create_your_own_image_classification_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "actual_image_classification.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "M2Wnbg9o945D"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2H672s_uQj7l"
      },
      "source": [
        "# Make your own artificial intelligence model\n",
        "\n",
        "\n",
        "###### (Model adapted from [Keras](https://keras.io/examples/vision/image_classification_from_scratch/) and [Tensorflow](https://www.tensorflow.org/tutorials/images/classification).)\n",
        "\n",
        "Make sure that you \"run\" each of the \"cells\". The runnable \"cells\" are the boxes that have the play button ![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB0AAAAeCAYAAADQBxWhAAABFUlEQVRIDe2U4QmEMAyFnc3/bqG76Ba6hVvofwdwAAfI8Q4Chybpa+EQwYAEa5OveYmt5AarbmDKC/2r6s+Qd993WddVpmn6erznGl3psizSdZ3UdX15sI7vrFHQvu8vIAs+DAPFTUJZoB6CAYdQSKbJcnxK6hDatq0JnedZmqYxv+Fw6HFkLhRT6VWHhNu2iXcoxEVT7UIjabWK4zjE63kksQsdxzGsVMHwFhgt8MyFspViWq02FFXK9NS7LIp7Cmm8QUlNL+Iic+VFUCSxJamuRdIibwjFBmtINLnlsT9lSWgOmAEiHwXFRkjm9RjrKUl/q6ehGoSpBgD/MXx082jM2WdDzwlK3l9oiWp0zCsvLVXJxg/D+/4z1R3HhwAAAABJRU5ErkJggg==)on the top left. To \"run\" a cell, click on the play button.\n",
        "\n",
        "![run_cell.PNG](data:image/png;base64,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)\n",
        "\n",
        "Side note:\n",
        ">   When you hover over the set of brackets, the brackets will turn into a play button.![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB0AAAAeCAYAAADQBxWhAAABFUlEQVRIDe2U4QmEMAyFnc3/bqG76Ba6hVvofwdwAAfI8Q4Chybpa+EQwYAEa5OveYmt5AarbmDKC/2r6s+Qd993WddVpmn6erznGl3psizSdZ3UdX15sI7vrFHQvu8vIAs+DAPFTUJZoB6CAYdQSKbJcnxK6hDatq0JnedZmqYxv+Fw6HFkLhRT6VWHhNu2iXcoxEVT7UIjabWK4zjE63kksQsdxzGsVMHwFhgt8MyFspViWq02FFXK9NS7LIp7Cmm8QUlNL+Iic+VFUCSxJamuRdIibwjFBmtINLnlsT9lSWgOmAEiHwXFRkjm9RjrKUl/q6ehGoSpBgD/MXx082jM2WdDzwlK3l9oiWp0zCsvLVXJxg/D+/4z1R3HhwAAAABJRU5ErkJggg==) Click on the button to run the cell. When you click, the button will soon turn into a stop button.![image.png](data:image/png;base64,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) Click on this button to stop the cell from running.\n",
        "\n",
        "You should run all of the cells in the order that they are in.\n",
        "\n",
        "If you are interested in learning and understanding the code or other information, please refer to the [Guide](https://) tab on the website for more information."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMoO-Rx0GWQk"
      },
      "source": [
        "## Connect to Google Drive\n",
        "Before we get into the fun, we need to have a place containing many pictures of cats and dogs. With [Google Colaboratory](https://colab.research.google.com/notebooks/intro.ipynb) (the thing you are working with right now), it is easiest to have the pictures in Google Drive. Here we are mounting or connecting Google Drive to Google Colaboratory so that you can access your Google Drives files from here.\n",
        "\n",
        "So, the first thing you want to do is to run the below cell.\n",
        "\n",
        "Then it will print out a link telling you to click on it. When you click on the link, you will be told to sign into Google or choose a Google account. Then you will get a screen that looks like the following:\n",
        "\n",
        "![gdrive_mount.PNG](data:image/png;base64,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)\n",
        "\n",
        "When you see this screen, click on the \"Allow\" button. This will take you to a screen that looks like the following:\n",
        "\n",
        "![gdrive_mount_code.PNG](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAf4AAAIDCAYAAADli3x/AAAgAElEQVR4Xu3dDXBV5b3v8V+IJigQsZJqlWPbRNtCTyW0U0JbgfYq2ClgrwRnQHrLi61Ie0Da8tK5BHqahDtCsEW4FbEehB4DzJjgbUHHAk4P4dgC57aAPTe02tDWE1+DihtRdgRyZ639wn7P3g9Ze+8n+5uZzliynrX+6/OsvX5rPetZO0Xd3d3d4gcBBBBAAAEECkKgiOAviH5mJxFAAAEEEHAFCH4OBAQQQAABBApIgOAvoM5mVxFAAAEEECD4OQYQQAABBBAoIAGCv4A6m11FAAEEEECA4OcYQAABBBBAoIAE0gr+8+fP6+2T7+jMmS75u7p07tz5AiJiVxFAAAEEEMhPgeLifiotKVH//iW68srB6ldU1GOhPQa/79S7eqPzLV31ocEqKbnU3cAllxT3uGIWQAABBBBAAAFvBc6ePefekHd1faA333xbH/7wEJUNGpByoymD/x3fKZ1+731de82Hva2ctSOAAAIIIIDARQu88urrGjBwgK4YNDDpupIGv3On/+677+najxD6F90TrAABBBBAAIEsCbzy6hsaNHCABiW5808Y/M4z/eN/69ANFddnqUw2gwACCCCAAAK9JfBi+9/dDC9K8Mw/YfCfePNtFRcX68rBZb1VA+tBAAEEEEAAgSwJvPW2T93d5935ebE/CYP/5Vde1+DBZRpw+WVZKpHNIIAAAggggEBvCZw+/b5O+ny67iNXpxf8x//2X7p+6LXM3u+tHmA9CCCAAAIIZFHAme3/UscrqvjYP6QX/M6zgRsrP5rFEtkUAggggAACCPSmQLIsTzjUT/D3Jj3rQgABBBBAIPsCBH/2zdkiAggggAACORMg+HNGz4YRQAABBBDIvgDBn31ztogAAggggEDOBAj+nNGzYQQQQAABBLIvQPBn35wtIoAAAgggkDMBgj9n9GwYAQQQQACB7AsQ/Nk3Z4sIIIAAAgjkTIDgzxk9G0YAAQQQQCD7AgR/9s3ZIgIIIIAAAjkTIPhzRs+GEUAAAQQQyL4AwZ99c7aIAAIIIIBAzgQI/pzRs2EEEEAAAQSyL0DwZ9+cLSKAAAIIIJAzAYI/Z/RsGAEEEEAAgewLEPzZN2eLCCCAAAII5EyA4M8ZPRtGAAEEEEAg+wIEf/bN2SICCCCAAAI5EyD4c0bPhhFAAAEEEMi+AMGffXO2iAACCCCAQM4ECP6c0bNhBBBAAAEEsi9A8GffnC0igAACCCCQMwGCP2f0bBgBBBBAAIHsCxD82TdniwgggAACCORMgODPGT0bRgABBBBAIPsCBH/2zdkiAggggAACORMg+HNGz4YRQAABBBDIvgDBn31ztogAAggggEDOBAj+nNGzYQQQQAABBLIvQPBn35wtIoAAAgggkDMBgj9n9GwYAQQQQACB7AsQ/Nk3Z4sIIIAAAgjkTIDgzxk9G0YAAQQQQCD7AgR/9s3ZIgIIIIAAAjkTIPhzRs+GEUAAAQQQyL4AwZ99c7aIAAIIIIBAzgQI/pzRs2EEEEAAAQSyL0DwZ9+cLSKAAAIIIJAzAYI/Z/RsGAEEEEAAgewLEPzZN2eLCCCAAAII5EyA4M8ZPRtGILHAa+9067kXzuq5P5/TX14/p9N+aUCpdMPVxfrSJ4t122cu1cD+dukdev2POtX1rq7sX6bPln/aruKpFoE+JkDw97EOZXfsFXj3jPSL/V1q+Y8PUu6EcxEwdVSJvjnmUmt29hu7F+vPb/9VI8uH6ZH/Vm9N3RSKQF8UIPj7Yq+yT9YJOKH//ab31f76+XDtV5cVacRHi3XN4CL95bXz7u9e93WHf+8E/8wxJVbsK8FvRTdRZIEIEPwF0tHsZv4KxIZ+xYf76bvjS1T10eK4oo/8/ZxW7/RrQP8i/fQbl1kz5E/w5+/xR2WFJ0DwF16fs8d5JrBqp1+7/3jWreqLNxZr6eT+KQPduVBwfmx6zk/w59lBRzkFLUDwF3T3s/O5FnAm8s342XtuGc7Q/iPfutyqQE/Xj+BPV4rlEPBegOD33pgtIJBU4Gd7urQjOJlvyaRS3XbTJb2m1f3uKZ1tf9FdX/HV16jfNdemte7ut/dJRUUq6n+91P9jPbZxRiDa3wjMTRhxfb+Ey/cU/Odfe0XnXn/NbXvpiM/2uE0WQAABcwGC39yOlghctMD3Hn9fz78UCM1n/+eAi16fswInRE//4lGd2b0ran39rv6IBn3neyr50pfjt3P2pM69sEjdr/4i6ndFV45Vv48vV9GV4+LaOIH/0F6/fv184DGF8+O8cfDd8aX6y+uByYiVVwfmKyQL/q7n/k2nHvqpzr/+angdRQMGasDMe3TZlGm94sFKEEAgWoDg54hAIIcCt/yv0+7Wb7q+nztZL9GPE6yR4ZpomZ98I/Bi/9m/vKCTP7hX3affTbpXgxavUP/bJl34/akjOvuH8dLZd5K2KR7+cxV9ZGb4906wf//x993vGEj1E9qvRMF/5te7dKqxLmnzy+6YpoHf/X4Oe4dNI9A3BQj+vtmv7JUlAukE/5b9XfrF/tTv9odGC96863adfyMwZO4EpxPw/QYOVNfRP+jdhx5Q9+nAhcbgBzbo0hGfC1wsPHeDdOYl97/7/cM/qaj86yq67KM6/8avdP54nXTO5/6u+LN7wnf+3370fR0PDu9P+Mwl7iOKgf2LdPTv5/TQ3q6wfrLgd0Yl3vzGf3eXKxowQAO/8wOVfmmcnMcTpx76ibp+2xpXpyVdSpkI5L0AwZ/3XUSBfVngrv/9nvtu/tVXFGnrdy9PuKvO3f4zz8cH/+snu8Pv9TvBH3kHffn/+JY7XB7544wGvH3vN9x/KvniWF1Rt0bdr27RubZvh0O/3yd+EtXGed5/zhkNcAJ6yGQVj2hxRx9W7wrc6k/5/KXuUH7kT+TvkwX/uz/7id5/cntcuDsXBO/t2K73dwR+5zzvH/zAw335EGDfEMi6AMGfdXI2iMAFgeVPnNFvXzzn/kPTdy/XNVcUpc0T+RqgE/zvrFgUvlMe8n+eVdHAQXHrOvn9ufrg+cPuv5fvPaRzR2vUfWKn+/8vGfeGdMnguDbnfn+Luk/uDyxzS5cia/7l9wckfAshdEGTLPjfvmeGzh5/UZdU3KgrH2nSB0d/7wZ+13P7wtvv9+Fr3IuXqMcSaeuwIAIIJBMg+Dk2EMihQOTdsTNkvnRyadrVxIZrKNRDYZpoRae3PKL3/vXRC8EfCvX+1+uSL/0l4bad4f7zf20IB39oQqLz+uHWf0o8ShFaJlnwd946yl2fM+HQ+Ymc3Nd/wkQ37EOPItIGYUEEEEhLgOBPi4mFEPBOIBTgzhYemNE/4Tf2xW695dAH4Wfp37m1RDWjLlXs3Xyiik+t/rHO7H4qPviDd/OJ2pxru1vdr/5rXPA7/5DsTYTQHIBkwX/i618JzzdwLwA+fI0b9pfXTE84UuGdPmtGoPAECP7C63P2OM8E/v2Fc/pRc+Dr+JzX4eqmpg5/Z/nVO8+E/2rf1u8Ghtvfb9mmdzf81F3PFT9eHffanjNx7s0ZX3dn/IdGBSLv5otvesKd2Bf1c/akzj53Y2DG/8CbdEn1/1XkRcePp/bXzZ+I/mrhyC8lShb8kY8lBs77ni6rmR61WadW5yfR44o86z7KQcA6AYLfui6j4L4oEDtz35kl/6VPOP+7EKpO4O9+/gM990JgToDzEzlC4EyMe2vuDPdO2nkX/oq6xvBwufO7d360RGfbX3DbhV/pO/M3nX3uE4GVXXKFim9qvvDOvvNu//NT1f12YIZ9vxvXqN/1C+QE+z2Pvhe+8Ii8UHF+t6L5TPiPDSULfueZ/skfzAtstvIT7lsGoZAP1Xr+3VMqW7KCIf++eMCzTzkVIPhzys/GEbggEPm8vyeX0BflxH7TX+y78c4z9H4DB4UD31nvpTeN1OCfbAxvInJmv/uPA0eo6NIrwoHv/FPR4DEq/tyz4TaxtTpf1DOwtEhHX7pwUeIsnOo9/sjHDm5dwW/s++DoH8Lb4V3+no4Efo9A5gIEf+ZmtEDAMwHnjnlLa1f4j/Yk2pDzh3y+O6E06RsATvhHvrMfuQ4nSAfM/HbcELob/n/+Qfid/cg2zrv9/SpWxM34d8L/Z3v8cV/i4/x1QefHec/fqbX+zv5Jv7kv8rW+yG067/YP+OY9cY8APINnxQgUkADBX0Cdza7aI+B8He5zL5zVa+8Evs7X+bnmin7u8H86f5XPeUbuf26fzr32itvWuet3viAn5ff1nz2p7s5fqvv9v6v7zN9V1P+j6ld+uzSoKimcU+evn//AHf53fm64up/7ZT63P3DavSD45phLNXNMid7yv6MPzp9VSb9LdWVpWdT6nKH9QK2vqmjgQLdWZ6Ifz/ftOV6p1C4Bgt+u/qJaBPJGwJlzUHV9cdyFiHMx8PWfBL4hMPTGQd4UTSEIICCCn4MAAQQyEnCC3Znc53zjoHN37/xVwcifyC8W2nj3Ze4oAD8IIJA/AgR//vQFlSBgjUDkXxUccX2xvvTJYp0+Ix35+7nwBL9Uf3jImh2lUAT6oADB3wc7lV1CwGsB567fCf/QH+qJ3Z4zwc/5a4PpzEfwulbWjwAC0QIEP0cEAggYC4T+gFD76+fdyXxO4N/8yWLVfL6E0DdWpSEC3goQ/N76snYEEEAAAQTySoDgz6vuoBgEEEAAAQS8FSD4vfVl7QgggAACCOSVAMGfV91BMQgggAACCHgrQPB768vaEUAAAQQQyCsBgj+vuoNiEEAAAQQQ8FaA4PfWl7UjgAACCCCQVwIEf151B8UggAACCCDgrQDB760va0cAAQQQQCCvBAj+vOoOikEAAQQQQMBbAYLfW1/WjgACCCCAQF4JEPx51R0UgwACCCCAgLcCBL+3vqwdAQQQQACBvBIg+POqOygGAQQQQAABbwUIfm99WTsCCCCAAAJ5JUDw51V3UAwCCCCAAALeChD83vqydgQQQAABBPJKgODPq+6gGAQQQAABBLwVIPi99WXtCCCAAAII5JUAwZ9