n",
            "Predicted Label: iPhone\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPad\n",
            "Predicted Label: iPhone\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPad\n",
            "Predicted Label: iPhone\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPad\n",
            "Predicted Label: iPhone\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9718umDF1qr0KIjuVR1rrcurOda/3+24R3yiKsS+l+JZv+Za2ntPs7Cyj0YhPfvKT/MIv/AIAr371q3nf+953UaXozc9GZjE+UiC8w/saq4EiyxGBolswtpH7tCUNTRENg+A9GhL64hVpknMtCol+I72cZlhFGoFpwLjomNuoXaNSyVLapmogxecwajCSY0Qijt8omjftOGrK6JdIgFAraEC6JYRAQBmJpxeU3qAPwNz8POvnz6SsuwYRuQDv349MiHmQ4hRpYm997gmYYGjhX5OZliKxM/MuvdK5Q8rC23k9/5Tb/PUk+1IKa22kcwP33nsv3/Zt38bHPvax1lxaXFzk7NmzF/195aIfUeYdRISiAA0O31QonhBgY2NEvRbjFMzNYWwOxpAnf0Ih0qYnEenQwNSszsQEliyiSSG031N7fFqGssySp3bXqyOUBoocS8zEqydnVDdZx7BFhuZ5XAGskJFhsozuoTloNlhejZlrqHL06NH43gfWzy2zvrZGyDPmrrDP23jLFNu2ZRK3jnIbn2yvf1GZYAsJ5p28vyFP0dH+sz/7M+69917e97738brXva79/Mk69/0/9E+B5Kxt/+qC5bytELGrEkZ88LuX/Kn3xlxoZk8fO0GF2rfJbPIxCaf99+QaE7PDTAWwpnBQAW5bOMJv//B/j2k8Nx+LRQkevP++Nrkfhaaq8EVGNdxi7ctfbs/1ZNK2ITntHDmGTXb/E5Xn7INfQ8REIC71zbS73ba5rdghVM5h5uYYG8PJ4QgzAbT2eHZ1UB4d1U/azqfDeHo61HbfSvHRj36U3/qt3+K9730vMzMz9Ho9xuMxnU6H06dPc+TIxWkMP/D+fwdAnmgUQkBDg4aG3FgisuQZbkSMfzHvceLoMTJrme3PUJYlVmImmnMR+Tl55jQnz0ZaSB081lvQ6JwaayDxpWJUuWrjc71en26vREQ43B0wO5ghK+IqUNUjziyfQ1U5s3yO2kXukxciq1SVZtCBsuD3/usf55/97v/M4NyQ/+ln/hUAd770ZYzWV1BVujbDbWzhxhXl7AyD48eAC5XiYixdnEfWNwkh8Pm//iuqBFbc9E3fzNxtz8KWndSuyXqmWDw+JBq7ASYJSSI8+KUvs7yyQn/Q59ihOYpkHtXVKLKW2UaYTlaemzv5JXNBYlbHtVeLfaURXES+Oqz2/HxfSrGxscE73/lO3v/+9zM/Pw/AK17xCj784Q/zPd/zPfzpn/4p3/qt33rR3xsZxqhsXoIxlP0uSMbWWs1wGHMt+iajTAULfvKN/4g3vObZ9HoZo9XP48aPY7WisIF89hkEMZzN3sjP/W//FwAf/dsvYUyDmIAbboIIplPik23hG09Rxc6989hR/sXd/5h+r8dgtodVJdSOMsuZ6XQZugofAh/6m7/if/yd30RV6R1ZYGN9BecabAVGYgSjsYof5MiEg1XV1N0IGVfOxWi8cwkivvwBFFemWGxgwtWaQKTW2hZp9R6CCJmlLa5g05eOCEUDWAJWPBmeEqWD0jTDdJyjmAouxpXU03oUl7AC9lNS4ErR3P2iv1diGe5LKf7kT/6ElZUVfvInf7L97Jd/+Zd5xzvewT333MOJEyf43u/93kucIaRk/0i+MxYwcfkPGlImWGh57YdnD3HzkUUG/ZwN06HespgglBmUCwUBS9FboNuPQcEgAlYxBryEFHtTgkmQo9c2MFhmGccPLzHbH9Cd7SIucqg6WcGh/gxjdXjvOTx/aNv8sLGtmqLLk3xoFVAj7b8lBCSLAQRNlo9MQKsneboXUCqAmAx08ZmyJa0y9Xcqzqe7BmE0sVLhhRYh22YWX6o9V0euHLHaTzs0saUvV/alFG9605t405vedMHnv/u7v3tZv+92uzEV1dqpJ6fRlEpFCoIqTeIn3TY/j//brzDCsfWVjzA++xU6fU/vGV3yF6yiNufQM45wezfe+Vxj2LKxy22nbE2GLIvkPheU3MXBNej1WDl3nmpzyMN//TVOPvQgw9UNemXJ8aWjHL75OAjMz821eP1wPCLv98mlD9tFLSJkK7JdlC9FiydQ8FMZVxPfZxJo2xk4m7y/Sja3XjjrXwsn/Hpl1R5IRLtTFDEvQFMhtOARG1EdQiT14TwurdllN7D1mQcZjUcMP/cw9ROP0RwObI0to3wDyXLmFm7iUC8OzyIzjPIMtUrZKQkuFl3Lsixez3nyFLzrdrp87f4HyG3GZz71Cb72hS8xXNugyHMOzc9z6KZjmMzy2n/yZmIihlKNxmQzve0SnxDH5aQC3zZvon3wRgQVs+OzK5IU0UZIrN8Jk3XXYVcjBoI+Lf4BXJ+I18HQPNJfTcXDgnMYm5EXeSztCGjTQB2pGCtmlblhhh12WK1uZ+wGrFVjHlxd54nHx+S55ZVbI5iJgYpxTzBlgdhII/d1g9aQGYsGxSnkWWKCivDwI49ggFMPPUK9tkWhBmkCy2fPc9/JR8nyjBe/8R9QdDqRpWoV5yPj1kZqb7whiavFdPCvfZs4XuzBNr0cEQTJMghRubdJhdMR5mk49oov8bTLVSlOcQ3kQJQi2sXaGsFBA0YVYw1iIvLj8G2mWJ3XjAbHsRk8NCg4t7XBQ2aDT47PUIUxnZBzR/+59OZjXGOcBbLcYjODd5OKegnbT9X8ylTrKKiyVY0wwOb6Bpm1zM/EahajpuahlTOYzPLY46fopNiMlkIz0QMXmBQwVJEdBb7aGMB2KOGSCM4lRWiVb+dKsdv2b6/O14VmXIdyIErRyg6K804uzzQ1woolKztYBVt2kY6DwmGyDoUVCptTaL5N5JucUWPUeS+a84QHpWisnaqQF2WswJHnIEJhhW6/v01cTGC+imzj+ukedkZMLm6PXw2u0uUqVXKlrlx2+xR67XyKp8t8upKJ6GCo40W0zfOQEaDNPwjB41KEuvE1mOgjzBWzHPvmbyILBU/Mr7B1bkTPL3OoOMltxwwda3jx6FmcPxtXiswVqMbyOcONDWyATre7XSHEGMqZSMVY9xVH5+exxvLsl3wTdtxQWIvNMrJuh1vtS0FgbBXT76JA8BWdXqqtOhrHmrdCLNlitguQhUnwb0LLUG2h0n302tRKYVraxQ5Fv8DR3t+Ae7p8ihvm05TUadiE5JhqGinWblfoVs3bogS/+ef/kf/q5V1mugN6r5zn+Z2j3G7m+Y5iiWNZBd4z+soDfPQLnwVgbANFG0nWlvrgNBA0Zs2t1iMA7j9zkn6nR55l3PTsm7G1ox6NCSL4Iqc/2yOEwCce+WpbLkYxMSKcOE+SxxI8klnUeDDX6EEndEt25GbcMJOuttwocXNDbsguOZCVoskSLyekKVwDOYY8yzAp+acotpv2pw//Hf/f2a+QicGNNgh1hXcN1nuWBoeSSdPhwZSP0TxzBnliDV97in6fPEWwhFhn1fQ6nE9porUzlE88gjWWzz50H1rV4GJ1iqzIqU00gb742APMPPMWAA4tzOOrWJjMNTX4mE5rihybh3aFM8YkGpamiuaTcjNPISy7ywYPQXd8NG2NXGnQavuHe3hJ1yF0erlyxZUir1E7Lind2QEC1KMqbkTimjhwppGZsP0g5oZrDEqLtZaZxeN0ih5Kh0oPsaEzBAKPbi0zGt4HwOD0I2ytKyEIi4cWKEzGuKooOwWI0OkVrCYma5n1WA81EuDwiaP0Ol1MUHJr6eQlq6NNVJVO2CAUcbCPm5qQqvUJsTKgSKy3ajPXIkPW2FYp8InaIQbzVLgKeyYZsZ/g8MUv8zTGKa5HORhHW6bKprQRWW2/212m0qhiEviTGUue5QQKPF0kdCLZz+RMqpyJ94lzkWbldInJ9ab3oRAjbT6CWIPNLEZjqc7JTkohxNlfJ+3bNXO2lUPS+1axpyLb02zVqy5fv5P4dSkHYz6pgwBePGoUCoMaoqkSUgZDJmTJDFnbfA6FWcaahrX1B9FVR+M61P4Qxew8iDKbb9EQKwp2s9voz9QEVYIaKoB+H1PkoEoYO7rp1hugyuKo8lvnkfEKTapxGgx4E9GwkVnFpiymopylUksISi4m5i4hSF7gZwQzjmbcVm4xWxWguNxgxWCDYPYFcer2/GGkLTQs1rTKpyHOBSmVPBZWmNbPKd/caHQoDTG9Fj/ZfCYgT5erqfswy+TK1zDV3SD5peVAlGJSnc9LbKyxBhXBE7Y3GTGGMKEzz3RYzxRjHBoMaI6jpHY9zDCAKN3BCIrIHN1qAibLEMCppzaCFJaQxzwLE5RyELP6JLPUCQbeajyh9jgErwGnjk4nwse9okSalPpajxlkA8RanAR8qsgnxqB5hkkVFBojlD5tkljEVcSw96aElyUTbhXbK86OlecCQuD0anbBydqW7Firde/jrxUd8Mp/tN/gy+XLgQbvJpyhljuk2+S27UcF2q2pTcAI2GKApcBkMxiWqLwDAuo3sFmDCNSySaklBoMTbaHfSbQ5Lyx5qhcl1iBVKilfBUQlluRXDyg9iauLmi7UkaA4Hjf0ZyyZKdikYSRhd5NvyNexPCWlGI/HvPGNb+THfuzHuOuuuy67mscki17aWW9SJFin+RCtj5AVOd5bUk2OmGmqsVJf8CSTIsemelJh6AnJp1BjEGtRI/h0WkdK1wYkKG5CBZEsVihMUbDgJRXiEzJbYvLYXdV4PDXLTs/8NzTiG0GekvH4nve8h7m5mG3867/+69x99938/u//Prfddhv33nvvxS9qLSbLsHlOlsX8ZmNzJC8xRQdTdJC8A1kBWUG336ca9xhu9RiOLcM6UIcGk40wpsaaBpN3sHML2LkF6sExXFngCkG6OaZbQpHjM8FlwjgXRhLiq67R2kPtaSTD2YJgM4KxaBCciy/yGYrZJYrZJULWTSmyZntr3JSrIEwBCXsoyVWFNq+Vg620YMfuomhX87Xv5l3j6+97pXjggQe4//77+Y7v+A6AK6rmkeVFe3MAyjYqtD2OpvT1rMNsziKqdLsb5EVNYVfplCt420FRqkYZr85Gm7u6GVvUWKkRa8GaaCalJKNGPaONmIrYrZVjnRmsGB7f3GSsm+SWuJWu0VSRUDk7rskHsWjz+NAM0s3JrMHVHklladvqfRNpvdvJfSa5WopxjRamG5DsPuVXfuVX+Nmf/Vk+8IEPADAajS67mse7X/k9e3wqF3/IXtq9tI0JiIRtfl7K6AlASIiM9yVW7iCpW+uctpU20bYurFHIE22i8SGeabopaZAHpeUexc3YUwKRKgTlttkFfut1/xBR5fggbnD52Jfvw6TiC2oEcT6iPMtDZHPlov2zp6hCCKhCYwv8oVgl5JH1EWb0QCxcsKPJk5jGhJoCpFpQAtSNw8/Mcd4Y1jdHTOpkxXDHzuy+WuHR+trvIHS9yL6U4gMf+AAveclLuOWWW/b8/smWxrd8POZSb+ceCGIyxNiWiRoUfEpSf92gx81HX0mWdZld2KDbqynLLQblBrZewHnPZx58mI8+9ggAp5s7CeMHQSvUhZjllxukE0PplShuOQbv7jx0C2+48y46ec5znnOImbwhVEOMNZiiZF1KfAj8H5/8HPd87ONxrHUFm6UttHzM4nvPq9/Ef/cffp+8cfz2t/03ALzg5X+PYmUDUaXpWbJzm2TrY8zcgOzmo1fW6d4jWyNCCDz21c+zvnIGFG59/gvoH38GtujQJEg2zwANiKvAJhIjRIxWwKjnsc9/kbXl88wPBhybn6Fqqrji4rB+p1X9WBO4Ob82MO2Vxm2upvn5QL13au++lOIjH/kIjz76KB/5yEd44oknKIriiqp5+JYdmWYxMTErzcQ8AQVcAJeg2+984SaL5RzCDEEMwQ3pWeHETJ9bj74Ur4Gb8sM8sh7Lxjz2+GfJyiVEelDV+NGQULmYS4GiWUOWInYvOHKUN738lQw6XebOfhL3yN8xXl1GxFDMLjB4/l0EDPN//1V87M//BkXJ52apwgpBG5zG0pmw7Us8bfKfroVzTWVfSvFrv/Zr7ft3v/vd3HTTTXzmM5+57GoeO2SvfMoWMJ/+bqqg73QEOR3fBsd3nOrio2Y3XjSpQTX9mpx3AorJjl9M2jFRiCe9068bueFTXCV5y1vewtve9rbLq+Zhktlk0rASQZFhiJ0AACAASURBVI3BGAs2wbhecalwwa1HugyfCLjGsVmvU7tVZnprLNlAPng+GfCs4yd49nNuBeCTa2PG2gHJKLslmcnx9RYVDgQaPGUqIdiRwKIRZgTc2b+hWP0UHVdT146NlRzfcSCW2170fWytraIKZVfYGq3gfYXtdrEpi++CLLj/hAfW17M8ZaV4y1ve0r6/3GoeOykKgsGgYhGbYbI8Bi0JbZLRow9tsHFqA+cC2WAdU6yThzNs1Gd5+GQJIgxnjkMe4eGhO8QWLlIWsoJuZskKodERKopYJfORTmKMQ6sRKvDIymeZXXyCo8ePUw7Bn6k4Nf5a3AzG1qw3G6gqi7PH6MwvoMERmkBotp3QHdynG/J1KQcW0Z7UQI0BvOm91SKtYrrqxWjoGY1iIeBOx5EXnkCD1y3G1SoqhqqcR9Pt+JDjxQNpg0OxKeXUAgGsabP9RGI1EQ2BkduimzuYzTDWYTdiqUvUYQh4Uj3ZzGLzArA0WsOUUtxYHL7+5WAKF2RF4jzlTDLJgjE4hDqZTLUGnIn2en/8XHx3Ha+beO8YbebM5It0xz30/BIqynD1NKdOR0TJdj3iigjXmhwhQygwxHKXqtCEWBHPZzkhzwi5pY/DnDzFcGOMCRk2LHB4bg4Vy+PDcxhbx1XMOdzIE7xDAxiNDF2xJc5DkwCSDCHTSLhrVFFRgm0D9U/eT9NIyyQ3wtBmJ06yCnfo4VTKxWRFhog+7dTXqSDWVKqsoLAblGnhq6sscvVCNldTDqbEjYlF0KxNEGzykr16fGgSXq6YPHGf7LO57YUZkkFTH8O7jPnuiC4brC4LKoHV+hzLZ9PsP3cOwix4i2oWlUNNVAyJ+2gHGykhW2I45yqqRpg58vcol/tobcB0ML0TDBaeTwDOjxpsrxMHkff4YUNwDjGCFBnxhnKcNDQTUmNSx+i0R6XA0lYnnMhe5taFxQ5oN+0Q2R5NMnWc7MAm5IJC09Nf7xiLyvbofDpH6b5yQK69p3YgSpHjdnCHQjtHKWIiczbDxEEErM1m3PmyO+j2Sjr0yCloRmc499CXOJuvoKKMZmcoj8b9n/1oNe5NEQLqHV4jh0mTg5/leescbzrHqY1V+nXJc5/5fczd/kZwseyfCxnjvIdXZWXssb3FOGkKSOYw4ghqW2bvboryzuG1Bzx2nUi7MY3qjdwMDmqlSHinSYVVjUbFEGJd1glhNqQ5YaXa4vzmBh1fMTBKIY5mPGK9qtgMDoyy5QNVeqBBJ/Vcp+qkatpqV3ZaA1XTsDbconGOsyJxK+CkFD4EVm2M7y6PG4JGxZ2cZ5rrBJM4xaXu/PpK69yrLYpipjnk2wdf/QbsKHF0mfI0dN+BKEUvS46p+GQ2aQq2aqpuLQQxmGR8/7sHP8WXzEk6ecbxzjHm8hlcs8rG2uOccwUqgXNrq3ytiStFPSrICQieHA8KLgRqAYIQKJjstvClM2f4w7/+FIWx5N3jdKQg8x6jAXzNuaoiAF+tNqlc3CZgs66YySErLI2zETkTwZQ5RvO2uve0bDMv9Ok1US6mpBdJa53UyrqgwPI1aPNuM/JyxMA1V4wDUYoss0BM5AHaDKygYdufm3ISzx/t8pdfux/jA0d5iFlKsgzy2Q5uYQkXlM8tV9RNNFILP0vGOkLAqo8ppzZsp2tIaBOYHlvfpHnwASyC73+RLHPkGmdL4+D8RnSuTzcWmS0TMJCieiZumyVZEfcA75SIuljyZtc9C0TuylNkiF4L2akQT+OFg16xSam7gYVrIAcDyU71/e5n0JLaLvdUOwbY1K/2MgEu3aRLfLN3ay46uJ+G7LCrIXs680zu9unQjn2YT0+DHIhSuHTZQDRHfHqFicsngki2vVoMMubMCWwQFvKSWZMxrgPna8cGQ7wq69phthOd555Txk5RQswDzwySm7i3tolFy+jFCoEEYX3cIKocm/UMOgbRWDy5qjwmbaTYG2UcOnwYgPXVM4zHG0Q8tkOeihr4pEATqNOxvc/ehK0b/xywwuyRJ7Hz/e6Bem0Gr+wDfrq6Pbf32Q5EKWzKZovb2AlG4sYtMQGU6Awb2yrF+Ycf4UyYx2DRgWGtEKq6YL3JqcvNCLk2XYyNsQdcQZYVqGRgLD5BvxMStVTjqbQNobKRgr56qs9YFUwg4BlTsTlKlpLpYFOOttWakVuN8LHMUmYFitIEj2rAJbp4mJ59243cry/yxwXJN9OBjlaun4DC1TWfriOl2I2nRxRnm26nTCLeaSFPe9VF5CegGMLkAU4jOtNWSxqAun2ZXfTC7XetIiqoj45HQNK+2YJqrHqBTtc+nURTpmDYC8bNhQPp+lKJPURvEAIPRCnmJle1ceBP8q6bEHDJ+fYpoQZgYXaWejNDg7Dqt9ioa5Q5tOySlbHYQLHZwYcn4mnzAbboRvJf8DT4VLgsmkU0DSSzyKvSNDWKUksPzTNC5vF4KoRK485Kg7xPnRJtyrykKTv4YHCp5q0gmMyiwe6sJTsVaIsxsusnFrDbp5j8Z9htQsVo0lW//j5WoKub1bH32fatFH/8x3/Me9/7XrIs4yd+4id43vOed9mFCxbL1BhjklIodQhUzlOlDQ4r79okI2tzsF0whi3TEGQEvkCaLcz6WcBiwyKmEykiTjzGdBAMTRhThwYxSi5xXTDek6VMPmvAW4OijDobbNlAyAKqAR9qQsqQ9VmPrbVons3Oduh25vChodES2+kgxlB2OwRRbNpQRcTsoLdrCIQQrgm8ecXSIh3TptP2lxdAstekDfsDJK5L9GllZYXf/M3f5A/+4A8YDoe8+93v5sMf/jB33303r3/963nXu97Fvffee9Ec7Y6dMo9E8Gl2iq522tY3NGlDeGLqaBZzuL1RnAmgHpoxjDZBLT0CJm0j4VUJGtm3MYNPMUYj2TAyLdpqHtYITZobG4Y0WU3INJ7fO9AsOtHG45PCGilidQ+TETTD5rYtm+mmNlTZKds8o+sJkt0B0l1H7TpI2ZdSfPzjH+euu+5iMBgwGAz41//6X/Oa17zmsgsXjLQABWNyBEMQoZbAmJqtMEaBLQSfqOPaVCxajzFC1p3B5F18XtJYgzKDYOlKYDWFq9fyit6ohw3CyASQgJ0yCVRpC5YNO4HuqAJVShU8hjp4DIINAnWk0jW2pkkrgDMF+B4SPEVWoLZLUKiCpZCCcZr9slojYIBC8HiJPT4poAC0ZUInshcy1fpGk2JoU8ebtmzgbqssmkKJKXNhApaJe4xbkzbeTBPQpIzo7pp64VoojFy5+aS6nXcf/8iO77b9S20JkZqcxvZYc+nr7kspHnvsMcbjMT/6oz/K+vo6b3nLW66ocMFq6CIIufQwWJCMWoR1hqzoJkGVdc3bPSEHo5M8o/SUVjg+d4TZXo9xXbG+uYGO4ibtPev5qxADcqe76xxbH1A6YbPnMNKQNYokCBgPearAsVnWHN3cIFPlJjdHaDKGNu4b3QHMKK5fX7WbNJ1FEKHJB+R1F6uKLTpUhcUjbPqMY5KxaWK35lsByhwEQrOFM0ooDZJfngEwvdG8iLRoXAhhe3uvNKgnEkEKJg5M+7klcrYmB2XGUmQ5mUmbWSYfK7Mm7rmxqy3XZA3Zh6KFtI/4pH1mAqio4pN5Gk+tZOk7FyKyYydMA0k2cdj7+vv2KVZXV/mN3/gNTp06xQ/8wA9cNBC0l/zL57483dDEzIgzoQ8BpxM4M7Q0gDx4enmJkVhf1oqJMYjF0HasYHhDqkM7Ek9xNEdUaEw0yWS6SQFs+p23SjeVtsyxiCpBImdqQs0AGCk0NnaXWhvrtKbZSI1wc6fL//qiv08OHO3EvfEeOHV/W/81aEDTTkpmo8KcizT3y4lZtKtKsvunq3l8bW0LM7wfnqSaR1xEJ+eBpnb4/oDaGFYaR0hbIGzj4tvSIDx6wDvBtbKbVxlavDL2QWu5Jr9ISUwGQSblXLxeUh/3daeLi4vceeedZFnGrbfeSr/fx1p72YULfvGhWGCgkxVIKlCsoWY0Wmc4WkVRKrfeZqb++B2v4T+/8yX0iw49U5KLxRNwBErNUZTN4Yh/9eF7APjg+FF0vY8Ey9jUZG5E6RzOghdYyYTBRtzJ6HWrgR85U9ILwk26wKEqkNVjTG4p5rqYbgengfc8dB//8ltyFHCL82QjjwlKYQtCbvlfXvxy/skX/pITavkfXvAqAP7Zc19Cp1AQZVRt4dbX0aomn5mhezSucBdu5Li9OkwkltEJmKYhhMCjn/87NlfOAPDM57+IzrHbkKKkScXW85wYWNQGoWwh55DKexrvePxLX2ZtZZnZXo/FTp/N4Wa8dmEQF3asFI+RczPNlQ6TayIiQp7n7fZmzjnW1tYQERYXF9na2gIme4OkCpOdbkoKi31Z1zXj8fiiir4vpXjVq17Fz/zMz/DDP/zDrK2tMRwOedWrXnXZhQsa4kw6rsZAwBpPaWqKvKZrNT7QGjKNBctedfh23JdPshGU/s230JufBxOdaLe+BQhLg1le88JXAPDBP/23rBcBcstssOQmo7ENJs20oWNZ99Fpfv7jlpf3jjMg4/zrX8LqTT2CGyP9nM7ROY6O+5ja8ep/8X6WyhoVsC4jtPX7Y7V0C8xmhnlyunnajy6L2wtLQr2sU6SJfw9aWp3bFdG+LpCxS4j3MQMToGkaxuNxO5FMFAJgY2MjVp80hpmZGYrZAYSA9546+JbpvJfsSymOHj3Kd33Xd/H93//9ALzjHe/gxS9+8eUXLrghN+Q6ln0bim9+85t585vfvOOzyy1cMOciwrS6cR7nG7zxSEfo9DK6ZQchlud3ow0AtoJnaX6OTAQs1PWIWpXGGmYOx2IFtcLDw3UANjJDRYRtgxGGOYy8MjeMZsFMY9kwcaVYmTGce/Yi4yxns3B0lof0GgfeYI/mMNOH2tGtYfjNx1HgDf44L3nc0asC95sxX8lG9MXysmKeRRWOSuzWTAJ1SqmtRYGA0YBcG7f1KckO1GZXslSMsV0fbbbWtrvcigi9wYClm24ihMD5kydZXFxsj5202Wtg3NSgSgiBRn3iqe0tB+I9PddUqMJGx+JVqNVTScDXQu3TnhTapyLCT3WeE8qcIAbtlNDpREfYgEtLp0Nx6W60m0UoVVOGn/r0SihEoo9PflebQG0CRxYO0enNQuOgb5GshK0aaTx5DUfzPkGEm12XW6jpq6eHsCSWvljukgGl8xytY4ebpmJYCCrgbUR2sGYHLf5ARPfgPF0KKLmOlKKuI5giIhRFQZ7nnH74Ybz3fOYzn+HWW2OZo+c973ntfY28Yzzc2t5WTYS8LGiTanbJwRACAYQImSF44mYm0VKUlFsBOo2cTAX7tjHHXfi87Py722TccfSOL6N9aUyq8hFsG22PX6dBniA+k8BdS8zBjtEWmJRIMDvPvP03oSDXtaRV4nptZQtPQ0uwDMlXaJoG59yFlRrTJDAdF5FLZP0diFI8u9cHBObnQISh85wdj3hiNOT82BEQthSGEmFC5wLL51YwqnTzkl7ZIRcDITDaTNCmzZlJzm85HqGUICZuLO8bSh/IJe7dXdkATey0TgULX9tgIBZ7q6OeiymnlTasnd+k83jcLdUBt55qUIFhsc79mSfvBOYxHBNLhnAUi6indGmnJu/aaLmYWLAhN/4iEe+DkQkfa0cAcbf5xPUThZ/EJCaK8NBDD/GhD30I7z1f/epXeeYznwnA4cOHOXr8OJm1ZMGjIilQKdjcIHkB1dae1zgQpXjOwgkE6BQx9rDV1Jza2KAMaxgd4VE2fUOVBvnDqyvMLx6nMJZxJmxoQwieEALdIkc1sLp6nocf+GK6qYqBWoxaRDxZqOlqIOQWLxKVK0SFe6JUvrx6jkEwdP79/0thckxwODyj0GBdgarycF/xG2NU4PRcztzcDGIsduzpNw4rMFMYQjBkKSjSiKeRaD4VQK6GIuxRzuNAZLq8zU6f4gIF0OvHfOp0OjRNQ9M0LC8v86lP/TUf+tCHUYXVtXVOnToFwAtf+EL+wWtfy8xggBEhNwYjFhGwO7Z8uFAORCkOdWcREbo2x4qhw5i68KzkNetBcaoU6lsm6/nRJtLvkRclLjeM8TS+AfXM9QcE7xlVQx4//RgAxnhyr1iUWjwqnhxwNs7aViHX6LucLz0Prw/pBWH+M+eZWanJQ3TSVZR69hABOLkwYBgciLAhgazXoZsVZIyiASiCyQxkkuxDCBLL2mhayo0KVs1kHF4XEskvU625zs2nPM/xPvb3cDjk5MmTPPHEGVRjAZezZ88iIjz44IOMtrboliXkOUYE25rDl1bygylxk3UREWyI3JxMDSWWQVEyK4FGA10q7Ch6Qqe21vjyqUcobcZsmdPJbKwzGxynyydQVVbPn2OUAkwuV/oeMlUqE2hiTDvuHAqUXphsVnFmIPxdVVMGWFgKlF1FNUXKVSkG8dDHjnTRRAvQUU3WD2TEGETI4rk1N+AMIS0EaYexbQ5S0PR6mjv8CuR6VgiIwbpJ8G1jY4OVlRW63VjDS4zFSDRdH3roYc6cOROtibl5OgOLn9IDl+JUe8nBVPPozMc36+vgPVkVmPMZN3UG2G5JEwLL0rBWR6r2F8+f4vHHz2AV+plSWiV4xXhHT8aIQMcO2ChjlJouzFQNRRC2TI2zcZ9tkxkkwPw44BIR6KuHhdPdMSbAkSMd1BaMs4BzDj+qOJRFh3v2xCzZqWiD9tdrZgaBWVUya/FFGR3zXgcUqiy59JmNAUMFG2JU2gWPaHiaB56SWCnJ2d/2JSZ0iJZNdLEA3nViPg1HQzY2NxiPxzz8yCN88YtfYG5ugIgwNzvH8vIyAH/xkY9y+zNuZ+nwYe546Z3c9qxntYlnYoSt4RCy3p7XOJh9tJt1BBhvnEcbhxuN8T7QyTPmDbig3Dw0GOK2vp3DFnFLCBZrzmDNJoWbY6ADuv0voWoYu+McTklGz1rvYPI1JPMcry0ulGhmMAgqiutU+FEc4CfOF/SLJYwYDh/ps3TkML2ZAZtbWzz2+CnGW+NIoFtZ5rDEdJuXHrmZpV6PMssR28EawWKZkznGIWc1braKFAP6aefVIjTUuWPcqckLT/9JnO1p9OQp53QruJg+mGJA0hL1VSOq1rEZil50Br1eVg8XHJIbClNSNTWPPnaKzEbG7+L8Iodm4oRbb9b8n//2HgT44R/9pyzNzWCMIc9zFhcX2Vxfg4XrSCm2kdOdlOgJTCYSb3KCK4saUt00kO1c4ohCbL+fmCUS9niELWt054zX7jGhioZtVENDmCC1TA5Baa/VmkU70nEukcTZTsTXx4y7W67Xdl1M2nxy2YZmL0DR0l/vfcss3k3V30sOpu6TzeIg63dRXxDKghCUIs8wk6V+foZnlLF5Zx4YsVpXBJRNO6I2q4h6VvwWzp1HxDIjN2FDF4CFNfBzFmzA24waS+UdzVYFAtY65srENgw5oWlA4dGHzvL4o49QFiXGGnqdLjf150DBDh3j1bhP3Vl7lmO9WYw1MUZhMowROmWJ7Sl5HpEtYwwk4ppRHwmoE8TnOpDpyWV7U86923a9KE2e54zHY3zdEJynLEtuPnGcEAIPPvggM/1oSs3OzuKcQ1X56Ec/xtLSIt/93d+Nc44nTj3O7MwMF0tuOBClqIaj7fwAkVgmPwSauma4uUVQxVcN3cEsAM+eCTxWr9GoJ5h1PFtgM5rQYf38AKMZC/0+i2lu6PnTrDiHd3F7YicZIwIjjamgkgudXtpz2xdsVY6gsLBwGF83NFVN3xbc0l/k+NwSAuQdpbHJF6o8p5fPk2cZs70eR+bmodPFWEtZllPJLRrZnKpYm9GkyPA1Sdi5QtEL3lxcIa4naapI1zDGMDPocfTIAhDX6H6v15qaeZnH0qsh8NWv3M8ff+CPOX/2LHNzc7ziFa/g7OnTcMtgz2scPEn+EtlXO00RbfMEUsrzzqOSnRP/7FGiftf5Ltx1aK/3uyp8T2jdl7qfvc59nZW1+UaQPf2sPbLxtqn4l3/uA1GKwwtLANS+IW5VqzjX4Dc2KUejhJIoC3mczVeLJyjsGkYdXRfwISczHZQFjO9gxLLQWcLZWOCsMussrM+Bz0AKguSMrTKOxgKOGjOOtHTnAuJjGZuOGkKwOG+Yo+CmbMChOma2dSRj4RnHAVjf2GRYVQQN1K5hNBqhg1m8cxhoqc3ee6yhpXdcj6oxbT7tKtgzdcz1Yz5Za5ibncVay+23386tz3gm//EvP4EV4Tk3n2C4NUSBM+fORZ9UBDFw//3389CDD3LixAmWlo5y4viJi15jX0qxtbXF2972NtbW1miahh//8R9naWmJn//5nwciGWuSr72XdHsDQAnVFj54fAiIKN1eb7KNNcWc5ejhmIjzlYc2aSqDD8KgEmonFGZIoacZ133EwOEwYkWOp457NkVHkCA0QahUqPA0uRA0MPIVzTjCt5ktKPsLkX1VlGgWoPAcGszwvMXj9CgAZbw2pEh5NqWx2H4/xiHqut3je5IVtp2qMBlwROWf2O7XwwDb0bZLK8X1RPMgKFluyG3G8SNH+aYXvJD/8Od/hQqsra2xsBSrOHqg1+tjrSHUNfVwRNPUnDu3zJ/8+w/xgz/4jy96iX0pxR/90R9x++2389a3vpXTp0/zgz/4gywtLfH2t7+dO+64g7e+9a38xV/8Bd/+7d++5+/b/OIJ4pSgTmMMeSoOUJLHaCSQqSUPglXBeyHzQq6ejtYU2kNCoKueoRYgUEiXwoRYfimRCwMKEvOPG93eozsz0LEZRiwhVSWUzNDJCrp5QUdzVBWXuFYicWcim9iurpnOSLtw+Z7moR2YzS5TKNsFcp0M9suWbVM2z3O63W5ciDUG9qxNFeCtJS9ihp4PAW8t3kfa+fLyyiWvsC+lOHToEF/5ylcAWF9fZ35+npMnT3LHHXcAsZrHxz/+8YsqxcYwBuWapkkZbHGlyIzgk8I47xlWcTY/NF6mDOdQbXCyRjBjLFsUMsZ2jyFGsOFRMuJKoaGHy+IsWODJGk8mDm9A1VJmHZrExZjrH2Lx0E0YY6mB4eaQejSmpxatHb1edMaKWcNWNQYUVzcRRTZKPnGuU4kbowE128qh6tuV4nodf8qToE/XEfepaRwahtRVhQC33nwzd97xQoIPfPm+Bzg0jnGh4WiEDwEjQpH2ZlcRnAbOnDvHX3zso9z13OfteY19KcUb3vAG/vAP/5DXvva1rK+v8573vIdf/MVfbL9/smoeW8met5F8EXkpqdGNibPq2NecH64BcGj4eQ53ziESMEUkbvuwBeFhZhdjGGql+hy+jimwmSyyWfYIEhmxY9tQhxpPFX0KG0gEXA71lzjSX8KYjLMbazy+sUq1scVQRjy2Pqa89XYQYfHQYcZNjULcx84pmEDZKel1e2nmygiuadEnYwwh1Z/1icf1tG9AfxFp/YSp0M3EhLoe2ncxEYVqXKEhkOU5z7z9GbzxP3sddV3ztUcfY2sYg7JVVeESR2rQ7RI0sFlXCELtl/nAh/4f7vqh/3bPa+xLKT74wQ9y4sQJfud3fuf/b+9cYy25rjr/23tX1alzzj330beffnbiOLYhiYF4ZhInQSKJSQwSZEjiiMhBSCCEiEM+EBnHskgkNETIBgGRJUQUhYxNNAjPfDASwh5gRkIQrAktPCHE8Th+drvdj/s+r6raj/mwq+rUedxnd997O9xlXZ8+deqxq2qvvdde67/+i+eee45Pf/rTtFqt8vdNRxXtM+pc5c/ko5VQ3nMU1QOs8Nin9pv/Eyrw4VgpQhAK61IcfdqqBjiSrIMy3oU7S8i0CiEPDvoO4Fn//HVNuehVMkRJv28jjDnemsUYW95Dve5jH0EgOHLIu2QPz06XI6qUMp+yIcThYlV6QU5//0WcLbJEAGO8CZYuIlfa23vo+VrEsTM2Dx8kHbyXVDvs3DyrUtBF4Gr1fL/xd6el4nxtsvty16VGyS0spEBMz/OjP/NRrLP8l3f/eOnksNb/DvngZC2Z9koSSElmzLqX2JFSnDp1ive+1zNW3HrrrV4r9QAesBmbhw2nEUAgPDOgsAZhNdpouqkvBNlJM0TND+dHnv+vzB2fQwYBUeNGVDhHNz1N37zAkbk3Y23G98//b5aX/Uyh5Qm6U0exMsDi+WQT3SfVbZyzZGaVMO8sjeA4jWAeISQvvXGG1954nU63S5qlGGu59bYfQgjJ299xO/18ak76aUnwHE/VabVatJRCI9G9BBn5x3rLzW9B9zs456hZg1tbw/T7RK0ZGsev3d5DtxaSZIdsHs5DJXM/dYDh1X/5vyydX6BVr3N0qkW/701azTjM40Lc4kh/bXvtvUISCEGv2yXLMsIoIohClpZXSJKEJ/7wy5w990a+p6TZaqGUIo4i1jpdzi0sEUhBsxbR0YYv//f/MfkaO2nYjTfeyLPPPsuHPvQhzpw5Q7PZ5Nprr+Vb3/oWd9xxB08//TSf+tSn1j+BzwdF91OwlqzbJu21sRhsUXC+HhO2vIs1mBJMXfNjqKiJOPweaJ4kTFbJsjXE/DVgeqTZNLjrAajJKUQt9IklgQ/i9JI23Z6HcbTbXUziZ6GV9nleW3wDZ+Fie5nEZYRSkmWGtU6bV8+cQUrB3JGjNGI/mk7FDaz2tPuh8rBk58AaDxEROSwdGBq9hVSIwBMy70jy2WLMulnP2ikAgMUiX0zedag+BeNEaOW1N5PtWl07WKaEYUgWBOic1UO5gJMnT2Kt5bOf/Sz/+M1/BODkTW/h1ltvJa7FaKNZWlxkcXGJNEtZWV7hbW9/27qkPTtSik984hM8+OCD3HvvvWit+eIXv8iRI0f4rd/6Lay13H777dx5553rHi/biV+EGgUEZDogMY5AGWbrESCoRzXSFd/sa1MBfY9NMkLjahmq3iS2ddZWFnBGM5e9gyj0WXiuJjlvUzQQ0sQyGwAAIABJREFUESBFnXrcoNU6jjYZ1tVYShcAqM1Ps7b0OgbL6eUFtDXEcYxSAcfmj1OzCqygfX6Ra2+5xSfLN6bo9XpYa1GihjIhIrCEKIIwopf4NVPWTwiD0HufegnOSJwJcGYHSUbWQaq9+YUoFcvKHBKfo1+rHXOjPlrWyhhL3ZykGM7PPJudb5taYTc55yTppwkqDGiGg67bXvVrz+tPHOUTPzfKIqMJhaU+P8s187NDv7y+zjV2pBTNZpM//MM/HNv+jW98Y8vnqGZ6jW4f5M+KiceUjHkjx2+2lhn8Pv7y1jvWTRqZD2Si7JaH6tKvM963qrInEW3ndJlwDoIs7dFZW8HaNllSQ0rJiWPXceSQD9699OKbic6tIGSb1urfeuCdMtRow9pFcI5gOeO09fm5r9hrOdbKiKSlNTWL0QGLiyt0OmuAQ6oaJ9/0FgB63Yx29wXSNOPwkSNk2qc6Gm1YWloijmOEEFx3nSsX3QUjojEGp1Ren8LmFJpyJLF+1x/vnkiVUOBKX+fSZeN27olSrK5eBAStxgxKBky3YurRLJ1uRr/XBgQvv/j/OBucBeDGtUXar3wbZw29zllkskqUdZlJlpiJHE5I3MwxZk7+LADz8SwXL2qMcxyzMXFtGisVGm9m1Go1VM1DSAJb49ob3ozWmlde/T4LCws5uhICFTIzM4OUkqmpqZKqsWCps9Yig8CjYo0mUIE3qXKiZx+k3B8zzXqhu9EYhEfYjPugNuuMu+XGLQiUtyvbad8ezRTD4L4yI6qS/eWcZ5B2+XesAWuwOkXoFJclkCXe4ygEmAzhTI4xmsxn5K/oZTCag5ASKVXZtgL+MLxf9XO8g4jK577z85dgyS2MsvspR/tKNGQL59ybzLvc/beWGISQhAFImSKERTiPgU97vdI9GN/0Xuzxtk8oyrpok6GzHlm2Qr/mX/WarLN66IcB6IXHMdkbWJvli94EpSJmZ+cREqLY5TMSJKnl5Mk3AYLZQ1P0em2cc0ipqMcN5ubmEEJw9Ogxpqa8r945gZTKw8ClRCqFswKlFHbMfBrgovazlEk7/suQ+ji3tZliu6bNlva/TMuUoWttcs69KRmceCSsFRohJCIOiGtQCwQ2lL5TRgFh4E2ctfo1dGu+opALG6Aib/vrhKmGxyZ1em1U03sXIi05HBmwGkGUAw4DoihGSkG9Iej3feRTa8Ph+TmkVMzM1rE289cXkiCIqOX4KykVvZ6HnQihSvJekSuFyXJzqcJ2PezZ2Y9kmQMZwDy8bNu7uk9gIJdD9kQpbB5NdELm6aXKw3ylIFBeKQglcd4h+y4iUXW/f/0QRA2STNM3FtGs4Zylx0WatcjDLdKUOJhCYkgSR+ohS3k6oucjlZVEoCAIUCogjpuAGcAf8NFq5xxaa7Ic/KdUWALPyjTaSlrksKklBu7SPZMCDriOh42KV2+HnXu3Ftq7IXuTjhp4+z0KakihiKOQMDBkqQ+AAUjhK+0ACCkIyHAWlOkgtUZmKWQp9bDhX6buMJMjbE0E/Z7BWk/IGSgFCLROEQK6HYPRvoNLWZQs9n/WucFizg3WB6US4JUiCIJB9mAuV2OncLhxc2KHC+39Oltst117ohRTDc+iUAubSKEIAwG2Q9pPSHq+3kActZhpejxVO24h0h7CWZp0iejT011Ud41WOOMHt36HKevNrTRLaPd7aKuRIqYe1wFJp7OKc5aVlUWU8g8qkBEixyc5WV2gU+KWQHiPVe59EmI4Ij2kChsqhtjCPldItnjJ0rExuv0KdfgtDyT5Anmn7djOcXuiFK2pFiDQCWCh2+nS7SzSaS+QpT2kkNQPTVPPy2Sdbl4PwRrCWcK6IAglhjWMluj4EMZaLi5kLL/uq/GcP3eW6VaCUlCLLNONWWpRHZP20Dqj3+lQr+fkAkKS9NpIqQhqgeeGyuMLQqqh9UFhIiRJQr/fxzlH2GiU8YvNpPC07YUIKPjfNpb9aj7l3srdmI32Zk1hc+yqExXXZ2XUyJODCsSjx39acBbhZJ6bMCg+X5g+gQRwHmgopPf8VFyonjpHlCbToD3+XNLJsi6aGAEKuTxZHsjPIX+g7OhtdbY989nmK6MdKMa+j1MsLfjMp0DUEEiUtNTCGNFokeSN77TbvPTSKwCcfNe1dPpLWGcI++BSQdbpkvZTAuZAwPFWxDWxXwt0umusHTmGCUKSnkU4S1xTHJprYa0GMY/MXaXnz6+wcP4VnHMcOjbPzOwMYRgipcqrafoe4Jwrq7+2WvXclBJ0kj5Jmg6CYFXXZi7ODbBEl5R9NwESs42Di8jcYA0xlFiUm4xsob+vs8N2O+tO3bg7uc7gBOzPOIVTPp8hmIq8j99Bkkh6SUaGBgeh7WDScwC0L75KPD2HEBFORjgktWgaW89YTTLAUa/FnF7y+y8tpcjeGkIGBFFAFi+y1m0jcs9WzTUpskiPzMxwZMabUq3pACcMqTHYNKHf71GfmkYIR7PZpJ/4g9r9ZVrTMwRBSKACnLJ0AS1BteqkC97dqyzo1MPNa1KRSvDsVZatGVxDT83Dxwuyti35+N3A7LAZgcy9Yc4irfUlj62vrkSuGBZLwbVwpWWnptB2Z+eh/bdw6N5R3JRrTjEyAK4LSGCoyPpla0DxIUZ/4bJdai9l5HEKJv/78siVt6suj7l6GQCBzz//PL/2a7/GL/7iL3Lvvfdy9uxZ7r//fowxHDlyhIcffpgoinjyySf5+te/jpSSe+65h49//OOTL5r1fZuSAJcpHAqhDUoKCGoIHGEgqClvDq32LUmgkcLilMBJ77rV1uKMBhzCpghhEEBjOkaFIUJK0jRhbXUNFQU0pnzSybHjJ6jVvWdrebXH0nIbay3d1FCv12jWI4yxdJM+K6tdQCBUXDJFaGPp9fr0en2iICwzvDaCTg/BP/aDpu3ADNlcruB9baZv29LHSwQEdrtdfvu3f5t3v/vd5bY/+qM/4pOf/CR33303v//7v88TTzzBRz7yER599FGeeOIJwjDkYx/7GHfddRezs7Nj52zmtnlYqyFkgJMhRkjiegOdJICnvxHWmytr3QwTaKSUNJp1ojBE1gRK1rBJD5wlWV4mTfxaxWQrJO0lsJ7FwwrAhKSii5CSlSAlbnpmOSljZqb8Y0hNgBWSJJNoY+n3LZkvSUGaOdbaHp5iCrNESYT05b6qwbvJ72EQ6NsLlRht12BZUaG5yf8bD13sE4qbzZpwmZq4abZLFEV85StfGUovfeaZZ/jABz4ADJg7nn32Wd7+9rfTarWI45gf+7Ef49SpU5MvKnypJSmk/5QiX9gGqCDwUGypIP+zFpwdgPSK4wLl/5SUFN4pnMXlwEFrUmxexwJncFbjjMZov93mZGxKCZQq4Bm5r8t6ahy/yPbPu0DGFskxRUR7O7I/vVVu8DEhlrfnsssN2nSmCIKAIBjerdfrlZ6Ygrnj4sWLHDp0qNzn0KFD6zN6HPGld7X0Hpw8KIAiRDofgMPNlN6RCJDSAQayVVK9Ro4KoSiw4sIYOe/znuO549Ry1nCEyM2bQQdOgSz1HVvZPlIFuR8/gHyNowJHsylwLgABki5pjn0C75JFgC7chNaQ9FY8NF3567z04gsUNSAkPn/EWYtYW0MtLGz26IfFOTC+0G0a1LAzfpB6ebmNaL+AWI+4QEjG0lGdI3UCc+gwK1LSkRLbVIUPamwm0ypgaXqefy9yyQvtjTLW1pPV095LRK0JUqGiOkG9QRCGKOlfinQpwvi0TtG/iNEZDpdzMguUhFBoyDo4a1lbeI1kzSth4DJcvw8uJzgOAjSQ5vGIvgYR+fzv6dljTM/MI4TANE8STB0lDEO0sawlGWtrHk2r4jrNlq/ZraIa5DCPmlIoHJ3OAlF9GhVIOgsXAXj7TTej88IzsVL0VtdI+gm1ZpPpDYgdJorWsLKGtZZXX/g3uqu+OMmNJ36I+vEbkGGFuCDAx3RMCirGOYdBI4tYkDW89i//l+WFC0zFMXONBt1uGwckLkWNpCwszxxhZmV9yqKrVRYOTyaP2JFSNBoN+v0+cRyXzB1Hjx7l4sWL5T7nz5/nR37kRyYe30lsbl8bXzVUaGqh52IKlQIcgQyQ+dtpZAlr3WWcs6RpD2t07lZMkKaHc5asfRGZdRFALYQo8rahtppMd/Ikd2+CxQSIHN9kO4Je5iPhOouYievUGzOYAHCC9krizSntMVpCCEQY0UszrHMEoS+CUk3WGc3B2P9SmE+D9cTV0vIrITsq03nnnXfy1FNPAfD000/zvve9j9tvv51vf/vbrK6u0ul0OHXqFHfcccfE44X0C1RvmimiMCAKFaECiUY6jdN9dH8N3V/DZl1c1sGmHVza9Z9ZF6d7oBPQCcJpFBaZE6wVjE8ip9GRwqGEj3qHShCIPPLtMqzuY7I+Nu1g0vyaSRtMHyUMgbAo4ROfnDVYU7D+jQSTclNp5G7L3xxXDkO0ExnKuKsG8lwVAzZA0P6g/a0nm84U//qv/8rv/u7vcubMGYIg4KmnnuKRRx7hgQce4M///M+55ppr+MhHPkIYhvzGb/wGv/RLv4QQYowgrSq1uoeET003USqgVo9p1COk62OSFZy1dBbP0MunbGXPkXW86WCy1EPPhUNKS44+J7YJQuT8sFb4PA38sjlQDhX4dQuADCQ2L2OVZCukHW+rG53QdUvYeguhAkTUoqVi8hxWdOJNqSzxZglCeua/nJxLG4O0g3FG5k6EQjGKSkn7QTGqnX30c1Lz9kObd0s2VYq3ve1tPPbYY2Pbv/a1r41t+/CHP8yHP/zhTS861/Iw7LimUcoRCk3YT7D9BVz7AtZo3LkXISf8Wgl8nWqBIFQCFUmUEISBKjFIxtQxeflLbS3t1Hf0IFBEtYAw9LkUzjmyLEG7nLlPKuI4ypM1F8kWFlhz0i+66/NMzZ8EIQjjOheXMkCQ0GRm/gRhEIHNyKxf72itCcIBxHy0iPxQ3sI+kiHFODCfdmY+XRYZehGF2TE6rU0yR0ZPUzEBRvYePXLrfbF6zkHbRnfZTorjoA37SyHG5Qq0b91T7s9nsScwjyhdxQGL51/GGE3aW0V3F4j0Mk3PbMpc2OFYzY/mr4uYIHcBB1L6yLGD1FhMQeYtIoTMWZMDhY0CnIMUR4pGZBrp+TyQxKgwTxiSITJnIA/0q8QyxckIbSWrnZTz3R4gaKYB8aG3AhCGDR+zcClSGHxxeYZMoxJ+noPeHJQcqPtZMXzTLvNcse6prvR8tLP72BOlkK//LQ5oGU9SYK3BBhlO5ZANoC3rdPN5TIqIoi5KagrYt0AoASqf7oRDiIIIMaMu5fB4nwfiKpsAMGSY/Li+OIpwvs41wtGsW1oqdwub16BfAwRSBSAbIBQLiSEVcCj2EBWdpUR5IciVnkYR+SWJNDRlRkwXRVRm900qBQYTFsHWIvMJy6RZnk2IZw4sgpqezJAiNSqQsvwihMIJr5xSCKxwaBwG7zwQGATe+eCsGEHz7m9F3li23+49AgTmxdXzIuult2gUiTfUXyZD2apUNd7blHeq6r8dpSL5726dAcS3pFrgZJDiUQTDKi2onr9y7uGOPgw0vBxe2nXzODYKsE/KMCo3uRHc4L/vVcWeKEWa+RnBlu4xW47kBQmAE3iiAihzFwoZppCZvN25AXvG6LiXx6DH2qWCMHfpasDiXEqWeSJmY1eRYhEQhGqWKIiRKiKyxncpp1BpB0GCydNVndG+qLvzHLhJPyHt9oiiJuElP8XLKwX2af/I3inm3uRTjHhhhBA+diFU7sL0fAAl4wYDpaiO+AUIz/9TVDaPgO5yEF5pAojhkb1sl/CYKykkgtzVik8gyrIOpuMj8VKFWDJQEZELUaqGcHMEyQrWWWTosyWUclC4aJ1BW0gNyJ2R3F1WKWImxXqn3LhvZom9a8OelgwejPIeo+PJMQrKyYFSWDfw9Q8fN+jcY+bECBp1K6mjrjhjPtILKZG5fe2c8cFCIXC6i9NtnIuQxN7ScxZpEqxzyNA7BQraHhBgCiNxP3Q41jW1r9qlw2WUPZopcin7h8+nHgD3iv3E8P5jZxATfx2ceFiRNlssDqLUvkNLoVDSw05Ca3PGQoHN1tC9ACkDnGwiwhTcPK6/gkUgcmoeJRwuVzDpFEKU9YM3bMdeyODZOJwbf65X70J7+7I3M0Vh5ogqZxJDUGyHwNn833J0RTD4pxvf6o8WamxrEbxbd1vu3fHrEYmQikD5RxQLTap98M6ki+isDUiEmkKEU7jZ6zErp0lFiFI5UwiWHMEI1lf4Mw723Hpyg5jQJDjHZTWh9os1tg3ZG5dsWT00VwrhVwFCDEwY5wYju1CbE44NqU3JzFdNdR1VHzeyfzUG4rDOop1D5PHNQIaI0HfnTGeYLM07V4JL2gibotfeIJV1wuYcAMZmeBUQYDRZ5v+MXr/e2hWTkcc2FHccVYpybTHY95JmiqtsktkbhsAoyD1Hamih7Jw3L3zsojKeysnKsNEaYRCXWE8Zxo8NwyBXCIO1vliiLEw4KX3ehRCEyiLzAIAwBlwPjUPYPgJVwgSEEGXMwFlXlqQydrfnivVpMyeB/sgd01flMH8ZZE8X2sMy7NsfXQOMvs71XnJ1j1GwW3muDUc9NzSMFt2iULKxQyt6t9Xus1+62SSU7LDsl5buruyJUgwAc97MkVLhK2nLstNZa7E5wM9Jk3uARpRkk75dDViNvXM39JF/SX0BUeeQDgS1ch+DwJq83dgSkRuqDKEsBlChIFQBKgxBQBDWcFbinEA6gXIiP+9+6GzDs8PQumo/NO+S5NJuYsdsHp///Oc9KjQIePjhhzly5MiW2Ty6ST/PnvP1KYSQSBkAEvK1RNWOFcKORWo3s3GFC4ZjyUNaVDlZZXva7/l8cRWAUES1WvmIMuPQOdwca8gNPnAWhUfJGqMxGI+kcgKDQxeWkrXYsuPtTyO7COCJq14xLq3xm6JkJ7F5/MEf/AH33HMPjz/+OHfddRdf+9rX6Ha7PProo/zpn/4pjz32GF//+tdZXl7eccMujwtQVP5Gv0/aPuHoMcjGZAjH+JEHcrXKpjNFwebxla98pdz2hS98oSxmMjc3x3e+850hNg+gZPN4//vfP3bOqXd8CItgTkPowJrzoC+gbEAgDmOE5LWowfG2Z9cTug0nDkEgWe326GcZUyKi5QI6nQ5OCNLZiLg4/0qfqLWEUIY3uhFdO8Ncc4ajscRay+LSCovCkzfPNAX63L+AyegldZIUrDZI4UF8tZygwKY9cH6mkKGiAEVIHWEUWOkwjZS4l2CUz+VOdY162kY4R39Ko50lXXOYyblXwMaDgcmy9ZOUcqdR7usa/1lCmi/7a4LSVWydIzUaESiv8tYiduAH2M04RtXck5UiOUDJP+ycy+uRbH+A2hGbRyOn0jfG8I1vfINPf/rT22LzMPFNgGDJFFCLkzhnkIAUQX5dwUo9R70qRWIMTjtkLBF1Sc9BgkDM+uBYZjKy/P57NUD4h2NqEoliDUcnp9a3s66Ema/hkNf6SqlWa6Qxfl0BaCHKmIIt/fdgKg86c+CERURzqBs+gbAKVfNK8dqL30PldDhW+iQkO1cn031Wv/e9zR79kDjnPHmBg6TRAnypsZdXOohuhc2jbFpuBhXeMzFwTAgcqVPII8dJhWDJB2vyMI1lFDxog5D2/IlttffKynDcaogqtvJTsX1UXyuc2xNlxwttYwz3338/73rXu3j3u9/NX/7lXw79vtHIcWwmASlxKyEYSd+2MW6JupO05BxOQHIo5uiF0/6A6cP827kzpEYzPT1LXG+gEkvTREwdP4GxhlcunCYMvNfquBZkqosTjjYttGxQ0z3i7gXv7jWW3syNAGQ2o9V/DYnl7IWzrCwvY7RB5kwdkQpwzpEmPd9hABkG5CUqCTKwQUp683/GnP5vyPY05s0/BcB1b/+PTCVrCBzdpmbh387QO9dm+poW195667aet8sy3MIy1lpe/t63wXmg4jVHb6Z+/EZkVEMLDx33fcESOg0u8jOIhDSHyEdYXj71LyxePEeoFNOhApvXGjQpODnkDGjPn2Bq4ezG7dvFmaI6W0opy0F71JVfzBTrwfRXjt848fw7VorPf/7z3Hjjjdx3330A22Lz+D+v9jDAxVcdWeLIVIKTmpox1FKNCATNN7X48aanoWn2NFNzR7HA1PQ8tbiB7BvCrgMdIXAcOvYm+l3PENhdbpPapvdBTB+nPjVPjT5BPy/kKEDV/ci3uriISFfAaaSMkFJihR3La3DW4cTuvfgrLpWYRBndZrDQHnV3bw6R2X3zad3ck3X+vd4xo7IjpXjyyScJw5Bf//VfL7fdfvvtPPTQQ6yurqKU4tSpUzz44IMTj//H1zK0hYUzNbJUEEw1iOqHCNIEvdQFCdL2OflDnhvp2n6buTedRIYhSjWQIiTptTl/9hyd9kVkoLjmXbdgZE5/2bYkpoZDIONr6NaPkuk+oZoCIYgihRPTAKipGrK7iLQZQVBDqQBrbBmbGHNZ/oCtoUc9fcPBu/0tk5g5JtXZru6zlTXGjtg8FhYWqNVqfOpTnwLgpptu4otf/OKW2TxWdERmHY35a5AupjXfZHauDv0+q2+soJ3m+/0XWcrt5sMi4WjrMEGtRnclIU0Mi2+s8vyp53n99dcJaiHvv/l64rmmv+l6lyANcUh6osZrbTjXccSqjhDQqoXkpj631udQUiGxZYFIKWU5krqhEfUHU8YhHlePTI7ID88Mhbm1Xh7OqOyYzWOSbJXNIxIZAsdcXRGIgFZdMF0HqRS1mYjMSs5qwKT5EQP6AKkUUkEY1ZianmGmnxBEIdaBysF72rlykWWsYy3NWOxkxJGPdyRWe3gG4BohPqFIM6lHDE2z+3/w3FhGzKJJpobbIbh9t8ynSZ154n1MMJ22KnsS0T6pLmAk3PWjP8qhqTmiKKU5ZanHUwgO000S/uR/fodgzS/uRKORAwQFQdxE1RTHbpzlxNxbIFRYZ/n+2sucaHmG8/OvvEQzDpBK8Eq7x/96YZl/eL1Nc34enEXoLrPWF1b50Z99G0YvI7MeAp2X/sofvPVuKP9AxbYDiPtNRhViHB078NRsd02xm1KFAI0ifYuyzqPrh+K7L/Emx+iHqrI32KdGDA76gaOrLG3Tod3rMyunaUQxiYAsbtA45F2bh6dSsrRDZlNePr3C0mqflpriRP0Y9Xod4wyraYI1CUIImgoi2UdKSZassLzU5uLFHqmsY60h6S2TCE9sltqUzLaRrpfPFrkUtnVBoCYLvHs+Wu06qO/yStHFJ60n3D5fU6ynoJNmjDEM3RZmjT3jfRogHtYfkfwLm3TsZBNg0veRI7fYtv2VrbwbMoR92t0r7/YFN5U9mSlecrNkOC6csQRBiptuIqemCC9Y3OIC2mpeWFb88IyPmkf9JZRyuAB6WYel7ipZaDA2oL/QRyrBiVuOgvER8DjtIutTIOFoQ/HuE01aWYriDZAOpjKmZnz8W/aXiVozSFtHdJN1lUoI6Y9lkK+xH1/o9mVkcVqExndtotjehdZ/P8NmUTWaXdQ/L/a75IX2lZCbTjTRCJ5b6dLpZiz0YfmioZ5mHOtZlIRbbpjltpavB2GWl0hWLuLCiJvedCNvvmUarERY6WnngUxqgmwNgP7aMktaY5Vi7vBRPnrHm7jnnY4g6/pwZk3hcir+paWX6BifR5GVYESXe5/8ucsHKQcPskDtui0Vp/7Bl90O3hVS7eBCiKFAXlUZqrKZW3ZPlEJJ7wkvPErGemIubT3tjXAOJQWqzAsauEWVlBAEOONJlIPAd1Bd1r8u7EaLcz5FKAoUkXAEOYTERQEuym+9HBgnIoZKm3SYX+pA9pNUF9Trkctttq0qe6IU72n0ccA7owzjBCleKQIhqOds4XFgMHnRlrWZFq69ihMCVjueCi+Hg/cLRxEObf3+drqFIcAJgemdo39hjZ416NT/7gQEkTfNSLp0ewpnBcmKgS4E1j8WAdgCUEWAsOXFCPORMQg7CKUxFqIsoKckKvOYr5poo2sRIHDdNsb0SMM1jJre0XPTWpfrHVklcZ74jgWD/N5i+BmMsAUkwgqBNQ6j/T7GOFQwOvJvvjjdyUyxE7DeKCDS5iWUq9+L9ox6n7Z6zT1RihO9132fth5BZI3BOj8LBDnlJBqy1LvXLDF0Viaeq+oDqviOEDYFHPRCnFIYrXN3nTeLRM5NK62FvOqRSRewrJbuB1cBBLKOHZq6DKu9uzjTIVrWyoeqpMjrfgsCHRCEiqCmkOH20ZvDjolRhBsbmObrxFkqbkybB+1c+bdJvGafyKgnab1A3nou2PUy5ffGfEpWfP5yoeHGgLWeZ0l7pRBC+O3g0aHbXpB1EDiEDhBKIYxBZpkfQQBpvVJgLbLfzw/q4nLu2FIqndcxPtJkmcUYiUKQWYlVAeRwEyEkns9KIKRCKV/sciMf+Zbvj+08kQmdvPicoGOTzJArIZfjOqNYpvXMpUnri2xsi5c9UYrV1c4QetFXHTX5GsE3SUo5UBrX2fY1jDHgHGEYIpXCGIMuAjtAFPpoubWOJElwOBLtMHbAMAJQxQCKAd1IKRkBWihiBFrEOBlji2w9DU7mWXraYrTFaFOm2W5Xqg4AAZfsIdoIvjLuGr8y5tNuzUD7PqJ9oe07XsHEZ63AWp+fLcs+OfDsSNXf9jVM5nOjg8AhlcVZ0GbQi0JdjDCQphKHIwgPI6Li4VUpd8ot49dxCkcIKkJOX0cQTRPUfV5JZiQ288VjwsyiU0OW6iEbeDtiq1T+YnLFoS3LGLbLS1EEZ3z3y995L/WcVaXalP1xvytF/eYfL/3Kxc1Ya4cWUUMviultT7OGZcCUIL/RBVrVdRflM0jdCoJtDr9WalCGjm0w/+YfQQUNaPjyumEcoxORX0+XbdndZBSyAAALjklEQVSJyVDl33XO5qbc9s5RVeoh86n4JkDkxNT7x8k23ppJ3qaNTLHtYp+2ZNw+//zzfPCDH+Txxx8f2v73f//33HLLLeX3J598ko9+9KN8/OMf5y/+4i/Wv6gKPJGx9ITGQqoc6Ff9HpTbZCARSmzrr3ru4nzlv9f7rbLPlv9UUPLf+m05I2D5gkbYvC9hcBzFJ13CiSrnG4ne7x9tyGVrDbqca6BNZ4pJxAUASZLwJ3/yJxw5cqTc79FHH+WJJ54gDEM+9rGPcddddzE7Ozt2zut/OK+a6ob7SOH9KFKIy7okcnXbN2ayBjhfhbXELVUuVphpjhzG5EBOBspuLDYB26X36mmOXP8mAiRdU8kZzr1dBJ5L1vfDHbxAV3VH5p151AklKLcPokCV3yliOVXTaXg3kaemXg1SVYTRmWIr8Qo9tsXLjogLAP74j/+YT37ykzz88MMA2yIu0AtnAOj3+6XZBD4cX5g1cRyjYg/FsEm0WTPHRIpFnzsNA3fqpNEkDxY6IKkF2IqXwo1q7YTtSkUELvIuWStBDnzlvW5Gpv2jrylDZjNSm6Hteq9jYyliC0VnFjk4casq5kl5St9u/mGxzlLctRCj1Ty2eO4djtSXa62y0aJ9u96+TfcOgoA4joe2vfTSSzz33HPcfffd5bbtEBdsVYbd72Kbf+MT7/Cv479N3Gnyqdc50b6zPcZkKypUxQeNJuas97eVfS7HMTs513q/rSc7Wmh/6Utf4qGHHtpwn41GgLPnfS63H/XwCzyKxvp9hFgdNHxHudEV+wjW76+VdZy3orZ3LU/ALMiyjDNnziIr1ocT58uzLQlICZCzx1jT8N3vfndb13HWYtMMB2Qz86T5M/n+0hpy7QUQcmCV5Ytnb0JJf1ei+igc/SCmft2NCARJOdt423X01bkwon/s+o2fww7Hg/1oqW1bKc6dO8eLL77I5z73OcATFNx777185jOf2TJxwczRExQJIc65cjaKooGZNAT6CuJJp9lQynVEYWPn3i4ozJABP1Dhfo0dqCK0S9G3qovb8YWuDiTaac689BLXXnuUZk2R5dZRKuOSIbAZwivffZbVi6c5eu2N3Hrbbdu6H93v0371DNZazrxxmijwHfGGE7dRO3YdIqxhhF/D+GZ6Ng9BhMNhJeic9id0hue++QznXn2VWCoaYTjAmQXgzHCuSP/YdcTnT2/Yvh151HagEds5ZqM1BkDnupsmHrdtpTh27Bh/8zd/U35///vfz+OPP06/398ycYHB43WczBd7MgAV+j/8jZuK3S1csH1YBD4l1eUrd7+mkBTwaFvZDwZmhRyBDRSfYuTclS+V/SzOqWJz7jodxENsbr87t38SlCZ1sZ2A6La6z6XKRkqx3vU3M5dGZUfEBV/+8pfHvEpxHG+ZuKBV8zXhXK3ScAQiMx6e7UAiS8p6QTpu/rjxzlkVqYbXFaUtWfw+IR5iGD3nsDE1MOcGqxZhDSr3fAQq9gBHneeWK81U3PDI3SxBqRphPI0K62Pw50n/HusAgURYQWYMeYElZKh8OWVj0YATAqV8vMG7rPKSAUIR4A9yqSGMmjRah3BZSj9NOdRoeMRy2seM17uZGD+qSnWhu9lovpFnaNI5qp1aCm8O+n0YfkeVUH/VFGeT84/KJRMX/N3f/V35760SFzTCEITA5KjPNE1JU9+RwihCALU4ppZ7omyVx7G01xk2c0bWHcVgXHqfKDrK0GmGHqpzdlBPovz/YKEmc20QBQJVOAIrkMYrsrN4tG9e3ssYQ9b1EJVep402EEYNpIq2HIktRQjCWg1rDcZZdG4SIkUJ/7BOIFSBoHWe/lK40ifgn4nAomhMzaAPWdYWF1i8uEhej4YaDlevLEDKZzMeABvtuJP2Gz2m2sGttWP3X+w/SmpWmr7WDvrASAeftLCG4UjRVgJ5ewMIzP3p/cS7ZHu9Hr1eDykljUYDIQSNuEac89XqSu70Rg+8KkUCO1Rcl0W4onqcGHyvFlMZHWQGzqe8w+X/VoFCO+8m1WmKihRTU1M45+j1eiwvLOKco9tZI03TbblQqyKEIAxDrBmOiDtb7ZiVBheNlmpsXxkGRPWYerPB2soSK+01ZOrz24/PzuQ0g9XRdfPOXsQJRpGqk6TYXryjUVDf6LWq6AedZuvGJNb7tM4OKUUh67le94a4II8YFXUflRQEyo8GsrD/ncOVi2FTOXTEnl/HWyQq28sOPXTs+GJaCTF2tmoHlqPbK4G0QYOqo1aOJbKOMIxwdYczJqf4356U3jlZ4MYm7LOOthWtK/hwFRIVBARRSBCFSCUx1vhOZwxuJIxSdYpUt1Wl6sTYSCmqnXk94rLR36pxBuvs0DquUMTRz+H7H+4lJaR87Ope9kYptAf4RcLhJKhIUZM1QBAEvnSw0H209SaVdtnEFeFG07lSquzRRYcafK/s6wYdWcmQsg7fyBRtK/s5N1ioWwmpTnHWkvY7RK6GznynF9pQC0IcMNVsEtfrhEEwCKdvQ4QUhFGEMSbnTx1G80J+2gpC2uUmHoBFkFiNwxErSWtuhkazgXOGxTdep3PhAkIIlrpt0pXu0DOdOXYdC+fPDz2P0dG8SsK9kWKIofaO48A2M5+EY2yWmKRoQ22oTp6VOMV6qV57ohTLb7xemgPFg4mEh5LbrI9zjk6SoPNocNSsjU2xVZm0rTgWxh/c6IhXfE/6miwbuGqrIEWtB+hWrXX58DOrMc7ROnyE733nNGEQoDNTXvfa629AKsXho0cIAkU+Fe742Yn8vPV6HSEEKooGeKtRw0wMwB4OSlLoxGhqcUTciDkeXk8oLC8/912sMZx/7VXWFi4MPafGbRlnz56dqBDFZ6fTmfjb+LsZrNOKdzTJfBq+DZ8P4Z0ZCikGylQ91uQ1BcGbZsW/VRCgAjV0jJSS9/6H9058xntGcXMg/17lyrttL1WEu1zgky3KP//zP+/m5Q7kQDaUd77znWPbdl0pDuRA9rscmE8HciAjcqAUB3IgI7Kr3qff+Z3f4dlnn0UIwYMPPsg73vGO3bz8ZZNnnnmGz372s9x8880AvPWtb+WXf/mXuf/++zHGcOTIER5++OEhgON+l0lloSfdz1bLQl/V4nZJnnnmGfcrv/IrzjnnXnjhBXfPPffs1qUvu/zTP/2T+8xnPjO07YEHHnB/9Vd/5Zxz7vd+7/fcn/3Zn+1F03YknU7H3Xvvve6hhx5yjz32mHNu8v10Oh33kz/5k251ddX1ej330z/9025paWkvm35FZNfMp29+85t88IMfBHzlo5WVFdrt9m5d/orLM888wwc+8AEAfuInfoJvfvObe9yirUuRXXn06NFy26T7qWZXxnFcZlf+oMmuKcXFixeZm5srv1+OzLy9lBdeeIFf/dVf5ed//uf5h3/4B3q9Xmkuzc/PX1X3Nim7ctL9XInsyv0oewPzYHN48X6WkydPct9993H33Xfz2muv8Qu/8AtDXE5X871NkvXu5wftPgvZtZliUknhggnkapNjx47xUz/1UwghuOGGGzh8+DArKyv0c/rNc+fODZkiV6M0Go2x+5n0Dq/2+5wku6YU73nPe3jqqacA+M53vsPRo0eZmprarctfVnnyySf56le/CsCFCxdYWFjg537u58r7e/rpp3nf+963l028ZLnzzjvH7uf222/n29/+Nqurq3Q6HU6dOsUdd9yxxy29/LKrEe1HHnmEb33rWwgh+MIXvsCtt966W5e+rNJut/nc5z7H6uoqWZZx3333cdttt/Gbv/mbJEnCNddcw5e+9CXCgkF9n8toduWxY8d45JFHeOCBB8bu56//+q/56le/ihCCe++9l5/5mZ/Z6+ZfdjmAeRzIgYzIQUT7QA5kRA6U4kAOZEQOlOJADmREDpTiQA5kRA6U4kAOZEQOlOJADmREDpTiQA5kRA6U4kAOZET+P8yHGhitgvn0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPad\n",
            "Predicted Label: iPhone\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPad\n",
            "Predicted Label: iPhone\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPad\n",
            "Predicted Label: Macbook\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPad\n",
            "Predicted Label: iPhone\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPad\n",
            "Predicted Label: iPhone\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPhone\n",
            "Predicted Label: Macbook\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPhone\n",
            "Predicted Label: iPad\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPhone\n",
            "Predicted Label: iPad\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPhone\n",
            "Predicted Label: Macbook\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BhxYucCkAv7fj7WlDr2oeK8pKmZ2d5WMf+xif//znGR0dBeDcc8/lxhtvBODb3/42L3vZy1bY1acbzsderpTZunWLC05p47LsaA1ekQWojvKP3Fn1o2d2He2CVXzudmxndZDWc/oQnXQUgRASlabSCgFBGLYJV8v71N9Y0Urxz//8z0xNTfGHf/iH+Xt//ud/zvvf/36uu+46tm7dyiWXXLJqnXxq4X7UUrnChi1b8TwPk3pXigNLSoGwsmvuwWphoZVlocHXbcXJ3u9VNidr52Rd5DpUS2BcnSopFdZIIKFUCtFG57kWSZL0Pe8JVigUl112GZdddtm897/0pS8dcYfWCqSS+GHoeE1AFEUds202kx7dQbCcmEaxfTaDd6pJnecrCkpG68hZsgXhgDSZSkokzoj2Pd/ZVTkb2PS74wk4xnO0u8FgMThCm0ljAFketu/7LqqcukUz0lyGFtN6vmrydGGp11+snZROpcoKOyilCMLQrQ6AVIpmFOXuYFJ3cMvVa/tmFVmneXTAYtI6ThoTuRRL3/PwPCiVSni+DzgKhhKplZ4hVauwFimO7nyzWJGCblUGjwRCyFYswko85VEKw1ytlELQTCn02XJhW8tOX8Uu1oWiCyyODu5Icq3f0xHyRMo8leR19Zb5g3eblZ/uFaUdIlWrOoWroFwJ4VbQlMkbJ0krQNLnWBeKDkgUSrj0Sul5aK2pVqt4ExVEELj3lUJagUQgTC/iRm8s5D162lC4hdxmyv+l1BDRWjEydVJJlRdJU0pBWqpnLYn4crFuU3RA4AZ71Iw4dOBAq+AA5LZF7n1awSBekwIB7Vpg9pjnnWo/oFjzSgpBdW6O6swMuku+SD/hmBWKtvk9J7LmSRDs2bOHm266iTiKGR8fJ2o2UZ5XEArZilUsdJ0jCMAVjy8+jgZ6nXe++uSehHRC4VJ1FaVSifvuv4+77rqLmdmZtu8yv8ZR6fnq49hVnwQ0FYQWl1SjIEkjuL6xBDoBYhpeE2GaDCSaQAnCwGMgDDBxgjGOFeopDykESrh01CiKyPTyubk5giDASwUqY5QuVHa/rZvLXFk6z9MZm1j8a7EIYhztXKDTd6wXuCC3kEhfEMcxCkNZSWITEMSK8twsZnof1lTRchysItDp9+tBJAwhauEOrAEcu0IBeT6OKLwWaWU0m+U/pLkU1hrCoMLs7CwDfgm0RmiDsJDYBGNdkC/LVc4GfqlUynMcsk1Sllr2Zqkepgzd4hCdwbpu8Yt2wcxz99yqmKVH5Fym1jGe5zlvXHEVS122re82PXfbF722cWwLRRcIUdhyK/W+ZIN5eGTYRbiNwSQJfroTkVISKSSBr/CDgCiKCMPQ5VCngycIgrYsuuxaS+nPcvrebaXoFbzrGdQTthXVTt92gTuR7wAgcCqkpxSGDjVvyT1em1gXik4InEcpTasUQrhaUMbkezOAi1mUvADSzywQJ0me8D87O8vw8DC+7+f53VnJSVg6SXA56LVSFEteFtv1IgS28spFzpQVUrrSVbRm/SAICMOQqjEY6fK83XcxP7DZT1gXinlIZ8g0yR/AJfzb3DbIiiLrKEZYUFIh0AS+n+/fkO0X12g0GBwcbBuIRVrFagqF6+v8lWLROy6qVanK2Po2BLmDVjibI6N2KKVQnjcvnbVnemGfYF0oeiBzuyZJgu/5iKSZG9HB4AgWqNfrVGfnqFVreMdv5Pa77qeSpoQqpdi7dy/79u1jYGCAE088kVKptOzVYiX9hu6Ge6ct0fUzBMaa9HXmXXMCYBKXciqUyrch8D2PKGqC8pBK0mw0jlrl86cK60LRC8KtGFprlOehUrejsxEUOoqp1+scPnyYqUOH2LBplJ07d7JhcjJv99hjjxEEAfv372fz5s2UC1UDj06XF/c0LeS1ms+ibRnKWe0orR3tJXPHep6HbtSwgJSKer3uhEKIvl0tjkgoGo0Gr33ta3nrW9/KOeec88yo5pHnN7e8RcYYx5oNAsrlcl4eUghBGIZMTGwgDAJOOOEERkZHKZVKeanN4447joMHDxKk0XBoLxyw2kG7Tg/TEZ4tN7SzU2UBO6PdapKplG4FFE4oEt239gQcoVB89rOfZWTEJa5/6lOfekZU88grIqWs0KxOqkzLQQohaDYjAs9jZGSESqlMpVzmUEny4l/6JYIgoF6vY4zhhBNOYHZ2lq1btwKtcvzZBihHQ33q5nJdrH0nWsel9HhBTqH3PA8hJYl230mlUsGvV7GFgnH9rj6tOKL98MMP89BDD/Erv/IrAPz4xz/mla98JeCqedxyyy2r0sGnGpnR6Hke5XI5rxSYGdpuQFuS1N1aqVQw1pIkMTpJmJmZYXh4mEqlwsFDh/I4RTF4B0dHIFblvKLlACiqT1kUv1jGM+M/SSlBuMy8oaEhPN/ra17gileKj370o/zJn/wJ3/jGNwBndC61msdaKVxgKGyoKFJXY/pam5jTL7mIE+ZmOS+JSKxmdHCEhi8L5WsEWI3wBUZIDgqLCH2mq1MIKfA3jbK/2XQkuqSG8AT46U4/6TWPTkTLLkmf792kOFcKl7Me4iLbqXvJhh5BZYTf/h9voR7FeMDwQJnhkWGmG5rZ+x5sC96BKwoh+0CxWpFQfOMb3+Css87i+OOP7/r5YrrsWihcYIEmEBoQKc0jFgasQRnLzGN7uO3ffsK/fOdfuPPgLp6cO8xvXfybvOmNbySpNiiHIaFSWG2Io4jpAZ9tXoW5atXFMjRE0w2G02BfEIRtu4uKTCDSMdLFH9Slx0u8t2zgzjvEzmuXcZqyY9z/mc0jsVgOBzCmPYyBONEYIEk0tWqDr37qi/xs9+NMSsUrzz6VS6+8jO3P34EaHUPio4xop3mItUPz6FW4YEVCcfPNN/P4449z88038+STTxIEwbKqeaxlCATCWupzVZ7cuxdrLM1mM41kg++FVEbK6GaEq8BvUZ7Ls9A6pjIQ0mxGAChPUK64DUwy47w4+IqD1Obpra2CAaLDyM2PtYUPu93DAok97ecotm0JhkkZGcJapE29SM0YLEgD2mqEdSG6sFxCUScOKuxHMGN9rAnxjY+WECkQylXzCKw8OgvjKmNFQvHJT34y//vTn/40xx13HHfccQc33ngjv/7rv97f1TzSRJlmo87BgwdzD1TmhWo0GsQGkmZEqCSlIMBTPhZNvV6nVClhjM7zDmZnZxkcHMyN7ExX73bZbs+FLrVyeMSCMgG02nd7Lz9H4f3idXpBConwJNYKEu0qnpdKpfQc6Xdki+E/kXOg0ld9gVWLU7zjHe/gPe95zzOimoeVgsRCo1HPXanSOiOyVC5Tkh5iwJDUG25XIyURvqQyUCGKo/QYlRvsSZI4t266K+pCWKqhvFj2XifNfCGy4LxiBjirQmQWgHAbWlorMAi8lPwRBEEerc/2Bs93M+oXCeiCIxaKd7zjHfnfz4RqHhmhzaVZqlbuhHHPvueBdivAzMFDDA0OMDA2iiAhDEJK5RJxHPPggw/heR6bN2/OOUJLjRt0Y692c7MuGJnuEJCFztX5XuqFLVA8rFv5SGM3ouWNUunur27PDF3Iu+hfqViPaHdA0PKX6HTPOyEc3SOKIjcQgNnZOQaHBmk2Gjz44EP4J2zm/kd3MTo2yk033cRznvPcfObcv38/J510EmNjY0gpuwrHQq7abgG5XgLRya/qvMZiAtEuGC3it5QCk7jVwAoBKvVQ2YLqpE1b4YJ+xbpQdKKgr2dMhWywhGHLaDbGUKvWqNeqHDx4gInN4+zeuZONtY3Ecczo6CiHDh1i165d+UBzm66QB7eWMut3/r2cImjzaRti3vvdhKT9GvPPSUHwvEI2ohCCRqNBEseOZaz6M7FzXSi6IB0uSJHaAOkPXi6X3aaHzSaDg4M05