test_test_dqn_model_multiple_t08_noBuf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "curses is not supported on this machine (please install/reinstall curses for an optimal experience)\n",
      "---------0------------\n",
      "[{'id': '1', 'model': 'AR', 'num_r': 4, 'rate': 1.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '2', 'model': 'AR', 'num_r': 4, 'rate': 2.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '3', 'model': 'AR', 'num_r': 4, 'rate': 3.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_0-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_0-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_0-2000\n",
      "Initialization OK!----> user 3\n",
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      "97:r:[   -6.64483473    -9.45731463 -2758.3281316 ],p:[0.77184319 1.03573942 0.06615   ],op:[0.00515681 0.01876058 0.        ],tr:[0.92559189 0.63974772 0.05749391],pr:[0.18377731 1.41681954 0.07013706],rev:[0.9999 2.013  3.0102],dbuf:[2.14417365e+00 5.10657317e+00 1.37889947e+04],ch:[3.79396009 4.05129639 3.99721894],ibuf:[26.0, 17.0, 9.0],rbuf:[2.12736732e+00 5.41259895e+00 2.88346903e+04]\n",
      "98:r:[   -6.58958314    -9.27490562 -2905.0767987 ],p:[0.76650032 1.01054335 0.04195   ],op:[0.00514968 0.02080665 0.        ],tr:[0.96020542 0.61148811 0.03973211],pr:[0.15819969 1.41716836 0.03599932],rev:[1.0061 1.9767 3.044 ],dbuf:[2.08191572e+00 5.12052808e+00 1.45237060e+04],ch:[4.00923209 4.04833533 4.11213876],ibuf:[13.0, 48.0, 27.5],rbuf:[2.00000000e+00 9.09924977e+00 2.97101857e+04]\n",
      "99:r:[   -6.53154636    -9.34796948 -2856.64251489],p:[0.75885765 1.02065589 0.04495   ],op:[0.00559235 0.01969411 0.        ],tr:[0.98134515 0.63044876 0.04335613],pr:[0.15509996 1.4204456  0.04270735],rev:[1.0153 2.0031 3.0174],dbuf:[2.07973181e+00 5.12584741e+00 1.42814146e+04],ch:[4.07288047 3.9860629  3.96808313],ibuf:[41.0, 37.5, 4.5],rbuf:[5.33023130e+00 3.41259895e+00 2.93178652e+04]\n",
      "---------1------------\n",
      "[{'id': '1', 'model': 'AR', 'num_r': 4, 'rate': 1.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '2', 'model': 'AR', 'num_r': 4, 'rate': 2.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '3', 'model': 'AR', 'num_r': 4, 'rate': 3.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_1-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_1-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_1-2000\n",
      "Initialization OK!----> user 3\n",
      "0:r:[ -4.24767348  -8.77768364 -23.89405125],p:[0.28159428 0.8413701  1.4954066 ],op:[0.21475572 0.0663299  0.0003434 ],tr:[0.07882126 0.52192076 1.14976262],pr:[1.41012961 1.59471418 1.84351279],rev:[0.9927 1.978  2.9943],dbuf:[ 1.38436738  7.58041818 59.64025623],ch:[4.0103975  3.98348593 4.19156891],ibuf:[27.0, 23.0, 26.5],rbuf:[ 2.41259895 14.52613465 50.3870613 ]\n"
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      "99:r:[  -4.22804544   -8.87559232 -342.27333695],p:[0.28119776 0.85922576 1.135     ],op:[0.21255224 0.05367424 0.        ],tr:[0.07145106 0.54628863 0.94117789],pr:[1.40881843 1.58353199 1.67400083],rev:[0.9997 2.0154 3.0238],dbuf:[1.39022719e+00 7.86196160e+00 1.66596668e+03],ch:[3.99893874 3.99821358 3.93899991],ibuf:[14.0, 38.5, 43.5],rbuf:[0.00000000e+00 2.27760179e+00 4.12971288e+03]\n",
      "---------2------------\n",
      "[{'id': '1', 'model': 'AR', 'num_r': 4, 'rate': 1.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '2', 'model': 'AR', 'num_r': 4, 'rate': 2.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '3', 'model': 'AR', 'num_r': 4, 'rate': 3.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_2-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_2-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_2-2000\n",
      "Initialization OK!----> user 3\n",
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      "99:r:[ -4.22941175  -9.2102809  -54.22351289],p:[0.44020557 0.93132905 1.77881602],op:[0.00094443 0.00832095 0.00043398],tr:[0.25095088 0.76515935 1.31041108],pr:[0.75035482 1.27967512 1.67313212],rev:[0.9984 2.025  3.0053],dbuf:[  3.50105877   8.46540449 199.94756446],ch:[3.9841476  3.93322663 3.96539957],ibuf:[19.5, 29.0, 9.5],rbuf:[  3.87058858   4.47164193 238.91151454]\n",
      "---------3------------\n",
      "[{'id': '1', 'model': 'AR', 'num_r': 4, 'rate': 1.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '2', 'model': 'AR', 'num_r': 4, 'rate': 2.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '3', 'model': 'AR', 'num_r': 4, 'rate': 3.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_3-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_3-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_3-2000\n",
      "Initialization OK!----> user 3\n",
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      "---------4------------\n",
      "[{'id': '1', 'model': 'AR', 'num_r': 4, 'rate': 1.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '2', 'model': 'AR', 'num_r': 4, 'rate': 2.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '3', 'model': 'AR', 'num_r': 4, 'rate': 3.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_4-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_4-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_4-2000\n",
      "Initialization OK!----> user 3\n",
