2017-1-5(1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/gensim/utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.\n",
      "  warnings.warn(\"Pattern library is not installed, lemmatization won't be available.\")\n",
      "Using gpu device 0: GeForce GTX TITAN X (CNMeM is enabled with initial size: 95.0% of memory, cuDNN 4007)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting to training the model..., saving to weights/201701051610_e20_1k1k_l200.p\n",
      "Training\n",
      "the length of the training data is  1998\n",
      "Before trianing, the error is:\n",
      "Training error: 0.257755994797\n",
      "Training_correct error 0.337905704975\n",
      "Testing_correct error: 0.335038632154\n",
      "top- 1 = [3] : 3\n",
      "top- 5 = [21] : 21\n",
      "top- 10 = [30] : 30\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     501.754755\n",
      "std      264.648264\n",
      "min        1.000000\n",
      "25%      290.500000\n",
      "50%      541.500000\n",
      "75%      711.000000\n",
      "max      978.000000\n",
      "dtype: float64\n",
      "top- 1 = [1] : 1\n",
      "top- 5 = [12] : 12\n",
      "top- 10 = [24] : 24\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      551.164000\n",
      "std       260.994743\n",
      "min         1.000000\n",
      "25%       362.625000\n",
      "50%       559.000000\n",
      "75%       775.500000\n",
      "max       985.000000\n",
      "dtype: float64\n",
      "\n",
      "Epoch 0 ...\n",
      "Epoch  0 Update  21 Cost  0.182155281305\n",
      "Epoch  0 Update  42 Cost  0.167487800121\n",
      "Epoch  0 Update  63 Cost  0.207024231553\n",
      "Training error: 0.125692605972\n",
      "Training_correct error 0.118412218988\n",
      "Testing_correct error: 0.114850081503\n",
      "top- 1 = [3, 48] : 48\n",
      "top- 5 = [21, 199] : 199\n",
      "top- 10 = [30, 304] : 304\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      52.060060\n",
      "std       68.136176\n",
      "min        1.000000\n",
      "25%        7.500000\n",
      "50%       28.000000\n",
      "75%       63.000000\n",
      "max      370.500000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46] : 46\n",
      "top- 5 = [12, 151] : 151\n",
      "top- 10 = [24, 232] : 232\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       61.457000\n",
      "std        73.761911\n",
      "min         1.000000\n",
      "25%        11.500000\n",
      "50%        34.000000\n",
      "75%        85.000000\n",
      "max       448.000000\n",
      "dtype: float64\n",
      "epoch took: 48.7678158283\n",
      "\n",
      "Epoch 1 ...\n",
      "Epoch  1 Update  84 Cost  0.10254971683\n",
      "Epoch  1 Update  105 Cost  0.11504471302\n",
      "Epoch  1 Update  126 Cost  0.0893101021647\n",
      "Training error: 0.117395535111\n",
      "Training_correct error 0.0951170623302\n",
      "Testing_correct error: 0.0903676599264\n",
      "top- 1 = [3, 48, 50] : 50\n",
      "top- 5 = [21, 199, 208] : 208\n",
      "top- 10 = [30, 304, 320] : 320\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      52.107608\n",
      "std       67.633366\n",
      "min        1.000000\n",
      "25%        7.000000\n",
      "50%       22.000000\n",
      "75%       72.500000\n",
      "max      324.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43] : 43\n",
      "top- 5 = [12, 151, 169] : 169\n",
      "top- 10 = [24, 232, 267] : 267\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       54.447000\n",
      "std        69.017665\n",
      "min         1.000000\n",
      "25%         9.500000\n",
      "50%        30.500000\n",
      "75%        61.250000\n",
      "max       425.000000\n",
      "dtype: float64\n",
      "epoch took: 49.2361490726\n",
      "\n",
      "Epoch 2 ...\n",
      "Epoch  2 Update  147 Cost  0.0750384479761\n",
      "Epoch  2 Update  168 Cost  0.0610469020903\n",
      "Epoch  2 Update  189 Cost  0.124674670398\n",
      "Training error: 0.0985486656427\n",
      "Training_correct error 0.101944170892\n",
      "Testing_correct error: 0.102379269898\n",
      "top- 1 = [3, 48, 50, 86] : 86\n",
      "top- 5 = [21, 199, 208, 293] : 293\n",
      "top- 10 = [30, 304, 320, 431] : 431\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      38.222723\n",
      "std       53.008296\n",
      "min        1.000000\n",
      "25%        4.000000\n",
      "50%       15.000000\n",
      "75%       50.250000\n",
      "max      398.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81] : 81\n",
      "top- 5 = [12, 151, 169, 246] : 246\n",
      "top- 10 = [24, 232, 267, 378] : 378\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       44.222000\n",
      "std        56.271624\n",
      "min         1.000000\n",
      "25%         5.500000\n",
      "50%        17.500000\n",
      "75%        62.000000\n",
      "max       432.000000\n",
      "dtype: float64\n",
      "epoch took: 48.7764930725\n",
      "\n",
      "Epoch 3 ...\n",
      "Epoch  3 Update  210 Cost  0.0899893343449\n",
      "Epoch  3 Update  231 Cost  0.0913775414228\n",
      "Epoch  3 Update  252 Cost  0.0783319026232\n",
      "Training error: 0.0997497886419\n",
      "Training_correct error 0.0821037888527\n",
      "Testing_correct error: 0.083479039371\n",
      "top- 1 = [3, 48, 50, 86, 76] : 76\n",
      "top- 5 = [21, 199, 208, 293, 310] : 310\n",
      "top- 10 = [30, 304, 320, 431, 411] : 411\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      37.868368\n",
      "std       57.583045\n",
      "min        1.000000\n",
      "25%        3.500000\n",
