Monkeypox_CA4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "oYC53ssJiz7P",
        "outputId": "63c2ddab-de5b-407c-e5e7-dd0243eba96a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!unzip -u \"/content/drive/MyDrive/AI for Diagnosis & Prognosis/Dataset/archive.zip\" -d \"/content/drive/MyDrive/AI for Diagnosis & Prognosis/Dataset/\""
      ],
      "metadata": {
        "id": "gb6sxnPKix9l"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, Dense, Flatten,Dropout\n",
        "from keras.models import Model\n",
        "from keras.applications.vgg19 import VGG19\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "dS8fvj7qjSS3"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "valid_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ],
      "metadata": {
        "id": "k2B-0AjNj0hW"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "training_set = train_datagen.flow_from_directory('/content/drive/MyDrive/AI for Diagnosis & Prognosis/Dataset/Fold1/Fold1/Fold1/Train',\n",
        "                                                 target_size = (224, 224),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'binary',subset=\"training\")\n",
        "test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/AI for Diagnosis & Prognosis/Dataset/Fold1/Fold1/Fold1/Test',\n",
        "                                            target_size = (224, 224),\n",
        "                                            batch_size = 32,\n",
        "                                            class_mode = 'binary')\n",
        "val_set = valid_datagen.flow_from_directory('/content/drive/MyDrive/AI for Diagnosis & Prognosis/Dataset/Fold1/Fold1/Fold1/Val',\n",
        "                                            target_size = (224, 224),\n",
        "                                            batch_size = 32,\n",
        "                                            class_mode = 'binary')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3SGAPw4Yj3CA",
        "outputId": "fbc6315a-1b2f-47fe-cc27-c031ed273091"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2142 images belonging to 2 classes.\n",
            "Found 45 images belonging to 2 classes.\n",
            "Found 254 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_y=training_set.classes\n",
        "test_y=test_set.classes\n",
        "val_y=val_set.classes"
      ],
      "metadata": {
        "id": "reQNiN6bkWym"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_set.class_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "o1jgRsEnk0Ht",
        "outputId": "21f2719c-a51e-4af2-b202-ad21b1796dc3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Monkeypox': 0, 'Others': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_y.shape,test_y.shape,val_y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "QRLrSRkIk1ms",
        "outputId": "7f76d7cd-6f23-49dc-f3af-b2c7ef10a0a8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2142,), (45,), (254,))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CUSTOMIZED CNN"
      ],
      "metadata": {
        "id": "fyc3w2Sy3eVG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import (Dense,Conv2D,AveragePooling2D,Flatten,Dropout, MaxPool2D)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32,(3,3), activation='relu', input_shape=(224,224,3)))\n",
        "model.add(MaxPool2D((2,2), strides = (2,2)))\n",
        "\n",
        "model.add(Conv2D(64,(3,3), activation='relu'))\n",
        "model.add(MaxPool2D((2,2), strides = (2,2)))\n",
        "\n",
        "\n",
        "model.add(Conv2D(128,(3,3), activation='relu'))\n",
        "model.add(MaxPool2D((2,2), strides = (2,2)))\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(64,activation='relu'))\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "Np_mAOqpT_Ha"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "  loss='binary_crossentropy',\n",
        "  optimizer=\"ADAM\",\n",
        "  metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "jBatxfRDlE-T"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Early stopping to avoid overfitting of model\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "early_stop=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=5)"
      ],
      "metadata": {
        "id": "AFKF09fhlI3A"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the model\n",
        "history = model.fit(\n",
        "   training_set,\n",
        "  validation_data=val_set,\n",
        "  epochs=20,\n",
        "  callbacks=[early_stop],\n",
        "  batch_size=32,shuffle=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xqLI17solJP-",
        "outputId": "b1555469-ad95-4f13-9ef2-4b49a4873a0c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "67/67 [==============================] - 341s 5s/step - loss: 0.7159 - accuracy: 0.6111 - val_loss: 0.6471 - val_accuracy: 0.5157\n",
            "Epoch 2/20\n",
            "67/67 [==============================] - 9s 132ms/step - loss: 0.6311 - accuracy: 0.6265 - val_loss: 0.6617 - val_accuracy: 0.4921\n",
            "Epoch 3/20\n",
            "67/67 [==============================] - 8s 124ms/step - loss: 0.5410 - accuracy: 0.7292 - val_loss: 1.0122 - val_accuracy: 0.4685\n",
            "Epoch 4/20\n",
            "67/67 [==============================] - 8s 121ms/step - loss: 0.4868 - accuracy: 0.7530 - val_loss: 0.8070 - val_accuracy: 0.5118\n",
            "Epoch 5/20\n",
            "67/67 [==============================] - 9s 131ms/step - loss: 0.4171 - accuracy: 0.8133 - val_loss: 1.3576 - val_accuracy: 0.4567\n",
            "Epoch 6/20\n",
            "67/67 [==============================] - 8s 119ms/step - loss: 0.3727 - accuracy: 0.8324 - val_loss: 0.9471 - val_accuracy: 0.5669\n",
            "Epoch 6: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracies\n",
        "\n",
        "plt.plot(history.history['accuracy'], label='train acc')\n",
        "plt.plot(history.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.savefig('vgg-acc-rps-1.png')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "GLwPiy6amusI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "080a40a2-f806-42b5-d39d-1aec4a5bc1e9"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loss\n",
        "plt.plot(history.history['loss'], label='train loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.savefig('vgg-loss-rps-1.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "HQ0bho9PmwaO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "outputId": "093a6557-f5e3-4509-ec8c-2fed3cbaf733"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = model.evaluate_generator(test_set)\n",
        "custom_accu = score[1]\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "metadata": {
        "id": "_4DcsqJvmwkT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "93df42ed-d227-45be-ad3e-30079fe21ef2"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-60-f6a0c1e48383>:1: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  score = model.evaluate_generator(test_set)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 2.256014108657837\n",