      "0:r:[ -6.08298263 -13.07171567 -18.3838156 ],p:[0.58935167 1.3097591  1.97076733],op:[0.12939833 0.0010409  0.06478267],tr:[0.66898313 1.01099371 1.53580088],pr:[0.74902991 0.9866147  1.69702799],rev:[1.0147 1.9958 3.0149],dbuf:[ 1.66491314 12.92657835 10.49707798],ch:[4.01757099 4.05441604 3.92881487],ibuf:[11.5, 6.5, 26.0],rbuf:[ 2.79468272 15.47506089  2.49674589]\n",
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      "99:r:[ -6.00683076 -12.85250428 -18.50600063],p:[0.57998683 1.30006786 1.98636629],op:[0.13006317 0.00328214 0.05848371],tr:[0.65202073 0.97485643 1.52745374],pr:[0.7488365  1.03391835 1.69815239],rev:[1.0032 1.9996 3.0237],dbuf:[ 1.63215379 12.12852138 10.73600317],ch:[3.92711944 3.93872667 3.96108405],ibuf:[35.5, 48.0, 45.0],rbuf:[ 1.         14.74166339 12.34306776]\n",
      "---------5------------\n",
      "[{'id': '1', 'model': 'AR', 'num_r': 4, 'rate': 1.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '2', 'model': 'AR', 'num_r': 4, 'rate': 2.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '3', 'model': 'AR', 'num_r': 4, 'rate': 3.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_5-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_5-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_5-2000\n",
      "Initialization OK!----> user 3\n",
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      "97:r:[ -6.18150164  -8.92219616 -55.24999883],p:[0.71079646 0.96764422 2.03885   ],op:[0.00305354 0.01735578 0.        ],tr:[0.86810875 0.59137399 0.72195762],pr:[0.18738761 1.44496818 2.24015009],rev:[0.9886 1.9817 2.9787],dbuf:[  2.35350818   5.2109808  194.69599413],ch:[4.21374727 3.95149352 3.82248503],ibuf:[0.0, 39.5, 27.0],rbuf:[  1.           4.688549   192.92296151]\n",
      "98:r:[ -6.36687371  -9.01699937 -55.25506171],p:[0.7331143  0.97934988 2.0178    ],op:[0.0021357  0.01835012 0.        ],tr:[0.86809136 0.61070647 0.79020634],pr:[0.19455918 1.44954657 2.21761967],rev:[0.9945 2.0004 3.0239],dbuf:[  2.42436856   5.17699685 195.56330856],ch:[3.95894364 3.96790099 4.08157998],ibuf:[41.0, 27.5, 37.0],rbuf:[  4.77628812   1.29816986 197.73988556]\n"
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    {
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     "output_type": "stream",
     "text": [
      "99:r:[ -6.31517288  -8.93487329 -55.18651632],p:[0.72688267 0.97076849 2.019     ],op:[0.00276733 0.01938151 0.        ],tr:[0.85451382 0.61289594 0.76349558],pr:[0.20739751 1.44000148 2.22512735],rev:[1.0003 1.9925 3.0041],dbuf:[  2.38986438   5.06836645 195.17258158],ch:[3.9355364  3.95436166 4.00231783],ibuf:[12.5, 37.5, 37.0],rbuf:[  5.23717495   5.97685139 191.77069192]\n",
      "---------6------------\n",
      "[{'id': '1', 'model': 'AR', 'num_r': 4, 'rate': 1.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '2', 'model': 'AR', 'num_r': 4, 'rate': 2.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '3', 'model': 'AR', 'num_r': 4, 'rate': 3.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_6-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_6-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_6-2000\n",
      "Initialization OK!----> user 3\n",
      "0:r:[   -3.51161085   -10.76506816 -1635.30563589],p:[0.32089324 1.13712174 1.46015   ],op:[0.05350676 0.01872826 0.        ],tr:[0.10884178 1.07867585 0.50629323],pr:[1.01105334 1.02720513 1.47831712],rev:[1.0052 2.0172 2.9822],dbuf:[2.58205423e+00 7.59134081e+00 8.11812218e+03],ch:[3.99342117 4.07679133 3.97490705],ibuf:[18.5, 33.5, 11.5],rbuf:[2.00000000e+00 9.31093896e+00 9.98739650e+03]\n",
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      "4:r:[   -3.51805777   -10.70940432 -1659.06313689],p:[0.32144818 1.12622066 1.4653806 ],op:[5.15018181e-02 2.01793374e-02 1.93963643e-05],tr:[0.10426774 1.05132838 0.50906662],pr:[1.00304154 1.02975685 1.48399177],rev:[1.0027 1.9861 2.9977],dbuf:[2.67228886e+00 7.69102161e+00 8.23669968e+03],ch:[3.9983006  4.11263579 4.09317078],ibuf:[15.5, 5.5, 4.0],rbuf:[1.22994693e+00 5.00000000e+00 1.00506557e+04]\n",
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      "6:r:[   -3.47996828   -10.65498149 -1654.66926437],p:[0.3173358  1.12086919 1.47835   ],op:[0.0512642  0.02373081 0.        ],tr:[0.10509308 1.05756026 0.50556129],pr:[0.99858582 1.03015979 1.49419762],rev:[1.0026 1.9818 3.0039],dbuf:[2.65584140e+00 7.49090746e+00 8.21421232e+03],ch:[4.03794007 4.08942824 3.87461384],ibuf:[12.0, 20.5, 34.0],rbuf:[1.00000000e+00 8.02284539e+00 1.00754109e+04]\n",
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      "98:r:[   -3.44730216   -10.74462818 -1630.31093256],p:[0.31477915 1.13150064 1.46805   ],op:[0.04947085 0.01914936 0.        ],tr:[0.0865461  1.03897387 0.4905524 ],pr:[1.0044718  1.05316007 1.48847424],rev:[0.9994 2.0032 2.9623],dbuf:[2.66651078e+00 7.69714092e+00 8.09283266e+03],ch:[3.86070061 3.93916324 3.94640258],ibuf:[0.0, 37.0, 29.0],rbuf:[1.13813906e+01 1.00000000e+00 9.86173365e+03]\n",
      "99:r:[   -3.51060167   -10.85729921 -1646.72847954],p:[0.32217359 1.14016247 1.47765   ],op:[0.04867641 0.02288753 0.        ],tr:[0.10594495 1.03371554 0.49892242],pr:[0.9982093  1.08004668 1.49575953],rev:[1.0061 2.0137 2.9943],dbuf:[2.71900835e+00 7.76449603e+00 8.17453640e+03],ch:[4.00618847 3.93374014 4.04302211],ibuf:[14.5, 1.5, 22.5],rbuf:[1.41259895e+00 7.28361558e+00 1.00186805e+04]\n",
      "---------7------------\n",
