transfer_learning_finetuning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "itSCyw3ipYfv"
      },
      "outputs": [],
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d salader/dogs-vs-cats"
      ],
      "metadata": {
        "id": "KW42KSn9pfyA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65abb533-1cdd-440e-aa3c-eb3c16021b89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading dogs-vs-cats.zip to /content\n",
            "100% 1.06G/1.06G [00:05<00:00, 196MB/s]\n",
            "100% 1.06G/1.06G [00:05<00:00, 197MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "zip_ref = zipfile.ZipFile('/content/dogs-vs-cats.zip', 'r')\n",
        "zip_ref.extractall('/content')\n",
        "zip_ref.close()"
      ],
      "metadata": {
        "id": "CXz412Aip5B4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense,Flatten\n",
        "from keras.applications.vgg16 import VGG16"
      ],
      "metadata": {
        "id": "8dAgedWkp8Pi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conv_base = VGG16(\n",
        "    weights='imagenet',\n",
        "    include_top = False,\n",
        "    input_shape=(150,150,3)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGeyBIoxp_ji",
        "outputId": "53603394-54df-4183-8972-60dead8a38e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 0s 0us/step\n",
            "58900480/58889256 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_base.trainable = True\n",
        "\n",
        "set_trainable = False\n",
        "\n",
        "for layer in conv_base.layers:\n",
        "  if layer.name == 'block5_conv1':\n",
        "    set_trainable = True\n",
        "  if set_trainable:\n",
        "    layer.trainable = True\n",
        "  else:\n",
        "    layer.trainable = False\n",
        "\n",
        "for layer in conv_base.layers:\n",
        "  print(layer.name,layer.trainable)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rsRlwtCUqB81",
        "outputId": "dfea1783-8823-4e98-859c-5a525fa8ae51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_1 False\n",
            "block1_conv1 False\n",
            "block1_conv2 False\n",
            "block1_pool False\n",
            "block2_conv1 False\n",
            "block2_conv2 False\n",
            "block2_pool False\n",
            "block3_conv1 False\n",
            "block3_conv2 False\n",
            "block3_conv3 False\n",
