From 0544b9ccf57f5de8caadd58372f2c5aead55c527 Mon Sep 17 00:00:00 2001 From: Stefan Hill Date: Sat, 28 Jan 2023 21:13:00 +0000 Subject: [PATCH 01/27] Added some data exploration --- data-exploration.ipynb | 21657 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 21657 insertions(+) create mode 100644 data-exploration.ipynb diff --git a/data-exploration.ipynb b/data-exploration.ipynb new file mode 100644 index 0000000..83c02ef --- /dev/null +++ b/data-exploration.ipynb @@ -0,0 +1,21657 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "id": "96bd31e1-02e1-45c9-8552-1782934a2fc6", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "images = np.load('data/images.npy')\n", + "labels = np.load('data/labels.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7921309c-2fe5-4428-a0e7-aade989641d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True:\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "for i in range(100):\n", + " print(f'{labels[i]}:')\n", + " plt.imshow(data[i])\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1ab445b7-9c8d-4f51-99a3-d6262cd47dbc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False:\n" + ] + }, + { + "data": { + "image/png": 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QvUm6V7NP66Y0+97GTZI+JGmHpOck/ZektUPU279I2i3pGc0Ga8OAertCs0/Rn5G0q/q6dtCPXaGvvjxufFwWSII36IAkCDuQBGEHkiDsQBKEHUiCsANJEHYgif8HOthHsBzHBzgAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False:\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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AFMAWVf2xiDwM4B8AnIyu+qCqPleqjlac5r/GebbVHmd/f9IeCz+btsejXx3tC9a+u/y3ZttDqfPMetwe6ZlZ+3hxaqI1WDueWmS2TbfYa9JrIWeJxKz1X4tyebgyAL6nqq+JSDuA3SKyI6r9SFX/o3TdI6JiyWV/9iEAQ9HX4yJyEID9756Iqs7nes0uIn0ArgLwSnTR/SLypohsFZHOQJvNIjIgIgNp2Kc/ElHp5Bx2EWkD8EsA31HVMQA/AXARgPWYO/L/cKF2qrpFVftVtT+J8HnSRFRaOYVdRJKYC/oTqvorAFDVE6qaVdVZAD8FsKF03SSiQsWGXUQEwGMADqrqo/Mu7513tTsA7Ct+94ioWHJ5N/56AN8AsFdE9kSXPQhgk4isx9xw3BEA3yxB/2rC92981qz/fuQSs37B4hGznjCWe748aQ8ZtsqgWb+jdcis9yRGzfqWqRuDtbgtmy9JnjLrLdfYdfqkXN6N/wOAhRYHr90xdaIaxDPoiJxg2ImcYNiJnGDYiZxg2ImcYNiJnOBS0mWw/et/adanVneY9WNftn9N1lLV1xxdZbbNZuz/95mP7W2Vm3snzHo6Fe77r9/qN9v+vm+NWZcXFpyOMc87MXVfeGQncoJhJ3KCYSdygmEncoJhJ3KCYSdygmEnckK0gCWSP/ediZwE8MG8i7oBVOuk5GrtW7X2C2Df8lXMvl2gqksXKpQ17J+5c5EBVbXPrKiQau1btfYLYN/yVa6+8Wk8kRMMO5ETlQ77lgrfv6Va+1at/QLYt3yVpW8Vfc1OROVT6SM7EZUJw07kREXCLiK3isjbInJYRB6oRB9CROSIiOwVkT0iMlDhvmwVkWER2Tfvsi4R2SEih6LPcZO6y9m3h0XkaPTY7RGRjRXq2yoReV5EDojIfhH5dnR5RR87o19ledzK/ppdROoxt6rAXwMYBLALwCZVPVDWjgSIyBEA/apa8RMwRORGABMAHlfVy6PL/h3AiKo+Ev2j7FTV71dJ3x4GMFHpbbyj3Yp6528zDuB2APeggo+d0a87UYbHrRJH9g0ADqvqe6qaAvAUgNsq0I+qp6ovAvj0djC3AdgWfb0Nc38sZRfoW1VQ1SFVfS36ehzAuW3GK/rYGf0qi0qEfQWAj+Z9P4jq2u9dAfxORHaLyOZKd2YBPap6bk+m4wB6KtmZBcRu411On9pmvGoeu3y2Py8U36D7rBtU9WoAXwVwX/R0tSrp3Guwaho7zWkb73JZYJvxP6nkY5fv9ueFqkTYjwKYvwriyuiyqqCqR6PPwwCeRvVtRX3i3A660efhCvfnT6ppG++FthlHFTx2ldz+vBJh3wVgjYisFpEGAHcB2F6BfnyGiLRGb5xARFoB3ILq24p6O4C7o6/vBvBMBfvyCdWyjXdom3FU+LGr+Pbnqlr2DwAbMfeO/LsA/rESfQj060IAb0Qf+yvdNwBPYu5pXRpz723cC2AJgJ0ADgH4XwBdVdS3nwPYC+BNzAWrt0J9uwFzT9HfBLAn+thY6cfO6FdZHjeeLkvkBN+gI3KCYSdygmEncoJhJ3KCYSdygmEncoJhJ3Li/wEGfIFId2IS1QAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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oqy06EfbzJL0+5fe96q713kPS47a32l7f6WamsSwiRovr+yUt62Qz06hcxrudPrDMeNc8d/Usf94oPqD7sKsj4gpJN0j6cvFytSvF5Huwbho7ndEy3u0yzTLj7+vkc1fv8ueN6kTY90laMeX384ttXSEi9hWXByU9ou5bivrAeyvoFpcHO9zP+7ppGe/plhlXFzx3nVz+vBNhf07SatsX2u6X9AVJmzrQx4fYXlh8cCLbCyVdp+5binqTpFuL67dKerSDvZyiW5bxLltmXB1+7jq+/HlEtP1H0o2a/ET+FUlf7UQPJX1dJOnnxc8Lne5N0kOafFk3rsnPNm6TdKakLZJ2SXpC0lAX9favknZI2q7JYC3vUG9Xa/Il+nZJ24qfGzv93CX6asvzxtdlgUzwAR2QCcIOZIKwA5kg7EAmCDuQCcIOZIKwA5n4f4pnek8onu5cAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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vCdlOeijvHsf/SudBs+1eszo98cxO5AmGncgTDDuRJxh2Ik8w7ESeYNiJPMGwE3liKvuzdwH4JYB2AApgo6o+LSKPA/g7AP3Bjz6qqi+XqqMVZ6x/fnyw2W7r3iYcAJBR+59hJGPXj2fmOGudiYtm2+FRe065fYUAcHGs1qz35QbdtaFGs226KezebdY6AXsvzjfbxurOm/X80FBBfaqkqVxUkwXwsKq+IyKNAHaKyNag9hNV/afSdY+IojKV/dl7AfQGXw+IyAEAnaXuGBFF6zO9ZheRRQBWAXg7uOlBEdktIptEZNLnsiKyQUR6RKQnA/+23CGqFlMOu4g0APgNgIdU9TKAnwJYCmAlxs/8T07WTlU3qmq3qnYnkSq+x0RUkCmFXUSSGA/6r1X1BQBQ1TOqmlPVPICfAbi5dN0komKFhl1EBMAzAA6o6lMTbu+Y8GPfxMycKEQ0Y0zl3fjbAHwHwB4R2RXc9iiAdSKyEuPDcUcAfLcE/asaY3euctbWtP6X2bYmZKLpiNpbF69dvMes31Z7yFlridvDVw997nWz3hqzh+b+tvO/zXqduIcsvzrvPbPtivQps54KmX7bkbzgrH278y2z7X/M7jbrM3LoTVV/B2Cywzpzx9SJZiBeQUfkCYadyBMMO5EnGHYiTzDsRJ5g2Ik8Iaohe/JGaJa06BfljrLdX5Tis2Y5aweeXGG2bemxRzhrz9nj8H3d9mNyYsA94Jy6aDZFLGv/+ydChpMHFtqD3c3vu6eZ9q+y/66W/Xbfxhrs+x5pc9dzaft3L/rhm2a9Wr2t23BZz0/6h/PMTuQJhp3IEww7kScYdiJPMOxEnmDYiTzBsBN5oqzj7CLSD+DohJtaAZwtWwc+m2rtW7X2C2DfChVl3xaq6qSLl5c17J+6c5EeVbVXCaiQau1btfYLYN8KVa6+8Wk8kScYdiJPVDrsGyt8/5Zq7Vu19gtg3wpVlr5V9DU7EZVPpc/sRFQmDDuRJyoSdhFZIyLvi8ghEXmkEn1wEZEjIrJHRHaJSE+F+7JJRPpEZO+E21pEZKuIHAw+h+wXXda+PS4iJ4Njt0tE7qlQ37pE5A0R2S8i+0Tk+8HtFT12Rr/KctzK/ppdROIAPgBwJ4ATAHYAWKeq+8vaEQcROQKgW1UrfgGGiHwZwBUAv1TVzwe3/RjAeVV9InigbFbVH1RJ3x4HcKXS23gHuxV1TNxmHMC9AP4aFTx2Rr/uQxmOWyXO7DcDOKSqh1V1DMBzANZWoB9VT1W3Azh/1c1rAWwOvt6M8f8sZefoW1VQ1V5VfSf4egDAR9uMV/TYGf0qi0qEvRPA8Qnfn0B17feuAF4TkZ0isqHSnZlEu6r2Bl+fBtBeyc5MInQb73K6apvxqjl2hWx/Xiy+Qfdpq1X1RgB3A/he8HS1Kun4a7BqGjud0jbe5TLJNuMfq+SxK3T782JVIuwnAXRN+H5BcFtVUNWTwec+AC+i+raiPvPRDrrB574K9+dj1bSN92TbjKMKjl0ltz+vRNh3AFgmIotFpAbA/QC2VKAfnyIi9cEbJxCRegB3ofq2ot4CYH3w9XoAL1WwL59QLdt4u7YZR4WPXcW3P1fVsn8AuAfj78j/AcAPK9EHR7+WAPh98LGv0n0D8CzGn9ZlMP7exgMA5gDYBuAggN8CaKmivv0KwB4AuzEerI4K9W01xp+i7wawK/i4p9LHzuhXWY4bL5cl8gTfoCPyBMNO5AmGncgTDDuRJxh2Ik8w7ESeYNiJPPH/8lSfxnFL+OQAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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mew9JP7P9rO2N3W5mDqsj4lDj+mFJq7vZzBxqp/FeSGdMM94zj10z05+3ig/o3u66iPiwpJslfbHxcrUnxfR7sF4aO53XNN4LZY5pxt/Szceu2enPW9WNsB+UtG7G7+9pLOsJEXGwcXlU0qPqvamoj7w5g27j8miX+3lLL03jPdc04+qBx66b0593I+zbJV1m+xLbg5I+J+nxLvTxNraXNz44ke3lkj6h3puK+nFJdzSu3yHpsS72MkuvTONdNc24uvzYdX3684hY8B9Jt2j6E/lfSfr7bvRQ0df7JP2y8bO7271J+oGmX9aNa/qzjTslvUvSNkl7Jf27pFU91Nv3JO2U9Lymg7WmS71dp+mX6M9L2tH4uaXbj12hrwV53Pi6LJAEH9ABSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBL/D3Zwi4GTa8wsAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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tK2m8YRKfD79/Hh970TMrvE13dRlfLx8ZftxCEs/sZjbXzH5pZq+Z2atm9qXs5fVmttHMdmW/80eFiBTVaF7GDwC4092XAbgEwBfNbBmArwHY5O7NADZlfxeREpWY7O5+2N1fyv7cBWAngDkAbgCwPvtn6wHcmKc+isg4eF/v2c1sAYALAWwGMNPdD2dDRwCM+IbbzNYCWAsAVeDvXUUkf0b9abyZ1QB4BMCX3f1dn3y4uyMwFcXd17l7i7u3VIJ/0CQi+TOqZDezSgwl+o/c/afZi4+a2axsfBYAPk1IRIoq8WW8mRmA+wDsdPdvDws9DuA2APdkvz+Wc2+q+Jl/4slwiSlTyZ+3Tp3Lyxk1Gd6+6dHwNFSfEF7iGgAmHuFbNvdNC5dhAKC2bYDGu2eH79uGDS38ug8mzA1OOB1U8aojel4JF2mqWnh5arCpl8b7J7ONj4FtL4dLmh/e+hXa9u5r/pPG17deRuNLm9tofOdgUzDWfOQC2tae3UbjIaN5z/5hAJ8BsN3M3r6VuzCU5D8xs9sB7Adwy5h6ICIFkZjs7v5rAKHRLHyHAREpGRouKxIJJbtIJJTsIpFQsotEQskuEomSmuLataqRxk81h5+b5j3Bpxyebayl8bee41sTL9j/fDDWt+YPaNvUaV5Pruzk03M7VoW3iwaADBme0LCFT4fsaUh4vucrbKOqnf/B6eVkmevmn9O2f1u2hsYHqviUaJsangK75OsJAwSu4eHWk1NofMZkvsT26hW7g7HT3fx+JfxLgnRmF4mEkl0kEkp2kUgo2UUioWQXiYSSXSQSSnaRSJRUnb3mDb5V7WAqvO7x4Sv4ksaYyudGL7zzFRpns77L+3jls2MxXxK5uym8RDYATOC7C1Ot1/M6+9Qt/Pn+9FI+PqFzIW+fOh6+bxtPL6dt+wf5GgQ+gR+3ylR4HYCB/Xyb7O/8/c00/oU/3UDj971xKY2fOhN+TMyYw5dvm/AyDQfpzC4SCSW7SCSU7CKRULKLRELJLhIJJbtIJJTsIpEoqTr72Xm8Vt45P/zcdHYVX5u9vJWvze4DfG32stpwvTldw+vBp8+lYVQnrN1ec4TXyvtrwselZgP/F5f38+tOdfDjcvRSvna7p8JjEJ46sIS2HRxMqOFP5nX2/t5w38qn8MfatPvC6xcAwL8su5rGL7n0dRpvOxO+/ao2Pm5D89lFhFKyi0RCyS4SCSW7SCSU7CKRULKLRELJLhKJ0ezPPhfAgwBmYmha9zp3v9fM7gbweQDHs396l7s/kUtnUhu20Pjc5+uCsd5LeDF74q5DNM6ryYD3htcgr32R78VdvZHP0y+bOoXG+xc20HiqI/yc3bGI73lfnuZjBMp7+RgAm8LXvK99KTxvu2sCn+ePM/zh2bTlDI2nusLzwjPdvG2SRV/ZTOPHLl1B46nnwpPSx1pHTzKaQTUDAO5095fMrBbAi2a2MRv7R3f/hzz1TUTG0Wj2Zz8M4HD25y4z2wlgTr47JiLj6329ZzezBQAuBPD2a5g7zOwVM7vfzKYG2qw1sy1mtiWN8EthEcmvUSe7mdUAeATAl929E8B3ASwCsApDZ/5vjdTO3de5e4u7t1SCv38UkfwZVbKbWSWGEv1H7v5TAHD3o+4+6O4ZAN8HcHH+uikiuUpMdjMzAPcB2Onu3x52+axhf3YTgB3j3z0RGS+j+TT+wwA+A2C7mW3LXnYXgFvNbBWGynH7AHwhD/17l8HOzmCs8iletksqrSWxyvChartpPm17tpFvB50kXcPLXygLx8un8am/ZXt5+evkCl6aq67lWxN3nheeZnrdcn5+ONAz4sdA79h500IarzkQngKbNKU5kSeUJElprVhG82n8rwGMdNRyqqmLSGFpBJ1IJJTsIpFQsotEQskuEgklu0gklOwikTBPqBeOpzqr99V2FekNXxoYFn5uYnVwAPB+PhUzqW6ai/LzFtN495J6GvdyflxONYdr4b0NfMJkqj2353tPKN6mwkMjULcvYRnr07wWXnmKb8PtL5PlnDP8tpNYRcIdL0/Ybpo9HnN4LG72Tej09hEfMDqzi0RCyS4SCSW7SCSU7CKRULKLRELJLhIJJbtIJApaZzez4wD2D7toOoATBevA+1OqfSvVfgHq21iNZ9/mu/uMkQIFTfbfuXGzLe7eUrQOEKXat1LtF6C+jVWh+qaX8SKRULKLRKLYyb6uyLfPlGrfSrVfgPo2VgXpW1Hfs4tI4RT7zC4iBaJkF4lEUZLdzK41szfMbLeZfa0YfQgxs31mtt3MtpkZX4w+/32538yOmdmOYZfVm9lGM9uV/c4XVy9s3+42s7bssdtmZmuK1Le5ZvZLM3vNzF41sy9lLy/qsSP9KshxK/h7djMrB/AmgE8AaAXwAoBb3f21gnYkwMz2AWhx96IPwDCzjwLoBvCgu5+fveybANrd/Z7sE+VUd/9qifTtbgDdxd7GO7tb0azh24wDuBHA51DEY0f6dQsKcNyKcWa/GMBud9/r7v0AHgZwQxH6UfLc/WkA7e+5+AYA67M/r8fQg6XgAn0rCe5+2N1fyv7cBeDtbcaLeuxIvwqiGMk+B8DBYb+3orT2e3cAT5nZi2a2ttidGcFMdz+c/fkIgJnF7MwIErfxLqT3bDNeMsduLNuf50of0P2uj7j7RQA+CeCL2ZerJcmH3oOVUu10VNt4F8oI24y/o5jHbqzbn+eqGMneBmDusN+bspeVBHdvy34/BuBRlN5W1Eff3kE3+/1YkfvzjlLaxnukbcZRAseumNufFyPZXwDQbGYLzSwF4NMAHi9CP36HmVVnPziBmVUDuBqltxX14wBuy/58G4DHitiXdymVbbxD24yjyMeu6Nufu3vBvwCswdAn8nsAfL0YfQj06xwAL2e/Xi123wD8GEMv69IY+mzjdgDTAGwCsAvALwDUl1Df/g3AdgCvYCixZhWpbx/B0Ev0VwBsy36tKfaxI/0qyHHTcFmRSOgDOpFIKNlFIqFkF4mEkl0kEkp2kUgo2UUioWQXicT/Aeot1o/i6p0gAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(1000,2000):\n", + " print(f'{labels[i]}:')\n", + " plt.imshow(data[i])\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e97df53f-109c-473d-8b08-e2cec19442f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 5.99769319e-04, 1.38408304e-03, 2.07612457e-03, 3.07574010e-03,\n", + " 2.02998847e-03, 9.22722030e-04, 9.84236832e-04, 1.27643214e-03,\n", + " 2.81430219e-03, 2.99884660e-03, 1.49173395e-03, 4.45982314e-04,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "5a564724-e468-491d-bdd6-1d6704c99a7c", + "metadata": {}, + "outputs": [], + "source": [ + "# we have to shuffle our dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "0336fc1e-8f05-4574-a267-7e867df7553f", + "metadata": {}, + "outputs": [], + "source": [ + "# balance the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "cd241834-c8c4-4f72-bfb9-d6abbf930261", + "metadata": {}, + "outputs": [], + "source": [ + "# train test split" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "9cada698-1f8b-49cd-90a7-d7d6fd287539", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "2b7e57ed-bc9c-43d9-abc2-7d4891bc8d7b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 