mapVCp4ahP4HhsnNzI4OMhLXvISJiYmCMOQMAwZHh7Oj4XlqUOdWElQbiE1q+2+FxC47DMplUs2SoN4YRjmAoEQNJtNTFoLql+xLhSdyP2mhQJeHQZrHEcMDw8zPjyM1QlSwF6lee6ZZ6KtTvVry9DQENu3b6derxPHMY1Gw9Gt0z0klkPHWCvInUlpvCVb/cCtgLmh3cdYF4oO5HljeWyg9RNnFAeRcqJinaDjGCUFtiSJ4ghtnFB4nk+Wj51l22Vkuu6M1PmCsVLG69GAzWIj2VZh1pEDs6Qr6BCKPhL0TvSn0nc00RblbfGerLWUy+XUtijl0Wnf951nxljiKG55q1LjvNls5jTror5+tKpyHD20G/hFfliGJI6JosjFw/tYKNZXil7IPIu2VQ81y88OfA+hFL4QWK3RSYIQGt/3EErkMYnMd5/Vo10oRrHcWX+p7ZdPIXcK0qJnF8xTn1xVxR5bH9M/jtpjWijm/0CSGItuRByemWN/rc6+ah01PEa1XkMFiuHRIaozs9RrVYQxjAwOMTA0hGjMEPhlGlFEtVZndLRMs1ljfHyCarVKEAS5ejE/8y6NBXT0plvexUKGebf2toOU1+0LyIJvKeUPlZ1XphELYdBYkBLfC5BaIz2F1hYSjQx8Em0QQlKdnSNuRGAsUok8kKehD4rbOBzTQtENSkiCygCTWzZz4onPYqZZZ39zyuVka52nmlYqZZJGRLPRzFmz9VqdoFxiYmKCw4cPUy6XOXDgAL7v56rTWjaq8751WVmcF06ljoYYhUUpHz8I5kXO+x3rQtEBIyzSZl6mhGajQa1epVar5TyfqNkkUB4bJjcQN5rEzQiESyKK5ubYvHkzTz75JNu2bQOcmhFFEVEUEcfxsiLb3eozLda+/b2F+UzZcYupWVnFEoRTmaRqZds5fpg7Pk5iTDfiVR9hXSg6kNUSN6ZV2MxTXs6B8n2fOrD3yb3c+tCPOLB3H8NDQzz73Bdx6JHdbNi4kampKf7lX/6FV77ylczNzWGtpV6vs2XLFiYmJhblPxXRKRDL5US58bnwMW3BO5yDqUs+XssBQTbmBZ7yUtXL7byqE933q8W692keMuaPQ5IkJGm1P6Vche2BwUHiJOGxxx+jWq2mnqaYnTt38vDDDzM7O8vjjz/Orl27eM7zn0+pXGbbtm2MjIysWvBt6ccufN488FZ43Q1Kuf3ujDEkcZxnExZz1q21JDrB2t7Gdj9gxSvFDTfcwDXXXIPnebzzne/k5JNPfmYULkhhrXEV/bROcw2c+1VrTVSv06jXGRkZYW56hlqthhCSrVu3MjQywoYNGzjhhBN43vOex6M7d6KThP3796O1ZnR0dMnfSzejeiE1p3usY+mR8sVmeJN+D8WU1Fzq0nwMo82i6tpax4qEYmpqis985jN8/etfp1ar8elPf5obb7zxGVG4IIMx1pHfjEF6bhCU0lkxThKGhoc59ZRTqc3NIRGEYcCmk56DHzpG7Etf+lImJyc5fPgwg8PD1Gq1Qo53C/lgXyAOvNrG+UL0DqcVObdspz+sGIAEiOOYwPedu1kqR4B8BuyQuiKhuOWWWzjnnHMYHBxkcHCQj3zkI1xwwQV8+MMfBlzhgmuvvbaPhaIVvAMQQiKFIExneKUUGzZMEEgPldogT5gGI6USVrgyLzt27KDZbLJ9+/b0HO1U7uw9ITLiRHfH6ZEKRGuQd/cMdQsmCunsA5HnZNMqTpAaFIlOMAbK5XK6w5NE6wSjNf1Ok12RUOzatYtGo8Ef/MEfMDMzwzve8Y5lFS7oC+R5zq3Idk7zyJqkmWZOR3HUaSuXRv0+EvQSlKM5Q1ubpauKXJ2UUuV7ALpru+qCx+RKAXD48GH+6q/+iieeeILf/u3fXpaveu1W82h5XyIbcd5lr+fsxq8SC0tsNHKgwv60xpGrVJHkk2JiLXtoQhcbc7HgWY+Xq46Ff5beHyZY9oqoFewDrLAIoRnYvoEPfOiDSNwWwpVyhZmoxr0P3OvUr8JN9YuTdkVCMTExwQte8AI8z2P79u0MDAyglFpy4YK1Us0jAoK2ah5uy2ClYefP7uYbf/9N7rr/Xp70Y/ZWp7n+r/6azWMTNKs1bGLwrMjVpyeJ2CLCfKXoRDfPTlF96hbRztr0wkIR7bYbZX7V8fzjzves60vqeMUCe4jYaHyMFSCk24cjiSmVKuw+OM2HP/ghAg7OsLwAACAASURBVATbN2zmzNNP58rf/1846TnPwyiBTv2bwrqI9loKXvaq5rEil+z555/PrbfeijGGqakparUa5557LjfeeCNAfxQuyJaF9j9ztSl/WSDx5Vt6uQ9ylSgzPmW+3VXv7LoFcxaW0f2eq88qotifTjKjMbaVnltobE1WgTx7s/9UqRWtFJs2beLVr341l156KQDvf//7Of300/uqcIEAAg1CglX5Dl94BtCucMGc1JiSxyCChIAB5RPN1dBxQqVSYXhwCGstM9PTWGMZHB1m35N7czq153k5idBa29qIfd4ORRnHqJDytwJ07t1tbV4EtP1qXeydfMCLogYo8gQKI1y/Msav73noOCH0A5IEAiWp1+tYqxHKgkp3U03n3bWzPiyOFdsUl19+OZdffnnbe/1WuED0eJE5JIslM7XWVMoVoijKa1wlccJIWiKTmToAk5OTVKtVms0mQ0NDNBoNgiAgSZI837vILG1dtF0gFo1cd5uAC4d0W5EWytmY5wTI24p8snd7VIi2Jp0u5u4uWVH4f+1jnebRCxZnUKcwxlXZHhkZYWpqin/7t3/j0MGDSCGZm5vjvNddxEO3/JSDBw5y2WWXce211/Ka17yGRqPBrl27mJycZNu2bQwODvYc8EumdKQc7M5PV+L16SxggHVGdPpp3k4pBVa2kSKlbG3Ykq80xmDzvcX7RQzasU7z6AFrWrkB2caOpVKJWq3G5q1bOfuFL0QpxfDwMJs3bUrL9btMtN27d3Po0CHuuOMOTjrtNKq1Glu2b2dkfLzn9YqUi4XoFq7x6g63btd2j/QzRL6DUxzH1Go1oihq66dNz6O1Sb1z/WdLZFgXinnI/PAmLz8P5HvelUolDh08SNRsuv0oRkbyZBspJaOjo2zZsoVTTjmFl7zkJdx9++1smJjgwfvu4+D+/W0b1HcTgE4e0nIfy77bzlWph05p0h0gfd8nDMP8PjL1KVspjHX1ePsZ6+pTJ1yB2FyV0DpBSEeGyxKEdJIwNDTEyc89GWMMzUYDhobYfNJzELiB8+IXv5hNmzaRJAlbNm2iXq9TLpSYzJANxqJna9HBvQTuU9GA7pUN1/U6FkTBrdzy1JqcR5XZRNlEEUcxYRi4gg3rhQuegbDt+rTWJne1FvOyh4eHUdJtFSyFYE9UZePQOMJCs9lk8+bNGGPyDVvADajOvbSLn3Ub1D27SXcVqvO4xVaQok2QG92Irl6rDC7VtrVyJDoB66dVTExeIqhfsS4UPdDaNrdA56BlkDq3qhuAzuPiqll08wr1YrgWB2s2my83f6Ktz10KI6wW5cIJl8SxWpwhXdzEJYMxtp9tbGBdKOYjI+h1pI92FjITQkAaqCo4L3N0qiy98hY6z1/8fLl2QlFVWqx8zmqiqKppo9dXimcsOqLP3YTCpDWe0gPQ2uRDoei/X0wgilipwVw8vv02VmelcKulBityNcmY1t59RftlIdWrH7AuFJ1IVwprbB6bsKSqUWoTZOqTES77zFMK0YgplcIW3aEQFCs+t1+q9V4xoLdSoSgKYlF9Wp2NGTNh7e71ylcK3f/pqMe8UKyEiZTP5ta5IHUqSFpnZWLEPF27c/B0nq/b85EE43oZ6ss6Y6GxlAKBanN8ZfttuGtlxxR5T6Jwov5Rp45toZCO02NbLx2ExACRToitIZESUQ6IVQi+Im42iBo1BssVKqUQIWBfUicIA6pRDWssXiwZHBzMix+0Bbrs/AodRQFaVBhE9+hdquC1veeKIXdRqXLXq0gHsm0NaCvzUvxu5WxijMBYZy9oa1C+oqkjQj+k2UgYHBzE90KiKEYXVMoWk6p/hlr/9PRooIuFLFolhNtUkDxH20JU0/zsZ3cyM3WIzRs3MjBYQW0e5z8f3c3g6BA7d+5k72N7uPjiiymVSmR5zVnhg6WoVkvrf8dxncKU3UqxXdZmAQYJptXGpJ4mISRKgjaaJE7yeEXXPJpuQt0/C8V6RLsrrM1pHsU976x1m0KWSm63IikVFnjggQc5fPgwP/rRjzh06BD79+/ntttucznMQYDnecRxTL1ez5myaxc2v2djHGXD4mwel1mZ7kGRJDkLOBP6NqHoY7tiRStFtVrlPe95D9PT08RxzNve9jYmJyf50Ic+BMDJJ5+c52v3HdKZEdse1MoeLoiXIIDDh6eolEts27aN8uAQzznpOcRxzK7Hd/HsZz87XxmAnAbiLrG0AXO0Xak9rpovLPlKZnFbd+HUvjAM8TxvngHfuq/+JgSuSCj+4R/+gRNPPJF3v/vd7N27l9/5nd9hcnKSq6++mjPOOIN3v/vdfO973+PlL3/5avf3qUGBMp4F8TKBAJe3cOZZZ3HK805moFKhUi7xRFLl2S96IY2kyfj4OCUR5CtNNpt2BuiWgm5tj6Z3RwBStVytBiBpxVKEEGmldeeJy3Z3omjY92duUY4VCcXY2Bj3338/ADMzM4yOjrJ7927OOOMMwFXzuOWWW/pUKFqhp8z1mnlYjDVu9k/rN/lyLKV6gKo2GBwYJDAhY2NjqMRV9YAlZNz18EZlx3UjDHYet1CljqVet3WRzLYqzPVCpJwwciEXwhniWSkgx5LVbZT7fsSKbIpf+7Vf44knnuDCCy/kyiuv5KqrrmJ4eDj//BlRzUM4NUKKVnqpNZnu7KrkxXFMHEU0oygvs2mtJYndBpBBEOQzqjvlygJzKxncy6F5tAltp+3eakRW0aMzLpFtiZwLhTX081KxopXim9/8Jlu3buWLX/wi9913H29729sYGhrKP++Xah7Q/tMJyN2SOpRc9vv/lThJsFIQRRG7D+/NjXA6ZvDYanY35vJBJWzH6DpC9bpbTvaSIsddmix6XEffYzRPJLPtTZx0IBp1Pv2ZTxCkQUzPU5hSyL0PPux2UV3wDtYmViQUt99+O+effz4Az3ve82g2m21elX6o5pGNC01rDChAJAYizR0//glf/ew17Js6RBRIHtn1GHd+/6fEjSYmjjBxjC8lSjhG6a7mLMeVBjDSCZVnOvegWDiivdj73YJyC60GvT7rVOW6nQ8j8yFsgCeSWTapgZzCYcjyJiAMQn7lv/8h2zZuZNPQEBvGR/jf/uR9PP8FZ2A8hcn6jbfm9qdY1WoeJ5xwAnfeeScAu3fvZmBggB07dvDTn/4U6JNqHj3RMip1ull6lnmX7U6U0R361buSoZN0KITLsut2V5kaKdLnzNbKvpPMBWv73cpmhSvFZZddxtVXX82VV15JkiR86EMfYnJykg984AMYYzjzzDM599xzV7uvRx9tHsWsIp5DzoOyFkkaVO42egrcJ5FV7egk6dGKq+UfH6VxtFheRns7QHTWkG0FGqVwkW4rWiuXTHd6te3N5yO/2eXfw1ONFQnFwMAAf/mXfznv/a9+9atH3KGnGgqnQhX8OyAkQiqkcftVxM0mSRQT1xtgLdVq1ZXVx20fLNKk6SiOKQUu2Sa2Fl8qgsKG9BlZTkrZUVPJMTGWOl6Wb6wXFQKbOpKKVBPanhNZjOS7L8hIt3+flJJyyW2IOTs7ixAWEydgLDqK8aRCCgXW1Z2VKY1mralOC2E9ot0VNjeks4AVkJeqOW7bNmbn5pBKUa5U8H0fKSWVcoUwDInjGM/z8H2f/fv3c+jQIbJ85qcnINcdixEGW8lUDhmTt1ar5bWDO1eixdzP/YBjm/vUC1pD6jgwxuAHbs+68vgISZLw8EMP8bd/+7f4vsfLX/7LVEoh8WDIY4/cxYMP3MfIyAgXvOpX+elPf8r09DRjY2Ns2bKFbdu2EYYhURQ9zTe4MDqLESDIBTqOY6rVal6kIU6/pyw28Uxwya6vFJ1IDWtjXIU7a2w+SA7v3Z+bkWeeeSbHHbeVcqnE9PQ0jUaDeqPO5OQkg4ODPPnkk9x55514nsfg4CB79uxh//79a04g5uVod3imiu0yWkcQBC43O0kKBnbWzjhCYf/KxPpKMR+upL7WGoELvGX1UnWc4JdKjIyMcPbZZ+N5kqHBQer1GnubVXaMTlIK3W5Hw6PjDA4OEgRBvjoMDQ0t2fB9KtCrH6aQKJTRzIuZh1lZ0CzxqihMed2nPpaKdaHohCWtcpeuFAUvUqVSoVqtUqlUEIDnuUh3qVQiJGZicAxPOp07jmO2bNkCtHTzftC3La54chttnna6SfZZ0d5otTe5IPUr1oWiC6y1JInLG/B8jyA1pLN6SEmS5BwoYzSqwH5NEqdiZBslZnGAYomc3uhthHcG2FbDYO91jm7cpW6VRzJBL+7hZ4x134s1/eB97Yp1oegCY90un4lO8D0/97LoRKOUIqq7PbWT2OTCYnGF05TA7e6TbsRuc7ViNfKkW+gWse4lcK00h6XzoTIspWZUGIY5OdBaJxTWdN9vox+wLhSdKPywWa5xMS/CNSlSLrqfZqGBd6Sz/HJdoEWhOBL1rWiQF22jzBEhM/VqxVdYG1gXii7QRrdcjalhWczAy3RpIQUy3VS9CAGtaHYHVsOm6Mz3Xuy81vbec6/rcYtwqTqvmeVXLDeJaq1iXSi6INOLIY02dxjJ2eDKyt90wmYHHkUshRTYatv9mK5tl3DNzr+LRaOzs/SzYBzbQmELLOmCv90ak5ersan6VBxQRa9Lt9m3VS+gJTz5JZc1IBdSwVo7BHVyqXod2ibMPfqR0+c7e5K6XduOSzlPWaRbSOmO7Ty8z+Tj2A7exVDDbQhJYhFGY0STxNQxjTpxtUEsYHh0A5vCMcDxvsY2TFIql3OSYKVcRkpJGIQMDw9TLpeJ45gwDNFa4/s+g4OD3XcwSmFwqZ/Fh17wIeY9TP43JNmjEIwsqoAZiizgJElcQM7Y9ocFGyeYOEFogwKUBbTBxglbN29hKq5TjRvISGNrTWySoIXrgyTtdJ/g2F4pukAqhfIUCJBKgtDpxumOOu77Pt/9zr8yOjLCnid28/jjj6Gk5AUXnM8jB2e47u+/wute9zqscMbn9PQ03/ve97j00kvZsWNH14T/nljCDGs7niGdrFs607K9QJ3tBS27IYoiZmdnkVIyNjaGtWnEv3CQ1i4TT4mO8/WJO2pJK8UDDzzAq171Kr785S8DsGfPHt785jdzxRVX8K53vSunLtxwww284Q1v4E1vehNf+9rXjl6vjyIErVTLLLNAKWdTeJ7nNjs0hjiOmZmZoVqtorXGWEutVmP79u0cf/zx3H///Xz/+99nbGyM8847