      "[{'id': '1', 'model': 'AR', 'num_r': 4, 'rate': 1.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '2', 'model': 'AR', 'num_r': 4, 'rate': 2.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '3', 'model': 'AR', 'num_r': 4, 'rate': 3.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_7-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_7-2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_7-2000\n",
      "Initialization OK!----> user 3\n",
      "0:r:[  -7.58514534  -12.37017453 -121.5145047 ],p:[0.88299647 0.77349288 1.5029    ],op:[0.01170353 0.00135712 0.        ],tr:[1.24086599 0.393154   0.90014847],pr:[0.32621092 1.59218726 2.00359913],rev:[1.01   1.9802 2.9916],dbuf:[  2.13772672  30.85687266 547.45652349],ch:[3.98938888 3.98668472 4.04530321],ibuf:[48.0, 26.0, 26.0],rbuf:[  2.4464011    8.16270237 904.52394944]\n",
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      "12:r:[  -7.51683983  -12.84752116 -139.81809658],p:[0.88454653 0.80337111 1.50125   ],op:[0.01370347 0.00032889 0.        ],tr:[1.24707951 0.42295988 0.88828248],pr:[0.31287675 1.59786109 2.00137137],rev:[0.9943 2.0196 2.9968],dbuf:[  1.65419913  32.0896058  639.04048291],ch:[3.92278321 3.98060715 3.95369733],ibuf:[23.5, 39.0, 10.5],rbuf:[   2.           38.08580888 1081.9615341 ]\n",
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      "98:r:[  -7.54709685  -12.57649218 -131.16472025],p:[0.88830654 0.79538757 1.5027    ],op:[0.01439346 0.00041243 0.        ],tr:[1.27809702 0.40361862 0.90404792],pr:[0.29747896 1.60557771 2.00457142],rev:[0.9853 2.0085 2.9951],dbuf:[  1.62748426  31.05046089 595.71560126],ch:[4.06208093 4.05531266 4.09681925],ibuf:[15.0, 38.0, 24.5],rbuf:[  1.          44.60700026 889.30660271]\n",
      "99:r:[  -7.57038435  -13.1348667  -116.49294014],p:[0.88961409 0.80150683 1.4979    ],op:[0.01283591 0.00079317 0.        ],tr:[1.28143304 0.40989798 0.90577642],pr:[0.30065376 1.60562242 2.00159205],rev:[0.9998 2.0117 2.9903],dbuf:[  1.75392176  33.58233352 522.5487007 ],ch:[3.98414887 3.86085896 4.02942739],ibuf:[33.0, 47.5, 1.0],rbuf:[1.14837867e-01 3.81469801e+01 8.30315260e+02]\n",
      "---------8------------\n",
      "[{'id': '1', 'model': 'AR', 'num_r': 4, 'rate': 1.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '2', 'model': 'AR', 'num_r': 4, 'rate': 2.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '3', 'model': 'AR', 'num_r': 4, 'rate': 3.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_8-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_8-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_8-2000\n",
      "Initialization OK!----> user 3\n",
      "0:r:[ -5.71923622 -19.52042444 -40.99295407],p:[0.55606724 0.88875    1.89755   ],op:[0.10518276 0.         0.        ],tr:[0.53458518 0.2549056  0.99437589],pr:[0.64710715 1.74280908 1.98931369],rev:[0.9936 2.0006 2.9957],dbuf:[  2.1461811   62.05212219 129.06277037],ch:[3.94291462 3.85060858 3.96650852],ibuf:[34.0, 38.5, 19.5],rbuf:[  1.54859124  67.353114   139.60425241]\n",
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      "99:r:[ -5.68359695 -18.99364819 -39.86860898],p:[0.5546524  0.87988313 1.8624    ],op:[1.02847595e-01 6.68711277e-05 0.00000000e+00],tr:[0.54395067 0.27226887 0.97905616],pr:[0.62780279 1.72376581 1.97870989],rev:[0.9941 1.9971 2.9689],dbuf:[  2.11798477  59.77024095 124.84704491],ch:[3.98697051 4.02710914 4.04042311],ibuf:[23.5, 44.0, 29.0],rbuf:[  3.          60.10767199 140.33947253]\n",
      "---------9------------\n",
      "[{'id': '1', 'model': 'AR', 'num_r': 4, 'rate': 1.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '2', 'model': 'AR', 'num_r': 4, 'rate': 2.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}, {'id': '3', 'model': 'AR', 'num_r': 4, 'rate': 3.0, 'dis': 100, 'action_bound': 2, 'data_buf_size': 100, 't_factor': 0.8, 'penalty': 1000, 'action_level': 5}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_9-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_9-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_08_nB_dqn/my_train_model_9-2000\n",
      "Initialization OK!----> user 3\n",
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     ]
    }
   ],
   "source": [
    "from mec_env_var import *\n",
    "from helper import *\n",
    "import tensorflow as tf\n",
    "import ipdb as pdb\n",
    "import time\n",
    "\n",
    "for k in range(10):\n",
    "    \n",
    "    tf.reset_default_graph()\n",
    "    \n",
    "    print('---------' + str(k) + '------------')\n",
    "    \n",
    "    MAX_EPISODE = 100\n",
    "    MAX_EPISODE_LEN = 10000\n",
    "\n",
    "    NUM_T = 1\n",
    "    NUM_R = 4\n",
    "    SIGMA2 = 1e-9\n",
    "\n",
    "    t_factor = 0.8\n",
    "    epsilon = 1.0\n",
    "\n",
    "    config = {'state_dim':3, 'action_dim':2};\n",
    "    train_config = {'minibatch_size':64, 'actor_lr':0.0001, 'tau':0.001, \n",
    "                    'critic_lr':0.001, 'gamma':0.99, 'buffer_size':250000, \n",
    "                    'random_seed':int(time.clock()*1000%1000), 'epsilon':epsilon, 'sigma2':SIGMA2}\n",
    "    \n",
    "    IS_TRAIN = False\n",
    "#     rate = 3.0\n",
    "    res_path = 't_M_08_nB_dqn/'\n",
    "    model_path = 'model_M_08_nB_dqn/my_train_model_'+str(k)+'-2000'\n",
    "    meta_path = model_path+'.meta'\n",
    "    init_path = ''\n",
    "    \n",
    "    user_config = [{'id':'1', 'model':'AR', 'num_r':NUM_R, 'rate':1.0, 'dis':100, 'action_bound':2, \n",