            "Test accuracy: 0.6666666865348816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#VGG19"
      ],
      "metadata": {
        "id": "kxRAhcvXnA2r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vgg19 = VGG19(input_shape=(224,224,3), weights='imagenet', include_top=False)"
      ],
      "metadata": {
        "id": "By5q7cBXmwmo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2e855526-3791-4348-e42a-8f1aa2cfb2a0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80134624/80134624 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in vgg19.layers:\n",
        "    layer.trainable = False"
      ],
      "metadata": {
        "id": "ep1HgSEXmwpD"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = Flatten()(vgg19.output)\n",
        "x=Dense(4096, activation='relu')(x)\n",
        "x=Dense(4096, activation='relu')(x)\n",
        "x=Dense(1000, activation='relu')(x)\n",
        "prediction = Dense(1, activation='sigmoid')(x)"
      ],
      "metadata": {
        "id": "-DwGjcLhmwrI"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = Model(inputs=vgg19.input, outputs=prediction)"
      ],
      "metadata": {
        "id": "j_oa1Honmwta"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1.summary()"
      ],
      "metadata": {
        "id": "pkEq_oommwvy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7e32e077-7926-4eac-9127-e4a9acd1d56f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\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_1 (Flatten)         (None, 25088)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 4096)              102764544 \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1000)              4097000   \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 1001      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 143,668,241\n",
            "Trainable params: 123,643,857\n",
            "Non-trainable params: 20,024,384\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1.compile(\n",
        "  loss='binary_crossentropy',\n",
        "  optimizer=\"adam\",\n",
        "  metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "0zqfANWTmwxv"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the model\n",
        "history1 = model1.fit(\n",
        " training_set,\n",
        "  validation_data=val_set,\n",
        "  epochs=50,\n",
        "  callbacks=[early_stop],\n",
        "  batch_size=32,shuffle=True)"
      ],
      "metadata": {
        "id": "JhWpKpR-mwzw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "54336ffd-bda4-4c15-8fe7-60dbdc44dbf0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "67/67 [==============================] - 26s 278ms/step - loss: 2.3679 - accuracy: 0.5882 - val_loss: 0.7894 - val_accuracy: 0.5118\n",
            "Epoch 2/50\n",
            "67/67 [==============================] - 13s 199ms/step - loss: 0.4556 - accuracy: 0.7908 - val_loss: 0.8293 - val_accuracy: 0.6260\n",
            "Epoch 3/50\n",
            "67/67 [==============================] - 13s 197ms/step - loss: 0.3048 - accuracy: 0.8739 - val_loss: 1.3466 - val_accuracy: 0.5433\n",
            "Epoch 4/50\n",
            "67/67 [==============================] - 14s 212ms/step - loss: 0.2491 - accuracy: 0.8889 - val_loss: 1.5249 - val_accuracy: 0.6220\n",
            "Epoch 5/50\n",
            "67/67 [==============================] - 13s 199ms/step - loss: 0.2650 - accuracy: 0.8805 - val_loss: 1.5319 - val_accuracy: 0.5157\n",
            "Epoch 6/50\n",
            "67/67 [==============================] - 14s 202ms/step - loss: 0.2360 - accuracy: 0.8978 - val_loss: 1.5757 - val_accuracy: 0.4843\n",
            "Epoch 6: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = model1.evaluate_generator(test_set)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "metadata": {
        "id": "-GtmZXO4mw15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "cb51ed08-ce19-4873-db3e-464d6b094330"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-3ec7817b7737>:1: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  score = model1.evaluate_generator(test_set)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.6662021279335022\n",
            "Test accuracy: 0.7333333492279053\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracies\n",
        "\n",
        "plt.plot(history1.history['accuracy'], label='train acc')\n",
        "plt.plot(history1.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "02E83_T2nWsq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "8bb9da0a-5e2c-4f41-c3d4-2e3fdf1ae2bd"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABWOklEQVR4nO3dd3SU1dqG8WsmvUNICC30DtKLYEEkiKAoVhSUYvcIR0+OesDe8bMgKig2LCiKBbABClEQEARBFKTXhBYSAqmkzcz3x4ZAhEACSd6Zyf1ba5aZTHsSYubOLs+2uVwuFyIiIiIWsVtdgIiIiFRtCiMiIiJiKYURERERsZTCiIiIiFhKYUREREQspTAiIiIillIYEREREUspjIiIiIilfK0uoDScTid79uwhLCwMm81mdTkiIiJSCi6Xi8zMTOrUqYPdXvL4h0eEkT179hAbG2t1GSIiInIGkpKSqFevXom3e0QYCQsLA8wXEx4ebnE1IiIiUhoZGRnExsYWvY+XxCPCyNGpmfDwcIURERERD3O6JRZawCoiIiKWUhgRERERSymMiIiIiKU8Ys1IaTgcDgoKCqwuQ0rJz88PHx8fq8sQERE34BVhJCsri127duFyuawuRUrJZrNRr149QkNDrS5FREQs5vFhxOFwsGvXLoKDg4mOjlZTNA/gcrlISUlh165dNGvWTCMkIiJVnMeHkYKCAlwuF9HR0QQFBVldjpRSdHQ0O3bsoKCgQGFERKSKO6MFrJMmTaJhw4YEBgbSvXt3li9fXuJ9CwoKeOqpp2jSpAmBgYG0b9+euXPnnnHBJdGIiGfRv5eIiBxV5jAyffp04uPjefzxx1m1ahXt27enX79+7N+//6T3f+SRR3jrrbd4/fXXWbduHXfddRdXXXUVf/zxx1kXLyIiIp6vzGFk/Pjx3H777YwcOZLWrVszefJkgoODmTJlyknvP3XqVB566CEGDBhA48aNufvuuxkwYAAvv/zyWRcvIiIinq9MYSQ/P5+VK1cSFxd37AnsduLi4li6dOlJH5OXl0dgYGCxzwUFBbF48eIzKFdK0rBhQyZMmGB1GSIiImVWpgWsqampOBwOYmJiin0+JiaGDRs2nPQx/fr1Y/z48Vx44YU0adKEhIQEZsyYgcPhKPF18vLyyMvLK7qekZFRljI9wkUXXUSHDh3KLUCsWLGCkJCQcnkuERGRylThHVhfffVVmjVrRsuWLfH392fUqFGMHDkSu73klx43bhwRERFFl9jY2Iou0y25XC4KCwtLdd/o6GiCg4MruCIREfEG+YVOtuzPZO7afUz6eQvxn6/mYHa+ZfWUKYxERUXh4+NDcnJysc8nJydTq1atkz4mOjqaWbNmkZ2dzc6dO9mwYQOhoaE0bty4xNcZO3Ys6enpRZekpKRS1+hyucjJL7TkUtqmayNGjGDhwoW8+uqr2Gw2bDYbO3bsYMGCBdhsNubMmUPnzp0JCAhg8eLFbN26lSuvvJKYmBhCQ0Pp2rUr8+fPL/ac/5ymsdlsvPvuu1x11VUEBwfTrFkzvvnmm1PWNXXqVLp06UJYWBi1atViyJAhJyxM/vvvv7n88ssJDw8nLCyMCy64gK1btxbdPmXKFNq0aUNAQAC1a9dm1KhRpfqeiIhI+TuYnc/KnWl8viKJcbPXc9uHK+j90gJaPTaXuPG/cNfHK3nxh43MWLWbLSlZltVZpmkaf39/OnfuTEJCAoMGDQLA6XSSkJBw2jedwMBA6tatS0FBAV999RXXX399ifcNCAggICCgLKUVOVzgoPVjP5zRY8/Wuqf6Eex/+m/pq6++yqZNm2jbti1PPfUUcKzvBsCYMWN46aWXaNy4MdWrVycpKYkBAwbw7LPPEhAQwEcffcTAgQPZuHEj9evXL/F1nnzySV544QVefPFFXn/9dYYOHcrOnTuJjIw86f0LCgp4+umnadGiBfv37yc+Pp4RI0Ywe/ZsAHbv3s2FF17IRRddxE8//UR4eDhLliwpGr158803iY+P5/nnn6d///6kp6ezZMmSsnwLRUSkjBxOF7sO5rA1JYut+7PNf1Oy2JqSTdopRjtC/H1oUjOUJtGhNIkOoVZ4YIn3rWhlbnoWHx/P8OHD6dKlC926dWPChAlkZ2czcuRIAIYNG0bdunUZN24cAL/99hu7d++mQ4cO7N69myeeeAKn08mDDz5Yvl+JB4mIiMDf35/g4OCTjig99dRT9O3bt+h6ZGQk7du3L7r+9NNPM3PmTL755ptThsARI0Zw4403AvDcc8/x2muvsXz5ci699