2017-1-9(1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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: 0.468234956264\n",
      "Training_correct error 0.0543815381825\n",
      "Testing_correct error: 0.049486130476\n",
      "top- 1 = [551] : 551\n",
      "top- 5 = [873] : 873\n",
      "top- 10 = [879] : 879\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      80.493493\n",
      "std      241.486438\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      977.000000\n",
      "dtype: float64\n",
      "top- 1 = [361] : 361\n",
      "top- 5 = [886] : 886\n",
      "top- 10 = [894] : 894\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       75.791000\n",
      "std       230.202321\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       981.000000\n",
      "dtype: float64\n",
      "\n",
      "Epoch 0 ...\n",
      "Epoch  0 Update  21 Cost  0.475922703743\n",
      "Epoch  0 Update  42 Cost  0.611287415028\n",
      "Epoch  0 Update  63 Cost  0.474363714457\n",
      "Training error: 0.456330955029\n",
      "Training_correct error 0.0360841006041\n",
      "Testing_correct error: 0.0358751639724\n",
      "top- 1 = [551, 553] : 553\n",
      "top- 5 = [873, 884] : 884\n",
      "top- 10 = [879, 898] : 898\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      11.942943\n",
      "std       46.688198\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      627.000000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361] : 361\n",
      "top- 5 = [886, 889] : 889\n",
      "top- 10 = [894, 902] : 902\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       13.228000\n",
      "std        49.200983\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       578.000000\n",
      "dtype: float64\n",
      "epoch took: 9.38283014297\n",
      "\n",
      "Epoch 1 ...\n",
      "Epoch  1 Update  84 Cost  0.261831760406\n",
      "Epoch  1 Update  105 Cost  0.473911732435\n",
      "Epoch  1 Update  126 Cost  0.428571224213\n",
      "Training error: 0.452415972948\n",
      "Training_correct error 0.0226097553968\n",
      "Testing_correct error: 0.0257809516042\n",
      "top- 1 = [551, 553, 559] : 559\n",
      "top- 5 = [873, 884, 910] : 910\n",
      "top- 10 = [879, 898, 939] : 939\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       4.622623\n",
      "std       21.499015\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      364.000000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364] : 364\n",
      "top- 5 = [886, 889, 911] : 911\n",
      "top- 10 = [894, 902, 925] : 925\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean        7.04400\n",
      "std        29.32074\n",
      "min         1.00000\n",
      "25%         1.00000\n",
      "50%         1.50000\n",
      "75%         2.00000\n",
      "max       330.50000\n",
      "dtype: float64\n",
      "epoch took: 9.36678385735\n",
      "\n",
      "Epoch 2 ...\n",
      "Epoch  2 Update  147 Cost  0.421020954847\n",
      "Epoch  2 Update  168 Cost  0.355433732271\n",
      "Epoch  2 Update  189 Cost  0.551509320736\n",
      "Training error: 0.449427515268\n",
      "Training_correct error 0.019885757938\n",
      "Testing_correct error: 0.0231666527689\n",
      "top- 1 = [551, 553, 559, 570] : 570\n",
      "top- 5 = [873, 884, 910, 936] : 936\n",
      "top- 10 = [879, 898, 939, 957] : 957\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       3.661662\n",
      "std       18.344077\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      325.000000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374] : 374\n",
      "top- 5 = [886, 889, 911, 918] : 918\n",
      "top- 10 = [894, 902, 925, 946] : 946\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        6.175000\n",
      "std        28.763722\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       352.000000\n",
      "dtype: float64\n",
      "epoch took: 9.32840299606\n",
      "\n",
      "Epoch 3 ...\n",
      "Epoch  3 Update  210 Cost  0.45769315958\n",
      "Epoch  3 Update  231 Cost  0.444663643837\n",
      "Epoch  3 Update  252 Cost  0.439707279205\n",
      "Training error: 0.446576654911\n",
      "Training_correct error 0.0176332294941\n",
      "Testing_correct error: 0.0181073732674\n",
      "top- 1 = [551, 553, 559, 570, 567] : 567\n",
      "top- 5 = [873, 884, 910, 936, 917] : 917\n",
      "top- 10 = [879, 898, 939, 957, 940] : 940\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       4.079079\n",
      "std       15.594153\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      214.000000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371] : 371\n",
      "top- 5 = [886, 889, 911, 918, 922] : 922\n",
      "top- 10 = [894, 902, 925, 946, 948] : 948\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        4.047000\n",
      "std        13.503597\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       154.000000\n",
      "dtype: float64\n",
      "epoch took: 9.35931706429\n",
      "\n",
      "Epoch 4 ...\n",
      "Epoch  4 Update  273 Cost  0.358215481043\n",
      "Epoch  4 Update  294 Cost  0.380258202553\n",
      "Epoch  4 Update  315 Cost  0.415090501308\n",
      "Training error: 0.444687902927\n",
      "Training_correct error 0.0181473698467\n",
      "Testing_correct error: 0.0193680264056\n",
      "top- 1 = [551, 553, 559, 570, 567, 577] : 577\n",
      "top- 5 = [873, 884, 910, 936, 917, 950] : 950\n",
      "top- 10 = [879, 898, 939, 957, 940, 963] : 963\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       2.506507\n",
      "std        8.554916\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      155.000000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379] : 379\n",
      "top- 5 = [886, 889, 911, 918, 922, 943] : 943\n",
      "top- 10 = [894, 902, 925, 946, 948, 971] : 971\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.919000\n",
      "std         9.023406\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       118.500000\n",
      "dtype: float64\n",
      "epoch took: 9.36144900322\n",
      "\n",
      "Epoch 5 ...\n",
      "Epoch  5 Update  336 Cost  0.480872899294\n",
      "Epoch  5 Update  357 Cost  0.320644319057\n",
      "Epoch  5 Update  378 Cost  0.30446138978\n",
      "Training error: 0.445303440094\n",
      "Training_correct error 0.0198452435434\n",
      "Testing_correct error: 0.0222689975053\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577] : 577\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952] : 952\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977] : 977\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       2.293293\n",
      "std        8.135720\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      124.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380] : 380\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952] : 952\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979] : 979\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.700000\n",
