Pneumonia.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Pneumonia.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ie4F2mo2p-an",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXEE_1mvZ1Kd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "7d5a907e-2905-4e6b-cb2f-ce545c439eaf"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/drive')\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEAAti5tZ4bx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0c10a76-28b9-41c9-c373-011f60665a68"
      },
      "source": [
        "import os\n",
        "os.listdir('/drive/My Drive/Colab Notebooks/chest_xray')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['train', 'test', 'val']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqjbKz12aArx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f3a442c0-c8b3-43bd-fbc1-5af0edb4311a"
      },
      "source": [
        "loc = '/drive/My Drive/Colab Notebooks/chest_xray'\n",
        "loc"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/drive/My Drive/Colab Notebooks/chest_xray'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1DsvlfFraRG6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import Model\n",
        "from keras.layers import Flatten, Dense\n",
        "from keras.applications.vgg19 import VGG19\n",
        "from glob import glob\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKB_I-xCa8SL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image_size = [224, 224]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqW0pWhBqM5w",
        "colab_type": "text"
      },
      "source": [
        "#VGG19"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y05EZd8ubQKP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data = loc + '/train'\n",
        "test_data = loc + '/test'"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-XOLjI8bX5H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "91d234fd-d319-461e-8df2-59c015dffaa7"
      },
      "source": [
        "vgg = VGG19(input_shape = image_size+[3], weights = 'imagenet', include_top = False)\n",
        "vgg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.engine.training.Model at 0x7faadf57bbe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CeRAmXZYcPEw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for layers in vgg.layers:\n",
        "  layers.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-tLkFyVcm9F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "58115c90-0a56-4c58-c659-c9b4bcd024aa"
      },
      "source": [
        "folder = glob(train_data+'/*')\n",
        "folder"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/drive/My Drive/Colab Notebooks/chest_xray/train/NORMAL',\n",
              " '/drive/My Drive/Colab Notebooks/chest_xray/train/PNEUMONIA']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ruFu4ZvIiz25",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "da6989fe-2986-472e-a79e-ad9841eb9d96"
      },
      "source": [
        "X = Flatten()(vgg.output)\n",
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'flatten_2/Reshape:0' shape=(None, None) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8sm7Tw-o-ts",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# n-2 layer\n",
        "x = Dense(1024, activation='relu')(X)\n",
        "\n",
        "# n-1 layer\n",
        "X = Dense(256, activation = 'relu')(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4umxkukpc9u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "49a06bce-4797-4959-dc26-2123b2ec94da"
      },
      "source": [
        "prediction = Dense(len(folder), activation = 'softmax')(X)\n",
        "prediction"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'dense_5/Softmax:0' shape=(None, 2) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmgV-iThpowO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "616e3b1c-44b3-4aa2-c11d-8d5c8c141a4d"
      },
      "source": [
        "model = Model(input = vgg.input, output = prediction)\n",
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.engine.training.Model at 0x7fada0041400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsQ2KsxpqCFY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c463a832-acfa-4ef1-8d41-0090b8e556e2"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv4 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv4 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv4 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 256)               6422784   \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 256)               65792     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 2)                 514       \n",
            "=================================================================\n",
            "Total params: 26,513,474\n",
            "Trainable params: 6,489,090\n",
            "Non-trainable params: 20,024,384\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eA-YiaSnqje7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss = 'categorical_crossentropy',\n",
        "             optimizer = 'adam', \n",
        "              metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4m_szSpUwh4R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ERfMqRexXfy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip=True)\n",
        "                                   \n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vo-MMjcaxvIA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c8135641-7490-4181-d35c-ba6a86e2fc22"
      },
      "source": [
        "training_set = train_datagen.flow_from_directory(loc+'/train',\n",
        "                                                target_size=(224, 224),\n",
        "                                                batch_size=32,\n",
        "                                                class_mode='categorical')\n",
        "\n",
        "testing_set = test_datagen.flow_from_directory(loc+'/test',\n",
        "                                               target_size=(224, 224),\n",
        "                                               batch_size=32,\n",
        "                                               class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 5239 images belonging to 2 classes.\n",
            "Found 624 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVEFTD9-ynwe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "00ba2870-beed-43c7-fd69-12f1a505281e"
      },
      "source": [
        "f = model.fit_generator(training_set,\n",
        "              validation_data = testing_set,\n",
        "              epochs=40,\n",
        "              steps_per_epoch = len(training_set),\n",
        "              validation_steps = len(testing_set)\n",
        "             )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "164/164 [==============================] - 1784s 11s/step - loss: 0.2220 - accuracy: 0.9076 - val_loss: 0.2559 - val_accuracy: 0.8846\n",
            "Epoch 2/40\n",
            "164/164 [==============================] - 103s 626ms/step - loss: 0.1645 - accuracy: 0.9391 - val_loss: 0.0832 - val_accuracy: 0.9215\n",
            "Epoch 3/40\n",
            "164/164 [==============================] - 108s 658ms/step - loss: 0.1145 - accuracy: 0.9553 - val_loss: 0.3516 - val_accuracy: 0.9167\n",
            "Epoch 4/40\n",
            "164/164 [==============================] - 108s 661ms/step - loss: 0.1048 - accuracy: 0.9609 - val_loss: 0.0597 - val_accuracy: 0.9087\n",
            "Epoch 5/40\n",
            "164/164 [==============================] - 109s 665ms/step - loss: 0.1042 - accuracy: 0.9618 - val_loss: 0.2540 - val_accuracy: 0.9103\n",
            "Epoch 6/40\n",
            "164/164 [==============================] - 109s 664ms/step - loss: 0.0956 - accuracy: 0.9597 - val_loss: 0.5523 - val_accuracy: 0.8910\n",
            "Epoch 7/40\n",
            "164/164 [==============================] - 109s 665ms/step - loss: 0.1005 - accuracy: 0.9643 - val_loss: 0.0653 - val_accuracy: 0.9006\n",
            "Epoch 8/40\n",
            "164/164 [==============================] - 109s 663ms/step - loss: 0.0860 - accuracy: 0.9685 - val_loss: 0.1202 - val_accuracy: 0.9071\n",
            "Epoch 9/40\n",
            "164/164 [==============================] - 109s 664ms/step - loss: 0.0850 - accuracy: 0.9668 - val_loss: 0.2679 - val_accuracy: 0.9199\n",
            "Epoch 10/40\n",
            "164/164 [==============================] - 109s 666ms/step - loss: 0.0813 - accuracy: 0.9676 - val_loss: 0.5007 - val_accuracy: 0.9006\n",
            "Epoch 11/40\n",
            "164/164 [==============================] - 109s 667ms/step - loss: 0.0814 - accuracy: 0.9685 - val_loss: 0.2418 - val_accuracy: 0.8494\n",
            "Epoch 12/40\n",
            "164/164 [==============================] - 110s 672ms/step - loss: 0.0783 - accuracy: 0.9716 - val_loss: 0.3141 - val_accuracy: 0.8942\n",
            "Epoch 13/40\n",
            "164/164 [==============================] - 110s 670ms/step - loss: 0.0817 - accuracy: 0.9683 - val_loss: 1.5700 - val_accuracy: 0.7917\n",
            "Epoch 14/40\n",
            "164/164 [==============================] - 110s 669ms/step - loss: 0.0837 - accuracy: 0.9666 - val_loss: 0.5917 - val_accuracy: 0.9183\n",
            "Epoch 15/40\n",
            "164/164 [==============================] - 110s 670ms/step - loss: 0.0671 - accuracy: 0.9754 - val_loss: 0.4137 - val_accuracy: 0.8958\n",
            "Epoch 16/40\n",
            "164/164 [==============================] - 109s 666ms/step - loss: 0.0706 - accuracy: 0.9735 - val_loss: 0.4540 - val_accuracy: 0.9215\n",
            "Epoch 17/40\n",
            "164/164 [==============================] - 110s 670ms/step - loss: 0.0631 - accuracy: 0.9748 - val_loss: 0.6364 - val_accuracy: 0.9054\n",
            "Epoch 18/40\n",
            "164/164 [==============================] - 110s 673ms/step - loss: 0.0660 - accuracy: 0.9754 - val_loss: 0.0812 - val_accuracy: 0.9167\n",
            "Epoch 19/40\n",
            "164/164 [==============================] - 111s 677ms/step - loss: 0.0792 - accuracy: 0.9714 - val_loss: 0.2493 - val_accuracy: 0.9103\n",
            "Epoch 20/40\n",
            "164/164 [==============================] - 111s 678ms/step - loss: 0.0694 - accuracy: 0.9729 - val_loss: 0.1393 - val_accuracy: 0.9183\n",
            "Epoch 21/40\n",
            "164/164 [==============================] - 111s 677ms/step - loss: 0.0682 - accuracy: 0.9725 - val_loss: 0.7278 - val_accuracy: 0.9135\n",
            "Epoch 22/40\n",
            "164/164 [==============================] - 112s 680ms/step - loss: 0.0684 - accuracy: 0.9744 - val_loss: 0.4820 - val_accuracy: 0.9279\n",
            "Epoch 23/40\n",
            "164/164 [==============================] - 111s 677ms/step - loss: 0.0634 - accuracy: 0.9769 - val_loss: 0.0724 - val_accuracy: 0.8814\n",
            "Epoch 24/40\n",
            "164/164 [==============================] - 109s 667ms/step - loss: 0.0628 - accuracy: 0.9756 - val_loss: 0.1045 - val_accuracy: 0.9167\n",
            "Epoch 25/40\n",
            "164/164 [==============================] - 109s 664ms/step - loss: 0.0590 - accuracy: 0.9771 - val_loss: 0.5558 - val_accuracy: 0.9087\n",
            "Epoch 26/40\n",
            "164/164 [==============================] - 109s 666ms/step - loss: 0.0626 - accuracy: 0.9777 - val_loss: 0.0849 - val_accuracy: 0.9183\n",
            "Epoch 27/40\n",
            "164/164 [==============================] - 110s 671ms/step - loss: 0.0552 - accuracy: 0.9800 - val_loss: 0.0800 - val_accuracy: 0.9215\n",
            "Epoch 28/40\n",
            "164/164 [==============================] - 109s 666ms/step - loss: 0.0499 - accuracy: 0.9824 - val_loss: 0.1733 - val_accuracy: 0.9183\n",
            "Epoch 29/40\n",
            "164/164 [==============================] - 110s 671ms/step - loss: 0.0552 - accuracy: 0.9798 - val_loss: 0.9492 - val_accuracy: 0.8397\n",
            "Epoch 30/40\n",
            "164/164 [==============================] - 109s 663ms/step - loss: 0.0695 - accuracy: 0.9735 - val_loss: 0.3267 - val_accuracy: 0.9135\n",
            "Epoch 31/40\n",
            "164/164 [==============================] - 109s 666ms/step - loss: 0.0498 - accuracy: 0.9819 - val_loss: 0.4283 - val_accuracy: 0.9183\n",
            "Epoch 32/40\n",
            "164/164 [==============================] - 110s 668ms/step - loss: 0.0462 - accuracy: 0.9838 - val_loss: 0.1283 - val_accuracy: 0.9167\n",
            "Epoch 33/40\n",
            "164/164 [==============================] - 108s 661ms/step - loss: 0.0499 - accuracy: 0.9824 - val_loss: 0.3071 - val_accuracy: 0.9263\n",
            "Epoch 34/40\n",
            "164/164 [==============================] - 109s 664ms/step - loss: 0.0446 - accuracy: 0.9836 - val_loss: 0.7054 - val_accuracy: 0.8894\n",
            "Epoch 35/40\n",
            "164/164 [==============================] - 109s 666ms/step - loss: 0.0419 - accuracy: 0.9840 - val_loss: 0.5988 - val_accuracy: 0.9071\n",
            "Epoch 36/40\n",
            "164/164 [==============================] - 109s 662ms/step - loss: 0.0452 - accuracy: 0.9832 - val_loss: 0.1290 - val_accuracy: 0.9054\n",
            "Epoch 37/40\n",
            "164/164 [==============================] - 109s 667ms/step - loss: 0.0463 - accuracy: 0.9800 - val_loss: 0.4528 - val_accuracy: 0.9199\n",
            "Epoch 38/40\n",
            "164/164 [==============================] - 109s 664ms/step - loss: 0.0561 - accuracy: 0.9792 - val_loss: 0.0162 - val_accuracy: 0.9167\n",
            "Epoch 39/40\n",
            "164/164 [==============================] - 109s 664ms/step - loss: 0.0520 - accuracy: 0.9800 - val_loss: 0.1626 - val_accuracy: 0.9199\n",
            "Epoch 40/40\n",
            "164/164 [==============================] - 108s 659ms/step - loss: 0.0564 - accuracy: 0.9779 - val_loss: 0.1656 - val_accuracy: 0.9151\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHxlWADo0FPc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "cc25225c-a984-4667-9d27-276c88147410"
      },
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "plt.plot(f.history['loss'], label='training loss')\n",
        "plt.plot(f.history['val_loss'], label='val loss')\n",
        "plt.legend()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fad37dca240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEBUKzThDFA0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "9ca0bfdb-dbcf-4abc-cf03-a54a1fdd4cd9"
      },
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "plt.plot(f.history['accuracy'], label='training accuracy')\n",
        "plt.plot(f.history['val_loss'], label = 'validation accuracy')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fad378cc7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgcS7BgLNNFB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "name  = 'Pneumonia_model.h5'\n",
        "\n",
        "path = loc+name\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sl3XO2jfPxKT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save(path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtEZr1KBP0On",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import load_model\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byRJ1IrBP2aF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f1404938-ba98-4b80-8ee6-50862f90f1df"
      },
      "source": [
        "os.listdir(loc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['train', 'test', 'val']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CspdXju9QQsB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "98ddadfb-fbe4-4c23-dcf6-16456c87d589"
      },
      "source": [
        "img = image.load_img(loc+'/val/PNEUMONIA/person1954_bacteria_4886.jpeg', target_size = (224, 224))\n",
        "img"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FAD1C95AB00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbeeLg4fRC1l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#  converting to array\n",
        "\n",
        "x = image.img_to_array(img)\n",
        "\n",
        "x = np.expand_dims(x, axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGhv2aFyRVQE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4e5c23ed-bbaa-4ed2-fa3c-b858a88ccc8e"
      },
      "source": [
        "image_data = preprocess_input(x)\n",
        "classes = model.predict(image_data)\n",
        "\n",
        "if classes[0][1] ==0.0:\n",
        "  print('Congratulation Free from Pneumonia')\n",
        "else:\n",
        "  print('Sorry , You have Pneumonia. \\nPlease consult Doctor as soon as')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sorry , You have Pneumonia. \n",
            "Please consult Doctor as soon as\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8qUT20gRunX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "ded78ea7-fccf-4d08-f224-1440d36677c3"
      },
      "source": [
        "import os\n",
        "\n",
        "for i in os.listdir(loc+'/val'):\n",
        "  if i != '.DS_Store':\n",
        "    \n",
        "    for j in os.listdir(loc+'/val/'+i):\n",
        "      if j != '.DS_Store':\n",
        "        \n",
        "        img1 = image.load_img(loc+'/val/'+i+'/'+j, target_size=(224, 224))\n",
        "        \n",
        "        x = image.img_to_array(img1)\n",
        "        \n",
        "        x = np.expand_dims(x, axis=0)\n",
        "        \n",
        "        image_data = preprocess_input(x)\n",
        "        classes = model.predict(image_data)\n",
        "        print(i,classes[0])\n",
        "        \n",
        "#  0: No pneumonia\n",
        "#  1: Pneumonia "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PNEUMONIA 1.0\n",
            "PNEUMONIA 1.0\n",
            "PNEUMONIA 1.0\n",
            "PNEUMONIA 1.0\n",
            "PNEUMONIA 1.0\n",
            "PNEUMONIA 1.0\n",
            "PNEUMONIA 1.0\n",
            "PNEUMONIA 1.0\n",
            "NORMAL 0.0\n",
            "NORMAL 0.0019224132\n",
            "NORMAL 1.0\n",
            "NORMAL 1.0\n",
            "NORMAL 0.0\n",
            "NORMAL 0.0\n",
            "NORMAL 0.0\n",
            "NORMAL 0.99683374\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xa209BEtTRXf",
        "colab_type": "text"
      },
      "source": [
        "# InceptionV3\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rm9mtm6lkFw1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, Activation\n",
        "from keras.layers import Dropout, Flatten,GlobalAveragePooling2D\n",
        "from keras.layers import Conv2D,BatchNormalization, MaxPooling2D\n",
        "from keras.applications.inception_v3 import InceptionV3"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGujMwusqwHa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a9241875-766a-4cf2-ab2f-decb9794f5ea"
      },
      "source": [
        "inc_model = InceptionV3(input_shape = (224, 224, 3), weights = 'imagenet', include_top=False)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8xDw6YXr3Ou",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c444f49-a5a0-43a6-c4d0-c3d1542171bb"
      },
      "source": [
        "x = inc_model.output\n",
        "x"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'mixed10/concat:0' shape=(None, 5, 5, 2048) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0p8Fsc1vtIHi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0906baf3-615b-4f71-b007-6b74761e066b"
      },
      "source": [
        "x = Dropout(.3)(x)\n",
        "x"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'dropout_1/cond/Identity:0' shape=(None, 5, 5, 2048) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sACAUHI6uT0_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a5ad5d5a-4237-4000-eade-214481543358"
      },
      "source": [
        "x = GlobalAveragePooling2D()(x)\n",
        "x"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'global_average_pooling2d_2/Mean:0' shape=(None, 2048) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIJTt__duXAW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "db002ea2-56b1-426c-f25b-b59f5315dde4"
      },
      "source": [
        "# densely-connected NN layer.\n",
        "x = Dense(128, activation = 'relu')(x)\n",
        "x"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'dense_3/Relu:0' shape=(None, 128) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emRSM4YRuY7G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "19c11e26-e91e-4d0c-8ff7-23ab05054cfe"
      },
      "source": [
        "# Normalize the activations of the previous layer al every batch\n",
        "x  = BatchNormalization()(x)\n",
        "x"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'batch_normalization_147/cond/Identity:0' shape=(None, 128) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbKI63xCub_P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1fc9ad03-fdee-42e1-82bb-eace51d1c2ae"
      },
      "source": [
        "#  Last Layer\n",
        "prediction = Dense(2, activation='sigmoid')(x)\n",
        "prediction"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'dense_5/Sigmoid:0' shape=(None, 2) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFMgPx23ufGm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "a21fe6c6-f4e7-488a-980a-9bb0ed17a246"
      },
      "source": [
        "model = Model(input = inc_model.input, output = prediction)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mrlyFIv54dt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss = 'categorical_crossentropy', \n",
        "             optimizer = 'adam',\n",
        "             metrics = ['accuracy'])"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YppOXPdcvVii",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3ef5433c-397e-4af9-a41c-8068d3211b78"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 111, 111, 32) 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 111, 111, 32) 96          conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 111, 111, 32) 0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 109, 109, 32) 9216        activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, 109, 109, 32) 96          conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 109, 109, 32) 0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 109, 109, 64) 18432       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, 109, 109, 64) 192         conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 109, 109, 64) 0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 54, 54, 64)   0           activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 54, 54, 80)   5120        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, 54, 54, 80)   240         conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 54, 54, 80)   0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 52, 52, 192)  138240      activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, 52, 52, 192)  576         conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 52, 52, 192)  0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 25, 25, 192)  0           activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 25, 25, 64)   12288       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, 25, 25, 64)   192         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 25, 25, 64)   0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 25, 25, 48)   9216        max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 25, 25, 96)   55296       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, 25, 25, 48)   144         conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, 25, 25, 96)   288         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 25, 25, 48)   0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 25, 25, 96)   0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 25, 25, 192)  0           max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 25, 25, 64)   12288       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 25, 25, 64)   76800       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 25, 25, 96)   82944       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 25, 25, 32)   6144        average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, 25, 25, 64)   192         conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, 25, 25, 64)   192         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_63 (BatchNo (None, 25, 25, 96)   288         conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_64 (BatchNo (None, 25, 25, 32)   96          conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 25, 25, 64)   0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 25, 25, 64)   0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 25, 25, 96)   0           batch_normalization_63[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 25, 25, 32)   0           batch_normalization_64[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, 25, 25, 256)  0           activation_6[0][0]               \n",
            "                                                                 activation_8[0][0]               \n",
            "                                                                 activation_11[0][0]              \n",
            "                                                                 activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 25, 25, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_68 (BatchNo (None, 25, 25, 64)   192         conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 25, 25, 64)   0           batch_normalization_68[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 25, 25, 48)   12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 25, 25, 96)   55296       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_66 (BatchNo (None, 25, 25, 48)   144         conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_69 (BatchNo (None, 25, 25, 96)   288         conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 25, 25, 48)   0           batch_normalization_66[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 25, 25, 96)   0           batch_normalization_69[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_2 (AveragePoo (None, 25, 25, 256)  0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 25, 25, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 25, 25, 64)   76800       activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 25, 25, 96)   82944       activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 25, 25, 64)   16384       average_pooling2d_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_65 (BatchNo (None, 25, 25, 64)   192         conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_67 (BatchNo (None, 25, 25, 64)   192         conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_70 (BatchNo (None, 25, 25, 96)   288         conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_71 (BatchNo (None, 25, 25, 64)   192         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 25, 25, 64)   0           batch_normalization_65[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 25, 25, 64)   0           batch_normalization_67[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 25, 25, 96)   0           batch_normalization_70[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 25, 25, 64)   0           batch_normalization_71[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, 25, 25, 288)  0           activation_13[0][0]              \n",
            "                                                                 activation_15[0][0]              \n",
