AlexNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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      "次数 0\n",
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      "test_accuracy 0.5\n",
      "loss 2.1190982\n",
      "time 10.586190223693848\n",
      "次数 1000\n",
      "output [[5.89056239e-02 9.41094458e-01]\n",
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      "test_accuracy 0.75\n",
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      "time 193.1195182800293\n",
      "次数 2000\n",
      "output [[9.98559058e-01 1.44096219e-03]\n",
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      "test_accuracy 0.8125\n",
      "loss 0.08852711\n",
      "time 390.6342055797577\n",
      "次数 3000\n",
      "output [[9.98003304e-01 1.99674326e-03]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.22389856\n",
      "time 589.5874428749084\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "次数 4000\n",
      "output [[1.0915474e-02 9.8908454e-01]\n",
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      "test_accuracy 1.0\n",
      "loss 0.16314217\n",
      "time 785.5492076873779\n",
      "次数 5000\n",
      "output [[9.99939442e-01 6.05293135e-05]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.0470541\n",
      "time 981.8034453392029\n",
      "次数 6000\n",
      "output [[9.84038472e-01 1.59614962e-02]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.0620595\n",
      "time 1178.0122754573822\n",
      "次数 7000\n",
      "output [[9.5945233e-01 4.0547676e-02]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.046812292\n",
      "time 1375.5586557388306\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 8000\n",
      "output [[1.8756978e-01 8.1243014e-01]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.14939758\n",
      "time 1570.8127694129944\n",
      "次数 9000\n",
      "output [[2.0465468e-05 9.9997950e-01]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.03655307\n",
      "time 1768.2562782764435\n",
      "次数 10000\n",
      "output [[9.99977708e-01 2.22711878e-05]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.025428347\n",
      "time 1967.8001415729523\n",
      "次数 11000\n",
      "output [[1.72377862e-02 9.82762277e-01]\n",
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      "test_accuracy 0.75\n",
      "loss 0.10424777\n",
      "time 2166.8704290390015\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 12000\n",
      "output [[3.3875766e-01 6.6124231e-01]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.04074084\n",
      "time 2366.325464963913\n",
      "次数 13000\n",
      "output [[9.99731600e-01 2.68362288e-04]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.045553725\n",
      "time 2634.7650804519653\n",
      "次数 14000\n",
      "output [[9.96612966e-01 3.38705722e-03]\n",
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      "test_accuracy 1.0\n",
      "loss 0.11656674\n",
      "time 2835.706684112549\n",
      "次数 15000\n",
      "output [[9.9998415e-01 1.5853129e-05]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.017219113\n",
      "time 3033.397040605545\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 16000\n",
      "output [[9.9910837e-01 8.9156366e-04]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.044597123\n",
      "time 3234.313696861267\n",
      "次数 17000\n",
      "output [[9.99777615e-01 2.22452421e-04]\n",
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      "test_accuracy 1.0\n",
      "loss 0.07400069\n",
      "time 3434.9996025562286\n",
      "次数 18000\n",
      "output [[9.99981999e-01 1.80134884e-05]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.0075414334\n",
      "time 3632.668951511383\n",
      "次数 19000\n",
      "output [[9.93362784e-01 6.63721329e-03]\n",
      " [9.99100447e-01 8.99552309e-04]\n",
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      " 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1]\n",
      "test_accuracy 1.0\n",
      "loss 0.17550284\n",
      "time 3832.428851366043\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 20000\n",
      "output [[9.52564836e-01 4.74351346e-02]\n",
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      "test_accuracy 1.0\n",
      "loss 0.05405877\n",
      "time 4028.7517178058624\n",
      "次数 21000\n",
      "output [[9.99983191e-01 1.67868839e-05]\n",
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      "test_accuracy 0.9375\n",
      "loss 0.011332517\n",
      "time 4249.592703580856\n",
      "次数 22000\n",
      "output [[9.77890551e-01 2.21094433e-02]\n",
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      "test_accuracy 0.875\n",
      "loss 0.016729865\n",
      "time 4448.956258535385\n",
      "次数 23000\n",
      "output [[1.0000000e+00 2.3517135e-08]\n",
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      " [4.7350053e-02 9.5265001e-01]\n",
      " [9.9977666e-01 2.2329070e-04]\n",
      " [9.9998331e-01 1.6713118e-05]\n",
      " [1.0000000e+00 5.7777050e-09]\n",
      " [9.9334586e-01 6.6542123e-03]\n",
      " [4.4686901e-03 9.9553138e-01]\n",
      " [9.9998999e-01 9.9594918e-06]\n",
      " [9.9999881e-01 1.2343213e-06]\n",
      " [9.9997902e-01 2.0994827e-05]\n",
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      " 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0]\n",
      "test_accuracy 0.875\n",
      "loss 0.030095192\n",
      "time 4643.619425773621\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 24000\n",
      "output [[9.9998808e-01 1.1959474e-05]\n",
      " [9.9999738e-01 2.6161838e-06]\n",