      "std         8.319929\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       124.500000\n",
      "dtype: float64\n",
      "epoch took: 9.36759305\n",
      "\n",
      "Epoch 6 ...\n",
      "Epoch  6 Update  399 Cost  0.212372750044\n",
      "Epoch  6 Update  420 Cost  0.558201789856\n",
      "Epoch  6 Update  441 Cost  0.40723118186\n",
      "Training error: 0.442311942577\n",
      "Training_correct error 0.0148917259648\n",
      "Testing_correct error: 0.0189990773797\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599] : 599\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972] : 972\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988] : 988\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       2.018018\n",
      "std        8.058291\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      132.000000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388] : 388\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972] : 972\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978] : 978\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean        2.91700\n",
      "std        12.06813\n",
      "min         1.00000\n",
      "25%         1.00000\n",
      "50%         1.50000\n",
      "75%         2.00000\n",
      "max       191.50000\n",
      "dtype: float64\n",
      "epoch took: 9.35038399696\n",
      "\n",
      "Epoch 7 ...\n",
      "Epoch  7 Update  462 Cost  0.358646035194\n",
      "Epoch  7 Update  483 Cost  0.383217155933\n",
      "Epoch  7 Update  504 Cost  0.219167843461\n",
      "Training error: 0.443549633026\n",
      "Training_correct error 0.0172637458891\n",
      "Testing_correct error: 0.0193698313087\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574] : 574\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951] : 951\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976] : 976\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       2.344344\n",
      "std        8.476305\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      153.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381] : 381\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955] : 955\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977] : 977\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.666000\n",
      "std         7.951079\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       111.500000\n",
      "dtype: float64\n",
      "epoch took: 9.34785819054\n",
      "\n",
      "Epoch 8 ...\n",
      "Epoch  8 Update  525 Cost  0.474592268467\n",
      "Epoch  8 Update  546 Cost  0.597170472145\n",
      "Epoch  8 Update  567 Cost  0.28885820508\n",
      "Training error: 0.441482067108\n",
      "Training_correct error 0.012865813449\n",
      "Testing_correct error: 0.0161275397986\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598] : 598\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973] : 973\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987] : 987\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       2.171171\n",
      "std       10.033223\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      173.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393] : 393\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971] : 971\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978] : 978\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.692000\n",
      "std         9.566015\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       149.500000\n",
      "dtype: float64\n",
      "epoch took: 9.36970114708\n",
      "\n",
      "Epoch 9 ...\n",
      "Epoch  9 Update  588 Cost  0.447171419859\n",
      "Epoch  9 Update  609 Cost  0.466343134642\n",
      "Epoch  9 Update  630 Cost  0.502109289169\n",
      "Training error: 0.440711289644\n",
      "Training_correct error 0.0126458564773\n",
      "Testing_correct error: 0.0154139054939\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596] : 596\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974] : 974\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984] : 984\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       1.849850\n",
      "std        5.195765\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max       94.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389] : 389\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965] : 965\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981] : 981\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.325000\n",
      "std         5.798189\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max        76.500000\n",
      "dtype: float64\n",
      "epoch took: 9.36231899261\n",
      "\n",
      "Epoch 10 ...\n",
      "Epoch  10 Update  651 Cost  0.473056077957\n",
      "Epoch  10 Update  672 Cost  0.478673577309\n",
      "Epoch  10 Update  693 Cost  0.247214481235\n",
      "Training error: 0.439794421196\n",
      "Training_correct error 0.0120016010478\n",
      "Testing_correct error: 0.0146716171876\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596, 602] : 602\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974, 981] : 981\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984, 987] : 987\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       1.800801\n",
      "std        5.688284\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      111.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389, 400] : 400\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965, 964] : 964\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981, 984] : 984\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.130000\n",
      "std         4.323864\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max        52.500000\n",
      "dtype: float64\n",
      "epoch took: 9.37154912949\n",
      "\n",
      "Epoch 11 ...\n",
      "Epoch  11 Update  714 Cost  0.476623475552\n",
      "Epoch  11 Update  735 Cost  0.515744328499\n",
      "Epoch  11 Update  756 Cost  0.217148691416\n",
      "Training error: 0.439870238304\n",
      "Training_correct error 0.0116343107074\n",
      "Testing_correct error: 0.0148625681177\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596, 602, 600] : 600\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974, 981, 977] : 977\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984, 987, 990] : 990\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       1.779780\n",
      "std        4.763132\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max       84.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389, 400, 393] : 393\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965, 964, 973] : 973\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981, 984, 983] : 983\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.254000\n",
      "std         5.442895\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max        85.500000\n",