            "                                                                 activation_18[0][0]              \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 25, 25, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_75 (BatchNo (None, 25, 25, 64)   192         conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 25, 25, 64)   0           batch_normalization_75[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 25, 25, 48)   13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, 25, 25, 96)   55296       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_73 (BatchNo (None, 25, 25, 48)   144         conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_76 (BatchNo (None, 25, 25, 96)   288         conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 25, 25, 48)   0           batch_normalization_73[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 25, 25, 96)   0           batch_normalization_76[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_3 (AveragePoo (None, 25, 25, 288)  0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, 25, 25, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 25, 25, 64)   76800       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, 25, 25, 96)   82944       activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, 25, 25, 64)   18432       average_pooling2d_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_72 (BatchNo (None, 25, 25, 64)   192         conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_74 (BatchNo (None, 25, 25, 64)   192         conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_77 (BatchNo (None, 25, 25, 96)   288         conv2d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_78 (BatchNo (None, 25, 25, 64)   192         conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 25, 25, 64)   0           batch_normalization_72[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 25, 25, 64)   0           batch_normalization_74[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 25, 25, 96)   0           batch_normalization_77[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 25, 25, 64)   0           batch_normalization_78[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, 25, 25, 288)  0           activation_20[0][0]              \n",
            "                                                                 activation_22[0][0]              \n",
            "                                                                 activation_25[0][0]              \n",
            "                                                                 activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, 25, 25, 64)   18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_80 (BatchNo (None, 25, 25, 64)   192         conv2d_62[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 25, 25, 64)   0           batch_normalization_80[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, 25, 25, 96)   55296       activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_81 (BatchNo (None, 25, 25, 96)   288         conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 25, 25, 96)   0           batch_normalization_81[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, 12, 12, 384)  995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_64 (Conv2D)              (None, 12, 12, 96)   82944       activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_79 (BatchNo (None, 12, 12, 384)  1152        conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_82 (BatchNo (None, 12, 12, 96)   288         conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 12, 12, 384)  0           batch_normalization_79[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 12, 12, 96)   0           batch_normalization_82[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 12, 12, 288)  0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, 12, 12, 768)  0           activation_27[0][0]              \n",
            "                                                                 activation_30[0][0]              \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, 12, 12, 128)  98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_87 (BatchNo (None, 12, 12, 128)  384         conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 12, 12, 128)  0           batch_normalization_87[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, 12, 12, 128)  114688      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_88 (BatchNo (None, 12, 12, 128)  384         conv2d_70[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 12, 12, 128)  0           batch_normalization_88[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, 12, 12, 128)  98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_71 (Conv2D)              (None, 12, 12, 128)  114688      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_84 (BatchNo (None, 12, 12, 128)  384         conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_89 (BatchNo (None, 12, 12, 128)  384         conv2d_71[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 12, 12, 128)  0           batch_normalization_84[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 12, 12, 128)  0           batch_normalization_89[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, 12, 12, 128)  114688      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_72 (Conv2D)              (None, 12, 12, 128)  114688      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_85 (BatchNo (None, 12, 12, 128)  384         conv2d_67[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_90 (BatchNo (None, 12, 12, 128)  384         conv2d_72[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 12, 12, 128)  0           batch_normalization_85[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 12, 12, 128)  0           batch_normalization_90[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_4 (AveragePoo (None, 12, 12, 768)  0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, 12, 12, 192)  147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, 12, 12, 192)  172032      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_73 (Conv2D)              (None, 12, 12, 192)  172032      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_74 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_83 (BatchNo (None, 12, 12, 192)  576         conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_86 (BatchNo (None, 12, 12, 192)  576         conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_91 (BatchNo (None, 12, 12, 192)  576         conv2d_73[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_92 (BatchNo (None, 12, 12, 192)  576         conv2d_74[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 12, 12, 192)  0           batch_normalization_83[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 12, 12, 192)  0           batch_normalization_86[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 12, 12, 192)  0           batch_normalization_91[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 12, 12, 192)  0           batch_normalization_92[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, 12, 12, 768)  0           activation_31[0][0]              \n",
            "                                                                 activation_34[0][0]              \n",
            "                                                                 activation_39[0][0]              \n",
            "                                                                 activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_79 (Conv2D)              (None, 12, 12, 160)  122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_97 (BatchNo (None, 12, 12, 160)  480         conv2d_79[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 12, 12, 160)  0           batch_normalization_97[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_80 (Conv2D)              (None, 12, 12, 160)  179200      activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_98 (BatchNo (None, 12, 12, 160)  480         conv2d_80[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 12, 12, 160)  0           batch_normalization_98[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_76 (Conv2D)              (None, 12, 12, 160)  122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_81 (Conv2D)              (None, 12, 12, 160)  179200      activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_94 (BatchNo (None, 12, 12, 160)  480         conv2d_76[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_99 (BatchNo (None, 12, 12, 160)  480         conv2d_81[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 12, 12, 160)  0           batch_normalization_94[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 12, 12, 160)  0           batch_normalization_99[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_77 (Conv2D)              (None, 12, 12, 160)  179200      activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_82 (Conv2D)              (None, 12, 12, 160)  179200      activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_95 (BatchNo (None, 12, 12, 160)  480         conv2d_77[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_100 (BatchN (None, 12, 12, 160)  480         conv2d_82[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 12, 12, 160)  0           batch_normalization_95[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 12, 12, 160)  0           batch_normalization_100[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_5 (AveragePoo (None, 12, 12, 768)  0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_75 (Conv2D)              (None, 12, 12, 192)  147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_78 (Conv2D)              (None, 12, 12, 192)  215040      activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_83 (Conv2D)              (None, 12, 12, 192)  215040      activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_84 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_93 (BatchNo (None, 12, 12, 192)  576         conv2d_75[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_96 (BatchNo (None, 12, 12, 192)  576         conv2d_78[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_101 (BatchN (None, 12, 12, 192)  576         conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_102 (BatchN (None, 12, 12, 192)  576         conv2d_84[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 12, 12, 192)  0           batch_normalization_93[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 12, 12, 192)  0           batch_normalization_96[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 12, 12, 192)  0           batch_normalization_101[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, 12, 12, 192)  0           batch_normalization_102[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, 12, 12, 768)  0           activation_41[0][0]              \n",
            "                                                                 activation_44[0][0]              \n",
            "                                                                 activation_49[0][0]              \n",
            "                                                                 activation_50[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_89 (Conv2D)              (None, 12, 12, 160)  122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_107 (BatchN (None, 12, 12, 160)  480         conv2d_89[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_55 (Activation)      (None, 12, 12, 160)  0           batch_normalization_107[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_90 (Conv2D)              (None, 12, 12, 160)  179200      activation_55[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_108 (BatchN (None, 12, 12, 160)  480         conv2d_90[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_56 (Activation)      (None, 12, 12, 160)  0           batch_normalization_108[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_86 (Conv2D)              (None, 12, 12, 160)  122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_91 (Conv2D)              (None, 12, 12, 160)  179200      activation_56[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_104 (BatchN (None, 12, 12, 160)  480         conv2d_86[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_109 (BatchN (None, 12, 12, 160)  480         conv2d_91[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, 12, 12, 160)  0           batch_normalization_104[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_57 (Activation)      (None, 12, 12, 160)  0           batch_normalization_109[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_87 (Conv2D)              (None, 12, 12, 160)  179200      activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_92 (Conv2D)              (None, 12, 12, 160)  179200      activation_57[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_105 (BatchN (None, 12, 12, 160)  480         conv2d_87[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_110 (BatchN (None, 12, 12, 160)  480         conv2d_92[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, 12, 12, 160)  0           batch_normalization_105[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_58 (Activation)      (None, 12, 12, 160)  0           batch_normalization_110[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_6 (AveragePoo (None, 12, 12, 768)  0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_85 (Conv2D)              (None, 12, 12, 192)  147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_88 (Conv2D)              (None, 12, 12, 192)  215040      activation_53[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_93 (Conv2D)              (None, 12, 12, 192)  215040      activation_58[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_94 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_6[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_103 (BatchN (None, 12, 12, 192)  576         conv2d_85[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_106 (BatchN (None, 12, 12, 192)  576         conv2d_88[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_111 (BatchN (None, 12, 12, 192)  576         conv2d_93[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_112 (BatchN (None, 12, 12, 192)  576         conv2d_94[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, 12, 12, 192)  0           batch_normalization_103[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, 12, 12, 192)  0           batch_normalization_106[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_59 (Activation)      (None, 12, 12, 192)  0           batch_normalization_111[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_60 (Activation)      (None, 12, 12, 192)  0           batch_normalization_112[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, 12, 12, 768)  0           activation_51[0][0]              \n",
            "                                                                 activation_54[0][0]              \n",
            "                                                                 activation_59[0][0]              \n",
            "                                                                 activation_60[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_99 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_117 (BatchN (None, 12, 12, 192)  576         conv2d_99[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_65 (Activation)      (None, 12, 12, 192)  0           batch_normalization_117[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_100 (Conv2D)             (None, 12, 12, 192)  258048      activation_65[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_118 (BatchN (None, 12, 12, 192)  576         conv2d_100[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_66 (Activation)      (None, 12, 12, 192)  0           batch_normalization_118[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_96 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_101 (Conv2D)             (None, 12, 12, 192)  258048      activation_66[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_114 (BatchN (None, 12, 12, 192)  576         conv2d_96[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_119 (BatchN (None, 12, 12, 192)  576         conv2d_101[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_62 (Activation)      (None, 12, 12, 192)  0           batch_normalization_114[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_67 (Activation)      (None, 12, 12, 192)  0           batch_normalization_119[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_97 (Conv2D)              (None, 12, 12, 192)  258048      activation_62[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_102 (Conv2D)             (None, 12, 12, 192)  258048      activation_67[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_115 (BatchN (None, 12, 12, 192)  576         conv2d_97[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_120 (BatchN (None, 12, 12, 192)  576         conv2d_102[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_63 (Activation)      (None, 12, 12, 192)  0           batch_normalization_115[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_68 (Activation)      (None, 12, 12, 192)  0           batch_normalization_120[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_7 (AveragePoo (None, 12, 12, 768)  0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_95 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_98 (Conv2D)              (None, 12, 12, 192)  258048      activation_63[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_103 (Conv2D)             (None, 12, 12, 192)  258048      activation_68[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_104 (Conv2D)             (None, 12, 12, 192)  147456      average_pooling2d_7[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_113 (BatchN (None, 12, 12, 192)  576         conv2d_95[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_116 (BatchN (None, 12, 12, 192)  576         conv2d_98[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_121 (BatchN (None, 12, 12, 192)  576         conv2d_103[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_122 (BatchN (None, 12, 12, 192)  576         conv2d_104[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_61 (Activation)      (None, 12, 12, 192)  0           batch_normalization_113[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_64 (Activation)      (None, 12, 12, 192)  0           batch_normalization_116[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_69 (Activation)      (None, 12, 12, 192)  0           batch_normalization_121[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_70 (Activation)      (None, 12, 12, 192)  0           batch_normalization_122[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, 12, 12, 768)  0           activation_61[0][0]              \n",
            "                                                                 activation_64[0][0]              \n",
            "                                                                 activation_69[0][0]              \n",
            "                                                                 activation_70[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_107 (Conv2D)             (None, 12, 12, 192)  147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_125 (BatchN (None, 12, 12, 192)  576         conv2d_107[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_73 (Activation)      (None, 12, 12, 192)  0           batch_normalization_125[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_108 (Conv2D)             (None, 12, 12, 192)  258048      activation_73[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_126 (BatchN (None, 12, 12, 192)  576         conv2d_108[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_74 (Activation)      (None, 12, 12, 192)  0           batch_normalization_126[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_105 (Conv2D)             (None, 12, 12, 192)  147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_109 (Conv2D)             (None, 12, 12, 192)  258048      activation_74[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_123 (BatchN (None, 12, 12, 192)  576         conv2d_105[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_127 (BatchN (None, 12, 12, 192)  576         conv2d_109[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_71 (Activation)      (None, 12, 12, 192)  0           batch_normalization_123[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_75 (Activation)      (None, 12, 12, 192)  0           batch_normalization_127[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_106 (Conv2D)             (None, 5, 5, 320)    552960      activation_71[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_110 (Conv2D)             (None, 5, 5, 192)    331776      activation_75[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_124 (BatchN (None, 5, 5, 320)    960         conv2d_106[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_128 (BatchN (None, 5, 5, 192)    576         conv2d_110[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_72 (Activation)      (None, 5, 5, 320)    0           batch_normalization_124[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_76 (Activation)      (None, 5, 5, 192)    0           batch_normalization_128[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 5, 5, 768)    0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed8 (Concatenate)            (None, 5, 5, 1280)   0           activation_72[0][0]              \n",
            "                                                                 activation_76[0][0]              \n",
            "                                                                 max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_115 (Conv2D)             (None, 5, 5, 448)    573440      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_133 (BatchN (None, 5, 5, 448)    1344        conv2d_115[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_81 (Activation)      (None, 5, 5, 448)    0           batch_normalization_133[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_112 (Conv2D)             (None, 5, 5, 384)    491520      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_116 (Conv2D)             (None, 5, 5, 384)    1548288     activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_130 (BatchN (None, 5, 5, 384)    1152        conv2d_112[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_134 (BatchN (None, 5, 5, 384)    1152        conv2d_116[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_78 (Activation)      (None, 5, 5, 384)    0           batch_normalization_130[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_82 (Activation)      (None, 5, 5, 384)    0           batch_normalization_134[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_113 (Conv2D)             (None, 5, 5, 384)    442368      activation_78[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_114 (Conv2D)             (None, 5, 5, 384)    442368      activation_78[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_117 (Conv2D)             (None, 5, 5, 384)    442368      activation_82[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_118 (Conv2D)             (None, 5, 5, 384)    442368      activation_82[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_8 (AveragePoo (None, 5, 5, 1280)   0           mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_111 (Conv2D)             (None, 5, 5, 320)    409600      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_131 (BatchN (None, 5, 5, 384)    1152        conv2d_113[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_132 (BatchN (None, 5, 5, 384)    1152        conv2d_114[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_135 (BatchN (None, 5, 5, 384)    1152        conv2d_117[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_136 (BatchN (None, 5, 5, 384)    1152        conv2d_118[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_119 (Conv2D)             (None, 5, 5, 192)    245760      average_pooling2d_8[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_129 (BatchN (None, 5, 5, 320)    960         conv2d_111[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_79 (Activation)      (None, 5, 5, 384)    0           batch_normalization_131[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_80 (Activation)      (None, 5, 5, 384)    0           batch_normalization_132[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_83 (Activation)      (None, 5, 5, 384)    0           batch_normalization_135[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_84 (Activation)      (None, 5, 5, 384)    0           batch_normalization_136[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_137 (BatchN (None, 5, 5, 192)    576         conv2d_119[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_77 (Activation)      (None, 5, 5, 320)    0           batch_normalization_129[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_0 (Concatenate)          (None, 5, 5, 768)    0           activation_79[0][0]              \n",
            "                                                                 activation_80[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 5, 5, 768)    0           activation_83[0][0]              \n",
            "                                                                 activation_84[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_85 (Activation)      (None, 5, 5, 192)    0           batch_normalization_137[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed9 (Concatenate)            (None, 5, 5, 2048)   0           activation_77[0][0]              \n",
            "                                                                 mixed9_0[0][0]                   \n",
            "                                                                 concatenate_1[0][0]              \n",
            "                                                                 activation_85[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_124 (Conv2D)             (None, 5, 5, 448)    917504      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_142 (BatchN (None, 5, 5, 448)    1344        conv2d_124[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_90 (Activation)      (None, 5, 5, 448)    0           batch_normalization_142[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_121 (Conv2D)             (None, 5, 5, 384)    786432      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_125 (Conv2D)             (None, 5, 5, 384)    1548288     activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_139 (BatchN (None, 5, 5, 384)    1152        conv2d_121[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_143 (BatchN (None, 5, 5, 384)    1152        conv2d_125[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_87 (Activation)      (None, 5, 5, 384)    0           batch_normalization_139[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_91 (Activation)      (None, 5, 5, 384)    0           batch_normalization_143[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_122 (Conv2D)             (None, 5, 5, 384)    442368      activation_87[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_123 (Conv2D)             (None, 5, 5, 384)    442368      activation_87[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_126 (Conv2D)             (None, 5, 5, 384)    442368      activation_91[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_127 (Conv2D)             (None, 5, 5, 384)    442368      activation_91[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_9 (AveragePoo (None, 5, 5, 2048)   0           mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_120 (Conv2D)             (None, 5, 5, 320)    655360      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_140 (BatchN (None, 5, 5, 384)    1152        conv2d_122[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_141 (BatchN (None, 5, 5, 384)    1152        conv2d_123[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_144 (BatchN (None, 5, 5, 384)    1152        conv2d_126[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_145 (BatchN (None, 5, 5, 384)    1152        conv2d_127[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_128 (Conv2D)             (None, 5, 5, 192)    393216      average_pooling2d_9[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_138 (BatchN (None, 5, 5, 320)    960         conv2d_120[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_88 (Activation)      (None, 5, 5, 384)    0           batch_normalization_140[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_89 (Activation)      (None, 5, 5, 384)    0           batch_normalization_141[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_92 (Activation)      (None, 5, 5, 384)    0           batch_normalization_144[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_93 (Activation)      (None, 5, 5, 384)    0           batch_normalization_145[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_146 (BatchN (None, 5, 5, 192)    576         conv2d_128[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_86 (Activation)      (None, 5, 5, 320)    0           batch_normalization_138[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_1 (Concatenate)          (None, 5, 5, 768)    0           activation_88[0][0]              \n",