      " [9.9977595e-01 2.2409245e-04]\n",
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      " [9.9999917e-01 8.6726612e-07]\n",
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      "test_accuracy 1.0\n",
      "loss 0.0061309254\n",
      "time 4840.878098011017\n",
      "次数 25000\n",
      "output [[1.00000000e+00 5.37799458e-08]\n",
      " [9.20732971e-03 9.90792692e-01]\n",
      " [5.25345095e-02 9.47465420e-01]\n",
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      " [9.99971271e-01 2.87690273e-05]\n",
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      " [9.99976635e-01 2.33408919e-05]\n",
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      " 0 1 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1]\n",
      "test_accuracy 0.9375\n",
      "loss 0.029133704\n",
      "time 5037.639114379883\n",
      "次数 26000\n",
      "output [[9.9998605e-01 1.3895370e-05]\n",
      " [1.6308542e-02 9.8369145e-01]\n",
      " [2.3402355e-04 9.9976593e-01]\n",
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      " 0 1 1 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0]\n",
      "test_accuracy 0.9375\n",
      "loss 0.009139538\n",
      "time 5232.5264139175415\n",
      "次数 27000\n",
      "output [[1.00000000e+00 2.88352614e-14]\n",
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      " 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1]\n",
      "test_accuracy 0.8125\n",
      "loss 0.0057942914\n",
      "time 5428.981654882431\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 28000\n",
      "output [[1.0000000e+00 5.6739868e-10]\n",
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      " [9.9996686e-01 3.3092660e-05]\n",
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      " [9.7226212e-03 9.9027741e-01]\n",
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      " [3.0582747e-03 9.9694175e-01]\n",
      " [9.9999988e-01 7.4504449e-08]\n",
      " [1.0000000e+00 4.2852437e-11]\n",
      " [9.9999988e-01 7.5505859e-08]\n",
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      " [9.9999976e-01 2.2277986e-07]\n",
      " [9.9995434e-01 4.5681150e-05]\n",
      " [9.9997056e-01 2.9479401e-05]\n",
      " [9.9999666e-01 3.3194208e-06]\n",
      " [1.7460430e-03 9.9825400e-01]\n",
      " [1.0000000e+00 1.3144498e-08]\n",
      " [9.9998009e-01 1.9956331e-05]\n",
      " [1.0000000e+00 1.4881952e-11]\n",
      " [9.9999976e-01 1.9350874e-07]]\n",
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      " 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.043343376\n",
      "time 5627.372099876404\n",
      "次数 29000\n",
      "output [[9.9999964e-01 3.1125509e-07]\n",
      " [1.0000000e+00 4.8535536e-09]\n",
      " [1.0990250e-02 9.8900980e-01]\n",
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      " [1.0000000e+00 2.7232682e-08]\n",
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      " [1.0000000e+00 2.3561122e-09]\n",
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      " [8.3856194e-06 9.9999166e-01]\n",
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      " [9.9999988e-01 8.7888388e-08]\n",
      " [5.5548913e-05 9.9994445e-01]\n",
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      " [9.9994409e-01 5.5944645e-05]\n",
      " [9.9999607e-01 3.9891338e-06]\n",
      " [4.6986733e-03 9.9530137e-01]]\n",
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      " 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1]\n",
      "test_accuracy 0.9375\n",
      "loss 0.10218768\n",
      "time 5822.671862125397\n",
      "次数 30000\n",
      "output [[9.9999750e-01 2.4756712e-06]\n",
      " [1.0000000e+00 5.4822152e-08]\n",
      " [9.9999976e-01 2.1024894e-07]\n",
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      " 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0]\n",
      "test_accuracy 0.875\n",
      "loss 0.02368345\n",
      "time 6021.231470108032\n",
      "次数 31000\n",
      "output [[1.68869726e-03 9.98311281e-01]\n",
      " [1.00000000e+00 1.72894477e-09]\n",
      " [1.48123351e-03 9.98518765e-01]\n",
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      " 0 1 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.04511945\n",
      "time 6217.819729566574\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 32000\n",
      "output [[1.00000000e+00 3.30294947e-09]\n",
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      " 0 0 1 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.017575383\n",
      "time 6413.807613611221\n",
      "次数 33000\n",
      "output [[4.49193455e-02 9.55080628e-01]\n",
      " [9.99999881e-01 1.38026209e-07]\n",
      " [9.99999881e-01 1.16799306e-07]\n",
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      " [6.11255527e-04 9.99388695e-01]\n",
      " [1.00000000e+00 7.96343325e-10]\n",
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      " [9.99996066e-01 3.89071738e-06]\n",
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      " [1.00000000e+00 3.82843490e-09]\n",
      " [9.99999762e-01 1.91173683e-07]\n",
      " [9.99999642e-01 3.50101061e-07]\n",
      " [1.00000000e+00 4.22247437e-09]\n",
      " [9.99956489e-01 4.35536567e-05]\n",
      " [1.00000000e+00 6.42995990e-10]\n",
      " [3.40104953e-06 9.99996543e-01]\n",
      " [1.00000000e+00 1.19311755e-10]\n",
      " [9.99997139e-01 2.80805875e-06]\n",
      " [9.99997854e-01 2.13198996e-06]\n",
      " [3.14417221e-02 9.68558311e-01]\n",
      " [8.54447950e-03 9.91455495e-01]\n",
      " [9.99998331e-01 1.63860932e-06]\n",
      " [9.99999642e-01 3.49117158e-07]\n",