NKT3v+WW24p+rhx48a89tprdO3alaysLEJDQ5k0aRIRERF89tln+Pn5AdC8efOixzzzzDP897//5d577y36XNeuXU/37RARkVLIyitk29GgcVzo2JGaQ77DWeLj6kQEFgsdTaJDaVIzlJphAW7T86nMYWTw4MGkpKTw2GOPsW/fPjp06MDcuXOLFrUmJiYWWw+Sm5vLI488wrZt2wgNDWXAgAFMnTqVatWqldsXcbwgPx/WPdWvQp67NK9dHrp06VLselZWFk888QTff/89e/fupbCwkMOHD5OYmHjK52nXrl3RxyEhIYSHh5fYDwZg5cqVPPHEE/z5558cPHgQp9P8cCcmJtK6dWtWr17NBRdcUBREjrd//3727NlDnz59yvKliojIcZxOF3szck3o2G9GN46GjuSMvBIfF+Brp1FUyAmho3F0SKlG7K12RhWOGjWqxL/IFyxYUOx6r169WLdu3Zm8zBmx2Wwe8Y0/lX/uirn//vuZN28eL730Ek2bNiUoKIhrr72W/PxTLzb6Z2iw2WxFAeOfsrOz6devH/369eOTTz4hOjqaxMRE+vXrV/Q6p2q3r1b8IiKll1vgYHtq9gmjHNtSsjlcUPJu06jQABM0/hE66lYLwm53j1GOM+HZ79oezN/f/5Tbm4+3ZMkSRowYwVVXXQWYkZKj60vKy4YNGzhw4ADPP/980e6l33//vdh92rVrx4cffkhBQcEJQScsLIyGDRuSkJBA7969y7U2ERFP5HK5SMnKY1vKiaFj96HDlLTnwdduo0GN4KLplKOho3F0KBFBJ45MewOFEYs0bNiQ3377jR07dhAaGlriolKAZs2aMWPGDAYOHIjNZuPRRx8tcYTjTNWvXx9/f39ef/117rrrLtauXcvTTz9d7D6jRo3i9ddf54YbbmDs2LFERESwbNkyunXrRosWLXjiiSe46667qFmzJv379yczM5MlS5YwevTocq1VxCoZuQUkHshh18Ec7DYb1UP8qR7sT/VgP6oF++PjwX+ZypnLL3SSmJZ9bErluNCRmVtye4aIIL9iaziOho7YyGD8fCq884ZbURixyP3338/w4cNp3bo1hw8fZvv27SXed/z48dxyyy307NmTqKgo/ve//5V7I7jo6Gg++OADHnroIV577TU6derESy+9xBVXXFF0nxo1avDTTz/xwAMP0KtXL3x8fOjQoQPnnXceAMOHDyc3N5dXXnmF+++/n6ioKK699tpyrVOkIjmcLvZl5JJ4IIfEtGwS03JITDtM4gHz8cGcghIfa7NBeKAf1YP9ikJKtWA/IoP9TwgtkSHHPvb3rVpvOp7sUE5+0S6Vo6FjW0oWO9NycDhPPsxht0G96sEnDR2RIf5us4DUajZXaZtjWCgjI4OIiAjS09MJDw8vdltubi7bt2+nUaNGJ7SdF/elfzexSk5+IUlph9l54GjYOHI5kMOug4dPuSsBICrUn3rVTYPBQzn5HMwpIP1wySHldEIDfE1oCfGn2pHAYoKLP9VDTv5xkH/5LJaXEx2/Tfaf0ysHTrNNtvE/dqs0iQ6lQY1gAstpc4MnOtX79/E0MiIiXsXlcpGSmUdiWg47D+QUDxxpOaRklrwjAcDPx0a96sHERgbTIDKY+pHB1K9x5L+RwYQEnPhrs9DhJP1wAQdzCjiYk8/B7Hzz32LXC4o+f+jI550us10zK6+QXQcPl/prDPSzHxl58Scy5Mhoyz9GXqodCTVHPw4N8NVf4cc5fpvs8aFj+4Fs8gtPvU22KHQUjXKEEhPuPttkPZHCiIh4nLxCB7sOHj4ynXJ86DCjHbkFpx7diAjyo0ENEzjq/yN01I4IKvPaD18fOzVCA6gRWvpmjU6ni8zcQg7m5JOWk8+hnHzSsguOjLYc+zgt+1h4OZiTT4HDRW6Bk73puexNzy316/n52E4z8nLiNFJ4oJ9H79BwuVzsTc89EjSy2Hbc7pV9GSV/7/x97TSOCjm2W+VI6GgUFXLSMCpnT99VEXE7LpeLgzkFR4JGNknHBY6ktBz2ZuSWuBMBzDx9nWpBJmjUODrKEVI0uhERbP2OBLvdRkSwHxHBfjSkdIdculwusvMdxUdeTvpxPgePhpmcfHILnBQ4zIjR6UaGitVog2rHrX05GmaKTSkdWQ9zdISmWpAfvpW8+PKf22S3pR4b8cjJP/U22cbRxUNH0+hQ6lQreyCVs6MwIiKWKHA42Xsol51HF4oeN8qRlJZDZt6pD4kM8fehfo0Q6kcGHRnVCCka5ahTLcgrF4babDZCA3wJDfAlNrL0B2PmFjiOjLaYURbzXxNgjn6clnNsVOZgdgFZeYU4XZCWbR63jexSv154oC/VjwSWyKMjMSGnnkYK8D31ugqXy0VqVv6xVufHhY5dB0+/TbZxdGjxkY6oULcIpWIojIhIhTm6Ffboeo2jQSMxLYfdhw6XuAPhqFrhgcXWbBwb5QjWToQyCPTzoXZEELUjSt+cML/QyaHDJpgUW/fyz49zjgWcowt5M3ILycgtZOeBnFK/Xoi/jxltOX7qKNiP7HxH0TRLxim2yYYH+hZbw3E0dNSvgttkPZHCiIicseO3wial5RwZ5SjdVlgwLazrn2SRaIMawdSrXrV3IVjN39dOzbBAaoaVfrebw+ki/fBxoy3HrXdJy8nnUHZB0fqYo9NKhw4X4HCa6afs/MPsPlTyQl6bDWKrBx83tXIsdNRQOPVoCiMickrlsRW2+M6UkKLAER0a4NELJKU4H7uNyBAz9VJaTqeLzLzCE9a7HP04wNfnyFbZEBrWCFFA9VIKIyJV3PFbYY+fStlZDlthYyODCdXuAzkFu91GRJAfEUGlX8gr3ke/JTxYw4YNue+++7jvvvusLkXcXHlshS22duMst8KKiBxPYUTES7hcLjYmZ7IpOatozcbRUQ5v2AorIt5LYUTEC2TnFfLorLXM+GN3ifepilthRcQz6LePBd5++23q1Klzwsm7V155JbfccgsAW7du5corryQmJobQ0FC6du3K/Pnzy/Q6K1asoG/fvkRFRREREUGvXr1YtWpVsfscOnSIO++8k5iYGAIDA2nbti3fffdd0e1LlizhoosuIjg4mOrVq9OvXz8OHjx4hl+5VISN+zK5YuJiZvyxG7sNujWM5NrO9Yjv25xXb+jAjH/1ZOUjcax9sh9z7r2At27uwsOXtebmcxvQq3k0DaNCFERExFLeNzLickFB6fe2lyu/YLP37DSuu+46Ro8ezc8//0yfPn0ASEtLY+7cucyePRuArKwsBgwYwLPPPktAQAAfffQRAwcOZOPGjdSvX79U5WRmZjJ8+HBef/11XC4XL7/8MgMGDGDz5s2EhYXhdDrp378/mZmZfPzxxzRp0oR169bh42NWq69evZo+ffpwyy238Oqrr+Lr68vPP/+Mw1FyR0OpPC6Xi89/T+Lxb/4mt8BJrfBAXruxI90aRVpdmohImXhfGCnIgefqWPPaD+0B/9OvBq9evTr9+/dn2rRpRWHkyy+/JCoqit69ewPQvn172rdvX/SYp59+mpkzZ/LNN98watSoUpVz8cUXF7v+9ttvU61aNRYuXMjll1/O/PnzWb58OevXr6d58+YANG7cuOj+L7zwAl26dOGNN94o+lybNm1K9dpSsbLzCnlk1lpmHpmW6dU8mvHXty/T2SgiIu5CY7MWGTp0KF999RV5eWbb5CeffMINN9yA3W7+SbKysrj//vtp1aoV1apVIzQ0lPXr15OYmFjq10hOTub222+nWbNmREREEB4eTlZWVtFzrF69mnr16hUFkX86OjIi7mXDvgwGTlzMzD9242O38b9LW/L+iK4KIiLisbxvZMQv2IxQWPXapTRw4EBcLhfff/89Xbt2ZdGiRbzyyitFt99///3MmzePl156iaZNmxIUFMS1115Lfn5+qV9j+PDhHDhwgFdffZUGDRoQEBBAjx49ip4jKOjUraFPd7tULpfLxfQVZlomr9BMy7w+pCNdG2paRkQ8m/eFEZutVFMlVgsMDOTqq6/mk08+YcuWLbRo0YJOnToV3b5kyRJGjBjBVVddBZiRkh07dpTpNZYsWcIbb7zBgAEDAEhKSiI1NbXo9nbt2rFr1y42bdp00tGRdu3akZCQwJNPPnkGX6GUp6y8Qh6euYavV5ugfVGLaMZf36FMnS5FRNyV94URDzJ06FAuv/xy/v77b2666aZitzVr1owZM2YwcOBAbDYbjz766Am7b06nWbNmTJ06lS5dupCRkcEDDzxQbLSjV69eXHjhhVxzzTWMHz+epk2bsmHDBmw2G5deeiljx47lnHPO4V//+hd33XUX/v7+/Pzzz1x33XVERUWVy/dATm/93gzu+WQV21Kz8bHbuP+SFtx5YWO1URcRr6E1Ixa6+OKLiYyMZOPGjQwZMqTYbePHj6d69er07NmTgQMH0q9fv2IjJ6Xx3nvvcfDgQTp16sTNN9/Mv//9b2rWrFnsPl999RVdu3blxhtvpHXr1jz44INFu2WaN2/Ojz/+yJ9//km3bt3o0aMHX3/9Nb6+yrCVweVy8enyRAZNWsK21GxqRwQy/Y5zufuiJgoiIuJVbC7XqfoyuoeMjAwiIiJIT08nPDy82G25ubls376dRo0aERhY+tMlxVr6dzu1f07L9G4RzcualhERD3Oq9+/j6U9cETezbk8Go6Ydm5Z5sF8Lbr9A0zIi4r0URkTchJmWSeKJb/8mv9BJnQizW6ZzA+2WERHvpjAi4gYycwt4aOZavv3TTMtc3LImL1/XnuqalhGRKkBhRMRif+9JZ9S0P9iemo2v3caDl7bgtvM1LSMiVYfCiIhFXC4Xn/yWyFPfrTtuWqYTnRtUt7o0EZFK5TVhxAM2Bclxqvq/V2ZuAWNnrOG7v/YCENeqJi9d155qwZqWEZGqx+PDyNETZvPz89W+3IMcbUl/9N+vKlm7O51R01ax40AOvnYbY/q35NbzG2ErxYnPIiLeyOPDiK+vL8HBwaSkpODn51d00Jy4L6fTSUpKCsHBwVWqgZrL5eLj3xJ5+tt15Duc1K0WxOtDOtKpvqZlRKRq8/h3ApvNRu3atdm+fTs7d+60uhwpJbvdTv369avMaEBmbgFjZqzhe03LiIicwOPDCIC/vz/NmjUr04m2Yi1/f/8qM4q1dnc690xbxU5Ny4iInJRXhBEwf2mrrbi4E5fLxcfLdvL0d+uLpmUmDulIR03LiIgU4zVhRMSdZOQWMOarv5i9Zh8AfVvH8NK17YkI9rO4MhER96MwIlLO1uwy0zKJaTn4+dgY078Vt5zXUNMyIiIlUBgRKScul4uPlu7k2e/NtEy96kFMHNKJDrHVrC5NRMStKYyIlIOM3AL+9+VfzFlrpmUuaR3Di5qWEREpFYURkbP0165DjJr2R9G0zEMDWjGip6ZlRERKS2FE5Ay5XC4+/HUHz85eT4HDRb3qQUwa0on2mpYRESkThRGRM5B+2EzLzP3bTMv0axPDC9e2JyJI0zIiImWlMCJSRn8mHWLUp6tISjusaRkRkXKgMCJSSi6Xiw9+3cFzR6ZlYiODmHijpmVERM6WwohIKaQfLuDBL//kh7+TAbi0TS3+79p2mpYRESkHCiMip7E66RCjpq1i18HD+PvYefiyVgzr0UDTMiIi5URhRKQELpeL95fsYNwcMy1TPzKYSUM6cU69CKtLExHxKgojIieRnlPAA1/+yY/rzLTMgHNq8fw17QgP1LSMiEh5UxgR+YfVSYe455NV7D5kpmUeubwVN5+raRkRkYqiMCJyhMvl4r3F23l+zgYKnS4a1DDTMm3ralpGRKQiKYyIAIdy8rn/i7+Yv95My1x2Tm3GXXOOpmVERCqBwohUeasSDzJ62h9F0zKPDmzNTd3ra1pGRKSSKIxIlaVpGRER96AwIlWSmZb5k/nr9wNwWbvaPH/1OYRpWkZEpNIpjEiVs3LnQf796ZFpGV87j13emqGalhERsYzCiFQZLpeLdxdt5//mmmmZhjWCmahpGRERyymMSJVwMNtMyyRsMNMyl7erzThNy4iIuAWFEfF6K3ceZPS0VexJz8Xf187jA1szpJumZURE3IXCiHgtp9PFO4u28eIPGyl0umgUFcLEIR1pU0fTMiIi7kRhRLzSwex8/vvFn/x0ZFrmivZ1eO7qcwgN0I+8iIi70W9m8Tord6Yxatof7D0yLfPkFW24oWuspmVERNyUwoh4DafTxdtHpmUcTheNo0KYOKQTreuEW12aiIicgsKIeIW07HziP1/Ngo0pgKZlREQ8iX5Ti8dbsSON0dP+YF9GLgG+dp7QtIyIiEdRGBGP5XS6mPzLVl7+cVPRtMykoZ1oVVvTMiIinkRhRDzSgaw84j//k4WbzLTMoA51eOYqTcuIiHgi/eYWj/PPaZmnrmzD9V00LSMi4qkURsRj/HNapkm0mZZpWUvTMiIinkxhRDzCP6dlrupYl2cGtSVE0zIiIh5Pv8nF7S3fnsboT1eRnJFHoJ+dp65oy3Vd6mlaRkTESyiMiNtyOl28uXArL/+4EacLmkSH8MbQzrSoFWZ1aSIiUo7sZ/KgSZMm0bBhQwIDA+nevTvLly8/5f0nTJhAixYtCAoKIjY2lv/85z/k5uaeUcFSNaRm5TH8/eW8+IMJIld3qss3o85XEBER8UJlHhmZPn068fHxTJ48me7duzNhwgT69evHxo0bqVmz5gn3nzZtGmPGjGHKlCn07NmTTZs2MWLECGw2G+PHjy+XL0K8y7JtB/j3p3+wP9NMyzx9ZVuu6xJrdVkiIlJBbC6Xy1WWB3Tv3p2uXbsyceJEAJxOJ7GxsYwePZoxY8accP9Ro0axfv16EhISij733//+l99++43FixeX6jUzMjKIiIggPT2d8HDtnPBWTqeLNxZsYfy8TThd0LRmKG8M7UTzGI