            "                                                                 activation_89[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 5, 5, 768)    0           activation_92[0][0]              \n",
            "                                                                 activation_93[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_94 (Activation)      (None, 5, 5, 192)    0           batch_normalization_146[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed10 (Concatenate)           (None, 5, 5, 2048)   0           activation_86[0][0]              \n",
            "                                                                 mixed9_1[0][0]                   \n",
            "                                                                 concatenate_2[0][0]              \n",
            "                                                                 activation_94[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 5, 5, 2048)   0           mixed10[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_2 (Glo (None, 2048)         0           dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 128)          262272      global_average_pooling2d_2[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_147 (BatchN (None, 128)          512         dense_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 2)            258         batch_normalization_147[0][0]    \n",
            "==================================================================================================\n",
            "Total params: 22,065,826\n",
            "Trainable params: 22,031,138\n",
            "Non-trainable params: 34,688\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kK8W2SkHvbT_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRC78sWe2DRF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "traindatagen = ImageDataGenerator(rescale=1./255,\n",
        "                                  zoom_range = 0.2,\n",
        "                                  horizontal_flip=True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "\n",
        "                                 "
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Xf4lBj53aWF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9433d99a-21cb-4c99-ad31-1cfc9841804b"
      },
      "source": [
        "training_set = train_datagen.flow_from_directory(loc+'/train',\n",
        "                                                target_size=(224, 224),\n",
        "                                                batch_size=32,\n",
        "                                                class_mode='categorical')\n",
        "\n",
        "testing_set = test_datagen.flow_from_directory(loc+'/test',\n",
        "                                               target_size=(224, 224),\n",
        "                                               batch_size=32,\n",
        "                                               class_mode='categorical')\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 5239 images belonging to 2 classes.\n",
            "Found 624 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZ5eZ3NR5Vbb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "outputId": "92026f39-3fff-470c-84dc-4fb5ff2f86b8"
      },
      "source": [
        "f = model.fit_generator(training_set,\n",
        "              validation_data = testing_set,\n",
        "              epochs=25,\n",
        "              steps_per_epoch = len(training_set),\n",
        "              validation_steps = len(testing_set)\n",
        "             )"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "164/164 [==============================] - 220s 1s/step - loss: 0.2144 - accuracy: 0.9112 - val_loss: 0.3125 - val_accuracy: 0.8814\n",
            "Epoch 2/25\n",
            "164/164 [==============================] - 184s 1s/step - loss: 0.1194 - accuracy: 0.9586 - val_loss: 0.0714 - val_accuracy: 0.8910\n",
            "Epoch 3/25\n",
            "164/164 [==============================] - 188s 1s/step - loss: 0.0958 - accuracy: 0.9660 - val_loss: 0.1772 - val_accuracy: 0.8638\n",
            "Epoch 4/25\n",
            "164/164 [==============================] - 189s 1s/step - loss: 0.0977 - accuracy: 0.9634 - val_loss: 0.3694 - val_accuracy: 0.8782\n",
            "Epoch 5/25\n",
            "164/164 [==============================] - 187s 1s/step - loss: 0.0656 - accuracy: 0.9794 - val_loss: 0.4022 - val_accuracy: 0.9183\n",
            "Epoch 6/25\n",
            "164/164 [==============================] - 187s 1s/step - loss: 0.0711 - accuracy: 0.9759 - val_loss: 0.6609 - val_accuracy: 0.7933\n",
            "Epoch 7/25\n",
            "164/164 [==============================] - 188s 1s/step - loss: 0.0725 - accuracy: 0.9750 - val_loss: 0.4409 - val_accuracy: 0.9247\n",
            "Epoch 8/25\n",
            "164/164 [==============================] - 189s 1s/step - loss: 0.0608 - accuracy: 0.9775 - val_loss: 0.7226 - val_accuracy: 0.7917\n",
            "Epoch 9/25\n",
            "164/164 [==============================] - 191s 1s/step - loss: 0.0497 - accuracy: 0.9819 - val_loss: 1.3738 - val_accuracy: 0.8542\n",
            "Epoch 10/25\n",
            "164/164 [==============================] - 193s 1s/step - loss: 0.0540 - accuracy: 0.9817 - val_loss: 0.5932 - val_accuracy: 0.9103\n",
            "Epoch 11/25\n",
            "164/164 [==============================] - 188s 1s/step - loss: 0.0595 - accuracy: 0.9798 - val_loss: 0.1580 - val_accuracy: 0.9135\n",
            "Epoch 12/25\n",
            "164/164 [==============================] - 189s 1s/step - loss: 0.0456 - accuracy: 0.9826 - val_loss: 0.6123 - val_accuracy: 0.8862\n",
            "Epoch 13/25\n",
            "164/164 [==============================] - 186s 1s/step - loss: 0.0473 - accuracy: 0.9834 - val_loss: 0.2368 - val_accuracy: 0.8926\n",
            "Epoch 14/25\n",
            "164/164 [==============================] - 188s 1s/step - loss: 0.0528 - accuracy: 0.9790 - val_loss: 0.3748 - val_accuracy: 0.7869\n",
            "Epoch 15/25\n",
            "164/164 [==============================] - 188s 1s/step - loss: 0.0602 - accuracy: 0.9759 - val_loss: 1.1269 - val_accuracy: 0.8686\n",
            "Epoch 16/25\n",
            "164/164 [==============================] - 187s 1s/step - loss: 0.0432 - accuracy: 0.9845 - val_loss: 0.2380 - val_accuracy: 0.9279\n",
            "Epoch 17/25\n",
            "164/164 [==============================] - 188s 1s/step - loss: 0.0562 - accuracy: 0.9794 - val_loss: 0.9650 - val_accuracy: 0.8221\n",
            "Epoch 18/25\n",
            "164/164 [==============================] - 189s 1s/step - loss: 0.0473 - accuracy: 0.9805 - val_loss: 0.3680 - val_accuracy: 0.9215\n",
            "Epoch 19/25\n",
            "164/164 [==============================] - 187s 1s/step - loss: 0.0376 - accuracy: 0.9870 - val_loss: 0.3199 - val_accuracy: 0.9343\n",
            "Epoch 20/25\n",
            "164/164 [==============================] - 189s 1s/step - loss: 0.0343 - accuracy: 0.9874 - val_loss: 0.0601 - val_accuracy: 0.9151\n",
            "Epoch 21/25\n",
            "164/164 [==============================] - 187s 1s/step - loss: 0.0516 - accuracy: 0.9807 - val_loss: 0.5704 - val_accuracy: 0.7772\n",
            "Epoch 22/25\n",
            "164/164 [==============================] - 189s 1s/step - loss: 0.0551 - accuracy: 0.9803 - val_loss: 0.1282 - val_accuracy: 0.9006\n",
            "Epoch 23/25\n",
            "164/164 [==============================] - 188s 1s/step - loss: 0.0444 - accuracy: 0.9819 - val_loss: 0.4606 - val_accuracy: 0.6747\n",
            "Epoch 24/25\n",
            "164/164 [==============================] - 187s 1s/step - loss: 0.0735 - accuracy: 0.9740 - val_loss: 0.3961 - val_accuracy: 0.9215\n",
            "Epoch 25/25\n",
            "164/164 [==============================] - 189s 1s/step - loss: 0.0467 - accuracy: 0.9840 - val_loss: 0.5377 - val_accuracy: 0.9135\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pMN5iES79cd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "67bcfff4-c98f-4a8e-e26c-573fc584ec2a"
      },
      "source": [
        "plt.figure(figsize = (20,8))\n",
        "plt.plot(f.history['loss'], label = 'Data loss')\n",
        "plt.plot(f.history['val_loss'] , label = 'Validation loss')\n",
        "plt.legend()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fef26bae828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rkiRJksqITSBJUu61tULv8fNPBsvIHBkzF0iSJEnKGptAkqTcOxEKPcAmUN1cqKgyF0iSJEnKIptAkqTcS6+CCTNh/LSBXV81EmrnQrtNIEmSJClbbAJJknIv3TLwPKAMJ4RJkiRJWWUTSJKUWwfaYP+2gR8Fy0g1wsE2OLw7N3VJkiRJZcYmkCQpt07kAV05uNfV94dD7zQcWpIkScoGm0CSpNxKN0PlCJh2+eBel2oK68712a9JkiRJKkM2gSRJuZVugfrLQ9jzYIytg7Epw6ElSZKkLLEJJEnKnd5u2PFbmDnIo2AZqUaPg0mSJElZYhNIkpQ7O5+CniODnwyWUd8IHc+EZpIkSZKkYbEJJEnKnXRLWAc7GSwj1QS9x6Hz2ezVJEmSJJUpm0CSpNxJN8PYepgwc2ivz0wIMxdIkiRJGjabQJKk3Nm2KhwFi6KhvX7KpVA50lwgSZIkKQtsAkmScuNwJ+zdNPSjYACVVTB1njuBJEmSpCywCSRJyo1MHtBQJ4NlpJpCwLQkSZKkYbEJJEnKjfQqiCph2qLh3ae+EQ53wMGd2alLkiRJKlM2gSRJuZFuDg2cEaOHd59Ufzi0uUCSJEnSsNgEkiRlX18vbF89vDygDCeESZIkSVlx3iZQFEX/GEXRriiKzvjVdxRFfxhFUWsUReuiKHo8iqKF2S9TklRUdm2A44dgxjDzgABGTYLxM8wFkiRJkoZpIDuBvgW88Rwf3wS8Oo7jJuDTwF1ZqEuSVMzSzWGdsTQ796tvdCeQJEmSNEznbQLFcfwosOccH388juO9/W/+GpiRpdokScUq3QKjp8Dki7Jzv1QjdD4L3Uezcz9JkiSpDGU7E+hPgAezfE9JUrFJN4c8oCjKzv3qGyHuhY6N2bmfJEmSVIay1gSKoug6QhPoI+e45r1RFLVEUdTS0dGRrUdLkgrJkb3Q+Uz2joIBpJrCai6QJEmSNGRZaQJFUXQ58PfAjXEc7z7bdXEc3xXH8dI4jpfW1dVl49GSpEKz/cmwZmMyWMbk2VA92lwgSZIkaRiG3QSKomgW8C/Au+M4fnb4JUmSilq6BYhg+hXZu2dFJUyd704gSZIkaRiqzndBFEXfB64FaqMoSgN3ANUAcRx/A/g4MAX4WhSyH3riOM7iGQBJUlHZtgqmXgYjx2X3vqlGePrfII6zlzUkSZIklZHzNoHiOL7pPB9/D/CerFUkSSpefX2wvQUue2v2713fBKu/DQe2wwQHUUqSJEmDle3pYJKkcrb7eTi6P7t5QBmpxrCaCyRJkiQNiU0gSVL2pFeFdeaV2b93akFYd67L/r0lSZKkMmATSJKUPelmGDkBplya/XvXjIeJDe4EkiRJkobIJpAkKXvSLTDjCqjI0T8v9U1OCJMkSZKGyCaQJCk7jh2EXU/DjBwcBctINcLuF+D44dw9Q5IkSSpRNoEkSdmxfTXEfbkJhc6obwRi2LUhd8+QJEmSSpRNIElSdqSbwzrjitw948SEMMOhJUmSpMGyCSRJyo50C9TOgVGTcveMiQ0wYpy5QJIkSdIQ2ASSJA1fHIfx8Lk8CgYhcDq1wAlhkiRJ0hDYBJIkDd/eTdC1G2Yszf2z6hth53ro68v9syRJkqQSYhNIkjR86Zaw5nonEIRcoOMHYd+W3D9LkiRJKiE2gSRJw7dtFVSPgamX5f5Z9U1h3bk+98+SJEmSSohNIEnS8KWbYfoSqKjM/bOmzgciw6ElSZKkQbIJJEkanuNdoSGTj6NgACPGwJSLHRMvSZIkDZJNIEnS8LSthb4emHll/p6ZanQnkCRJkjRINoEkScOTXhXW6XmYDJZR3wh7N8PRA/l7pqTs6doDvT1JVyFJUtmxCSRJGp50M0y6EMbW5e+Zqf5w6F1P5++ZkrKj+yh8ZRGs+j9JVyJJUtmxCSRJGro4hm3NMCOPR8Eg7AQCc4GkYrTraTi6H9ItSVciSVLZsQkkSRq6/Wk41J6/UOiM8dOhZqK5QFIxam8Na+ezydYhSVIZsgkkSRq6dHNYZ+QxDwggiqC+CdptAklFJ7ODr/M56OtNthZJksqMTSBJ0tClW6CqJkzryrdUYzhW4jeRUnFp698J1HssBLxLkqS8sQkkSRq69Cq4YDFUjcj/s+sbobsL9mzK/7MlDU1fbzjGOf2K8LZHwiRJyiubQJKkoek5Bm1r838ULCOz+2in4dBS0dj9QmjeNr0rvN3xTLL1SJJUZmwCSZKGpn0d9B7Pfyh0Rt08iCrNBZKKSSYU+sJXwdh6dwJJkpRnNoEkSUNzIhQ6z+PhM6proHaOE8KkYtLeChXVUDsX6uZAx8akK5IkqazYBJIkDc22VTB+BoyfllwN9Y3uBJKKSVsrTJ0fcsRq50LHsxDHSVclSVLZsAkkSRqadEtyeUAZqUY4kIauPcnWIen84jgcI512eXi7bi4cPwgH25KtS5KkMmITSJI0eAfbYf9WmJnQUbCM+kw49Ppk65B0fgfboKsT6k9qAoHh0JIk5ZFNIEnS4J3IA0ooFDoj1RRWc4GkwtfWHwqdaQLV2gSSJCnfbAJJkgYv3RzCXTPfzCVlXArG1JkLJBWD9lYgemkH39ipUDMBOm0CSZKULzaBJEmDl24JuR7VNUlXEnKBdq5LugpJ59PeCpMvgpHjwttRBHXzQji0JEnKC5tAkqTB6e2G7auTGw3/cvWNsGsj9PYkXYmkc2lrhfqmU99XO8edQJIk5ZFNIEnS4OxcDz1Hkp8MlpFqhN5jsPu5pCuRdDZH9sG+LS9NBsuomwuHO5zwJ0lSntgEkiQNTqGEQmek+vNFzAWSCld7/5HN+oWnvt9waEmS8somkCRpcNItMDYFE2clXUlQOyeEVJsLJBWuTBPoTDuBwCNhkiTliU0gSdLgpFeFXUBRlHQlQdWIEC7rTiCpcLW3hubx2Kmnvn/CTKgebTi0JEl5YhNIkjRwh3fDnhcLJw8oo74RdtoEkgpWWyvUX376+ysqYMol0LEx/zVJklSGbAJJkgZue0tYC2UyWEaqEQ7thEMdSVci6eW6j4Ymz8uPgmXUzYVOdwJJkpQPNoEkSQO3bRVElXDBoqQrOVV9fzi0uUBS4enYAHHvmXcCQWgC7d8Gxw7lty5JksqQTSBJ0sClmyG1AEaMSbqSU6WawmoukFR42lrDWt905o9nJoTtfi4/9UiSVMZsAkmSBqavF7avLpzR8CcbMwXGTTMXSCpE7a0wYhxMmn3mj9c5Jl6SpHyxCSRJGpiOjXD8IMwssDygjFSjO4GkQtTWGnYBVZzly87JF0FFlU0gSZLywCaQJGlg0s1hLcSdQBBygTqfgZ5jSVciKaOvF3auP3soNEBlNUy+2HBoSZLywCaQJGlg0s0wanL4qX0hSjVCX4/fSEqFZM+L0H347HlAGXVz3AkkSVIe2ASSJA1MuiXsAoqipCs5s3rDoaWC07Y2rGebDJZROzc0jHqO574mSZLKmE0gSdL5HdkXMoEK9SgYhOMkVTWGQ0uFpL0VKqqhbt65r6ubG8bI73khP3VJklSmbAJJks5v+5NhnbE02TrOpbIKps6H9nVJVyIpo31d+HNZNeLc1zkhTJKkvLAJJEk6v3QLEMH0K5Ku5NxSjWEnUBwnXYmkOO6fDHaeo2AAUy4FIjO9JEnKMZtAkqTzS68KP82vGZ90JedW3wRdu+Fge9KVSDrYBl2d554MljFiNEycGY6dSpKknLEJJEk6t76+/lDoAj4KlpFqDKu5QFLy2lrDOpCdQBDCoTvcCSRJUi6dtwkURdE/RlG0K4qiM35FHQVfiaLo+SiKWqMoWpL9MiVJidnzAhzdV9ih0BmpBWE1F0hKXubPYX3jwK6vmwu7n4O+3tzVJElSmRvITqBvAW88x8ffBFza/7/3Al8fflmSpIKxbVVYZ1yZbB0DMWoiTJjlTiCpELSvhckXwchxA7u+bi70HIV9W3NblyRJZey8TaA4jh8F9pzjkhuBu+Pg18DEKIqmZatASVLC0s0wcjzUzkm6koGpb4R2m0BS4gYaCp1R64QwSZJyLRuZQNOBbSe9ne5/nySpFKRbwlSwiiKJkUs1hiMl3UeSrkQqX0f2wb4tAwuFzqjrbzR32gSSJClX8voVfRRF742iqCWKopaOjo58PlqSNBTHDsGu9TCzCI6CZdQ3QtwHuzYkXYlUvjJHMusXDvw1oybB2JTh0JIk5VA2mkDbgZknvT2j/32nieP4rjiOl8ZxvLSuri4Lj5Yk5dSO1aGhUgyh0BlOCJOSd2IyWNPgXlc7x51AkiTlUDaaQD8Cbu6fEvZKYH8cx21ZuK8kKWnp5rBOvyLZOgZj0mwYMdZcIClJ7a1hV8+41OBeVzc3ZALFcW7qkiSpzFWd74Ioir4PXAvURlGUBu4AqgHiOP4G8ABwPfA80AX8ca6KlSTlWboFplwKoycnXcnAVVTA1MvcCSQlabCh0Bm1c+HYATjYDuOdMyJJUradtwkUx/FN5/l4DPyXrFUkSSoMcRzGw1/6+qQrGbz6Rlj3z+HXEEVJVyOVl+6j4UjX3DcO/rV1/RPCOp+xCSRJUg4UyagXSVLe7d0MXZ0wY2nSlQxeqhGO7Yf9285/raTs6tgAfT2DzwOCl5pAhkNLkpQTNoEkSWeWbglrMU0Gy8h882kukJR/J0Khh3AcbGwKRk6Ajo3ZrUmSJAE2gSRJZ5NeBdVjoG5+0pUM3tTLgMhcICkJ7a0wYlwIaR+sKIK6OdDpTiBJknLBJpAk6czSzTB9CVSeNz6u8IwcC5NnQ/u6pCuRyk/7upDLVTHELzMzE8IkSVLW2QSSJJ2u+0j4Rq4Y84AyUo3uBJLyra83HMMcylGwjNq5cHgXdO3JXl2l7ugB2PJ40lVIkoqATSBJ0una1oZg1xlFmAeUUd8EezbBsUNJVyKVjz0vQvdhmDaMJtCJCWEeCRuwX38dvnk9HGxPuhJJUoGzCSRJOt22VWEt9p1AxLDr6aQrkcpH+zBCoTNq54TVI2EDt/1JIHY3kCTpvGwCSZJOl26GiQ0wdmrSlQxdfWNYzQWS8qetFSqqoW7e0O8xcRZUjXIn0GC0rQ3r1ieSrUOSVPBsAkmSTpduKc7R8CebMDOMmjYXSMqf9laYOg+qRgz9HhWVUHuJO4EG6mA7HOo/BrbFJpAk6dxsAkmSTrU/DQd3wIxlSVcyPFEEqQUhpFZS7sVx2AlUv3D496p1QtiAZXYBXXRdaHof2ZdsPZKkgmYTSJJ0qnRzWIs5DyijvhF2roe+vqQrkUrfwXbo6hxeKHRG3VzYvxWOHx7+vUpd21ogglf8ZyCGbb9JuiJJUgGzCSRJOlW6BapqINWUdCXDl2oMk4r2bkq6Eqn0nQiFzsLfHScmhD03/HuVuh1rYMolcNG1IY9py6+SrkiSVMBsAkmSTrVtFUxbNLxMj0KRCYc2F0jKvbb+JlCqcfj3qnVM/IC1rYVpC6F6FExfYi6QJOmcbAJJkl7Scyx8Q1EKR8EApl4GUYW5QFI+tK+FyRdBzfjh32vyRRBVQsfG4d+rlB3uhANpuGBReHvWVbBjNRzvSrYuSVLBsgkkSXpJ+1PQe6z4Q6EzqkeFYxLuBJJyr30d1GchDwjCTsTJFxkOfT5ta8I6rT+Mu2EF9PXA9pbkapIkFTSbQJKkl2RCoYt9PPzJUo3uBJJy7eh+2Ls5O3lAGXVzPQ52PpnJYJnm28wrgcgjYZKks7IJJEl6SXoVjJ8O4y9IupLsqW8MU4aO7k+6Eql0ta8L67QsjIfPqJsLe16EnuPZu2ep2bEGJs2GURPD26Mmhr/zDIeWJJ2FTSBJ0kvSzaWTB5SRmXK2c32ydUilLBMKna3jYBDCoft6QiNIZ5YJhT7ZrOXh7/Le7mRqkiQVNJtAkqTg4E7YtxVmlNBRMHhpQphHwqTcaV8HY1MwLpW9e9bNCWunuUBn1LUH9m15KRQ6o+Eq6O566aiYJEknsQkkSQoyeUClEgqdMW4ajJoMO9clXYlUutpbs5sHBFDb3wTqMBfojNr7d1+daScQwJbH81uPJKko2ASSJAXpZqiozm6mRyGIorAbyJ1AUm70HAuj3LN5FAxgxBiYMMudQGezIzMZ7GU7gcalYPLFsLfNKM8AACAASURBVNVwaEnS6WwCSZKCdAtMuxyqa5KuJPtSTbBrA/T1Jl2JVHp2PR2ye6ZluQkE4UhYx8bs37cUtK0NTbLRk0//WMPysBOory//dUmSCppNIEkS9PbAjtWldxQso74Reo7A7heSrkQqPZnJYNneCQQhHLrzeZsZZ9K29uyNt4blcHSfDTRJ0mlsAkmSYNf6ECRaqk2gVH84tLlAUva1tcKIcWFUebbVzQkN3P1bs3/vYnZ0P+x54fRQ6IxZV4XVUfGSpJexCSRJOikUusTGw2fUzYWKKnOBpFxobw277Spy8GVl3bywGg59qszuq5fnAWVMuhDGXWAukCTpNDaBJEkhD2jMVJjYkHQluVE1Mhwr2WkTSMqqvt7QXM3FUTA4aUKYx5pOcSIU+ixB/lEURsVveRziOH91SZIKnk0gSRJsWxWOgkVR0pXkjhPCpOzbswm6D+cmFBpC6PGYOieEvVzb2rDTZ+zUs1/TsBwOtsHezXkrS5JU+GwCSVK569oTsiVK9ShYRqoRDu4Iv15J2dG+Nqz1Tbl7Ru1cj4O9XNuas+cBZcxaHlaPhEmSTmITSJLKXbolrDOvTLaOXKvvD4duNxxaypq2Vqiohrr5uXtG3dywE8hjTcGxQ9D53NmPgmXUzYNRkwyHliSdwiaQJJW79CqIKuCCxUlXklup/p0K5gJJ2dPeClPnQdWI3D2jbm6YhnVoZ+6eUUza1wHx2UOhMyoqwpSwLe4EkiS9xCaQJJW7dDOkFsCIMUlXkltj62BsylwgKVviOOwEqj/PjpThOhEObS4QEPKA4Pw7gSA0gfa8AAdtoEmSAptAklTO+noh/STMKPGjYBmpRtjpcTApKw62Q1dnbvOAIOwEAug0FwgITaAxU2Fc/fmvbVgR1q2P57YmSVLRsAkkSeWs4xk4fjBMBisH9Y3h19zbnXQlUvFrbw1rriaDZYybBiPHuxMoIxMKPZBpjtMuh+rRHgmTJJ1gE0iSylm6Oazl0gRKNUHvcXcUSNnQ1t8ESjXm9jlRFI6EdWzM7XOKwfGu8HkYyFEwgMrqEPq/xZ1AkqTAJpAklbN0c5geM+XipCvJjxMTwswFkoatvRUmXwQ143P/rLq5Nm8Bdq6HuO/8odAnm7U8BOIf2Ze7uiRJRcMmkCSVs3RL2AU0kGMFpWDKpVA50lwgKRvaW3OfB5RROydMByv3RkbbmrAOdCcQQMNVQAzbfpOTkiRJxcUmkCSVq6P7w7GCcjkKBlBZFcZZuxNIGp6j+2HvZqjPcR5QRt28sJb7bqC2NTB6CkyYMfDXTF8KFdUeCZMkATaBJKl8bX8SiGHG0qQrya9UUzgaIWno2vt30w1mR8pw1GXGxJd5LlDb2vA5H8zuzRGj4YLFsNVwaEmSTSBJKl/pFiCC6VckXUl+1TfC4Q44uDPpSqTilWkC5es42MSGcJSznCeE9RyDXRuG1nhrWA7bV0P3kezXJUkqKjaBJKlcbVsVjljUTEi6kvzKTDIyF0gaurZWGDMVxtXn53kVlVB7aXkfB9u5Hvp6BhcKndGwHPq6+5v/kqRyZhNIkspRHIfJYOV2FAycECZlQ3srTMtTHlBG3dzy3gnUtjasQ9kJNPMVQGQukCTJJpAklaXdL8DRfTDzyqQryb9Rk2D8DHOBpKHqORayefIVCp1ROxf2bYXjXfl9bqFoWxN2bk66cPCvHTUx7ILcahNIksqdTSBJKkfpVWEtp8lgJ6tvdCeQNFS7NoRjSfnKA8qomwPEsPu5/D63UAwlFPpkDVeFY8C93dmtS5JUVGwCSVI5SjfDyPHhJ+vlKLUgZIt0H026Eqn4tLeGNV+TwTIyf191lGEuUM/xkAk0lDygjIbl0N0V8pwkSWXLJpAklaN0M0xfAhVl+s9AqhHiXsdNS0PR1gojxsGk2fl97pSLIaqEzjLMBerYCL3Hh9d4m7U8rB4Jk6SyVqZf/UtSGTt+OPxEeUYZ5gFlZI6xmAskDV77unCkMt9N5KqRMHl2eTZvM6HQFywe+j3GpWDyxYZDS1KZswkkSeVm+2qI+8o3Dwhg8kVQNcpcIGmw+vpC8zTfeUAZtXPL8zhY25rs7L5quAq2PhF+HyVJZckmkCSVm3RzWMtxPHxGRSWkLnMnkDRYe16E44fyPxkso24O7Hmh/MKN29bCtMuHv/tq1nI4src8d1NJkgCbQJJUftItMOUSGD056UqSlWoMx1riOOlKpOLR3n8saVpSTaB5YTLZnk3JPD8JvT1h1+JwQqEzGswFkqRyZxNIkspJHIfx8OV8FCyjvgmO7oMDO5KuRCoe7eugohrq5ifz/No5YS2nnSydz0LPkexMY5t0IYybBlueGP69JElFaUBNoCiK3hhF0TNRFD0fRdHtZ/j4rCiKHo6i6LdRFLVGUXR99kuVJA3bvi1wuKO8j4JlpBrD6pEwaeDaWsNunKoRyTw/0wQqpwlhbWvCekEWdgJFUdgNtOVxd0FKUpk6bxMoiqJK4KvAm4DLgJuiKLrsZZd9DPhhHMeLgT8AvpbtQiVJWZBuCWs5TwbLSC0Ia/u6ZOuQikUcQ3trckfBAEaOhfEzyiscum0tVI8Jx3izYdZVcHBH+KGAJKnsDGQn0JXA83EcvxjH8XHgHuDGl10TA+P7/3sC4N56SSpE21ZB9WiY+vJefhmqGQ8TG9wJJA3UwfawkzCpUOiMurnltRNox5pwfLWiMjv3a1gRVkfFS1JZGkgTaDqw7aS30/3vO9kngD+KoigNPAB8MCvVSZKyK90MFyyByqqkKykM9U2OiZcGKrNrLsmdQBCaQB3PlseY877e8HnPRh5QRt08qJloE0iSylS2gqFvAr4Vx/EM4HrgO1EUnXbvKIreG0VRSxRFLR0dHVl6tCRpQLqPhKMc5gG9JNUYxk0f70q6EqnwZSaDZfK0klI7JwQl7992/muL3e4XoPtwdptAFRXhSJhNIEkqSwNpAm0HZp709oz+953sT4AfAsRx/ARQA9S+/EZxHN8Vx/HSOI6X1tXVDa1iSdLQtLWG0cozzQM6ob4R4j7YtSHpSqTC19YKk2aHo5RJqpsb1s4yyAXKZij0yRqWhwb4wZ3Zva8kqeANpAnUDFwaRdHsKIpGEIKff/Sya7YCrwWIomg+oQnkVh9JKiTpVWGd7k6gE05MCDMcWjqvpEOhM+rmhbWjDHKB2tZCVQ3Uzs3ufRuWh3Wro+IlqdyctwkUx3EP8AHgIWADYQrY+iiKPhVF0Q39l/05cGsURWuB7wMr49i5k5JUUNLNMHEWjEslXUnhmNgAI8aZCySdz9H9sHdz8qHQAKMnw+ha6NiYdCW5t2NNaFZnO8dt2sIwJMAjYZJUdgb0L0ocxw8QAp9Pft/HT/rvp4EV2S1NkpRV6ZaQA6GXVFSEUfFOCJPOLdMoLYQmEPRPCCvx42B9fWH3VdO7sn/vymqYsQy22gSSpHKTrWBoSVIh278dDmwPX/TrVPWNsHM9uIFVOrv21rAWwnEwCOHQHc+U9p/bvZvg2IHs5wFlNCwPzb0j+3Jzf0lSQbIJJEnlIN0cVptAp0s1hm+09m1JuhKpcLW1wpipMK4+6UqCunlwdB8cLuEIykwodDYng52sYTkQw7ZVubm/JKkg2QSSpHKQbobKkVDflHQlhSfzOTEXSDq79nWFswsIoG5OWEs5F6htLVSOgLr5ubn/9KVQUe2RMEkqMzaBJKkcpJvDkYKqEUlXUnimzgcic4Gks+k5Bh0bCquJnJmWVcoTwnasgamX5e7v7RGj4YLFhkNLUpmxCSRJpa7nePhmwqNgZzZiDEy5OOx0kHS6XRugr6dwQqEBxl8QJvuVajh0HIedQLk6CpbRcBVsXw3dR3L7HElSwbAJJEmlbuc66D1mE+hcUo3uBJLO5kQodI4bEoMRReFIWKnuBNq3JWQe5SoUOqNhBfR1h+mRkqSyYBNIkkpd5ot7m0BnV98IezfD0QNJVyIVnvZ1MGIsTJqddCWnqp1buk2gtrVhzXXjbeYrgAi2PpHb50iSCoZNIEkqddtWwbgLYML0pCspXKn+rJNdTydbh1SI2lrDbrmKAvuysW4OHGqHo/uTriT7dqyBiiqYuiC3zxk1EVILYMuvcvscSVLBKLB/zSVJWZduhhlLk66isNU3htVcIOlUfX3hqGQhTQbLOBEOXYK5QG1rw1Sw6prcP6thOWxrht6e3D9LkpQ4m0CSVMoO7QrZEjOvTLqSwjZ+OtRMNBdIerk9L8LxQ4UVCp1R198E6iyxI2FxDG1r4II8ZTDNugq6D0P72vw8T5KUKJtAklTK0s1hNQ/o3KIojL9utwkkneJEKHQBNoEmXQiVI6FjY9KVZNeB7dC1G6blOBQ6o2F5WB0VL0llwSaQJJWydHPIlSikqT6FKtUYMoH6epOuRCoc7a3h75C6eUlXcrqKSphySekdBzsRCp2nJtC4eph8EWwxHFqSyoFNIEkqZemWcIyjelTSlRS++kbo7oI9m5KuRCocba0hm6ZqZNKVnFndnNI7DrZjDUQVIbA5X2Yth62PhwwoSVJJswkkSaWqtwe2P+lRsIFK9YdD7zQcWgJCNk17a2EeBcuomwd7t0D3kaQryZ62tSH0esTo/D2zYTkc2Vt6DTVJ0mlsAklSqdr1dNjZYhNoYOrmQVRpLpCUcWgnHO4IeVmFqnYOEEPnc0lXkj1ta+CCPB0Fy2i4KqzmAklSybMJJEmlKhMKPdMm0IBU14RvKJ0QJgVt/aHQhTgZLOPEhLASyQU62B6ab/nOcZs0G8ZNswkkSWXAJpAklap0C4ypg4kNSVdSPOob3QkkZWRGhhfyTqApl4T8nI4SOca0Y01Y8xUKnRFFYVT8lsfDMUBJUsmyCSRJpSiOQ8jnjGXhi3sNTKoRDqSha0/SlUjJa2sNO0RqxiddydlVjQw1lkqWTdtaIEqm8dawHA7ugH1b8v9sSVLe2ASSpFK08X7Yuxnm35B0JcWlPhMOvT7ZOqRC0L6usHcBZdTNLZ2dQG1rofZSGDk2/89uWB5WR8VLUkmzCSRJpaavDx7+y3BMouldSVdTXE5MCPNImMrc0f2wd1NhTwbLqJ0Du18IExGLXdua/OcBZdTNh5qJsOVXyTxfkpQXNoEkqdRs+DfYtR5efTtUViVdTXEZm4LRteYCSZk/A/UJNSQGo24u9HWHplUxO9QBB7Yn1wSqqAi5QFvdCSRJpcwmkCSVkr5eeOTzYdx549uTrqb4RFE4ErZzXdKVSMlq758MVgw7gTITwor9SFhbfxB3vkOhT9ZwFex+Hg7tSq4GSVJO2QSSpFKy/l+hYyNceztUVCZdTXFKNcKujaVxtEQaqvZ1Ybrg2FTSlZxf7ZywdmxMto7hastMBkuw8dawIqyOipekkmUTSJJKRW8PPPI5mLoA5t+YdDXFq74Jeo+Fn4ZL5aqtFeovL47pgiPHwfjp0Pls0pUMT9samHwR1ExIroZpC6F6tEfCJKmE2QSSpFLx1D+FxsV1/yNkO2hoDIdWues5Bh0biuMoWEbtnNI4DpZUHlBGZTXMWGo4tCSVML9LkKRS0NsdsoDqL4d5b0m6muJWOwcqqsNxGKkcdWyEvp7w90mxqJsHnc+F6YjFqGsP7NuabB5QRsOKEAx+dH/SlUiScsAmkCSVgrX3hMk41/1FcRzfKGRVI8I3lO4EUrlq6w+FLqom0BzoPgwH0klXMjQnQqELYBrbrKuAGLatSroSSVIO2ASSpGLXcxwe/Su4YAnMeWPS1ZSG+kbHxKt8tbfCiLEhn6ZY1GYmhBVpLlAhNYFmLIOKKo+ESVKJsgkkScVuzXfDMYLrPuouoGxJNcKhdjjcmXQlUv61tYY/A8WULZYZE99ZpLlAbWtg4iwYPTnpSmDEaLhgMWwxHFqSSlER/esuSTpNzzF49E6YcSVc8tqkqykd9f3h0OYCqdz09YWjkMUUCg0wphZGTynecOhCCIU+WcNy2P4kdB9JuhJJUpbZBJKkYrb67pCBYRZQdqWawmoukMrN3k1w/BDUNyVdyeDVzi3OJtDR/bDnxcIIhc6YtRz6ukMjSJJUUmwCSVKx6j4Cj30xfLF+0bVJV1NaxkyBcdPMBVL5yWTTFFModEbdnHAcLI6TrmRwMkHcBdUEegUQeSRMkkqQTSBJKlZPfgsOtrkLKFdSje4EUvlpbw2hwFPnJ13J4NXOhSN7iy/Lq21NWAvpONioSZBaYDi0JJUgm0CSVIyOd8Fj/xsuvBpmX510NaWpvjEcLek5nnQlUv60r4O6+VA1MulKBq9Yw6Hb1sL46TC2LulKTjXrqjAmvrcn6UokSVlkE0iSilHLP8DhXWEimHIj1RgyMYrtG0ppONpaizMPCF5qAnVsTLaOwWpbW1hHwTIalkP3YWhfm3QlkqQssgkkqTB0Phfybfp6k66k8B07BL/8Mlz8Gmi4KulqSlfmG2FzgVQuDraH5nKxTQbLGD8dRoyFjmeTrmTgjh0M//4V0lGwjIblYTUXSJJKik0gScmLY/jX/ww//xT85v8kXU3ha/476OqEa/8i6UpK2+SLoarGXCCVj0xAcTGGQkPIRqu9tLh277U/BcSF2QQaVw+TZsOWx5OuRJKURTaBJCVv/b+EMbTjp8N/fBr2bEq6osJ19AD86q/h0tfDzGVJV1PaKvvDcdvXJV2JlB/tmSZQY7J1DEfdvOLaCZQJhb6gAI+DATSsgK1PQF9f0pVIkrLEJpCkZPUcg599MuSv3PITiCrhx/+t+Eb85stv/k+YfnPt/0i6kvKQmRDm/x9VDtpbYdKFUDMh6UqGrnYOHNwBR/cnXcnAtK2Fsamw66YQNVwFR/ZAZxE11iRJ52QTSFKyVv0d7NsCr/sUTJwFr/8UbPoFrL476coKz5F98MTfwNzrYfqSpKspD/VN0LU7ZKVIpa6ttXiPgmWcmBD2XLJ1DNSONYUZCp1xIhfIUfGSVCpsAklKTtceePSv4OLXwiWvDe9bsjKMPf/3j8GBHYmWV3B+/fXw0213AeVPqv9YjLlAKnVH98PeTcUbCp1Rm5kQVgS5QMe7Qn5RIeYBZUyaDWPrw5EwSVJJsAkkKTmPfTFMRnn9p196X0UF/O5fQ2833P/nHsPJ6NoDv/4azL+h+L9JKyapBWE1F0ilbuf6sNYXcENiICZdCJUjiiMceud6iPsKNw8IQth2w/IQDu2/x5JUEmwCSUrGnk0h32bR//PSN9oZUy6G13wUnnkAnvrnZOorNE98NTTM3AWUX6MmwoRZ7gRS6TsxGawp2TqGq7IKplxSHDuBMqHQhbwTCEIT6MB22Lc16UokSVlgE0hSMn7+Saishus+euaPv/L9MP0KePD/hcOd+a2t0BzeDb/5Bix4G6QuS7qa8lPf2D/GWSph7a0wpq5wA4oHo3ZO8TSBRteGyZiFbNZVYfVImCSVBJtAkvJvWzOs/1dY/kEYf8GZr6mohBv+NoxE/8nt+a2v0Dz+13D8MFxb5p+HpKQaYfdz0H0k6Uqk3MmEQkdR0pUMX93cMHCg+2jSlZzbjrVhF1Chf86nXhYmxhkOLUklwSaQpPyK4xD6PGYqLP+v5742dRlc899h3f+FZ36Sn/oKzaFdYYJa07temnqj/KpvDLkduzYkXYmUGz3HoWNj6eSN1c4Jf2Z3P590JWfXfRQ6NhT+UTAIWX2zroIt7gSSpFJgE0hSfm34MWz7NVz3FzBy7Pmvf9VtMHUB3PdnYXpNufnVX0PPUXj1R5KupHw5IUylrmMD9HUXfx5QRt28sHZsTLaOc9m1Hvp6CjsU+mQNy8OOyEO7kq5EkjRMNoEk5U/PcfjZHeEL9MXvHthrqkbAjX8Dh9rhpx/PbX2F5mA7NP89LLwJai9JupryNWk2jBhrLpBK14lQ6CLYlTIQUy6BqAI6n026krNrWxvWYtgJBDBreVjNBZKkomcTSFL+PPlN2PMivO7TYYLLQE2/Aq76L/Dkt2DTozkrr+D88kvQ2w3XfDjpSspbRUXIxHAnkEpVeytUj4HJFyVdSXZU18DEhsIOh25bCzUTQ53FYNpCqBoVRsVLkoqaTSBJ+XFkHzzyeZh9DVz6usG//tq/CN+g/OiDISS51O3fDi3fhMV/CJNnJ12NMhPC4jjpSkrL/nSYfqdkta8L/x+vKKEvC+vmFvZOoB1riiMUOqNqBMxcZhNIkkrAgP61j6LojVEUPRNF0fNRFJ1xPE0URb8XRdHTURStj6Loe9ktU1LR++X/hiN74fWfGdoXvSNGww1/A3s3w8N/mfXyCs5jXwzBplf/96QrEYRcoGP7Yf+2pCspHXEM33oL/NPKpCspb319/U2gEgmFzqibC53PQW9P0pWcruc47Hq6ePKAMmYtDzsiyzGfT5JKyHmbQFEUVQJfBd4EXAbcFEXRZS+75lLgfwAr4jheAHwoB7VKKlb7tsKvvwEL/2B4+QcXvgqW3gK//hqkW7JXX6HZtxVW3w1L3g2TiuSoQKnLBOaaC5Q97a2wd1M44rlnU9LVlK+9m+D4odKZDJZROzeEXe/dnHQlp+vYAL3HiycPKKNhefjhxLZVSVciSRqGgewEuhJ4Po7jF+M4Pg7cA9z4smtuBb4ax/FegDiOHR0g6SU//3TY/fOajw3/Xr/zSRh3Afzbf4GeY8O/XyF69M7w+br6z5OuRBlT+3/2YS5Q9my4L4T3RhWwxg3EickEFJfiTiCAzgLMBToRCl1kO4FmLIOKKo+ESVKRG0gTaDpw8v73dP/7TjYHmBNF0a+iKPp1FEVvzFaBkorc9tWw7ofwyvfDhBnDv1/NeHjLl8Lo38e+OPz7FZo9m2DNd+GKP87O50vZMXJsmBLWvi7pSkrHxvtg1lVw8WtCE6ivN+mKylP7uvCN/dT5SVeSXbWXhrUQw6F3rIGR48PfKcVkxGi4YLFNIEkqctlKAKwCLgWuBW4C/i6KookvvyiKovdGUdQSRVFLR0dHlh4tqWDFMfz7/4TRtfCqP8vefee8Hi7//dAEKrXjOY/eGb4hy+bnS9lR3wg71yddRWnY/ULIRJn3Flj8R3AgDS8+knRV5am9FermQdXIpCvJrpoJYddoITaB2taGnVfFGMQ96yrYsRq6jyZdiSRpiAbyr892YOZJb8/of9/J0sCP4jjujuN4E/AsoSl0ijiO74rjeGkcx0vr6uqGWrOkYvHsT2DLL+Ha28MOnmx64+dh1KRwLKwQgz+HYvcLsPb7sPRPYPy0pKvRy6WaYM+LcOxQ0pUUv433h3Xem2Hu9eHP8prvJltTuWprLb2jYBl1cwrvOFhvTzhWWmyh0BkNy0Oe0fYnk65EkjREA2kCNQOXRlE0O4qiEcAfAD962TX3EnYBEUVRLeF42ItZrFNSsentDruAplwCV6zM/v1HT4brvwBta+CJv83+/ZPwi/8FlSPgVWbrF6SZy4AYNv0i6UqK38b7Qtj2pIawA6Xp90JGUNeepCsrLwfb4fCu0guFzqjtnxAWx0lX8pLOZ6DnaPGFQmfMeiUQeSRMkorYeZtAcRz3AB8AHgI2AD+M43h9FEWfiqLohv7LHgJ2R1H0NPAw8OE4jnfnqmhJRWD1t2H3c/C6T0FldW6ecdlbw3GSRz4Hnc/n5hn50vEMrPu/cOWtMHZq0tXoTC68OuxYefrfkq6kuB3cGaYLzfvdl963+I+g9xg89c/J1VWOMhlXmel3paZuTph8duDlG9gTVKyh0BmjJoWg/K02gSSpWA3oMHIcxw/EcTwnjuOL4zj+bP/7Ph7H8Y/6/zuO4/i2OI4vi+O4KY7je3JZtKQCd/QAPPw5aFgRjnrkShTBm78YdhL86IPQ15e7Z+XaL/4XVI2CFf8t6Up0NpXV4fjSMw+W7mS6fHjmASAOn8uMaZeHRsRv/7/EyipLJyaDlWoTaF5YOzYmW8fJdqyB6jEw5eKkKxm6huWhkVsqR7ElqcwUYSKdpIL3q7+Grk54ff9o+FwaVw9v+MvwU8mWf8jts3Jl59Pw1L/AK98HY2qTrkbnctlb4dgBQ4yHY+N9MOlCSC049f2L3x2OdzqBLX/aW8PvRc2EpCvJjdr+MfEdzyZbx8na1oamZ0Vl0pUMXcNVYYdVe2vSlUiShsAmkKTs2r89ZPQ0vhOmX5GfZy76Q7joOvjZJ2Dftvw8M5t+8XkYMRau+kDSleh8Zr8aRk6A9fcmXUlxOrofXvxFOMb58gZx07tCJtZvDYjOm/Z1pRsKDaGpPmpS4YRD9/WGxkmx5gFlzFoeVnOBJKko2QSSlF3/8RmI++C1H8/fM6MIfvevQ/jnfR8qrBDQ82lfFzJmrnp/CLtWYasaAfOuh2fuh57jSVdTfJ77KfR1w/zfPf1joyeH46OtP/Bzmw9HD4Rpd6XcBIqisBuoUHYC7X4euruKvwk0fhpMmg1bn0i6EknSENgEkpQ9ba1hxPkr3hem/uTTpAb4nTvg+Z/B2iKKJXv4c2FnySvfn3QlGqjL3hp2tGx6NOlKis/G+2BMHcxYduaPL343HNkDzz6Y37rK0c6nwlqqk8Ey6uYWTibQjjVhLdZQ6JM1LA87gYrphy5K1tbfwNdXOAVSKgA2gSRlRxzDv38MRk2Eq/88mRqW3QozXwE/uR0O7UqmhsHY8duwo2T5B8LnTcXh4utgxDh42iNhg9J9NOwEmnv92fNQLr4Oxl1gQHQ+tPXnuZTyTiAITaAje+BwZ9KVhDygqlFQOyfpSoavYXn4vHYUyFE7Fb613w/N5w0/TroSqezZBJKUHc//DDb9Al79keQaGhUVcMPfQvcReOC/J1PDYDz8OaiZGHZOqXhUjYS5bwq7Wnq7k66meGx6NITJnukoWEZFJSy6Kfx9cqAtf7WVo/Z1YVfWuPqkK8mtE+HQBdCsaFsL9Y1QWZV0JcM366qwOipeAxHH8PzPw39v+FGytUiyCSQpC3p7ZJAfRgAAIABJREFU4N//Z8gIWPonydZSNweu/UjI2Xm6gL/QSLfAcw/Biv8KNeOTrkaDddmNcGQvbH4s6UqKx8Yfhx1Us68593WL/jDkiq39fn7qKlfta8No+FxPcExaXf+um6TDofv6+ieDFXkeUMbki2BsveHQGpjO52D/Vhg3LQwHOLIv6YqksmYTSNLwrfkudGyA130yBOcmbfl/Dd/c3P/n4Rv1QvTwX8LoKXDlf066Eg3FJa8NE92e/rekKykOfb2w8QG49HVhJ9W5TLkYGlaEI2HmjeRGz3HYtbH0j4IBjJ8B1WOS3wm0dxMcP1gaeUAQmocNV8EWw6E1AC/07wJ6w2fDcIBnf5JsPVKZswkkaXiOHYKHPxuyeObfkHQ1QWU13PhV6NoND3006WpOt/XX4QuiFR+CkWOTrkZDUT0K5rwBNtwXdsLp3Latgq5OmP+WgV2/6A9hzwuw7Te5ratcdWwI34iVeig0hGPCtZcm3wTa8duwlspOIAij4g+kYd/WpCtRoXv+ZzDlUrjsbTB+emHv1JbKgE0gScPz+N/AoZ3w+s8U1rGCaQvhVR8Ku5Se/1nS1Zzq4b+EMVNh2XuSrkTDcdmNobGx5VdJV1L4Nt4HlSPgktcN7PrLbgw7rX77ndzWVa7a14W1voQaEudSNxc6Ex4T37Y2/Bmom5dsHdnUsDysHgnTuXQfgc2/hEt+JzRl5/9u+EHYsUNJVyaVLZtA/z979x0eVZ01cPx70wgkIZQkIC1AgJAElF6lCwpCgr2Bvazr6q66rq5rWcvuqutrXVesWFBxRUWqQkCKIr1DAiR0CCT0hBDS7vvHyUgxgZSZ+U05n+fhGUm594Bh5s65pyilqi93Hyx6Q1ZmN+9hOprf6v8X2cIy9U9wMtd0NGL7TzJA++IHIaSO6WhUTbQZCsF1tCXsfGxbtsG0GlD5+Ve1wiFpNKz/Vt8ouELWWmmRatDadCTuEdUOju2BgmPmYshaDY2SPKNl2lliEiE0UpNA6tx2/AzFBZIEAqkaLy6ALbPMxqWUH9MkkFKq+n78h2xHuuRp05GULzhUtoUd3Q1znjUdjbwZ/vGfMkyz222mo1E1FVJHZtykTZWZN6p8+9fDkR2VbwVz6DwWio5rks0V9q2VLVUBfnIZ6Ki+ObDFzPlt27eGQjsEBMiWME0CqXPJmANBodCyr/y+RS/ZTKhbwpQyxk9e/ZVSTrd/gwxu7XGXZ99NbtETet4DS981P8By23y5I9bvYZkpo7xf4mg4ni1znlT50qcDFsSPqNr3Ne8JDdvI84xyntJSaQfzh6HQDtFla+JNbQg7sgMKjvrOUOjTtegNB7dAXo7pSJSnykiVYf+O656AQGg/EjbPklYxpTxFaSms/9ovllJoEkgpVT2zn4JaEdD/EdORnN/gJ6FeC5jyB3MXHI4qoLpNoestZmJQztd2mNzh1GqViqVNk4ROeEzVvs+yZED0zkVwMNM1sfmjw9ugMM8/hkI71G8FAcHmhkPvXS2PvlYJBKfmAu3ULWGqHEd2yjyuNkPO/HjCKKn0zJxrJi6lzlZaClPvh0m3e94sURfQJJBSquoy58oTZP9HoE4D09GcX61wGPU6HMyA+S+aiSFzjmw66v/n86/IVt6jVrjMOUibIhcQ6kyHt8P+dVVvBXO46AawAmTAu3KOfWvlsXFHs3G4U2AQNIwzNxw6aw0EBMlMIF9zQScIqq0tYap8GWWr4R3zgBxa9YfQetJOrZRppSXw3X1SeTzgsd/+vPogTQIppaqmtARmlVXW9LjbdDSVFzcYOo+Bn984dVfWXRxVQJEtoNMY955buV7iaMjNgt1LTUfiedKny2P7y6v3/XUvkAHcq7/QuUvOkrVWEhIxiaYjca/oeMhJN3PurNUQk+CbNwCCQqBZN6nYU+psGakQ2VyGs58uMFhahDfNgOJCM7EpBXJtMfn3sOZzGPg4DPqrZ207dhFNAimlqmbNRBn0OuRp77ugHfY8hEXBd3+QgdbusvkH2LMCBjziW5thlGh3KQTW0paw8qRNg5ikms0N63wT5O6FzB+dF5c/27dWBiV72/N3TUXFS2VaUYF7z/vrUGgfnAfkENtX5kyZ3L6mPE9JEWydL61g5b2pTkyWWVnbF7g/NqUASorh23tg7UQY/AQMfNR0RG6jSSClVOUV5sPc56FpV+hwleloqq52fbj8FWlP+fk195zTtmWLWv2W0tqifE9oXbnI3fidtoSdLi9H5oRUtxXMod1wqNMQVn3qnLj8nb8NhXaIjge7FA65eb7U0d2Qf9A35wE5xPaWv9tdWg2pTrNrKRTmVtxa03oQhITDRt0SpgwoKYZv74Z1X8mNbW+YcepEmgRSSlXe4rfkjvyw5723VDJhJCRdAfNfgmw3tAakT5c77wMelfJn5ZsSU+DYHqn4UmLzTMCWLTA1ERQCF14nbQP5h5wSmt/K3Q95+/1rHpCDox3F3cOhs9bIoy9XAjXrLi2GO342HYnyJBmp8nPRqn/5nw8OlUra9Ona7qvcq6QIvr5DNoENfRb6PWQ6IrfTJJBSqnLysuGn1+QNnWMbiLca/hKEhMm2MFdeeJSWwrx/yZrrjte67jzKvHaXyfahjZNNR+I50qbJHCxnJBw63QQlhXLHTlWfYyi0P20Gc4hqC1gGkkCrwQqExh3ce153CgmTJJduCFOny0iFZj0gNLLir0lIhvwDOlhcuU9JkWwA2zgZhv0D+v7RdERGaBJIKVU58/4FxQVwyTOmI6m58Bi47EXYvQyWvuu686RNkflJAx6T7TTKd9WuJ8PHN06RFkB/dzIXtv4olXfOqBps3EHeZGpLWM04qlL8sRIouDbUj4UDBiqBouPl/L4strdUQrp75pLyTHnZknQ+ezX82doOle1yadoSptyguBC+ulV+3i79F/T5g+mIjNEkkFLq/HI2wYqPoettENXGdDTOceG10HYYzHkWDm1z/vFLSyRxFhUPHa50/vGV50lMgaM7Ye8q05GYt2W2VO7UtBXsdJ3HyDwbRyJDVd2+dTKf7Fx35n1ZVDzkuHlNvK8PhXaI7Sv/5rUlVgFkzpXH863aDgmTRFHaVJ2pp1yr+CR8dQukT5OOgN6/Nx2RUZoEUkqd3+ynIbgODHzMdCTOY1kw8lUp05/6R+dXb2z4VtYRD3wMAgKde2zlmeKHy/wDbQmTGQ91GkKLXs47ZserZQvbqs+cd0x/s2+tf1YBOUS3g4MZMhDUHY5lyQwmXx4K7dC8pzzqqngF0goWFl25IfSJKZCbBXuWuz4u5Z+KT8KXY2W24IiXoec9piMyTpNASqlz27ZQBrz2e0jWq/uSyGYw9BnYNh9WfuK845YUSxVQTCIkjnbecZVnq9MAWg2QLWH+3BJWXAhbZpUlxZyYAK1dX9rL1n6pLSfVUXAMDm2Fxn6QkKhIdHsoOQlHdrjnfI6qtSZ+UAlUpwHEJOlsFyWV0BlzIG4IBFTirWa7S8tm6n3n+tiU/ykqgIk3wZYf5OZvj7tMR+QRNAmklKpYaSnMegLqNoNe95qOxjW63gaxF8uf89he5xxz/SS52zzwr5W7AFK+I2k0HN5+agCvP9q2AE4eg/ajnH/szmOg4IjczVNVs3+9PPrjUGiHqHh5dNdw6KzVgAWNfHgo9Olie8tacHdVWinPlLUaThw6fyuYQ2gktB4oc1r8+QaKcr6iEzDxRsiYDaNeh263m47IY+i7E6VUxdZPkhfzIU/67lDLgABIfkNmGUx/uOYXICXFMO8Fablw5jwU5R3iL5cWQ3++o5k+DYLD5KLe2VoNkKT0am0Jq7J96+TR39vBwH3DobPWyFayWuHuOZ9pLXpDYR7sX2c6EmVSxhzAgrhBlf+exGQ4slNnvinnKcyHL26Q+VTJ/4Gut5qOyKNoEkgpVb6iAhma3PhC319v3jAOBv1NqgvWf12zY62dCIe3yfG0Csj/hDWEVv1gw2T/vKNZWir/jtpeAsGhzj9+QCB0ulHeZBzd7fzj+7KstVAnCiIuMB2JOaGREN7YfcOh9672j6HQDrF95FFbwvxbxhxpgazKCAHHDZS0qa6LS/mPwnz44jrYOg9S3oIuY01H5HH0HYpSqnxLxsHRXTDsef9IZvT6PTTpAjP/AscPVu8YJUUw/0Vo0hnaXebc+JT3SEyBQ5mwf4PpSNxv9zIZhOuKVjCHTjcCNqz5wnXn8EX71kgrmGWZjsSs6HgZ2u9qedmQu9c/hkI71G0i2+c0CeS/ThyG3Usr3wrmENYQWvbVVfGq5gqPw+fXykzT0W9D55tMR+SR/OCdnVKqyo4fhIX/B20vhdYDTEfjHoFBkPIfGZ76/aPVO8aqCVLOPOhv+kbLn7UfBVaAf7aEpU+TAZ/thrnuHA1aQct+siXMH6utqqO4ELLTK7epx9dFx8OBLa7/2ckqmwvmD0OhTxfbF3b+ov82/dXW+WCXVj0JBJCQDAc2y3OVUtVxMg8+uwZ2/AxXvgudbjAdkcfSJJBS6rfmvyh9/UOfNR2JezVKgn4Pw7qvYNP3Vfve4pOw4GVo1r16Fz/Kd4RHyxshf0sC2bYkgVr1k7YbV+o8RtouteKgcnLSobTIv+cBOUS1g8Jc5y0CqEjWKnn0t7/zFr0h/6C8mVf+JyMVakVC025V/96EUYCl1UCqek7mwmdXSxL6yvfgQh8fZVFDmgRSSp3pQAYs/wC63AIx7U1H4379HpbV7tMehIKjlf++lZ/Asd0w6HGtAlLSEnZgE2SnmY7EfbLTZAW5OwaiJyRDSIQOiK4sx7Y6f2pNqkh02YYwVw+HzloDDeJcnxD1NL/OBfrZbBzK/Wy7bDX8QKmurqqIxtC8J2zUJJCqooJjMOFq2U541QfQ8WrTEXk8TQIppc405+8QFCrrzf1RUIhsEcjbB7Ofqtz3FBVI+1yL3tC6CtswlO9y3NH0p2qg9GmABe0vd/25QupAhythw7dy90+dW9Za2djWIM50JOZFl93ccPWa+L1r/DPp1qA1hDeCHb+YjkS5W3aazMGqSTV0YrJslzu01XlxKd9WcBQmXAV7lsPVH8q1gTovTQIppU7Z8YtsZuj7R4hoZDoac5p1lUHRKz6CbQvO//UrPoLcLJ0FpE6JaCxJQX9LAjXrLn92d+g8ForyJRGkzm3fWmjcwT+G/J9PWDSE1nNtEij/EBzd6X/zgEBeA1v0lpYM5V8yUuUxbkj1j5FQtlRAq4FUZRQchU+vhL0r4ZqPIGm06Yi8hl4NKKWEbcOsJ2R9cO/7TEdj3qC/Qf1WMOV+WTVZkcJ8+OkVGVTbqp/74lOeL2k0ZG903zpqk47slPYXd1QBOTTrJvNdVmlL2DmVlsK+9f43m6YillU2HNqF/y6zVsujP1YCgcxEO7pLnheU/8icA9EJENm0+seo10I2rOpcIHU+J47AJ6Pl2uPaT04lEFWlaBJIKSU2fCullIOfgJAw09GYF1IHkt+Ew9vhx39U/HXLP5CV2IMed1toykv8ekfTD6qB0qfLozsvwixLBkTvWizbnlT5Dm+TQci6GeyUqHaurQTKWiOP/vp3HttbHrUlzH8UHpdB/W1qUAXkkJAMe1bA0d01P5byTfmH4JMU2L8ervvUvTegfIQmgZRSstkq9e8QkwQX6TrFX7XqB11vg8X/hd3Lf/v5k3nw02syB8gxDFMph7pNyoZc+kkSKDoBGrp55syF14MVCKsmuPe83uTXodB+mpAoT3R7yD8Axw+65vhZa6BeLNRp4Jrje7qYRBmIrcOh/cf2n6Ck0DnbUROS5TFtas2P5esy5sg1Rkmx6Ujcx5EAyt4I130G8cNNR+SVNAmklIKl78GRHTDsOQgINB2NZxn6rLTIfXefJMtOt+w9eSOhVUCqIokpMuTyYKbpSFzn+EF5s2fiTlxEI2g7DNZM9K+L4KrIWgsBQZKkU8LVG8L2rvbfVjCQ64jmvXQukD/JSIXgOjIPqqai2kgiUecCnVvBUfjfzfLrzc7wy399f1HC8YPwcbJUcl7/BbQbZjoir6VJIKX8Xf4hWPCSDPJzRhmvrwmtCyNfhZx02QDmUHAMfn4d2gyF5j3Mxac8m+OOpi9XA22eCXYpJLhhNXx5Oo+RbX6Zc8yc39PtWwdR8RAcajoSzxHVTh5d0RJ24oi04PnjUOjTxfaWuUt5OaYjUe6QkSqzEZ31PJOQLEnEvGznHM8XrfoMCvNg6HMQ0QR++Cu8kiSbbY/tNR2d8x0/AB+PgoNb4IYvoK0Tqs78mCaBlPJ3C/9P7hwMe850JJ6r3aXQ8Vr5u9q3Xj629B04cVirgNS51WsOTbv5dhIofTrUbQYXGHrT2+5S2fi06lMz5/d0+9ZqK9jZIptL1YIrhkP/2n7nx5VAIMOhQauB/MGhrfLLGa1gDonJgC1bJ9VvlZbIdWjzXtD3AbjjB7hzDsQNgkVvwmsd4Zt75CaAL8jLkQTQoa1ww0S9ae0EmgRSyp8d2gZL3oFON0KjJNPReLbLXpC1wt/dJ9VTi96E+BHQtIvpyJSnS0yRbUGHtpmOxPkKj0PmXGkFsywzMQQGw4XXwabv5U6hOiV3vwyu99cBxRUJCICotlLh6WyOodCmkqKe4oJOEFRbk0D+IKOsCtOZb8xjEqFBnLaEVWTLLFlc0ut3pz7WrBtc+zE8sAq63ykzlcZdLO1TW1JlC7A3ysuGj0fKNdSNX0qiS9WYJoGU8mdznpE3UIP+ZjoSzxfWEEa8JG/mxw+XXuyBj5mOSnmDRMeQSx+8mM1IheICc61gDp1ugtIiWPs/s3F4GsddYK0E+q2oeMhxQSXQ3tVSGRcW5fxje5OgEHlTqsOhfV/GHKjfEhq0dt4xLUteO7cvlBtv6kxLxkHdptC+nNfe+i1h+Ivw0AYY8rRUPH52Ffy3N6z89LfzLT1Z7j746HI4shPGTILWA0xH5DM0CaSUv9q1TNbC97lfthip80u6EuIvl7vHCaO03F9VTv2W0KSzb7aEpU+H2g2gheHteI0SoWlX2RLmrXc7XWFfWVVKow5m4/BE0e3g2G7Z8uhMWWv0tcEhto8kIguOmY5EuUrxSdi2QFrBnF0NmpAMpcWwaaZzj+vtstNh6zzofofcyK1I7frQ7yH441oYPU4Gtk/5A7zaARb82/OTa8eyJAF0dA+M+RpaXmw6Ip+iSSCl/JFtw6wnICwG+jxgOhrvYVkw8hXoeA1c8ozpaJQ3SUyBPSvkbpavKCmCzd/LetbAINPRSDVQ9gap1lMia62sKq9dz3QknifKsSHMidVAJ3PhYIYOhXaI7SND43ctNR2JcpWdi6HouHPnATk06QyRLXyzirYmloyDoFDocmvlvj4oBDrdAL/7CcZOhsYdYe7z8EoiTH/YM7eXHtsrCaDcfTD2G3kuUU6lSSCl/FHaVNi1WIYa1wo3HY13iWgMV70PDeNMR6K8ya9bwnzoYnb7QmmLNLEavjwdrpIL41UTTEfiOXQodMWi28ujMzeE7VsH2FoJ5NCsOwQEwc5FpiNRrpKRCgHBshnM2SxLqq4z52o1mcOJw7BmotyMDGtYte+1LJmnM/YbuPcXec1c+Qm82RUm3iQJPU+opD26G8aPkFlAY76BFr1MR+STNAmklL8pLoTUp+UCuPNY09Eo5R8axsndN19qCUubJhuW4gabjkTUrifJtnVfQVGB6WjMO5krm1Qaa0KiXA1aSYLigBOTQDoU+kwhYZIQ26HDoX1WxhyI7e26G4qJyVBSKIOQVdlMnxPQ856aHadRIox+C/60TlrGtv8EH14K718ioyJKip0Tb1Ud2SUVQPkHYey30KKnmTj8gCaBlPI3K8bLG4Ohz3lGC4dS/iIxBXYvlf52b1daCptmyDaY4Nqmozml801SnaRrhWHfenls3NFsHJ4qMFi2DzlzOPTe1RDeGCIaOe+Y3i62D+xZrolZX3Rsr7TgxrlwXXezHhDeyLduoFRXSTEsfQ9iL3be83pEYxjyFDy0EUa8DPkH4Ktb4c0usHic82emncvhHfDRCMg/LG1rzbu779x+SJNASvmTE0dg3gvQqj+0HWo6GqX8S+IV8pg21WwczrB3JeRmlb+ZxKSW/WWGhLaESSsYaDvYuUS3c34lkM4DOlOLPlLJsXel6UiUs2XOlUdXzANyCAiQ15mMVCjMd915vMHmmXB055lr4Z0lJAx63AX3r4RrP5Xk0PePwquJkPp3GdLsSoe3SwVQwVG4eTI06+ra8ylNAinlV356RfqJhz3v/C0OSqlzi2oDMUmwcbLpSGoubaq00rS71HQkZwoIgE43yuaUI7tMR2NW1lqoEwURF5iOxHNFt5fKWGesTC48LgklnQd0Jsc8D10V73syUqXyrVGSa8+TmAxF+XI+f7bkHbnJ0W64684RECh/33fMgjtmQ6sB8PPr8FpH+PZ3pypMnenQVhh/ubQw3zwFmnZx/jnUb2gSSCl/cWSnlHZedL1epCplSmKKDF909V01V0ufLutaa9c3HclvdboRsGHNF6YjMcsxFFoT/hWLipftVc7YjrN/gxxL5wGdqU4DiEnUuUC+pqQYMn90zWr4s8VeDLUb+EYVbXXtWy/LGHrc6b5RDs17wHWfSnVQt9tlscW4vvDJaEnIOWOI9MFM+GikbJi7ZYpWUrqRJoGU8hdznpMX6sFPmI5EKf+VmALY3j2zJmcTHNziea1gDvVj5e7lqgkyu8gfFRdCdprOAzqf6Hby6IyWsL2r5VFvsvxWi96yJt7UsFnlfHtXQsERmQvnaoFB0H4EbP7eOVV73mjJOFnE0OVm95+7QSsY8RI8tAGGPC2vLROugrf7wKrPqv//5ECGtIAVnYBbpupzp5tVKglkWdZllmVtsiwrw7Ksx87xdVdZlmVbltXNeSEqpWpsz0pY9z/o9XuIbGY6GqX8V0x7aUHx5iGXjruxnrIavjydx8CRHf7bgpKTDqVF0FjnAZ1Tw7aA5Zzh0FlrpP2ubpOaH8vXxPaBwlzYv850JMpZMlLBCoDWA91zvoQUOHkMts53z/k8yfGDsvXywuvMVt/Wri+bxP60Dka/DVjw3e+lVWzBy5B/qPLHytksCaCSIrh1mt6wMOC8SSDLsgKBt4DhQCJwg2VZieV8XQTwR2CJs4NUStWAbcOsJ+Xi9OIHTUejlEpMkeREXrbpSKonfTo07erZb3YTRkGtSP8dEP3rUGi9s3pOIXWgXgtJmtWUYyi0tt/9VmwfedSWMN+RkQpNu0m7nzu0HgC16kKaF99Aqa6VH0NxQc3XwjtLUIi0Xd/7M4z5RmZCzX0OXk2CGY/IjJ9zydkEH48Eu0QSQK6eKaXKVZlKoB5Ahm3bW23bLgQmAinlfN1zwIuA7oBUylOUlsjdgx0/wcDHILSu6YiUUokpMjvEG+cbHN0jbQCe2grmEFwbOl4lFVcFR01H43771kFwGDRobToSzxcdDwdqWAlUVAA5aZp0q0jdJlC/pf9W5vma/ENSYe6OVjCHoFrQ7jJIn+FfbYUlRbDsfam4ikkwHc2ZLEt+BsZ+C/cugqQrYPl4eKMLfDkGdpZTF5KdLjOAbBtumeZ5fyY/UpkkUFPg9BUbu8s+9ivLsroAzW3bnu7E2JRSVXUyV7bizHsRPr0SXmwJ39wlwy+73mo4OKUUIENSG7bxzpaw9LKXeU9PAgF0GgPFJ2DDt6Yjcb+stXJ3NSDQdCSeL6odHNgiN02qK3sDlBbrUOhzadFHhuI7Y5isMitzLmC7djV8eRKT4cQhubHpL9KnwbE90NMFa+GdqVESjP6vtIpd/CBsWwgfDoP3h8q1TmkJ7N8oLWBWANw6XdrjlTE1Hi9uWVYA8ApwayW+9m7gboAWLVrU9NRK+TfbhqO7YdcS+bVzMexfLxUGWPJGs+PV0LwntB0GgcGmI1ZKgdw9SxwNP70Kxw9AWJTpiCovfaq8aXYM1PVkTbtAdIK0hPlTEry0VCqBLrrOdCTeIToeSk7KDKnqVk7pUOjzi+0Daz6XhJs3PH+oimXMkfkwTTq797xxQ2Q48sYp7ptFZNqSd6SKru0w05FUTt0L4JKnod/DsPpzWPwW/O9m+TOczIXAEKkAimpjOlK/V5kk0B6g+Wm/b1b2MYcIoAMwz5I+6MbAFMuykm3bXn76gWzbfhd4F6Bbt256K0Cpqigpkgv7X5M+SyB3r3wuOAyadYP+j8hKx2bdITTSbLxKqYolpsDCl+Uun7ckKPIPwfafoe8fTUdSOZYlA6Jn/U1mEETHm47IPQ5vkyG8OhS6cqLL7kbnbKp+Eihrjbwprqc3OCv061ygnzUJ5M1KS2UeUNxg91cahtSBtkPldXPEyxDg40uu966Gnb/Apf/0vqrOWuHQ827ofof8/1r0H7l5PfZbaBhnOjpF5ZJAy4C2lmW1QpI/1wM3Oj5p2/ZR4NfbmJZlzQP+fHYCSClVRScOw65lp5I+e1ZAUb58LrK5XFA17wktekJMkqzQVEp5h8YdoX4rKZP2liTQ5h9kkKM3tII5XHgdpD4t1UDDnjMdjXvsK9vAdIEmgSolqiwhkbMJ4odX7xhZq6UKSIdCV6xBawiLkTe13W4zHY2qrv3r4Xi2+1vBHBKS5XVz1xKI7W0mBndZ8o7c5O08xnQk1RcQKDe9ElMkCaTPkR7jvO8abdsutizrD8APQCDwoW3bGyzLehZYbtv2FFcHqZTPs22Zpu9o69q15NS2EitQLua73CxJn+Y9IbLpuY+nlPJsliUXRYvelAobd21YqYn0aRDRxP0tADURHi3DRNdMhCFP+Udb7L618roRrQM3K6V2PQhvVP3h0MWFMuui9++dG5evsSy5ebVjkelIVE1kpMpj3GAz5287TFqK0qb4dhIoLwfWT4Iut/hOZb8mgDxKpUoHbNueAcw462NPVfC1A2sellI+rqhA7hw62rp2LYH8A/K50Eho1uOlUTwaAAAgAElEQVTUPJ+mXSEkzGy8SinnSxoNP78Gm2Z4/p2+wnyZA9F5jPeV4HceIwmsLbOh/QjT0bhe1lppcQoONR2J94hqJ5VA1ZGTBqVFOhS6MmL7wMbJcGSnts55q4w5Uska0djM+UPrSgIqbaq0SflqYmHFeCgp9Jy18MrnaP+IUu6Ql1PW1rUYdi2FvavkyR2gQZzc2WhRVuUTFe99b7KUUlV3QSd5I7RhsucngTLnyqatBC9qBXNoM1TaUFZ/5h9JoH1rzd2l91bR7aVarDrtCjoUuvJ+nQv0iyaBvNHJXLmO7f0Hs3EkJMPm7+VaumkXs7G4QnEhLPtAWu6i2pqORvkoTQIp5WylpXBgU1lb11J5wTy0VT4XGCKtFD1/d6q1KzzabLxKKTMcLWGLx8kMsNr1TUdUsfRpEFoPYvuajqTqAoPgouth8X8hLxvCY0xH5Dq5+yFvv9ypV5UXHS/DtHOzoG6Tqn1v1hqoFVn9odL+JCZR/q52LtLtdd5o2wIoLTY3D8ghfjgEBElLmC8mgdKmQN4+6Pkf05EoH6ZJIKVqqvC4DG12tHXtXgoFR+VzdaKgRS/oepskfJp0gqBaZuNVSnmOxNEyF2jT99DpBtPRlK+kGDbNlNk63jpTp/MYWPQGrP0S+txvOhrXsG1pLwRo4oNvjFzp9OHQVU4CrZa5fb7aluJMAYFS9bzjF9ORqOrISIWQcLmeNalOA2jZT1bFD3na9/7tLRkHDdtA3BDTkSgfpkkgpaqjtLRsvfN02cRil8jHoxMg6YpTVT4NWvvei5NSynmadoW6zWTbiacmgXb8DAVHvLMVzCE6Hpp1ly1hvf/ge8/LpaUw8xFY9j70uFtuPqjKi46XxwObIW5Q5b+vpAj2rYced7kmLl8U2we2zILjByAs6vxfrzyDbUsSqNUACAoxHQ0kJsO0ByF7IzRKMh2N8+xeAbuXwfB/62gI5VL606VUdfzyJvz4DwiuDRc/CDdNgke3w32LYdTr0OlGaBjne280lFLO5WgJy5wDBcdMR1O+9GkQFOr9c2Y6j5Gti3tWmo7EuUpLYOr9kgDq8wAMf0lfe6oqvJEsZXBs5aysnE1QclKHQldFq/7y+HYfmPGIbAsrLTUbkzq/gxky0LuNh1SntB8JWFIN5EuWjIOQCM+9KaR8hiaBlKqqXUthzrMymO62mTDkSWg71LPneSilPFdiigyK3/y96Uh+y7al4jFuiPdvKUy6EoJqw+oJpiNxnpIi+OZuqXAa8BgMfVYTQNVhWbKUIaeKa+Kz1shjE00CVVrTrnDDRKmWXvkJjB8OryTAzEelTUwTQp7JsRreU5JA4TFSVZbmQ0mg3H2w4Vu5YVErwnQ0ysdpEkipqsg/BJNuh7pNIflNvdhWStVcs+4QcYG0hHmavavg2B7vbgVzCK0rCbd1k2TlvbcrLoSvboX1k+CSv8Ogv+prUk1Et5OlDlWRtVpmpDSIc01Mvip+OFz3KTySCVd/CM27w4qPYPxl8GoSzHxM5ixqQshzZMyROTX1W5qO5JSEZGkHO5BhOhLnWP6hDN7W9lLlBpoEUqqybBu+u08y9deMh9r1TEeklPIFAQFyMbtltqzg9STp08AKlKHQvqDzGDh5TP5c3qzoBHx5k/w5hr8kbcmqZqLi4XiO3OyprKw1solNZ3dUT61w6HAVXDcBHsmAqz6QbU/LP4QPh8FrHeD7x6UCWxNC5hSdgO0/md8KdjbHzYk0D7yBUlXFJ+Xnvt2lMk5CKRfTVy2lKmvx27BphpTbN+1qOhqllC9JGi2zRbbMMh3JmdKmScl9nQamI3GO2L5QL1bap7xV4XH4/FpJGo56HXreYzoi3+AYDp1TyWqg0hJZDKHzgJyjVgR0vBqu/0wSQle+BxdcBMvegw+Gwmsd4Ye/we7lclNOuc+ORVB8wvOSQJHN5HrcF+YCbfhWktD6fK7cRJNASlXGnhUw+ymIHwG97jUdjVLK1zTvKcNpN0w2HckpB7ZIe0zCKNOROE9AgFQDbZsPh3eYjqbqCo7BhKvkrvwV46DrraYj8h2/bgirZBLowBYoypdEhXKu0Lpw4bVwwxeSELriHWjcAZa8A+8PgdcuhFlPyLWZJoRcL2MOBNaSJLqnSUiWtkxvfD53sG250RwVD62rsJ1QqRrQJJBS53PiCHx1G0Q0hpS3dOaCUsr5AgIl2bJltlR6eAJHy1T7y83G4WwX3QBYsPpz05FUTf4h+CRF1gdf/SFcdL3piHxLZAsZHF7Z4dA6FNo9QiPlZ/3GLyUhNPptiEmAxePgvcHw+oVyk27PSk0IuUpGKrTsCyF1TEfyW4nJ8pg21WwcNbFrqSSyet6j7zGU22gSSKlzsW2Ycr8MRr36Q99piVBKeZ7EFCm53zLbdCQibZq0ukQ2Mx2Jc9VrDq0HShLIW+aMHD8AHyfD/vUyPyXpCtMR+Z6AAIhqU/lKoKzVkjRq2Na1calTateDTjfCTf+DR7ZAyn+leuKXt+C9QfD6RTD7adi7WhNCznJkp/yb8LRWMIcGraFRR+9OAi0ZB7UiNbGv3EqTQEqdy7L3Zf3kkKegeQ/T0SilfFlsX6gT5Rlbwo5lwZ7lvrEVrDydx8DRnbB9gelIzi93H4wfAQczpBoifrjpiHxXVHzlZwI5hkIHBrk2JlW+2vWh800wZhL8eQsk/0e2Vy16E94dAG90htRnIGutJoRqImOOPHpqEgikGmjXEnmu9DZH98hrfpexEBJmOhrlRzQJpFRFstbAD49Dm6HQ+37T0SilfJ2jJWzzD7KNxaRN0+WxvQ/NAzpd+5HSZuLpA6KP7ILxw6UadcwkiBtsOiLfFt0eju6Ck3nn/rrSUkku6Dwgz1CngbyJHvuNJIRGvSGrzH9+Hd7pB292hTnPySBvTQhVTeYcqNsMotqZjqRiCcmA7Z3VQMs/AGzocbfpSJSf0SSQUuUpOAZf3Sp35a94R9e/KqXcIzEFio7LDAaT0qZBg7hTw3J9TXAodLxG3jScOGI6mvId2iYVQMcPwtjJ0PJi0xH5vuiyN7oHt5z76w5thcJcTQJ5orCG0PUWuHky/HkzjHxNWkB/egXGXQz/6QZzn4f9GzQhdD4lRbB1PrQZ4tmzamLaS5Iqzcu2hBWdgOXjZelM/VjT0Sg/o+9slTqbbcO0P8mmgas/kAsKpZRyh5b9oHYDsy1hJw7D9oXSCubJF/411XkMFBfA+q9NR/JbOZulAqgwD26ZAs27m47IP0Q51sSfZzh01mp51KHQni0sCrrdBjd/Bw9vhpGvQt0msPD/4O0+8FYP+PGfkJ1mOlLPtHsZnDzm2a1gDgnJsP1nSZp7i3WT4MQhXQuvjNAkkFJnW/GRvCkY9DjE9jEdjVLKnwQGSfJl0/dQVGAmhi2zobTYd1vBHC7oBI06eF5L2P4N8NEIKC2BW6drosGdGrQGKxBy0s/9dVmrZWV2dHv3xKVqLjwaut0Ot0yFhzfB5f8H4Y1g/kvw317wVk+Y9wJkn+f/vT/JSJV/D60HmI7k/BKTwS451crs6WwblrwDMUly80cpN9MkkFKn27cevn8MWg+Cix8yHY1Syh8lpkirSeZcM+dPmwrhjaFpVzPndxfLgk43wd6VsH+j6WjE3lXw0eUQEAy3zYBGiaYj8i9BIdAwDg6crxJoDTRKgsBg98SlnCs8BrrfCbdOk4TQiJel/X/eC/DfnvBWL5j34vkrwnxdRio07ynz0zxd4wuhXixs9JKWsB2LYP86XQuvjNEkkFIOJ/NkDlBoJFz5rs4BUkqZ0WoAhNYz0xJWdEIu/NuP8I/nwAuvlYTL6s9MRwK7lsoa+FoRkgCK0tXjRkS1O/eGMNuWJJDOA/INEY2gx11w23R4OB2G/1s2j837F7zVXdrF/FFetvyctxliOpLKsSypBto6z3PnvJ1uyTj5Oet4jelIlJ/ygys8NygtMR2BqinbhukPw6FMuOp9uUuklFImBAZD+8th00woPunec2+dB0X5sj3LH4RFycr1NRNlCKop2xbCJ6MhLBpumwkNWpmLxd9Fx8vg5+LC8j9/eDsUHNU2PV8U0Rh63g23z4SHNsq2xoWvyJB2f5P5ozx6SxIIZC5QaZFs2PRkR3ZC+jTocguE1DEdjfJTmgSqiZJimHgTzHnGdCSqplZ/BmsnwoBHoVV/09Eopfxd4mg4eVQ2s7hT2jSoFelfMwo6j4H8A+beOGSkwmdXywaj22ZAZDMzcSgRFS+zRQ5llv95x1BorQTybXWbwPCXICAI5jxrOhr3y0iVFrnGXvRz3rQbRFzg+VvClr0PWNKSqJQhmgSqicAgqF0Pfvmv9g17s+x0mP5nedPT/xHT0SillAzirBUJGye775wlxbBpBrQbJrNR/EXcEJmBZGJAdPoM+OIGaf26dbpUIiizoh0bwipoCctaIy2EMTqvyefVbQJ97ocN38DuFaajcZ/SUsicI1VA3tQWHBAg1VsZqTLiwRMV5sOKj2UBRL3mpqNRfsyL/mV7qCF/h+A6MPMv0lKkvEthvswBqhUubWABgaYjUkopCKolbUrp0ypuS3G2XYtlXa2/tII5BAZBpxtgyyzI3e++867/Bv43Fhp3lI1FYVHuO7eqmGMWU0XDobPWQEyC/BtVvq/vA9KmOesJ/7nOz1oN+Qe9YzX82RKSobhAEkGeaO2XUHAEev7OdCTKz2kSqKbCo2Hw32Drj7JRRXmXmY/IKtgr39U7sEopz5KYIrNHti9wz/nSpsnaa2+88K+pTmOkBWjtRPecb/UX8PUd0KwHjJ0sA0KVZwgJg8gW5VcC2TbsXa2tYP6kVgQM/CvsXCSVkv4gYw5gQdxg05FUXWwfaWPzxJYwx1r4xhdCi96mo1F+TpNAztDtDohJgh8el8oS5R3WfCnl//0e9s4XOqWUb4sbDCER7tkSZttSdRQ3SCoj/U1UG2jeS14TXH23f/l4mHyvzJ8bMwlC67r2fKrqoivYEHZ0t1TL6VBo/9LlFtkaN/tpswPk3SUjVX7GvbE6MSBQFits/gGKCkxHc6ZtCyAnTaqAdC28MkyTQM4QGAQj/g1Hd8FPr5qORlXGgS0w7UFo0Ufu8CillKcJDoX4y6RCp6TYtefat1Zew/ytFex0nW+SFqDdy113jsVvw7Q/QdthcMOXUnWiPE90ezi45bfbX38dCq1JIL8SGASXPCM/Eys/Nh2Na504AruXeXdFaGIyFOZJl4YnWTJOqpQ6XGU6EqU0CeQ0LftCx2vg59dltajyXEUnZA5QUC2ZAxQYZDoipZQqX2KKVB5sX+ja86RNAytA5hD5q6QrZMbfqk9dc/yFr8D3j8nMiusmSJJPeaaodjJX5MjOMz+etQasQGiUZCYuZU78cIjtC/NegIJjpqNxnW3zpTU2zotWw5+tZX8IjYSNHtQSdmgbbJoJ3W7T537lETQJ5ExDn4PAYPj+cdORqHP5/q+wf73MAYpsajoapZSqWJtLIDjM9S1h6dNkRoE3lv87S60ISQSt/wYKjzvvuLYNc/8Bc56Rm0VXj/ev7WveyLEh7Ozh0HtXS5VQcG33x6TMsiwY9hwcz5Ebvr4qI1U2UzbrbjqS6gsKgfgRMsPJU9r3lpUtn+l2u+lIlAI0CeRcdS+AAX+BzTOlF1V5nvVfw4rx0PeP0Hao6WiUUurcgmvLyva0qb9tTXGWg5mQvdG/W8EcOt0EhbnOW/Rg2zD7SVjwEnQeC1e8o9Wn3iCqnTzmpJ/6mG1LO5jOA/JfTbtCh6vhl7fg2F7T0TifbctQ6NYDvP95KiFZtnBtc9NihXM5mQcrP5XK3rpNTEejFKBJIOfrea9cPMx81PMGkvm7g5kw5Y+yjWXwk6ajUUqpykkcDfkHYMci1xw/fbo8tr/cNcf3JrF9oEFrGRBdU6WlMOMRWPQm9LgbRr0hd4KV56vTAMJiIOe0SqDcLKkC0c1g/m3IU9IuNfcfpiNxvpx0OLbHu+cBOcQNlipaT9gStuYLOHlU18Irj6JJIGcLCoHhL8LhbfDLf0xHoxyKT8Kk2+QC/OoPpW1PKaW8QduhEFQbNk52zfHTp8nK2vqxrjm+N7EsqQbavlBmOFRXaQlMfQCWvQd9HoDhL0GAXnJ5leh4OHDahrCsNfKoSSD/Vj9WkrqrP4N9601H41wZqfLYxovnATkEh0oVbfp011XRVkZpKSx9F5p08e4WO+Vz9IrEFeIGSxnigpfhyC7T0SiAWU/IBdzot6Fec9PRKKVU5YWESSLIFS1hufth11JtBTvdRTfIkOzVn1fv+0uK4dt7ZMD0gMdg6LO6DtgbRbWTSiDblt9nrQEsaNzRaFjKA/T/swwenv2U6UicKyMVohMgspnpSJwjIVmq93b+Yi6GrT/KbDFdC688jCaBXOXSsjLRWU+YjUPJQNWl70Kv+6D9CNPRKKVU1SWmQN5+2LXEucfdNAOwIUGTQL+KbCo3c1Z/XvWkW3EhTLoV1n0FQ56GQX/VC39vFR0vLRy5++T3e1dLYigkzGxcyrza9aH/I5A5BzLnmo7GOQqPS8uxL1QBObQdBkGhZreELRknraVJo83FoFQ5NAnkKvVaQL+HpXx/6zzT0fivw9vhu/ulDPOSvxsORimlqqndpWUXs07eEpY+Deq3gphE5x7X23UeA8d2V+31u6gAvrxJKrYuexH6PeSy8JQb/LohrKwlLGuNDoVWp/S4C+rFwqwnzbYbOcv2n6Gk0LeSQLXCZdV92lRpy3K3g5mwZRZ0vwOCarn//EqdgyaBXKnP/VC/Jcz4i9wdVO5VXAhf3Sb/fY2u5FVKebFaETKsc+N3zruYLTgKW+fLQGitVjlT/Ai527/6s8p9feFx+Pxa2DIbRr4GvXQAqNeLKksC5WyGvGzI3avzgNQpQbVkSPT+9bBmouloai4jVWbPtehjOhLnSkyWf7t7V7r/3EvfhYBg6Hqb+8+t1HloEsiVgkPlbuCBTbD0HdPR+J/Uv8uTfsp/JBmnlFLeLDFFNhTtXuac422ZDaVFkDDKOcfzJUG1oOO1kDYN8g+d+2sLjsGEq2WY9BXjoJte8PuEiMZQq65cw/06FForgdRpOlwlleZzn4fCfNPR1ExGKrTqJ+9dfEm7yyQR4+wq2vMpOAarPoMOV0JEI/eeW6lK0CSQq8VfBm0vhXkvnOorV66XPgMWvyUbHBKTTUejlFI11+5SCAxx3sVs+jSZVaAbS8rXeQyUnIT1X1f8NScOw6ejYfdS2Tx50fXui0+5lmWVDYfeJPOAQIdCqzNZFgx7XipNFv/XdDTVd2grHMr0jdXwZ6tdD1oPkFXxjiHv7rD6cyjMhZ73uO+cSlWBJoHc4bJ/SZ+tr20R8FRHdsHke2Xl8dDnTEejlFLOERop8w02flfzi9miAqkEih8OAYHOic/XXHChvOlfNaH8zx8/AB+Pgn3r4LoJkHSFe+NTrhfdXpJAWauhYRsIrWs6IuVpWvaF+Mvhp9cgL8d0NNWTMUcefTEJBLIl7PB2ea52h9JS6QBp1gOadnXPOZWqIk0CuUPDOOjzAKz9UibvK9cpKYJJt8uQvms+8r2yVqWUf0tMkYHFe1bU7Djb5kNhnraCnU/nsZIAOPvNQ+4++OhyOJABN0yUZJryPdHt4Hi2XLvpPCBVkaHPQFE+zH/RdCTVkzFHxiY0aG06EtdofzlYAVIN5A4Zs6W6SquAlAfTJJC79HsI6jaDGY9ASbHpaHzX3OekLD/5dUm+KaWUL4l3zDeYXLPjpE+DkAho1d85cfmqjtdIC96q0wZEH90N44dL1emYSb61TUedyTEc+sQhTQKpikW1ha63worxkhj2JsWFsG2BVJn66oKAsCiI7eu+VfFLxkHEBXLTRikPpUkgdwkJg0v/IVsEVow3HY1v2jwLfn5dpvB3uMp0NEop5Xy160PrgTVrCSstkblp7Ybp2trzqdNANoWt/VLeLB3aJgmg4wfh5snQ8mLTESpXim536r91KLQ6l4GPQVAopD5tOpKq2bUYio77biuYQ0KyDHnP2eTa8+Rsgsy5shY+MNi151KqBjQJ5E6JKdBqgFSrHD9gOhrfcnQPfHsPNOogM5iUUspXJabAkZ3SplQdu5ZA/gEpkVfn13msVIL88iaMHwEn8+CWKdC8h+nIlKvVi5U39iAzopSqSHgM9P2TVFl60+iHjFSpLm3Vz3QkrpUwUh5dXQ205B0IrKVr4ZXH0ySQO1kWjPg3FB6HOc+YjsZ3lBTD13dC8cmyOUC1TUeklFKu0/5yCAiCDdVsCUufLi1ObYY6Ny5fFTcIIprAnGehtBhunQ5NtCrELwQEQsO2Mi+ldn3T0ShP1/s+aQOa9aR7N1HVRMYcaNELakWYjsS16jaRQc1pLlwVf+IIrPkCOl4tLWhKeTBNArlbdDz0uhdWfgq7azjYU4l5/4Kdi2Dkq9KXrZRSvqxOA5nlU52WMNuGtKnSUqabjionIBD63C/boW6bAY0STUek3GngozD4SdNRKG8QUgcGPwF7lsOGb01Hc37HsmRMha+3gjkkJsuQ/0PbXHP8VRNkQLgOhFZeQJNAJvT/C4Q3ghl/ljWCqvoy58LC/4POY+Ci60xHo5RS7pGYAoe3VX3l7f71cGSHtoJVVe/fwx+W640Gf5QwSu7sK1UZF90AMUlS8V980nQ055bp46vhz+bYhumKLWGlJbIWvkUfHSKvvIImgUwIrQvDnoO9K2H1BNPReK/cffDN3VJdNfwl09EopZT7tB8JVqBUA1VF+nTAkmHHqmp8dXOOUsp5AgJh2LNweDss+8B0NOeWMQfCG0OjJNORuEf9lpKgSZvq/GNv/l5m9WkVkPISmgQypeM10KI3pP4d8g+Zjsb7lJbIHKCTeTIHKCTMdERKKeU+YVGymWrj5Kq1hKVNk/kP4TGui00ppfxZm0ug9SBY8JLMifFEpSVSTd/Gh1fDlychGXYvk4UyzrRkHNRtJjdolPICmgQyxTEk+sRh+PGfpqPxPgv+DdsXwuUvQ0yC6WiUUsr9ElPgYAZkb6zc1x/eDvvXaSuYUkq52rDnJAG08P9MR1K+PSuh4IgkgfxJYoo8pk9z3jH3b4RtC6DHnRAY5LzjKuVCmgSqoYKikup/c+OO0P1OWP4BZK11XlC+btsCmPcCXHgddLrJdDRKKWVGwijAqnxLWFrZRa/eqVRKKddq3FHmAy0ZB4d3mI7mtzJSwQqQiiV/EtUWohOcuyp+yTgICoUutzjvmEq5mCaBasC2bW7/aBn3fb6Sg3nVHP426HFZOzrjEe9ZJ2lSXo60gTVsA5e/4l8lrEopdbrwGIjtW/kkUPp0aNQBGrRybVxKKaVkU5gVAHOfMx3Jb2WkQtOusm3S3yQmy1bhvJyaHyv/EKz9H1x4rX/+XSqvVakkkGVZl1mWtcmyrAzLsh4r5/MPWZa10bKstZZlzbEsK9b5oXqeUhv6toli1oZ9DH11AdPXZlX9ILXrwyV/h12L5UlEVay0FL69GwqOyhygWuGmI1JKKbOSRkNOOmSnn/vr8nJg5y9aBaSUUu4S2RR63wfrvpL2K0+Rfwj2rPCfrWBnS0gGu9Q5LWErP4HiE9DzdzU/llJudN4kkGVZgcBbwHAgEbjBsqzEs75sFdDNtu0LgUmAX6xqCgywuG9QG6bd349m9Wtz3+cruXfCCnJyq1gV1GkMNOkCs5+EgmOuCdYX/PSKDLG77AVo3MF0NEopZV77kVSqJWzTDMDWeUBKKeVOff8EdaJg9lOeU/GfORew/TcJ1CgJ6req+ar4kmJY+h607Oc/G9aUz6hMJVAPIMO27a22bRcCE4GU07/Atu0fbdvOL/vtYqCZc8P0bPGNI/jm3j48ell75qRlM+zV+UxZsxe7sk/2AQEy4DgvG+a/6NpgvdWORfDjPyDpSuh6q+lolFLKM9S9QLZ9nS8JlD4d6rWQORVKKaXcI7QuDHxMlpls/sF0NCJzrnQiNOlsOhIzLEtawrYtkAU91bVpOhzbrVVAyitVJgnUFNh12u93l32sIncAM2sSlDcKCgzg3oFxTH/gYmIbhvHAF6v43YQVZOcWVO4ATbtCl7EyXOx8Zf3+5vhBmHQH1G8Jo17XOUBKKXW6xBTI3gAHtpT/+ZO5sPVHaD9Knz+VUsrdut4qsyxnPyXVIybZtswDaj0IAgLNxmJSQgqUFsOmGrxlXfKO3FyJH+68uJRyE6cOhrYsawzQDfh3BZ+/27Ks5ZZlLc/JccIwLg/UtlEEX9/bh8dHtOfHTTkMe3UBk1ftqVxV0JCnISQMZv7Fc0pGTSsthcm/g/wDMgcotK7piJRSyrMkJMtjRdVAW2ZDSaG2gimllAmBwTL/88AmWPWp2Vj2r4e8/f7bCubQtAvUbVb9LWFZa2HHz9Djbv9OpimvVZkk0B6g+Wm/b1b2sTNYlnUJ8Dcg2bbtcofi2Lb9rm3b3Wzb7hYdHV2deL1CYIDF3f3jmPFAP1pHhfGnL1dz1ycryD52nqqgsCgY/CRsm1/5bS++7pc3YcssuPSfcMFFpqNRSinPE9kUmvWAjZPL/3z6dJlJ0aKXe+NSSikl2o+E5r3gx39KdaYpGany2GaIuRg8gWVBwihpjavO/48l70BwHeg8xvmxKeUGlUkCLQPaWpbVyrKsEOB64Iy0qWVZnYF3kARQtvPD9E5tYsL56nd9eOLyBBZuyeGSV+bz9Yrd564K6na7zGz44W9QeNx9wXqiXUsh9Rl5ku5+p+lolFLKcyWmwL51cDDzzI8XF0oiPX643q1USilTLAuGPQ/Hs2HRm+biyJgDjTpCRGNzMXiKxGQoOSmvkVVx/IBsfLvoBpmtpJQXOm8SyLbtYuAPwA9AGvA/27Y3WJb1rGVZZTXo/BsIB76yLGu1ZVk1HLfuOwIDLO7s15qZf+xHu0YRPPzVGu74eDn7jlZQFRQQCMGC3FkAACAASURBVCNelkFjC//PvcF6kvxDMOl2iGwGyf/RORZKKXUuiWUvx2dvO9m2AE4e09XwSillWvPukDhakkDHstx//pO5sPMXrQJyaN4TwmKq3hK2Yrwkj3rc7Zq4lHKDSs0Esm17hm3b7WzbjrNt+x9lH3vKtu0pZf99iW3bjWzb7lT2K/ncR/Q/raPD+fKe3jw1MpFFmQcY+up8/rd8V/lVQS16wYXXy4vE2Xd1/YFtw3f3Qe4+uGY81K5nOiKllPJs9VpAky6/bSVOnwoh4dB6oImolFJKne6Sp6GkCOb90/3n3rZQhiH7+zwgh4BASBgpc/OKTlTue0qKYNkHMlg7pr1r41PKhZw6GFqdW2CAxe0Xt+L7P/YnoXFd/jJpLbeOX8beI+U88Qx9BgJrwcxH/W9I9OK3YdMMGPqsbE1TSil1fkmjYe8qOLxdfl9aCukz5II/ONRoaEoppYAGraHHXbBqAmSnuffcGalyU6B5T/ee15MlJEPRcWmTq4y0KZCbpWvhldfTJJABLaPCmHh3L55JTmLptkNc+uoCvly288yqoIjGMPAxyJgNm783F6y7rf9GVmjGj4Be95qORimlvMevW8LKStt3L5P5EwmjzMWklFLqTP0fgZAIud51F9uW9xSt+kNQiPvO6+laXixzfc5upa7I4nFQvxW0HebauJRyMU0CGRIQYHFLn5b88Kf+JDWty6Nfr+PmD5ey5/SqoJ73QFS8VAMVnWezmLcrzIcpD8Ck26BJJ0h5S+cAKaVUVTRoJVsUHS1h6VMhIBjaDjUbl1JKqVPqNID+D8tA4q3z3HPOg5lwZKfOAzpbYDDEXw6bvpdFCueyZwXsXirvzwL0LbTybvoTbFiLhnX4/M5ePJeSxIodh7n01QV8vqSsKigwGEa8BEd2wKI3TIfqOtnp8N5gWPkx9P0T3DZTXiCVUkpVTWIK7FkOR3ZB2jS56xsaaToqpZRSp+txD0S2gFlPSuuuqzlWw8dpEug3EkbByaOwbf65v27Ju9JO1+lG98SllAtpEsgDBARYjO0tVUEXNovk8W/XMfaDpew6lC/DPBNHy6awwztMh+pctg0rP4V3B8LxHBjzddkspGDTkSmllHdKHC2P81+Ew9tk6KVSSinPEhwKQ56EfWth3f9cf76MVGjYRipG1ZniBkl73tmLFU6Xux/Wfw2dbtIbK8onaBLIgzRvUIfP7uzJP67owKqdh7nstQV8ungHpUOfAysAZv3NdIjOU3AMvrkLpvxBVmbe+7NuK1BKqZpqGAeNOsKqTwFL5qsppZTyPB2ulhbeOc9VfjtVdRQVwPaf9Dq7IkG1oN2lkD4dSorL/5oV46G0SNfCK5+hSSAPY1kWN/WM5YcH+9Mltj5PTl7PTV/t5UjXByBtauWn13uyvavh3QGSUR/0BIydLIOwlVJK1Vxiijw2667PrUop5akCAmDY83BsNywZ57rz7FwExSe0FexcEpPhxCHY8fNvP1dcKGvh2wyFqDbuj00pF9AkkIdqVr8On9zegxeu7Mi6PUfp/3MHjtVpgT3z0fMPLvNUti1T9T8YCsUn4dbpMOARCAg0HZlSSvmOpNGAdSoZpJRSyjO16g/tLoOFr8Dxg645R8YcCKwFLfu65vi+oM0lEFS7/C1hGyfLps1euhZe+Q5NAnkwy7K4vkcLZj3Yn86tGvHAkeuwDm7h8I+vmw6t6vIPwcSb4PtHIW4w/O4niO1jOiqllPI9UW3lObbnPaYjUUopdT6XPAOFebDgJdccPyNVrrlDwlxzfF8QEgZtL5GFCqcP6rZtWPw2NGwLrQebi08pJ9MkkBdoUq82H93WnRFX3sKPdCPkp38zMXUxpaW26dAqZ+diGNdPVmFe+i+4YaJu/1JKKVdq3EGH7CullDeIaQ9dboZl78sqd2c6sgty0nUeUGUkpEDePti97NTHdi+HvSt1LbzyOfrT7CUsy+Labs1Juu0tQqxSas9/huve/YVtB46bDq1ipaWw4GUYP0LejNwxC3r/HizLdGRKKaWUUkp5hoGPS8tW6t+de9zMslmimgQ6v3aXQmDImS1hS8ZBrbpw0fXm4lLKBTQJ5GViYtsT1P9BUgIXEb5vCZe9toD3F26lxNOqgnL3w4QrYO5zMp/ingXQtIvpqJRSSimllPIsEY2g7wOSgNi5xHnHzUiFus0gOt55x/RVoXWh9SDYOEXawI7tlXlAncdCrQjT0SnlVJoE8kLWxQ9CZAvebTiR/nH1eX56GteMW0RmTp7p0ETmXBjXV17ERr0BV30gT6xKKaWUUkqp3+r9BwhvBLOekCRETZUUwdb50GaIVuFXVmIyHN0JWath+YdQWgI97jQdlVJOp0kgbxRSBy77J8EH03k3YRWvXncRmTnHGfH6Qt5dkGmuKqikGFKfgU+vhDoN4e4foest+sKjlFJKKaXUudQKh0F/g91Ly99SVVW7l8PJY5IEUpUTPwKsQFg3CZaPl81tDVqbjkopp9MkkLdqPxLiBmP9+E+uaBvC7Af7079dNP+ckc7V4xaRkZ3r3niO7IKPRsBPr0CXsXDXjxCT4N4YlFJKKaWU8ladx0B0gswGKi6s2bEyUiWh0WqAU0LzC3UaQMuLZSNY/gFdC698liaBvJVlwfCXoOgEpD5DTN1Q3h3bldev78S2A8cZ8cZPvD0vk+KS0vMfq6bSpsG4i2H/Rmn9Sn5TqpWUUkoppZRSlRMQCEOfhUNbYcX4mh0rIxWa94Da9ZwTm79ITAa7BKLbawJN+SxNAnmzqLaybWv1BNi1DMuySOnUlNkPDmBwfAwvfp/OVW8vYvN+F1UFFZ+EGX+BL2+C+i3hnvnQ8WrXnEsppZRSSilf13YotOoP816AgqPVO0Zejsy10VawqktIhpAIuPhBHWmhfJYmgbxd/0cg4gKY8WcZXgZER9Ti7TFd+M+Nndl1+AQj3/iJt37McG5V0MFMeP8SWPoO9Pq9rH9vGOe84yullFJKKeVvLAuGPgcnDsHCV6p3jMy58qir4asuPAYe3aZr4ZVPCzIdgKqhWhEw7Hn4+g5Y+Ql0uw0Ay7IYeWETerVuyNPfbeDfP2ziy2W7uLBZJG1jImjXKJy2jcKJbRhGcGAVc4Fr/wfTHoTAYLhhIsQPd8EfTCmllFJKKT/UpBNceJ3Mpul+J9RrXrXvz0iFOlHQ+CLXxOfrAoNNR6CUS1m2M1YQVkO3bt3s5cuXGzm3z7Ft+OhyyN4I96+UoWZnmbkui0krdrM5O5ddh078+vHgQIvWUeG0aRROu5gI2jYKp11FyaHC49L+tXoCtOgNV70Pkc1c/adTSimllFLKvxzZCW92g6Qr4Mp3Kv99paXwcluIGwxXvee6+JRSHs2yrBW2bXcr73NaCeQLHEOi3+kPc5+Dka/+5kuGd7yA4R0vACC/sJjM7ONsyc5l8/48MrJzWbf7KDPWZeHICQYHWrSKCqNtowjaxoTTNXQvPVf8meDDGVj9H4EBj0Gg/vgopZRSSinldPVaQK974efXZQboBZWs6tm3RjZbaSuYUqoC+i7eVzTuAD3ugiXvQJdbpIy0AnVCgujYLJKOzSLP+PiJwhIyc/LYvD+XLdl5bNmfy7pdR4jcMIHuQZ9wlDo8XPI4+9b0pG3W2rKqIUkStYyqRluZUkoppZRSqnz9HpJxD7OehJu/q9yg4oxUeYwb7NrYlFJeS5NAvmTgX2H91zDjEbj9BwioWlKmdkggHZpG0qFpWXKo4ChM/SNs+Jbcpv1ZmvQcScdCCdmfy/q9R5mx/lTlUFCAVA61axRBmxhJDjnaykKCNDmklFJKKaVUlYRGwoBH4ftHJbnTduj5vydjDlzQCcKjXR+fUsoraRLIl9SuB5c8A9/9HtZOhE43Vv9Ye1b8f3v3HyTHedd5/P30/J7Z2dmVdqVIK1l2bAdjyQ6RhUNxiSuXhCTGgPmZSyg4pwIEKFJwxz8Bih+GukBywAEhQFWAQKAIISQhmALOkF+QOupMJNkXWzK2hbFk7UralbSzs7Oz86v7uT+6p6dndma1v6TR7nxeVVPd/XRP72N5e3v3M9/nafjUu6H4Mrz5UfLf+BM85Dg8FDmkVTnUGlb2wqVy33Dozr0jwYTU/rxDtyocEhERERFZ3bF3+0/j/cef96t7nFj/Y5eL8PK/+o83FxHpQyHQTvPqd8KJP/JvFHc95H+CsB6eB//3d+Bzj/qPnn/3/4aD9/c8dEXlUCAaDr1wqczzl8qcminx989c7AiHbp3I+U8pCyekzm8oHLLW4nqWpmfxrL90XYsbbQ+Wrtdq8/A8aHpe2NY61rX++zvO53m4HrieF57P9SyHJnIcPThOIaunCIiIiIjIFosn4U2/AH/5CDz1Z3D0v/Y/9j/+Cayr+YBEZFUKgXYax4Fv/lX4yH+GL30A3vYra3/v0hX47I/CC4/DXd8CD38YMuPr7kK/cKjacDkzW+bMrD/v0POXypzuEQ4dGM/gOMYPZdx2ENMd5IRhzmAecNfhjj0jHL1ljPsOjXP0lnFunxzBcdYwbltEREREZDV3PwwH7ocvvB+OfBckc72PO/N5SBXgwNff2P6JyLaiEGgn2v8auO9d/iTRr/l+2Hv3td/z0v+BT/+g/zSBB3/Vn2R6LZPPrUM60T8c+ve5clA1tMjZqxUMfiDkOIa4Y4g5DjEH4o5DzDHhK+4YHGO6jm3va70v5jgrjzGGWCzYNsF7Yq3zdX6dWNe5Y0HA8/zFRU6em+fkuSL/cPoSnzx+HoDRdJzX3OIHQvcdGufVBwvk06oWEhEREZF1Mgbe8j/go2+Bf/kwvOF9K4+x1g+BXvmAnuArIqsy1g6mjOLYsWP2+PHjA/naQ6FyFX77KOw9Ao/8Tf9Ax3Phn38N/ukDMH4bfM8frf0RlNLBWsuLl5c4cXaeJ8/Nc/JskednF7HW/+f/mr15jh5qB0O37s5itjhoExEREZEd6i++D858AX78Scjv7dw3+2/wu6+Fb/0t/8NgERlqxpgT1tpjvfYpJt6psrvgjT8Hf/uTcOozfulot9IF+MwPwUtfhnv/Czz065DK3/i+7hDGGG6fHOH2yRHefuwgAKVqg6fOFTlxdp6T5+b5m6dm+PgT5wDYlUty9JaxsGLo1QcLZJO6JEVERESkhzf/Ijz39/ClX4Fv/c3OfeGj4d904/slItuK/uLcye57F5z4Y3j8Z+HOt0JqpL3vhc/BX/0wNCrw8O/6TxJTVcqWG00neOBVkzzwKv8xnZ5neWG2zMlz82Ew9LlnZwGIOYav3ZfnvlvGw4qhA+MZVQuJiIiICOy+HY79AHzlD+AbfhQmv6a978znYPIuGDs4uP6JyLag4WA73bkn/PHDr/vv8OZHwW3A538J/uVDsOewP/wregORG25+qc6TL/vDx06em+epl4tU6i4Ak/lUx4TTR6YKpBOrPBpURERERHaupSvwoa+DQ/8JvvcTflt9CT54K9z/Hnjr+wfaPRG5OWg42DC75bXw6u/1J5G79fXwxV+G6eNw7N3w1l+GRGbQPRx647kkb7xrL2+8yx/b3XQ9nru0yMmz/oTTJ8/N8/ipSwAkYobD+wvhvEJHD42xr6D/hyIiIiJDIbfb/3D3878I//FluO31/gNe3DrcoaFgcvO5VKpSrDR45WSORMwZdHcEVQINh8VL8OFjUCtBahS+7UNw+DsG3StZh7nFGk+em+fEuXmePFvk/50vUmt6AOwrpDsmnL573yjJ+OB+wHqepdb0WG64/qvuUm34r3C76VGtu+ExrX21hodnLa5n8ax/Ls9aXGuxFlyvtd7rmMi6Fxwfrvvv8zzwrO38GtHjg/f360Nr2+I/AW5XLsnuXIpduSS7RpLsziX99cirtX+Q/09ERERkB2ksw28fg9wE/NAX4fGfhhMfg/e9BIn0oHsnQ2x2scrT5xd4enqBp88v8NXpBeYWawAkYw6vesUIh/cVODw1yuH9o3ztvlHNiXqdrFYJpBBoWHz1k/DMp+HBD8L4rYPujWxSvenx7IVS+Hj6k2fnmS4uA5CKO9wzVeC+Q+P+pNOHxtiTT9N0PapNr2co4wcxXldQ43YFNX6wU61Hgxtvxbla4dR6JeMOqbhDzDHEjMEYQ8whsm5wDDjBfscYnKAt5gTHdKwbHAf/uPD97eNb748Z+pzLBOsE52qdx38K68Jyg6tLda4s1bkavOYrdfr9SM2n4uwaaQVDrZAoxa5cgl25VEeAtHskqRuiiIiI9PfUn8NnfwS+8w/aT/n9vk8NulcyRC6Xa+2w5/wCz0wvcLFUBfypZu+YHOGeqQL3HCgwnk3y7IUSp2ZKnJpZYL7SCI+7bSLH4f0Fjuwf5fD+Aof3jzKeSw7yP21HUAgkMgQuLlT9UCiYcPqZ6RJ11w9kEjFDw93YtZ5JxMgkY2QSMVIJx98O2lLx1j6/PR28WsenE46/HbS11lvHpONOsIzhONt/AmzXs0E4VONKub4iJLqyVGc+bKtxdane9/9LOuGEVUTjuc4qo92RsGhXcMxoOq5JxEVERIaF58FHHvCf9lu5DG/7IHzDjwy6V7JDXV2qB4FPMQx+Zhbagc9tEznunSpwz4Ex7pnyg5xcqvcHmtZaLixUw0Do1EyJ0zOl8ANtgP2FNHcHgdDh/aMcniqwv5DW77rroBBIZAjVmi7PTJd48tw8l8t1skk/lOkMYbrDGScS4MRIxR39sL2OrLUs1ppcLbfDomhI1BEgBaHScsPtea64Y/qERX610Xguya6sP2xtVzbJWFZD1HYaay0N1wZDK11GMwlNJC8DV2u6lKtNlmou5VqTpXqTcq1JpeYylk2wfyzDvkJa36siG/Hil+BPHvbX33sCJu4YaHdkZyhWgsAnUuUTDWhum8j5FT5Blc/h/aPk04lNf935pTqnL7SDoVMzJV6cK+MFccVYNhGEQu1w6LaJEWI74IPk60EhkIjIDrFcd7laqQfBUa2jyqgVJs1XWsFRjVK12fdc+VTcD4eC13g2GQ5P25VLBNvtSqTRdGJHVGwNQvdwzOhQzOgwy+g8Wv661x5+2T2PVo+hnK7XeU/Pp+NM5lPsyaeYzKfZE66n2JNPh/vGsgkFvgL4854t1SOhTc0PbVrr/rYbtq/c3w57lmrNNVehTowkmRrLsD94TXUs0+zKJfU92ket6XK5XGep1iSfjjOaTpBNxvTvNSw+/g64+iL82BN+SYbIOiwsNzg17c/d05rL59zVSrj/0O4sR6YKQZVPgcP7CxQymw981qpSb/LshUVOR4Kh5y4uhqMdMokYd+3Ld4RDr9qb1wcLKAQSERlaDdejWGl0zFsUrTgKtyt15pcaXFmqUW30ntfJMTCeTbaDo3DdD4x2jyTbwVGwvNn+ELHW0gwmL681XOquR63hUWt61JsetaYbLP31Wrje3t8Z2Hg95tfqnhTdC39ZWY+YY8gmYqQjwyszXUMuM5H90WGXybhDabnBbKnKXLnGbKkWLntVkyVihskRPxyazKfZM5piciQVWfqB0eRIShVkN5mG61Gpt4PBSt0Nq22ioU252uzR7oc2S7Umi0Fbpd672rCbYyCXijOSipMLXvlUnFwq1tE+koqTS8YYSScYCfblUnGyyRjFSoPp+WVmisvMLCwzXawyPV9hplhd8X2aTjjtUKiQYWq8FRilmRrLsK+Q2VHfm9WGy+VyjcvlOpcXa1wu15gLlpfLdebKNS4v+tf1Yo+wP+YYRtNxRjMJRtMJRjN+ODSaTlDIJnrvi2xnEjfXz25ZRaMKbg3ShUH3ZFNaD/1ouJa669FovZpd265HvWkj616wv7OtYzs4T8e2a2k0o9vt9zRdy0gqzljW//1mLPg9ZyyTYCybZDzrV1iPZROMZbZPZfVitcEz0yWeni7y9HSJp88XeelKO/A5MJ7h3gMF7pnyh3QdmRplLHvzzc3TcD3OzJY7hpM9O1Niseb/LIw7hjv2jHRUDN29RdVK24lCIBERWbNWtdF8ZDja1a7A6Eq5td1gvlJfUYHSkoo7HaFQd9VRK1BKJ2Jh+NIKW2pBeFJruJH23m3RwCbaFq63Ap+m13fy7vXIdM19FR1G2blvtcDGWXF89Jjr8RhVay3lWpO5xRqzi7WuZZW5SNvVpXrPc4xlE0FFUTpSZdR+tdqHfZ6qcHheV5VXvyqwSn1llVf3sho9Llg2+1x7vaTizjVDm3a7v68jzAmWI6k46cT1Gy5srfUDouIy08UgJArWp4tVZorL4dNmWoyByZFUGBRNjWfYX0j72+N+WyEz2Iq3aLATBjqtgKdc4/JiPVzvFexAUN03kmIiCGUnRpJMBAFuLhWnXGtSWm5QqjYoLTeDZYNStbO939DilrhjglCoX1ikEEl6a7oeF0tVpueDa7a1LC4zW6pRD0OadvDSCmGu15+lybhDMuaQiBkSMYdEzCEZ79qOOSTi/nbMGMq1JsWK/ztOsdJY9cOcjsAosgwDo0h7K1TKp67vPbJca3KqNaQrqPJ58fJSuH9qLBMO52oN7drOkzF7nuXl+UpHMHRqptRxrzi0OxtWDN0dhEN78jv3aXoKgURE5LrxvGBuo8i8RlcjFUfRAKn1Wm2Y2mocA6m4P0l5Mua0lx1tsXBfKt56+XNctZ5Cl1zRFgv3deyPfJ1ULBaecxj+uGm4Xlh9EK0kmitXmS21w6O5xVrPX45TcaddSdQVGPntfsVROhHD8yyutXjW4nn4616wbf1J171gv+v5x3jWRo7rccyKc3We1/UsNmjz1/2la4msB8d4lqbrdVSALTeakTDHo1p3qTSaHdVh/cLR1bQqvrLJ+IqAMZuMrZhoP5tcGTbmooFNOs5IMk42dX2CxUGpNlwuLlQj4VArLGq3dT+tMpuMdQ01S3dsv6KQXve/UbXhdlTodFbs+MFOq2qn9Sl1t9F0nIl8yg9zgmBnMtieaAU+ef8pkls1xKHe9FisNljoERC1g6P+QVK/itGWVohUWCUwKnS9xrL+Mp9OaI6Pm9hy3e245rrDnoul6oqffa3hnntH/fm//BCmHcD4IUywHe/aDto6tnu+vx3ihNsxEz71dTOstVTqbhgItZbFSp35yPZ8sF0MPkhb7XeduGPC7/nxYK7GaIXReLA91hUspeIrfwZU6k1Oz5T46vl26PPvc+UwVNtXSIdDuo4Eoc/ESGpT/ybbxWypuiIYig53m8yn2pNPB5VDt+zK7ojf8xQCiYjITaU1TK0VDi033D6BTWc4E99Bf8TuFNZaSsvNsJJoNlJV1F1ttLDcGHR3N6Vf6JLpE85EtzNJh0wi3vU+h0wyHm6n4o7m3doi1lquLNU7/kiNBkQzxWWudFW7GQN78+kVQ83ijhOGOh2Bz1qDnR5VO61wZyuDnRup1nRZrDb9EGnVICna7m8vLDeoN1cPkfLpeEcw1H4lewZHhYwfLOVTcV1Dm9D6eX6+WOlZyTM9v/K6iTmGV4z6182BSOVdtApvO36Pb4XWU2P9kMgfdt8rMCp2BUndAXZUNhnrqCyaXaxyZrY9efKefKo9pOvAKEemCju62mUjStUGp2faj6s/PVPihdlyGF5+/AdfyzfeMTHgXm6eQiAREREZuNZwmNlIdVG96eEY/w8Jx/ivmENk3eA4xj/G+J/oxhz/GGMMMdP6pJfIenCMCdqd4Dym/3md4Ov767SPMSbs3074ZFDaqg23IxTy5yRqz1F0oVjtqHIbDSZan+gajtVdtTMxkuz5ab20VRsuC8t+JVKx0gjXw1elvrIteK022bhj6Fll1KvqaLRjO0nuJpvD7nrwPMtcucb5joCnM/BZqvefj+vAeHu45dRYlqnxDHvzKX1As8WWg6qjtVQejWUS3HNgLJy4ee+oAp+NqDZcnr+0yKmZEg/du4/RHTB/kEIgEREREZF18DzL5XKNpmfZrWDnpmCtZTkIkLrDo1K/QCnyWm2IZscQtkyCsUzCn58tmDsmGRl6lOgYqrTKvpgJhjL12RfOTdO5b6PD4epNjwsLfphzvqMKzl92B5sAhUwiEux0Bz0ZPZlPZJtaLQSK3+jOiIiIiIjc7BzHsEefqt9UjDFkk3GyyTj7Cpl1vdday1LdpRipMiqtUo00X6lzoeF2PGWq9USpevBEqev1WbpjiMxzEwmMgpAoHt2Om3CentnFWkefjPGHB02NZbj3wBgPHmmFO+mwkmckpT8HRYaNrnoREREREdnRjDHh0+0OjG/NOf1Hmnc+Xrz95Kse+1wveCz5yn0rHmXu9X+sedPzqAfrtYZHKh7j9XdOhhU8rbl5XlFIq4JNRFZQCCQiIiIiIrJO/nxjsaGd+FhEtifN4iUiIiIiIiIiMgQUAomIiIiIiIiIDAGFQCIiIiIiIiIiQ0AhkIiIiIiIiIjIEFhTCGSMeZsx5jljzBljzE/12J8yxvxFsP8JY8ytW91RERERERERERHZuGuGQMaYGPA7wIPA3cA7jTF3dx32A8C8tfYO4DeAD251R0VEREREREREZOPWUgl0P3DGWvuitbYOfAJ4uOuYh4GPBeufAt5kjDFb100REREREREREdmMtYRAU8DLke3zQVvPY6y1TWAB2L0VHRQRERERERERkc27oRNDG2PeY4w5bow5Pjc3dyO/tIiIiIiIiIjIUFtLCDQNHIxsHwjaeh5jjIkDBeBK94mstR+x1h6z1h6bnJzcWI9FRERERERERGTd1hICfQW40xhzmzEmCbwDeKzrmMeAR4L17wa+YK21W9dNERERERERERHZjPi1DrDWNo0x7wUeB2LAR621p4wxvwQct9Y+Bvwh8KfGmDPAVfygSEREREREREREbhLXDIEArLV/B/xdV9vPR9arwPdsbddERERERERERGSr3NCJoUVEREREREREZDAUAomIiIiIiIiIDAGFQCIiIiIiIiIiQ0AhkIiIiIiIiIjIEFAIkxuvTgAAA/lJREFUJCIiIiIiIiIyBIy1djBf2Jg54OxAvvjWmwAuD7oTIjuAriWRraPrSWRr6FoS2Rq6lkS2zrWup0PW2sleOwYWAu0kxpjj1tpjg+6HyHana0lk6+h6EtkaupZEtoauJZGts5nrScPBRERERERERESGgEIgEREREREREZEhoBBoa3xk0B0Q2SF0LYlsHV1PIltD15LI1tC1JLJ1Nnw9aU4gEREREREREZEhoEogEREREREREZEhoBBoE4wxbzPGPGeMOWOM+alB90dkOzPGvGSMedoY85Qx5vig+yOyXRhjPmqMmTXGPBNp22WM+UdjzAvBcnyQfRTZLvpcT48aY6aD+9NTxphvHmQfRbYDY8xBY8wXjTGnjTGnjDE/EbTr/iSyDqtcSxu+N2k42AYZY2LA88A3AeeBrwDvtNaeHmjHRLYpY8xLwDFr7eVB90VkOzHGPACUgT+x1h4J2v4ncNVa+4HgQ4pxa+37BtlPke2gz/X0KFC21v7aIPsmsp0YY/YB+6y1J40xeeAE8O3Au9D9SWTNVrmW3s4G702qBNq4+4Ez1toXrbV14BPAwwPuk4iIDBlr7T8DV7uaHwY+Fqx/DP+XBRG5hj7Xk4isk7X2grX2ZLC+CDwLTKH7k8i6rHItbZhCoI2bAl6ObJ9nk/8zRIacBf7BGHPCGPOeQXdGZJvba629EKxfBPYOsjMiO8B7jTFfDYaLafiKyDoYY24FXgM8ge5PIhvWdS3BBu9NCoFE5GbxOmvtUeBB4MeCknwR2STrj/vW2G+Rjfs94Hbg64ALwK8Ptjsi24cxZgT4NPDfrLWl6D7dn0TWrse1tOF7k0KgjZsGDka2DwRtIrIB1trpYDkL/BX+kEsR2ZhLwRjy1ljy2QH3R2TbstZesta61loP+H10fxJZE2NMAv+P1j+z1n4maNb9SWSdel1Lm7k3KQTauK8AdxpjbjPGJIF3AI8NuE8i25IxJhdMdIYxJge8BXhm9XeJyCoeAx4J1h8B/nqAfRHZ1lp/sAa+A92fRK7JGGOAPwSetdb+r8gu3Z9E1qHftbSZe5OeDrYJwWPYfhOIAR+11r5/wF0S2ZaMMa/Er/4BiAMf1/UksjbGmD8H3gBMAJeAXwA+C3wSuAU4C7zdWqvJbkWuoc/19Ab8cnsLvAT8cGROExHpwRjzOuDLwNOAFzT/DP5cJro/iazRKtfSO9ngvUkhkIiIiIiIiIjIENBwMBERERERERGRIaAQSERERERERERkCCgEEhEREREREREZAgqBRERERERERESGgEIgEREREREREZEhoBBIRERERERERGQIKAQSERERERERERkCCoFERERERERERIbA/wfLvUb1juiBEAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2RuWrHeQ1qA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "ff6d43af-270d-4a3d-9166-7f0facc2dd70"