    "                    'data_buf_size':100, 't_factor':t_factor, 'penalty':1000, 'action_level':5},\n",
    "                   {'id':'2', 'model':'AR', 'num_r':NUM_R, 'rate':2.0, 'dis':100, 'action_bound':2, \n",
    "                    'data_buf_size':100, 't_factor':t_factor, 'penalty':1000, 'action_level':5},\n",
    "                   {'id':'3', 'model':'AR', 'num_r':NUM_R, 'rate':3.0, 'dis':100, 'action_bound':2, \n",
    "                    'data_buf_size':100, 't_factor':t_factor, 'penalty':1000, 'action_level':5}]\n",
    "\n",
    "    print(user_config)\n",
    "    # 0. initialize the session object\n",
    "    sess = tf.Session() \n",
    "\n",
    "    # gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)\n",
    "    # sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) \n",
    "\n",
    "    # 1. include all user in the system according to the user_config\n",
    "    user_list = [];\n",
    "    for info in user_config:\n",
    "        info.update(config)\n",
    "        info['model_path'] = model_path\n",
    "        info['meta_path'] = info['model_path']+'.meta'\n",
    "        info['init_path'] = init_path\n",
    "        user_list.append(MecTermDQN_LD(sess, info, train_config))\n",
    "        print('Initialization OK!----> user ' + info['id'])\n",
    "\n",
    "    # 2. create the simulation env\n",
    "    env = MecSvrEnv(user_list, NUM_R, SIGMA2, MAX_EPISODE_LEN)\n",
    "\n",
    "#     sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "#     tflearn.config.is_training(is_training=IS_TRAIN, session=sess)\n",
    "\n",
    "#     env.init_target_network()\n",
    "\n",
    "    res_r = []\n",
    "    res_p = []\n",
    "    res_b = []\n",
    "    res_o = []\n",
    "    res_d = []\n",
    "    # 3. start to explore for each episode\n",
    "    for i in range(MAX_EPISODE):\n",
    "\n",
    "        cur_init_ds_ep = env.reset()\n",
    "\n",
    "        cur_r_ep = np.zeros(len(user_list))\n",
    "        cur_p_ep = np.zeros(len(user_list))\n",
    "        cur_op_ep = np.zeros(len(user_list))\n",
    "        cur_ts_ep = np.zeros(len(user_list))\n",
    "        cur_ps_ep = np.zeros(len(user_list))\n",
    "        cur_rs_ep = np.zeros(len(user_list))\n",
    "        cur_ds_ep = np.zeros(len(user_list))\n",
    "        cur_ch_ep = np.zeros(len(user_list))\n",
    "        cur_of_ep = np.zeros(len(user_list))\n",
    "\n",
    "        for j in range(MAX_EPISODE_LEN):\n",
    "\n",
    "            # first try to transmit from current state\n",
    "            [cur_r, done, cur_p, cur_op, temp, cur_ts, cur_ps, cur_rs, cur_ds, cur_ch, cur_of] = env.step_transmit()\n",
    "\n",
    "            cur_r_ep += cur_r\n",
    "            cur_p_ep += cur_p\n",
    "            cur_op_ep += cur_op\n",
    "            cur_ts_ep += cur_ts\n",
    "            cur_ps_ep += cur_ps\n",
    "            cur_rs_ep += cur_rs\n",
    "            cur_ds_ep += cur_ds\n",
    "            cur_ch_ep += cur_ch\n",
    "            cur_of_ep += cur_of\n",
    "\n",
    "\n",
    "            if done:\n",
    "                res_r.append(cur_r_ep/MAX_EPISODE_LEN)\n",
    "                res_p.append(cur_p_ep/MAX_EPISODE_LEN)\n",
    "                res_b.append(cur_ds_ep/MAX_EPISODE_LEN)\n",
    "                res_o.append(cur_of_ep/MAX_EPISODE_LEN)\n",
    "                res_d.append(cur_ds)\n",
    "                print('%d:r:%s,p:%s,op:%s,tr:%s,pr:%s,rev:%s,dbuf:%s,ch:%s,ibuf:%s,rbuf:%s' % (i, cur_r_ep/MAX_EPISODE_LEN, cur_p_ep/MAX_EPISODE_LEN, cur_op_ep/MAX_EPISODE_LEN, cur_ts_ep/MAX_EPISODE_LEN, cur_ps_ep/MAX_EPISODE_LEN, cur_rs_ep/MAX_EPISODE_LEN, cur_ds_ep/MAX_EPISODE_LEN, cur_ch_ep/MAX_EPISODE_LEN, cur_init_ds_ep, cur_ds))\n",
    "\n",
    "    name = res_path+'test_1000_' + time.strftime(\"%b_%d_%Y_%H_%M_%S\", time.localtime(time.time()))\n",
    "    np.savez(name, res_r, res_p, res_b, res_o, res_d)\n",
    "    \n",
    "    sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'res_rec' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-425790d5479b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m't_10/test_1000_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%b_%d_%Y_%H_%M_%S\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocaltime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_everything\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_rec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavez\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'res_rec' is not defined"
     ]
    }
   ],
   "source": [
    "name = 't_10/test_1000_' + time.strftime(\"%b_%d_%Y_%H_%M_%S\", time.localtime(time.time()))\n",
    "a,b,c,d = plot_everything(res_rec, win=10)\n",
    "np.savez(name, a,b,c,d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 't_10/test_1000_' + time.strftime(\"%b_%d_%Y_%H_%M_%S\", time.localtime(time.time()))\n",
    "a,b,c,d = plot_everything(res_rec, win=10)\n",
    "np.savez(name, a,b,c,d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'env' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-5b799163863e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcur_init_ds_ep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mcur_r_ep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muser_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mcur_p_ep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muser_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mcur_n_ep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muser_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'env' is not defined"
