{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dkv8XgIRo5MB", "outputId": "5a5bf9a5-55ba-4d94-fd9a-755d4562e7bc" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/gdrive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/gdrive', force_remount=True)\n", "\n", "import sys\n", "sys.path.append('/content/gdrive/My Drive')\n", "\n", "import os\n", "os.chdir('/content/gdrive/My Drive') " ] }, { "cell_type": "markdown", "metadata": { "id": "9tDnbB-hHAIY" }, "source": [ "Source code berikut berfungsi untuk menghubungkan antara google colab dengan gdrive akun milik Anda. Diharapkan di gdrive anda sudah terdapat folder Dataset. Source code berikut juga mengubah working path anda ke path \"/content/gdrive/My Drive\", karena google colab ini menggunakan linux sebagai os-nya maka command-command linux dapat digunakan disini." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lPPa701nGvo_" }, "outputs": [], "source": [ "def list_files(startpath):\n", " for root, dirs, files in os.walk(startpath):\n", " level = root.replace(startpath, '').count(os.sep)\n", " indent = ' ' * 4 * (level)\n", " print('{}{}/'.format(indent, os.path.basename(root)))\n", " subindent = ' ' * 4 * (level + 1)\n", " for f in files:\n", " print('{}{}'.format(subindent, f))" ] }, { "cell_type": "markdown", "metadata": { "id": "xGvBC2CWJQmR" }, "source": [ "function yang akan digunakan untuk mengecek path tree dari sebuah folder.\n", "Source: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0Rfi9yMiGySO", "outputId": "381e7fe3-4b8f-4840-fdca-c64e2dbbe267" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset/\n", " Tenebrio/\n", " Tenebrio1.jpg\n", " Tenebrio2.jpg\n", " Tenebrio3.jpg\n", " Tenebrio4.jpg\n", " Tenebrio5.jpg\n", " Tenebrio6.jpg\n", " Tenebrio7.jpg\n", " Tenebrio8.jpg\n", " Tenebrio9.jpg\n", " Tenebrio10.jpg\n", " Tenebrio11.jpg\n", " Tenebrio12.jpg\n", " Tenebrio13.jpg\n", " Tenebrio14.jpg\n", " Tenebrio15.jpg\n", " Tenebrio16.jpg\n", " Tenebrio17.jpg\n", " Tenebrio18.jpg\n", " Tenebrio19.jpg\n", " Tenebrio20.jpg\n", " Tenebrio21.jpg\n", " Tenebrio22.jpg\n", " Tenebrio23.jpg\n", " Tenebrio24.jpg\n", " Tenebrio25.jpg\n", " Tenebrio26.jpg\n", " Tenebrio27.jpg\n", " Tenebrio28.jpg\n", " Tenebrio29.jpg\n", " Tenebrio30.jpg\n", " Tenebrio31.jpg\n", " Tenebrio32.jpg\n", " Tenebrio33.jpg\n", " Tenebrio34.jpg\n", " Tenebrio35.jpg\n", " Tenebrio36.jpg\n", " Tenebrio37.jpg\n", " Tenebrio38.jpg\n", " Tenebrio39.jpg\n", " Tenebrio40.jpg\n", " Tenebrio41.jpg\n", " 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Tenebrio270.jpg\n", " Tenebrio271.jpg\n", " Tenebrio272.jpg\n", " Tenebrio273.jpg\n", " Tenebrio274.jpg\n", " Tenebrio275.jpg\n", " Tenebrio276.jpg\n", " Tenebrio277.jpg\n", " Tenebrio278.jpg\n", " Tenebrio279.jpg\n", " Tenebrio280.jpg\n", " Tenebrio281.jpg\n", " Tenebrio282.jpg\n", " Tenebrio283.jpg\n", " Tenebrio284.jpg\n", " Tenebrio285.jpg\n", " Tenebrio286.jpg\n", " Tenebrio287.jpg\n", " Tenebrio288.jpg\n", " Tenebrio289.jpg\n", " Tenebrio290.jpg\n", " Tenebrio291.jpg\n", " Tenebrio292.jpg\n", " Tenebrio293.jpg\n", " Tenebrio294.jpg\n", " Tenebrio295.jpg\n", " Tenebrio296.jpg\n", " Tenebrio297.jpg\n", " Tenebrio298.jpg\n", " Tenebrio299.jpg\n", " Tenebrio300.jpg\n", " Tenebrio301.jpg\n", " Tenebrio302.jpg\n", " Tenebrio303.jpg\n", " Tenebrio304.jpg\n", " Tenebrio305.jpg\n", " Tenebrio306.jpg\n", " Tenebrio307.jpg\n", " Tenebrio308.jpg\n", " Tenebrio309.jpg\n", " Tenebrio310.jpg\n", " Tenebrio311.jpg\n", " Tenebrio312.jpg\n", " Tenebrio313.jpg\n", " Tenebrio314.jpg\n", " 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Zophobas270.jpg\n", " Zophobas271.jpg\n", " Zophobas272.jpg\n", " Zophobas273.jpg\n", " Zophobas274.jpg\n", " Zophobas275.jpg\n", " Zophobas276.jpg\n", " Zophobas277.jpg\n", " Zophobas278.jpg\n", " Zophobas279.jpg\n", " Zophobas280.jpg\n", " Zophobas281.jpg\n", " Zophobas282.jpg\n", " Zophobas283.jpg\n", " Zophobas284.jpg\n", " Zophobas285.jpg\n", " Zophobas286.jpg\n", " Zophobas287.jpg\n", " Zophobas288.jpg\n", " Zophobas289.jpg\n", " Zophobas290.jpg\n", " Zophobas291.jpg\n", " Zophobas292.jpg\n", " Zophobas293.jpg\n", " Zophobas294.jpg\n", " Zophobas295.jpg\n", " Zophobas296.jpg\n", " Zophobas297.jpg\n", " Zophobas298.jpg\n", " Zophobas299.jpg\n", " Zophobas300.jpg\n", " Zophobas301.jpg\n", " Zophobas302.jpg\n", " Zophobas303.jpg\n", " Zophobas304.jpg\n", " Zophobas305.jpg\n", " Zophobas306.jpg\n", " Zophobas307.jpg\n", " Zophobas308.jpg\n", " Zophobas309.jpg\n", " Zophobas310.jpg\n", " Zophobas311.jpg\n", " Zophobas312.jpg\n", " Zophobas313.jpg\n", " Zophobas314.jpg\n", " Zophobas315.jpg\n", " Zophobas316.jpg\n", " Zophobas317.jpg\n", " Zophobas318.jpg\n", " Zophobas319.jpg\n", " Zophobas320.jpg\n" ] } ], "source": [ "from imutils import paths\n", "\n", "imgpths = paths.list_images('Dataset')\n", "list_files('Dataset')" ] }, { "cell_type": "markdown", "metadata": { "id": "LBwdJESJJv6D" }, "source": [ "Simpan nama folder dari satu dir yang sama dengan working path. Contoh pada source code di atas, daataset disimpan pada folder Dataset. *imgpths* menyimpan nama folder, sedangkan untuk code berikutnya berfungsi menggunakan function *list_files* dari code sebelumnya dengan input nama folder letak disimpannya dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "GoXTCLPyzZ06", "outputId": "d55e78aa-7162-4807-ac7d-bea5d0358d28" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset\n", "Tenebrio\n" ] }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Zophobas\n" ] }, { "output_type": "display_data", "data": { "image/png": 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jopDwsmChdgnA1TyCYeRuh2yczQCVoHRHP0nYNJ58LL+nEKgW8sgrzQYYijc4laJcPSJmibZi4YDJmQQLJCx7fDgctkjKuCSKUYEWcghn4XMaqRxy2Dsly1+Eq86pyeCUkYbZWM2kWmaPAUHIOiksqYAxLcI97UwE6MqMTJWJyLquMQTjLUSFByCtRBAJrtTd6c1C5wgMiwM00H15AQ+ZiECszfa6HTbQDwrc6owewgikFzT37zxAwpzsqzudEke/JTU45Uwj5GlSuKHlXYnQyEghbdkdwm4h0D0AmO0UqfUZSoARThhQ4tBlDaG3YbveL/D1MpMJc1jch536sG2bQT8VAdG2bWmpNRCz+S/3Ynl9QMOcS2EadsYhnQHAErAyHGvlBMiD5IApLky+TDiKvOfJ7bsbHm9JI66Ilbscn4kX9+7dzalNn9wVKkCsjqTAbYQpw4NtVrd0C0iJeFk3LZmgnQ3slDChn/AHNY8JoMPLsEOUJoeZU1+TqXGncboezE8XcVXNZI2l00w0DZiXLWm5fsvFhElCziXvTMlwOixLJmC3bctG2lPUrI5ZVqTKATlIEVEiXhbetkuTLemTArACRLFyQoi8ZHgUuCP1ZQdEpogCmTXRNosbKh72pMxYhP35iIgSlJgztEnfaDA5LZIgFwqJ0rZHQMYLuw6SIQ4dyMrMy1LiHsYpmR3BbKeyzyhyPBouTGP8lMKX7XTHGvKXHPQYpXvPMr1EdkMYzlzsciIba7hiJlLotm3L4tkiGZePUs78/hiTDSU5osyAWibMBsDjSigzmxR6gzZYCyNM2iAWslqDJfeWebexMjGMDURQX9C1jwsQp99N5KEWD0b4U3oZyagCbGjJOiMHMwJaWVhe4IiZoAwD9lFSoSBfz7CGiTVB2ihPXVk1gnk0zdnzYDIkNXd3XhSORkMIMH1MyERkCKLdd6f8uThaUoDK82bUIqF1lhAW8eVHheddxfI4qlDdbAY9/Zttwn0Li61VZcp0YRXtxinNdtMHJEFScFPEtRhd2pWzdovFFFzz8RF0lHqPDYlBsZTguptykDyijMyibsF9GOedmU2NjEMzITkZtw0gDlvvPqkJTj5ik+nrjymkZsWIICK2pJ1ka/hJu8QHQmfAwgNN5VKVhMk5WTMJQROk4tkySBK6fygcP1CoOc15jMRmwQSxhJOIpPNvul0RW0RBLrUaH8QELGHFxELSswwdGCAW6e3RsNyDvnXh6x01z6irp9rVIlgKr1dZO3/Yvcqi0MBbYck9BQ+wMz7wPLyKa4TtFEAHTcly6LYWrvEHiMtSGgOZuzVNYe0zRwwusy/5iFdVFD4bcElQk6MNCjrWbbywrwPBk9do4aJZOGTFgaOAwfftHWJ3IuQwKMnrwB6cdnLItXbp6UarG5scPOCeq4TSZEdz0XpL5xlwVBmk7BA+sDGFa/WenXqRZFjDKwWrYsml+wvLGwDQWOenWBxEuCu7eeHFk477bE1KVcaxoXYNqViYyZ5RMwkLZI3GGLREQLbce9RC37SMckhEWsnAEppchY5/CjMRiaZrs+W8Rl0NSQzFXXK9bTBvodYpTK39ciJ7ETSUAcMe4Yny17wZo56MQqDpl/qoVNNMONhKXJVU1QihNgU0r6NpjotJ94ar01Q8BzPlQmOglGam/ra1QKainVGWAhKZsLcGk7FGJyYmWszX5RCZ2UEV+TKVuTMmErvBCV3aOQlNTRREAf7KU3tic2lyMFqUMekVzKCKS1TYnQ9gDXaRddVftk1Sj7ug9NZszYeZt02JPH26bVuDLy2oVI1kvgp1p1njJ0eovjqpXghMsm2Rzu+iBltWimc560G6fnYKB9+HOLo1XCxYU9cn2R/4ZIPLrDDAnrB2uxe32d/o1V1pLZv9i4VIK7sCqWxuGAtfu6ha4MumKHAv7jCwUXa0fB79KqAiiWay90kiZ6FvgmKXUiA0ElTxvdQ070nrmyo3xVkxZtdTKkmmpoQdgAMBgaFQMWYZ1hZLduYM2lw0e3QYZ9UDKdxeEBFgrmqEa8rJshI+9iRan/IIId0CrilSzjlY9AeKNJWb9FgXthHG81RzIaLFA1GTpynlPQlrmEBJlM2tdsny0ASGAkyG3haQAhJamPLR4vzEtiGmAa32al2+kogAsQXa3SDD7hKw2NzNjAU/Mo1ERP1PF2PsXHajBnnBj4pWC13Ie2ALUmyRtkgXYkFGh9IhVuEY7DozReUkYpUW5IXy5v4m49fNYdJTszI/fu0KE/ZMAVpTvGMUVs9pVsMHaxQfAbIm4ewegXJJlflsifLfSrt7a0HC3papryB7JhNoAKpC8EjCvFiSoLMqZlXSQ6Mn6jyuwWstxs6iP0EN/7Uesla1lkDUWda0KW7Tpo0axk9VSWoKFvdJdg7AczHmgchRBgYuNg/b4QqwLKSKjBOd/UEL2TQXFfPXbgKSPmRhULgFFd10QyhnmuQkqlqhXxdPZ7kCAUkttd5sgI0/wYQrGbcVj3wqJuj0pb4Y3LjbvJhjdisE19BKoJa689PtFhoUmqRhko/S6bRvFgfmyl1rvAdrTeZsG1It5VKgpXQEXYitE5FuvEuz4R7AhIwBK6cp7pq3zDITNJjvFTbDIkGgs4igHPeOhEq1s7811oKoAfCFObYOwcv/YgloI1mWZdvEig1DqjjTvES0Dozvvjpgkzk1YFLcZFssGzMrlNjrtLqkmtKgJXZHGSjWA7QQK+liNtsWdqCJD5pm1LDLaDfniK7HOaM0F5PXS7Udl02aKJTjn2xesDn9vqZExc3VYeYSg5dbSqYtv3pRjGN2ZSIlVoVtIyJi3wXE5HvDCFnIpY7YurufyKUggsDKfZhIwRoAxKTT5QmgLtuqgDsyZNkM37GJKIOSVcmjA1Q7VppSLYdxKsSgUQcSYuddIZPkCM1pyc/dIMxyS0mnO80QAiJkRiOB5WC2Zi/AUfLVbwyZof5IEQxuLuqxGJyj3VgHpIaZug3ovWUQ2tx0rfZQw/LkKRvbZSVGCmbKSkzjgG1tsq0U3l4jg3qZWBAqZK4rgBcO1TIkiOwe51HOAajMkagQIJajtZLRTaNuFK0ivloIXA+1XTowNXbpbys5XA5LrVSh29tmGIwBkW8ZrAPCaKUyzSyk9CEMTwMCRJluzZ03OY380r8T0aCbDd9QDCj9UIpp2qqERdo0w27oISd8AbRZMcuKocCXZnm++RmJqkoCIJleCZRGtqybztzJ1bhut6kycEmaKIO8hEUIZP4q7PGgwBVFWc0zVGGFJ5Nk+IPpaW2uGrk05aydaz5+tOs54ADvcMaLiokXM5e3gI1CW0MjZElrQL5Pq0SnbS8uHo/2xPCUyS48tHbDBdwtVSH6QwUfJeCrmmjAtDNKwnMKTiwMY5vat0cGl+q3sjcOUMBht04OsQH3VsqrxZ6q6kgrag9dzs2nwDVeA75kFIJgpaZxcssAK/vzLE8FQcHy/DcVQcnruuKWBPqa/LmrorrxtHXSpUpr9UFhi9C+vMdWuT/5i7zgiAe7uiVvOiBJdhzWwRd2Rp75BACyBVpTQidPSFWzE2V78t/kd2yJcZ03c+cFylnz0KicUDcxH7URlvSrB3u9O7SOGZXgGWjfmTAQs5AlCExZ8y0xHKhtePclY80NxsFvikBSIE4tBYhCvMIAZ507IjpxUbJNOkEsJyDTtjlA1MjKuu6JLsyXSPaa9qQ6ZiCZvE53v/o0iUSVo8TRy5/N6O+Y2rSiCMfLEqkBjV7JN6KYEg+xFUAqsQ01XJ/VJHkxcRnBxPYNG1F9AMDhPocNIq/NUitvD1xvIKRjNd9DC7fwFcrlLBpCpyblFLPGuNVOCs4jh83Mtou/GSdBlcGAYFvyA26DiPz0hKSZ7asTicSBKFSJyUqhoOZdI+HIHOXqZZYpV71CuiJuiaVPAXxLGdxiMUF8Qd5K1rcsoUqyBB80RI2ZV3anYFtgTVRrYcc2vjXu1kRbVs3koyQwaaGhnZlKUo2iBujCC5HtK4+asDhdogvoJFuDUbGGEDVG3rsq4M1SjICpa2p5Twrn29pMdpQ7BrpUaR9eoQAYVO5jblGwFRe5myIPSFRcFMCEbes+t4JoAkRFHO/7MAUkmy1P0WZPwkG2ERxZDE+DBGiU3pPXzmj3I7EcbKvRzd+1qCRwc12ZbA0RrQnPXZlb9sGHEpFAuo+k57IwRVFUMAahz84Ap2kk682kMMhWc6wmn2O4HClQbQtNaJ/JavptxEYcJaIsx7O9NNq1wZRERudQhEtxCZFVtAqkbthsKF7okRNXt3kefLW2FIDGUrovTjvBLWVJLeIuydacqQdrqrZbJxcZPZbnKNlTRSxmgEEEiWN/0P1X2OMFLCIbAOZl2y7MbKnopI+ZNgkNlEalnJzkRrAmXtyTBeRbtoLi4yqQ5v4eZA27GVGmZYmSvYLVxgoiiiSCz1BVtyivds9dIlirlpNINdktS9BKuwCLtiCKZCF5GBI7qhPVWWYu+zLCcHxSfBHwC6Nh88F02gRpNUwdupZnBq2GReTAw6WKiJZlsX1B2QDRYsEy8xJkrJKe5L1RnplXIrb/McGq5P1u6mMlS8pw0ZkoShpBtKtK6uyITsOEa3GhY5U1TiKK/oMclizRrK0KKcnvzqoxH54jMJvAtKCSpQYm2PxWAqm+7Wc/jRAI7T9jBPVuonJpelyzo1Yk5jMjbnLSbPOu0+4+ulTF7hqrnki/GWw22QHU6zxN0tSNTwzJPlaIHK1qZj+6n1GFbYPoI5yJFqkUIIdEFhfZLhumOq0rJwK3BTm/pAkwp8SLL8yskIx7RFulQ4xwhW1TMxVpmN/K9zhqs/J72mf3YOSA0ZJeZfzVz/PRQKmmefYkqNbDUAm/YXBtDTuLcwbRc9Y66IcHUMQBh7qUMLOtbSG8MW3bZbtsx9MJg3+niX8TvRBmNeAvZqkiYqItNtHbxm5RtWPSiH1Lp8M9SWcnwTghgmUbNBY21M/UcLDbDgVpKmeDkYrOcistOJNpNZEuYbFPP+vDCloVDJ0gb3BNocjDlyjEh7DCSkfdp8NqmDyTFpd71rTI7dmBzKlafVY5IQpbYWYpJ0NEUPDCXMVSlrEcGJzXVWVdVt+h2UyIUw1A7LdTQGx7ZEOKcTNRBBn2YV6EtXc3fbEuJI6xS/oOGt8kq+fJULGMlWn4mR9EJMqxPy3ylSCOWNhCfSYISRBtHlL/k7q2AZ68lSq2Zi8JoYyM8kET3pQ21downcsDfbKtirqoEYUWbnEi2vBDQczmDj7IzscQrWoCV0db/KGBc/0KygCQAwEaoIxd1BHHUPtzcnxhGzx0aKTkPJIiRSnSs/avtiSoQQsCYhmO13U9mp1LCk7i1Uc4uMWd70b0qo0CWkKX6T1LIVshx2Z2XlSgKfQIm5gg3UqnOCfSU7bkR74INA5zM7RuyyFx8ASDZOfoTfOZhpJRbhWkwGy3XLAiZd93jqh4wZ75BQbDTauZNT+KKHBZYKmabgi5i0wWUY3Vp8Zjz+C3CJ/al0k0U8m6eDHRYV1TatXP19RI1PhEiXw3JzOzhSFUMJzZcDG1UWeIwBhPO5qsZn76wvaYgvHxpwhnUwt7eX5OmMniHs/AkGeeQD3Z5tTNEaLRG8EgSsiLMty+FJiYxLpRlNdudiAVptqZdOZ8PndnZSdMEdHCtW9lOhQoR7ySqp17QIiIRpPUuZEmhm45gcTDzdd0+UjK9v1u0lbQiPykl2h2t4Y4fqg8i/noVnnY7CVRFXupQxyNQ8Zy/LQsnHadyJAHd6W8YwCjQe3z7X92xahIkYkcTtlciBcW2bbtYnkEhPEAeR4i6izTE6UVd8Q5Fu2YMCJRQI4kwwZt+DoNK8XJF10fNGFQb71t95hLidRAUNkw632F46Fqydngp1HowkuEw6FY8AMnkY5pkqoctNryaubCjIvIpCnNwd3AQoy+b+Cu43QkrCnRpIi2VSNF4g2wP6EdN6oysKWgdHGJmQ07IrV92I8c6iv3ebyRi3OQ1vQqf/XTAAjw0NL+P71pm3KwDaq4XLYMhxLXmxtUVaHak2My7btJawFAEz8F2ccuAYIlxpfTkVRhuYGEX1ynBMyAL6dviQ/ynYMlWy7/psqg4mhCB0JkWkKPsgNKjxvylp5LLcFnyxdj8oNig0Nvpze491AcAOtZnzyBI/FpdpiAJlKOWZaIPqQ7mx2u+/r/8GwyyuvKXRM4D8NRtUNftxSPQV6pYm9r0I/3BP7hp//w/e/+/QsvvfjHf/ynQS5F32Bd/jQ5Rto2xxZx2lHT4sGchMoi3bGIqFJaNSKyU4B7UWST0dKpqCBFd3iESJ6FwxrBbDjyKu8IH9T7MMPRXTgxI1KCQ4S/41yt2QFa+ViTBVgY7qegNJ/YPVQQXaM+syuCxoKuK303DGFr7oiFqVWZUoIDEBFEJKo1XRua2Yo1k8Hdg4n9LPfeBROKZYVjSOmb3/72X//lv7u9efrKa69+5Q++en3//t4jJ7NiqHEefuPgsiyXyyWz38RgtYUoFt22bVvWVQWxub5VN0T4NR2Xnz3mGGIR2sAvqde6ezY9V8rLx3W5MaulzfYka5vGc9XwpZOlutOkKmLIZk7jim/cMOETR4Dma+2WNKhxP4VIJZyzG7r5GWz4pMoUn5m7QYes+MtfFXbGn2WTtfG7Zd8GW6KIOkzP2qgysSAcGUVDqgCWdf2HH//D1//9X59vL8Tr06e3jx8/fvDw4XgeTi2C9dKG7TLbdPXDfxBLMWTiBVfaAgBu7K3cz7BagDNHandlueyzBiMiS0mWc/T7EdalMy/YFQzTlucuQEBMTLwYtrPRR1H+gJaoNZu+ymUrBCvG7c/6YXIhrE2kCv6rluxS5K8jBrAyiiFc2BtmiU2t/WCMNGPamrMqHUQG8snTT07HKyNAmh87Qt3RGLO9D0I1AnRtYMSEUMoaffjee//5r79+e3NjezYv2+XJ06fTaPsUUvpN7cFQEbRtW8S+ZTf3LQZc48PxGAId9CQiTdEJMcm2LNvnKbGSsNV3iwB2clDCeLjl0MxtNpEi+MatIscUgQNedOBPRlBKI34q0qASTb33+C2EL3yZAyinJmJ/S7UdySIKS6EhdilAGhaxtE0bdSZd6lxMyYvRwIEOAMqwPPRdAU+Q1kmZ2qQBIAPdtSsZakd3LMsisv3sRz+4PPnkhftXT24vt+dtJbKz3RKGXy6XRKjdIarqsiyBkMJXMqmIghSbn50bq8hQc2fw/RPaqaDYohF4qWpuVMwvGS6sBBBxmnH3O5QMSFPi0sPMkXhReIWrL2cg3EFXazdqsULH05Gpo2wlrdMSxBgc0EgZoXR5neWpJflT+USu3D1ygdLELo9e6XKTnmgvWO6hYhux5Qz8N8LpdJUDoDzJg0gr4U7BtyKso5Osp1NYSv7RRx/+4mc/u719KorTuhyWhYDb29v++JzEDpnz+EYhICUlYt02qKmdveDEM009ZZBWhznfeyJwO2eLOaTN5HcNtPwFEa2BfAuoqoCYiFOrPFlKi2MdU+owS95iCVNnAkRpQaT7Om9SpRK90d27sSlnK3EyoI0qz0dgXrLbXnkStyFinBTgujOlXFVb8Eg5qWkdY/KekyD6U2ZzHHs5lCpnZxUWbcfAoGb9gFBWVfne33/vl796Z7tsIkK8XZ0Ox8MhCyJaCqC+5FCNt5vaydbNS/pigJ0qH5NINEQASP3lUErs+1pbyq3Q2PRJZq1QO0kmzoPkeDGK902uYcwgizGUugq2kM0uVuBO4SfbKX4TlEnZ0vBbKRDRWN1skDO8sY2Q42YX+DyEokskxSovAjAkRzstUta79CSrRjNccUZeLFgDiqiZYdtRnWe2eLwQ5XFng8aH/YBNj3l566c///53vv/0vJn/oU1vztv1g8ODBw8Se6RI6WzOgdglTQuriG6CRmTYhgz1LdEUGwPNd5GFrFZhYPWYIUiJBntKbFI8P+eHfDVAmWlZiTlFgiKvEXvWkhDqGU4r7jEKNYtCIEbbjD8JeFKB4zCSpIcXFPEy0Sj+1Lb3iGPpg3taQd22LfAkblQ++Uso0uv6ALZtO5/P2Vf+1CVvHAPy2fyp5URgQTmReXO3/xSgY4K/yRtqekNE2/n83W9959GjR45/oAIVBS3Lvet72emdAxu01xrM4D14Fw4pSW+pVLgragVRnvEpFmGa+8RisjdTWNfMAFG8hTCuxvsnTNZYfdWJI4fdwMowmcLJY98I1DMsLHaq+HOe281MYTrrFL8kWiD3ml6QW2IpQ9Xr0Txr1Q0nM6/rOnCiCWhK2J2QK2/oayyoI4qqJbYzadUBVG+/6zo1W/jzX7z1j2/9IkBcqCUTMa/r2scztTDJK9IMNyKrl1pEOGGgOb255eipve9OlAAsrCLYyqh3giQd1JZ0DKpneikcnUusTs+3Yfn7ARulx7lpf7AY2VXK5CD+JjspsZoqHetEz8cDGOXN9YlHmAZjSZGOrwwQMijpghBs61q4/7UTt3M6VjbIpcLTVZQi4uKoiracavN2rLVd/ulnPzmwvnB9vLlsn9yct03NcqzreljX6EXG+GPIlPZxGmcZtKliYd02lTr2zPiiNMiwdSGblNC1/IDokHzvsyiLxZGBJCJHnw6vhMiLYt1bN2K6boXpzkb9u5fHzjClmwod86sYxR9IYyvjT4n6B70ZRWropcvWb/dECM3rVNu32RlZPJAhJQ2SRKRI4YMLmRMzK0kqCwFm/vDdd37z63+Cno8HXB0Px8P68dPbpzfndeHlcDgcjg6xdyC6G1TPkWZVjAhiew9oGJwJgfrSUcGmBoSICJsDf3P1M8Lp2rgiZIXy+ILsyUUOhtytJa0Rx7FHRN2Aw9/LljIxWPiJ8SbHbiyT4UWibnimmNG6sxP0J5GdPWP3FKpi9eN2w3CAWLNGo1EcfMr59uZ4uuqLThhU1om7LIu/stSvx7YJ9kXD0bdSvakDtF0u3/n23//q1+9ebm+JsKzrcjg8uHdcCCvTuh7sOH6r+bSR2gvXaIxCgsJBfPZUZZ5dSD35TJhmSpQ78XO5KnjTShd7jyZ/gjjRr3gWmkTxLkx4GDUkqFL8MvVQnLD0Sy5XjwFUGQkAsbbQTUgwNeqYWwbc2GakH1mira5CgQVU76sdZZr6oTVJkf1CTdfFbuRVVVV6Qi1dT9zgw5BahifFRrYxzGuxYqUuE2AVcdFC9NMf/uS73/77J0/P3uzlwme5Oh2v751AfHU6LetC5OaK4ij5LhZtwM9Il4CWZdFt8+kHNuhZ++JU6IsxQVtHE60QJ7Os6HtdTELtkFNb6XVJBOJVzRP1bYhNXFTRTjAKQebQlSY69XFppgW1Ztzry7iGHed0BAzHDt3bYOYXcE7/dvPZTRTtYDh266Sne1fQ4Uq2M50kq1bAyYhUJOAJ+lh0i5c1dgT75MmTv/vmtz75+JM6zIhwuchTnB+sy3o83n/wYFlXrSNDh9OXxgGkhx3VCb4uEicUaU+0m2KTLSt55sm8k311mhjd7sQMaudjpUYGjeydkgSNpRga5AmBBO1nrrqo8Ixocwg7R61vg2Wc1iP+0XYYRuuxSBMXy+mkJYsY1A17+sc0RWlgOpX7d4qIkqqYbJx15DgUozmMCXM7fCAkCfuO0M4r66Jgu5x/8sMf/fIff7n5UVhI0l0umyoO6/rKq684RWAm3G+ZbGc2HvK31a9GMgnZ6m5OYfArzJKfHEaV26y+kqq9+s96XMdoSAEyVwYD30S5WzVrZTulU8+0pxCjrM/6uFzOImILHWmi/N8gtMvlsCZDcOg1yISlvfLJdkO68TJOplL+1tbRgNtniZdWYFS4Ow0bweg8N2L+rAdTrUEvS4nX23uFQJfaYIwuy/Lhe+/+/be+9eTxExGh0LNE6Aocr65ef/31mLuvh3abtLNYpd5pUL13AhEtRFvKr+8OarHiaKUqCTwpzThx5OG2KTFEltHyxgrBMXXpIbK9kDWfitiJUG+DtdLvZeFlLIahVgzuqkDlJU3v3ZsACFGklGPyZLp2SdJ2+EL6d5OqlPW90eoUT3szkSlIGZZipGa6uaGFyE8rCeCrs5GCH2MFszoKJvrFz372/rvvW8OS3sfuYVbi6+ce3ru+ZyKrQ8Av7KcvG8K7AwNRe/um9ZsLKwg05jxCGBQA8cpcbaSYPGBSKqvLhlf3MvtbRgh+TovGqlIbIojcF7cQnsAcfDYiM+CWb1krqeFiUcfR2jQGS0bRQcpQuDmK3eE6SU/KQdeQlPgY4mDJunCY0KvvlqnltpSSURx9PCmOXNtWNX2iHfsBIAMvP8UFVM1pxYfM/MF77/3gez/YLrdXx0VWvj1fRLHJZjxgXnldX3ntU+u6ZgaB4hwR2XL3GCK6RCpeSsA0cTcb6uLtu2lM9OFZeNvHhCgd0x6UeKhCeR5Mkn1F7ikM+51IyYJBHbkFR16ECPqAMm5gpvRTPrkBwKWaxnkV5mQZzcGENktu6ohDCrSbMd675vHj02ZCmtDup3qKyck/I9NOQfZXM/j3jFU7kJBtu2yXw+HU+deNEzWbR8jXPCszP3700df/3b//5c/fevr0KRGuTofr072b2+3R06eqWJflcDoer64+9alPpd9K+8K8HI7U4lBW3cIKEN0VkUCU1F8xm7U9BII7bnIaNUuFOKFmhyh8TbqsPrCq856J6wAFtx72B5fPCkohN/YUC/3PFDhuIMpNVNmWFqAlGOTWZ1AnrYx7y8Bhu9qvEbI4NTWW8Q1PaOX0iPx8poISQawOOgcKEkgXVY2GIiEUj9sANtkOA5jb1USgSrrjBt7Ot3/zn/7jj773g5ubGwEg+vjp7f17uHd1uL2sT24vInJ1Or708ssvv/wS1INi+/hLXBQAMkLM4LpPITQ9A0ML0eyNJqTbFmEiWV2GxhmN6TR3yu+sR8v62mL16pzg4krLWBAaEmqci+TraMwUUMhCCxEpU89vda7Hv73bkjYQ50uXKVeZ4r3ltikD7dMtFrVMWyOuW9mMHEfdpY8/eXQ8nY6HYzVI+VbRSlszkegG9cicEctnDcwu63q1EFRip0K+2Tb4ER5GRFRlWVaAodsPv/fd737r20+ePpGo9hORx0/PC/H9e4eLyLou62F983Nvnk6nfHNYMDjfjNnUZoxFMOiJEkH9BHUvCyHYuVRaBpV94XhjsYWPlLPeGvWCRw1BVJ3eCF+oOWWoWxHA86QpeQmayNM2EdfpWE6D3qbXlITBa7LXEEyOMy3mXoym70VuAGnu3MwhjUQJn0Ihy7ou03lJVDJtHjAFwlsmP09ANV6D2ot3vby2wJnmNqAQM9ku59unV9cPiOitn//8P/z7r3/44aOekTKU/eTm5v711XPXR+X16vr+Zz772c5Lm6/6DjD7UxOYjMLUVJpZZbPN0chCHVWRi6pG3TbboEXUA1nybFwzW0WtId4EiGh1VgaliQgMZnIYC9AoE1SL9uXInKBQirdITFaK9kx1zxkaRt1FRmsupU2Ax2YnD1hfVGOrrVdHGcl5UmUoQNf37hEztMy73VGGjZgQJzigQEZAV3+RcxTNDYnTDDIwmhBeltPVPSJ6/OiDb3/jP3/4/vuXy6bwCls/tVqxbXJ7Pt+7vlI+fvqNN557/rk+9+TayGhtln74tUjkNNHYHJHJdM3v9oO2ByNAweTfJ/RmSfyVnKvT5HMrWBc7d04Yrlk/IIi9AzQvxikXc74e4TcxYqM+eao3avnlPOOq39+XmDSyNTq+Tiy/22qKZ5qD2lkzDfa3QKUsJrAjEoC3rV4YNpQSQH3jQ9vE0ijp5k/85Rp2kpuqgpf15ubpt77xjXf/6a3rA+u2PLndLLa3xO7KJJtul+14PPHp/he/9M+W9spTq8u7wyA1JqZwE5UfsIk53LTrAID1cBCRbdvQBIbGBJg16NmuOmsp3SIUZJs3VpMFpgXRq5kJ+AFPTBSp/NDmoBZ1EbZDS7XhqkHyxhqV5Nhkijp1uqhRS6bSvHpYt+2db+7xdn/Ei4okhgaweDWikB2XKhIm3KihUH97z/n2lhde13XeWliN11Etjdb+phxnrVsHWpbl6dMnX//Lv/z+N//29vHjZeHnrq/W5fbJ00uS0fpdlhXL8vqbn/n065/OSvZt2zLc7oI+USxMqo1to1wBY8ImgB3h7hezEtUwHID+QuEQDBAvZG/1KcSVAg0rYyZzha30ysxXrsBQOJIUTGSxSrgwaiFbBnY5vbtdGNpKy/S50yBNv2ad6rMebKJm24yxbSoiy7rGNntPeESJLBEBHLlmrehE/egHPRwPXVwQb+lV1fP5vCxL5vebkXAeG+Pz0EZivpxv//Y//NUPvvV3T588vr29ENF9ohefu16Xm0ef3Pi7RqBEtJxOtBy/9OXfy/TV9EkkZ//ajh0iMC/56iWNrOZo0gYnSuPKTLd2RHGIDQUs00JBY+DJ0A3w8/XSt6kqlYB6zFY1gBT7alPUQgt7ZXAK0CxSaE7wcrnklclWpe1JLvIu5YHwWdODiDWAYLDnYg7H9ereiUhBSgYiCYijFAeU0EKfzMsiz75vE6E4x+dwOGSS4nxzW+MJWmR5NPm76eW73/ybH/7d352fPIYoL6yKJ09uz+fL/XvHZSFRK9Kk5XBYr64+/dk3X3n1VbSEsA3GCdLTcYC9gRzQOKfU++2RRFC4CqUVs7exO5POHeSUBASj2xcP2rg8xSS+dUbQzFFQvb8e9lbcEYcVRhwg8CBlx+OR22dv0vrFFLW86PrUFqf7p3ig7tBsKkZocq3QbGxpDDPD44X0fkeZw2StzWtd18PhMA2sz8LDABQ7VeX73/7WN/7q6x9/9Eg2AbAwLUwi+sknT0Xk/r0jbPvowtf3r+8/9/wffPUrh3UdQNvkF6J3oyV2yaBKIoyoX4kUno3jSXRaL7aiUHPH8FFf0VJE1MyWx0ppSV0lYg3hGror/BvXuZ8kg1DRgcfZP9qxTvOvo9Xpv3YJG7BqLJZ3U5wf9l0r5SOJQLRAIepFSI6jcp0IwBBpOsaiKGHo8UE/1if6IlVdT4dKPVjCgmiTzSbOhH/62c//5q//+oN337HzQkwkDwvd3p7P5/OTJ3S6Oh1XvjnL/eurew8e/N5X/uDV115LL7YvHduF/8hBNqszE8deO03kSS1HA2HPqLlFJrbxl40Ym2uL096/Il4rZ38auoLm6oojM24sH4A8/CUlfYGn43SErUphqnbaZ4oNU4by+tSmN9j2p2QE5ALnM0prWh6a+ldEvX8bVPaVXAGIaLPKx7xII96qBy0x77A55D7sx6MP3//eN7+xPf34dFhvbs8ACHpYVl5IVZ48lZvb7XDQ69Nx4cv1/QdvfPZzf/AHf8BEW3SRXfu/mJShfPR0McSrR4jBOn+FU+YeRvPmW7gcTlMrBtysbjVChBAYJcVKcf6LqgLCtKBtHswBpUfaiwWRH0ozIiFNV+gy3mQLraB0hH7ULdZ0cwcBRJ6HLKFlq0Z130O+DzIWDwAvWSZi5U0uBKZlJSggCj2fz4fDQVvaCRFwWzaiR16TBR0lzFsQ2aAKUolGfvOrX/2nv/x/f/2LHzH04YMTPsHTp2e2aj/ZzE1v23a5bFen4/H63v0XXvraH//RVaTae0f7wdg9TRn8tqSwO+WoOK3roWQa9TyTQSKi7XxmXmhZiCyLRLB3TQzmsqZPtsW+l/f7Dd08IKVqFCkK0zXYITNo5veaVNWZnAPS7CzpwpQ2A+NnECxgXN/M9uA4w89pjpoD8vO0yI+KjCJGsGPl0S6qxquddgPAGOH3oWr4QYfzsXD70Qfv/sd/+//8/Mc/3M63AO5fXz24PlkXqmqqvy7LjYiqXD+4luP1V//oa6+9+opXSYyUoGalcuR9/H1I0/ecXZPC8mHES76TIjs9Xl15hjigUVpAitKabFwBsfcVNgX1Z3h8k4CxShHYilw81BLWLYtaxsT57m4LkaKgbsYDNOR3GjvttCiB8kwpakvjYOqTbkVEawDk7+AjpgVZLuFcOR6Ps6C0GCoFndpFteJHnRJXAEbLwXy+ufn2f/wPb7/1Mzlfbm4vtrT32muvLOvhw48e3Z4v5jJ4WXjj9Xhaj1e/8wdf+fLvfili1Khh2QFNaR3txWhPwF3ZqjJB1N/MK+qZZAUpBHWeCnQSbcMe8HMJpRnLMPMtfwion2jYDVhwwEp0NCymwCq6K0UW4V2UzwSZOyTMeSaB0mhN1ghjUE2V/7QbItW+k0Xy9UwiJttNbbtyiHRZeIn4wWp1OvU7A7h1tmdPjQpElEczOXSb74f+4Fvf/On3v3u5uQFwWFdVPH16vrk9X9+/x8TbptsmF9HL5XK8ulpO965ffOX3//Cr66GdrDFu86Jabr+7mHM/qYngwMAjj+YIwU8NMpmX7OUYFBZ5WRYe0G2buPuMANie28hti/tcAODR5z7ARtmMBMiooY902fOSdmyebii+0iBtPpJ8iG3bti0Ze2BYOd6G0vtgesf9CxNlmjGPDe7i5YDA+6pxZH7kpz/8wd99/a8++fADkW1hHA5877SK6Dtvv/fJxx/fu3e0FxRCcTgcrh88vH7+ha/98Z+cTlei/oaBSUrqO8WGg1GYMi8wkTS/pITlQlmQRMfvZhTtjjFdVpsv7ubgamyP2tJMHFBSNv+1hgAoN0gVuD56mvNPe0mZyLSnQjJmTxRgmh20FcCTTcVLrdxLmkdL7ErTS+LjNWud6OF+QERQFZHD8UhN49PK2vfMOHQXpKrLsrz79q+++fW//vC9d+VyAbAs68K4un8iosdPbt//4NFzD64P63J7vqzr8uDhg6vnn/+jf/kvP/PZz0icf9T7wjgGiuOEeqfUlk2n1adMlFC5dRSSinUuGjGZOR3YzZ5sKtBnbOweyXtXpGiESDlBh7EClYXz6/E7VQWVb2TtAjF9MH4ms5ffe4A5PJWdk59nSc39E2ucmOeWjJclp8RZfNimMA2P254i94NMx6sTsdeAo2lCF7Js6fb2NubO2+X8o2//7c1H7907rkSOL9ZlWVe+Oh2I+OZWnjw9Hw/rui7X96+vn3v+y1/72pd+78uqum0XSpKH11Yo75a5kt5aCHggfgpWRx1zI05AP98lN+oFig1Ri7xJPqJVuhdjAGAvy60/PHazkr2Qp0h8pa/p5reLGQUlosGY0k507pxVJ8fkN5tgBo7R6nNuluYGEZRC5EuyvaS1YYHqsQm58YSbMI3t90E6oZhZdfvOf/6PP//et0nOD66PD+6fFgYzjsfFamzWw6KiN+fLsvLDh/evnnvujS9+6St/+IehVK3YRDNFdBeEaIYNugPZIxnRxAsYLI213tUvhbhy7oRxDRAg39ozeErFGi4hrpDH8OQVf2qHAQ0fs3X5nsvmjvpAbXzWVE+xDkQZ3V/n9yRhQUNfBA2brEnZ0KoKMNNJIxtEOe82G81JIdxldWrv5wEWXgR1cGgOKd5H6l1dnU5Q0EI/+vvvf+Mv/+3Now8AXN+/d33vyl8ZpiqbQnVhK+TS4+l4uH744htv/tGf/snpeNSx7GfweqOf2pMUo9wnGafkSHeRndT7xu2zXS4a+fo8kyYi1qKw5be2TYnA2sRKdJQ7r+eqkzZj+1KIYNHXgR61AfWFPEW9oXQvRvsQIXWl3xaWxHhoT7YEUi6HtzhXVaeAvOsz7SptZg3OP3zTZp3jNd2Ti9Dmo5988vGP/u5vzk8eP3l6fvL0/NGHH7/86Td+50tfXJbl8ZOb8/liqd3DysfjgQ5Xr77x5r/68z9/cP/BfKyI91GwOkdotJjMZ7DJ8uFz5r1LXpGaHADtc9/pB23uUOUI83riihpwoijXGd+Eol1WqHVUOMzbi70RbayzaSk5a3n2iW0YOXQHSdsjhjLCBsGAhydMWGkBLcSrg6vu17r0TGKUOPdZpE/lUEAQhaSBoPvwOAHRwr/40Q8/+M0vmfR0OoLofNneffeDBy+/xutyuWznTS6bQPV0Olw9eEhX97/6J3/6wovPIyKvPSnsLEWMBgkNzk8a8lsoOQloNke7GflICOvhwBER231JtJGwJZ95KAjHQqFbg2FWCk+FxfOTmoQOj8zbNdVjluli/0Lh6SglWHtH7uZ8QwmRK0Q4ZuMNc+bZicYFVIoqkSJoa1l8b7vLnEpti20csEUb1XohL8VZS3jy8aOffPc756dPSfWwLsx8vlze/qdfvfvOu9dXh+Pp6pPHT6B6OBxOD55brh9+9Y//+NVPvSrih8Vnges07Ak4UzNXJW1MtGlInE8n55LiNT9lfzZrfkfLABErbU6MUQKoeVLrYk18pFG41zumQrU7jSdEWJbSWlwM6Y60+26VNP/kktfWfgplE6nsi+OkbjtHBCAG2Ss5M7ViP6f3zf5cEnOE5UldZTHSzVYPNN7VBhsXF54LA+aWjJl+8r3v/foXP9PLhYkWxvHADx/ee+e9Rx89enx7s15fX9lwTtfXp4fPfe53v/yVr32F8rBQrxHbwZ2WRtq7y+FmIvRFvHhqj8zyonvP7CJQpnYxQCxFWwNMd3rtJMjqZUt2j1FitJOEmpPbhXhtSZehWGCJVZ+7pGc/sSaXgxfu92RaAGHMlEHI000ZAMRisUKMvQOJrSMSdGyQaFiLcHG0ZYp4p6M3p3G6DTMBuTBMBJFIHS3LR++984Nv/s3jRx9BhYhPx/V4OvK63L8+PrnZPn5yVloOhwN4OT144Y3P/86/+LM/OxwOIkLkL0LtlikCi3D/zeonFwdbq9PCcMXyVn26x2RJLhNn2+isQXSNkuui0ihDMdShYSJ/74MN3h4rs2kd5v3qUeLeThI8aZboJw6a3o0gTVqXoT6gPUU88IbEukCUsXoWXQkKy2DNEolcP6CIT/cdIaxgdphhR0vqp9xH6BIXG62IiX710x8//fiD4/GgIDv5d1lX3WQlujqtAD15erscDtcvvPTip17/0z/7s+t7dkytZeXmyjNTpDT8e4rVdBIJpP7T7p47H4xvGiukjXihbx3v2+HZY0g0d2YntcF8idfxAk0CEIfcwHd0D82NVqk+k1rQ+Jmkas9j+zlhrCrq7PL5fjInTEQRdVLvsAdxfe1PhyPj7TWZ+UnBzXypeUw7UxS5N8brdKJXJjrfPP3NWz+7Wum5B/ceXl+99OLzX/mv/lyh23ZRlcPKp9NKTMfr6+dfee1f/Zt//dxzz21bbhmypd87WJ5hy8TOTn1XhjTtYUhyvsXTncCZ6oR1HGLG1KpqBNQh9rN01TEW+an3DQw1WOXTajUqk2tT1TxG2WkRqGUvd/3xSTCTKL5Vi9z+kR1j77YxqmCIyMG7FWOwW2+MGtYgC1GdwYXmEBMwwc677mZD6/QiVZzPt4fDYTd328GL733zP//iR9/fbp4Q84svPU/L8o8//IGI3l42Aanq6bDcf3j/rMsXvvzlT73+qe1y8Q37cawsAUI1WtXYU5YBcfAATcjSradYEJM51lDIgubpCvoxcWguL9uR6PBO95k2IoDpwOiUJ8szNUwXxlTH5eui+NhHsLD+c4cp67Zw/AyFDHW6qdr/vCbBsCOBmSP7P2SCqR0rT6FrthbsdIdrdGoNhbspG9df0lF8dFKcTqekQ6B1ZuZ15fff+c3f/tXX333n/fc/+PiDDz6+OcuXvvLPD+v68cePb24v58tG0ONpPd67//wrn/rdL39ZfG3bTUsn0kRhsqkFdDZm5jSLsN0PtLOAKBZLcuQpE8XuFqulmJqzIjiepaaQvQUnBcokKWG1wWYiduC2apKf/M9BvFKecovOMNWd461YbE8UF4PGUjdeGIhOdcpy9OAhxmil7FuelUXTeVEYbkMCFAkfpxC1HUtEILq9vZFtOxwO6Ys7arao5Zc//fGTTz66CM5nAcnl7Xcvf/Mt2W6Pp9Mnnzy5vT0fDuvV9X1c3fujf/EnDx/ev1ziiFFTITvKhkCCAOEKg5DBaJ8OkR2BKlCYRbCXSOykhMfK906BbhS7fHCcYmMLJ5vZbCNXWwno45kDAgWBVvfhe8bEH75TSofwsAy1ldmMqalBVtrnWYUMVH7KpSWthv/E3ORB84Qj6wOj8hGNSCWcgDtXO9qqUZPSKcQ0kU8RSOudaSNh+ne6vXnyq5/9ZCW5Oh2OK9/cnrfL9qt/fIuZrq/vMS+CCx8OfLx+43e++Lu/97vx+q4oUDK7SzqouFtJ+H+iT420HQFKSkAdwEbuvZoEaDaqTTImu7X/7isWUe4RZMlt4oj30VFqGpJJIqvTOsLLshU5tYo4nKsVPtmUvWBsBEw7qeqcSA/SL1L7zlWoTgSGtOKYGCfRwnnLwG/tfEemnwxp+dttfJ/FFPI0o+lkMTU9HA6XS20WTTbEXJYP33/3/d/8E8l5ZdCy3LtaRfHeB48f35yf3l7uX18dDofldL1cP/yjf/Gnh3URUXt9ZgA/oZhyYlNt+loRttGcMmU6GB7PAU0LV2A/lqi7vFTaPCYuaGeBkvVeBnCgqEFVxPrs+FGFZd4FZhAGH4x0q75T2ASPeFzDdZthDQYbciQ8s3zwoTq9HjdKMVG6hahk3HvWdBVpc8epKflqehgVUzxqK5jMtHUAi/K9pZgMUijzsjjGR9qMlsV5+62ff/LBB9vlQkzLQsfDgZluL3L74ZObmwvh9rkXX8DV/d//w3/+8isvyyYAkZIqXS5nIuJlUVXbeJ6208kUr8tUHY1z0LMDDAfHDcW7vQn7NMDt9CoNwqfYuU+0SuVR/cQPcNI0fo0ykXkPkbCtzC4A1FY9URahIbjkGCUNBgM1u7rRA3a61I3RYv/ZszhwSYlRuctHkoYiDRcPxD9leEswwwrYN8oUDsLxod50lV1ZZobrIO5S1tvbp2/9w48ef/LEX1t6YmIWlcPK966OIhBAD4eXXvv0V772VRuUWl2mBWLQ03JSaL1EJGLbpBoRCaTe1zqnEgdhIoDifAezuzJY5RKKiR3O+MBVFFKF3Wp3iCuAPJ1LI1+hMKSy+IKu2ptsXGALI5d0BxfbaydK9rsUzYZqmlUYjLvXhrvJRJOgSda9YD90oaSgO2Wz2LZMYoYtNs0OXEmdRkphMtjfiJyzmhwKMz/68IMP3313XVfiBcDn/+CP77/w8uW8GfQ/HPne/XtPL/qFL33p2s6ltTwzSFWPV8fD8ahQJdtWgF4YkOjLTFGHQROb91odGphahjufRQuOSl4zHM72R3Fk8nRf2qqJ+CvKhfVOqTcH2GbZAHHR8ThA2l0ZzB4wy9Y4T71j02Kxk+BvNigg6JbTNCckop5sZBOUs6AmUp1P1HbhMS+iorJVW2YP4o1+A6xhevTeu7jcPHx4LZuI6MMXXn7yyaOb28tFlKDHA19d39d7z33+819oQQ/5a3JVAb1ssiyLxw47O3LnJwmjmwYB717qIZAA4xHL3fvvd6hWIHqHVTMUEjiP6p4GSFRXj/kCoVty2X8N7xHZTEPutoh/BwsnqdoL0F5X2lRx54+kdewxKh4gAPX+0og2JjmO3isCypKHPrAcgWwbyCFj/sa2IqHxlqJ5fHS+ufnxd77z4bvvyeVy7/reV/74ax998Ou333rrkye3CmybHK/vYT2+8ZnPvPjyiznONjw3e8+iTN6ZG9S7bqB5nyRSr2rPHc5toq5Ud75aARMfd1kJuE0hNCXwA6EVGUv2wxSYeanHW8WLOonjBKPAYUWavT1Imt0l8tMkQ+ZmAIhw+oDFook38nmKNDzQa0dDU80GLJF9BbzesB+AofFZlsXXcFShyr6bGhRah9a3jWpdlrd/+csffe97Hz568v6HT3719gff+MZ3Hj36+HR1OpxO2yab6LoelfhLv//767JgshZI0zDbdexEn2aO+5/cZ9d4kWQZRIWKs3cpYfP4z7YCUORZqW0WJaNEtNZC1PxJxlDyLIc6jqGwXMpEekz7tV3ov+9h2Thh6u51IFB+C4LWHV1908h1dvadAgk7k6wtwJHLZbNXTg5GIvq1O3/zT2/pdmZeBHK5yNu/fu/tt987ndbT6cCHg5w34ePDF15583Nv7vbFQytvYDXh5rO8u93LkprNiEGSv0W6Daxx0418O/q2L9BMdmiyW7ZjcDJX4SvTXihQ73jKvtXeCZ1mp5sKmOuzl/V0BKO+aJh/U9iuYYhA/zPIAZDyHku1Sc766uGCtA5cOj2Mi8SEWajMhqoVIMCNeQqQ4Vy1ar6UldEOGXfsxas5Eget7fUCAC6Xy3tv/9PKcjryYTluqpdNntxc3v/oCfPTB/evjtfXZz588fe+fP/+9eXiazgoFbdQVxKcP4smGSFOKMphL2E4ntkNQmADmlVinOyAOKkVF+0BVkf3CLI6TkBgPo8KCf5igT4w3xJL2oyMt7h/k0wfbLvSWVKuDcWk/lBSHL27mngZn7JV7Q4Q+cL1IOHwA5sm6sSqBUXw20uZ7eNvpPYQsmiqrSqXmc+3tx+9/55sZwLWhe7fv3rxhfsPH5zWdbk5y6PHtzicluPpi7/7xW71Ue948sky12rmMz9z8BEuq+al40+FR+JpHUD9XT43NU3bePYSRoAOm3/rzbjWIBMpEwCxSoGBozGFhIli7794dtzi004zvRv3flbwN0ACNlAiEfHz/oYGyuCXVyaNAgfT98SS2ekzU8MpKTWkPL6LBp5xK5NqEwEzP3n8yUcffnBzc3uRi0CXhRam48rX9473ro7rwtumzz///MOHzzUsRdNAcFdRaM58T73plrtAt4ZGKSoiDrveEMhvbdlxRcGbtEpebOqxuFdRWL/hrqp8pcVc3migHiLKWpUZQvo1bVxvg54mcOfokWdUKbsRVxXZalTRSRbCDQ8jolfto0Z7I8iOWPFgmQp7koiXxRZfEzV2P9jOx7L/6Lu//tWjDz56erM9eXo5XN27fvicBzpMpwM/uH/FvLzxmc9c3bu6y9HX+w2fQZmJTjVsv9Bp7q8DUlcxP+8/oVAARwxsqrZ1CH1smtsIjdC4TylZQfYkjn1qV2ResufcCcarwW080/rfHcZgRCR3kswnH7HJQCkACl6W4/EEoMFBmgS68PkQB9wB1afuJ7r2OzVPq50oUiYh5cw4pp98+AGgy7IsC189ePjSG2/S4SheuIyrqytelk+9/jrtBmJyu98R+lsICz86IKuitWHKHoJlwWAcsdoxBuZeujwV7xID3jkYx3yec1CvmtG+laiVu/QwsJyNj4AC39CY5B3ZRKC+GuLM2N9srZYO5sTGewjwQz1AlOoRY4opDnK8GxLCvIzodcStORrNiECzzCDq2XfrBOebm1/+/Bd6OR9XenD/3quvvvL+279++vT2o09ub27OANF6PF0/eP0zb9gOnDvHeeew9zYJDgVU/YX1sukmsp3PN2bY9Q7rUqoLd4U6tDm45aKStcOWlNkB/KAewYrCIYSAPU5roXyLvSM+Fc4zYX0w1avfOeBBKjmx+yPPWXylOyEZ+fZSaO4lf4bKqgmnDkQYjBx5BCSux8/49N9y1vkCi4GCodlu1aOqySaV1P/Fj3/8D9//weOPH2+XbT2sv/nV29BNLtv5vG2bAriAPv2pTz3//EOMLxBEk9du2icDOd0fiSE5HFZVZeINGzOZksWMQh/CdHA7unJHEKpF1nFnTtoq2qUkYj+chkex5yQedr6sxbCUTq2Cvv30NE9TdlEwileibzZRu6O26pdxT9i+L7e0/g+6QDtFXE9yA/cd/nffcgrKCCq7ZyzNnsQ9aS0ib/30x9vlhngB69Pb8y9+9ot1XYj5eDishyMvfLvpZz//uXVd8uzxeYKtWQAT+6c/jakqflChvbILtGpfmQ4PaaDCkw4yqM2zJFgb+foPOU4RgR2IRu1ZPwMrOJTViD4GW6fNgyhsbdApLQUuggy9iXEwOp8PcdcQ7yKuuK/rvPbJ9ojJxMwOeK5C5N7UXqrc0nib7iOmoVDU19o9kyHZz0W2y0fvvbdtZyI6ng4vv/ba8y++eHuRJzfn8/m8rHz18Lnjvesv/M7vOKHHNZv9ICebfceMElGVCSJShleb2k/2SHv5SsNg6UZkKhYCKPfTBdq5a9a5DJ6ODNOX1BBuwEWRo8lGukhRpbWqKx0b9j7niOxO2jWeRcrMKizGTc3U8nuViAsSN3AWZnmHqIYBJyjfkQ2BCPuPmQiwF8FlEfYnH3/0zm9+c/P05rJdNpF//Rf/4+e+9MXj6bAJ3Zwv26ZnwWfe/Nwrr768ex8J0BjQnWxXg/7IhH6cLFp6NczGHGYEaQhk0jvtxDCoxKHS3HSvD4Hy8MYO+6xkLkZZTLez3bV1Vt6wt5pjgstyIwG6BU24/FuwTqfXXRcRBiW35jZywiCVbyWlHKq6eXOdIyui9V6mjiTyWJE00C6LlPzbGULylx6CmW6fPn3//Y8+eXL75Haj5fDhBx+/+5t3eOHj8bAsK4hubs8vvfLysvQqWaUM0J8BNgaitw9FIBYCmAVos/cnFKmcyCSAIDW25mmbMSOIbFv9mq0xZrAT3IftYQQTa5EMxX0t+5fiX/0Ok7Rx1Dbs3/6Jue+IdLeBrYPlCf5Gm9x6NPQ3KWZcUJBmpi0pnd112SrnGxI2qtRwz37YwVgG7IwFvj3L6cHzRHj/vfcuF3u9JR1OV7yuL738cipKqW61P0vYYGiZpmG0St1A6M0naSRxeiPlH6lIUqRUjlV8lsh25ghpaHoCf7Cy5BAjwbCuYE0334raM5SIyczpsNT9X1A1hDUexWB0oDsJaTf67Mt0pUb1MQAkZrpUtb9gDXfhFWurnxNhRQGJVbAbHrXv+YvdJXL53t996+bxY1NWEf3B33/3yc35yc3lfNkUxMfT1fWDT336NSsKzfGEu9AY0TNB50yTcGpaGpRPmJEqkLrXon2LcHFHuj5/uCa+ezwGEPAj3MLOyAFYo4BgxIkNME7z1l093zNp0Rt7xiOqG5SIlmaBbEJxQnirPqfdcMyqMQAsKYm2X4/bsk5+9nHWfuSYJbjyC7EvBe++/esffve7t5ez6fLbv/r1+++99/Tm9ny+MOHe6eqi+MKbb7704ovps3qThSz3Xd+J/cyXeFaB7J3n+8drZyvB98e41e5gQEF+KIGZjDgkh9hSCcM6d8gSJA5vNTq0d/VWZiu2/AJAHFNGNFG5wDIKeNW/d5qE5ITjMPgO/WfaNxLRTURKiS3wGWxETGAioobSRw+Ju5mXdY3jmZ7Vs/mzOAGl8LWr8OyeOqwmoo/ee+/xo0d2mvLp6t5Ln3r96dPz7e22CUALH68E/NU//Fq+6mLsdx9pGoidSapVCuEWWwnny/nSXy4XVcsAVT0HEjllzqnub3+2zb53oWJ1nKqFVto9tkqBlNwQMgIIau+A7CA53WIaMUTgZS+NpAGLjZ9Gmi5RpV5hCPNP2wSW+EOHuOwuqeqcdvrVmBMh+nx092D/U+2lD/lnczc0HtFRDwFM9PFHjz755DFAt+fz65/7wn/zP/xFpL2Jl1Vpefm1177whc+J5xLRqEpUm5I7JiRqKdM2xw5BXPG6CyNYYojg1wkd/fzWj4MnBYmwVYJZANTQUkpWllQ2sNi+NORq3+30J2UtlUwWESgiBpcl26GrIeM7uhf5FaoaC8kIuW6zzTA7K6BVCVIr8E5LnaOzfLwm4kJYbxmqcTyLplCNbDg1fwc/7ACaaGhks53y8/Gjj588ub29yPmC0/X9H//wh7fnix36sKxHAb308stXVyfEuoLPpetMcaGL129zBWbXDuthWddMUeaGV7/DihrGVTe6i4DDFQ9Wawx5i3WTd0oF24i8qO9ZqwdVFViNtcX8rijuphNPE7VTjSY8PgqZa4IbarICX/tvZAQSArWH7jSEz/Sk8482XGce5Rcbd/vTYYSomgOl2BRQrwMRKgisqmIn/FqVn6g8evTxprg9Cx3Wn//0p48/eazgTTYCeF3WhZ577rkkdEjtHhVR3jLeOfjKQBZVXpJc0iZSJa3NVOVkaXegbY2inEMtAv42kttDvvt3updUxejJAhKCcos2VRVSEWrbZJWxU0chbYRN1EPORvdjvNdIHQ96Rrv0RCaagQmUZGvtvzU4SjiJVIiaO2lYVCT2BBRq7zEMAlVmgYg9bwMlwgfvvf/D7//oct6Y+bAenzx+cjmfz5tcRAR0OB2XdX399dcTe0yT2n+0q/v4SxCVdMfDuMXO+OxG3k0KRYlsl9H+5wQq7I7RzHd/Wka9aOkdpjLXT/mCZ0FbivHV8gIgNUuKgpdkYv/Xehg7nghBqrYiTv6Ki8x3lK+IFnRobgZJjvcV0Br4yE9qJ6pZ/aAhbgumFflOK/fs4aCRqLn73JunT7bt/P6773zw/vuXy3a5XN783Of+/L/+NzEUXteVeb3/8Pk3P/eZgFMUWhZDpDaDBrAmioXfIEDzHNSeBmkknoRKMxTL8Y/2z5/jAYSGtHW3046smfTEnEAMR1sTRkPs3qFqU6fAktNoiJDp57tBT17X0SpX635cucmTnRxaax6iJP3ePu2djdRQaVGEBRqHlGTN97+nHVV7UL0KKx6ROn68PZ6AhoCPH310vj3b625ffvWVw8KquhAx0+nqdBH53Bc+//DhwzAH7EwMgSitKSnpRB7CH//vaJonag4RiwmDrbSMlunOT8cuZsgTnbTH/YrV105UzmKmiAdtIMwE1vhh6lWpDnOPGdciZw2pP4LkgYmz7a4e/fBvQZEIeFbf0dcodjSyPRNMAQ368uokVVOoQYCVMSKyfLlkEXar3nluTZ1O95j5/Xd+cz7f2uHKp6urX/zs5741XnVZl/O2vfLqqxllR8CfRNhL0qihdeLOTsa0EGprh+dFeg111dpNNRPcVKshZjePLlbdg1f4Ze3HnRWyjgEfGGBSOwVvGKsnItINdl4GvRLENDvl2AVpw3f6dYdBbhY7Xf5IZ1h5xZ3tNJrU9anxrAkdEY/PtDc8BqSYiGsCer69feftd7bLBuDe9fVv3nn3Jz/6icTmz/VwWNblhReeV9naYkB2/V82IdH3XRcpzPxIjUSY+Yd58pmSd4nX4MWaG87fB4I0H9SnMBLK/2cnjKVWxVYJBTTe9zw0HFWo1oV2XYlBZNYtl6IoTVz336OQKTc7BfV0sAZ+f4art9vVwJJQSEAbY0Nu9nqFLol+lEuMIzY9976GN3srVOTJJ4/teJJPv/nZ3//aVy+y2cvoeVnWw+H+/QevvvbKQDcwwN1RTzxOP+uTN/4oIF675Ebby6p8KaKtjNKQJaZ8gqceCY2nTpxwZW3FP8QnwWXbDokwaDvdsAeSntwKUShPT4j/T/RozhLaJDuN0jP1L56KTmttPwnaHbtrUARf3qwSPIysiaJJRna1U+QqJMj/ObTqd3U2NyPtsjlSkPwQ5MP5fKvAp994nYCbpzfW7OF4sIvPP/8cHF3hWbTpdKMShTK+OSSv2yijYslFe4u6724qN6Zc3OztjKSx2rvOjHSLze25SN3JX1HJZdCiWIxTgdWLDvMEpvQjivhlJr2DgOD73sbGzV6oFy9fTk9JuTsIKXgxsXHzOwGAgBhQUon6sDqmQUCc58b1zmOWkRSeQu7oOnMNmcLRdKleYTBkOg6n00uvvEKg42H9/re+861v/C0vi+1uOJyOm+jnPv/5w+GwbZkSawADw3myU8vD4PszcIgBJo33e5viEUOnkxkGuZiPGFFAoGSVfPaK3BJOhxFqL8jwU/u1KDaE6jGCkKqYiya92FeYCMBdOV+iZiyC7Ih/71j2gteimg6kuW6q0Sx+NNlGHGNt5PAZOv7NKZaGWCDpDWd2KzS8ubdCEaMY5pKmJj9SlQK6Gm0u23a53D64WlamRx+8+8lHH3qyi/nqeDocDp/57Bvhh7VRvMarfWmyJjh/nOb7SvnaJU+78xX6m5F8d3Ki2PrXa7OkggNbwkImsQgVQWsj57A7toLBzOs2qtpLbodN7xS+UUZfUPi6N96WINrox0hQhzrGGHoJQGjLaFLRrkevjoHz3+FmH32ZhDbOnmVAu15TBgYVit5MbKJ9+tU//vLH3/3uc/evXnh4dToszLQyrcz3rk7E9Ok3Xn/11VfixLOJJjT0NQ6+f4botUvKCBn95zZJGnpsc7ijlwYr2spL9iiyffTB+737CBiHce28ebTr2798iJTPo6pV7sid9MENAXwGt0FVGmyzBcBlvig7A6IUJHqXuDnrG6MFCuKmiZ4E0eFn5j93TucOwxx+cxbKhg1ElJnef/e9f/zHX3/09PzZ3/nc53/nzece3Ls+rad1uXc6quJP/sWf3L9/b9fFzNopbt2NJXB+ICeLwXWTKp1zPkuJiGF7RWWMn3nsEUPZq42cRJYU1jSIRHTv+vqu/dnDyOcoJGhO+Xo4S330u3wdKNzkTAgf/KiFXepbmvSuoZVARJu1Gp09mg80Oic66cI0ZwemfFgcw9IVq9tXFU+Qho+oKTi72nYUImzbBqIb5V+9//HPf/nurTKWZYsw+nTv6tOvv661sjGI8mQ1frugd1VMN+6bizCU8o3N2kCqYu4OW0VNW5Lg5aV8pszEyzLxbjD2d2390PA+CvgZPeUsFHZStI2TcAc8d5tkSFf93bY+L4KqsiXnWwIwblebDuXdbYiqpjFNjm3Nbpa2mJIOQpoTa1Y5F0rLpA2mLh4uwsUUiEhUF+J+IwG8Huh4Oj9+8pO3fq1vvQ0IRNaFV14evvDi9fW9MDfl+2bOPhuzT/cMA/MZ16LhXU0Q7EBvHdrpzYogkkQKAsTqplRlbrA/m164q+h+FhQn1WwirNGmupigkg72rw4D9RbDSdLYbrMHiKJ3CvjU4cWoahSEUtZSIzSyDpYvXJ6Rp0ElQOM9H4VITI1amtQs/ESX2QQqSJGrPdngc889uLq+Bi8b6LLJZZMNRIcrrIfPvvnZq6uryfnn9LKX3tHsxIOMw0jGtvKbAxgt96ixOOY+aAcSbPJpQRBqrsqOsxwLjplV42z8t5v8Z8GkRO3tpKiEaTE9Cw3medbcxstNuU0gbHWhxXO5zZoGXva0i1ZT+QuamZHwXzoKqQZUwugxO42GccZST/3q9VgzREK62svN/SNd3bs6HA+8Lst6OBxPV/dOV1enf/alLy6Lv2Q1/fs8gB262g8yx/+sR5DF2n76lRc4tCOyqKUn+7OltM531SkYtP67vVXrsO16wmhEMvJEK4Ff02wPgUjDK4pBh1TVF1bj2AQaNXKQaAEtFD0gEsQ7gbRVsFCJvCwi+Ya68MlzX5ME9Jgrt893zukIDgam+vpaWNrRHxHz0ydPvv7v/sNnP/OZq+de/Ief/txKc9dluXdc3vzsZ15//dMInB2Ny9zDKO7duLVRAOonE1AcuYssSyyP0WfQWqLwPsDINT+FIE/PdsFUgcZbOEHRRRUiwA52byFXTTA69Os2coCAFVUzbwlST3tTydMIGJplmeRg4tkg1MG4BJaaqCjGKgomtfdeaB5xkUk8tdS5O9RJobvNLzVQTa3ohNgbgxwDeUQ5gwlVZaL333ufjvf++//uv316c/N//Z//97u/+c2yLq+/8ZlHH7z/8MGDBw/ug8wKDp7tbjj0X/jVjVJzxEXkPgf1VIGTUVTIpBKzgfEnLMp2ydMIoI2i2vKitS/cWpmGWsa+CVlaMAR49zwNBe4dnezwV5brP8u/xiMNbJGDuAIwgwiak3E/7ly0l/OaKVXyAt9YJdOonUqSjHrZGt/hleYtBj8VfrpAmyZOBgBs2/biyy/+xf/0F4fDKqL/y//6P7/3zrvrYX3tU699/7vf/9Y3v/306dP79+9PGc0wQPVv14SeNhsGg7JJPeaPeyjUzeyEukHxZEFq7twyEVkpnBiENCHyctl+s/3BBFUVRAF31a7F30nGVOvsbqWYSLwYiZobRExjBOnWiC0NNI7uvyhiJ2nCgsE4w1xqVj+ncAc/jIa2+yBxGBe+gKvqzJgdFt5/GgWjnUCOnkAweqtqQDrb/GO+6eVXXnrxpRet/d/9vS8fD8fsV2QoBd4PKf+cxnnnPQiHOPxg5HVj5dXA3TsmsJ0b1LRXROBNhEaLCAdLAiI7tUNJoRRneSTmoqxW6DKXDa2zddLciBOz3UXOWrLlupE3d4rQaC1SXrtxDjuuBmyaSWv4lBMR5n6cNN2UTU++tTNn5FmJb6L8CCCaSyWQaLYZVCuBSEsP6PX9+1/+yu8nTzt989nO3b1U/ZaPpa9yCr3lAt3qgGnoTp1UGpbP3rOS1QohitR0rD7kx4K6DTQY5vIg7htpF52knrOfJcNEBIaXt/fUVS8ljwEJfMVq5GITpvCqYZQUUMF8SFrnmU48mEYcMuROeCrB68q9N1G9qXRVGss18QJOgEhUNxERmU4+nGlewup5DNq9dfaZIcLsm8ahAg5/RrKUwqjjmy5A6oCDd20ZVCYRubm58TdOARRvPqOImAK8I9yAI31TuMQ/GASQAAiw7WGWqmXe2bZM2Iua2APLSbbK/PjO9CiK/+2a18XILtzlmnwzUXrJQRREHEZkgzsRTFSkbZL7YakmsFD1lyI52ywHn0dJ9az9JAdhq9Lm+U93SODc+d2mffgeU8mETolUfOcxSI8H/elR0wr8rKu/cTjz081Ou7dtYtzOziAAysHrDo2nt164kAKqusbdJqjJ9Zn5lP8UDspMyGwkinCI10dlMOmp+Um6COqlNEmXAYpl8Oz3qgqoW4gg5BAkPgO1WNcWdRedrIzDYJWFPL2IqDX7W0zR/tNvmNzZ7LlqeM4LIPxHN3I2rzJaTqh2+nwGgEjLx0y+IxRqfq06NQvtnVish0qamBP0rwSqYKJPJ8NDF+d6URtFqbT9GqqMRMxNN5PS1CVxdEyJfrpOA2KHACgNcuCq4qsNikbNaNCn3RcsugHok2w+oilgnFtLO9XxRyJzU41HlaZBgz6vSSAmARqkpEHMO+/sASwRbZeLyBay3wxwN0uIXAw6N+2+WHK1HVBNzpz+QITndhSUUhRgDrruG14IqJ3m+ylbB55kbzdwXHQgV0IaTdfzIZJBCBdAtJnTyJhmVM0veQq0JKU5sdE2TGodb/VoC8Z7LlLEvZ0Z6hZb2oOtce86OlXVzktrgRwAT1I1ydZk22Lkdwx1D0n9OwIqxaarvT1LR9BamJNzzbK5XdCsACNAmexQK39qXHugcCytr71NhXs9H5KRLaZsBe6y5vzD2al4LU01KCJMpI0MBPKqzp3TTB43rfJSY9tPXNNWh8hxPxSFVGgM9EzWicgP4Ry7mwxCN5z5p+nVpuqr1SLhTBVSxX0EFh1SRybUaTadn7AWBiFoyjB82QtiZ1LdqWDmjBuKVT66+eAa8mwu5RsyNXcttPdAt5E0XEhsmheiZDeTXyOzfAIaujVgk52hVd5psR5QZVTzBETl33D2GxTqO0xrTrhcLqK1l/9ZJIvviRs86SsDrdWNhfTThQ1GSIzZTUryKfkpUf2SP4mIhp3ME1EcmYvtoPENWzEcKKwIzNkc9ipPhBjsB2G+rQxGfN8btu6y9xQz8U13oNMyRsVbvf0cSzKK/N6xBsr7j7bT8JoqaUITn9Qovr2LcH/7OQQjAZCSvUg9J0NxKGpA5GyaJoJGNyaWzSTM9KrxNR9nYjLdNu6G0OaZNAJbVQ3ud6hkNOpb9CdpMwlrbs5gkzorutImLfTRR4/kstlAAI8VUg+mqU6CtRepYaa7EsVqISK1LEaimuLMzR3BzUcEr5S6I6NeMxn1YeQbkwCrcXBdy4FQr42ekcAEvkcK9JN6fD+IcU5LnIrsfW7MsZVIRzcxTzR2jMV/bOr2Ap+g1wywgNC5Otc0HFxGxSLNXmjOLbPAHXLGNEPda4y9gscs5qaQLRIP3sUmHiqGp+mNqKqfFzJCwC5A/39iSQrlGMtmkzLDF3Q3PUG6IEbMwaGIht/SJCFUyernqr1msoYScwP//o57oOPRaYRU4B00Ggw3etlspjrLjJlyVOZh7iBH6rOEA9P4adOG2ZtGJsyiXExK25MzTJ/XTYUYilRPxN9hrrWMGVLa86oUHZn5/v37xJXdsS+b56+hTZ06Uycj1IFmsU53onCXD5obHHvJfrMALcxNibJbO6rBkrsjUKwQU6xGN/aVS+o8nV43t+ddtz4cBRMKgMReKwqyNyL5frVR4AarHoh/Bykm+sIsQ9NnlzdP8s5cMXqp/2dW8WLSyIOEppW+bGJt1E0zpqZ8sWNEROp+RIa+5d8v27Zt28T7oSysW2jdj67krMU0A20D/gxy02c3XWkdhNspIbRaUgntpx0detJLRyKbAciiwRpSn8t0xXibNn41nxS7/PMxZ0sdP2fKwNA6McvmEZZsj+FHkpmTMxvkbDB7SpbkXYi8AMtpZGuf9nTmxURTjolokrgqgGsER5fXkQ/q/5eSal2ETCWHHGNRLwiblGFSpy4Wex3rzaaGybYx8SaSsptHTuzbsevirLQfxRjl5tgVuQdwEUYC8JCv51AJYRMVechKKb3rQx+DHVvaTEUH9UxiZylbxVONQi1ybTsHy7SOyY+8MpFvEOow0iVVAexid5xOGqzjJBplRy0vvSHQjovNek+M8RM0TaFyBBFDevzaAH9/Mex0HsTeOHUiYCcT4xWng2ySA0YThd/SQsR0GlMpKlmEP8SV5V0A90bWjjfimYIIdRBo226i/DfUkqJoL/9F+Fnr27U6XGscm5l1KX3zgsm4wg8OinnkpLWBoYkWqhoboMcKdoQlKJpqbGyS+FvtPS4OFEazj0D9IcP+hSLbZD6ughR1tyyReI38HpQq3FPLfth/I2moTTGeJVKdFAnnrdimI1ynVfioZVm6QlIqQ4V32lfGnTDFaR8PVUGK2okY5utotCihSRjXzmeLYIPcRBRgK2eFxV51BqaqQmL7axCkHRKRDstNS/WX0xBozIK6QE2+/25Cg8JGoyVTUBitTakshZbh6fQwrmen4p+tGtihn7QeRA3NevRXfbg8+fpqFmeFVx23lCVm2puubi22bbu5uZlu6GJU93MF4+4hMreZRcnwWEtFp46Kl+2r/S5logQQGeJ+DfIMme0s0SaqczSTbQjd0PLL0JCEVdSLLYjC83l8r5PTEVEGlAXEptgdcNSrSseiqGgNsQfM6qk9dCeiWJNr28V8c4dvnPesPRZVqzeDyGazzaNgHMBF161SvlhYDPAoItLpRCD0Iy6cwSHgPgWDh0HH8p4hHzMSH4XseDxOUmWDFGdxvL8YQL7zkfL1LOkYwq6HHnb3l+OhlMUoNM2foVagKVzFHXm082AODQ9Q1hgSoQPHlnlOJ6jur1RVWTquaBqVpjgvZeouli1trCG/eQJxnoDSxI5K58H+rPhqaWRbAuTYgUTlZm0IClGIzOce+GP9wPG9SCE39mRRgFaGNsbsAYp9t9RFsx7wlQHcgdj0GVC9VL85wf5sfHcxClsehTEa7LT/ik6SO0HSZ5wEZnsaiMYEcgplWtxOMf9zwqzhKZFamlodrHKfQmQva6d+Tw6oE6hGkCV+sZU7FDqmEf5tNPId6U8LnKoQRF4JDuFKRFQyR2poq3x/cWsg+CxVORnVOwQzFdTEKARZ67FsYpCHmYUTHphMWpet6Xr86T/AcG74XIpTnBBZxQ7UqkGpYZRrNo77WWOhEs84NmEYj+jEshQdDc3scD7vpvC9fnZDg851fPJExfy7sVPrTErtDVNGW5NmZ/wYFlxhm0oCNbd6WBMx954qBPWTxFRIN6ion1Q7cMb/mg4M2jsvDAJS6z8I6vsA86lo/BnLdsMArK8s1Jm85DC2AN2ybbqJAmL7F4zNPjmvBc7kXp9LqUET08FFemDTon4ahNKrljORqpqGYjC6qsn9mkJg9jQe1sgKJYD70l122EFSG0fhu0Yd+99054A82nUtPCUKIiWJI69BRFa96K/ToT6ijP7SUpJIVt7r2HQZ/F7HPElAjMLxRGPOaFTmJ4YP7SLE0SBR+JBZRUF0c/N0Xdb2oAzQoPyLUmzTQNu6Y621qpUBErRigjQX4RjKVINBFvzairthAOslxSvX9VOIw5IAA4uVFBfV1SFGjJLYcwGdPehwFV0I068RM6V+OzH20Qp8k0hibbNtgaJTKFpsEqbWBo2AlGF9qUsV6m/3xQmQ9xiISrZ7Ol7DWHbJCAo2m/8sMeqWjzy8GBx3s5cgkbR/FNlJhKWUMEXphG1FMns0Kk1CNqhNua8IA3y7IjwZqegjZ+bp5dU6GubmFUfY1K0MEUX9tHpokJU32pIB6bOlsEifydCHzozp1IedLd48qWqkFhxS+LlFNCxdW1tSJSzV2/w2OScBnOhT73up8k7zcRnaqRYIMCflQxbs1nNU9XK5TLPOTrvlDtMIgS7L0gcmXihcW9qrnSBun0uK137YJp2ZI+we3d1Wmc5BdBZmC/vNztfwVKdHdMSsbqPUnGAbSpJxIc6TVTNHGyIDFckUZwqv267U6LH/aTTBxXYdGgXLATkBtJOSqrxsJ0kpgJNJmG6r+WcCxp6KQKM3GTQJH6eeF8B4psi+L4wicvc98RGdi0/2Zn6QS617dhBlYPZEc5BvVumYBTEFImqLP7R5UvQSpJ3PGsWoJ9rLdwO7rMEPyoW5NPVkr0onf41dmTuTAlcFAvnhQ1nyAa861NzvMJw9PIZIAi+NUR9lnNru2inpxif58JFrpg2fKVX778mD8O8FODLh1umYN6f0jTbPP36EVaP+/Hgw3hvRuQXnGYXVhQdW/hd1HahnZ71q05T87qaFsuSxNxKTRzDFz0NSLfpity7e6Ulk+6lgEhynzXgWquKGRCcOa3t0wNTRTqlSgpVx3BMJBoZBEVXnxWJoOyfTns3KrUYLb6oc0nQ4W0rAZMnuUGjkeGL+QABTr4lj7ufu/jY7hO6M7pp7XaTh/vijq5BTctpoNT1Vj4coMsGWwJiCoo1yRF61tkVRByGXjUIxIv9kJOC7QbMPciIpEO8dyQUcIYgf1OgsYXtx9J6MYcWDEGJ+Y7PtrHy3/cDEdVTajqDudgNNVS7nGcbfHmu7KFRHkZ7sx54r4Ra9BYTy0WgiKSote4NTU/m9S1W/WcNWNejWYtXYFNSbrp2bqhHiAKOMBgIOv5rWJySzQqDIVDu5yQNQBXTUyYgkiNi2YnnHOzDXT/2gHv+uVorvTi08MEVaKS0YZVAEJWYVKVlUcddnaQLN+uaqws8BdXYm80EtosxEn4bzvcvpBJqcEboV3YiIm9W7MNmduASu2OV82oPO2Yxjuuj0uUyntE09xkWqmMOFxQ2G3WGJxMTpxcXJ14cO2VUyIluxqBTRFXbq4KZZpR0C1DI1LSYNqSKwYjMB3eKAWG3eUDFRKfgGBbBuiAUzX6KiYQsMfIVIRJh855CDIa8WjwxByDhzqootS90hW4MJSXVwtziGjfCyGhNfzkrwnVXu8byqS3nvtzN7enYHHRD6Tuklp3t6O3tJymczJkrh6GvnwWSImm67yneiDWbpLisIEYLvoTLZUT/w2oPBDC+JSGQjX/DMObpT9liKcrUeoAgvAqYA8SKTGIPkgZCAxtKZqLKJqZQptBUTM0K+vQjwrQQScZqOU83Zcjjmya5itC79V80rqh6xpp23BIjYSxASBhSZkBUTjVN3rCaOny4fY5X66IWQfsqLt7R1H7nKO4pnYpqqWlWDVnqjqKVRNUXMkaNqciYd2OuGjdzGRBE8aS6umzkTEdm2bculdDeI2rZPavaIHpq4VAnJhtrU08aQaSrqFGjDW5tEVnmhiW0Syk/z8FO5GodMjgOFMHMIB5gos5T7SshBEa2UxwVSND2gu7bY/uY7+ChtSYlsE4h0DmksJxw98UlbXUa619ZatkC9mLEFud2cDGLh5CYXRyuf91x2kFfbeCrPCSDKVOBmRqa4pN8c66q27uO7e5G5oTBIYVN8oKzYYjOlhnWNOSiIcr02jUEOGGkvyor7XbJthrzW9EeAklaCQKkqFBx17WyPlQJP2kCJi0MPOp96IKORj3aSQTlezGlNV8SdHFWhlnBPupsfabrg8cckWJkOQPt0cY8rMv4JxOkre2vX1ED2TcWv/lyLCYJHqSl9QKNw5/0UR3ck1NP2cUGjqPQjtyche1RqAAfeXrSjsECRFJvYIQiDsxbxI+bF9qzGns/8lPsi3yG8mjJRDgoBcUxQbLMztcLKjpZ8PaQcHPeXbgTIMUWi2OLbGRaEjjjMhEKVlEzGNI59isoWAHak5J0eFkhnPUKTLsFdnqbvUefJqltefJYsYpTaSRSmINRck2880mZmw8HMAc34ST0chDK+a/pcD58oKKDN0LaimuhDRwfiRGsJZ3Mptr9mCE1UNapJfWxBcUNwK5Ed6Kww3E4CWlRir5lGFAkEWu8wB3ESg9lFZ20PndI5mZEdHeLiDwMJkznToyP5dGtbXDQCyUb3SCwiqdkVOqgv+4LESSBsSOpO4Fk31L9DTcROICqkkMwFBFt9r5/YwKfciobrGRESJspM/fpgtAiRN4wlFVALTqEg8r29FfzXfDleZJrCaN6o1qRHLhteYYAr4ZHN7ZQbUXzV6QVzkcVqoGUUqeuKcV0n/fYAKDIcRYKkaV5pni/+F0wdlHD3Ntfof44hJ71PEYwRik/7rqcmbt2JrPuVPiOD75uKvd29yiEjnTE31Txgp8ZeK+BQVKGbJT77nW1leSRF2HjAI7qBRyH35t9Ettjl5auZGpnryu+T83LVDVhaSFzVI2GigjxL7DHXALypYTsgpakzaBKXXMy/ELg9r5tRSU5k4yHAo71k39Jqs3VKUY2k2wA069I/hQ/qu40hmxuE3oaXqpV99QYxx5t+XQp3W4XqAJtSXCYPu7dMQZ8MjOwfr/lHoIEuQfH4kGUlEwXp+3E0Nc2jTmegmRsKG1q4XgEm2vorGlTXDG7CvYJIlwUqsd+DKG2YxbXJ6YEEQ8YFsCPXvF9oO5uw62VWSnXRnDa0dFUuIBYg0WdYjVSM2FuYZChlt0vMEArnyMvwOIlEJIXXsjgTv1NWhrbcixCYyeue3eem3IzdaX92UpKwKD2x6TmkeQ67uTteiUw92YOjZJd4WSOZzooVCGlJEwoWdLNqJWbFVGobCbW59uYjiIgnGA5HWMXgSIOZNsUq9Q7q5gQ6V+7gxwiWnS7wWsA6ygvDbRNr+xWNrG/HYWUKG1BFsL/JdJ0223TJ/7JnNUTG0zwR90N7UWWlMDo7RzFtodxdMpf4R4FNJDNaPT0xEES9UsNLgJ7Re/2bPnwYsKXsIzINGlFYeCJaQVUsbTmGyD/MhsTIZKvWVhJtLTpoVsW4WZ7IlgWQtipTtGG7C9mk+8ju0iJquMSqVQq+hC9SDRA5CVBv6g4TMip0+5O6NdRQcUR2LBfFdxLWeqcUqcArk20LsNEAUP0aQn/HEt4oB05U24FCGLtoFBA/sTdwsa0DRg1Zqlk3tzaA8t2qGmmyUJxWF+Quz2VmBQCxDe4mWxAV8vdjlt/JIRoG8spDTlulfpf6xikXZ9ZMfbLpLjnUSMFNIu5tVRkVAxM9AKHYQePmxEnWeYDx0y+OCjAHXBFSIWBWtZHBUOdZNULkEEIjC26JEvK8TBhCT7w5mEcfOdJKdTXbjVNKXCPfzkQCl2SiDNH8Lhp60U7ekprSkMEeT2vn4X/VsgekuZefDNOL6mqGzEFoLB+b4E/nS0+yNYFkYoi9UE5dQPNHN3ImInEen+7M4XTFbGynZg+h+/XeiFNdZGEeDUh3o5rs1JGUfoM/1FxJ7fBzBUr/mIphWNpNUeCwyiGbgWWG2wAOTDPkGiWPOWxCsW1bpxJG7+x2yPiXZiwa0Cz/klmFsoU+8SQlNAov63IzgQBA7NUN4kGAb5FTVfXMe+Qb+46COVFU+CmBFypwc29tDINjfJdjqUNUqDWIJl552kc27uQw1SNPd3D0qDokXVQbcgEWYhWleH3srO5DbBlpj6bHTdQChKU3oVInR51iTzWPoAgHpwaNS9etsJGX4Hzk5XNDhMvwYKuGOLS/pUKEnA5ChG2zIyPjlEY3bJIN5aa6zsScuN8lm5GEiTRK7MsG5wAC/wQ7SuzI81jWTUuDDmilcw7wN1JD1RMWDZqI5yYQPFZx4RjW8wHY3utaB1UAi9ccVr1szjkEsYCPxHHZ0DijTBLAF+1cZszJDrJU55AQWjaOyAy7elTdFyHHx9UtmgV3PCqhBnZ2k+UKCXd+gbq0vTYWmnRNnTXJGTahZuMisl0uRpfcb+wCgxD68OT+bA6ykSiJafbJqQHlwZI5mDH9S78R4NH0vwhllFy90chfVuXVDqaICC88CGoQ0q1dHGynEMai4wnPZboRakb5nNZW+TBRGHpwMDKdne8SFyYNe6hUcMn1JtSucWGQG5tauMu2/piiI5K56MEXNwHQmJv/4kNwfUtuFdvgJ0a52qQPdxFvUqVB5HgLbvfjlLbKcW9TaVuBsd9k27qKpjIbQXt3ac3bNMvGiwoRJEuUGhHWpCxZhZWfJpyS4wlWX76V2K0dGuFKzwSm7vhVN5d+G59FiKK+YghPFSQ3J8mAD2kIG5NREssgAQ1DhNixc+NKFYlTID+OpU8NANuFw4w2sjiq0dhnWFalENK82BXyZMzOhIk9Z70k/3K0OSTcIbKSw0qNSsnbdsmFTGgZyMkcLIKeOpE6hZGsIgjqbwUz27TZfKY8e/OnJX4uWIV1NEo+KWUedogBUS36NgYg+a2qtc82TZ0vNuaSqIJ8a6jtXyMigTIxCCQK9sR0q2OZSTAcvFa42GR3UJocbW4bzG16cIdP6JGXcVuiGpbc03UpkVh3b/oT4kKJNlJWQCXVQ2JsoBIcRW3bhrayPnCdSDXoHFJXyZu7pBMgYiLpWLmZ//jGiP33rRGuAzLDNJPXe1pfSdJ4JB73egMIsBoj2dJFfToCIcm3Erga79cc1CptdNuE4kVFoRxQWHGjqijz4hCKYgO0FQZqLHiLMvnWkDvpm8SZpS25pzPDVK0UouxOVHJ4hrV/TDrdB5phkMyxGa0lPW+Efm5ye2PGEN/bHW6mTyctcV60A5im61NWr9/v4zNzpfVBFfPU/rNgoCQKlSjypza2TH5qe+dq7tHTmGpVe7VOY9KxOmF5rFRxZ6fbYhqEacyXzuJFBYBiGvaaT0WhGe+TKfMrfqeoZDEFhekZnEozEpPhzIs5BZ8qu9fgfmQBmQUePKNlKbdWEJF7KRCb4kxE7KLfo37yEdL+99PGMpAIdXUqqfYoootE8+Dz8Yj2saIoG+QlzAlRlrjXh8gldegCVTiDkKfWi6iK4TwY+dRzXzboqJh35fSoIuAmAPiRfcpEm/o7Jql7R+jmUlcDHdxcEqZLa48jehRmJCGNQwDVD6Co9C4IqZ0hUjtJKh9eChfk1kajIpxUC8bfWB/Qdn8MID+xTRdeqmtbN4tJzhInlNtIAzQ0xr8xPlVUJsnda2P5KEICzHYrv5C7jZIJqlTIgEf3jCMiAtmJchkmRV1U+WvPSHg2alPdEKJAziXJ+6J9sxz+2jhVd5Rrk49QXqLKysAc1rBkOyJpJ2+s24U8RVYwIr+cKqttdut5LL/HDwxUQDUSjE6vwVDl9aRdR2YpfECdg6hQRF60Mz4MtBsbsdNtiET9MC1fvaJYM0bRoTOPyqKT5bayj7zNnOnjx4+vr6/VFmGoNrjaaXKAn22p6v6Ykg2amCdr3UR062FjipdOwufvxvI1KUJm13LVqDtrA0weWEa5KLQdQODgzEmBVHtEuLvm375HI1KjRKy2xZDIT2Y2IctTjZPHKJo7aVK1kLzPNRyrbQ13Q2GlQvwMJniDGGTF+pJ4QWMnZROUEKQwsiJCNC8hNPVoECFEULYt/RG12FYH709xcBcy3mns0elBxLrT6XRKMTB5ghPANYSCjQXacpqT9Hj602g2WsrRDQ6JE8DM0TI7IrdYCQjiuU5azQOnLPebBJ04lVvs4SklhDl1w6MxLyYnxGAbWiKyXQuMreHZQxaHYAIZLdpPqtZNBBdmCv0ZI6Cwx1ab1gJ7J2jArGxSnVteNN17tDFw2ZXdJpm42M70Cq7bdVXESzG9Fdq517QuycJGf7iFazFjUspQucehsVRKljXdttaEGyEHgxDE7vhkdRxUVGaMo7qOfEuEZvKMyOv6YmuXy4L/GkfUUNUMN9GPucVmChsfuR4wezjAca6j3R8GyV0+QqQoosomt1164gopQGJldGa0zAKh9WHEZaZtg6uthfLO+NgQkV6+4zCgZRDKZkBJxV9P08Sx8HT0TSVbSa5hYB0elYi4kQryj3MB/GWT3ZbkDarSlwLdbsHfR5bJqpgI7Ky5jqhUfeM4AfWSihyA1O5Oml5aXhX0hHb0eFpDqny7xCSt2YzdWXTrXeaza5qSzFRVdlvV842qSqJEzAtBFUK+7V6YFoDs5A5FpSqD51bjENV8ydFNFNAlCJDKkoLVwKRZMbcp47Jx4jC0G7qj9oU8npkehOiUotTaGI6JRBeUfNT+P6TPf+u40wlsVEhQ38Ku4iJ13UcARCKNNZ9km8SpG9rLDWLYIkMNso2DrWxVdWI/VENqnQcil5LUsjbu8e3ONCiNhgCI295xcxnrJlY4RbnSJzTsOGh+TpHvLIiNJqrqOXM4+lXfzRyusHMJGuvhmdVsIWd6gQbMSdUrrMPzQaMUs93cETQK2PnQbSV0IGtYi4EJQAiJtZDnXcfx9N6ClO8LcY5+64xhTcGkOHNlykWhnGwCGXN8aeY1cF2vkZJt68Xpka70nS25wSWSvVBV3R2oRMC2bZTVA5bmIs5SM7gpzm3PCfY1YgkJnvu6tTclisBYnCQYwr3Ic9ofRJyeHsHfJELFsZH5UHiEpZsdXEdEUD+yyEwEKFf1x667w7Y+Qo78Us1jlJXxihqY0NgnaM1m4tisTEKeZFXaJAenuSiRB65GBmHXaeYLqmTZ011jvNa/mJCSIoohYn1QFGN+xzyWXTTgVa21DIzlVqiqaCoHYW5Xpba+W8OiAmKVTQcbqQo/RySiB8RotYl7Zd6TO6vrvite0KK900ZzIU8kDUwDzn1Xrju7FuQH/0WVlGlJSEOwXYsDY5LiPWRzT28hGMXagssfwj5NO1FtyD2109jZ7xvhvHqeInu3WN0PdLQIbgqKJ6HciyZ51CYpwTlUz7S4V5J0qM4uT4I4NqAaXrgQqg3T0Wb9OylbPWs/hZptlfp0gmijFcPDJxNjc6wWsmq+bDRGDYfEUMtjeQEnM2JP7EAsc23JLpNQqj81LEq9uhwh5+0pas6o2LDZOWvoJMjAG5n9z0FJGLwU9zSRIUMatorq9IsBnMFLCTD1a4uvFIF3YSZNpzogiVrZLdZOHdngoFFg3iRPKZbtA0tNBfjBpNkMFzpz0xVrOEP6qUlbOc1Ib5psIMCWP2w8srJ5BeAlqboZqjcz6Suu6tKepHF0p4CtSrTlXnhbXatUPVmcteVMOc5NIRyRlgZisAmoqN7tLAJ5AGAC15z3upX+US1loxBgs9NIMQSnrQvtMwhspiFt6CNpKo7+vScyxtBPg5R3G9q+EdxvaELWpY3Z3nKjuaKXImIdUOOCPdVpknoOpHvqNX0hdqF2MOdl/4u3t1rLzGzXmVTjxNsagC8qlN1tFIBHWZ5wibkTAKyNcp2OAxU00ldqXXOCUBFP5ETas7CQGNbX2Cmfrdl/FPVCvJDLGkDioZasCHsA2J6OUGcxyBz1TPWsNlxFFBGiLX5zqX1BA60b/KJrcobeructK1H4I3TA6wIoEl1dPkx8U/biuxkOV61WTgTEScndi2VTSasskilz1BTYpifNdGXzVFsnKN4SF25Oh9llj1k1E0SG+nGejiJMWVZfWADSriZfs62GaeKiSURCIKelMSaWYs2RiqZZSuBij2kPDpBVO81R+t3knl8s5cGxXBr1G06q2UCWeGWgjtThbkQlxYs8yFdAiNgEI6KVBJeIe4ZPMiwdXIqpLUrApYo60k5uTb7VfpK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\n", "text/plain": [ "" ] }, "metadata": {} } ], "source": [ "from PIL import Image\n", "import os\n", "from IPython.display import display \n", "from imutils import paths\n", "\n", "imgpths = paths.list_images('Dataset')\n", "print('Dataset')\n", "count=0\n", "kelas_seb = None\n", "for dirimg in imgpths:\n", " kelas = dirimg.split(os.path.sep) [1]\n", " if kelas_seb is None:\n", " print(kelas)\n", " kelas_seb = kelas\n", " if kelas_seb != kelas:\n", " print(kelas)\n", " count=0\n", " kelas_seb = kelas\n", " a = Image.open(dirimg)\n", " \n", " count=count+1\n", " if count <= 3:\n", " im =a.resize((200,200))\n", " display(im)\n", " else:\n", " continue\n" ] }, { "cell_type": "markdown", "metadata": { "id": "nJ623PhvM8-P" }, "source": [ "Source code berikut berfungsi untuk mengambil nama folder yang ada pada folder Dataset dan mengambil setiap nama file gambar, kemudian untuk setiap folder atau kelas yang ada di Dataset akan ditampilkan gambar yang ada di subfolder berikut masing-masing 3 gambar." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "d6b4Z3UYAf90" }, "outputs": [], "source": [ "imgpths = paths.list_images('Dataset')\n", "count=1\n", "kelas_seb = None\n", "for dirimg in imgpths:\n", " dir = dirimg.split(os.path.sep)[0]\n", " kelas = dirimg.split(os.path.sep)[1]\n", " nama = dirimg.split(os.path.sep)[2]\n", " if kelas_seb is None:\n", " kelas_seb = kelas\n", " if kelas_seb != kelas:\n", " count=1\n", " kelas_seb = kelas\n", " pat = os.path.join(dir,kelas,nama)\n", " nama_baru = kelas+str(count)+'.jpg'\n", " os.rename(pat,os.path.join(dir,kelas,nama_baru))\n", " count=count+1\n" ] }, { "cell_type": "markdown", "metadata": { "id": "CPiPQpsNQNRi" }, "source": [ "Source code berikut berfungssi untuk mengubah nama file dari setiap gambar yang berada dalam folder kelas menjadi berformat \"(nama kelas)1.jpg\", \"(nama kelas)2.jpg\", dst." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Il7vqTg5hCnl" }, "outputs": [], "source": [ "def unique(list1):\n", " \n", " unique_list = []\n", " \n", " for x in list1:\n", " if x not in unique_list:\n", " unique_list.append(x)\n", " return unique_list" ] }, { "cell_type": "markdown", "metadata": { "id": "HNuCoYweUPpq" }, "source": [ "Sebuah function untuk mendapatkan nilai-nilai unik dari sebuah list, menjadi list baru yang berisi nilai-nilai yang tidak berulang." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QEws_NMZDel4", "outputId": "e5338c70-407f-4b63-9d88-456253e26ed2" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['Tenebrio', 'Zophobas']" ] }, "metadata": {}, "execution_count": 7 } ], "source": [ "import numpy as np\n", "\n", "kumpl_gmbr = []\n", "kumpl_lbl = []\n", "imgpths = paths.list_images('Dataset')\n", "\n", "for dirimg in imgpths:\n", " gmbr = Image.open(dirimg)\n", " gmbrz = np.array(gmbr.resize((224,224))) / 255.0\n", " kumpl_gmbr.append(gmbrz)\n", "\n", " lbl_kls = dirimg.split(os.path.sep) [-2]\n", " kumpl_lbl.append(lbl_kls)\n", "kelas_asli = unique(kumpl_lbl)\n", "kelas_asli" ] }, { "cell_type": "markdown", "metadata": { "id": "PTEcxC5LY1BJ" }, "source": [ "Source code berikut berfungsi untuk mengumpulkan semua gambar dataset dalam satu variabel dan melakukan rescale serta normalisasi dengan membagi nilai warna dengan 255. Kemudian menyimpan label dari setiap gambar sesuai dengan posisi gambar berada di subfolder kelas mana. Lalu, mendapatkan nilai unique dari label ini sehingga dapat mengetahui ada berapa kelas dalam dataset tersebut." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OoJ86g7frOuW" }, "outputs": [], "source": [ "def image_to_feature_vector(image, size=(224, 224)):\n", "\t# resize the image to a fixed size, then flatten the image into\n", "\t# a list of raw pixel intensities\n", "\treturn cv2.resize(image, size).flatten()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uxQr7s6VrgOh" }, "outputs": [], "source": [ "def extract_color_histogram(image, bins=(8, 8, 8)):\n", "\t# extract a 3D color histogram from the HSV color space using\n", "\t# the supplied number of `bins` per channel\n", "\thsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n", "\thist = cv2.calcHist([hsv], [0, 1, 2], None, bins,\n", "\t\t[0, 180, 0, 256, 0, 256])\n", "\t# handle normalizing the histogram if we are using OpenCV 2.4.X\n", "\tif imutils.is_cv2():\n", "\t\thist = cv2.normalize(hist)\n", "\t# otherwise, perform \"in place\" normalization in OpenCV 3 (I\n", "\t# personally hate the way this is done\n", "\telse:\n", "\t\tcv2.normalize(hist, hist)\n", "\t# return the flattened histogram as the feature vector\n", "\treturn hist.flatten()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "R6gvmlqm23JI" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lTWLrEhRBHeY" }, "outputs": [], "source": [ "#import numpy as np\n", "\n", "def plot_confusion_matrix(cm,\n", " target_names=kelas_asli,\n", " title='Confusion matrix',\n", " cmap=None,\n", " normalize=True):\n", " \"\"\"\n", " given a sklearn confusion matrix (cm), make a nice plot\n", "\n", " Arguments\n", " ---------\n", " cm: confusion matrix from sklearn.metrics.confusion_matrix\n", "\n", " target_names: given classification classes such as [0, 1, 2]\n", " the class names, for example: ['high', 'medium', 'low']\n", "\n", " title: the text to display at the top of the matrix\n", "\n", " cmap: the gradient of the values displayed from matplotlib.pyplot.cm\n", " see http://matplotlib.org/examples/color/colormaps_reference.html\n", " plt.get_cmap('jet') or plt.cm.Blues\n", "\n", " normalize: If False, plot the raw numbers\n", " If True, plot the proportions\n", "\n", " Usage\n", " -----\n", " plot_confusion_matrix(cm = cm, # confusion matrix created by\n", " # sklearn.metrics.confusion_matrix\n", " normalize = True, # show proportions\n", " target_names = y_labels_vals, # list of names of the classes\n", " title = best_estimator_name) # title of graph\n", "\n", " Citiation\n", " ---------\n", " http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n", "\n", " \"\"\"\n", " import matplotlib.pyplot as plt\n", " import numpy as np\n", " import itertools\n", "\n", " accuracy = np.trace(cm) / float(np.sum(cm))\n", " misclass = 1 - accuracy\n", "\n", " if cmap is None:\n", " cmap = plt.get_cmap('Blues')\n", "\n", " plt.figure(figsize=(8, 6))\n", " if normalize:\n", " plt.imshow(cm/7, interpolation='nearest', cmap=cmap)\n", " else:\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", "\n", " if target_names is not None:\n", " tick_marks = np.arange(len(target_names))\n", " plt.xticks(tick_marks, target_names, rotation=45)\n", " plt.yticks(tick_marks, target_names)\n", "\n", " if normalize:\n", " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", "\n", "\n", " thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " if normalize:\n", " plt.text(j, i, \"{:0.3f}\".format(cm[i, j]),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", " else:\n", " plt.text(j, i, \"{:,}\".format(cm[i, j]),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", "\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "K1T9uFKJqxBJ" }, "outputs": [], "source": [ "import numpy as np\n", "import cv2\n", "\n", "kumpl_gmbr = []\n", "kumpl_lbl = []\n", "imgpths = paths.list_images('Dataset')\n", "\n", "for dirimg in imgpths:\n", " gmbr = Image.open(dirimg)\n", " gmbrz = np.array(gmbr.resize((224,224)))\n", " nil_pix = image_to_feature_vector(gmbrz)\n", "\n", " kumpl_gmbr.append(nil_pix)\n", "\n", " lbl_kls = dirimg.split(os.path.sep) [-2]\n", " kumpl_lbl.append(lbl_kls)\n", "kelas_asli = unique(kumpl_lbl)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "s0X3gjsGW4s8", "outputId": "45b8c0c8-b87a-4b07-a419-d422ea2fce39" }, "outputs": [ { "output_type": 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'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas',\n", " 'Zophobas']" ] }, "metadata": {}, "execution_count": 12 } ], "source": [ "kumpl_lbl" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3qQGRXu1YNhD", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "fa88950d-9289-427f-95d4-24f03addda0c" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['Tenebrio', 'Zophobas']" ] }, "metadata": {}, "execution_count": 13 } ], "source": [ "import numpy as np\n", "from PIL import Image\n", "\n", "\n", "kumpl_gmbr = []\n", "kumpl_lbl = []\n", "imgpths = paths.list_images('Dataset')\n", "\n", "for dirimg in imgpths:\n", " gmbr = Image.open(dirimg)\n", " gmbrz = np.array(gmbr.resize((224,224))) / 255.0\n", " kumpl_gmbr.append(gmbrz)\n", "\n", " lbl_kls = dirimg.split(os.path.sep) [-2]\n", " kumpl_lbl.append(lbl_kls)\n", "kelas_asli = unique(kumpl_lbl)\n", "kelas_asli" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-Q_7PLITZlvg" }, "outputs": [], "source": [ "from sklearn.preprocessing import LabelBinarizer\n", "\n", "lb = LabelBinarizer()\n", "kumpl_lbl = lb.fit_transform(kumpl_lbl)\n", "if len(kelas_asli)==2:\n", " kumpl_lbl = np.hstack((kumpl_lbl, 1 - kumpl_lbl))" ] }, { "cell_type": "markdown", "metadata": { "id": "EZYHSZIq42EU" }, "source": [ "VGG19" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NietlXmg2i3M", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "2e6a20cf-3502-4e31-8f29-d9b6d5b11ad5" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n", "80142336/80134624 [==============================] - 1s 0us/step\n", "80150528/80134624 [==============================] - 1s 0us/step\n", "Epoch 1/25\n", "18/18 [==============================] - 23s 267ms/step - loss: 0.7483 - accuracy: 0.5885\n", "Epoch 2/25\n", "18/18 [==============================] - 5s 267ms/step - loss: 0.4822 - accuracy: 0.8003\n", "Epoch 3/25\n", "18/18 [==============================] - 5s 267ms/step - loss: 0.3709 - accuracy: 0.8854\n", "Epoch 4/25\n", "18/18 [==============================] - 5s 268ms/step - loss: 0.2879 - accuracy: 0.9132\n", "Epoch 5/25\n", "18/18 [==============================] - 5s 269ms/step - loss: 0.2431 - accuracy: 0.9201\n", "Epoch 6/25\n", "18/18 [==============================] - 5s 270ms/step - loss: 0.2043 - accuracy: 0.9358\n", "Epoch 7/25\n", "18/18 [==============================] - 5s 270ms/step - loss: 0.1822 - accuracy: 0.9340\n", "Epoch 8/25\n", "18/18 [==============================] - 5s 270ms/step - loss: 0.1871 - accuracy: 