      },
      "source": [
        "plt.figure(figsize = (20,8))\n",
        "plt.plot(f.history['accuracy'], label = ' Data Accuracy')\n",
        "plt.plot(f.history['val_accuracy'] , label = 'Validation accuracy')\n",
        "plt.legend()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fef28d76400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZozoSJFiaK7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import image\n",
        "from keras.applications.inception_v3 import preprocess_input\n",
        "import math"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwfhJXgiirDv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "55d3dbbd-14bc-4f32-9e8b-9d64a51ee79b"
      },
      "source": [
        "img = image.load_img(loc+'/val/PNEUMONIA/person1950_bacteria_4881.jpeg', target_size=(224, 224))\n",
        "img"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FEF28DC3898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmPryA_SjzuZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img = image.img_to_array(img)\n",
        "img = np.expand_dims(img, axis=0)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4v7CBMPLkxCn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e5cac0fb-4678-484c-e262-622da18a462f"
      },
      "source": [
        "img = preprocess_input(img)\n",
        "pred = model.predict(img)\n",
        "if pred[0][0]> pred[0][1]:\n",
        "  print('Congratulation Free from Pneumonia')\n",
        "else:\n",
        "  print('Sorry , You have Pneumonia. \\nPlease consult Doctor as soon as')\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sorry , You have Pneumonia. \n",
            "Please consult Doctor as soon as\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaKqBNCYk7gG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "4f0bb491-2e6a-4096-d261-f9ebb40a20b9"
      },
      "source": [
        "for i in os.listdir(loc+'/val'):\n",
        "  if i != '.DS_Store':\n",
        "    \n",
        "    for j in os.listdir(loc+'/val/'+i):\n",
        "      if j != '.DS_Store':\n",
        "        \n",
        "        img1 = image.load_img(loc+'/val/'+i+'/'+j, target_size=(224, 224))\n",
        "        \n",
        "        x = image.img_to_array(img1)\n",
        "        \n",
        "        x = np.expand_dims(x, axis=0)\n",
        "        \n",
        "        image_data = preprocess_input(x)\n",
        "        classes = model.predict(image_data)\n",
        "        if classes[0][0]> classes[0][1]:\n",
        "            print(i, 'Congratulation Free from Pneumonia\\n')\n",
        "        else:\n",
        "            print(i, 'Sorry, You have Pneumonia. \\nPlease consult Doctor as soon as\\n')\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Congratulation Free from Pneumonia\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "NORMAL Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n",
            "NORMAL Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5JJmGHFsKFN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "name  = 'Pneumonia_model.h5'\n",
        "\n",
        "path = loc+name\n",
        "\n",
        "\n",
        "model.save(path)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FvjKljmK-4X8",
        "colab_type": "text"
      },
      "source": [
        "# Resnet 50\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KthLW0pmlkCr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import ZeroPadding2D\n",
        "from keras.layers import Conv2D, BatchNormalization, DepthwiseConv2D, ReLU\n",
        "from keras.applications.resnet50 import ResNet50\n",
        "from keras.layers import Dense\n",
        "from keras.layers import GlobalAveragePooling2D, Dropout\n",
        "from keras.optimizers import Adam\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlyWiVZqAfdf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "d4f6413a-45ee-42d0-9d27-91497eceb421"
      },
      "source": [
        "inc_model = ResNet50(input_shape = (224, 224, 3), weights = 'imagenet', include_top=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
            "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94658560/94653016 [==============================] - 8s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5br0R_OA1Rx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a73bc135-a3ab-4ac1-e523-c299e265f533"
      },
      "source": [
        "x = inc_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "out = Dense(2, activation='softmax')(x)\n",
        "out"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'dense_6/Softmax:0' shape=(None, 2) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPyGr2mPIL39",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6872d8a4-b3ca-4571-bf8f-3b15b29632ad"
      },
      "source": [
        "model = Model(input = inc_model.input, output = out)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
            "                                                                 bn2a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
            "                                                                 activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
            "                                                                 activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 56, 56, 256)  0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
            "                                                                 bn3a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
            "                                                                 activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
            "                                                                 activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
            "                                                                 bn4a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
            "                                                                 activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
            "                                                                 activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
            "                                                                 activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
            "                                                                 activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
            "                                                                 activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
            "                                                                 bn5a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
            "                                                                 activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
            "                                                                 activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_2 (Glo (None, 2048)         0           activation_49[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 1024)         2098176     global_average_pooling2d_2[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 512)          524800      dense_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_6 (Dense)                 (None, 2)            1026        dense_5[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 26,211,714\n",
            "Trainable params: 26,158,594\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUHNQ6RrIkFj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "opt = Adam(lr=0.0001, decay=1e-5)\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer = opt, metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avenVVvCKIUF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip=True)\n",
        "                                   \n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XjDXzaq9KLhf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6a7b0023-0653-496a-e7d2-eb2e1d5f2d17"
      },
      "source": [
        "training_set = train_datagen.flow_from_directory(loc+'/train',\n",
        "                                                target_size=(224, 224),\n",
        "                                                batch_size=32,\n",
        "                                                class_mode='categorical')\n",
        "\n",
        "testing_set = test_datagen.flow_from_directory(loc+'/test',\n",
        "                                               target_size=(224, 224),\n",
        "                                               batch_size=32,\n",
        "                                               class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 5239 images belonging to 2 classes.\n",
            "Found 624 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWSHRvcmIlsz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751
        },
        "outputId": "699e793d-868a-45e4-9f2e-5bd0cbedd852"
      },
      "source": [
        "info = model.fit_generator(training_set, \n",
        "                           epochs=20, \n",
        "                           validation_data = testing_set,\n",
        "                           steps_per_epoch = len(training_set),\n",
        "                           validation_steps = len(testing_set),\n",
        "                           )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            " 65/164 [==========>...................] - ETA: 19:08 - loss: 0.0665 - accuracy: 0.9778"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:616: UserWarning: The input 78 could not be retrieved. It could be because a worker has died.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "164/164 [==============================] - 1804s 11s/step - loss: 0.0722 - accuracy: 0.9758 - val_loss: 1.1345 - val_accuracy: 0.6250\n",
            "Epoch 2/20\n",
            "164/164 [==============================] - 139s 850ms/step - loss: 0.0547 - accuracy: 0.9788 - val_loss: 1.3701 - val_accuracy: 0.6250\n",
            "Epoch 3/20\n",
            "164/164 [==============================] - 143s 874ms/step - loss: 0.0426 - accuracy: 0.9845 - val_loss: 1.1881 - val_accuracy: 0.6250\n",
            "Epoch 4/20\n",
            "164/164 [==============================] - 144s 875ms/step - loss: 0.0373 - accuracy: 0.9861 - val_loss: 3.1328 - val_accuracy: 0.6218\n",
            "Epoch 5/20\n",
            "164/164 [==============================] - 143s 873ms/step - loss: 0.0362 - accuracy: 0.9872 - val_loss: 6.3026 - val_accuracy: 0.6250\n",
            "Epoch 6/20\n",
            "164/164 [==============================] - 143s 875ms/step - loss: 0.0362 - accuracy: 0.9863 - val_loss: 0.1503 - val_accuracy: 0.9071\n",
            "Epoch 7/20\n",
            "164/164 [==============================] - 143s 871ms/step - loss: 0.0317 - accuracy: 0.9874 - val_loss: 0.0112 - val_accuracy: 0.9183\n",
            "Epoch 8/20\n",
            "164/164 [==============================] - 143s 874ms/step - loss: 0.0287 - accuracy: 0.9884 - val_loss: 0.0351 - val_accuracy: 0.9311\n",
            "Epoch 9/20\n",
            "164/164 [==============================] - 144s 878ms/step - loss: 0.0235 - accuracy: 0.9912 - val_loss: 0.1901 - val_accuracy: 0.8013\n",
            "Epoch 10/20\n",
            "164/164 [==============================] - 145s 886ms/step - loss: 0.0307 - accuracy: 0.9895 - val_loss: 1.6759 - val_accuracy: 0.8397\n",
            "Epoch 11/20\n",
            "164/164 [==============================] - 145s 884ms/step - loss: 0.0240 - accuracy: 0.9916 - val_loss: 1.0029 - val_accuracy: 0.9247\n",
            "Epoch 12/20\n",
            "164/164 [==============================] - 145s 884ms/step - loss: 0.0275 - accuracy: 0.9916 - val_loss: 0.3445 - val_accuracy: 0.9103\n",
            "Epoch 13/20\n",
            "164/164 [==============================] - 143s 874ms/step - loss: 0.0221 - accuracy: 0.9922 - val_loss: 2.0346 - val_accuracy: 0.8093\n",
            "Epoch 14/20\n",
            "164/164 [==============================] - 143s 870ms/step - loss: 0.0243 - accuracy: 0.9912 - val_loss: 0.6427 - val_accuracy: 0.8942\n",
            "Epoch 15/20\n",
            "164/164 [==============================] - 143s 870ms/step - loss: 0.0243 - accuracy: 0.9920 - val_loss: 0.2675 - val_accuracy: 0.8125\n",
            "Epoch 16/20\n",
            "164/164 [==============================] - 144s 878ms/step - loss: 0.0166 - accuracy: 0.9941 - val_loss: 0.2980 - val_accuracy: 0.9375\n",
            "Epoch 17/20\n",
            "164/164 [==============================] - 145s 881ms/step - loss: 0.0172 - accuracy: 0.9941 - val_loss: 0.0349 - val_accuracy: 0.9006\n",
            "Epoch 18/20\n",
            "164/164 [==============================] - 145s 882ms/step - loss: 0.0219 - accuracy: 0.9912 - val_loss: 0.1836 - val_accuracy: 0.8878\n",
            "Epoch 19/20\n",
            "164/164 [==============================] - 145s 883ms/step - loss: 0.0107 - accuracy: 0.9962 - val_loss: 0.2476 - val_accuracy: 0.8878\n",
            "Epoch 20/20\n",
            "164/164 [==============================] - 145s 884ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 7.9515e-04 - val_accuracy: 0.9215\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DsKgpS0xIlwV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "318d5d77-ceff-41c1-9b97-b6b499a0d0ed"
      },
      "source": [
        "plt.figure(figsize = (20,8))\n",
        "plt.plot(info.history['loss'], label = 'Data loss')\n",
        "plt.plot(info.history['val_loss'] , label = 'Validation loss')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6fe9f2fe10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFsK0OMIIlzh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "74a37169-c051-4ead-ed70-e554530e324b"
      },
      "source": [
        "plt.figure(figsize = (20,8))\n",
        "plt.plot(info.history['accuracy'], label = ' accuracy')\n",
        "plt.plot(info.history['val_accuracy'] , label = 'Validation accuracy')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6fe99510f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEfb28G-_coV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import image\n",
        "from keras.applications.inception_v3 import preprocess_input\n",
        "import math"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "co6dTL04_dqM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "6ea86d94-8da8-4eea-ac0d-0321ad03400b"
      },
      "source": [
        "img = image.load_img(loc+'/val/PNEUMONIA/person1950_bacteria_4881.jpeg', target_size=(224, 224))\n",
        "img"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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PLAIKL6W0vb19cXGxs7PjJD5/YgtWVlY4tCxXo3zj9fW1t8n3z1/xN1CQVgqIET7G9vY2j4NBQ9ljXe2/gsEZ9kohP4LPPQpgaQTj/yjNUS59ZxwC7gMzhCnBM0N2raW9skUUgbu7u9lshkV7fHystV5dXUUUPYYgThchCsjE+vo6D+NrNkpu4eeBLlUxdDDQOWe8IrgjVUlXDisv7v/m5ub09NTg0WKxQA1z2Ag50XM4G1h8rAQ6ZhB1bVSCA5tDgjGJBlAEtmelNI2wDsOAsKIOs6B+/kXs0HwGExplEVntpmn29/dPT0/v7u6wVEhq13Vra2u4AfjHHDnW0CArO85NIkDsPhHMKOSchKcVCufclhCvmgCDDWIHa614t2aEpZChtEuAYay14ovj7TgmqzGKj2I6KrPHA2xubnJDXKsXTcZCycKtrKzkQJpCF+K4TKdTbhdOBubm/PwcR5OD4UvFhLi9aWR3EMOIEGR1dRXTybG+ubkhDNzb2zs6OuKG7T/hriCCu7u70+n06uoKKpoVDDdmB4akiy3+4+Mjcp+FjRujGYXBEUpmZYnte6Hw2A8+AkZDYAHrh33lv1UUSSs50DeCNt6Pz3B8fPzhwwfrvKZpXr9+zTHD9GNPOYG3t7e9qG7A8j5sAD2cnLW1tcvLSxYQ7xB5QFOAOfA4BgdNpQUYwTKg4CEA3N/fY4GtjB0P8ZjcahEu+UsmPgtYLoEehlfUi0dsH84mhqtjwYdhwB10lLC9vQ15zBq6VZY8iY1mocSSpuCSs+VGN7D79kYeHx+dv8F2EPIjXs6eF2UXWdnd3d1hGE5PT43A8V1cv+971Kd3l53GInOuvNwlJJE5k+YNsQJ+A1cAaHSkQvZysVgAm9voc8PYFtJUe3t7bMEwDGBqfd/v7e2Nos+hBW5vb29vb6fT6cbGxvn5OfcPHsnS3d3d1QAmZvGSqnBWUOfoAmH3BhHQkBCfajJ/UAeT0G6S/uwaihxp4TejACZgBOSerTen6WfJIrZc9/f3+JFYIs7EYrHY2NiwHLfiNLEQfip8kd3d3c3NTdt37gwVtbKywhJjjptAmhzFcJvP54iaZboXM5qoPInExGt7exsbzW2wZNYEjhNZCxDBu7u7m5sbRJBv90OBKKGNcBiyEmxIv/Ol2FAnGzvx2VJI6iT5eYarptMpPrHNut0qTF5SKgEtfnZ2xspjlHk0NmJzc9Om1gvF+jv+wH23o49Ks1ObxV+2JUQc7Z9gfxyB4FxxqHZ2dvq+h/rDR5BRdPPNzQ2HmdOLSbGeQolWJeWRYB78M3xQryBvxU+ySCUBTJhv7rIqg9eLN8VpPjo62tjY2N/ft7pdit+flVqcTCbUQuAYeLmTeAn2DrmBQRAY25kEqvmYAhpPJpO7u7sffvjBMjSI0Yduw+5Mp9Pt7e2DgwNjhKYAu+CEnbP/mgJNtoryM4oKzaGqIWjlqy3WfItVkenedpTNw8BVJRLFGUXor6+vz87O8GSspA8PD7e2tvy9pKZubm5AGLD4eBHX19dWpb5JHFY0WRWqzwdbJRfY7iSYfBC5EwTDCGgJmUUWBF4S7i8PZSE29onoW8xGlYJ8nrD8TF69GdyTTZsztjbNPMDKygrr5UDVp9OXRW44ScBAKysrxsO5eBauhrg7NrfnZ6zELuyLFy94VMzoOI6Xl5ePj4+cBMBIU92iq72/v//VV1+RqgHkwtYQKvmeffEcwHDfQxUex/45uPGeFWXwBiFxNuU8uI1DI5ofNoelcIh9e3t7eXl5e3tLhRbxKFfwPXDNq6srtg/RdA0TUJ39SD8gEukVLqK35pyJ5Kpe9tQJw0F7uH+knxUgh5dCKZGVICgeqThra15ZCcLnAupgbVS9B0orh4xfegrGxr0hftzd3eU6d3d3hvGcHeHZ0IssBM7H+vo6S4mAGtuyT+NEUavaJkOnbAlWhv/y/kFUc+cXzAvhng3UscR7e3vb29vkqxxs+rQQgvCkZnn+GGy2bQTguLFGeXnLtEH7pIKZEhgq6M5BYAXkNMs3QoYUNk1zd3d3eXlZa93e3mZt4bwW0fnIfM7nc2rQ0BS4414cMnw23IZRuecsGlRR8VmkC7biuyHooyBMvFvkh+SFH20Uu80ehdUcUl4CcwMl0lrJRxmtAZlvxFEYAluMU8jmNapEG8eRo4zA2QPDNwU+8AYgdnbvBnEmUkBhzWavyqZUpXBaFRtZe6G5B+EsAFsEE8vl8uzsDAvIM1onpYA+tm37+vXr6+trzND29jb+a9M0Nzc3Gxsb/pStP5a0Eb/Emg/fn4jQKsFeOIvmg+fbqOIHOk9tZjHbxjrjRyL6owA+FnAcx7W1NVeJcH3ydicnJ5wZYgn7zeg8b71DH6tkFGqr1F08TllYL5oICUPFmDg3CqpDv/rprFbx3R0hOIbhllr/VAJ71+68rVijusG+7zF/DoqzShO5jo1mFVyfBPh7FYpYYdbBFhqwSVAMBJS/OiFhQ2Bwh7Xe3t62dBp8AO/gImdnZ5TVsqPspU1PVTXM9va2E6F7e3tXV1c2W3gjmC3nr0ugOdq95mzjmZmyVEJywY7pIPoPugR7h5fMffLsXi42G59kY2MD7IY6LdQYXgEeJyH8MAyXl5deqCJ6RydeTlbiMClZiB4pgfLbiiFgWJT/VmUQjCJFj6ioMgz0PoVicTugHKfoSdpw3d3dFaPuz5RoUSqZWzdKV4WocwiKOLa2bhCn2cgxMP4HEV2NNdomNqLQJhEunaAbVSM6BnpiI8adlWhKCX1ZRGhPAu24Q07CfD6nvs8xWQokOnuuhtYfHx9hNoHXWqoItDFqthVVIRE3hvKOgXATKkW5zyyOiEMo3mk6SH2aQGI1hmHY3t5GefN1d3d3rpluVT/TKtF6f39vu5zFrkoqiI0KniPHzw4HvbOOIkh+8nXW7j4hVsMYN26bE96r0GAQP85GqQaaiDXo7u5ui3hZd8brIqPA734q9L/pP4PgMQw9C1RE+UZd9apf44ShgIuQ5ForXA3EHUnivpvQLCCGz1Hv8jMNJhwGXl9fY4VRFaBOPAjGjlDJcUkrtkpSCi7uomm8KE7+6lwLD46CR3UVwahVnGWi70EIawq536o68VFgvv0ZHsEu+1J00uPjY9bcO313d0dBi10F38yowM77W0rhOl7MGti6jTK3ll0ua9Q2pQTaaJc6pXR7e8t3LVUAMypz4WCRR8gqr7Bq86kGxLBw11p/lm5nPWoNYc9pVJE4AtEoedU8rc/c3NxMKYEkcyZ4sCL2HaKDGzcKCfIKoq7izeC5R+fYCpXKECPDfiffi25eLpfn5+dwQFNKV1dX+/v7Tajesmu1sbFRlFjPOaMtfFm+GkVL1IK72Yk/lZ92IsBQsEr2FFlS1ElSzQIuigstOvFyOBjI62Qy2dnZYcV60TG5z6urq729PVw9mya8f7SD3btWBRH8l5WMzkmj3BUxUxXHsoq8RykEx563XVxcuFML+4J7k1VimsSAsRG2TcdUDgqOn6n2nwTUJ8w/FyU/WCYemHvtVbhoRZ1SgqpYlQPMOa+vrxv4BKRYXV2FNOBVRiWzLktVA3ei4VhkfSoGwZnIMXKfBBSwQGaRjeN4c3MDnGG9iGS8f/9+fX19a2uLz97d3WFYTdrf2Niw4zsKf0CNEW/VWm9ubvq+h0RikCGJeOpkEouA0xaxcR4KJJhDi0Cwqrb4LsDHaXZBEgFHp/r0Dx8+vHz5Eo8FP35lZQXyIZmqUawGvt38Jgd2P0KPpQzDAIs8yZXsRGLCSSP91vf9+fk57yd4N8pB/NCHtB/OACcEB4mb9H7ZFbRjkH4u1WkNGgMRo2WDUrdVr0g+SgLY8eGM1oKT1VqhxC7UO8T+36iANAti9JmpykP2agUzipbBzbSiDxfxqnh4qKLoY9c3YzscF7Pim5ubpsTjPzw8PFCyE8MFPMteaX3ECOHOqqBwaJz1apVEHcRWJmgtyspExwuZu7+/z8rf4JUCw9lf+tECti3CBIpE2nAQaA1FerlcYpG9oQi9K0a8rdZ/GxsbfEWS0c/qI8Lbjo+PJ5PJyclJ27Y//PADdgmZbsSjIFNlpwV9MVExDOLkGxhDUXENxR0/K6CNOlX0oaLD0paFG8f6oSpmVy96TnTsqqhcCKtNTNSLrfL+jjRt7Pgv34u/5VjHPlynthaQlZBjzqvxhGEYsM6IYCkF9JRDEpnLtVZS0sQcOJ08eycuYhImj7TFxUmiACMN7BMLSLReApsuKfjjVvneQcAnUZpBg+l0yiOMqjvAx8CyXV9fb29vZ9VS83tMv7eA4ooIaXOijAdzY1XI9xAoyY3S0Xt7eysrK1dXV+jyYRhub285jevr6xCMulCwz2edvwVfxw3AUXYo7ABxjJ1F4su+c5J3nGT3e1FlLexohSEwyR3w0lAOw2c529nZYaV8ZJfqKRC/fRC3vCrlU5QI5YJNaJHQdR2qbjabXV5e2u40qiHh9GOIk9oooGI3NzcN7nZqmFEVunI/eC824pbIVuUTo/IaLD3CavKoI5hSCgR1ZBGY0141trtXl4CUkllzbCc6ouu6ra0tGJxoKaR8HMfLy0tnwpzbNC8xKYE3iEhgqKEKiHCKwXpqCFAaIusyUSAO4s5hGABSbFE58I2SiEu1eMDy2IeGvZUU3kUNmmImKfqgKQCi3qpBrFhkaxkYytiFtbU1zorjFdbFwuQ0BkrUWtDnmDvmGdjaQXXrrNHGxgYUO78fxYNieHh4uLq6goPiTA/3D3qHJTIKxpIZ5em6jh2Fpk4i2/cMs4ROlC5B5ob5fS8eJ7eKq11K4SNWPEm4Wxu6M5A04ll8Al3FZWQbhUSjxq2trb29vVIKqPvDw8P19TWZG5hykArwFPf393lAy2IRsYuvrsL77Hs4ZoLBWJR7a/RKCkv29vZevnxp6iOrbZGw55MC1sY3crd9qNqoAYD/UR5+zsSn0O/ODoTtMgIBEdBI6iByu/VzEeseaZjNZpCh7CpgZSZqesPtut9nDnz7lNLm5qa5WEgDqprn5IWOb0OxVFFZzPX1NatvTjvre3193anmtYQS/mEYoDihOPnqvu8h9tr95Svw9lp1sEJR0TjTiJjDzUbcSu+6Pwg/CwIe5wT4ptaKCmepEVxgqZTS6enp9fU1VpKNA2hDKOHpFaUucZ+MLkVcyYFUVZhfVRFgtdWLF8fGPT4+8l1kXGvI1Rlq4NShOJdqX2WzY8XMb1IoME4OkuzOF+V4rORrSCbZI2FdOOvsZQ7JKwN4SYzSpWruetWdWaX73AD+/ajYQ9jOOzF2WWlcLD42kfUyTsHX8WaCgySMA6tqeCWJG+oFIn9tc2GnjYW+uLiAusYZQPIcF8ONcrrV6+nIw+alDZ1jk+qx+r4niVBFpLBLioiA3RYh3ug29OvW1tbl5SU3SY6xaZqNjY3JZIIb6sNQAt+qUYY2/tUxU1YixvB+rw5+LDW1aEvxdaB6ffjwwfY2C7kbQ29DlLqN5Ci2rvVFH7LQwzB83gfFVLmMZhkI1cvlkqx0GxpCODzi8QiESyCtFYHDjTK8PLkRkySOUlJqJwVAbnNzs1P/IwsEGK1bprRqoVaUpHHYtBSBiF8itX1g8VDqiWsF8dlNGeImLdWV0sYkCYWpArEbdVbLCt7t59hlt+xiCoqKQkf1cnKxVC/Og1mtOzs7VHfMZrOLiwueHSIzyUZu6f7+/vr6GkY2Wnl/f//k5MT623f7o6ISDBLFq1PDXlv2oq44ReV+WUXYnPPDw8PLy0tCn6okH29Iqo1GAKIPafKXjTbvqdDtHHjWgNZ6G1plV43ZclHjtCgPdEAbOBx4ytyTgdkqOJBaJcfFvie7R3wpp9bMSBJR7BxtF3g8xNc3eXV1NSrN4wDIrkKrvmLLwF6rIhP6ANiOl0DFWKrhkXOznMAm9PCw6bfAOURAytEZl5eXo9o0X11dQYRDd47i3qeUQJeqkj1ozVE9Hexrzefz7e3t3d3dXsludLD5R3YHs1gBReQKzrmtioNF7w5/IifCX+2AcUE8N9QEDMAcKGNsxFJ1wlklXGTFLTBLtUTw7tdf5oNWBe98k+P6opbm9hoxZL56Uoo2h75ftdbNzU2g+5QSOBRSkoRfxqNiWw8MVMX0QcldXl6iMEZ1mx/FJ8IfQATp8mBtgeLhvObQmBcTYUGsoqTEWMo2EWSNA5nVL9IfxEjF074IHQPs+fC9aPfLy0unlKyPF2qHZJW8UO3E5eXlu3fv3r59Sz2Cv/f6+nocR2osZ7MZdR1N0xCiLdUeIn2ONVIVaViYuE+bO25goa5HPnVd19GAYwyd49l03j+IBGeFTWTGIuRApnNENIZM0o9EWrsLFhF/oKjCs1VJuPWNhR0Bcj4dpg9K1IrQB90GJVq6pZrq4KTbqX/x4sVM/dUp1r66urq4uEDUrKh65fG4VN/3ZqpbhhZqq4v+Jt5vmgYAP6shclHClr1plE0ZhbPYd4yWcSmmNpppsVjQeQGJ7EVe8X7gLNohm6j7A2feRVTDMFxeXuIt2G0dx/Hs7Oz8/Pzrr79eLpcHBwc0ZnIsC/rTti0gpQ/21tYW9qFTs19HBUkhUVQTNtN2nUnd+TghrxyhpCRwVo6X1UhivrJWHOBB7Hp2zfYz7hcy9hkf1KtfRAN1D4WsbMqotsJ+P5E4JJqi0jCDETnn6BLwhPbP7JwBwlmDHh4eYjVYdOY/sKNO1RT1GbQHbPUDmt2IgNOqZbDtA9GegQ8ibjvctulFuS5brqykP6Agoknh3lQjUPz+HBIWKSVzOKyDB+XAHCVYHJEzrD++zTiOHz9+HISc//DDD3RPwVIXQdFAVMZx2CMaLCaVcFUFvtZbEZBequTIGFCn3qLcMHdVxV0i+W5Hlv+m0G7Sisyekh16W/n0NIRPxkHL07yi/9yEPpcWu6S8uU1GFlhtQUQdcl6NJuLE8B7CFwtZVQq4iNZFvpstpGrv4uLC8H5VPxKDkVbVPpGuBfBHFmJ3r6ysbGxsfPXVVwcHB9vb24eHh76HVhVzTejoOaoKxVrZ6x6NDI8MRcs6qVWbcGumMbAiSqCxFRWEjOob4zZJfN3a2trt7a1RSZhT9/f33377LdkjFJLtMgSi9fV127dODHTuh4AyBdibfUSjIzT4S3j/YKILNcgwHoKz1D+dgWGfkBWjMsm2Lgt6S3IAHMvaquefq+q0M2efiYtynlJKgOH0HLTT3QS2B6piPp/TgbGqjMtBmHN9Y0jBt2qRR3sqEEegn/fv33vpfeJ7FUDmUGRtk4Qpx7pBBXLShZuERsnS0Mi3VX8E/BN0wP39PaUUgwCyJI8WWbRvMFHfV1xwX3wSiq2zGt/xfke4Vehe1GoTzcepakJhKCrCNMMw/OEPf/jNb37D8vIbRHyuVv/8CUDKUhWVVlb6utVMCGQF5wGDiXSy6SgLqIZ938NoMeZo9w+pcK5hqUE53k1WBjxnru5iVa+fcNAUfFAf9BqGE/iXrWb2WH02Ytcjl35nSokWXOTxLOuooirMpajEx9aEwQApJcrAP3z4sHzahnwMr14FpTgufCNoP2JHn1t0AzV04C/Ga2utt7e3qG1AAztqSOpcTaxbcVamasPeq7iC6/B7m4XhKSZq7QuSgJ9aQlYW8UpKUxGJd+Kun52d3dzc5ADQ0C6Bi3z48OHg4GAYBo5NFkqN+vQj2L6T3+cAEIGhF3u1PiCinWjQFCpwpikL9pjJzJHUyCrQA6y11pirfwxuAAvSiwSX5JQ7RHtu4j/7ctXBqKyJFd4QSB6sNevVhKrqRvy3pbrN20HhzaOSFlWsIj51dHTEKqyurk6nU9M+fOaMzHOgp+rRzJ1A6Nzb23MVHpgUH8dQOp+xVJO9pIl1WeW2Tgx2amxrbCWLluDQJAsJymGmFA/oRPkYgFXj/7bFOWeCG/v0ScElAj1TL0uT5yk/4pZIyr9//94nB6talEbnNx5s16mEzdRbB3wsr7VaDYX8ttHYendxwzRNQxupFDyfrMIBAz5+qKSM2hjylD5+f0ZAl+qu5g2zJxclfaEyc7tW+MgEyI1SPnYc8T6zUrQOk7PS63Yr19bWPnz4EGNz6/9evR4cLVLlPFUnJqIiu79ZVFyIRaOwAvaJTDrKw00hiajohsJ6Ad31fX9/f49CQgtykayyjVGggR0s/4l7tgUgCmGtUAc7Ozt0S1zR/CcO7Shi9c3NjXVhFk7HTqEFf/jhhyy2jQ0xviZCvLW11YUJgEkxkwHjpmnYryz6SBL3EtwKc8+aWARtx5OKkEZRfKq6xVin+gBX4eI/6kuJ72cENH+CLvn31kNJvMD0tIWTVaZloig9vQzscY4dvcrRBN6eVozJFy9ecLt7e3v0WoLTbgs+Ks29vr7OcAlcLrs1hjaI95NyS3wFCsM5wL29PdhMW1tb+/v78F0QQY44387HOQlWcl6BKoi000ixRuijBSWF/hxOQ1s3w1kGBmdvqI89PDw8PDxcWVmByXZxcQFVhRXwGWARkLy+73/44YchcCcWol3j3CMrC/Vx8b0V5ehHlaAh6Dc3N1C5jRX2eiEhxrxbVUHZk8G4k1xoRY4ZRCPMAYqu4svavtdPu9s9e0VBNABE5MEdd6Hzgi2d5Xs2m21sbNjp7tSb6hnkOQT4+vj4mLV7+fLlYrF4eHjA7Jof7sQp3htaBKkalAjp1EYKHUD1sJ9oHEcCXqgY9DWIImiTFB01u2L400t1++j7Hn3Mnwz497H9lQAQx4g18InIbs81L5AojTPcqO3F+vr669ev5/M52HvRZN4qUjAakfZstOj5+PHjwcEBEoMwlVJYW8Ox0SJVNZUoGqRUxUk3+5a7XYrt37Yt1iOlBJaM/LFZVroWRB/XoiTZGPBOi0FV4P+TgNqpSsI+k1AxA+ajML/J05k94zjGIDEpUYaW4oEhh6fQEBodECvpaq2u3Dg4OKi1ArPZRWNugfWQzURSqEg+yeh3q7YCqNLV1dWrqyvuBIbvqBk/qFKKzT12Y2trKyuZjudQlfkklmJvsKptGDNnT6uKTduG6ciGkxrRW5OILAT1eBHIkElD1EvUWg8PD3HmwDfwRuAr+Q4dcsENTXLVcsibUBxmZzEHWD4FjoFN6DAMFrsuzCQxxwU4YggJ4SGUU/PmhQrR8B84w4aZ+a4x5Hh/PN6fKs6il012EmqdA8qf1XBif3+/E3HaMAoLGofgjiHtRAwIuM0WDsNAm2q/B26edbM5HykE+52mY+FCOOwY1amCbO9sNqNlKdWPnu6DCsFo2v5yiOdq4d6oyAb9Yb/QenoUxSFSDQ3xcj/2elsVDGZlPkGgWg0HAzHIOTO0CZ8Pab65udnb29vf37+5ubm+vkbvIi7cOfEfNFb2mLeZPozuwBa1bQtwUQU2F43ysO6wJkKRA3I58AWZwddfqkVXFrOW0ziZTCj4tHUdA8+mhCLeMbDon0hjPDrGL3JgJ/SqlS6qR8siIzshhHLNgT0e0Yq5BtEmEVt4p6m44zi6lhLtS0hkvI1NdRBmC8L9kLSExss1+76n6oNoxi4RwfKHDx94M0aAn5MgQE4LqtE92apSu9ZP3MmKuuhXTdfMggC57VFzB7OIeSiwGurLcmgR4AoQ1pARmjnnu7s7Nvv+/n5jY+PVq1eNEpWtaGUop0aU56n67mJwRr38FVzZVr5XurwIr3XUgbWBBWaLCibAs+BpLNVuiXgDTZ/UKiwqOFvURjyeFNqFWAmmz0bxVpkpTJQz68QmfhnmvzsstcChGucaCIRI8YQcYvuUDhWprB1UnWd/PCr8TtNU3acEB5G5CK4VNE+x15wAb8Ogfn+cb6tku1+G5WqoWB+U7x2V+ynqEd6IfjEq49A87XXailrQipOGz5DFBoruIOlZD4hpxTmaPp1ZM47j/v4+zhXAbRsGJETIJTpXNggGB2xbs0bMRMtuAepCi0nbTzv6KFQLxqCpS1k5PJ9wi4cDRyudJmTFa8hltv6tf7B/Gne0iJdeRWIomjrn7gyOD9AHZgdXAcVofo/q8ld06jbIocQPm6mVq/eeBXX8CCYwqiiPMShZBf+9+qMOYXoTp6IRKckwNW+msHNQfpLNwA6WwG3tnjYLMZLlkDGqh4naJ7Wi2hQ1p7VwcH0/Mnt2enrK3e7t7dVaTZzF1HLUmd4bh+ux8visWGduwLVBrbiqRek9bNpcjd470bRBpnuVIxvCjBZj0LCElNLd3Z0lJ4tgMIZ+fVX9N0fxEJLQg6Vo4zx+FMI/036Re8riRtjeFeHwTM8YA4ppeoGTDTYWPAw5MR+1qj4tXHDQPMVB7WfxaFmsu7s7vElaJtkxZdEdTODAEWmhWb1hNs0LTedAgMiasE/z+fz29ha3wcEWYVx0qbOyyRPN98hhJCQoG05FPI32oOKBt8rHYW1UXHB9ff327dvLy8udnZ1pGE9YRAEbhoESykb51XEcQfd4rqlmEkOfXYR+bMjH1tbWIK5CowI3BGimcdyjWohVsWbn6rTIK8lRNEbeq6o7gkpLUaV61W9ZeAz+lKdlSJ/BQS2/jpGT7LilIYlKjJWJPmsRDbZXGxMgIYdQC020yIGXha1nxU1DTikBVC3UGxtnFA/h5uaGfb27u2PdYeuM4whREouPiCCjd3d3c72Kih9GVR2wOl41wgVKk+05FPVdz6E1ISQYJznZGB6qPKV0GRUhCvEJsYfqAzOZTAgxF4vF5eXl2dnZw8MDGL7jMzttfd/v7u4yENu1ACUMoPITPT4+gldM1aeIu7WQFY2zsV9rQ2wHnc3iHLpKx07FoJ7zY+g+MmoCUQ2FX/bXcwAQanjlZ2QR31889Fbpo7gRgH9ViZwSKr+6rqMkzffUiha0VA8M2KJgNMDRDjOTvF73Gndn7kEtC9lObJxxRxNguZMIhSS1D3amm6YmPJpjaiCFaGIwfDgA1pErGhQRcV8cdG8zPxTlGFlGn3kUj9OqvShnFggWBKoKS4RUjeN4eXn58uVLLEBV3r8PLdVfvXqVc2bAV6ukaFG9P9Jze3vLVjIjj9jfoTDo5hCmADvGAGEwyIUvZ43ehD6s9lMH5ZlY6kHssByoMznQkmrgxf+oQZ2JtiBnhVpFTDA7T/ze2V6MEertGbmm1/D0KhyghurQVtyzqVrVcbZMkEP4sM5cnypvXr4CohbhLeNqizDQCCEGz0epIKxt21JfNZlMrq6u6BITw0yvNa+kxMwowLmKQl+VZLdiHkQdrKKMYBmbUNhZRO4eQ2N26ypOiKWKi5yenqJN6eVk/xj35urqqta6u7u7qknmRQ3VetWTPDw83N/fuxcIf8VJKGoaj3py9TaWii1IyiaOoWgzKW5uwjivpQiZvVpFJ4UcNdDw0lM6FavHgq+srBSr6Pgy1GK12qhOrar1UimF9HdWHbcD1YXGSi/V66cX94w3EFft7Ox4jcaQYxjVtQeIxJiF+7f4/AyighcBv8gBTHI0QRHOP5lMoJS3bct/BzXrIp8U8XZ7e3zdQqPADAj4oFcBHVXkrKrqeO9BoyRKo4I152Pm6sfeh+7JvXpgTdTudaKO+oNK4JlWuLKysrOzs7GxQYC4tbWFjoDnSgDH1/nimLjr62t/C/sL79Oujh0Pqw8wI/bFwQ3eqr2vUbnfMTSGzpra0YSW2SmEiVFM4+vx8bHE5UuqcSNyxJyhIaJHUkOGzbayaKCWrVgS976oW8E8DLaaaAJfVg7XSoti8Ea1OKxCq7EVjWoYLCJLdebF2poPhgLgqBDyW4tzQPHJcPiGwJUe1ONlqlHp3LNDw04N82tIxuRAO/RJa1QOkRTb2sBFh6/RTDd/l3+utTLQpw0jKNgpXOqmaYw3YZdJFNm570NbG54dS0VvErN8THRKYcbrGCp2olQtNS9lrpk7eHdV+TYDSUntTJLiGR4QF9E2PR54f9ePPihuh8U22ri//du/7VQA6a8nlNne3jb1cymO96jiFSSJAZiIMtfhu9fW1haqNDX2WWt9eHiARzhqVp9BAG8nK2gOUQq9yrJayzYqVuRPtAGz8bU34niC6J5Ah32y04lSaUWDqCFp5DW1vilie6GlJprLERfdYP4YmKxGlFOAYJ1KHccR8gOnaH19HVYongkHOKYxkxpfYqDy04zXoIKNyWRyeXnJgFM8hIh3JmVVlupgPFFTYpY0ah9OZs6ZSkaUPXnjQUCvl8JqlfNvkziG+Xc/GfOsQUTkqQ8PD50//NOf/vT//t//+4d/+AdHxFm9QGzfrXjwZUeNU6mCnMbQq5fnYeGqcpWYftI5pqLh1Dbqemw73oXeFYj4Qkzbpbq42JFNYbbT4+PjwcEBfW4HDRlzQNqEfKZ5MHw1UXzRFAF6enVqpuLndTRaQlPIoubOOYSSOEitEvT2t1JIpayvr4NwOQxdqEWM9aiZoDc3N5TLWrWjMpahIbeHow7Kg2CUwSX29/dBYcHa7IrYBBeNgLK7mUUzJxRzPzbc2V5NCL0RrOREnbbG0HUGhbrQOHGLJsfpOQ4aTe3f/u3fzmaz//E//sf79+9t3HMYamGRN3Ek8mV6UT0cwaSUng05JUS4ubm5u7sD+3QSEmKOL2I7WITYRWPdiuSfVM7BWcKv2t/fZ/AI0UYbKt9zzpD67u7uerV13t7enkwmZF/hxxg6GUKutQmjS3LAPbg3540M0UdBdAo+h+ShlTEisrOzg5RHr6nruo2NDVRUVodv3KdhGGAnOtwZxF7DIrGejUDQquZFi8UC73wYht3d3U5F8VY0S/VQ5yk4w6iGKuK5DznfGxWTD8YY8PKk8sZlIHmlT14/ZpJqqDxOYYwGyoBukdYBQNAMj/M2EzO6z0c8gvZFptMpXWFxGNDwFxcX/HU2m02fDjmutW5tbbnKudV4VhQzPjtLjzpZLpf0b9rZ2fERQqxJh9Za2V3Hc1dXVxN1Twa7Id5CgHZ2dhxp5ZD7Nad9DMXgDgJQsUv1CHeSogZ6r11Ja9BGjWhqoJlO1F0ipcSkUAwFvVFb8Ri5Dpw39C5hqLVaVXsLCujsRCFJp6enx8fHGHqWom1b5i7YA7FErqysMLEX970EfnEfGKIlFACmUF8/qA1qDc1HYVBE19Pv+XwmyTL+n/7Tf+KeRvXk5Y53dnaGwP2xUzUoz2TnOvrFfKqKcLlcLk9OTpDXpYYK2+BiiB06tJpNllKi/tiuFZpsY2Pj4OCAHmuOMDhgJJaQS4/LRtl3mj/pIHdQ4jgpZJxo6EwWr3EIRdUpDP7hJhvxeixztj/2c0xCKKGamc3mwHBNXJEqCgSONbPFUFS2JIM6uvE2qLSkAwZxI9NTJLEK9EgpnZ+fsxSc4UH57ao6T6OtYCkLzR/jv1lF0lnI7kQtOWzfJqpe9GlPygrFxXz2+in37w9k1SfknC8vL0spVGbhKFi0s8q73C7UFr9XOj6rz34pBZowBghd9f79e8by4UqiHbFlTiJn8ZENlBA/4gPs7e3t7u7SJQsdbCeM4ICoy2234JTc3d3RC46AF5uVxN2cqOLWxsvyZ+8FgfAigl3Y5eKvUfXaxHuFDdjZ/nAR/EiWtFU9U5W/3jQNiD2WeqGGFxw2fPf7+3t4zSDqRXUpSV57Uit+0hlVbU3v7+/p55pCKE1q2hCYl8IqqQYoptfcAdKkrQrFDGj4LFncc8gNWYp8zc/4oBa+HKCpb7/9Nge6JHk8q8xR8PJSrWJHD1osxaoeP4HsaFX7JEAQl0DYkXDUac3NYhE0bG5uMqCW+2TU+DiO6Jsi2hurs7GxgYXqug71uVAnToyRi5Cyqt1H8ZJaNcOwkLXqiRI1eqNK80aJwapsYRVdnCvYNx1EYYnxhD1pbpUEjw8G90ldh9Mr6EgzDTy4cRgGn/yk5tyWuSQvzv7PMAz0x8vq1pZFqkwK/lC3XRggvdRwYrtSfi48eFunKlBpoVEKJaCkFrxPpfFnOywPYkTDEqrKx+AXGsqC6M7u+lase7jU6uoqkd04jjjjlBG7n5s1/0J9YKrK4orqLogZDw8PURLeBjQKKhAyIgBNr6mNqHmMQK2VBExV/g3Xze4vFUtRg/ZiRWWRNU0mcjhMor+qs5r9UVSOGTCDpikjx2YYsphou6TcOpJKeTQm9eTkBOYHuh8u81INowelHC2FXJDfmDFNhZA93ana3c9mM1L5GxsbOP2G/9gmRznE4wZBLcSmKZH6ySpyGtQmcRkKJKO6WYTx3VVhllX45wXUfuf//b//Nz2doJA1cAgYyDi2TZ6jyFFVxaWUnZ0d8BpUJu+xyUihp8XGxgbb1qt4PKlKsGma6+trJIPZe/g9htM4RR4PgMDx17Ozs6urKzwNGFJLNS0DwyfsAFCkbYSBFd4M77hR5hq1wX326kfgyDerIxyi4xPrjMag0Y9RVgCGOPD88vr6mrvNOX/xxRekhUCXcZzW1tY8JQIHETcADMQyenFxwRl2/JpDrVyrYv/Ly8vXr1/De6L//CiE0qCSrwkQxgnPyjmNAVcuyqXV8LLJrYoLm6aB/PrZ10980BoCeVTmwcEBUTlTdXmGra0tC+j29jZ0CryZrOqzrJwKKL09Koow/+Vf/iWLluYnH9S2hHjcJFykdq52HQcHB+zZ0dHR+fk52zMMg1kRpJtRxn/84x+zMkNd1+GM4uyOypy1Yp6nQGJw3La1tWVn0RH68LT5bStCu+VyDCnmEhi+yLrpUUkxsg9hUWeO6M0TvFuZ7ezs4JSTDsCe5NA6OWkeg2tCEHF3ZYKG0qtwyiGaV4kPsn1AJfy+CX3mbY79vK06gBpbyBqys1SNodk8foOLwJyw9Mqwhj9r4u/u7v77f//vs9nsP/7H/4gdJxmzu7vLSk1UVzQMAyfAt96puhLxZV48jdwZ0OuIeK65VVwBAJIzjWeZ5KYA8xrBSfKrppp1Mo4jKfvr62t0iXNX19fXjZKrg8bNO0Nda2XLOSrEKPx8fX3NBCMX9LFwxs9bTVsclWCLIWqj/IoVBjrYNsd+mz2KKmgJ79ORU9K801GQ4YsXL5qmOTs7I+EENunwhfu3+8QQo5QSpOxpGMvGJgJocDj5Ugto13WUksbUruOtMYwct9SiYvEW8lNygnGepMqkKlTh8xrU4RL///DhQ0qJKsG+7//dv/t3nrg6qukN759MJkzl6sSTHdRMhh1FEGE0IwSQNR3l7ezs+MAhvuvr67gmM827SMqjerN5nrOzsxyqR+AiMfOFfqIYUMZ9FE2mKmqHxMMiiyBcpZTLy0tcVUwtagMjjg/Qti1sDLZ2op6JUb8uxcllBbICdqyeQbelKi1LaNPiKxelTLnaQn0uxnFka1D84zgeHx9fXV1dX1/f3t7ypJF5zfWRzqV65XFOLPTDMNDE3u9B+44CDQe1qK1Co0dBsMMwUFzuaK+qccFS42YiDLxUZwc7mq2m69qM15CRz8+i+Nls9tVXX7169Yol+N3vfucJJp0m4+KZRQSUdJlDKG9/27b0VuDWUVf0h0afk/9omgYY6OHhgblbKeC67ESjIiSr6o2NDRicj4+Pp6en7969e3x8ZGxcCvQwtD5OiIWeFAPxwfb2dquKSlP7bm9vaXnMV7B25+fnbIkfcAhNRPowxMhyacSkVUP0+rQ7VQrzqJbqSNhrPkSjyp6ZRpQwssIxO9p9ZWXl8PCQ4M9YRBZNmCAyialJziyJccFGW4E1Ivm79hA6YlGSuaq0yCSpVvxRhJXNcoXJGCgg0XZn5c8G8TDZ8U816BMBNY6QlNcZlOknSoCx5i+LtCvjXlMNafUz48vznOBznKSu68h82hkFwFuqswN7M1Fxdyv+BAf69vYWD8ZiwVdcX18Pw4AyZqHZErBrLxAlyMA329vb9t6WGq/DZ72OIDuIvkOiUQSOUbQJM2P8G6f1LZSW1FGtjqoSB3AuKRZYW1sDMOFbwJ7m8zlJCnjHC01bLKW8efMmhSaGVZMhGhW+JRUEm1y7UH8RXA6SbVVVdX55Hw2NGbK1Rkxy0IumD/iAZWWGmlBSNqhXTXQAPiOgy9BR15//zW9+QxjoT3Jqt7e3LT3okoU6QWblEqwe2raFWuFdJ+3L7rr9kEs9WRRyoRwJInS3KiaWov83ezPVGKtGnb2IwUspp6enxjhIjXBv/HJtbQ2VzP7Rh7GoeMMcItejTTSxrwr5GkOPHTaGpSthpkcKTYCN1Dgb12h21EzTeYZANsUy0MQGcmdSHsg+H2vLrWKg8EOAJqrgZKTW+UmO/d3dHeRlm9ekMXyWBNh6beC1eCmyyOn4D2gNZ+nq04KTIii+hiRnDmTqJH/02Wscx5YWGvZBbbxms9k//uM//tt/+28b8So4TAaPVtQak7U2xJXDpEBE0K46kCRWFUOGLfD6Tp625QXIGNXyD1Y5ooBpw90h8GTXze8EKUNns0bscSkFqtT9/T0O5aAmW+Rg1tfXHfoQqyEEnSqeR1WE+uhH29Sr+t4u0FKsvyzk1b2KGrXZQIZywFKIo91vMWbOUujlWcVwzTnzTnITyH3SOMYkxKDWend3h/fPiXp4eKCTBTINQsJ+2d8gQmLBibHA5mzTUko4TjwRrpFF31aihLZfhoSfpezjeq6trf141J5J7vb2Nqfn/PzcIOju7i7E2CEUMKBssPJzDVDzqk00AWNUs9nDw8OlOhr4zUn9221AOfF939NFYz6fgx6jWamnWWpOVxXnd2trixQIBMLFYnF7e4swmeJQ1R8Blclto7+pb6zKjxcNAuX6wGdZwHsjFogdvho4BkNIsPmEe8V6FTkNYZR8VvLwUZPdlhqyw4EnQ/vNN9/88Y9/5Ika0akmKovFLRnHkYJSn4r0FPNCevDgh2FAm040VM0i0YrVn9SfrIZcVBExMqs3DgLN1GCyCcw8rwKz4nkugfcUZTee9sfHx59q+aKA3t/f//a3v00p/eY3v7EFh6G8VMvFrB6LPu6kQE0MzaqzHpSGJqu7VOMewE70HJaFakzAjtvb25OTk9PTU8AjFhociu8lZVpVbU2wwp1QYYPjhf6YhTGYjmSxdEnkASIG7nmubmw5ZJ9b9eS3wRlD19KqFDF3NYpqHaMBfySrTSGbutD8cL7FTfbQDpwl/GaM1bt3777//nsE1KB9CsQx1uHg4CCpqpZv6bpuc3MzaabAUlNvYBuurKzgDqVQFMDpjUgFgewy1BAP4vU1qldhry0hccXQRL0Y5W2YLfHpq3W4x/+zsHo247/9t//2H/7Df/BqmgCB29GGMvkqqnmjzvutSnwmk8nm5iajOdrQEHUMpZU8Es0vU0pUI7A6vco8aq1wwJZqWWo3YxRFqG3bm5sbMJdWjYCTWpo1TcPEuuVyeXBwgLaDqHF7e7u7u9voNVWvDlSCVWlRGjOuW6/KcYvpKCJBVAm90u4TtYvHqx5D+Q47zS9t9DldZBlsnVJKQCLUxbN6eIFLTYrPOW9ubtIwh/M8U59rAsRWha88DvkzPCWEHnQF4WvFGWDZUd6+/1E1dCnMY7C1dJBjoRzU9akP9SS8/N/82R71/mKaavBLpptldf4w+WCpSUVsQ6diHaN67q9kVQHoY04Jfl7XdRcXF8BMOfQOKCLwEpz5/CzUJYFF5OTwHgoaLRBra2vb29v39/dOYOKeOolMuuvo6Ag6j8Wdh8UP7sUQ7dV4YgwvO5Gsg0Oo6CCmlKZqX1NDjw37nZxkEonoGJzd2WyGr0xKiSF0UOjBK4ZhuLq6IpDiUzgJlgl68OJgeONub2/Jk/V979IrrHPRgGoi8fX1daCVopIbgi0+a+MwaOxvG+rsrKdYBweRvfqJzkPfrs++nkyaS3KlEQUP8e00ThSR4p24O0mEmhoYU62YV2xJFjEPCaa7Ir9hpR41czJrlHyr6lA8/cfHRzYjpcSJdL1yCZ0R8BBQjTs7Oy9evKAdXK/a85kaPXcq+sYSDSqGdsTaa/JTo9rLXrOOkhz8pPxkFqrnkMLY0BAaGT9zA1JgjqbQMtzPxUegaBmSxPdolQTOmjX47bfffvPNNwSvLLuTnMMwrK6ukqNGg6Jibm5ukkaL4O8SJhINLzQDKanbGVtWQqbGVqWIIZkC0jmqRgiFMgSqcqsx5lVFXVWJtCiv1UMUPvv67W9/+3/+z/9JKuNsxPFOcpOt87unE5x6NR2xo4OmsT5mI09OTlAkhOeYG8Zy4ujYv8FDR4UQIaKcwOGRM7tiqBx7pQDa6CQmITUqdUIQmSIyV1/tODKQtE1SAMRmI5S9WiHb57Fv2gc6bBJcmkN7giKajyPIFJoEYsdZAfeLJObLKme9v7+3i4XBbZrm/v7+u+++oyNz13U4lzC8EH00DuJ+d3eHP9CoHoEyBHg829vbyBxKAfQNZKCqoM9BeoySwQfGkDDb2NgAXsiBIhcdJExufoojPdGg1p1JVA+egRz6//7f/zuLGoiMGlDkniaazGI/jAUi5K8a57O+vr6iQbnDMJydnd3e3t7c3LAxQBj2UHHUgD/6vp9pHMzt7e319TV+JLZ7b2/v+PiYHqXWUkt1WUEF8lBYOuolilJtJL5pvLGyssJ78ExadZjiUqj5JvC2hkDMMaQ3CqHkDPDOJkwAi79s1ZnD11loZnUNrWCA7vnqqRodVg3KrsJZAQS+++47l8zTlYRzaJ0ScWvDIxyAXk2+3dTTt0Fo1aqx/1KTf0fhozxUVIpJ0wrif4t4pUvV9C3DTI9ozH+M66O00iq3qivT7e0tQ8BJtjpCWmoWIPqJm2vUwYbtfPXqFZpjHtoi8y1XV1etqFnjOIJozjWGBqtECiCqLh/clBKhKB3dqba7uLgYQt+HrB5/+GQWC2QCFYUp7DSA2SaVhA36BlZXp6Kf/JRLP4ZWXlEXxuVGTJ+tew2gcqOKgxhGNCKg7O3tWQ6GMByCPBMXQfgwGo+Pjzs7O7e3tyD8KSVywlX82vl8vrq6+vLlSwSOO4Rdb9PMuhFo51DZ51HkSWUUfqJGrVyoM2lFhZ6rW3TWsDLb0hwa2PDLzzqjP0b4JycnnJI//OEPx8fH9sa++uqrr7/++r/+1/+6vb39X/7Lf/EzOPLy1V0psba2Rt8HY0AW07u7u9vbW2e9YUiNobyGiz9qZAyHLOdMuzbQWVQInEi3GbPTxk67WTPHbFB/kaI0T6cm5TzI+vr67e0t45Qa8Yzw/6JK8LJwitC+g+qhG1HWoyrtVGKaAibq25hoeH0OuftB7VLQpo5Zr66uqGnJGtHbNA2sTSh8QA0kRObz+du3bw8ODrDOqBgsAJd1FgOe8qiSjKLm0aSIqGaeTqck2zwVI6l6NgWaIsrLi0NAjC6AY96pyMQH2GsbwbsnAsr7aDo1ajhLEqa/urr6D//wD7/97W//83/+z33gFi00ZHui2XNJlXGGoE3oRPIYWoDztFwu9/f3PdowVjVNp1NOuQ09WhwVzslmU1uNbcWjyOJmk4/tQ/GGlXHbtvhzraawufYj3nxV5RobRnjeqmp2EEpPFWVkh0RdG4UyBZrIs1DJHm0WOcvqn8w7VLrHx0fUeQ7trX0kuO27uztmOjZNw3mjv82bN2+KpuAhPWtrayAnL168QJqjfHDsm6aBuEgxGSu8u7uLqDk8stJtQvN5n0AekDL/UcSDMXQ8zmEo2afSmYyDDipKtEK2w8TwslGld76nRvSlorpE5GlnZ2dU3hkwbLFYnJ+fz+dz2K8TtVR98eLF+fk56WBswePjI7a7bVuD7b2KllCKOF6ckKXarbPrRpGWqiekqv3+/v7m5gbWCOHwwcEBfuft7S2BiIGtKnqyvYtWk2JQokuVW1mFFHUWGZQpmajhkeNWg5oxJmjVWyBr8qK/uus6Gkix/tvb2/QjTypwaESAIh5fLpdkrUmMkdfF4J6fnzPxAx+M1XYdTquMORFtUr9plvT8/JzzD1uSE75Uc4deDBtEfxCncRRvkEfDG3GMmEM/Nru5PtgOLn/SoJ998fkxYLBYkKRBi516PHcab59FGiem6USzPzs7SyFxbMtVNeCm1go5vGi+G8EgeHujwgCczkZcTAcHVcQAE+856KQGUNiE8FUpNcQdzWqzVUq5ubkxY4scKULZaQZrI0Las4UyUDKo/rNqJpNPdaM+bw4dfB6S4iEvLM8CW4UsBqcFZwxzMVEJL8jDiubCg0XgouAmQdkmJQFdASs0qrE1UosgokeqcrMfP3784osv7NdtbGycnp5yoria83AppbkmiyY1DeDBTTmw4qwho5GezoiPrxIF1raphimaWdRxx4/+Mn9wHEfG8jHfiEAeLPPbb7+9u7tzmUs8JQ8PDxSFoSC3t7ffvHkDkJRSWmg+dimFDWjUmW2xWFxdXX38+JHA8/HxEW8BM3d2dnZycvLhw4dSCt/bNA1YN54oAuGYD9VVBLbXWqEBVNWvejWyppEXtU/KgWNreR1C1QcaMYe+TlacvUg2o9JRWfzlUZwMEkI0VaQ75KtXr/b39zmr9nnW1tY8RhELjrWBaDsMw93d3c3NDcPMd3d3JxpWMajqEBAjhaKlKlbow8PDhw8fWPxxHNmUQS0h7H3y4Miiw0fYPwiPRdwrmRRSW2RrID09MfFRGfgSTdMcHh5aUYPSkZ0z+JzVBY67aZW3BLa4vr6+uLhoQ7F5o2LfQUNeDg8P8bdGNVchHkQxEwA5GFxqcjDQnctx+r7/8OHD1tbWxsYGcgbgt76+fnV1dX5+PplMXrx40amcw8+yUFdHEPuk5petxiegO90BwZLXaW6iHdz4r115Ti/BECd8EC+kKEdvqxLjD/YJXY7HyQjT1dVVMkYbGxvIFub4+Pj4+vra8AjLiBHnSOAFsnSEPmbHJnXjSSlB9eJg3N7e4v6i0enaklLa39+3PeQZZ5oUnxT54f+Q4CDWTKEoyGCI5c2IW3zlnIvpdllYjH8mDT0MAw41n4EHlDWLkv1jcV+9elVVK722tnZ1dXVyclKFS7cqqrJ1Rnvt7+/TYWamzvPWWLPZ7Pr6mg3o1XZ0NpvF+7m/v3///j2lwzPN46mqz0JvwXJCCOhNwAo6DT1oOqr9p0H1UqalTdTccKlBtDim4DjRjmdR6GNSIKn/mUO3opxW+gQmxJkDBTNq42RjK7ILHuqrV6+A3Nu2vb+/J/nnRlfUBbQasztVj32+gicCgc9qXt40DR0uslLKi8UCbgqnwpaQy2aVp/rIFY0SHUXA29/fT+ojYpRjGaYefiqdKaXNzc32+vraZ314Wtl0eHhoM+Qwc1DvzEaZcR8C7LUN8cePHwmAHh8fSfkgpnQG3N/fN3IJemJU1d4MBgUbgV/Lg5llHMlmE41p87egFNvQswpjR4xVNXsE42gsloddqPa8fcoe5AyY4OOfB9XaZ4WP1hCNQE0r1Kgh7EZbbVeR05KwlDaMOqaNCr7KilorYvRHtaYZ1SXdxQIlkOGJULe2tpDFIk5xr9FvBEnAsT6u+FGHh4c0yfEpwqAb1BuGwalyZx+rmqXZdNiBtEX67Ovm5ubH/js19GBOgscnKt0HqXFlo9UMlhSFiklF8+/v7//pT38yzcLq0DbdG4awImFF49FHdSWu6spiNUbEw1BKhy8LzbtomoZ51Ds7O+Y3gEhj9TjWyPFSo31GceORV+4BG+rb6Pv++voaxkwTOtw2miHr6Luo/UQTskejpmSkQHlkzQ3xkFzhg7giRCHAc8iZ3VO+jhvmvPV9v7e3d3V1hXHf2dm5ubkp6qVqo1zVmQzSiSlLfSjtJ5NM2G5hWC6X5+fn+/v70+n0/v5+f3//u+++G0VgGNVazG50pwmz5Fx2dnbevn07qO+L/cZRKZJnlt0//0RwrIHQiu0wI6koMYiQObVgvWU0xJtN+OnL3tzcbGxsbG9v19A4JGmIR6vOCOhLOETojEF9k3mxBPA/lmJiI6NNKFZ0zN6ogCuprznyh1M7jiOMHtfXc8xAmB8eHuzD8XF3KLA2Mk40hPRSDQUhVqsc+Ll6Fo/qX9d1HcZhEKUS+aBFBREbAuSQ0Xc4iLGG0IOVTlSGCr+O/cKU1VpxYak9YtG2traKptWwyNZB5C/wJTDN4ICnp6e0OBiUekgCm5Zq+WlMyujH6uoq0x3sY1iV/pwGTc4k2dxwOXIqaG+ss+XYazRRXRXyinZcLBavX79Gkuaa24xUwUWwiPDMDkQISyH4PTw8kKOPAET0XFvx8P202GhqnnDIYJYQKDw+Pu7u7oJ38hUTzUBnFRhBaYS5DaPuzNWymBZxfDpVV/ZhTqk9UUttDt0c/Aj21WxhsqrPHAv2KiIlMIVN3GjQt22dnchOHZkJrU5PT7uug520DC1zx3Hc2NgAGeDA0DzVut9+J5MO2Uf6AN/d3VHH55B3rnFtbpXQq6eNC62yyDf16XQogK1laLz4GQGN/2nEW0OSHJtTl2NXrBUljKeNMoGHvlDHfI7y1tbW3t4eoJrDiBw6TrHEHDK6z5G7w0ZjKTBknGlCgXiokqZ8QzrZ2dmBnjfX0Jk//elPmDlMJzobgGkIfed6jVapovkAMhuFcZhcQtFwVu7HoVsKMycN0Y+qNPcbIuViFNTvtJltH3H0IMIrlXF4Vhwtetg6OnSg5tjUGBYVSJ7SSbkV5BI4NBw2W223fb27uwMhceiJyKI4qjpHj4Lo8YntiANvkXZeqN+YLfMvaNCfurvzf/QZCTE0HOTq/HQsyziOxIONKMmj6Mk0UMBGzOfzra2tnZ0dsufzMJ4MiceksgdMVymhgysLOqh8NOfMTjiSLaJ8uwAaYSI+wzxZZwzDsLGxAZ2Px7QK7FRytFAVb6My0UGsAJ8lv2EQV3+iKepWBtENHdQTnTsZRGGZq6LLvqC90rmmTQwaWkfA0aihDcV0bNBSzYU44cTs2JmdnR1mfTQqMc2ao5dSMh/v/v6exec9UP64HyJUU1HRiCQLLa9EbDydA3xbTqST99dQDz2qRX966nT+kgatyvIRfBhcNa2rESEFzY86mc/nOIVTNUgylvT69Wt8rFFMvE7T2e7u7lZXV2mNcnFxAXJEAe79/b3pBTkMgxoFX/NFM00CWS6XNCez77jUyAe8Bazwy5cvHa9wS/QRGdSkqdUYNcc0zrMjAY54GhWCWf9FhYqRcRhOpmqu3lVJBWhZFXZWJDlg1Ag3zH/8IreNRiVfXFwAfO7u7tKBcDKZECxTM4RPSciIUGJbOBgoe6wwWvPdu3f0/yZszSqWZzU2NzdhNLtawT0P0SDOHZiz4d2xpUphNlJ62l06ku6eCOhnhddShQPKctupLxolY3VipGbQYPfJZPLq1avHx0cibvusPjdt29IddzabXV5e+hY5so5tHx8fceoXaqNnhx2QHAnA6+dEAQb1avWRc8YtxljXWre2tnZ3d9F2EEewm0zHcqbEC4d0Wuib0OnApjCJfLjUdJ4qTh0HicKSoopbfMql6s4G8bVZ4YUaBTdNs7u7e3l5yerNNRq+U/v3xWLx/v37Tv1Ey1NeFQ0g0GoHBwek9AyOck449jBCrq+v4TNwb/ZJRjXfK2pp4WNcNHnCtY0INyjBNMyBQc1h6+yP2v82MOcfPqNBUyhCaNWcyA88DAM7ZFYBXiYuXafqzV4Vsd99911RO/e4ZKw1a0QdEog0GzxV0wf3ZOORjFJVNfEn3EEVYfhYnZ2dHdB4p1hSSlBPpuqVMtH4KcKOvu8J/AclxFuNxVlfX4eib3NmJ8cPZaBj1OSAEoD3Vixsu6GD+u23oSdCDQn9HIZX2cHFK8X/a5TddqCDbtvZ2WHLQABR7Y+aMXR4eHh1dUWlRwqd6KYaYTPVvI67u7uDg4MsXB2Yc29vb6Jyv1KKDV1W/pZ1QLU16mZT1RXBFjgiG6hbDuRndeVzRr2/BmiNZ3YcPQr+7MOESVyoJhQGUX9IPg0tgp/X9/3Dw8P333//3Xff3d7efvjwYTab2ZEA05mEDtxO1mG1Syku4MLdRnY3Nze3traOj4/d14AVpxCnaNwJkQeyDleIXgPIayeqGz4uxhGRpUfaGEauI4WOTJeaE+ddiTJndDOH0Yy9ur6k0K2ghvoe/5fok3YSSf14G/Xis2eytrZG+JjUvXYcRxxQdqHruo8fP7pmC88n58wgFMPJtdaLiwtSx3Fx2FnfGMJtP9CeUh/a2nBsnCm9v78/Ojpq1Y6lqrg3h1cUxecCmp7WLk1UAEVJgPPjSTxZJ83wMzBz1ru4B5yMXs0gR43mgLvQa2QC3wVgiWiaDFprZW/wE0DXPLjyxYsXlJBTHALwVkqhbUFMw15fX7sA3JB1r3LhuEBZCHwRUcanqKh/Zw3p4/yU5RCD9EaVjeUTbk2jXFFSibo/NWreqRVYp55ZtjBZXNLpdEpVHdsHVAcLDDIoNdMsAhPASC/jICHHIHRZJOXpdEoURVMWYNpWrDyjznYx2Sl8vKxsbSP2ZlGRcVHHlxrG832qMn9WgyaF8Hjl+JEGcaJjhNYk4s4hWZLV/ZVdxzHKOaP2Pnz48O7du4eHh4uLC04/cWhKyRNOl6rE7dSJgJbhMMMx0NPpdGtr6+joCM5OVibdwAeSB1tlHEcQ76y5Zsixnbbd3d2pZpoTzSRFsmwV/z4rtRnVKcS+VBPmCqTg/o+h1iCFmhvbKzugjYaEjBpUnEQ1x6ybpdCr2NqFXEl9yB4fH09OTi4vL9+/f59EPa6a215UVpBzfvnyZdM0BF4wCigaW2j8s3E6NpEdGYbh9vZ2Z2cnh4EnWf39ounoVRWIb91pYltcolKei9+fEdCi3GinElIOQRZvt1WVLVk4EDXkg6PMXWIrZ7MZHcLu7u5OTk7Q9mTPzKu/uroiuuTjJE5RwLu7u33fI8RQjI+Ojra3t5E8okXy16urq+SxrMCM+5ApwHzTyAAWBVBOq6RzVfMc+PYGDUopnkiUReJcqJoA6bE+SAGcsoeaVdQaJRsxNQjgi7dti19URb8nHgV4T3Iq+BbXAiwWi729PQ4wWgPhePfu3fn5+fX19VQjv3qVH7IyDKIopcB3BjOeqFZxMpm8f//+48eP19fXpRQ6avFcczUp9/P62LtDAjoVCo4dWYjSNhQ2GuVn6j1SDJLs8cw1LKto3HSrcQjO8o0idHFbVfRepAR6Ff7KN998MwzD6empU1CjWhailVkXdBXnFWsOhgrlFoiubdu9vT1KgbP66nTi2E7VRhS5NwZJl0Yy8rxIh9QwA65TyRjQCZEsbx7UX9wQm91KAzS4vA7Lkorcc5iUYg+VL7J3ZCg7B5zEkSKIMuqgqEUFz2inwkHq5eUl2SPwZvdqvLu7Y+JUr4GZOYwu39zcvL6+dhYqejLc/8XFxa9+9aukkk6DSjHwyMLUfLBhYpgnacO7tbWFrzU8pS/9nIw+CZKqOKcO1nBiGjXjw4Kz0AS22H2g+KrutV7r09NTusojcyA1u7u7dD8syp0u1SWrqg3+q1evdnZ2pmoFhZpEPdNUI+dMRUStFaV4f39PqzOULsdjocIdkmHoJyIntqEPI2bsSSfVYNhEkEZyhJ6FPw+qMYcEaCzMVizq9UEMQyItTmynXu52XotIMDiLcOTAfdB/PBTQJlhH27bX19cnJyeN+ITWIARMHz9+NIScxZ7mPSCsXZiWZvqBsQKYzjwsOhLxINzkoVhG4sgqsAw0hhVAEcB6cSIgIhXRffcuPIGZ7FCiRYBYrT45cCQqcs5EZ+vr6y9fvuTjyB8nGHzu/PwcpQutk4XDH+cjDsXm8/n6+vrx8bGD/ai/e42bpwnbVOV4kGobcS5JHxCWsprT6fTg4ICaJH7JkHo/LC97Zq06F1SBf48aCdKqiLtqLE4OI8qHkHE1hur8kH0AO5Sj5qjw/k7cH1+KAzBRkT7wMLvgNBUZh4uLC5CW9fV19+rhPBMGOeCbqnc4YRnYOzfmnm0sspUl6omoH50yqgEHFGZgJgQGzHumCQ1ZzO5RnVp4LndgTZ8UIHz6ar12PuXYzVFMKmxcrZVdTCKA8lfEBS+YIANVdH19zZoul8u9vT3AzqiAvaMPDw/MAwCxd+jdqm8Ctt6xJIfeDD3SyjidRfPcQelYnevra/Q9q8AwWbDDSHk8Ojqqyux5dVxP50jc5ImkjJHTAUl2fFDXhiF0sMmhX2unlovR2tolXar3IkrIpFXaSa+oQ6DXkNjo+vp6a2uL7r6oiVENgkbxp7hJMGMeFr3I6tG34uLiohMzleflNgD86eWRhV/61TQNkmqAyc5Mr3EiSW0d9vb2vvvuuywGSaN8VRFEnwOm0RpttqSit503wwZZ1VV1aOHBXr16xWItNDih73vaKFQxVsDY8ZfhfDhIX1lZefXq1aDmNt4bGxfvZQk1nBPNYe+6ziDLOI60KiFiOzs7452I+69+9SuAbqyJkVGrBJQHXW2d9LLzZF3I1UY1UcrKnXoNrTyqx6EGlMNeaQ3NSJLoJt4LKwVLbSNuP+oQxTaZTK6urlxIiSu5v7/vBrlO8+IsLdRL38gMOpVzyHZTrIzFoGS+V1uo2Wx2c3NDvSjHptb6+vXrd+/e3d7eUlvM5nJNpyFqrXd3d0dHR6Rp0OWcQKvw+LKJX11d/dF4pRD/LzQUa9D0WO4SXYVYGOhKgkXMvvv+++99hkytffny5R//+EfbrL7vt7e3oRf5S0nNj+NILpgVfNSUKl4sBHKAIBLp233E68DR7PveKYNROQXwUVS1hcYJJBNDkQMrxSTHuijNy8+9RvtYQGMmyTgo6+B7MKbYqrmVvca4PUm4Sq11uVxub28zi2ypARqTyYTg+vr6mhySO3cW9Zl61HQO7p9ajloriSLOuflyfOnR0dHV1ZVL5BB3R+WYyl7saUMcrL/ThIQBDw8PRO6+E4diY2gVVvXyGeaHx8fHn8yZ41YeowmJQeTPWU3C7fX19devXyNh2PHHx8dvvvkGChZBD1JFHLO7u3t1dQW5em9vj09RwOXNo0AKMDlShMiqJREguAFgo1VNe8FAPDw8gNsdHh5eXFwAlLZtC4mJoBWquX0+zAVsFWu7XrUuKE5nuRbqKJ1V4Wk336hTVIRZtLeq2qyiEouipqpWyUU9I+yxofOQY/vESRSqmQYVHB4eppQ+fPjAVCDwPvAj8HO+4vb2tldX6LOzM0BAyy45Ki775ZdfslncZ6dhI/hIPBQrP5vNjo6OPIfJARDePHaGCBsfADk+PDx8//69KYLjL8+L5zWG3H+jqbSsS/OUX4jgsuWM2EGwvv76a9waqCtN05Ci4FJd1zGUHDyMazKge9BsAxydRsUeneiVxDdz9bdmG6DfI8QIGUZnqm6XaGt8lbZtAThnmkaCM2fbPeplLbhUTQiGotOgIw63Dz2bkcJUAFt2uwGN2iXXkJi2Zfc6D2KUeiNa9R1BblZXVzmWREse/l5rpfLYBoGz6qoEl9bQDYW7AgRYXV3d3983d3hQanBvbw9yiQFdhIRUPhEFFxw1GImwhGgJA+X3syM8qTOIbMQgar1FMRqlzzRuqBrpx4okdeN1zMu+4us42qUa/ebmJhbuUL+CIE5V5U0Uj8L2oiPrXdfRAwjLgtExB8AOHy7RGLixVa3FUHtYdljGTdNsbW1h8R0AcTP4AyBEjZhvhAXoDBZ9sVhwzKZqSpqeppQaNc+poYLeHqSVEAojGvdlIJPzMyVv4zjGobclJAJc826byMrAW2CtJpPJ999/z9LB8yK4prnVM5+ElB4GHbTY6gCZ7vuek1/Eanh4eDg/Pz86OsIVHobh4ODA7cqqaGVYG/AEAptes03w8T5+/Fh/hiPyRIPa6lfh+1WpFPI9NJBGTHnaJDeZPR7H8Q9/+APODfKaVGRD31TeDMoDMoct4/qQFJFmNDT1x4OGV3RKRpdSyEU5YLft6FTAzuojkeRF2LmspE5WPQOOSlKE24gbVQUSoerG0Dx6DEVzNQyBcExqJVrUfsjb5tSoNYqDM/tejhswmqaneKfBgD0bbhR/ahxHZ9dKKfv7+7e3t5eXl60qg7PaneITwwGHaEcu4/Xr1xb3cRyhmzHb124MAkq/oM3Nzc3NTWeDY+SeQlt0nto19RxXntTOTw7sLTumPwpo/I8Pq5VlI96NQwSMIIjGUhUI5+fn5Axzzt9///3h4SEyCqOb0GdQDzc4RFzcBGyaXbVte3V1RaucVi3iOQwcZcy0E5igRa7UNmyEHNOQiLkfS7UTS2Kl4FIzVI5FhHrnJS4avYXXu9SQgCQ2cVy6UY29Ld+9uoaPyukZbbHLNajAY1Cng0GFSkmAP3gzx35UIQOtbJI6l7hoaVCLe+h2L1++pMDBytheCqZjPp8zDmB7exsKuaWNn1NKu7u7j4+P79+/Zx2KUqlXV1dkni8uLlg67qfTLHQy0o2aDBSxRnh2vs4JGsfoz17F+tKq1MFBpwa7kDngAWXFwoNKwnPOZ2dnrCmryVmhcJt8BnoXcwmqVzQHG7iYeBznFS+TLwUbApAvpeBUITpbW1tra2s3Nzf4W5eXl3MN0+XIdpoDFhGWKnyHGzPOnNUipYTOADhzTejF4HX0D7aYUbKrJvguNSQ9agGrzFEItk32KGyrVx2wRRzJYCqpO440TcMDOqzm92jinDNakPMMQFHVvKRtW9KBo/IFPnV2BhbqvJIUofbqkMDQQNuTo6OjJB5gdM1HoTTWjq1qK+JqfLpEPwnos1dWhtcOUFHfovv7+6urKwxxp+mXtPTwDs3nc3IbsdkaP/AzLxZubW2N7iMUH9I9mcy7aYhJ3VQuLi68QBjri4sLHE3oTojs5uYmXSHce2y5XOLROs3Tqa8Ol+J5yZQU9W9ib2KVEn+1IE7E7S8ha5dkzUc1CRuFLiV3DS6lhpHPy9AuD9zHvnIVQzSpiCKlhIV1JZ2BGxD1XsVDJBQw0C9fvuxVBrRYLG5vb0kLj+PILuScaWs1m81ub29hnHEyWYTj42PEkdgLBOa7777DA7m/v4dVEx1cnA2Hqv79s/AonvZPX60l0qoUMfeHG/FzsyKvovbKhCOoWK/IxsbG0dGRc4NZjQUHdbPOIsillD58+IClRlPiLO7t7fF+NCV6EagfljjpKzQxewmlF7cMSIhakffv37uME/wrh2yHQQAK9pNGfFvDtRoq7Nxmo7SHzaVViAXUCiA/pYrawJnCvHzavTGFRL+v4CNBoJM1aKWUcnJy4swFKmAcR/i7xkkI+1JKL168IM/EFvAG1hOKDAu7UNM8R3ujWmwcHx+T07cwkAfe3t6mS6P9bydiuFXnpVKYHIm9snqOhsX25LkG9Z8jNL1QYWRRPfhUM4QaTVVjp1m7L7/8cj6f0xDUgTZuK1aD/MfZ2dn5+TmdlehNB2BBCsQ5OniKSAmSV9SawqezaAYzrBE6lh0eHnI/jdp+s3kztYaEz7uysrK1tUUITGhPFF+U1LXmK8LMAVPiQll2UYFJKVn/aVD1klWptaMXfxSTN4eYNQUyXpLzCoObBb++vrbXnlWFZw7RMAyEVl3XnZ6erq2tHR8fk3YHZRvVZmIMsx6tQfhhOp3ixaJ9GlXJsfU//PCD22J62OQooHcS+g/gOiISrUiJpr3GRXiiQS18/B+UrlWjVNuXGmoOuaGk8Yz2okDUluoRcH9/D1ED0vugurC3b99yWZfbEzDCayZyQpmxQJPJBKCOyIBTPtfESJtXhAOi/t7eXhItiCjH3BeUAQlrfKxWRXP2KKrAVOcmknrWFTVBGEV0j0KGYitC4qpeRZh8NHNWB0Mo6RyUtnVUi9udxTdwOqdt293dXRykcRzBOsAlULRec0gO29vbaMcvv/yS5NAY0uWtuvk5KQDS50I8umOzth8+fHim5oFp6b1jc2fnvqjULj4XZ7jXpCWbnedRfJRWux1sW8Sf8yeviRoDudyMJnXEaykl0lxIG0bn8vISA5RzZrLWUu0USynkOThehN4+Oaenp5ubm8xIJUAmYHJg4WFCrThs+JSYLRZlpjG1Hn9ozq/zTA5FWUGWz2FBUnLI78nKYDUqRK4qtSmhzCilNFMLUiOpEZp2gG9FGNVJq7I7VtsYbc4ZGg0zZTCdHFrqBVBaHPuJWnXycZorOaXciMBlpunNzQ2yzg5ap04mkzdv3tB+tSh7B6qzsrLC83KTc3XEtibmN7BVCEhmmnz3c68fB+z5KLfqPe47ZndxVqw2rGk4W3hCxJK4a5zFq6srcHvSCTh80AhY4kGTKDheqFhGrvR9T7s8NDTZV5zFVmXBVmaceKKczc1NRnoOymb16lbFocLuw71CfOFQpkBHcmLCEToKOPrrVoosmk9LUc+FrLo5S7CFb3g6RraGLiPWpiWQpEzWaTUvlNi8qmGW8ZM+DL4HlXv16hUlDEXjX8HpXrx4QcF3VhqTiAp27xiaUJMgZItJamCReLHpvYgstVbWdlTvRVNCOw1H9FG07jMkEn3Q6XTaXl5eeqBRq65gLIqJ4k451lAItlCt/nw+Pz4+/vbbb01rd9ZkZWXl6urKjjALTYJuEF+T+zadwq46z+ZQaWVlhdGUVSg6ipbzmlJaLBawcggnq0YcjepfbOd1HMeFatj39vayYJ1G9AMwV9sKnt0nNoUsaLR0JXRXjdG3fUrjiIPaP41iiiT5mkvVzvMR59Ke3cNE8+uxFTg8g0gnbM1Sk7VYsQ8fPhwcHNBtpSrFcHx8fHFxkeS2rWnUJ/KBMF1dXdHeES3o3ndoDWMR3OEYStMGVU+QLByVdVuofN4FjJauZ/Z9Pp//SLeryvRn4UpYh1ZFSOgPUIapejbVWgHYMQ20l2Gn2eMffvjh3bt3zEXwgTYsAr9kuVyCqGFM9/f30aCwDfiW+XxO4+qpZity5jBeo9gbJKWqoOYaQAn2lZQBIBdR12Kx2N3dXdF01KxKEhbBSCQvO+WWPHQ58mqDPogh6huLth71lsQBN+ZfhL/amjUqm+zVYm0UX8KuUUqJCUlVHQnmalBzdXVlRQM2Qv0gVn6uHquQnSHQtJotWzV2FhU7m81evHiBDEwmEzi1jhaGMJCSdeCdwK58F5xru7kobGQ0aTpUDUN8flrh6OM7zhpEX/AeTNSvdRpq80DsgcF2d3fxKlJKiN3XX3+N2bq4uHj58mWt1WRvnuHs7GwIffzpqlprhVBn+iN0ZhANPPekMUJQRu7u7pxH5QhB52nVUJy9vLq6wiVg9mFcUHxlJI9+OI2qZnvxj5LiUzbM/03yEUvoAWi5TBouGj8+hinlTZiuZAegapyfD9ioWcIldI5ALFg9KGDOdFCcBMmtlMLWUI25VH1cVXl30zQHBwfn5+eoiUHVL0QFWLnz8/PXr1/zIPY4HbBmcYlWNKgy5wxtoNa6qhm1GNXFYkFaO3o4OYDzz4OkGl5IXquXYW0fUB6vKFlCQ8PFYrG9vX12doaCLKV88803VK+jzL7//vuvvvpqCN15yHkaWtre3t7Z2YESapIHD3l5edmrKhw2HXoaDAGiJGloUgNw56grKKGv1XQ6JfXFeqGQwAqmaoRWNHiJA4Oycbw4hLG+NcAaSEAKWaVO/T/sqlqtWhlXYQXPYMJRLags+gQTJr/ZQNt7oZ0iD4K7SY7N+hVzubq66ka4ScNDFosFJOX9/f2Tk5P5fE50a9Tl6uqKpaYxDk4/z0IhLqlmsC2oTIh4Vilpr2ZHPs+4GRT2OMsTFYF5C09w0CreAw/PipgdhwUk4mOTACazpg4Qpjw+Pv7Lv/wLmSGmW6MViPtMaUFjUcjGcxJ7Apd2mgALtAnUPAwDtG0ejCLmiTrorq+v01nE8c2q5ryPenGaWYupGofAg+F+4t6boJ3Fh0dSHb5k1RfYaakh95YDN7kIHskBjm7F4kuhoUtVi6FG+VUOGOvTix8NDJmVStja2jo4OEDn5ZzxNQktlsvl+fk50Q+yMp1Oj46O3GSBMiaSqOB0EMA56kUtqkk+YfEHNUHC2nSaTxmhX8c6ScGQiQ3jODJ2dRSubPEbPxl43PqI2/xXzU3D6czqR2AnF5T75cuXuI/wqxGvb7755u3bt/SlqLUiQEVNgU9OTmhN02vIBigJ/dKH0K8LwSIA5PkXmne4XC4pU35UF3psUC9OF64q+rKGSB916BgF3up0OmWeGm5MErjbajqovXPfoX2hpTq3WIhZ0xLaNFhwW01RdxRvpWL1yeL7Dvl24sWU0t7eHv4o7lobsoUkk9bX19+9e0dqg8SHpfxRI2W5OAiU/TFcC3xWyjxw0jge+Ff4BihXB2Rgn1ibRrNxkWzOEvuLjpxpbiVKh6UooYZ7FJPOr/ysqtPrOyqp6pDCxxePB2gdrd6rQeZ8Pv/666+xjM4z8TxdaLLaqTGB8+ZQEvnqyWTi+dhLlTKjP/AsNzY2MItoOAe/Ozs75Dk4kQ4XlsvlxcUFE6HJ6XnveV5CClvYQX3nqnpA+JRONUemCIq3kW01f3sM+Wgz9NJT/Lkqxh9U9ZaV52zVILcXddISTxlgo0S2NRO+OLDawcGBGYP8kiN0eHiIewoJHyZRoybaMJeJccdxPDw8vLm5OTk5IRhyq4Xz83Natv/mN79pmubVq1c3NzcXFxfUkRMDmZvH+jupO4hzg7Ye1ZrFIleecpZ/ksZPfzWqSxaw4lJT8fACW/XjzKFnBnfwzTffNOrJiJI30FPVE+/i4qKqNQ3NpAmGoNUgQIQ1RILYoIWaAdEtkUFBa5pJnNUBa1BGcRxHOoPe3t6+e/fuw4cPJOtAlZlA5+hwomqqQXSbXnMqcmjaXcW4Q74xkV6TGqbt2OexoR+UMsmB+jRqtkYE3nlMu6pTFdF7X4rAUTQF+D9it7m5iT/GcqFBkfKzszOfJfA4xqGbrO0zycdXVlZgkMznc0z/KJR6sVgwPsHlOj5g+B64H1YuxAnL5fL6+vr8/DwpU28nJ/0iWeSnDGcN+HxWNtbxwai0GBQNqnhHEXYmk8kf//jHh4cHSrGWmhfBETcY0XUdPvtvf/tbIpvpdLq5uYmG3tvbw08wu5skJyHCysoKPSyTai9hqNAV7P7+noZk/BduACIFXb9ozCYave97Crvmmrs80WyXRsloO9yYfk5LDWixQytMhL1eAuQsDD89TQ75bGShnjUM/hpUgFtCjsABnJW9kQRXIROlARJR6kn3Jew1nQEQF4fq8JSTJo1wMovG89G1ahQTD74SlpDGLV4WbqYLnctREOhUWh65p24JQ7NQBxY5W/YUegL/9L4knlinXhfobTwVo8Fd1x0fH3MrSUHi+/fvnZW2Ak+Kt3ge1NJXX32VUqJAttb68eNHcpgUznPoO7VhmaoDDLaAzhnjOO7t7aEGEC+iAWajFHUkpWKOP4H29apc4+JV9CVkgu61BCvR+mMQFurV7Qd0TJMVoSdxRNCFPu2dykd7scd9/VFUvaW6WrRiUST5Ho3oB1XTyRoRTA20cRHKaSaaSuMiAqDly8vL3d3dIirtzc0Nu7a6uvrq1atSysXFBd48hUrjOBK/fvjwAQcgqWE07jsHeAyFN4h7EgNuqabJTZhBzG5yJEr5jAF/9mprSNz5yDrVMVVHOACjhWaUkK5kR29ubq6ursi2kZal6qjve84TwfIXX3yBcsIZbdv28PBwa2vr8fGRgA54Iqvv9VzT5M/Pz1m+Vj3f6CYCLRUHAJejKIFRlDRiXBMsm6xWv5eXl2/evJmo14P/5CPqUIZlYdeTfD7LXAo8phryk1XU2EFt6+pTvkgb6ujjaakh896rssy6AHHMYW5ESglBMWC0u7sLILpcLvHRUX44AAu1ZmcBG+UhgagQKeyh3Zu2bff392nw1Cin8Otf/5q65FevXm1sbIDYECqMIT+SlF1jGTtVQBTxvqtqXy2O9td/Wo30CX8kqdyCE0Bv4lZdwA8PDx81NRAh/vbbb33ppml2dnaguyINbdtyQJN6K4xKon78+BFxzOqNiMuFH72xsXF5eYlziRvEeiF5l5eXaM0XL16UUpgyY6QDq0SER2qeiM2qBcCF/F6sqmnC4NcxNOQYxPi0H/npWe9DG9S44llzJflNFv7C8vaqKgbocJ6sE9uXjRjUiruqRQdntdGgBcTXWHJKieGlxM6dql5NhEBT4n0620lndGNeSaV8e3t77969Y7ubpgF2ZPTC8fExCQLsBpQ0ntRIBdqNU4Eptu4sIRfvdYvS+GNDvSSDMlEJJWvHQrSqdmiaBgJHznlnZ6eUQi2fNStIxIsXL+i3uL29/fLlS/wY5w/j+p6fn29vb29tbQ3DQB0jfaqapqHrH5An1clGalpNDqANGHnU6XQK5SylhBeVNY2vUdF3fpolr8rXR9MMUptSQqM0ItLbh7ECK6G5jeNxR+IxZq9PB6Nb+w6hJskfNArWqgEd9wB4VFRaySObdoj0AHS4OLNoUgex1HQ6hQ7Xtq3JRIgp2ZDpdEroeXFxwaqiQcZxPD4+JvNyeHhYVRTummOW6FHzqLJ6Sdv8AhdacdiPsul2mOV/fxRQ/1SFwCFPxnH4etRDo5pRb9j3339vVdooiYelwN2cqj1srXVjY4N+gkgk8dZyuby9vV0ul7j2qLqmaVZXVy8uLhBBvt3BVqOkKwobvchzuksZo5LYRXQ2RaeYMBIKhKLkn+K6DJoLkzQ6e6Ke9gZueg1pqCpv8Bo6N2ETlp+i1kkdOnNg7th/yCL4uQALvIbHb8TWyyGDjzSnlCjshrVzeHgIxEZUTtkC5DWWiHkdSDCOGa1p1tfXDw8PgT9xK7FsdG915xVklEFN7DLVv6tqI5zVyZGgYqHS+CKmB+BDCcM4PzXmT2iIY6A8seVV3BY0KBg4VpJEZaPRq45Pc87b29u//vWv7VuQ+YWOBG9goQ4+tPG9vLzkvon4XDRj7MN1M8ilVVQ0cx7bQ39G6mIJkrgTyPMTNWilItFXS3L+LFWciiQYsoSixFEZjehcWl6zquGKGo34U6MY7ITSNnCobQdAvtrk6dxbevW34vO3atfqpNc4jsgx4TOGDuSYY1w0vRexRkFifGieQ7oIkfIjc+SIIqBKIWG4E9h03g8C6sNmWMCgG8DcQm24h0/m7zw38Z/97agR503oDMrTgmnxnh9++AHfGRMGjw5ufM6ZKJsrkyvPGoVGYq3v+7u7u6urKxxNVBr14LSm9lhzFMkQhrIxcm6puuckRjD419nZ2evXrykeb9SHbHV1dUWN3VAhLFYRycMOaNRz1qklZPCySiO8bg7DLdD+q+Mwf1EVwTQpLCthXGoKTewt+oZQSBG1akbEalhkQXYeHh729/dxcpbLJfDQ27dvwedLKYgjCAamH4T/4eEBs+N4y5glLYPIZhPL44HUWk9OTra2tpyVACLgsFFYjC9hp9YApVkB/GYIxTAOW9v2KdnWocBSkwVxMUmO0Q6lKnEHDA7ehp4gqcCRYsCPP25zCUcGpwcNDw4wDAM+PiFhzpmEcqvOSicnJ0v1w8HBpcDDwNZcE95Zl8lk0okGXzUEp4oHZIgka05XhNYNnqO5WxEdUmjjbauU9WrFKXEUbO/T0ZLdrBIyotEBcJw7iD+O/nP0Buo5V0NangKLhE0jKYrg0n1yGIbXr19fX1/TvmUUTslTQ4Mc1bKUQLZtW1hLAIuY43EcX758eXZ2NlHJP6fl5OTk1atXKB3Q+0HVqo4yWSh8LUcF6ZPRXvG/0+m0eHfjK/KAjKRkDdVs1VIBxtSogXy7u7uMJWXyAe5j1qAM9oNaOaC1Rr2PgQVSSufn573qY3hC4HRKw1AhgFajWl88PDy8ffsWvc5Cd123vr5O12DkG2dgqeLxVc3jstFZqLa4KP0YfXmvchV0Yv1aniJ5MRTlZQcLI+C/tuplvBQ7OIWk66BUtTntKSUsj1EqcjMgRCkl7M/GxsbOzg5UJpzIqVqz55y3trZ+85vfdOo9zQ08PDxcXV1ZpeE1LZfLk5MTxtZQ4MWKkYN4/fp1q/kePNfXX39da93e3ua/RGk50A5t8TkVdmai0+ml9urNZrMnRa5e3yKkikxSCeNmgBJAH9Gyt7e3KysruNUYSnz22Wz25s0bKpa6rjs9PQU8X6pHABLj7n7cH0N6CMNB4zmUVdQTqppQaYgj+g+gCo8KDrV79VR12CKnip2aTqdMEWiaBgaqkzQpMGsckXhxBpHulirSaALpewzjjuyX27McA9Lst32Ks1isjRIgNy6MQYFxqJAw6wtOIAE7C8spRfovLi4ODg5evHjBgrhOyCS9VvWArHNSAtP5Au6NDBa9SdhNnH7u3z/gUBF22+GJBzsuWhRWK9TWYtuo4sLbM+iVQr87w/WU7XZdd3d39zd/8zdJuTsEa3NzE7iBJiJv375FlPHcMUyLxQJtz+obz3r37h0Fop0mb6AnMG2oCiBMfrm7uwtQQlRUBXejNff29ogtmGa5t7cXk+lJYDIobAo1G9GIs15N6PMW/42nOqrYrFT+M2lLoY99p27lCKsj94X6PKaABrj4uG1/Yppj/QE48WpoO19rJZyHQXZzcwMldLFYHB8f7+zsfPPNN3yFdU3SzMJhGPDK8MfQylM1YFqozyt7jSf27t07j6fhRZA3D1NJa0CXjevV0PemBsjpx7PqI+u9SfKQilAnLIgxbWw6COX9/f3vfvc7GIRIzO7urgs8iOk+fPgwmUwuLy8ZzomKZQkIj/ggwhrrQm1Jwe0wzcQ67jdEfRarj6rY3t4m6qdtmCPxB03zjdJm0XFyKAY6BkEcA/GqodVyFQHPP9vl92JarXphiyYl+OdWFLB4DIoKjrPKiI1CZGGNSUxZAkq0rOMSPIGJ2nvgMnLUDw4OiHrnGjdv7AwCJFQSqA7DMODOOhhyKSweGu/Z3NwkU2qo2Gtl/BjRHEVUiK9nGvQnAbWqeKZsSynYgomGmdoe8X2Hh4eY3RTmXxH1w/5/9+5dUnSSVEPNyrq+mzSD7SkmmB09PDykHwYGBZwrpbSxsQFoiu+xsbFxeHhYStne3p6r/7zBFCRjqkkUXlY7LSziRP3iRnUO+hRdb/RiiVrVWHLlJuTxvaoOvEY1R7AiSE85oMRA3DBxTxUs2mmee1ItFDAL+FoJQz5nas56eHgIosRXI9arq6t0eoPiubm5SYxbSjGDGC1rYgdGvNbq2ZZLTe6DyU/SuFeXRr4O9URo1avRxqiuDciAD7BF7pn6TLH1jYWS5RvCEKCkiuS1tbXT01Pkj7CJ7rrgcEhAo+4RzIDiyJrTNWjAppHUTt2sJ5rH4Cx5zpk+v4vFAqa9Z/Xt7++b68WjztWV3YcHUGZVMyXatrVfZTGaqEUy0mCn0FBaI5pLEp5q1WgWiEOoGlqI2df0AaghZuea41PWX9UYoKXaxbRiD2ZhwNxhr8rEEiAtLmKy/dbWFvlJEzrnml22vb3Ne5jL8cUXX3zzzTelFLx8HhzoHnG8vb1Fp3IqiJ4pv9nY2Li5uYGwe3NzA84DFR06qU0HN0moh6qOAbrxps9o0BpCJ6MDXj6fzpTSUr2EUkrT6fRXv/rVqBAe8AwK3HK5PD097dVAaxiGg4ODFEagouQgYdjMDZpYx1nkizipYBOYmI2NDXxNdoj4xmXdC1WotaEtMr8kcYURwKkwR8lBSVYVRw2cYvtJfHChgloS0IvFAhtnhG8psvMQGmYUtTEbVT3i9xgh4YZXV1dblZ3YXid51T/qlbY1OwKbY58Bq9L3/c3NDTjRx48fadkAaWZ/f79XJbs94C+//DJrTo0L520zqUzEGE7UehwIBVOGCSVKY+lM1XPJ0aA+h4Zv/Ro+ac7/XIOOgVBTAsPN1hzdhhwgkRAIejUFZ4dyzvAFb29v6XZEbg203zMiSIWRtzVjA2RuVbP02kAZpP7dT87iot5wD4bQVpPaUTab5OpkMrm5ubHPB1w6qIaYzcb0jGESXBaumZ9C7o6BylP6bFZ5uAMgx1vxv3YGeo1Ky0+JoaMyq0nIsXcOO5bVEYgFT+q8jJ1tVTfL+jw+PtL5+osvviB/xvr0fQ/E+/j4yOK8evXKfOekwa0sI04qE+erOg+jDghnPQ6ALKMjnqIqNJu1qDJjpOHV+4wGffaqgrLbUEZDRNbqhU3n5ZHXvC4uLs7OznAuAS+RclKLXApCXVV7S+oEiHho/0cu6uDg4Msvv9za2mLVuq6jgwAPRmMWe+iueUcNOCvrE0UsZfQOqxShJWS0U92SH7AEJOiZh+Q8cPzloMSmdac9IkffEW0dA9+iF/G+qLEUKxk/zp9G1ana7neaRY0H5ZLA1dXV+/v7b7/9Fp8V0MObu7W1RSDRNM3r168Z+8kiN02zu7u7t7fHTp2cnDAOGHTJEknROfV6nAo2N2vocA1d0FoRj1icZ6r08xrU3mdS5o24lbIK/uvCIMeSTJEzL3VjY+P9+/fOekH+AOKZTCakg1+9evXhw4dGjZU5hQR9lMXknHd3d4+OjsCY5vM5MAfhIU9oF57vxeXiIkVEDZwzcH4ehPpS+/4cG9wDlqZo1nRVHhI3g/ez4ixFp55httS9Gl5wKd9bqwIjcgHRGc1q89upUsXIKBsBiGMzQtQYkQF7vRx447I1gJfgmm/fvt3Z2Xl4eKCk22gMgB2LYGIuGCo6ldNFLRRGHBE0VAcgQxUuKJ4dIeOpOCoc5l4vq1jOzM+d//Rsygc/2Gp4J3xkKdff3t7G2zg4OJjNZjBkP3z4gPCBkvYaRUxp8jiOcDqB2bjLrMQ3kkrPW44vuwvFE8IHOq8XA3CpOoROXVvx2ZGku7s7fAnQFswfPgYHmvjM2GHTNJRD9arrteKsAokdaT0L3u3gIy5NSOhbKdYwXc4LTsDRh2k4Pif4IYiRhdgBXAp9xfhSwueFZmJj/as6FGHWe1UjkW+jwzwhC51Hu66DZTYMw5s3b96+fUt7bx6WUHVtbe3du3dXV1f/5t/8m1YDF3GUYUQU1aXYbxw0woWXXXA0i0PAz6rPFE289Shbgtn16eSbXFjID8MwuKzi+++/Z54piGmj8QBJs+bZgN3d3YXmB6AVVlZWXr9+/eLFC5gcHCbwf8Cgvu9XV1epOUTETfioqsp1rIM+yCLPA+ucn59fXV29ffv28vKy1jrTwKukAWJczVlHXmOgvhuh7FTWjNrAkkI6sQvkZbRGqQHqt/5zAF7DEBxrKQcZ/qD9rpSSq/iTQGvusIrvN6hwuW3bo6MjZOX29pYaawDOUgqIXt/3GKga6qffvHlDZ3cULb4ZBNDHx8c//vGP