      " [9.99121249e-01 8.78771883e-04]]\n",
      "[1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1\n",
      " 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.009565856\n",
      "time 6610.102232694626\n",
      "次数 34000\n",
      "output [[1.00000000e+00 3.05248804e-08]\n",
      " [1.31154731e-02 9.86884475e-01]\n",
      " [9.99998569e-01 1.43211332e-06]\n",
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      " [8.49593356e-02 9.15040672e-01]\n",
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      " [1.00000000e+00 3.49110736e-12]\n",
      " [6.28693283e-01 3.71306688e-01]\n",
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      " [1.00000000e+00 7.23416674e-11]\n",
      " [9.99764144e-01 2.35913481e-04]\n",
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      " [1.00000000e+00 1.83788522e-08]\n",
      " [1.97085319e-03 9.98029172e-01]\n",
      " [9.99951243e-01 4.87689649e-05]]\n",
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      " 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 1 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.013883725\n",
      "time 6806.801918029785\n",
      "次数 35000\n",
      "output [[5.78718755e-05 9.99942183e-01]\n",
      " [9.67057765e-01 3.29422466e-02]\n",
      " [9.99998569e-01 1.48076992e-06]\n",
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      " 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.05309019\n",
      "time 7005.076126337051\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 36000\n",
      "output [[3.7300205e-03 9.9626994e-01]\n",
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      " 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 1 0]\n",
      "test_accuracy 0.875\n",
      "loss 0.01052284\n",
      "time 7202.735651016235\n",
      "次数 37000\n",
      "output [[1.00000000e+00 2.51196064e-09]\n",
      " [3.15305515e-05 9.99968410e-01]\n",
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      " [9.99831915e-01 1.67999809e-04]\n",
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      " [1.00000000e+00 4.44832082e-08]\n",
      " [1.00000000e+00 1.13991729e-13]]\n",
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      " 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\n",
      "test_accuracy 0.9375\n",
      "loss 0.002105972\n",
      "time 7399.353382587433\n",
      "次数 38000\n",
      "output [[1.00000000e+00 6.70448919e-10]\n",
      " [1.09822104e-04 9.99890208e-01]\n",
      " [9.99998212e-01 1.80506595e-06]\n",
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      " [9.98463392e-01 1.53661007e-03]\n",
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      " [1.00000000e+00 1.40267424e-08]\n",
      " [1.00000000e+00 9.69310188e-10]\n",
      " [1.00000000e+00 1.55767532e-09]\n",
      " [8.62665176e-02 9.13733482e-01]\n",
      " [9.99999166e-01 8.00845669e-07]\n",
      " [1.00000000e+00 4.79530415e-10]\n",
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      " 0 0 0 0 1 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.0046908376\n",
      "time 7596.414712190628\n",
      "次数 39000\n",
      "output [[1.0000000e+00 3.7158106e-12]\n",
      " [5.4238294e-03 9.9457610e-01]\n",
      " [6.2462136e-02 9.3753791e-01]\n",
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      " [9.9999630e-01 3.6638316e-06]\n",
      " [1.0000000e+00 7.3561135e-10]\n",
      " [9.9999845e-01 1.6032559e-06]\n",
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      " [1.0000000e+00 3.4287639e-09]\n",
      " [5.5013516e-05 9.9994493e-01]\n",
      " [1.0000000e+00 3.8639058e-08]\n",
      " [9.9999964e-01 3.7640217e-07]\n",
      " [9.9999833e-01 1.6470289e-06]\n",
      " [9.9987078e-01 1.2921641e-04]\n",
      " [9.9896801e-01 1.0320580e-03]\n",
      " [9.9999988e-01 8.8620965e-08]\n",
      " [9.9953723e-01 4.6275111e-04]\n",
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      " [9.3611099e-02 9.0638888e-01]\n",
      " [9.9999976e-01 2.0567295e-07]]\n",
      "[0 1 1 1 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0\n",
      " 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0]\n",
      "test_accuracy 0.9375\n",
      "loss 0.008037074\n",
      "time 7792.042877197266\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 40000\n",
      "output [[5.49376309e-01 4.50623721e-01]\n",
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      " 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.024191001\n",
      "time 7987.952616453171\n",
      "次数 41000\n",
      "output [[1.0000000e+00 1.3838920e-11]\n",
      " [1.0000000e+00 9.6399919e-12]\n",
      " [1.0000000e+00 2.8841392e-16]\n",
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      " 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0]\n",
      "test_accuracy 0.9375\n",
      "loss 0.0057835868\n",
      "time 8183.707427978516\n",
      "次数 42000\n",
      "output [[1.00000000e+00 9.46654310e-09]\n",
      " [1.00000000e+00 2.09154937e-12]\n",
      " [9.99961495e-01 3.85003586e-05]\n",
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      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1]\n",
      "test_accuracy 0.9375\n",
      "loss 0.008220826\n",
      "time 8379.421169996262\n",
      "次数 43000\n",
      "output [[5.2434612e-02 9.4756544e-01]\n",
      " [9.9617225e-01 3.8277076e-03]\n",
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      " [9.9999988e-01 1.1713562e-07]\n",
      " [1.0000000e+00 4.1758210e-09]\n",
      " [3.8863793e-02 9.6113616e-01]\n",
      " [1.0000000e+00 5.9703707e-13]\n",