2GiIh4otK+f5dpmiY/P5+VK1cSFxd37AnsduLi4li6dOlJH9OzZ09WrlxZNJWzbds2Zs+ezYABA0p8nby8PDIyMopdxLsdnZZ56UcTRK7pVI9vRp2nICIiUgWUaZomNTUVh8NBTExMsc/HxMSwYcOGkz5myJAhpKamcv755+NyuSgsLOSuu+7ioYceKvF1xo0bx5NPPlmW0sSDLd16gHs/07SMiEhVdUYLWMtiwYIFPPfcc7zxxhusWrWKGTNm8P333/P000+X+JixY8eSnp5edElKSqroMsUCDqeL1xI2M/TdZezPzKNZzVC+GXW+goiISBVTppGRqKgofHx8SE5OLvb55ORkatWqddLHPProo9x8883cdtttAJxzzjlkZ2dzxx138PDDD2O3n5iHAgICCAgIKEtp4mFSMvP4z/TVLN6SCsB1nevx5JVtCPbXbnMRkaqmTCMj/v7+dO7cudhiVKfTSUJCAj169DjpY3Jyck4IHD4+PgCUce2seIlft6Yy4LVFLN6SSpCfDy9d154Xr2uvICIiUkWV+bd/fHw8w4cPp0uXLnTr1o0JEyaQnZ3NyJEjARg2bBh169Zl3LhxAAwcOJDx48fTsWNHunfvzpYtW3j00UcZOHBgUSiRqsHhdDHxpy28mmAWqTaPCWXSkE400yJVEZEqrcxhZPDgwaSkpPDYY4+xb98+OnTowNy5c4sWtSYmJhYbCXnkkUew2Ww88sgj7N69m+joaAYOHMizzz5bfl+FuL2UzDzum/4HS7YcAOD6LvV48oq2BPkrkIqIVHVl7jNiBfUZ8WxrdqVzy4crSMnMI8jPh2evasvVnepZXZaIiFSw0r5/a5JeKpTD6eKBL/8kJTOPFjFhTBrakaY1NS0jIiLHKIxIhZr5x2427MskLNCXz+44l+oh/laXJCIibqbC+4xI1ZVb4ODlHzcCMKp3UwURERE5KYURqTBTlmxnb3oudasFMbxnQ6vLERERN6UwIhUiLTufN3/eCsD9/ZoT6KddMyIicnIKI1IhXv9pM5l5hbSuHc6V7etaXY6IiLgxhREpdzsPZPPxsp0APDSgFXa7zeKKRETEnSmMSLl74YeNFDhcXNg8mvObRVldjoiIuDmFESlXfyQe5Pu/9mKzwdj+La0uR0REPIDCiJQbl8vFuDkbALimUz1a1Va3XBEROT2FESk3Cev3s3x7GgG+duL7Nre6HBER8RAKI1IuCh1Oxs1ZD8At5zeiTrUgiysSERFPoTAi5eLz33exNSWb6sF+3H1RE6vLERERD6IwImctO6+QV+ZvAuDffZoRHuhncUUiIuJJFEbkrL27aDspmXnUjwxmaPcGVpcjIiIeRmFEzkpKZh5v/WLavj94aQv8ffUjJSIiZaN3DjkrE+ZvIiffQfvYalx2Tm2ryxEREQ+kMCJnbMv+LD5bkQTAQ/1bYrOp7buIiJSdwoicsRfmbsDhdBHXKobujWtYXY6IiHgohRE5Iyt2pPHjumTsNhjTv4XV5YiIiAdTGJEyc7lcPDfbNDgb3LU+TWuGWVyRiIh4MoURKbM5a/fxR+Ihgv19+E9cM6vLERERD6cwImWSX+jkhbnmMLzbL2hMzfBAiysSERFPpzAiZfLp8kR2HMghKjSA2y9sbHU5IiLiBRRGpNQycwt4NWEzAPfFNSM0wNfiikRExBsojEipTV64lbTsfBpHhzC4a6zV5YiIiJdQGJFS2Zt+mHcXbQdgzKUt8fPRj46IiJQPvaNIqbwybxN5hU66NqxO39YxVpcjIiJeRGFETmvDvgy+XLkLgLEDWqntu4iIlCuFETmt5+dswOmCAefUolP96laXIyIiXkZhRE5pyZZUFmxMwddu48F+La0uR0REvJDCiJTI6XQxbo5p+37TuQ1oGBVicUUiIuKNFEakRN/+tYe1uzMIDfBl9MVNrS5HRES8lMKInFReoYMX5m4E4O6LmlAjNMDiikRExFspjMhJffTrTnYfOkyt8EBuOa+R1eWIiIgXUxiRExzKyef1n0zb9/hLmhPk72NxRSIi4s0URuQEbyzYSkZuIS1iwrimUz2ryxERES+nMCLFJKXl8MGSHQCMGdASH7sanImISMVSGJFiXv5xI/kOJz2b1OCi5tFWlyMiIlWAwogUWbs7nVmr9wDwkNq+i4hIJVEYEQBcLhfPzTYNzgZ1qEPbuhEWVyQiIlWFwogAsHBTCr9uPYC/j53/XtLC6nJERKQKURgRHE4Xz8/ZAMDwng2IjQy2uCIREalKFEaEr1btYsO+TCKC/BjVu5nV5YiISBWjMFLFHc53MP7HTQCM6t2UiGA/iysSEZGqRmGkipuyZDv7MnKpWy2Im3s0sLocERGpghRGqrADWXm8uWArAA/0a0Ggn9q+i4hI5VMYqcJe/2kLWXmFtK0bzhXt61hdjoiIVFEKI1XUjtRsPl62E4CH+rfCrrbvIiJiEYWRKurFHzZS6HRxUYtoejaNsrocERGpwhRGqqA/Eg/y/Zq92Gwwpn9Lq8sREZEqTmGkinG5XIybbRqcXdupHi1rhVtckYiIVHUKI1XMvHXJLN+RRqCfnfhLmltdjoiIiMJIVVLocPL8XDMqcuv5jagdEWRxRSIiIgojVcr035PYlpJNZIg/d/ZqYnU5IiIigMJIlZGdV8gr8zYD8O+LmxIeqLbvIiLiHhRGqoi3f9lGalYeDWsEM6S72r6LiIj7UBipAvZn5PLOom0APHhpS/x99c8uIiLuQ+9KVcCEhM3k5DvoEFuN/m1rWV2OiIhIMQojXm7L/kymr0gC4KEBrbDZ1PZdRETci8KIl/u/uRtxOF30bR1Dt0aRVpcjIiJyAoURL7Z8exrz1iXjY7fxv0vV9l1ERNyTwoiXcrlcPDt7PQA3dI2lac1QiysSERE5OYURLzV7zT7+TDpEsL8P98Y1s7ocERGREimMeKH8Qicv/GDavt9xYWNqhgVaXJGIiEjJFEa80Ce/7WTngRyiwwK4/YLGVpcjIiJySgojXiYjt4DXEkzb9//ENSckwNfiikRERE5NYcTLTF6wlYM5BTSJDuH6LvWsLkdEROS0FEa8yN70w7y3eDsAY/q3wtdH/7wiIuL+9G7lRcb/uIm8QifdGkYS16qm1eWIiIiUyhmFkUmTJtGwYUMCAwPp3r07y5cvL/G+F110ETab7YTLZZdddsZFy4nW783gy1W7AHjoMrV9FxERz1HmMDJ9+nTi4+N5/PHHWbVqFe3bt6dfv37s37//pPefMWMGe/fuLbqsXbsWHx8frrvuurMuXo55fs4GXC64rF1tOsRWs7ocERGRUitzGBk/fjy33347I0eOpHXr1kyePJng4GCmTJly0vtHRkZSq1atosu8efMIDg5WGClHizensnBTCn4+Nh7s18LqckRERMqkTGEkPz+flStXEhcXd+wJ7Hbi4uJYunRpqZ7jvffe44YbbiAkJKTE++Tl5ZGRkVHsIifndLoYN8e0fR/avQENapT8fRUREXFHZQojqampOBwOYmJiin0+JiaGffv2nfbxy5cvZ+3atdx2222nvN+4ceOIiIgousTGxpalzCrl6z938/eeDMICfPl3H7V9FxERz1Opu2nee+89zjnnHLp163bK+40dO5b09PSiS1JSUiVV6FlyCxy89MMmAO7u3YTIEH+LKxIRESm7MrXnjIqKwsfHh+Tk5GKfT05OplatWqd8bHZ2Np999hlPPfXUaV8nICCAgICAspRWJX20dAe7Dx2mdkQgt5zXyOpyREREzkiZRkb8/f3p3LkzCQkJRZ9zOp0kJCTQo0ePUz72iy++IC8vj5tuuunMKpViDuXkM/GnLQDE921OoJ+PxRWJiIicmTIfXBIfH8/w4cPp0qUL3bp1Y8KECWRnZzNy5EgAhg0bRt26dRk3blyxx7333nsMGjSIGjVqlE/lVdykn7eQkVtIy1phXN1Jbd9FRMRzlTmMDB48mJSUFB577DH27dtHhw4dmDt3btGi1sTEROz24gMuGzduZPHixfz444/lU3UVl5SWw4e/7gRg7IBW+NjV4ExERDyXzeVyuawu4nQyMjKIiIggPT2d8PBwq8ux3L2f/cHXq/dwftMopt7aTd1WRUTELZX2/Vtn03iYNbvS+Xr1HgDG9G+pICIiIh5PYcSDuFwunpttGpxd1bEubetGWFyRiIjI2VMY8SALNqawdNsB/H3t/PeS5laXIyIiUi4URjyE47i27yN7NqRe9WCLKxIRESkfCiMe4quVu9iUnEVEkB//uqip1eWIiIiUG4URD3A438HL8zYCMPripkQE+1lckYiISPlRGPEAU5ZsJzkjj3rVg7i5RwOryxERESlXCiNuLjUrjzcXbAXggX4tCPBV23cREfEuCiNu7vWEzWTlFXJO3QgGtqtjdTkiIiLlTmHEjW1PzeaT3xIBGDugJXa1fRcRES+kMOLGXvxhA4VOF71bRNOzSZTV5YiIiFQIhRE3tXLnQWav2YfdBmP6t7K6HBERkQqjMOKGXC4X4460fb+ucywtaoVZXJGIiEjFURhxQz+uS+b3nQcJ9LPzn75q+y4iIt5NYcTNFDic/N+cDQDcdn5jakUEWlyRiIhIxVIYcTPTVySxLTWbGiH+3NmrsdXliIiIVDiFETeSlVfIhPmbALg3rhlhgWr7LiIi3k9hxI28/cs2UrPyaRQVwo3d6ltdjoiISKVQGHET+zNyeeeXbQA82K8Ffj76pxERkapB73hu4pX5mzlc4KBT/Wpc2raW1eWIiIhUGoURN7A5OZPpK0zb94cGtMJmU9t3ERGpOhRG3MD/zd2A0wX92sTQpWGk1eWIiIhUKoURiy3bdoD56/fjY7fx4KUtrS5HRESk0imMWOj4tu83doulSXSoxRWJiIhUPoURC32/Zi9/7konxN+He/uo7buIiFRNCiMWySt08MLcjQDc2asJ0WEBFlckIiJiDYURi3yyLJHEtByiwwK47YJGVpcjIiJiGYURC6QfLuD1nzYDEN+3OcH+vhZXJCIiYh2FEQtMXriVgzkFNKsZynWd61ldjoiIiKUURirZnkOHmbJ4OwBj+rfEV23fRUSkitM7YSV7+cdN5BU66d4okotb1rS6HBEREcspjFSidXsymPHHLkBt30VERI5SGKlEz8/dgMsFA9vXoX1sNavLERERcQsKI5Vk0eYUftmUgp+PjQcuaWF1OSIiIm5DYaQSOJ0uxs3eAMDN5zakfo1giysSERFxHwojlWDW6t2s25tBWKAvoy9uanU5IiIibkVhpILlFjh46QfT9v1fFzWleoi/xRWJiIi4F4WRCvbhrzvYk55LnYhARp7X0OpyRERE3I7CSAU6mJ3PxJ+3APDfS1oQ6OdjcUUiIiLuR2GkAk38eQuZuYW0qh3OoI51rS5HRETELSmMVJCktBw+WroDgLH9W+JjV4MzERGRk1EYqSAv/rCRAoeLC5pFcWHzaKvLERERcVsKIxXgr12H+ObPPdhs5jA8ERERKZnCSDlzuVw8N3s9AFd1rEubOhEWVyQiIuLeFEbK2c8b97NsWxr+vnb+q7bvIiIip6UwUo4KHc6itu8jz2tI3WpBFlckIiLi/hRGytFXq3axeX8W1YL9+NdFavsuIiJSGgoj5SQnv5CXf9wEwOiLmxER5GdxRSIiIp5BYaScvLdoO/sz84iNDOKmc+tbXY6IiIjHUBgpB6lZeUxeuBWAB/q1JMBXbd9FRERKS2GkHLyWsJnsfAft60Vw+Tm1rS5HRETEoyiMnKVtKVlM+y0RgDH9W2FX23cREZEyURg5Sy/M3Uih00WfljXp0aSG1eW4p8Rl8NXtkLTc6kpERMQNKYychZU705j79z7savtesp1LYepVsOZzmHIpLJ4ATqfVVYmIiBtRGDlDpu27aXA2uGsszWLCLK7IDe36HT65DgpyIKwOuBww/3GYdj1kp1pdnYiIuAmFkTP0w9/JrNx5kCA/H+6La251Oe5nzx8w9WrIz4RGF8K/V8HAV8E3ELbMg8nnw85fra5SpOwK82DFe5D4m9WViHgNhZEzUOBw8n9zzajI7Rc0IiY80OKK3My+NWZqJi8d6veEGz8DvyDoPAJu/wmimkPmXvjgMvjlRU3biOfYvx7e6QPfx8MHA+DvmVZXJOIVFEbOwGfLE9memk2NEH/u6NXE6nLcy/4N8NGVcPgg1OsKQz8H/5Bjt8e0gdt/hvY3gssJPz0DH18NWfutq1nkdFwu+O1tePsiSF4Ddj9wFsKXt8DqaVZXJ+LxFEbKKCuvkAnzNwNwX1wzQgN8La7IjaRugY+ugJwDULsDDP0SAk6yliYgFK6aDFe+AX7BsO1nM22zbWGllyxyWln7zTqnOQ9AYS406QP3/QUdbzaBetbdZtpGRM6YwkgZvb1wKwey82kcFcIN3dT2vUjaNvhwIGQlQ0xbuHkmBFU79WM6DjWjJNGtzOM+uhJ+HgdOR6WULHJam36AN3rA5h/BJwAu/T8TssPrwMDXoNud5n7fx8OvE62tVcSDKYyUQXJGLu8s2g7Ag5e2xM9H3z4ADiXCh1dA5h6IbgnDvobgyNI9tmZLs46k482ACxY+b0JJxt4KLVnklPJz4Pv/mhGRnFSo2QbuWADn3gX2I//f2+3Q///g/P+Y6z8+DAtfMFM6IlImejctg1fmbeJwgYPODarTr02M1eW4h4w9ZkQkPQlqNIVh30BIVNmewz8YrpwIV78DfiGwY5GZttmSUDE1i5zK3r/M2pAV75rr595jAnNM6xPva7NBn8eh9yPm+s/PwvwnFEhEykhhpJQ2JWfy+e9JADw0oCU2m9q+k5lsgsjBHVC9IQz/FsLOIqS1ux7u/AVizjF/jX58DSQ8BY7C8qpYpGROJyx5Dd65GFI3QmgtuGkGXPoc+J1ix5zNBr0egEueNdeXTIA5/9MuMZEyUBgppf+bswGnC/q3rUXnBqWcgvBm2almseqBLRARa4JIeJ2zf96opnDbfOhyC+CCRS/Dh5dD+u6zf26RkqTvhqlXwrxHwVkALS6Du3+Fpn1K/xw9R8Fl483Hy9+Cb/+t9U8ipaQwUgpLtx4gYcN+fO02HujXwupyrJeTBh8NgpQNprPq8G+hWjku5vULhMtfgWvfB/8wSFxqpm02/Vh+ryFy1N+z4M2esP0Xs7tr4KtwwycQcgZnTXW9FQZNBpsd/pgKM+8ER0G5lyzibRRGTsPpdDFuznoAhnSvT+PoUIsrstjhQ6ahWfIaCI0xQSSyUcW8Vtur4a5fzDbhw2kw7Tr48VH9cpfykZcJs+6BL4ZD7iHzc3bnItOc72ymYTvcCNdOAbsvrPkCvhhhuraKSIkURk7juzV7+WtXOqEBvvy7TzOry7FWXiZ8ci3sXQ3BNcxi1aimFfuakY3h1h+PbaH89TV4v7/ZwSNypnb9DpMvgNUfAzY4Px5unVd+P89troLBn5jtwBu+g8+GmB06InJSCiOnkFfo4MUfTNv3u3o1Jio0wOKKLJSfDZ9cD7tWQGA1s323ZiWdVOwbAANegMEfQ2CEqWHy+bDh+8p5ffEejkKz/fa9S+DgdgivByO+g7jHwde/fF+rxaUwZLqZ+tky32wTzsss39cQ8RIKI6fw8bJEktIOExMewK3nN7a6HOsUHIZPb4DEXyEgAobNglrnVH4drQaaYfS6nSE33fy1OWcMFOZXfi3ieQ7uMOch/fysOUG67TVw9xJoeH7FvWaT3mZHjn+Y2bI+9Soz1SkixSiMlCD9cAGv/2Tavsf3bU6Qv4/FFVmkMA+m32QW9/mHwk1fQZ2O1tVTvQGMnAs9Rpnrv70JUy6BtO3W1STuzeWCP6fDm+dD0jITDK56G6557/RdgstDgx4w/GszorhrhdkOn32g4l9XxIOcURiZNGkSDRs2JDAwkO7du7N8+fJT3v/QoUPcc8891K5dm4CAAJo3b87s2bPPqODK8saCLRzKKaB5TCjXdKpndTnWKMyHz4ebIWa/YNMGO7ar1VWZ4fR+z5rTgIOqw54/4K0Lza4IkeMdPgRf3Qoz74D8TIjtDncvhvaDz26RalnV7QwjvoeQaNj3lznxN3Nf5b2+iJsrcxiZPn068fHxPP7446xatYr27dvTr18/9u8/+amr+fn59O3blx07dvDll1+yceNG3nnnHerWrXvWxVeU3YcO8/6SHQCM6d8S36rY9t1RaH6Jb5oDvoHmjb9BD6urKq5Ff7hrsXmDycswuyK+/y8U5FpdmbiDHUvM2qK1X4HNB3o/DCNmmwZ9VqjV1rx+WG2zLf79/nAoyZpaRNyMzeUqW9/i7t2707VrVyZONIdCOZ1OYmNjGT16NGPGjDnh/pMnT+bFF19kw4YN+Pn5nVGRGRkZREREkJ6eTnh4+Bk9R1nEf76aGat206NxDabd3r3qdVt1OmDG7eaXuI8/3PgpNI2zuqqSOQrMOoDFr5jrtc6B6z6EGk2srUus4SiABeNg0XjABdUbmaMG3GFUD8yU4kdXmB1hEbFmMbh+VsVLlfb9u0x/8ufn57Ny5Uri4o69MdntduLi4li6dOlJH/PNN9/Qo0cP7rnnHmJiYmjbti3PPfccDkfJnQnz8vLIyMgodqksf+9JZ+Yfptvn2KrY9t3phK9HmSBi94XrP3LvIALg4wdxT8DQr8yW431rzLTNmi+trkwqW+oWeK+v6dyLCzrcBHctcp8gAqYvz8g55iyn9CR4fwDs32B1VSKWKlMYSU1NxeFwEBNT/PyRmJgY9u07+fzntm3b+PLLL3E4HMyePZtHH32Ul19+mWeeeabE1xk3bhwRERFFl9jY2LKUeVaen7MBlwuuaF+HdvWqVdrrugWXC767D/6cZoa1r33fTIV4imZxZtqmwXmQn2Wmmb75t9kNJN7N5YKVH8BbF5g1RIHVzOjYoEkQEGZ1dSeKqGcCSc3WkLXPrCHZ+5fVVYlYpsIXQzidTmrWrMnbb79N586dGTx4MA8//DCTJ08u8TFjx44lPT296JKUVDnzqr9sSmHR5lT8fexVr+27ywVzHoRVH5pW1le/Da2vsLqqsguvY5qxXfggYDNfzzsXQ8omqyuTipJ9wOz4+vZeKMiBRheac2XaDLK6slMLrWkWtdbuADkHzBlMu363uioRS5QpjERFReHj40NycnKxzycnJ1OrVq2TPqZ27do0b94cH59jW2NbtWrFvn37yM8/eX+IgIAAwsPDi10qmsPpYtwcM1Q6rEcDYiODK/w13YbLBT8+AsvfBmxw5RtwzrVWV3XmfHzh4ofh5pkQUhP2r4O3e8HqT62uTMrblgRzrsyG78DuB32fhpu/hgj3XSBfTHAkDP/GLMLOTYeProQdi62uSqTSlSmM+Pv707lzZxISEoo+53Q6SUhIoEePk++0OO+889iyZQvO447T3rRpE7Vr18bfv5w7Hp6FWX/sZv3eDMIDfRl1cQW3OHcnLhf89DQsNQuSGfiqOVvDGzTpbaZtGvUyfzHPugtm/ct0kxXPVpALc8fCx1ebaY6o5nB7Apz3b7B72O63wAgTnBtdaKYXP77WbKcXqULK/H9tfHw877zzDh9++CHr16/n7rvvJjs7m5EjRwIwbNgwxo4dW3T/u+++m7S0NO699142bdrE999/z3PPPcc999xTfl/FWcotcPDyjxsBuKd3U6oFu09IqnALXziy2A8Y8BJ0Hm5tPeUtLMb8ou/9sJl+Wv0JvN0bktdZXZmcqeR1Zupt2Rvmetfb4I6FULu9tXWdDf8QGPI5NLsECg/DpzfquAOpUnzL+oDBgweTkpLCY489xr59++jQoQNz584tWtSamJiI/bi/TGJjY/nhhx/4z3/+Q7t27ahbty733nsv//vf/8rvqzhL7y/ZwZ70XOpWC2J4z4ZWl1N5Fr8CC54zH1/yLHS73dp6KordB3o9CA16wle3QepG82Y24AXoeHPlNr+SM+dymanEHx8FRx4ER8GVk8wZMN7AL8gcrvfVrbD+G5h+s1m75clTpiKlVOY+I1aoyD4jadn59HrhZzLzChl/fXuurirdVpdOgh8eMh/3eQwu+K+19VSW7FSYcQdsPTLVeM71cPl499xxIcdkJsPX/zo2fdG0Lwx6wywC9TaOQvO1/jUds4ZrInS8yeqqRM5IhfQZ8UYTf9pCZl4hrWuHM6iDhyx6O1vL3zkWRHqNqTpBBCAkyrS17/O42b685nN4+yLTm0Tc08Y58GYPE0R8A6H/izD0C+8MImAWYA+aDJ1HAC74+h7z/6yIF6vSYSTxQA5Tl+0A4KEBrbDbq8Bw/aqPYPb95uPz/wMXndg11+vZ7XBBPIycDeF14cAWeKcPrHjPTAWIe8jPge/+Y06MzjkAMW3hjgXQ/Q7vn1qz2+HyCXDuv8z12ffDklctLUmkIlXpMPLCDxsocLi4sHk05zeLsrqcivfnZ6YJGMC59xwZHfDyX+qnUv9cs9um+aVmDcL38fDlSLPFUqy1Z7Xpovv7FHO9xyi4/Seo2crSsiqVzQb9noMLjvzxMO8x+HmcArN4pSobRvIKHaRk5mGzwdj+La0up+Kt/Qpm3Q24zO6Dfs9W7SByVHCkOQTwkmdN+/u/Z5o3wT1/WF1Z1eR0wOIJ8G4cHNgMobXMbqh+z4JvgNXVVT6bDfo8Chc/aq4vfB7mPapAIl6nSi9gdblcrNubQZs6EeX2nG5p/bfw+XBwOaDTMLj8Vc/rxVAZdv0OX4yE9ERzQOAlz0C3KjAl4C7Sd8HMu2DHInO95eVwxesmMAosexPmHplW7XqbWTuj/4/FzWkBaynYbDbvDyIb55o3WJcD2t2gIHIq9brAXb+YN0FHvmmPP/0mOHzQ6sq8398zTSfVHYvAL9iEkMEfK4gc79y7TVNCbLDiXfhmlBlJEvECelfyZlsS4PObwVkAba42PRkURE4tqLp5E+z/ghkd2fAdTL5QZ4ZUlLxMmHk3fDHCrNWp08ms4+k0TCNSJ9N5BFz1ltkJtvoT0zfHUWB1VSJnTe9M3mr7IvhsiPkLv+XlpnmST5l73FVNNht0vxNu/RGqNzTTNlP6wa+va66+PCUth8nnHzkl2m4Wat76I9RoYnVl7q39YLjuA3MWz98z4PNhpj2+iAdTGPFGictg2mAozDU7Ra59H3z8rK7K89TpCHf+Aq0HgbPQHCb46Q2Qk2Z1ZZ7NUQgLnocpl8LBHRARa06v7fOofk5Lq/UVcMM003dl42zzc5mfY3VVImdMYcTb7PrdHLRVkA1NLobrPgTfKnTWTnkLjDB/hV42HnwCYNNc89d84jKrK/NMadvh/f6wYJxZx3TOdWZapkFPqyvzPM0vMefZ+IXAtp/h42sgN8PqqkTOiMKIN9mzGqZeDfmZ0PACc86FX6DVVXk+mw263gq3zYfIJpCxG94fAIvGw3GnUcspuFyw+lOYfAHsWg4B4XD1O3DNuxBUzerqPFfjXmbrc0A4JP4KUwdp5E48ksKIt9i31vwiykuH2HNN7wz/YKur8i6128GdC81f8y4HJDwJn1wLWSlWV+beDh+EL2+BWXeZoFy/hxkNaXe91ZV5h/rdYfg3ZvH17pXw4RX6mRSPozDiDfZvgI+uNL/063Yx53YEhFpdlXcKCDN/0V/xupmv35pgpm12LLa6Mve0fRG8eb5ZaGnzgYsfMetDqjewujLvUqcjjJgNITUheQ18MAAy9lhdlUipKYx4utQt8NEVkJMKtdvDTV9BYPmebCz/YLOZrae3/wxRLSBrH3w4EBa+oL4PRxXmw/wnzPclYxdENoZb58GFD4Ddx+rqvFNMaxg5x5y3lLrJrM05uNPqqkRKRWHEk6VtN7/ss5LNIWI3z9L8e2WKaQ13/AwdhoLLCT8/C1OvMsfdV2Wpm+G9OFj8CuCCjjfDnYugXmerK/N+UU1NIKne0OxUen8AHNhqdVUip6Uw4qkOJZm54cw9EN3SBBF1q6x8/iEw6A1z5LtfMGxfaKZtti2wurLK53KZg+0mXwB7/zRrGK6fCldO1LRhZarewASSqOZmVGrKpZC8zuqqRE5JYcQTZewxIyLpiWZ3x7CvITTa6qqqtg43muPta7aG7P3w0SD46VnTU6MqyE41Tfa++w8UHoZGveDuX00/DKl84XXMGpKYtubn8YPLzG47ETelMOJpMpPNiMjB7VCtAQz/FsJqWV2VAES3MMfcdxoOuOCXF8x6Hm9fSLhlvjlXZuPsYwcM3jzLvCGKdUKjze+HOp3gcJr5vZG03OqqRE5KYcSTZKeaXTMHNpuulcO/hYi6Vlclx/MLgiteg2veA/9Q2LnETNtsnm91ZeWvIBfmjDHNtrKSzWLe2xKg52idgeQugiPNyGn9nmbb/0eDYPsvVlclcgL9xvAUOWnmF0nKegirbfoKaHuk+zrnWrhjIdQ6B3IOwCfXmN0l3nKoWfLf8E5v+O1Nc73bHaYHS+121tYlJwoMh5u+hMa9TWfmT66DzfOsrkqkGIURT5CbDh9fbfoHhNQ0IyKRja2uSk4nqincOh+63mauL37FzN2n77K2rrPhdMKyN+Ht3rB/HYREw5AvYMCLZlRI3JN/iGmE2Ly/ObPq0xth3TdWVyVSRGHE3eVlmrNm9vwBwTXMiEhUM6urktLyC4TLXjZnBAWEQ9JvZtpm4xyrKyu7zH1mhGfuGHDkQbN+cPdSc0aKuD+/QBg8FdpcBc4C+GIE/PWF1VWJAAoj7i0/25y+u2s5BFYzc781W1ldlZyJNoPMCcB1OppOuZ/eAD88bJqDeYIN38MbPWDrT6bz7GUvw5Dp2sXlaXz8zHqm9kPMkQYzboeVH1pdlYjCiNsqOGyGUncuMX9R3zzTrD8QzxXZCG75Ac79l7m+dCK8f6lpTuWu8rPh23vNtt3DaeZn8I6FZurJZrO6OjkTdh+4chJ0uRVwwbf/NlNvIhZSGHFHhXkw/SbTQMs/1LR4r9vJ6qqkPPgGwKXj4IZpEBhhDjabfCGs/9bqyk605w9460JY+YG53nO02S1Ts6WlZUk5sNvN6FaPUeb63DGw6GVra5IqTWHE3RTmm7ncLfNNR88hn0NsN6urkvLW8jJzcm29rmbL5fSbYPaDJohazemARePh3Tg4sAXC6pgpwkueMWFKvIPNZv5Ne40x1xOegp+eMZ10RSqZwog7cRTCV7ea5lG+gXDjp9DwPKurkopSrb5p291ztLm+/C147xJI22ZdTUePGUh4EpyF0OoKuHsJNL7Iupqk4ths0HssxD1prv/yolnLpEAilUxhxF04HTDzTlj/jeliOfgTvQFUBT5+5q/TIZ9DUCTsXW2mbdbOqPxa1n4Fb54HOxeDX4hZV3D9RzrzqCo4/z7o/6L5eNkk09bf6bS0JKlaFEbcgdMJ34yGtV+C3ddsA20WZ3VVUpma9zPTNvV7QH4mfDnSvCEUHK74187NgBl3wpe3mCmjul3grkXQ8SYtUq1Kut8BV0wEbLDyfZh1d9U5W0kspzBiNZcLvo+H1Z+Azcdsu2s5wOqqxAoRdWH4d3B+vLn++xSzbiN1c8W9ZuKRvid/fQY2O1z4INwyF2o0qbjXFPfV6Wa45l3zu+ivz+CrWzxn+7l4NIURK7lcMOd/5q8QbHDVW6YfhVRdPr4Q97jZQRUcBclr4a1e8Nfn5fs6jkL4eZzZWnxop1m/MmI2XPywmTqSquuca830nI8/rPvaLK4uyLW6KvFyCiNWcblg3qNm0SKY+fl211lbk7iPpnFm2qbhBeY8kRm3w9ejID/n7J87bZsJIQufB5cT2g02r9Wgx9k/t3iHVpebBfS+gbD5B5h2vek5I1JBFEas8tMz8Ovr5uPLJ0DHoZaWI24ovLbZUttrDGCDP6bCOxfD/g1n9nwuF/zxCUy