3BJ+FHdIyOsqv0ZUmzZMdGed5+rPU5Vn8kH9JQGNP7TiPfBLLkd3GqtuNmkcx//5P/8n9gi/k0+ZBUjaneA0pcSoJGK6ra2t/f19agZSSp5kTEQ5n8+3t7dNqBnC3KaFyqvRvrhKfATC4uXlJWWEf/rTn05OTmizQ6f9XhVzePT8nORQDuqbZ8moTws2RmWWLdaO9xFiA66jqN8pGCjnbCxwjqWqcNNWI0Ad0aenaX3OKkbW+pstGAP5l2ReSgl2GBAepTXX19cslH1Z9AJqG932xRdf8L1YfAqbWOGbmxvCczvKyAOay0GkD0+SiTec5yV6tj4/K6AOr5J6A40BWG5Vfs7vrS0mk8k//uM/WvM1TfPw8HBycpLEciVkRvigHUwmk9///vd//dd/TV+K+/v7w8NDlCsgHBNkBmXbSEtU9YwgTRV5KnjGVCCcnJxA5mftmGmb1bygaRr3ay6qWzL8kRRLOnHsmNo2HQEdxB+15UqBUMcyGiV1kMQ6s1B2GQ2Dd5pI4Z0b1dMB8CiLstOoLhl5ih+044T/UFRLTVyfc6Yy1lwtpM2WAccXng2B1Js3b6jJZPAS5G5E7Q9/+ANhCaNq8BwMcvnmq3ClRrTMIZAZ7AbUz2FMn9egz3biUbP3OCLEGfeaJUw7imVo2UpvTlM9wNXAzMgC/83f/I0z5niW8/mcjrWrq6v49VmjjIwhsPSE6gT1qMkPHz7AsV0sFkdHR0PoCobYHRwcVOVjCBGKiFqtqMqNekqCfzlRBM7voKcGJL8Rqz96nF7ANvQh4iNx0X3xXp3J5vM53AaDsrgNKSRRS2CQ2DEo4outra1BlMH6E7VMp1MSvxzszc3N6+traOAG+XHT/YBgAniWi8UC7J0e7Vl5dqvDf/qnfxqGgbAYg4lOyaHqnx9skbh5k0hG8W8+K50ppRYH2QJaNaI9KdE3VT34qElkLBYZzrOzM1P5kwgf9/f3DJGgVRUA00RTOxBx1gVJxY/sVLJI8jdppAHe/ULzUEhUoKSBWsGqNjc3idmJh4ZhgGAL0WQ+n+/t7eGomdqcw7Boa6alZipXlZX1aiLiqCip2I1Tity0YtDZK4j4gK0eCsZCPIbixlGTrGJI26jYy05nqwztoEmqLCCANMdyHEdiTZv7ly9fwsFbLBZU2gxiIfViNg2irfhUAAL+5je/+frrr8dxJCNod4tyZCBVE6gbdZRZqoJqUJ+cUSPQfcDs8X/WAe26rjhciB/wVUYB1+wfvWVG0fDo3b9UK6UsUjdSghe4sbHBkHGohLQDoPvc8fExImj+gTUElLmlBt8wGOX09BQGPvA+tJ1eRH2ALdaa1bFZx7I36jnKrZLZb1WHPoSedaP4Iq0IHBYXe0Hpaaa+iGLcBGZdku8+qCqrBnZzNFmNWClsITk5TmkNICLPUgWEDaHaKbpAqMxaK8s7nU5pAjqoDRsJ+iTot2kayBJoWexP3/eUznJc/+Zv/oZzxaGig9Xd3d3/+l//q21bslODGjONmqdaQ8GWVdugrIql8+fU54/l8/FXFm303KhZ0E3TMLCQM4TL7IbzRvKc2losFuvr64Q4FxcXLCsNGqgnrLXCn59MJisrK/v7+xSwEiMjUjTDAMUYx3E2m338+NGtFWn9VUrhlohG6fWPX1GUxiilHB8fw/QZlM/wWvdqDEaIWsTh8uLyOJZdh6iNeif5cJaQGe81W8JYpuGFFNJRtphZBICs2nlstxMZVrdW4UX4/6hxP/ZN7R0lFWGzFNTPYL5QdXxkOp3SLpllub6+tt9FTPn4+OgGmsxFYZv6vj89Pd3e3p6qaXDRhIKinvy2D71qG2N49Av2/bl0JlGdB03knaiNb62V6nKO2sbGhnlx+/v7U3UKsMv161//msIDFgJwfqHuvV3XobldyFZrpVBkNpvxA22XoZucnJygLJGAm5ubew3oJjuFOqfxU1IgwnJ74dDHCw1CjlwhO6MG51HqSzVU70TGqwL2aqDejGp7FG2WiyI4Fd6kJGMVQyg+0qmni1W4Qx8fHq7pw0lr7Yn6GqeQaHVPyVKKm/AvNeF8Z2eHvCWGgkiIJbKHSkc3xJcF+dWvflVrZQAGAtN1HR1KvBSoAFJNdmftAjkq9XkbfyZ+/1FG438cUnkP0BCDmJcst3M8nG9CdfaeIOl3v/sdmFnOmSgKdxunZKLGYGy/p0PAzaOnfa2V1lYAIklTaaoivqOjo67rMDQ8vDsDOpYCuF6qyzPMf5oX4KQOqvYkfqqhg/oQxiNlcTKSLEwO9BHrgKJuSn5DVu3lJMyqi5Jq4KwNSf+F2jc0IolzTTAyABMECArYUsXmWb1osrIMsJWJUPHXaQuf5A1jcDY3N4Ho379/D1b69u1bVFJVngJ8BtC6qGgMr5S2QrR/44YbMdpYE/auVwla85RR/68QUKsfRwNdqCybqKcDx6LXYF1OsFuvHB0dQesC2mjb9uLiYghNJlAtVrrz+ZwEz+PjI+qQyZx0UoUIslCTNOg8MAHAmwAHOOt4xkRm+ACUPbBqfBfQBtqiqlWTgwN+M9f0aUukc3d2H8dQS+mgNYce3lVEZkeyRb0wo623vTPk1GhwIMJaxauwF7HQEC1UlBlhSekPjEarDugU5EwmE4j0WRlaSgu9Su/fvz87O/vhhx+ur69ptMTj7O/vxy3b2tqCrTcKSG6aBrYeGBavVnNZk1qaOaojROlDSedfKqBeMo7XVH1Ko8OE4Tg5OcF2c4K5M2rhnVUfhoFsED8Q06ysrFxfX0/Vbeby8pLn2dra2tjYeHh4oB3w9fU1hp6MXEqJkhejIQ8PD0wbr+Ia22km+rHuhLPDV/CAXtwxQCEmkbSh+yZqFQwlxtdVcXdRRn6i5k196BCWAxjUavS0hbVXITz6b6EO397RpWokHAzUUHAyV/fNHAY/OEozm8cAJ4NMi1plISW0CSEg4ybv7+9PT08B85umYc3pIsbRenx8/PWvf11E9f/qq6+IOJeh64nXKoVCN3s1NSRpfzlI+rwGLaG/iA1lVZUSqRf6SSRNCJhOp1999RUEVSQ4pQSbleqLvu/paHd2dgYOBwuk73sodiBErCbqDS/HA6lwZ9mt+XwOModjyi2h5k2aRpXiDJEvJfZniLTdrBiqN6FhHfJtTKBXx1CfWHuTScWZtjxNmItgnWcrjDwZJ8lhFJiR7UbjZgZ1nU4Ky+wr835TYGcalTvTvLIk0NE6m2D08vISlYlbxdNRAu+0LVAJ6SLX+QDgI8S//e1vB01J5OIM72vVvC4ruWObY/c6C6VKvxghfUZAxwDXZcWSyB9RHl9GfMOKrK2t7ezsvHr1ihuifxAySo4b+ev7/vb2lhF69nQZ44dN5wYI/RpNfYQJe319jb2gEG9ra4t+JEkoCW4rFL6vv/4aaz4MAxjTOI7YMs73VM2risoq2BjUjGW0qJjms2d9UMX6j4sokf2sPuCv3gn7A89UdVIoVgP7BL+fnGQvYn8S/R4rySk1j9YoI+c/SichKZxG4k7M3Sgsfa4hn0gkXUWZOG9U1e/c2dmh7xDvp5bBTjwkvWi+8UnGkPxMf86+PxdQR5c8mL0uLAj/BWKgoQj24uDggLHYWSxGlBxjXyaaykXxBu7m+fk5zxNzjMRMy+Xy6OiI5FNWf6KU0vr6+unp6cHBAVEX+oMQbRzHg4MDwD9ID6yUh/hyn2tray9fvsTHX1tbg1biS1k4mjB8thePLj2tKLQms/FKMmHGKXMI8FOwTil0iGjFnPfVcmju0DQN0uP3F/UdyeIsJ1WtsCMcy6rq3niTyDRK7uDgoFFbvPl8fnl5iVdGTgjMCCX68PDw4cMHP0KrlgW11r/6q7+iEXMRheXh4cH4jPEfH3KHHzbxUSf+RQI6BhJXDtNIUY2DihNQpRg1xthvbm7OZjOwp17zNlNK3D2BM3EiRdy2uVHI8HXAmXEiAe2I9EelZPCW+JbHx8fr6+vt7W3wlJmmd1LAyYocHx/v7+97cOUizFmbai4CLp2PNY88qEmqV7AGmN2uOX6eF2cMaFR52gjT+jU6Cb5+Fd+0qt7GGRBHbOYNldCVxDuNFz6ERnkkVpK6Pa6uru7t7a2vr1OuSFsAGAv4Qg6tAJWQNqwff93a2qKYExIThYoltHCyfaADl8/8MgycWIo1Nvx8Cv7zAmoPyY/EQez7HpJRSombphfF7u7uTPNJd3d3odZDGgAbSildXFzQgRZfllnZrDh1cBR+8O1bW1vr6+s0XqT5wvn5OQqGvWGZ5ppJmsP8KHzijY2Ng4MD+rPt7OwcHBw45kNXQZ3GSQLzI5XCoqdAmskaqm7pAQ3Iobm6JQ+ZtmuUQ/GgvcDydMS3hdWWxywWG2UHhUkOQKf6JGT3Gb8EGTV11Xf++PgIYgrDge4VTdMQJ6SUsPgk7RBKQ9r4taenp+M4oiM3NzcpGR2GgdlrPKw7R6CPY+dXoxxs5V+SRvpRWcT/2GmIm+QWXCw6Lsjd3R1lVhZrHKnj42PSP/TvBGwHJeWa8Anevn376tUrQ+VFaJl3btQUCOKtUoobmCHrL168wO/ECXahEot1dXVFQUijYbLgLHTPI3olSAI6QX8AffMsz0geFibEFMG1IrQ+GwLVw+becf0oZCoaNevjrNjOO2diXg7VoVWVn60mjVs6fQ92M/C2iSIWiwXQNW3AWhW37O/v06EOzZKEHoAxE7YSwk6n06OjI5CmIqIg1DPQ8aVarRgKxfcoaoaQA6vQN/yv1qA+wXbRuq7j7nmZ8jNoHmEshUOqHh4evvvuO4iDbJUjR76Lk4rzMNXAil4VZDa+SB7K4OLi4v7+fmdn5/DwkHK8o6MjGjSwE53Y9Zg2+PlWrri/UCWcmyHmRX3iA0Q/0orTgjWKUzyENiFV+V4f4yEQjljAT9/fiDNVlJ4eQ36VB88hNco1e3H+7ZAQ51nKSymurUVGrdKcnHRmlYX9u7/7u9evX/urSWNeXl5SfgRfkb4bP/zwA3o3q3xlNpsdHR1xqyRrjM83IgABSxfBwL1m6/zZCOm5gFq6fZVa64pGCRZl5Gaz2V/91V8hYUTorqOotRLNfPjwAQiN4BocFBXyqMl55+fn8J5wxkfh5xhxR9MppRcvXhwfH7948QK7D6OPkVPcqmPwVjX40+mUSIi8POrcvh33k+QOLjW92GbIqXBLbVbPdiTVCLzXuglJ5xr6jTke8vl/sgFhPiLbaTWZlIWyNbPfZQj2mW+KAW01a5DLWhPzBgCWosKsqnr5L774gvpEGBf0CEFrsK13d3enp6fr6+uQSFhbFoG9oMm3s6B4LEkukHGMGioI8i9GSMllx+lp5/qq+XboyxUNZwEcPjg4+OMf/0jsDJTjKGdlZeXjx48ENzQGMv9gRdNLSaBhX87Ozsg5IQqE/ywrqD48LuczRpWKJ803AnnlaCJkgF+lFMoboCyxcEUwOKz+qrHSKGxO/BjqMdqnbPZRuZNonpZPB0p/Kn/RjUlPa794LdSRaxnI+dGCs1Z+G3ditqj9oipAgM/Ow0g4lChaBkaOMTgy1WThDw8PaU/ECSGuJeGMi7VcLv/pn/7pd7/7HZuC4MK2YXEGvTBoVZGQjeeoUcXpL8CY0i+UHbOsmA/n9Lgnoksg9CLuID7+d999B7pGL3RCKKwSTRYazWNY0fQ6uv9gvwih2PKdnZ0XL15g39fX1z0zBT2N+DLkc642uVZChtZTSicnJ61ahUHghaSTlblGQBFi2yCkYaJuH9ZVpjzb7zTgb2Vp97SEqqMU6ug79cjtVHhup8IAE1+KW8wb7OpEAm4KfbViTFNCD6wUcAOY0W3bYt9KKbR03d/fxxf6+7//e+ad2mfoRL9vmoaw/fT01A+IT4VQpqeFhK0aw9sO+7YjuPFnBBR95rfGjzn54cI02zt0FakdDorzltQZ4+6MGnsKSoXDyorjAiYNUKuhJ9Hh4SHzEQfxJByEYfqn0yk5OoIbipPoHc6pMPMjpXRwcECo6wqqWise1fn5+cXFBTNWzKu1BfeKo0qbQHSwfFuSUqgRi3ITHRVvm3F+2y4rFZt4/8m+L0+HV83bFmqj0ijzVFUkxFcQL9vrMFQOCEP2krJHjAk4xuvXr9kmQkwEFNcTr9TDNm1qcPaysIusWXumO44B601PjfYvvLa2tlpw72cv+2E8AIEwnk1RB4daK927MaMwNefz+erqKuYDmWB76FkFjwEQ2KeccBvJI9uB+ViELu7cwzAM7uOfUqKpGAoVD/jm5mZ9fZ2Wn2SeUkrn5+dN09zd3T1qaiqCjuw26k2C3XfTsiYwiYqm1DVqx9yr7UqrKmQ7Bj7bltcmlKxYamNq1M6uA6n6NIoiDG9VeWJXBOP7LJBirQBPuCx+JGqFXeM07u3tYX/QO9hxBiH89re/ZTfPzs7I2u/s7KSU2J2c84cPH168eMFU1Zzzzc0NyrgKSWB/zc6JoecYsmW//Lq+vv7JQ+dXY8jXxSiBBSWf1qgzAkn2cRxPTk6QAPaJnI0ZT0YceD0bigponHMmFURU7nE+5gtz9BeLBY3Am6ZBTO/u7gD2uez9/f319fXZ2dnZ2dl33313dnZGXsDWgD1mrDcWED2RUlqqHNa+ER5n+7Q2w3+1BJvbwduGkNWMS+qwYHzK03OyoA0Vnpb1GAkh650qGYvIeynAq41GCOXQnmRUP042bhzH09NTuBPAc7QjqGJYN01zdHQUS9cpeHL6Golki0spmCAyzIbVkBw0GofH9IYcCMuf1Y/JQ8t/QX57zaFHYSwWC9pJYghQrsQiSZFH13WwLa+vr7lFEBZIn6wXiHFVd40XL17s7e1Rfoliw+gTx6SUqMOaz+fU4hGnj+LkIj2Xl5c4RmhHg9611t3dXRpktirCLJojQyONosg9CyoaA6VyNptRaV0EA0WPMz0tY2DdHFEhHEhnG0aIZOHVpuMMwwC5s3vao6GqEwRn0kGx1UpRvajJWU3T0G4Ni8FKGqIyhjqZTGg1j9JxxRgACxd59eoVepSeF13XkVtBTN+/f/+73/0upQSdMqUEaJpUYmAPbamKuayGbWPon/rLr+cCOioL4t9wocfHR3znQQQ2bgIi5tXV1c7ODirNdR3YeraB3cVNNCLjNsoAQzASYmE1i24QuFGzk6ZpSEednZ2Bw/GpqQbHY8KapqF5GG0zsnAiivXISsOPTBq7XQQYsSsYjaIMuMPbEjLdSdyRaN+jL9ur9sOWytq0BIwJN3quaR4GrbwFTahUsdfryINnR+dNNLrSviwhgUGoiV6np6eHh4fArpPJhFbDRWSOcRxfvXo1jiPwJ9bM21FrfffuHbluaFCY2VZFv4jmRH1Mx6dd7AwVfyqUNQBKn9egNdS2VoHV7pnmaLRt29PT02EY1tfXGd7IAUopgahhRgGqcs47OzvERkRCOefr62umluzu7h4cHGDfKZdjn3B/s3pIm45A1rSUQikta9SqEXPOmem05AJ2dnb4imEYNjc3UQCk+B4eHpgIio/bCGnv1FkX6elVEJwU/Yxq7TmqhDAJj3x2tptQDx4luKhLjJ0fBA6Gm/PMKSWOJQFAtPjD07mJJnwhpshHq0ILSFs559PTU8AZnH7wEB6WFdve3nax7osXL7grTGLWXBtgKd68trbGSCDs2zAMXN/+Er6yjUkMyv/s6zMCWpXwNH5h4HfQ7Atk8Z//+Z+prsKPAUXnse/v76lw//jxo8WanDudWHA9v/jii/39fZMzuCwPAPhaAo44DAMpKzdO91k0Skfag3QRkRMTbx23teqEgQFC3ZqK1asd7ijK5iAc0YsTtaAz4xbuT3VADhGrrXZ8j70xOw+8p1cfTX7vUKyKDZiCuefjoMicK+pXOQO2D/aqMXSDGtxVMWx2dnawPBQe4cWx7HQWodEVw4YeHh6+/fbbv/qrv2JJiYnHUGxjJL+q8HAMYNNfIqa/5IMSNk41X8IgdkqpaRrYrOwfp4oNw1IT6MTcph0dIDEyPQw8ZgWLOqjYt5to7mAW7sO5JPZExVZlklgX/K2u6y4vL602jOpxwMxRwvrMNWPE34ITBlzqADwFK28cxxam+aT5tz19h1b2casSdSkQdEpgGlQ1GxvVk2cQScXuXQp5gaoy/KS0DUKG4ziqITfP3nUdhCY8TngktHEdVPzZqlkuOHxKiQ5FPPJMs7kg3n/55ZdTddb1d1l3DCoyLqF9wy9ESL8koDmUeoHUePIQStT9vdbW1v73//7fLju+uLggLl7VCGiO7DiO9LnNOcOu7/t+e3ub/DuoL73HrI3ssZm8U5UowwfnrrxbrrV41JTzlNJCo6HZKjA8dLajXSbdG8xKYWIB+23XIkYhDrStaD/19C2REZ7sRdrPoSHeqJS6/XKnJcGeXRs0BFogJ7wLYxotlw6QObHIChUd2OWs2d2wuvx0+Dwu40G+eWTo3hRNvH//ns21OzuO48ePH3/3u98hA/h7DFgjWk1P0XuW6Jkv9OnLZudnNWhVFawXdwzdDbquw85yLEaN1bLT4/7ky+Xyyy+/BCeaTCavXr2iQ9j4tHccXhct7FqNxCyhZ+eo7ABBPcCQU/Zt24J58VpdXWVWIhtAJGtD7Gc0XoaYcirawAHNYWChPcIcBpn6HP7cKltiOjUgqYHnkJSifJaq7jRX3ITuFPImeEoxLkmhANWkTGvlFU3vLOoyzgICGPMDGFDReEjjR+wvfN+VlZU3b95cXV0B5Bmieffu3d///d9TQeq8QEqJ1mV2fnhSbuyXpTO+ntPtUuiiljQDfQx9VPj9yckJRhaDa4nkCdfW1s7Pz3nUly9fYjF///vfD8NwcHDAIXYUYmVAURvuLEcTtcf3MrDmmX9mDl4WrgaKPor+nJRVwzMGh0uidzjjYNcNn6RTJwx74Z1GtLRigtqlq0+LjFNolWhX3l6B+SWdZpM6CLMribuG8nau2KDVGEifRN9L9WUY1NjDZCXunznnlJE1TbO9vX17e3t4eHh+fo7HiVvJ9c/Pz2GKlVJQhEXVL2jcg4MDYG+Wly8i8Bo0gppdwL7jRURndFSpwr9aQC2mVpxYCh6sqMn0fD53Xwo+wqoB36SUHh4e6Hy0ubm5WCwODw9pO92oSqRRuxs2HunvNKgdXdio/SR73Gn6eTyUg6ZFod0RwaUmV4/iC6Pjl6qBzCErMdVcskFd4HKYUzoGTifqZAzNMzB8OTRfsKuaA4BirWnPDBirqpdn1izQGvB53tOJR+dDxQL6avUpUDoE4ggwk++ZLt2++PX1NYmfnPPq6iowCxA1N1NK2dracm2PoaW+71++fFnUeZknenx8fP36NecfiTctHWXs/apPC5X+1QLKs9mfJexY0UQi9omi9RcvXnz//fcgl8SYTrLxtp2dnVLKl19+SYubUZniTmResx/wclBm4EpFTV0Q0FaNMJvA1GRfGyXH+SAyF2PqIlZrE+hwHAO2E2cLKe/F3MuqVZ+oCa19Estro1RkDpS2GGh7SdPTiL4XO26pIl3fp829o10H/q3KOUAq7LL7IgBnWbmVEur42DuictwtK3XfKiAJ52GiVrRuAFNrBUzE1z8+Pj49PeVSTdN8+PDh3//7f09MggEE28YagD0vNJvhLxFQH++f9UGLerL1fQ9r2lvuUUkpJUMJ9gqurq5+/etf0+bv1atXZG+nmn3YqkEwYjpoaKd3BbXRhPY7jSpaWnUicDTD3vTqH9aEJJDxxaoGfUmYDho6pWTUdlWT0RrNR+OuzF2qikm9PmNI6owBw/cR8mm3wo4+gLVplObIIYqh/VIz4hvV9+UwrsBuBgrCruSgUuYsno2fiK30MaMsLD+dy7NcLi8uLhBW0OvFYsEO9n2/v79PNSlciGEYmJbEvuecwbAw+iAqz87tX/j6/LttSQd1auAQdCKJknu1D7exscHqX15eHh8fc1vQiADeiVpwAJC/Xs127Y3xcterRrSpUUMIbNCTCpJSYBvh/dgPxuns1Pq1qkOs2ZyQTSGbRbmsiocadTEvyhv5Jr1KNtmdGgcPoRCsPsX5LK/W+kmNbvBuLZEGrWzQLP32tltNgsuhFMdsEgyUlTreCz8Ta1P+4BMyqH6BbcLHRfRBl0g7W3c8Pj6+efPGe+RqGZ9PChiNbY2hc9ifDeHj6zMalOPrRYwblkVFgSR/cHBwenrqo3l/f4/uLKX83d/93YsXL4iEBiWCCZuyerBXdZ5BR9qRwKC0oo3ZarSqJ84akYG0FWV0RpVHO/yMgMBSkzci2YI7H9XyyoKYxaYb1T2BSCLJc/XVktAia75o06teUUz7QMVvntL1LY6IL34waaSkmne7vL5OkkaPDU1rrTMNeZ9Op/ycc6YxovVrqzFXPA6b63AQYQLSJvOSVGTS9/3vfve7t2/f5pxfvHgxn8/BwlFP5pVz8xE8iVbls6/4189rUBtHujOYHTKO48nJCfoJZMdh7zAMb968ub+/n8/nv//971++fGlqFnoX8BIl2ojSi5FyfFpKwYNMIgIntRJ3SrcK/2oE4HnbuIL1eqcO2XwLQCzeFbwHhBVNg5UoKvHun7bzREqIDkEW7d4VvXJAUlJAhewFxgvmkF6aaPpgFvnDLngvvk6rfgr1kzRSEk3dTk7f9/f397j17oZpiA0LA9KHUcKtAigdQyqb48q8IbCa7e1t4kif/L29Pf5LgAUIY43Wq4W5WVHpL+gmEl8/0WY/FfBGPLQq4lIphdxmVfkf0ck4jq9fvwaV+Ou//muoWVgcCNGN5k9SkLVUZTTyN6gQ27bYvl2j/jD8hvth6f2npAgDu29aCctBwbR1GGcAkk4WZZ0D4CYRfRjz0yhBmoLC5qhkpSRGVX8/W8CoC+3T+5ejyjOSuio0avv27MgNSmSztYY4kkDZ9JSpnlWvhyqlvsCIgTsA0JOaL2013wL9RxzGhjZNQyPwRpln2rZBBoLbkAJpcFTLVeo5ew0C6DWf85nn8wuvcRx/KdUZbWKvrjqEdZyJ29tbrPyvf/3rk5OTcRx//etfU088aPhs1eQrIyY8fBI+P4T6r6Kst5PjjdgbDqWLqr1ADKJH2KrGoAltjqN0DuoWa92cQzbIWI/zH/aSh9DK3pqMG5urH6K9w7gBJYCgeNJo9+hh856pptWPSm9mFaVE52GpXuD+Pdm1Tg3uchhkD/xupz+lxFCrqlwXRDDC/0YtVRA1SOVg7+61NI4jrYc8SiDn/ObNm7nmHE80zZb+BigLXzMFx/0vfLWfegM+9D6IiA7bRhCHQ7OxsXF9ff3q1SvwhaOjo9evXzdCEA2tDyqSrGrvTSZ3RcN+WrU8qLV6SI0BqYiNG5qxnNklRQkBFwya/27hHtRvI6WElLC4fd/j+47ql4SOTylBv7U8ORNjBIe7ApaKZsvHwy57UUMHxKgP7Hpe0e8f1KXCb/ARKmqpMijx63OSniauovQv1YkNy0PBhpeCPxVVLKFisdedWI7Hx8csOG/e3d3NojVZ+1DZ24lgvrOz8+2336ZAnbb/82d90Lgsn9egPOGgts2N+ho4+XF/f7+9vb1YLPb29mAKr62tffXVV1TGEejkAK+UwNtNKQ3DAF/BZt0b5sLqeEJS8GM6UXr9KQ4uAp1CJYZTdg6xOV0TjWpFDRv5IhBxRtSCZTU2iN7mGNymIKJLtr/+iDVuCtUgg+bdlJ+Z3mmVY4+IIrCsVpL23b2qlmZWYAjjckjB232nyQDiS3P3qt6oVBbw1KOKvBea08D9ZPWLLerGhaL1wD4ca1R7Va4uHra/8PX5TFJVhSQ6yVm+QbMdEC/oBRym3/3udwA3VZW+BibyU5QbH3RUIbK9lqhHizizDpNHDV8bAl/YNs6a0nFxEpWmE+OJB4m21WEs4j5Ryzg/aafBI/5Guxx9aBwXVZdlt3maU6hKIztrygc5eM9iLB+8GoYM8TbGXzuQtydaAwiQgjNqu0EMwH6hIPyRYRjW1taoVUL4qlpOwKdxcSkRs78oixFmvZhVLz4qg23EflAa6S/XoJ8X0Byw4qIkG6EuDCuQy83NTcr8CIcPDw83NjaIXaaaVkYy0LoBUTDsHLfNAeao9KPj2RhXpYBmF9HzUnDdcgDDiX4G5TBLaMpgM13EAe1Dg+qqDvM1FPrwjU1obm0TnMO8bjudJdSBVEXEFp3ofjQhd+8FGQWfdWLX8zg5hFwWxyiUVbCgnZBGFYg8aSP0Hm1SVJJP0OOfQVEuLi4894O9eHx83NvbQ/tSu8wpoltYzpnW99guvo7os6ps/y+XzlLKTwKanwJ4fra2bScainV+fk53yel0urW1xV3SHYlg2YBIrw4tt7e3lM/b1DqRk6RmapgqlEP9ja2Vc0WItWn29hny04k5bRi9lQMawBoR55mnnEPK1CJltZpFsHdrIUtYUrjtr0iB/xb91KSkZTz8S830yEIJaqgMKSqcSuq2V0MvcCuweAaGUDTni9jbwZ70gdPkkBGiWUppd3f34uICBYRmYbM4b1mu2t3dnatMvbZJLrLBVK5TQ9Vr+iS79tmX31B/gW6XUqL3bFZRNgeRYVCg1gRrx8fHdrR9lzWUKXqbs/KTo1ggPsH2r/3LVgw06yTDy1EyvKmWp/w5iGcYBlfkoZwwQBwGL3Gv8VxZSZEmZNh9bz7SOXS5Zhf52eDU8PMdBotYQj6flqqoQUtoJpyDOxsdd//s6zjvUEO6KOcM7cEHhoib04hmhSDCtwC9Ydl78VlJIyF/znzy/ka5t0HsKh/OJELtGHqt/SWvnwT02WeMWtsYVQ3L2tnZgbJFe2+4MCh52xo7/rHM0uuOLBo4LAF5jgtq/bqqUZO2vDaLvuH81Nvz29CXNQzEtrUdVIs4qp7LtxQVdkQlvVZx731UHBtZlG1zLeJjGKUyDAORNUvta9o+WHX5zCTB8jmkKmqIF4tSjl4i3jCqNQ1S4sQjaQtbXgD5QbyIUgp20jYaG8jtGTkhL4r28aGy4KLLxjCC5+c06Ke//1kN2qvRXA7zVaEe0ucbDbSzs2OWMf5lE/oScs68IkY3rTziHUczwT04nH+2VdY61qwLTWDyVzvCSAGvSQHZtgW0yUtBQXqlrLdSUJlVKXhf9tmnqoBGn1j/yaLpUxExwokG1iTBERZ6r5LdFfde9NnwB9NTrc/627LlEJjaT7Ab07YterQXox4xIDDgPRAYSinQo4wAIgymgLUi+Ve9fk7efu71ZOfiH7KGVxQFLkUxB6BXKYX+huAXOCtWUaOy7UMoGE+Kh5D+QaMmS4jJrANi5rfRy3ucQpcYFs7C14srGffMOgaFgRx7p1NK3j/fUhuqOmOwnEMGIToS9al7Gn0P+7he5ywHugTaXpJewElwsFwC6OYj2jTNJHTUjkIwBCZXCTT7RnXVeDuDauSzcGXqXZtANPENW1OQebbnWhRAt21LN4ekhmq1VvcjcvBef94B9SN8XkA/+wH/jJnG572+vh5FqWzV/6OE4MDfMdXMDbY5yk1WZLDUNL6sBkAldPrMIlX54iWwZp6pRitOizt/akR8KQGaebYuMd89ir7uG7aU/LRwupNnv+dlqxotmsO+Z98+PiX4ONFVA2bkfR2fNjlqQz3+GLrClIB/GYO03u0Dn9VJDbSji1iYjo5GJ2BwIXijWis6X+QwWIs7iUcrnpz/Hxr0Mybeh89f6a+BCIiSA5ADTuNPnSjxFohBVYI+jmRuWB0nzbmCNwPVi9ZJIYeZnsYlUTLM6/H2F4HkzdNsvpWf0UdvNhqlhKDYTqHNq5c4HphPDUV66oOini0ifZjGYgDOqnEUCSiro4SPdzx4TejsEHmrGKgh1KE7jnHT0FZFiL4siZVGeXl08O7uLqPo7HtUef/INB0P+DnOR7RJsVmIhj59ztdMT6Mgv+2XonhblkFMHOBuSNS0X+Q5k9IV3uwxFOUYqy+KnIoaeDzb9RLm5FVhxd6GKOXN07qf+NjRDkal/tMzh1JDZ5mjIq9Kz44q+XhmH/LTtHuU8hTQjGeL6ZVJoYtgRIWjno76L30yOCE+u1c7K8mZdD7jltvz4Vvmar/ve7DVQuLtWdZaATJHYe85Z6L7cRxp2DaOIz2CTPCzOPpBPrVdf8mrwA96tpS8HGFE6gbJq0GV7PafamglXMWIwxykAHRbQKuCJAufw1j/qQTkqA0TW2y/nvkMLKjv3wcgy4uKcpCDSxotRg0wuxVMDOOikk7BOUuh0VcK+qMGJ9WL7C+yobAHEq2zP9U/7Z+TVShitVeV/ItW1YgBUp6CL94E0jRRdq+pr45oUZxEukT0y+WS/jyYIFtOCjgJVCi446jATqS93hBGmf2cOMZFRn5+mhf/6cuGtQjv9TCatbU12/eFBrskJtArNPauGFTyL+1ccn0jkTm0hcIpLE9TJr6fRvPlLWpWk1FEvP3PliAaSvTxVA3hexWqRyl/dpF4BiK0NIZUkI+BP9WEjJ/VoTW06Ui+T99hCe0gefk2/I0pwE8+OXY/Rs1CTsEdsmbpwqjPQc1joJATxbvEuaoAy7qw73v6wnqteDPBPlJun+Ffq0EfHh6erCAvlqAJVUGDhklubGzQaoKJT5T8kXl/dhGEYAw0nBqwuiqnDSls1AbW+i8a/a7rWs2PyuIHLTSfNAWAMIeSgyQnoYTwyHo6npZnEV4M7XlFOxV/GUWtKsGYxQiJqt3r0IcZ0lyWs2pgwbL1bDsdGDghlEKp+/C09i0eYH/WJn6i5kJVLXBrrUTljbq592FEeSmFAbLmjA8a9cln3759S/iPEKPLDIElZUPyU8f0s69nfy3lcxGoFyiHCLrTYOOc8xdffLG6ukoh/BjS2QiTK9GygjsewzRku4AmYo3ioUXhroE5FrVUEdofLTXC14SEvv8aQQP/199SQsARleUzO/7TepVSnuYwPzXcz1bSV3AQ1oowNSoJ8ulGNCqw8cvyF2+vEYUlftASVoUhdKolsmjaw/Yd9n3vXuxGjrx9JGXiqKCqWrkql91+RSkF4x4hv2fr9he+frZoLj31k5iLQJN5uh25AafR5ol6TLaq+/EOeXWWKgMH6reZi/5fCpS5FOLlHJIrNqlZsXCUBp8lfzY9RfujwmvUqa95mpiJr/EpPOQr2Czkz4EpEeD0OYnHqco18nWSolK7xf7B6SVrU8f+WdM+m9AgbQzo2DiO9PW0mpiqLT8QUqMc1TKMK0ZYO03BJJ/H/cdWe23bXlxcuAs9ks0EixJCN2MXnxrtuLDP/vp5AfWWOJsMPMafzAxyXssbn1TDblTF0sZqukAC+Y4uVBXLrlGXIntUvuk2dHizMragRxDUMuQ3N4GAYvXZhsp6/zJ/Aic9s+/pacZr/Pnc3afmrIYwyEC9FbOvUxW0+dh4BfylFJuXAO6OoejH0swhWaqrilfMgGsKp6KoFbJvxrnAoubOeAIppbOzMx7k8vKS4iRbpGUYlzOGrtP/2tfnQWbbHbCGrFEPa2trR0dHnYrRPBee9Y2gMTG+myBUVYeN42hZBwS1Tm3EPbPrE3fdMbXtSA7hnrfce2nORw7wTRGvr1FOxReJ+c+oBa35cnChovSPn2M/5IB31qdQSwo5p3jbBumqMky+ActZCoIbl8VrgpaFdJxS6tXBK1K5rSPoUMLLRzcKd86ZONg3SascvoXqEXI3hqjMZeYeWPn44L8gi5895L+kQUf1Gubw8RuGhlf1jYcV75twFDKIwFLFq8ih5qHRtNmkZkyGIatK8n1a7IPaE81yAIx01qf9EaIFtGA9Mx/+TZHrnDUW9llAHW13lHVbZN+VfxlFPIpmCljvp46Eb69/Wlk2ampbCvV0UUajZehDs8gcxt652gR+bRUs5VNK8LRU7zFSmkRRWX0h5/P5/v7+/v4+bXKjp+tGbsMwuINu0iiYEnqs/qtenxFQPy0/tCoYaNuWZrAICo0lmHz8bCcadY0bVbRvvWWU1E6qYZ36NFNqmbCSs4rN8iXqU66hJYBvZ41KoFmMgdifJMrRBx1CN44coGZ722ywAxerLv7KN0ZvO4UeJNaaraq7cmAkpaAszbGwmkiBVGDhrnJsHI7Ew+waYl+K8wP1rAZk0FkopDD2F8FXtn13ELzQuHL2cXt7Gw5eG9rCZTUvyYEf/FkpjKv9TMt+PpOEOA6BDOv+XsRxWZ1hSmh/VYN3lUOz1qQm6kVc4F5FPLVWW5xo/tLTQDULMDcxLD7Ss7jb+41SdIBZQklxCrnQZ58dFdvG33x6hqPwxYWOXNhPtyEJYY1wWPqEGAVYY+3ALxeatu1LRQMSD/OoRhJxR5qnkxqLUJRhGOCeWzF5p2KMgUjM53O6fZNEtBPchmKvVnVLpZTpdErtcv7Eif8LX5/3QdHSxs+5S3pFgHi5/XhR22krsyTN1GqQVBZsUcIgNmvEpdp1EBVZ/iKebM3UqzAjHoAc8jf5aaLIGtfvNBpVA0EkpiRsQO0A5BCoWXXZ4qenIVT8VApeVxPYTDlnlE0O4aMvEp1OOwNjSLo+2yyrSdQenRqap9RSPx0bwZ/cBmuhgYsppfX1dfDOlBLQTa2Vqg9sHf7e8fHxKI6bv+gZOD2O4+3tbVzDn7PyzxRn/PlncVBfjqMA/89uDd2WG/UCTkrJeFc4Ri62ioc4KWYsIdFn37QEYKJVxZzFy7YPObMKfBbK+NWqv4N/E+1O/cQLjCrnsyv42aW0DeWXfsxnYVC8VA6xcPokevAyWt3Gi+QAMNsBiN/oD6an3XWS2B780sYdZ2AYBlMc2f2macitu1UHyohqR4/vRhK8R044lVLgklrRfFbYfvn1S3Q7F7KQyV2GYQNeKW4FeqjV1SiIirv3JvmDVaQbOwBWAKPI3tbH7Ggbqr2ibFWN4bGQPXuK8WnvhqwxoZ8VUG/es+v4ys8E1HeYP3Ew0lOWcfyrBTTq1/h1TaAkR+wsBXCjBPzfH+xVP+R7bjQdNMuTbgLTtBE1qes65vbmkKm3O4EdjwE+AYkn9XBl5KTve1Lz9tftV/yCsP3c6zMCauAdxxFL3Ybea9fX14Mo9K04lARx9qaLetF4v60UXX1mOciB9hH1hJfYysnG3YyyrB6i3pj0VGJ8V5xyC42DFRugVqNY7AN4M1JwCVo1J/M17UXkp4ykEsg0duNKiP2bp1TAHBo5OZzKAehuAocmrsmgljJN00DmqAK5nB+2zeGQuyFhFhybFXomxQksNSVy4zhyWccn9PxO8vUx/fimLEIN7c0+VRzx9Uy5xDf/kgblBHjhktqlOqZp1Hu3eZpwGzUQsVOjf3+xc7VJfmcKkXsjgDfexqh6MWN+KRB1HTY5H52V2YtP7lcMaywr1vG+sbhkWZ5i+XOvLED32YqnkM4oYofFl8+D3xPv07pgGVpwWRN7batqjOKC+yJGAN2IChPveaqlFATRxhp93HUdgRGFuxjurAaLm5ubVH2gpNz0YFRRwC8I2F/yej7lI8n+Zvl5tsXjOE7ULhX9igtVlMmczWbw4bPgt17tEtrQy68LA1ZKcJxtAuzSNQFArcI14+9TIEy0YezGGGYA56edYaIC80W8kRZx/muXLnoI49Psl1evBEDHJtJviIjBqFp4f3X+BBMoIVtribRqb0IrHoeVSYn4GhK8Xo0+kPabpqHNoA+k+ciAUCmcUhL0fptxTYzqwcHBxcVF27YIelW+oIqmnX8GP/r09cxT+nEdPvs+HylGxSVNU2jblmnb0XfMavKWglfahKZwjSoEeML50znb/iEa3PQJWN1psAEvJw74r/NG5SkNNIcE9y+vjjVofgqIREX46RKXz6FRlukc3O4/+/pU4zZPWz755YMXdXyjZDo7lbSPZtmhtp2yNzCE95lDxZKzlF3X0YnEyFSR22oVMJlMVldXaegAHwoGvisp0GXRJ/lXvUpcwSjpdnrodlIVxJHgQrNienDkkRiex2FjCWgLH1mqOVsKGX+fcntgWePS4h5HUWvVXySJ0ZNCHYi/1zJnIYgK0m+OUuJ7szf5TEDjFaKdbZ4mzePZeCZ8vrfySVdROxtZ7q/fHEGSZ1QHhy9FzF3793aCkR67IsQMSfUOE3VjHZX1ceuDJGjWFSNFyIzntLuku6qBP0R4mnn5yv9a6dza2ir95/oxW4lyN5jprDoej9LKwb8u6tfgBEncbMdSTWAMcRCrYqMxsPLizQxipj2zp9xAo/YKcWs/+0S/vBZNAL2jCsxPEYBnrxTk+7MbEL/3LzFzKRySoiCpqLA23lXcdQu6vyi6oQilCwfiGeZJLV5+rkazy5J40OYVWIP6oOL4IZe9ZooiteR3/pKn/uzr8vLyJ1LCszXNCjuSylkc1LNYt7e3PHkbeqyhI8EdWvVPLKLfFk0QtDKwd2h3yksQFa3Do2eSkYMDl5/a06jkLMdj4LA9U9vx/RbQGsa3peDPPVsubmkUJOmvsEGMe2+kojwd4h0vWALUYDGKEEcTKDVeW/+yUWp0DBlL/+wvjTqejVuqoyUJT65j9ymppWs8QqUU4i2nG8zo8xP9/zPuPMLP8kH9JIDt8/mccfAlkDvTU/uVlf6JfIWiDqBO01lnF6HNdlP8kbhtRa94h1GN/SV+nqG4mDE3fBONbHS2mlCk8Ux/l0/ot5/ehsWrCblT/4DW4WZGvXzxrNq9RomMpJ3mg3Gz0BpD4NUnHTybeMcSKcC3KZzP6IP6ePgOc6gUANejnpMUPA0cl6Fx9qBRIeNTpsS/9vX57naN+BM5Z5AmGvrc3997zwBpe3UhbNTm3ephDEwzbr0NxZNVExZ9iL24Y0jA1MCNsLH71MJGG+c/RakaRaGI3kIOLp21ZhEKES/oi/i5aqAqez8sgnGT/JuiuD6Co9avftLylBjqx0EKR5XD+3hYK9tLiXcYJZVviRoOqeW1srJC3rwqZh3VptSqpyizbaITaIAXkAgYYwuGZdX7c3nO+KTPFjz9HA4aN9JDhVuNYyKPDCTrUMlFZ1X5riLArwnpmU+tZPmE04AK8bJGN2sMTI5napXNs+a2RD4zjtbQFjVL+fi5nMdfYp4MIfGqwRccQ8NBG+VnKjDe5xiaODx7OkvqZ19+onj8vETW00nhb1LkWmuN2UirfFRpq7bojYYDjqoncfUE30Kx2qCJLtwqbEz7S392JT/zXPE/XoJRrXw4JdzWfD6nGalrWN1j108+qNqOg0h2gSNlBzRaZxa0DX0Z4koZT4lmqAnlV8926FkcPTwl86Yg5f66ZxLgp7AP8Owr+lB38emi1eCt2oBa/fAbJ2ZToBCkp4rfMm2laK2Ms2RpTgENGNRf18YnLqbvtg8d/Hx6UY30Mioa2WP94sCIxbEA2GAiwSjXola3fAs6tQl53V94fXoIf8kHddbLVs+GyXJAxbSbg5bQ6jwqp1Y9Y6scJlttf2kT8tG2dOyifd8cBgc+8/m8Z5wHf/azLuyzl6XNGuhT+Ys3WUL5gDX3n/2KZ7o8fYLORiMYKzT81Y2yqZbpCEWPIfxKCm6M5zswt6Idn0KBOWfPeOjCOF2XktuvcA91iPS+ASosTBhNwRXxcfrXvv4/1JfgjfayxyMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224 at 0x7F6FE995E128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vk8weceQ_9EV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img = image.img_to_array(img)\n",
        "img = np.expand_dims(img, axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlUTIG3X_9MF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c0173368-acaa-48eb-92f8-905241ef9a08"
      },
      "source": [
        "img = preprocess_input(img)\n",
        "pred = model.predict(img)\n",
        "if pred[0][0]> pred[0][1]:\n",
        "  print('Congratulation Free from Pneumonia')\n",
        "else:\n",
        "  print('Sorry , You have Pneumonia. \\nPlease consult Doctor as soon as')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sorry , You have Pneumonia. \n",
            "Please consult Doctor as soon as\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srKr6aGv_9Jj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        },
        "outputId": "f4ac47b4-a5e1-4c99-b0f8-41bd50ed900d"
      },
      "source": [
        "for i in os.listdir(loc+'/val'):\n",
        "  if i != '.DS_Store':\n",
        "    \n",
        "    for j in os.listdir(loc+'/val/'+i):\n",
        "      if j != '.DS_Store':\n",
        "        \n",
        "        img1 = image.load_img(loc+'/val/'+i+'/'+j, target_size=(224, 224))\n",
        "        \n",
        "        x = image.img_to_array(img1)\n",
        "        \n",
        "        x = np.expand_dims(x, axis=0)\n",
        "        \n",
        "        image_data = preprocess_input(x)\n",
        "        classes = model.predict(image_data)\n",
        "        if classes[0][0]> classes[0][1]:\n",
        "            print(i, 'Congratulation Free from Pneumonia\\n')\n",
        "        else:\n",
        "            print(i, 'Sorry, You have Pneumonia. \\nPlease consult Doctor as soon as\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "PNEUMONIA Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "NORMAL Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n",
            "NORMAL Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n",
            "NORMAL Sorry, You have Pneumonia. \n",
            "Please consult Doctor as soon as\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n",
            "NORMAL Congratulation Free from Pneumonia\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uluWNv6L_9Hj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "name  = 'Pneumonia_model_restnet_.h5'\n",
        "\n",
        "path = loc+name\n",
        "\n",
        "\n",
        "model.save(path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlCkD-22H1BY",
        "colab_type": "text"
      },
      "source": [
        "# Manual NN\n",
        "\n",
        "### google teachable machine learning model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjygcqPNIMkU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Conv2D, Input, ReLU, Dense, Flatten\n",
        "from keras.layers import BatchNormalization\n",
        "from keras.activations import relu\n",
        "from keras.layers import DepthwiseConv2D\n",
        "from keras.layers import ZeroPadding2D\n",
        "from tensorflow.keras import Sequential\n",
        "from keras.layers.merge import Add\n",
        "from keras.layers import GlobalAveragePooling2D\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Model\n",
        "from keras.optimizers import Adam\n",
        "from keras.preprocessing.image import image\n",
        "from keras.applications.inception_v3 import preprocess_input"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YuCOue3cYHQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_img = Input(shape=(224,224,3), name='Input')\n",
        "\n",
        "x = ZeroPadding2D(padding =([ 0, 1 ], [ 0, 1 ]),  name='ZeroPadding2D1',  trainable=True)(input_img)\n",
        "\n",
        "x = Conv2D(activation='linear',bias_initializer='zeros', data_format='channels_last', filters=16, strides=(2,2),  padding='valid', use_bias=False, kernel_size = (3,3), dilation_rate=(1,1), trainable = True, name='sequential_5/model2/Conv1')(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=8, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=48, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = ZeroPadding2D(padding =([ 0, 1 ], [ 0, 1 ]),  name='ZeroPadding2D2',  trainable=True)(x)\n",
        "\n",
        "x = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='valid', strides=(2,2), trainable=True, use_bias=False)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=8, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=48, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=8, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "\n",
        "x = Add()([x, x1])\n",
        "\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=48, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = ZeroPadding2D(padding =([ 0, 1 ], [ 0, 1 ]),  name='ZeroPadding2D3',  trainable=True)(x)\n",
        "\n",
        "x = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='valid', strides=(2,2), trainable=True, use_bias=False)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=16, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "\n",
        "\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=96, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=16, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "\n",
        "x = Add()([x, x1])\n",
        "\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=96, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=16, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "\n",
        "x = Add()([x, x1])\n",
        "\n",
        "\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=96, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = ZeroPadding2D(padding =([ 0, 1 ], [ 0, 1 ]),  name='ZeroPadding2D4',  trainable=True)(x)\n",
        "\n",
        "x = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='valid', strides=(2,2), trainable=True, use_bias=False)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=24, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=144, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=24, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "\n",
        "x = Add()([x, x1])\n",
        "\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=144, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=24, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "\n",
        "x = Add()([x, x1])\n",
        "\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=144, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=24, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "\n",
        "x = Add()([x, x1])\n",
        "\n",
        "\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=144, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='valid', strides=(1,1), trainable=True, use_bias=False)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=32, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=192, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=32, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "\n",
        "\n",
        "x = Add()([x, x1])\n",
        "\n",
        "\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=192, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=32, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "\n",
        "\n",
        "x = Add()([x, x1])\n",
        "\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=192, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = ZeroPadding2D(padding =([ 0, 1 ], [ 0, 1 ]),  name='ZeroPadding2D5',  trainable=True)(x)\n",
        "\n",
        "x = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='valid', strides=(2,2), trainable=True, use_bias=False)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=56, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "\n",
        "x = BatchNormalization( axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x)\n",
        "\n",
        "\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=336, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=56, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "\n",
        "\n",
        "x = Add()([x, x1])\n",
        "\n",
        "\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=336, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = DepthwiseConv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), trainable=True, use_bias=False)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "x1 = ReLU(max_value=6, trainable=True)(x1)\n",
        "x1 = Conv2D(activation='linear', bias_initializer='zeros', data_format='channels_last', filters=56, strides=(1,1),  padding='same', use_bias=False, kernel_size = (1,1), dilation_rate=(1,1), trainable = True)(x1)\n",
        "x1 = BatchNormalization(axis=3, momentum=0.999, epsilon=0.001, center=True, scale = True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', trainable=True)(x1)\n",