            "block3_pool False\n",
            "block4_conv1 False\n",
            "block4_conv2 False\n",
            "block4_conv3 False\n",
            "block4_pool False\n",
            "block5_conv1 True\n",
            "block5_conv2 True\n",
            "block5_conv3 True\n",
            "block5_pool True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_base.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMNnxAe8qxaj",
        "outputId": "9629c8b0-30dd-49fe-b2a0-bad513983e2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 150, 150, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 75, 75, 64)        0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 75, 75, 128)       73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 75, 75, 128)       147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 37, 37, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 37, 37, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 37, 37, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 37, 37, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 18, 18, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 18, 18, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 9, 9, 512)         0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 7,079,424\n",
            "Non-trainable params: 7,635,264\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(conv_base)\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256,activation='relu'))\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "MuCh9EqrsMPK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# generators\n",
        "train_ds = keras.utils.image_dataset_from_directory(\n",
        "    directory = '/content/train',\n",
        "    labels='inferred',\n",
        "    label_mode = 'int',\n",
        "    batch_size=32,\n",
        "    image_size=(150,150)\n",
        ")\n",
        "\n",
        "validation_ds = keras.utils.image_dataset_from_directory(\n",
        "    directory = '/content/test',\n",
        "    labels='inferred',\n",
        "    label_mode = 'int',\n",
        "    batch_size=32,\n",
        "    image_size=(150,150)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "js3QmFghq1AC",
        "outputId": "81f5993e-83cd-4521-b802-47cb82a7a5fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 20000 files belonging to 2 classes.\n",