+AHatgIAIMy149dum54nI8ZrGmRE6/1DT82jq1eacLJEKoDBihYUvwKKXzMf9X4QuI62tR9yX3cdsvRz2NYTGmFOb3+ltQkVZ5KSZ/jVf/wvys6DBeXD3YjMkL1KShufDzbNMWE1aBh9daX6WRMqZwkhlWzwBfn7WfHzJM2YFu8jpNO5lplIaXwQFOSZUzLwL8rJO/9jtv5gtu+tmmd1afR4zJz9Xq1/RVYs3iO1qfl6Ca5iuvB9cBln7ra5KvIzCSGVa9ibMf9x8fPEjx5pdiZRGaE24aab52bHZ4c9PzShJ8t8nv39hPvz4qGlilrkHIpvArfPggv+aEReR0qrd3ixwDq0F+9fB+/0hfbfVVYkXURipLCveM+c/gNk+eeED1tYjnsluNz87w7+DsNqQusmsI1n5QfGumSmb4N0+8OtrgAs6DTenBuuMIzlTNVvCyNkQEWuOCXi/v3sf8igeRWGkMqyaanqJAJx3L/R+yNp6xPM1PM9M2zSNg8Jcc7LuV7eaBmYr3jMH3O37y3R1HfwxXPEaBIRaXbV4uhpNTCCp3shsCZ/Sv2L74EiVYXO53P8QgoyMDCIiIkhPTyc8PNzqcsrmz+mmzTsu6H63ObFVXS2lvDidZvQj4SlwOcA/zHRwBWjcGwa9aXbliJSnzH1mMWvKBgiJNotca7W1uipxQ6V9/9bISEVaOwNm3QW4oMutCiJS/ux2c67IyDkQXs8EER9/6DcObpqhICIVI6wWjPgeap0D2SlmUevuVVZXJR5MIyMVZf138Pkw89dqx5th4GvmjUOkouSkmWMFmvSBmNZWVyNVweGD8PG1sPt3CAiHoV9A/XOtrkrciEZGrLTpB9PTweUw3S0HvqogIhUvONLs0FIQkcoSVB2GzYIG50NeBky9CrYtsLoq8UB6hyxvW3+C6TeDswBaD4Ir39A2ShHxXgFhZkSkSR/TA+eT680fZCJloDBSnnYshk+HgCMPWl5uTr/08bW6KhGRiuUfbM6yaXm5+f332RD4e5bVVYkHURgpL4nLzF8EhYeh2SVw7RSdfioiVYdvAFz3AbS9BpyF8OVIWP2p1VWJh1AYKQ+7VppFXAXZZjvl9VPN/5giIlWJjx9c/Q50vMmcCD3rLvh9itVViQdQGDlbe1bDx1eZLZUNL4AbpoFfoNVViYhYw+4DA1+Hbnea69/9B5ZOsrYmcXsKI2cj+W+YOsgcqx3bHW78zMydiohUZXY79P8/OO8+c/2Hh+CXFy0tSdybwsiZStloDiA7fBDqdDKrydVuW0TEsNkg7gno/bC5/tMzMP/J4mcoiRyhMHImDmw1QSQnFWq1g5tnQGCE1VWJiLgXmw16PQiXPGuuLx5vDgx1Oq2tS9yOwkhZHdwBHw6ErH1Qs405kyGoutVViYi4r56j4LKXzce/TYbv7gWnw9qaxK0ojJTFoSQTRDJ2Q1RzGPY1hNSwuioREffX9TZzcKPNDqs+MgeIOgqtrkrchMJIaWXsNUHkUCJENoZh30BotNVViYh4jg5D4Jr3wO4La76AL4ZDYZ7VVYkbUBgpjaz98NEVcHA7VKsPw7/VaagiImei7dUw+GNzuvSG70y31oLDVlclFlMYOZ3sA/DRlZC6yRzRPvxbiKhndVUiIp6rRX8YMh18g2DLfPjkOsjLsroqsZDCyKnkpMHUK2H/OgitBcO/geoNra5KRMTzNbnY7ET0D4Mdi8yJv4cPWV2VWERhpCS56fDxNbBvDYREmxGRGk2srkpExHs06AnDv4bAarBruVmXl33A6qrEAgojJ5OXaYYN96yCoEizWDW6udVViYh4n7qdYcT3EBwF+/6CDy6DzH1WVyWVTGHkn/JzYNoNkPSbaWQ2bBbEtLa6KhER71WrLYycA2G1IWU9vD8A0ndZXZVUIoWR4xXkwmc3ws7FZh7z5plQu73VVYmIeL/o5iaQVKsPaVthSn9I22Z1VVJJFEaOKsyD6TfBtgXgFwI3fWWGD0VEpHJENjKBpEZTSE80gWT/BqurkkqgMALgKIAvRsKWeWar2dDPoX53q6sSEal6IurBiNlQs7U5duPNHvBuX1j4AuxeqXNtvJTN5XL/IxQzMjKIiIggPT2d8PDw8n1yRyF8dQus+xp8Asze9ya9y/c1RESkbHLS4PNhZtvv8YKjoGkfaBoHTfroSA43V9r376odRpwOcz7Cmi/A7gc3TIPml5Tf84uIyNlJ32Uao22eB9sWQn7mcTfaoG4nE0ya9jUf230sK1VOpDByOk4nfDMaVn9szkm4/iNoeVn5PLeIiJQ/R4HZ6bhlPmyeD8lrit8eVN00U2va14yehNa0pk4pojByOvk58Mm1kLgUrp0Cba4qn+cVEZHKkbEXtiaYUZOtP0NeevHba7c/EkzioF5X8PG1ps4qTGGkNPKzIXGZSdAiIuK5HIWw+3cTTLbMg71/Fr89MAIa9z4ypROnw04ricKIiIhUXVn7YUuCCSZbf4LDB4vfHnOO+UO0WV+I7Q4+ftbU6eUURkRERMBsVti9ygSTLfPNxxz31ucfBo17mWDSNE4ns5cjhREREZGTyU41oyVb5pvRk5zU4rdHt4JmR3bo1D8XfAOsqdMLKIyIiIicjtMJe/8woWTzPLPuxHVcYzW/EDNqcnStSfUG1tXqgUr7/n1GHVgnTZpEw4YNCQwMpHv37ixfvrzE+37wwQfYbLZil8DAwDN5WRERkfJlt5ujP3o9CLfNgwe2mh2W7YdASE0oyIaNs+H7eHi1HUzsCnMfMuGlINfq6r1Gmfc5TZ8+nfj4eCZPnkz37t2ZMGEC/fr1Y+PGjdSsefI93eHh4WzcuLHous1mO/OKRUREKkpwJLS9xlycTtPLZPORtSZJyyF1k7ksmwR+wdDwAjNi0iwOIhtbXb3HKvM0Tffu3enatSsTJ04EwOl0Ehsby+jRoxkzZswJ9//ggw+47777OHTo0BkXqWkaERGx3OFD5jDVLfPNJXNv8dsjG5t1Js36QoPzwD/YiirdSmnfv8s0MpKfn8/KlSsZO3Zs0efsdjtxcXEsXbq0xMdlZWXRoEEDnE4nnTp14rnnnqNNmzYl3j8vL4+8vLxiX4yIiIilgqpBm0Hm4nJB8t/HgkniUkjbBsvfMhffQBNIju7QqdEUNCtQojKFkdTUVBwOBzExMcU+HxMTw4YNJz/muUWLFkyZMoV27dqRnp7OSy+9RM+ePfn777+pV+/k26fGjRvHk08+WZbSREREKo/NBrXamsv590FuBmz/xWwf3jwfMnaZ7rBbE8z9qzU4FkwaXQj+IZaW727KNE2zZ88e6taty6+//kqPHj2KPv/ggw+ycOFCfvvtt9M+R0FBAa1ateLGG2/k6aefPul9TjYyEhsbq2kaERFxfy4XpGw8EkzmmVETR/6x2338oUHPYwf8Rbfw2lGTCpmmiYqKwsfHh+Tk5GKfT05OplatWqV6Dj8/Pzp27MiWLVtKvE9AQAABAdrXLSIiHshmg5otzaXnaMjLgh2LjrWqP5Ro1p5sWwA/PgIRsaYbbNO+ZhtxQJjVX0GlK1MY8ff3p3PnziQkJDBo0CDALGBNSEhg1KhRpXoOh8PBmjVrGDBgQJmLFRER8TgBodCiv7m4XHBgy7EdOjsWQ3oSrPzAXOy+UL/HkR06faFma68dNTlembf2xsfHM3z4cLp06UK3bt2YMGEC2dnZjBw5EoBhw4ZRt25dxo0bB8BTTz3FueeeS9OmTTl06BAvvvgiO3fu5Lbbbivfr0RERMTd2WwQ1cxcevzLnCC/Y/GRhbDzzCLYHYvMZf7jEFbn2Bk6jS8yB/55oTKHkcGDB5OSksJjjz3Gvn376NChA3Pnzi1a1JqYmIjdfqyX2sGDB7n99tvZt28f1atXp3Pnzvz666+0bt26/L4KERERT+QfDM0vMReAA1uPHfC3fRFk7oE/ppqLzccc6tfsSDfYWu28ZtRE7eBFRETcUUEu7FxiRk02z4MDm4vfHhpzZBFsH2jc2zRsczM6m0ZERMSbHNxx7HC/bQtNq/qjbHao2+XY9uHaHUyre4spjIiIiHirwjyzZXjLfNPXJGV98duDo47t0GlyMYTUsKRMhREREZGq4lCSabC2eZ4ZNcnPPO5GG9TtZIJJ0zjzsd2nUspSGBEREamKCvNh1/Jj24eT1xa/PSjSjJYcXW8SevJDbsuDwoiIiIhAxt5jW4e3LoC89OK31+5ggknHoeV+8rDCiIiIiBTnKIRdK0ww2TIf9v557Lbh35pzc8pRhbSDFxEREQ/m4wsNephLn8cgM9msNdm2EGLPtawshREREZGqKiwGOgwxFwtZvwlZREREqjSFEREREbGUwoiIiIhYSmFERERELKUwIiIiIpZSGBERERFLKYyIiIiIpRRGRERExFIKIyIiImIphRERERGxlMKIiIiIWEphRERERCylMCIiIiKW8ohTe10uFwAZGRkWVyIiIiKldfR9++j7eEk8IoxkZmYCEBsba3ElIiIiUlaZmZlERESUeLvNdbq44gacTid79uwhLCwMm81Wbs+bkZFBbGwsSUlJhIeHl9vzSnH6Plcefa8rh77PlUPf58pRkd9nl8tFZmYmderUwW4veWWIR4yM2O126tWrV2HPHx4erh/0SqDvc+XR97py6PtcOfR9rhwV9X0+1YjIUVrAKiIiIpZSGBERERFLVekwEhAQwOOPP05AQIDVpXg1fZ8rj77XlUPf58qh73PlcIfvs0csYBURERHvVaVHRkRERMR6CiMiIiJiKYURERERsZTCiIiIiFiqSoeRSZMm0bBhQwIDA+nevTvLly+3uiSv88svvzBw4EDq1KmDzWZj1qxZVpfkdcaNG0fXrl0JCwujZs2aDBo0iI0bN1pdlld68803adeuXVFzqB49ejBnzhyry/Jqzz//PDabjfvuu8/qUrzOE088gc1mK3Zp2bKlJbVU2TAyffp04uPjefzxx1m1ahXt27enX79+7N+/3+rSvEp2djbt27dn0qRJVpfitRYuXMg999zDsmXLmDdvHgUFBVxyySVkZ2dbXZrXqVevHs8//zwrV67k999/5+KLL+bKK6/k77//tro0r7RixQreeust2rVrZ3UpXqtNmzbs3bu36LJ48WJL6qiyW3u7d+9O165dmThxImDOv4mNjWX06NGMGTPG4uq8k81mY+bMmQwaNMjqUrxaSkoKNWvWZOHChVx44YVWl+P1IiMjefHFF7n11lutLsWrZGVl0alTJ9544w2eeeYZOnTowIQJE6wuy6s88cQTzJo1i9WrV1tdStUcGcnPz2flypXExcUVfc5utxMXF8fSpUstrEzk7KWnpwPmTVIqjsPh4LPPPiM7O5sePXpYXY7Xueeee7jsssuK/Z6W8rd582bq1KlD48aNGTp0KImJiZbU4REH5ZW31NRUHA4HMTExxT4fExPDhg0bLKpK5Ow5nU7uu+8+zjvvPNq2bWt1OV5pzZo19OjRg9zcXEJDQ5k5cyatW7e2uiyv8tlnn7Fq1SpWrFhhdSlerXv37nzwwQe0aNGCvXv38uSTT3LBBRewdu1awsLCKrWWKhlGRLzVPffcw9q1ay2b960KWrRowerVq0lPT+fLL79k+PDhLFy4UIGknCQlJXHvvfcyb948AgMDrS7Hq/Xv37/o43bt2tG9e3caNGjA559/XunTjlUyjERFReHj40NycnKxzycnJ1OrVi2LqhI5O6NGjeK7777jl19+oV69elaX47X8/f1p2rQpAJ07d2bFihW8+uqrvPXWWxZX5h1WrlzJ/v376dSpU9HnHA4Hv/zyCxMnTiQvLw8fHx8LK/Re1apVo3nz5mzZsqXSX7tKrhnx9/enc+fOJCQkFH3O6XSSkJCguV/xOC6Xi1GjRjFz5kx++uknGjVqZHVJVYrT6SQvL8/qMrxGnz59WLNmDatXry66dOnShaFDh7J69WoFkQqUlZXF1q1bqV27dqW/dpUcGQGIj49n+PDhdOnShW7dujFhwgSys7MZOXKk1aV5laysrGIpe/v27axevZrIyEjq169vYWXe45577mHatGl8/fXXhIWFsW/fPgAiIiIICgqyuDrvMnbsWPr370/9+vXJzMxk2rRpLFiwgB9++MHq0rxGWFjYCeudQkJCqFGjhtZBlbP777+fgQMH0qBBA/bs2cPjjz+Oj48PN954Y6XXUmXDyODBg0lJSeGxxx5j3759dOjQgblz556wqFXOzu+//07v3r2LrsfHxwMwfPhwPvjgA4uq8i5vvvkmABdddFGxz7///vuMGDGi8gvyYvv372fYsGHs3buXiIgI2rVrxw8//EDfvn2tLk2kzHbt2sWNN97IgQMHiI6O5vzzz2fZsmVER0dXei1Vts+IiIiIuIcquWZERERE3IfCiIiIiFhKYUREREQspTAiIiIillIYEREREUspjIiIiIilFEZERETEUgojIiIiYimFEREREbGUwoiIiIhYSmFERERELKUwIiIiIpb6fwneln5EiEeWAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loss\n",
        "plt.plot(history1.history['loss'], label='train loss')\n",
        "plt.plot(history1.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "a59gi-i6nWvF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "63c8ef08-3d87-4772-ee0f-d95ca2eea754"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = model1.evaluate_generator(test_set)\n",