     ]
    }
   ],
   "source": [
    "cur_init_ds_ep = env.reset()\n",
    "    \n",
    "cur_r_ep = np.zeros(len(user_list))\n",
    "cur_p_ep = np.zeros(len(user_list))\n",
    "cur_n_ep = np.zeros(len(user_list))\n",
    "cur_ts_ep = np.zeros(len(user_list))\n",
    "cur_rs_ep = np.zeros(len(user_list))\n",
    "cur_ps_ep = np.zeros(len(user_list))\n",
    "cur_ds_ep = np.zeros(len(user_list))\n",
    "cur_ch_ep = np.zeros(len(user_list))\n",
    "    \n",
    "for j in range(1000):\n",
    "    # first try to transmit from current state\n",
    "    [cur_r, done, cur_p, cur_n, cur_ts, cur_ps, cur_rs, cur_ds, cur_ch, cur_of] = env.step_transmit()\n",
    "        \n",
    "    cur_r_ep += cur_r\n",
    "    cur_p_ep += cur_p\n",
    "    cur_ts_ep += cur_ts\n",
    "    cur_ps_ep += cur_ps\n",
    "    cur_rs_ep += cur_rs\n",
    "    cur_ds_ep += cur_ds\n",
    "    cur_ch_ep += cur_ch\n",
    "        \n",
    "    print('%d:r:%f,p:%s,n:%s,tr:%s,pr:%s,rev:%s,dbuf:%s,ch:%s,ibuf:%s' % (j, cur_r, cur_p, cur_n, cur_ts, cur_ps, cur_rs, cur_ds, cur_ch, cur_init_ds_ep))\n",
    "        \n",
    "print('r:%f,p:%s,n:%s,tr:%s,rev:%s,dbuf:%s,ch:%s,ibuf:%s' % (cur_r_ep/MAX_EPISODE_LEN, cur_p_ep/MAX_EPISODE_LEN, cur_n_ep/MAX_EPISODE_LEN, cur_ts_ep/MAX_EPISODE_LEN, cur_rs_ep/MAX_EPISODE_LEN, cur_ds_ep/MAX_EPISODE_LEN, cur_ch_ep/MAX_EPISODE_LEN, cur_init_ds_ep))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.78826445 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "graph = tf.get_default_graph()\n",
    "inputs = graph.get_tensor_by_name(\"input_1\"+\":0\")\n",
    "scaled_out = graph.get_tensor_by_name(\"output_1\"+\":0\")\n",
    "print sess.run(scaled_out, feed_dict={inputs: [[7.22254768,2.3384,2.7784888e-05]]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:r:-50.8910,p:1.4033,tr:1.4707,pr:1.3505,rev:2.9594,dbuf:72.5261,ch:0.00002568,ibuf:4\n",
      "1:r:-57.9454,p:1.3913,tr:1.6529,pr:1.2218,rev:3.0360,dbuf:74.1439,ch:0.00002761,ibuf:30\n",
      "2:r:-51.0844,p:1.1953,tr:1.5476,pr:1.2320,rev:2.8984,dbuf:73.1955,ch:0.00002990,ibuf:39\n",
      "3:r:-18.2811,p:1.3469,tr:1.5860,pr:1.3673,rev:2.9762,dbuf:61.5868,ch:0.00002683,ibuf:35\n",
      "4:r:-17.5547,p:1.2180,tr:1.7657,pr:1.2518,rev:3.0115,dbuf:55.9275,ch:0.00003238,ibuf:63\n",
      "5:r:-55.6239,p:1.4116,tr:1.4240,pr:1.3390,rev:2.8791,dbuf:69.1011,ch:0.00002492,ibuf:48\n",
      "6:r:-37.3900,p:1.2706,tr:1.3830,pr:1.5578,rev:3.0240,dbuf:59.5109,ch:0.00002915,ibuf:8\n",
      "7:r:-32.6223,p:1.4738,tr:1.3452,pr:1.6902,rev:3.0674,dbuf:63.5657,ch:0.00002512,ibuf:71\n",
      "8:r:-20.8140,p:1.2057,tr:1.4394,pr:1.5456,rev:3.0451,dbuf:49.6221,ch:0.00002902,ibuf:38\n",
      "9:r:-19.3836,p:1.1973,tr:1.4459,pr:1.4838,rev:2.9358,dbuf:56.0717,ch:0.00002922,ibuf:86\n"
     ]
    }
   ],
   "source": [
    "res_rec = []\n",
    "res_init_buf = []\n",
    "# 3. start to explore for each episode\n",
    "for i in range(MAX_EPISODE):\n",
    "    \n",
    "    cur_init_ds_ep = env.reset(3)\n",
    "        \n",
    "    res_init_buf.append(cur_init_ds_ep)\n",
    "    \n",
    "    cur_r_ep = np.zeros(len(user_list))\n",
    "    cur_p_ep = np.zeros(len(user_list))\n",
    "    cur_ts_ep = np.zeros(len(user_list))\n",
    "    cur_ps_ep = np.zeros(len(user_list))\n",
    "    cur_rs_ep = np.zeros(len(user_list))\n",
    "    cur_ds_ep = np.zeros(len(user_list))\n",
    "    cur_ch_ep = np.zeros(len(user_list))\n",
    "    cur_of_ep = np.zeros(len(user_list))\n",
    "    \n",
    "    res_rec_tmp = []\n",
    "    for j in range(MAX_EPISODE_LEN):\n",
    "        \n",
    "        # first try to transmit from current state\n",
    "        [cur_r, done, cur_p, temp, cur_ts, cur_ps, cur_rs, cur_ds, cur_ch, cur_of] = env.step_transmit(False)\n",
    "        \n",
    "        res_rec_tmp.append([cur_r, done, cur_p, temp, cur_ts, cur_ps, cur_rs, cur_ds, cur_ch, cur_of])\n",
    "        cur_r_ep += cur_r\n",
    "        cur_p_ep += cur_p\n",
    "        cur_ts_ep += cur_ts\n",
    "        cur_ps_ep += cur_ps\n",
    "        cur_rs_ep += cur_rs\n",
    "        cur_ds_ep += cur_ds\n",
    "        cur_ch_ep += cur_ch\n",
    "        cur_of_ep += cur_of\n",
    "        \n",
    "        if done:\n",
    "            res_rec.append(res_rec_tmp)\n",
    "            print('%d:r:%.4f,p:%.4f,tr:%.4f,pr:%.4f,rev:%.4f,dbuf:%.4f,ch:%.8f,ibuf:%d' % (i, cur_r_ep/MAX_EPISODE_LEN, cur_p_ep/MAX_EPISODE_LEN, cur_ts_ep/MAX_EPISODE_LEN, cur_ps_ep/MAX_EPISODE_LEN, cur_rs_ep/MAX_EPISODE_LEN, cur_ds_ep/MAX_EPISODE_LEN, cur_ch_ep/MAX_EPISODE_LEN, cur_init_ds_ep[0]))\n",
    "                \n",
    "name = 'test_1000_' + str(time.asctime( time.localtime(time.time()) )) + '.npy'\n",
    "np.save(name, [res_rec, res_init_buf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'data_buf_size': 100, 'penalty': 1000, 'rate': 3, 'action_bound': 1, 'id': '1', 't_factor': 0.9, 'dis': 100}]\n",
      "INFO:tensorflow:Restoring parameters from model/my_test_model\n",
      "--Return--\n",
      "None\n",
      "> \u001b[0;32m<ipython-input-3-a12846566f97>\u001b[0m(30)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m     29 \u001b[0;31m\u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m---> 30 \u001b[0;31m\u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m     31 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> graph\n",
      "<tensorflow.python.framework.ops.Graph object at 0x11f3544d0>\n",
      "ipdb> graph.trainable_variables\n",
      "*** AttributeError: 'Graph' object has no attribute 'trainable_variables'\n",
      "ipdb> tf.trainable_variables()\n",
      "[<tf.Variable 'fully_connected/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 'fully_connected_1/weights:0' shape=(3, 300) dtype=float32_ref>, <tf.Variable 'fully_connected_1/BatchNorm/beta:0' shape=(300,) dtype=float32_ref>, <tf.Variable 'fully_connected_2/weights:0' shape=(300, 2) dtype=float32_ref>, <tf.Variable 'fully_connected_2/biases:0' shape=(2,) dtype=float32_ref>, <tf.Variable 'fully_connected_3/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected_3/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 'fully_connected_4/weights:0' shape=(3, 300) dtype=float32_ref>, <tf.Variable 'fully_connected_4/BatchNorm/beta:0' shape=(300,) dtype=float32_ref>, <tf.Variable 'fully_connected_5/weights:0' shape=(300, 2) dtype=float32_ref>, <tf.Variable 'fully_connected_5/biases:0' shape=(2,) dtype=float32_ref>, <tf.Variable 'fully_connected_6/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected_6/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 't1_Wc:0' shape=(400, 300) dtype=float32_ref>, <tf.Variable 't2_Wc:0' shape=(2, 300) dtype=float32_ref>, <tf.Variable 't2_bc:0' shape=(1, 300) dtype=float32_ref>, <tf.Variable 'dense/kernel:0' shape=(300, 1) dtype=float32_ref>, <tf.Variable 'dense/bias:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'fully_connected_7/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected_7/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 't1_Wt:0' shape=(400, 300) dtype=float32_ref>, <tf.Variable 't2_Wt:0' shape=(2, 300) dtype=float32_ref>, <tf.Variable 't2_bt:0' shape=(1, 300) dtype=float32_ref>, <tf.Variable 'dense_1/kernel:0' shape=(300, 1) dtype=float32_ref>, <tf.Variable 'dense_1/bias:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'fully_connected/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 'fully_connected_1/weights:0' shape=(3, 300) dtype=float32_ref>, <tf.Variable 'fully_connected_1/BatchNorm/beta:0' shape=(300,) dtype=float32_ref>, <tf.Variable 'fully_connected_2/weights:0' shape=(300, 2) dtype=float32_ref>, <tf.Variable 'fully_connected_2/biases:0' shape=(2,) dtype=float32_ref>, <tf.Variable 'fully_connected_3/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected_3/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 'fully_connected_4/weights:0' shape=(3, 300) dtype=float32_ref>, <tf.Variable 'fully_connected_4/BatchNorm/beta:0' shape=(300,) dtype=float32_ref>, <tf.Variable 'fully_connected_5/weights:0' shape=(300, 2) dtype=float32_ref>, <tf.Variable 'fully_connected_5/biases:0' shape=(2,) dtype=float32_ref>, <tf.Variable 'fully_connected_6/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected_6/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 't1_Wc:0' shape=(400, 300) dtype=float32_ref>, <tf.Variable 't2_Wc:0' shape=(2, 300) dtype=float32_ref>, <tf.Variable 't2_bc:0' shape=(1, 300) dtype=float32_ref>, <tf.Variable 'dense/kernel:0' shape=(300, 1) dtype=float32_ref>, <tf.Variable 'dense/bias:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'fully_connected_7/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected_7/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 't1_Wt:0' shape=(400, 300) dtype=float32_ref>, <tf.Variable 't2_Wt:0' shape=(2, 300) dtype=float32_ref>, <tf.Variable 't2_bt:0' shape=(1, 300) dtype=float32_ref>, <tf.Variable 'dense_1/kernel:0' shape=(300, 1) dtype=float32_ref>, <tf.Variable 'dense_1/bias:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'fully_connected/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 'fully_connected_1/weights:0' shape=(3, 300) dtype=float32_ref>, <tf.Variable 'fully_connected_1/BatchNorm/beta:0' shape=(300,) dtype=float32_ref>, <tf.Variable 'fully_connected_2/weights:0' shape=(300, 2) dtype=float32_ref>, <tf.Variable 'fully_connected_2/biases:0' shape=(2,) dtype=float32_ref>, <tf.Variable 'fully_connected_3/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected_3/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 'fully_connected_4/weights:0' shape=(3, 300) dtype=float32_ref>, <tf.Variable 'fully_connected_4/BatchNorm/beta:0' shape=(300,) dtype=float32_ref>, <tf.Variable 'fully_connected_5/weights:0' shape=(300, 2) dtype=float32_ref>, <tf.Variable 'fully_connected_5/biases:0' shape=(2,) dtype=float32_ref>, <tf.Variable 'fully_connected_6/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected_6/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 't1_Wc:0' shape=(400, 300) dtype=float32_ref>, <tf.Variable 't2_Wc:0' shape=(2, 300) dtype=float32_ref>, <tf.Variable 't2_bc:0' shape=(1, 300) dtype=float32_ref>, <tf.Variable 'dense/kernel:0' shape=(300, 1) dtype=float32_ref>, <tf.Variable 'dense/bias:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'fully_connected_7/weights:0' shape=(3, 400) dtype=float32_ref>, <tf.Variable 'fully_connected_7/BatchNorm/beta:0' shape=(400,) dtype=float32_ref>, <tf.Variable 't1_Wt:0' shape=(400, 300) dtype=float32_ref>, <tf.Variable 't2_Wt:0' shape=(2, 300) dtype=float32_ref>, <tf.Variable 't2_bt:0' shape=(1, 300) dtype=float32_ref>, <tf.Variable 'dense_1/kernel:0' shape=(300, 1) dtype=float32_ref>, <tf.Variable 'dense_1/bias:0' shape=(1,) dtype=float32_ref>]\n",
