Custom_Images_Malaria.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.1"
    },
    "colab": {
      "name": "03-Deep-Learning-Custom-Images-Malaria.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfAte04UBkhT"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.image import imread\n",
        "# Technically not necessary in newest versions of jupyter\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uwPpEm-BBkh9"
      },
      "source": [
        "my_data_dir = 'C:/Users/Amir/Desktop/AI Institute Classes/Deep Learning/TF_2_Notebooks_and_Data/04-CNNs/cell_images'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGvNJb3gBkiC",
        "outputId": "b45d6fcb-9e82-4698-8f21-5b6908a7717c"
      },
      "source": [
        "# CONFIRM THAT THIS REPORTS BACK 'test', and 'train'\n",
        "os.listdir(my_data_dir) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['test', 'train']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4n36eaMBkiI"
      },
      "source": [
        "test_path = my_data_dir+'\\\\test\\\\'\n",
        "train_path = my_data_dir+'\\\\train\\\\'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gl4x4UrQBkiK",
        "outputId": "e564f42c-293e-477d-c88f-5c40a2a12052"
      },
      "source": [
        "os.listdir(test_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['parasitized', 'uninfected']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1pUn5aIBkiL",
        "outputId": "e6e90011-d1c8-46ac-cdd1-b3dbb67ba8e2"
      },
      "source": [
        "os.listdir(train_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['parasitized', 'uninfected']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pLfEOB2BkiO",
        "outputId": "40a3c61f-9706-43f7-f1ce-bfd8e45601b3"
      },
      "source": [
        "os.listdir(train_path+'\\\\parasitized')[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'C100P61ThinF_IMG_20150918_144104_cell_162.png'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIRhU7gWBkiP"
      },
      "source": [
        "para_cell = train_path+'\\\\parasitized'+'\\\\C100P61ThinF_IMG_20150918_144104_cell_162.png'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIzl8CiPBkiQ"
      },
      "source": [
        "para_img= imread(para_cell)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9bG5dZ0BkiR",
        "outputId": "5369300d-74b2-4680-8ddf-92c3331b21b2"
      },
      "source": [
        "plt.imshow(para_img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x1306e2b86d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Da56EibUkL/Jvs/xnlTbWyONLCA5GCxOwxDrzxCqx5KWFdk1/Zv2raPWXdBHppqUKq1/o65L4cZHQWPqGhkuEG4rCLymu+LeEgihp0FGO7pJr0FmzXqNGuRIuWemJxU70fGGraxphhdnJSFTTkM6mS8Ngr+EoGEs27pJiUFVpx/DhAlfmxzlK8fyW+l+jpjVl3Boux93f4bihxEcCkZuRxBJ0OEXmSeOGmvCzB7ngJFJkxWgCs8+ziCTyWYmNz9nomUxbk0Xt/KVUjckkjTFBZkzKTHnbOMoO/150IKpoi/O6a9cAkY1Xq0HGrWmb2bmHI/KM4xja67p5Qs41JrVdJtOl+6jday5e2MI+BW7byefzetmsKpJHxT9f3FATnpVgOZIHDk52EMvke9VD+cxe7mX43KTDZUy2eLC9nilSyeSXv5ylyWutTRRzjKUXeheYQoe+UsJEu8KmXJpcPFa50i7nDrh9XnRLbay51yWZfem6EoWfyer9sq4kLOpBJVPe4XhWaDJ8Q8MlwrEpvLi8xB8C8Ieq+goRuQfAAwDuBPARAN+rqgfHbWct1rB6uawfVu/MfOYpObP37DhTayd3PMnlWr7W+rZBYRIzEcJb3Kwq8r23JeeSJV3GUr9r59dQx1LbzK7nOo0S+5vXlXM3LL6UUJPVc+/GpXtPxtMbb1Cm8ADC1lonQs6PZ/06Z/7+JCj83wbwCfr+IwB+TF0yyT8F8LoTaGMVTGZy8Q8nf0i1ycBy5jAM2G632G63QeYsfULblHSx+KJOOgUFoDZ+2EyUmIyMAYwBM/NeUeY/XL7WL8asjYXfSmPoJy2z3rWJkNeZf88TVvL32nMaxzHxdVjbXq380vjw88zHl8+XkleqKmT6YPqdicaavpwWjjXhReQ5AL4NwE9O3wXAXwbw81ORtwH49uO00dDQcHI4Lkv/TwD8PQC3Td+fAeAx1eBC9AhcRtkzwVz7jnDMuqZkdaXVdsi82kosubU2+Lnv7e0lrOmM5SQW1lJdpT37ImRyy+6rJKYwJXL9mke09f9LIkjO2pbY3EDVdmxWqSnIajn1cs4oUYLRuOeK0xL7n6PGrtdMcbmSco3Jjvs+TK/6bJw03SxVEr3OA0ee8CLyCgBfUNUPi8i3+NOFokXeRU4heyzLXSBx14iBGBOTBmj5BTBdn7Fdvi7SzAIhzNSw3aZedV527TdTPX5yAZ43F6SyXk1zzeIFg19OAwM7TC9Wn3qv5eVLY1UKhV2yCvCEzH0N/P9dE622KOQLErfP7XVdtzMiba5B537W2q9p/0v3AKT6ABGBkWixEUsmSMNaelr4TDTR6jnozI+bauqVIvJyADcBuB2O4t8hIv1E5Z8D4I9KF6vqWwC8BQDklLLHNjQ0pDjyEqOqP6iqz1HVuwG8BsC/UtXvBvDrAL5rKnY/zjiZZE25tmYPdYkK5BSgVqZUT01JxeX8756C+TJrlFL+vmrBJdfc7y4F0pKij8vk9167vqQsrLWZ97VWb+meWFHGSsFdisHSeNba8EpE731ZU+omyj8bP+eB03C8eSOAB0TkHwL4LbgMs2cCa23iuSYcW0g593uqUQ0Pl60nyUtRPl87jsktCg42IjGp40yOnk7zCyeZLMs3LMXDxReasaTNZyQ7BY+oXc7l41of8jZqSTCW+rumDUbNVTprZHatqiamVC7DIcwtOdtYfubnoKk/kQmvqu8H8P7p+GEALzmJehsaGk4WN5xrbZKQgV0XLVGYCoU3YornXTWTYo8oLrN+yVodVvE5W8jUiO3KrKAyrGCssNIzljYrUlJe5deXFGongVLbOWWsWRGWrltzvsSNrL2/NdzQ3FIy577c2Kapx4MymJ/H9UrhLxKSl6KaFY4erlVKI13wI0cWnhkSFgbnRDO9CKbsYOLbYlMT11sqw23W9rbnC4GFnf2e3CeWJw+/0DN5eYd72JKYs2aycV9rOpNcs17K65cvYqVdd7v0EaX+OnZ9fn9u3Ehj7/thleLhAYBBjItFY20Vn33v290xGWWef9/3Fvt2Emi+9A0Nlwg3FIUXpTRQABREOYgtT3V5vKLzsRTtvk4BNx13BtZvYxW2z9YVQCUq7s/ztlvvx59TNu5HXl+tnV1Yq/RaW2ddmbmbPefvLObwONRi8y+1XeO+lkSbWl0luGuZ7U/CXoYjk10T2j6jnXM31IRnuMGMx8yWJg+v+iCZva8zQiW2P/c18mY3IN2Iwy8rl+EQWox8g0ypH77eGli04HM5W51dVKy7NAmK1y9cx+x5vojl8n2N3a4tiH48cw+9XIQp1T8royiWSxeSShlLwTSE7i8Zp7OZ8I2lb2i4RLhu00V/+hfeHq9nSk52eDsQpdCyw4WO6ZZYpi6xfF3Jw/Hjg1bfjrNILjzM7C7K/SkpqHJW35/PY+LDHI71XlKozSiY9fdVDtuV11Uc5wXOgC0Svp1wW6XowoeM555zHTUKn1+T9L0QSDTvn1A+uRGanA/3xnHsDeXbM9ReUm+He7/z+9bc5o2dLrqk3VZVEtAFQJwoduQkjpTcwURHGDvayouQeurxi26Mf0nTcE4zebAjdnV6X0cdw6RXqy5l9ATeBurLc7hlC5vpDdIXcdfkZ3Z5ia1N9/8TaytkrcgsDLU97mtl7ZrPfE2HscsSsSSL52JVrZ7oLJX2p+MU42T1lTDh3cQu9ctIFDStFXgdk4tS7N+rkw1l3Vj6hoZLhOuWwqdUndhw0sx7ODvq3Bbry5XZ+MNrwBfPe51NxpoyNeTAlQzuUy3X3GHZ3NoOtZLWe5eIsJZ6l/qd11Ort8bB1NoAUgepmlUjv7a2pTfX8vu6EotBUlVsJ/UOJo5Jy/XClC0SJ4HrZsI//Etvh0FZ081sfE1mFNPvnKg5e3fYhWAJJecQfy9Ayq7X2sivLdWz6xouX3uha22U6l4qszRJucySxeEwloBan0rt7low8onN1hiejGvuPb/HKLdTOW6P6jHmZKdoY+kbGi4RrhsKDzWIm8xifje3wtK6lbD3Dn6FPSrFrlGgJY5gxmIXCFLN/p2zvDVlVf5/F1u9pNyqUdLSfftjS7bl2jW76loSGZY06LXyu9rfVabIYmdlojVmd/QaYwzUcGSiyphWuKFaIJSj4rqZ8KqKkSKsMEvf8RtHqtJkaLX80EWdfzzgJIPaRCvFR1/L2jsdQvYdadSZ2iK0i3UtyXq7rsmPl2TqxS2jdH1NB1Dr09KCtGuxqrHbeZ9qY7gktuxy9KnF1Gc9kevfvD/++tCGNcEbFB09U1N+TieBxtI3NFwiXFcUHoWV2MWSi2WS9XCMVJkXa9GUwy5td521TVhDQXOKVNMYl5Q5a2Lf+/KH0dIvad+rys4Ftrpm669ZFfK28vJL1GyJQ+BzS1wCt1/j0mr+FnyvaywE3Jbqbo4t+W1h3I+L62bCzx648bKNDRMbWNLI0iBquVzeTu2F5rKll9W/FLtY9PwFLb1IS+z0GpGiVqampedrSi93tU0Tx7ZUZ17vLk38rv7Xri0tpkumrSWrSa0Ph3k2vn7W8oe6DWvmOVDq6Ux24Dqa8EA6iH5QRNKMqkzK82GLE75MnZgSA6m3W2m1r014f01pMrP5jY+XlIqlMVhCTSav1bOL6tQW0dq98wu7izLmMQEO288193FYirz2+pmi1XK5+LwliWlQDqnGC7Axpzfhmwzf0HCJcP1Q+BHBwcbFlZ+or7UJJTZM+Wk5E0vUBnG7oqCsjQdSKlTiCFQQOAoOjwUBpIvXDHYsauM517yqJttiS5Sfj7uuS3QO7NMdclFlYLvFkjY9jz1fYr+XvAGLvvQm5coiKwuQ/XSuVyBqFzi5yt7xmhiV9zVvo6Slzzm0EjeTWzD8M7DWhn0RxhiMw4jOxzfoiKOR9J0siSCH9aDchetmwr/gVd+TfH/oXW8Lx8lkonfB0IxnGSkJa8UPLNu9xkhYN/8iLCQ15JeE5bacFd5lwsrZxpoZj/u8Rj+xhHxC1Exjpbq5j3m5w+hHSuV4AvurROsKPK4jb2eNaLQL+eJY63etXf5lrU7juDhubrk7ROTnReTfisgnROQ/EZE7ReTXRORT0/+nn1RnGxoajofj8gs/DuD/VtU/B+A/hssi+yYA71OXPfZ90/cTR4nl8iyvaprptKaIKa2+rGHf9dkFLltKgsBllvrE9+aPfUZbj1oihIQqa/ysgb+WkzMs1e/vp+u68MkTQJQ+eX2lZBDGmFDnLq1+6b3In9lhnmntvpc5KQPAwAe5zO+tZALORYfToPRHDoAhIrcD+BiAe5UqEZHfA/AtqvqoiNwF4P2q+nU76jp0Jz71zrfGLxz0YhJ/VDWJb5eg4t64hq2yyCZRQdtcCtlU2+NdyqNW09jztcMwpP2lamviSKKrwIKWfUFrnpcpXV8yadX6BMxl/hqLzuPLuo8wbloez7wftfta+q3Ut6UxCEFqRaAadThWKExZR6Ke0eT8LjHl+fd9d/7zqgAYx6Hw9wL4YwD/XER+S0R+UkRuAfC1qvooAEz/n1W6WEReLyIfEpEPHaMPDQ0Nh8BxlHY9gBcD+O9V9YMi8uM4BPuux04mSRQz8heQlNQVr6xRtF2sIgBYTbX4azmkEldQU4LlmnG223M4qJpWmo9r3MIa345SG/nvJaxVyJXqq+V+8+zwUnu1OJBMuWviHfdhLZa4Fg/nNUnPAOVnVhvnGedwTGXjcSb8IwAeUdUPTt9/Hm7Cf15E7iKW/gvH6mEFpUmztCc8TxFdQu3Bq8R3SVA2k+2S6XjSct9LD7mm2c1l2mRhWPHyJXWsfLHXTPY1GvCTeHF3jpuzsbqyC3EI14gquxZSX6Y2ST1hcH019M504NU2LsCmeD4hKkaqi9paHCd77L8D8DkR8fL5SwH8LoAH4bLGAueQPbahoaGOY0WtFZG/AOAnAewBeBjA98MtIu8B8DwAfwDgVar6pR317OzEJx94G2SKeCMZi5bujY9gpV1O4ZlK87mSllSlrsgqbZul+5qVy23ya7a1ljiXXJzQcdlfPEfCacgyVd7F8q55h46qcS5RXFbyrVFQ5grNGsVe6m9NJCgFAhURDDY6g/U9uVD3ZGWgqLWW3jHTS3EffJJAJXtXn/eK155+1FpV/SiAUiMvPU691fa8Rx0w07QDkwxIp22S0I8OVzxkxx7Ozyf9OQQbzZNnzWJTku2GYUgSWrCoUGPpa5rkxGEke5Hy48PKliV2eGlhW6M7WZqku54nj+caC4T/fc1zL4lqxhh416AkFBvcUJeOl0SsUH7HvoM1aL70DQ2XCNeNa61YnRQbAGwazcZHvJlpY5kLqFC3nMULLBok+GxbSalLLedc7oNeyl6aX7fGZu3voxOTiC/5Gl/NhUdUNrCQOpQpeIHTMGZT7FfN8lDjEnZRfnbOyeuq3VPtvvn7SG3ssjrsYvdzV9pRfZ4ADVyWHdMxHNTC9JEz2+kfbxV22ivir8v7bsyce1iDCznhP/1zPwsglXFdhphpAmX3V9OUr5Eb+QXLvddKbeTXLrVVmsw1Nn6p7lLa69IE2mVySpyDRgXJLFxzPO8SoVXZ6l1OLnw+11fwfeQOR7UX+ah6gFJdNawxNVZFI6ojfzeY3Y+aeRL1Kv1I+m0VdtINSJcFgVmJxtI3NFwiXEgKn9hb7UR9rdPOAwVTZIUVrik4lr4XKTmlWGJNaU4NdkVWKaGqcPKnFUVxpMbqcpna7/64xhHk4yEFhZuqQv15iSHEkjol5U7CluQCBayhRBlzBWDgKJQUdZyjYEcbpfZy7BJTavb5EmcXKTwrFrlMqhQsPU871kXGJVzICa/TJFcrgY2vltUoOwFI5PYkcV9lktfMLDlKDyln75Y01bu0q/lDWzI98fnaC8UyckljXxMBSlFZReP1iakxtI3wnKy1MV/ebAF2/11OtTgedkiTSYYWlI6thmiuLiahhDL+MduKpaOEXeLPGrh3b3cZhSBuoonP1RhJJnypLwpNvCvD+FsNVijp1jPqjaVvaLhEuJAU3rPuBoBNbJuRpYtpocuLrKpiJGcUk7stEsscqGRXT3tc7GdF87zm2lJ/uY++Jqftn9fr2/bfOxNt8spUmqmeSpJ6Otp3iWvJKbxrXu1UAAAgAElEQVSN/gwiXUKJQjFi3a1VdMwFIFI0Zlm9r1WNS+Fx8b+VogBxmZpWv8SJ1ZSwR7Vvl/pcQrUfFUuO6ShjMLH3CZt/iFftQk54ZkdrMqoW5GVmsdY+t+SFq+woSSZ2ZXCPrSeQ3Ec//JIENUxYdxaXRwNDT94fddKH+xqHEeN2LiqocIJDFpGm5JxexFJN5PC4YAg09NFgGNgZhV5M6xebNFW1rTysfKJGGb6ybyF7JTik1hLWigFHgXt34vfEwxEIrH66YSrVAZTMjnzeHIK4NJa+oeES4UJSeLbDsgbWK+EkW4VZox24TJE0sCOhytIhrpZr1/ma8m+NPT9h47VkbU+5i5lTymiDFcMCMNPjtKOS0k+DQs35Gfj7Yy2vwpjCfYg66hSo0AhM+QDclk9M5+MzMGJikEkFtGMWNlIwUvAX/Qym5tkjIByPQ+ovkSgSSy68Uqfy+fjuyuCbI7DeC8reJQLMyjmuiy0SXgnqA5e6624gLX0JTiYfACCREXOkWuTdDgk181D+InA/dl1bQu1F2vVS5XoCzp3H0EEC+zweDHjyyWsAnP+9R9d12Gyc19zBtWvEkitt1ki9uYijh7UIPuIi/o8XpaaxMlEEcex62Yc+tSJ4OTZb1FB21mEvRtfP5aCZ0i0/m9Jz5u/Hkfn9hI8TOEa/QeW9YnDuAhZxuO3DiCGNpW9ouES4kBQ+sVlTvDq/OuWrPiuvErdMYpFy33ZeHYPd2CLEE5fM2SFRGCbKtXV29UQJQzZoD2MMVDzrPRAV6GD9LsFRoIO7dqMb3HrL0/Dk408BAJ588kk8/sRVAMD22gC79fcnMNO24sEoDnpH8aU36PY8VQasHabyFt1EEYdxRGc2gcILTEjxpbTXQCTdxeXPG2PQSTc7765xx53pophhFZDI4FvRVOnnx2s0QUGno8Xov1BcONvbEOdfYDKGKFWEJkpiVrCR0q8kKvD76dIQzzmCruvQITopWbKrQ4l7M2m9voypWYIM5sb7FbiQE57Z0CDDOWGSjjErk5tlxqkeP6F50tZYxVimPNBqdeeEX6vtrbGihl6w3nTYTo4pZuwwXPMsvcVXrn0Zw5Put6tfuYbHv0wTfpzMdRD0/R4A4ECv4clrTwIAbr/zCm674+apH0DnN3cIYIyXGTfuPmj8OOFGVbcQuwgthDpw+odpkmFMxtP0qUjm6+0lTtoRFd2JsoNT1BPoOFbZ9vwc18fWg5LpUDVOclTeFy+yBPmcN8OwbkFQ7GNuqSj1+TC4kBO+JjvVzA98tjeRophNKu+wPZNfmBrlj9Dk6LCyk2rmaedfEpHUtDZRYiMmLFbWArDTZNwCMrgJNxwMeOrqFvaqq+ArX7yKa49vXUVWA9V8arCwcAuBioX2rvzj+Cr63snzezDouj3fJfgR9RO+28QxTV5Knb+gec57u53rLvKFWcEJHLviQmIlqi1ETeJmnZpVJ1OVIiQZVZPqY/L3KtHL0LnkDags8msnY0lux4oJbzPl3FEneujHsa5uaGi4rnChKbz74v7lVDJl5SrXVmHCQssaVPbcKHln+Qt25W5PqPlCn0QkoShxhTfYTNzJcKDY6KRZPxghB678cFWxfWzA9gnHCYyPKcYnXHvbp65hnFjmg2HA1rqLZE9w69OvTMc97LVJlrypD7K2bDqE2+sEnTFBrs0pm5evRSSG2KK4BWoVOsyp+ryu6Zy1GCzFJDAI/vMKBM/J6ngq63NiUMh8P8Za3UupTI3FXuL2nJg0PdtqqYrVJ3u3+R2Jbc9z+dVwISc8s3uejTfGACNr54gl1/J5fgi99EH+nC8Yk7yVhbiOE5ZZr7KsdhhWKxEJkgfqH6IJoonVAXuyDwDojMV2Yu+2T20xPjHiyS86pd1Tj21x9ctOPh8OYpLKq9trwb31yu37wK2TMkkRNq2otUFv0neA2fRJPydJA2oVHSjWWrAQxgQg46gQiucWlGtKFQHBNRoAlBZI6WK9VjWdIJRMNHjRwUS3Xo2iDGvp7DBGf4dg4y7L9CV2fXFxKBChkmjIdUWzYwUm1is2jRcQF0NOcFqrqFD1+qINDQ3XO45F4UXkfwDw38AtVh+Hi1p7F4AHANwJ4CMAvldVD45Yf+IPPTLlKCjOrLVJCCiRSFHYAaS2ZZQVTkK/JURggeWrsYM1CsGKIaYKoort5I8+XLOwW0fF9UnBV/9kouJPjLj6pav46pecQu6pL2/xxJeeAABsNvvBwUYPRoyTyc3cdlMw0XVi0E/32nXRG0yMwWbfcRTo3DgNGpVqyv76nsKPZCqltN5Q9xvg/N+96Q9A4BREJISJilQQ4XoPY0zg0KxmabZC5+uUeElL78EK3KWyyXMunJsdZyY3Fg939Ze97nKl3WEcbjyOPOFF5NkA/haAF6nqkyLyHgCvAfByAD+mqg+IyE8AeB2Af3aYupMc3RUWnfqRsk0FW6aqkytLbBWHAx7H6AWVBx047ECvlRNTrbc7Z62F3U528e2IJ74y2dq/eA1f/WN3bJ9QXH3sKg4ed/c4HtjAVl+9ehVd5yY8jMFNN10J7V275jzwbsY+jPjHHze5sM3Z+SREU5zSBnWxwKh+fBFcfFUl2pytwk4y/DAMQZ4XEdhpeLuum8JtTaKTAn6dFtDz5LiCVuJEMzyGPMpjuCczJaH0bdf0CbV3jDEzma1IjZ1PeGbpa1r6kqzOfX3et712sZ81HJel7wFcEZEewM0AHgXwl+Gy0ADA2wB8+zHbaGhoOCEcmcKr6h+KyP8Kl2ziSQC/CuDDAB5TVc+7PQLg2Yetu6alT7YJFjTH+eocotkiXTV5JR9H8orqIyVAQqHrEWvKq3Kdbcx/K1F4Zi2NMejN9JjGazDTGn316lfx5FefwlOT7V23gr09Z0t/7LGv4PHHXe6PO575dDztztvd/d1kgoNN3/eB7e+6LlwrHcL21r6b7mtS4kGVIgrxuEv4biB46mCS4KxEW/iY+cWTg77N97V6PwDTIdAkS34UrJVW8pREej7UtqCYqz3PXAlX1cx7h5osZnwiao7s0JVyBIeh8Edh4XMch6V/OoD7ANwD4DEAPwfgWwtFi70UkdcDeP2KduLxZNoYM88p//xVEZJVdNKlXl40WOwea+0wY98BwGjUH4DlKCBZCLwVIXfFzTd3ePnVHcfzUVtsQjhl1aiNtdDAq/b7e7hys3tk1/a2GPUqrh1MrP+BQvbcBL79aU/DTbfe4sZhD+j