0.9253\n", "Epoch 9/25\n", "18/18 [==============================] - 5s 271ms/step - loss: 0.2249 - accuracy: 0.8993\n", "Epoch 10/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1445 - accuracy: 0.9427\n", "Epoch 11/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.1096 - accuracy: 0.9705\n", "Epoch 12/25\n", "18/18 [==============================] - 5s 271ms/step - loss: 0.0917 - accuracy: 0.9792\n", "Epoch 13/25\n", "18/18 [==============================] - 5s 271ms/step - loss: 0.0818 - accuracy: 0.9861\n", "Epoch 14/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.0784 - accuracy: 0.9861\n", "Epoch 15/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0925 - accuracy: 0.9792\n", "Epoch 16/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0787 - accuracy: 0.9757\n", "Epoch 17/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0589 - accuracy: 0.9948\n", "Epoch 18/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0553 - accuracy: 0.9983\n", "Epoch 19/25\n", "18/18 [==============================] - 5s 270ms/step - loss: 0.0699 - accuracy: 0.9844\n", "Epoch 20/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0657 - accuracy: 0.9809\n", "Epoch 21/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.0400 - accuracy: 0.9965\n", "Epoch 22/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.0323 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0305 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0291 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.0276 - accuracy: 1.0000\n", "Score for fold 1: loss of 0.26910457015037537; accuracy of 90.625%\n", "Epoch 1/25\n", "18/18 [==============================] - 6s 274ms/step - loss: 0.8245 - accuracy: 0.5556\n", "Epoch 2/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.4812 - accuracy: 0.8212\n", "Epoch 3/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.3752 - accuracy: 0.8455\n", "Epoch 4/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.3030 - accuracy: 0.8837\n", "Epoch 5/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.2651 - accuracy: 0.8958\n", "Epoch 6/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.2426 - accuracy: 0.8941\n", "Epoch 7/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1829 - accuracy: 0.9340\n", "Epoch 8/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1633 - accuracy: 0.9427\n", "Epoch 9/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1500 - accuracy: 0.9427\n", "Epoch 10/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1728 - accuracy: 0.9271\n", "Epoch 11/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1460 - accuracy: 0.9375\n", "Epoch 12/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1028 - accuracy: 0.9688\n", "Epoch 13/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0732 - accuracy: 0.9844\n", "Epoch 14/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0698 - accuracy: 0.9809\n", "Epoch 15/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0565 - accuracy: 0.9896\n", "Epoch 16/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0511 - accuracy: 0.9913\n", "Epoch 17/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0415 - accuracy: 0.9948\n", "Epoch 18/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0397 - accuracy: 0.9913\n", "Epoch 19/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0346 - accuracy: 0.9948\n", "Epoch 20/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0304 - accuracy: 0.9948\n", "Epoch 21/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0269 - accuracy: 0.9965\n", "Epoch 22/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0198 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0171 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0156 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.0138 - accuracy: 1.0000\n", "Score for fold 2: loss of 0.16893555223941803; accuracy of 95.3125%\n", "Epoch 1/25\n", "18/18 [==============================] - 6s 273ms/step - loss: 0.9716 - accuracy: 0.5938\n", "Epoch 2/25\n", "18/18 [==============================] - 5s 271ms/step - loss: 0.4372 - accuracy: 0.8073\n", "Epoch 3/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.3384 - accuracy: 0.8750\n", "Epoch 4/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.3189 - accuracy: 0.8594\n", "Epoch 5/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.2780 - accuracy: 0.9010\n", "Epoch 6/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.2594 - accuracy: 0.8958\n", "Epoch 7/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.1965 - accuracy: 0.9323\n", "Epoch 8/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.2146 - accuracy: 0.9201\n", "Epoch 9/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.1733 - accuracy: 0.9288\n", "Epoch 10/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1519 - accuracy: 0.9601\n", "Epoch 11/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1240 - accuracy: 0.9653\n", "Epoch 12/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1259 - accuracy: 0.9635\n", "Epoch 13/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1228 - accuracy: 0.9514\n", "Epoch 14/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1068 - accuracy: 0.9635\n", "Epoch 15/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0961 - accuracy: 0.9757\n", "Epoch 16/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0871 - accuracy: 0.9792\n", "Epoch 17/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0729 - accuracy: 0.9826\n", "Epoch 18/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0606 - accuracy: 0.9878\n", "Epoch 19/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0570 - accuracy: 0.9931\n", "Epoch 20/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0492 - accuracy: 0.9948\n", "Epoch 21/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0471 - accuracy: 0.9965\n", "Epoch 22/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0398 - accuracy: 0.9965\n", "Epoch 23/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0369 - accuracy: 0.9983\n", "Epoch 24/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0431 - accuracy: 0.9983\n", "Epoch 25/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0360 - accuracy: 1.0000\n", "Score for fold 3: loss of 0.1377037614583969; accuracy of 95.3125%\n", "Epoch 1/25\n", "18/18 [==============================] - 6s 274ms/step - loss: 0.8410 - accuracy: 0.5990\n", "Epoch 2/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.4384 - accuracy: 0.8090\n", "Epoch 3/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.3516 - accuracy: 0.8420\n", "Epoch 4/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.2950 - accuracy: 0.8715\n", "Epoch 5/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.2386 - accuracy: 0.8993\n", "Epoch 6/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.2137 - accuracy: 0.9149\n", "Epoch 7/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.2164 - accuracy: 0.9115\n", "Epoch 8/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1608 - accuracy: 0.9392\n", "Epoch 9/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1298 - accuracy: 0.9531\n", "Epoch 10/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1265 - accuracy: 0.9479\n", "Epoch 11/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1073 - accuracy: 0.9653\n", "Epoch 12/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0900 - accuracy: 0.9670\n", "Epoch 13/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0767 - accuracy: 0.9774\n", "Epoch 14/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0694 - accuracy: 0.9861\n", "Epoch 15/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0995 - accuracy: 0.9549\n", "Epoch 16/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0909 - accuracy: 0.9583\n", "Epoch 17/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0562 - accuracy: 0.9826\n", "Epoch 18/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0314 - accuracy: 0.9948\n", "Epoch 19/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0240 - accuracy: 0.9983\n", "Epoch 20/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0191 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0200 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0162 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0125 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0103 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0088 - accuracy: 1.0000\n", "Score for fold 4: loss of 0.09713289141654968; accuracy of 95.3125%\n", "Epoch 1/25\n", "18/18 [==============================] - 6s 274ms/step - loss: 0.6027 - accuracy: 0.6771\n", "Epoch 2/25\n", "18/18 [==============================] - 5s 271ms/step - loss: 0.3370 - accuracy: 0.8594\n", "Epoch 3/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.2548 - accuracy: 0.8889\n", "Epoch 4/25\n", "18/18 [==============================] - 5s 271ms/step - loss: 0.1874 - accuracy: 0.9288\n", "Epoch 5/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.1554 - accuracy: 0.9566\n", "Epoch 6/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.1494 - accuracy: 0.9479\n", "Epoch 7/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.1306 - accuracy: 0.9531\n", "Epoch 8/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1137 - accuracy: 0.9618\n", "Epoch 9/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0951 - accuracy: 0.9740\n", "Epoch 10/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0742 - accuracy: 0.9861\n", "Epoch 11/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.0737 - accuracy: 0.9844\n", "Epoch 12/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.0580 - accuracy: 0.9931\n", "Epoch 13/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0767 - accuracy: 0.9792\n", "Epoch 14/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0433 - accuracy: 0.9896\n", "Epoch 15/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.0332 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0288 - accuracy: 0.9983\n", "Epoch 17/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0263 - accuracy: 0.9965\n", "Epoch 18/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0243 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0248 - accuracy: 0.9983\n", "Epoch 20/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0198 - accuracy: 0.9983\n", "Epoch 21/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0156 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0181 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0148 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0173 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0115 - accuracy: 1.0000\n", "WARNING:tensorflow:5 out of the last 9 calls to .test_function at 0x7fda90ce8a70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Score for fold 5: loss of 0.21195587515830994; accuracy of 96.875%\n", "WARNING:tensorflow:5 out of the last 9 calls to .predict_function at 0x7fda90bebcb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/25\n", "18/18 [==============================] - 6s 276ms/step - loss: 0.6558 - accuracy: 0.6215\n", "Epoch 2/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.4121 - accuracy: 0.7899\n", "Epoch 3/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.3000 - accuracy: 0.8802\n", "Epoch 4/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.2180 - accuracy: 0.9115\n", "Epoch 5/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.2067 - accuracy: 0.9062\n", "Epoch 6/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.2295 - accuracy: 0.9028\n", "Epoch 7/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1416 - accuracy: 0.9549\n", "Epoch 8/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1270 - accuracy: 0.9583\n", "Epoch 9/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1111 - accuracy: 0.9653\n", "Epoch 10/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1026 - accuracy: 0.9583\n", "Epoch 11/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1072 - accuracy: 0.9670\n", "Epoch 12/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0840 - accuracy: 0.9740\n", "Epoch 13/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0737 - accuracy: 0.9740\n", "Epoch 14/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0636 - accuracy: 0.9844\n", "Epoch 15/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0580 - accuracy: 0.9861\n", "Epoch 16/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0462 - accuracy: 0.9931\n", "Epoch 17/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0367 - accuracy: 0.9965\n", "Epoch 18/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0312 - accuracy: 0.9983\n", "Epoch 19/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0268 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0260 - accuracy: 0.9983\n", "Epoch 21/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0321 - accuracy: 0.9965\n", "Epoch 22/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0214 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0202 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0168 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0142 - accuracy: 1.0000\n", "WARNING:tensorflow:6 out of the last 11 calls to .test_function at 0x7fda90958b00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Score for fold 6: loss of 0.14098680019378662; accuracy of 98.4375%\n", "WARNING:tensorflow:6 out of the last 11 calls to .predict_function at 0x7fda90958ef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/25\n", "18/18 [==============================] - 6s 276ms/step - loss: 0.9647 - accuracy: 0.5417\n", "Epoch 2/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.4199 - accuracy: 0.7882\n", "Epoch 3/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.2832 - accuracy: 0.8993\n", "Epoch 4/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.2463 - accuracy: 0.9045\n", "Epoch 5/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.2176 - accuracy: 0.9219\n", "Epoch 6/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1842 - accuracy: 0.9358\n", "Epoch 7/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1809 - accuracy: 0.9427\n", "Epoch 8/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1570 - accuracy: 0.9462\n", "Epoch 9/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1339 - accuracy: 0.9531\n", "Epoch 10/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1100 - accuracy: 0.9792\n", "Epoch 11/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1053 - accuracy: 0.9688\n", "Epoch 12/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0911 - accuracy: 0.9774\n", "Epoch 13/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0834 - accuracy: 0.9809\n", "Epoch 14/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.0740 - accuracy: 0.9931\n", "Epoch 15/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0783 - accuracy: 0.9774\n", "Epoch 16/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0618 - accuracy: 0.9896\n", "Epoch 17/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.0557 - accuracy: 0.9931\n", "Epoch 18/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.0577 - accuracy: 0.9878\n", "Epoch 19/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.0523 - accuracy: 0.9931\n", "Epoch 20/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0450 - accuracy: 0.9913\n", "Epoch 21/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.0393 - accuracy: 0.9948\n", "Epoch 22/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0402 - accuracy: 0.9931\n", "Epoch 23/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0320 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0284 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0215 - accuracy: 1.0000\n", "Score for fold 7: loss of 0.2048337310552597; accuracy of 92.1875%\n", "Epoch 1/25\n", "18/18 [==============================] - 6s 275ms/step - loss: 0.6446 - accuracy: 0.6545\n", "Epoch 2/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.3788 - accuracy: 0.8490\n", "Epoch 3/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.3055 - accuracy: 0.8767\n", "Epoch 4/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.2447 - accuracy: 0.9115\n", "Epoch 5/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.2017 - accuracy: 0.9219\n", "Epoch 6/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1715 - accuracy: 0.9392\n", "Epoch 7/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1515 - accuracy: 0.9462\n", "Epoch 8/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1152 - accuracy: 0.9705\n", "Epoch 9/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1036 - accuracy: 0.9722\n", "Epoch 10/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1143 - accuracy: 0.9583\n", "Epoch 11/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0701 - accuracy: 0.9844\n", "Epoch 12/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0543 - accuracy: 0.9948\n", "Epoch 13/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0442 - accuracy: 0.9913\n", "Epoch 14/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0599 - accuracy: 0.9792\n", "Epoch 15/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0569 - accuracy: 0.9861\n", "Epoch 16/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0554 - accuracy: 0.9844\n", "Epoch 17/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0391 - accuracy: 0.9896\n", "Epoch 18/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0434 - accuracy: 0.9913\n", "Epoch 19/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0181 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0128 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0159 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0169 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0096 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0074 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.0085 - accuracy: 1.0000\n", "Score for fold 8: loss of 0.3237378001213074; accuracy of 87.5%\n", "Epoch 1/25\n", "18/18 [==============================] - 6s 276ms/step - loss: 0.8046 - accuracy: 0.5694\n", "Epoch 2/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.4961 - accuracy: 0.7188\n", "Epoch 3/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.3710 - accuracy: 0.8594\n", "Epoch 4/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.3106 - accuracy: 0.8889\n", "Epoch 5/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.2966 - accuracy: 0.8785\n", "Epoch 6/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.2470 - accuracy: 0.9097\n", "Epoch 7/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.2146 - accuracy: 0.9271\n", "Epoch 8/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1856 - accuracy: 0.9358\n", "Epoch 9/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1894 - accuracy: 0.9201\n", "Epoch 10/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1813 - accuracy: 0.9323\n", "Epoch 11/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1435 - accuracy: 0.9479\n", "Epoch 12/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1266 - accuracy: 0.9583\n", "Epoch 13/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1200 - accuracy: 0.9583\n", "Epoch 14/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1205 - accuracy: 0.9618\n", "Epoch 15/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1312 - accuracy: 0.9462\n", "Epoch 16/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.1084 - accuracy: 0.9601\n", "Epoch 17/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0765 - accuracy: 0.9774\n", "Epoch 18/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0734 - accuracy: 0.9826\n", "Epoch 19/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0546 - accuracy: 0.9913\n", "Epoch 20/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0473 - accuracy: 0.9931\n", "Epoch 21/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0404 - accuracy: 0.9948\n", "Epoch 22/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0363 - accuracy: 0.9948\n", "Epoch 23/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0341 - accuracy: 0.9948\n", "Epoch 24/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0333 - accuracy: 0.9948\n", "Epoch 25/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0284 - accuracy: 1.0000\n", "Score for fold 9: loss of 0.15690341591835022; accuracy of 93.75%\n", "Epoch 1/25\n", "18/18 [==============================] - 6s 275ms/step - loss: 0.6007 - accuracy: 0.6632\n", "Epoch 2/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.3651 - accuracy: 0.8733\n", "Epoch 3/25\n", "18/18 [==============================] - 5s 272ms/step - loss: 0.2853 - accuracy: 0.8993\n", "Epoch 4/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.2441 - accuracy: 0.9062\n", "Epoch 5/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.2574 - accuracy: 0.8889\n", "Epoch 6/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.2010 - accuracy: 0.9132\n", "Epoch 7/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.1576 - accuracy: 0.9358\n", "Epoch 8/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1626 - accuracy: 0.9427\n", "Epoch 9/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.1278 - accuracy: 0.9601\n", "Epoch 10/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1082 - accuracy: 0.9740\n", "Epoch 11/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0992 - accuracy: 0.9670\n", "Epoch 12/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0890 - accuracy: 0.9774\n", "Epoch 13/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0883 - accuracy: 0.9740\n", "Epoch 14/25\n", "18/18 [==============================] - 5s 273ms/step - loss: 0.0650 - accuracy: 0.9861\n", "Epoch 15/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.0598 - accuracy: 0.9878\n", "Epoch 16/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.0958 - accuracy: 0.9601\n", "Epoch 17/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.1348 - accuracy: 0.9444\n", "Epoch 18/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.0635 - accuracy: 0.9774\n", "Epoch 19/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0391 - accuracy: 0.9913\n", "Epoch 20/25\n", "18/18 [==============================] - 5s 276ms/step - loss: 0.0286 - accuracy: 0.9983\n", "Epoch 21/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0295 - accuracy: 0.9948\n", "Epoch 22/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0221 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0283 - accuracy: 0.9983\n", "Epoch 24/25\n", "18/18 [==============================] - 5s 274ms/step - loss: 0.0159 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 5s 275ms/step - loss: 0.0140 - accuracy: 1.0000\n", "Score for fold 10: loss of 0.14960581064224243; accuracy of 96.875%\n", "------------------------------------------------------------------------\n", "VGG19\n", "Score per fold\n", "------------------------------------------------------------------------\n", "> Fold 1 - Loss: 0.26910457015037537 - Accuracy: 90.625%\n", "------------------------------------------------------------------------\n", "> Fold 2 - Loss: 0.16893555223941803 - Accuracy: 95.3125%\n", "------------------------------------------------------------------------\n", "> Fold 3 - Loss: 0.1377037614583969 - Accuracy: 95.3125%\n", "------------------------------------------------------------------------\n", "> Fold 4 - Loss: 0.09713289141654968 - Accuracy: 95.3125%\n", "------------------------------------------------------------------------\n", "> Fold 5 - Loss: 0.21195587515830994 - Accuracy: 96.875%\n", "------------------------------------------------------------------------\n", "> Fold 6 - Loss: 0.14098680019378662 - Accuracy: 98.4375%\n", "------------------------------------------------------------------------\n", "> Fold 7 - Loss: 0.2048337310552597 - Accuracy: 92.1875%\n", "------------------------------------------------------------------------\n", "> Fold 8 - Loss: 0.3237378001213074 - Accuracy: 87.5%\n", "------------------------------------------------------------------------\n", "> Fold 9 - Loss: 0.15690341591835022 - Accuracy: 93.75%\n", "------------------------------------------------------------------------\n", "> Fold 10 - Loss: 0.14960581064224243 - Accuracy: 96.875%\n", "------------------------------------------------------------------------\n", "Average for each Fold:\n", "> Accuracy: 94.21875 (+- 3.1289038116407477)\n", "> Loss: 0.18609002083539963\n", "------------------------------------------------------------------------\n" ] }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } }, { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential_9\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " vgg19 (Functional) (None, 7, 7, 512) 20024384 \n", " \n", " flatten_9 (Flatten) (None, 25088) 0 \n", " \n", " dense_27 (Dense) (None, 50) 1254450 \n", " \n", " dense_28 (Dense) (None, 20) 1020 \n", " \n", " dense_29 (Dense) (None, 2) 42 \n", " \n", "=================================================================\n", "Total params: 21,279,896\n", "Trainable params: 1,255,512\n", "Non-trainable params: 20,024,384\n", "_________________________________________________________________\n" ] } ], "source": [ "#from sklearn.model_selection import KFold\n", "from sklearn.model_selection import StratifiedKFold\n", "from statistics import mean, stdev\n", "import numpy as np\n", "\n", "from tensorflow.keras.applications.vgg19 import VGG19\n", "from tensorflow.keras import layers, models\n", "from tensorflow.keras.losses import sparse_categorical_crossentropy\n", "\n", "#SF = KFold(n_splits=10, shuffle=True)\n", "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=1)\n", "acc_per_fold = []\n", "loss_per_fold = []\n", "kumpl_lbl = np.array(kumpl_lbl)\n", "# kumpl_lbl = kumpl_lbl.reshape(kumpl_lbl.shape[0], 1)\n", "kumpl_gmbr = np.array(kumpl_gmbr)\n", "fold_no = 1\n", "\n", "num_label=len(kelas_asli)\n", "all_cm=np.zeros((num_label,num_label))\n", "\n", "for latih_idx, uji_idx in skf.split(kumpl_gmbr, np.argmax(kumpl_lbl, axis=1)):\n", " vgg = VGG19(input_shape=[224,224] + [3], weights='imagenet', include_top=False)\n", " vgg.trainable = False \n", " flatten_layer = layers.Flatten()\n", " dense_layer_1 = layers.Dense(50, activation='relu')\n", " dense_layer_2 = layers.Dense(20, activation='relu')\n", " prediction_layer = layers.Dense(len(kelas_asli), activation='softmax')\n", "\n", " vgg_19 = models.Sequential([\n", " vgg,\n", " flatten_layer,\n", " dense_layer_1,\n", " dense_layer_2,\n", " prediction_layer\n", " ])\n", "\n", " vgg_19.compile(\n", " optimizer='adam',\n", " loss='categorical_crossentropy',\n", " metrics=['accuracy'],\n", " )\n", " h = vgg_19.fit(kumpl_gmbr[latih_idx], kumpl_lbl[latih_idx,:], epochs = 25, batch_size = 32)\n", " scores = vgg_19.evaluate(kumpl_gmbr[uji_idx], kumpl_lbl[uji_idx], verbose=0)\n", " print(f'Score for fold {fold_no}: {vgg_19.metrics_names[0]} of {scores[0]}; {vgg_19.metrics_names[1]} of {scores[1]*100}%')\n", " acc_per_fold.append(scores[1] * 100)\n", " loss_per_fold.append(scores[0])\n", " fold_no = fold_no + 1\n", "\n", " #for cm only\n", " from sklearn.metrics import confusion_matrix\n", " predict = vgg_19.predict(kumpl_gmbr[uji_idx])\n", " cm = confusion_matrix(np.argmax(kumpl_lbl[uji_idx], axis=1), np.argmax(predict, axis=1))\n", " all_cm=all_cm+cm\n", "\n", "print('------------------------------------------------------------------------')\n", "print('VGG19')\n", "print('Score per fold')\n", "for i in range(0, len(acc_per_fold)):\n", " print('------------------------------------------------------------------------')\n", " print(f'> Fold {i+1} - Loss: {loss_per_fold[i]} - Accuracy: {acc_per_fold[i]}%')\n", "print('------------------------------------------------------------------------')\n", "print('Average for each Fold:')\n", "print(f'> Accuracy: {np.mean(acc_per_fold)} (+- {np.std(acc_per_fold)})')\n", "print(f'> Loss: {np.mean(loss_per_fold)}')\n", "print('------------------------------------------------------------------------')\n", "plot_confusion_matrix(all_cm,normalize=False)\n", "vgg_19.