test_iris_nn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://guo-pu.blog.csdn.net/article/details/107141991"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0, loss: 0.2821310982108116\n",
      "Test_acc: 0.16666666666666666\n",
      "--------------------------\n",
      "Epoch 1, loss: 0.25459615513682365\n",
      "Test_acc: 0.16666666666666666\n",
      "--------------------------\n",
      "Epoch 2, loss: 0.22570249810814857\n",
      "Test_acc: 0.16666666666666666\n",
      "--------------------------\n",
      "Epoch 3, loss: 0.21028399839997292\n",
      "Test_acc: 0.16666666666666666\n",
      "--------------------------\n",
      "Epoch 4, loss: 0.19942265003919601\n",
      "Test_acc: 0.16666666666666666\n",
      "--------------------------\n",
      "Epoch 5, loss: 0.18873637914657593\n",
      "Test_acc: 0.5\n",
      "--------------------------\n",
      "Epoch 6, loss: 0.17851299047470093\n",
      "Test_acc: 0.5333333333333333\n",
      "--------------------------\n",
      "Epoch 7, loss: 0.16922875493764877\n",
      "Test_acc: 0.5333333333333333\n",
      "--------------------------\n",
      "Epoch 8, loss: 0.16107673197984695\n",
      "Test_acc: 0.5333333333333333\n",
      "--------------------------\n",
      "Epoch 9, loss: 0.15404684841632843\n",
      "Test_acc: 0.5333333333333333\n",
      "--------------------------\n",
      "Epoch 10, loss: 0.14802725985646248\n",
      "Test_acc: 0.5333333333333333\n",
      "--------------------------\n",
      "Epoch 11, loss: 0.14287303015589714\n",
      "Test_acc: 0.5333333333333333\n",
      "--------------------------\n",
      "Epoch 12, loss: 0.1384414155036211\n",
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      "--------------------------\n",
      "Epoch 13, loss: 0.13460607640445232\n",
      "Test_acc: 0.5333333333333333\n",
      "--------------------------\n",
      "Epoch 14, loss: 0.13126072846353054\n",
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      "--------------------------\n",
      "Epoch 15, loss: 0.12831822223961353\n",
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      "--------------------------\n",
      "Epoch 16, loss: 0.12570795230567455\n",
      "Test_acc: 0.5333333333333333\n",
      "--------------------------\n",
      "Epoch 17, loss: 0.12337299063801765\n",
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      "--------------------------\n",
      "Epoch 18, loss: 0.12126746587455273\n",
      "Test_acc: 0.5333333333333333\n",
      "--------------------------\n",
      "Epoch 19, loss: 0.11935433372855186\n",
      "Test_acc: 0.5333333333333333\n",
      "--------------------------\n",
      "Epoch 20, loss: 0.11760355532169342\n",
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      "--------------------------\n",
      "Epoch 21, loss: 0.11599067971110344\n",
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      "--------------------------\n",
      "Epoch 22, loss: 0.11449568346142769\n",
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      "--------------------------\n",
      "Epoch 23, loss: 0.11310207843780518\n",
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      "--------------------------\n",
      "Epoch 24, loss: 0.11179621890187263\n",
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      "--------------------------\n",
      "Epoch 25, loss: 0.11056671477854252\n",
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      "--------------------------\n",
      "Epoch 26, loss: 0.10940408334136009\n",
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      "--------------------------\n",
      "Epoch 27, loss: 0.10830028541386127\n",
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      "--------------------------\n",
      "Epoch 28, loss: 0.10724855214357376\n",
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      "--------------------------\n",
      "Epoch 29, loss: 0.10624313168227673\n",
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      "--------------------------\n",
      "Epoch 30, loss: 0.1052791029214859\n",
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      "Epoch 31, loss: 0.10435221716761589\n",