23flnPucZwMaNz01P62H2Xb37V/aCc023MUGZbs2kY9AoGgVPUkHYA686YvCTcWsDG48tMPjwVWPUjAOATrvzpBcEi561uPnWK2FR66yi86JCp8HFeKT0ywoEEaLbM2ERY+82NVGt4K0vtclcEt3yicbXjl70MqYYasrVT2KjRj1RZ1w/XUFKZIFonVAjSbiz4zreHEdp918C+Iyq/vHUkfcC+E8B3CEi/UTlnwPgj0oX67GzxzY0NBwWx5nwfwDgm0XkZjiW/qUAPgTg1wF8F5ym/n4cM5lkpMQWQpQAllZoUkWwzZJXxkQzn+1dZrYqtMsL6YLyLfSPYtp76s6KIr/Kc9itcRzRdX2hfxKY3NT1FBgmaWn/1iu4/emD20gCQA+AK/uOqm/HAVd65ydvZYDs+XsacOtt7nx3xeDKLY4a7t+8h36i9mo0bOLx/gfeltD3faDe4zhSmDENrPtwsMV2onrYSrDvyyiBcpvEmSd9Zuw8w0rUwW7D8+w2XaTwFJ/d0LEaopLE6ud28SXNd4nC1ziCtWKcISXxoDYoRE2Fi7BWcfcrv7ta92FxHBn+gyLy83CmtwHAb8FR7P8TwAMi8g+ncz91jDbCZOMJD8Sorl3XJWV6b7rJ/OU5+4jR8r5iDnG1xMbFc/G7IQuBj43HCwAf+0kuEpMGbrfbaMIa4n0PY3SisVAcqNOym70Otz7jNnSTfH318Ws4eGoy2RmLzcZdc9Nt+7j9DrcQyE0W+09zk7y/RdDd5K41PbHYHSBeJpbpvv3iSuOg4xgWLx3GxCIS8vspYLw6XpEsqEE2sAorcYFkcc2qDfWOWWy8suyLpEwQzwopunNzLv/GdfD//Dj/XnLamX/XnYtE+u4dj4XPcdxkkn8fwN/PTj8M4CXHqbehoeF0cCFca7/+3nvwyz/8ZgCeopSjw7CmM4llTtrtGD3VzliyRDwoaE1VTLTvVlZ7ridV9tENqeMW/CWG7MmQlPIH7YWN8d+NAqMvowO6PUdxNzcLbh2cTf3gqwPGPUGnIUoAbO/G5JYrV0J/b7vzCm6/85bp+g1uf6Y7PsBTMDdN49JHhaj2RNng0nsFiksuuFC2dEQuhMUXEQNjaKuzRBEH9Jy8/62IgRWKw94R9RUtplBmMFfF25hdOK7U1u4fR3LNwjPP21kqk9eRvBqsPO7YqUYRfOIzZ5uTxIWY8EvgGzfkHOIfvtO4765DpIPfRJFvpogsPSN1RollU71ADakZkT37TNA2Oz3DZBLs+hCO2uk7vRdcF/YK9PsGm2fcCgDY3jJiONiiv8WV23/6Hl0jIfjD3pUem2nBuP3Om6GTln5jOvRXHHu/2TdB4z5gS+M0QgxCYhAghLRzbXlrXddFX3pYGqN0Asb1UdJnlim3vUWj77tk62yU9WsOK2k9vFAtTWp+x2qhsGsopd2eT1IbfqyLDWXR4aS12c2XvqHhEuHCUPiSeyxriEUkC5E0Z3WMMVG7bQQYecVkZUlfXFlzp4g8q6nrpyEOwab9zvpWUgx1XRrggYmIL+NYZHdub28P1yZFne0AdI567u9v0I8dboLTumO0gVvYbq8FR5oRAzaTBt7uR/b6yu03RVt/R5wK9afEVvprRmaZNz32J+egg3FLEpmEWPTMYruLpjESOIM03DMTjPDU0HQSuB6m8LnPfOlZ1iLh+vLM0pewJk8gWxtKv/tj7kueUtzD7WcILEJ836q9OBou3ISfvszOOY39vKy1NropGNqXXQA/qMQURNtm15hrYj0biqKT5lvnNrqO4rYpp0NOtfdpBJmpvS560NneYgz+6AprxqgnUEAmTnr/Sh/Y5950uDKx7rInuPlmt0BoF0OJDYkjk4a922GjDLPGNAZez9CJgQTHHcWGwlGH+9N0wxLvWdAuTmoXU6+cmy58Z7mbJlzHmYMyjT0f5xN+zYYZRiICFN4LFucOAxZ/ePKfJBpL39BwiXBhKLyHKkX4JAcbIDqyjOPIOzADMVzSiDL1zil8qXzpeo/gDELBNgFD8dhTW+84jsF33FobqI8q+YGPUbEDxNh8wzCEFFIigv3JWWYcR2ywCSyvWMVNvdvWanUb+ri/vwn92LvSQ6aYdqMdg0OKMTEiCyAzu7R3GbU2cgJm08GEuHsKE9xvLRBs+hyU0qCfXrecqvOz2I5jCPRo7TaMR2IJ6EghR09cLCngiEqOmSuta6/MLJc4ufwdSbgyilTLGLlfdD7Zgk2cDnN7KuRMVEmmclRcmAnvb2sY0r3OhsMcs7zM2U2oHmYHrUSNKwplPEoa33yyF1+E/J0J7KsmD1BEgkOQWhs04h2ZF53T0MQi9ybI43ngzsHz7b0z9/lILqaXwMZf2bslvjCbPrKsIjjwaZ2hEB9BdUhDJPHGHQDY33OvyTgcBB5aR4tNWIA1bLsVIxgn/36IhFDY/X6P7dZN4G5vDz15GGKa2KN1IbK9lt7S87DjEJ+b0oSABSY9gdsINd1LF/3q+V74/qZOhiN+zk4MKy8K/I6NtmRFSOM4WElFhrBoAjHyD8vwZp2F4ChoLH1DwyXChaHwvLL6HUgd2diNMYlDTgj1rAjBF9zXqN1MFXNpFJG8Xb6Wr/HHHOI6uJEKXW/TFTnnCoKHaRa/nNvzlJ8pW17Ga7RFBMI5yDqy+5tYjp2McrEkcCB9tPUbop6e/RzJb8DXoBRP3lFV/2y6kMhCVcLWXufe6tn4VIEqnhMzZlIERg4qWAK6LradvAdRydebTRT7DKfDXqaY/JyToCmEMhseE2UsOukkisWOyhIbb1lhfLJbYhkXbsLn5yxtktnlgZeazzJ/+dGWB48ykJgFv+UlWX8XaprgmZjC9RcsEkB8d4wxMJs+mskwJgsZO5148OT3fuu+rpA3XhCdcEaLjQoGdhTSKN/7mkfSwKtRksFtzOQjkmShkY6cnbyHoUuBk4wP75mIoknqmJJ70QGT1cI7MmURZ3LwBDNm/l6lZeu56ZfK1/QBfBzfhXSBOUk0lr6h4RLhQlB4XgF5t9Q4jmHrq7W2uDrNKGDFjk5cf0KRXHyJMofA1LdEiZN+GHIe0nm5Ul1M+bm/qgq/OSxnw7344jmekr03uXcKeeyElkmxCAAVasZ9tYLAWTkxYOIEiPWGaNDk65ByGr4Nq5bs+0DSbWbvNWWZO0tRjrylg3SMBibsQXCKzqjsjJS77pvB9wtEhRxr09k117VD222DuZw4xUxk5FTQSIJZkBiGMrW/YVl6RiLfgR5+gRV27O9utqeWPCKXtfl86Xg2MQtl8nbYESNfCEoTzVob2N+8rG/FqkKI5TUbiphCMrnTevOEjeMWX+gxpFQWjXn3+r5PzKS1+4MxYTHWToPvvZguSEzWatigYySaMB3b78fJQro+MWh5kcBuY2SiYXstPk9jQpna1DjcpInmyV1mOZG4pyMnMDWkkZCob5VLlzz+joILMeEdsYqUzo7RDdV7sXZdF8JX+XLhv+5WckjlN35QuZy9ZpXlxYllTKBgtpuQxOajXOoh7LNqEuHVy9TIFIk1LgR2DBt00KeyukeuzGNFUty95uRmUxgfHhfj3txwHKycEnfOGTWIc5QWHmjYkaPWwsg8PBjg9QqT/oFNjcniH/u2pHOpLbq5rsUGRWz0zOM+5cc1JO3RMZ/O5fxoKm4yfENDwxFxISh8jZ9xFMwdW2uLrGXOStU04GtYdL5+Va8XKIVjvz27JonW3XuHmc6E0EY5hc8jvIT/FfaSKVMeRqtUJtEfKIJZUam9nKKzfoXBFJ77m5j+kFKtkFue9oT7zSicy05DkopYf9/3ZD6zsH6DDtZxZTWw+TXX2/AzYNm+ZH1ZeqdSc3GFG63oA04CF2TCR3nSxXmLLrQICpkxZEqZy/CuliVFmdBLWZvSS6GGS6jZuPPvqjqFXK6/iBwMY9b2DoWhK5Iqy3gMfdsjxQEwkDDJDZu8wBN+Ox2zTZ/CX/FORt+1zqCTVHQA0smfn+edhAO57yZmOaHrs8U8KHlB4b2VJmx/+EWcJ797L+Y6IKffKC8K+XsRZfV0ISiZl7k8KwhPAo2lb2i4RLggFF5CBlGYGJ8dRoIZKqcQSTqqhBhG9jVnTZUofE1rXlpRc4UK71svhV3yftWJRryb7wlwpqZItWzYQhvNiDk7yL1NFY55CCh/f7bovDIMFI+fWFOlDSgGBlAExalBFxSqsIrUYSVStIGiArHCr2SCtNaGjSYqJg0/BoHxm4gODqLJjJ6HY+m9Ztck4xW4xoFTiM2fuRexcueZuKEomvtc+en3PvW3L7177oaJK7CxLjYuJWIAe22S6fQksHPCi8hPA3gFgC+o6tdP5+4E8G4AdwP4LIBXq+qfiuvxj8Pll7sK4L9W1Y+s6QjLcR45+1uzi4ZwthJDVufsUo6arFdimxN7fhK0oEsWHu5nTfaa9yv2g/fMJ/dKpXMzkGcpu01HLrAaNt9w37fbbSbrT5tOyF3YjvGafnIDFUr8yO2nu+piyDG/i9DboN0Fdc35ZprUom4yldj4jvLDpWwwjTNN+GSByVj6mfhVmPCzRSHRfyCUKb0vOZte1SdpRUwj9+vRDvPfj4E1LP1bAbwsO/cmAO9T1RcCeN/0HXCZZ144fV6PQyaRbGhoOF3spPCq+hsicnd2+j4A3zIdvw3A+wG8cTr/M+qWsA+IyB0icpeqPnqYTpXsw87evlurWTo/06avaNu378+VHFa4DF+7S7OacAwSteMl5SOz6SWrQOAEmI02XdiKmifj8MEtJTVah/O8IchTJt7qWRvTqvMSlzesaC2PM2vgN13cUiuIVLbvox1+a4eoXLNx7Hncksyz/t4qL0FNiQZSUArtv9j17uV1anQnSFKSJxwQjbPfRnxSOGptX+snsao+KiLPms4/G8DnqJxPJrk44X/74c/gz/z1mF3joXe9Pf7ovbNQ3smUPFiS55c0oFyHIH3hSixh3qZHLqvNXCpZu87a2YU6S+dLIoXva5iAbKJTDRO6J8vDoBqclyTTiVBrSbtuoY0LcKIPKWjNkww6C1aFlBMvxyeojT8HHuFFryZKpaz3xGIjjiOXK3m2ucVqPhl3iY18feldFJrkYmgXHcVfXIrNdxSctNKu9ISLPZaVySQbGhpODked8J/3rLqI3AXgC9P5RwA8l8odKZlkooGflFK8+cUiKh9yzWrMR6XIws8XKU4ShBCpprVE+dc47RRX9Mr1jESVV1AqOnYyKiWTFFYjbfbAmFA9X6bve0okETcDM2upIgnLyde7WADT5ZZtBpnI488SS8/IxywRnUYbtiknTkOZ9aS0P106Q67K3GCh/cJz43oZ7OsRvheQimp8LobU4n5ZZu8Lfhfuh4vhePMgXKLIH0aaMPJBAD8gIg8A+CYAXz6s/A6kLHYp+ig7a6hqkoivJHcdBYmXWCaP8cQuLQT+mCcdLyzKdVXaL7OAZbkXcMPUBeelaNfbDjEW3N5mg964sE/DNg+xPR3P9iVwFNlYDsg2gvj/GVtcW7gS8cemY1cbU5bhIytMdRlmt3kRS2Xopb7kJtwIXh6X36vSglJi162WTZUJq78gFh0Fa8xy74JT0D1TRB6ByyX3wwDeIyKvg8si+6qp+K/AmeQegjPLff+J9rahoeFYWKOlf23lp5cWyiqANxy3U1W2l9ik1PFmTon5e67gqtWfs5el8jUNPPcjL8O/HRa5ZjyhmMSFbDb7MQzX6BxS/DWd9OG83wHWdTGmvo6soJzn3Uuj80awWBVj2AN2nI9h6DPcHoLO9LOKPAsflLMVt1J2GupMH30DOlZE7tpWGgSP5Gwy1gXimt8T31vp/Kw8UW925S7VB8xFmePignjapUgG3Tt2WRuZqkybLtl1gH8Au7fN5g+qVI43jbBVYOZRld3DGlkPhcWEr8s91PL2g/PMGO9js9mbhZqO9+HaODg4iP2w3A1OEumO2W8lihACDU5OqfPKmkUzCU1NJkRD15iuC4uNtTaU22w28b4PyBGKQn6hE3STqXEcY7jrcJcFk2tpcubl82vzcSkhuXdewI3MLDsAoLRYnfTmmeZL39BwiXAhKbxHohTqBHY7p1rOThy1yDEYxlzpVlKQJOeJ8uQsOtfB52tluN1dyj1gUgMFF+HIksNo2E8wjiO2T0Vqten3iFIiJvDgOOiIMdLNGPu4JzcFiolRAjHTcYwBM6RzlHwTwz71m7ir0ZD93BIVDYo/GxVchiwMBgLvJzTaMfHV12EMSrxhGKLLr7Wh3nEb03pBuxir33BW4T5611DUWs+V1XbxJc+WA7FwhB5ye/avUU6pE3u+SX0F2CISgmwYDZF7jHT07uJEcaEn/BKrnciydCyF8n6BqNVXYqW5/VwGr5nicitCcs0hI6OkrGVZYzvaIWy4MZZ8zbO4cn6/uLVxMeqkD0kweMOMjhomip1m4nAQo9aGejVfUPn+YnnO2xfTYdsQ/UZVgWliCZyZNPjiZ1t24zZa2kO/twkJJ/q9qL0fwtZeAKKzRZpFo5ooxShpzd1zjs5gflFQIGSMUdVJMz+JSdCwoUhg4RdES5aKe74jOqGdNBpL39BwiXAhKXzOEodzwZOzrgyLhE2S6KZrUdP41xQ7UcE1JudYwSYicctvBTl15+ipXnukqi5lNKJoEZRz1N3tdkyUYh692QTl3HY7YrudKLGlcqx0g9tO6+NpihHYLUdsJasEPzOfC4/ECd5hKhByRgDGkTgp8iFg5yAIcS1GsJky6nZ7fUwptaGEISO5+Jo5hS9RdT7PeeNGtWmyE+XAGGWuMdX2p/kAfDlOq2b6LuxnOE1cVxPeEEPC7DYPbsmhI5/ENRY9N3vtMr8Mw5C8SDzJ5haDZfMLkDrkGOoTguwL9JqGVxI2gU2/iVoKWcXbWLeBXRaYOHltua9GvKebnxzAMMSAmD4ZJTrS8kv4A9WUVfXnRUy4WTva8Jx8wkhfl7VRnwCxyQLqj3kCcRgtMTHttS069pBHJu0V8DAmeu25MfPmzw3dh8Tc9v6Gfa1emhCZovSy3D9N/k2cfktWnZNEY+kbGi4RLiSFX6MsY5TcN135ucZ9qa4lm3oJuYZ3qT8lpV3NjXR2LTvC5BFQiHsJ2nXlfsXtrSNpwMdxjA4y7C47Rs2wd0P1ud/UIFgSDLHuSv21Gllh5rhSbi0+j2EYgzZ8GIbwAYDRHoRy3SaOX78XFZQwinGS3WSM7XEOtzz2oEuztQ39Yp+FxJKT7RwM5T31NgZSCHrpxixq2dP3x4b3UozB8++r+bWdDi78hGeUti6ylj4vI9i9cKxitSuTkUNGcRs5C587dyTnQ3TatD0ln3ah8jCRFXUhqKaJNowYJ236mJgk06g8fqIMBwcxT530dI/j7N550lpvIhwUYkr6C0DHIYxP6L3QokATXkeL4dpBqGd7cBBST48yYH/f5bzv+z7IuGbTAZ1fBGuRaqNokJpFRxrR+rNx75X33c+2YKOMoOtAKlqO0LCIdn3qOHXWuJAT3iP3qKvJ3SgsEO7B+DzlAHR5f3x+fb7yl8rzbq5cf8D1WWsT01rNmyvtG5l/iFNJ7rszMRiDVahP60SBIEQF49Z7440xK4xGuTLfERfaDdRyQ/2K4atC6idrfS6JRMlItw0gtU2H9rYDnnrqKX8Sw7iFD9+7v7+PW265BQCwd3N0oe32okedGqoTNkjmAp7I7lxh/T8AABUKSURBVFd/D/kCnuh9SPb21xhjAlUfxjE1uYHKs9xOi6wAyXvM+oezRpPhGxouEeQsNIM7O1GLyE/4vZ99a2IaSdj+wpbGfPVVm67sJdlSsu8cYafkeMFlSkicXwpmPREJbPFMx8CBI4laDIm3YRcSKWIbWfpr17bO08w1jO120syTbG8kOqnwduPtNjqseBY6BBE2Bl67nVsrusnh3nnglfUVzHp7L7/h2gF8gkqoQkWDmW3/5g1uvvlmAEC3H2Xybo/GtSPffx2Kz4m5NX+vJTEHAAYbI/zyBiSOx8Dms1GHUG/NWmTJLHf3d56aU82HVfUbdxW60Cx9AmNC+GOjEnN/Z2YtftW6ae+393byEMTwyQCZAbsuDYjhFwXMJ6Svt6RvyNljYwx6irjKL0aMLpuFZ/LKMSMhXp0YgZq4mWSzAeyUg24UwE7sbb/fh0mqasMmErUW49ZP+HRBSlxYJZ28MVxz3MjD+ouu64K3XIcuWLxUNSjHZkk7gtuqoOv3wrEajXH3NgLZTG1vEFxPx45FrKjDEX76yklF0nDiIjHPvYiA4wKnUYGJKNCE50VeQr1p/rmRvA3TDL7ni8bSNzRcIlw3FJ7ZJE4jBEVwMnGmlIhkJWYNLNLQVv7YIPWrZpa+tI0xj7yTixElpdhMsVi1BkQuocRF5KyrdArjxQALYOIcBhspoEVUfCrGYAngewVAG1DMrH0/Cpa8xHJtcy3ijR8P1rhbO4T9TqYXmF6wN3nRYQP0e5MXHZkj02dBZksaPiUtPffDi1SJ5SM5pudBvvE1aw5ITVijn/Vrzx7XjQyf41PvcJFtrY1eZQxrbZpP3qbmG36ps76EMkUZHvUJn9ex5kH7q+ba+1Loo9Ry4FJoT66kFuFYpPP7V7DdbqHDdL0VTBYzDNttEHlETbLvPC5UzlznxacSux+OJ852richrf/kWebEARJN9iMbDRNleNmQHqGLC8ZgeX972bSVLKZZWGme2IlnIWnjQ39QeDYT7n3t9xTbPieskuEbS9/QcIlw3bD0OaSf2NEh7kkWkUDZnDLGF5bFjQklFpT9tTnqzJIlYMmJp7YFN7mnCuvO9eb1hC3fhmkd5dvror5STKRmPfmzCyw2XdTIh3uFhRGBSvTl5354C0MnMTegAIHau76y/3ukpKqOio86wvQ+Sg3Q9yZQeEPedVYPIseVOQft5qTmY56IeJ51N6lIWK3X7ObcLiqu2wlfko+RyGYI4Y51MvfECZUGpAi76Mc0ZFWJpcu17Ek9uWmNWEWPtR5/tZetxN4DSPKqG2jwROtUoD7vG+D9ZmA2HYZhun5EmBN9B6iNqaatHdF1cULG/fQSFpW+69N+gT3vvPhDGVskJp/Y22ywnVh06YBur4NMfbdCYbihUVOu5THVPC55GLN001ViZpuNOy3ofEzXn3BuiDNFY+kbGi4Rjpo99h8B+GsADgB8GsD3q+pj028/COB1cHTjb6nqvzyNjt/7qvmmg4fe/Y6wQosxMBIpVRqZ1STUN2xdHG2RgiaKHbI/M5bYytw1N+EECue9O+aueqq+2MSadtLDq8EFgHr7vgo2E+W2g0ZqbxXSe9dPJyIlGm3L4zNR7z7aoI2R4L6rqoH7NQbglNIwk52676J23RioCLYjOeJ4DqGj+wYoBVNK+T0SUahPXVgVgBSVfTamLa/gIii5j4M1FP6tmGeP/TUAX6+q/xGATwL4QQAQkRcBeA2A/2C65n8Xn0f4DCAd/JsFTBr06FRjYPrOfYxj3WQKfRT2lnuTjcQUv7k5h1EKg8QfjxKrXypbEhHyD9fBMNKHj6ALH2CSeTG6tNA6YNQBVhTSG0hv3O6zDdBt4ETuzgKdhdkA/b5BtxF0G4HpEY6lU5geMD0AY6E9oL3TpvvrpVeYjYHZGJeyeTqPzkKN8/23dsCoinFazEzvNPUw2SKJKa11ZgZNPs6jav6BDc/bEYQ0dJZf9L233NrP9YidE15VfwPAl7Jzv6qqPlj5B+BSSgEue+wDqnpNVT8Dl5DiJSfY34aGhmPgJJR2fxPAu6fjZ8MtAB4+e+wMcsrJJL1NF4AjD1qOKMIOMoLU37sUwRYVv/oSN1BTACbnCoq93E7MbRTvc3kgQpmuE0jY1hb+OCvGFJlWO7JUeJ8dsrGzmOQVb6qabCH1gXQShyNoIC/J2JiYIktM6v/AVgURCtJJY6Fqi+OSo1aGI9sAKIpSOez1SdwBHHPCi8gPwel93+FPFYoVR1oXkkkeFS/4rnRjwiff/bO+o5Npai7/GqX4b13MTY5h3PkiJXvvJd0SyZ5r7KDjfw+/+RdfNcjXkBi5tevSWGc170EO9eTFFF9vydxnetJRkIua5S2/6h1s+tieeJ95CVrxzphwXlWDvG3tGHzm9/b24ENJXbt2LdxT13VhvvksK94sZ3VI7onleba4RF1LOvnDIlrQ0sexlmCWc6LcPCU5APyZV9+PGwFHnvAicj+cMu+lGkdndfbYhoaGs8eRJryIvAzAGwH8F6p6lX56EMA7ReRHAfz7AF4I4P87di+PCE9FVBWwxCoi27rqmZBxTCgjs+jhGCieB1CkRkDdHz7/7uPAs+svEHO75eIAt8/532bKPXKK2dWPRIkY/NTLAR9qrqcc4MHv0h1pbLtOgmvtdmuDVaDmeLSEIkXP+mQTMcxFn+GQYzwSiZh2HbPuNRw1e+wPAtgH8GvTAH1AVf9bVf0dEXkPgN+FY/XfoGGz89mDQ1YreMNNyuYG9p62qI4avdXiiw9gxcvIk8f/L5WtyflJn7JouPlCEheobHttoY+15ApL5wyZ4nIxBXx/dO9JO35L6ziEsXMx5WJdeRYY7kNky1PW3SOf8GW9SfTGVHEOQGkbfE2BENxAOGr22J9aKP9mAG8+TqcaGhpOB9ftbrnTwkM/6/SPSQ456h0r5LqFba9AncIX2WeJLqm5xr/kRuoUTyRq2NT3X4ma+mPOgpuLNVxvSOZAO+f89R4cFTZHEpVnOjeLSeiddozBOFHSvu+dHZ5QovAgl9sRaZKHMtdCHJ1Mz6XjHAL0vCZqf9bRZE8AN1jEmzPCGi+6krlu18JZk0vDvnxVJFF5vPxJE0VE0r3q1F8xGjTlfH3OHieZVSpiSs3cyKZKvidmw0Vi2C6ohvx1ThyJ4aiDP9ZoE31FvthFs94YU1KDE1NGVEUswbK9Lfj4X78ONWvRfOkbGi4RGoXPUHSl1bIG/jC0IGfzQ1107CljYhWQNHKO10SqSGpJmCi++8JcShrLjY+LVoWMo2DMRJCg1UzPs5Jxy9yDEhfgs6lmob65xTwOXWTL6Rhlboa5DquZElGEfOkl+AdcBrQJn6Gk6WaWd63JqDRZSmXSBSae7wpBL1UVI5nocg1+3MdObWQOP7mWPwebsHyZmGABUewwMdyViIQNOrmWPUmKOcnNvdkkUYpiZCGTpK4OdQMQyrE+WN7klHsozu+VtfJ+geCxvvsU0zNfNDSWvqHhEqFR+AoSllCQBEBghdph66pxBUx1/DX+f4m99/UEi0G2bTf8TsEoDEyRwnO9+b6BcRxDiO38npK+TJTfjmNQyA3DkGTBTTiVJB3V1D9jZs4usS/pWBXBaaeoiHSGnp9CdQzbYE2trhsUbcJn0N6b4lJHHc/WswNPzpKHOqZJ0/mURHDRW6aCyYsfWctUFq1p/TkwJwD0XuueadCTewqytoWfOSISrnX99bHB6Doouo1J+suoJeGw6iLYmC5q5q3aIA2oahQTJDZpMSbOTzBxf4Fki1VcjNOc7qzv8GUGbOPiVAlaelnQJnwGttf6V1H4vRYFT5poNprX41/WPP1gjdqXvcTShWSbebsZMk+VfAGSOkgp1lWUdqXrSv3KUfKOY71CVb7OzYBC1Jm5rKQxDasE78ib60SifZ5x73deqGizZ4omwzc0XCI0Cp+h6I+OSPms2Ki1J0ojifkJAPKILWXvuhrrPlYotMmobs2ENrs2O1ahrPALloeaiTA3661ps9q/jjTtfG8iwQwpBmQhiH3vjIlafupTR77+wDjTfVxWtAmfwxTYWMcvA3ARmsILTqx+8tLnbL5IDBUtkuQqYU3dElsdupdNuJrisPRiV73YeANKoQ5L30M4amPiQpGJBMLlfdvcJ9KJsJiRt+vmrOfd5yIDkC4KxsQNT6aLQ8tRii/7hG8sfUPDJULbPLMCn3nvu8kTLfVKC84uY6S0dvRa+sh2+/xpOUtfQ+KbTmbARAnG2zoTBqM8nDWlXq3tEnbly8v7roHKliP35iZB3jvwwr/xfdV+fOYX3hmuCVuaszTQ/vju73hNtZ4bCG3zzElBSDOvtLfaKrHnZAP2mvyR5kHJHs3IdQca+eJoQ+44dXPWRz4/luO8cdu1UFklvQLrC/we8Wo4qanP/j+z9zVvw5HZ+65b5bLMnnPI+u/OjzPrSENj6RsaLhUahV8BJU+7hJoYG7TzQj7gFnA2ZKbwFS1xoORZFBYU2GVjTOJjz0q7hDKb2OFUIZdSwtXadM89aF0DX7ueVYqlEkYEytF9upWKtaDY09CK6RxlX+rPZUeb8CuQTg6Sg0HmOg7EIPPsNLmHFzCXa0vn89/zFNZ+Aah5vdW08Wv0B6E/dD5q0OsyfMjekmnga45F3YaDUaybqJdELj9xNJa+oeESoVH4FUiopGSUcqLcHVNwozDTVk/Abw2NPycurSWHHMq0mgSqRFTOqSqsWgx+EwrKW205Yi4rFmdUObtf7pNPPsE+9+M4hnjupjOJgowIfKqxL7UoCCYGdTJDY8dPETspvIj8tIh8QUR+u/Db3xURFZFnTt9FRP6piDwkIv9GRF58Gp0+a+Ss8K48ZKGs0fjZUdbL5PmedUbOEnNf1lzD9df6U/qojlAdnbWiM/EzId+lx+1Y+uT9Yu1/bjK8CObiGxFHTSYJEXkugL8C4A/o9LfCxaJ/IVwaqX92/C42NDScFNaEqf4NEbm78NOPAfh7AH6Zzt0H4GfULc8fEJE7ROQuVX30JDp7Xrj7vr+efP/sL7tUejXWM7ifTt/zeOmHxZI2/DCUcJe93Z/PFYa2xqJXdtYlLsUV/4OmdDsfHDXzzCsB/KGqfix7AZ8N4HP03SeTvK4nfI7oYIMgsLrEi9kEqsmvPKHYbFZxTOFjZoOTPnWcuJH0BGCNf9rfUjv5guJk+PKCk/i0+/+aBsZI6pV1AUMaTg+HnvAicjOAHwLwV0s/F84VSZCccvbYhoaGOY5C4V8A4B4Anro/B8BHROQlOEQyST2F7LFnB+/s0q1Sli39VhULYoFCKmm4HWqsLFvRa4Ylas/tcXccC1+3nyeaffo9Rt/l+hR66F42nDQOPeFV9eMAnuW/i8hnAXyjqv6JiDwI4AdE5AEA3wTgy9e7/F5CnAAxZJSKORPP7erGGNcxAKlokEezqUWaYeRyt9ewG2NSB6Lp/D3fed1labm0WGOWexeA/xfA14nIIyLyuoXivwLgYQAPAfg/APx3J9LLhoaGE8FRk0ny73fTsQJ4w/G7dX0gtXFThtKQjK6cZjlhhStsQS1QxS6qDDhnoKBhtxShh8WAmSPM1ONM0WaMSUSHVPF2nUliDc3T7ijwjiZ93xcnJpWc/iuYmUombUWsjUFuyznoS847XH9J65474ZQbJsF7WhRYJm/+MNc3mi99Q8MlQot4c0r4zC+981BBHnNKHSLpZIq2KhZ22NX6kf/urw22/qyciODeS5SW6TpDi3hz3qix0MtiwNn0hzFzCso8BS8CUWg4GTSWvqHhEqFR+DPAGkpeY6vzepYUb0vusUAaKtpWnHZK7dZSWDVcf7goMvwfA/gqgD85775keCYuVp8uWn+A1qe1OO0+PV9Vv2ZXoQsx4QFARD60Rulwlrhofbpo/QFan9biovSp8WoNDZcIbcI3NFwiXKQJ/5bz7kABF61PF60/QOvTWlyIPl0YGb6hoeH0cZEofENDwynj3Ce8iLxMRH5PXKTbN51TH54rIr8uIp8Qkd8Rkb89nf8HIvKHIvLR6fPyM+7XZ0Xk41PbH5rO3SkivyYin5r+P/0M+/N1NBYfFZGviMjfOetxkkIk5dq4iMOpRlKu9Ocfici/ndr8RRG5Yzp/t4g8SWP1Eyfdn0Xk4ZHP8gMXrf3TAO4FsAfgYwBedA79uAvAi6fj2wB8EsCLAPwDAH/3HMfnswCemZ37XwC8aTp+E4AfOcdn9+8APP+sxwnAXwLwYgC/vWtcALwcwP8F51/0zQA+eEb9+asA+un4R6g/d3O5s/6cN4V/CYCHVPVhVT0A8ABc5Nszhao+qqofmY4fB/AJuOCbFxH3AXjbdPw2AN9+Tv14KYBPq+rvn3XDqvobAL6Una6NS4ikrKofAHCHiNx12v1R1V9V1WH6+gG4cG/njvOe8LUot+cGcSG5vwHAB6dTPzCxZT99luzzBAXwqyLyYXFBPwHga3UKGzb9f1b16tPFawC8i76f5zgB9XG5CO/Y34TjMjzuEZHfEpF/LSL/+Vl25Lwn/Ooot2cBEbkVwC8A+Duq+hW4RBovAPAX4EJt/+Mz7tJfVNUXwyX4eIOI/KUzbr8IEdkD8EoAPzedOu9xWsK5vmMi8kMABgDvmE49CuB5qvoNAP5HAO8UkdvPqj/nPeFXR7k9bYjIBm6yv0NV3wsAqvp5VR1V1cLF6HvJWfZJVf9o+v8FAL84tf95z5JO/79wln2a8K0APqKqn5/6d67jNKE2Luf2jonI/QBeAeC7dRLgVfWaqn5xOv4wnA7rz55Ff4Dzn/C/CeCFInLPRDVeA+DBs+6EuC1iPwXgE6r6o3SeZb3vADDLr3eKfbpFRG7zx3BKoN+GG5/7p2L3I838c1Z4LYidP89xItTG5UEA3zdp678ZZxRJWUReBuCNAF6pqlfp/NeIuOycInIvXFq2h0+7PwHnpS0kbebL4bTinwbwQ+fUh/8Mjs37NwA+On1eDuDtAD4+nX8QwF1n2Kd74awWHwPwO35sADwDwPsAfGr6f+cZj9XNAL4I4Gl07kzHCW6xeRTAFo6Cv642LnAs/f82vV8fhwupfhb9eQhOd+Dfp5+Yyv5X0/P8GICPAPhrZ/n8mqddQ8Mlwnmz9A0NDWeINuEbGi4R2oRvaLhEaBO+oeESoU34hoZLhDbhGxouEdqEb2i4RGgTvqHhEuH/B7uTJ21L9MNoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XY7ySkcjBkiS",
        "outputId": "96577cb3-37c2-4bc8-8042-3578554f1d17"
      },
      "source": [
        "para_img.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(148, 142, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkHn7dtjBkiT",
        "outputId": "fa5a45db-09e3-47e8-ffff-cb9fd1147802"
      },
      "source": [
        "unifected_cell_path = train_path+'\\\\uninfected\\\\'+os.listdir(train_path+'\\\\uninfected')[0]\n",
        "unifected_cell = imread(unifected_cell_path)\n",
        "plt.imshow(unifected_cell)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x1306e357978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "D3yiZp5tBkiU"
      },
      "source": [
        "**Let's check how many images there are.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q18lAog6BkiV",
        "outputId": "276c7b84-d731-4925-b9ce-7120c003e08e"
      },
      "source": [
        "len(os.listdir(train_path+'\\\\parasitized'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12480"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfngD8UmBkiW",
        "outputId": "449809af-6d34-48f6-ab40-ee2bbff9ef84"
      },
      "source": [
        "len(os.listdir(train_path+'\\\\uninfected'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12480"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQfcZ8L6BkiX"
      },
      "source": [
        "**Let's find out the average dimensions of these images.** In real images, all the images always are not in a same shape, thats why we need to take an average to resize our images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MRHyO4AWBkiY",
        "outputId": "f1c69b9f-a3c4-41a0-996b-e2ca05ebc2c4"
      },
      "source": [
        "unifected_cell.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(139, 160, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvlgWHGmBkiZ",
        "outputId": "95a75e74-ea13-45fb-fef7-a2b5d4e282e1"
      },
      "source": [
        "para_img.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(148, 142, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Yk7k5iDBkia"
      },
      "source": [
        "# Other options: https://stackoverflow.com/questions/1507084/how-to-check-dimensions-of-all-images-in-a-directory-using-python\n",
        "dim1 = []\n",
        "dim2 = []\n",
        "for image_filename in os.listdir(test_path+'\\\\uninfected'):\n",
        "    \n",
        "    img = imread(test_path+'\\\\uninfected'+'\\\\'+image_filename)\n",
        "    d1,d2,colors = img.shape\n",
        "    dim1.append(d1)\n",
        "    dim2.append(d2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0aIamVHBkia",
        "outputId": "991feb89-a53d-4cd6-9265-e6e2275435c6"
      },
      "source": [
        "sns.jointplot(dim1,dim2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x1306e659ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1-9fq_8Bkib",
        "outputId": "0fc74d19-74ac-4c5a-85dd-e98409033491"
      },
      "source": [
        "dim1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1C6ZFAnkBkic",
        "outputId": "0da8f8c4-4921-4c83-a06b-72986c86fe08"
      },
      "source": [
        "np.mean(dim1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "130.92538461538462"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEn3tmxEBkid",
        "outputId": "d9532b87-ba12-4cf7-f423-1fdf0684d4a4"
      },
      "source": [
        "np.mean(dim2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "130.75"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxYl-XoJBkie"
      },
      "source": [
        "image_shape = (130,130,3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4DMk8K61Bkig"
      },
      "source": [
        "## Preparing the Data for the model\n",
        "\n",
        "There is too much data for us to read all at once in memory. We can use some built in functions in Keras to automatically process the data, generate a flow of batches from a directory, and also manipulate the images.\n",
        "\n",
        "### Image Manipulation\n",
        "\n",
        "Its usually a good idea to manipulate the images with rotation, resizing, and scaling so the model becomes more robust to different images that our data set doesn't have. We can use the **ImageDataGenerator** to do this automatically for us."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWOzwQcjBkih"
      },
      "source": [
        "**in MNIST dasaset we had 28*28= 784, in CIFAR dataset we had 32*32*3= 3072, but in current example we are dealing with 130*130*3= 50700 and we can fit the model in one time. we need to use batches to spilit the dataset for training.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0U-pnxyfBkih"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4OIzY6NBkii",
        "outputId": "d2331bc5-9b0d-4372-ab22-7936c1faa08d"
      },
      "source": [
        "help(ImageDataGenerator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on class ImageDataGenerator in module tensorflow.python.keras.preprocessing.image:\n",
            "\n",
            "class ImageDataGenerator(keras_preprocessing.image.image_data_generator.ImageDataGenerator)\n",
            " |  ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None)\n",
            " |  \n",
            " |  Generate batches of tensor image data with real-time data augmentation.\n",
            " |  \n",
            " |   The data will be looped over (in batches).\n",
            " |  \n",
            " |  Arguments:\n",
            " |      featurewise_center: Boolean.\n",
            " |          Set input mean to 0 over the dataset, feature-wise.\n",
            " |      samplewise_center: Boolean. Set each sample mean to 0.\n",
            " |      featurewise_std_normalization: Boolean.\n",
            " |          Divide inputs by std of the dataset, feature-wise.\n",
            " |      samplewise_std_normalization: Boolean. Divide each input by its std.\n",
            " |      zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.\n",
            " |      zca_whitening: Boolean. Apply ZCA whitening.\n",
            " |      rotation_range: Int. Degree range for random rotations.\n",
            " |      width_shift_range: Float, 1-D array-like or int\n",
            " |          - float: fraction of total width, if < 1, or pixels if >= 1.\n",
            " |          - 1-D array-like: random elements from the array.\n",
            " |          - int: integer number of pixels from interval\n",
            " |              `(-width_shift_range, +width_shift_range)`\n",
            " |          - With `width_shift_range=2` possible values\n",
            " |              are integers `[-1, 0, +1]`,\n",
            " |              same as with `width_shift_range=[-1, 0, +1]`,\n",
            " |              while with `width_shift_range=1.0` possible values are floats\n",
            " |              in the interval [-1.0, +1.0).\n",
            " |      height_shift_range: Float, 1-D array-like or int\n",
            " |          - float: fraction of total height, if < 1, or pixels if >= 1.\n",
            " |          - 1-D array-like: random elements from the array.\n",
            " |          - int: integer number of pixels from interval\n",
            " |              `(-height_shift_range, +height_shift_range)`\n",
            " |          - With `height_shift_range=2` possible values\n",
            " |              are integers `[-1, 0, +1]`,\n",
            " |              same as with `height_shift_range=[-1, 0, +1]`,\n",
            " |              while with `height_shift_range=1.0` possible values are floats\n",
            " |              in the interval [-1.0, +1.0).\n",
            " |      brightness_range: Tuple or list of two floats. Range for picking\n",
            " |          a brightness shift value from.\n",
            " |      shear_range: Float. Shear Intensity\n",
            " |          (Shear angle in counter-clockwise direction in degrees)\n",
            " |      zoom_range: Float or [lower, upper]. Range for random zoom.\n",
            " |          If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`.\n",
            " |      channel_shift_range: Float. Range for random channel shifts.\n",
            " |      fill_mode: One of {\"constant\", \"nearest\", \"reflect\" or \"wrap\"}.\n",
            " |          Default is 'nearest'.\n",
            " |          Points outside the boundaries of the input are filled\n",
            " |          according to the given mode:\n",
            " |          - 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k)\n",
            " |          - 'nearest':  aaaaaaaa|abcd|dddddddd\n",
            " |          - 'reflect':  abcddcba|abcd|dcbaabcd\n",
            " |          - 'wrap':  abcdabcd|abcd|abcdabcd\n",
            " |      cval: Float or Int.\n",
            " |          Value used for points outside the boundaries\n",
            " |          when `fill_mode = \"constant\"`.\n",
            " |      horizontal_flip: Boolean. Randomly flip inputs horizontally.\n",
            " |      vertical_flip: Boolean. Randomly flip inputs vertically.\n",
            " |      rescale: rescaling factor. Defaults to None.\n",
            " |          If None or 0, no rescaling is applied,\n",
            " |          otherwise we multiply the data by the value provided\n",
            " |          (after applying all other transformations).\n",
            " |      preprocessing_function: function that will be applied on each input.\n",
            " |          The function will run after the image is resized and augmented.\n",
            " |          The function should take one argument:\n",
            " |          one image (Numpy tensor with rank 3),\n",
            " |          and should output a Numpy tensor with the same shape.\n",
            " |      data_format: Image data format,\n",
            " |          either \"channels_first\" or \"channels_last\".\n",
            " |          \"channels_last\" mode means that the images should have shape\n",
            " |          `(samples, height, width, channels)`,\n",
            " |          \"channels_first\" mode means that the images should have shape\n",
            " |          `(samples, channels, height, width)`.\n",
            " |          It defaults to the `image_data_format` value found in your\n",
            " |          Keras config file at `~/.keras/keras.json`.\n",
            " |          If you never set it, then it will be \"channels_last\".\n",
            " |      validation_split: Float. Fraction of images reserved for validation\n",
            " |          (strictly between 0 and 1).\n",
            " |      dtype: Dtype to use for the generated arrays.\n",
            " |  \n",
            " |  Examples:\n",
            " |  \n",
            " |  Example of using `.flow(x, y)`:\n",
            " |  \n",
            " |  ```python\n",
            " |  (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
            " |  y_train = np_utils.to_categorical(y_train, num_classes)\n",
            " |  y_test = np_utils.to_categorical(y_test, num_classes)\n",
            " |  datagen = ImageDataGenerator(\n",
            " |      featurewise_center=True,\n",
            " |      featurewise_std_normalization=True,\n",
            " |      rotation_range=20,\n",
            " |      width_shift_range=0.2,\n",
            " |      height_shift_range=0.2,\n",
            " |      horizontal_flip=True)\n",
            " |  # compute quantities required for featurewise normalization\n",
            " |  # (std, mean, and principal components if ZCA whitening is applied)\n",
            " |  datagen.fit(x_train)\n",
            " |  # fits the model on batches with real-time data augmentation:\n",
            " |  model.fit(datagen.flow(x_train, y_train, batch_size=32),\n",
            " |            steps_per_epoch=len(x_train) / 32, epochs=epochs)\n",
            " |  # here's a more \"manual\" example\n",
            " |  for e in range(epochs):\n",
            " |      print('Epoch', e)\n",
            " |      batches = 0\n",
            " |      for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):\n",
            " |          model.fit(x_batch, y_batch)\n",
            " |          batches += 1\n",
            " |          if batches >= len(x_train) / 32:\n",
            " |              # we need to break the loop by hand because\n",
            " |              # the generator loops indefinitely\n",
            " |              break\n",
            " |  ```\n",
            " |  \n",
            " |  Example of using `.flow_from_directory(directory)`:\n",
            " |  \n",
            " |  ```python\n",
            " |  train_datagen = ImageDataGenerator(\n",
            " |          rescale=1./255,\n",
            " |          shear_range=0.2,\n",
            " |          zoom_range=0.2,\n",
            " |          horizontal_flip=True)\n",
            " |  test_datagen = ImageDataGenerator(rescale=1./255)\n",
            " |  train_generator = train_datagen.flow_from_directory(\n",
            " |          'data/train',\n",
            " |          target_size=(150, 150),\n",
            " |          batch_size=32,\n",
            " |          class_mode='binary')\n",
            " |  validation_generator = test_datagen.flow_from_directory(\n",
            " |          'data/validation',\n",
            " |          target_size=(150, 150),\n",
            " |          batch_size=32,\n",
            " |          class_mode='binary')\n",
            " |  model.fit(\n",
            " |          train_generator,\n",
            " |          steps_per_epoch=2000,\n",
            " |          epochs=50,\n",
            " |          validation_data=validation_generator,\n",
            " |          validation_steps=800)\n",
            " |  ```\n",
            " |  \n",
            " |  Example of transforming images and masks together.\n",
            " |  \n",
            " |  ```python\n",
            " |  # we create two instances with the same arguments\n",
            " |  data_gen_args = dict(featurewise_center=True,\n",
            " |                       featurewise_std_normalization=True,\n",
            " |                       rotation_range=90,\n",
            " |                       width_shift_range=0.1,\n",
            " |                       height_shift_range=0.1,\n",
            " |                       zoom_range=0.2)\n",
            " |  image_datagen = ImageDataGenerator(**data_gen_args)\n",
            " |  mask_datagen = ImageDataGenerator(**data_gen_args)\n",
            " |  # Provide the same seed and keyword arguments to the fit and flow methods\n",
            " |  seed = 1\n",
            " |  image_datagen.fit(images, augment=True, seed=seed)\n",
            " |  mask_datagen.fit(masks, augment=True, seed=seed)\n",
            " |  image_generator = image_datagen.flow_from_directory(\n",
            " |      'data/images',\n",
            " |      class_mode=None,\n",
            " |      seed=seed)\n",
            " |  mask_generator = mask_datagen.flow_from_directory(\n",
            " |      'data/masks',\n",
            " |      class_mode=None,\n",
            " |      seed=seed)\n",
            " |  # combine generators into one which yields image and masks\n",
            " |  train_generator = zip(image_generator, mask_generator)\n",
            " |  model.fit_generator(\n",
            " |      train_generator,\n",
            " |      steps_per_epoch=2000,\n",
            " |      epochs=50)\n",
            " |  ```\n",
            " |  \n",
            " |  Method resolution order:\n",
            " |      ImageDataGenerator\n",
            " |      keras_preprocessing.image.image_data_generator.ImageDataGenerator\n",
            " |      builtins.object\n",
            " |  \n",
            " |  Methods defined here:\n",
            " |  \n",
            " |  __init__(self, featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None)\n",
            " |      Initialize self.  See help(type(self)) for accurate signature.\n",
            " |  \n",
            " |  flow(self, x, y=None, batch_size=32, shuffle=True, sample_weight=None, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None)\n",
            " |      Takes data & label arrays, generates batches of augmented data.\n",
            " |      \n",
            " |      Arguments:\n",
            " |          x: Input data. Numpy array of rank 4 or a tuple. If tuple, the first\n",
            " |            element should contain the images and the second element another numpy\n",
            " |            array or a list of numpy arrays that gets passed to the output without\n",
            " |            any modifications. Can be used to feed the model miscellaneous data\n",
            " |            along with the images. In case of grayscale data, the channels axis of\n",
            " |            the image array should have value 1, in case of RGB data, it should\n",
            " |            have value 3, and in case of RGBA data, it should have value 4.\n",
            " |          y: Labels.\n",
            " |          batch_size: Int (default: 32).\n",
            " |          shuffle: Boolean (default: True).\n",
            " |          sample_weight: Sample weights.\n",
            " |          seed: Int (default: None).\n",
            " |          save_to_dir: None or str (default: None). This allows you to optionally\n",
            " |            specify a directory to which to save the augmented pictures being\n",
            " |            generated (useful for visualizing what you are doing).\n",
            " |          save_prefix: Str (default: `''`). Prefix to use for filenames of saved\n",
            " |            pictures (only relevant if `save_to_dir` is set).\n",
            " |          save_format: one of \"png\", \"jpeg\"\n",
            " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
            " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
            " |            `validation_split` is set in `ImageDataGenerator`.\n",
            " |      \n",
            " |      Returns:\n",
            " |          An `Iterator` yielding tuples of `(x, y)`\n",
            " |              where `x` is a numpy array of image data\n",
            " |              (in the case of a single image input) or a list\n",
            " |              of numpy arrays (in the case with\n",
            " |              additional inputs) and `y` is a numpy array\n",
            " |              of corresponding labels. If 'sample_weight' is not None,\n",
            " |              the yielded tuples are of the form `(x, y, sample_weight)`.\n",
            " |              If `y` is None, only the numpy array `x` is returned.\n",
            " |  \n",
            " |  flow_from_dataframe(self, dataframe, directory=None, x_col='filename', y_col='class', weight_col=None, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None, interpolation='nearest', validate_filenames=True, **kwargs)\n",
            " |      Takes the dataframe and the path to a directory + generates batches.\n",
            " |      \n",
            " |       The generated batches contain augmented/normalized data.\n",
            " |      \n",
            " |      **A simple tutorial can be found **[here](\n",
            " |                                  http://bit.ly/keras_flow_from_dataframe).\n",
            " |      \n",
            " |      Arguments:\n",
            " |          dataframe: Pandas dataframe containing the filepaths relative to\n",
            " |            `directory` (or absolute paths if `directory` is None) of the images\n",
            " |            in a string column. It should include other column/s\n",
            " |              depending on the `class_mode`: - if `class_mode` is `\"categorical\"`\n",
            " |                (default value) it must include the `y_col` column with the\n",
            " |                class/es of each image. Values in column can be string/list/tuple\n",
            " |                if a single class or list/tuple if multiple classes. - if\n",
            " |                `class_mode` is `\"binary\"` or `\"sparse\"` it must include the given\n",
            " |                `y_col` column with class values as strings. - if `class_mode` is\n",
            " |                `\"raw\"` or `\"multi_output\"` it should contain the columns\n",
            " |                specified in `y_col`. - if `class_mode` is `\"input\"` or `None` no\n",