        "\n",
        "\n",
        "x = Add()([x, x1])\n",
        "\n",
        "\n",
        "x = Conv2D(filters=336, kernel_size=(1,1), strides=(1,1), padding='same', data_format='channels_last', dilation_rate=(1,1), activation='linear', use_bias=False, kernel_initializer='VarianceScaling',bias_initializer='zeros', trainable=True)(x)\n",
        "\n",
        "x = BatchNormalization(axis=2, beta_initializer='zeros', center='True', epsilon=0.001, gamma_initializer='ones', momentum=0.999, moving_mean_initializer='zeros', moving_variance_initializer='ones', scale=True, trainable=True)(x)\n",
        "\n",
        "x = ReLU(max_value=6, trainable=True)(x)\n",
        "\n",
        "x = DepthwiseConv2D(activation='linear', bias_initializer= 'zeros', data_format='channels_last', depth_multiplier=1, dilation_rate=(1,1), kernel_size=(3,3), padding='same', strides=(1,1), use_bias=False, trainable=True)(x)\n",
        "\n",
        "x = BatchNormalization(axis=2, beta_initializer='zeros', center='True', epsilon=0.001, gamma_initializer='ones', momentum=0.999, moving_mean_initializer='zeros', moving_variance_initializer='ones', scale=True, trainable=True)(x)\n",
        "\n",
        "x= ReLU(max_value=6)(x)\n",
        "\n",
        "x = Conv2D(filters=112, kernel_size=(1,1), strides=(1,1), padding='same', data_format='channels_last', dilation_rate=(1,1), activation='linear', use_bias=False, kernel_initializer='VarianceScaling',bias_initializer='zeros', trainable=True)(x)\n",
        "\n",
        "\n",
        "x = BatchNormalization(axis=2, beta_initializer='zeros', center=True, epsilon=0.001, gamma_initializer='ones', momentum=0.999, moving_variance_initializer='ones', scale=True, trainable=True)(x)\n",
        "\n",
        "x = Conv2D(activation='linear', bias_initializer='zero', data_format='channels_last', dilation_rate=(1,1), filters=1280, kernel_size=(1,1), padding='same', strides=(1,1), trainable=True, use_bias=False)(x)\n",
        "\n",
        "x = BatchNormalization(axis=2, beta_initializer='zeros', center=True, epsilon=0.001, gamma_initializer='ones', momentum=0.999, moving_variance_initializer='ones', scale=True, trainable=True)(x)\n",
        "\n",
        "x= ReLU(max_value=6)(x)\n",
        "\n",
        "x = GlobalAveragePooling2D(data_format = 'channels_last',trainable=True)(x)\n",
        "\n",
        "x = Dense(activation='relu', units=100, batch_input_shape=(None, 1280), bias_initializer='Zeros', trainable=True, use_bias=True)(x)\n",
        "\n",
        "x = Dense(activation='softmax',  bias_initializer='zeros', units=2, trainable=True, use_bias=False)(x)\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "betFeI3w67A_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0783ed4f-2835-4041-d0ff-0f3fcabd04b5"
      },
      "source": [
        "model = Model(input = input_img, output=x)\n",
        "model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "Input (InputLayer)              (None, 224, 224, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "ZeroPadding2D1 (ZeroPadding2D)  (None, 225, 225, 3)  0           Input[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "sequential_5/model2/Conv1 (Conv (None, 112, 112, 16) 432         ZeroPadding2D1[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 112, 112, 16) 64          sequential_5/model2/Conv1[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_1 (ReLU)                  (None, 112, 112, 16) 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_1 (DepthwiseCo (None, 112, 112, 16) 144         re_lu_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 112, 112, 16) 64          depthwise_conv2d_1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_2 (ReLU)                  (None, 112, 112, 16) 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 112, 112, 8)  128         re_lu_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 112, 112, 8)  32          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 112, 112, 48) 384         batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 112, 112, 48) 192         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_3 (ReLU)                  (None, 112, 112, 48) 0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "ZeroPadding2D2 (ZeroPadding2D)  (None, 113, 113, 48) 0           re_lu_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_2 (DepthwiseCo (None, 56, 56, 48)   432         ZeroPadding2D2[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 56, 56, 48)   192         depthwise_conv2d_2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_4 (ReLU)                  (None, 56, 56, 48)   0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 56, 56, 8)    384         re_lu_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 56, 56, 8)    32          conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 56, 56, 48)   384         batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 56, 56, 48)   192         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_5 (ReLU)                  (None, 56, 56, 48)   0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_3 (DepthwiseCo (None, 56, 56, 48)   432         re_lu_5[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 56, 56, 48)   192         depthwise_conv2d_3[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_6 (ReLU)                  (None, 56, 56, 48)   0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 56, 56, 8)    384         re_lu_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 56, 56, 8)    32          conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 56, 56, 8)    0           batch_normalization_6[0][0]      \n",
            "                                                                 batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 56, 56, 48)   384         add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 56, 56, 48)   192         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_7 (ReLU)                  (None, 56, 56, 48)   0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "ZeroPadding2D3 (ZeroPadding2D)  (None, 57, 57, 48)   0           re_lu_7[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_4 (DepthwiseCo (None, 28, 28, 48)   432         ZeroPadding2D3[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 28, 28, 48)   192         depthwise_conv2d_4[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_8 (ReLU)                  (None, 28, 28, 48)   0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 28, 28, 16)   768         re_lu_8[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 28, 28, 16)   64          conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 28, 28, 96)   1536        batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 28, 28, 96)   384         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_9 (ReLU)                  (None, 28, 28, 96)   0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_5 (DepthwiseCo (None, 28, 28, 96)   864         re_lu_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 28, 28, 96)   384         depthwise_conv2d_5[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_10 (ReLU)                 (None, 28, 28, 96)   0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 28, 28, 16)   1536        re_lu_10[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 28, 28, 16)   64          conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 28, 28, 16)   0           batch_normalization_12[0][0]     \n",
            "                                                                 batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 28, 28, 96)   1536        add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 28, 28, 96)   384         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_11 (ReLU)                 (None, 28, 28, 96)   0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_6 (DepthwiseCo (None, 28, 28, 96)   864         re_lu_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 28, 28, 96)   384         depthwise_conv2d_6[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_12 (ReLU)                 (None, 28, 28, 96)   0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 28, 28, 16)   1536        re_lu_12[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 28, 28, 16)   64          conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 28, 28, 16)   0           add_2[0][0]                      \n",
            "                                                                 batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 28, 28, 96)   1536        add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 28, 28, 96)   384         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_13 (ReLU)                 (None, 28, 28, 96)   0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "ZeroPadding2D4 (ZeroPadding2D)  (None, 29, 29, 96)   0           re_lu_13[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_7 (DepthwiseCo (None, 14, 14, 96)   864         ZeroPadding2D4[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 14, 14, 96)   384         depthwise_conv2d_7[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_14 (ReLU)                 (None, 14, 14, 96)   0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 14, 14, 24)   2304        re_lu_14[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 14, 14, 24)   96          conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 14, 14, 144)  3456        batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 14, 14, 144)  576         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_15 (ReLU)                 (None, 14, 14, 144)  0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_8 (DepthwiseCo (None, 14, 14, 144)  1296        re_lu_15[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 14, 14, 144)  576         depthwise_conv2d_8[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_16 (ReLU)                 (None, 14, 14, 144)  0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 14, 14, 24)   3456        re_lu_16[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 14, 14, 24)   96          conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 14, 14, 24)   0           batch_normalization_21[0][0]     \n",
            "                                                                 batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 14, 14, 144)  3456        add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 14, 14, 144)  576         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_17 (ReLU)                 (None, 14, 14, 144)  0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_9 (DepthwiseCo (None, 14, 14, 144)  1296        re_lu_17[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 14, 14, 144)  576         depthwise_conv2d_9[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_18 (ReLU)                 (None, 14, 14, 144)  0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 14, 14, 24)   3456        re_lu_18[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 14, 14, 24)   96          conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 14, 14, 24)   0           add_4[0][0]                      \n",
            "                                                                 batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 14, 14, 144)  3456        add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 14, 14, 144)  576         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_19 (ReLU)                 (None, 14, 14, 144)  0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_10 (DepthwiseC (None, 14, 14, 144)  1296        re_lu_19[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 14, 14, 144)  576         depthwise_conv2d_10[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_20 (ReLU)                 (None, 14, 14, 144)  0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 14, 14, 24)   3456        re_lu_20[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 14, 14, 24)   96          conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 14, 14, 24)   0           add_5[0][0]                      \n",
            "                                                                 batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 14, 14, 144)  3456        add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 14, 14, 144)  576         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_21 (ReLU)                 (None, 14, 14, 144)  0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_11 (DepthwiseC (None, 12, 12, 144)  1296        re_lu_21[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 12, 12, 144)  576         depthwise_conv2d_11[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_22 (ReLU)                 (None, 12, 12, 144)  0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 12, 12, 32)   4608        re_lu_22[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 12, 12, 32)   128         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 12, 12, 192)  6144        batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 12, 12, 192)  768         conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_23 (ReLU)                 (None, 12, 12, 192)  0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_12 (DepthwiseC (None, 12, 12, 192)  1728        re_lu_23[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 12, 12, 192)  768         depthwise_conv2d_12[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_24 (ReLU)                 (None, 12, 12, 192)  0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 12, 12, 32)   6144        re_lu_24[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 12, 12, 32)   128         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 12, 12, 32)   0           batch_normalization_33[0][0]     \n",
            "                                                                 batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 12, 12, 192)  6144        add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 12, 12, 192)  768         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_25 (ReLU)                 (None, 12, 12, 192)  0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_13 (DepthwiseC (None, 12, 12, 192)  1728        re_lu_25[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 12, 12, 192)  768         depthwise_conv2d_13[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_26 (ReLU)                 (None, 12, 12, 192)  0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 12, 12, 32)   6144        re_lu_26[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 12, 12, 32)   128         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 12, 12, 32)   0           add_7[0][0]                      \n",
            "                                                                 batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 12, 12, 192)  6144        add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 12, 12, 192)  768         conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_27 (ReLU)                 (None, 12, 12, 192)  0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "ZeroPadding2D5 (ZeroPadding2D)  (None, 13, 13, 192)  0           re_lu_27[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_14 (DepthwiseC (None, 6, 6, 192)    1728        ZeroPadding2D5[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 6, 6, 192)    768         depthwise_conv2d_14[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_28 (ReLU)                 (None, 6, 6, 192)    0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 6, 6, 56)     10752       re_lu_28[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 6, 6, 56)     224         conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 6, 6, 336)    18816       batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 6, 6, 336)    1344        conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_29 (ReLU)                 (None, 6, 6, 336)    0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_15 (DepthwiseC (None, 6, 6, 336)    3024        re_lu_29[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 6, 6, 336)    1344        depthwise_conv2d_15[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_30 (ReLU)                 (None, 6, 6, 336)    0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 6, 6, 56)     18816       re_lu_30[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 6, 6, 56)     224         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 6, 6, 56)     0           batch_normalization_42[0][0]     \n",
            "                                                                 batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 6, 6, 336)    18816       add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 6, 6, 336)    1344        conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_31 (ReLU)                 (None, 6, 6, 336)    0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_16 (DepthwiseC (None, 6, 6, 336)    3024        re_lu_31[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 6, 6, 336)    1344        depthwise_conv2d_16[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_32 (ReLU)                 (None, 6, 6, 336)    0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 6, 6, 56)     18816       re_lu_32[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 6, 6, 56)     224         conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 6, 6, 56)     0           add_9[0][0]                      \n",
            "                                                                 batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 6, 6, 336)    18816       add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 6, 6, 336)    24          conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_33 (ReLU)                 (None, 6, 6, 336)    0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "depthwise_conv2d_17 (DepthwiseC (None, 6, 6, 336)    3024        re_lu_33[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 6, 6, 336)    24          depthwise_conv2d_17[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_34 (ReLU)                 (None, 6, 6, 336)    0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 6, 6, 112)    37632       re_lu_34[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 6, 6, 112)    24          conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 6, 6, 1280)   143360      batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 6, 6, 1280)   24          conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_35 (ReLU)                 (None, 6, 6, 1280)   0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 1280)         0           re_lu_35[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 100)          128100      global_average_pooling2d_1[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 2)            200         dense_1[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 530,348\n",
            "Trainable params: 520,348\n",
            "Non-trainable params: 10,000\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"In..., outputs=Tensor(\"de...)`\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2e39k9Zch4z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip=True)\n",
        "                                   \n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2tUm9-B6p11",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "ef5d3bca-13f4-46ab-ebec-775ff0048840"
      },
      "source": [
        "training_set = train_datagen.flow_from_directory(loc+'/train',\n",
        "                                                target_size=(224, 224),\n",
        "                                                batch_size=32,\n",
        "                                                class_mode='categorical')\n",
        "\n",
        "testing_set = test_datagen.flow_from_directory(loc+'/test',\n",
        "                                               target_size=(224, 224),\n",
        "                                               batch_size=32,\n",
        "                                               class_mode='categorical')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 5239 images belonging to 2 classes.\n",
            "Found 624 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZdC7NLc748U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "opt = Adam(lr=0.0001, decay=1e-5)\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer = opt, metrics=['accuracy'])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lkYjSb7t7keS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "outputId": "af0ff7db-396e-42ec-9b92-c44ea580c038"
      },
      "source": [
        "info = model.fit_generator(training_set, \n",
        "                           epochs=25, \n",
        "                           validation_data = testing_set,\n",
        "                           steps_per_epoch = len(training_set),\n",
        "                           validation_steps = len(testing_set),\n",
        "                           )"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            " 70/164 [===========>..................] - ETA: 14:16 - loss: 0.3989 - accuracy: 0.8031"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:616: UserWarning: The input 1 could not be retrieved. It could be because a worker has died.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " 79/164 [=============>................] - ETA: 13:20 - loss: 0.3920 - accuracy: 0.8046"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:616: UserWarning: The input 47 could not be retrieved. It could be because a worker has died.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "136/164 [=======================>......] - ETA: 4:24 - loss: 0.3658 - accuracy: 0.8169"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:616: UserWarning: The input 122 could not be retrieved. It could be because a worker has died.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "164/164 [==============================] - 1633s 10s/step - loss: 0.3612 - accuracy: 0.8208 - val_loss: 0.6904 - val_accuracy: 0.6250\n",
            "Epoch 2/25\n",
            "164/164 [==============================] - 149s 907ms/step - loss: 0.3056 - accuracy: 0.8584 - val_loss: 0.6898 - val_accuracy: 0.6250\n",
            "Epoch 3/25\n",
            "164/164 [==============================] - 153s 933ms/step - loss: 0.2810 - accuracy: 0.8729 - val_loss: 0.6903 - val_accuracy: 0.6250\n",
            "Epoch 4/25\n",
            "164/164 [==============================] - 152s 930ms/step - loss: 0.2723 - accuracy: 0.8769 - val_loss: 0.6907 - val_accuracy: 0.6250\n",
            "Epoch 5/25\n",
            "164/164 [==============================] - 154s 936ms/step - loss: 0.2704 - accuracy: 0.8792 - val_loss: 0.6887 - val_accuracy: 0.6250\n",
            "Epoch 6/25\n",
            "164/164 [==============================] - 153s 936ms/step - loss: 0.2603 - accuracy: 0.8899 - val_loss: 0.6886 - val_accuracy: 0.6250\n",
            "Epoch 7/25\n",
            "164/164 [==============================] - 152s 925ms/step - loss: 0.2506 - accuracy: 0.8904 - val_loss: 0.6848 - val_accuracy: 0.6250\n",
            "Epoch 8/25\n",
            "164/164 [==============================] - 153s 934ms/step - loss: 0.2414 - accuracy: 0.8988 - val_loss: 0.6839 - val_accuracy: 0.6250\n",
            "Epoch 9/25\n",
            "164/164 [==============================] - 154s 942ms/step - loss: 0.2449 - accuracy: 0.8901 - val_loss: 0.6857 - val_accuracy: 0.6250\n",
            "Epoch 10/25\n",
            "164/164 [==============================] - 152s 929ms/step - loss: 0.2230 - accuracy: 0.9040 - val_loss: 0.6737 - val_accuracy: 0.6250\n",
            "Epoch 11/25\n",
            "164/164 [==============================] - 152s 927ms/step - loss: 0.2169 - accuracy: 0.9074 - val_loss: 0.6945 - val_accuracy: 0.6250\n",
            "Epoch 12/25\n",
            "164/164 [==============================] - 154s 938ms/step - loss: 0.2141 - accuracy: 0.9143 - val_loss: 0.6673 - val_accuracy: 0.6250\n",
            "Epoch 13/25\n",
            "164/164 [==============================] - 154s 937ms/step - loss: 0.1972 - accuracy: 0.9168 - val_loss: 0.6965 - val_accuracy: 0.6250\n",
            "Epoch 14/25\n",
            "164/164 [==============================] - 152s 930ms/step - loss: 0.1944 - accuracy: 0.9210 - val_loss: 0.6762 - val_accuracy: 0.6250\n",
            "Epoch 15/25\n",
            "164/164 [==============================] - 154s 938ms/step - loss: 0.1891 - accuracy: 0.9206 - val_loss: 0.6604 - val_accuracy: 0.6250\n",
            "Epoch 16/25\n",
            "164/164 [==============================] - 153s 935ms/step - loss: 0.1862 - accuracy: 0.9244 - val_loss: 0.6857 - val_accuracy: 0.6250\n",
            "Epoch 17/25\n",
            "164/164 [==============================] - 153s 933ms/step - loss: 0.1770 - accuracy: 0.9290 - val_loss: 0.6649 - val_accuracy: 0.6250\n",
            "Epoch 18/25\n",
            "164/164 [==============================] - 153s 935ms/step - loss: 0.1670 - accuracy: 0.9349 - val_loss: 0.6869 - val_accuracy: 0.6250\n",
            "Epoch 19/25\n",
            "164/164 [==============================] - 155s 942ms/step - loss: 0.1601 - accuracy: 0.9382 - val_loss: 0.6925 - val_accuracy: 0.6250\n",
            "Epoch 20/25\n",
            "164/164 [==============================] - 152s 926ms/step - loss: 0.1581 - accuracy: 0.9370 - val_loss: 0.7017 - val_accuracy: 0.6250\n",
            "Epoch 21/25\n",
            "164/164 [==============================] - 153s 936ms/step - loss: 0.1515 - accuracy: 0.9380 - val_loss: 0.8898 - val_accuracy: 0.6250\n",
            "Epoch 22/25\n",
            "164/164 [==============================] - 153s 932ms/step - loss: 0.1491 - accuracy: 0.9401 - val_loss: 0.6232 - val_accuracy: 0.6250\n",
            "Epoch 23/25\n",
            "164/164 [==============================] - 154s 939ms/step - loss: 0.1521 - accuracy: 0.9401 - val_loss: 0.6321 - val_accuracy: 0.6250\n",
            "Epoch 24/25\n",
            "164/164 [==============================] - 152s 925ms/step - loss: 0.1393 - accuracy: 0.9462 - val_loss: 0.8369 - val_accuracy: 0.6250\n",
            "Epoch 25/25\n",
            "164/164 [==============================] - 154s 936ms/step - loss: 0.1470 - accuracy: 0.9422 - val_loss: 0.7874 - val_accuracy: 0.6250\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXXYY153KLF5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "36062f10-a83e-47b9-cbf4-eff8d3de7652"
      },
      "source": [
        "for i in os.listdir(loc+'/val'):\n",
        "  if i != '.DS_Store':\n",
        "    \n",
        "    for j in os.listdir(loc+'/val/'+i):\n",
        "      if j != '.DS_Store':\n",
        "        \n",
        "        img1 = image.load_img(loc+'/val/'+i+'/'+j, target_size=(224, 224))\n",
        "        \n",
        "        x = image.img_to_array(img1)\n",
        "        \n",
        "        x = np.expand_dims(x, axis=0)\n",
        "        \n",
        "        image_data = preprocess_input(x)\n",
        "        classes = model.predict(image_data)\n",
        "        print(i, classes[0])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PNEUMONIA [0.23284408 0.76715595]\n",
            "PNEUMONIA [0.23170176 0.7682982 ]\n",
            "PNEUMONIA [0.23287757 0.76712245]\n",
            "PNEUMONIA [0.232384   0.76761603]\n",
            "PNEUMONIA [0.23256584 0.7674342 ]\n",
            "PNEUMONIA [0.23122728 0.7687727 ]\n",
            "PNEUMONIA [0.23316854 0.76683146]\n",
            "PNEUMONIA [0.23342638 0.76657367]\n",
            "NORMAL [0.23408565 0.7659144 ]\n",
            "NORMAL [0.23275615 0.7672438 ]\n",
            "NORMAL [0.2315217  0.76847833]\n",
            "NORMAL [0.23329152 0.7667085 ]\n",
            "NORMAL [0.23388569 0.7661143 ]\n",
            "NORMAL [0.23395818 0.7660418 ]\n",
            "NORMAL [0.23401417 0.76598585]\n",
            "NORMAL [0.2334296 0.7665704]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZvyJpiFemNA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "name  = 'Pneumonia_model_manual_.h5'\n",
        "\n",
        "path = loc+name\n",
        "\n",
        "\n",
        "model.save(path)"
      ],
      "execution_count": 88,
      "outputs": []
    }
  ]
}