      "dtype: float64\n",
      "epoch took: 9.37122988701\n",
      "\n",
      "Epoch 12 ...\n",
      "Epoch  12 Update  777 Cost  0.349685847759\n",
      "Epoch  12 Update  798 Cost  0.41899189353\n",
      "Epoch  12 Update  819 Cost  0.651082873344\n",
      "Training error: 0.442777216434\n",
      "Training_correct error 0.0169982947409\n",
      "Testing_correct error: 0.0192343778908\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596, 602, 600, 585] : 585\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974, 981, 977, 948] : 948\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984, 987, 990, 963] : 963\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       2.586587\n",
      "std        9.248017\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      137.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389, 400, 393, 384] : 384\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965, 964, 973, 949] : 949\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981, 984, 983, 955] : 955\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        3.143000\n",
      "std        11.876683\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       191.000000\n",
      "dtype: float64\n",
      "epoch took: 9.35645604134\n",
      "\n",
      "Epoch 13 ...\n",
      "Epoch  13 Update  840 Cost  0.448370724916\n",
      "Epoch  13 Update  861 Cost  0.37948101759\n",
      "Epoch  13 Update  882 Cost  0.286985814571\n",
      "Training error: 0.438947170973\n",
      "Training_correct error 0.00986039172858\n",
      "Testing_correct error: 0.0131382290274\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596, 602, 600, 585, 609] : 609\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974, 981, 977, 948, 983] : 983\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984, 987, 990, 963, 990] : 990\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       1.679680\n",
      "std        4.889152\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max       97.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389, 400, 393, 384, 405] : 405\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965, 964, 973, 949, 977] : 977\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981, 984, 983, 955, 984] : 984\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.152000\n",
      "std         5.207496\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max        90.500000\n",
      "dtype: float64\n",
      "epoch took: 9.35424184799\n",
      "\n",
      "Epoch 14 ...\n",
      "Epoch  14 Update  903 Cost  0.408278107643\n",
      "Epoch  14 Update  924 Cost  0.390450924635\n",
      "Epoch  14 Update  945 Cost  0.434762895107\n",
      "Training error: 0.441195875406\n",
      "Training_correct error 0.0130709782243\n",
      "Testing_correct error: 0.0159921068698\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596, 602, 600, 585, 609, 580] : 580\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974, 981, 977, 948, 983, 956] : 956\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984, 987, 990, 963, 990, 969] : 969\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       2.512513\n",
      "std        9.159251\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      155.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389, 400, 393, 384, 405, 377] : 377\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965, 964, 973, 949, 977, 946] : 946\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981, 984, 983, 955, 984, 962] : 962\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.972000\n",
      "std        10.124214\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       159.000000\n",
      "dtype: float64\n",
      "epoch took: 9.38115715981\n",
      "\n",
      "Epoch 15 ...\n",
      "Epoch  15 Update  966 Cost  0.340007305145\n",
      "Epoch  15 Update  987 Cost  0.380504816771\n",
      "Epoch  15 Update  1008 Cost  0.785713851452\n",
      "Training error: 0.44123339653\n",
      "Training_correct error 0.0129157509655\n",
      "Testing_correct error: 0.0163329895586\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596, 602, 600, 585, 609, 580, 595] : 595\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974, 981, 977, 948, 983, 956, 970] : 970\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984, 987, 990, 963, 990, 969, 985] : 985\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       2.013013\n",
      "std        7.782703\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max      143.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389, 400, 393, 384, 405, 377, 393] : 393\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965, 964, 973, 949, 977, 946, 963] : 963\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981, 984, 983, 955, 984, 962, 981] : 981\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.674000\n",
      "std         9.176597\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max       139.500000\n",
      "dtype: float64\n",
      "epoch took: 9.36289787292\n",
      "\n",
      "Epoch 16 ...\n",
      "Epoch  16 Update  1029 Cost  0.44670778513\n",
      "Epoch  16 Update  1050 Cost  0.376128971577\n",
      "Epoch  16 Update  1071 Cost  0.383386343718\n",
      "Training error: 0.438953787088\n",
      "Training_correct error 0.0107462769374\n",
      "Testing_correct error: 0.0141916107386\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596, 602, 600, 585, 609, 580, 595, 612] : 612\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974, 981, 977, 948, 983, 956, 970, 989] : 989\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984, 987, 990, 963, 990, 969, 985, 991] : 991\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       1.570571\n",
      "std        3.918927\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max       79.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389, 400, 393, 384, 405, 377, 393, 404] : 404\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965, 964, 973, 949, 977, 946, 963, 970] : 970\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981, 984, 983, 955, 984, 962, 981, 983] : 983\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.041000\n",
      "std         4.302305\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max        77.500000\n",
      "dtype: float64\n",
      "epoch took: 9.40234994888\n",
      "\n",
      "Epoch 17 ...\n",
      "Epoch  17 Update  1092 Cost  0.56717133522\n",
      "Epoch  17 Update  1113 Cost  0.346813678741\n",
      "Epoch  17 Update  1134 Cost  0.586138427258\n",
      "Training error: 0.440263867378\n",
      "Training_correct error 0.0111255291849\n",
      "Testing_correct error: 0.0144896162674\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596, 602, 600, 585, 609, 580, 595, 612, 604] : 604\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974, 981, 977, 948, 983, 956, 970, 989, 985] : 985\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984, 987, 990, 963, 990, 969, 985, 991, 991] : 991\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       1.743744\n",
