vgg16.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "vgg16.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPb9bFKcn68lbp7ZGmquXQ3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/deepuvkrish/brain_tumor_detection/blob/main/vgg16.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMWCpOzgX6ua"
      },
      "source": [
        "### Installing required modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3EsHvH2X5zV"
      },
      "source": [
        "pip install -q tensorflow"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYPGB7_lYEhl"
      },
      "source": [
        "!pip install -q kaggle"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJbGmIDNYflx"
      },
      "source": [
        "### Importing required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5tU-rO6HYUBL"
      },
      "source": [
        "import keras, os\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0WgpKI5ZgKr"
      },
      "source": [
        "### DATASET loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "75ImFWzEZmPc",
        "outputId": "6cdc739e-d388-4db0-d324-071e058ccf8a"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-02d1da0c-ae84-4149-b470-855e9d52537e\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-02d1da0c-ae84-4149-b470-855e9d52537e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"deepakkv38\",\"key\":\"108c6cad6346ab3739f7531c490ef2bc\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXOGuxE5ZtMV"
      },
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCSc_-qEZ1e8",
        "outputId": "ec81935c-b55e-481b-ead3-429fe1cf0553"
      },
      "source": [
        "!kaggle datasets download -d navoneel/brain-mri-images-for-brain-tumor-detection"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading brain-mri-images-for-brain-tumor-detection.zip to /content\n",
            " 60% 9.00M/15.1M [00:00<00:00, 13.3MB/s]\n",
            "100% 15.1M/15.1M [00:00<00:00, 19.3MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQ7yzzDrZ5jP",
        "outputId": "13c5a048-2803-4245-97f1-40288700db6a"
      },
      "source": [
        "from zipfile import ZipFile\n",
        "file_name=\"brain-mri-images-for-brain-tumor-detection.zip\"\n",
        "\n",
        "with ZipFile(file_name,'r') as zip:\n",
        "  zip.extractall()\n",
        "  print(\" Extraction completed\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Extraction completed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlN8tq0cZRDS"
      },
      "source": [
        "### DATASET Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IadprBFVZE4d",
        "outputId": "5316bf12-5951-46bf-9077-55fc35b9f099"
      },
      "source": [
        "trdata = ImageDataGenerator\n",
        "traindata = trdata().flow_from_directory(directory=\"/content/brain_tumor_dataset\", target_size=(224,224))\n",
        "\n",
        "tsdata = ImageDataGenerator\n",
        "testdata = tsdata().flow_from_directory(directory=\"/content/brain_test\", target_size=(224,224))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 253 images belonging to 2 classes.\n",
            "Found 253 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0C-Wy87tbjEa"
      },
      "source": [
        "### VGG16 Architecture implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efNL7Tvmap0r"
      },
      "source": [
        "VGG = keras.applications.VGG16(input_shape=(224,224,3), include_top=False, weights='imagenet')\n",
        "\n",
        "VGG.trainable = False"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tdwPTTqb8_B",
        "outputId": "c98eceed-fb9b-42ee-fcee-8beb4c63679d"
      },
      "source": [
        "model = keras.Sequential([\n",
        "    VGG,\n",
        "    keras.layers.Flatten(),\n",
        "    keras.layers.Dense(units=256, activation='relu'),\n",
        "    keras.layers.Dense(units=256, activation='relu'),\n",
        "    keras.layers.Dense(units=2, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer = 'adam',\n",
        "              loss = keras.losses.categorical_crossentropy,\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg16 (Functional)           (None, 7, 7, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 256)               6422784   \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 256)               65792     \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 2)                 514       \n",
            "=================================================================\n",
            "Total params: 21,203,778\n",
            "Trainable params: 6,489,090\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GltVNL2UdHVt",
        "outputId": "4f9d317a-408f-48fa-aab9-05b979f2cb50"
      },
      "source": [
        "hist = model.fit_generator(steps_per_epoch=len(traindata),\n",
        "                           generator=traindata, \n",
        "                           validation_data= testdata, \n",
        "                           validation_steps=len(testdata),\n",
        "                           epochs=5\n",