      " [1.0000000e+00 2.1969091e-09]\n",
      " [1.0000000e+00 1.7364469e-09]\n",
      " [1.0000000e+00 6.1016203e-10]\n",
      " [1.0000000e+00 5.7229865e-09]\n",
      " [1.2645163e-02 9.8735482e-01]\n",
      " [9.9998748e-01 1.2515856e-05]\n",
      " [1.0000000e+00 8.4324407e-14]\n",
      " [9.9965060e-01 3.4938595e-04]\n",
      " [9.9999642e-01 3.6307085e-06]\n",
      " [3.2435359e-08 1.0000000e+00]\n",
      " [9.9999988e-01 7.4750410e-08]\n",
      " [1.0000000e+00 3.7274153e-09]\n",
      " [1.0148512e-07 9.9999988e-01]\n",
      " [1.0000000e+00 7.0858033e-12]\n",
      " [9.9999988e-01 1.0641759e-07]\n",
      " [9.9999857e-01 1.4570511e-06]\n",
      " [1.0000000e+00 1.7119197e-14]\n",
      " [1.0000000e+00 7.0661149e-15]\n",
      " [1.0000000e+00 8.0812104e-09]\n",
      " [9.7232741e-01 2.7672587e-02]\n",
      " [1.0000000e+00 1.4299085e-08]\n",
      " [1.0000000e+00 3.0983313e-14]\n",
      " [1.0000000e+00 4.7426215e-17]\n",
      " [1.0000000e+00 5.8501884e-11]\n",
      " [1.0000000e+00 4.6770211e-08]\n",
      " [1.1196486e-03 9.9888033e-01]]\n",
      "[1 0 0 0 0 0 1 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1\n",
      " 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1]\n",
      "test_accuracy 0.9375\n",
      "loss 0.038520757\n",
      "time 8575.405590772629\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 44000\n",
      "output [[1.00000000e+00 3.79685026e-14]\n",
      " [1.00000000e+00 5.05779363e-10]\n",
      " [1.00000000e+00 4.47662574e-10]\n",
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      " [1.00000000e+00 3.71967207e-10]\n",
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      " [1.00000000e+00 1.77707779e-11]\n",
      " [1.00000000e+00 3.56126406e-10]\n",
      " [9.99992967e-01 7.07899426e-06]\n",
      " [1.00000000e+00 1.32889297e-10]\n",
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      " [1.00000000e+00 2.46456272e-10]\n",
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      " [1.00000000e+00 9.62598712e-09]\n",
      " [1.00000000e+00 1.64331754e-12]\n",
      " [1.00000000e+00 1.48268713e-15]\n",
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      " [1.00000000e+00 5.56960901e-12]\n",
      " [9.99999404e-01 5.62493142e-07]\n",
      " [1.92695856e-01 8.07304144e-01]\n",
      " [9.28232893e-02 9.07176733e-01]\n",
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      " [9.99996543e-01 3.50356004e-06]\n",
      " [3.48273776e-02 9.65172648e-01]\n",
      " [1.73418764e-02 9.82658088e-01]\n",
      " [1.00000000e+00 3.76140482e-12]\n",
      " [9.99993086e-01 6.96169900e-06]\n",
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      " [1.01925525e-06 9.99998927e-01]\n",
      " [4.70041741e-05 9.99953032e-01]\n",
      " [9.99961495e-01 3.85581225e-05]]\n",
      "[0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0\n",
      " 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0]\n",
      "test_accuracy 0.9375\n",
      "loss 0.044809416\n",
      "time 8771.93892288208\n",
      "次数 45000\n",
      "output [[1.0000000e+00 3.6326708e-12]\n",
      " [9.9999809e-01 1.8959278e-06]\n",
      " [1.0000000e+00 2.8543565e-09]\n",
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      " [7.3410501e-03 9.9265891e-01]\n",
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      " [9.9967611e-01 3.2385424e-04]\n",
      " [9.9998903e-01 1.0929999e-05]\n",
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      " [9.9949265e-01 5.0736323e-04]\n",
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      " [1.6893463e-03 9.9831069e-01]\n",
      " [9.9999940e-01 6.3007900e-07]\n",
      " [9.9999928e-01 7.7321488e-07]\n",
      " [6.3470118e-03 9.9365294e-01]\n",
      " [1.0000000e+00 7.9215656e-09]\n",
      " [1.0000000e+00 4.2090029e-14]\n",
      " [1.0000000e+00 5.6713900e-10]\n",
      " [9.9994516e-01 5.4819335e-05]\n",
      " [8.6879855e-01 1.3120149e-01]\n",
      " [1.0000000e+00 3.5554337e-14]\n",
      " [9.9999809e-01 1.9409736e-06]\n",
      " [1.0000000e+00 8.3761931e-10]\n",
      " [5.0176243e-12 1.0000000e+00]\n",
      " [9.9999750e-01 2.5417696e-06]\n",
      " [1.0000000e+00 1.0183735e-09]\n",
      " [6.1685307e-04 9.9938309e-01]\n",
      " [9.9999988e-01 1.5502610e-07]\n",
      " [3.1284951e-06 9.9999690e-01]\n",
      " [9.9887639e-01 1.1235853e-03]\n",
      " [1.0000000e+00 1.7792919e-08]\n",
      " [1.0000000e+00 4.7680089e-13]\n",
      " [9.9990606e-01 9.3953968e-05]\n",
      " [1.0000000e+00 3.2215311e-08]\n",
      " [1.0000000e+00 9.8557082e-09]\n",
      " [1.0000000e+00 5.6847397e-15]\n",
      " [1.0000000e+00 1.0103028e-15]]\n",
      "[0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0\n",
      " 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.009889466\n",
      "time 8968.526618242264\n",
      "次数 46000\n",
      "output [[1.0000000e+00 1.4486420e-11]\n",
      " [9.9998200e-01 1.8048120e-05]\n",
      " [9.9999833e-01 1.6803434e-06]\n",
      " [1.0000000e+00 1.1357705e-16]\n",
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      " [1.0000000e+00 5.9113157e-12]\n",
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      " [2.3060641e-03 9.9769396e-01]\n",
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      " [4.4117009e-08 1.0000000e+00]\n",
      " [9.9990678e-01 9.3259870e-05]\n",
      " [1.0000000e+00 1.4822732e-09]\n",
      " [9.9947292e-01 5.2715425e-04]\n",
      " [2.0722531e-02 9.7927743e-01]\n",
      " [1.0000000e+00 1.3736358e-11]\n",
      " [9.9999821e-01 1.7719819e-06]\n",
      " [9.7084492e-01 2.9155074e-02]\n",