      "std        5.244226\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max       95.500000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389, 400, 393, 384, 405, 377, 393, 404, 392] : 392\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965, 964, 973, 949, 977, 946, 963, 970, 976] : 976\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981, 984, 983, 955, 984, 962, 981, 983, 984] : 984\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        2.211000\n",
      "std         4.914329\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max        67.500000\n",
      "dtype: float64\n",
      "epoch took: 9.36519289017\n",
      "\n",
      "Epoch 18 ...\n",
      "Epoch  18 Update  1155 Cost  0.410684883595\n",
      "Epoch  18 Update  1176 Cost  0.410524994135\n",
      "Epoch  18 Update  1197 Cost  0.585417330265\n",
      "Training error: 0.438837110996\n",
      "Training_correct error 0.0108242332935\n",
      "Testing_correct error: 0.0133363232017\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596, 602, 600, 585, 609, 580, 595, 612, 604, 603] : 603\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974, 981, 977, 948, 983, 956, 970, 989, 985, 981] : 981\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984, 987, 990, 963, 990, 969, 985, 991, 991, 987] : 987\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       1.588589\n",
      "std        2.548214\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max       40.000000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389, 400, 393, 384, 405, 377, 393, 404, 392, 402] : 402\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965, 964, 973, 949, 977, 946, 963, 970, 976, 966] : 966\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981, 984, 983, 955, 984, 962, 981, 983, 984, 985] : 985\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        1.915000\n",
      "std         2.785953\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.500000\n",
      "75%         2.000000\n",
      "max        41.500000\n",
      "dtype: float64\n",
      "epoch took: 9.36075496674\n",
      "\n",
      "Epoch 19 ...\n",
      "Epoch  19 Update  1218 Cost  0.471842974424\n",
      "Epoch  19 Update  1239 Cost  0.324994981289\n",
      "Epoch  19 Update  1260 Cost  0.357892602682\n",
      "Training error: 0.440403997898\n",
      "Training_correct error 0.0127952545881\n",
      "Testing_correct error: 0.0150398714468\n",
      "top- 1 = [551, 553, 559, 570, 567, 577, 577, 599, 574, 598, 596, 602, 600, 585, 609, 580, 595, 612, 604, 603, 601] : 601\n",
      "top- 5 = [873, 884, 910, 936, 917, 950, 952, 972, 951, 973, 974, 981, 977, 948, 983, 956, 970, 989, 985, 981, 978] : 978\n",
      "top- 10 = [879, 898, 939, 957, 940, 963, 977, 988, 976, 987, 984, 987, 990, 963, 990, 969, 985, 991, 991, 987, 989] : 989\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       1.620621\n",
      "std        3.012951\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max       52.000000\n",
      "dtype: float64\n",
      "top- 1 = [361, 361, 364, 374, 371, 379, 380, 388, 381, 393, 389, 400, 393, 384, 405, 377, 393, 404, 392, 402, 395] : 395\n",
      "top- 5 = [886, 889, 911, 918, 922, 943, 952, 972, 955, 971, 965, 964, 973, 949, 977, 946, 963, 970, 976, 966, 969] : 969\n",
      "top- 10 = [894, 902, 925, 946, 948, 971, 979, 978, 977, 978, 981, 984, 983, 955, 984, 962, 981, 983, 984, 985, 986] : 986\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean        1.98700\n",
      "std         3.05322\n",
      "min         1.00000\n",
      "25%         1.00000\n",
      "50%         1.50000\n",
      "75%         2.00000\n",
      "max        41.00000\n",
      "dtype: float64\n",
      "epoch took: 9.36931109428\n",
      "saving the model...\n",
      "Evaluate the model using fast estimation...\n",
      "count    999.000000\n",
      "mean       1.620621\n",
      "std        3.012951\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        1.500000\n",
      "max       52.000000\n",
      "dtype: float64\n",
      "count    1000.00000\n",
      "mean        1.98700\n",
      "std         3.05322\n",
      "min         1.00000\n",
      "25%         1.00000\n",
      "50%         1.50000\n",
      "75%         2.00000\n",
      "max        41.00000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "run main.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ef/5ZysriGYEAANSQzMxCyfXTt1gzpaUVyhhVaklL81zH7t019/M6MTFRV1xx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\n324nJyfb/x040E5OTra/bN9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wCHz9YK3VjiM7VL6zXGsq1uil6pc0ffx0LZi6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\nHkRo1yG/9rsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f702cffc6d0>"
      ]
     },
     "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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wadKkp7Jt4cKFCAsLKzXdTz/99FTHUOdpWtx9fHw0RKGLiwvCw8PRq1fJQysr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\nIXP/BuC0zP1xwC0xxg0xxvnA6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f702cfe6e10>"
      ]
     },
     "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": 1,
   "metadata": {
    "collapsed": true
   },
   "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: 0.256023526192\n",
      "Training_correct error 0.333335667849\n",
      "Testing_correct error: 0.342972397804\n",
      "top- 1 = [1] : 1\n",
      "top- 5 = [17] : 17\n",
      "top- 10 = [26] : 26\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     458.362362\n",
      "std      279.827936\n",
      "min        1.000000\n",
      "25%      224.000000\n",
      "50%      461.000000\n",
      "75%      693.750000\n",
      "max      978.000000\n",
      "dtype: float64\n",
      "top- 1 = [2] : 2\n",
      "top- 5 = [16] : 16\n",
      "top- 10 = [29] : 29\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      475.959000\n",
      "std       282.316227\n",
      "min         1.000000\n",
      "25%       237.125000\n",
      "50%       476.500000\n",
      "75%       722.375000\n",
      "max       985.000000\n",
      "dtype: float64\n",
      "\n",
      "Epoch 0 ...\n",
      "Epoch  0 Update  21 Cost  0.251657098532\n",
      "Epoch  0 Update  42 Cost  0.24139970541\n",
      "Epoch  0 Update  63 Cost  0.272840440273\n",
      "Training error: 0.221390083432\n",
      "Training_correct error 0.190529316664\n",
      "Testing_correct error: 0.193975538015\n",
      "top- 1 = [1, 8] : 8\n",
      "top- 5 = [17, 37] : 37\n",
      "top- 10 = [26, 70] : 70\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     254.861862\n",
      "std      216.797657\n",
      "min        1.000000\n",
      "25%       75.500000\n",
      "50%      194.000000\n",
      "75%      397.000000\n",
      "max      903.500000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5] : 5\n",