            "Found 5000 files belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalize\n",
        "def process(image,label):\n",
        "    image = tensorflow.cast(image/255. ,tensorflow.float32)\n",
        "    return image,label\n",
        "\n",
        "train_ds = train_ds.map(process)\n",
        "validation_ds = validation_ds.map(process)"
      ],
      "metadata": {
        "id": "DjbuBEQkr3DE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=keras.optimizers.RMSprop(lr=1e-5),\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['accuracy']\n",
        "  )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxUjDM6Nr5Qp",
        "outputId": "1f31ef8f-8966-40dd-ecee-397450f449c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/rmsprop.py:130: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(RMSprop, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_ds,epochs=10,validation_data=validation_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tiat79J_sG6W",
        "outputId": "59a3af41-7bca-4ff5-bf14-60fd56523c35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "625/625 [==============================] - 87s 119ms/step - loss: 0.2385 - accuracy: 0.8968 - val_loss: 0.1606 - val_accuracy: 0.9352\n",
            "Epoch 2/10\n",
            "625/625 [==============================] - 76s 120ms/step - loss: 0.1311 - accuracy: 0.9468 - val_loss: 0.1308 - val_accuracy: 0.9470\n",
            "Epoch 3/10\n",
            "625/625 [==============================] - 75s 120ms/step - loss: 0.0921 - accuracy: 0.9643 - val_loss: 0.1247 - val_accuracy: 0.9488\n",
            "Epoch 4/10\n",
            "625/625 [==============================] - 83s 132ms/step - loss: 0.0650 - accuracy: 0.9772 - val_loss: 0.1391 - val_accuracy: 0.9452\n",
            "Epoch 5/10\n",
            "625/625 [==============================] - 75s 120ms/step - loss: 0.0430 - accuracy: 0.9864 - val_loss: 0.1278 - val_accuracy: 0.9512\n",
            "Epoch 6/10\n",
            "625/625 [==============================] - 76s 121ms/step - loss: 0.0280 - accuracy: 0.9924 - val_loss: 0.1371 - val_accuracy: 0.9528\n",
            "Epoch 7/10\n",
            "625/625 [==============================] - 75s 120ms/step - loss: 0.0169 - accuracy: 0.9955 - val_loss: 0.1633 - val_accuracy: 0.9478\n",
            "Epoch 8/10\n",