X3UExCCCAAAIIeCtA8Hvry9oRQAABBBDIKwGCP6+6g2IQyFTAp/bDx+QfWq3h5Zm29Xj5zjYd6CjV8JGVKvN4U6weAQTSFyD407diSQSyIOBT+74dWvvwNu07eFynJA2quFVT752reVOrFJvt7Ztu060Nx6WSadp2tF6jS7NQYjqb8O3RolHfUUuXVFX/Gz05Y2g6rVgGAQSyIEDwZwGZTSCQnkCHds69XQv2OHEf/1My7B5t2bZYoyNunw+v/rSmPNwllUzRxkOrNCFfbq07d2lm9ffUKqmi9hk9O6cyPQKWQgABzwUIfs+J2QAC6Qm0bbhNExuPSxVT1LhukSYPL5dzA+/vOKi1C+bo4SNdKhl7v57dXKML98+dajtwXP6hVRo5NF9u9wP762s/orbOElWOHh43UpGeCEshgIAXAgS/F6qsE4GMBY6o7lN36rGuEWrY06wZcTfI7Wqa8lXVddyqNTs2aDIj5xkL0wABBAICBD9HAgL5INDZopnVP1SrZmrH8VqNTFST3y+V5tddfT7QUQMCCGQmQPBn5sXSCHgk0NMdf5LNHm7Qx2u2SGPv16HNNTFD6j617VyntQ8/pX3HTqjLnSg4SuPuWqAVcyq0b9YXtbh1jBoPbtJUd9bgEdVV3KnH3IuPBSqNaluiIcPGas6SZZozbqj7CCL1T6ea3fVLs1te1IrglUxn8xyNWrJfY1f/VlvGHdem+nVq2ndIxwOzGFU9eZaW3jddI/PtDYWedpffI2CRAMFvUWdRat8WCD/jLxmhezeu0rxxabwGlyz4/W3aMP1OrT7ixL2TqcPkTAHobD+mE84/VUzUpPKntOtgouC/VTU1B9XScsoN42GBhjrmNpQq7n1CTy+p6iH8Uwd/9aQp8u3eoWNdzgVFpcrlV8exwFsMKhmjdfs3aTLh37cPePYuZwIEf87o2TACsQId2r34O5rfcsy9O1fJEI2dukBT7xqjCcOT3GUnDP5O7Zz1ZS1o7dKg8XXa3jhdw8Oz/X1qa16p+Ut26Li7+UTB78a7ah78F62JmEzQuW+5ps3eruMaoYbWZqV+Qy918Lu7V7VI2zfP1chQbb42NS24U7WtXSq563E931CdxsgCRxECCGQqQPBnKsbyCHgs0Hl4m9bWN2rrkYjX+gaN0pKNazVvdMxtcKLgb9uomyet0ctVddq7Y7oSvUjX0TxHtyzZr64kwV+x+Jd6dt7wmD3160DtZzV9a5fGPvhbbUl5S95D8JdM1MZDa+NfP+zYpjvGrtCRknu040+LE8918Nif1SPQ1wUI/r7ew+yfvQL+DrXt3qsNj63TruBFQEXNQ9rSOP7C63wJgj/0bv+kh3+v9Ulf7A/NKUh0xz9Kja1NmprgzYGoZ/SBiQFJfnoI/tmP66/LqxO0jZxnkGSSo709SuUI5IUAwZ8X3UARCKQW8LVt06LpK+R8t8/4B3+vRyYHx8fjgj8UuMPUsOdXCV4LDG0ntFyyyX2JQ7e3gt+d3JfwwoHg57OAgNcCBL/XwqwfgTQF/B3t6iyvdCfhJfrxH1ium+7arq6xP9WhzZMCM/gJ/jR1WQwBBEICBD/HAgI5F/DrcP0XNOWxU6qqf0ZPxn97T6DC0Lv+ka/ueTLUn/y7BLjjz/nBQgEIXLQAwX/RhKwAgYsX8B9u0C01W/RyyRg17t2U8Pm6MyFvzJL90qSHdHTd+MBfvLuIyX3uuhJO7iP4L75HWQMC+StA8Odv31BZQQk4d/23a8pjzl/aG6F7N6/VwtHBV/j8HTrcvF6Lljuv4F2n2S2/1oqRwecBnrzOR/AX1KHHzhacAMFfcF3ODuevQMx7/HGFDtL41b/SI5HT7Y2+wKdEw+6t09S2H6o+6Tf3Mbkvf48TKkPg4gQI/ovzozUCvS7ga9ultQ8+op37At+yVzJkmMZNnql599bEf5VtRl/ZO0gV1RM1Z8kCzRip4FfqMqu/1zuQFSKQ5wIEf553EOUh4I1AmzaM+bpWvzxT247XarQ3G2GtCCCQhwIEfx52CiUhcFEC/jY1Lf6h6g6P0pa9tRqd4PXA8GTC8T/V0Y2TAhMF+UEAgYIQIPgLopvZycIS8Gn3gi9o7q6u+O/Dl+R8GdD8KSvU2hUzUbCwkNhbBApWgOAv2K5nx/u0QJK/zufvOBb4E7hKMFGwT4OwcwggEBIg+DkWEOizAj617dyoDVv3at/BwJ+8dScKTpiuOfdNV+zf++mzDOwYAghECRD8HBAIIIAAAggUkADBX0Cdza4igAACCCBA8HMMIIAAAgggUEACBH8BdTa7igACCCCAAMHPMYAAAggggEABCRD8BdTZ7CoCCCCAAAIEP8cAAggggAACBSRA8BdQZ7OrCCCAAAIIEPwcAwgggAACCBSQAMFfQJ3NriKAAAIIIEDwcwwggAACCCBQQAIEfwF1NruKAAIIIIAAwc8xgAACCCCAQAEJEPwF1NnsKgIIIIAAAgQ/xwACCCCAAAIFJEDwF1Bns6sIIIAAAggQ/BwDCCCAAAIIFJAAwV9Anc2uIoAAAgggQPBzDCCAAAIIIFBAAgR/AXU2u4oAAggggADBzzGAAAIIIIBAAQkQ/AXU2ewqAggggAACBD/HAAIIIIAAAgUkQPAXUGezqwgggAACCBD8HAMIIIAAAggUkADBX0Cdza4igAACCCBA8HMMIIAAAgggUEACBH8BdTa7igACCCCAAMHPMYAAAggggEABCRD8BdTZ7CoCCCCAAAKFGfydLZpZ/ZQmH9ykqeUcBMkFjqiuYp2GJ3XqVNuBTpWPHq7cMvZUZxb7uLNNBzrLNXq4gYh7XB7TwuO1GpnFkvvSpjqb52jUrok6tLlG5fKr4/AxaXiVhpb29l56ue7erdU1ObZAf11e1bsr9nBt/o4jatMwjTTsuMP1N2rtsN9qCyf4hL3UB4O/U82zvqjFrRH7WzJEVVMXaeWSGg0vk0TwJ/7Ixrn0EKidu3TPmPUa/dSvNafSw7NAj6vOn+Dv3DlPNz9Yraf3zpJD4pyApuiJ9E66GQZ/Ruvu0bBvLBAd/Ee0YcQMtTf+TmsmOB/8i/g53KCP10g7whdlvbjuiygrnaa5Cv6LOT4Pb/isph1fpf9oHK8yBc7pOyclC/L43xP8qY+MPhv8kQeJ39ehA6vv1qy2Wdq7Y7oqCf7eCf50zjpZWSZ/gj92dzM6+RH8F320RAf/Ra/uwgrigr8X1+3xqmwM/mgSgr+3D5GCCH4Xzd+qZcNWaOiuf9O88vih/o59DVq2eJtaT0hDxi7WI+tmaWTwJsHf3qK6xWvUfMT55QhNbVirlROGBvuiQ7trF2pZ81GdkDOysEzrGyYp9Ft/2zYtqm3UriOnNKhqmlauq9fk0C+jetOv9uYGLarfriOnSjRk7AKtb5yr0aERY98RNdU2aNWuozo1aIQmLanTmhnD5Y5gBgNj8sN+NS122g9S1ey1emT5WJW7v9umCa3NmhHcbnvT7bq19R4d3ThJofsg9+SwZH+4orGrnavrl92h/sqtU9S2eoW2HvG7Nls2ztJwZ8NxF1CpLaJ3t13N9UtVt/WoTpUM0dj71mrNvOrwI4Po/piuNQ21Ghd286tt0zzNXL1fJ0pH6K7lk+Rf0qrR4UcSHdpXv0KLmva7fTJ2yQatn1MV3tdkHyJ/22bNX7BOe46f0qCKW7WwcZXmjCxT24Yv644Ta/Xn4FCpb/dCjXiwWnufmu7e1at9m742vk1Lj9Vr+M7QUHOlNlTcqcfCG5sZvlvsPLBRixasU+uJLveYWNFYq6mVpeF+nNEyVE1zG9V6olRVd63S+obx4eMpsDrnQifxun2Ht2lZfeh4m6ilDas0w+2syJ/AibRp/G/0ZPigcPahVfOObNDkMimVf9zdVNRxELgIG7q6Wjvr16hseaK7tE4d2LBCyx7eq+OnBqli/AKtXxc8ptx9W6ehD47VvnrHQBoyqVbbG6fLIUp5rDu/jhrqDzip5UWtCD47SWrvnCKSfM7di7cLHanZ7vri192jWcXjmnqsUcu2HpV/yBgt3bxBc+L6xj1ZBc4Fq3fIPeVUTXHPG4FTTsAn6WfSWaRjj5YtWBr8vC7Q0uGtWuxfnHDUye3L636qca0rtcrFnqgV21ZphovtgB7UhtoV2rDnuE4NqtD4+36q9XOC5x2n0oSfmeNJj890z4fhC+Z72zWz+ocKD+COvT/4GCd4PLvHXvzvO5z9Sumd/BwRuFCaqHWda7Ro10Rtd0d5UiwfM8rX2yHtxfoI/nLJf6BBt9RKa3bUanSZT22bFmpia/A5oW+PFo1ao7LNT2jFaOeM2KKZt27ThL2BIG3fdJu+1nqPnt5Yo0q1q3nu7Vpb/YT+fd7w4LJPafJTGzS1UurY3aBp9aVav7dWI2POxZ075+jmByu0JVRD00Ld0TJez+6YrqFqV9OU29U8vklb5lWpzHdQdVO+ocOzn9GTMyqDJ8MVUv0TWjN1uMo6W1U36261zXZO7KUxw2SBk/7umt/rEecMH5UHsRdEgZNb09g6PbluuoaXtqtp7le1ofqXgf2LCf6UFjHBs3PWl7W2cpOeXF6tMl+bmhbcqebxv3aDyL9vuW6q79KajXWaXCm1N8/T12qv0yNH6zWuVHKC9/OrB+mRzbUaV+5TW/NS3bFcWukGv18H6m/TMq0Kr3vTgq9rX9JhwmBhMf3sO9CgO+56WQudIGxv0CdrK/W0G/R+7av9R83aOkaNwQsN90Jg10QdXTde/qjgiR/q9x9u1NemH9M895goVee+5Zo2V1p5tF6jfYGTWMdd/6ItzkWbr1WrptytzuW/1/oEQ9Vxownt23THxBZN2LZJ80aWKbAPRzRnT7OcwySqq2PqdE92e6a4F4OlPfj3HPzOMbNI2xumq7K8TGUxx3rUcVLq04H6OzXTX6fnG6pVGryo2Rc0GOoPHBt1QzcFfu8ec8mO9aEpgz+lvT/151wJhvojLyp6Ombdvmoao4Yda90Lsfamebr14Wo9tX+uhsec2X27l7rHd+BcIHU0z9EtW0Pngh4+k2rThlvv1IHZT2i9c2PQETgXbB2X+HGTW9e+adrsfJaG+tXmnHfqr9MW53gsbdemW2/Xvnt/pUemVqrUPe/Mkb/hD1o52vkgRptFfWbKEjzmcs+d6Z0Po49tszv+5N6pzxGBm6CXddeDD2nhuKEqL1Pqc0rnEe1rH6LRo4cGbsQs+CmQ4A+G+WMV2uGErnuCDU3uCxxUh2f/p1Y6qeJecDujA5vdO8jJ5X75fFJZ+OwVPAhrfqstk8vdZ7jLrntGTwcfcjuTUg53DtHIkUPVVv9pLav4lZ4On3Xb1TTxdnU0/D8tjZq95fz7V9W2JKIGtWv36kMqv3e6RjrBs6BcT0aeJNo26uYpnVr/p1qNDN7xzzteq9GhHNs5TyMeHuvelZY7/91ya+BK2bdL91Q9pcnBO7ueg3+FKvf8KhwcgTuqKTq0eVJwNOHCJMlUFlEfiIg75BC52vdo1YFyzZtxnXbHXZj4tHPu59Q83rl7VIILl4Oqq9gYmIQop2+PaM6xwEWC2537lutTj1UHak72oXQND2nOsVXBdj61H3AmhlWrsjTyeDiiuk+1SDP2q7P6V24gH67/tNYO+zd3IlHsUHP0CSxw0bBq2DMRx4RPh5s2qnPcYk0oja/daT+t9An9eUn8xKzY4HfqmD8keNEZ3M/Y0Yrw7rvHwQ5NcC9eAr47JzkXg/4EvpH+gWM+auJU3B1/9DETS+73+eQvjbggcEJ1eql2/GmxRrrB36jhrU2aGhrhcY71SR1a6fSp+9k9pmTHelnSO/4e7Iem/pynDv5EF9PxZssqIvo9aDbVPcfECPl98inCJ2pZxyfFZ/Jw/Lmio3mGxhxLfse/athvtP0CtjaM+bo6GpxzkeTz+VVaVhYOtKjjMbtYeEIAACAASURBVNVnJkHwO8dneufD2IsGs+BP6t3DOULOMXRklv7UMDZiRNXgnJLHFwB9NvijJvepxB0uWxEaZo87UUUOm4Z6K+KOrq1Fq2oDQ/1dwV8HhsLL3aGumdMb1T5yiuZNmqRxE6pV6d5IJ5hkGGwbGCqMPCoSnOwifu2GSeSB6PzOt0fzq7ZpXDjsYmaDd7Ro2thjWuoMU0WE/bgDC92LgMhh/vCm0pjcFxVsMcsnt4j5BDgn+tXDtH9bTcwQtrNc/InN+VcnwCaeWKu/LpfqKhaq3HlkE75VinjG3+FMwtoS/5GLHSKMW6JTuxffrfm7SzRhao0m1IzRhOGhK3ifdi/4nPbV/KdWlm/WzQ9X6OnZB/X5lrF6vuE6NU/8nrQucHGUOvgDx0TbvS9qRegKLbKOBM/4U80RSHRXFHUB6xwmzmjE1jHRw6PuNiPCftxBzXcuAtyLwZ78q9II/lRvggQ+GxeG+kMAoUchieZrxF7YJT/WhyYN/h7sHZEUn/PUwX+xZrEHozOK1ahl7lB/6IwTOh/F+0Qec25otU53R5+iHuMlmdWfaBLcgfobtSE0Iz5yqD9U5uzQ6EGqz0zi8I4+LwdWGH8+7J3gT3px2sM5wjWM9HJHe0zOKfmb/H02+JPPAI19Nu18kGKDJDJ1nTuMR1S59XEtHV2u0oQzTH1qP9yqAy07tLb5ZY1b97jWTAjcme67K/EwbfQhYRD8nbs0s3pH4JVE9wo25mTo3lW3a6X7fCpwkt9d81tNbf2imqoTDPO75+NEQ/3RJ/FUwR/Yp0QWMbc0BsF/ePWnNcXflF7wLyhPOISazsfQ39GmAweeUvPWbdqtBXpyW+DZs7PfNx9foKevW6G15Y9r/bgjWjaiVVP3V2ntlJe1NDgak2/B7zxCGtUSer0tWsDnjATtmaJDNa0atbU6eDGYOMQu+F9s8Pt1uP42TWufH3g85ozKRA2jJwj+4AjcyDSOdePgd4/9FJ/zlEP9F2sW3S/uMfRwhbZtW6zR5aG5H6GRNa+DPzAysqnKGdF8WXW3zlD7fcGh/iRvqST7zCS6ME3vfJiF4E9xjoibDOn0/UWcU9I572R7GYK/vENNU76i5pqIiU6RveB0+oPDIu6YOtQ8/Sva6Qz1Ty29MBwcvLx2T6busPoktdd+VjMVfDaZsmcTDfU778i/rLKRVRralt5Qf+T73+7w9urh4Qlo7p1fizRpn18TDgUmcMXf9F5M8EcMjcdZOO9UR/wkGup33n/vKNPIkaXaeTFD/f5tumNsi6ZGTGZM50MV+Ygm8ISgzR3y7FwXHJ1xal5wUJWlPo3bGJhLsK/2s9qtSdqqieFhwcyH+p33wY/IX1mtSn/8BVz6d/zOyTKDoX73Gs0ZNdohTWqVf/zvgnM+0hu2jhoeTjC5L9V3P7ijHvdFjHodaNDH7wq9Kpcg+COPl+BQf7JjPbOh/gj79lSf8/KYixMHL3JyX3pmyR+PRB+hcXfh7ujdU3IeC0wtTx385QZD/dHvu0eci4Y7NxfRNxTOaMD04OupPX1mYh9zHUj7fOhx8PdwjogL/g6zc0o6551cLUPwlwcniy3waYU78aZMvrZtml/r09JtczW8Y7NuGd+qGbucma7ORLMVmrZ8r0a6Q/2l2r3gC1pVtkHbG8aqXD7tq79d9/iWBd4/bduoWybt0OjHHtfKceXyOxNt5u7QuM1rNSHmJjidyX27awITdsp8R7Rh1p3aXRM/uS/w+8Dkv/Z7I2ZUuyf572hXyiFvZ0h1qUpbfq2Fw0tVWtrDSSbqhO8Mh6ewiDrCO9Xz5D4FJu85Eyh3LtXMxYPcCXCJJvcd3jRP0xrLgpP7AnXM99UGJiQ6kzWbFmqZb5G2zxuuzt2NatJ0LQ2/lREszOmrKQc1J3gM+Nu36Z7xmzVuT+g7Cpxn+3fqscq66Iupe5/SpIcvjOrEBr/7fHXPdB1yhl5LS6UeJ/dFn2hTBX/sukvdyX17NDV0HB/eqJk1ezQ1weS+0OiM8whj7q4Lj7Wcfw9MVEvu7+7j1mrt2DxXI0ud2c53a9bW64KTHXt6tdKn3Yu/oLrStdruvnVyUKvmztNjx6YH33oIBGp4cl9owmxlk55dXhU1uS/RsZ5qVn/KyX0pP+fl7kTdaWP3asbBtZpQFvpsXHhjoCez1PMiok//zuTHW1un6ynnTQZn/2u/odo9VUl9o/c5ZnJf+y4tm/s9taQ1uS84kfbBCm135kK5Ex5XqnTd41oxrlydBxp1z9wtOjYjONTfw2cm7vjM4HwYe9w7FxyLSp/Qs/cNU6nzOYr5if19au/U5whf3BcepV6+lMl9ubp+idxuTxNBEn+BT8fO5ZpfH3imFvWKlfN2zO7lmu+8Jud3XjtbpQnH71ZTVXCijj/wWtoq53W+rkGqmrRYKxudGfDBU+vhzVq0OPCKWMmQMVq4bpXmhd/Ri6w78nW+wHpWNEwPv1Io3xFtWrxUa93XapzX+Wq1ckbwFbXgJJtxq0u0030dcJCqF2/Q+ojX40LPdAMT5JJNcXNek5ujaQ2H5L/XmVDmPE/PYKi/B4uooyO4rPs6X+z+OObh/nBer8z8db6dtQtVF+qTu5ZpzfLAsHKqyXLOq17LajeG+2pGY51WhN8hDAyBLrsu+EaDszMJvg8i7j3yzj1aNGuhWo5Va11wIteFV8pi9i3DZ/xKsG5fxPE2qGqili6v04zQe6kJPp4XRqiiR2VS+3do9+LvaH7LscDrrbOr1N54POkdadxm/W3aNPdu9/WxUud1xtll2nSfz30dcpx7sdmo0tVj1bbaeeVRGlZTp/UNwccCPRzr6b/OF39cpfycK