            " |                extra column is needed.\n",
            " |          directory: string, path to the directory to read images from. If `None`,\n",
            " |            data in `x_col` column should be absolute paths.\n",
            " |          x_col: string, column in `dataframe` that contains the filenames (or\n",
            " |            absolute paths if `directory` is `None`).\n",
            " |          y_col: string or list, column/s in `dataframe` that has the target data.\n",
            " |          weight_col: string, column in `dataframe` that contains the sample\n",
            " |              weights. Default: `None`.\n",
            " |          target_size: tuple of integers `(height, width)`, default: `(256, 256)`.\n",
            " |            The dimensions to which all images found will be resized.\n",
            " |          color_mode: one of \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". Whether\n",
            " |            the images will be converted to have 1 or 3 color channels.\n",
            " |          classes: optional list of classes (e.g. `['dogs', 'cats']`). Default is\n",
            " |            None. If not provided, the list of classes will be automatically\n",
            " |            inferred from the `y_col`, which will map to the label indices, will\n",
            " |            be alphanumeric). The dictionary containing the mapping from class\n",
            " |            names to class indices can be obtained via the attribute\n",
            " |            `class_indices`.\n",
            " |          class_mode: one of \"binary\", \"categorical\", \"input\", \"multi_output\",\n",
            " |              \"raw\", sparse\" or None. Default: \"categorical\".\n",
            " |              Mode for yielding the targets:\n",
            " |              - `\"binary\"`: 1D numpy array of binary labels,\n",
            " |              - `\"categorical\"`: 2D numpy array of one-hot encoded labels.\n",
            " |                Supports multi-label output.\n",
            " |              - `\"input\"`: images identical to input images (mainly used to work\n",
            " |                with autoencoders),\n",
            " |              - `\"multi_output\"`: list with the values of the different columns,\n",
            " |              - `\"raw\"`: numpy array of values in `y_col` column(s),\n",
            " |              - `\"sparse\"`: 1D numpy array of integer labels, - `None`, no targets\n",
            " |                are returned (the generator will only yield batches of image data,\n",
            " |                which is useful to use in `model.predict_generator()`).\n",
            " |          batch_size: size of the batches of data (default: 32).\n",
            " |          shuffle: whether to shuffle the data (default: True)\n",
            " |          seed: optional random seed for shuffling and transformations.\n",
            " |          save_to_dir: None or str (default: None). This allows you to optionally\n",
            " |            specify a directory to which to save the augmented pictures being\n",
            " |            generated (useful for visualizing what you are doing).\n",
            " |          save_prefix: str. Prefix to use for filenames of saved pictures (only\n",
            " |            relevant if `save_to_dir` is set).\n",
            " |          save_format: one of \"png\", \"jpeg\"\n",
            " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
            " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
            " |            `validation_split` is set in `ImageDataGenerator`.\n",
            " |          interpolation: Interpolation method used to resample the image if the\n",
            " |            target size is different from that of the loaded image. Supported\n",
            " |            methods are `\"nearest\"`, `\"bilinear\"`, and `\"bicubic\"`. If PIL version\n",
            " |            1.1.3 or newer is installed, `\"lanczos\"` is also supported. If PIL\n",
            " |            version 3.4.0 or newer is installed, `\"box\"` and `\"hamming\"` are also\n",
            " |            supported. By default, `\"nearest\"` is used.\n",
            " |          validate_filenames: Boolean, whether to validate image filenames in\n",
            " |            `x_col`. If `True`, invalid images will be ignored. Disabling this\n",
            " |            option can lead to speed-up in the execution of this function.\n",
            " |            Defaults to `True`.\n",
            " |          **kwargs: legacy arguments for raising deprecation warnings.\n",
            " |      \n",
            " |      Returns:\n",
            " |          A `DataFrameIterator` yielding tuples of `(x, y)`\n",
            " |          where `x` is a numpy array containing a batch\n",
            " |          of images with shape `(batch_size, *target_size, channels)`\n",
            " |          and `y` is a numpy array of corresponding labels.\n",
            " |  \n",
            " |  flow_from_directory(self, directory, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', follow_links=False, subset=None, interpolation='nearest')\n",
            " |      Takes the path to a directory & generates batches of augmented data.\n",
            " |      \n",
            " |      Arguments:\n",
            " |          directory: string, path to the target directory. It should contain one\n",
            " |            subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images inside\n",
            " |            each of the subdirectories directory tree will be included in the\n",
            " |            generator. See [this script](\n",
            " |              https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)\n",
            " |                for more details.\n",
            " |          target_size: Tuple of integers `(height, width)`, defaults to `(256,\n",
            " |            256)`. The dimensions to which all images found will be resized.\n",
            " |          color_mode: One of \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". Whether\n",
            " |            the images will be converted to have 1, 3, or 4 channels.\n",
            " |          classes: Optional list of class subdirectories\n",
            " |              (e.g. `['dogs', 'cats']`). Default: None. If not provided, the list\n",
            " |                of classes will be automatically inferred from the subdirectory\n",
            " |                names/structure under `directory`, where each subdirectory will be\n",
            " |                treated as a different class (and the order of the classes, which\n",
            " |                will map to the label indices, will be alphanumeric). The\n",
            " |                dictionary containing the mapping from class names to class\n",
            " |                indices can be obtained via the attribute `class_indices`.\n",
            " |          class_mode: One of \"categorical\", \"binary\", \"sparse\",\n",
            " |              \"input\", or None. Default: \"categorical\".\n",
            " |              Determines the type of label arrays that are returned: -\n",
            " |                \"categorical\" will be 2D one-hot encoded labels, - \"binary\" will\n",
            " |                be 1D binary labels, \"sparse\" will be 1D integer labels, - \"input\"\n",
            " |                will be images identical to input images (mainly used to work with\n",
            " |                autoencoders). - If None, no labels are returned (the generator\n",
            " |                will only yield batches of image data, which is useful to use with\n",
            " |                `model.predict_generator()`). Please note that in case of\n",
            " |                class_mode None, the data still needs to reside in a subdirectory\n",
            " |                of `directory` for it to work correctly.\n",
            " |          batch_size: Size of the batches of data (default: 32).\n",
            " |          shuffle: Whether to shuffle the data (default: True) If set to False,\n",
            " |            sorts the data in alphanumeric order.\n",
            " |          seed: Optional random seed for shuffling and transformations.\n",
            " |          save_to_dir: None or str (default: None). This allows you to optionally\n",
            " |            specify a directory to which to save the augmented pictures being\n",
            " |            generated (useful for visualizing what you are doing).\n",
            " |          save_prefix: Str. Prefix to use for filenames of saved pictures (only\n",
            " |            relevant if `save_to_dir` is set).\n",
            " |          save_format: One of \"png\", \"jpeg\"\n",
            " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
            " |          follow_links: Whether to follow symlinks inside\n",
            " |              class subdirectories (default: False).\n",
            " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
            " |            `validation_split` is set in `ImageDataGenerator`.\n",
            " |          interpolation: Interpolation method used to resample the image if the\n",
            " |            target size is different from that of the loaded image. Supported\n",
            " |            methods are `\"nearest\"`, `\"bilinear\"`, and `\"bicubic\"`. If PIL version\n",
            " |            1.1.3 or newer is installed, `\"lanczos\"` is also supported. If PIL\n",
            " |            version 3.4.0 or newer is installed, `\"box\"` and `\"hamming\"` are also\n",
            " |            supported. By default, `\"nearest\"` is used.\n",
            " |      \n",
            " |      Returns:\n",
            " |          A `DirectoryIterator` yielding tuples of `(x, y)`\n",
            " |              where `x` is a numpy array containing a batch\n",
            " |              of images with shape `(batch_size, *target_size, channels)`\n",
            " |              and `y` is a numpy array of corresponding labels.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Methods inherited from keras_preprocessing.image.image_data_generator.ImageDataGenerator:\n",
            " |  \n",
            " |  apply_transform(self, x, transform_parameters)\n",
            " |      Applies a transformation to an image according to given parameters.\n",
            " |      \n",
            " |      # Arguments\n",
            " |          x: 3D tensor, single image.\n",
            " |          transform_parameters: Dictionary with string - parameter pairs\n",
            " |              describing the transformation.\n",
            " |              Currently, the following parameters\n",
            " |              from the dictionary are used:\n",
            " |              - `'theta'`: Float. Rotation angle in degrees.\n",
            " |              - `'tx'`: Float. Shift in the x direction.\n",
            " |              - `'ty'`: Float. Shift in the y direction.\n",
            " |              - `'shear'`: Float. Shear angle in degrees.\n",
            " |              - `'zx'`: Float. Zoom in the x direction.\n",
            " |              - `'zy'`: Float. Zoom in the y direction.\n",
            " |              - `'flip_horizontal'`: Boolean. Horizontal flip.\n",
            " |              - `'flip_vertical'`: Boolean. Vertical flip.\n",
            " |              - `'channel_shift_intensity'`: Float. Channel shift intensity.\n",
            " |              - `'brightness'`: Float. Brightness shift intensity.\n",