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      "--------------------------\n",
      "Epoch 32, loss: 0.10345886647701263\n",
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      "--------------------------\n",
      "Epoch 33, loss: 0.10259587876498699\n",
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      "--------------------------\n",
      "Epoch 34, loss: 0.10176052898168564\n",
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      "--------------------------\n",
      "Epoch 35, loss: 0.10095042176544666\n",
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      "--------------------------\n",
      "Epoch 36, loss: 0.10016347281634808\n",
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      "--------------------------\n",
      "Epoch 37, loss: 0.09939785301685333\n",
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      "--------------------------\n",
      "Epoch 38, loss: 0.0986519306898117\n",
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      "--------------------------\n",
      "Epoch 39, loss: 0.09792429022490978\n",
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      "--------------------------\n",
      "Epoch 40, loss: 0.09721364825963974\n",
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      "--------------------------\n",
      "Epoch 41, loss: 0.09651889838278294\n",
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      "--------------------------\n",
      "Epoch 42, loss: 0.09583901055157185\n",
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      "Epoch 43, loss: 0.09517310559749603\n",
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      "--------------------------\n",
      "Epoch 44, loss: 0.09452036581933498\n",
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      "--------------------------\n",
      "Epoch 45, loss: 0.0938800759613514\n",
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      "--------------------------\n",
      "Epoch 46, loss: 0.09325156174600124\n",
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      "Epoch 47, loss: 0.09263424947857857\n",
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      "--------------------------\n",
      "Epoch 48, loss: 0.09202760085463524\n",
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      "--------------------------\n",
      "Epoch 49, loss: 0.09143111668527126\n",
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      "--------------------------\n",
      "Epoch 50, loss: 0.09084436297416687\n",
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      "--------------------------\n",
      "Epoch 51, loss: 0.09026693925261497\n",
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      "--------------------------\n",
      "Epoch 52, loss: 0.08969846926629543\n",
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      "--------------------------\n",
      "Epoch 53, loss: 0.08913860656321049\n",
      "Test_acc: 0.6\n",
      "--------------------------\n",
      "Epoch 54, loss: 0.08858705498278141\n",
      "Test_acc: 0.6\n",
      "--------------------------\n",
      "Epoch 55, loss: 0.08804351650178432\n",
      "Test_acc: 0.6\n",
      "--------------------------\n",
      "Epoch 56, loss: 0.08750772476196289\n",
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      "--------------------------\n",
      "Epoch 57, loss: 0.0869794450700283\n",
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      "--------------------------\n",
      "Epoch 58, loss: 0.08645843528211117\n",
      "Test_acc: 0.6\n",
      "--------------------------\n",
      "Epoch 59, loss: 0.08594449050724506\n",
      "Test_acc: 0.6\n",
      "--------------------------\n",
      "Epoch 60, loss: 0.08543741703033447\n",
      "Test_acc: 0.6\n",
      "--------------------------\n",
      "Epoch 61, loss: 0.08493701927363873\n",
      "Test_acc: 0.6\n",
      "--------------------------\n",
      "Epoch 62, loss: 0.08444313891232014\n",
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      "--------------------------\n",
      "Epoch 63, loss: 0.08395560272037983\n",
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      "--------------------------\n",