      "ipdb> exit()\n"
     ]
    },
    {
     "ename": "BdbQuit",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBdbQuit\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-a12846566f97>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;31m# 1. include all user in the system according to the user_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/bdb.pyc\u001b[0m in \u001b[0;36mtrace_dispatch\u001b[0;34m(self, frame, event, arg)\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'return'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_return\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'exception'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/bdb.pyc\u001b[0m in \u001b[0;36mdispatch_return\u001b[0;34m(self, frame, arg)\u001b[0m\n\u001b[1;32m     89\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe_returning\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquitting\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mBdbQuit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrace_dispatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mBdbQuit\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from mec_env_var import *\n",
    "from helper import *\n",
    "import tensorflow as tf\n",
    "import ipdb as pdb\n",
    "import time\n",
    "\n",
    "MAX_EPISODE = 50\n",
    "MAX_EPISODE_LEN = 1000\n",
    "\n",
    "NUM_T = 1\n",
    "NUM_R = 1\n",
    "SIGMA2 = 0.3e-9\n",
    "\n",
    "config = {'state_dim':3, 'action_dim':2};\n",
    "train_config = {'minibatch_size':64, 'actor_lr':0.0001, 'tau':0.001, \n",
    "                 'critic_lr':0.001, 'gamma':0.99, 'buffer_size':250000, \n",
    "                 'random_seed':1234}\n",
    "user_config = [{'id':'1', 'rate':3, 'dis':100, 'action_bound':1, \n",
    "                'data_buf_size':100, 't_factor':0.9, 'penalty':1000}];\n",
    "\n",
    "print user_config\n",
    "# 0. initialize the session object\n",
    "sess = tf.Session() \n",
    "saver = tf.train.import_meta_graph('model/my_test_model.meta')\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "saver.restore(sess, tf.train.latest_checkpoint('model/'))\n",
    "\n",
    "graph = tf.get_default_graph()\n",
    "    \n",
    "# 1. include all user in the system according to the user_config\n",
    "user_list = [];\n",
    "for info in user_config:\n",
    "    info.update(config)\n",
    "    user_list.append(MecTermLD(sess, info, train_config))\n",
    "    print 'test'\n",
    "\n",
    "# 2. create the simulation env\n",
    "env = MecSvrEnv(user_list, NUM_R, SIGMA2, MAX_EPISODE_LEN)\n",
    "env.init_target_network()\n",
    "\n",
    "res_rec = []\n",
    "res_init_buf = []\n",
    "# 3. start to explore for each episode\n",
    "for i in range(MAX_EPISODE):\n",
    "    \n",
    "    cur_init_ds_ep = env.reset(3)\n",
    "        \n",
    "    res_init_buf.append(cur_init_ds_ep)\n",
    "    \n",
    "    cur_r_ep = np.zeros(len(user_list))\n",
    "    cur_p_ep = np.zeros(len(user_list))\n",
    "    cur_ts_ep = np.zeros(len(user_list))\n",
    "    cur_ps_ep = np.zeros(len(user_list))\n",
    "    cur_rs_ep = np.zeros(len(user_list))\n",
    "    cur_ds_ep = np.zeros(len(user_list))\n",
    "    cur_ch_ep = np.zeros(len(user_list))\n",
    "    cur_of_ep = np.zeros(len(user_list))\n",
    "    \n",
    "    res_rec_tmp = []\n",
    "    for j in range(MAX_EPISODE_LEN):\n",
    "        \n",
    "        # first try to transmit from current state\n",
    "        [cur_r, done, cur_p, temp, cur_ts, cur_ps, cur_rs, cur_ds, cur_ch, cur_of] = env.step_transmit()\n",
    "        \n",
    "        res_rec_tmp.append([cur_r, done, cur_p, temp, cur_ts, cur_ps, cur_rs, cur_ds, cur_ch, cur_of])\n",
    "        cur_r_ep += cur_r\n",
    "        cur_p_ep += cur_p\n",
    "        cur_ts_ep += cur_ts\n",
    "        cur_ps_ep += cur_ps\n",
    "        cur_rs_ep += cur_rs\n",
    "        cur_ds_ep += cur_ds\n",
    "        cur_ch_ep += cur_ch\n",
    "        cur_of_ep += cur_of\n",
    "        \n",
    "        if done:\n",
    "            res_rec.append(res_rec_tmp)\n",
    "            print('%d:r:%.4f,p:%.4f,tr:%.4f,pr:%.4f,rev:%.4f,dbuf:%.4f,ch:%.8f,ibuf:%d' % (i, cur_r_ep/MAX_EPISODE_LEN, cur_p_ep/MAX_EPISODE_LEN, cur_ts_ep/MAX_EPISODE_LEN, cur_ps_ep/MAX_EPISODE_LEN, cur_rs_ep/MAX_EPISODE_LEN, cur_ds_ep/MAX_EPISODE_LEN, cur_ch_ep/MAX_EPISODE_LEN, cur_init_ds_ep[0]))\n",
    "                \n",
    "name = 'test_1000_' + str(time.asctime( time.localtime(time.time()) )) + '.npy'\n",
    "np.save(name, [res_rec, res_init_buf])\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model/my_test_model\n",
      "[[0.1545126  0.15780352]\n",
      " [0.15616506 0.15971294]\n",
      " [0.06559879 0.09880391]\n",
      " [0.06487427 0.10064688]\n",
      " [0.15168335 0.16229852]\n",
      " [0.06257845 0.09748891]\n",
      " [0.06430701 0.09924787]\n",
      " [0.06548443 0.10012874]\n",
      " [0.15782878 0.15414344]\n",
      " [0.15429462 0.17596859]\n",
      " [0.05945097 0.0991616 ]\n",
      " [0.06383383 0.09632684]\n",
      " [0.06522399 0.10438306]\n",
      " [0.06095436 0.09916781]\n",