        "                           )\n"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 4s 534ms/step - loss: 0.0788 - accuracy: 0.9881 - val_loss: 1.8960e-04 - val_accuracy: 1.0000\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 4s 536ms/step - loss: 0.0290 - accuracy: 0.9921 - val_loss: 1.4159e-06 - val_accuracy: 1.0000\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 4s 526ms/step - loss: 0.0113 - accuracy: 0.9921 - val_loss: 1.0530e-05 - val_accuracy: 1.0000\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 4s 535ms/step - loss: 0.0052 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 0.9960\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 4s 559ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 9.4844e-07 - val_accuracy: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "alWg2LPOdqrV"
      },
      "source": [
        "model.save('vggclg.h5')"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4yYWd-SgjFu"
      },
      "source": [
        "#### Plotting data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "D4o8t14lgfAU",
        "outputId": "11af1880-2d05-473c-dfa8-36867804dd5a"
      },
      "source": [
        "plt.plot(hist.history[\"accuracy\"])\n",
        "plt.plot(hist.history[\"val_accuracy\"])\n",
        "plt.plot(hist.history[\"loss\"])\n",
        "plt.plot(hist.history[\"val_loss\"])\n",
        "plt.title(\"Model Accuracy\")\n",
        "plt.ylabel(\"accuracy\")\n",
        "plt.xlabel(\"epoch\")\n",
        "\n",
        "plt.legend([\"accuracy\",\"Validation_accuracy\",\"loss\",\"validation_loss\"])\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "Vaz_B-TDhiNG",
        "outputId": "75b979b5-c97b-4873-fc87-c2a2e0c0f65a"
      },
      "source": [
        "from keras.preprocessing import image\n",
        "img = image.load_img(\"/content/Image363.jpg\", target_size=(224,224))\n",
        "img = np.asarray(img)\n",
        "plt.imshow(img)\n",
        "img = np.expand_dims(img, axis=0)\n",
        "\n",
        "from keras.models import load_model\n",
        "saved_model = load_model(\"/content/vggclg.h5\")\n",
        "output = saved_model.predict(img)\n",
        "\n",
        "if output[0][0]>output[0][1]:\n",
        "  print(\"Cancerous tumor not detected..!!\")\n",
        "else:\n",
        "  print(\"Cancerous tumor detected..!!\")"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:11 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f423f32e680> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "Cancerous tumor detected..!!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-l1tR4WjyWd",
        "outputId": "1b9fd782-363c-4c9b-ea33-cd15c199b993"
      },
      "source": [
        ""
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f42457ec200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "Cancerous tumor detected..!!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YpcDpYuKl9a6"
      },
      "source": [
        "### VGG Classical method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayH5ZB1BuPff"
      },
      "source": [
        "pip install -q pyyaml h5py"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZS7nXp7mD3J",
        "outputId": "a100f3d9-cddf-4f26-80ad-cf655f31855f"
      },
      "source": [
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), input_shape=(224, 224, 3), padding=\"same\", activation='relu'))\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(units=256, activation='relu'))\n",
        "model.add(Dense(units=256, activation='relu'))\n",
        "model.add(Dense(units=2, activation='softmax'))\n",
        "\n",
        "from keras.optimizers import Adam\n",
        "opt = Adam(lr=0.001)\n",
        "model.compile(optimizer=opt, loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_12\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_85 (Conv2D)           (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "conv2d_86 (Conv2D)           (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_30 (MaxPooling (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_87 (Conv2D)           (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "conv2d_88 (Conv2D)           (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_31 (MaxPooling (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_89 (Conv2D)           (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "conv2d_90 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "conv2d_91 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_32 (MaxPooling (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_92 (Conv2D)           (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "conv2d_93 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_94 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_33 (MaxPooling (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_95 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_96 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_97 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_34 (MaxPooling (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_7 (Flatten)          (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_21 (Dense)             (None, 256)               6422784   \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 256)               65792     \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 2)                 514       \n",
            "=================================================================\n",
            "Total params: 21,203,778\n",
            "Trainable params: 21,203,778\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IGnuXJEHwJYQ",
        "outputId": "95840a4a-26de-4a85-d35a-bfb297ef34d2"
      },
      "source": [
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "checkpoint = ModelCheckpoint(\"vgg16_1.h5\",\n",
        "                             monitor='val_accuracy',\n",