      "50%       16.000000\n",
      "75%       42.000000\n",
      "max      378.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66] : 66\n",
      "top- 5 = [12, 151, 169, 246, 242] : 242\n",
      "top- 10 = [24, 232, 267, 378, 344] : 344\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       43.143000\n",
      "std        60.577844\n",
      "min         1.000000\n",
      "25%         6.000000\n",
      "50%        21.250000\n",
      "75%        46.000000\n",
      "max       417.000000\n",
      "dtype: float64\n",
      "epoch took: 49.0867660046\n",
      "\n",
      "Epoch 4 ...\n",
      "Epoch  4 Update  273 Cost  0.102640353143\n",
      "Epoch  4 Update  294 Cost  0.0877730324864\n",
      "Epoch  4 Update  315 Cost  0.119521379471\n",
      "Training error: 0.086856238544\n",
      "Training_correct error 0.107121005654\n",
      "Testing_correct error: 0.120220825076\n",
      "top- 1 = [3, 48, 50, 86, 76, 118] : 118\n",
      "top- 5 = [21, 199, 208, 293, 310, 392] : 392\n",
      "top- 10 = [30, 304, 320, 431, 411, 533] : 533\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      24.363864\n",
      "std       37.543915\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%        9.000000\n",
      "75%       26.500000\n",
      "max      279.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92] : 92\n",
      "top- 5 = [12, 151, 169, 246, 242, 285] : 285\n",
      "top- 10 = [24, 232, 267, 378, 344, 455] : 455\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       35.453000\n",
      "std        50.465682\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        12.500000\n",
      "75%        41.250000\n",
      "max       324.000000\n",
      "dtype: float64\n",
      "epoch took: 49.1464388371\n",
      "\n",
      "Epoch 5 ...\n",
      "Epoch  5 Update  336 Cost  0.0586893036962\n",
      "Epoch  5 Update  357 Cost  0.0592846162617\n",
      "Epoch  5 Update  378 Cost  0.172971352935\n",
      "Training error: 0.0897238701582\n",
      "Training_correct error 0.0772184580564\n",
      "Testing_correct error: 0.0757179409266\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93] : 93\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359] : 359\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495] : 495\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      28.575576\n",
      "std       44.863448\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%       10.500000\n",
      "75%       31.000000\n",
      "max      277.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79] : 79\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279] : 279\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443] : 443\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       35.241500\n",
      "std        50.618402\n",
      "min         1.000000\n",
      "25%         4.500000\n",
      "50%        13.750000\n",
      "75%        41.000000\n",
      "max       273.000000\n",
      "dtype: float64\n",
      "epoch took: 49.4259550571\n",
      "\n",
      "Epoch 6 ...\n",
      "Epoch  6 Update  399 Cost  0.0754013508558\n",
      "Epoch  6 Update  420 Cost  0.0876249596477\n",
      "Epoch  6 Update  441 Cost  0.165213420987\n",
      "Training error: 0.0925588086247\n",
      "Training_correct error 0.075753249228\n",
      "Testing_correct error: 0.0882906317711\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92] : 92\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406] : 406\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582] : 582\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      24.823824\n",
      "std       42.170965\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%        7.500000\n",
      "75%       21.000000\n",
      "max      344.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80] : 80\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306] : 306\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470] : 470\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       30.191000\n",
      "std        47.151795\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        11.000000\n",
      "75%        25.000000\n",
      "max       333.000000\n",
      "dtype: float64\n",
      "epoch took: 49.4044981003\n",
      "\n",
      "Epoch 7 ...\n",
      "Epoch  7 Update  462 Cost  0.0874141305685\n",
      "Epoch  7 Update  483 Cost  0.0745683759451\n",
      "Epoch  7 Update  504 Cost  0.084833085537\n",
      "Training error: 0.0767989233136\n",
      "Training_correct error 0.0734879598022\n",
      "Testing_correct error: 0.0873811766505\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202] : 202\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515] : 515\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690] : 690\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      18.065065\n",
      "std       33.711923\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        5.000000\n",
      "75%       14.500000\n",
      "max      263.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135] : 135\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423] : 423\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557] : 557\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       27.840000\n",
      "std        46.303525\n",
      "min         1.000000\n",
      "25%         2.500000\n",
      "50%         8.000000\n",
      "75%        27.250000\n",
      "max       291.000000\n",
      "dtype: float64\n",
      "epoch took: 49.7146921158\n",
      "\n",
      "Epoch 8 ...\n",
      "Epoch  8 Update  525 Cost  0.0818914100528\n",