/g8u+WYRj8YHAaPeVUwlVkmwS8FXotdFHztb2HjeLXP3qaR7iTbHi7GHewMX+dbVXq/xrWt0VoX23l1sC7wemnwdb17ZoVeLV2mOWVbtMC36MIIV6rPTA/HZ6rzYWzwB+YPrdRB/z3BSaDRR1Xs75VyAqqLpGTniMTfe5BieV7ny4fgL8Aa0vriF+eOdZ2G7y/krykOzXVIZ95FYRxHkW8k5Mce9zBikNaxnh97YkMVefsNd2++KT3zjA2EyWu86irpq1/Ny33og0P9eensbVE9nAyd16c6di/U1/ZM0X9EfGmPt0Xl4dqdO5B6nxauS/Q2QR7W62VJzitjHXs0f+JeTU0258PL7SddN8GfTfa8Df5Dh6Tq6mxS9P62Ro2SDh7s/fX2whoJ/l5AzPkqUgb/Ea361J3aVDlF6zeuCn77V84rpoAcC7iz9DdVqmbdQ1oTO98hp7UR/NnkJ/g91Cb4PcRl1QgggAACfUuAO35P+5M7fk95WTkCCCCAQMYCBH/GZJk0IPgz0WJZBBBAAAHvBQh+T40Jfk95WTkCCCCAQMYCiYL/8sulz3xGuuGGjFfnaYP33pP++EfpL3+J3gzP+D1lZ+UIIIAAAn1JIFHwX3ed9KMfSd/+dn7t6csvS//8z9KjjxL8+dUzVIMAAgggYI0Awe9pVzHU7ykvK0cAAQQQyFiA4M+YLJMGBH8mWiyLAAIIIOC9AMHvqTHB7ykvK0cAAQQQyFiA4M+YLJMGBH8mWiyLAAIIIOC9AMHvqXGfC/4DBw5o1apVnqKxcgQQQAAB7wRufPttrd63L3oDzOrvNfA+F/w7duxQTU1NrwGxIgQQQACB7AqMkhT3522uvTbw2hyv8110ZxD8F03IChBAAAEEelPANPj/9OcX9OimX+iE82d9M/jZ/OiGDJaOWZT3+M3teqsld/y9Jcl6EEAAgdwImAb/D5Yu05tvvpVx0QR/gKyou7u7O1Yv2fBAxsoeNiD4PcRl1QgggEAWBEyDf9a35rnVXVSQZ7p/3PFnKtb7yxP8vW/KGhFAAIFsChD83mrzjN9bX9aOAAIIIJChQG8Fv8kz/yFXXaVvzfmmPvXJT6RXNXf86Tl5uRR3/F7qsm4EEEDAe4HeCn7TZ/5O+K9Z1ZDejhL86Tl5uRTB76Uu60YAAQS8F+it4Dd55p9xG4Lf+wOipy0Q/D0J8XsEEEAgvwUIfm/7h2f83vqydgQQQACBDAUI/gzBMly87wX/A3epZtG2DBlYHAEEEEAgXwQIfm97os8F/wPTP6JF21/zVo21I4AAAgh4JkDwe0brrrjPBT/P+L09YFg7Aggg4LUAwe+tMMHvrS9rRwABBBDIUIDgzxAsw8UJ/gzBWBwBBBBAwFuBhMGfxp/ljX0VL+NX8yRl3IbX+bw9GNJZO0P96SixDAIIIJC/AkmD3/mzvN/6VtLCcxb8P/6x9POfR9c1apR0MO6PC+cFOnf8edENFIEAAgggEBIg+L09Fgh+b31ZOwIIIIBAhgKmwR+7mYyH7U2H+rnjz7CHe3lxhvp7GZTVIYAAAlkWIPi9BeeO31tf1o4AAgggkKEAwZ8hWIaLE/wZgrE4AggggIC3AgS/t74Ev7e+rB0BBBBAIEOB3gr+efO/p/ffP5Ph1qWrrvqQHli1Mr12zut8PONPz8qrpXjG75Us60UAAQSyI9Bbwf+nP7+gn2/aojfffCvtwp3Q//acmfrUJz+RXhuCPz0nL5ci+L3UZd0IIICA9wK9FfzeVyqJ4M8Kc8qNEPy57wMqQAABBC5GgOC/GL2e2/a9Z/ybl6hmdmPPe84SCCCAAAJ5KUDwe9stfS/4d+xQTU2Nt2qsHQEEEEDAMwGC3zNad8UEv7e+rB0BBBBAIEMBgj9DsAwXJ/gzBGNxBBBAAAFvBQh+b30Jfm99WTsCCCCAQIYCBH+GYBkuTvBnCMbiCCCAAALeChD83voS/N76snYEEEAAgQwFCP4MwTJcnODPEIzFEUAAAQS8FSD4vfXte8HPe/zeHjGsHQEEEPBYgOD3FrjvBT/v8Xt7xLB2BBBAwGOBpMG/bJk0ZYrHW89w9W+8Ia1fL/3859ENR42SDh7McGXZWZzgz44zW0EAAQQQSFMgYfBfeaX0ta9JH/tYmmvJ0mJnzkh/+IP0m98Q/Fkij9sM39WfK3m2iwACCPSOQMLg751VZ28t3PFnz5rgz541W0IAAQS8ECD4vVC9sE6G+r31Ze0IIIAAAhkKEPwZgmW4eJ8L/t/97ne6//77M2RgcQQQQACBfBG48eRJrWltzZdyzOpgqN/MzaSVz+fTSy+9ZNKUNggggAACeSDQ/8UXNXTp0nAlxcXFuvTSS/OgsgxK+Md/lLZuzaBB9hbtc3f82aNjSwgggAACngi8/Xb8LHlPNuThSp23EL7yFQ83YL5qgt/cjpYIIIAAAghYJ0DwW9dlFIwAAggggIC5AMFvbkdLBBBAAAEErBMg+K3rMgpGAAEEEEDAXIDgN7ejJQIIIIAAAtYJEPzWdRkFI4AAAgggYC5A8Jvb0RIBBBBAAAHrBAh+67qMghFAAAEEEDAXIPjN7WiJAAIIIICAdQIEv3VdRsEIIIAAAgiYCxD85na0RAABBBBAwDoBgt+6LqNgBBBAAAEEzAUIfnM7WiKAAAIIIGCdAMFvXZdRMAIIIIAAAuYCBL+5HS0RQAABBBCwToDgt67LKBgBBBBAAAFzAYLf3I6WCCCAAAIIWCdA8FvXZRSMAAIIIICAuQDBb25HSwQQQAABBKwTIPit6zIKRgABBBBAwFyA4De3oyUCCCCAAALWCRD81nUZBSOAAAIIIGAuQPCb29ESAQQQQAAB6wQIfuu6jIIRQAABBBAwFyD4ze1oiQACCCCAgHUCBL91XUbBCCCAAAIImAsQ/OZ2tEQAAQQQQMA6AYLfui6jYAQQQAABBMwFCH5zO1oigAACCCBgnQDBb12XUTACCCCAAALmAgS/uR0tEUAAAQQQsE6A4LeuyygYAQQQQAABcwGC39yOlggggAACCFgnQPBb12UUjAACCCCAgLkAwW9uR0sEEEAAAQSsEyD4resyCkYAAQQQQMBcgOA3t6MlAggggAAC1gkQ/NZ1GQUjgAACCCBgLkDwm9vREgEEEEAAAesECH7ruoyCEUAAAQQQMBcg+M3taIkAAggggIB1AgS/dV1GwQgggAACCJgLEPzmdrREAAEEEEDAOgGC37ouo2AEEEAAAQTMBQh+cztaIoAAAgggYJ0AwW9dl1EwAggggAAC5gIEv7kdLRFAAAEEELBOgOC3rssoGAEEEEAAAXMBgt/cjpYIIIAAAghYJ0DwW9dlFIwAAggggIC5AMFvbkdLBBBAAAEErBMg+K3rMgpGAAEEEEDAXIDgN7ejJQIIIIAAAtYJEPzWdRkFI4AAAgggYC5A8Jvb0RIBBBBAAAHrBAh+67qMghFAAAEEEDAXIPjN7WiJAAIIIICAdQIEv3VdRsEIIIAAAgiYCxD85na0RAABBBBAwDoBgt+6LqNgBBBAAAEEzAUIfnM7WiKAAAIIIGCdAMFvXZdRMAIIIIAAAuYCBL+5HS0RQAABBBCwToDgt67LKBgBBBBAAAFzAYLf3I6WCCCAAAIIWCdA8FvXZRSMAAIIIICAuQDBb25HSwQQQAABBKwTIPit6zIKRgABBBBAwFyA4De3oyUCCCCAAALWCRD81nUZBSOAAAIIIGAuQPCb29ESAQQQQAAB6wQIfuu6jIIRQAABBBAwFyD4ze1oiQACCCCAgHUCBL91XUbBCCCAAAIImAsQ/OZ2tEQAAQQQQMA6AYLfui6jYAQQQAABBMwFCH5zO1oigAACCCBgnQDBb12XUTACCCCAAALmAgS/uR0tEUAAAQQQsE6A4LeuyygYAQQQQAABcwGC39yOlggggAACCFgnQPBb12UUjAACCCCAgLkAwW9uR0sEEEAAAQSsEyD4resyCkYAAQQQQMBcgOA3t6MlAggggAAC1gkQ/NZ1GQUjgAACCCBgLkDwm9vREgEEEEAAAesECH7ruoyCEUAAAQQQMBcg+M3taIkAAggggIB1AgS/dV1GwQgggAACCJgLEPzmdrREAAEEEEDAOgGC37ouo2AEEEAAAQTMBQh+cztaIoAAAgggYJ0AwW9dl1EwAggggAAC5gIEv7kdLRFAAAEEELBOgOC3rssoGAEEEEAAAXMBgt/cjpYIIIAAAghYJ0DwW9dlFIwAAggggIC5AMFvbkdLBBBAAAEErBMg+K3rMgpGAAEEEEDAXIDgN7ejJQIIIIAAAtYJEPzWdRkFI4AAAgggYC5A8Jvb0RIBBBBAAAHrBAh+67qMghFAAAEEEDAXIPjN7WiJAAIIIICAdQIEv3VdRsEIIIAAAgiYCxD85na0RAABBBBAwDoBgt+6LqNgBBBAAAEEzAUIfnM7WiKAAAIIIGCdAMFvXZdRMAIIIIAAAuYCBL+5HS0RQAABBBCwToDgt67LKBgBBBBAAAFzAYLf3I6WCCCAAAIIWCdA8FvXZRSMAAIIIICAuQDBb25HSwQQQAABBKwTIPit6zIKRgABBBBAwFyA4De3oyUCCCCAAALWCRD81nUZBSOAAAIIIGAuQPCb29ESAQQQQAAB6wQIfuu6jIIRQAABBBAwFyD4ze1oiQACCCCAgHUCBL91XUbBCCCAAAIImAsQ/OZ2tEQAAQQQQMA6AYLfui6jYAQQQAABBMwFCH5zO1oigAACCCBgnQDBb12XUTACCCCAAALmAgS/uR0tEUAAAQQQsE6A4LeuyygYAQQQQAABcwGC39yOlggggAACCFgnQPBb12UUjAACCCCAgLkAwW9uR0sEEEAAAQSsEyD4resyCkYAAQQQQMBcgOA3t6MlAggggAAC1gkQ/NZ1GQUjgAACCCBgLkDwm9vREgEEEEAAAesECH7ruoyCEUAAAQQQMBcg+M3taIkAAggggIB1AgS/dV1GwQgggAACCJgLEPzmdrREAAEEEEDAOgGC37ouo2AEEEAAAQTMBQh+cztaIoAAAgggYJ0AwW9dl1EwAggggAAC5gIEv7kdLRFAAAEEELBOgOC3rssoGAEEEEAAAXMBgt/cjpYIIIAAAghYJ0DwW9dlFIwAAggggIC5AMFvbkdLBBBAAAEErBMg+K3rMgpGAAEEEEDAXIDgN7ejJQIIIIAAAtYJEPzWdRkFI4AAAgggYC5A8Jvb0RIBBBBAAAHrBAh+67qMghFAAAEEEDAXIPjN7WiJAAIIIICAdQIEv3VdRsEIIIAAAgiYCxD85na0RAABBBBAwDoBgt+6LqNgBBBAAAEEzAUIfnM7WiKAAAIIIGCdAMFvXZdRMAIIIIAAAuYCBL+5HS0RQAABBBCwToDgt67LKBgBBBBAAAFzAYLf3I6WCCCAAAIIWCdA8FvXZRSMAAIIIICAuQDBb25HSwQQQAABBKwTIPit6zIKRgABBBBAwFyA4De3oyUCCCCAAALWCRD81nUZBSOAAAIIIGAuQPCb29ESAQQQQAAB6wQIfuu6jIIRQAABBBAwFyD4ze1oiQACCCCAgHUCBL91XUbBCCCAAAIImAsQ/OZ2tEQAAQQQQMA6AYLfui6jYAQQQAABBMwFCH5zO1oigAACCCBgnQDBb12XUTACCCCAAALmAgS/uR0tEUAAAQQQsE6A4LeuyygYAQQQQAABcwGC39yOlggggAACCFgnQPBb12UUjAACCCCAgLkAwW9uR0sEEEAAAQSsEyD4resyCkYAAQQQQMBcgOA3t6MlAggggAAC1gkQ/NZ1GQUjgAACCCBgLkDwm9vREgEEEEAAAesECH7ruoyCEUAAAQQQMBcg+M3taIkAAggggIB1AgS/dV1GwQgggAACCJgLEPzmdrREAAEEEEDAOgGC37ouo2AEEEAAAQTMBQh+cztaIoAAAgggYJ0AwW9dl1EwAggggAAC5gIEv7kdLRFAAAEEELBOgOC3rssoGAEEEEAAAXMBgt/cjpYIIIAAAghYJ0DwW9dlFIwAAggggIC5AMFvbkdLBBBAAAEErBMg+K3rMgpGAAEEEEDAXIDgN7ejJQIIIIAAAtYJEPzWdRkFI4AAAgggYC5A8Jvb0RIBBBBAAAHrBAh+67qMghFAAAEEEDAXIPjN7WiJAAIIIICAdQIEv3VdRsEIIIAAAgiYCxD85na0RAABBBBAwDoBgt+6LqNgBBBAAAEEzAUIfnM7WiKAAAIIIGCdAMFvXZdRMAIIIIAAAuYCBL+5HS0RQAABBBCwToDgt67LKBgBBBBAAAFzAYLf3I6WCCCAAAIIWCdA8FvXZRSMAAIIIICAuQDBb25HSwQQQAABBKwTIPit6zIKRgABBBBAwFyA4De3oyUCCCCAAALWCRD81nUZBSOAAAIIIGAuQPCb29ESAQQQQAAB6wQIfuu6jIIRQAABBBAwFyD4ze1oiQACCCCAgHUCBL91XUbBCCCAAAIImAsQ/OZ2tEQAAQQQQMA6AYLfui6jYAQQQAABBMwFCH5zO1oigAACCCBgnQDBb12XUTACCCCAAALmAgS/uR0tEUAAAQQQsE6A4LeuyygYAQQQQAABcwGC39yOlggggAACCFgnQPBb12UUjAACCCCAgLkAwW9uR0sEEEAAAQSsEyD4resyCkYAAQQQQMBcgOA3t6MlAggggAAC1gkQ/NZ1GQUjgAACCCBgLkDwm9vREgEEEEAAAesECH7ruoyCEUAAAQQQMBcg+M3taIkAAggggIB1AgS/dV1GwQgggAACCJgLEPzmdrREAAEEEEDAOgGC37ouo2AEEEAAAQTMBQh+cztaIoAAAgggYJ0AwW9dl1EwAggggAAC5gIEv7kdLRFAAAEEELBOgOC3rssoGAEEEEAAAXMBgt/cjpYIIIAAAghYJ0DwW9dlFIwAAggggIC5AMFvbkdLBBBAAAEErBMg+K3rMgpGAAEEEEDAXIDgN7ejJQIIIIAAAtYJEPzWdRkFI4AAAgggYC5A8Jvb0RIBBBBAAAHrBAh+67qMghFAAAEEEDAXIPjN7WiJAAIIIICAdQIEv3VdRsEIIIAAAgiYCxD85na0RAABBBBAwDoBgt+6LqNgBBBAAAEEzAUIfnM7WiKAAAIIIGCdAMFvXZdRMAIIIIAAAuYCBL+5HS0RQAABBBCwToDgt67LKBgBBBBAAAFzAYLf3I6WCCCAAAIIWCdA8FvXZRSMAAIIIICAuQDBb25HSwQQQAABBKwTIPit6zIKRgABBBBAwFyA4De3oyUCCCCAAALWCRD81nUZBSOAAAIIIGAuQPCb29ESAQQQQAAB6wQIfuu6jIIRQAABBBAwFyD4ze1oiQACCCCAgHUCBL91XUbBCCCAAAIImAsQ/OZ2tEQAAQQQQMA6AYLfui6jYAQQQAABBMwFCH5zO1oigAACCCBgnQDBb12XUTACCCCAAALmAgS/uR0tEUAAAQQQsE6A4LeuyygYAQQQQAABcwGC39yOlggggAACCFgnQPBb12UUjAACCCCAgLkAwW9uR0sEEEAAAQSsEyD4resyCkYAAQQQQMBcgOA3t6MlAggggAAC1gkQ/NZ1GQUjgAACCCBgLkDwm9vREgEEEEAAAesECH7ruoyCEUAAAQQQMBfIKPiP/+2/dP3Qa3XJJcXmW6QlAggggAACCORE4OzZs3qp4zVVfGxo3PaLuru7u2P/9eVXXtfgwWUacPllOSmYjSKAAAIIIICAucDp0+/ppO9dXfeRD6cX/CfePKni4n66cnCZ+VZpiQACCCCAAAI5EXj75Ds6f75bV31ocHrBf767W8ePv6QbKj+ak4LZKAIIIIAAAgiYCzjP92+ouF5FRUXpBb+zlO/Uab377mldm2CYwLwUWiKAAAIIIICAlwIvv/KGysoGaNDAAQk3k/AZf2hJn+9dnTr9XsJnBF4WzboRQAABBBBAIHOBV159QwMHDlDZoMSh76wxZfA7C5w6dVqvvXFCQz40WCUlJSotLWG2f+Z9QQsEEEAAAQR6XcCZve/3d6mr6wOdeOukrrl6SNI7/dDGewx+Z0Fn4v9bb5/UGX+Xu4Fz5873evGsEAEEEEAAAQQyEyguF9d7kgAAAHFJREFULnZvyPuXluhDV16R8Jl+7BrTCv7MymBpBBBAAAEEEMhXAYI/X3uGuhBAAAEEEPBAgOD3AJVVIoAAAgggkK8CBH++9gx1IYAAAggg4IEAwe8BKqtEAAEEEEAgXwUI/nztGepCAAEEEEDAA4H/D+0wG5sPhFElAAAAAElFTkSuQmCC)\n",
        "\n",
        "Then click on the copy button and switch back to this tab that has your Google Colaboratory open and paste it into the box that below where it says says \"Enter your authorization code:\" and press enter.\n",
        "\n",
        "\n",
        "That's all."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOSy4UaGQmj8",
        "cellView": "form"
      },
      "source": [
        "#@title Connect to Google Drive. Run this cell (click on the button on the top left of this cell):\n",