13., 56., 71., 71., 128., 111., 37., 11., 1., 1.]),\n", + " array([0.13616301, 0.34718647, 0.55820992, 0.76923337, 0.98025682,\n", + " 1.19128028, 1.40230373, 1.61332718, 1.82435063, 2.03537409,\n", + " 2.24639754]),\n", + " )" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(list(map(lambda x: np.sum(x), data))[500:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb08ceb6-b52a-4830-9563-f1c42dd63b8a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 [Default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From dcf42741dbb4b9ed19fc9bdd94a4f4a4f5d603fc Mon Sep 17 00:00:00 2001 From: Stefan Hill Date: Sat, 28 Jan 2023 21:24:28 +0000 Subject: [PATCH 02/27] Moved iquhack into example folder --- .../data-exploration-checkpoint.ipynb | 21678 ++++++++++++++++ examples/MIT iQuHACK remote challenge .docx | Bin 0 -> 3981436 bytes examples/MIT iQuHACK remote challenge .pdf | Bin 0 -> 5046596 bytes examples/data/images.npy | Bin 0 -> 12544128 bytes examples/data/labels.npy | Bin 0 -> 2128 bytes examples/in-person/README.md | 17 + examples/in-person/requirements.txt | 100 + examples/mock_data/data1.json | 1 + examples/mock_data/data2.json | 1 + examples/mock_data_creator.ipynb | 79 + examples/part1_example_cirq.ipynb | 226 + examples/part1_example_qiskit.ipynb | 233 + examples/part2_example_cirq.ipynb | 221 + examples/part2_example_qiskit.ipynb | 230 + examples/quantum_classifier.pickle | Bin 0 -> 497 bytes examples/quantum_classifier.qasm | 4 + utils/.ipynb_checkpoints/utils-checkpoint.py | 54 + utils/utils.py | 54 + 18 files changed, 22898 insertions(+) create mode 100644 .ipynb_checkpoints/data-exploration-checkpoint.ipynb create mode 100644 examples/MIT iQuHACK remote challenge .docx create mode 100644 examples/MIT iQuHACK remote challenge .pdf create mode 100644 examples/data/images.npy create mode 100644 examples/data/labels.npy create mode 100644 examples/in-person/README.md create mode 100644 examples/in-person/requirements.txt create mode 100644 examples/mock_data/data1.json create mode 100644 examples/mock_data/data2.json create mode 100644 examples/mock_data_creator.ipynb create mode 100644 examples/part1_example_cirq.ipynb create mode 100644 examples/part1_example_qiskit.ipynb create mode 100644 examples/part2_example_cirq.ipynb create mode 100644 examples/part2_example_qiskit.ipynb create mode 100644 examples/quantum_classifier.pickle create mode 100644 examples/quantum_classifier.qasm create mode 100644 