      "Epoch  8 Update  546 Cost  0.102885998785\n",
      "Epoch  8 Update  567 Cost  0.124444670975\n",
      "Training error: 0.0649016872048\n",
      "Training_correct error 0.0729025006294\n",
      "Testing_correct error: 0.0839079767466\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205] : 205\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564] : 564\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738] : 738\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.638639\n",
      "std       22.141127\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        4.000000\n",
      "75%       11.000000\n",
      "max      162.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156] : 156\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468] : 468\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596] : 596\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       19.704000\n",
      "std        34.629317\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%        19.625000\n",
      "max       206.000000\n",
      "dtype: float64\n",
      "epoch took: 49.2566800117\n",
      "\n",
      "Epoch 9 ...\n",
      "Epoch  9 Update  588 Cost  0.08970028162\n",
      "Epoch  9 Update  609 Cost  0.0695525407791\n",
      "Epoch  9 Update  630 Cost  0.0319570749998\n",
      "Training error: 0.0614430606365\n",
      "Training_correct error 0.0697435438633\n",
      "Testing_correct error: 0.0887727290392\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225] : 225\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610] : 610\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736] : 736\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.092092\n",
      "std       22.614483\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       11.250000\n",
      "max      208.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166] : 166\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479] : 479\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614] : 614\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean       21.41400\n",
      "std        38.95274\n",
      "min         1.00000\n",
      "25%         2.00000\n",
      "50%         6.00000\n",
      "75%        22.50000\n",
      "max       271.00000\n",
      "dtype: float64\n",
      "epoch took: 49.4886729717\n",
      "\n",
      "Epoch 10 ...\n",
      "Epoch  10 Update  651 Cost  0.063362672925\n",
      "Epoch  10 Update  672 Cost  0.0694444775581\n",
      "Epoch  10 Update  693 Cost  0.0757331997156\n",
      "Training error: 0.064365811646\n",
      "Training_correct error 0.0788674727082\n",
      "Testing_correct error: 0.0984400510788\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200] : 200\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598] : 598\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745] : 745\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.458458\n",
      "std       22.218322\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       11.000000\n",
      "max      173.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148] : 148\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475] : 475\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605] : 605\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       21.715000\n",
      "std        38.724454\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%        19.000000\n",
      "max       289.000000\n",
      "dtype: float64\n",
      "epoch took: 49.8208749294\n",
      "\n",
      "Epoch 11 ...\n",
      "Epoch  11 Update  714 Cost  0.0437170118093\n",
      "Epoch  11 Update  735 Cost  0.114784456789\n",
      "Epoch  11 Update  756 Cost  0.0514871068299\n",
      "Training error: 0.0676115751266\n",
      "Training_correct error 0.0683713331819\n",
      "Testing_correct error: 0.0836198031902\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161] : 161\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536] : 536\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698] : 698\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      13.920921\n",
      "std       23.795582\n",
      "min        1.000000\n",
      "25%        2.000000\n",
      "50%        4.500000\n",
      "75%       14.000000\n",
      "max      294.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115] : 115\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452] : 452\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622] : 622\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean       20.02600\n",
      "std        33.57005\n",
      "min         1.00000\n",
      "25%         2.00000\n",
      "50%         6.25000\n",
      "75%        17.50000\n",
      "max       217.00000\n",
      "dtype: float64\n",
      "epoch took: 49.266272068\n",
      "\n",
      "Epoch 12 ...\n",
      "Epoch  12 Update  777 Cost  0.0336507894099\n",
      "Epoch  12 Update  798 Cost  0.0849446207285\n",
      "Epoch  12 Update  819 Cost  0.0798107907176\n",
      "Training error: 0.0687031969428\n",
      "Training_correct error 0.0725525915623\n",
      "Testing_correct error: 0.0905041620135\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169] : 169\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532] : 532\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702] : 702\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      13.523524\n",
      "std       23.272234\n",
      "min        1.000000\n",
      "25%        2.000000\n",
      "50%        5.000000\n",
      "75%       13.000000\n",