        "vgg19_accu = score[1]\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "metadata": {
        "id": "XE_xIH4znW3q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d9b4e36c-2581-4074-8fb2-5024058f8ca8"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-59-7dad6ec71727>:1: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  score = model1.evaluate_generator(test_set)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 1.2004477977752686\n",
            "Test accuracy: 0.7333333492279053\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# VGG16\n"
      ],
      "metadata": {
        "id": "H3hQWED53HYh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vgg16 = VGG16(input_shape=(224,224,3), weights='imagenet', include_top=False)"
      ],
      "metadata": {
        "id": "zhvn3SjDnV6_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "50572b72-5103-40ce-8324-1e483e53f7e2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58889256/58889256 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in vgg16.layers:\n",
        "    layer.trainable = False"
      ],
      "metadata": {
        "id": "7fESuPCinV9k"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = Flatten()(vgg16.output)\n",
        "x=Dense(4096, activation='relu')(x)\n",
        "x=Dense(4096, activation='relu')(x)\n",
        "x=Dense(1000, activation='relu')(x)\n",
        "prediction = Dense(1, activation='sigmoid')(x)"
      ],
      "metadata": {
        "id": "qLtUjNDHnWCM"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model3 = Model(inputs=vgg16.input, outputs=prediction)"
      ],
      "metadata": {
        "id": "-0H0p5N5qFRg"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# view the structure of the model\n",
        "model3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "o0qyrzL_qLjv",
        "outputId": "0ed68300-6cda-4c59-fadd-fbc6675157cd"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (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_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_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_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 25088)             0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 4096)              102764544 \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 1000)              4097000   \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 1)                 1001      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 138,358,545\n",
            "Trainable params: 123,643,857\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model3.compile(\n",
        "  loss='binary_crossentropy',\n",
        "  optimizer=\"adam\",\n",
        "  metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "Yl5TvQp1qPtC"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the model\n",
        "history2 = model1.fit(\n",
        " training_set,\n",
        "  validation_data=val_set,\n",
        "  epochs=50,\n",
        "  callbacks=[early_stop],\n",
        "  batch_size=32,shuffle=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "_Oql1A8rqX0Q",
        "outputId": "a513e95e-65ac-4217-8703-86968c1cd59f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "67/67 [==============================] - 15s 219ms/step - loss: 0.1859 - accuracy: 0.9216 - val_loss: 1.1945 - val_accuracy: 0.4961\n",
            "Epoch 2/50\n",
            "67/67 [==============================] - 14s 206ms/step - loss: 0.1622 - accuracy: 0.9337 - val_loss: 1.9364 - val_accuracy: 0.5827\n",
            "Epoch 3/50\n",
            "67/67 [==============================] - 13s 197ms/step - loss: 0.1921 - accuracy: 0.9192 - val_loss: 1.8005 - val_accuracy: 0.5866\n",
            "Epoch 4/50\n",
            "67/67 [==============================] - 13s 197ms/step - loss: 0.1485 - accuracy: 0.9328 - val_loss: 2.0746 - val_accuracy: 0.6102\n",
            "Epoch 5/50\n",
            "67/67 [==============================] - 13s 197ms/step - loss: 0.1024 - accuracy: 0.9585 - val_loss: 2.2649 - val_accuracy: 0.5472\n",
            "Epoch 6/50\n",
            "67/67 [==============================] - 13s 199ms/step - loss: 0.1697 - accuracy: 0.9379 - val_loss: 2.5692 - val_accuracy: 0.4843\n",
            "Epoch 6: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = model3.evaluate_generator(test_set)\n",
        "vgg16_accu = score[1]\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "_Iy3xq2BqZ2p",
        "outputId": "bb6b188c-cca8-4b39-c259-c7b38248c69d"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-58-adcefa686442>:1: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  score = model3.evaluate_generator(test_set)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.6823620200157166\n",
            "Test accuracy: 0.5555555820465088\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracies\n",
        "\n",
        "plt.plot(history2.history['accuracy'], label='train acc')\n",
        "plt.plot(history2.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "xK7MVDn2qy8j",
        "outputId": "11c4895f-8b2a-4386-858d-0024d78b3a8c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loss\n",
        "plt.plot(history2.history['loss'], label='train loss')\n",
        "plt.plot(history2.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "B77fU8zxrA8m",
        "outputId": "edb17565-5c6d-4be0-9aab-b4de1b83ea79"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RESNET50"
      ],
      "metadata": {
        "id": "7w-u9v3w3NVD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "resnet = ResNet50(input_shape=(224,224,3), weights=None, include_top=False)"
      ],
      "metadata": {
        "id": "IUPche2tw8fH"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in resnet.layers:\n",
        "    layer.trainable = False"
      ],
      "metadata": {
        "id": "c6MR_FgMw8hV"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = Flatten()(resnet.output)\n",
        "x=Dense(4096, activation='relu')(x)\n",
        "x=Dense(4096, activation='relu')(x)\n",
        "x=Dense(1000, activation='relu')(x)\n",
        "prediction = Dense(1, activation='sigmoid')(x)"
      ],
      "metadata": {
        "id": "oqfqNZfdw8jk"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model4 = Model(inputs=resnet.input, outputs=prediction)"
      ],
      "metadata": {
        "id": "cnfX2R1tw8lq"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model4.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0iprDjiYw8ny",
        "outputId": "d8ed3c53-f0c5-412f-c766-18fd93c3eebb"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_3[0][0]']                \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
            "                                                                  'conv2_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
            "                                                                  'conv2_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