utils/.ipynb_checkpoints/utils-checkpoint.py create mode 100644 utils/utils.py diff --git a/.ipynb_checkpoints/data-exploration-checkpoint.ipynb b/.ipynb_checkpoints/data-exploration-checkpoint.ipynb new file mode 100644 index 0000000..89b667a --- /dev/null +++ b/.ipynb_checkpoints/data-exploration-checkpoint.ipynb @@ -0,0 +1,21678 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "id": "96bd31e1-02e1-45c9-8552-1782934a2fc6", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "images = np.load('data/images.npy')\n", + "labels = np.load('data/labels.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7921309c-2fe5-4428-a0e7-aade989641d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "for i in range(100):\n", + " print(f'{labels[i]}:')\n", + " plt.imshow(data[i])\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1ab445b7-9c8d-4f51-99a3-d6262cd47dbc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False:\n" + ] + }, + { + "data": { + "image/png": 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QvUm6V7NP66Y0+97GTZI+JGmHpOck/ZektUPU279I2i3pGc0Ga8OAertCs0/Rn5G0q/q6dtCPXaGvvjxufFwWSII36IAkCDuQBGEHkiDsQBKEHUiCsANJEHYgif8HOthHsBzHBzgAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False:\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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qP3xX1/DMD8nYt62z9pbQSeCRnSgQTHaiQDDZiQLBZCcKBJOdKBBMdqJAMNmJAhFMnT12Hd3aP1jtrvBCt10vnlb7HACflpT7d1NPHd2n2bhuAGhVe8tnSyZl1/Dfmlhlxottvm58g28/aI9GrKP78MhOFAgmO1EgmOxEgWCyEwWCyU4UCCY7USCY7ESBCKbOHpunlm5J5e2eb5+Mp6e8JW33hVvSYv9eWdi33Zyye+mLJXcdviUT79yHmZ7K+/i9f09fHd4Xj/F4qRUe2YkCwWQnCgSTnSgQTHaiQDDZiQLBZCcKBJOdKBCL2Z99LYDHAOQxt533dlX9vog8COBPAJzbHPwBVd1Zq4nWXJz+Zk9NtaXFroO/X2w349Mle935srr/Z/v62Sdn7Os+PrvUjI+U2sx4cdZdZ7fmDQDTRo0eAFb0nDHjppj97I1YR/dZzEk1swDuU9XXRKQdwKsisiuKPayqf1O76RFRtSxmf/YhAEPR12Mish/A6lpPjIiq62O9ZheRdQCuAfBydNFXRWSPiDwqIp2OMX0iMiAiA0VUflonEcWz6GQXkTYATwP4uqqeBfADABsBbMXckf+7C41T1e2q2quqvVnk4s+YiCqyqGQXkSzmEv1xVX0GAFT1hKqWVLUM4IcArq/dNIkoLm+yi4gAeATAflX93rzLe+b92OcB7Kv+9IioWhbzbvynAXwRwF4R2R1d9gCAu0VkK+bKcUcAfLkG86sf8fzfK7tbPTMb1plD/3DDa2Y8m7KXJb62810zfvWSd5yx9aveN8femn/TjP9Rhz2+f2rUjF++Yp0z1rv0iDl279gaM97dMmHGkV/pDJVODJtDz8ctmX0W8278iwAWKkqevzV1ogDxDDqiQDDZiQLBZCcKBJOdKBBMdqJAMNmJAsGlpCOSslse1dh1WTN2K+bO9y4342d32VsTZybtdsrHr7rBGUtN2//P/72/x4z/a/etZvzkZzzLORfct7/n4BZz6PhV9hLcS5dNmvGedqMX44Q59ILEIztRIJjsRIFgshMFgslOFAgmO1EgmOxEgWCyEwVCtI5L4orISQBH513UDcBumE5Oo86tUecFcG6VqubcLlbVFQsF6prsH7lxkQFV7U1sAoZGnVujzgvg3CpVr7nxaTxRIJjsRIFIOtm3J3z7lkadW6POC+DcKlWXuSX6mp2I6ifpIzsR1QmTnSgQiSS7iNwmIv8nIgdF5P4k5uAiIkdEZK+I7BaRgYTn8qiIDIvIvnmXdYnILhE5EH1ecI+9hOb2oIgMRvfdbhG5PaG5rRWRn4vImyLyhoh8Lbo80fvOmFdd7re6v2YXkTSAXwH4LIBjAF4BcLeq2rsV1ImIHAHQq6qJn4AhIp8BMA7gMVW9IrrsrwCMqOpD0T/KTlX9RoPM7UEA40lv4x3tVtQzf5txAHcC+BISvO+Med2FOtxvSRzZrwdwUFUPq+oMgCcB3JHAPBqeqr4AYORDF98BYEf09Q7MPVjqzjG3hqCqQ6r6WvT1GIBz24wnet8Z86qLJJJ9NYD5+xkdQ2Pt964Afioir4pIX9KTWUBeVYeir48DyCc5mQV4t/Gupw9tM94w910l25/HxTfoPupGVb0WwOcAfCV6utqQdO41WCPVThe1jXe9LLDN+K8led9Vuv15XEkk+yCAtfO+XxNd1hBUdTD6PAzgWTTeVtQnzu2gG322dyiso0baxnuhbcbRAPddktufJ5HsrwDYJCLrRaQJwBcAPJ/APD5CRFqjN04gIq0AbkXjbUX9PIBt0dfbADyX4Fw+oFG28XZtM46E77vEtz9X1bp/ALgdc+/IHwLw50nMwTGvDQD+N/p4I+m5AXgCc0/riph7b+NeAMsB9AM4AOBnALoaaG7/BGAvgD2YS6yehOZ2I+aeou8BsDv6uD3p+86YV13uN54uSxQIvkFHFAgmO1EgmOxEgWCyEwWCyU4UCCY7USCY7ESB+H9GYd97gLDbxgAAAABJRU5ErkJggg==\n", 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\n", 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oqy06EfbzJL0+5fe96q713kPS47a32l7f6WamsSwiRovr+yUt62Qz06hcxrudPrDMeNc8d/Usf94oPqD7sKsj4gpJN0j6cvFytSvF5Huwbho7ndEy3u0yzTLj7+vkc1fv8ueN6kTY90laMeX384ttXSEi9hWXByU9ou5bivrAeyvoFpcHO9zP+7ppGe/plhlXFzx3nVz+vBNhf07SatsX2u6X9AVJmzrQx4fYXlh8cCLbCyVdp+5binqTpFuL67dKerSDvZyiW5bxLltmXB1+7jq+/HlEtP1H0o2a/ET+FUlf7UQPJX1dJOnnxc8Lne5N0kOafFk3rsnPNm6TdKakLZJ2SXpC0lAX9favknZI2q7JYC3vUG9Xa/Il+nZJ24qfGzv93CX6asvzxtdlgUzwAR2QCcIOZIKwA5kg7EAmCDuQCcIOZIKwA5n4f4pnek8onu5cAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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W7Mdlz/BlZl3a2txFT5+d89mJ6LzFsBMFgmEnCgTDThQIhp0oEAw7USAYdqJAlLM/+xwA3wPQBUAB9Krqt0VkNYC7ARyNPvVhVd1Qq4GmWey5z/PtheN9a7dn4G76jpRy5rG+PdCbMu513wF7XXgAOFy6xFnLeubanyrY89nntwyadZ013V08etRdAyANdjR0zJ6Ln0blXFQzBuABVX1NRKYA2CoiG6PaN1X10doNj4iqpZz92QcADETv50VkDwD3EiBElEq/0t/sIjIXwCIAr0Q33Sci20VkjYh0OI7pEZE+EekrwL60kohqp+ywi0g7gB8BuF9VTwF4DMAVABZi/Mz/9cmOU9VeVe1W1e4c4l2HTUSVKyvsIpLDeNB/oKo/BgBVPaKqRVUtAfgOgCW1GyYRxeUNu4gIgCcA7FHVb0y4feaET7sdwM7qD4+IqqWcV+NvAHAngB0isi267WEAK0VkIcbbcfsB3FOD8QXh8/Pt35NTPO2vuY3HnLVLsvZUzgOFqWbd11prE3uaqjUFdnqzvRR0vmi33pZP2W7W/3PuZ521lm3modAaTmlOSjmvxr+AyVc2D7KnTnS+4hV0RIFg2IkCwbATBYJhJwoEw04UCIadKBBcSroaSvZyzT7b/naRWb/1T641643N7umWl3WcNI/tasmb9aWdb5r1/rFJp0S8b+fZ2c7akZGLzGNfXG9/3/+z/dNmveW/XjXrFi14prAaW3iPf4H09el5ZicKBMNOFAiGnSgQDDtRIBh2okAw7ESBYNiJAiH1nLcrIkcBvDPhpmkA3JOxk5XWsaV1XADHVqlqju1yVZ10De26hv0jdy7Sp6rdiQ3AkNaxpXVcAMdWqXqNjU/jiQLBsBMFIumw9yZ8/5a0ji2t4wI4tkrVZWyJ/s1ORPWT9JmdiOqEYScKRCJhF5FbRORNEXlLRB5KYgwuIrJfRHaIyDYR6Ut4LGtEZFBEdk64rVNENorI3uitPaG8vmNbLSL90WO3TUSWJTS2OSLynIjsFpFdIvKV6PZEHztjXHV53Or+N7uIZAH8AsDvAjgIYAuAlaq6u64DcRCR/QC6VTXxCzBE5LcADAH4nqp+MrrtHwEcV9VHol+UHar6YErGthrAUNLbeEe7Fc2cuM04gNsA/DESfOyMca1AHR63JM7sSwC8par7VHUUwFMAlicwjtRT1c0Ajn/o5uUA1