      "max      288.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127] : 127\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485] : 485\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672] : 672\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       17.531000\n",
      "std        29.637196\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.500000\n",
      "75%        15.000000\n",
      "max       190.000000\n",
      "dtype: float64\n",
      "epoch took: 49.3607320786\n",
      "\n",
      "Epoch 13 ...\n",
      "Epoch  13 Update  840 Cost  0.072304174304\n",
      "Epoch  13 Update  861 Cost  0.0690368041396\n",
      "Epoch  13 Update  882 Cost  0.0413294769824\n",
      "Training error: 0.0671595260501\n",
      "Training_correct error 0.0689151287079\n",
      "Testing_correct error: 0.0870760679245\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186] : 186\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595] : 595\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716] : 716\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.617618\n",
      "std       21.336281\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       12.500000\n",
      "max      245.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143] : 143\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529] : 529\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665] : 665\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       15.855000\n",
      "std        25.879059\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.000000\n",
      "75%        16.000000\n",
      "max       181.000000\n",
      "dtype: float64\n",
      "epoch took: 49.7553060055\n",
      "\n",
      "Epoch 14 ...\n",
      "Epoch  14 Update  903 Cost  0.0230688545853\n",
      "Epoch  14 Update  924 Cost  0.0697318464518\n",
      "Epoch  14 Update  945 Cost  0.0603713318706\n",
      "Training error: 0.055987522006\n",
      "Training_correct error 0.0552660189569\n",
      "Testing_correct error: 0.0641145408154\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233] : 233\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653] : 653\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779] : 779\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       8.545546\n",
      "std       13.568803\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.000000\n",
      "75%        8.500000\n",
      "max      157.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162] : 162\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530] : 530\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698] : 698\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       11.858000\n",
      "std        17.776947\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         4.750000\n",
      "75%        13.125000\n",
      "max       120.000000\n",
      "dtype: float64\n",
      "epoch took: 49.5908508301\n",
      "\n",
      "Epoch 15 ...\n",
      "Epoch  15 Update  966 Cost  0.0660632997751\n",
      "Epoch  15 Update  987 Cost  0.0347093641758\n",
      "Epoch  15 Update  1008 Cost  0.039114356041\n",
      "Training error: 0.0527125261724\n",
      "Training_correct error 0.0673270151019\n",
      "Testing_correct error: 0.07998406142\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259] : 259\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714] : 714\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831] : 831\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       7.254254\n",
      "std       13.664529\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        6.000000\n",
      "max      155.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193] : 193\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630] : 630\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753] : 753\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       11.165000\n",
      "std        18.126012\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%        10.000000\n",
      "max       151.000000\n",
      "dtype: float64\n",
      "epoch took: 49.4205899239\n",
      "\n",
      "Epoch 16 ...\n",
      "Epoch  16 Update  1029 Cost  0.0442724451423\n",
      "Epoch  16 Update  1050 Cost  0.0652790665627\n",
      "Epoch  16 Update  1071 Cost  0.0747208446264\n",
      "Training error: 0.0483071468771\n",
      "Training_correct error 0.0516675412655\n",
      "Testing_correct error: 0.0678435266018\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272] : 272\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735] : 735\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843] : 843\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       6.183183\n",
      "std       11.160784\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        6.000000\n",
      "max      164.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194] : 194\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636] : 636\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802] : 802\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        9.038000\n",
      "std        14.761859\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.500000\n",
      "75%         8.000000\n",
      "max       135.000000\n",
      "dtype: float64\n",
      "epoch took: 49.4318339825\n",
      "\n",
      "Epoch 17 ...\n",
      "Epoch  17 Update  1092 Cost  0.0414074324071\n",
      "Epoch  17 Update  1113 Cost  0.071006372571\n",
      "Epoch  17 Update  1134 Cost  0.0419829301536\n",
      "Training error: 0.0467457361519\n",
      "Training_correct error 0.0499443151057\n",