      "Epoch 64, loss: 0.08347426354885101\n",
      "Test_acc: 0.6333333333333333\n",
      "--------------------------\n",
      "Epoch 65, loss: 0.08299898356199265\n",
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      "--------------------------\n",
      "Epoch 66, loss: 0.08252961561083794\n",
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      "Epoch 67, loss: 0.08206603862345219\n",
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      "Epoch 68, loss: 0.08160812221467495\n",
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      "Epoch 69, loss: 0.08115577697753906\n",
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      "--------------------------\n",
      "Epoch 70, loss: 0.08070887625217438\n",
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      "--------------------------\n",
      "Epoch 71, loss: 0.08026730827987194\n",
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      "--------------------------\n",
      "Epoch 72, loss: 0.07983098551630974\n",
      "Test_acc: 0.6666666666666666\n",
      "--------------------------\n",
      "Epoch 73, loss: 0.07939981296658516\n",
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      "Epoch 74, loss: 0.0789736919105053\n",
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      "--------------------------\n",
      "Epoch 75, loss: 0.07855254225432873\n",
      "Test_acc: 0.7\n",
      "--------------------------\n",
      "Epoch 76, loss: 0.07813626900315285\n",
      "Test_acc: 0.7\n",
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      "Epoch 77, loss: 0.07772481068968773\n",
      "Test_acc: 0.7\n",
      "--------------------------\n",
      "Epoch 78, loss: 0.07731806673109531\n",
      "Test_acc: 0.7\n",
      "--------------------------\n",
      "Epoch 79, loss: 0.07691597566008568\n",
      "Test_acc: 0.7\n",
      "--------------------------\n",
      "Epoch 80, loss: 0.07651844993233681\n",
      "Test_acc: 0.7\n",
      "--------------------------\n",
      "Epoch 81, loss: 0.07612543925642967\n",
      "Test_acc: 0.7333333333333333\n",
      "--------------------------\n",
      "Epoch 82, loss: 0.0757368616759777\n",
      "Test_acc: 0.7333333333333333\n",
      "--------------------------\n",
      "Epoch 83, loss: 0.07535265013575554\n",
      "Test_acc: 0.7333333333333333\n",
      "--------------------------\n",
      "Epoch 84, loss: 0.07497274875640869\n",
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      "--------------------------\n",
      "Epoch 85, loss: 0.07459708396345377\n",
      "Test_acc: 0.7666666666666667\n",
      "--------------------------\n",
      "Epoch 86, loss: 0.074225596152246\n",
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      "--------------------------\n",
      "Epoch 87, loss: 0.07385822664946318\n",
      "Test_acc: 0.7666666666666667\n",
      "--------------------------\n",
      "Epoch 88, loss: 0.07349492143839598\n",
      "Test_acc: 0.7666666666666667\n",
      "--------------------------\n",
      "Epoch 89, loss: 0.07313562091439962\n",
      "Test_acc: 0.7666666666666667\n",
      "--------------------------\n",
      "Epoch 90, loss: 0.07278026547282934\n",
      "Test_acc: 0.7666666666666667\n",
      "--------------------------\n",
      "Epoch 91, loss: 0.0724287973716855\n",
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      "--------------------------\n",
      "Epoch 92, loss: 0.07208118494600058\n",
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      "Epoch 93, loss: 0.07173734344542027\n",
      "Test_acc: 0.8\n",
      "--------------------------\n",
      "Epoch 94, loss: 0.07139724027365446\n",
      "Test_acc: 0.8\n",
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      "Epoch 95, loss: 0.07106082234531641\n",
      "Test_acc: 0.8\n",
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      "Epoch 96, loss: 0.07072803378105164\n",
      "Test_acc: 0.8\n",
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      "Epoch 97, loss: 0.0703988391906023\n",
      "Test_acc: 0.8\n",
      "--------------------------\n",
      "Epoch 98, loss: 0.07007317896932364\n",
      "Test_acc: 0.8333333333333334\n",