kbvr8X4D0vdOcaWCqo6oKqvRe/nAZzbZjzRx84YV10kEfZZAA5M+Pgg0rXfuwJ4VkS2ikhP0oOZRJeqDkTvHwbQleRgJuHdxruePrTNeGoeu0q2P4+LL9B91I2quhjArQDujZ6uppKO/w2Wpt5pWdt418sk24y/L8nHrtLtz+NKIuz9AOZM+Hh2dFsqqGp/9HYQwDqkbyvqI+d20I3eDiY8nvelaRvvybYZRwoeuyS3P08i7FsAXCki80SkEcAXAaxPYBwfISJt0QsnEJE2ADcjfVtRrwewKnp/FYCnExzLB6RlG2/XNuNI+LFLfPtzVa37PwDLMP6K/NsA/i6JMTjG9WsAXo/+7Up6bACexPjTugLGX9u4C8BUAJsA7AXwUwCdKRrb9wHsALAd48GamdDYbsT4U/TtALZF/5Yl/dgZ46rL48bLZYkCwRfoiALBsBMFgmEnCgTDThQIhp0oEAw7USAYdqJA/B/s+7MUMs8waAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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mew9JP7P9rO2N3W5mDqsj4lDj+mFJq7vZzBxqp/FeSGdMM94zj10z05+3ig/o3u66iPiwpJslfbHxcrUnxfR7sF4aO53XNN4LZY5pxt/Szceu2enPW9WNsB+UtG7G7+9pLOsJEXGwcXlU0qPqvamoj7w5g27j8miX+3lLL03jPdc04+qBx66b0593I+zbJV1m+xLbg5I+J+nxLvTxNraXNz44ke3lkj6h3puK+nFJdzSu3yHpsS72MkuvTONdNc24uvzYdX3684hY8B9Jt2j6E/lfSfr7bvRQ0df7JP2y8bO7271J+oGmX9aNa/qzjTslvUvSNkl7Jf27pFU91Nv3JO2U9Lymg7WmS71dp+mX6M9L2tH4uaXbj12hrwV53Pi6LJAEH9ABSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBL/D3Zwi4GTa8wsAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(1000,2000):\n", + " print(f'{labels[i]}:')\n", + " plt.imshow(data[i])\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e97df53f-109c-473d-8b08-e2cec19442f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 5.99769319e-04, 1.38408304e-03, 2.07612457e-03, 3.07574010e-03,\n", + " 2.02998847e-03, 9.22722030e-04, 9.84236832e-04, 1.27643214e-03,\n", + " 2.81430219e-03, 2.99884660e-03, 1.49173395e-03, 4.45982314e-04,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "5a564724-e468-491d-bdd6-1d6704c99a7c", + "metadata": {}, + "outputs": [], + "source": [ + "# we have to shuffle our dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "0336fc1e-8f05-4574-a267-7e867df7553f", + "metadata": {}, + "outputs": [], + "source": [ + "# balance the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "cd241834-c8c4-4f72-bfb9-d6abbf930261", + "metadata": {}, + "outputs": [], + "source": [ + "# train test split" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "9cada698-1f8b-49cd-90a7-d7d6fd287539", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "2b7e57ed-bc9c-43d9-abc2-7d4891bc8d7b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 13., 56., 71., 71., 128., 111., 37., 11., 1., 1.]),\n", + " array([0.13616301, 0.34718647, 0.55820992, 0.76923337, 0.98025682,\n", + " 1.19128028, 1.40230373, 1.61332718, 1.82435063, 2.03537409,\n", + " 2.24639754]),\n", + " )" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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