        "                             verbose=1, \n",
        "                             save_best_only=True, \n",
        "                             save_weights_only=False, \n",
        "                             mode='auto', \n",
        "                             period=1)\n",
        "early = EarlyStopping(monitor='val_accuracy', \n",
        "                      min_delta=0, \n",
        "                      patience=20, \n",
        "                      verbose=1, \n",
        "                      mode='auto')\n",
        "hist = model.fit_generator(steps_per_epoch=len(traindata),\n",
        "                           generator=traindata, \n",
        "                           validation_data= testdata, \n",
        "                           validation_steps=len(testdata),\n",
        "                           epochs=10,\n",
        "                           callbacks=[checkpoint,early])"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
            "Epoch 1/10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "8/8 [==============================] - 8s 1s/step - loss: 0.6705 - accuracy: 0.5810 - val_loss: 0.6844 - val_accuracy: 0.5455\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.54545, saving model to vgg16_1.h5\n",
            "Epoch 2/10\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.6632 - accuracy: 0.6561 - val_loss: 1.4993 - val_accuracy: 0.6126\n",
            "\n",
            "Epoch 00002: val_accuracy improved from 0.54545 to 0.61265, saving model to vgg16_1.h5\n",
            "Epoch 3/10\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.7452 - accuracy: 0.6403 - val_loss: 0.6708 - val_accuracy: 0.6522\n",
            "\n",
            "Epoch 00003: val_accuracy improved from 0.61265 to 0.65217, saving model to vgg16_1.h5\n",
            "Epoch 4/10\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.6635 - accuracy: 0.6482 - val_loss: 0.6195 - val_accuracy: 0.7312\n",
            "\n",
            "Epoch 00004: val_accuracy improved from 0.65217 to 0.73123, saving model to vgg16_1.h5\n",
            "Epoch 5/10\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.5738 - accuracy: 0.7233 - val_loss: 0.5778 - val_accuracy: 0.7391\n",
            "\n",
            "Epoch 00005: val_accuracy improved from 0.73123 to 0.73913, saving model to vgg16_1.h5\n",
            "Epoch 6/10\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.6020 - accuracy: 0.7312 - val_loss: 0.5375 - val_accuracy: 0.7549\n",
            "\n",
            "Epoch 00006: val_accuracy improved from 0.73913 to 0.75494, saving model to vgg16_1.h5\n",
            "Epoch 7/10\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.5541 - accuracy: 0.7352 - val_loss: 0.5204 - val_accuracy: 0.7470\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.75494\n",
            "Epoch 8/10\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.5148 - accuracy: 0.7668 - val_loss: 0.4869 - val_accuracy: 0.7787\n",
            "\n",
            "Epoch 00008: val_accuracy improved from 0.75494 to 0.77866, saving model to vgg16_1.h5\n",
            "Epoch 9/10\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.5312 - accuracy: 0.7668 - val_loss: 0.5162 - val_accuracy: 0.7826\n",
            "\n",
            "Epoch 00009: val_accuracy improved from 0.77866 to 0.78261, saving model to vgg16_1.h5\n",
            "Epoch 10/10\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.5253 - accuracy: 0.7470 - val_loss: 0.5291 - val_accuracy: 0.7826\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 0.78261\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "BbM_0vvmxEiA",
        "outputId": "cb158105-3493-4c42-d38c-e216ec825fe9"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(hist.history[\"accuracy\"])\n",
        "plt.plot(hist.history['val_accuracy'])\n",
        "plt.plot(hist.history['loss'])\n",
        "plt.plot(hist.history['val_loss'])\n",
        "plt.title(\"model accuracy\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Accuracy\",\"Validation Accuracy\",\"loss\",\"Validation Loss\"])\n",
        "plt.show()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXjU1dn4//fJzCQz2QkJYQskLIGQTAZCWBTZinUXBFygCgKKlbZY2z597LftT/3a2tpH+6uPtlqVzQXBpRV3raCICsomzGRhTcIaQkIg60wyy/n+MQshJCEJM5ks53VduZiZz3Ynl557Pud8zn2ElBJFURSl5woJdgCKoihKcKlEoCiK0sOpRKAoitLDqUSgKIrSw6lEoCiK0sOpRKAoitLDqUSg9ChCiDVCiD+2ct8iIcTVgY5JUYJNJQJFUZQeTiUCRemChBDaYMegdB8qESidjqdL5tdCCLMQokYIsVIIkSiE+FgIUSWE2CiE6NVg/5lCiFwhxDkhxGYhRFqDbWOEELs9x70B6Btd6yYhxB7PsVuFEJmtjPFGIcT3QohKIcQxIcSjjbZf5TnfOc/2RZ7PDUKIvwohjgghKoQQX3s+myaEON7E3+Fqz+tHhRBvCyFeE0JUAouEEOOFENs81ygWQvxdCBHa4Ph0IcRnQohyIUSJEOK3Qoi+QohaIUTvBvtlCSFKhRC61vzuSvejEoHSWc0FfgikAjcDHwO/BRJw/3f7AIAQIhVYBzzo2fYR8L4QItTTKG4AXgXigLc858Vz7BhgFfBjoDfwAvCeECKsFfHVAAuBWOBGYJkQ4hbPeQd74n3WE9NoYI/nuKeAscCVnpj+G3C18m8yC3jbc821gBP4BRAPXAHMAH7iiSEK2Ah8AvQHhgGbpJSngM3A7Q3OuwBYL6W0tzIOpZtRiUDprJ6VUpZIKU8AXwHfSSm/l1LagHeAMZ797gA+lFJ+5mnIngIMuBvaiYAOeFpKaZdSvg3saHCN+4AXpJTfSSmdUsqXgTrPcS2SUm6WUlqklC4ppRl3Mprq2fwjYKOUcp3numeklHuEECHAEuDnUsoTnmtulVLWtfJvsk1KucFzTauUcpeU8lsppUNKWYQ7kXljuAk4JaX8q5TSJqWsklJ+59n2MnAXgBBCA8zHnSyVHkolAqWzKmnw2trE+0jP6/7AEe8GKaULOAYM8Gw7IS+srHikwevBwK88XSvnhBDngCTPcS0SQkwQQnzh6VKpAO7H/c0czzkON3FYPO6uqaa2tcaxRjGkCiE+EEKc8nQX/akVMQC8C4wSQqTgvuuqkFJub2dMSjegEoHS1Z3E3aADIIQQuBvBE0AxMMDzmdegBq+PAY9LKWMb/IRLKde14rqvA+8BSVLKGOCfgPc6x4ChTRxTBtia2VYDhDf4PTS4u5Uaalwq+HlgHzBcShmNu+usYQxDmgrcc1f1Ju67ggWou4EeTyUCpat7E7hRCDHDM9j5K9zdO1uBbYADeEAIoRNCzAHGNzj2JeB+z7d7IYSI8AwCR7XiulFAuZTSJoQYj7s7yGstcLUQ4nYhhFYI0VsIMdpzt7IK+P+FEP2FEBohxBWeMYkDgN5zfR3we+BSYxVRQCVQLYQYCSxrsO0DoJ8Q4kEhRJgQIkoIMaHB9leARcBMVCLo8VQiULo0KeV+3N9sn8X9jftm4GYpZb2Ush6Yg7vBK8c9nvDvBsfuBJYCfwfOAoc8+7bGT4DHhBBVwMO4E5L3vEeBG3AnpXLcA8Umz+b/Aiy4xyrKgb8AIVLKCs85V+C+m6kBLniKqAn/hTsBVeFOam80iKEKd7fPzcAp4CAwvcH2b3APUu+WUjbsLlN6IKEWplGUnkkI8TnwupRyRbBjUYJLJQJF6YGEEOOAz3CPcVQFOx4luFTXkKL0MEKIl3HPMXhQJQEF1B2BoihKj6fuCBRFUXq4Lle4Kj4+XiYnJwc7DEVRlC5l165dZVLKxnNTgC6YCJKTk9m5c2eww1AURelShBDNPiasuoYURVF6OJUIFEVRejiVCBRFUXq4LjdGoCjKeXa7nePHj2Oz2YIditJJ6PV6Bg4ciE7X+nWGVCJQlC7s+PHjREVFkZyczIVFVpWeSErJmTNnOH78OCkpKa0+TnUNKUoXZrPZ6N27t0oCCgBCCHr37t3mO0SVCBSli1NJQGmoPf89qETQwezFxVR+9lmww1AURfFRiaCDlb34Iice+DnOyspgh6IofrNhwwaEEOzbty/YoSjtoBJBB7PtNYOU2HJzgx2KovjNunXruOqqq1i3rjWrfLaP0+kM2Ll7OpUIOpDLZsN24AAAVrMlyNEoin9UV1fz9ddfs3LlStavXw+4G+3/+q//IiMjg8zMTJ599lkAduzYwZVXXonJZGL8+PFUVVWxZs0afvazn/nOd9NNN7F582YAIiMj+dWvfoXJZGLbtm089thjjBs3joyMDO677z681ZMPHTrE1VdfjclkIisri8OHD7Nw4UI2bNjgO++dd97Ju+++20F/la5FPT7agWz5+eBwuF/nqESg+Nf/fT+XvJP+7XIc1T+aR25Ob3Gfd999l+uuu47U1FR69+7Nrl272L59O0VFRezZswetVkt5eTn19fXccccdvPHGG4wbN47KykoMBkOL566pqWHChAn89a9/dcczahQPP/wwAAsWLOCDDz7g5ptv5s477+Q3v/kNs2fPxmaz4XK5uOeee/jb3/7GLbfcQkVFBVu3buXll1/2zx+mm1F3BB3IZjYDED5hgrojULqNdevWMW/ePADmzZvHunXr2LhxIz/+8Y/Rat3fNePi4ti/fz/9+vVj3LhxAERHR/u2N0ej0TB37lzf+y+++IIJEyZgNBr5/PPPyc3NpaqqihMnTjB79mzAPaEqPDycqVOncvDgQUpLS1m3bh1z58695PV6KvVX6UBWswVt375EzfgBJX/6M/aS0+gS+wQ7LKWbuNQ390AoLy/n888/x2KxIITA6XQihPA19q2h1WpxuVy+9w2fgdfr9Wg0Gt/nP/nJT9i5cydJSUk8+uijl3xefuHChbz22musX7+e1atXt/G36znUHUEHsprNGDIz0RuNgOoeUrq+t99+mwULFnDkyBGKioo4duwYKSkpmEwmXnjhBRyertDy8nJGjBhBcXExO3bsAKCqqgqHw0FycjJ79uzB5XJx7Ngxtm/f3uS1vI1+fHw81dXVvP322wBERUUxcOBA33hAXV0dtbW1ACxatIinn34acHcrKU1TiaCDOM6exX7sGIZMI/q0NNBqsVpUIlC6tnXr1vm6ZLzmzp1LcXExgwYNIjMzE5PJxOuvv05oaChvvPEGy5cvx2Qy8cMf/hCbzcakSZNISUlh1KhRPPDAA2RlZTV5rdjYWJYuXUpGRgbXXnvtBXcdr776Ks888wyZmZlceeWVnDp1CoDExETS0tJYvHhx4P4I3UCXW7M4OztbdsWFaaq//JJjP76fQa+8TMT48RTMmYM2theDVq0MdmhKF5afn09aWlqww+i0amtrMRqN7N69m5iYmGCH02Ga+u9CCLFLSpnd1P7qjqCDWM0WCAnBkO7uxzVkGLHm5NDVErGidBUbN24kLS2N5cuX96gk0B5qsLiDWC1mwoYNIyQiAgBDppFzb76J/cgRQtUazIrid1dffTVHjjS7OqPSgLoj6ABSSmxmC/pMo+8z74Cx1ZITrLAURVEAlQg6hP3YMZznzmEwZvo+Cxs6FGEwYLWYgxiZoiiKSgQdwrrX3dgbTOcTgdBq0Y8ahU3dESiKEmQqEXQAq8WMMBgIGzbsgs8NRiO2vDyk3R6kyBRFUVQi6BA2swV9+ihEo+ntemMGsq6OukOHghSZolye6dOn8+mnn17w2dNPP82yZcuaPWbatGl4HwG/4YYbOHfu3EX7PProozz11FMtXnvDhg3k5eX53j/88MNs3LixLeG36MEHH2TAgAEXzHrurgKWCIQQq4QQp4UQLfZ9CCHGCSEcQohbAxVLMMn6emx5eReMD3gZMt2fqbpDSlc1f/58X8VRr/Xr1zN//vxWHf/RRx8RGxvbrms3TgSPPfYYV199dbvO1ZjL5eKdd94hKSmJL7/80i/nbIp35nWwBfKOYA1wXUs7CCE0wF+A/wQwjqCyHTiIrK/H0OCJIS/dwIFoYmNVqQmly7r11lv58MMPqa+vB6CoqIiTJ08yefJkli1bRnZ2Nunp6TzyyCNNHp+cnExZWRkAjz/+OKmpqVx11VXs37/ft89LL73EuHHjMJlMzJ07l9raWrZu3cp7773Hr3/9a0aPHs3hw4dZtGiRr+zEpk2bGDNmDEajkSVLllBXV+e73iOPPEJWVhZGo7HZhXQ2b95Meno6y5Ytu2CNhZKSEmbPno3JZMJkMrF161YAXnnlFd8s6gULFgBcEA+4S2p7zz158mRmzpzpK3txyy23MHbsWNLT03nxxRd9x3zyySdkZWVhMpmYMWMGLpeL4cOHU1paCrgT1rBhw3zv2ytg8wiklFuEEMmX2G058C+g9RWquhireS9w/tt/Q0II9EajuiNQ/OPj38ApP/+31NcI1z/R7Oa4uDjGjx/Pxx9/zKxZs1i/fj233347Qggef/xx4uLicDqdzJgxA7PZTGYT/x8A7Nq1i/Xr17Nnzx4cDgdZWVmMHTsWgDlz5rB06VIAfv/737Ny5UqWL1/OzJkzuemmm7j11gs7E2w2G4sWLWLTpk2kpqaycOFCnn/+eR588EHAXato9+7dPPfcczz11FOsWLHionjWrVvH/PnzmTVrFr/97W+x2+3odDoeeOABpk6dyjvvvIPT6aS6uprc3Fz++Mc/snXrVuLj4ykvL7/kn3X37t3k5OSQkpICwKpVq4iLi8NqtTJu3Djmzp2Ly+Vi6dKlbNmyhZSUFMrLywkJCeGuu+5i7dq1PPjgg2zcuBGTyURCQsIlr9mSoI0RCCEGALOB54MVQ0ewmS1oevdG279/k9sNxgzqDh3C5SmSpShdTcPuoYbdQm+++SZZWVmMGTOG3NzcC7pxGvvqq6+YPXs24eHhREdHM3PmTN+2nJwcJk+ejNFoZO3ateReYnW//fv3k5KSQmpqKgB33303W7Zs8W2fM2cOAGPHjqWoqOii4+vr6/noo4+45ZZbiI6OZsKECb5xkM8//9w3/qHRaIiJieHzzz/ntttuIz4+HnAnx0sZP368LwkAPPPMM5hMJiZOnMixY8c4ePAg3377LVOmTPHt5z3vkiVLeOWVVwB3AvFHHaVgzix+GnhISukSQrS4oxDiPuA+gEGDBnVAaP5jtVgwZGbS3O+oNxrB6cSWn0+45xuQorRLC9/cA2nWrFn84he/YPfu3dTW1jJ27FgKCwt56qmn2LFjB7169WLRokWXLBndnEWLFrFhwwZMJhNr1qzxrV7WXmFhYYC7IW+qj/7TTz/l3LlzGD2TPmtrazEYDNx0001tuk7D8toul8vXfQYQ4akwAO6uoo0bN7Jt2zbCw8OZNm1ai3+rpKQkEhMT+fzzz9m+fTtr165tU1xNCeZTQ9nAeiFEEXAr8JwQ4pamdpRSviilzJZSZl/uLVBHclZVUV9Q0OT4gJfBO8NYdQ8pXVRkZCTTp09nyZIlvruByspKIiIiiImJoaSkhI8//rjFc0yZMoUNGzZgtVqpqqri/fff922rqqqiX79+2O32Cxq9qKgoqqqqLjrXiBEjKCoq4pDnabxXX32VqVOntvr3WbduHStWrKCoqIiioiIKCwv57LPPqK2tZcaMGTz/vLsTw+l0UlFRwQ9+8APeeustzpw5A+DrGkpOTmbXrl0AvPfee9ibeUy8oqKCXr16ER4ezr59+/j2228BmDhxIlu2bKGwsPCC8wLce++93HXXXdx2222+9RouR9ASgZQyRUqZLKVMBt4GfiKl3HCJw7oUW04OSIm+iSeGvLTx8Wj798OmSlIrXdj8+fPZu3evLxGYTCbGjBnDyJEj+dGPfsSkSZNaPD4rK4s77rgDk8nE9ddff0GJ6T/84Q9MmDCBSZMmMXLkSN/n8+bN48knn2TMmDEcPnzY97ler2f16tXcdtttGI1GQkJCuP/++1v1e9TW1vLJJ59w4403+j6LiIjgqquu4v333+d///d/+eKLLzAajYwdO5a8vDzS09P53e9+x9SpUzGZTPzyl78EYOnSpXz55Ze+9ZYb3gU0dN111+FwOEhLS+M3v/kNEydOBCAhIYEXX3yROXPmYDKZuOOOO3zHzJw5k+rqar+V1w5YGWohxDpgGhAPlACPADoAKeU/G+27BvhASvk2l9CVylCX/fMFSp9+mtTvvkXTQvXD4w/8HFt+PsM+67YPTykBospQ90w7d+7kF7/4BV999VWT29tahjqQTw217kFi976LAhVHMFktFkKTk1tMAuCuRFr1n//gOHsWba9eHRSdoihd0RNPPMHzzz/vl7EBLzWzOECklFjNey+oL9QcfYZ36UpVd0hRlJb95je/4ciRI1x11VV+O6dKBAHiOHUKZ2lZi+MDXvqMdBBCLV2pKEpQqEQQIN6ngFp6YshLExlJ6JAh2NSTQ4qiBIFKBAFiNe9F6HSENXjKoSUGo1q6UlGU4FCJIEBsZgthaWmEhIa2an+9MQNnWRmO4uIAR6YoinIhlQgCQDqdWHNzm6wv1BxfJVK1UI3ShXgLqSldm0oEAVB36DCytrZV4wNeYSNGgE6HTS1dqShKB1OJIAC8jXlb7ghCQkPRjxyp7giULklKya9//WsyMjIwGo288cYbABQXFzNlyhRGjx5NRkYGX331FU6nk0WLFvn2/dvf/hbk6JVgFp3rtqx7zYRER6MbPLhNxxmMGVS8+x7S5UKEqByttM1ftv+FfeVN19dvr5FxI3lo/EOX3O/f//43e/bsYe/evZSVlTFu