      " [4.0959404e-04 9.9959046e-01]\n",
      " [6.8274263e-04 9.9931729e-01]\n",
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      " [7.0870563e-02 9.2912942e-01]\n",
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      " [1.0000000e+00 7.4935064e-10]\n",
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      " [9.9999845e-01 1.5978540e-06]\n",
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      " [4.3248036e-07 9.9999952e-01]\n",
      " [1.0000000e+00 9.4716004e-09]\n",
      " [1.0000000e+00 4.7566280e-09]\n",
      " [9.9999976e-01 2.3303619e-07]\n",
      " [9.9950981e-01 4.9022358e-04]\n",
      " [9.9999082e-01 9.1720685e-06]\n",
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      " [1.0000000e+00 5.7957838e-13]\n",
      " [9.9999905e-01 9.7703139e-07]\n",
      " [9.9997008e-01 2.9897963e-05]\n",
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      " [1.0000000e+00 5.8600802e-12]\n",
      " [9.9993503e-01 6.4912922e-05]\n",
      " [9.9999547e-01 4.5594825e-06]\n",
      " [3.4588427e-04 9.9965417e-01]\n",
      " [9.9925584e-01 7.4412324e-04]\n",
      " [1.4187324e-02 9.8581266e-01]\n",
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      " [1.0000000e+00 9.9713899e-09]\n",
      " [5.8781596e-05 9.9994123e-01]\n",
      " [1.0000000e+00 2.7523514e-10]\n",
      " [2.0069891e-04 9.9979931e-01]\n",
      " [9.9994564e-01 5.4410080e-05]\n",
      " [1.0000000e+00 2.9134526e-13]\n",
      " [9.9985552e-01 1.4441842e-04]\n",
      " [9.9991882e-01 8.1158258e-05]]\n",
      "[0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 1 1\n",
      " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.0028107439\n",
      "time 9164.577709436417\n",
      "次数 47000\n",
      "output [[6.0569023e-04 9.9939430e-01]\n",
      " [1.0000000e+00 4.5975901e-14]\n",
      " [1.0000000e+00 1.4217222e-20]\n",
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      " [1.0000000e+00 7.5071007e-09]\n",
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      " [9.9999952e-01 4.4174942e-07]\n",
      " [9.9999905e-01 9.2904054e-07]\n",
      " [3.1887401e-02 9.6811265e-01]\n",
      " [9.9968731e-01 3.1276370e-04]\n",
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      " [9.9072158e-01 9.2784315e-03]\n",
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      " [9.9999309e-01 6.9234366e-06]\n",
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      " [9.9981385e-01 1.8616611e-04]\n",
      " [9.9999869e-01 1.3392720e-06]\n",
      " [1.0000000e+00 9.9286890e-09]\n",
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      " [1.0000000e+00 1.4781184e-15]\n",
      " [9.9901950e-01 9.8049815e-04]]\n",
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      " 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.013684914\n",
      "time 9361.774624109268\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "次数 48000\n",
      "output [[9.9988830e-01 1.1173457e-04]\n",
      " [9.9996269e-01 3.7293386e-05]\n",
      " [9.9981052e-01 1.8956591e-04]\n",
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      " [8.5719832e-04 9.9914277e-01]\n",
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      " [9.9903178e-01 9.6820004e-04]\n",
      " [9.9859315e-01 1.4068143e-03]\n",
      " [1.0000000e+00 2.7174828e-08]\n",
      " [1.0000000e+00 3.8134747e-08]\n",
      " [7.8447267e-02 9.2155272e-01]\n",
      " [2.9330609e-05 9.9997067e-01]\n",
      " [3.7790199e-03 9.9622101e-01]\n",
      " [9.9986112e-01 1.3886251e-04]\n",
      " [1.0000000e+00 2.1540872e-10]\n",
      " [1.0000000e+00 2.9551776e-08]\n",
      " [1.0000000e+00 1.3923081e-08]\n",
      " [4.0419804e-06 9.9999595e-01]\n",
      " [1.0000000e+00 2.7769270e-08]\n",
      " [1.0000000e+00 1.8082857e-10]\n",
      " [9.9782664e-01 2.1733916e-03]\n",
      " [9.9998248e-01 1.7466908e-05]\n",
      " [9.9999523e-01 4.7488074e-06]\n",
      " [9.9983704e-01 1.6298410e-04]\n",
      " [1.0000000e+00 5.3712800e-08]\n",
      " [1.0000000e+00 8.2063725e-09]\n",
      " [9.9998939e-01 1.0550661e-05]\n",
      " [2.0075072e-02 9.7992492e-01]\n",
      " [8.0373784e-06 9.9999201e-01]\n",
      " [9.9974686e-01 2.5317859e-04]\n",
      " [9.9956757e-01 4.3238368e-04]\n",
      " [2.8921465e-10 1.0000000e+00]]\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
      " 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1]\n",
      "test_accuracy 1.0\n",
      "loss 0.007661769\n",
      "time 9558.496435403824\n",
      "次数 49000\n",
      "output [[1.00000000e+00 5.71633478e-08]\n",
      " [1.00000000e+00 7.14154846e-10]\n",
      " [1.00000000e+00 1.70404274e-10]\n",
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      " [9.99988198e-01 1.17466625e-05]\n",
      " [1.00000000e+00 4.21353832e-08]\n",
      " [1.00000000e+00 4.73898532e-10]\n",
      " [9.99997377e-01 2.59814033e-06]\n",
      " [9.99987483e-01 1.25208462e-05]\n",
      " [1.00000000e+00 4.08115625e-10]\n",
      " [6.67019822e-06 9.99993324e-01]\n",
      " [9.99929190e-01 7.08198277e-05]\n",
      " [1.00000000e+00 2.28805197e-09]\n",
      " [1.00000000e+00 8.14960321e-11]\n",
      " [1.19127791e-10 1.00000000e+00]\n",
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      " [9.99999881e-01 6.71463098e-08]\n",
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      " [1.00000000e+00 1.87880147e-08]\n",
      " [1.00000000e+00 2.41306388e-08]\n",
      " [1.00000000e+00 1.99335872e-08]\n",
      " [9.99729216e-01 2.70794495e-04]\n",
      " [1.00000000e+00 4.34024594e-09]\n",
      " [9.99998689e-01 1.27757664e-06]\n",
