diff --git a/notebooks/tutorial-instructor.ipynb b/notebooks/tutorial-instructor.ipynb index 0934ecd..7cea426 100644 --- a/notebooks/tutorial-instructor.ipynb +++ b/notebooks/tutorial-instructor.ipynb @@ -99,15 +99,15 @@ }, { "data": { - "image/png": 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XWmuGkUVrTXQmAgAAAJAbs4vL2je1oBsn57RvaiGTrjhxte+ktSay0h6JZAAAAE1EybePU5T2natP72utNVvdTaC1JuLGHQAAANDWoubbxynu9p201kQWuAMAAADaVth8+03d6xM9KU+ifSetNZE2AgAAANC2fPLtkwwAkmzfSWtNpIUUIAAA0Jai5NsntZ4nlo95XUv+PtoJdwAAAEBbipJvH+dJerMC5CBo34l2QwAAAADaUhL59mEdmDkcqAahEdp3oh2RAgQAANpSkvn2QQQtQG7EJL1+27m695GnMptZANTDHQAAANCWfPPm48q3D1OAvFrXurVaPnZcn/7a4y94Ps2ZBUAj3AEAAABtabCvS9sb9MdvZHW+ve/kYJ8CZEm6/PyzZCYtN0hDSnNmAdAIdwAAAEDbGh8Z0NjEdKCT+JX59lEnB/sWIH/hoSfVaqlpzSwAGuEOAAAAaFvD/b26YXSr1ljz960xaffoNg3398YyOdi3kDhoxlBtZgGQBQIAAADQ1nYMbdH+nWWVG6QDlUs92r+zrKuHzgs9ObjRSX9chcTNJDmzAGiGFCAAAND2hvt7Ndzfq9nFZU3NL+nosePauG6thvt7X5DzH9fk4LRSc+KeWQAEkasAwMzOlPRmSb8kaaukTZJ+JOnrkvZJ2uecO7Hi/S+RtNDkIw84596W1HoBAEC8Bvu6Gm6Yo0wOXv2ZtQJk3+FfQcU5swAIKlcBgKS3SvqIpO9IulPSYUl9kkYlfUzSL5jZW51zq2P/r0m6rc7nfSPBtQIAgBTFPTk4TAGyKXj+/0pppBoBq+Xt/3Wzkt4o6R9WnfT/rqR7JL1FlWDgllXXfdU5d31aiwQAAOmLe3JwrQC5VU3BGpOuec2gPvhPs6G/my5AyEKuioCdc593zv39ys1/9fnvSvqL6m8vT31hAAAgc0lMDg5agLxrZCDyzAIgLXm7A9DMj6uP9cL4c83sNySdKel7kr7inDuU2soAAEDikpocHLQA2XdmAZC2jggAzGytpF+t/vb2Om95bfXXymvukvR259zhgN9xsMFLFwRcJgAASJBP4W6YU/hmBchSuJSh2swCIAu5SgFqYrekV0j6rHPuH1c8/4yk90u6RFJ39derVSkgvlzSpJm9ON2lAgCApIyPDLQcGlaTxCl8mJkFQFZyfwfAzHZJ+m1JD0oaW/mac+4JSX+w6pK7zexKSV+SVJb065L2tPoe59wlDb7/oKSLw68cAADErR1O4YOmDAFZyXUAYGa/pcrm/ZuSRpxzge75OeeOm9nHVAkALlOAAAAAAOTDjqEt2ty9QXsn5zRdJx2oXOrRrpGBxFNwWqUMAVnJbQBgZtdI+pAqvfxHqqf9YTxZfSQFCACAmLTLqTen8EBjuQwAzOxaVfL+vyrptc45n8kfP1t9/FZsCwMAoKCm5pe0Z3KubgHu9lKPxlM4ca+HU3jgZLkLAMzsfZL+SNJBSVc2S/sxs7Kk+5xzP1r1/BWS3l397SeSWisAAEVwYOZw05z7exaOaGxiWrtHtwUufuXkHkhOrgIAM3u7Kpv/ZyV9UdIus5NK/R92zn28+s8fkPTyasvPx6rPbZN0RfWf3+ec+3KSawYAoJNNzS+1LLiVpBNOuu7WQ9rUvb7pnYB2vZMAdJJcBQCSStXHUyRd0+A9X5D08eo/75f0ZklDkn5B0oskLUq6SdKfOee+mNhKAQAogD2Tc4EGX0mVIGDv5FzDDXwSdxIa4Q4DiixXAYBz7npJ14d4/4SkiaTWAwBAkc0uLocauiVJ0wtHNLu4fNJmO+47CY1whwHonEFgAAAgZVPzPj04pA/e8ZBmF5df8JzPnYSwDswc1tjEdMOgpXaH4aaZR0N/NpAnuboDAAAA2sfRY8e9rrv9/kXdfv/icyfuZ3WdFtudhEbC3GG49pZD+vq3v6+zu04jPQgdiQAAAAB42bgu2jaiduL++m3nel0/Nb8UeGMe5g6Dk7T/fz3ygudID0InIQUIAAB4iWMzfMJJf/+1x72uDXoHwqdWYTXSg9BJuAMAAAC8DPZ1aXupJ/LmOuDB/ElW3oFo1tXHt1ZhtagFyEC7IAAAAADexkcGNDYxHTi9Jk7D/b2Buvr41irU06qVKZAHpAABAABvw/29umF0q9acNJczWeVSj+47/FSgrj5zTxyN9btrBchAXhEAAACASHYMbdH+nWWVSz2pfN8ak6644OzAXX18awyaiSutCMgCKUAAABRcHFNxh/t7Ndzfq9nFZX3wjod0+/2Liax1jUm7R7fp5nsfC9XVp2vdWi3HmAoUZ1oRkDYCAAAACiqJqbiDfV16z5XnJxIAlEs92lWdG/CfbzkU6trlY8dlJrmYahWitkAFskQKEAAABZTkVNxad6C4vOvyl+mOd1+mA7/xqucKf328fttPxlarQBEw8owAAACAggkzFfe6Ww95bbjHRwZi22yf3XXaC1KSfNNvBs/uiqVWoVzqYTIwco37VwAAFEyYqbi+bS9r3YGCBBqtrN7w+6bf1OobarUKtbqHJ5aP6RPThwOlB60xadfIgNf3A+2CAAAAgALxmYpba3sZ9tR7x9AWbe7eoL2Tc5qOMCxs9YbfN/1m5XWDfV0v+HlesemMlsFKrQCZ9B/kHQEAAAAF4ps/PzW/5JX2svLE/VP3PaaP3PUtr89YyWcCcau0nVbBSq0Amc0/OgEBAAAABeKbPx+17eVgX5eufd2FOvjIv8aycQ8zgTho2k699CDftqhAOyMAAACgzcW5IY2SPx+HuDbuQWsMfNJ2VqcHAZ2GAAAAgDaVRJ/+OPLno4hz407aDuCHAAAAgDZ0YOZw001yrU//7tFtunrovBe81uyOQRL582HFuXEnbQcIjwAAAIA2E6ZP/7W3HNI9Dx/Ry889XRtOPUW33PvtlncMksifDyvujTtpO0BwBAAAALSZMH36naSbDz6mmw82f9/qOwZJ5c+HxcYdSB8BAAAAbcSnT39Qtcm+m7rXkz8PFBgBAAAAbcS3T39QKyf7kj8PFBMBAAAAbSRqv/0gVk/2JQ0HKJY1WS8AAAA8L65++60kfacBQPviDgAAAG2globz8NK/pfJ9Ue40kDIE5BsBAAAAGWo27CtJQe40rN7oB20zCqC9EQAAAJCRVsO+ktRso+4TlDQbTAagvRAAAACQgaDDvpLQbLJvlKBkZZtR7gQA7YsiYAAAMhBm2Fecmk32jSMoqbUZBdC+CAAAAEiZ77CvK//d2bII39tqsm9cQUmtzSiA9kQAAABAynxbcL7qZb3a/ZatWuMRBZRLPdq/s9wwPz/uCcS0GQXaFzUAAACkzLcF59Fjx/WO4ZI2d2/Q3sk5TdfZsJdLPRq9eJOe+dGzodp0xr1hT2OgGQA/BAAAAKTMd9hX7brh/l4N9/fG2o8/7g17WgPNAITHv50AAKTMt0PO6usG+7piG8AV94adLkBA+yIAAAAUUpbTbAf7urS91BMq575Z6844xLlhT3qtAKIhAAAAFEqzIVdpTrMdHxnQ2MR0oK47zVp3xsUnKKknjbUCiIYuQACAwjgwc1hjE9MNN7m1abY3zTya2BpmF5e1b2pB9z7ylH5p20/KWnT0adW6M07jIwNeHYZq0lwrAH/cAQAAFELQIVcnnHTtLYd0z8NH9PJzT48tNajZnYeudWu1XKcIt1zq0a6U7khIlTSgG0a3eg0DS3utAPwRAAAA2k4S+flhhlw5STcffEw3H6z8Pmpq0IGZw0031cvHjsskveGiczVw9sbUaxJW2jG0JZE2owDaBwEAAKBtJJWfH3XIVS01aPfotoaDtBoJeufBSfrMoce1f2c581P0JNqMAmgfBAAAgLbQ6pQ86iY8qhNOuu7WQ9rUvT7UBj3MnYcTTto7OZd5AFATZ5tRAO2DImAAQObC5Odfd+uh0Bv6uIZc1TboQfnceZheOKLZxeWwSwOAwAgAAACZ8zklDyPOIVdhNui+dx7iuGMBAI0QAAAAMpXGKXncKTVBN+i+dx7iumMBAPUQAAAAMpXGKXltyFVcgmzQZxeXdf/j3/f6/DjvWADAavwXBgCQqbROycNM3m2l2Qa9WSejoJIqAqarDwCJAAAAkDHf0+6w10UZclXvs+pp1ckoiHKpJ/ZNeVLtVQHkEylAAIBM+W48fa7bMbRF+3eWVY6QDtRogx60k1Eza0zaNTLg/wF1HJg5rLGJ6YZ3JGrtVW+aeTTW7wXQvrgDAADIVC0/P0zKTJRT8tVDru7/9tO65d7HFGTf3myDHqaTUaPP3j26LdaT+LDtVcPOOACQT9wBAABkbnxkQGss2HvjOiUf7OvSO4ZL+tOrL9Lut2xt+f3NNuhRJw2XSz3av7McesBZK0m3VwWQT9wBAABkLmh+fhKn5FIlNWhz9wbtnZzTdJ2NfLnUo11N8uR9Oxm97uV9es+V5ydSiBulvSqFwUBnIwAAALSFqJvwqFanBoXplOPbyejl556R2GY7SntVAgCgsxEAAADaRpRNeFwG+7pCf1danYzCYAgZgEYIAAAAbcdnE56lNDsZBdWOQQmA9kARMAAAEflMGk6i3/9K7RiUAGgPBAAAAMQgi05GzbRjUAKgPRAAAAAQwezisvZNLejeR57SL237SVmEdqJxa7egBEB7INEPAAAPU/NL2jM5V7fVZte6tVquU0ybdCej1bJurwqgPREAAAAQ0oGZw0031cvHjsskveGiczVw9sbUOxmtlHV7VQDthwAAAIAQpuaXWp6oS5KT9JlDj2v/znLmm+t2aK8KoH0QAAAAEMKeybmWm/+aE07aOzmXeQBQk7f2qgCSQREwAAABzS4u1835b2Z64YhmF5cTWhEAhEcAAABAQFPzS6leBwBJIAUIAJAbWeewH63T2SfJ6wAgCQQAAIC216zl5vZSj8ZT6mKzcZ3fX5u+1wFAEvgvEgAgcVFO7lu13Lxn4YjGJqa1e3Sbrh46L8ZVn8w3yGiXImAAkAgAAAAJinpyH7Tl5gknXXfrIW3qXp/oZnuwr0vbSz2hCoHLpR467wBoKxQBAwAScWDmsMYmphtulmsn9zfNPNrwM3xabiZtfGRAayzYe9eYtGtkINkFAUBIBAAAgNiFPbmv1yXHt+XmB25/MNG2m8P9vbphdGvLIGCNSbtHt5H+A6DtEAAAAGIXx8m9b+vMj9z1L7ryQ3fr6o9+JbH2mzuGtmj/zrLKpZ66r5dLPdq/s5x4TQIA+KAGAAAQqyjDslbmykdtnRl3cXC9QuYDv/GqzFuTAkBYBAAAgNjMLi7rg3c85HXt1PzSCzbOcbTOjKM4OEgh8zuGS1GXCgCpIQAAAETWbJMc1OoT/7hy52spRj6f104tSAEgLtQAAAAiadXtJ6jVJ/61lptxqKUYhRFHITMAtCMCAACAt6Cb5CDqndCHabnZStgNeju2IAWAOOQqADCzM83s183sU2Y2b2Y/MLPvm9mXzGynmdX9eczsUjP7rJkdMbNnzOyQmV1jZqek/TMAQCcJs0luptGwrKAtN4MIU1QcpZAZANpdrgIASW+V9FeSypKmJX1Y0i2SXiHpY5JuMrMX/DVhZr8s6W5Jl0n6lKQ/l3SqpA9J+mRqKweADuOzSa6n1bCsVi03gwpTVOybzkMaEIA8yFsR8KykN0r6B+fcidqTZva7ku6R9BZJo6oEBTKz01UJGJ6VdLlz7p+rz79P0uclXWVmb3POEQgAQEhxbHZN0uu3nat7H3lKs4vLDVtoDvf3ari/V7OLy/rUfY/pI3d9K/R3hSkC9m1BGrV1KQCkIVcBgHPu8w2e/66Z/YWkP5F0uaoBgKSrJJ0l6a9rm//q+4+Z2e9LmpT0LnEnAABCi7rZ7Vq3VsvHjuvTX3v8Bc/XWmvW27AP9nXp2tddqIOP/Guouw+NUowa8W1BGkfrUgBIWt5SgJr5cfVx5d9IV1Qfb6/z/rslPSPpUjM7LcmFAUAn8t3svuLc02UmLTcIIGqtNW+aebThZ4QpDm6VYlSPbwvSuFqXAkCSOiIAMLO1kn61+tuVm/3zq4+zq69xzh2XtKDKXZCXJrpAAOhAvpvd+7/ztFzE1ppBi4PXmLR7dFvotfq0IA17lwEAstIRAYCk3aoUAn9sxMWPAAAgAElEQVTWOfePK54/o/r4/QbX1Z7/iVZfYGYH6/2SdIH3qgEgx3w2yV3r1rbc/Ne0aq3Zqji4XOrR/p1l7wFdSd9lAICs5D5Z0cx2SfptSQ9KGgt7efUxhiZ2AFA84yMDGpuYDtQK1NQ47aeRWmvNRifrK4uDp+aXdPTYcW1ct7ZhMXEYtbsMreYc+N5lAICs5DoAMLPfkrRH0jcljTjnVleE1U74z1B9p696X0POuUsarOGgpItbrxYAOk+YTfLrt517UsFvEFPzSy0384N9XYmk3+wY2qLN3Ru0d3JO03WKjsulHu1qULAMAO0qtwGAmV2jSi//b6iy+X+iztsekvQzkgYlHVx1/VpJJVWKhsP3kwMASAq+Sb73kae8Pj9Kt6E47gwkeZcBALKQywDAzK5VJe//q5Je65xr1Iz685L+g6TXSfrbVa9dJmmDpLudcz9Maq0AUARBNsm+U3J9ug1NzS9pz+Rc3VahzdqMNpPUXQYASFvuAoDqEK8/UuVE/8o6aT8r3SzpA5LeZmY3rhgEtk7SH1ff85Ek1wsAnabZJr/ZJjmt1poHZg43TUmqtRndPbrNu0AYAPIstgDAzM5ukIYTGzN7uyqb/2clfVHSLrOTWjQ87Jz7uCQ55542s3eqEgjcZWaflHRElWnC51efP5DkmgGgU0Q9Va91DUpygNfU/FLLegTp+Tajm7rXk78PoHDivAPwqJndJumjjSb2xqBUfTxF0jUN3vMFSR+v/cY5d5uZvVrS70l6i6R1kuYlvUfSXueCNqQDgHyLksMe16l6mK5BPq0190zOBfps6fk2owQAAIomzgBgVtJbJV1lZv8i6aOSPu6c+15cX+Ccu17S9R7XTUn6xbjWAQB5EvXkPs5T9SRba84uLoe6uyC1bjMKAJ0otkFgzrmtkn5O0n5JmyT9N0mPmdnfmNllcX0PACC4AzOHNTYx3XBjXDu5v2nm0Yaf4XOq3kxSA7waTQ1O6joAyKtYi4Cdc1+W9GUzG5f0q5L+o6R/r0oR7qykv5D01845v15wAIDA4ji5T+pUPYnWmr7tQqO0GQWAPEqkC5Bz7vuSbpR0o5ldKumdkq6W9EFJN5jZTZL+rNaVBwAQvzjy4aOcqgfZyMfZWtOnXWiU6wAgr2JLAWrie5KeknRMlUnwp6pyd2DazG4zs/r3gAEA3qKc3K+Up1P1tNqMAkDeJRIAmNmLzOxtZnanpG+q0rHnSVU67/RKukLSP6rSjvPPk1gDABRZXPnweTpVr7UZDSNsm1EA6ASx/hfazPpVyfv/NUlnqtKv/zZJ/905N7nirXep0pf/ZlWm9AJA4bVbPvzs4rKeWD7m9TlZnaon3WYUADpBnIPAPifp51VJ83lc0vsl/aVz7vEmlx2U9Oa41gAAeRS1TWc9UU7um60niCxP1ZNsMwoAnSLOOwBXSLpT0n+XdJtz7tkA1/y9KsECABRSXAO2VvPd2D597MeBT9DraYdT9R1DW7S5e4P2Ts5puk4QUy71aJdHUAUAnSLOAOBC59xDYS5wzn1D0jdiXAMA5EacA7ZWq+XDhznFv+CcLu35XPDOQau106l6Em1GAaBTxBYAhN38A0DRxdGms5mw+fC17/HRrqfqcbYZBYBOQfNjAMhAUgO2ap9dO/X+pW0/qc8c+o5ci3z4a14zqA/+02yo9UjSuy5/md78yk1ssgEgRwgAACADSQzYala827VurZbrdAaqndyv7v8f1Nldp7H5B4CcIQAAgAz4tum8e/bJunnsrYqJl48dl0l6w0XnauDsjSflw9/7yFNe68li4BcAIBoCAADIgG+bzjsfelJ3PvTkC9qDBi0mdpI+c+hx7d9ZPilXP08DvwAA0SQyCRgA0FzUYtlae9CbZh71KiaOaz3tVvQLAGiNAAAAMlBr0xnFCSdde8sh72LiqOvJcuAXAMAfAQAAZGR8ZOC59pu+PLt21i1CDrOedhj4BQDwQwAAABkZ7u/VDaNbIwcBPuoV7wZdTzsN/AIAhEf1FgBkaMfQFm3u3qC9k3OaDpnKE0Wj4t1W62nXgV8AgOAIAAAgY8P9vRru79Xs4rJu+OwDuvOhJ1P5ziDrqQ0UW902FACQXwQAANAmBvu6dNngWYkHAEGLdwf7utjwA0AHIgAAkBlOmE/mm1pjJrkAFcEU7wIACAAApG5qfkl7Jufqtq9cOeCqiGrtOMO09iyXejR68aaWw8Ao3gUASHQBApCyAzOHNTYx3XCDu3LAVVH5tOPcMbRF+3eWVW7Qy79c6tH+nWVdPXRejCsFAOQRdwAApGZqfqnlKbVUGXB13a2HtKl7fSFPq2vtOMOe6PsW75KKBQDFQgAAIDV7Judabv5rTjhp7+RcIQMAKVo7zqDFu6RiAUAxEQAASMXs4nKovHZJml44otnF5cKeRifZjvPAzOGmdxhqqVi7R7eRNgQAHYYAAEAqpuaXvK8ragBQ0+hE3zcwIBULAIqNAABAKo4eO57qdZ0sauoOqVgAUGwEAABSsXGd339ufK/LWlKFtVFTd0jFAgDk829WALnje4Kct5PnJAtr40jdIRULAMAcAACpqA24CqNc6snVpjPpGQc+qTurkYoFACAAAJAanwFXeRH2dD7sSXyU1J2VipaKBQA4GQEAgNTUBly1CgJWD7jKgzhO55uJkrqzUlFSsQAAjREAAEjVjqEt2r+zrHKDdKByqUf7d5Zz1Xs+rtP5ZuJK3SlCKhYAoDnu6QJIXZIDrrLgezr/wTse0nuuPD/Qzxxn6s74yIDGJqYD3bHIWyoWAKA1AgAAmWk04CpvfE/nb79/UbffvxioO1CcqTu1VKxWNQt5TMUCALRGChAARBS1QDZId6C4U3c6MRULABAMdwAAIKI4Tsib9e6viTt1p9NSsQAAwRAAAEBEtdP5sIXAq9W6AzUKAJJK3emUVCwAQDAEAEBBcMqbrDCn883UugM1S93Z3L1BeyfnNF0n4CiXerQrwrRhAEDnIwAAOtzU/JL2TM7VPZ0OUnyKYIKezgcxNb/UNDgjdQcAEAUBANDBDswcbrohrRWf7h7dRrFnDFqdzgcVtKsQqTsAAB8EAECHmppfCnQaHaT4tEiinqqvPJ3/4B0P6fb7F0OvIWpXIQAAmuFvGaBD7ZmcC5yK0qr4tAjiTpUa7OvSe6483ysAKPKfAwAgeQQAQAeaXVwO3ZGmVfFpJwuSKvUrH5vWGy46VwNnbwx8Z8CnO1Cz3v0AAMSBAADoQFPzS97XFW3zGTRVykn69Ncef8FzQe4MxN27HwCAqJgEDHSgoEWkcV3XyuzisvZNLejGyTntm1rQ7OJyIt/jI0yq1GpBJvjWugOtseafFbZ3PwAAvrgDAHQg3yLSuItP270FqU+q1GpBiqjp3Q8AaCcEAEAH8t1IxrkBbZcWpM26+vimSq0WpIia3v0AgHZBAAB0oKyLT9uhBWmruw9vuXiT7p59MrbvC1pETe9+AEDWqAEAOtT4yEDLvPOauItPfVqQxunAzGGNTUw3DIDuWTiia2/5uu58KL4AQIrvjgIAAEkiAAA6VFbFp1FakMYh6N2HJCRVRA0AQJwIAIAOtmNoi/bvLKtc6qn7ernUo/07y7Hm4EdpQRqHKF19omKCLwAgD/jbCuhwaRefZtmCNI6uPlHQxQcAkAcEAEBBpFV8mmUL0ixz8JngCwDICwIAALFKsgVpq7sYWeXgM8EXAJAnBAAAYpVEC9KgA8XizsE/v2+jZp84KtekpoAJvgCAvCEAABC78ZEBjU1MByrGbXV6HmSg2K98bFpvuOhcdW94keeK63vb9i0a7Otigi8AoKMQAACIXa0Faat2nK1Oz4O29HSSPv21x/0X3EAtxahZEfXs4rL2TS0w2RcAkBsEAAASsWNoizZ3b/A6Pa9ttie+uJBZS8/VaUmri6in5pf0+7d9o2VaEgAA7YYAAEBiwrYgbZbrn6Y40pLGJqa1e3RbrDMWAACIAwEAgMQFaUHaalOdlrjSkk446bpbD2lT93ruBAAA2goBAIDMBd1UR/H2S39KLznzxdpw6im69d5vexf1hpk0fMJJeyfnCAAAAG2FAABAbHynDYfZVPt6yZkv1juGS5Iq9Qk+a/WZNDy9cESzi8sUBgMA2gYBAIDIgvbpr8dnU+1j9ZAwn8nIvpOGp+aXCAAAAG1jTdYLAJBvB2YOa2xiuuEmvlYQe9PMo3Vf991UhxXHkDDfScNZTSgGAKAeAgAA3sIWxNbb7Ke1OY4jD983iIh7QjEAAFEQAADw5lMQu1oam+PVPf19+QYRFAEDANoJAQAAL1EKYldKenPcqqd/GIN9Xdpe6gl1TVzBBwAAcSEAAOAlSkHsSj6b6s0/sV5vuOgnZdb8fa16+vsYHxnQmhbfu/L74wo+AACICwEAAC9xFsSG3VR/4KptuvHfX6xP7Cyr3CB4KJd6tH9nOfZJvMP9vbphdGvL9SYRfAAAEAcq04Cc8u25H5c4C2Jrm+pWBcWrN9XD/b0a7u9N/X+LHUNbtLl7g/ZOznkPFAMAICsEAEDOROm5H6e4C2KjbKp9evpHlVXwAQBAVAQAQI4cmDnc9JS81nN/9+i22FNfVqvl7ocpBG5VEJvHTXUWwQcAAFEQAAA5Ebbn/qbu9YnfCRgfGdDYxHSgVqBhCmLZVAMAkByKgIGciKPnftwoiAUAIH9yFwCY2VVmdqOZfdHMnjYzZ2afaPDel1Rfb/Trk2mvH/ARV8/9JOwY2qL9GXTjAQAAfvKYAvT7ki6SdFTSY5IuCHDN1yTdVuf5b8S4LiAxUXrup5FKk8fcfQAAiiqPAcC7Vdn4z0t6taQ7A1zzVefc9UkuCkhSnD33k0TuPgAA7S93AYBz7rkNv7UaAwp0iDh77gMAgGIryu7gXDP7DUlnSvqepK845w5lvCYgsLh77gMAgOIqSgDw2uqv55jZXZLe7pw7HOQDzOxgg5eC1CAAkSTRcx8AABRT7roAhfSMpPdLukRSd/VXrW7gckmTZvbizFYHrDC7uKx9Uwu6cXJO+6YWTurgMz4y0LLdZk2YnvsAAKBYOvoOgHPuCUl/sOrpu83sSklfklSW9OuS9gT4rEvqPV+9M3BxxKWiwKbml7Rncq7u6f72Uo/GRwae67Jzw+jWlsPA6LkPAACa6fQ7AHU5545L+lj1t5dluRYU24GZwxqbmG6Y2nPPwhGNTUzrpplHJdFzHwAARNfRdwBaeLL6SAoQMjE1v9TyNF+qTPW97tZD2tS9/rk7AfTcBwAAvoocAPxs9fFbma4ChbVncq7l5r/mhJP2Ts69IK2HnvsAAMBHR6cAmVnZzE6t8/wVqgwUk6RPpLsqoFLwG6ajjyRNLxw5qTAYAAAgrNzdATCzN0l6U/W351QfX2VmH6/+85Jz7r3Vf/6ApJdXW34+Vn1um6Qrqv/8Pufcl5NdMXCyqfkl7+s49QcAAFHkLgCQ9NOS3r7quZdWf0nSI5JqAcB+SW+WNCTpFyS9SNKipJsk/Zlz7ouJrxao4+ix46leBwAAUJO7AMA5d72k6wO+d0LSRJLrAXxsXOf3r57vdQAAADXsJoAEtOrQ49ujv116+9OBCACA/CIAAGIUdKjXYF+Xtpd6QhUCl0s9iWyyw2zmg/58AACgfREAADE5MHO4aV//2lCv3aPbdPXQeRofGdDYxHSgVqBrTNo1MhDresNu5sP+fAAAoD11dBtQIC6zi8vaN7WgGyfntG9q4aR2nGGHek3NL2m4v1c3jG7VGmt+zRqTdo9ui/VkPewEYp+fDwAAtCfuAAArrE6H2XDqKbrl3m+3PCX3Heq1Y2iLNndv0N7JOU3X+Y5yqUe7Yk6r8ZlAHHVoGQAAaB8EAICap8M0Ujslv+Y1g95DvQb7ujTc36vh/t7UCmvDbuZ3/88H9PVvPx3qO1b+fAAAoL0QAKDwWuW2N3PCSR/6p1mv71091GuwryvxDbPPBOKwm/8ahpYBANCeqAFAoQVNh2nG99IshnqlmZvP0DIAANoTdwBQaGHSYeKW1FCvZqlEaW7KGVoGAEB74m9oFJZPOkyc4i6SDdLWM81NOUXAAAC0J1KAUFhZtqqMe6hX0Laey553ALZuOiPU+5MaWgYAAKIjAEBhxZ0O06Kd/3PiHuoVpq3nhz83qwvOCbcxL5d6dN0vXNByXkFNEkPLAABAfAgAUFhxp8Nc89qBTIZ6hW3rWVtHELXNfJZDywAAQLwIAFBYcW5Sy6UejY8Mav/Ossqlnobv2b+zrKuHzovte33qGB787rLGXxM+WNkxtCX1nw8AAMSPImAU1mBfl7aXeiIXAq9MeUl7qJdvHcPp616k/TvLoScQp/3zAQCA+BEAIHHtvFkcHxnQ2MS0dyvQRikvaQz1kvzrGI4eOx5pM5/WzwcAAOJHAIDEBGlLmXWueC233WcYWKNT8jT51jGsvK7RZr6dAzcAAOCPAACJODBzuOmmutaWcvfotsxzxncMbdHm7g1N02FGL96kZ370bOTNcNybat/go9l1eQjcAACAPwIAxC5MW8rrbj2kTd3rM99QJp3bntSm2qeOoVmP/jwFbgAAwA8BAGIXti3l3sm5zAOAmiRy25PeVIepY2jWoz+PgRsAAAiPNqCIlU9byumFI5pdXE5oRdkKu6n26eoTV49+n8ANAADkD3cAECvftpRT80sdWWAax92QIGlJQeoYmhUsRwncOvHPDQCATkYAgFhFaUvZaaJuqsPWDUSpYyBwAwCgOAgAEKs42lJ2iiib6vsOP+VdN+BTx0DgBgBAcXTerguZSqItZTtrdtruuzm+/9tP69b7Hku1GJfADQCA4uBvb8Qq7raU7SpIeo7v5njm4SOpd1EqWuAGAECR0QUIsRsfGWjZkaamWVvKdnVg5rDGJqYbBjm19JxlzzsAjxx5JtT74+iiVAvcwshj4AYAAAgAkIC42lK2ozBtPT/8uVldcE64DfJP9WzwXldUnR64AQCACgIAJGLH0Bbt31lWucGpcrnUo/07y7mbJhu2raekUJvqoZCn8DVxFON2cuAGAACeRw0AEhOlLWXSfNbk09bzwe8u692vHdCezzUPHGqb6n/70XHdfDDUV0iKrxg36jwBAADQ/ggAkDiftpRJCdtbf/W1Pk5f9yLt31kOtKn2zeWPc0PezoEbAACIjgAAhXFg5rB3b30pWq/8oJvqduqi1E6BGwAAiA8BAAohTPFuo976cfTKD7KpHh8Z0NjEdKBaA4pxAQBAWBQBoxDCFu/unZw76fm0euVTjAsAAJJEAICO51O8W6+3fpq98ju1ixIAAMgeKUCITTsWjc4uLuuDdzzkde3U/NJJ608zPYdiXAAAkAQCAEQWpbNOFmsKql7Rby09p1U9QZzpORTjAgCAOBEAIJKonXWyWFNQjYp+6ZUPAADyjAAA3uLorJPVmoJotlbScwAAQF4RAMCbT2edpAOAMGtqJmjxLuk5AAAgb+gCBC9xddaJcz0fuP2BSDn/NfTWBwAAnYw7APAyNb/kfV2cJ+ZxFPuuRG99AADQ6QgA0FCz/PZ6HXKC8L2unriKfWso3gUAAEVAAICTBGnr2ahDTiu+160WZ7Hv617ep/dceT65/AAAoBCoAcALHJg5rLGJ6YYpNbW2nsueJ/lxna7HVewric0/AAAoFAIAPCdMW88Pf25WF5wTbtMctLNOKz4FyI3EtSYAAIC8IADAc8K29ZQqRbNBxNlZx7cAeTW6/QAAgCIiAIAkv1P1B7+7rPHXDLQMAuLurBNHITHdfgAAQFERAECS/6n66etepP07yyqXeuq+Xi71aP/Osq4eOi/K8l4gaiFxEmsCAADIC7oAQVK0tp7D/b0a7u9t2jY0Tr6n9u+6/GV68ys3kfMPAAAKjQAAkvxP1VdeN9jXlcrmerCvS9tLPaFSlsqlHl37ugsSXBUAAEA+kAIESf6n6lnl0I+PtK49qKHYFwAA4HkEAJD0/Kl6GFm20Bzu79UNo1tTL0AGAADIOwIAPCdvp+o7hrakXoAMAACQd9QA4Dm1U/VWw8Da6VQ97QJkAACAvCMAwAvsGNqizd0btHdyTtN1imzLpR7tGhloi83/SmkVIAMAAOQdAUBBhDkh51QdAACgcxEAdLip+SXtmZyr2zJze6lH401O8zlVBwAA6DwEAB3swMzhpvn89ywc0djEtHaPbmurQlnuPAAAACSHAKBDTc0vtSzmlaQTTrru1kPa1L0+9bz+1Rv9Daeeolvu/bbX3QoAAAAEQwDQofZMzrXc/NeccNLeybnUNtfN0pIaade7FQAAAHnDHIAONLu4HGpzLUnTC0c0u7ic0Iqed2DmsMYmpkOvT3r+bsXU/FICKwMAACgGAoAO5LtBTnpjHTQtqZna3QoAAAD4IQDoQEePHU/1uqDCpCU1k9bdCgAAgE5EANCBNq7zK+3wvS4In7SkZkgDAgAA8EMA0IF8i3mTLAKOe8Oe9N0KAACATkUA0IEG+7q0vdQT6ppyqSfRXvtxb9iTvFsBAADQyQgAOtT4yIDWWLD3rjFp18hAouuJe8POPAAAAAA/BAAdari/VzeMbm0ZBKwxaffotsQ31HF+ftJ3KwAAADoZAUAH2zG0Rft3llVukA5ULvVo/85yKoO1fNKS6knjbgUAAEAnI5G6ww3392q4v1ezi8uaml/S0WPHtXHdWg3396Z+ij4+MqCxiWnvVqBp3a0AAADoZAQABTHY15V52kwtLclnGFi51KNdIwNs/gEAACIiAECqdgxt0ebuDdo7OafpOnMByqUejV68Sc/86NlM71YAAAB0KgIApM43Lakd0pgAAADyjgAAmQmaljQ1v6Q9k3N1JwlvL/VonNQgAACAwOgChLZ2YOawxiam627+Jem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\n", 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\n", 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\n", 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" ] }, "metadata": { "image/png": { - "height": 263, - "width": 392 + "height": 261, + "width": 390 }, "needs_background": "light" }, @@ -203,14 +203,22 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "705.1093\n" + "700.0426\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/maer3/anaconda/envs/dl-workshop/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU.\n", + " warnings.warn('No GPU/TPU found, falling back to CPU.')\n" ] } ], @@ -235,13 +243,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "55d8e71199d64071a2afbf6a792dabb1", + "model_id": "ff819088cb36420daef119fae8ae8d9c", "version_major": 2, "version_minor": 0 }, @@ -343,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -380,7 +388,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Congratulations, you have just implemented **stochastic gradient descent** (SGD)!\n", + "Congratulations, you have just implemented **gradient descent**!\n", "\n", "Stochastic gradient descent is an **optimization routine**: a way of programming a computer to do optimization for you so that you don't have to do it by hand." ] @@ -389,20 +397,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Minimizing $f(w)$ with `autograd`\n", + "## Minimizing $f(w)$ with `jax`\n", "\n", - "Autograd is a Python package for automatically computing gradients. This way, we do not have to specify the gradient function by hand. With autograd, our example above is modified in only a slightly different way." + "`jax` is a Python package for automatically computing gradients; it is known as an \"automatic differentiation\" system. This way, we do not have to specify the gradient function by hand-calculating it; rather, `jax` will know how to automatically take the derivative of a Python function w.r.t. the first argument. With `jax`, our example above is modified in only a slightly different way." ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "df50ae1202384f6d8715facb5e765275", + "model_id": "1222a223b35b444e9e12bedce5a1e704", "version_major": 2, "version_minor": 0 }, @@ -417,18 +425,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n", + "-1.5000029\n" ] - }, - { - "data": { - "text/plain": [ - "array(-1.5000029, dtype=float32)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -450,9 +449,7 @@ " w = w - df(w) * 0.01 # 0.01 is the size of the step taken.\n", " \n", " \n", - "# We call on JAX's device_get function to retrieve the \n", - "# result of optimization, so that we can inspect it.\n", - "jax.device_get(w)" + "print(w)" ] }, { @@ -461,6 +458,12 @@ "source": [ "# Back to Optimizing Linear Regression\n", "\n", + "## What are we optimizing?\n", + "\n", + "In linear regression, we are minimizing (i.e. optimizing) the loss function w.r.t. the linear regression parameters.\n", + "\n", + "**Keep in mind:** The loss function is the parallel to the $f(w)$ polynomial function that we were playing around with above.\n", + "\n", "## Ingredients for \"Optimizing\" a Model\n", "\n", "At this point, we have learned what the ingredients are for optimizing a model:\n", @@ -469,6 +472,8 @@ "2. Loss function, which tells us how bad our predictions are.\n", "3. Optimization routine, which tells the computer how to adjust the parameter values to minimize the loss function.\n", "\n", + "**Keep note:** Because we are optimizing the loss w.r.t. two parameters, finding the $w$ and $b$ coordinates that minimize the loss is like finding the minima of a bowl.\n", + "\n", "The latter point, which is \"how to adjust the parameter values to minimize the loss function\", is the key point to understand here. " ] }, @@ -483,13 +488,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "859434cb61e14a0e9499ea7d97514883", + "model_id": "29056d70828f487e8f1233ca54b68dac", "version_major": 2, "version_minor": 0 }, @@ -517,7 +522,10 @@ " \"\"\"\n", " return p['w'] * x + p['b'] # take out in student version.\n", "\n", - "# Initialize values of w and b. We will store the parameter values in a dictionary.\n", + "# Initialize values of w and b. \n", + "# We will store the parameter values in a dictionary.\n", + "# The dict keys are the name of the parameter,\n", + "# and the dict values are the parameter values.\n", "params = dict()\n", "params['w'] = npr.normal() # FITB\n", "params['b'] = npr.normal() # FITB\n", @@ -533,13 +541,7 @@ " y_est = model(params, x) # FITB\n", " return mse(y, y_est) # FITB\n", "\n", - "from jax import grad\n", - "\n", - "# `grad` as a function returns another function, let's call it `grad_func`.\n", - "# `grad_func`'s signature is identical to the original function passed into it.\n", - "# However, it's return statement returns an object of the same data structure\n", - "# as the first element passed into it, except now each element is a gradient scalar/tensor.\n", - "dmseloss = grad(mseloss) # derivative of loss. \n", + "dmseloss = grad(mseloss) # derivative of loss function\n", "\n", "# Optimization routine\n", "losses = []\n", @@ -548,8 +550,8 @@ " grad_p = dmseloss(params, model, x, y)\n", "\n", " # Update the gradient values for each parameter.\n", - " for k, p in params.items():\n", - " params[k] = params[k] - grad_p[k] * 0.001\n", + " for name, value in grad_p.items():\n", + " params[name] = params[name] - value * 0.001\n", " losses.append(mseloss(params, model, x, y))" ] }, @@ -562,20 +564,20 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { - "height": 263, - "width": 390 + "height": 261, + "width": 388 }, "needs_background": "light" }, @@ -591,16 +593,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'b': array(19.865015, dtype=float32), 'w': array(2.013633, dtype=float32)}" + "{'b': array(19.729565, dtype=float32), 'w': array(2.0043414, dtype=float32)}" ] }, - "execution_count": 29, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -608,7 +610,7 @@ "source": [ "from pprint import pprint\n", "\n", - "jax.device_get(params)" + "pprint(params)" ] }, { @@ -703,13 +705,13 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f67f1d8254ec4b209646aa238ea1016d", + "model_id": "a58ed727bb7b48759b86a5a6ff021bc1", "version_major": 2, "version_minor": 0 }, @@ -760,30 +762,30 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { "image/png": { - "height": 250, - "width": 373 + "height": 248, + "width": 372 }, "needs_background": "light" }, @@ -809,7 +811,7 @@ "\n", "Expressed in equation form, it looks like this:\n", "\n", - "$$L = -(y \\log(p) + (1-y)\\log(1-p)$$\n", + "$$L = -\\sum (y \\log(p) + (1-y)\\log(1-p)$$\n", "\n", "Here:\n", "\n", @@ -837,13 +839,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7cc28ec6452477a90767cf61e714ca7", + "model_id": "efca0d431d7348ac93e8b8f3af3defb7", "version_major": 2, "version_minor": 0 }, @@ -875,7 +877,7 @@ "# Exercise: Define logistic loss function, using flattened parameters\n", "def logistic_loss(p, model, x, y):\n", " preds = model(p, x)\n", - " return -np.mean(y * np.log(preds) + (1 - y) * np.log(1 - preds))\n", + " return -np.sum(y * np.log(preds) + (1 - y) * np.log(1 - preds))\n", "\n", "# Exercise: Define gradient of loss function.\n", "dlogistic_loss = grad(logistic_loss)\n", @@ -887,31 +889,34 @@ "\n", "# Exercise: write SGD training loop.\n", "losses = []\n", - "for i in tqdmn(range(5000)): \n", - " grad_params = dlogistic_loss(params, model, x, y_true)\n", - " for k, v in params.items():\n", - " params[k] = params[k] - grad_params[k] * 0.01\n", + "for i in tqdmn(range(5000)): \n", + " # Evaluate gradient\n", + " grad_p = dlogistic_loss(params, logistic_model, x, y_true)\n", + " # Update parameters\n", + " for name, value in grad_p.items():\n", + " params[name] = params[name] - value * 0.001\n", + " # Keep track of losses\n", " losses.append(logistic_loss(params, logistic_model, x, y_true))" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'b': array(3.4998062, dtype=float32), 'w': array(9.200708, dtype=float32)}" + "{'b': array(2.6803613, dtype=float32), 'w': array(9.481778, dtype=float32)}" ] }, - "execution_count": 33, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "jax.device_get(params)" + "pprint(params)" ] }, { @@ -923,30 +928,30 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "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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pSr3009VQU7jPujPrkiRJmpiKCesAKaXPRMRG4ArgjQWXHgCuHN4eM1PUF7TBuHWjJEmSJqqS2mCIiHcD3wauJJtRbwZOAR4E/iMiPj6RcVJKp4z0Au45SKWPqb6miv5W/q7ePnr7UjnKkCRJ0gxTMWE9Is4GPgb8V0rpnSmlB1NK7Sml24CXA48B74qIY8pZ51REBPU1hbPrtsJIkiRpfBUT1oHfy49P2PYxpdQO3EJW78mlLKpY3L5RkiRJk1VJYb0+P462PWP/+a4S1FJ0bt8oSZKkySp5WI+I2ohYHRErh126MT/+eUQcMew7LwLOADqAm0pQZtEN3b7RsC5JkqTxFWU3mIg4Hzg//7gsP54eEVfm73eklC7O3x8BrAceAlYUDPNtsn3Unwesj4irgC3AGrIWmQDek1LaWYyaS23I9o3uCCNJkqQJKNbWjScBrx127pj8BVkwv5gxpJT6IuLFwFuAPyRbVNoE7AK+B1yWUrq2SPWWnG0wkiRJmqyihPWU0qXApRO8dyPZLPlI17qBz+SvWaXep5hKkiRpkippgemsNqRn3a0bJUmSNAGG9RIZss+6bTCSJEmaAMN6iTTYBiNJkqRJMqyXSEONC0wlSZI0OYb1EimcWXfrRkmSJE2EYb1E3LpRkiRJk2VYLxF71iVJkjRZhvUScetGSZIkTZZhvUTqXWAqSZKkSTKsl4hPMJUkSdJkGdZLpHDrxk7bYCRJkjQBhvUSGbJ1ozPrkiRJmgDDeokM3Q3GmXVJkiSNz7BeIkP2WbcNRpIkSRNgWC8R91mXJEnSZBnWS8StGyVJkjRZhvUSsWddkiRJk2VYL5GGmoLdYHpsg5EkSdL4DOslMmSBqT3rkiRJmgDDeonUD9ln3TYYSZIkjc+wXiJu3ShJkqTJMqyXSF11FRHZ++7eRG9fKm9BkiRJqniG9RKJCLdvlCRJ0qQY1kuocPtGd4SRJEnSeAzrJVS4faMz65IkSRqPYb2Ehm7faFiXJEnS2AzrJTT0Kaa2wUiSJGlshvUSKtxr3e0bJUmSNB7Degk1uBuMJEmSJsGwXkJDn2JqG4wkSZLGZlgvocKZ9U7bYCRJkjQOw3oJucBUkiRJk2FYLyG3bpQkSdJkGNZLaOjMumFdkiRJYzOsl9CQsN5jG4wkSZLGVpSwHhEXRMTlEXFjROyNiBQRX5vGeGdGxHciYnNEdObHayPixcWot1zculGSJEmTUVOkcd4PnAjsAx4FVk91oIh4P/D3wA7gf4DNwGLgZOBs4HvTrLVshmzd6My6JEmSxlGssP4OspD+APAc4LqpDBIRryIL6j8CXpFSaht2vXaadZZVvTPrkiRJmoSihPWU0kA4j4gpjRERVcDHgHbgNcODev5zuqdaYyVw60ZJkiRNRrFm1ovhWcDRwLeBxyPiJcBTgA7glpTSzeUsrhgahjzB1Jl1SZIkja2Swvoz8uNW4DbgqYUXI+IG4IKU0vbxBoqIdaNcmnIvfTEM2WfdJ5hKkiRpHJW0deOS/PiXQCPwPKCVbHb9h8BZwLfKU1pxNNTYBiNJkqSJq6SZ9f4kG2Qz6Hfkn++KiJcD9wHPiYjTx2uJSSmdMtL5fMZ9bbEKniwfiiRJkqTJqKSZ9cfz44MFQR2AlNIBstl1gFNLWlURDWmDMaxLkiRpHJUU1u/Nj7tHud4f5htLUMtBUTizfsA2GEmSJI2jksL6DUAPcFxE1I1w/Sn5cWPJKiqyprqCsN7VU8ZKJEmSNBOUPKxHRG1ErI6IlYXnU0o7gP8LzAf+bth3ng+8ANgD/KBUtRZbU93gEoH2LttgJEmSNLaiLDCNiPOB8/OPy/Lj6RFxZf5+R0rp4vz9EcB64CFgxbCh3gmcBlwSEWcBtwBHAS8HeoE3ppRGa5OpeI1D2mAM65IkSRpbsXaDOQl47bBzx+QvyIL5xYwjpbQtIk4D3k8W0J8JtAHfBT6aUvpFkeoti8YhbTCGdUmSJI2tKGE9pXQpcOkE791Itj3jaNd3kc2wv7MIpVWU2uqguiro7Uv09CW6evqoq6mkZQOSJEmqJCbFEooImmyFkSRJ0gQZ1kvMVhhJkiRNlGG9xArDervbN0qSJGkMhvUSc0cYSZIkTZRhvcSabIORJEnSBBnWS2xoG4xhXZIkSaMzrJdYY+3gbpm2wUiSJGkshvUSsw1GkiRJE2VYL7HCBaa2wUiSJGkshvUSG7LPum0wkiRJGoNhvcSGPhTJfdYlSZI0OsN6iTXZBiNJkqQJMqyXmG0wkiRJmijDeok1uhuMJEmSJsiwXmJNPhRJkiRJE2RYLzEfiiRJkqSJMqyXmG0wkiRJmijDeokNbYNx60ZJkiSNzrBeYoVPMD3Q3VfGSiRJklTpDOsl5kORJEmSNFGG9RJzNxhJkiRNlGG9xIa2wRjWJUmSNDrDeom5G4wkSZImyrBeYnXVVVRXBQA9fYmuHheZSpIkaWSG9RKLCJpq3b5RkiRJ4zOsl0FT/WBY328rjCRJkkZhWC+D5vqagff7O51ZlyRJ0sgM62XQYliXJEnSBBjWy6C5rjCs2wYjSZKkkRnWy6C5oGd9nzPrkiRJGoVhvQwKe9bdDUaSJEmjMayXgQtMJUmSNBGG9TIoXGC6z551SZIkjcKwXgZNdQX7rDuzLkmSpFEY1stgyNaN9qxLkiRpFEUJ6xFxQURcHhE3RsTeiEgR8bUijHthPlaKiDcUo9ZKYM+6JEmSJqJm/Fsm5P3AicA+4FFg9XQHjIgjgcvzMVumO14lGRrW7VmXJEnSyIrVBvMOYBUwD3jTdAeLiAC+DOwEvjDd8SpNc2HPum0wkiRJGkVRZtZTStf1v89y9rS9FTgHODs/ziq2wUiSJGkiKm6BaUSsAf4R+GxK6YZy13MwuHWjJEmSJqJYPetFERE1wFeBh4H3TWOcdaNcmnYvfTG4daMkSZImoqLCOvB3wMnAs1NKB8pdzMFSOLPebs+6JEmSRlExYT0iTiWbTf9kSunm6YyVUjpllJ+xDlg7nbGLoXlIG4xhXZIkSSOriJ71gvaX+4C/LXM5B11jbTX963A7uvvo6e0rb0GSJEmqSBUR1sn2UV8FrAE6Ch6ElIAP5Pd8KT/3mbJVWSRVVUFT7WDfenu3i0wlSZL0RJXSBtMJ/H+jXFtL1sf+M+BeYFotMpWiub6G/V1ZSN/f2cO8htoyVyRJkqRKU/KwHhG1wEqgO6W0ASBfTPqGUe6/lCys/3tK6d9KVefB1lJfw7a2TsAdYSRJkjSyooT1iDgfOD//uCw/nh4RV+bvd6SULs7fHwGsBx4CVhTj589EQx+MZBuMJEmSnqhYM+snAa8ddu6Y/AVZML8YDXCvdUmSJI2nKAtMU0qXppRijNeKgns3Dj83wbFnTQsMDH+KqWFdkiRJT1Qpu8HMOUPaYHwwkiRJkkZgWC+T5vrCNhh71iVJkvREhvUysQ1GkiRJ4zGsl0lL/eC+6m0d3WWsRJIkSZXKsF4mrQ2DM+ttHc6sS5Ik6YkM62ViWJckSdJ4DOtl0tpgG4wkSZLGZlgvk3nOrEuSJGkchvUyGTqzbliXJEnSExnWy2RIz3qnbTCSJEl6IsN6mbjAVJIkSeMxrJfJ8DaYlFIZq5EkSVIlMqyXSV1NFfU12V9/b1/iQHdvmSuSJElSpTGsl5GLTCVJkjQWw3oZDd2+0UWmkiRJGsqwXkaFi0z3OrMuSZKkYQzrZWQbjCRJksZiWC+jlnrbYCRJkjQ6w3oZude6JEmSxmJYL6OhbTDOrEuSJGkow3oZObMuSZKksRjWy8iwLkmSpLEY1stoXkEbzF7bYCRJkjSMYb2MnFmXJEnSWAzrZeQCU0mSJI3FsF5GzqxLkiRpLIb1MjKsS5IkaSyG9TKa1+gCU0mSJI3OsF5GQ3aDOdBNX18qYzWSJEmqNIb1MqqrqaKprhqAvgT7umyFkSRJ0iDDepktKGiF2dNuK4wkSZIGGdbLrLBvfc8Bw7okSZIGGdbLbEHTYFjf7cy6JEmSChjWy2y+M+uSJEkahWG9zBY01g28N6xLkiSpUFHCekRcEBGXR8SNEbE3IlJEfG2SYyyKiDdExFUR8UBEHIiIPRHxs4j4s4iYlb9YzC9sgznQVcZKJEmSVGlqxr9lQt4PnAjsAx4FVk9hjFcB/wJsBq4DHgaWAq8A/g14UUS8KqU0qzYjtw1GkiRJoylWWH8HWUh/AHgOWdierPuAlwLfTSn19Z+MiPcBtwCvJAvu35l2tRVkvls3SpIkaRRFaS1JKV2XUrp/OrPeKaWfpJT+uzCo5+e3AF/IP549jTIrUuFuMM6sS5IkqdBM6QPvT7Gz7hGfhTPrbt0oSZKkQsVqgzloIqIGuCj/+IMJfmfdKJem0kt/ULkbjCRJkkYzE2bW/xF4CvC9lNIPy11MsbnAVJIkSaOp6Jn1iHgr8C7gHuDCiX4vpXTKKOOtA9YWp7rimG/PuiRJkkZRsTPrEfEW4LPA3cBzU0q7ylzSQdFaX0NE9n5fZw/dvX1jf0GSJElzRkWG9Yh4O/DPwG/JgvqWMpd00FRVxZBWmL3OrkuSJClXcWE9Iv4G+DRwO1lQ31bmkg66ITvCGNYlSZKUK3lYj4jaiFgdEStHuPa3ZAtK1wHnppR2lLq+cljgIlNJkiSNoCgLTCPifOD8/OOy/Hh6RFyZv9+RUro4f38EsB54CFhRMMZrgQ8BvcCNwFujv5l70MaU0pXDT85083yKqSRJkkZQrN1gTgJeO+zcMfkLsmB+MWM7Oj9WA28f5Z7rgSunUF9FW9DkXuuSJEl6oqK0waSULk0pxRivFQX3bhx+boJjRErp7GLUW2nmNw7+zrS7vauMlUiSJKmSVNwC07loYcHM+i7bYCRJkpQzrFeAQ5oHw/rj+51ZlyRJUsawXgEWNhfOrBvWJUmSlDGsV4AhYX2fYV2SJEkZw3oFOKSgZ/1xZ9YlSZKUM6xXgEUtg2F9pz3rkiRJyhnWK8CQmfX9XaSUyliNJEmSKoVhvQI01FbTVFcNQE9foq2zp8wVSZIkqRIY1iuEi0wlSZI0nGG9Qrh9oyRJkoYzrFeIhT4YSZIkScMY1ivEwiZ3hJEkSdJQhvUKcYgz65IkSRrGsF4h7FmXJEnScIb1CuFuMJIkSRrOsF4hhjwYyZl1SZIkYVivGItaXGAqSZKkoQzrFWLIzLphXZIkSRjWK8aiwp51w7okSZIwrFeM+Y21VEX2fm9HD109feUtSJIkSWVnWK8QVVXBwub6gc/OrkuSJMmwXkEObR0M69vbOstYiSRJkiqBYb2CLC7YEWb7vo4yViJJkqRKYFivIIUz6zvabIORJEma6wzrFeTQloI2mH22wUiSJM11hvUKYs+6JEmSChnWK8hiZ9YlSZJUwLBeQYb2rBvWJUmS5jrDegVxZl2SJEmFDOsVxJ51SZIkFTKsV5AFjbXUVAUAbR09dHT3lrkiSZIklZNhvYJUVQWLCh6MtMNWGEmSpDnNsF5hhiwy3eeDkSRJkuYyw3qFGbLI1L51SZKkOc2wXmEKn2JqG4wkSdLcVpSwHhEXRMTlEXFjROyNiBQRX5viWMsj4oqI2BQRnRGxMSI+ExGHFKPWSrfYHWEkSZKUqynSOO8HTgT2AY8Cq6cySESsBG4ClgDXAPcApwJvA14YEWeklHYWpeIK5cy6JEmS+hWrDeYdwCpgHvCmaYzzebKg/taU0vkppfeklM4BPg0cD3xk2pVWuMIFptv2GtYlSZLmsqKE9ZTSdSml+1NKaapjRMQxwHnARuBzwy5/ANgPXBgRzVMudAZYOq9h4P2WvR1lrESSJEnlVkkLTM/Jj9emlPoKL6SU2oCfA03AM0tdWCkdNr8grO8xrEuSJM1lxepZL4bj8+N9o1y/n2zmfRXw47EGioh1o1yaUi99KS2ZV7DAdF8nvX2J6vypppIkSZpbKmlmfX5+3DPK9f7zC0pQS9nU11SzsDl7imlvX3KRqSRJ0hxWSTPr4+mfXh63Lz6ldMqIA2Qz7muLWdTBsHReA7v2Z08v3bKnY0gfuyRJkuaOSppZ7585nz/K9XnD7pu1lhW0wrjIVJIkae6qpLB+b35cNcr14/LjaD3ts8ay+Y0D711kKkk1Uh4lAAAgAElEQVSSNHdVUli/Lj+eFxFD6oqIVuAM4ADwi1IXVmrL3L5RkiRJlCGsR0RtRKzOn1Y6IKW0AbgWWAG8ZdjXPgg0A19JKe0vSaFltGz+YBvMVmfWJUmS5qyiLDCNiPOB8/OPy/Lj6RFxZf5+R0rp4vz9EcB64CGyYF7ozcBNwGURcW5+32nAc8naXy4pRr2VzgcjSZIkCYq3G8xJwGuHnTsmf0EWzC9mHCmlDRHxdOBDwAuBFwObgcuAD6aUdhWp3oq2bL5hXZIkSUUK6ymlS4FLJ3jvRga3YRzp+iPA64tR10w1pGd9TwcpJSJ8MJIkSdJcU0kLTJWb31hLQ232P017Vy9tnT1lrkiSJEnlYFivQBExZHbdRaaSJElzk2G9QrnIVJIkSYb1CnVYwSLTzbsN65IkSXORYb1CHb5g8Cmmj+0+UMZKJEmSVC6G9Qp1xCGDYf3Rxw3rkiRJc5FhvUItP6Rp4P1ju9vLWIkkSZLKxbBeoY6wDUaSJGnOM6xXqMKwvnl3B719qYzVSJIkqRwM6xWqsa6aRc11APT0Jba6faMkSdKcY1ivYMsPsRVGkiRpLjOsV7DCHWEec0cYSZKkOcewXsEK+9YffdwdYSRJkuYaw3oFc0cYSZKkuc2wXsGOKNhr3QcjSZIkzT2G9Qq23J51SZKkOc2wXsGOGLYbTErutS5JkjSXGNYr2LyGWuY11ADQ2dPH9rbOMlckSZKkUjKsV7ijFjUPvH9olzvCSJIkzSWG9Qp31KLBRaYbd+wvYyWSJEkqNcN6hVtROLO+05l1SZKkucSwXuGeVDizvtOZdUmSpLnEsF7hCmfWH7ZnXZIkaU4xrFe4FQUz67/bsd/tGyVJkuYQw3qFO7S1nsbaagDaOnrY3d5d5ookSZJUKob1ChcRQ3eEsW9dkiRpzjCszwDuCCNJkjQ3GdZnAGfWJUmS5ibD+gxQ+BTTh51ZlyRJmjMM6zNA4Y4wD/oUU0mSpDnDsD4DHHNoy8D7Ddv3uX2jJEnSHGFYnwGWzqunpb4GyLZv3N7WWeaKJEmSVAqG9RkgIli5ZHB2/f5t+8pYjSRJkkrFsD5DHFvQCvOAYV2SJGlOMKzPEMctNaxLkiTNNUUL6xGxPCKuiIhNEdEZERsj4jMRccgkx3l2RFyTf78jIh6OiO9FxAuLVetM5My6JEnS3FOUsB4RK4F1wOuBW4BPAw8CbwNujohFExznTcCNwLn58dPA9cBzgO9HxCXFqHcmOragZ/2B7YZ1SZKkuaBYM+ufB5YAb00pnZ9Sek9K6RyysH088JHxBoiIWuCjQAdwSkrpwpTSe1NKFwJPBzqBSyKivkg1zyhHLmyirib7n2t7Wyd7DnSXuSJJkiQdbNMO6xFxDHAesBH43LDLHwD2AxdGRDNjWwjMB+5LKd1beCGltB64D2gEWkb47qxXXRUcs3jwr9BWGEmSpNmvGDPr5+THa1NKfYUXUkptwM+BJuCZ44yzDdgOrIqI4wovRMQq4Djg9pTSziLUPCMVbt+4wbAuSZI069UUYYzj8+N9o1y/n2zmfRXw49EGSSmliHgL8DVgXURcBWwCjgBeDtwF/OFECoqIdaNcWj2R71eq4wrC+r1b28pYiSRJkkqhGGF9fn7cM8r1/vMLxhsopfStiNgEfAO4qODSVuDLZItW56zVy+YNvF+/eW8ZK5EkSVIplGKf9ciPadwbI/4E+BHZTjBryNpn1pDNyP8z8P9P5AemlE4Z6QXcM5U/QKV48mFDw3pK4/6VSpIkaQYrRljvnzmfP8r1ecPuG1Hel34FWbvLhSmle1JKB1JK9wAXkm0N+aqIOHv6Jc9Myw9ppKU++8eQx9u72bq3s8wVSZIk6WAqRljv37ll1SjX+xeLjtbT3u88oBa4foSFqn3ADfnHU6ZS5GxQVRWsXtY68NlWGEmSpNmtGGH9uvx4XkQMGS8iWoEzgAPAL8YZp3//9ENHud5/vmsqRc4WawpaYe42rEuSJM1q0w7rKaUNwLXACuAtwy5/EGgGvpJS2t9/MiJWR8TwnVluzI8XRMTTCi9ExEnABWR97z+Zbs0zWWFYv2eLO8JIkiTNZsXYDQbgzcBNwGURcS6wHjgNeC5Z+8slw+5fnx/7F5+SUrolIr4MvB74Vb5140NkvwScD9QBn0kp3VWkmmekNYfZBiNJkjRXFCWsp5Q2RMTTgQ8BLwReDGwGLgM+mFLaNcGh/oysN/11wAuAVmAv8DPgSymlCe0GM5sdv6yVCEgJHty+j47uXhpqq8tdliRJkg6CYs2sk1J6hGxWfCL3xijnE3Bl/tIImupqOHpRMw/u2E9fgnu3tHHikeNuYS9JkqQZqBT7rKvITjhicJfMOx8bc0dMSZIkzWCG9RnoxOWDYf2OR3aXsRJJkiQdTIb1Gaiw7cWwLkmSNHsZ1megEw6fR3VV1vb/wPZ97OvsKXNFkiRJOhgM6zNQU10Nxy1pAbJdYX7zqH3rkiRJs5FhfYY6qbAV5lFbYSRJkmYjw/oMVdi3fqdhXZIkaVYyrM9QTyvYEeb2hw3rkiRJs5FhfYY6fmkrDbXZ/3yb9nSwZU9HmSuSJElSsRnWZ6ia6ipOPvKQgc+3bNxVxmokSZJ0MBjWZ7BnHL1w4P0tv9tZxkokSZJ0MBjWZ7DTCsL6r373eBkrkSRJ0sFgWJ/BTn7SAmryhyPdu7WN3e1dZa5IkiRJxWRYn8Ga6mo44YjBXWF+tdHZdUmSpNnEsD7DnbpicJHpr1xkKkmSNKsY1me4Z6wY7Fv/xYMuMpUkSZpNDOsz3GlHLyJvW+c3j+3h8f32rUuSJM0WhvUZbn5TLSceuQCAlODnG3aUuSJJkiQVi2F9FjjzuEMH3t94n2FdkiRptjCszwJnHbd44P2N928npVTGaiRJklQshvVZ4MQjF9BaXwPApj0dbNi+v8wVSZIkqRgM67NAbXUVp69cNPD5hvu2l7EaSZIkFYthfZY4c9Vg3/p1924rYyWSJEkqFsP6LHHu6iUD72/esJM9B7rLWI0kSZKKwbA+Sxy+oJGnHjEfgJ6+xE+dXZckSZrxDOuzyHlPXjrw/tq7t5axEkmSJBWDYX0Wef4Jg2H9p/dso7Ont4zVSJIkaboM67PI8UtbedLCJgD2d/Vy04adZa5IkiRJ02FYn0UiYkgrzHfv3FzGaiRJkjRdhvVZ5vdOPHzg/Q9+u4WOblthJEmSZirD+ixz4vL5HLUoa4XZ19nDT+5xVxhJkqSZyrA+y0QELzvpiIHP19z+WBmrkSRJ0nQY1mehl5002Apz3T3b2dPuA5IkSZJmIsP6LLTy0JaBByR19fbx33duKnNFkiRJmgrD+iz1irWDrTDfuOVhUkplrEaSJElTUbSwHhHLI+KKiNgUEZ0RsTEiPhMRh0xhrKdGxFci4pF8rG0RcX1EXFSseme7V5y8nPqa7H/euzbt5c5H95S5IkmSJE1WUcJ6RKwE1gGvB24BPg08CLwNuDkiFk1irNcBvwbOB24EPgl8GwjgxcWody6Y31TLS5522MDnr//y4TJWI0mSpKko1sz654ElwFtTSuenlN6TUjqHLLQfD3xkIoNExDOBfwN+C6xKKb0mpfS+lNKbU0pnARcWqd454Y9Pe9LA+/+6YxN7O1xoKkmSNJNMO6xHxDHAecBG4HPDLn8A2A9cGBHNExju40A18CcppS3DL6aUTJuTsPZJh3D80lYADnT38q1bHy1zRZIkSZqMYsysn5Mfr00p9RVeSCm1AT8HmoBnjjVIRCwHzgRuBe6KiOdGxMUR8a6IODciXAw7SRHBRc86auDzFT/7Hd29fWN8Q5IkSZWkGAH4+Px43yjX78+Pq8YZ5xkF9/8kf/0T8AngR8DtEXHsRAqKiHUjvYDVE/n+bPLKtctZ2FwHwGO7D/C932wuc0WSJEmaqGKE9fn5cbTtRvrPLxhnnCX58dXAGuAV+djHAl8Fngp8NyLqpl7q3NNQW81Fpw/Orn/xhgfdxlGSJGmGKEVrSeTH8RJidcHxDSmlq1JKe1NKG4DXkrXHrAJeOd4PTCmdMtILuGeKf4YZ7aLTV9BQO7iN44337yhzRZIkSZqIYoT1/pnz+aNcnzfsvtE8nh87ge8VXkjZVPA1+cdTJ1vgXLewuY5XP/3Igc+f/N/7nF2XJEmaAYoR1u/Nj6P1pB+XH0fraR8+Ttvwhaq5/jDfOInalHvT2Supyx+SdMcju/nx+m1lrkiSJEnjKUZYvy4/njd8x5aIaAXOAA4AvxhnnDuBHcDiiFg6wvWn5MeNUy917jpsfiN/ctpg7/on//c++vqcXZckSapk0w7reU/5tcAK4C3DLn8QaAa+klLa338yIlZHxJCdWVJKPcC/5h8/Xhj8I+KpwOuAHrKnmWoK3nT2Shprs6UB6zfv5b/v3FTmiiRJkjSWYi0wfTOwDbgsIq6OiI9GxE+Ad5C1v1wy7P71+Wu4fyCbgb8IuDUiPhURXwV+CTQAf5NSeqBINc85h7bW8/ozVgx8/uj37qG9q6d8BUmSJGlMRQnr+ez604ErgdOAdwErgcuA01NKOyc4TjtwLtmMfBPZTP1LgZuAF6eUPlWMeueyN529ksUt9QBs2dvB56/bUOaKJEmSNJqibd2YUnokpfT6lNJhKaW6lNJRKaW3pZR2jXBvpJRilHHaU0qXppRWp5TqU0rzU0rPSyl9v1i1zmWtDbX8zQuPH/j8xRsf5OGd7WWsSJIkSaMpxT7rqjCvXLucE4/MnlHV1dPHJVf/xq0cJUmSKpBhfQ6qqgo++NITqMr/bePG+3fwzVsfKW9RkiRJegLD+hx10pEL+LNnHz3w+cP/s57New6UsSJJkiQNZ1ifw9513vEcvbgZgLbOHt797Tvde12SJKmCGNbnsIbaaj72yqcRBe0wX7jB3WEkSZIqhWF9jjv16IW86TkrBz5/8tr7uHXjEzbwkSRJUhkY1sU7n7+Kpx91CAC9fYm/+sav2dbWUeaqJEmSZFgXNdVVfPaPTmZ+Yy0Am/d08BdfXUdHd2+ZK5MkSZrbDOsC4IgFjXzmD08a2M7x1w/v5r3/6f7rkiRJ5WRY14DnHr+ES17y5IHPV/36MT7zo/vLWJEkSdLcZljXEH96xgr+8BlHDnz+7I/v54qf/a6MFUmSJM1dhnUNERF86GVP4czjFg+c+9D/3M23fMKpJElSyRnW9QR1NVX864WncEq+QwzA33znTgO7JElSiRnWNaKmuhqueN0zWHPYPAD6Evz1t+/kqzdvLGtdkiRJc4lhXaOa31jL1/7sVJ6cB3aAv73mLj7/0wfcJUaSJKkEDOsa06KWer7xxmdy0pELBs59/Af38r6rfkt3b18ZK5MkSZr9DOsa1/ymWr72htN45jELB85945aHef2Xf8WeA91lrEySJGl2M6xrQlrqa/j3Pz2Vl598xMC5nz2wg5d/7ues37y3jJVJkiTNXoZ1TVh9TTWfevWJvPP5qwbOPbhjP+d/7ud881eP2McuSZJUZIZ1TUpE8NZzj+OfX3MyTXXVAHT29PHu79zJu755B3s7bIuRJEkqFsO6puT3nnY4//V/ns2qpS0D5/7z14/xgk/fwA33bS9jZZIkSbOHYV1TduySFq5+yxm8cu3ygXOb93Rw0RW38L6rfuMsuyRJ0jQZ1jUtTXU1fPLVJ/K516xlYXPdwPmv//JhzvnE9Vz160ftZZckSZoiw7qK4iVPO4wfvv0sXnDC0oFzO/Z18o7/ewd/8MVfcO+WtjJWJ0mSNDMZ1lU0h7bW84U/OYXL/+hkls6rHzh/y+928aLP3sC7v30Hm3YfKGOFkiRJM4thXUUVEfz+iYfz43edzRvPPJqaqgCgL8E3b32Usz/xU/7he+t5fH9XmSuVJEmqfIZ1HRQt9TVc8pIn8723ncmZxy0eON/V08cXb3iQMz9+HR/9/nq2tXWUsUpJkqTKZljXQbVqaStf/bPT+PobTuPE5fMHzu/r7OFfr3+QZ3/sOv726t/yyK72MlYpSZJUmQzrKolnHbuYq99yBv/yx2s5dsng3uxdPX189RcP8Zx/uo6/+Oqt3PTADnePkSRJytWUuwDNHRHBi556GC84YRn/u34rn7/uAe54dA+Q9bT/8K6t/PCurRy7pIWLTj+Kl598BK0NtWWuWpIkqXwM6yq5qqrgBScs47wnL+XnD+zkC9dv4GcP7Bi4/sC2ffzdNXfxD99bzwtOWMYr1y7njGMXU50vVpUkSZorDOsqm4jg2cct5tnHLeaBbW189eaH+Pa6R9nf1QtAR3cf19y+iWtu38TSefWcf/IRvPTEw3nyYfOIMLhLkqTZL+ZSf3BErFu7du3adevWlbsUjWJfZw9X3fYoX7/lEdZv3jviPU9a2MSLnrKMFz5lGScducDgLkmSKs4pp5zCbbfddltK6ZTpjGNYV8W6e9NevnPbo1xz+2Ps2DfyvuyHzW/g3DVLOHvVEk5fuYjmev+xSJIklZ9hfQoM6zNTd28fN9y3nf++YxM/Xr+Nts6eEe+rrQ6esWIhZx9/KGetOpTjl7Y66y5JksqiWGG9aNOQEbEc+BDwQmARsBm4GvhgSunxKY55FnAd2RaTH0kpvb9I5WoGqa2u4tw1Szl3zVI6e3r5+QM7+P5vtvC/67eyu7174L7u3sRNG3Zy04ad/MP37mFhcx2nrljIaccs5NSjF7Jm2TyqXKQqSZJmkKKE9YhYCdwELAGuAe4BTgXeBrwwIs5IKe2c5JitwL8D7UDLOLdrjqivqeac1Us5Z/VSunv7uHXj4/z0vm1cf+927tnSNuTeXfu7+MFdW/jBXVsAmNdQw6lHL2TtUYdw0vIFPHX5fLeGlCRJFa1YM+ufJwvqb00pXd5/MiI+BbwD+Ajwl5Mc87PAfOCj+felIWqrqzh95SJOX7mI975oDVv2dHDDfdv56X3buHnDTh4vmHUH2NvRw4/Wb+NH67cBEAHHHtrCiUcu4MQjF3DykQtYtbSVuhqfFSZJkirDtHvWI+IYYAOwEViZUuoruNZK1g4TwJKU0v4JjvkyshaaC8l+ofgyRWiDsWd97ujrS9y/bR+//N1Ofvm7XfzywV3s2Nc57vdqqoJjl7Sw5rB5rDmslTWHzWP1snkc2lpfgqolSdJsUUk96+fkx2sLgzpASqktIn4OnAc8E/jxeINFxBLgS8DVKaWvRcTrilCj5piqquD4Za0cv6yVi05fQUqJB3fs59aNu7j9kT3c/shu7tvaRm/f0F9We/oS92xp454tbVz168Hzi1vqWXNYK8ctaWXlkmZWHtrCykNbWNxS5yJWSZJ00BQjrB+fH+8b5fr9ZGF9FRMI68AXyRaUTrZtZkBEjDZ1vnqqY2pmi4iBgP0Hz8jOtXf1cNemvdzxyG5uf2Q3dzy6m0d2HRjx+zv2dXLj/Z3ceP+OIefnN9ay8tA8vC/Jxl+xqIkjFzbRUFt9sP9YkiRplitGWJ+fH/eMcr3//ILxBoqIPwVeBvxBSmlrEWqTRtVUV8MzVizkGSsWDpxr6+jm3i1trN+8l7s3t3HPlr3cu6WN9vypqsPtOdDNbQ/v5raHdz/h2tJ59Ry1sJkjFzZx1KImnrSwiSflx0XNzshLkqTxleIJMv2JZMzm+IhYAXwG+FZK6ZvT+YGj9QblM+5rpzO2ZrfWhlqevmIhTy8I8H19iYd2tXPvlr1s2L6fDdv28cD2fWzYto/9o4R4gK17O9m6t5NbNu56wrXmumqOXNjE8kMaOXxBwWt+A4cvaGRJaz011S50lSRpritGWO+fOZ8/yvV5w+4bzRXAAeDNRahJKpqqquDoxc0cvbh5yPmUElv3drJh+77stW0fG7bv56Fd+9m0u+MJ/fCF9nf1DvTGj6S6Klg2r4HDFzQMCfKHzW9k6bwGlsyrZ1FznYFekqRZrhhh/d78uGqU68flx9F62vutJQv820dpD7gkIi4BrkkpnT/pKqUiiwiWzW9g2fwGzjh28ZBr3b19bNp9gId3tfPQznYeyY8P78pe+0Z5Cmu/3r7EY7sP8NjuA8DIzxSrCljUUs+S1v5XA0vn1XPovAaWtNZnob61nkNb66k11EuSNCMVI6xflx/Pi4iqEbZuPINsxvwX44zzFaBphPPHAWcBtwPrgF+PcI9UUWqrqzhqUTNHLWrmzOOGXksp8Xh7Nw/vamdzHsg37e5g0+4DbN5zgMd2d0xom8m+BNvbOtne1sld49w7v7GWRS11LG6uZ1FLXfZqrmdxSx2LWrJZ+kUt2ed5DbU+6VWSpAox7bCeUtoQEdeS7fjyFuDygssfBJqBfy3cYz0iVuffvadgnLeONH6+deNZwHenu8+6VAkigoXNdSxsruOkI0ded93R3cuWPVmAf2z3ATbn7zft6WDb3g62t3Wyc3/XhH/mngPd7DnQzYPbx3/UQU1VVl9/eO8P8ovy9wub6wfqX9hcx7yGGhfLSpJ0kBRrgembgZuAyyLiXGA9cBrwXLL2l0uG3b8+P/pfeGkEDbXVrFjczIphffKFunr62LGvk21tnWzb28HWtk627+1g695OtrV1sK0tW+C6c38nk3n2WU9fysZsG392H7Jwf0hzFuQPaapjYcvg+0Ut+bE5O7+wqY5Dmutsy5EkaYKKEtbz2fWnAx8CXgi8mOzJpZcBH0wpPXE7DEnTUldTNbD4dCy9fYnd7V3s3N/Fjn2d7NzXxc59nfnnwfc782tt4/TTD9fTlwbacSZqXkPNkNn5hQMz9rUDxwVN2ax9a0MtrQ01NNZWO4MvSZpzirZ1Y0rpEeD1E7x3wv/FTSldCVw5taokVVdF3sZSz6qlrePe39Hdy679Xezc18WO/cPDfSe79ncNeY22B/1Y9nb0sLejh4072yf8nZqqoKWhhnl5eG8tCPKF57L3Q++Z11BDi4FfkjQDlWKfdUkzSENt9YRm7Pv1h/uRXjv3d/H4wPtOHm/v5vH2rkm15fTr6Uvsbu9md3v35L+cqwporquhub6G5vpqWhpqaamvprmuhpb6/vM12bmB94PHlv7v5eds55EkHWyGdUnTMtlw39uX2HOgm135rP3j7YOhfmdB0N9zoJu2jh7aOrrZ29FDV0/f+IOPoy9BW2fPpFt9RlNXUzUQ4AsDf0tDDS35LwVNddU01VfTVFtNU/65ua6GxsJjfTVNtTU01Vf7C4AkaQjDuqSSqq4a3A3n2CUT/15nT28e3rMAXxjkh5/rv29vwbl9nT10dE8/8Bfq6uljV08Xu8bfZGfCaquDpro85NdV0zQQ7KuHnq+vGfILQP+9zXXV+S8AWdtP/y8M9TVVtgBJ0gxkWJc0I9TXVFPfUs3ilvopj9HT28f+rl72dfawv7Nn4Ji97x1ybt+Q673D7u1hf1fvmE+pnaru3jSw1WYxRUBjbRbqG2qraazNQv3A+2Gfm+qGXa+rorF26HefMFZNtXv0S1KRGdYlzRk11VXMb6xifmPttMdKKdHR3ffE4N9VEPw7emjv6qW9u4f2zl7au3o50J2F/wNdvezv6hk4tndl1w/GLwBZvQz8jIOpvqaKxrqhvwCM94tBY10VjXU1BdeqnvBLQENtNQ212Xn/lUDSXGJYl6QpiIgsiNZVc2jr1Gf7C6WU6Orty4J9dy/tnVmI7w/1WdgeDPbtXSMH/+zYm38n+1eAYvT8T0RnTx+dPX3sprj/MlAoIvuloKG2P8jnIb62moaawaDffz67VvWE0N9Qm/1rQf2wcQbuye+vr6nyXwwklY1hXZIqRERk7T411RxS5LF7/l97dx4jSVnGcfz3TM/MshyrgHhETUBF3YTEGBR1iShEjQcGDzCaqOuBkUQQlESM8UBjFBEVoyZGUUDhH8GIxiCioqyKtyIeK4cLCnKsssDusj1Hd7/+8T41U11TPdOzO131dvf3k7ypnrfeqq6tp6r2ebuudkczrY6ac23NzLfVnI/JfDP/2f+eKYzL/72nMP3MfKdrXlUIQZqZ7/g9CIPrFOQtdA6mujsJ67Lk3s8oLOk8FDoJ6/Odhx6dhHWTDTXoHABwJOsAMAYmGxM6sBGfXjMoIQTNeodgz0Iy390xKE38e3QMmvMdNedanpjHcTOtTmVnCfKyMwYPNav5vskJW/hVf91k7BR0DScncuMXzxysm5pYqMsS/zhdfnyje/qp7vZ0FIC0kKwDANaEmS38QrzWZwby2p2g2Va7O4mf72imFT/Pen1zPtfG28+WtG96m9lCfTbtbA2dg1Yn+E3OlX+1JieskMwv7Sh0dQamSjoPy3QGlus80FEAliJZBwAMlcZE9njLar6v04lnDPJJf7GTkCX6zfnuRD+OW9q+ORfPEhQ7D7M+3JsXh62VVieo5fc9VC3rKGT3H6wrO7vQ55mE7PO0l2w+04043XRjsX7a5zHVMG5eRnJI1gEAWMbExOLNxFUIIWi+Hc8eZJffZGcMsrrsF//ZLOH3Yb4ufs6my09TmIfPd2bMOwqZ7sQ+dhKKif3i51yHoOGdhMZi8t/dNlfnbacbi52NYtvJCToOiEjWAQBIiJlpetI0PTmhgyr+7hCCWrkzCfnEf6UOwsySDsJiRyHrDCx0QHJnEvKdhwE9uXRV5vy+iF01L0f21KN8h2FdP8n/VDx70N2uu6ORjc+mLW/T/ZmOQ31I1gEAgKTYUZhqmKYGfDNymXxHYdnOQFnnId8ZmM+dXWjHxHu21dFcq5373NFcO3Ya5hbatDXfTqC34LqeejTTqntxupL3dT0S+rJx+bMGvdosdjhKzmLk22TtxuxdCyTrAACgdnV2FDKdTnzXQZbQz3qCX5bYzy10FDrdnYAVppsta9teOo9WCqcZcuba8d+hGm56LlPWUSh+XjwzMaH9pyd1wSnPqHux9wrJOgAAgOL9CftNxCca1a3dCV0J/eySswLlSX7WNt9RiMN2rk1uPrlps0U9NWMAAApDSURBVPkVOxwpnXHIrLbzcMB0g2QdAAAAa6PRdWPzVK3Lkp1xyCfz3Ul/u2fS39Vp6GrT7qNNcT7tve48TE9ODGDNVINkHQAAAD2ldMZB6t15KF5ulK+fGOJr3EnWAQAAMDRS6zwM2vCeEwAAAABGHMk6AAAAkCiSdQAAACBRJOsAAABAokjWAQAAgESRrAMAAACJIlkHAAAAEkWyDgAAACSKZB0AAABIFMk6AAAAkCiSdQAAACBRJOsAAABAokjWAQAAgESRrAMAAACJIlkHAAAAEkWyDgAAACTKQgh1L0NlzOz+9evXH7Jx48a6FwUAAAAjbOvWrWo2mztCCIfuy3zGLVm/XdIGSXfU8PVP9+E/avhuVIMYjwfiPB6I83ggzqOvzhgfLmlnCOGIfZnJWCXrdTKzP0hSCOHoupcFg0GMxwNxHg/EeTwQ59E3CjHmmnUAAAAgUSTrAAAAQKJI1gEAAIBEkawDAAAAiSJZBwAAABLF02AAAACARPHLOgAAAJAoknUAAAAgUSTrAAAAQKJI1gEAAIBEkawDAAAAiSJZBwAAABJFsg4AAAAkimR9wMzsCWb2dTO728xmzewOM7vQzA6ue9nGkZkdamanmtl3zOw2M2ua2UNm9gsze7uZle4TZrbJzK42sx1mtsfMbjKzs8ysscx3nWhmP/P57zaz35jZ5hWWb7OZ/dbbP+TTn7iv/25EZvYmMwteTu3RZuBxM7OGbz83+Ta4w7evTfv6bxxXZvZ8M/u2md3jx9p7zOxaM3t5SVv25yFjZq/weN7l+8w2M7vCzJ7Xoz0xTpSZnWxmXzCzn5vZTj8eX7bCNEnGs7JjeQiBMqAi6cmS7pMUJF0l6TxJ1/nf/5B0aN3LOG5F0mm+/u+WdLmkT0r6uqQHvf5K+cvCctOcJKklabekr0n6tMcvSLqix/ec7uP/J+lLkj4n6U6vu6DHNBf4+Du9/Zck3e91p9e97oa9SHqix3mXr9NT64ibJJN0Re448Gnfrnb7dnZS3etq2IqkD/r6/K+kiyV9QtJXJP1O0vmFtuzPQ1YkfSq3/i/y/0uvlDQnqSPpjcR4eIqkG30d7ZK01T9ftkz7JONZ5bG89qCNcpH0Qw/iGYX6z3r9l+texnErkk6Q9EpJE4X6x0r6t8fltbn6DZK2S5qV9Kxc/X6SbvD2ry/M63BJM76TH56rP1jSbT7N8wrTbPL62yQdXJjX/T6/w/fl3z7OxQ+qP5b0Tz+gLknWq4qbpDf4NL+UtF+u/tm+nW2XdFDd62xYiqRTfH3+qGy9SZrKfWZ/HrLix+a2pHslPbow7nhfz9uI8fAUj9uRflx+oZZJ1lOOpyo8ltcetFEtkp7kQbxdSxPDgxR7Xg9LOqDuZaUsxOUDHrMv5Ore5nWXlrQ/wcddX6j/mNd/tGSa0vlJ+obXv7Vkmp7zo/Qd2zMVf4E7TtK5Kk/WK4mbpC1ef3zJND3nRymN64SkbX4sPayP9uzPQ1YkPcfX13d7jN8paRcxHs6ilZP1ZONZ5bGca9YH5wQfXhtC6ORHhBB2KfbE9pf03KoXDD3N+7CVq8vieE1J+y2S9kjaZGbr+pzmB4U2+zIN+mBmGxVPm38+hLBlmaYDj5tvJ5sUt5ufr+J7UG6TpCMkXS3pAb+u+RwzO7PHtczsz8PnVsXLXY4xs0flR5jZcYo/fv04V02MR0uS8az6WE6yPjhP8+EtPcbf6sOnVrAsWIGZTUp6s/+Z31l7xjGE0FI8czKpeCaln2nuUfwV8Almtr9/9wGSHi9pt48vYlvZSx7Xbype4vSBFZpXEbenSGoonrZvLZ2EWK/Ss314n6Q/Svq+YsfsQkk3mNn1ZnZYrj3785AJIeyQdI6kx0j6u5l9xcw+aWbfknSt4uVP78xNQoxHS6rxrPRYTrI+OI/w4UM9xmf1j6xgWbCy8yQdJenqEMIPc/V7E8d+p3lEYci2svY+LOmZkt4SQmiu0LaKuBHrtfVoH54mab2kFyn+0nqU4j1DxyneAJZhfx5CIYQLJb1GMSl7h6T3K96rcKekS0II23PNifFoSTWelW4DJOv1MR+GWpcCMrN3Szpb8W7uN612ch+uJo57G3u2lVUws2MUf03/TAjhV2sxSx8OMm4cF1Yne2ybSTo5hPCTEMLuEMLfJL1a0l2SXtDr8X4l2J8TZGbvU3z6yyWKT1k7QNLRivcrXG5m569mdj4kxqMh1Xiu6bGcZH1wij23og2FdqiBmb1L0ucl/V3xJpEdhSZ7E8d+p9nZZ/uVevAoyF3+coukD/U5WRVx47iwth7w4bYQwp/zI/xMSnaW7Bgfsj8PGTN7oeKjG78XQnhvCGFbCGFPCOGPih2y/0g628yyyyCI8WhJNZ6VHstJ1gfnZh/2ul7pSB/2uqYdA2ZmZ0n6oqS/Kibq95Y06xlHTwiPULwhdVuf0zxO8Vehu0IIeyQphPCw4n84B/r4IraV1TtQcf1vlDRjiy9CCpI+4m2+6nUX+t9VxO02xcfQPcm3n36mQW9ZzB7sMT5L5tcX2rM/D4/shTQ/LY7wdf5bxVzmmV5NjEdLqvGs9FhOsj442YHlJVZ4K6aZHSTpWElNSb+uesEgmdk5ii89uFExUd/eo+l1PnxpybjjFJ/oc0MIYbbPaV5WaLMv06C3WcWXU5SVP3mbX/jf2SUyA4+bbyc3KG43z1/F96DcFsX/qI80s+mS8Uf58A4fsj8Pn+wpH4f1GJ/Vz/mQGI+WJONZ+bG8yudpjlsRL0VKsiheFhEk/V7SISu03aD4VsTVvJDhCPGCjWSLej9nvZK4qb8XaWyoez0NS5F0ma/PjxfqX6z4bP0HJT3S69ifh6xIep2vy3slPb4w7mUe46b8jeDEeLiK+nspUpLxrPJYXnugRrko3ghznwfzKsVX21/nf9+cHVwolcZks6/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+ "image/png": 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\n", 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" ] @@ -1163,17 +1168,17 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[638, 61],\n", - " [ 69, 287]])" + "array([[642, 57],\n", + " [ 49, 307]])" ] }, - "execution_count": 37, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1187,7 +1192,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1196,20 +1201,20 @@ "Text(33.0, 0.5, 'actual')" ] }, - "execution_count": 38, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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1bwE3AMDItbnZf2atqn44yalJbthGm+OSnJ/koUnOTPLOJPdNcnpVnbxEn5OTnJ5k36H9mUkeluT84X5rJuAGABi7VrP/zFBVVZLTklyT5B1LtNk/yclJrk1ycGvtpa21lyd5eJIvJzm+qg5Z0OfQJMcP1x/eWnt5a+2lSQ4a7nPycN81EXADALDR/XqSo5L8UpIbl2jzwiS7Jnlba23L/MnW2nVJ3jR8ffGCPvPff2toN99nS5I/GO73S2ucu4AbAGDsNnJJSVX9WJI3J/n91tpF22h61HB83yLX3rugzVr6rJhVSgAAmLmq+tRS11prBy3zHrsk+dMkX03y6u00f9BwvGyR8a6qqhuT7FdVu7fWbqqqPZLcL8kNrbWrFrnfF4fjgcuZ67YIuAEARq7NzbbmeoZ+I8kjk/xUa+3b22m793DcusT1rUn2GNrdtMz2SbLP8qa6NAE3AAAzt9ws9lKq6lGZZLXf0lq7eAZTmv+toq2w30rb34GAGwBg5DbaxjdTpSSXJXntMrttTXLPTDLX1yxyfa/heP1U++T2TPdC28uAL5uXJgEARq61mvlnjfbMpHb6x5LcPLXZTUvyuqHNO4dzvzd8/8JwvEPNdVXtm0k5yZWttZsmz9xuTPL1JHsO1xd64HC8Q034SslwAwCw0dyS5L8tce0nMqnr/vtMguz5cpMLkhyW5Oipc/OeONVm2gVJnjf0OW2ZfVasWltzWcoofPfqy/1BAcuy535HrPcUgE3ilpu/tiHeVrzy0UfNPM7Z7+MXdHm2qjopkyz3i1prfzx1/oAkn89kne6D5tfirqq7J/lkkgckOXS6HnzY+OYjmWx885Pza3EPm918KpOs+IOn1/VeDRluAAA2vdbaFVX1yiSnJLmkqs5O8p0kxyTZL4u8fNla+2hV/X9JXpHk0qo6J8ldk/xCkh9M8h/XGmwnAm4AgNHbwMsCrkhr7dSq2pLkhCTPz+R9xc8lObG1dsYSfY6vqkuTHJfkV5LMJfl0kt9trf3VLOalpGSZlJQAy6WkBFiujVJS8tWDf3rmcc6PXPLBDfFsG4FVSgAAoCMlJQAAI7ezlJRsVDLcAADQkQw3AMDIyXD3JeAGABg5a2j0paQEAAA6WlOGu6rmkqzmd6LWWpNdBwDYAJSU9LXWoPeirC7gBgCAUVhTwN1aO3JG8wAAYJ20JsPdkxpuAADoSB01AMDItbn1nsHOrUvAXVX7JvnpJPdLsusiTVpr7Td7jA0AwMrMKSnpauYBd1W9PsmrFty7cvvLlfM/C7gBANjpzbSGu6qek+S1ST6c5JhMguszkvxikncmmUtyVpKjZjkuAACr11rN/MPtZp3hfkmSK5Mc3Vq7taqSZEtr7awkZ1XVu5L8dZI/n/G4AACwIc16lZKHJXlPa+3WqXN3nv+htfb+JO9P8soZjwsAwCq1uZr5h9vNOuC+S5Jrpr5/O8neC9p8NskjZjwuAACr1NrsP9xu1gH3VUn2nfr+1SQPX9DmfkluDQAAjMCsA+7PZFJWMu+CJI+tqudV1R5V9XNJnjm0AwBgA1BS0tesA+6/SvKQqjpg+P7mJFuTnJ7k+iR/mcnKJSfOeFwAANiQZrpKSWvt9EyC6/nvX6uqn0xyfJIHJNmS5A9ba///LMcFAGD1bHzTV/et3VtrVyQ5rvc4AACsjnWz+5p1SQkAADBlphnuqvqR5bZtrX11lmMDALA6lvHra9YlJVuSLOdfWeswNgAAbDizDnr/JIsH3Psk+fEkP5rkwiRfmfG4AACskpcm+5r1KiUvWOpaVd0pyWuTvDjJsbMcFwAANqod9tJka22utfb6TMpO3ryjxgUAYNtaq5l/uN161FF/NMnz12FcAAAW4aXJvtZjWcAfTLLHOowLAAA73A7NcFfVzyT5hSSf3ZHjAgCwNC9N9jXrdbgv2MY4P5xkfp3uN8xy3B3hbvd97HpPAdgkzv3BI9Z7CgBsILPOcB+5xPmW5Lok709ycmttqcAcAIAdzEuOfc16WUBbxQMAbDJKSvoSIAMAQEczDbir6oKq2uaSf1X13G3UegMAsIO1Dh9uN+sM95FJ9t9Omx9N4o0iAABGYT02vrlbklvXYVwAABahhruvHgH3on+LUFWVybKAT0rytQ7jAgCwClYp6WvNJSVVNVdVt1XVbcOpk+a/T38yyWpfnuTHk5y11nEBAGAzmEWG+6LcntU+PMlXk2xZpN1tSa5J8sEkfzyDcQEAmIG59Z7ATm7NAXdr7cj5n6tqLslprbVNt5MkAAD0MOsa7gOSfHPG9wQAoKMWNdw9zXpZwH9NsndV3XWxi1W1a1X9SFXtNuNxAQBgQ5p1wP0bSb6QZM8lru+R5J+TvHrG4wIAsEpzbfYfbjfrgPuJSf62tXbtYheH83+b5MkzHhcAgFWaS838w+1mHXDvn+Sy7bS5LNvfjRIAAHYKs35p8i7Z/soyLYkabgCADcJLk33NOsN9eZIjttPmyCRfmfG4AACwIc064P7LJAdV1X9e7GJVvSrJTyQ5b8bjAgCwSnMdPtxu1iUlJyd5TpLfrqp/n+QDSb6e5H5JfjaTbd2/muT/nfG4AACskpKSvmYacLfWrquqI5P8WZJDMslmt+R7/xY/muS5rbXrZjkuAABsVLPOcKe1tiXJYVX1E0kek2SfTHaf/Fhr7dOzHg8AgLVRAtLXzAPueUNwLcAGAGDUugTcVbVvkp/OpHZ710WatNbab/YYGwCAlZHh7mvmAXdVvT7JqxbcuzKp5Z7+WcANALABeGmyr5kuC1hVz0ny2iQfTnJMJsH1GUl+Mck7M/kF6qwkR81yXAAA2KhmneF+SZIrkxzdWru1qpJkS2vtrCRnVdW7kvx1kj+f8bgAAKzSnAR3V7Pe+OZhSd7TWrt16tyd539orb0/yfuTvHLG4wIAwIY064D7Lkmumfr+7SR7L2jz2SSPmPG4AACs0lxq5p9ZqKrfqaoPVtXXqurbVXVtVX2mql5XVfdYos+hVfWeoe1NVXVpVb2squ68WPuhz5Or6sKq2lpVN1TVx6vq2Jk8RGYfcF+VZN+p719N8vAFbe6X5NYAAMC2vTzJHkn+JsnvZ7K54q1JTkpyaVX98HTjqnpakouSHJ7kXUn+IMldk7w1k/cI76CqjktyfpKHJjkzk/cO75vk9Ko6eRYPMesa7s9kUlYy74Ikv1JVz0vyv5IcmeSZST4y43EBAFiltv0m62Wv1trNC09W1W8leXWS/5Lk14Zze2USLN+W5MjW2iXD+ddmEpMeU1XPHt4tnL/P/klOTnJtkoOHDRxTVW9I8skkx1fVua21i9fyELPOcP9VkodU1QHD9zcn2Zrk9CTXJ/nLTFYuOXHG4wIAsEpzHT6zsFiwPfifw/GBU+eOSXKvJGfNB9tT95iPPV+y4D4vzGTPmLfNB9tDn+uSvGn4+uJVTX7KTAPu1trprbXdW2tXDN+/luQnk7w9yQeS/FGSn2ytfWyW4wIAMCpPGY6XTp2bX3b6fYu0vyjJTUkOrarpTRm31ee9C9qsWret3ecNwfdxvccBAGB15mr26wJW1aeWutZaO2iF9zohyZ6ZLMZxcJKfyiTYfvNUswcNx8sWGe/WqroiyUOS3D/J55fR56qqujHJflW1e2vtppXMeVr3gBsAANbohCQ/NPX9fUle0Fr7t6lz8yvjbV3iHvPn91lhnz2GdgJuAABWp8dLkyvNYm/nXvdJkqr6oSSHZpLZ/kxVPbm19ull3mY+jb+Sx11NnzuY9UuTAABsMhv1pcmFWmv/0lp7V5InJLlHkj+ZujyfpV64B8y8vRa0W0mf61c41e8j4AYAYFNprX0lyecyWR3vnsPpLwzHAxe2r6pdkhyQyRrel09d2laffTMpJ7lyLfXbiYAbAGD05mr2nx3gvsPxtuF4wXA8epG2hyfZPclHW2u3TJ3fVp8nLmizagJuAAA2nKp6cFXdZ5Hzdxo2vrl3JgH0dcOlc5JcneTZVXXwVPvdkrxx+Pr2Bbc7LcktSY4bNsGZ73P3TDbWSZJ3rPVZvDQJADByc9kxKekVOjrJ71bVRUm+nOSaTFYqOSKTpf2+keRF841ba9dX1YsyCbwvrKqzMtlB8qmZLP93TpKzpwdorV1RVa9MckqSS6rq7CTfyWQTnf2SvGWtu0wmAm4AgNHboFu7/20mmyYeluQRmSznd2Mma2b/aZJTWmvXTndorZ1XVUckeU2SZybZLcmXkrxiaH+HR22tnVpVWzJZevD5mVSAfC7Jia21M2bxIAJuAAA2nNbaZ5O8dBX9PpLkSSvsc36S81c61nIJuAEARm4HveQ4Wl6aBACAjmS4AQBGrtdGNUzIcAMAQEcy3AAAI7dBVynZaQi4AQBGzkuTfSkpAQCAjmS4AQBGzkuTfclwAwBARzLcAAAjJ8Pdl4AbAGDkmpcmu1JSAgAAHclwAwCMnJKSvmS4AQCgIxluAICRk+HuS8ANADBytnbvS0kJAAB0JMMNADByc5YF7EqGGwAAOpLhBgAYOS9N9iXDDQAAHclwAwCMnAx3XwJuAICRsyxgX0pKAACgIxluAICRsyxgXzLcAADQkQw3AMDIeWmyLwE3AMDIeWmyLyUlAADQkQw3AMDIzclxdyXDDQAAHclwAwCMnJcm+xJwAwCMnIKSvpSUAABARzLcAAAjp6SkLxluAADoSIYbAGDk5mq9Z7BzE3ADAIycdbj7UlICAAAdyXADAIyc/HZfMtwAANCRDDcAwMhZFrAvGW4AAOhIhhsAYOSsUtKXgBsAYOSE230pKQEAgI5kuAEARs5Lk33JcAMAQEcy3AAAI+elyb4E3AAAIyfc7ktJCQAAdCTDDQAwcl6a7EuGGwAAOpLhBgAYuaaKuysBNwDAyCkp6UtJCQAAdCTDDQAwctbh7kuGGwAAOhJwAwCMXOvwWauqukdV/XJVvauqvlRV366qrVX191X1H6pq0Ti2qg6tqvdU1bVVdVNVXVpVL6uqO29jrCdX1YXD/W+oqo9X1bEzeIwkSkoAANiYnpXk7UmuSvKhJF9N8kNJfj7JHyd5YlU9q7X2vfi+qp6W5NwkNyc5O8m1SZ6S5K1JDhvu+X2q6rgkpya5JsmZSb6T5Jgkp1fVw1prJ6z1QQTcAAAjt0FruC9L8tQkf91a+95CKlX16iSfSPLMTILvc4fzeyV5Z5LbkhzZWrtkOP/aJBckOaaqnt1aO2vqXvsnOTmTwPzg1tqW4fwbknwyyfFVdW5r7eK1PIiSEgCAkZvr8Fmr1toFrbXzp4Pt4fw3krxj+Hrk1KVjktwryVnzwfbQ/uYkJw5fX7JgmBcm2TXJ2+aD7aHPdUneNHx98dqeRMANAMDm893heOvUuaOG4/sWaX9RkpuSHFpVuy6zz3sXtFk1JSUAACPXY6fJqvrUkuO1dtAa7rtLkucPX6cD5QcNx8sWGe/WqroiyUOS3D/J55fR56qqujHJflW1e2vtptXOeafPcFfV71bVl9d7HgAAzMSbkzw0yXtaa++fOr/3cNy6RL/58/usos/eS1xfljFkuO+ZZP/lNNzWb2J3vst9ZzUfAIANpcfW7mvJYi+lqn49yfFJ/jnJ81bafTiuJJ2/mj53sNNnuAEA2LbW4Z9Zq6qXJvn9JJ9L8rjW2rULmmwvG73XgnYr6XP9CqZ6B5suw11Vf7LCLocut+G2fhPb5a7325Dr5QAA7Oyq6mWZrKX92SQ/3Vr710WafSHJwUkOTPJ9VQtD3fcBmbxkefmCPvcc+ly8oM++SfZIcuVa6reTTRhwJ3luJmn92l7DKYJlAIAl9CgpmZWq+n8yqdv+hySPb61dvUTTC5I8J8nRSf58wbXDk+ye5KLW2i0L+hw29Fm41vYTp9qsyWYMuL+V5Mokv7bM9q9K8oR+0wEAoIdh05o3ZJKxfsIiZSTTzknyO0meXVWnTm18s1uSNw5t3r6gz2lJ/nOS46rqtKmNb+6e5NVDm3dkjTZjwP2PSR7RWvu75TSuqhf0nQ4AwOY21zZeMUBVHZtJsH1bkg8n+fWqOxQ4bGmtnZ4krbXrq+pFmQTeF1bVWZnsIPnUTJb/OyeT7d6/p7V2RVW9MskpSS6pqrNz+9bu+yV5y1p3mexpoQIAAA7vSURBVEw2Z8D9D0kOq6oHtNYs9wcAsEYbL9xOMqm5TpI7J3nZEm3+Lsnp819aa+dV1RFJXpPJ1u+7JflSklckOaW1O/5m0Vo7taq2JDkhk/W975TJi5knttbOmMWDbMaA+++SPDaT3zqWE3Cfl2RLzwkBADBbrbWTkpy0in4fSfKkFfY5P8n5Kx1ruTZdwN1aOzfJuSto/+4k7+43IwCAzW1uo+a4dxLW4QYAgI42XYYbAIDZ6rFRDbeT4QYAgI5kuAEARm4jb3yzMxBwAwCMnJcm+1JSAgAAHclwAwCMnJcm+5LhBgCAjmS4AQBGzkuTfQm4AQBGrjUlJT0pKQEAgI5kuAEARs6ygH3JcAMAQEcy3AAAI+elyb4E3AAAI2cd7r6UlAAAQEcy3AAAI+elyb5kuAEAoCMZbgCAkbPxTV8y3AAA0JEMNwDAyFkWsC8BNwDAyFkWsC8lJQAA0JEMNwDAyFkWsC8ZbgAA6EiGGwBg5CwL2JeAGwBg5JSU9KWkBAAAOpLhBgAYOcsC9iXDDQAAHclwAwCM3JyXJrsScAMAjJxwuy8lJQAA0JEMNwDAyFkWsC8ZbgAA6EiGGwBg5GS4+5LhBgCAjmS4AQBGrlkWsCsBNwDAyCkp6UtJCQAAdCTDDQAwck2GuysZbgAA6EiGGwBg5Lw02ZeAGwBg5Lw02ZeSEgAA6EiGGwBg5JSU9CXDDQAAHclwAwCMnBruvgTcAAAjZx3uvpSUAABARzLcAAAjN+elya5kuAEAoCMZbgCAkVPD3ZcMNwAAdCTDDQAwcmq4+xJwAwCMnJKSvpSUAABARwJuAICRm2tt5p+1qqpjqurUqvpwVV1fVa2qztxOn0Or6j1VdW1V3VRVl1bVy6rqztvo8+SqurCqtlbVDVX18ao6ds0PMEVJCQAAG9GJSR6R5IYkVyZ58LYaV9XTkpyb5OYkZye5NslTkrw1yWFJnrVIn+OSnJrkmiRnJvlOkmOSnF5VD2utnTCLB5HhBgAYudbhnxl4eZIDk+yV5CXbalhVeyV5Z5LbkhzZWvsPrbVXJvnxJBcnOaaqnr2gz/5JTs4kMD+4tfbS1trLkzw8yZeTHF9Vh8ziQQTcAAAjtxFLSlprH2qtfbG1Zd3smCT3SnJWa+2SqXvcnEmmPLlj0P7CJLsmeVtrbctUn+uSvGn4+uJVTv/7CLgBANjsjhqO71vk2kVJbkpyaFXtusw+713QZk3UcAMAjFyPZQGr6lNLjtfaQTMe7kHD8bJFxrq1qq5I8pAk90/y+WX0uaqqbkyyX1Xt3lq7aS2Tk+EGAGCz23s4bl3i+vz5fVbRZ+8lri+bDDcAwMi1NtfhnjPPYq9FDceVpPJX02dRAm4AgJGb2/w7TW4vG73XgnbzP99z6HPNNvpcv9bJKSkBAGCz+8JwPHDhharaJckBSW5Ncvky++ybZI8kV661fjsRcAMAjF5rbeafHeyC4Xj0ItcOT7J7ko+21m5ZZp8nLmizJgJuAAA2u3OSXJ3k2VV18PzJqtotyRuHr29f0Oe0JLckOW7YBGe+z92TvHr4+o5ZTE4NNwDAyG3EGu6qenqSpw9f7zMcD6mq04efr57fer21dn1VvSiTwPvCqjorkx0kn5rJ8n/nZLLd+/e01q6oqlcmOSXJJVV1dm7f2n2/JG9prV08i2cRcAMAsBH9eJJjF5y7//BJkq8kOWH+QmvtvKo6IslrkjwzyW5JvpTkFUlOWWzHytbaqVW1ZbjP8zOp/vhckhNba2fM6kEE3AAAI7cONdfb1Vo7KclJK+zzkSRPWmGf85Ocv5I+KyXgBgAYubkNGHDvTLw0CQAAHclwAwCMXNuAL03uTGS4AQCgIxluAICR24gvTe5MBNwAACO3Edfh3pkoKQEAgI5kuAEARk5JSV8y3AAA0JEMNwDAyNn4pi8BNwDAyCkp6UtJCQAAdCTDDQAwcpYF7EuGGwAAOpLhBgAYOTXcfclwAwBARzLcAAAjZ1nAvgTcAAAj17w02ZWSEgAA6EiGGwBg5JSU9CXDDQAAHclwAwCMnGUB+xJwAwCMnJcm+1JSAgAAHclwAwCMnJKSvmS4AQCgIxluAICRk+HuS8ANADBywu2+lJQAAEBH5a8QYHWq6lNJ0lo7aL3nAmxs/nsB4ybDDQAAHQm4AQCgIwE3AAB0JOAGAICOBNwAANCRgBsAADqyLCAAAHQkww0AAB0JuAEAoCMBNwAAdCTgBgCAjgTcAADQkYAbAAA6EnADAEBHAm5Yoarar6r+e1X976q6paq2VNXvVdXd13tuwMZRVcdU1alV9eGqur6qWlWdud7zAna8XdZ7ArCZVNUDknw0yb2TvDvJPyd5VJL/lOToqjqstXbNOk4R2DhOTPKIJDckuTLJg9d3OsB6keGGlfnDTILtX2+tPb219qrW2lFJ3prkQUl+a11nB2wkL09yYJK9krxknecCrCNbu8MyVdX9k3w5yZYkD2itzU1d+4EkVyWpJPdurd24LpMENqSqOjLJh5L8WWvtues8HWAHk+GG5TtqOH5gOthOktbat5J8JMnuSR6zoycGAGxcAm5YvgcNx8uWuP7F4XjgDpgLALBJCLhh+fYejluXuD5/fp8dMBcAYJMQcMPs1HD0YgQA8D0Cbli++Qz23ktc32tBOwAAATeswBeG41I12g8cjkvVeAMAIyTghuX70HB8QlV93/92hmUBD0vy7SQf29ETAwA2LgE3LFNr7ctJPpBk/yQvXXD59Un2SPIn1uAGAKbZ+AZWYJGt3T+f5NFJHpdJKcmhtnYHkqSqnp7k6cPX+yT52SSXJ/nwcO7q1toJ6zE3YMcScMMKVdUPJ3lDkqOT3COTHSbPS/L61tq16zk3YOOoqpOSvG4bTb7SWtt/x8wGWE8CbgAA6EgNNwAAdCTgBgCAjgTcAADQkYAbAAA6EnADAEBHAm4AAOhIwA0AAB0JuAEAoCMBNwAAdCTgBgCAjgTcAADQkYAbYJWq6gVV1arqBQvOb6mqLeszq5XbbPMF2GwE3AAbzFKBPACb0y7rPQGAndBPr/cEANg4BNwAM9Za+/J6zwGAjUNJCbBhVdX+Q2nF6VX14Ko6r6quraobq+rvq+oJC9p/rxSjqo6uqguramtVtQXtHjzc82tVdUtV/UtV/Y+qetAS8/h3VfUXVXXdMPZHq+rntjHvJWuiq+oXquqDw3PcPLT986o6eLh+YZLThuanDc8z/9l/6j67VNWvVdXHqur6qrqpqj5TVcdV1R3+214Tx1XVPw3jfr2q3lZVey/1HADMhgw3sBkckOTiJJ9N8l+T7JvkF5K8t6p+sbV29oL2xyQ5Osl7k7wjyf7zF6rq6CT/K8ldkpyf5EtJ9kvy80l+rqoe11r79FT7Bw5j32O43z8k+XdJzhu+L0tVVSaB9LFJrh7m8G/D2I9L8oUklyQ5Pck3kzwtybuH8eZ9c7jX/Nx/duj3P5LcPNzn1CSPTvK8BVP4vSS/nuSqJH+U5LvDGI9Octck31nuswCwMgJuYDM4PMnJrbVXzp+oqrdlEgi/o6re21q7fqr9k5I8qbX2vumbVNXdk/x5kpuSHN5a+9zUtYck+XiSP07yE1Pd/iCTYPtlrbXfn2r/tEyC7uV6USbB9ieTPL61tnXqXndOcu8kaa2dPonN87Qk57XWTl/kXq/JJNh+2zCv26bu80dJXlhV57TW3j2cPzSTYPvLSR7VWrt2OP+aJB/K5BeYr6zgWQBYASUlwGawNckbpk+01i5J8mdJ9knyjAXt370w2B48f2j/uulge7jfPyV5Z5JHVtX/lSRVtV+Sxye5IpPgdrr9u5P83Qqe4T8Ox1+dDraHe93WWrtqOTcZykWOS/KNJC+fD7bn75Pk+CQtyXOmuv3ScPyt+WB7aH9zkv+ygmcAYBVkuIHN4NOttW8tcv7CTLLGj0xyxtT5Tyxxn0OG4yOq6qRFrh84HH8syeeG+ybJ308HtgvGP2LJWQ+qao8kD03yL621z2yv/XYcmEnG/YtJThyy4Qt9O5NnmDefsV/sF4QPJ7l1jXMCYBsE3MBm8C9LnP/GcFz44t83FjYc3GM4vmg74+254L7bG3979hmOX19m+22Zf4YHJnndNtrtOfXzks/RWrutqq6ZwbwAWIKSEmAz+KElzt9nOG5dcL4tbLig3SNaa7WNzxkL2m9v/O355nC83zLbb8v8nN61nWc4YJE+d3iOoe77HgvPAzA7Am5gM/iJqvqBRc4fORyXW6bxseH42GW2n7/vTw2B6VLjb1Nr7cZMVlj5oap65PbaJ5kvX1lszH/OJIB/zLBayXLMr7qyWPnLY+NvOwG6EnADm8HeSX5j+sSwbvVzMsnevmuZ9zktk2D1dVX1qIUXq+pOVXXk/PfW2pVJ/iaTZQmPW9D2aVlG/faUU4bjf1249vUw7r5Tp+ZLPH5k4U1aa7dmsvTfvklOqaq7LfIc+86/+Dk4fTi+pqp+cKrdbkl+ewXPAMAqyGoAm8FFSX65qh6d5CO5fR3uO2Wy6sf12+o8r7V2TVUdk0mA/rGq+mCSf0oyl0lwe0gm5RW7TXV7aSbLD/7esNHOP2ayDvczMlkL+ynLfIY/TvJTmayU8sWqencm63DfN8lRSf57kpOGthdnsnThy4YAeb72+tRhhZPfTPKIJC9O8pSquiCT+vB7Z1LbfVgmSwd+bnjuj1TVqZmslPLZqjont6/DfV0ma3MD0ImAG9gMrsgkuHzzcNw1kzKJN7TW3r+SG7XWPlhVD09yQiZrWT82k01f/neSC5Kcu6D9F6vqMcPYP5NJGcmlSZ6e5F5ZZsDdWmtJjq2qDyT5lST/fniOqzJZKeQvp9peV1XPzOSlyF9Kssdw6cwkW1tr362qpyd5bpIXJHlyJi9J/lsmf1avzWTJxGn/KcllmfwC8auZZNHfleTVmfwSAUAnNfn/AICNZ9jK/IokZ7TWXrCukwGAVVLDDQAAHQm4AQCgIwE3AAB0pIYbAAA6kuEGAICOBNwAANCRgBsAADoScAMAQEcCbgAA6EjADQAAHQm4AQCgIwE3AAB0JOAGAICOBNwAANCRgBsAADoScAMAQEcCbgAA6Oj/ADT15X764wEaAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "image/png": { - "height": 263, + "height": 261, "width": 366 }, "needs_background": "light" @@ -1260,6 +1265,111 @@ "In its current state, it is not artificial intelligence. You should not be afraid of it; it is just a really powerful model that maps X to Y." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bonus: The Anatomy of a Deep Learning Framework\n", + "\n", + "Frameworks are opinionated beasts. There are some things we might want to do to ease our lives. Here's some utility functions and where they might go inside a framework." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def noise(size: tuple):\n", + " \"\"\"\n", + " Random draw from a standard normal distribution.\n", + " \n", + " Should exist in utils.py.\n", + " \n", + " Intended to be used as a utility function\n", + " to initialize neural network weights.\n", + " \n", + " :param size: Weights matrix shape.\n", + " \"\"\"\n", + " return npr.normal(size=size)\n", + "\n", + "def consecutive_dense(shapes):\n", + " \"\"\"\n", + " Return a dictionary with 'w' and 'b' parameters\n", + " of the correct shape.\n", + " \n", + " Should exist in utils.py.\n", + " \n", + " The param dict p will have something like this as its structure:\n", + " \n", + " {\n", + " 'layer_0': {\n", + " 'w': (some random number),\n", + " 'b': (some random number),\n", + " },\n", + " 'layer_1': {\n", + " 'w': (some random number),\n", + " 'b': (some random number),\n", + " }\n", + " }\n", + " \n", + " :param shapes: Input -> Output column dimensions, as a\n", + " sequential list of integers.\n", + " :returns: p, a dictionary, with the appropriate nested structure\n", + " \"\"\"\n", + " # Defensive programming checks.\n", + " for i in shapes:\n", + " if not isinstance(i, int):\n", + " raise TypeError(f\"element {i} in shapes is not an integer\")\n", + " \n", + " p = dict()\n", + " for i, (input_, output_) in enumerate(zip(shapes[:-1], shapes[1:])):\n", + " p[f'layer_{i}'] = dict()\n", + " p[f'layer_{i}']['w'] = noise((input_, output_))\n", + " p[f'layer_{i}']['b'] = noise((output_))\n", + " \n", + " return p\n", + "\n", + "\n", + "def identity(x):\n", + " \"\"\"\n", + " Identity function.\n", + " \n", + " Should exist in layers.py\n", + " \"\"\"\n", + " return x\n", + "\n", + "\n", + "def dense(p: dict, x: np.ndarray, nonlin=identity):\n", + " \"\"\"\n", + " \"Dense\" neural network layer.\n", + " \n", + " Should exist in layers.py\n", + " \n", + " :param p: Parameters dictionary. Should have 'w' and 'b' in it.\n", + " :param x: Data and/or activations from previous layer.\n", + " :param nonlin: A function\n", + " \"\"\"\n", + " # layers.py\n", + " a = np.dot(x, p['w']) + p['b']\n", + " return nonlin(a)\n", + "\n", + "def sgd_update(p, g):\n", + " \"\"\"\n", + " Update parameter dictionary using SGD.\n", + " \n", + " Probably should exist in optimizer_utils.py.\n", + " \n", + " Assumes p and g are both identically keyed,\n", + " with only two layers of nesting.\n", + " \"\"\"\n", + " for layer, params in g.items():\n", + " for name, value in params.items():\n", + " p[layer][name] = p[layer][name] - value * 0.001\n", + " \n", + " return p\n" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/notebooks/tutorial-student.ipynb b/notebooks/tutorial-student.ipynb index bfdd5a8..814dd7a 100644 --- a/notebooks/tutorial-student.ipynb +++ b/notebooks/tutorial-student.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import jax.numpy as np\n", "import numpy.random as npr\n", @@ -97,14 +88,14 @@ "outputs": [], "source": [ "x = np.linspace(-5, 5, 1000)\n", - "w_true = ____ # exercise: specify ground truth w.\n", - "b_true = ____ # exercise: specify ground truth b.\n", + "w_true = _____ # exercise: specify ground truth w.\n", + "b_true = _____ # exercise: specify ground truth b.\n", "\n", "def noise(n):\n", " return npr.normal(size=(n))\n", "\n", "# exercise: write the linear equation down.\n", - "y = ___________________\n", + "y = _____\n", "\n", "# Plot ground truth data\n", "plt.scatter(x, y)\n", @@ -130,7 +121,7 @@ "# Plot a very bad estimate\n", "w = _____ # exercise: fill in a bad value for w\n", "b = _____ # exercise: fill in a bad value for b\n", - "y_est = ________________ # exercise: fill in the equation.\n", + "y_est = _____ # exercise: fill in the equation.\n", "plt.plot(x, y_est, color='red', label='bad model')\n", "plt.scatter(x, y, label='data')\n", "plt.xlabel('x')\n", @@ -162,9 +153,12 @@ "outputs": [], "source": [ "# Exercise: implement mean squared error function in NumPy code.\n", - "# It should take in y_true and y_pred as arguments.\n", - "def mse(_______, _______):\n", - " return _________________________\n", + "# It should take in y_true and y_pred as arguments,\n", + "# and return a scalar.\n", + "def mse(y_true, y_pred):\n", + " # Your code here\n", + " \n", + " return \n", "\n", "# Calculate the mean squared error between \n", "mse(y, y_est)" @@ -187,7 +181,7 @@ "source": [ "@interact(\n", " w=FloatSlider(min=-10, max=10, step=0.1), \n", - " b=FloatSlider(min=10, max=30, step=0.1)\n", + " b=FloatSlider(min=0, max=30, step=0.1)\n", ")\n", "def optimize_plot(w, b):\n", " y_est = x * w + b\n", @@ -279,21 +273,27 @@ "outputs": [], "source": [ "# Exercise: Write f(w) as a function.\n", - "\n", + "def f(w):\n", + " \"\"\"\n", + " Note: We don't use this function in this\n", + " cell.\n", + " \"\"\"\n", + " return _____\n", "\n", "\n", "# Exercise: Write df(w) as a function. \n", - "\n", - "\n", - "\n", - "\n", + "def df(w):\n", + " \"\"\"\n", + " Derivative of f with respect to w.\n", + " \"\"\"\n", + " return _____\n", "\n", "# Exercise: Pick a number to start w at.\n", - "w = ___________ # start with a float\n", + "w = _____ # start with a float\n", "\n", "# Now, adjust the value of w 1000 times, taking small steps in the negative direction of the gradient.\n", - "for __ in range(______):\n", - " _____ = _____ - ____________ * 0.01 # 0.01 is the size of the step taken.\n", + "for i in range(1000):\n", + " _____\n", " \n", "print(w)" ] @@ -311,9 +311,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Minimizing $f(w)$ with `autograd`\n", + "## Minimizing $f(w)$ with `jax`\n", "\n", - "Autograd is a Python package for automatically computing gradients. This way, we do not have to specify the gradient function by hand. With autograd, our example above is modified in only a slightly different way." + "`jax` is a Python package for automatically computing gradients; it is known as an \"automatic differentiation\" system. This way, we do not have to specify the gradient function by hand-calculating it; rather, `jax` will know how to automatically take the derivative of a Python function w.r.t. the first argument. With `jax`, our example above is modified in only a slightly different way." ] }, { @@ -332,11 +332,11 @@ "df = grad(f)\n", "\n", "# Exercise: Pick a number to start w at.\n", - "w = -10.0\n", + "w = _____\n", "\n", "# Now, adjust the value of w 1000 times, taking small steps in the negative direction of the gradient.\n", "for i in tqdmn(range(1000)):\n", - " w = w - df(w) * 0.01 # 0.01 is the size of the step taken.\n", + " _____ \n", " \n", "print(w)" ] @@ -347,6 +347,12 @@ "source": [ "# Back to Optimizing Linear Regression\n", "\n", + "## What are we optimizing?\n", + "\n", + "In linear regression, we are minimizing (i.e. optimizing) the loss function w.r.t. the linear regression parameters.\n", + "\n", + "**Keep in mind:** The loss function is the parallel to the $f(w)$ polynomial function that we were playing around with above.\n", + "\n", "## Ingredients for \"Optimizing\" a Model\n", "\n", "At this point, we have learned what the ingredients are for optimizing a model:\n", @@ -355,6 +361,8 @@ "2. Loss function, which tells us how bad our predictions are.\n", "3. Optimization routine, which tells the computer how to adjust the parameter values to minimize the loss function.\n", "\n", + "**Keep note:** Because we are optimizing the loss w.r.t. two parameters, finding the $w$ and $b$ coordinates that minimize the loss is like finding the minima of a bowl.\n", + "\n", "The latter point, which is \"how to adjust the parameter values to minimize the loss function\", is the key point to understand here. " ] }, @@ -374,48 +382,37 @@ "outputs": [], "source": [ "# Exercise: Define the model\n", + "def model(p, x):\n", + " _____\n", + " return _____\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "# Initialize values of w and b. We will store the parameter values in a dictionary.\n", + "# Initialize values of w and b. \n", + "# We will store the parameter values in a dictionary.\n", + "# The dict keys are the name of the parameter,\n", + "# and the dict values are the parameter values.\n", "params = dict()\n", - "params['w'] = ____________ # FITB\n", - "params['b'] = ____________ # FITB\n", + "params['w'] = _____ # draw one value from standard normal\n", + "params['b'] = _____ # FITB\n", "\n", "\n", "# Differentiable loss function w.r.t. 1st argument\n", - "def mseloss(flat_p, unflattener, model, x, y):\n", - " \"\"\"\n", - " :param flat_p: parameters to optimize\n", - " :param unflattener: A function to unflatten the parameters into its original data structure.\n", - " :param x: input data\n", - " :param y: correct outputs\n", - " \"\"\"\n", - " p = unflattener(flat_p) # FITB\n", - " y_est = _______________\n", - " return _______________\n", - "\n", - "from jax import grad\n", - "\n", - "# `grad` as a function returns another function, let's call it `grad_func`.\n", - "# `grad_func`'s signature is identical to the original function passed into it.\n", - "# However, it's return statement returns an object of the same data structure\n", - "# as the first element passed into it, except now each element is a gradient scalar/tensor.\n", - "dmseloss = grad(mseloss) # derivative of loss. \n", + "def mseloss(params, model, x, y):\n", + " _____\n", + " return _____\n", "\n", + "dmseloss = grad(mseloss) # derivative of loss function\n", "\n", "# Optimization routine\n", "losses = []\n", "for i in tqdmn(range(3000)):\n", " # Evaluate the gradient at the given params values.\n", - " grad_p = _________________________________\n", + " grad_p = _____\n", " \n", " # Update the gradient values for each parameter.\n", " for k, p in params.items():\n", - " _________________________________\n", + " _____\n", + " \n", + " # Keep track of losses.\n", " losses.append(mseloss(params, model, x, y))" ] }, @@ -590,7 +587,7 @@ "x = np.linspace(-5, 5, 100)\n", "w = 2\n", "b = 1\n", - "z = w * x + b + np.random.random(size=len(x))\n", + "z = w * x + b + npr.random(size=len(x))\n", "y_true = np.round(logistic(z))\n", "plt.scatter(x, y_true, alpha=0.3)" ] @@ -605,7 +602,7 @@ "\n", "Expressed in equation form, it looks like this:\n", "\n", - "$$L = -(y \\log(p) + (1-y)\\log(1-p)$$\n", + "$$L = -(y \\log(p) + (1-y)\\log(1-p))$$\n", "\n", "Here:\n", "\n", @@ -639,34 +636,32 @@ "source": [ "# Exercise: Define logistic model\n", "def logistic_model(p, x):\n", - " \"\"\"\n", - " Logistic regression model.\n", - " \"\"\"\n", - " z = ____________________\n", - " y = ____________________\n", - " return ____\n", + " _____\n", + " return \n", "\n", "# Exercise: Define logistic loss function, using flattened parameters\n", "def logistic_loss(p, model, x, y):\n", - " preds = _____________\n", - " return _______________\n", + " _____\n", + " return \n", "\n", "# Exercise: Define gradient of loss function.\n", - "dlogistic_loss = _________________\n", + "dlogistic_loss = _____\n", "\n", "# Exercise: Define parameters, and then flatten them.\n", "params = dict()\n", - "params['w'] = ____________\n", - "params['b'] = ____________\n", - "p_flat, unflattener = flatten(p)\n", + "params['w'] = _____\n", + "params['b'] = _____\n", "\n", "# Exercise: write SGD training loop.\n", "losses = []\n", "for i in tqdmn(range(5000)):\n", - " __________\n", - " __________\n", - " __________\n", - " losses.append(_______________)" + " # Evaluate gradient\n", + " grad_params = _____\n", + " # Update parameters\n", + " for k, v in params.items():\n", + " params[k] _____\n", + " # Keep track of losses\n", + " losses.append(logistic_loss(params, logistic_model, x, y_true))" ] }, { @@ -784,33 +779,24 @@ "metadata": {}, "outputs": [], "source": [ - "def noise(size):\n", - " return np.random.normal(size=size)\n", - "\n", "# Exercise: Initialize parameters\n", - "params = dict()\n", - "params['w1'] = __________________\n", - "params['b1'] = __________________\n", - "params['w2'] = __________________\n", - "params['b2'] = __________________\n", + "params = _____\n", "\n", "# Exercise: Write model together.\n", "def model(p, x):\n", - " # \"a1\" is the activation from layer 1\n", - " a1 = __________________\n", - " # \"a2\" is the activation from layer 2\n", - " a2 = __________________\n", - " return a2\n", + " _____\n", + " return \n", "\n", - "# We do not need to rewrite the logistic loss: this is because it has been defined above already!\n", + "# We do not need to rewrite the logistic loss;\n", + "# this is because it has been defined above already!\n", "\n", "# Exercise: Write training loop.\n", "losses = []\n", "for i in tqdmn(range(20000)):\n", - " _______________\n", - " _______________\n", - " _______________\n", - " losses.append(_______________)" + " grad_p = _____\n", + " for k, v in params.items():\n", + " params[k] = params[k] - grad_p[k] * 0.01\n", + " losses.append(logistic_loss(params, model, X.values, y.values))" ] }, { @@ -839,8 +825,8 @@ "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", - "y_pred = model(params, X)\n", - "confusion_matrix(y, np.round(y_pred))" + "y_pred = model(params, X.values)\n", + "confusion_matrix(y.values, np.round((y_pred)))" ] }, { @@ -890,13 +876,6 @@ "\n", "In its current state, it is not artificial intelligence. You should not be afraid of it; it is just a really powerful model that maps X to Y." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -915,7 +894,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.7.3" } }, "nbformat": 4,