3DimTJnC66+/zrXXXsvvfvc7nE4ntbW17NmzhxMnTpDjmTfT1AplSsdSrU0AWC0WDEZjsxVHm6M3ZuKqqaHeU2RKUbqKr7/+mvnz56PRaEhMTGTq1Kns2LGDcePGsXr1ah599FEsFgtRUVEMGTKEgoICli9fzieffEJ0dHSww+/x1B2Bn7lqa6k7cICo+3/c5mMNxgzAPQchbOhQf4emdHOt+ebe0aZMmcKWLVv48MMPWbRoEb/85S9ZuHAhe/fu5dNPP+Wf//wnb775JqtWrQp2qD2auiPwM1teHrhc7nUG2ig0JYWQiAhViVTpciZPnswbb7yB0+mktLSULVu2MH78eI4cOUJiYiJLly7l3nvvZffu3ZSVleFyuZg7dy5//OMf2b17d7DD7/HUHYGfWfe2faDYS2g06NPTVakJpcuZPXs227Ztw2QyIYTgf/7nf+jbty8vv/wyTz75JDqdjsjISF555RVOnDjB4sWLfYu2/PnPfw5y9IpKBH5mtVjQDRiAtnfvdh1vyDRS/vIruOrrWz0ZTVGCpbq6GnCvv/3kk0/y5JNPXrD97rvv5u67777oOHUX0LmoriE/s5nN6Nswf6AxvTETabdTt3+/H6NSFEVpnkoEfuQoK8N+8iSGTFO7z+EbMFbdQ4qidBCVCPyoLRVHm6Pt1w9NfLyqRKooSodRicCPrOa9oNGgHzWq3ecQQmDIyMCaoxKBoigdQyUCP7KZLYSlphJiMFzWefSZRuoPF+CsrvFTZIqiKM1TicBPpMuFNScHQzvmDzRmMBpBSmy5uX6ITFEUpWUqEfhJfdERXJWVrVqj+FL0Ge4BY1WJVOnspk+fzqeffnrBZ08//TTLli1r9php06axc+dOAG644YYmaw09+uijPPXUUy1ee8OGDeTl5fneP/zww2zcuLEt4Tdp8+bN3HTTTZd9nq5EJQI/8Tba7ZlR3Ji2Vy90SUmqEqnS6c2fP5/169df8Nn69euZP39+q47/6KOPiI2Nbde1GyeCxx57jKuvvrpd5+rpVCLwE+teMyHh4X6rEWQwGrGqOwKlk7v11lv58MMPqa+vB6CoqIiTJ08yefJkli1bRnZ2Nunp6TzyyCNNHp+cnExZWRkAjz/+OKmpqVx11VXsbzCP5qWXXmLcuHGYTCbmzp1LbW0tW7du5b333uPXv/41o0eP5vDhwyxatIi3334bgE2bNjFmzBiMRiNLliyhrq7Od71HHnmErKwsjEYj+/a1vlrrunXrMBqNZGRk8NBD7rpOzZXUfuaZZxg1ahSZmZnMmzevjX/VjqdmFvuJ1WJBn5GB0Gj8cj690UjlRx/hKCtDGx/vl3Mq3dupP/2Junz/lqEOSxtJ39/+ttntcXFxjB8/no8//phZs2axfv16br/9doQQPP7448TFxeF0OpkxYwZms5nMZkqv7Nq1i/Xr17Nnzx4cDgdZWVmMHTsWgDlz5rB06VIAfv/737Ny5UqWL1/OzJkzuemmm7j11lsvOJfNZmPRokVs2rSJ1NRUFi5cyPPPP8+DDz4IQHx8PLt37+a5557jqaeeYsWKFZf8O5w8eZKHHnqIXbt20atXL6655ho2bNhAUlJSkyW1n3jiCQoLCwkLC+sSZbbVHYEfuOrrse3bd1nzBxrznktNLFM6u4bdQw27hd58802ysrIYM2YMubm5F3TjNPbVV18xe/ZswsPDiY6OZubMmb5tOTk5TJ48GaPRyNq1a8m9xEMU+/fvJyUlhdTUVMBd5mLLli2+7XPmzAFg7NixFBUVtep33LFjB9OmTSMhIQGtVsudd97Jli1bmi2pnZmZyZ133slrr72GVtv5v293/gi7gLr8fLDb0bej0Fxz9GlpEBKCzZJD1PTpfjuv0n219M09kGbNmsUvfvELdu/eTW1tLWPHjqWwsJCnnnqKHTt20KtXLxYtWoTNZmvX+RctWsSGDRswmUysWbOGzZs3X1a8YWFhAGg0GhwOx2Wdq1evXk2W1P7www/ZsmUL77//Po8//jgWi6VTJwR1R+AH52cU+y8RhISHEzZ8uLojUDq9yMhIpk+fzpIlS3x3A5WVlURERBATE0NJSQkff/xxi+eYMmUKGzZswGq1UlVVxfvvv+/bVlVVRb9+/bDb7axdu9b3eVRUFFVVVReda8SIERQVFXHo0CEAXn31VaZOnXpZv+P48eP58ssvKSsrw+l0sm7dOqZOndpkSW2Xy8WxY8eYPn06f/nLX6ioqPAV5+usOm+K6kKsZjPaPn3Q9e3r1/PqjRlUf7YRKWWbVztTlI40f/58Zs+e7esiMplMjBkzhpEjR5KUlMSkSZNaPD4rK4s77rgDk8lEnz59GDdunG/bH/7wByZMmEBCQgITJkzwNf7z5s1j6dKlPPPMM75BYgC9Xs/q1au57bbbcDgcjBs3jvvvv79Nv8+mTZsYOHCg7/1bb73FE088wfTp05FScuONNzJr1iz27t17UUltp9PJXXfdRUVFBVJKHnjggXY/GdVRhJQy2DG0SXZ2tvQ+g9xZHL72OkKHDyPp73/363nPvvEmpx55hKGf/YfQpCS/nlvpHvLz80lLSwt2GEon09R/F0KIXVLK7Kb2V11Dl8l57hz1R45cVsXR5pxfulI9RqooSuCoRHCZvJO+/PnEkFfY8OGIsDBsamKZoigBFLBEIIRYJYQ4LYRoshUTQtwphDALISxCiK1CCP9/pe4AVosZhPCVhfAnodOhT0tTA8ZKi7pa964SWO357yGQdwRrgOta2F4ITJVSGoE/AC8GMJaAse01Ezp0CJrIyICcX59pxJaXh7zMx9yU7kmv13PmzBmVDBTAnQTOnDmDXq9v03EBe2pISrlFCJHcwvatDd5+Cwxsbt/OSkqJ1WIh8jIfTWuJwWjk7CuvUnf4MPoRIwJ2HaVrGjhwIMePH6e0tDTYoSidhF6vv+CJp9boLI+P3gM0+6CxEOI+4D6AQYMGdVRMl2Q/cQJneblfKo42x1vW2maxqESgXESn05GSkhLsMJQuLuiDxUKI6bgTwUPN7SOlfFFKmS2lzE5ISOi44C7BZvZfxdHm6AYPJiQ62jdpTVEUxd+CekcghMgEVgDXSynPBDOW9rCaLYiwMPSemiaBoJauVBQl0IJ2RyCEGAT8G1ggpTwQrDguh9VsRj9qFEKnC+h19EYjdfsP4GpnrRZFUZSWBPLx0XXANmCEEOK4EOIeIcT9QgjvXO+Hgd7Ac0KIPUKIzjVd+BKk3Y4tLy8g8wcaM2QawenElp8f8GspitLzBPKpoRaXKJJS3gvcG6jrB1rdwYNIm82vFUebo884P2AcPmZMwK+nKErPEvTB4q4qEBVHm6NL7IM2MVEtXakoSkCoRNBOVosZTa9e6Nr4vG576Y0ZvqeUFEVR/Eklgnaymc3oM40dVh7aYMyk/sgRnBUVHXI9RVF6DpUI2sFZXUPdocMYjIHvFvLyVSLNUd1DiqL4l0oE7WDLyQEpAzqjuDFvUTtViVRRFH9TiaAdrBbPjOIAVBxtjiY6mtDkZFWJVFEUv1OJoB1sZjO6QYPQ9urVodfVZxqxqUSgKIqfqUTQDlazpUMeG23MkGHEcfo09pKSDr+2oijdl0oEbWQvKcFRUtIhM4ob815T3RUoiuJPKhG0kXf94GDcEYSlpYFWqyqRKoriVyoRtJHNbAGdzt0od7AQT6VTm6pEqiiKH6lE0EZWsxn9iBGEhIUF5fr6TCNWSw7S5QrK9RVF6X5UImgD6XRiy8kJyviAl8FoxFVVRf2RI0GLQVGU7kUlgjaoLyzEVVODvgNnFDemN6oBY0VR/OuSiUAIcbMQQiUMwLrXM1DcgTOKGwsbOhQRHq4qkSqK4jetaeDvAA4KIf5HCDEy0AF1ZlaLmZCoKEKTk4MWg9BoMIwapSqRKoriN5dMBFLKu4AxwGFgjRBimxDiPiFEVMCj62SsZjMGYwYiJLg3SHqjEVt+PtJuD2ociqJ0D61q0aSUlcDbwHqgHzAb2C2EWB7A2DoVl81G3f4DQR0f8DJkGpH19dgOdMmlnhVF6WRaM0YwUwjxDrAZ0AHjpZTXAybgV4ENr/Ow5eWB0xnUJ4a8zg8Yq3ECRVEuX2vuCOYCf5NSGqWUT0opTwNIKWuBewIaXSfinVHsbYSDSTdgAJpevXxVUBVFUS5HaxavfxQo9r4RQhiARJekB1YAACAASURBVCllkZRyU6AC62xsZgvafv3Q9ekT7FAQQniWrlSPkCqKcvlac0fwFtBwGqvT81mPYjWbg1JfqDkGYyZ1hw/jqqkJdiiKonRxrUkEWillvfeN53Vo4ELqfBzl5diPH+8U4wNeemMGuFzusQtFUZTL0JpEUCqEmOl9I4SYBZQFLqTOpzOND3gZPLGoiWWKolyu1owR3A+sFUL8HRDAMWBhQKPqZGxmC4SEYEhPD3YoPtrevdH1768GjBVFuWyXTARSysPARCFEpOd9dcCj6mSsFgthw4YREhER7FAuoM/MVI+QKopy2VpzR4AQ4kYgHdALIQCQUj4WwLg6DSklNrOZqGt+GOxQLmIwZlD1ySc4ysvRxsUFOxxFUbqo1kwo+yfuekPLcXcN3QYMDnBcnYb96FGcFRWdanzAyzexLEfdFSiK0n6tGSy+Ukq5EDgrpfy/wBVAamDD6jyCuTTlpehHpYMQaulKRVEuS2sSgc3zb60Qoj9gx11vqEewmi0Ig4GwYcOCHcpFNJERhA0bqtYmUBTlsrQmEbwvhIgFngR2A0XA65c6SAixSghxWgjRZL+FcHtGCHFICGEWQmS1JfCOYjOb0aePQmhbNZzS4fQZRqwWC1LKYIeiKEoX1WIi8CxIs0lKeU5K+S/cYwMjpZQPt+Lca4DrWth+PTDc83Mf8HyrIu5Asr4eW34+hkxTsENpliHTiLO8HMfJk8EORVGULqrFRCCldAH/aPC+TkpZ0ZoTSym3AOUt7DILeEW6fQvECiE6VZeTbf8BZH19p5pR3Jg+wzuxTHUPKYrSPq3pGtokhJgrvM+N+s8A3JPTvI57PruIZyGcnUKInaWlpX4Oo3lW816gcw4Ue+lHpCJ0OpUIFEVpt9Ykgh/jLjJXJ4SoFEJUCSEqAxzXBaSUL0ops6WU2QkJCR12XZvZgiY+Hm2/TnWjcgERGkpYWpqqRKooSru1ZqnKKClliJQyVEoZ7Xkf7YdrnwCSGrwf6Pms03AvTWnE/zdD/mUwGrHl5iKdzmCHoihKF9SaCWVTmvrxw7XfAxZ6nh6aCFRIKYsvdVBHcVZWUl9YiMHUebuFvPTGDFy1tdQXFAQ7FEVRuqDWPBP56wav9cB4YBfwg5YOEkKsA6YB8UKI48AjuJe6REr5T+Aj4AbgEFALLG5j7AHlna3bGWcUN+Ydw7BacggbPjzI0SiK0tW0pujczQ3fCyGSgKdbcdz8S2yXwE8vdZ5g8c0o7gKJIDQ5mZDISKwWM7FzZgc7HEVRupjWDBY3dhxI83cgnY3VbCE0JQVNtD+GQwJLhISgz8hQlUgVRWmXS94RCCGeBbzTVkOA0bhnGHdbUkqsZjORk64MdiitZjBmcGbNy7jq6wkJ7VELyCmKcplaM0aws8FrB7BOSvlNgOLpFBzFxTjLytB34vkDjemNRrDbqdu3r1PPe1AUpfNpTSJ4G7BJKZ0AQgiNECJcSlkb2NCCx1vNsys1qL4BY7OlS8WtKErwtWpmMWBo8N4AbAxMOJ2D1WxG6HToR4wIdiitpk1MRJMQryqRKorSZq1JBPqGy1N6XocHLqTgs5nNhI1KQ3ShvnYhBAZjpio1oShKm7UmEdQ0LBEthBgLWAMXUnBJhwNrbi4GY9frXjEYM6gvLMRZVRXsUBRF6UJaM0bwIPCWEOIk7qUq++JeurJbqjt8GGm1dokZxY3pjZkgJbbcXCImTgx2OIqidBGtmVC2QwgxEvB2mO+XUtoDG1bwdKWJZI0ZMtIBd0lqlQgURWmt1tQa+ikQIaXMkVLmAJFCiJ8EPrTgsJnNhMTEoBs8ONihtJkmNhbd4EGqEqmiKG3SmjGCpVLKc943UsqzwNLAhRRcVrOlS1QcbY4hw4g1R80wVhSl9VqTCDQNF6URQmiArvM4TRu4amqoO3iwU69IdimGTCOO4mLsp08HOxRFUbqI1iSCT4A3hBAzhBAzgHXAx4ENKzhseXngcnWpGcWNeaul2tRdgaIordSaRPAQ8Dlwv+fHwoUTzLoN30BxV04EaWmg0aj5BIqitFprVihzAd8BRbjXIvgBkB/YsILDaragGzgQbVxcsENptxCDgbDhw9WAsaIordbs46NCiFRgvuenDHgDQEo5vWNC63hWi5nw0aODHcZlMxiNVP7nP0gpu+ygt6IoHaelO4J9uL/93ySlvEpK+SzQbRfFdZSW4jhZ3KXHB7z0xgxcFRXYjx4NdiiKonQBLSWCOUAx8IUQ4iXPQHG3/Xrp7VPvyuMDXg2XrlQURbmUZhOBlHKDlHIeMBL4AnepiT5CiOeFENd0VIAdxbrXDBqNe7C1iwsbNgyh12OzmIMdiqIoXUBrBotrpJSve9YuHgh8j/tJom7FZjETNiKVEEPXfyBKaLXoR41SdwSKorRKm9YsllKelVK+KKWcEaiAgkG6XFgtOV2y4mhzDMYMbHl5SIcj2KEoitLJtWfx+m6nvqgIV1VVtxgf8NIbM5E2G3WHDgU7FEVROjmVCGg4kazrlpZozGDMAM7/boqiKM1RiQBPxdHwcEKHDAl2KH6jGzSIkJgYbGqcQFGUS1CJAPeMYr3RiNBogh2K3wghMGRkqFITiqJcUo9PBK66Omz793erbiEvfaaRuoMHcVm77cqiiqL4QY9PBHX5+WC3d4sZxY0ZjEZwOrHld8vSUIqi+EmPTwRWc/eZUdyYPsM9YGxT3UOKorRAJQKzGW2fPugSE4Mdit/p+vRB27evL9kpiqI0RSUCixmDqfvdDXgZjEasOSoRKIrSvIAmAiHEdUKI/UKIQ0KI3zSxfZAQ4gshxPdCCLMQ4oZAxtOY4+xZ7EeOou9GM4ob0xuN2I8cxXnu3KV3VhSlRwpYIvCsbfwP4HpgFDBfCDGq0W6/B96UUo4B5gHPBSqepniXc+yO4wNe3qehrDm5QY5EUZTOKpB3BOOBQ1LKAillPbAemNVoHwlEe17HACcDGM9FrGYzCIE+I70jL9uh9Onu301VIlUUpTmBTAQDgGMN3h/3fNbQo8BdQojjwEfA8qZOJIS4TwixUwixs7S01G8BWs1mQocOQRMZ6bdzdjaaqChChwxRlUgVRWlWsAeL5wNrpJQDgRuAV4UQF8XkqXiaLaXMTkhI8MuFpZTYzBYMmSa/nK8zMxgzsFrMSCmDHYqiKJ1Qs2sW+8EJIKnB+4Gezxq6B7gOQEq5TQihB+KB0wGMCwD78eM4z57tljOKG9MbM6l49z0cJSXo+vYNdjiK4j9SgssJznrPj/3C1y5705/7/m3ic1cTn0tXsH9TtyHTYOSNfj9tIBPBDmC4ECIFdwKYB/yo0T5HgRnAGiFEGqAH/Nf304LzFUe770CxV8NKpCoRKJ2WywnlBXDKAiW5UJIDFSeab5y9rwMlRAuaUAjRQUiwO088wuO7ViKQUjqEED8DPgU0wCopZa4Q4jFgp5TyPeBXwEtCiF/gHjheJDuo/8JmtiDCwggbPrwjLhdUYSNHgk6HzZJD9DXdbpVRpSuyVbgb+1M5UOJp+E/ng73WvV1oID4VeiWDNszdIGtCQaNt8Fp3/t8QXaPPvK+b+fxS+3emxr8DBPKOACnlR7gHgRt+9nCD13nApEDG0Byr2Yw+PR2h0wXj8h0qJCwM/YgRqhKp0vFcLjhb6P52fyrn/L8VR8/vY4iDvhkwdjEkplMXP4qDrgHkna7jVKXtEuf3/LQ/QMDm+WleZJiW/rEG+sfq6RdjoHdEKCEh4nIu3KkENBF0VtJux5aXR69584IdSofRGzOofP8DpMuF6EHfdJQOVFfl+ZbfoGunJA/sNe7tIgR6D4ekcZC9GJmYQWnEcHIrw8k7VcW+U1Xk51VSUHoSl+zQJ8nbLFQTQt8YPf1j9fSPMdDPkyD6x+rpH2ugX4yBaL0WIbpGsuiRicB24ACyrg59Dxgo9jIYMzm3bj31RUWEdaMFeLoNlwtqz4C13N0VoouA0AjQGaCzNSYuF5w7cuG3/JIcOFt0fh99LPQ1QtYCSMygLn4Uh+RA8krryS+uIj+/kn1fVHK2Ns93yMBeBtL6RXNDRl/S+kWT1i+aAb0MhAT595dSUmlzcPKcleIKGyfPWTlZYaX4nI3iCivfFZZzqtKG03Vhr3ZEqIZ+sQb6xbiTRf9Yd8LwJo7+MQYMoZ1jDZSemQg8XSQGU/d/dNTLO2Bss1hUIugoLpe7Ya8+DTWn3f9Wn4bqEqgpPf++5jTUlIF0NnES4U4IDX90ERd/FhoBoZGgCz//OjS8mc8j3MmmNQ1sXTWczmvU6OdBfZUnvBCIGwr9x8CYBcjEdM5EppJTFUn+qWr2naokf18lh0tLcLpOAaDXhTCibzTXeRr8kX2jGdkvimh9Z+2mFcRFhBIXEUrGgJgm93C6JKVVdb4E0ThZ5BdXUVZdd9FxvcJ1vjuJfp4EMcBzR9EvRk/fGD06TeDv4HtkIrDuNaPp1QvdgMbz27qv0CFDCAkPx2q2EDNrFmXWMuxOO/0i+wU7tK7F5QLr2Qsb9hpP415d2uh1adONuyYUIvpAZB+IGQD9R0Nkovu9Ic79JEx9jbtLpb6JH3sN2M5B5Qmor4X6avfnzosbmmaJkPNJoXGSCA13P8FzOg/KC3E/xwGExbj78kfPh8QM6hNGcYgk8ksd5BdXkn+gkvwvqyiv2ee7zIBYAyP7RnHNKO+3/CgG945A04361wE0IYK+noabQU3vU+dwUlLhSRYVVk56EkZxhY3jZ61sLyyn0ua44BghICEyjH6xBgbE6rnB2I+bMvv7Pf6emQgsZgyZmV2m/84fhEaDPj2dc3t28o9v/j8+KPgAp8vJ1YOv5l7jvYzq3bgMVA8hJTjq3I1pTVkTDXyjb/E1peByXHyeEJ2nMU+AqP7Qb7S7Yfc2+A1f62MC093jdDRIHg0ShL3B60t9XnsGKo65/y59jWByN/pnolLJrY4m/1QV+cWV7DtQxaHTpThc7ik/YdoQRvSN4odpiYzsF+Vu9PtGExPeWb/ld7wwrYZBvcMZ1Du82X1q6hy+JFFcYeXEORvFnmSx71QVxgGxAYmtxyUCZ3U19YcLiL7++mCH0qHyzuRhji1j1O5C/nPoGLeNvI1wbThv7H+Dz458xpX9r+Re471kJ2Z3zgQp5flvyvXVnobL89pe2/Tnl2wEPfs1N1koROdpwBMgqi/0y2y6YY/s4+4TD/bfTaMFTYw70XhIKbE7JXany/PT8LWLekfT287V2tlfUkX+oUryv6qkrHq/75x9o/Wk9YviByP7MLJfNKP6RZHcOwJtB3RhdHcRYVqG9YliWJ+oDr1uj0sEtpwckLJHTCSTUrKzZCcrLCvYenIr02JCMTnh3fRn6Jvtfmr3HuM9vLn/TV7Ne5Ulny4hMz6Te4z3MC1pGiEXV/vwL1sl7F0PZw4106jXXNiwN9mH3gxNaBP94xEQ3b/5vvbw3p6GPdHd+Bt6XXbjLqXEZndRU++gts5Jrd1BTZ2T2noHtfXuf2vqnFjrndTUO7DWO6lzuHC4XNg9jXR9k424Z5uj8bYLG3q7s/3TckK1IaQmRjJtRB9ft05a32h6RYRe1t9E6Xx6XCLwLU1p7L5PDLmkiy3Ht/CS5SXMpWbi9HH8POvnzJ06hVNvzyLs4FHwJIKo0CjuMd7DnWl38u6hd1mdu5qff/FzhsYMZYlxCdenXI8uxM+395Un4bt/ws7VUFfp/gbr65/2NNCRfc/3Vzfe1tRAaePPNW2L2dtgexvomkoHtWXnqK0730D7GnNvA+5tzC/Yp0EjX+eg1u6kLVMkw7QhhGpDCNWEoNOEoNMK978hDV5rQjDoNETrtb73Oo1nm+9YgdazLVRz/jj3doE25Pzr8+cIIVTr3hYRpmVw7/AOGahUgq8HJoK96AYPQhMbmL62YHK4HHxS9AkrLSs5dO4QAyIH8LsJv+OWYbeg1+qRUlIaF4fVbKHX/PkXHKvX6rlj5B3MTZ3Lp0WfsjJnJb/7+nf84/t/cHf63cwePhuD1nB5AZbkwdZnwfKW+9v9qFlw5QMwIOvyznsZjpXX8vLWIt7ceeyigbqWhGndjWV4qMbzoyUiTENseCgRYe734aEaIkI1hPv20xIRqsEQqmlwrNa3j0Gn6XaDqErX0OMSgc1sIXzChGCH4Vd1zjo2HNzA6tzVnKg+wdCYofzpqj9xXcp1F3ybF0JgMBqxtbB0pTZEy41DbuSGlBvYcnwLKywr+PP2P/OC+QXuTLuTeSPnER0a3ezxF5ESir6Cb56BQ5+5u2myF8PEn0BcyuX82u0mpeTbgnJWf1PIxvwSQoTguoy+pPeP8TXsEWFad4Mdqr3gM2/jrRpspTvpUYnAXlKC4/TpbtMtVF1fzZsH3P37ZdYyMuMz+e9x/91i/77eaKR6yxac1TVoIiOaPbcQgqlJU5maNJVdJbtYYVnBs98/y6qcVdyeejsLRi0gIbyFkuBOB+S/604AxXvcfe7Tfw/j7oHwuMv91dvFZnfy3p6TrPqmkH2nqugVrmPZtKEsmJjsfuxPUXqoHpUIrHv3AnT50tPltnJey3uN9fvXU1VfxcR+E/nL5L8wru+4Sz7xY8g0gpTY8nKJGD++VdcbmziWsYlj2Ve+j5WWlbyc9zJr89cya9gsFqcvJim6QbXx+hrY/Sp8+w84dxR6D4ObngbTPPcs2SAoqbTx6rYjvL79KOU19YzsG8Vf5hqZNXoAel3nmNmpKMHUoxKBzWIBnY6wtLRgh9IuxdXFvJz