      "Testing_correct error: 0.0650392621756\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263] : 263\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749] : 749\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864] : 864\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       5.903904\n",
      "std       10.514103\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        5.250000\n",
      "max      145.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198] : 198\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685] : 685\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807] : 807\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        8.325000\n",
      "std        13.669537\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       123.000000\n",
      "dtype: float64\n",
      "epoch took: 49.5866370201\n",
      "\n",
      "Epoch 18 ...\n",
      "Epoch  18 Update  1155 Cost  0.0281577594578\n",
      "Epoch  18 Update  1176 Cost  0.0279857199639\n",
      "Epoch  18 Update  1197 Cost  0.031181935221\n",
      "Training error: 0.0448585934937\n",
      "Training_correct error 0.0507593862712\n",
      "Testing_correct error: 0.0675635188818\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263, 297] : 297\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749, 776] : 776\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864, 887] : 887\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       5.160160\n",
      "std        9.167385\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max       96.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198, 225] : 225\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685, 701] : 701\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807, 817] : 817\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        8.575000\n",
      "std        15.275134\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         2.500000\n",
      "75%         7.000000\n",
      "max       134.000000\n",
      "dtype: float64\n",
      "epoch took: 49.1194779873\n",
      "\n",
      "Epoch 19 ...\n",
      "Epoch  19 Update  1218 Cost  0.0584981888533\n",
      "Epoch  19 Update  1239 Cost  0.0524895340204\n",
      "Epoch  19 Update  1260 Cost  0.0314953140914\n",
      "Training error: 0.0442051738501\n",
      "Training_correct error 0.0425166003406\n",
      "Testing_correct error: 0.0482059717178\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263, 297, 274] : 274\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749, 776, 778] : 778\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864, 887, 881] : 881\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       4.926927\n",
      "std        7.862557\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max      100.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198, 225, 206] : 206\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685, 701, 689] : 689\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807, 817, 814] : 814\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        6.672000\n",
      "std         9.635105\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       109.000000\n",
      "dtype: float64\n",
      "epoch took: 49.3292078972\n",
      "saving the model...\n",
      "Evaluate the model using fast estimation...\n",
      "count    999.000000\n",
      "mean       4.926927\n",
      "std        7.862557\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max      100.000000\n",
      "dtype: float64\n",
      "count    1000.000000\n",
      "mean        6.672000\n",
      "std         9.635105\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       109.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 尝试在sentence.py中把maxlen调整成了maxlen=200以后并没有什么区别\n",
    "run main.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TvXt3Onbs2Gh7nTp1YuLEiWRkZACwZcsWDh06xFVXXVVv/Zdffpnjx49TUFBQ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\nkkqQYVCSJEmSStD/BwA2Ou7wFIBcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8963996c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (15.0, 7.5)\n",
    "plt.title('MSE distribution')\n",
    "plt.plot(sls.mse,'ro-', label=\"Training data (similarity = 1 + similarity = 2)\")\n",
    "plt.plot(sls.mse_train,'bo-', label=\"Training data (similarity = 1)\")\n",
    "plt.plot(sls.mse_test,'go-', label=\"Testing data (similarity = 1)\")\n",
    "plt.legend()\n",
    "\n",
    "plt.ylabel('MSE')\n",
    "plt.xlabel('Epoches Times')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Gf0N/AhoH8NIjL7Gq/yoaWhufc6A0Pt4+RL5/95+jlbujFnlXDDIzM3nmmWdo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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89634c8810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (15.0, 7.5)\n",
    "plt.plot(sls.tops[1],'ro-', label = \"TOP-1 @ Trianing data (similarity = 1)\")\n",
    "plt.plot(sls.tops[5],'bo-', label = \"TOP-5 @ Trianing data (similarity = 1)\" )\n",
    "plt.plot(sls.tops[10],'go-', label = \"TOP-10 @ Trianing data (similarity = 1)\")\n",
    "plt.plot(sls.tops_test[1],'ro--', label = \"TOP-1 @ Test data (similarity = 1)\")\n",
    "plt.plot(sls.tops_test[5],'bo--', label = \"TOP-5 @ Test data (similarity = 1)\")\n",
    "plt.plot(sls.tops_test[10],'go--', label = \"TOP-10 @ Test data (similarity = 1)\")\n",
    "plt.ylabel('Amount of documents belongs to top[1,5,10]')\n",
    "plt.xlabel('Epoches')\n",
    "plt.title('Tendancy of accuracy during training')\n",
    "plt.legend(loc = 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "run main.py"
   ]
  }
 ],
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