        "# Mount Google Drive to Google Colaboratory\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "print(\"All good!\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2Wnbg9o945D"
      },
      "source": [
        "# Images setup\n",
        "Here you will create a folder for retrieving your images that you want to classify (if you don't have a dataset for images that you can classify, head over to the [Example](https://) tab on the website or to this [notebook](https://colab.research.google.com/drive/1ZfscSQPfgaPnjLtGJe9_xSui-KHY8xjf?usp=sharing)).\n",
        "\n",
        "Create a folder inside Google Drive. Make sure that this folder is not inside another folder but rather directly in Google Drive. Then create a folder for each of the class/categories that you want to classify your images into and name your each folder as the class it represents. Then put all of your images into the respective folders.\n",
        "\n",
        "Let's clarify this with an example. Let's say that you want to make a computer tell a cat from a dog. You can you create a folder called \"cats_v_dogs\" in your Google Drive. Make sure that this folder is not within a folder. Inside the \"cats_v_dogs\" folder, create a folder for your cat images called \"cats.\" Again, inside the \"cats_v_dogs\" folder, create a folder for your dog images called \"dogs.\" Then upload all of your cat images into your \"cats\" folder and upload all of your dog images into your \"dogs\" folder.\n",
        "\n",
        "By now, you are done with setting your images and your file system should look like the following.\n",
        "\n",
        "```\n",
        "My Drive/\n",
        "        |_cats_v_dogs/\n",
        "                |_ cats/\n",
        "                    |_ cat_pic1\n",
        "                    |_ cat_pic2\n",
        "                    |_ cat_pic3\n",
        "                    |_ etc.\n",
        "                |_ dogs/\n",
        "                    |_ dog_pic1\n",
        "                    |_ dog_pic2\n",
        "                    |_ dog_pic3\n",
        "                    |_ etc.\n",
        "```\n",
        "Now you are ready to create your model!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HoNgnMSpXGaI"
      },
      "source": [
        "# Model creation and testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJVSv9Fk_skn",
        "cellView": "form"
      },
      "source": [
        "#@title Please type in the name of main folder (in the above example, the name would be \"cats_v_dogs\") and then run this cell (click on the button on the top left of this cell):\n",
        "\n",
        "# Import necessary packages\n",
        "import os\n",
        "import tensorflow as tf\n",
        "\n",
        "folder_name = \"\" #@param {type:\"string\"}\n",
        "\n",
        "# Change working directory to the your folder.\n",
        "%cd /content/gdrive/My Drive/$folder_name\n",
        "# Get the current working directory\n",
        "cwd = os.getcwd()\n",
        "\n",
        "# Create and configure Datasets\n",
        "batch_size = 32\n",
        "img_height = 180\n",
        "img_width = 180\n",
        "\n",
        "# Training set\n",
        "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    cwd,\n",
        "    validation_split=0.2,\n",
        "    subset=\"training\",\n",
        "    seed=123,\n",
        "    image_size=(img_height, img_width),\n",
        "    batch_size=batch_size,\n",
        ")\n",
        "\n",
        "# Testing/validation set\n",
        "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    cwd,\n",
        "    validation_split=0.2,\n",
        "    subset=\"validation\",\n",
        "    seed=123,\n",
        "    image_size=(img_height, img_width),\n",
        "    batch_size=batch_size,\n",
        ")\n",
        "\n",
        "# Configure dataset for performance\n",
        "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
        "train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\n",
        "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
        "\n",
        "print(\"All good!\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Ck-Ejju-7Ey",
        "cellView": "form"
      },
      "source": [