      " [9.99993682e-01 6.37528956e-06]\n",
      " [9.99999523e-01 4.57570280e-07]\n",
      " [1.00000000e+00 1.41053891e-10]\n",
      " [9.99999046e-01 9.89016144e-07]\n",
      " [1.00000000e+00 7.08219305e-09]\n",
      " [1.41916715e-03 9.98580813e-01]\n",
      " [9.93265748e-01 6.73423195e-03]\n",
      " [1.00000000e+00 2.31696537e-10]\n",
      " [1.00000000e+00 8.48035064e-09]\n",
      " [5.08404709e-02 9.49159503e-01]\n",
      " [1.42115474e-01 8.57884586e-01]\n",
      " [1.00000000e+00 8.17317858e-09]\n",
      " [3.09068322e-01 6.90931678e-01]\n",
      " [9.99996185e-01 3.82532016e-06]\n",
      " [4.20946577e-07 9.99999523e-01]\n",
      " [2.40278605e-05 9.99975920e-01]\n",
      " [9.99973893e-01 2.60540510e-05]\n",
      " [1.00000000e+00 3.23152963e-13]\n",
      " [1.00000000e+00 5.46083889e-09]\n",
      " [9.64282546e-04 9.99035716e-01]\n",
      " [2.61239963e-09 1.00000000e+00]\n",
      " [1.00000000e+00 1.71756991e-08]\n",
      " [9.99998093e-01 1.95077200e-06]\n",
      " [1.00000000e+00 1.22775339e-13]]\n",
      "[0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0\n",
      " 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 1 1 0 0 0 1 1 0 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.0055852123\n",
      "time 9754.907831668854\n",
      "次数 50000\n",
      "output [[1.00000000e+00 9.72166480e-15]\n",
      " [5.72389886e-02 9.42761004e-01]\n",
      " [1.00000000e+00 5.32298898e-08]\n",
      " [9.99999881e-01 1.05109798e-07]\n",
      " [9.99991536e-01 8.45779778e-06]\n",
      " [1.00000000e+00 2.29432935e-15]\n",
      " [9.99999881e-01 9.03309356e-08]\n",
      " [1.00000000e+00 1.07466926e-13]\n",
      " [1.00000000e+00 1.41653562e-13]\n",
      " [9.99674916e-01 3.25111148e-04]\n",
      " [9.99863029e-01 1.36958231e-04]\n",
      " [1.00000000e+00 4.19581985e-13]\n",
      " [1.83915813e-03 9.98160899e-01]\n",
      " [3.07242190e-05 9.99969244e-01]\n",
      " [9.98066753e-06 9.99989986e-01]\n",
      " [1.00000000e+00 7.81833248e-18]\n",
      " [1.00000000e+00 3.03170222e-13]\n",
      " [1.00000000e+00 1.27978228e-09]\n",
      " [1.06546046e-04 9.99893427e-01]\n",
      " [1.00000000e+00 7.39903798e-12]\n",
      " [1.00000000e+00 5.87658477e-10]\n",
      " [1.00000000e+00 3.12115403e-11]\n",
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      " [8.98849422e-08 9.99999881e-01]\n",
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      " [1.00000000e+00 1.54279555e-18]\n",
      " [1.00000000e+00 5.38253990e-12]\n",
      " [7.77855190e-03 9.92221415e-01]\n",
      " [2.02640236e-04 9.99797404e-01]\n",
      " [1.00000000e+00 7.40653385e-20]\n",
      " [1.00000000e+00 1.02235339e-13]\n",
      " [2.16297829e-03 9.97837007e-01]\n",
      " [1.00000000e+00 5.78553698e-12]\n",
      " [1.00000000e+00 4.13243982e-17]\n",
      " [9.99997735e-01 2.23064421e-06]\n",
      " [1.00000000e+00 4.32598313e-08]\n",
      " [1.37234300e-01 8.62765729e-01]\n",
      " [1.00000000e+00 7.18817672e-10]\n",
      " [1.00000000e+00 1.09215972e-16]\n",
      " [7.18319789e-03 9.92816806e-01]\n",
      " [1.00000000e+00 1.81811532e-09]\n",
      " [1.00000000e+00 4.73834803e-08]\n",
      " [3.51227857e-02 9.64877248e-01]\n",
      " [1.00000000e+00 3.78752389e-08]\n",
      " [9.99998569e-01 1.37459142e-06]\n",
      " [1.00000000e+00 2.31447708e-15]\n",
      " [2.01491741e-04 9.99798477e-01]\n",
      " [1.24888061e-06 9.99998808e-01]\n",
      " [3.43111822e-07 9.99999642e-01]\n",
      " [1.00000000e+00 1.06543156e-10]\n",
      " [4.98381034e-02 9.50161934e-01]\n",
      " [9.99726713e-01 2.73246056e-04]\n",
      " [1.00000000e+00 3.58478425e-09]\n",
      " [1.00000000e+00 7.34635334e-14]\n",
      " [1.00000000e+00 1.12654919e-16]\n",
      " [1.00000000e+00 1.84557469e-12]\n",
      " [9.99982834e-01 1.72124892e-05]\n",
      " [1.00000000e+00 5.91011268e-11]\n",
      " [9.99999881e-01 1.00637372e-07]\n",
      " [1.00000000e+00 9.15919285e-11]\n",
      " [1.00000000e+00 6.99303943e-16]\n",
      " [1.00000000e+00 2.39711251e-09]]\n",
      "[0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0\n",
      " 0 1 0 0 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0]\n",
      "test_accuracy 1.0\n",
      "loss 0.006977653\n",
      "time 9954.14196896553\n",
      "INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.CancelledError'>, Run call was cancelled\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import time\n",
    "from tensorflow.python.framework import graph_util\n",
    "import cv2\n",
    "\n",
    "begin=time.time()\n",
    "\n",
    "# def img_preprocess(img):\n",
    "    \n",
    "#     return tf.image.convert_image_dtype(img, dtype=tf.float32)\n",
    "\n",
    "def train_doc():\n",
    "    \n",
    "    filename_queue = tf.train.string_input_producer(['E:\\\\friedhelm\\\\face_detection_VGG\\\\face_train_224.tfrecords'],shuffle=True)\n",
    "\n",
    "    reader = tf.TFRecordReader()\n",
    "    _, serialized_example = reader.read(filename_queue) #返回文件名和文件\n",
    "\n",
    "    features = tf.parse_single_example(serialized_example,\n",
    "                                   features={\n",
    "                                   'label':tf.FixedLenFeature([],tf.int64),\n",
    "                                   'img':tf.FixedLenFeature([],tf.string),\n",
    "                                   })\n",
    "    img=tf.decode_raw(features['img'],tf.uint8)\n",
    "    label=tf.cast(features['label'],tf.int32)\n",
    "    img = tf.reshape(img, [224,224,3])   \n",
    "#     img=img_preprocess(img)\n",