      "top- 5 = [16, 23] : 23\n",
      "top- 10 = [29, 50] : 50\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      291.273000\n",
      "std       235.115686\n",
      "min         1.000000\n",
      "25%        96.875000\n",
      "50%       229.500000\n",
      "75%       451.250000\n",
      "max       963.000000\n",
      "dtype: float64\n",
      "epoch took: 9.42312502861\n",
      "\n",
      "Epoch 1 ...\n",
      "Epoch  1 Update  84 Cost  0.173480719328\n",
      "Epoch  1 Update  105 Cost  0.227109074593\n",
      "Epoch  1 Update  126 Cost  0.209477975965\n",
      "Training error: 0.235600933433\n",
      "Training_correct error 0.153468713164\n",
      "Testing_correct error: 0.155288174748\n",
      "top- 1 = [1, 8, 4] : 4\n",
      "top- 5 = [17, 37, 35] : 35\n",
      "top- 10 = [26, 70, 67] : 67\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     298.236236\n",
      "std      232.106766\n",
      "min        1.000000\n",
      "25%       95.500000\n",
      "50%      263.000000\n",
      "75%      471.000000\n",
      "max      897.500000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3] : 3\n",
      "top- 5 = [16, 23, 37] : 37\n",
      "top- 10 = [29, 50, 72] : 72\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      317.561000\n",
      "std       241.350061\n",
      "min         1.000000\n",
      "25%       107.750000\n",
      "50%       291.000000\n",
      "75%       472.125000\n",
      "max       952.000000\n",
      "dtype: float64\n",
      "epoch took: 9.43398308754\n",
      "\n",
      "Epoch 2 ...\n",
      "Epoch  2 Update  147 Cost  0.152037858963\n",
      "Epoch  2 Update  168 Cost  0.211680442095\n",
      "Epoch  2 Update  189 Cost  0.233046606183\n",
      "Training error: 0.187974974513\n",
      "Training_correct error 0.242754876614\n",
      "Testing_correct error: 0.231702938676\n",
      "top- 1 = [1, 8, 4, 13] : 13\n",
      "top- 5 = [17, 37, 35, 61] : 61\n",
      "top- 10 = [26, 70, 67, 113] : 113\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     174.578579\n",
      "std      177.242147\n",
      "min        1.000000\n",
      "25%       35.750000\n",
      "50%      115.000000\n",
      "75%      257.000000\n",
      "max      848.500000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15] : 15\n",
      "top- 5 = [16, 23, 37, 63] : 63\n",
      "top- 10 = [29, 50, 72, 120] : 120\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean      216.51600\n",
      "std       200.48973\n",
      "min         1.00000\n",
      "25%        47.50000\n",
      "50%       154.75000\n",
      "75%       349.12500\n",
      "max       879.00000\n",
      "dtype: float64\n",
      "epoch took: 9.41788101196\n",
      "\n",
      "Epoch 3 ...\n",
      "Epoch  3 Update  210 Cost  0.128209650517\n",
      "Epoch  3 Update  231 Cost  0.189863979816\n",
      "Epoch  3 Update  252 Cost  0.143652036786\n",
      "Training error: 0.209973186255\n",
      "Training_correct error 0.144986107945\n",
      "Testing_correct error: 0.158282533288\n",
      "top- 1 = [1, 8, 4, 13, 6] : 6\n",
      "top- 5 = [17, 37, 35, 61, 33] : 33\n",
      "top- 10 = [26, 70, 67, 113, 70] : 70\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     223.489489\n",
      "std      191.909551\n",
      "min        1.000000\n",
      "25%       65.000000\n",
      "50%      174.000000\n",
      "75%      331.500000\n",
      "max      888.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5] : 5\n",
      "top- 5 = [16, 23, 37, 63, 27] : 27\n",
      "top- 10 = [29, 50, 72, 120, 52] : 52\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      261.356000\n",
      "std       219.097853\n",
      "min         1.000000\n",
      "25%        79.000000\n",
      "50%       199.250000\n",
      "75%       403.000000\n",
      "max       930.000000\n",
      "dtype: float64\n",
      "epoch took: 9.40751385689\n",
      "\n",
      "Epoch 4 ...\n",
      "Epoch  4 Update  273 Cost  0.199347779155\n",
      "Epoch  4 Update  294 Cost  0.297611951828\n",
      "Epoch  4 Update  315 Cost  0.225044250488\n",
      "Training error: 0.164968177676\n",
      "Training_correct error 0.234963208437\n",
      "Testing_correct error: 0.219791069627\n",
      "top- 1 = [1, 8, 4, 13, 6, 8] : 8\n",
      "top- 5 = [17, 37, 35, 61, 33, 67] : 67\n",
      "top- 10 = [26, 70, 67, 113, 70, 125] : 125\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     119.911912\n",
      "std      120.956532\n",
      "min        1.000000\n",
      "25%       25.000000\n",
      "50%       79.000000\n",
      "75%      183.750000\n",
      "max      724.500000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16] : 16\n",
      "top- 5 = [16, 23, 37, 63, 27, 73] : 73\n",
      "top- 10 = [29, 50, 72, 120, 52, 126] : 126\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      141.915000\n",
      "std       146.831868\n",
      "min         1.000000\n",
      "25%        29.375000\n",
      "50%        91.000000\n",
      "75%       206.375000\n",
      "max       714.000000\n",
      "dtype: float64\n",
      "epoch took: 9.44917702675\n",
      "\n",
      "Epoch 5 ...\n",
      "Epoch  5 Update  336 Cost  0.113318875432\n",
      "Epoch  5 Update  357 Cost  0.128445804119\n",
      "Epoch  5 Update  378 Cost  0.139468535781\n",
      "Training error: 0.138220712543\n",
      "Training_correct error 0.0922933667898\n",
      "Testing_correct error: 0.0988744050264\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20] : 20\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90] : 90\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140] : 140\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      92.508509\n",
      "std       87.854111\n",
      "min        1.000000\n",
      "25%       22.000000\n",
      "50%       65.000000\n",
      "75%      143.500000\n",
      "max      456.500000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13] : 13\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67] : 67\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118] : 118\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      118.969000\n",
      "std       119.504093\n",
      "min         1.000000\n",
      "25%        29.500000\n",
      "50%        75.500000\n",
      "75%       173.500000\n",
      "max       720.000000\n",
      "dtype: float64\n",
      "epoch took: 9.46497297287\n",
      "\n",
      "Epoch 6 ...\n",
      "Epoch  6 Update  399 Cost  0.08879083395\n",
      "Epoch  6 Update  420 Cost  0.0998856425285\n",
      "Epoch  6 Update  441 Cost  0.205095753074\n",
      "Training error: 0.13291439414\n",
      "Training_correct error 0.156636953354\n",
      "Testing_correct error: 0.18975739181\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11] : 11\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81] : 81\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157] : 157\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.00000\n",