            "                                                                  'conv2_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
            "                                                                  'conv3_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
            "                                                                  'conv3_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
            "                                                                  'conv3_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
            "                                                                  'conv3_block4_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
            "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
            "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
            "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
            "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
            "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
            "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
            "                                                                  'conv5_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
            "                                                                  'conv5_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
            "                                                                  'conv5_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " flatten_3 (Flatten)            (None, 100352)       0           ['conv5_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " dense_10 (Dense)               (None, 4096)         411045888   ['flatten_3[0][0]']              \n",
            "                                                                                                  \n",
            " dense_11 (Dense)               (None, 4096)         16781312    ['dense_10[0][0]']               \n",
            "                                                                                                  \n",
            " dense_12 (Dense)               (None, 1000)         4097000     ['dense_11[0][0]']               \n",
            "                                                                                                  \n",
            " dense_13 (Dense)               (None, 1)            1001        ['dense_12[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 455,512,913\n",
            "Trainable params: 431,925,201\n",
            "Non-trainable params: 23,587,712\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model4.compile(\n",
        "  loss='binary_crossentropy',\n",
        "  optimizer=\"adam\",\n",
        "  metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "wqK-nXvIw8p0"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the model\n",
        "history3 = model4.fit(\n",
        " training_set,\n",
        "  validation_data=val_set,\n",
        "  epochs=50,\n",
        "  callbacks=[early_stop],\n",
        "  batch_size=32,shuffle=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WIXFXHHCw8tq",
        "outputId": "250b7eed-7a42-4383-b973-4f7e309298db"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "67/67 [==============================] - 22s 238ms/step - loss: 1.3805 - accuracy: 0.5233 - val_loss: 0.7415 - val_accuracy: 0.3386\n",
            "Epoch 2/50\n",
            "67/67 [==============================] - 13s 197ms/step - loss: 0.6806 - accuracy: 0.5518 - val_loss: 0.6819 - val_accuracy: 0.5709\n",
            "Epoch 3/50\n",
            "67/67 [==============================] - 13s 198ms/step - loss: 0.6615 - accuracy: 0.6018 - val_loss: 0.6461 - val_accuracy: 0.5748\n",
            "Epoch 4/50\n",
            "67/67 [==============================] - 13s 194ms/step - loss: 0.6417 - accuracy: 0.6321 - val_loss: 0.6601 - val_accuracy: 0.5630\n",
            "Epoch 5/50\n",
            "67/67 [==============================] - 13s 196ms/step - loss: 0.6113 - accuracy: 0.6746 - val_loss: 0.7022 - val_accuracy: 0.4685\n",
            "Epoch 6/50\n",
            "67/67 [==============================] - 13s 196ms/step - loss: 0.6072 - accuracy: 0.6601 - val_loss: 0.7543 - val_accuracy: 0.5197\n",
            "Epoch 7/50\n",
            "67/67 [==============================] - 13s 196ms/step - loss: 0.5639 - accuracy: 0.7120 - val_loss: 0.6852 - val_accuracy: 0.5197\n",
            "Epoch 8/50\n",
            "67/67 [==============================] - 13s 195ms/step - loss: 0.5620 - accuracy: 0.7185 - val_loss: 0.8758 - val_accuracy: 0.4685\n",
            "Epoch 8: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracies\n",
        "\n",
        "plt.plot(history3.history['accuracy'], label='train acc')\n",
        "plt.plot(history3.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "EfIbWwYcw8vj",
        "outputId": "a311d3f6-f0ac-42dd-9941-4e00b1911c52"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loss\n",
        "plt.plot(history3.history['loss'], label='train loss')\n",
        "plt.plot(history3.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "d-kKwD02w8xo",
        "outputId": "bc068dee-1585-4078-fec3-05010ca35b76"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = model4.evaluate_generator(test_set)\n",
        "resnet_accu = score[1]\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "4NMNWjpLw8zz",
        "outputId": "fbd2dd7b-1335-4345-958b-10f7a0f22d11"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-56-8e8c9d9f02fb>:1: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  score = model4.evaluate_generator(test_set)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.7097933292388916\n",
            "Test accuracy: 0.6666666865348816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "data = {'Custom CNN':custom_accu, 'VGG16':vgg16_accu, 'VGG19':vgg19_accu,\n",
        "\t\t'RESNET50':resnet_accu}\n",
        "model = list(data.keys())\n",
        "accuracy_values = list(data.values())\n",
        "\n",
        "fig = plt.figure(figsize = (10, 5))\n",
        "\n",
        "plt.bar(model, accuracy_values,\twidth = 0.4,color=['#ff6361', '#bc5090', '#ffa600', '#58508d'])\n",
        "\n",
        "plt.xlabel(\"Models Trained\")\n",
        "plt.ylabel(\"Accuracy Attained\")\n",
        "plt.title(\"Comparing Diffferent Model's Test Accuracies\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "xkafDVyew81-",
        "outputId": "29b64318-c601-4277-96d9-3322039ee9ab"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iorEyZilw83m"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TlsZRGmhw85h"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IGLJ9MbSw87n"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PhWGVNcUw89p"
      },
      "execution_count": 47,
      "outputs": []
    }
  ]
}