3Mv868C/qnHXMGDSDe4z3kBGf0epz6D13QzaLpdWJwGtk3EienPokyyuXszp3NRsObeBfB//FtYOv5Z6hsxmxfyPsWOGe7JQ0Aa79M4y4IWhVHL8/epbV3xTxkaUYp5RcnZbI4knJXDGkd+d8RFZRgqRHJQLrXjP6ESMICQsLdihtUlBRwCrLKj4s+BCAG4fcyJKMJQyJbXupCG2vXugGDryspSsHRQ/ikSseYZlpGa/tepY3Ct7n46KPmVxr5Z5BYxh71f+BQcEZh7E7XXxkKWb1N0XsOXaOqDAtd1+ZzN1XJLc4kUdRerIekwik04ktJ4eYW24JdiitlluWywrLCjYd3USYJozbR9zOovRFl10WQm/MwLb3MhazlxKOfkufrc/wy/0fcY9Wz/rUiawNLWNR/WHG5P6De0PqmDxgcod98z5TXce67Ud59dsjlFTWkRIfwf+dmc7csQOJDOsx/5krSrv0mP9D6gsKcNXWdvqKo1JKdpzawQrLCrYVbyNKF8W9xnu5M+1Oeht6++UaBmMmVR9/guPMGbS923BOlxP2fQhbn4HjO9wTrqb8NzHj7+PHkQksdFj598F/syZ3DT/d9FNSe6VyT8Y9XJN8DdqQwPynll9cyepvCtmw5yT1DheTh8fzxJxMpqYmdKt68YoSSD0mEVhzcoHOuzSlS7rYfGwzKy0rMZeZ6a3vzYNZD3LHiDuIDI3067V8S1daLERNm3bpA+xW2PM6bPu7eynBXslww1Mw+kfuyVve82oN3Jl2J7ePuJ2PCj5iVc4qHvrqIZ79/lkWZyxm1rBZhGkuv1vO6ZJszC9h9TeFfFtQjkGn4baxA1l0ZTLDEzt2ar6idAeig1aG9Jvs7Gy5c+fONh8npcR+5Ai6QYM61cIsdpedTwo/YVXOKt8ksMXp7kZTrw3MI42umhr2jxtP/LJlJCz/WfM71pyBHS/B9hfdxcj6Z8GkByBtJoRc+mkbl3TxxbEvWGlZiaXMQm99bxaMWtDu5FZhtfPWzmO8vK2IY+VWBsQaWHjFYOaNG6SKmynKJQghdkkps5vc1lMSwZ7Te3g592X0Wj0GrcH3430frg2/4H1T2/Vavd/q79gcNjYc2sCa3DWcqD7BsNhhLMlwl3QIVDdKQwU3z0Q6nST87KdEXHUVmugGk8TKC2DbP+D7teCwwvBr3Qlg8KR21d6RUrL91HZWWFbwbfG3ROmimDdyXqu7uwpKq1mztYi3dx2ntt7J+OQ4Fk9K5oejElWhM0VpJZUIgK9PfM1fd/4Vq8Pq+7E5bEja9vuHacIuShLN/TTcrtfoMejcCeXA2QO8lvcaZ2xnyEzI5N6Me5maNDXwRd4aqHj3XUr+9GecFRWg0RA+diyRY4YSGWoh9PRnCK0OMm+HK5ZDn5F+u25uWS4rc1ay8chGQjWhzB42myUZSy4aAJdSsuVgGau/KWTz/lJCNSHcbOrP4knJzS4OoihK81QiaIaUEpvT5ksKDZNE44TR3LbG2y947Wx+QezOUPZZOp1Yv/+e6g1rqP5yC3WldgB0CZFEzriWqGtuIDw7GxHq/2qTBRUFrM5ZzQeHP0AIwa2pt7LUuJQIbS/+tfsEa74p5HBpDQlRYdw1YTA/mjCIhKiu9divonQmKhEEiUu6LkgMlpOlvP19AQeL7SToBxEfGUpCVBjxkWG+f+Mjw+jjeR3Q9UztVnfxt63PQtkBiB6IffhdVJ3tS/XX26j99jtkfT0hERFETJpE5LRpRE6ZjDY+3q9hFFcX84L5Bd45tAEhNTjPXUnV6clk9nN/+7/R2J9Qrer+UZTL1VIi6DFPDQVDiAghNETPFwcrWLP1KDuKzhIeGsFVw+KprnNQUFrD9sJyztbamzw+IlTTZKJwvw4lPiqMBM/7S5ZKqD4Nx76Do9/Cse3u+j/Oekg0wpyXIH02Oo2OOCBu4WJctbXUfPsd1Zs3U715M1X/+Q8Igd5oJHLaVKKmTSMsLa1VdzMul8TmcDZZh7/SJjl1+GaqD6UQmrAJbeyXJPTezjXpC7g63aSSgKJ0AHVHECDnautZt/0Yr24r4mSFjaQ4A3dfkczt45IuWqTb7nRxprqesuo6SqvqKPX8W1ZdR1l1PaVVNso82881kzSiwrTEexJEn0gtadpiRjnySbHmkFixl/DqowBITSiifxYkjYdhV0PKFN8AsJQSq915wUIptfUOausc1O8/gHb7N4Tv+paIwv0IKbFGx3F8ZBYFQ00cHDiSSnTuWv513sb+fMPfkthwHfPHD2LBxMHUyhM8t/c5PjvyGdGh0SzOWMyPRv6IcJ2aFawol0N1DXWggyVVrN5axL93H8dmd3HFkN4snpTMjLREv5Qurne4OFNTR1lVPaXVNsqq6jl3rpywkj30PrubgdUWhtXlE0ktAKUyml2uEexyDWeXK5UcmUKY3kBCZBih2hBfw19b78DaykVUYm1VZJfs44rT+Ywp2Y/BUYddo6Vg4EgKho3h+MgsXAmJGBrU2o9oVLff0KA2/9CEyIvuaHLP5PL37//O1ye+pre+N0szl3Jr6q1+mYegKD2RSgQB5nJJNh84zepvivjqYBmh2hBmjx7AoknJpPVrQ+3+1pDSvbj4se2ebp7voCTHs+6ugD6j3N/2B02krl82Z3T9KfXcTZT57jTqKa2qo87h8iyicuECKW1ZREXW11O7a5e7++iLzdiPuu88wlJTiZw6lcjp0zCYTAhN+8Y7vj/9Pc9+/yw7Tu0gMTyR+033M2vYLP+vmqYo3ZxKBAFSXefg7Z3HeHnbEQrLakiMDmPhFcnMHz+IOH+t6+q0wykzHP3O3egf+w6qit3bdBEwMBsGTXQ3/gOywRC8ldeklNQXFlH95ZdUb95M7a5d4HCgiY0lYspkoqZNu3jOQivP+92p73h297OYy8wkRSWxzLSMG1JuQNOKiW2KoqhE4HdHz9SyZmsRb+08RlWdgzGDYlk8KYXrM/pe/hqvteXub/vHvnP/e2KXe1IXQMwg37d9ksZDn3TQdN7xfmdlJTXffOMecN7yFc6zZ91zFrKy3E8hTZ9GaEpKqx+flVKy5fgWnv3+Wfaf3c/QmKH8dMxPmTFoRofOwVCUrkglAj+QUrLt8BlWfVPEpn0laITgxsx+LJ6Uwuikdn4LlxLOHDrfxXPsO/ejnAAhWuibeb7RT5oA0f399wt1MOl0YjWbqd7svluo278fAF1SEhGTriQ8exzh2WPR9e17yXO5pIvPjnzGP/b8g8KKQtLi0vjZmJ91aLVTRelqVCK4DDa7kw3fn2DN1iL2naoiLiKUOycM4q6Jg0mMbkctoNL97gqe3m/91nL354Ze7sbe2+j3z4LQ7vukjP3kSaq3bKH6i83U7tyJq6YGAN3AgYRnZxOePZbw7Gx0gwc327g7XA4+LPiQ5/c+z4nqE4xOGM3yMcsZ369tC+4oSk8QtEQghLgO+F9AA6yQUj7RxD63A48CEtgrpfxRS+fsqERQXGHl1W1HWLf9KGdr7aT1i2bxpGRmmvq3fXnDumrIfQd2vwLHt7s/6z3cvXhL0gRImuhe0rETFcPrSNLhwLZ/P9adO6nduYvanTvd3UiAJiGe8LHZ7uQwLpuw4cMvKhpod9p559A7vLD3BU5bTzOh3wSWj1mOKcEUjF9HUTqloCQCIYQGOAD8EDgO7ADmSynzGuwzHHgT+IGU8qwQoo+U8nRL5w1kIpBSsvvoOVZ/U8jHOaeQUvLDUYksnpTChJS4tnU7SAnHd8Lul91JoL4a4lNhzALIvAOiEgPyO3QHUkrqCwp8SaF2504cxe4B8pDoaMKzsnx3DPr0dITO/QSRzWHjzf1vsjJnJeW2cqYOnMrPxvyMkXH+q5UUKHannaNVRzlVc4q03mnE6eOCHZLSzQQrEVwBPCqlvNbz/v8ASCn/3GCf/wEOSClXtPa8gUgE9Q7v8oaF7D1eQZRey7xxSSy8IpmkuDZ2z9SUwd718P2rULoPdOGQPgeyFrq7fVQfdrvYT5zwJYXanbuoLywEQBgMGEwmT3dSNgZTJjatZG3+WlbnrqaqvoprBl/DT8f8lCExbV/a099sDhtFlUUUnCvgcMVhCs4VUFBRwNHKozikAwCBIK13GpP6T+LK/ldi6mNSj8sqly1YieBW4Dop5b2e9wuACVLKnzXYZwPuu4ZJuLuPHpVSFNlPVgAAEItJREFUftLEue4D7gMYNGjQ2CNHjvglxrLqOl7/7iivfXuE01V1DEmIYPGVyczJGkhEW5Y3dDnh8Bfw/Suw7yNw2d2PcmYthIw5EKYWS/E3R1kZtbt2+5JD3b597rswnQ5Dejrh2WNh9Cj+pc9jzZG3sDlt3DTkJu433U9SVFLA46ux11BYUcjhc4c5XHGYwnOFHK44zPGq476KtyEihEFRgxgSM4QhsUMYEjOEhPAE9pzew9aTWzGXmnFKJxG6CMb3He9ODAOu7JD4le6nMyeCDwA7cDswENgCGKWU55o7rz/uCHJPVrD6myLe2+te3nBqagKLJyUzZXgblzc8ewT2rHXX7a88DoY4MM2HrAXQJ+2yYlTaxllZifX77313DNacHLDbQQi0w4dycHAo70cfJm8AXD1mLkszl9I34tJPKF1KRV0FBRUFHD53mIKKAt83/VM1p3z7aEO0JEcnMyRmCENjhzIkdghDY4YyOHowoZrm55v8v/buNrat677j+PdPUiIpydYj5Vi26ycplmQ7lpQ0iO1hGJJgy9ohG9ACXZs13bBXRddmw7C168ttfTNsQ9etyNB1KYY2awYk3dYFe2iXrkVgO/EDJdmxFMey4tiRbIu0LJmS+Mz/XlyKlGwptWLJl9b9f4ALUVcSdXxhnd8959xzzs3MTY5fOc6R8SMcHTvK+Ow4ANs2bONQ2yEOtx3m0c2PUltVu+x7GDOvkruG/h54U1W/U/z8NeArqnpiuff9sEGQLyg/HrrKC0cucvxdZ3vDTz68lc8d2kF76wp2y8ql4e1XIfpdGP2pc273407lv+djELAlECpBIZkkefoMcydPMHfyJMmBQTTpzMcYbxLe2eaj/rFDPPn0l4js2vuB4z+qyvXU9du6cy5MXeB66nrp+0L+EDvrd5Yq+vm7/G0btt31ZkOqyns333NCYfwoJ66eIJlLEvAF6In0cHiL043U2dRpcyrMktwKggBOt88TwBjOYPFnVPXsgu95CmcA+XMi0gL0Az2qen2p94QPHwT/cuISX37lDFsawvz2IWfxt/rwCvpdr511Kv/TL0HyhjO5q/cZ6HkGGqypXuk0myU1NMTcyZNMvnGEuVMnqS4u4JdqqqXxscNsfOQxkt3budicZzRxsXyXPz3KdHq69F51VXWlSn5hhd9W13bPKuFMPkP/RH+ptXDuhjMvoynUxMG2gxxuO8zBtoO0hFd32XBz/3Lz8dGPAV/H6f9/QVW/JiJ/CpxU1R+Kcxv2V8BTQB74mqq+