    "    min_after_dequeue = 10000\n",
    "    batch_size = 64\n",
    "    capacity = min_after_dequeue + 10 * batch_size\n",
    "    image_batch, label_batch = tf.train.shuffle_batch([img, label], \n",
    "                                                        batch_size=batch_size, \n",
    "                                                        capacity=capacity, \n",
    "                                                        min_after_dequeue=min_after_dequeue,\n",
    "                                                        num_threads=7)  \n",
    "    return image_batch,label_batch\n",
    "    \n",
    "\n",
    "def test_doc():\n",
    "    \n",
    "    filename_queue = tf.train.string_input_producer(['E:\\\\friedhelm\\\\face_detection_VGG\\\\face_test_224.tfrecords'],shuffle=True,seed=77)\n",
    "\n",
    "    reader = tf.TFRecordReader()\n",
    "    _, serialized_example = reader.read(filename_queue) #返回文件名和文件\n",
    "\n",
    "    features = tf.parse_single_example(serialized_example,\n",
    "                                   features={\n",
    "                                   'label':tf.FixedLenFeature([],tf.int64),\n",
    "                                   'img':tf.FixedLenFeature([],tf.string),\n",
    "                                   })\n",
    "    img=tf.decode_raw(features['img'],tf.uint8)\n",
    "    label=tf.cast(features['label'],tf.int32)\n",
    "    img = tf.reshape(img, [224,224,3])   \n",
    "#     img=img_preprocess(img)\n",
    "    min_after_dequeue = 1000\n",
    "    batch_size = 16\n",
    "    capacity = min_after_dequeue + 10 * batch_size\n",
    "    image_batch, label_batch = tf.train.shuffle_batch([img, label], \n",
    "                                                        batch_size=batch_size, \n",
    "                                                        capacity=capacity, \n",
    "                                                        min_after_dequeue=min_after_dequeue,\n",
    "                                                        num_threads=7)  \n",
    "    return image_batch,label_batch    \n",
    "    \n",
    "    \n",
    "def model(x,prob,is_training):\n",
    "    \n",
    "    with tf.variable_scope('conv1',reuse=tf.AUTO_REUSE):\n",
    "        weight1=tf.get_variable('weight',[11,11,3,64],initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        bias1=tf.get_variable('bias',[64],initializer=tf.constant_initializer(0.1))        \n",
    "        conv1=tf.nn.conv2d(x,weight1,strides=[1,4,4,1],padding='SAME')\n",
    "        he1=tf.nn.bias_add(conv1,bias1)\n",
    "        bn1=tf.layers.batch_normalization(he1,training=is_training)\n",
    "        relu1=tf.nn.relu(bn1)       \n",
    "\n",
    "    with tf.variable_scope('pool1',reuse=tf.AUTO_REUSE):  \n",
    "        pool1=tf.nn.max_pool(relu1,ksize=[1,3,3,1],strides=[1,2,2,1],padding='VALID')\n",
    "\n",
    "    with tf.variable_scope('conv2',reuse=tf.AUTO_REUSE):\n",
    "        weight2=tf.get_variable('weight',[5,5,64,192],initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        bias2=tf.get_variable('bias',[192],initializer=tf.constant_initializer(0.1))        \n",
    "        conv2=tf.nn.conv2d(pool1,weight2,strides=[1,1,1,1],padding='SAME')\n",
    "        he2=tf.nn.bias_add(conv2,bias2)\n",
    "        bn2=tf.layers.batch_normalization(he2,training=is_training)\n",
    "        relu2=tf.nn.relu(bn2)     \n",
    "    \n",
    "    with tf.variable_scope('pool2',reuse=tf.AUTO_REUSE):  \n",
    "        pool2=tf.nn.max_pool(relu2,ksize=[1,3,3,1],strides=[1,2,2,1],padding='VALID')\n",
    "  \n",
    "    with tf.variable_scope('conv3',reuse=tf.AUTO_REUSE):\n",
    "        weight3=tf.get_variable('weight',[3,3,192,384],initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        bias3=tf.get_variable('bias',[384],initializer=tf.constant_initializer(0.1))        \n",
    "        conv3=tf.nn.conv2d(pool2,weight3,strides=[1,1,1,1],padding='SAME')\n",
    "        he3=tf.nn.bias_add(conv3,bias3)\n",
    "        mean3,vias3=tf.nn.moments(he3,0)\n",
    "        bn3=tf.layers.batch_normalization(he3,training=is_training)\n",
    "        relu3=tf.nn.relu(bn3)    \n",
    "        \n",
    "    with tf.variable_scope('conv4',reuse=tf.AUTO_REUSE):\n",
    "        weight4=tf.get_variable('weight',[3,3,384,256],initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        bias4=tf.get_variable('bias',[256],initializer=tf.constant_initializer(0.1))        \n",
    "        conv4=tf.nn.conv2d(relu3,weight4,strides=[1,1,1,1],padding='SAME')\n",
    "        he4=tf.nn.bias_add(conv4,bias4)\n",
    "        bn4=tf.layers.batch_normalization(he4,training=is_training)\n",
    "        relu4=tf.nn.relu(bn4)          \n",
    "        \n",
    "    with tf.variable_scope('conv5',reuse=tf.AUTO_REUSE):\n",
    "        weight5=tf.get_variable('weight',[3,3,256,256],initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        bias5=tf.get_variable('bias',[256],initializer=tf.constant_initializer(0.1))        \n",
    "        conv5=tf.nn.conv2d(relu4,weight5,strides=[1,1,1,1],padding='SAME')\n",
    "        he5=tf.nn.bias_add(conv5,bias5)\n",
    "        bn5=tf.layers.batch_normalization(he5,training=is_training)\n",
    "        relu5=tf.nn.relu(bn5)    \n",
    "\n",
    "    with tf.variable_scope('pool3',reuse=tf.AUTO_REUSE):  \n",
    "        pool3=tf.nn.max_pool(relu5,ksize=[1,3,3,1],strides=[1,2,2,1],padding='VALID')\n",
    "        \n",
    "    with tf.variable_scope('fc1',reuse=tf.AUTO_REUSE):     \n",