      "mean      92.85986\n",
      "std       86.06248\n",
      "min        1.00000\n",
      "25%       24.00000\n",
      "50%       71.00000\n",
      "75%      134.00000\n",
      "max      444.00000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16] : 16\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76] : 76\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124] : 124\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      128.016000\n",
      "std       120.254555\n",
      "min         1.000000\n",
      "25%        29.750000\n",
      "50%        88.000000\n",
      "75%       196.250000\n",
      "max       620.000000\n",
      "dtype: float64\n",
      "epoch took: 9.44030189514\n",
      "\n",
      "Epoch 7 ...\n",
      "Epoch  7 Update  462 Cost  0.158953100443\n",
      "Epoch  7 Update  483 Cost  0.150326967239\n",
      "Epoch  7 Update  504 Cost  0.208725348115\n",
      "Training error: 0.149434939027\n",
      "Training_correct error 0.0807736217976\n",
      "Testing_correct error: 0.0905024632812\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18] : 18\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79] : 79\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151] : 151\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     110.995996\n",
      "std      112.062444\n",
      "min        1.000000\n",
      "25%       21.500000\n",
      "50%       73.000000\n",
      "75%      169.500000\n",
      "max      543.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13] : 13\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85] : 85\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138] : 138\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      159.422000\n",
      "std       159.991644\n",
      "min         1.000000\n",
      "25%        26.500000\n",
      "50%       100.750000\n",
      "75%       252.500000\n",
      "max       725.000000\n",
      "dtype: float64\n",
      "epoch took: 9.41560697556\n",
      "\n",
      "Epoch 8 ...\n",
      "Epoch  8 Update  525 Cost  0.0983357876539\n",
      "Epoch  8 Update  546 Cost  0.123154610395\n",
      "Epoch  8 Update  567 Cost  0.103565230966\n",
      "Training error: 0.110778510571\n",
      "Training_correct error 0.0948333144188\n",
      "Testing_correct error: 0.103301428258\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22] : 22\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110] : 110\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181] : 181\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      72.652653\n",
      "std       74.072055\n",
      "min        1.000000\n",
      "25%       15.000000\n",
      "50%       48.000000\n",
      "75%      105.000000\n",
      "max      435.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17] : 17\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95] : 95\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162] : 162\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      105.217000\n",
      "std       110.803052\n",
      "min         1.000000\n",
      "25%        19.000000\n",
      "50%        65.500000\n",
      "75%       155.000000\n",
      "max       638.000000\n",
      "dtype: float64\n",
      "epoch took: 9.42818808556\n",
      "\n",
      "Epoch 9 ...\n",
      "Epoch  9 Update  588 Cost  0.123290605843\n",
      "Epoch  9 Update  609 Cost  0.0754237174988\n",
      "Epoch  9 Update  630 Cost  0.0684768930078\n",
      "Training error: 0.129836693406\n",
      "Training_correct error 0.150396570563\n",
      "Testing_correct error: 0.128794521093\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11] : 11\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78] : 78\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141] : 141\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      79.933934\n",
      "std       76.610283\n",
      "min        1.000000\n",
      "25%       24.000000\n",
      "50%       57.500000\n",
      "75%      112.750000\n",
      "max      458.500000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20] : 20\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116] : 116\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183] : 183\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       96.131000\n",
      "std       103.948237\n",
      "min         1.000000\n",
      "25%        18.000000\n",
      "50%        65.500000\n",
      "75%       141.375000\n",
      "max       583.500000\n",
      "dtype: float64\n",
      "epoch took: 9.4212679863\n",
      "\n",
      "Epoch 10 ...\n",
      "Epoch  10 Update  651 Cost  0.185507401824\n",
      "Epoch  10 Update  672 Cost  0.0855669230223\n",
      "Epoch  10 Update  693 Cost  0.164071395993\n",
      "Training error: 0.135360375047\n",
      "Training_correct error 0.133478283882\n",
      "Testing_correct error: 0.117597863078\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11, 11] : 11\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78, 75] : 75\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141, 145] : 145\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      85.082082\n",
      "std       77.810074\n",
      "min        1.000000\n",
      "25%       20.500000\n",
      "50%       64.000000\n",
      "75%      123.500000\n",
      "max      409.500000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20, 13] : 13\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116, 84] : 84\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183, 135] : 135\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      115.597000\n",
      "std       117.646591\n",
      "min         1.000000\n",
      "25%        24.000000\n",
      "50%        75.000000\n",
      "75%       174.500000\n",
      "max       748.000000\n",
      "dtype: float64\n",
      "epoch took: 9.41287994385\n",
      "\n",
      "Epoch 11 ...\n",
      "Epoch  11 Update  714 Cost  0.0978763774037\n",
      "Epoch  11 Update  735 Cost  0.138628259301\n",
      "Epoch  11 Update  756 Cost  0.0748111158609\n",
      "Training error: 0.0959019362926\n",
      "Training_correct error 0.07092987746\n",
      "Testing_correct error: 0.0736088529229\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11, 11, 40] : 40\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78, 75, 176] : 176\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141, 145, 267] : 267\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      51.115115\n",
      "std       55.124620\n",
      "min        1.000000\n",
      "25%        9.000000\n",
      "50%       30.000000\n",
      "75%       76.000000\n",
      "max      334.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20, 13, 29] : 29\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116, 84, 173] : 173\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183, 135, 278] : 278\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       75.649000\n",