            " |      \n",
            " |      # Returns\n",
            " |          A transformed version of the input (same shape).\n",
            " |  \n",
            " |  fit(self, x, augment=False, rounds=1, seed=None)\n",
            " |      Fits the data generator to some sample data.\n",
            " |      \n",
            " |      This computes the internal data stats related to the\n",
            " |      data-dependent transformations, based on an array of sample data.\n",
            " |      \n",
            " |      Only required if `featurewise_center` or\n",
            " |      `featurewise_std_normalization` or `zca_whitening` are set to True.\n",
            " |      \n",
            " |      When `rescale` is set to a value, rescaling is applied to\n",
            " |      sample data before computing the internal data stats.\n",
            " |      \n",
            " |      # Arguments\n",
            " |          x: Sample data. Should have rank 4.\n",
            " |           In case of grayscale data,\n",
            " |           the channels axis should have value 1, in case\n",
            " |           of RGB data, it should have value 3, and in case\n",
            " |           of RGBA data, it should have value 4.\n",
            " |          augment: Boolean (default: False).\n",
            " |              Whether to fit on randomly augmented samples.\n",
            " |          rounds: Int (default: 1).\n",
            " |              If using data augmentation (`augment=True`),\n",
            " |              this is how many augmentation passes over the data to use.\n",
            " |          seed: Int (default: None). Random seed.\n",
            " |  \n",
            " |  get_random_transform(self, img_shape, seed=None)\n",
            " |      Generates random parameters for a transformation.\n",
            " |      \n",
            " |      # Arguments\n",
            " |          seed: Random seed.\n",
            " |          img_shape: Tuple of integers.\n",
            " |              Shape of the image that is transformed.\n",
            " |      \n",
            " |      # Returns\n",
            " |          A dictionary containing randomly chosen parameters describing the\n",
            " |          transformation.\n",
            " |  \n",
            " |  random_transform(self, x, seed=None)\n",
            " |      Applies a random transformation to an image.\n",
            " |      \n",
            " |      # Arguments\n",
            " |          x: 3D tensor, single image.\n",
            " |          seed: Random seed.\n",
            " |      \n",
            " |      # Returns\n",
            " |          A randomly transformed version of the input (same shape).\n",
            " |  \n",
            " |  standardize(self, x)\n",
            " |      Applies the normalization configuration in-place to a batch of inputs.\n",
            " |      \n",
            " |      `x` is changed in-place since the function is mainly used internally\n",
            " |      to standardize images and feed them to your network. If a copy of `x`\n",
            " |      would be created instead it would have a significant performance cost.\n",
            " |      If you want to apply this method without changing the input in-place\n",
            " |      you can call the method creating a copy before:\n",
            " |      \n",
            " |      standardize(np.copy(x))\n",
            " |      \n",
            " |      # Arguments\n",
            " |          x: Batch of inputs to be normalized.\n",
            " |      \n",
            " |      # Returns\n",
            " |          The inputs, normalized.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data descriptors inherited from keras_preprocessing.image.image_data_generator.ImageDataGenerator:\n",
            " |  \n",
            " |  __dict__\n",
            " |      dictionary for instance variables (if defined)\n",
            " |  \n",
            " |  __weakref__\n",
            " |      list of weak references to the object (if defined)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z20mfNS9Bkii"
      },
      "source": [
        "#we dont need to normalize the dataset because its already normilized"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YEEYD9gBkij",
        "outputId": "42b39d3d-73f7-4c0f-ddf8-67c2d7982795"
      },
      "source": [
        "imread(para_cell).max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9098039"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NvD405hBkij"
      },
      "source": [
        "image_gen = ImageDataGenerator(rotation_range=20, # rotate the image 20 degrees\n",
        "                               width_shift_range=0.10, # Shift the pic width by a max of 10%\n",
        "                               height_shift_range=0.10, # Shift the pic height by a max of 10%\n",
        "                               #rescale=1/255, # Rescale the image by normalzing it.\n",
        "                               shear_range=0.1, # Shear means cutting away part of the image (max 10%)\n",
        "                               zoom_range=0.1, # Zoom in by 10% max\n",
        "                               horizontal_flip=True, # Allo horizontal flipping\n",
        "                               fill_mode='nearest' # Fill in missing pixels with the nearest filled value\n",
        "                              )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rTWe1jQBkik",
        "outputId": "b63a41a4-4eea-4754-9988-62d69e2520db"
      },
      "source": [
        "plt.imshow(para_img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x13074d43828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "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": "TBda8RRtBkik",
        "outputId": "60ae0c65-04d9-43dc-a1b9-523dbd34df75"
      },
      "source": [
        "plt.imshow(image_gen.random_transform(para_img))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x13074db35f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "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": "kQvQPsb8Bkil",
        "outputId": "fb1d463a-6fee-4e6b-f220-6a5a0594428d"
      },
      "source": [
        "plt.imshow(image_gen.random_transform(para_img))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x13074e125f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "As409kSYBkil"
      },
      "source": [
        "### Generating many manipulated images from a directory\n",
        "\n",
        "\n",
        "In order to use .flow_from_directory, you must organize the images in sub-directories. This is an absolute requirement, otherwise the method won't work. The directories should only contain images of one class, so one folder per class of images.\n",
        "\n",
        "Structure Needed:\n",
        "\n",
        "* Image Data Folder\n",
        "    * Class 1\n",
        "        * 0.jpg\n",
        "        * 1.jpg\n",
        "        * ...\n",
        "    * Class 2\n",
        "        * 0.jpg\n",
        "        * 1.jpg\n",
        "        * ...\n",
        "    * ...\n",
        "    * Class n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBaqRZdYBkim",
        "outputId": "3ed0f641-10c8-497e-dd4c-6ae27d09f083"
      },
      "source": [
        "image_gen.flow_from_directory(train_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 24958 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.preprocessing.image.DirectoryIterator at 0x13074cde358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BfozP8-bBkim",
        "outputId": "d4393433-4efe-4bca-af6b-c0e243650a9b"
      },
      "source": [
        "image_gen.flow_from_directory(test_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2574 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.preprocessing.image.DirectoryIterator at 0x13074dc5cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLy6PFw3Bkin"
      },
      "source": [
        "# Creating the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zql2-e_PBkin"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense, Conv2D, MaxPooling2D"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JPIVOs-Bkin"
      },
      "source": [
        "#https://stats.stackexchange.com/questions/148139/rules-for-selecting-convolutional-neural-network-hyperparameters\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "\n",
        "model.add(Dense(128))\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "# Dropouts help reduce overfitting by randomly turning neurons off during training.\n",
        "# Here we say randomly turn off 50% of neurons.\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "# Last layer, remember its binary so we use sigmoid\n",
        "model.add(Dense(1))\n",
        "model.add(Activation('sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ry8BBPyVBkio",
        "outputId": "87155785-bf41-4439-bfc3-5882cfb1458d"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 128, 128, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 64, 64, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 62, 62, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 31, 31, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 29, 29, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 12544)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               1605760   \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 129       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 1,662,209\n",
            "Trainable params: 1,662,209\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AD9yOjUVBkio"
      },
      "source": [
        "## Early Stopping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64usJ8VzBkip"
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTJ3UpNkBkip"
      },
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss',patience=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FQKda84RBkip"
      },
      "source": [
        "## Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "As4M_bhCBkip",
        "outputId": "be568b05-d973-43b8-8ccc-66cd45ecd607"
      },
      "source": [
        "help(image_gen.flow_from_directory)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on method flow_from_directory in module tensorflow.python.keras.preprocessing.image:\n",
            "\n",
            "flow_from_directory(directory, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', follow_links=False, subset=None, interpolation='nearest') method of tensorflow.python.keras.preprocessing.image.ImageDataGenerator instance\n",
            "    Takes the path to a directory & generates batches of augmented data.\n",
            "    \n",
            "    Arguments:\n",
            "        directory: string, path to the target directory. It should contain one\n",
            "          subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images inside\n",
            "          each of the subdirectories directory tree will be included in the\n",
            "          generator. See [this script](\n",
            "            https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)\n",
            "              for more details.\n",
            "        target_size: Tuple of integers `(height, width)`, defaults to `(256,\n",
            "          256)`. The dimensions to which all images found will be resized.\n",
            "        color_mode: One of \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". Whether\n",
            "          the images will be converted to have 1, 3, or 4 channels.\n",
            "        classes: Optional list of class subdirectories\n",
            "            (e.g. `['dogs', 'cats']`). Default: None. If not provided, the list\n",
            "              of classes will be automatically inferred from the subdirectory\n",
            "              names/structure under `directory`, where each subdirectory will be\n",
            "              treated as a different class (and the order of the classes, which\n",
            "              will map to the label indices, will be alphanumeric). The\n",
            "              dictionary containing the mapping from class names to class\n",
            "              indices can be obtained via the attribute `class_indices`.\n",
            "        class_mode: One of \"categorical\", \"binary\", \"sparse\",\n",
            "            \"input\", or None. Default: \"categorical\".\n",
            "            Determines the type of label arrays that are returned: -\n",
            "              \"categorical\" will be 2D one-hot encoded labels, - \"binary\" will\n",
            "              be 1D binary labels, \"sparse\" will be 1D integer labels, - \"input\"\n",
            "              will be images identical to input images (mainly used to work with\n",
            "              autoencoders). - If None, no labels are returned (the generator\n",
            "              will only yield batches of image data, which is useful to use with\n",
            "              `model.predict_generator()`). Please note that in case of\n",
            "              class_mode None, the data still needs to reside in a subdirectory\n",
            "              of `directory` for it to work correctly.\n",
            "        batch_size: Size of the batches of data (default: 32).\n",
            "        shuffle: Whether to shuffle the data (default: True) If set to False,\n",
            "          sorts the data in alphanumeric order.\n",
            "        seed: Optional random seed for shuffling and transformations.\n",
            "        save_to_dir: None or str (default: None). This allows you to optionally\n",
            "          specify a directory to which to save the augmented pictures being\n",
            "          generated (useful for visualizing what you are doing).\n",
            "        save_prefix: Str. Prefix to use for filenames of saved pictures (only\n",
            "          relevant if `save_to_dir` is set).\n",
            "        save_format: One of \"png\", \"jpeg\"\n",
            "            (only relevant if `save_to_dir` is set). Default: \"png\".\n",
            "        follow_links: Whether to follow symlinks inside\n",
            "            class subdirectories (default: False).\n",
            "        subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
            "          `validation_split` is set in `ImageDataGenerator`.\n",
            "        interpolation: Interpolation method used to resample the image if the\n",
            "          target size is different from that of the loaded image. Supported\n",
            "          methods are `\"nearest\"`, `\"bilinear\"`, and `\"bicubic\"`. If PIL version\n",
            "          1.1.3 or newer is installed, `\"lanczos\"` is also supported. If PIL\n",
            "          version 3.4.0 or newer is installed, `\"box\"` and `\"hamming\"` are also\n",
            "          supported. By default, `\"nearest\"` is used.\n",
            "    \n",
            "    Returns:\n",
            "        A `DirectoryIterator` yielding tuples of `(x, y)`\n",
            "            where `x` is a numpy array containing a batch\n",
            "            of images with shape `(batch_size, *target_size, channels)`\n",
            "            and `y` is a numpy array of corresponding labels.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-VAORMiBkiq"
      },
      "source": [
        "batch_size = 16"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vf2xZ70YBkiq",
        "outputId": "d8a27b32-0c54-4cf5-e108-21f792965ce7"
      },
      "source": [
        "train_image_gen = image_gen.flow_from_directory(train_path,\n",
        "                                               target_size=image_shape[:2],\n",
        "                                                color_mode='rgb',\n",
        "                                               batch_size=batch_size,\n",
        "                                               class_mode='binary')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 24958 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKGq9U54Bkir",
        "outputId": "22579c4e-6027-4fe7-dff5-283934ef967b"
      },
      "source": [
        "test_image_gen = image_gen.flow_from_directory(test_path,\n",
        "                                               target_size=image_shape[:2],\n",
        "                                               color_mode='rgb',\n",
        "                                               batch_size=batch_size,\n",
        "                                               class_mode='binary',shuffle=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2574 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBExQyXsBkis",
        "outputId": "bbc28951-1bed-40c8-c64f-912ab852557b"
      },
      "source": [
        "train_image_gen.class_indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'parasitized': 0, 'uninfected': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7hVrgnCBkit"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "u_Ffr8mrBkiu"
      },
      "source": [
        "#results = model.fit_generator(train_image_gen,epochs=20,\n",
        "                             # validation_data=test_image_gen,\n",
        "                             #callbacks=[early_stop])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJ3F-iqjBkiw"
      },
      "source": [
        "#from tensorflow.keras.models import load_model\n",
        "#model.save('malaria_detector.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQmquurIBkiw"
      },
      "source": [
        "# Evaluating the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SczNUiAjBkix"
      },
      "source": [
        "from tensorflow.keras.models import load_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVWXujMxBkix"
      },
      "source": [
        "model = load_model('malaria_detector.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEfB__aUBkjK",
        "outputId": "61918a34-24ce-4dcc-e2a8-4dc342795b33"
      },
      "source": [
        "#model.metrics_names"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['loss', 'accuracy']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsrW33utBkjL",
        "outputId": "d79a6134-c0ad-4665-8dae-25325a545b1d"
      },
      "source": [
        "model.evaluate_generator(test_image_gen)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-67-31acfd80dad9>:1: Model.evaluate_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.evaluate, which supports generators.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[36.75750732421875, 0.8714063763618469]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBpUQtXZBkjM"
      },
      "source": [
        "from tensorflow.keras.preprocessing import image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFqwCIuoBkjM",
        "outputId": "36ba4a5c-796a-462b-da83-bf9978b87749"
      },
      "source": [
        "# https://datascience.stackexchange.com/questions/13894/how-to-get-predictions-with-predict-generator-on-streaming-test-data-in-keras\n",
        "pred_probabilities = model.predict_generator(test_image_gen)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-69-f14b427af105>:2: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.predict, which supports generators.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IW_o9sOTBkjN",
        "outputId": "9425dcab-b0e4-482d-e367-a9fb59688e05"
      },
      "source": [
        "pred_probabilities"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.],\n",
              "       [0.],\n",
              "       [0.],\n",
              "       ...,\n",
              "       [1.],\n",
              "       [0.],\n",
              "       [0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwW7kJM5BkjT",
        "outputId": "e5b21217-5193-4aaf-b84d-3a1c17ea5660"
      },
      "source": [
        "test_image_gen.classes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyqbY8nvBkjb"
      },
      "source": [
        "predictions = pred_probabilities > 0.50"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pmKfBt72Bkjc",
        "outputId": "7a565157-5e8b-4192-a978-e250c23fdb70"
      },
      "source": [
        "# Numpy can treat this as True/False for us\n",
        "predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[False],\n",
              "       [False],\n",
              "       [False],\n",
              "       ...,\n",
              "       [ True],\n",
              "       [False],\n",
              "       [False]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aP4W2_fIBkjd"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NfAExhqjBkjd",
        "outputId": "2d4d9c94-cf90-4295-f5ff-4cb397dfa6ba"
      },
      "source": [
        "print(classification_report(test_image_gen.classes,predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.98      0.88      1274\n",
            "           1       0.97      0.76      0.85      1300\n",
            "\n",
            "    accuracy                           0.87      2574\n",
            "   macro avg       0.89      0.87      0.87      2574\n",
            "weighted avg       0.89      0.87      0.87      2574\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXDGOUnnBkje",
        "outputId": "ddd6949d-b7dc-4629-9493-9d1daec4823a"
      },
      "source": [
        "confusion_matrix(test_image_gen.classes,predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1245,   29],\n",
              "       [ 308,  992]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNX2GMLBBkjf"
      },
      "source": [
        "# Predicting on an Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3kh_nJ4Bkjf",
        "outputId": "151fe576-1b48-43ba-9011-fad403754d8e"
      },
      "source": [
        "# Your file path will be different!\n",
        "from tensorflow.keras.preprocessing import image\n",
        "para_cell"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'C:/Users/Amir/Desktop/AI Institute Classes/Deep Learning/TF_2_Notebooks_and_Data/04-CNNs/cell_images\\\\train\\\\\\\\parasitized\\\\C100P61ThinF_IMG_20150918_144104_cell_162.png'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eC_Vms_jBkjg"
      },
      "source": [
        "my_image = image.load_img(para_cell,target_size=image_shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2A0vqUF9Bkjj",
        "outputId": "6b6619c6-ed84-4d11-9e9d-86ea54efa612"
      },
      "source": [
        "my_image"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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qMWEQyw0O1VHFnahpXoArVvgIqszyDYM/MkpuTojEK2YmgDqLrBPfPDZ4lSM5rDCt/6ZiwEf7DfsGQunp0SedpMTNhWIeC7ylKuJPZlZrvWnL9pKLW4s8GSqB5mtrJN8yZ77W4addXG/wNMPpejJc3Qg8iMNXwEdi4k0xJe+qRbfU8s0Zg1Mmz56SMEgWLW4pbfVCzRugwRBNp53jRDTJqM0UHevJdYLzxyAOh2wo05NumxfnJZ1lwOTF8dchu/YTElNF/+1ffgfAr//2H8bXjwhmRPoMPg6dBXDh+7GOExo0lPfNugqOgwVz8xgRHgyPejdCjvfuUx1V3GkBGQ0nozlDbRYV5kXuv8TZhFv220Tw/GvGxPzg8hLlxunkaFRAzL2fKp3O5PzX/+VPibI7UW33Dy3+f3VgZBJ+LyNMguKkA3ycYJhafT43J5zjuTfB3W/HYfMnufGYuLgemRpwJakmR15LoWtlMyvbSFZvpdDd46QirB9fJGlLl5ZKnizJWwZraxrgjnkDAVFDz5sLInptyBEQJ7mRledcQWyGZK11ZkbxSPihA+iW1XgTKxpxEd7i4GsQz69zbXnOm2OSnY3jZTZOE7ALIkiHk2FzX3PaK/fNBRpGgUrBzhgdMccmBQGaOI3ko8poAiKn6TOSrgU3XIV2YL5U2qURSXVVjczi3InZmuE4UZsBmlHSK6OOEmnuZd7Y0lG4DL4TE8SHJncIkjeRrHJbyo0YpcvVbCnqIakeM9/R8OM//7Yk36o6PDEhdSK6jM9ZDJKhkOk3L3fC6LQxJN2bWWTOiSmOmoLvsDen2VcYgJ42NFOt1Jx8UsZNlj2Nm0yQ6Wnu5aTzw1PpkooiJ0QHygX3xtKtxwXHP7vjbA1c2QnXLNwvwDmKcB0PuN5eVN8g+UEnZspEkNew0/sslZDkrrkDDqcPB+LIOzrpKkkUbJRy3sTQaRdZgk00QPuCMctOyTC5Z2mBty4ndjTIOaLkOwq6jHYByMHLm4yJRMs3xfQJBPHhpB6R5MAUR1mwaMjiHQrDSkBvQ7ZHD8ez0MlS8trGXIgbaKQGDQl+XOeJbm7yh6STRyNRYvT7S32L+sWarStcLmfJgW93uJtZiXxdjXRqf8Zk7X7IR4gzDVHXNREwwXe9k2tmCq/Vdd6bwYqKpCgvW7kSbFvbti1utzLxISAOKu9o0wKJahG/CmujObsKbdrr+2Ods4PPgEPUAu2nZjKDXu/rBkoGkQ0lBPfR/CQqRkf2gCPpe8jIS4LL0SuRgP2kLUeeD0dWYCpyPa3mJgXF74aD2cw0JGGTA4bFWw8XSxHdioZXXsoUOyIB0b3BihklsMFockFQCYgTYJx53JWQpnpBqy2Dr5MqD0JprvCU5Jn7OkmFMzQgpcjmuq7uHgE2Xhusklqr5ZZb5Loh6DP5Zyq7XgqSqrhmlH6xdzGoStLaQ/3ww/soky2lrOv6cH8vSauhl7YDhMdRZYgESDlocW6Irt1aHfJNHJHnuZ6w5ZCyGiR51ds+s/uBFm+xPkd9Ma5M3lydFfOcVbQky86U+ioDnRHzmVeWUuSwYkyG0PWyJj44dCD6mRlvrZoZWLw1Napy1Wp+9+GHH37wT6+2++qbJBpKNT0slYuVC71UyUthba3Y6gRhtKI4xW895mxmhSU+AyBVrEhEcwFOcdS6oRkcat7gMGnVsiyETTib7YgUu2TOih3ei3w7WM3k7lBrLeyDuEdSa2330q3bJjsaUm32blAy2ayt1hBklo5OzjD6TnFXqvgs+lPs00h3X6xstVkr9V6Q+4evXv3s/oN/fLXdV28s4LJcHnT/4f2H73/5+Rd+6cV6KU6YlVJWSXHULuLq3OPtCPEaD5WkJkeTVNYeKia50CS0LEWlML50LCc8cFWykToibQ/haWYA+yn2bC/dFhi3uCE/OC6yIslWkoyCorgyZ29is0DvX4KroYmeoUyIYrRWqzvgxTewWn2or/7vw8/+/tX9BxtcEl9Xd7wSXYs+wM+XZV1fPItdBBrKWqa+DdLbLcJtP+TSB5xcd0FPSKB6yzZJBE1Ac9murpSd9qOpmpVK+qApjnCFBleuKrpCA4aJnUgjT70TNIlhCB9cTalXH52WlZ8aaxmAs3VlfdCq9eGh8QH1lV7/w337qdo/aXt935oeat38gRe+96XnvCx+3woL12IGFJZ0IDAmhIeIMLlUdQYiUbfWCyaMAlo7xsmFCMPNfjfXMnZ+zfvK1zhhdobazgF7yUxIxZb8hiHu0bqvMLkPyaRbuGT+9RQA4y2+ux4aVjZpgC1WXPXCu2K+uW+vtw///vXrn26v/vHD+tDc/dV272jP37/DeyqC11ZrXQpsXfoyCuS9nBCCO1oTvUnR4glAAXoXCZEIJ1azqqj7DYQNO1wAvDZDgJODeHbQnxCDY4nQpGaezEiL0+QM4ckhP3c0dJHq8ggRJ4T3vKWHEOzGWaQwI0A94ou31pehPz9MZm+t3tf68/azn/3sw7+///n/ee3/pJ/+3Qfttf/8Z/ettVJWmda71cTtvr7HF5dysVC+62prBFxhEc5yNG+qUqVc7vKqWqtqD2xEhtVWooCFcLKgoHRlaxiuRiNtWZZlWZgc+ynYDpx9K/2OAuY2FlMo9fBfB/vs23pDKPXwFncJOGdWUiHRPrUspZSCDv3z+k4qDokxgzc1YnaLLWj3Bnv16uc//+CVNl4ul5/+9GcffPAPv/QrX/ril98vz6wsZVmWdV0vlwsLavWlgCTXBWGrDJ/AwNcPD3CiudowTJ1xSsArABYrgEX9q6tTXiklmn1mGjrBIcN9vyBVdUrytvcH6Fcyw+RcAXSjQIbG3aptkKDWCouG/BnxsjpjPiZy8q+ESGMdT0eZ2bglzmRYc5doZg6BXO4uz18s95ft/qHqQbys73/xi8/ee1kuKHfrr/zqL2Ntz7642B3LWspqKPAwKdUkRTpKxs2rb+5V2FBrYwsXDTJxYXM8W+8MVlzFqRKeYYt0tNzLxS6XRZKsZxInB0wa2sXykFFtq0od92gFQJObWTGQAo1kn9LoR4V0o1yM3o+WkIyO7z0MMNsl5cJTAhi1m0d68dYmDsLSJUtrDrRSlh4pMnp3Mt0MVfXuvefvfemlHvD87uXW6vPlhbPyArG+94UX5bk9f7mud4tMonP0q1iWxataa3SheX3Ytq1iY62VLYRNaHKYGQWqi9PqW2utrMXMVGBmVoBiBIQ92JU5e4qmndKzB5qYPg7ZV3kogYk2d33lt/7wUW4IqMXxzGgDBsnd59tDFN5HzoqkToSTNEL498KUkcrr+V73ZVlLKdu2qcGrINXWQvU86N4u5Uv/6v1XH9w/vP6wmq+rP/vC3fu/9JLP/O6L6/KS5dkiEwtYyuHASyyujaIeAYK5zQZxYUE5+wELl7fW4szaUW50MppCZiq8Wc+QoZ/hriTNOJQlbyjIs/Gy/Pi/fi8MIRyD8hGJHF6AJhqUjlECEKO05OA/D2EVT+52s4UxQrXWKMBF0YQmuWq5lPUF36vPH35eyxcMcF/ay+fPSX7hy8/f//LL9cX6/q+8fMBreyYYtYTt4lJvdFlrRVoe2d9eAoCM5HOwpnscdSlwd/FcUTopxoZLLIV5dZZOGbInfOQ/WZleczuHWycaNO22iQP2Ni62c9L+V8CmQZ3utVR/UE9pBnV1MsoU3FtTKYu7pApYKcWl5c7WX35ve9l4V+6+dAGsFC7Lcnm+rJfy/pdfaG2rleX5ujwrFZvkUqMh4vNWABgWmFmDS8M1dvXUcuzFAMC9LksRZm/M7GH1nU7Ky2KHqQgjjx7X26OBcQY537tzybVzu0Q1Sp53hkrcXaAtpTWXEY0AzGC2zLxrd5nMZlYrypnd3b0nLG2055F6UKWjyin31nR5fqfqfuet6tl7S/nyF0xoTWT4WXr+/MJLe/HihQpqrZseyKDWSE33GjUJtdbn773cbCulYSPKgz/Mky9mZiqFbKUU2lAVg1GCpuabOo7QP7zZLNNZTDvavweILM7uJZj0l2B2ZXlVQrlw8tt4TBjXYg9NTOxN7kuOwjkfohH+Gysk2WVraw3eSayUotY7Zi5Waq0mkrx7ubbWmjtdz5Y712Zmd3erpMvzhYuaN6GVYkBqAlAsThbZWqxW1SY5TCy0hYAtWMxMRbHy6ve2FPcNo+EQw8wPUDoXj8yEJhkhpXWyXJqQg+0OUxsmS64T6D2nCAsP/owGOMGImwIgRvlixOsBjCYsI3Gxn+ufjDxx0Jc419wf3uG10OReluK1uftClcVa26NYlQ0LFpgtNMPzy0szs3Vxd5APcoe4lJ4NHAn3u8vS6gNENV9LefXqNY3toYIsFy53y7Zt5XJZyuLu8K2Q7jUqVrzVUgoEMzM4QsHbEgf0Mjkj1Sbn1ELvUJN6/xJxhLxzkoUDX0gyY+uABjPz5qWUttViBmPX1Uaot82PJMBEbE4RKx145uwoNgyYST7d0jBMkJOU0XlUbsVIWileZGYyohwOP3WXhTD1ZPqM3QsQInvjYSFLXLr87GcCzYz0Gm82UETASrgLAGBeSllsDWDxCmSxx1mIlihdUzXmMerS9kLKPbR+QkPOJcXVyN08h46fnLgXchkxunzmhd7ENpIfl/WepHxagoCZLZe1Ya/vi7t8nBPt7gshY22+ivHyvGj15h3B6vUpxUhG1oElXqbCIptysvf26P7HCKyJ9F7InrcTQDQ7BAtOkmBuNguudMG5ZVpHQ2utsARBRY5i8tsOnamnACd6OlcHJOmq+CXeA5L1f/w1dN28t78oJIjLmZfYH2pRERMZBMDUeTRmI9DD2q2/lqyY6jjcaHJ5JEe7ICGNFiRc3CTB6Q0ADEaxNdkSZHv7iPxsWxdfe0nDYOspKiSht2+KivFDVOqMhrxnM4NYcinV1QstsnK+1s84VlzlyXXVKCr4o3DXNAI8PMTVSNpSXIpCxJHMkZmJoszd4+i1kt3SpUSRnLRihLtoAXq6uxWKJSK8Vsy3amZ1uzczmFmxpNdus/WIqO5A4Mjsn8h8RHEimnn+/YCGUopvTtJbnB6lZ9k33tux03vbs+d2lVOdy5yqYucSdGewrIsNTS6pPbSpFeZdFCVpawAaD/kMRcMINFIR9DVy27Z5wYRIYM5Q5rTzLStzYSggeGfPWmuSt1YlrlhO28kf5pZDH0dyH0cx3luJcIZs+xa6Ork+ghj0FVKyteZNS67HvhIpp3NDOayYsZ2l6mSOYnvtkI+29Bqpi0xKUpcq/fCP9Zagcfpx2iHuAf1Q40XsLUFRrJ8XTz1ffDazH9ZdHCuSxvFvkmQbkoPL7XxJ4uz9VOTQwLv0lnrd6gTF/H0q+R0NZuYCzJo7jKaDTTJA1CMEnYiOrTlzbwkm1TfxtMuTUjrcPd7kRQnTWJqxH5LuM8ZgpE9dGodQ4JFIpsEgyGEo3iIcaTRIqlGqdVRF7t4gc5A00KzUh4fw4Un2boalRzNbjVTKTojxvogJ53AmHIia1AyoXgDgApSN12492pVukNTLkHukc28DC2C08juc3MNRMZx4FukldhMNJ1KaQk6z6+y4MdZa1rAa1Frnkm3bgvdba23bvIHkEvIHZUpndw8PKV5prBnSGky5WolOQsEH0cN2UKsBwDg1PLlhZ+sdDcTezfEQXp34mESZJUqUDbTrZg2ZBgMoSODOgmI+7MSrc5O8VTaZcTCvd2q66Bi2R8ai97LDsm3bDLAvyxL3LcuiHGtLCZUD1AaEEJ2Ex8v/lmVpra1l2baNY2Z337y6uznWcgF6VUcHa9pzEG58DD1wDaioj+rnDZKfG+tZylWXgN/4xn+OTz/63rcwKpmULLOg+2sBx2Qdz3iLhknKK+stFhH7yFvKuJzOQWgblygtZiSrpNa4n1xPUX71Utch5W83WB203lGe9eosDsq8O2aI3PJ+pmpX73boIHIitQmuDvzox9Krux83WLuDqiHvBkwBIHIzvsMLvWi3V4vkQDwy36QlxnKUVPEMVe0rHvmlMNscbaqWZVlqrfE+SCMj3ce5bPfW4lztXvTGoSTnsjlKuwz9SIBfucTzBWKT6ycBnWgr91M6EBNBRF0h4is4Arxd1D9+IHeQ8J68bBIPraauwDo2lo/l7ngaLDI32XF8DK1P/HHk7Ehe1jt3r1VV6oUMNPpCW9z9YasDa9bg7snwcIfDyBr5kpExyS20tvrg7hqV5JMtSlkulwtLf0NMF0o9Jz+zuqEfdrhPlktUO0SxaZ+niwQAh2PYZzQEIFqNvj9d1PAg+g/xkJMawDCow6Q5ycozyRwdvWnMBDq3bQvNvK6XyWetNdJGk9e+ryhLWKYp0jvSDZ2RiGbG3cLDIGml1Frdfdu2dV1ba+3BAZR1sdXKsrS2nWhl5/guXHeHINPf/NNsDposoLc4nh5Opm9JwgSxyDAir1nsRpQ/+7ccxz3n1xmj3+m99xMPMww9KO1srW2vNwB3l2ckZj42knbWjOSFz7ZtY6Naa60ZC0Gtxd2XtTf487YBoAxOjOIEA9XQvMmhKrlqrT3KLWtblQSVygbjencH9WjotNRP0YFSiozTFyF3YRVx74l4ke6CyYpZRLeuHJIDGjLZdpHdTZEz5nUk53lvJo1OCvn1X0cqOBBRYu/aNvNSSpmhIffmHu9rWbw2yNQEdYOkPvQ+llCsJ54Svm5oDknegwrNJdValXI40WDeLuu6rstlqeEVstPWlLp5v3OP6EUnTZKMQnhfHmUtRhIOKGzyr/7OoRLgBhom2q8bxESs08+uHybCJgSVjhfOYzwcRn07Bn/i3x7jjKM4BTNUDmDb2vQTF1slbFvbtmbyQR+lJ22MvkWITQirJooIxxme+aE1elWP0AJgMzMY18ulXJZ1XbGyhuCyHQ2ZFczMoSYfEcb91XXZGOFocBe5HFsO/R3fjAbD7qNPigZ6WeGk+kkOmQlihOs0uTJpl6OPgx4R4mDpRebuvjWSlM0+9u4ubbVWwnxr8z2ExrCtI/0ZtUbNLGR3HLPRpCUI3lyuWl2CR2EGPUhk+nEEaTI7vTIw6nh2TVNrBbSua9dKERMcTkbsvSu8dcGVgnwUDTyaQBNeSezc6JudL56fM/NmeZqPQZwu6Mi27ut3HdOFjAi22uSKqBchbyAxXl/rMoBu04eQh36cdBrp/Vqbmj+8fqi1Nn/QKLJfLiV6azdvbMkhH3ZgaxtnTwd3G84HSSeiwWOiyOnfeSjoX//t338MATfQgCRMMuUO4d4AKL0meAIRqYYna4t8WWia/gt9uA2I9EpvkFkaicUufr95bZsHW6hXHzkpK7aQlNf5iFJWku61bnFYKr2itxDYTVLf6uvXr7b60Fpb7paXL19eXiyllHJZbClanE5HK4D3eo6goVTL1E1Yv1xWJ7b00gGSLGGsWpUjIkvlEMt5bJx19v/89jdD5HWApgOX8WPPkMzeBCn8u9tXKd4Xo2vaGey1xGQRETKTVDeXZFiwtfbQ7u83swWubauUSTL2d2KF3Tk6MsX8gw7Qck9LjbFtW71/kJrg67revVhfvHhR7grJcjEAXiSpqReFkrshlBV19dZPdBEAekm16pTAZhZ5+698/bZCvh5XSsOsqllkAYcEjw0VW0MChB+LUCel+BA4J4Mq4y92skSLPbK1PZ4IuhldjUZZdXdSjXDz5W5xh+RlWeTeNhlhZhHm4zCOzTgP4bo7HAUlwqGtbXvUvXkpLMvFVizLwpVciRVWrJVQAIJEWO8ko7a/nyaK0tBNVY66pklwLGVKkIbGecbk46EhkNnD3cKoZN2fF4iZ/rANTdDSy3OmkZrl/nQv/MhhGIeEs9pnkaHQgaYaUfFeW9ik3vB2vj8u7jXAa+X01VPe26xXybtXGdY7u1wuWLFc1tBGw7oDIys0mlVM/p5MH9hStME6ADrqfVJ177uMR6/+4Xe+5aNXfeyq9GBD+P37e0A4cgzdqNAhVJ5Wvw8lUYv8gtOoWkQcPXOyoGHbNlXS2SrqthVb2+beC1Li5RXKzDHqM7qkAmDrUkpxr+6+3hUWrOvKFcuyRAlI9W2AEhN5GKHTDvohUZ0HtQfga7//R+8E9OvxeD+lxVR7yTQaGC+AIdf1YOMieQY9iHQ0Z3fQJ+u7P+LIAUjNGswCJJL6wQpar3SCsNoS3pzgRmq4hV3rwAhYNNzsqYhIiK5NzRZb7mxdV+sHER9aaxNzR3I5x+xkjAKfw2WPdDF8p/EoGqYYQWfJHqyBdXtHkkC0PQI8oRkfjsbuHlc58UfeUtzYK50IgxAHK+LgQIWtpVZHxVIgL60191aKoUx+xV28dHGUxZuRRhDFymVdN9/KpbDQ2TuKgYhyMXQ+mCXV+yGRCK6N39WjdT0+9smx8Dgavva73dT90fe/EwRnLK21VtsIN5qZxXEJ8pD9v4YvToGQZMXy5gUxjAQKl/B4RTdxLfagBheXUgrRhrXmsQzZAjPKKYVKR9RmwxqXYoLIrfWUsxUifLPhbc/CHyyjHALgXiwzXo4x1vzxYZ/GW7xGowBuYX7TYL12sT9+vlttQnBCv0N5sMiu7sgZCnb3WVqpEQ0kSRRhHJ6gIltiiC4auLMS4QyKi3qMfeHSWlts6eZN89Ir0iWLoDedFYUsqFvthwFYEFGgMUCwV0YdylhKKTfljz+BTHpcRV+Pv/7+twFQ0cPJDeGjltaa6l4VcNINSoeu5lQj9NijbNGIAckYW8plmlUYqPXUYL1bBP3QzpKRPSnD3dH8crlIvL+/j3NzMgUR9A5ZYdqVvvggi1B1KLtQihelR+BF6ork337jTz4p+Md4hzfkRm1W2yqnOwYhXsE6CF/DVZ4m6dQQ07Kcf41T2t0nmrXMJEZWclebXT/t03am6ZrrRhebiBHZ0mu2S6F73TbnUsqy3FLIe9i4i9n+FJEjDAPEXVdFdJ90vAMa+mGNZhPQ7h5x9RbcPFLWvJKe1wocA6yeaqHj4tYatMcAJivkr/MRRsJTW0/SEguiWGsVqYG7u9tYYVwfHIArNJAegdr59l7rJuyhzv5JxhOg9Uff/k639wUAtdZyPEuMIxrm1+hgMe1xDDJ3IN5j5/MwhNRr5YZRy1GHYKNDOFJnTwARF2EqNqXLzBp0ebbGNRMN8WLXhvRWG6CfzAhWHAenSML4xlDdu453e233zXGi02n7X1sREyW9z3d6EauG82FmcqcJ3NN2wB46Bvd+h0hybyKy32XC6CUmea2VLGi969iUjUKLpcxKnonI/iadPEYpxicH2mk8ARp2c2gUQz62zLR5SAqPbHKDmUXRemhOB9DfGbpXMXU5NmKOJ/YKBAfdhmzZAisjLd0jLktXHvPGrgSSWcFRa4QSZXuRmHp6BMR4AjTsUgJSzwh6Vn+TbDMJh8c7b48flYJLM94ep6NMu41USumJPKnWulxKT/OwpzNba7QSNpgkmNlS2lb7ebKjJ19KaZ2HDACLeWuKY0VAP/ZA0vhYCvOTj6fghkUjFBiVqQdFHfxRaAKcjk7UGInCQ04qwlaLmR8PNkkho0RyMXNvvTYCKuuhsHOavK7NCiWP/rCSWMBeQ6dIMYnOZGU5947tJCs2M6N9zGjdO40nQAO7AUcGBDjyUIcLfJaqnUygzCJbawCWZbFT2m7YrOUYP89WUxZQmJVRya+c7BiREgxzAEA03ptsHWNWQ3/t6580cvfG8TRCKT5EHLKh74pdLgOjpYnGu98zB7QE6HmOO5NehrJIpR+BnfOmyXuNocNUxboLQiLOZEZzNqBE4Sg5sgV7QcKnPZ4ADfv7CQyQCqPZxIi3jNwR2L2tMICQEt1zq5aac8TY7ZaDeYNs1fgAK6MGZlT/h9YdAbhuEHHc2CvmbTgQQCDGLM4rTnPss0DDUz7jJ//t+5LUPMrw4rBi1O/0g1zjOOZhBdPIGbV7swtcZposi+aNeZ6cWZrT6nhSiOOEkqTf+IM/Piz+L74bhkMbrSi+8ju/94TA+ejxFNwwRic+k4uO/pLuaOUaDcssEdeeK2a/k8XmSaL+8tnUOT3AOs2nrJbjsyNM2aHbUwgkS7AmsVy19hysg5GJuv2S8k9tPCUaegs+knREzJnGXg0GpFpojgt17LCIEZvrcmm8OkxSP8aculbu8mpv3ZUKczjq+AHM60nj7TddfuXrT+wYv9N4Wm7oVAmAZmVUNZtMTnfv3fBcs3c5LXqZ9NrvYKCH9lBG3w9yHFawXf1OLT3mCMe4tdY06nYPNnFfYPQhlka7g1+c8ZRo0AiNKfUv7pLaROhwZu8q1DFp3Mzy20xOFmf+fUinRlJxTmkohokwnmytY27qF2Q8PVH8zV9+H75D2YfdMkXKjLVhOLGn+oHIEyABDskwzRo4Lmg4+OddLCYPIP70Warcdx1PqhsAdI+3q7yd6JJw4DheZ0lzjksGrIvN0NOQYFONnrWuJWK3aJmXE3nkiE394o6nRwPgZNHjlmUGIoZLseveSBw9OvUwq8Ki2bvy7mqAI8I6lLT0KTD9046nRwN7Flfi1fHfN43dzgEwyFxpnPhgyp89+CN99XO1eT7eeHo0+DjfiqicH7gI55fZjuw3nIv1c9CJ6X0JvRIQwCFHRkMj1X33X3S6vz0+i1X/5AffzaZRVpuYwD1aQTgS/smy2oN04xeSX/vUotCfwfg0dMONkW2e/OFdZ+i3Y3eSP95sv2jjM0IDki0fI9ud84KThZr/pJ4C6q8AzFi56RX/8xr/D5wwIeNsabrkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=130x130 at 0x1300015B5C0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsesajl9Bkjk",
        "outputId": "27eefb79-06eb-46b6-e11e-e987c00e3fb7"
      },
      "source": [
        "type(my_image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PIL.Image.Image"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUMLz8MaBkjl"
      },
      "source": [
        "my_image = image.img_to_array(my_image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EorVUizWBkjr",
        "outputId": "31b276e9-b996-4654-c450-1274010bb143"
      },
      "source": [
        "type(my_image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McYRVQtaBkjs",
        "outputId": "094a04e3-7403-489c-c4a0-f7958dd3c9fb"
      },
      "source": [
        "my_image.shape #it should be (1,130,130,3) we have to expand the array"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(130, 130, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTSvfNVoBkjt"
      },
      "source": [
        "my_image = np.expand_dims(my_image, axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9fEsz8NBkjt",
        "outputId": "fb73832a-8f72-4d79-9661-fed9b76dd3be"
      },
      "source": [
        "my_image.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 130, 130, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "leF2dBD5Bkju",
        "outputId": "c6082eca-ba69-4d3d-cc99-c658e1c379b7"
      },
      "source": [
        "model.predict(my_image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qquTVYzfBkjv",
        "outputId": "a719627f-58c8-48cf-8b6a-d5de8d7bf45b"
      },
      "source": [
        "train_image_gen.class_indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'parasitized': 0, 'uninfected': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fZgrUl4Bkjv",
        "outputId": "ce99c343-9c73-461e-f451-581e2652cbf6"
      },
      "source": [
        "test_image_gen.class_indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'parasitized': 0, 'uninfected': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jn70_P45Bkjx"
      },
      "source": [
        "# Great Job!"
      ]
    }
  ]
}