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "2yLwpwlJ5JNQ" }, "source": [ "Inception V3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "x7kGbpLL3Lbj", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "f116614e-d844-4695-d8b0-0add543ada75" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n", "87916544/87910968 [==============================] - 2s 0us/step\n", "87924736/87910968 [==============================] - 2s 0us/step\n", "Epoch 1/25\n", "18/18 [==============================] - 10s 165ms/step - loss: 1.8373 - accuracy: 0.7240\n", "Epoch 2/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.1413 - accuracy: 0.9410\n", "Epoch 3/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0859 - accuracy: 0.9722\n", "Epoch 4/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0421 - accuracy: 0.9931\n", "Epoch 5/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0239 - accuracy: 1.0000\n", "Epoch 6/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0145 - accuracy: 1.0000\n", "Epoch 7/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0112 - accuracy: 1.0000\n", "Epoch 8/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0093 - accuracy: 1.0000\n", "Epoch 9/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0081 - accuracy: 1.0000\n", "Epoch 10/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0069 - accuracy: 1.0000\n", "Epoch 11/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0054 - accuracy: 1.0000\n", "Epoch 12/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0049 - accuracy: 1.0000\n", "Epoch 13/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0043 - accuracy: 1.0000\n", "Epoch 14/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0036 - accuracy: 1.0000\n", "Epoch 15/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0033 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0029 - accuracy: 1.0000\n", "Epoch 17/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0025 - accuracy: 1.0000\n", "Epoch 18/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0023 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0021 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0018 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0016 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0015 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0013 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 9.8948e-04 - accuracy: 1.0000\n", "Score for fold 1: loss of 0.08001065254211426; accuracy of 98.4375%\n", "Epoch 1/25\n", "18/18 [==============================] - 8s 167ms/step - loss: 1.1987 - accuracy: 0.7188\n", "Epoch 2/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.1593 - accuracy: 0.9323\n", "Epoch 3/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.0492 - accuracy: 0.9826\n", "Epoch 4/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.0281 - accuracy: 0.9931\n", "Epoch 5/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0252 - accuracy: 0.9913\n", "Epoch 6/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0156 - accuracy: 0.9965\n", "Epoch 7/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0086 - accuracy: 0.9983\n", "Epoch 8/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.0029 - accuracy: 1.0000\n", "Epoch 9/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0027 - accuracy: 1.0000\n", "Epoch 10/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.0023 - accuracy: 1.0000\n", "Epoch 11/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.0020 - accuracy: 1.0000\n", "Epoch 12/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.0019 - accuracy: 1.0000\n", "Epoch 13/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.0018 - accuracy: 1.0000\n", "Epoch 14/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0016 - accuracy: 1.0000\n", "Epoch 15/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0015 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0015 - accuracy: 1.0000\n", "Epoch 17/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0014 - accuracy: 1.0000\n", "Epoch 18/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0013 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0012 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.0010 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 9.7076e-04 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 9.2155e-04 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 8.6910e-04 - accuracy: 1.0000\n", "Score for fold 2: loss of 0.040939606726169586; accuracy of 98.4375%\n", "Epoch 1/25\n", "18/18 [==============================] - 9s 164ms/step - loss: 1.2766 - accuracy: 0.7587\n", "Epoch 2/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.1492 - accuracy: 0.9479\n", "Epoch 3/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0454 - accuracy: 0.9861\n", "Epoch 4/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0195 - accuracy: 0.9948\n", "Epoch 5/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0027 - accuracy: 1.0000\n", "Epoch 6/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0028 - accuracy: 1.0000\n", "Epoch 7/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0017 - accuracy: 1.0000\n", "Epoch 8/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 9.9209e-04 - accuracy: 1.0000\n", "Epoch 9/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 9.6143e-04 - accuracy: 1.0000\n", "Epoch 10/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 8.4387e-04 - accuracy: 1.0000\n", "Epoch 11/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 7.9998e-04 - accuracy: 1.0000\n", "Epoch 12/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 7.0835e-04 - accuracy: 1.0000\n", "Epoch 13/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 6.5488e-04 - accuracy: 1.0000\n", "Epoch 14/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 6.3867e-04 - accuracy: 1.0000\n", "Epoch 15/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 5.8007e-04 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 5.5158e-04 - accuracy: 1.0000\n", "Epoch 17/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 5.1928e-04 - accuracy: 1.0000\n", "Epoch 18/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 4.9700e-04 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 4.7086e-04 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 4.4898e-04 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 4.2632e-04 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 4.0697e-04 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 3.8440e-04 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 3.6936e-04 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 3.5216e-04 - accuracy: 1.0000\n", "Score for fold 3: loss of 0.1294606328010559; accuracy of 96.875%\n", "Epoch 1/25\n", "18/18 [==============================] - 9s 170ms/step - loss: 3.1631 - accuracy: 0.7118\n", "Epoch 2/25\n", "18/18 [==============================] - 3s 166ms/step - loss: 0.3240 - accuracy: 0.9149\n", "Epoch 3/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.1267 - accuracy: 0.9566\n", "Epoch 4/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0203 - accuracy: 0.9913\n", "Epoch 5/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0084 - accuracy: 0.9983\n", "Epoch 6/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0040 - accuracy: 1.0000\n", "Epoch 7/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0020 - accuracy: 1.0000\n", "Epoch 8/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0015 - accuracy: 1.0000\n", "Epoch 9/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0013 - accuracy: 1.0000\n", "Epoch 10/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0012 - accuracy: 1.0000\n", "Epoch 11/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 12/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 9.9978e-04 - accuracy: 1.0000\n", "Epoch 13/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 9.9043e-04 - accuracy: 1.0000\n", "Epoch 14/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 8.8170e-04 - accuracy: 1.0000\n", "Epoch 15/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 7.9341e-04 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 7.3814e-04 - accuracy: 1.0000\n", "Epoch 17/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 6.9287e-04 - accuracy: 1.0000\n", "Epoch 18/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 6.5609e-04 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 6.1907e-04 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 5.7216e-04 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 5.4667e-04 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 5.1492e-04 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 4.8960e-04 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 4.7956e-04 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 4.5005e-04 - accuracy: 1.0000\n", "Score for fold 4: loss of 0.012595536187291145; accuracy of 100.0%\n", "Epoch 1/25\n", "18/18 [==============================] - 9s 165ms/step - loss: 2.8072 - accuracy: 0.6649\n", "Epoch 2/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.6752 - accuracy: 0.8802\n", "Epoch 3/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0974 - accuracy: 0.9705\n", "Epoch 4/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0206 - accuracy: 0.9913\n", "Epoch 5/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0056 - accuracy: 1.0000\n", "Epoch 6/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0023 - accuracy: 1.0000\n", "Epoch 7/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0012 - accuracy: 1.0000\n", "Epoch 8/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 7.6567e-04 - accuracy: 1.0000\n", "Epoch 9/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 5.6527e-04 - accuracy: 1.0000\n", "Epoch 10/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 5.4266e-04 - accuracy: 1.0000\n", "Epoch 11/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 4.7842e-04 - accuracy: 1.0000\n", "Epoch 12/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 4.5438e-04 - accuracy: 1.0000\n", "Epoch 13/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 4.2503e-04 - accuracy: 1.0000\n", "Epoch 14/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 4.1036e-04 - accuracy: 1.0000\n", "Epoch 15/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 3.7776e-04 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 3.5664e-04 - accuracy: 1.0000\n", "Epoch 17/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 3.5027e-04 - accuracy: 1.0000\n", "Epoch 18/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 3.2311e-04 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 3.0666e-04 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 2.9354e-04 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 2.7957e-04 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 2.6846e-04 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 2.5793e-04 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 2.5057e-04 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 2.4060e-04 - accuracy: 1.0000\n", "Score for fold 5: loss of 0.2480836808681488; accuracy of 92.1875%\n", "Epoch 1/25\n", "18/18 [==============================] - 8s 163ms/step - loss: 2.9457 - accuracy: 0.6771\n", "Epoch 2/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.2903 - accuracy: 0.9097\n", "Epoch 3/25\n", "18/18 [==============================] - 3s 161ms/step - loss: 0.1575 - accuracy: 0.9375\n", "Epoch 4/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0252 - accuracy: 0.9931\n", "Epoch 5/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0091 - accuracy: 0.9965\n", "Epoch 6/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0029 - accuracy: 1.0000\n", "Epoch 7/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0018 - accuracy: 1.0000\n", "Epoch 8/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0014 - accuracy: 1.0000\n", "Epoch 9/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0013 - accuracy: 1.0000\n", "Epoch 10/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 11/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 12/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 9.3617e-04 - accuracy: 1.0000\n", "Epoch 13/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 8.8149e-04 - accuracy: 1.0000\n", "Epoch 14/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 8.1175e-04 - accuracy: 1.0000\n", "Epoch 15/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 7.9319e-04 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 7.1285e-04 - accuracy: 1.0000\n", "Epoch 17/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 6.8141e-04 - accuracy: 1.0000\n", "Epoch 18/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 6.4118e-04 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 6.0944e-04 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 5.7662e-04 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 5.5997e-04 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 5.3163e-04 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 5.0389e-04 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 4.8295e-04 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 4.6343e-04 - accuracy: 1.0000\n", "Score for fold 6: loss of 0.05135352909564972; accuracy of 98.4375%\n", "Epoch 1/25\n", "18/18 [==============================] - 8s 172ms/step - loss: 3.7095 - accuracy: 0.6597\n", "Epoch 2/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.4240 - accuracy: 0.8924\n", "Epoch 3/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.1097 - accuracy: 0.9618\n", "Epoch 4/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0390 - accuracy: 0.9931\n", "Epoch 5/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0148 - accuracy: 0.9965\n", "Epoch 6/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0110 - accuracy: 1.0000\n", "Epoch 7/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0053 - accuracy: 1.0000\n", "Epoch 8/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0041 - accuracy: 1.0000\n", "Epoch 9/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0037 - accuracy: 1.0000\n", "Epoch 10/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0031 - accuracy: 1.0000\n", "Epoch 11/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0027 - accuracy: 1.0000\n", "Epoch 12/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0025 - accuracy: 1.0000\n", "Epoch 13/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0022 - accuracy: 1.0000\n", "Epoch 14/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0020 - accuracy: 1.0000\n", "Epoch 15/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0018 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0017 - accuracy: 1.0000\n", "Epoch 17/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0015 - accuracy: 1.0000\n", "Epoch 18/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0014 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0013 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0012 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0012 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0010 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 9.3452e-04 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 8.7789e-04 - accuracy: 1.0000\n", "Score for fold 7: loss of 0.1126035526394844; accuracy of 98.4375%\n", "Epoch 1/25\n", "18/18 [==============================] - 9s 165ms/step - loss: 1.1445 - accuracy: 0.7986\n", "Epoch 2/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.1921 - accuracy: 0.9410\n", "Epoch 3/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.1590 - accuracy: 0.9410\n", "Epoch 4/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0735 - accuracy: 0.9705\n", "Epoch 5/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0453 - accuracy: 0.9896\n", "Epoch 6/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0358 - accuracy: 0.9844\n", "Epoch 7/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0067 - accuracy: 0.9983\n", "Epoch 8/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0012 - accuracy: 1.0000\n", "Epoch 9/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 1.7655e-04 - accuracy: 1.0000\n", "Epoch 10/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 9.1835e-05 - accuracy: 1.0000\n", "Epoch 11/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 5.8142e-05 - accuracy: 1.0000\n", "Epoch 12/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 4.7870e-05 - accuracy: 1.0000\n", "Epoch 13/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 4.5335e-05 - accuracy: 1.0000\n", "Epoch 14/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 4.3436e-05 - accuracy: 1.0000\n", "Epoch 15/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 4.2213e-05 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 4.0742e-05 - accuracy: 1.0000\n", "Epoch 17/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 3.9665e-05 - accuracy: 1.0000\n", "Epoch 18/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 3.8431e-05 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 3.7583e-05 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 3s 167ms/step - loss: 3.6811e-05 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 3.5904e-05 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 3.4977e-05 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 3.4184e-05 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 3s 166ms/step - loss: 3.3381e-05 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 3.2675e-05 - accuracy: 1.0000\n", "Score for fold 8: loss of 0.41219767928123474; accuracy of 89.0625%\n", "Epoch 1/25\n", "18/18 [==============================] - 9s 165ms/step - loss: 3.2400 - accuracy: 0.6510\n", "Epoch 2/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.2691 - accuracy: 0.8854\n", "Epoch 3/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.1316 - accuracy: 0.9531\n", "Epoch 4/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0718 - accuracy: 0.9878\n", "Epoch 5/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0424 - accuracy: 0.9931\n", "Epoch 6/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0203 - accuracy: 0.9983\n", "Epoch 7/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0144 - accuracy: 1.0000\n", "Epoch 8/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0060 - accuracy: 1.0000\n", "Epoch 9/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0040 - accuracy: 1.0000\n", "Epoch 10/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0029 - accuracy: 1.0000\n", "Epoch 11/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0022 - accuracy: 1.0000\n", "Epoch 12/25\n", "18/18 [==============================] - 3s 162ms/step - loss: 0.0019 - accuracy: 1.0000\n", "Epoch 13/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0017 - accuracy: 1.0000\n", "Epoch 14/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0014 - accuracy: 1.0000\n", "Epoch 15/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0012 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 0.0011 - accuracy: 1.0000\n", "Epoch 17/25\n", "18/18 [==============================] - 3s 166ms/step - loss: 9.9088e-04 - accuracy: 1.0000\n", "Epoch 18/25\n", "18/18 [==============================] - 3s 166ms/step - loss: 8.9608e-04 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 8.1585e-04 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 7.3215e-04 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 6.6474e-04 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 6.1246e-04 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 5.6350e-04 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 3s 166ms/step - loss: 5.2372e-04 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 3s 166ms/step - loss: 4.9033e-04 - accuracy: 1.0000\n", "Score for fold 9: loss of 0.05367988348007202; accuracy of 98.4375%\n", "Epoch 1/25\n", "18/18 [==============================] - 9s 171ms/step - loss: 2.0773 - accuracy: 0.7656\n", "Epoch 2/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.9577 - accuracy: 0.8958\n", "Epoch 3/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.2405 - accuracy: 0.9583\n", "Epoch 4/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 0.0205 - accuracy: 0.9913\n", "Epoch 5/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 0.0049 - accuracy: 0.9983\n", "Epoch 6/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 7.4370e-04 - accuracy: 1.0000\n", "Epoch 7/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 9.2987e-05 - accuracy: 1.0000\n", "Epoch 8/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 6.1821e-05 - accuracy: 1.0000\n", "Epoch 9/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 5.7507e-05 - accuracy: 1.0000\n", "Epoch 10/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 4.7033e-05 - accuracy: 1.0000\n", "Epoch 11/25\n", "18/18 [==============================] - 3s 163ms/step - loss: 4.4195e-05 - accuracy: 1.0000\n", "Epoch 12/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 4.0977e-05 - accuracy: 1.0000\n", "Epoch 13/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 3.8687e-05 - accuracy: 1.0000\n", "Epoch 14/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 3.6790e-05 - accuracy: 1.0000\n", "Epoch 15/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 3.4991e-05 - accuracy: 1.0000\n", "Epoch 16/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 3.3216e-05 - accuracy: 1.0000\n", "Epoch 17/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 3.1750e-05 - accuracy: 1.0000\n", "Epoch 18/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 3.0615e-05 - accuracy: 1.0000\n", "Epoch 19/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 2.8886e-05 - accuracy: 1.0000\n", "Epoch 20/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 2.7988e-05 - accuracy: 1.0000\n", "Epoch 21/25\n", "18/18 [==============================] - 3s 164ms/step - loss: 2.6822e-05 - accuracy: 1.0000\n", "Epoch 22/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 2.5772e-05 - accuracy: 1.0000\n", "Epoch 23/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 2.5011e-05 - accuracy: 1.0000\n", "Epoch 24/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 2.3953e-05 - accuracy: 1.0000\n", "Epoch 25/25\n", "18/18 [==============================] - 3s 165ms/step - loss: 2.3413e-05 - accuracy: 1.0000\n", "Score for fold 10: loss of 0.10655650496482849; accuracy of 98.4375%\n", "------------------------------------------------------------------------\n", "InceptionV3\n", "Score per fold\n", "------------------------------------------------------------------------\n", "> Fold 1 - Loss: 0.08001065254211426 - Accuracy: 98.4375%\n", "------------------------------------------------------------------------\n", "> Fold 2 - Loss: 0.040939606726169586 - Accuracy: 98.4375%\n", "------------------------------------------------------------------------\n", "> Fold 3 - Loss: 0.1294606328010559 - Accuracy: 96.875%\n", "------------------------------------------------------------------------\n", "> Fold 4 - Loss: 0.012595536187291145 - Accuracy: 100.0%\n", "------------------------------------------------------------------------\n", "> Fold 5 - Loss: 0.2480836808681488 - Accuracy: 92.1875%\n", "------------------------------------------------------------------------\n", "> Fold 6 - Loss: 0.05135352909564972 - Accuracy: 98.4375%\n", "------------------------------------------------------------------------\n", "> Fold 7 - Loss: 0.1126035526394844 - Accuracy: 98.4375%\n", "------------------------------------------------------------------------\n", "> Fold 8 - Loss: 0.41219767928123474 - Accuracy: 89.0625%\n", "------------------------------------------------------------------------\n", "> Fold 9 - Loss: 0.05367988348007202 - Accuracy: 98.4375%\n", "------------------------------------------------------------------------\n", "> Fold 10 - Loss: 0.10655650496482849 - Accuracy: 98.4375%\n", "------------------------------------------------------------------------\n", "Average for each Fold:\n", "> Accuracy: 96.875 (+- 3.2775276505317237)\n", "> Loss: 0.12474812585860491\n", "------------------------------------------------------------------------\n" ] }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } }, { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential_19\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " inception_v3 (Functional) (None, 5, 5, 2048) 21802784 \n", " \n", " flatten_19 (Flatten) (None, 51200) 0 \n", " \n", " dense_57 (Dense) (None, 50) 2560050 \n", " \n", " dense_58 (Dense) (None, 20) 1020 \n", " \n", " dense_59 (Dense) (None, 2) 42 \n", " \n", "=================================================================\n", "Total params: 24,363,896\n", "Trainable params: 2,561,112\n", "Non-trainable params: 21,802,784\n", "_________________________________________________________________\n" ] } ], "source": [ "#from sklearn.model_selection import KFold\n", "from sklearn.model_selection import StratifiedKFold\n", "from statistics import mean, stdev\n", "import numpy as np\n", "\n", "from keras.applications.inception_v3 import InceptionV3\n", "\n", "\n", "\n", "from tensorflow.keras.losses import sparse_categorical_crossentropy\n", "\n", "#SF = KFold(n_splits=10, shuffle=True)\n", "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=1)\n", "acc_per_fold = []\n", "loss_per_fold = []\n", "kumpl_lbl = np.array(kumpl_lbl)\n", "kumpl_gmbr = np.array(kumpl_gmbr)\n", "fold_no = 1\n", "\n", "num_label=len(kelas_asli)\n", "all_cm=np.zeros((num_label,num_label))\n", "\n", "for latih_idx, uji_idx in skf.split(kumpl_gmbr, np.argmax(kumpl_lbl, axis=1)):\n", " incv3 = InceptionV3(input_shape=[224,224] + [3], weights='imagenet', include_top=False)\n", " incv3.trainable = False \n", " flatten_layer = layers.Flatten()\n", " dense_layer_1 = layers.Dense(50, activation='relu')\n", " dense_layer_2 = layers.Dense(20, activation='relu')\n", " prediction_layer = layers.Dense(len(kelas_asli), activation='softmax')\n", "\n", " incv3_new = models.Sequential([\n", " incv3,\n", " flatten_layer,\n", " dense_layer_1,\n", " dense_layer_2,\n", " prediction_layer\n", " ])\n", "\n", " incv3_new.compile(\n", " optimizer='adam',\n", " loss='categorical_crossentropy',\n", " metrics=['accuracy'],\n", " )\n", "\n", " h = incv3_new.fit(kumpl_gmbr[latih_idx], kumpl_lbl[latih_idx,:], epochs = 25, batch_size = 32)\n", " scores = incv3_new.evaluate(kumpl_gmbr[uji_idx], kumpl_lbl[uji_idx], verbose=0)\n", " print(f'Score for fold {fold_no}: {incv3_new.metrics_names[0]} of {scores[0]}; {incv3_new.metrics_names[1]} of {scores[1]*100}%')\n", " acc_per_fold.append(scores[1] * 100)\n", " loss_per_fold.append(scores[0])\n", " fold_no = fold_no + 1\n", "\n", " #for cm only\n", " from sklearn.metrics import confusion_matrix\n", " predict = incv3_new.predict(kumpl_gmbr[uji_idx])\n", " cm = confusion_matrix(np.argmax(kumpl_lbl[uji_idx], axis=1), np.argmax(predict, axis=1))\n", " all_cm=all_cm+cm\n", "\n", "print('------------------------------------------------------------------------')\n", "print('InceptionV3')\n", "\n", "print('Score per fold')\n", "for i in range(0, len(acc_per_fold)):\n", " print('------------------------------------------------------------------------')\n", " print(f'> Fold {i+1} - Loss: {loss_per_fold[i]} - Accuracy: {acc_per_fold[i]}%')\n", "print('------------------------------------------------------------------------')\n", "print('Average for each Fold:')\n", "print(f'> Accuracy: {np.mean(acc_per_fold)} (+- {np.std(acc_per_fold)})')\n", "print(f'> Loss: {np.mean(loss_per_fold)}')\n", "print('------------------------------------------------------------------------')\n", "\n", "plot_confusion_matrix(all_cm,normalize=False)\n", "incv3_new.summary()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "LarvaVGG19IncV3withCV.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }