From b4b7a818e1858b765b336bf189e070b972ce400e Mon Sep 17 00:00:00 2001 From: wassname Date: Tue, 3 Nov 2020 05:45:26 +0000 Subject: [PATCH] missing pandas --- .../Intro_to_NN_Part_2.ipynb | 905 +++++++++--------- .../Intro_to_NN_Part_2.py | 14 +- 2 files changed, 481 insertions(+), 438 deletions(-) diff --git a/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.ipynb b/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.ipynb index dc3234f..d411f7d 100644 --- a/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.ipynb +++ b/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.ipynb @@ -5,8 +5,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:34.137781Z", - "start_time": "2020-10-13T11:01:33.730220Z" + "end_time": "2020-11-03T05:41:51.020358Z", + "start_time": "2020-11-03T05:41:50.508159Z" } }, "outputs": [], @@ -17,6 +17,8 @@ "from torch import nn\n", "import torch.nn.functional as F\n", "\n", + "import pandas as pd\n", + "\n", "import numpy as np\n", "from tqdm.auto import tqdm" ] @@ -142,8 +144,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:34.157752Z", - "start_time": "2020-10-13T11:01:34.139511Z" + "end_time": "2020-11-03T05:41:51.065950Z", + "start_time": "2020-11-03T05:41:51.021948Z" } }, "outputs": [ @@ -168,8 +170,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:34.545071Z", - "start_time": "2020-10-13T11:01:34.169245Z" + "end_time": "2020-11-03T05:41:51.389498Z", + "start_time": "2020-11-03T05:41:51.067512Z" } }, "outputs": [ @@ -208,8 +210,94 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:34.550634Z", - "start_time": "2020-10-13T11:01:34.546598Z" + "end_time": "2020-11-03T05:41:51.406041Z", + "start_time": "2020-11-03T05:41:51.391255Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class: Continuous\n" + ] + }, + { + "data": { + "image/png": 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VlvfntrxWfc3Mt2o76axqPpAmEXZm6NVd//WS2TJEqxrs10JR6fDPAye0IM5/NzA+4RytkVLoiI97/GxAiNCKuYCQ5+O9UJYzicREoZLONvr+pSMFRz5nReWeIi/+5z6NusrSTWWHZl3py+HwK93aqh+Xj0Y0EeCv+/kRn9Y3Smxq/Ycuja2sGGag5bNC8koDJoYS1nYOIluq1TWb5tdTcD/ZbY8q/jn9ajCXhVwWuZO9c0768sAU3EspzANXWUt9eth7T7DQ36DSLLZyLHcOrOD6vKyEuOLfJWfnwtmi213JfiWxazLYoSY04qatFHm78VCnK+0bpDNOYoN4IdU/fm1oWqEeid1QJFjU1Sh5I5QY/FBTOTfewL92yN1ppek1bQwaFxAzVR3OivQMHy7m08fmT93PT8H8JYNgyamlo8eyCZk4AkvAl+EAq0opcgnO3nWE8Pn2UUt5aAAvifrGU+7X+yKoAgn+c6jQUFSsNuCOR80Kmj771mLtiJcTcO70I/3sPRg8+uwY3wucoBDiGTn3GCJuFxQr68PdqGGm8Hub5uETeHPMLp4VsOeeT0zKRFigTqVkC6kPf3vRTjcWg6pECStXXMm3Ec50JPGpbXCCdMUTJjffP813h9J6L/OlpoJpvXuOEzUbCjOJVxnP3NriH9tpDwL9yfYzU9LT1KVlXAFetkT0vxi6RzKlxVFxsdalPO1KX48Y5M2Am+zSfnI1zNs3qP804ajKAjV9f0Kz54wdFqpZs3TM8aM0/Nj73ASgN+aMG2WPzs7yrI43i+G8cHJvrybw51V7TSyAcR/XEnFq1ocAEcwXPCiTeL8Fmrkyuyfnh1nUawMvrUdV+WzDPUKxhgVRO77KrUfkU/dV0/oBaMhBzvOl3FC/nqpD1iTmGsO/gYUysUPqZXzXTEWZN59IIJXI8DCUE7u/Uvpfr/pcGbMW2hh2hm+p1UbsXs+IqcS6Fa07Mdb1ob4XXtao21rpR++Zk2hMSfptnm03gMB/4mSiUc4Mda3iVJEBapYZ4jmmAahYZaMshnBX0dvRWtRmaifnPb+Y+tLALJBE+vduAyO+DDvP0lvXQVQnEUclWO1w5Yib+MHLjPAZhX99P647qeoWxKq2qlRKO2vPSI30btVuZDGZv7Ya3UiJoULxzoNxqo9Ta85P+PQg3Mq1VVEjkNJEXcfxWE31pT/QLw7amcxrjY0F7SRTJgH8OznepvaLthJUF2eYk8reOOFhkQe0zZ0k/MZKEgTYizW+FKx0J3YCrXxU6tAxNvzjwZLxMm8o94YCcBRh1HpU5BHoKUNOxmfe+s2XShzAf/2kwxJy9wBznNVgWaQ09Ls1/VDlP0jUwlneKBGQxKyC1TD0QonbWUuz/9inN8P9r/qrUmPdptvsoeV4IVh1Z/oWP6uLDzMmmudO1q64eqnBX0Xe0L3+KuQzddu1G6jFGDDAUbtTyGaD5Du5aQEGsM21Ohsxuj5J09etP32/ftmro5M1kzmm6oTOHNU1an2X5o3O6GrL+eghmH7/NMUm/M8i4qRoORACDYhy1OgJNyQ7JL0Vm/teqIW9402HpXIrUCEc7Bw35FtFCoSt1BPbkT/d8Si1eHrtaGst4Jv2wUyiuvNlLdL3ZxuAaMtQA38j3mVpf2wTbuGQJStLBVY1TPrb2hP4jdTqHJEErSxVbz52fzxy86s9PlDsxTeFh0glD4ZR1Dlha/LxfbCVotouyr332kKxJcCL4uJFCNUG/hW2cHn9dFuf/ZFlXPaEhSn42nSuHBRohAswuH86LOlR0Iruag8vlChz+bzlIESL27jmnz4qA+ovBujmiCWmlFcjIHg76skQjhuF5h5ojBR+w6GGUwCKuoWqAeQwrChOpwi77TIFRmyQrd59EImwrd0VdJcBOvsRaNYVf6snDBjcXrZtx49DEy/6usTZZflMmpl6T8IB6fKVRDMR7uA/5TszkTwcK4s32+BNmI9BIeDlINoq8IHvPuxe+e8O53PszeaTOTva5Q6/BpwgiPKhU5lGFVok3XMa6eQrOonwkPcN4LtSJmK6VaHCSaxLHPibtRrkcoJg5jwEXeGSt6Otfzsqd3K/1GCRKMmykU3f6Ubl+NYer2jXws+nCA3TMU3rds1N4+U3lqBLVsOmpDJ/D3v2OpGknl1y+6V0UPJ78JcbPF1oyjnvXPbkr7tsBqWSmCQ2ceTK28Pxp6OTPzz9mTr6hn5/vF2ecIEzTZCAZ3RjaI/lqIk64koN3MCxSgQi5kXa1EzHfLc9qDgYlfKXbQFflmCy4etYa+KXcE5eU78zUKa01MDDNMsPJu9f7UeZad3DaRCOV+nH7Psuu8TSAo9IPxO6lRF1x19IB3ftT8xaU6kteQijnrtuZ62P31V0HdUM/hLQSilIpyA4S4OXmX5WMQ/uHH1rmFzq1IsOTPaufzRpvzoSzj+GbyeDH9gyUKQh2c8sXc7LfgQO1sMqIwqQgYnrujO7Ru/XixZucYI8eIgO4b/ch/PJqmv5Uv8SHKH3DheF9AmYBdpb80bx7mi1O2aP487G7mQDEsfuuJX6YnCclEnS0WZyIWfJUP6x7j4awygBC9I8sN3NSWMfCrKbK4JFM/hr00WriYNVoe6wc8/FT7y4ViZLdpzUQIkdjQmN19ewas3c4WM9jILXqzbEMUznwqMuBN1QUc/1da2LTa0TLCd9v9wT3NdpAyWQbcJwF1/Cf3+1Own20q2s+o5wW+/tvBGPToty2i46KDYPNwYu9fSu9t7B4Q97B9s1yuIqkckmGRZi1CEi5zXF7ifBzBMz2hEFwYecOny8iO7qONEqxBoXlRdjAMLMhbantAeSrnZqeZ2o8kNfz6EQrkiUF9xtKrUwpfnwfualHDw0eEERuI6O9z7T9eYZ4OTINq0wSpfqeqEmPnsPRUm0nh/ZfVc5g7+ujB/tQnHySC4rx3wA9SVZPPLO3OEqibRbt8xNHuqD3UGRvogLfgxIT7PTrQvedTw7fGtI950cfLrQ1WuqPFVArvY9iiffNaz+VJdQBowtmv/01f1hyf3YuJP9uAHxpuCBxEGsrDhVTYWJOhAAPaVMlaCibjfDQBPwO498bcd3ECWzieov4xIAMKxC0HlN7YOjkTfk9j1waVF/ttxg4Q5+SO7HPJbm006JrKg6yaUPFaunpHYkVkzOlULfW26m2dtnW6A8CA/qNtcWF4FeZ8pBFUb8d5LEyqfwo+fmbCRyz2WBDcgiEpO7ZEXmIPU1o4bwG+dnvQXsWfWzjfemqQ/tFkkJsQqmKld3z5apn3fpJRZu2rRciEKOOisAAABvBq9+Js6kfePeft4MkQhIqYNyhsh6vX26gmQX72VMbKyhWmvwv2t779te2blfiwXfZTtv97beXDh8xsEgtDCLuVQra0kLAAAG69s2kE5ed/FPHoA/wSAAcxxeVcNoJV1JTT7bCl4sOFDnu63RMynSUC58b9FrrkLuchh/PtCv6jpIPB8yAABQNtKhzDntd3FvHx4ODxLvmK6H/zf+7oKrtXBttU9gWQDa6hWmmhQ22FE+wP7+oPDUwSAQSKtBzwcdfg0A6GaDMEJCyXft75xtFppcH3Wi/sjbDoyDRgT/j2a1yj+7zJfjuOJ8k1XEY2WX5BM+N4USdo5Ru1+YntTm/ea6qtIGHvAH/cXdWGEnQb8zT55bxvB1VwX/v+Vy07azhp6vlKjg6j3DZDxNkcisinoRdQuk2cA23ENVW3bQJi8etpsFrAu1gZ9PML9ydIHLuoXE/0mO7CQbAABaVJyne5vVqlnGsypYuDmMVKgrfRE5TqqudbGdCo3WC8Y6PcznHVVSBUV8KxSrdFE85EXLPJ23dciarSL/yXctAcBiP/6jH/mWrC7FHCWZfKO1c1spkax0zDLvV7ox9BvGyztY9KnwSmgErS9xgknLkmyFIMx2V7RNakOWZf1UzXQhFuB/upGbaYOlCN7VXT+5T8NInJikDDlBbXB9Um40Ra2lntt0KJdTj9+J7ZMtKKPT+gOcH899QVy3qy270axd2UY4Q7IBuma3PaxMlKcNixj8swY3V5JUWhulE9R5Xx+zDz/OGLpRCIzVig1CJpRcY8GiXvSkQhEQ0ntDKA6MNoDxvCHrNN9WXFxrqiLUmkU6XVnQWZlwmMxpn3KOCf/0OMc7vUB2+2zwbjDscU4b4TliQlAXmEnrTm7W4rhsWiGwodNv3AkRj3ZN+9WivijVq6nhR/tK1wTjHbaaWlXETN+gKq6NwGuQO+aoIcJ/KEGn4BXBTlGDNbou78h1aMf5JJJFFXs8zKaQGGoMtOo5x/YsfrXSwi858lgxJdelTquB1nVDW9yQnSQDrCRwq5eCMa5NAXF0m6YB/FdiDpTIxH77sPLI+9SKPdeyc9rYnipyyGnUONpAnZdVm5gZk2ZfR9IcKGBvwDxx088iQ75huDV2ipF5kppRK5Ts3Gw5lINZEq+5soK/AFYVoqytkCQjEY6tiew4WxMFHW6X0Vxsxm8Tw8Ujqe3f7pr1Y6XIubLHcYbBIjlpQoUKlhaeFlVP9KqNkTYg4xlhjwmfFClmhiWq8OewKTItcw2512vhSMuVzQpgxklpHOWAAWqgs0NSqOEiDu9hlExPdd6riJxtrBKPNqID8ZGZ2GsB+XIRgFpaGHFmpFFb8rQraBOCEPwV7QiKde1gLc8xTHYkyv1ysOakkB14olLJ6cmp/xWZrZI7xTZpT7PFLnfLQbS0LKpzAkNFlLEV39Qp1mzMU6y3RCpdWGuqLfJ8JmP4XyVSkEBLCIyjwtxFKuHNXJcRcIzjDmahcsPWn/n82nB1r9drtU1LacpzjcBtRX3ZDzgCVliJu1Vr86VcKZFLDZnwI2ZB0KIy07QW/oofgR2w444vSTVRMZHxiUT2OMXF6zzd1HT3tMCHQv9MPkQxywuc19viqBBzZdPKFd8qtSR0qVyprVqtysKBBo80XkBMzpOWUeDSFlW0I5le7bShzuhW0aGou1ZUFcm71UX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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(4):\n", + " x, y = landmassf3_train[i]\n", + " print(\"Class:\", landmassf3_train.classes[y])\n", + " display(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2020-11-03T05:41:51.440450Z", + "start_time": "2020-11-03T05:41:51.407154Z" } }, "outputs": [ @@ -230,11 +318,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:34.646797Z", - "start_time": "2020-10-13T11:01:34.552585Z" + "end_time": "2020-11-03T05:41:51.487311Z", + "start_time": "2020-11-03T05:41:51.441705Z" } }, "outputs": [ @@ -244,7 +332,7 @@ "deep_ml_curriculum.data.landmass_f3.LandmassF3Patches" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -264,14 +352,82 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:34.733743Z", - "start_time": "2020-10-13T11:01:34.648377Z" + "end_time": "2020-11-03T05:41:51.580998Z", + "start_time": "2020-11-03T05:41:51.488516Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class: Continuous\n" + ] + }, + { + "data": { + "image/png": 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\n", 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/AVYb//M9ev2/58sA7H50Xv6SdPoXT4AfFFaW6p3wiUHWJp9vofsnXLAvHabnUzR5mN3YmxKbC8KUd6Ld4/HGWsFGkySykFBdvh+A1j9oAQA2AHj7Tb/K2C//U/Awu/Dl32GXlkAWnO7Ep8/kk9+6v/oqsf/sdV6v4M/xwiJ8BNHHDZkfH82YF5XSvSzivnHS1Y5MwvbTxH9pVX06leFXyOMLW61xRz1aUE698enGA9P3N+kPxv8Y3P3pQtD+Vf+Ft4XvPb78+gs/+uNHj3fBd1586pUjxarUVs7MjfDhCVz+0o319+9Yx9/4WX7open15Gj32C51R/kq8RrHfX+6XP8m98G5LXA2B8Bed/eUGCuAXi20/ujpF1/ffeNa/aDy98XJ7w0c/J0b/f/4PQAK/3Dt4IhTcCHk+Qp7IHTuEmijdqE+exiu8KPOxpewyWhry9On5MJDl3+5PAuvBeqrmN3Cxw+FOQDSTzPqPHNSUJzbx+9/+1sP/1TmP9jlFn/mrLl//Tz9u388AoB5pfLX97gvx/pYdHvChjq0N1X0unJ0K20wl1sfHeRSGkxm1xcFdHpxBfQvH3ap15mu905z0/wre/vP3vs6RzbbJY4Dk3e/G175Tnq2de3hu3DlT+6DtB7Hf/gGAP+mZf/dn4+XL5ZSObZl3zfbaWkBoRft2zsLX9MlQttKf1LkrMHJFQscsZcvtabjjCar3nvJuC5QNfIo2V9cnMjvmgjpOwz615Vb2sv4rcL6F9/57/PuMx9+8jdg7cVtauoSX7ux8T1USfDcJS9Vig5y4R37gXnuZnTcaXv9/VI15h84Uza9s/7y87fjAjcqiYONqbNAe2Z0bgCkw+Dek/3S1cHK9a2deIH/2zsbz/D7/9fLrndnzPWv6nfScH29waLvo5LNKFQS4sLEnaE/18IW/0YtntG9Me9QBWr1sZaL89134qMxYX8U3/z5bv+Dys/ovZhCQZ89rZg6RVUuzP8eXj/46XuFZwb3y6+M6aSiVOfC0Rm+eFU9sqxqikF5coLY3GSDCJ7f3Kr3PynVW3vdUYkm1/rFmYOzF+Jgd/poBgDY+PrD8WOnoWWn+ZfqvdxeRpenn5Y3zDdWGO4D8uXaY124gqIHxXJgFJmRcZk+G1ejS4HOpPGeoPBRm0/QQvnyNF7+HFeUGXiy2N0NNOizVGP97K5xHmg3q6cffo4Wu/1Mpc275fxw09m42jtqjAbXTr3q2uXXdq3mbBhRUbXouJlsp53YrJXDbkrkZ80tWgaYnQ/gG3ncKY4/2YzqRxSH3/y8Xii011ol4rCPv7HQyog7v5VebpVci6gmH+6tUOVK6cgQmQd8+yeAOLeiHgjjZBxV5bmQ8R1Ol6f1yvxgZPHVzOKbWBjDmTxCze4fSHP7rzbeeiS89s0PUoWk5r96k333E/drxSpj3kG7F7+w7Gxz9/KLVGt3caUT2h9k2x1dcG7qe0zvL+QCpTY3ktq+WS56k1Kh2qqyfXKTjvNK30aeGXsnOXr7sKN9sLh9RJxeIk/cF7/w2SnRuTXupdxo+D1jpvfav7C8M3pMnolPGV2yfqLvmwx311pI6Zv7sSlEdLxZUJu7gxM2gYJBZqXsxDbpwmSHK/fS6kRIRX8Tfvvs5vGT75Q+BNJadJtbqYVnxn6fW7z8dGcqA8DvfP1q/1Y1c1uh5SwlwU7R1V9TD4grPK/qB4/P00WpqILbZ9PzigriMT+fZftWWeEOT9iaxcxZHFeONhFdXSZfW/hfFtvme0P6XvXFBt8NFsndR7tgg5SdG+rbHykFukWNU55+ZFW16xZF/5ziUPt3UUxhtVQx9gY7pcoXLc/LRVnrpB7LQw9vREWhLDY8wHAz+M2qqV1+8HHTG7dJET4onVtJUz3oP1jMOcrlgksf71MbtCH3QblO6c4zuDq/B1eJdzM7p1lxghfU9O7spXY16R2W42jJ8SKWZEVkKEYmKSaT6zTjolnpCX3v6BqNJxNtZeE7jIMZDX4yqF9UqR/haJ5I169EerfH8qEZrLMJ3OUq7lsWSK81opRKWc4gGnPPQ+GPyKmYU3BUSc+NYIHgrMQrxdNEwB4XwH8U5z2Pu+iwhbMBtfqtiT/grQ4+vWpWSw9rSbNH14odYjZrHiV+Mlc7VPyg7ZwQv5gs4mnOeE3rrv1aPnb9nVo5J+uBXwxUm6OmbKhnRWoSuK00g2j1wfK2ho5l7VnrkU51epVJaBTXNG3Gsa9sGBO92jZUfpF1GWQJGCxR8+9Mky+ubbs706AmKbE5g77+WDNv8sv3MnkomMR7bTk4URdkPClwApI4i0FWJVmuDMAiNRSXheRhxpLD6jnq0vQraOobzpFpzEKYk/V+jeOK6aw24S8CtrV+J35ACdEZPjWIhTtx6ZzXJHJ1ZgMjhbMqJjVRLYxCmue3I1aL4L/dmLHkVCro3arS5WY0RQ6K1PR8sLXXHWIn5+BAyNmUlaukF8ay5tgLi+WPmU94iSYSHUbJWvt+sSlGXclWxjk5ECHJBrAuYGUcQ8TM2R7fRS/J1f1AWegbSUDkBjsxWG7PAJl4NqPt3vT8muDwZCvvF22UH5+0maZn9cNJpJJt3U+ksoJRuF1zdyJDizHlQEgpc0PmgE5m5TQNWPwRMWz0Ue0oltzKbBJKuSEN4NmI1FKBMuJpiVRK2epq8CWCLrgE147l9aMhOy8lR/slFVkRrNEBK8DeUSuOulDMRa8KoiKnsHSW9GYY8HEqEDPCHYXwP1lxdSIeRgSb2fOGzERM3i46A8rnTDTfIjrGMqRCYxJfGonPUW8mFIt2RgWGDElhITwcq2X8GVVngRDnFFRyChFIdrqJn0GCZyCHg5B2E/Rmo9gZRKGjsmky3Ly8RHA+6TkZaTtN1Yu8u4RF8wHkwW0HxnNSR7fnykQwbJUcf+z4YcbOi7E0lsgpSSkwVQQSq4BUnRwaZAHbhibLQoxSqziO0qq2KfTVYEsKIjbd7ULXTdkF4vQkBNQhI0FVofe45C+57enMCM4ry2NNnI0dYrktagtzQeKbocDJFBWTSkLEI8rwGIpSuCDFvmbyCP4rncSowDY2mH01jm97EU/0I7Y8XKyIuoetLRjEuU+xEoLpJ3614WFCks6FQy7HflYAWcxBNzS7SgPQBAegPPF1vRaZEiyUOCcABDGBLPw/bkfKlEgJUgCL9+9SFJOVKkKezwMn0hKjTlR77iwM0IZJxuJJW6bUE44IdF7UrJkM8t4kZ+K2voYtVyXz3PaTcaVQnkppMxKniSTYfkdDZAnkcUw+3IkXyKJ5ReVTrFQZa26a1MjIEiYZ0BlFC43HMao3gC4xask/6TXCqZ3oGHJ5TNWNokDbXuSQOTQrVKkYiRyg2alhhFwxgYhDZ47sFGUZcwRH2avVTAjzWZ+ay205Nw1MB0HG1lKFNQ/4yJ3U3ZF3xhqEwKcTgKNpLBUojosc7pQAJcwKWVEDwMzHFR25/llAB1FGKQzCM45gF/jtKpyFx47px5iwCIE/lMGtmK9GJaBqYObCwgUm3DPV9tm0U44aq0GZwTYXB2lBZPZoo+FXRQHlvM12PDfLse2YRY7CioCnDG+i+fU8d0eQ4sIo4s2QByKj8QwOamejMg8IViScnu6yFSEU1WyiqY4MtFhKXE4Xc0bCU4HMxFQSC+oMTsc6emqF7SCbCi6QqhYBktzIPPjG9gE8+RyqSSgKRS7hgkKlaY7OcGyoqjUqIaJH+oSPGMqQVyKbUsY8VTnOqf3KpE5UUq8zL/AkLOSw9PhwGrmsYyuIjKiCOCNRmMWxOmpYqDO15RwzUx8EJgGjiT8Lwf7TRBlopcAJ4twd1ms0wJgWzC6TokgCHJDHYpiiQCFQ3Jy3allI4dHpyRNRyyhaKe61M4pgs3xXVMKAFUUS/ubBXFl2uIBUiSkX5aaXcHkSQiFXhJSRcRIBAUEiQJAZjzRRinKbnNUszW4luE2kIYdPyEKYx8ePw3SzENixAnWFw07mMsP5YmYlZUgi2OTCGKaAlujMpMpt23U65SU7k+JILKijQCiaE5QDRmeYoqQGwPapmaDApsJQmp0Syb4/EJXc8CKOSMesS6Vg/RTTYZ7ml1p5QM5cm0P8xjCiQEgaAYP8VVluBfFSgso2k0A67NuCaKckDPkQBxwf+jMuJivVpOrCEx6dTItG2CtH/boclGShM4E1QXM1ivW1hNUhbcy8KPVyjCgTybJqgYnpCaJ3AsqRrE2HBUwjP8QEveqfogLkOFyPBl3AqGQ9puqTBCcdaqaOF3okBxXfIBAQuSKQygDE9IkCMUkU7WEyojhQZhSkDQUpxUDAMkzTo77NY6El4x4Zph5d4YnYUDqaUEWAxngGQomrz56OIkTRcgYkEjXjqs03pwjsy3y9BFk8IM04lwZE1sohxwuqVfdUpNGWZ+3lFUZh3SmxEvp56CjVPqdz9ZTC2UN6M3GBmhox4DaJBHkjx5rCMp3VnHmiFdZHCBOoLJom54e5C3Jdi+aCPBdVFYlCAJCYOTH8b6wXcD2SKsY4cmsLuZc5ac4DOKurvVEWHixVQJaz6Rixchnmo6HBVeJIghFpCpYMODuJxGkhjLx6as9QxglS3ggsuiHEhOuxICStOEanWRMU2HAW9w2WdUZemCHOtotYBPsjk3IV+HAZZxYlVIFrGD62ZmKNBrqZxITM9rMjnhJd1z0rLwKUogpW5BRnGlkQfJ94YlYlAIW5GUpDixlmE9sISZk+6ugBFOtMPsu4oCsIGVGNwkHOIU5WjTQYdHmp7T9FZDShSNnnqPFElkSz2CcYfkrNs3KsYc+uhO1w4sA0VUWVCYrUDDGyThmxRxO0AJM0gjQBIjKcRHkCy1TIJHHJS2LCPuvFtCoVAiiCMzbnJZbOwqMas4Fyid+SSF50QIUGDIWInJik44jIszabsT4xHg7hP2FnISB5kAVxIos+AykiCxwUxCzP+4lqE7QfxBnwmAbiCrQ+jSiDyLIig0OmI2tK7orlFdw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\n", 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\n", "text/plain": [ - "" + "" ] }, - "execution_count": 6, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class: Continuous\n" + ] + }, + { + "data": { + "image/png": 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P0h5gt+xAO5o2aCXigZbZ2M/TGv7j7jI6Pyz5+GlAqkGd61F7l1g37muAVzkzk+TAioZFLOs43rm5ch65ohhOkRQqvtMOan48ijEu5ViYxmyp6H1XSASY5ufm8FLCP+r57GtCFO32vtNBuO04+rQPLquZiOkmz3Ypte1ZuLCDcCFHBCcBcuZtGIxy31q5T8M3Ub4toCDT9y3MhZnUcML39DTbT5L1MoL/I3kt7onS5D44RbE37Il90o3REMcUohC7Ltyg4Jl1ELNAJwdGtPTrNv/cwzdZQBHpkxGf+B3Pd1nbxtNYrk9THOIGn/imFflxB/85aLfnh16yMAF2wDWe0uTyEBZqzFK7ly8f8YxUaLoDSvGh5f1RkxVzWyB2WWQES4RBBifFhiveo0Z+kOxTMz4bN1F6HLfc6+yphX+rT+9fAnPJYEhorHGhPk3ukVASdtR6e4B5WR27aQenO+iHaPTAa91HRblMmrNG90rakB04zTErrOT5rvLTW1aVLmmPluZglM/g37mwOP00XLRtIGLRFmCftw+O7mLuqvby6asPulXGR5v1UQfzdEgAO3A9kLhMO6WEU+68WYiDuwAZBNCg41pXk26ExZaeuFNgp9HJBfz9q35mps48uVlpVgg34OGrs8oUYTxT6qqK8302E0HxqqZ4TI4D9hWgcQmjUJ1WJ2kkiacjGYPV/cE1xBaDkMlwebadXOs8WtOzbg1f3UM1/MreIszC+DgbZL7tTzM39yUthOMn73Gya0KytyOlK8s2E0IjNOOHhVT0tKNSbSfrkcc9WwcEM5Y+j+QgbydMxEDtSXXnXn8Df+8135mD259Hx0m8JCaR9Pn60u+hey3RHe92fNzDRB4TQCLhOclRMmnC2cZJs5tXAz/A2RinUVFUeElkpM3ybgO2T3B5PKK7g9qvegN/cxLT/Q8A5Cno5/EW1m0a702/87LsTEvUVEh5OT0mkWI44RUbWmm6Wsw2kEOU6tye4Qxf3EWRMUn9BKo3ULV48aibp1b7zk/VyYL88BrC7Q0B/Rjv9l/1oN7Ku5kyMSBneZyyJI2m3Ww4FAGAYT+AqoK+ljk4T0jK54OKI+X0E3xTNYTqw65FY/g46H76+NQxdTmLF/D0BP4HMzxM9hiIroE8cEo8qeh5LGBIkonGwDRhg9ast5I7BU8Ey/skZpHqMKr4N6ORr2CLc72uZDIggWmcgGMz8J7BIj5bJrSVEv79bWKfOSbT+YajSEsa+fQphToEZcDgNalhARyrkFqIUxfSXLcT1Pr7hgLzKxI3qg/ZeWN33Dgn88wNzWccRvHykwjMuUH8GDqi0/mH7kU2KcFyNw5D5dEQ4c/2gqD5EZwhqp/RRYPUh75iAR+3A2lnoxjXjGcYJT9MjtWi4+XDKChAdoQ7VN/xj4rFdYTT1lZHj0xXw/8yPcy74LP1nyMVEqoQaOGZ7jKRTCSa+/4habZNJ88H2Xb9Wig3edZk2T47Hcz+Wb6tKTkcv3h79dExjzYItDCNfnQezKx8DaPNOOietAj+UbFrsz+51VjPX6IrIEUwg8mFgvL2/Lg/jujbpmR8YcDzYPv4Igkphp5YxX7l7eKrFZiMpSfR9/I+MQ1Foj95gcy+q8ZyyDrrLcAhhv+aLnR4t10Xf/nkw8caUDqQVTPwfjwe7KHWV8/X4gRdw0O6kNC8DFLtKvBw7GG7zS4f7pLIc3t+NcFrqVsus0gh9rrXYc3F0KWQpAR6An/Xxer2Clrpe//t18utYyFMjoib4wcPaLa8gFdormQNuA5jxJ5Ctx/NO91KOONivVhGFUslGA6K4ZhHVLOqrPq08TIywyKwvESjIkn9aoWBP/E/35UhNMot0LXILEnzHPrZYn84r8fpboRNqZrTxzo4Zv1cJjaZdtvvLVE8UZPN6NgJFrEP25FUvojSGKdRwNSYUXFp4W8Nqv+0ZDlOCF0u1QSTM3w3H7pF8vam93YT92Ptxq4LpcG0IkEwApZ8BoTWfrrVSQfjdZ5ZL8s29A0wQyYjmnQSwkF7W1MZd/Cj2XU646c9/MRHUnZzsDq5W0PY25WKRdVH0Ybq14kBsjGneyowdxkRyUBFI3K/ccgzaiStN2w/oHEivI0ohryxWhtCJIig8fAPl5PkeOb7vj30GPYAtD2lVoRVO9smK4iqdhIsF0mk4awFggOEeCPrgVscDANj2jLQ+tADwqGeMBLV44AdbMmQYxgo6hWHD3UQ5cN7XK94KU07Zv54Ba3C+LRqhqOsjZjSjwAFEDKNIHWO1cg2qR6k6RAWGm9iM1LKOAtSuRHMjiPBKGoIwCPeU2LSBP7hg+Lj5wjQaAlxR3TSIlUaHS6z91MlBZdzwxhwfc9gSSPQeqgN70XUJA5qJUBvIkigkszXqfeVYwxTE6h2okWJkYHlEv7TcIdAcg4TBKhGFA/aHHteDKSL8mxqJ4bt4a1BQEXeY+idhzjoSeUw8lEnNYSZT4P2uKcKUuSgQnjonU2ZZDHIgYYKkziBPL0Zo/bghjauk5G105uY601yUYqhebKjB6NmMeOW4hpLw4IPLq1jb0Jc1FimDetCBIeQaO8IoSWISaRzyRJdtj3r+gB/R5/xMO7NAFgYEPUFivJlU0eNoDs3glYAglOKshGMng4c9dwr3BQCI4sAHRGCrRstFINFRCOMYkpPTBTUGMbKas9Gaog6ZtuHMV2dpT6ehptNyuO6XwVIdSP7JIWQ95QwwEdjgiNg1A6GUGgyOkRb5xQyg5PGM4yMZCQG6KVpbBNKZVvESdxgBsj1tTPzeaZnBrLcQFrL43DQOQiOMMwgsCNwtnVjAFb2BHgIPHN5tRuAh06BJrKByQXtIwaQDBz2VXNHlFcCR4mhQkeBwj9IBpeLJu0OUvndMap5x2AOADZo4MYY6iODBtTFCshgaIQoMDTgnUraMffMxSgifOZ6ZnmwqkfdFhMUYEARoX5ENPKI7DcQ930LwKBdr3IFh9SkYgg0KO8GARhwQQNO8Chs2hNsFa5Fi3EcER9HMuKUhmPXkt4brHQDzDidjpQBD60unOadGODvVIY5GOs46gScRUER3nKNO4aGfAAY6QCs4h5YxAwwo7ND8ECaonC0gJhgg7qwawPRuPMUBUfFJJjIsagNmo097KOBoAxOEc7j3QTDmN3mjRtj5RixObQm3UejztUIWxjyPtRUAZAGxhMm2OgEaHStzeABSSw1xcAjBISRNQvdqEZiS45s4ih5LsPUdxD5lrSsLzkw48KGDtWj3bPyGDuz8z4QgvY2IHjCuKAh7ZLBaLUNrhuyngvOJQR6oR3x0CjjvDJaR5CgiLm09/A/0411jWoTj9qOF3sJeglBn9aQdrG1gZPdTEABmWaO8/kQ9dDYBvS6k3ucj8VEy4kdHbcQsG6EpuMt8D62IPcEcYCthuSO70tpgUwUpS6Bkbd8CEFHsUlyApyJxIKxvESYADTQhyYoaysxRBChpZRhqoTpzNjBMHY0OKLFCAsMMsxoMLWzRA8RuY0CFxDzvjOI6mpioZTOCUn72BEGSQhFXemGOqKDc49RL5BhiYyE0HOneLvlo2JkBKRldho7OGtEHnqEjMKdjpyOASLcI2Fx/9hizTOyoBwPEXBpF6TXxginrCHNdtqDDjdFWOQEBJR6MTrshlaxylkcYxoTdh77ggwISz3qwVjt5SBDCJa3JB7SSlMbJoXEDmUlQCjY2CrMejqCVgGi88IWNfG8o1y0aa2l7z1qMK1AZKKJ5/HgMcHpiFo1htEPFrAAEA7StAjWzBCrGHS0iBJEnS6ffIdntRNdkBgyThVDlNOY9YVhtujpsN843Bq/j4BjEcoUjrStwYig2o1mCABgyxBDghqo5Qgi5TtOsOhznFHV2I5MejWzUeojwjH3ERUmDs4n/dZoaKlFA7GDToDKUKNyq+KhEW7n+2o2SNdA5jhOHMMUwDaGCgETWeOsi0kkYZTIUY0FpQkFEeTKZz0Ng8MGKuq8HXU1pGUEOTZukkaydjMXGceHxpeZAh4nHEstEM2QTfWYNNYBG4jhhnY+UQwR7CaajTw3RdCVhA41Y9gZj50NGvKRegWUyFmWeiYjnALoHSh1iBoOGolHV7hJ0JHlEmnk9RNtq8Y5XtOsM7AzDDDhCTMNMF1Bwb0bxwj71jFoAOXGAsVdIA5gnEqzZFq3jDyFWhHmNI78GLGwUBGkfDRKoR7r13TcT+AQGYR7Sv0QgwQO2DjC0ncFTbW2OwwyiwKgyIgQjTByIqmywASm3vrQurEWfA8hjuJYcRN8buFMsQdYGjcirSlqiGeAuwwSmQacKIJhSZGlpMxkAMhQNZMYNdRfaG8ppnUcdREAziSs59a4sXc0A/hGTCjziYF6VESzzqybrSbII85gftPGDALnYu2EVoJqBGhCe0F2x6SCWhA9C6BFBvMe5y1ylDhoa2B5sINOpRJS0Zx3ixgpuX0MytBeY7dCY6ozwiDOicnFLvLBw6PqdAN65gJNCyCBJwUMABPlofFNlNSw6RAdnciMSU3suVTwwkRAoxh2EKPduttn9Y7ieoYQG4A4ITzVwjmI9U71CgcKbM+ATwgiiGNWG9ySy6RpE7xZoBHvBHADAq6HgGLLBJ2MHACUmd7Qpso61ZOy0xbnweecRpSkERM96sfBubVwNYzaGdSQ0hkiErswuhYNTvTk2HszEAHtiNA2eENyDAXCHQ4NP4h2IOgR2pG7NkSollbwoj31Ok364Fnoyg4NnjtCDE6xzLwkjTQh074marA2IBtCIG80j62dvkaIxw4WqJXU8tACWEO7TmwbsSfJYR8VB8KlZxIknQCVVcq5zh+xJxJLmzFmC+U0HOAARmfHsmIOFRRS4xiGP3UsKyGrJzYkUs/cDnmNxyGFDXbOEoU5I1RWLLYBw44FaKkjo/ce0sHKKWuXakCpMzXWqjXUIEc1oy0RKpegj7WnFv5HxO1uWfLpbk9xR7UfrTOasSBHPdWUqLBoJdKaFi1wHbXapoZGHaIDpH0RuYG3vbCgGwNzsZ1o4KXRCZJKw8p6bhgc/z9OHGz+pvHAzAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "x, y = landmassf3_train[4]\n", - "print(\"Class:\", landmassf3_train.classes[y])\n", - "x" + "for i in range(10):\n", + " x, y = landmassf3_train[i]\n", + " print(\"Class:\", landmassf3_train.classes[y])\n", + " display(x)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2020-11-03T05:41:51.618493Z", + "start_time": "2020-11-03T05:41:51.583011Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda/envs/py37_pytorch/lib/python3.7/site-packages/torchvision/datasets/mnist.py:45: UserWarning: train_labels has been renamed targets\n", + " warnings.warn(\"train_labels has been renamed targets\")\n" + ] + }, + { + "data": { + "text/plain": [ + "Continuous 0.529358\n", + "Discontinuous 0.291396\n", + "Salt 0.108453\n", + "Faulted 0.070792\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Note that this is an unbalanced dataset, so we expect an accuracy of at least 52%, this is out baseline\n", "labels = pd.Series(landmassf3_train.train_labels).replace(dict(enumerate(landmassf3_train.classes)))\n", @@ -310,11 +579,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:34.801249Z", - "start_time": "2020-10-13T11:01:34.735665Z" + "end_time": "2020-11-03T05:41:51.659500Z", + "start_time": "2020-11-03T05:41:51.620070Z" } }, "outputs": [ @@ -324,7 +593,7 @@ "['Discontinuous', 'Faulted', 'Continuous', 'Salt']" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -361,11 +630,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:34.874511Z", - "start_time": "2020-10-13T11:01:34.803122Z" + "end_time": "2020-11-03T05:41:51.734285Z", + "start_time": "2020-11-03T05:41:51.660732Z" } }, "outputs": [ @@ -1066,11 +1335,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:34.925623Z", - "start_time": "2020-10-13T11:01:34.876534Z" + "end_time": "2020-11-03T05:41:51.752419Z", + "start_time": "2020-11-03T05:41:51.736345Z" } }, "outputs": [], @@ -1108,11 +1377,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:36.543151Z", - "start_time": "2020-10-13T11:01:34.926857Z" + "end_time": "2020-11-03T05:42:13.413381Z", + "start_time": "2020-11-03T05:41:51.753666Z" } }, "outputs": [ @@ -1138,11 +1407,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:36.898830Z", - "start_time": "2020-10-13T11:01:36.544561Z" + "end_time": "2020-11-03T05:42:17.213478Z", + "start_time": "2020-11-03T05:42:13.414691Z" } }, "outputs": [ @@ -1173,7 +1442,7 @@ "1" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1200,11 +1469,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:36.947568Z", - "start_time": "2020-10-13T11:01:36.903252Z" + "end_time": "2020-11-03T05:42:17.272027Z", + "start_time": "2020-11-03T05:42:17.214781Z" } }, "outputs": [ @@ -1212,7 +1481,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor([[-0.0955, 0.0532, 0.0204, -0.0987]], device='cuda:0',\n", + "tensor([[ 0.0177, 0.0667, -0.0199, -0.1097]], device='cuda:0',\n", " grad_fn=)\n" ] } @@ -1241,21 +1510,21 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:36.963650Z", - "start_time": "2020-10-13T11:01:36.948781Z" + "end_time": "2020-11-03T05:42:17.278181Z", + "start_time": "2020-11-03T05:42:17.273282Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1281,11 +1550,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:37.236500Z", - "start_time": "2020-10-13T11:01:36.965111Z" + "end_time": "2020-11-03T05:42:17.506335Z", + "start_time": "2020-11-03T05:42:17.279301Z" } }, "outputs": [], @@ -1308,11 +1577,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:37.247794Z", - "start_time": "2020-10-13T11:01:37.238271Z" + "end_time": "2020-11-03T05:42:17.513911Z", + "start_time": "2020-11-03T05:42:17.507813Z" } }, "outputs": [], @@ -1351,11 +1620,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:37.300329Z", - "start_time": "2020-10-13T11:01:37.249243Z" + "end_time": "2020-11-03T05:42:17.580445Z", + "start_time": "2020-11-03T05:42:17.515248Z" } }, "outputs": [], @@ -1379,11 +1648,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:01:49.416804Z", - "start_time": "2020-10-13T11:01:37.301715Z" + "end_time": "2020-11-03T05:42:27.227016Z", + "start_time": "2020-11-03T05:42:17.581731Z" }, "scrolled": true }, @@ -1391,7 +1660,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9e5a344cc65441498d0141c7bc5e3e62", + "model_id": "7f39c62372a34dee8e4fdd6f53e8a6bd", "version_major": 2, "version_minor": 0 }, @@ -1406,26 +1675,26 @@ "name": "stdout", "output_type": 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loss: 0.00611\n", + "[1, 180] loss: 0.00602\n", + "[1, 190] loss: 0.00594\n", + "[1, 200] loss: 0.00591\n", "\n", "Finished Training\n", "2371 4417\n", @@ -1450,11 +1719,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:02:31.312762Z", - "start_time": "2020-10-13T11:01:49.418135Z" + "end_time": "2020-11-03T05:43:00.875475Z", + "start_time": "2020-11-03T05:42:27.228366Z" }, "scrolled": true }, @@ -1462,7 +1731,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "29c13920b3a7479988c9c36c46d78360", + "model_id": "ddd017ba32344eaaacd5548203b0d300", "version_major": 2, "version_minor": 0 }, @@ -1477,106 +1746,106 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1, 10] loss: 0.00588\n", - "[1, 20] loss: 0.00584\n", - "[1, 30] loss: 0.00607\n", - "[1, 40] loss: 0.00597\n", - "[1, 50] loss: 0.00604\n", - "[1, 60] loss: 0.00601\n", - "[1, 70] loss: 0.00586\n", - "[1, 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"[4, 180] loss: 0.00579\n", + "[4, 190] loss: 0.00579\n", + "[4, 200] loss: 0.00584\n", + "[5, 10] loss: 0.00578\n", + "[5, 20] loss: 0.00576\n", + "[5, 30] loss: 0.00596\n", + "[5, 40] loss: 0.00589\n", + "[5, 50] loss: 0.00594\n", + "[5, 60] loss: 0.00593\n", + "[5, 70] loss: 0.00576\n", + "[5, 80] loss: 0.00584\n", + "[5, 90] loss: 0.006\n", + "[5, 100] loss: 0.00594\n", + "[5, 110] loss: 0.00593\n", + "[5, 120] loss: 0.00581\n", + "[5, 130] loss: 0.00593\n", + "[5, 140] loss: 0.00585\n", + "[5, 150] loss: 0.00587\n", + "[5, 160] loss: 0.00586\n", + "[5, 170] loss: 0.00581\n", + "[5, 180] loss: 0.00579\n", + "[5, 190] loss: 0.00579\n", + "[5, 200] loss: 0.00584\n", "\n", "Finished Training\n", "Testing accuracy on unseen data...\n", @@ -1607,11 +1876,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:02:31.323080Z", - "start_time": "2020-10-13T11:02:31.314288Z" + "end_time": "2020-11-03T05:43:00.885805Z", + "start_time": "2020-11-03T05:43:00.876853Z" } }, "outputs": [], @@ -1670,11 +1939,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:03:06.466022Z", - "start_time": "2020-10-13T11:02:31.325667Z" + "end_time": "2020-11-03T05:43:28.944080Z", + "start_time": "2020-11-03T05:43:00.887623Z" }, "scrolled": true }, @@ -1682,7 +1951,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5c393b5324ce4659ade19c41a87d5ce8", + "model_id": "71cddfba7cc043c7a483d18942abd653", "version_major": 2, "version_minor": 0 }, @@ -1728,7 +1997,7 @@ "59.157799411365176" ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1742,11 +2011,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:03:06.510194Z", - "start_time": "2020-10-13T11:03:06.468783Z" + "end_time": "2020-11-03T05:43:28.977303Z", + "start_time": "2020-11-03T05:43:28.946269Z" } }, "outputs": [ @@ -1807,7 +2076,7 @@ "1" ] }, - "execution_count": 21, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1856,248 +2125,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-10-13T11:08:21.574558Z", "start_time": "2020-10-13T11:03:52.622772Z" } }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dbc78f3990644a4b94c94c6618ca47f9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 10] loss: 0.0546\n", - "[1, 20] loss: 0.00867\n", - "[1, 30] loss: 0.00245\n", - "[1, 40] loss: 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loss: 3.62e-09\n", - "[9, 100] loss: 5.83e-07\n", - "[9, 110] loss: 1.54e-06\n", - "[9, 120] loss: 1.64e-10\n", - "[9, 130] loss: 0.00011\n", - "[9, 140] loss: 0\n", - "[9, 150] loss: 1.04e-10\n", - "[9, 160] loss: 0\n", - "[9, 170] loss: 1.8e-07\n", - "[9, 180] loss: 7.72e-07\n", - "[9, 190] loss: 4.52e-09\n", - "[9, 200] loss: 0.000218\n", - "[10, 10] loss: 2.37e-05\n", - "[10, 20] loss: 8.21e-08\n", - "[10, 30] loss: 1.83e-06\n", - "[10, 40] loss: 0\n", - "[10, 50] loss: 0\n", - "[10, 60] loss: 2.38e-10\n", - "[10, 70] loss: 6.94e-05\n", - "[10, 80] loss: 1.04e-07\n", - "[10, 90] loss: 0\n", - "[10, 100] loss: 1.56e-05\n", - "[10, 110] loss: 0.000305\n", - "[10, 120] loss: 3.43e-09\n", - "[10, 130] loss: 1.27e-06\n", - "[10, 140] loss: 0\n", - "[10, 150] loss: 4.68e-09\n", - "[10, 160] loss: 0\n", - "[10, 170] loss: 2.74e-08\n", - "[10, 180] loss: 3.66e-06\n", - "[10, 190] loss: 8.94e-11\n", - "[10, 200] loss: 0.000204\n", - "\n", - "Finished Training\n", - "4412 4417\n" - ] - }, - { - "data": { - "text/plain": [ - "99.88680099615124" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [] }, { @@ -2109,11 +2144,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": { "ExecuteTime": { - "end_time": "2020-10-13T11:03:06.518361Z", - "start_time": "2020-10-13T11:03:06.511841Z" + "end_time": "2020-11-03T05:43:29.004642Z", + "start_time": "2020-11-03T05:43:28.978505Z" } }, "outputs": [], diff --git a/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.py b/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.py index 8454dc5..c4fa0c8 100644 --- a/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.py +++ b/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.py @@ -21,6 +21,8 @@ from torch import nn import torch.nn.functional as F +import pandas as pd + import numpy as np from tqdm.auto import tqdm # - @@ -144,6 +146,11 @@ print(landmassf3_test) # - +for i in range(4): + x, y = landmassf3_train[i] + print("Class:", landmassf3_train.classes[y]) + display(x) + # Let's have a look at the dataset print(landmassf3_train.data.shape) print(landmassf3_test.data.shape) @@ -154,9 +161,10 @@ # # Let's display the first one: -x, y = landmassf3_train[4] -print("Class:", landmassf3_train.classes[y]) -x +for i in range(10): + x, y = landmassf3_train[i] + print("Class:", landmassf3_train.classes[y]) + display(x) # Note that this is an unbalanced dataset, so we expect an accuracy of at least 52%, this is out baseline labels = pd.Series(landmassf3_train.train_labels).replace(dict(enumerate(landmassf3_train.classes)))