9EHv+WGDYDad4/XzMZ7sWsH2hqmb8NYrzmOf41HwV0Pnx52+/52/5NnHPdcDLRQYjf6Un736PDp4lu7L0DDj/C3MBuHcFuHSjjBz3dsJ7dvHjk17nL78+l201rRW3MS1eDLOsfFjHBk/wrHxY0ymnPkpnU2dHGo7xKG2Q/S29n5gV5RZ32xC2UqowqVjzt3/0L9Bds5ZyK3vWeexzxp7rG+9OTd5jn8efpGmyRxd78MDo1OEzr5LfvSi8w2BAKG93dT09hF+uI+avj4CzT9/r2W3FLTA25Nvc3T8KEfGjjAwMUBOc4QDYT76wEdL4wvbNy4/Wc+sPxYEdyJxDQa/7zz2eX0EqjfA/k9A77Owpc8e+/Sg/NQUc/39JKP9zEWjpM6cQTMZAKq3byfc10fNw32E+/pWtGbSvTabnS0NOh8bP8alhLNC7Ja6LYsGnTdU29Nt65kFwXLyORj5X6fyP/dfoHn4yEFn0tfe34BqexrDlBUyGVJvnSXZH2XuVJRkNEp+ynnAzd/YSLi31wmG3j5C+/biW4O9nlfD5cRljo4d5cj4EY5fPc5sdha/+DkQOVAaX+hu7raVXdcZC4JbXb8A/d9zWgCJK1AbcR777P0sRB5cnYKadW9+2e1k9FQpGDLFOS5SXU1o/35q+nqdlkNvL/4G95YIX062kGVwYtDpRho/wvD1YRRlY/VGelp76In00NPaw97mvbZL3H3OggCczdqH/8MZ+L34OogPOn7Zqfwf/BXw28xNc/dy8bjTnXQqylx/lNTZIcg5M4ar23eXxxkefpiqrVsrrjtpMjXJG+Nv8ObVNxmcGOTC9AUA/OJnT9OeUjD0RHp4oPaBiiu/WZ4FATgtgH//AjTucCr/ns/c18s6m/tDIZkkeeYMyWiUuWiUZP8AhUQCKC6o11seZwh1dpbWTaoU0+lpTsdOMxAbYHBikNPx0ySL+2O01rQuCobOpk6q7IaqYlkQgLMC6HgUtv+CPfZpXKOFAunzI053UrSfZDRKdmwMKK6btG8fVVu2EIhECLS2Fj+WX/uC7k5YzBVynL9xnoHYAAMTAwzGBhmbccof9AfZ27y3FAwHWg/Y4nkVxILAmAqWvXbNaTGccp5Myl67Ri4eL3UpLeTbuNEJhohzVM2HxcLgiETw1dy7/vyJuQkGY4MMTDjhMDQ5RK7glH37xu0ciBwohcPuht0VPfNZVUlkE8Tn4sST5SOnOYL+4KIjFAhR7a8m5C9/DAYWf0+Vr2pVus9UlcLMDIjgr1vBSggLWBAYc5/RQoH81BS5iQlysRi5iRi52ETxY6x8PhZDs9nbft5XV7dkQNza0vDV1q56P38ql2Lo+tCiVsP8BLcNVRt4KPIQB1oP0BPp4aHIQ/dkraRMPrOoYo8n41xPXieejBNLxkqv48k4mUJm1X6vIKXAmA+HMNU0pP3Up/zUp3xsSCobklAzl6d2rkB4NkdoNktwNkPVTJqqRIrATBLJF+DZT9L11T/7cGX5gCCo3A1vjfEw8fkINDURaGqCzuX3U1BVJzCKoVAOjXJgJAcHncBIpW7/PTU1BCItt7cuNm8m1NlJ9Y4diH9lj5GGAiH6NvXRt6mvVMbLiculYBiIDfD8wPMoik98dDR00NPaU2o5bK27s0H0ghaYSk/dVrEvVbnfzNxc8j0ag400h5uJhCNs37idlnALzeFmWsIti45qfzXpXJp03jlS+RSZXJpUYprc1CSZG5Pkp6YoTE2j0zdhOoFMJ5DELIGbSQIzc1QlpqmeSVOdur2lNy8bgJmwkAjBRBhmaiHRAjNhSIR9PLgry1osZ2ktAmM8QFUpJBKLAmJRaBQ/z8Zi6Nxc6eckHCa0Zw+h7m5C3V2EursJtrcjdzlHIpFJcCZ2phQOp+Onmc0625U2h5pLXUmb6zYzmZq87W4+nowzmZws7eGwUDgQXlSJN4fKFXukJuJU9KEWmsJNy+7zoPk8mUuXSI+MkBkZIXvlKvmpKee4caP0eqnW2DxfXR3+hoby0dhYfF2/+HxDA4HiR6mpKYVgrpAjk8+QyqdKIVRXXfeh14+yriFjzB3Lz8ySHXuf1PAwqaEh0kPDpIaHS/tKU1VFsL3dCYaubickOvfc1bhEvpBnZGqkPNYQG+By4nLp6z7xlSr0pe7YFx41gZo77u7SQoHs2Bjp8yOkz58nPTLiVP6jo2g6Xfo+f1PTgop8mcp84dfr6yvuCTALAmPMXdFCgezly6SGhkgNOQGRGh4mP+n0/SNC9c6dhLq6yq2Hrq67mkQ3393THG6mMdh4VzOdVZXc+Hipoi9V/KOjpeXJAQKbNxNsb3eOjg6CHe0Ed+3CV3v/rzJgQWCMWXWqSu7atUXBkBoaKu0vDVDV1kZobzfBrq5iSOwl0BpZs4loqkpuYmLBHf75YvfOhXKLBghEIk4l39FB9XzF396Of8P6XW/JgsAYc8/kbtxwupSKwZAaGiZz8WLp6/7mZqfVsKD1ULVt24rCQVXJx+Plu/uRctfO/IS9+d9VvsMv3uXv3l2Ry32sNQsCY4yr8jOzpM+9TepsueWQvnChNFfCt2EDoc7OUjAEu7oI7tqFBALkbtxwKvliRZ8pVvzzC/4B+Ovrqe5Y0KXT7nTrBJpsQts8CwJjTMUppNOk3zlPanhBOJx7p/SYqwSD+Gpry+MQOE/iOBV98Q6/WPH7W1ps3aOfw+YRGGMqji8YJLx/H+H9+0rnNJcj8+67TjCcHSI/kyC4uzxwG9i0ySr8NWBBYIypGBIIFCv9Duqfftrt4nhG5S76YYwx5p6wIDDGGI+zIDDGGI+zIDDGGI+zIDDGGI+zIDDGGI+zIDDGGI+zIDDGGI+775aYEJEY8N6H/PEWIL6Kxbnf2fVYzK5HmV2LxdbD9diuqpGlvnDfBcHdEJGTy6214UV2PRaz61Fm12Kx9X49rGvIGGM8zoLAGGM8zmtB8C23C1Bh7HosZtejzK7FYuv6enhqjMAYY8ztvNYiMMYYcwsLAmOM8TjPBIGIPCUi50RkRES+4nZ53CQi20Tk/0RkSETOishzbpfJbSLiF5F+EXnV7bK4TUQaRORlEXlbRIZF5KDbZXKLiPxB8W/kLRH5voiE3C7TWvBEEIiIH/gm8KtAN/BpEel2t1SuygF/qKrdwGPAFzx+PQCeA4bdLkSF+Bvgv1W1EziAR6+LiGwBvgQ8oqr7AD/wm+6Wam14IgiAR4ERVR1V1QzwEvDrLpfJNap6RVWjxdcJnD/0Le6Wyj0ishX4OPBtt8viNhGpB34R+EcAVc2o6pS7pXJVAAiLSACoAcZdLs+a8EoQbAEuL/j8fTxc8S0kIjuAXuBNd0viqq8DfwwU3C5IBdgJxIDvFLvKvi0itW4Xyg2qOgb8JXAJuAJMq+qP3C3V2vBKEJgliEgd8Arw+6p60+3yuEFEfg2YUNVTbpelQgSAPuB5Ve0FZgFPjqmJSCNOz8FOoA2oFZHfcrdUa8MrQTAGbFvw+dbiOc8SkSqcEHhRVX/gdnlcdBh4WkQu4nQZPi4i33O3SK56H3hfVedbiC/jBIMXPQm8q6oxVc0CPwAOuVymNeGVIDgBdIjIThGpxhnw+aHLZXKNiAhOH/Cwqv612+Vxk6r+iapuVdUdOP8vfqKq6/Ku706o6lXgsojsKZ56AhhysUhuugQ8JiI1xb+ZJ1inA+cBtwtwL6hqTkR+D/gfnJH/F1T1rMvFctNh4LPAGREZKJ77qqr+p4tlMpXji8CLxZumUeB3XC6PK1T1TRF5GYjiPGnXzzpdasKWmDDGGI/zSteQMcaYZVgQGGOMx1kQGGOMx1kQGGOMx1kQGGOMx1kQGHMLEcmLyMCCY9Vm1orIDhF5a7Xez5jV4Il5BMasUFJVe9wuhDH3irUIjLlDInJRRP5CRM6IyHERaS+e3yEiPxGR0yLymoh8pHh+k4j8q4gMFo/55Qn8IvIPxXXufyQiYdf+UcZgQWDMUsK3dA19asHXplV1P/B3OKuWAvwt8E+q+hDwIvCN4vlvAD9T1QM46/XMz2bvAL6pqnuBKeATa/zvMeYD2cxiY24hIjOqWrfE+YvA46o6Wly076qqNotIHNisqtni+Suq2iIiMWCrqqYXvMcO4Meq2lH8/MtAlar++dr/y4xZmrUIjFkZXeb1SqQXvM5jY3XGZRYExqzMpxZ8PFZ8fZTyFobPAK8XX78GfB5KeyLX36tCGrMSdidizO3CC1ZlBWf/3vlHSBtF5DTOXf2ni+e+iLOj1x/h7O41v1rnc8C3ROR3ce78P4+z05UxFcXGCIy5Q8UxgkdUNe52WYxZTdY1ZIwxHmctAmOM8ThrERhjjMdZEBhjjMdZEBhjjMdZEBhjjMdZEBhjjMf9P+pJVWKFGZPhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "sXeYggG-xYRx",
        "outputId": "4d35a191-ab43-47e2-bb4b-8067b443b99d"
      },
      "source": [
        "from keras.preprocessing import image\n",
        "img = image.load_img(\"/content/Image401.jpg\",target_size=(224,224))\n",
        "img = np.asarray(img)\n",
        "plt.imshow(img)\n",
        "img = np.expand_dims(img, axis=0)\n",
        "from keras.models import load_model\n",
        "saved_model = load_model(\"vgg16_1.h5\")\n",
        "output = saved_model.predict(img)\n",
        "if output[0][0] > output[0][1]:\n",
        "    print(\"\\n CANCEROUS TUMOR NOT DETECTED..!!\\n\")\n",
        "else:\n",
        "  print(\"\\n CANCEROUS TUMOR DETECTED..!!\\n\")"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:11 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f4242ebb5f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "\n",
            " CANCEROUS TUMOR NOT DETECTED..!!\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5vuS1hGm056"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}