        "#@title Please type in the number of classes or categories that you have (if you have cats and dogs, then this number would be 2) and then run this cell (click on the button on the top left of this cell):\n",
        "number_of_classes =  #@param {type:\"integer\"}\n",
        "\n",
        "if number_of_classes != len(os.listdir()):\n",
        "  print(\"Error: Please enter the number of casses or categories that you have (this number should be the number of subdirectories in your main folder):\")\n",
        "else:\n",
        "  # Create model\n",
        "  data_augmentation = tf.keras.Sequential(\n",
        "      [\n",
        "       tf.keras.layers.experimental.preprocessing.RandomFlip(\"horizontal\"),\n",
        "       tf.keras.layers.experimental.preprocessing.RandomRotation(0.1),\n",
        "      ]\n",
        "  )\n",
        "\n",
        "  def model(input_shape, number_of_classes):\n",
        "      inputs = tf.keras.Input(shape=input_shape)\n",
        "      x = data_augmentation(inputs)\n",
        "      x = tf.keras.layers.experimental.preprocessing.Rescaling(1.0 / 255)(x)\n",
        "      x = tf.keras.layers.Conv2D(32, 3, strides=2, padding=\"same\")(x)\n",
        "      x = tf.keras.layers.BatchNormalization()(x)\n",
        "      x = tf.keras.layers.Activation(\"relu\")(x)\n",
        "      x = tf.keras.layers.Conv2D(64, 3, padding=\"same\")(x)\n",
        "      x = tf.keras.layers.BatchNormalization()(x)\n",
        "      x = tf.keras.layers.Activation(\"relu\")(x)\n",
        "      previous_block_activation = x\n",
        "      for size in [128, 256, 512, 728]:\n",
        "          x = tf.keras.layers.Activation(\"relu\")(x)\n",
        "          x = tf.keras.layers.SeparableConv2D(size, 3, padding=\"same\")(x)\n",
        "          x = tf.keras.layers.BatchNormalization()(x)\n",
        "          x = tf.keras.layers.Activation(\"relu\")(x)\n",
        "          x = tf.keras.layers.SeparableConv2D(size, 3, padding=\"same\")(x)\n",
        "          x = tf.keras.layers.BatchNormalization()(x)\n",
        "          x = tf.keras.layers.MaxPooling2D(3, strides=2, padding=\"same\")(x)\n",
        "          residual = tf.keras.layers.Conv2D(size, 1, strides=2, padding=\"same\")(\n",
        "              previous_block_activation\n",
        "          )\n",
        "          x = tf.keras.layers.add([x, residual])\n",
        "          previous_block_activation = x\n",
        "      x = tf.keras.layers.SeparableConv2D(1024, 3, padding=\"same\")(x)\n",
        "      x = tf.keras.layers.BatchNormalization()(x)\n",
        "      x = tf.keras.layers.Activation(\"relu\")(x)\n",
        "      x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
        "      if number_of_classes == 2:\n",
        "          activation = \"sigmoid\"\n",
        "          units = 1\n",
        "      else:\n",
        "          activation = \"softmax\"\n",
        "          units = number_of_classes\n",
        "      x = tf.keras.layers.Dropout(0.5)(x)\n",
        "      outputs = tf.keras.layers.Dense(units, activation=activation)(x)\n",
        "      return tf.keras.Model(inputs, outputs)\n",
        "  model = model(input_shape=(img_height, img_width) + (3,), number_of_classes=2)\n",
        "\n",
        "  print(\"All good!\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCcFl8mL_J8Q",
        "cellView": "form"
      },
      "source": [
        "#@title Please enter the number of times you want your model to be trained (too small of a number would make a poorly performing model and too large of a number would take too long to train; 15 is a good number) and then run this cell (click on the button on the top left of this cell):\n",
        "number_of_epochs = 15#@param {type:\"integer\"}\n",
        "\n",
        "# Compile, fit, and test model\n",
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(1e-3),\n",
        "    loss=\"binary_crossentropy\",\n",
        "    metrics=[\"accuracy\"],\n",
        ")\n",
        "\n",
        "model.fit(\n",
        "    train_ds, epochs=number_of_epochs, validation_data=val_ds,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afOBCBPHS1QK"
      },
      "source": [
        "# Now look at the number on the right next to the last \"val_accuracy.\" That is the performance of your model. Multiple that number by 100. Your model classifies correctly that percent of the time!"
      ]
    }
  ]
}