    "        weight6=tf.get_variable('weight',[6,6,256,1024],initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        bias6=tf.get_variable('bias',[1024],initializer=tf.constant_initializer(0.1))        \n",
    "        conv6=tf.nn.conv2d(pool3,weight6,strides=[1,1,1,1],padding='VALID')\n",
    "        he6=tf.nn.bias_add(conv6,bias6)\n",
    "        bn6=tf.layers.batch_normalization(he6,training=is_training)\n",
    "        relu6=tf.nn.relu(bn6)     \n",
    "        fc1=tf.nn.dropout(relu6,prob)\n",
    "\n",
    "    with tf.variable_scope('fc2',reuse=tf.AUTO_REUSE):     \n",
    "        weight7=tf.get_variable('weight',[1,1,1024,512],initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        bias7=tf.get_variable('bias',[512],initializer=tf.constant_initializer(0.1))        \n",
    "        conv7=tf.nn.conv2d(fc1,weight7,strides=[1,1,1,1],padding='VALID')\n",
    "        he7=tf.nn.bias_add(conv7,bias7)\n",
    "        bn7=tf.layers.batch_normalization(he7,training=is_training)\n",
    "        relu7=tf.nn.relu(bn7)     \n",
    "        fc2=tf.nn.dropout(relu7,prob)\n",
    "        \n",
    "    with tf.variable_scope('fc3',reuse=tf.AUTO_REUSE):     \n",
    "        weight8=tf.get_variable('weight',[1,1,512,2],initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        bias8=tf.get_variable('bias',[2],initializer=tf.constant_initializer(0.1))        \n",
    "        conv8=tf.nn.conv2d(fc2,weight8,strides=[1,1,1,1],padding='VALID')\n",
    "        logit=tf.nn.bias_add(conv8,bias8,name='he')     \n",
    "    return logit  \n",
    "    \n",
    "def train(x,y_,prob,is_training):\n",
    "    \n",
    "    y=model(x,prob,is_training)\n",
    "    _y=tf.reshape(y,(-1,2))\n",
    "    output=tf.nn.softmax(_y,name='logit') \n",
    "\n",
    "    with tf.name_scope('train_loss'):\n",
    "#         loss_all=tf.add(-y_*tf.log(y_to_loss+1e-9),-(1-y_)*tf.log(1-y_to_loss+1e-9))\n",
    "#         y_=tf.stop_gradient(y_)\n",
    "        loss_all=tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_,logits=_y)\n",
    "        loss=tf.reduce_mean(loss_all,name='train_loss')\n",
    "        tf.summary.scalar('loss',loss) \n",
    "        \n",
    "    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "    with tf.control_dependencies(update_ops):\n",
    "        opt=tf.train.AdamOptimizer(0.01).minimize(loss)    \n",
    "\n",
    "    with tf.name_scope('test_accuracy'):\n",
    "        test_accuracy=tf.reduce_mean(tf.cast(tf.equal(y_,tf.argmax(output,1)),tf.float32),name='test_accuracy')\n",
    "        tf.summary.scalar('test_accuracy',test_accuracy) \n",
    "\n",
    "\n",
    "    saver=tf.train.Saver()\n",
    "    merged=tf.summary.merge_all() \n",
    "    \n",
    "    image_batch,label_batch=train_doc()\n",
    "    image_batch1,label_batch1=test_doc()    \n",
    "    \n",
    "    with tf.Session() as sess:\n",
    "        sess.run((tf.global_variables_initializer(),\n",
    "                  tf.local_variables_initializer()))\n",
    "        coord = tf.train.Coordinator()\n",
    "        threads = tf.train.start_queue_runners(sess=sess,coord=coord)\n",
    "       \n",
    "        writer_train=tf.summary.FileWriter('E:\\\\friedhelm\\\\face_detection_VGG\\\\train\\\\',sess.graph)\n",
    "        writer_test=tf.summary.FileWriter('E:\\\\friedhelm\\\\face_detection_VGG\\\\test\\\\')\n",
    "        \n",
    "        for i in range(50001):\n",
    "            img,label=sess.run([image_batch,label_batch])\n",
    "            label=np.reshape(label,(64))    \n",
    "            sess.run(opt,feed_dict={x:img,y_:label,prob:0.7,is_training:True})\n",
    "            if(i%100==0):\n",
    "                img1,label1=sess.run([image_batch1,label_batch1])\n",
    "                label1=np.reshape(label1,(16))\n",
    "                \n",
    "                summary=sess.run(merged,feed_dict={x:img,y_:label,prob:0.7,is_training:True})\n",
    "                writer_train.add_summary(summary,i) \n",
    "                \n",
    "                summary,_=sess.run([merged,test_accuracy],feed_dict={x:img1,y_:label1,prob:1,is_training:False})\n",
    "                writer_test.add_summary(summary,i) \n",
    "            if(i%1000==0):\n",
    "                print('次数',i)    \n",
    "                print('output',sess.run(output,feed_dict={x:img,y_:label,prob:0.7,is_training:True}))\n",
    "                print(label)\n",
    "                print('test_accuracy',sess.run(test_accuracy,feed_dict={x:img1,y_:label1,prob:1,is_training:False}))               \n",
    "                print('loss',sess.run(loss,feed_dict={x:img,y_:label,prob:0.7,is_training:True}))               \n",
    "                print('time',time.time()-begin)\n",
    "                saver.save(sess,\"E:\\\\friedhelm\\\\model\\\\model.ckpt\")\n",
    "    writer_train.close()\n",
    "    writer_test.close()\n",
    "    \n",
    "def main():\n",
    "    \n",
    "    with tf.name_scope('input'):\n",
    "        x=tf.placeholder(tf.float32,name='x')\n",
    "        y_=tf.placeholder(tf.int64,name='y')\n",
    "        prob=tf.placeholder(tf.float32,name='prob')\n",
    "        is_training = tf.placeholder(tf.bool,name='is_train')    \n",
    "    train(x,y_,prob,is_training)\n",
    "\n",
    "if __name__=='__main__':\n",
    "    main()\n",
    "# tensorboard --logdir=C:\\\\Users\\\\312\\\\Desktop\\\\\n",
    "# tensorboard --logdir=train:\"E:\\\\friedhelm\\\\face_detection_VGG\\\\train\\\\\",test:\"E:\\\\friedhelm\\\\face_detection_VGG\\\\test\\\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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