D2MMMzMzxHG8Kv3sZvB2/r2a6HbeLNU2dzykhdV0GqfoVyy6UtRqNT7ykY9wzjnn5O996lOf4oorruDiiy/mE5/4BNdffz2XXHIJn/nMZ7j++uvxfZ83vvGNXHjhhYyOjh7VGzgaSLQL3GWJNAOp6pMNjNNPPx2lFM997knUqlWEUhy2TTaNbOCkHScSNZtceOGFxHFMpVJh27ZtJElCEARt9slqoJs6VHxeLWSZlUIIF9FPCxY0o6ilFqYrQZzmqPur2oOnDouuFEEQ8IUvfKEtvfTHP/4xr3zlK4FW5Y4777yT008/naGhIUqlEmeffTa333770ev5UYK1FpNotxFJmkwTBmH+w8dxTBCGlEolAj9gbGyMyYkJpJAEYYgABoeGkFKyefNmPM9jeHg4nxxWY6XI1KNeAlF8vVrCUYxP+L7fmiSszeMTGbLgXb9i0ZUiqwJXRMalh1bljgMHDjBe2OhwfHy8Z0WPNVO4wICWaVwh1b0tlqQZse0Fp/G7z9qGljAyMsbzn/ccfrH/8Y5B5vw+Qri6s49VDzsdvuFUsH0HGm1qWJGK3Ynlqttdh1y3kAOt6y94Ntv2qg0Jhv2i6V6YjnMpyx/84VuYq84Rej6eEIxObuDRffuRU1NA5tFaRhbV04wjNrR7Uwt6zxRrpnBBE+ZCkMJSig1SGDSaPQ8/ws+/fTNf//o3qJYkr3/jpXzls9fwj9/6ltOhcXEMkRHJrWVPY5at4QAyq5UheuearU6ikcsRXyiolgfXRHeFoFjXdiG160lTZ5ModT+HNXzsb/4f/vUH3+W5mzYzhuJ//e+/z/N//SIGtm7EIAgsoL0159bpVbhgRd3MNlcvlUp55Y6NGzdy4MCBvM2+ffs466yzVtbbpxHOWGwNmLwo2JJqNrm1RrgDlnzNlag5QiiWFCSz9DR6M3ds9nf2vLz6VCL3Plnrblsbk2+H1o9YUZzi3HPP5cYbbwRalTvOPPNM7rrrrtwjc/vtt/OiF71oVTv7lCCli+eMVoHTm2WrZE0+aAQdf6+84NmR4mmLIItWndxMoJI4Rq/5QtK9sehKcffdd/PRj36U3bt343keN954I3/xF3/Be9/7Xq677jq2bt3KJZdcgu/7vPvd7+b3fu/3EELMK5DWL8jysnXG9EzZn22lK/OSkqJtQXAlYvIXXc/fy7W5XEHKdiftNLTnnUe48jSL9eVIvFZ57nm6VCRpnKJfsahQnHbaafzd3/3dvPe/9KUvzXvvoosu4qKLLlqdnj1NcL536+qjpiuEoJPfY3MByCth0BKKjOLRC0si5S2tt0s6x0J2XzebYrkCmruacd9H5pLtV6wx0+fpRcYhynT14mDqmZCTe61InThuUJmOYxdjo3adWReQFcd9akWt552z8NxpUxQJgt0Eo3NrsvZj538nnW1NZlNk1yn2u/ctrRkc29wnHwaAEMAToARo0IkhUaDKIbYeUSmV8SaGCaTFxg2+dt3fc/3XruPxxx8DoFqrkcQJc/UGDz3yOKXBEarTMySNJjZOCKTi0L796GZEyfPxEHgIpAVhLMJYxyfq8pA9HsIahDVIo/Pn4t8ifcg2gUi9ZTm7ytWAktI9lBIoJbtqfkXBmZ2dZW5uzgm/1gyVKzQPTNOMI6qBYJaYSGuUIQ/gWdXa6Gut49heKXLXuaC1K7yrLm6NY8hmZSGVUlSrVWZnZ4njmOc+97ncddddPPTQQ4RhyOiJx7H38V8QBAEPPvggd912G2effTaf//znOeWUU5ibm+O8887j5JNPdlcpzriF/7v3bz7yQUpHtcGOv6Gl4nUuVq3BX1TDul8vo3Nk9asy75wslM7M7DGTBvVaRPLut7dWcWwLRTek7tgkSTDaIP2AKIoolUoYYxgfH+eiiy5i48aNPPbYYzz88MMpI7bExAkncPDgQY477jhKKXnwj/7oj5iamsJay8TERJsLdDHj9unyZHVDll1XzD3Pviff90l0knug4jhetymeUbAWa9O6TzgKQzYQPN9HKcXExARCCEZGRjjttNMAmFGaDSOjDA4OorXm+OOPZ2pqiolNmxgdHUX5PjodLL10+fldWdjofSoFpmgXZZwwIGfJGpuuFMbmLNl+xbpQdEG2sby1bgBkmx6aJMFIiVIejXqDgcoA46NjaK2Zm9mP1YZyWEIg8jwKU6j84RXo1/m1FgjcLfTZQrkZRwO5uiRbm9hkW5cppTCYXEi1Nj373Q9YF4oOWK0xSYwxOo9RZDuDCiHRiW6Vq7ct120x8o21RFGElJJmvZ4HA4tM22xQLRbNXurgWmgLsG5R66xdZ653eoau/ehFPvR9H4ErqUk5cNRxs64+PWNgjcEkSZqO2ipkFgQBSritvawxeEqhE02cVskD8FKVgtRIhxblOtO1OwfsYoO+l4q0mHrSK+7QObC7q3GdTsnuql6mUgVBkCdiZX3L1M/cyl4bptGSsC4U8+Aq5OXqQsGmcJGBrMS8Sx8V+T9Xth8A0c5MXUlAbDEsRygy/b9bmyXZNvk2G93vIWNMZ0Li9t+2hcBPf6lS60LRAZsS6Io6dJIk+KnHJSfMsUBQrqOMwNGoH7uYkC1ns5b5bdvPnd1rLwRBkActXdnNzKbIBCMj5c8/91rEulDMQyuR31rnm4/imLBUcsk0QiAzUiDthMD2ZJul0zxWgoUM7c6Zv9eqUoxctx/ToT7ZXpt+OQwMDACumkfmfco2y8xPsfDtrCkcs0Jhu71IOW22oD4JKTFa59yetuNyElyX9/KXhZwHuohKR/u2bi0gPL0G+nJIfl1VJwvzY88tG6kbPOXlZoPNgp8pzcOVEMrusT9si2OX5iGgJgENSkOCJMLDRhBWLaWGwPqKRihoJnUqWKqNOpFOaOqE2XoNjQVPuR1WgQQLSjLXqGOVxCoJniIcqHBoZppqo47wPfxyCeF7xNZA1q4Lijp/58N5eEzbo9Oz1VnWZiHhaD1bSCkk2QMAneQPHTWJ6jVsEqOwBIlFCI9ZYkToE8SgDs7BXB3jKSIpSKzF69xGeY1iSSvFAw88wFvf+lZ+93d/lyuvvJI9e/bwvve9L/fff/zjH2dycpIbbriBv/mbv0FKyaWXXsqb3vSmo93/VYdjKBRmRuv2zVZSMjQ0hO/7XHPNNVhrOf/889m2bRtzc3M0TZN7d97LxMQEW7dupdGoceDAAQYHBxkaGso3cBkZGWF4eJhNmzblUWFjXC2lThWll11wNIN2nQtf8ZpFQzqLZGcM2WK/+jlGASus5vHJT36SSy+9lNe85jV85Stf4Utf+hJvf/vbnxHVPKx19oRJA1DGWhqNBuOVoXyGrtfrnHbaaczOzvKjH/2IoaEhJp5zAqObNvH973+fHTt2cPjwIRqNBnNzc3iex8GDB5mYmGDXrl0EQcD555/f0sWFyCv+dWI1izEfSZusXTHGAU6NK5fLjicmZFtEu8UmI1WfuiqQaw6LCkVWzeMLX/hC/t4HP/hBwnTXz7GxMe655562ah5AXs3jggsuOEpdPzoorhQW96NHcYzv+8zMzBCGIa973evY8qxn8cjDD1Mul9m8eTPTSjM+PsqLX/xiHn30UUZGRvjZz37GWWedhRCC7du3MzIywtzcHEqpvMhDMXbRzZhdjVl3oXN0i2GIFus7bTTfxZtFtev1Rj4WHGW+XZVLz/zMMrS7VfOoVCqAI4d99atf5W1ve1tfVvMwgMy0JJEOhDhBbx3lZb91CS9ovhrpKfwgoByWOEgT0YwQQyGPHNgNI2XCkTKHpSYyCbvr09ixAbaPnQJYXn/if8krAmaG+uhGV37zyajqrpsNxB4zaM/hZHsf03lcr3Nk+eiFN1LXanu7BNivkvlt076HWwb53Bc/S1AKUFIyWCqTjI5x7+5dsFfluSYSlpW7/nRhxd4nrTVXXXUVL33pSznnnHP41re+1fb5QrPTWqjmYXF1ZCuuvBOR2/oZ/eQh7rnn37n5hhu49eH/JJwc47itWznr5FO4/LWXUKvVGKgMEMdxHscQUvJEY5rjSiMA+L6HEuRt6vV6PrlktOsoitp4UIHnz1OVlrtKFHX6NqO8B+WiM0aReYwE7UrOfpUwqb15586OPzhnect/+wOedfKJDAcB559yOpf9xm/yvJOfi50YJrYWoy0VPPDWjm9nVat5ALzvfe/jhBNO4O1vfzvAM6aaRytGYVGpH79arbZlmWX5FZkHiA4OUxwnIF1RMK01Siq3j5422NQF67YEE/ieD7RXMi9iMY9Rr88W8zgV23W8m8fccmGRvV29IuWGIcBok3qYHaUlSRLSdcLlifeJDrUisb3hhhvwfZ93vvOd+XvPlGoeOh3IkBVYds6GrNxlpkoWdevOY5MkIWpGbpAgUFI6wbCWJE7Qic5GXdtn3QZzsXpIWyWRLujlvl0WeqxWvbhUWjumrICcRGmtI0rmORUi3TSyD1QnWGE1j4MHDxKGIW9+85sB2LFjBx/60IeeEdU8cu5OGmzKfuBsm9zVOH+3kpLd9P6FcqWXg+WeI5vti5uwFM+Vfeae3SoghMSkQjjf0O4vrLiaRzc8E6p5yHQHVKMdTcHRvxs9aRVZfoFIRFsbf6GIcxd+Xmc1ysWwlIg2rMCla9130L4ytCqLtwtExg52tlIm7FqbRQmLaxnHLM2jF/KgbmHuTpLEURm6oFcew0IqTrdUTSvnt1+tlWJZEN28Wu0qW1EgLLQlHrnPn+ErxTEBm/+HTrcKFsLZAlproiiiMlDpeqhON6K3tpU7AYAxHVzZ9FJFOkbbJ92FIp+hs/86qxFQ4OQuJVC34BW7n3v+sUXWa1Eo3PfQpj71R7yuDce0UCjIf2mRl35JSHSDKGkgAh8R+CRGIGRAXWsqAwM0m828ikXmhSKaQ5XL+WBIIhfwSwquW2MMwhO50V4UEN8rtapl+I6mTvraGEOtUSdJkrQqoaBp3WpTKpWQShFHUfsGKimL1yV6FDLvMpvGugi0M5CdqpglVBX5Ui43IkEHbq/sbJIAkQvDwMQAsUzwygFSSRr1OnMzszTrESEKgXRf89rxxi6IY1oousFaV7bFJc64ARuEITfddBN7HnuUubk57r77bh555BHCMGRsbIxSqcSb/ut/4ZP/x8d47LHH2LBhAwPlCtPT05TLZcbGxpDpqpOViCkapNZadNSileQlYpTEALFO8kSnRGuq1Sq+cALpeR6+7xNFUR4XccLQqlbYNE0nEKbgkcpeF13QabHketRs9Utr/ugD7+VdH/loLhS5dy4V2o3btuGnHKiwFBIGYcqHsr3JVGsY60LRgSTRrsiA5xOGJYzno5OEO+64g2//f/8MwIYNG5ibnQUh2HfgAEopXttocPsddyCk5MChQ5AG6LKBmwlA5rK1xgXVctepkfnM63keQkqEkni+T7lcplQqUWvU2bdvH8YYxoZHaDabJEl7aZnijkPZs/AAbGov2XxlyIUkJSXKVNDKlXLbStGMIh57/PE8il0M3gE8vncviXabSEZRhPJSV3VBV+snC2NdKDqgdUKcZD9wE2Udr2fv3r1UhgeQUjJVnWV0fIQk0SRJnKtSA+OjHDp0iJGRYWqzVUpDA25WlZLACzHGInWCSmfqrOqFtZZmPUao1LBWHlYJEm2I4iaxMDRMQjOOaZgEYyyHq7OAoDJQoRSWaDYbiMQjFGk8OjeYLZF1uydZa8BYpLUImyXEWax2o8lI2wAAENhJREFUZX2EEHhSIX3PFXC2FmEMQgqs74KNTmXMPFGpQ0KDLx1jtlarMzc351atdkog/bJkrAtFNxRUB2EdIdBaixcENBsNpKewQhDFkYvaWlevNUpihoaHKFcqzFWrLl9CpBlpUrjNfKSHR+rBKerupOoGYNO0TqsESvnERnNg35MEQcCvXPAKNm3ezFBlgF/8Yic//OEPaSaxU2WUBJGVvRR5Gqif6vK5AWwKxD9rMVIjrBNIIRUaixXunkw6tBNrXGUTIRDSZeIZa8CmhdI0NJtNvKLhkK5M2Z/9YnSvC0UH4jh2pVoESJlW7IhjPM+j1migdZL+XUdbR9vQqfcpimOUUszWqmncwQ0IYzRxrNMYgHSRcoRLLkpHi0K1leoUUiCVIooiJjZM8puXvpEdJ53E/v37+fnPf84jczWkkoyMj9JsNJ1KlB4jcFQTgUAqmSZBWSeUFshiMZn6lPMCWzypFlPY/dPp/bhCcToN2AmQAt3UeKmdpDyVOx/iJM49WX1iYwPrQjEPnUW/IuNsgIy8J6QTApPWdcqYpdZaEqNJjMZGRcWh3cukUK39pUWWnikRbpJPP3D/WQG1RoMTx8d43etfzyOPPsod//qv7Nu3j8d2PpJuWK8oVUo0m5HLY7DWBd9EarRrgyl6n7B52dwWQb7IdWrX/m1h1XGCgnMEiIy/Rf59CeGEUEqJTg34wg31DdaFogNKKTzfx0tn3IzmYeIY6wGkpW2ESCO42aByu6pmg0z3iOhmJMLM/ZlxmYx2R2aCYq3T1cMwwPd9/vPee/nOd77Drbfeyvbt25FSUKs7xm4QukJtURy1zp/mlmdUFWi5ZFsS28X87aHidONc9XOAbiGsC0UHMu+PVCqfHXWSoJMEqRRWu7pQboBoZ38AWEsSZwatJcs7E6nhmw32PCYgpWPhSplnrCXpiqKUQiqFUpLNW7ZgreVLX/wipXKZ8bExpqamCPyAoaGhfDOZUhBiTJlGo0G9XgdrMBbCIKAW10g7ltsReQyQlr7fFqjrsJGzSiaZuxfrApFrpQD0amJdKDrgVA7tSrQ4EoMTBinbS9i0zb5p02zlSEeZKJTDyYJqxjpxEWl7azRWWsKwjK+1I9UBCEEYBAhriSNnSB/cv59S6p5NmpGrVWsyJq6iXC7jeR5xHBM1o9zVqqRMVSZnP+RSYJyKledP2PkCki0cEjFvmzBr+i8GsRQsyf554IEHeNWrXsWXv/zltvd/8IMf5PstgKOUv+ENb+BNb3oTX/va11a3p08RomaTarVKo9HEGIvnKSrlMoODg4SeT6A8PCHxhHTBLkT+JboNVZwg5Dq2bA+yhWFIEAR5XdksQmwSjac8ymGJgXKZgXKZRr3Bvr372LfnSQ4fmqJUKrNhwwYmxsbYNLmRDeMTDJQr+J4H1jJYGWBoYNCpccZQCksoIQn9gCBwjzAICH2f0HdZctKZB20rR7dxnt1HGx8rjXH0VxRicayocAE499tf//VfMzk5mbfrq8IF3X7HlLeTaO28MGmAyvN9rKcxSQLWCUEeGS4Q6HL2KJAxbKXI8ilagykrxZkNQGMt0zMz6CQtYZ+6cYdHRgjLZaI0j7sUhti0xq0xhlqtltsns7OzbN++nTAM2bNnTx49N9agCuS9tAP5vbWhQAfPV7vso+whWluXtaIPIm9DW/uW8GTtF9/o/unHigoXAHzuc5/jiiuu4OMf/zhA3xUuEECocXvAC3BpMorNm7ex9dkncd9DOxnYu5/Q+Jz2ipfxpa/8HSPlAZRSNJp1RwOxGb08HRTWYLV22lRqF/iB2/RlrlrNqRpZ7VVwg9+TkhiD8KWLVqSDSIY+oxvGGR0dbcvnMMYQhiFhGDpqe7NJFEWcsONE9u3bx96DLjc+86Als93TUd3s7+ceIyDlNTXnLRex0OhUJbQyc+VaPOVRQYE2DCSKkUoFb3CIypbNDIxPIBJBYBIIPWKTEMjVyUs5mlhR4YKdO3dy33338a53vSsXiuUULlgraEuhSY1HIQWlUokgCJidm6M0PsyWyY3YOEGVwBeSeqLxQxcs00nSNntn5y0F5Zye0Ww2KQVlhADP87vmZniVcv53kXc0UK6wdfMWwjDk8PQ0lXLZcZ98n4EBF2E/rA8zWBngjNNO58CBA9z987vy3YZKpRJzszNd779YUK1erxNFEWGgaEbz244OjzA+Pk65UgFrmZ6e5uGHHyYcHqZerRMoL6/OPjIywujYGGG53PYd90q5XWtYkaH9Z3/2Z7z//e9fsM1C7rq1Us0jsyKLJARrLcHxk5z/xtdy1sWvQCrJccdvY/uznpXSw2OUVK3dT1PD+Lhtx/E//8+Pp14atwIIBB0aBL307251n3KiXoE/VUz2ccelKpmUKUFxiKuu+h/5ey6G0P32i4ULoihye29IMc9Tu+34bXzggx/M7SIpJUYbqjVXkcQZ8ml1cyEYHx+nNDrKw3seb91vH3mpli0Ue/fu5Re/+AV//Md/DLgCBVdeeeX/3965xcZRnQH4O3Nmd2fXTrzGt2ASy1EujaMSoIE2TYCKJFCFlJRbIyFFPHATVROlEgisKBVVLyAUwwvPKQ+AKqAPLaoQRBFVK1BwlSBFFDVRzKVxVSdxnI0dz95mZ04fzszsxRtAwVl7w3wvs7vS7px/d/6d//xXdu3a9bUbF8yLbh7+b+WCjl7jbzhdmPjvGO8f/hdvvv46wpQ8/IufUzw3xb7nXySXz2H6JlHJ1ReRlJLfDv2e3/3qN8TicR28MowqD5UbzGy4SBxAlSq8Wf4xyKaNxWJ0dXVVmV22bVMqlUilUiQSiVBpMpkM4+PjuK6LZVnYto1lfXmVnhCCUqkUZtkqr9rEef7F5xl8YpBUKkVLS0uopGNjYxQKBdKWjqW0p9NIabL7l7tZe+31tLS1Ebqy5lEXj4BZ6+bR09PDwYMHw+cbN27k1VdfJZ/Ps3fvXqamppBS8tFHH7Fnz55LX/HlRoBTfljFwo52+r6znK6rF3Fq7BR/eeNP3PC9Gzh99iydHR2kFrTieh4Fp6DjDX5cI97SAtLARXcVlH7j4aBoyYybfhFOtY2v97RBkK18xJRIw6CkPM6cm/D3FqZ2GxsCTAPXgOl8lnyhQNbOUnSKyEQMSRw7lyOeSuIEcYoZ34ERbqqNmEUilmR6+sLMIB3geB4XsllKSg9pKRaLICUL29sxCjkuXLhAZ3c3/f399C5ZTCIRx/NKGKap3cB+QLEZuKTGBS+99NIMr5JlWU3XuEAPXalRCuUSi8e4alEPS5cv41zmHKOff0Epm2dx3xJs20bGTeJSYnoJ/YMHBUEJ0x+sDjErUY4um9qUKimdcSoMGbbSAd9rJco2SxAVL7hFhCvKe4xJL9yPJJNJPOVRsPUI42CzDTqVRAiBZyhc4eFdxPMu/ICc8hRuSaHwUEJWTV8Kjo5bwlAGKp+jWHKIx+Pcf9/PWL16gF8/8QTFYpG2dBs33nQjHV2dOgfLT4PxhapKN5/PfOPGBe+99174uBkbF+jAVIWvXgg84ZHuvIp1t25Aofjn39/n1P/GaGlNkc3ZFIr6LiDjOnjmOA7K83CKBVwVjAUDt+TgOCViMZN0up1kMsnCtjYWLligI9mGgWHooJghyt0MAgemlBJpaDvdMPwgoJ+IZyWTKL9mI1Awzy8HrZVPevWVQnudJKhyYROAMMoXrxDQ0XEVjz76MFJKSo5OqZ+cnOR85hwnT35Bui1N64JWbr/9djb86Fbau7ogJsEwyjso35HRBB7ZKKJt4LfODH49aaBQtLQt4LoffJ+UlWL0+KecGTvFycwp4nFJLmsDioQ/W7voOCjPxcnaOhVCCAwpiHkehvBISINlfYvpW7KEnkU9pNPtOohmWX5tgsCMJSr24H7yYG2wjPLGWt89ygmMoWOjNvSgQKr6blAhyp9dOWcobhlhOasQAjOZ4J5tP0EpxfnMeUZPnuTYsWP84eWXEYZg87qbWb5yBXfdfTddvVcjY2a5lsI3z5pAF0K+1Uoh8NMt/KhumPFqgBKCWDJB37Kl3LT+hxw/+jHZUY/z588zNaUr3NysjRAQRytWMsgFEoIFLSnfXDBoaUmxOJ2mt62NhKconJvAEQY531sjhKDk+uaK7w1CKfKFAk6xqOMenhf2VQrzraAqqU/XQ9ROIQLPre/xCkpLA/dvUIVXcgvltyvFpm138dfX3yCfz3Nm/AzHjx2jUCiw7Jpeli7t5/rrbmDtTTfS3t2FNPX4YH9B4ffcTHzrlSIwnYKrzBUKx98nGEgWdney/aGHcMcnePPPf+Szzz7n0xMnyGQylIpFSo6OQptC0FkRmJJ2sTxwPVfk6N/+wRHfw1NZqx0E8yZzOb2mijLS4FiZUVuZWVuWQi9e10fM9DRJ4yLje/27jQjNM79RgmuUmyhIyc2bN/Kfox8jhKC1tZUfr9tAKplk2fLlDKxezbotWxExE/ySpMArZxhlk1DU18t5ybdaKWYMxvWT+5T/X6eEoTfSpkQu6uanW7dyYuQE77ekGD8zTs62EUqPCk4lk6z97rW6XyyKeCJGoVggm80ihNDF/KbfsCCYfVFRZ2GmUlV10cExqI+Q0kRKPdge8HOOyoE+hQo7fdRIhVL5i3wDIjT1q+44WDrO4qeoWAmLNQMDWJaFlUwSj8XovaaXe+67H6wExGO+w0GC5xccVcQ7ymrbHHcNoRqcFH/kyBHWrl3byFN+bSr99lU1CPoRCA8RFuooUB6lYhHXKTJy8hQrrun2LwCFqxv9V/zj672DEAaC4N8+sN2DTCFtehF6tHR1nvKDgf4H+eupHc7ohcMXBRW3PgVhS8KKgGNI8HnB3qLyBuR/F/8+dpyBgVVV763sXeUKo8pUqsy6rVWC+eR9uti1OCdKERExX5gXShERMd9pjhBjREQDiZQiIqKGhnqfnn32WY4ePYoQgj179rBmzZpGnn7WGB4eZvfu3axYsQKAlStX8sgjj/DUU0/hui5dXV3s27evKoFvvlNvLHQ9ea6EsdBfiWoQw8PD6rHHHlNKKTUyMqK2b9/eqFPPOh9++KHatWtX1WuDg4Pq7bffVkop9cILL6jXXnttLpZ2Sdi2rXbs2KH27t2rXnnlFaVUfXls21Z33HGHmpqaUrlcTm3dulVlMpm5XPploWHm06FDh9i8eTOgJx9NTk4yPT3dqNNfdoaHh9m0aRMAt912G4cOHZrjFX19gurK7u7u8LV68lRWV1qWFVZXXmk0TCnOnj1Le3t7+LwZKvO+jJGRER5//HEeeOABPvjgA3K5XGgudXR0NJVspmliWVbVa/XkacbqykthziLaqok9wf39/ezcuZMtW7YwOjrKgw8+WDWdqJllq8fF5LnS5Axo2J2i3kjhoBNIs9HT08Odd96JEIK+vj46OzuZnJwkn9fpFKdPn64yRZqRVCo1Q556v2Gzy1mPhinFhg0bePfddwH45JNP6O7uprW1tVGnn1Xeeust9u/fD8D4+DgTExPce++9oXwHDhzglltumcslfmPWr18/Q54rZSz0V9HQiPbQ0BCHDx9GCMEzzzzDqlWrGnXqWWV6eponn3ySqakpHMdh586dDAwM8PTTT1MoFOjt7eW5556btTHDl5va6sqenh6GhoYYHBycIc8777zD/v37EUKwY8cOtm3bNtfLn3WiNI+IiBqiiHZERA2RUkRE1BApRUREDZFSRETUEClFREQNkVJERNQQKUVERA2RUkRE1PB/K7bw/sCQQeEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPhone\n",
            "Predicted Label: iPad\n",
            "==================\n",
            "==================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Image : iPhone\n",
            "Predicted Label: iPad\n",
            "==================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "va3ueBvgClnE"
      },
      "source": [
        "Upon inspecting our wrongly classified images, here are the takeaways:\n",
        "- If the iPad image comes with a keyboard, there are rare instances that the model classifies it as a Macbook.\n",
        "- If the iPhone model has no notch (the iPhone 6, 7, 8 design), there are rare instances that the model classifies it as an iPad.\n",
        "- Thus, iPad is the class with the lowest accuracy at 86%. Although, a 86% accuracy is already relatively high.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0l0I3qfFWkE6"
      },
      "source": [
        ""
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rzel4UJ1EKwa"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "- Convolutional Neural Network is suitable for image classification task\n",
        "- Transfer learning helps us by allowing us to use a portion of trained layers\n",
        "- Using callbacks make our training process 'easier' as we don't have to adjust things manually per epoch\n",
        "- Using the top 500-600 images from Google Image Search, our model can classify Apple products (iPad, iPhone, Macbook) with 88-89% accuracy (on validation set)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1c08B1ykpzy"
      },
      "source": [
        "model_inception.save_weights('model_inception_weights.h5')\n",
        "model_inception.save('model_inception.h5')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Na11V96p654l"
      },
      "source": [
        "## Epilogue: Function to Predict Image\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKfBJRyh1Edv"
      },
      "source": [
        "def predict_image(image, withpath, model = model_inception):\n",
        "  if withpath == True:\n",
        "    from PIL import Image\n",
        "    im = Image.open(image)\n",
        "  else:\n",
        "    im = image\n",
        "\n",
        "  resized_im = im.resize((150, 150))\n",
        "  im_array = np.asarray(resized_im)\n",
        "  im_array = im_array*(1/225)\n",
        "  im_input = tf.reshape(im_array, shape = [1, 150, 150, 3])\n",
        "\n",
        "  predict_array = model.predict(im_input)[0]\n",
        "  import pandas as pd\n",
        "  df = pd.DataFrame(predict_array)\n",
        "  df = df.rename({0:'Probability'}, axis = 'columns')\n",
        "  prod = ['Macbook', 'iPad', 'iPhone']\n",
        "  df['Product'] = prod\n",
        "  df = df[['Product', 'Probability']]\n",
        "\n",
        "  predict_label = np.argmax(model.predict(im_input))\n",
        "\n",
        "  if predict_label == 0:\n",
        "    predict_product = 'The uploaded image resembles a Macbook.'\n",
        "  elif predict_label == 1:\n",
        "    predict_product = 'The uploaded image resembles an iPad.'\n",
        "  else:\n",
        "    predict_product = 'The uploaded image resembles an iPhone.'\n",
        "\n",
        "\n",
        "  return predict_product, df"
      ],
      "execution_count": 85,
      "outputs": []
    }
  ]
}