      "--------------------------\n",
      "Epoch 99, loss: 0.0697510140016675\n",
      "Test_acc: 0.8666666666666667\n",
      "--------------------------\n",
      "Epoch 100, loss: 0.06943229213356972\n",
      "Test_acc: 0.8666666666666667\n",
      "--------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 101, loss: 0.06911696959286928\n",
      "Test_acc: 0.8666666666666667\n",
      "--------------------------\n",
      "Epoch 102, loss: 0.06880500447005033\n",
      "Test_acc: 0.8666666666666667\n",
      "--------------------------\n",
      "Epoch 103, loss: 0.068496348336339\n",
      "Test_acc: 0.8666666666666667\n",
      "--------------------------\n",
      "Epoch 104, loss: 0.06819095648825169\n",
      "Test_acc: 0.8666666666666667\n",
      "--------------------------\n",
      "Epoch 105, loss: 0.06788879167288542\n",
      "Test_acc: 0.8666666666666667\n",
      "--------------------------\n",
      "Epoch 106, loss: 0.06758981943130493\n",
      "Test_acc: 0.8666666666666667\n",
      "--------------------------\n",
      "Epoch 107, loss: 0.0672939820215106\n",
      "Test_acc: 0.9\n",
      "--------------------------\n",
      "Epoch 108, loss: 0.06700124125927687\n",
      "Test_acc: 0.9\n",
      "--------------------------\n",
      "Epoch 109, loss: 0.0667115617543459\n",
      "Test_acc: 0.9\n",
      "--------------------------\n",
      "Epoch 110, loss: 0.06642490904778242\n",
      "Test_acc: 0.9\n",
      "--------------------------\n",
      "Epoch 111, loss: 0.06614123564213514\n",
      "Test_acc: 0.9\n",
      "--------------------------\n",
      "Epoch 112, loss: 0.0658605070784688\n",
      "Test_acc: 0.9\n",
      "--------------------------\n",
      "Epoch 113, loss: 0.06558268051594496\n",
      "Test_acc: 0.9\n",
      "--------------------------\n",
      "Epoch 114, loss: 0.06530772615224123\n",
      "Test_acc: 0.9\n",
      "--------------------------\n",
      "Epoch 115, loss: 0.06503560487180948\n",
      "Test_acc: 0.9\n",
      "--------------------------\n",
      "Epoch 116, loss: 0.06476627103984356\n",
      "Test_acc: 0.9\n",
      "--------------------------\n",
      "Epoch 117, loss: 0.06449970323592424\n",
      "Test_acc: 0.9333333333333333\n",
      "--------------------------\n",
      "Epoch 118, loss: 0.06423585396260023\n",
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      "--------------------------\n",
      "Epoch 119, loss: 0.06397469434887171\n",
      "Test_acc: 0.9333333333333333\n",
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      "Epoch 120, loss: 0.06371618993580341\n",
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      "--------------------------\n",
      "Epoch 121, loss: 0.06346031092107296\n",
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      "--------------------------\n",
      "Epoch 122, loss: 0.06320700887590647\n",
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      "Epoch 123, loss: 0.06295627448707819\n",
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      "Epoch 124, loss: 0.0627080425620079\n",
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      "Epoch 125, loss: 0.06246231310069561\n",
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      "Epoch 126, loss: 0.06221904046833515\n",
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      "Epoch 127, loss: 0.061978183686733246\n",
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      "--------------------------\n",
      "Epoch 128, loss: 0.06173973251134157\n",
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      "--------------------------\n",
      "Epoch 129, loss: 0.06150364316999912\n",
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      "Epoch 130, loss: 0.061269884929060936\n",
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      "Epoch 131, loss: 0.06103843171149492\n",
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      "--------------------------\n",
      "Epoch 132, loss: 0.06080925837159157\n",
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      "--------------------------\n",
      "Epoch 133, loss: 0.06058232951909304\n",
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      "--------------------------\n",