            "625/625 [==============================] - 76s 120ms/step - loss: 0.0111 - accuracy: 0.9972 - val_loss: 0.1592 - val_accuracy: 0.9530\n",
            "Epoch 9/10\n",
            "625/625 [==============================] - 75s 120ms/step - loss: 0.0077 - accuracy: 0.9983 - val_loss: 0.2120 - val_accuracy: 0.9450\n",
            "Epoch 10/10\n",
            "625/625 [==============================] - 75s 120ms/step - loss: 0.0052 - accuracy: 0.9989 - val_loss: 0.1859 - val_accuracy: 0.9520\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['accuracy'],color='red',label='train')\n",
        "plt.plot(history.history['val_accuracy'],color='blue',label='validation')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "M8c6eVF4sVTT",
        "outputId": "fc469f63-f452-47e8-846a-c8d2223e606e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'],color='red',label='train')\n",
        "plt.plot(history.history['val_loss'],color='blue',label='validation')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Ap3-7R0swWTo",
        "outputId": "1fb28104-1095-489f-d5cd-8cee052cef7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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EMkSkf67nTorIKt9tdqAC91f58vCf/+io/saN2pUza1Yx3zQlRbeinzMnIDEaUxTp6ToR7IortP3x5Zc6R75CBa8jM8Gm0EQvIlHAs0AfoDkwSESa5zpsBzAMeCOPtzjmnGvju/UtZrxF1r+/rghs0kQXuY4apQtIiqRXL6hTx7pvjGfeeguaN9fF2vfdBytWQIcOXkdlgpU/LfqOwBbn3Dbn3HFgGtAv5wHOue3OuTVAZl5vECwaNYLPP9dZOc88o5e3W7cW4Y2io2HwYB3lTU8PeJzG5GffPp2K2L8/JCRAair88586bGRMfvxJ9PFAzl03dvoe81d5EUkVkS9F5Mq8DhCRm33HpKalpZ3BW5+5cuV08cg772iSb9tW5/GesZQU7aOfOjXgMRqTm3NaB6Z5c63C8cgjutq1dWuvIzOhoDQGY8/xze28HhgvIr/LfYBzboJzLsk5l1SrVq1SCEnHU1et0kGsAQNg5Ehd7OG3Vq2gTRvrvjElbtcunTk2ZAice67+ux0zRi8sjfGHP4l+F1A/x88Jvsf84pzb5fvvNmAh0PYM4itR55yjqwfvugteeEHXQn3zzRm8QUqKXjuvX19iMZrI5Rz897/aiv/kE70SXbIEEhO9jsyEGn8S/XKgiYg0FJFywEDAr9kzIhInIjG++zWBZCCosmLZsro0/P33deODdu30Etkv11+vzSpr1ZsA274devaEm27S7sU1a3RsKRDVIk3kKTTRO+cygNuAecAGYLpzbp2IPCAifQFEpIOI7ASuBV4UkXW+lycCqSKyGlgAPOqcC6pEn+Wyy/SSuG1bvUS+6SatdFCg2rW1QtTkyVrd0phiyszU0gXnnafTJZ97TvemtxWkpjjCrtZNcWVk6HS1Rx7R/vvp0wu5VH7rLZ0CMXeuTrs0pog2b9YiZIsXa2t+wgTtXjTGHxFV66a4oqN1c+K5c3UqW1JSIT0zl1+uhb2t+8YU0cmT8O9/6/j+mjW6wnXuXEvyJnAs0eejZ0/tyunYEYYN03HXnKVVT4mJ0c0uZ83SgiPGnIH163U9x1//qnVq1q/XDUKsMKoJJEv0BahXT6sB3nefbmaSlKTVAH8jJUXnZs6YUeoxBrs9e+Drr3UGicl24oReObZtq/Xi33gD3n1X/80ZE2iW6AsRFaUrD+fPh0OHtIU/cWKuxJWUpB351n0DaK3x11/XFmp8PLRsqQls+HAd8zh40OsIvZV1pXjPPXDlldqKHzTIWvGm5Fii99PFF+sfaJcuWg52yBBNaID+haak6CTnLVs8jdMrGRla423wYN1lMSVFVx7fc4/2OXfrpi3WAQN027rkZN0BLDX1t3uRhqOjR3Uu/J13ak2aPXt0HP/NN3XyljElyjkXVLf27du7YHbypHMPPuhcmTLONWni3MqVvid27tQH//EPT+MrTZmZzqWmOnf77c7Vru0cOBcX59wttzi3ZIk+n1NGhnNLl+pX1KGDcyL6mlq1nBsyxLkpU5xLS/Pmdwm0Xbucmz5dv5v27Z2LitLfVcS5G25wLj3d6whNuAFSXT551aZXFtFnn+nldnq6rli85RaQ3r10X9lt26BM+F4sbd+ufcqTJmnp53LldPLR0KG6rCAmxr/3SUuDjz7SK4F582D/fr046tBBN3fv00fvB/siocxM7X75/HO9LVmSve9BhQrQqZNeCSYnw/nnQ9Wq3sZrwlNE7RlbmtLS4IYbdCrctdfCSz1nUHXEdbBgAXTv7nV4AXXwIMycqcl98WJ9rGtX7cK69lqth14cmZlaanfOHP0+ly3Tx6pX1xlQffpkV4f22rFjsHx5dlJfulTHb0Djy0rqXbpoOaSAblBvTD4s0ZegzEwYOxb+/nc45+xM3tx3EUnXNYJXXvE6tGI7fhw+/FAX/r73nv7ctKm23AcPhgYNSu6z09N1ADwr8e/bp4+3a5fd2u/cuXQKe6Wlnd5aX7Eie3OxxMTTE3ujRjaoarxhib4ULF2q28nu3ZXBo9H3cN3qe4hvWink/uid0+0XJ03SGTIHDuhg4aBB2npv3770E1lmJqxenZ30ly7VRUZVq+rMnqzWfvyZFM/Oh3Na2C4rqX/+eXahu3LltCspK7FfcAHUqFH8zzQmECzRl5L0dBh+1UHeW6z9GJUqOc49V2jWTFvCTZvqzvdNmkBsrMfB5rJ5s7bcJ0/WIYYKFXTq39ChmkyDqSTuoUPa2p87V5P/7t36eKtW2a39Cy7wbzOO48e1hZ6zGyZrS4QaNTShZ93at9etKY0JRpboS5FzsPgfH7Hu4XfZeNZFbEq8kk1bo/nuu9Pn3p99NqdOADlPBPHxpddiTkvT6X2TJ2ufuAj06KEt96uvhsqVSyeO4nBOF2TNmaO3JUt0qmflyvq79Omjyf/ss/X4gwc1mWcl9uXLs/chaNz49G6Ypk2tG8aEDkv0XnjzTe3I7tgR5szhWLmqbN6ss1Q2bdJb1v2jR7NfVqmSbi6R8wQQyKuAY8d0h6LJk7VFnJGhuxQNGaLdM4Ho/vDSkSM6Xz2rtb9jhz7evLkm7XW+uqrR0drfn5XYk5ODY6DXmKKyRO+Vt9/Wjvs2bXT+YB5TU5zTrofcyX/jRk1SgbgKyMyERYu0333mTE2G8fF6HhoyRFeuhiPn9HvMmr4ZFZXdWu/QIfi6z4wpDkv0XnrvPS1j3KIFfPzxGY3eHTtGka8Csrodpk7VjVR27tTujGuu0X73bt2Cf366McZ/lui9Nneujmyee66OIhZzzfuZXAVERWkf9dChcMUV1oo1JlxZog8Gn3yimbZBA71/1lkl8jE5rwKOHtUVq1ZLxZjwZ4k+WCxapHsWxsfr/nChPvJpjAkatsNUsOjWTUcF9+zR+1lTQowxpgRZoi9tycnaT79/P1x4YXb1K2OMKSGW6L3QsaN23Rw5osk+QmvYG2NKhyV6r7Rrp8n+l1802W/c6HVExpgwZYneS61bw8KFuqKpWzddy2+MMQFmid5rLVrobJzoaLjoIi3TaIwxAWSJPhg0barJvkIFTfYrVngdkTEmjFiiDxaNG2uyr1pVyy4uW+Z1RMaYMGGJPpg0bKjJvmZNLQK/ZInXERljwoAl+mBz9tma7OvV0yI1Cxd6HZExJsRZog9G8fGa4M85By69VBdYGWNMEVmiD1Z162qyb9JEK5PNmeN1RMaYEGWJPpjVqqWLqlq00DLHs2d7HZExJgRZog92NWpoWeM2bXTXkLfe8joiY0yIsUQfCqpV092pOnaEAQNg2jSvIzLGhBC/Er2I9BaRTSKyRUTG5PH8hSLylYhkiEj/XM+liMhm3y0lUIFHnCpVtMRxcrJu9jppktcRGWNCRKGJXkSigGeBPkBzYJCINM912A5gGPBGrtdWB+4DOgEdgftE5Lc7ZBv/VKoEH36oq2dTUuDll72OyBgTAvxp0XcEtjjntjnnjgPTgH45D3DObXfOrQEyc722F/Cxc+6Ac+4g8DHQOwBxR66KFXXD8V694MYb4YUXvI7IGBPk/En08cD3OX7e6XvMH8V5rclPhQrwzju6B+3IkfD0015HZIwJYkExGCsiN4tIqoikpqWleR1OaIiJgZkz4aqr4PbbYexYryMyxgQpfxL9LqB+jp8TfI/5w6/XOucmOOeSnHNJtWrV8vOtDeXKwZtv6kycu+6Chx/2OiJjTBCK9uOY5UATEWmIJumBwPV+vv884OEcA7A9gb+dcZQmf2XLwuTJ+t+774bjx+G++0DE68iMMUGi0ETvnMsQkdvQpB0FvOycWyciDwCpzrnZItIBmAXEAVeIyP3OuRbOuQMi8i/0ZAHwgHPuQAn9LpErOhpefVWT/f33a7J/6CFL9sYYwL8WPc65D4EPcz12b477y9Fumbxe+zJg8wBLWlQUTJyo3TmPPKLJ/oknLNkbY/xL9CZElCkDzz+vyf7f/9aNx598Ulv6xpiIZYk+3IjAU0/prJyxY3Wnqtdeg+a517gZYyJFUEyvNAEmot02M2bAt99Cu3bawj950uvIjDEesEQfzvr3h3XrdKeqv/4VuneHrVu9jsoYU8os0Ye7OnVg1iztvlm7Flq10n5857yOzBhTSizRRwIRuOEGTfTJyfCnP2mtnO+/L/y1xpiQZ4k+ktSvr6WOn38eli6F887Tlr617o0Ja5boI40I3HILrF4NrVvDsGG6TeHevV5HZowpIZboI9XvfgcLFuhsnHnztHU/Y4bXURljSoAl+kgWFQV33gkrV0LDhnDddTBoEBywKhXGhBNL9AYSE+GLL+Bf/9LSxy1awAcfeB2VMSZALNEbFR0N99wD//d/UKsWXH657mD1449eR2aMKSZL9OZ0bdvC8uXwt79pRcyWLeHTT72OyhhTDJbozW/FxOgmJp9/DuXLQ48e8Oc/w08/eR2ZMaYILNGb/HXurAO1t98O//kPtGmj8++NMSHFEr0pWGwsjB+v3TcnTkDXrjBmDPz6q9eRGWP8ZIne+Oeii2DNGh2gfewxaN8evvrK66iMMX6wRG/8V6UKTJigUy8PHIBOneCBB7Slb4wJWpbozZm79FL4+mtdYHXffXD++bB+vddRGWPyYYneFE316jBlipZN+O473dxk7Fjb3MSYIGSJ3hRP//7auu/TB+66C7p1gy1bvI7KGJODJXpTfHXqwNtvw+uva9Jv3Rqeew4yM72OzBiDJXoTKCIwdKgm+i5d4NZbdXOTHTu8jsyYiGeJ3gRWQgLMnQsvvKCF0lq2hIkTre/eGA9ZojeBJwJ//KPOu2/TBkaM0MHajz7yOjJjIpIlelNyGjXSzU2mTtUqmL166W31aq8jMyaiWKI3JatMGRg4EDZu1N2sli/XCpnDhsHOnV5HZ0xEsERvSkdMjO5mtXUr/OUv2spv0gT+/nc4fNjr6IwJa5boTemKi4MnnoBNm+Dqq+GRR6BxY3jmGTh+3OvojAlLluiNNxo00JW1qak6M2fUKN3C8K23wDmvozMmrFiiN95q3x4++QTefx/KldOVtsnJVvfemACyRG+8JwKXXaazcV56CbZv12R/zTWwebPX0RkT8izRm+ARHQ033aTJ/f77Yd48aN5ctzFMS/M6OmNCliV6E3wqVoR779XiaDfdBM8/D7/7ne5j+/PPXkdnTMjxK9GLSG8R2SQiW0RkTB7Px4jIm77nl4lIA9/jDUTkmIis8t1eCGz4JqzVratJfu1a3eHq7ruhaVN49VUrqWDMGSg00YtIFPAs0AdoDgwSkea5DrsROOicaww8CTyW47mtzrk2vtstAYrbRJLERHj3XVi4EM46C4YPt5IKxpwBf1r0HYEtzrltzrnjwDSgX65j+gGv+e7PBHqIiAQuTGPQWvdffgnTpsGRI1ZSwRg/+ZPo44Hvc/y80/dYnsc45zKAw0AN33MNRWSliCwSka55fYCI3CwiqSKSmmaDbqYgZcrAgAGwYQOMG2clFYzxQ0kPxu4BznbOtQXuBN4QkSq5D3LOTXDOJTnnkmrVqlXCIZmwEBMDd9yhJRX++ldt5VtJBWPy5E+i3wXUz/Fzgu+xPI8RkWigKpDunPvVOZcO4JxbAWwFzi1u0MacEhcHjz+uJRWuucZKKhiTB38S/XKgiYg0FJFywEBgdq5jZgMpvvv9gU+dc05EavkGcxGRRkATYFtgQjcmh3POgcmTraSCMXkoNNH7+txvA+YBG4Dpzrl1IvKAiPT1HfZfoIaIbEG7aLKmYF4IrBGRVegg7S3OuQOB/iWMOSWrpMIHH2j3jpVUMAZxQdbaSUpKcqmpqV6HYcJBRobOub/3XtizB3r2hD/8Afr1g/LlvY7OmIASkRXOuaS8nrOVsSZ85Syp8NBDuvnJwIE6F/9Pf9IZO0HW0DGmJFiiN+GvYkWdjfPttzB/vhZQe+UV6NhR+/PHjoW9e72O0pgSY4neRI4yZaBHDx203bsXXnwRqlSBu+6ChATo2xdmzbLZOibsWKI3kalqVbj5Zh2k3bBB5+KnpuquV/HxMHq0rbg1YSMkBmNPnDjBzp07+eWXXzyKKvyUL1+ehIQEypYt63UowSMjAz7+WLt13n1XW/Zt2mhtneuvh5o1vY7QmHwVNBgbEon+22+/pXLlytSoUQMroVN8zsenSdQAAA1/SURBVDnS09M5cuQIDRs29Dqc4JSerqttX3kFVqyAsmXhiis06ffurQO9xgSRkJ9188svv1iSDyARoUaNGnaFVJAaNeDWW7U7Z80auO02WLxYk339+vA//wPr13sdpTF+CYlED1iSDzD7Ps9Ay5ZaQG3XLnjnHejUCZ58UlfeduoEL7wAhw55HaUx+QqZRO+1Q4cO8dxzz53x6y699FIOWRIID2XL6mKrd97RpD9uHBw7BiNH6iYpgwZpjXzbFMUEGUv0fsov0WdkZBT4ug8//JBq1aqVVFjGK7Vra/XM1au1e2fECN3jtlcvaNBAd8Oyjc1NkLBE76cxY8awdetW2rRpQ4cOHejatSt9+/aleXPdbOvKK6+kffv2tGjRggkTJpx6XYMGDdi/fz/bt28nMTGRESNG0KJFC3r27MmxY8e8+nVMoIhofZ1nntEyC9Ona1fPo4/CuedC167w8su6UYoxHgmJWTcbNmwgMTFRfxg9GlatCuyHtmkD48cXeMj27du5/PLL+frrr1m4cCGXXXYZX3/99alZKwcOHKB69eocO3aMDh06sGjRImrUqEGDBg1ITU3l6NGjNG7cmNTUVNq0acN1111H3759GTJkSGB/lzNw2vdqAmv3bpg0SWftbNoEsbFaYG34cLjwQl28ZUwAhfysm2DUsWPH06YmPv3007Ru3ZrOnTvz/fffszmPy/aGDRvSpk0bANq3b8/27dtLK1xT2urVg//9X12MtXQpDBmiffsXXQR16ui8/Fdf1ROCMSUs9CYDF9LyLi0VK1Y8dX/hwoXMnz+fL774gtjYWLp3757n1MWYmJhT96OioqzrJhKIwPnn6+3JJ2H2bPjwQx20nTpVj2nZUitr9uqlXT1WWdMEmLXo/VS5cmWO5NPPevjwYeLi4oiNjWXjxo18+eWXpRydCQmxsVo98/XXtSW/ciU89hjUqqV9/D176o5ZvXvrSWHdOquuaQIi9Fr0HqlRowbJycmcd955VKhQgTp16px6rnfv3rzwwgskJibStGlTOnfu7GGkJiSUKaNjQ23a6OKrn36CRYu0pT9vHtx5px6XkKAngJ494ZJLdCGXMWco9AZjTcDY9xrEduzITvrz5+uCLBFIStIunp49oXNnndtvDDYYa0zoOfts3TRlxgzYvx+++ALuu08T+8MP68ydmjXhqqvg+edhm23FbPJnXTfGBLuoKG29d+6syf7QIfj0U23tz5uns3kAfvc7be336qWzeypX9jZuEzQs0RsTaqpV07r5V1+tg7WbN2vC/+gjeO01eO45ra55wQXZ3Tzt2tnc/Qhm/+eNCWUiugL3z3+G997T8soLFuhGKkePaimGDh20ZMOgQTZ3P0JZi96YcBITA9276+2RR+CHH3QzlY8+0tu0aXpcixbQqhU0aaIniiZN9BYX52X0poRYojcmnNWuDYMH6805WLtWu3kWLIAvv9TEn3PmXc2av03+554LjRtDpUre/R6mWKzrpoRU8v1R7N69m/79++d5TPfu3ck9lTS38ePH8/PPP5/62coemyIT0Vb8XXfp6txt27TM8vr1OqD7xBPa7x8To1M677kHBgyAtm11YLdePb1SGDECHn9cX7NuHdgGNkHPWvQlrF69esycObPIrx8/fjxDhgwhNjYW0LLHxgRMTAwkJuott59+gi1bdLD3m2/0v5s36366aWnZx4nodNC8rgQaNLC5/kHAEr2fxowZQ/369bn11lsB+Oc//0l0dDQLFizg4MGDnDhxggcffJB+/fqd9rqcVS+PHTvG8OHDWb16Nc2aNTut1s3IkSNZvnw5x44do3///tx///08/fTT7N69m4suuoiaNWuyYMGCU9Uwa9asybhx43j55ZcBuOmmmxg9ejTbt2+nT58+dOnShaVLlxIfH8+7775LhQoVSu/LMuGhYkVo3VpvuR06lJ34c54IpkyBw4ezj4uKgoYNs08AOU8E9evr86bEhVyi96hKMQMGDGD06NGnEv306dOZN28eo0aNokqVKuzfv5/OnTvTt2/ffLfpe/7554mNjWXDhg2sWbOGdu3anXruoYceonr16pw8eZIePXqwZs0aRo0axbhx41iwYAE1a9Y87b1WrFjBK6+8wrJly3DO0alTJ7p160ZcXBybN29m6tSpvPTSS1x33XW89dZbnpZDNmGoWjWdzdOhw+mPO6cLvHJfBXzzDSxcCDm6IYmJ0ZNA3bo6llDQrUoVvXIwRRJyid4rbdu25YcffmD37t2kpaURFxdH3bp1ueOOO/jss88oU6YMu3btYt++fdStWzfP9/jss88YNWoUAK1ataJVq1annps+fToTJkwgIyODPXv2sH79+tOez23JkiVcddVVp6poXn311SxevJi+fftaOWTjHREt0larls7jz8k5ndqZM/lv26Yzg1at0v/mN/5UrlzhJ4OsW61aVgE0l5BL9F5WKb722muZOXMme/fuZcCAAUyZMoW0tDRWrFhB2bJladCgQZ7liQvz7bffMnbsWJYvX05cXBzDhg0r0vtksXLIJiiJQHy83rp3z/uY48e1//+HH/K+ZT23YQPs25f/QHCVKv6fGKpXD/supJBL9F4aMGAAI0aMYP/+/SxatIjp06dTu3ZtypYty4IFC/juu+8KfP2FF17IG2+8wcUXX8zXX3/NmjVrAPjxxx+pWLEiVatWZd++fcyZM4fuvj+ErPLIubtuunbtyrBhwxgzZgzOOWbNmsWkSZNK5Pc2ptSUK5d9MiiMczpgnN9JIeu2davWCkpLg8zM375PmTKa7KtV++2tatW8H895q1gx6LuVLNGfgRYtWnDkyBHi4+M566yzGDx4MFdccQUtW7YkKSmJZs2aFfj6kSNHMnz4cBITE0lMTKR9+/YAtG7dmrZt29KsWTPq169PcnLyqdfcfPPN9O7dm3r16rFgwYJTj7dr145hw4bRsWNHQAdj27Zta900JnKI6Nz+SpWgUaPCj8/MhAMH8r9SOHxYu44OHdIupqz7OccV8hIVVfgJoaDnK1Uq8fIUVqY4gtn3aowfjh8//SSQdcvrsbyeP3q04PcvU0a7mqpVg06dslcvn6GCyhRbi94YYwpSrlz2AHNRnDgBP/7o34kiISGwsfv4lehFpDfwFBAFTHTOPZrr+RjgdaA9kA4McM5t9z33N+BG4CQwyjk3L2DRG2NMsCtbVncG83B3sEI7hkQkCngW6AM0BwaJSPNch90IHHTONQaeBB7zvbY5MBBoAfQGnvO9nzHGmFLizwhAR2CLc26bc+44MA3ol+uYfsBrvvszgR6iq4b6AdOcc786574Ftvje74wF21hCqLPv05jI4U+ijwe+z/HzTt9jeR7jnMsADgM1/HwtInKziKSKSGpazhoaPuXLlyc9Pd2SU4A450hPT6e8LSoxJiIExWCsc24CMAF01k3u5xMSEti5cyd5nQRM0ZQvX56EEhr4McYEF38S/S6gfo6fE3yP5XXMThGJBqqig7L+vLZQZcuWpWHDhmf6MmOMMfjXdbMcaCIiDUWkHDq4OjvXMbOBFN/9/sCnTvtZZgMDRSRGRBoCTYD/C0zoxhhj/FFoi945lyEitwHz0OmVLzvn1onIA0Cqc2428F9gkohsAQ6gJwN8x00H1gMZwK3OuZMl9LsYY4zJQ0isjDXGGFOwglbGBl2iF5E0oODqYAWrCewPUDihzr6L09n3cTr7PrKFw3dxjnMuz+W7QZfoi0tEUvM7q0Ua+y5OZ9/H6ez7yBbu34VtDm6MMWHOEr0xxoS5cEz0E7wOIIjYd3E6+z5OZ99HtrD+LsKuj94YY8zpwrFFb4wxJoewSfQi0ltENonIFhEZ43U8XhKR+iKyQETWi8g6Ebnd65i8JiJRIrJSRN73OhaviUg1EZkpIhtFZIOInO91TF4SkTt8fydfi8hUEQm7an9hkej9rJkfSTKAvzjnmgOdgVsj/PsAuB3Y4HUQQeIpYK5zrhnQmgj+XkQkHhgFJDnnzkNX/w/0NqrAC4tEj3818yOGc26Pc+4r3/0j6B/yb8pDRwoRSQAuAyZ6HYvXRKQqcCFatgTn3HHn3CFvo/JcNFDBV5AxFtjtcTwBFy6J3q+695FIRBoAbYFl3kbiqfHA/wCZXgcSBBoCacArvq6siSJS0eugvOKc2wWMBXYAe4DDzrmPvI0q8MIl0Zs8iEgl4C1gtHPuR6/j8YKIXA784Jxb4XUsQSIaaAc875xrC/wEROyYlojEoVf/DYF6QEURGeJtVIEXLok+IHXvw4mIlEWT/BTn3Ntex+OhZKCviGxHu/QuFpHJ3obkqZ3ATudc1hXeTDTxR6pLgG+dc2nOuRPA28AFHscUcOGS6P2pmR8xfPv1/hfY4Jwb53U8XnLO/c05l+Cca4D+u/jUORd2LTZ/Oef2At+LSFPfQz3QMuKRagfQWURifX83PQjDwemg2EqwuPKrme9xWF5KBoYCa0Vkle+xvzvnPvQwJhM8/gxM8TWKtgHDPY7HM865ZSIyE/gKna22kjBcJWsrY40xJsyFS9eNMcaYfFiiN8aYMGeJ3hhjwpwlemOMCXOW6I0xJsxZojfGmDBnid4YY8KcJXpjjAlz/w+Z4DbsCsz41AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yb8ocI6PwX-d"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}