      "std        95.347438\n",
      "min         1.000000\n",
      "25%         8.500000\n",
      "50%        32.000000\n",
      "75%       102.000000\n",
      "max       506.000000\n",
      "dtype: float64\n",
      "epoch took: 9.42934393883\n",
      "\n",
      "Epoch 12 ...\n",
      "Epoch  12 Update  777 Cost  0.0589326843619\n",
      "Epoch  12 Update  798 Cost  0.155569002032\n",
      "Epoch  12 Update  819 Cost  0.162111014128\n",
      "Training error: 0.128627866507\n",
      "Training_correct error 0.175012469292\n",
      "Testing_correct error: 0.206931918859\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11, 11, 40, 20] : 20\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78, 75, 176, 116] : 116\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141, 145, 267, 196] : 196\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      73.607608\n",
      "std       75.311608\n",
      "min        1.000000\n",
      "25%       15.000000\n",
      "50%       50.500000\n",
      "75%      106.250000\n",
      "max      489.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20, 13, 29, 14] : 14\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116, 84, 173, 90] : 90\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183, 135, 278, 132] : 132\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      115.261000\n",
      "std       107.986555\n",
      "min         1.000000\n",
      "25%        25.500000\n",
      "50%        86.500000\n",
      "75%       172.125000\n",
      "max       472.000000\n",
      "dtype: float64\n",
      "epoch took: 9.42159199715\n",
      "\n",
      "Epoch 13 ...\n",
      "Epoch  13 Update  840 Cost  0.0779199004173\n",
      "Epoch  13 Update  861 Cost  0.0688901990652\n",
      "Epoch  13 Update  882 Cost  0.076161518693\n",
      "Training error: 0.095212303102\n",
      "Training_correct error 0.0896815881133\n",
      "Testing_correct error: 0.106938906014\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11, 11, 40, 20, 35] : 35\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78, 75, 176, 116, 163] : 163\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141, 145, 267, 196, 243] : 243\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      56.379379\n",
      "std       59.249859\n",
      "min        1.000000\n",
      "25%       11.000000\n",
      "50%       36.500000\n",
      "75%       82.500000\n",
      "max      368.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20, 13, 29, 14, 29] : 29\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116, 84, 173, 90, 128] : 128\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183, 135, 278, 132, 213] : 213\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       98.666000\n",
      "std       106.431326\n",
      "min         1.000000\n",
      "25%        14.000000\n",
      "50%        57.000000\n",
      "75%       163.000000\n",
      "max       488.000000\n",
      "dtype: float64\n",
      "epoch took: 9.42395615578\n",
      "\n",
      "Epoch 14 ...\n",
      "Epoch  14 Update  903 Cost  0.0628397241235\n",
      "Epoch  14 Update  924 Cost  0.104264914989\n",
      "Epoch  14 Update  945 Cost  0.143438011408\n",
      "Training error: 0.100691311061\n",
      "Training_correct error 0.095749117434\n",
      "Testing_correct error: 0.104808270931\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11, 11, 40, 20, 35, 32] : 32\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78, 75, 176, 116, 163, 154] : 154\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141, 145, 267, 196, 243, 231] : 231\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      59.293293\n",
      "std       64.978943\n",
      "min        1.000000\n",
      "25%       11.500000\n",
      "50%       34.500000\n",
      "75%       88.250000\n",
      "max      348.500000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20, 13, 29, 14, 29, 28] : 28\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116, 84, 173, 90, 128, 143] : 143\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183, 135, 278, 132, 213, 215] : 215\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       92.140000\n",
      "std       105.503941\n",
      "min         1.000000\n",
      "25%        14.000000\n",
      "50%        47.250000\n",
      "75%       134.000000\n",
      "max       456.000000\n",
      "dtype: float64\n",
      "epoch took: 9.4343521595\n",
      "\n",
      "Epoch 15 ...\n",
      "Epoch  15 Update  966 Cost  0.0686482489109\n",
      "Epoch  15 Update  987 Cost  0.046213619411\n",
      "Epoch  15 Update  1008 Cost  0.110384769738\n",
      "Training error: 0.105883665383\n",
      "Training_correct error 0.133898794651\n",
      "Testing_correct error: 0.119663268328\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11, 11, 40, 20, 35, 32, 45] : 45\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78, 75, 176, 116, 163, 154, 162] : 162\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141, 145, 267, 196, 243, 231, 237] : 237\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      60.879880\n",
      "std       65.321665\n",
      "min        1.000000\n",
      "25%       12.000000\n",
      "50%       38.000000\n",
      "75%       89.000000\n",
      "max      379.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20, 13, 29, 14, 29, 28, 31] : 31\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116, 84, 173, 90, 128, 143, 151] : 151\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183, 135, 278, 132, 213, 215, 236] : 236\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       81.195000\n",
      "std        94.777258\n",
      "min         1.000000\n",
      "25%        11.000000\n",
      "50%        44.000000\n",
      "75%       117.125000\n",
      "max       458.000000\n",
      "dtype: float64\n",
      "epoch took: 9.43506002426\n",
      "\n",
      "Epoch 16 ...\n",
      "Epoch  16 Update  1029 Cost  0.0738225132227\n",
      "Epoch  16 Update  1050 Cost  0.0791482329369\n",
      "Epoch  16 Update  1071 Cost  0.122023738921\n",
      "Training error: 0.138372257352\n",
      "Training_correct error 0.175732344389\n",
      "Testing_correct error: 0.14814285934\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11, 11, 40, 20, 35, 32, 45, 14] : 14\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78, 75, 176, 116, 163, 154, 162, 79] : 79\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141, 145, 267, 196, 243, 231, 237, 131] : 131\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      79.864865\n",
      "std       69.967866\n",
      "min        1.000000\n",
      "25%       25.500000\n",
      "50%       61.500000\n",
      "75%      115.500000\n",
      "max      419.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20, 13, 29, 14, 29, 28, 31, 13] : 13\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116, 84, 173, 90, 128, 143, 151, 91] : 91\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183, 135, 278, 132, 213, 215, 236, 152] : 152\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean       95.70200\n",