      "Epoch 134, loss: 0.06035762187093496\n",
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      "--------------------------\n",
      "Epoch 135, loss: 0.060135108418762684\n",
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      "--------------------------\n",
      "Epoch 136, loss: 0.05991474911570549\n",
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      "--------------------------\n",
      "Epoch 137, loss: 0.05969652533531189\n",
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      "Epoch 138, loss: 0.05948041286319494\n",
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      "Epoch 139, loss: 0.05926638375967741\n",
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      "Epoch 140, loss: 0.05905440915375948\n",
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      "Epoch 141, loss: 0.058844463899731636\n",
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      "Epoch 142, loss: 0.05863652843981981\n",
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      "Epoch 143, loss: 0.058430567383766174\n",
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      "Epoch 144, loss: 0.05822655465453863\n",
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      "Epoch 145, loss: 0.05802448093891144\n",
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      "Epoch 146, loss: 0.057824309915304184\n",
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      "Epoch 147, loss: 0.057626024819910526\n",
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      "Epoch 148, loss: 0.0574295949190855\n",
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      "Epoch 149, loss: 0.0572349950671196\n",
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      "Epoch 150, loss: 0.05704221222549677\n",
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      "Epoch 151, loss: 0.05685121566057205\n",
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      "Epoch 152, loss: 0.05666199512779713\n",
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      "Epoch 153, loss: 0.05647451803088188\n",
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      "Epoch 154, loss: 0.056288767606019974\n",
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      "Epoch 155, loss: 0.05610471125692129\n",
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      "Epoch 156, loss: 0.055922345258295536\n",
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      "Epoch 157, loss: 0.0557416332885623\n",
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      "Epoch 158, loss: 0.05556256230920553\n",
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      "Epoch 159, loss: 0.05538512021303177\n",
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      "Epoch 160, loss: 0.05520927347242832\n",
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      "Epoch 161, loss: 0.0550350034609437\n",
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      "Epoch 162, loss: 0.05486230552196503\n",
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      "Epoch 163, loss: 0.05469114240258932\n",
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      "Epoch 164, loss: 0.05452151130884886\n",
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      "Epoch 165, loss: 0.05435337871313095\n",
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      "Epoch 166, loss: 0.054186731576919556\n",
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     ]
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_acc: 1.0\n",
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      "--------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: UTF-8 -*-\n",
    "# 利用鸢尾花数据集，实现前向传播、反向传播，可视化loss曲线\n",
    " \n",
    "# 导入所需模块\n",
    "import tensorflow as tf\n",
    "from sklearn import datasets\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    " \n",
    "# 导入数据，分别为输入特征和标签\n",
    "x_data = datasets.load_iris().data\n",
    "y_data = datasets.load_iris().target\n",
    " \n",
    "# 随机打乱数据（因为原始数据是顺序的，顺序不打乱会影响准确率）\n",
    "# seed: 随机数种子，是一个整数，当设置之后，每次生成的随机数都一样（为方便教学，以保每位同学结果一致）\n",
    "np.random.seed(116)  # 使用相同的seed，保证输入特征和标签一一对应\n",
    "np.random.shuffle(x_data)\n",