      "std        95.00136\n",
      "min         1.00000\n",
      "25%        22.00000\n",
      "50%        64.00000\n",
      "75%       140.25000\n",
      "max       497.00000\n",
      "dtype: float64\n",
      "epoch took: 9.44844412804\n",
      "\n",
      "Epoch 17 ...\n",
      "Epoch  17 Update  1092 Cost  0.152907967567\n",
      "Epoch  17 Update  1113 Cost  0.0934856459498\n",
      "Epoch  17 Update  1134 Cost  0.262294322252\n",
      "Training error: 0.0897378996015\n",
      "Training_correct error 0.0713714361191\n",
      "Testing_correct error: 0.0717159435153\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11, 11, 40, 20, 35, 32, 45, 14, 29] : 29\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78, 75, 176, 116, 163, 154, 162, 79, 164] : 164\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141, 145, 267, 196, 243, 231, 237, 131, 257] : 257\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      45.735736\n",
      "std       46.451515\n",
      "min        1.000000\n",
      "25%       10.000000\n",
      "50%       31.000000\n",
      "75%       69.000000\n",
      "max      358.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20, 13, 29, 14, 29, 28, 31, 13, 33] : 33\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116, 84, 173, 90, 128, 143, 151, 91, 153] : 153\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183, 135, 278, 132, 213, 215, 236, 152, 254] : 254\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean       73.04000\n",
      "std        86.48335\n",
      "min         1.00000\n",
      "25%        10.00000\n",
      "50%        39.00000\n",
      "75%       104.00000\n",
      "max       471.00000\n",
      "dtype: float64\n",
      "epoch took: 9.4118411541\n",
      "\n",
      "Epoch 18 ...\n",
      "Epoch  18 Update  1155 Cost  0.056145131588\n",
      "Epoch  18 Update  1176 Cost  0.0740430057049\n",
      "Epoch  18 Update  1197 Cost  0.0531010627747\n",
      "Training error: 0.0881845727563\n",
      "Training_correct error 0.102489724755\n",
      "Testing_correct error: 0.0974953621626\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11, 11, 40, 20, 35, 32, 45, 14, 29, 51] : 51\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78, 75, 176, 116, 163, 154, 162, 79, 164, 190] : 190\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141, 145, 267, 196, 243, 231, 237, 131, 257, 278] : 278\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      46.719720\n",
      "std       52.435054\n",
      "min        1.000000\n",
      "25%        8.500000\n",
      "50%       30.000000\n",
      "75%       66.500000\n",
      "max      408.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20, 13, 29, 14, 29, 28, 31, 13, 33, 28] : 28\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116, 84, 173, 90, 128, 143, 151, 91, 153, 178] : 178\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183, 135, 278, 132, 213, 215, 236, 152, 254, 277] : 277\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       69.514000\n",
      "std        85.716656\n",
      "min         1.000000\n",
      "25%         8.000000\n",
      "50%        32.000000\n",
      "75%        96.000000\n",
      "max       491.000000\n",
      "dtype: float64\n",
      "epoch took: 9.41775298119\n",
      "\n",
      "Epoch 19 ...\n",
      "Epoch  19 Update  1218 Cost  0.0804895088077\n",
      "Epoch  19 Update  1239 Cost  0.0693650692701\n",
      "Epoch  19 Update  1260 Cost  0.0549708046019\n",
      "Training error: 0.118798777461\n",
      "Training_correct error 0.114684522152\n",
      "Testing_correct error: 0.0989385396242\n",
      "top- 1 = [1, 8, 4, 13, 6, 8, 20, 11, 18, 22, 11, 11, 40, 20, 35, 32, 45, 14, 29, 51, 17] : 17\n",
      "top- 5 = [17, 37, 35, 61, 33, 67, 90, 81, 79, 110, 78, 75, 176, 116, 163, 154, 162, 79, 164, 190, 114] : 114\n",
      "top- 10 = [26, 70, 67, 113, 70, 125, 140, 157, 151, 181, 141, 145, 267, 196, 243, 231, 237, 131, 257, 278, 186] : 186\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      65.865866\n",
      "std       62.335262\n",
      "min        1.000000\n",
      "25%       16.000000\n",
      "50%       47.000000\n",
      "75%       97.000000\n",
      "max      369.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 5, 3, 15, 5, 16, 13, 16, 13, 17, 20, 13, 29, 14, 29, 28, 31, 13, 33, 28, 18] : 18\n",
      "top- 5 = [16, 23, 37, 63, 27, 73, 67, 76, 85, 95, 116, 84, 173, 90, 128, 143, 151, 91, 153, 178, 122] : 122\n",
      "top- 10 = [29, 50, 72, 120, 52, 126, 118, 124, 138, 162, 183, 135, 278, 132, 213, 215, 236, 152, 254, 277, 191] : 191\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       86.940000\n",
      "std        95.034062\n",
      "min         1.000000\n",
      "25%        14.000000\n",
      "50%        53.750000\n",
      "75%       131.875000\n",
      "max       508.000000\n",
      "dtype: float64\n",
      "epoch took: 9.40775108337\n",
      "saving the model...\n",
      "Evaluate the model using fast estimation...\n",
      "count    999.000000\n",
      "mean      65.865866\n",
      "std       62.335262\n",
      "min        1.000000\n",
      "25%       16.000000\n",
      "50%       47.000000\n",
      "75%       97.000000\n",
      "max      369.000000\n",
      "dtype: float64\n",
      "count    1000.000000\n",
      "mean       86.940000\n",
      "std        95.034062\n",
      "min         1.000000\n",
      "25%        14.000000\n",
      "50%        53.750000\n",
      "75%       131.875000\n",
      "max       508.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "run main.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gIiIiLUBpXp4jiSvXHijNygJj6vRVmpVVdRv5jbeF1ZAhQxg5ciQAbdq0IT4+nsTERIwx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AAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dd3b4ae10>"
      ]
     },
     "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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BgYE26167do3OnTuzbds2FixYQEJCArGxsYwZM4apU6cye/Zsq+cNHjzY0v6m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\n81BmbJ3B+hHrea37a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fJNv/8O//63VigHtKQ/NHQuCQlmGjWqRErKKK5e9WLJEu28ZkWS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kTv+gTDBlChQtKgVgUBB8/DFs3gyu9mfeqlAoFAqFQqEwCJX5yyB7ruxh0YlFzOow6//t3Xd4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\nA9gJYU4B5+Z6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5db34ed450>"
      ]
     },
     "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": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sentences import prepare_data as prepare_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sentences.maxlen = 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences.maxlen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}