    "np.random.seed(116)\n",
    "np.random.shuffle(y_data)\n",
    "tf.random.set_seed(116)\n",
    " \n",
    "# 将打乱后的数据集分割为训练集和测试集，训练集为前120行，测试集为后30行\n",
    "x_train = x_data[:-30]\n",
    "y_train = y_data[:-30]\n",
    "x_test = x_data[-30:]\n",
    "y_test = y_data[-30:]\n",
    " \n",
    "# 转换x的数据类型，否则后面矩阵相乘时会因数据类型不一致报错\n",
    "x_train = tf.cast(x_train, tf.float32)\n",
    "x_test = tf.cast(x_test, tf.float32)\n",
    " \n",
    "# from_tensor_slices函数使输入特征和标签值一一对应。（把数据集分批次，每个批次batch组数据）\n",
    "train_db = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(32)\n",
    "test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)\n",
    " \n",
    "# 生成神经网络的参数，4个输入特征故，输入层为4个输入节点；因为3分类，故输出层为3个神经元\n",
    "# 用tf.Variable()标记参数可训练\n",
    "# 使用seed使每次生成的随机数相同（方便教学，使大家结果都一致，在现实使用时不写seed）\n",
    "w1 = tf.Variable(tf.random.truncated_normal([4, 3], stddev=0.1, seed=1))\n",
    "b1 = tf.Variable(tf.random.truncated_normal([3], stddev=0.1, seed=1))\n",
    " \n",
    "lr = 0.1  # 学习率为0.1\n",
    "train_loss_results = []  # 将每轮的loss记录在此列表中，为后续画loss曲线提供数据\n",
    "test_acc = []  # 将每轮的acc记录在此列表中，为后续画acc曲线提供数据\n",
    "epoch = 500  # 循环500轮\n",
    "loss_all = 0  # 每轮分4个step，loss_all记录四个step生成的4个loss的和\n",
    " \n",
    "# 训练部分\n",
    "for epoch in range(epoch):  #数据集级别的循环，每个epoch循环一次数据集\n",
    "    for step, (x_train, y_train) in enumerate(train_db):  #batch级别的循环 ，每个step循环一个batch\n",
    "        with tf.GradientTape() as tape:  # with结构记录梯度信息\n",
    "            y = tf.matmul(x_train, w1) + b1  # 神经网络乘加运算\n",
    "            y = tf.nn.softmax(y)  # 使输出y符合概率分布（此操作后与独热码同量级，可相减求loss）\n",
    "            y_ = tf.one_hot(y_train, depth=3)  # 将标签值转换为独热码格式，方便计算loss和accuracy\n",
    "            loss = tf.reduce_mean(tf.square(y_ - y))  # 采用均方误差损失函数mse = mean(sum(y-out)^2)\n",
    "            loss_all += loss.numpy()  # 将每个step计算出的loss累加，为后续求loss平均值提供数据，这样计算的loss更准确\n",
    "        # 计算loss对各个参数的梯度\n",
    "        grads = tape.gradient(loss, [w1, b1])\n",
    " \n",
    "        # 实现梯度更新 w1 = w1 - lr * w1_grad    b = b - lr * b_grad\n",
    "        w1.assign_sub(lr * grads[0])  # 参数w1自更新\n",
    "        b1.assign_sub(lr * grads[1])  # 参数b自更新\n",
    " \n",
    "    # 每个epoch，打印loss信息\n",
    "    print(\"Epoch {}, loss: {}\".format(epoch, loss_all/4))\n",
    "    train_loss_results.append(loss_all / 4)  # 将4个step的loss求平均记录在此变量中\n",
    "    loss_all = 0  # loss_all归零，为记录下一个epoch的loss做准备\n",
    " \n",
    "    # 测试部分\n",
    "    # total_correct为预测对的样本个数, total_number为测试的总样本数，将这两个变量都初始化为0\n",
    "    total_correct, total_number = 0, 0\n",
    "    for x_test, y_test in test_db:\n",
    "        # 使用更新后的参数进行预测\n",
    "        y = tf.matmul(x_test, w1) + b1\n",
    "        y = tf.nn.softmax(y)\n",
    "        pred = tf.argmax(y, axis=1)  # 返回y中最大值的索引，即预测的分类\n",
    "        # 将pred转换为y_test的数据类型\n",
    "        pred = tf.cast(pred, dtype=y_test.dtype)\n",
    "        # 若分类正确，则correct=1，否则为0，将bool型的结果转换为int型\n",
    "        correct = tf.cast(tf.equal(pred, y_test), dtype=tf.int32)\n",
    "        # 将每个batch的correct数加起来\n",
    "        correct = tf.reduce_sum(correct)\n",
    "        # 将所有batch中的correct数加起来\n",
    "        total_correct += int(correct)\n",
    "        # total_number为测试的总样本数，也就是x_test的行数，shape[0]返回变量的行数\n",
    "        total_number += x_test.shape[0]\n",
    "    # 总的准确率等于total_correct/total_number\n",
    "    acc = total_correct / total_number\n",
    "    test_acc.append(acc)\n",
    "    print(\"Test_acc:\", acc)\n",
    "    print(\"--------------------------\")\n",
    " \n",
    "# 绘制 loss 曲线\n",
    "plt.title('Loss Function Curve')  # 图片标题\n",
    "plt.xlabel('Epoch')  # x轴变量名称\n",
    "plt.ylabel('Loss')  # y轴变量名称\n",
    "plt.plot(train_loss_results, label=\"$Loss$\")  # 逐点画出trian_loss_results值并连线，连线图标是Loss\n",
    "plt.legend()  # 画出曲线图标\n",
    "plt.show()  # 画出图像\n",
    " \n",
    "# 绘制 Accuracy 曲线\n",
    "plt.title('Acc Curve')  # 图片标题\n",
    "plt.xlabel('Epoch')  # x轴变量名称\n",
    "plt.ylabel('Acc')  # y轴变量名称\n",
    "plt.plot(test_acc, label=\"$Accuracy$\")  # 逐点画出test_acc值并连线，连线图标是Accuracy\n",
    "plt.legend()\n",
    "plt.show()"
   ]
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