      " [0.06635379 0.0955418 ]\n",
      " [0.06379106 0.09433711]\n",
      " [0.06133086 0.09875894]\n",
      " [0.06503102 0.09639146]\n",
      " [0.15047684 0.17412661]\n",
      " [0.15663381 0.15740666]\n",
      " [0.17768952 0.15899988]\n",
      " [0.15730077 0.14491995]\n",
      " [0.05902164 0.10017106]\n",
      " [0.06845198 0.09686822]\n",
      " [0.15724926 0.14774598]\n",
      " [0.1529071  0.16426612]\n",
      " [0.1520101  0.15706535]\n",
      " [0.16091335 0.16260217]\n",
      " [0.15082897 0.16070077]\n",
      " [0.06310375 0.09398151]\n",
      " [0.06018374 0.10056869]\n",
      " [0.05968894 0.10066012]\n",
      " [0.15474424 0.16255529]\n",
      " [0.15111688 0.1622602 ]\n",
      " [0.0838669  0.18973866]\n",
      " [0.06870251 0.24295035]\n",
      " [0.06702217 0.10505467]\n",
      " [0.1529     0.16945271]\n",
      " [0.07725129 0.2267289 ]\n",
      " [0.16294895 0.17434688]\n",
      " [0.15574709 0.16130401]\n",
      " [0.20783955 0.15714663]\n",
      " [0.06550175 0.09858679]\n",
      " [0.15049149 0.16456898]\n",
      " [0.07544824 0.21661521]\n",
      " [0.06828475 0.20191087]\n",
      " [0.06303965 0.09975735]\n",
      " [0.06077179 0.09510674]\n",
      " [0.05939222 0.21417175]\n",
      " [0.15744181 0.15481968]\n",
      " [0.06703371 0.0961935 ]\n",
      " [0.06101098 0.09532821]\n",
      " [0.06000968 0.0968477 ]\n",
      " [0.06272067 0.09651975]\n",
      " [0.06293141 0.09775309]\n",
      " [0.1807509  0.14914069]\n",
      " [0.07988207 0.21801658]\n",
      " [0.15504603 0.15840368]\n",
      " [0.06574407 0.10044469]\n",
      " [0.15782984 0.14918709]\n",
      " [0.06163548 0.09617905]\n",
      " [0.18549454 0.16540246]\n",
      " [0.06691907 0.09619211]\n",
      " [0.15158403 0.16156551]\n",
      " [0.16312805 0.1411491 ]\n",
      " [0.06408553 0.09957531]\n",
      " [0.07417839 0.24105006]\n",
      " [0.15845358 0.14733854]\n",
      " [0.06268133 0.09865659]\n",
      " [0.06259628 0.10060268]\n",
      " [0.15697578 0.15612301]\n",
      " [0.06315472 0.09874184]\n",
      " [0.15155551 0.16466443]\n",
      " [0.0684339  0.09670408]\n",
      " [0.15804742 0.1471258 ]\n",
      " [0.06777399 0.09937201]\n",
      " [0.15745883 0.15512241]\n",
      " [0.07787099 0.18941617]\n",
      " [0.15094519 0.16575828]\n",
      " [0.06204953 0.10006336]\n",
      " [0.06457884 0.09637766]\n",
      " [0.1566805  0.15900572]\n",
      " [0.073722   0.1387465 ]\n",
      " [0.06777364 0.09883787]\n",
      " [0.15473479 0.16418679]\n",
      " [0.15471494 0.16764955]\n",
      " [0.06499669 0.10125799]\n",
      " [0.06494455 0.09975021]\n",
      " [0.06567012 0.10388392]\n",
      " [0.06477854 0.10146907]\n",
      " [0.06582473 0.09566869]\n",
      " [0.15664805 0.15316115]\n",
      " [0.17256567 0.13805512]\n",
      " [0.15063478 0.16546924]\n",
      " [0.06475607 0.09760302]\n",
      " [0.07123706 0.2395148 ]\n",
      " [0.15113892 0.16684961]\n",
      " [0.17407049 0.14677933]\n",
      " [0.0675661  0.09759188]\n",
      " [0.07825781 0.18605559]\n",
      " [0.15697493 0.15677188]\n",
      " [0.15818304 0.15161465]\n",
      " [0.0642926  0.09740894]\n",
      " [0.15328537 0.15881251]\n",
      " [0.15233518 0.16093989]\n",
      " [0.06764258 0.10350794]\n",
      " [0.06079651 0.09745083]\n",
      " [0.06548005 0.09991478]\n",
      " [0.1559824  0.14784561]\n",
      " [0.06798785 0.10175251]\n",
      " [0.06210332 0.09805966]\n",
      " [0.16539049 0.16531672]\n",
      " [0.15323895 0.15794131]\n",
      " [0.0623428  0.1021018 ]\n",
      " [0.15152028 0.16410151]\n",
      " [0.15177967 0.15767501]\n",
      " [0.15320167 0.17085268]\n",
      " [0.05883736 0.09802791]\n",
      " [0.06626944 0.09521814]\n",
      " [0.06083923 0.09588645]\n",
      " [0.06024697 0.09469387]\n",
      " [0.06523327 0.10074306]\n",
      " [0.06548014 0.09800895]\n",
      " [0.08346169 0.21074566]\n",
      " [0.14996743 0.16694221]\n",
      " [0.26543832 0.14737494]\n",
      " [0.07574388 0.22089282]\n",
      " [0.06471444 0.09477579]\n",
      " [0.15128718 0.16148189]\n",
      " [0.06413429 0.09822165]\n",
      " [0.15587641 0.16933708]\n",
      " [0.0639132  0.09917262]\n",
      " [0.15261845 0.16389917]\n",
      " [0.06560063 0.09578133]\n",
      " [0.06569213 0.09937915]\n",
      " [0.06490492 0.09844251]\n",
      " [0.06553614 0.09942034]\n",
      " [0.06005635 0.09906808]\n",
      " [0.06251406 0.09922477]\n",
      " [0.29507977 0.12825869]\n",
      " [0.06702092 0.10323571]\n",
      " [0.07640952 0.19645761]\n",
      " [0.15605778 0.15931195]\n",
      " [0.06109808 0.09953243]\n",
      " [0.07209607 0.13881615]\n",
      " [0.06631714 0.09578627]\n",
      " [0.0659247  0.09970091]\n",
      " [0.15689035 0.1505612 ]\n",
      " [0.18062364 0.16705288]\n",
      " [0.07783278 0.23469695]\n",
      " [0.06340884 0.09759663]\n",
      " [0.0680376  0.09931169]\n",
      " [0.06241155 0.09544186]\n",
      " [0.15444903 0.16025631]\n",
      " [0.15795444 0.15656045]\n",
      " [0.15573372 0.16422701]\n",
      " [0.06666558 0.10012089]\n",
      " [0.06004781 0.09751993]\n",
      " [0.061175   0.09455636]\n",
      " [0.05042117 0.11676585]\n",
      " [0.15796886 0.15529789]\n",
      " [0.07204937 0.22754507]\n",
      " [0.06449411 0.0990058 ]\n",
      " [0.06473488 0.09588123]\n",
      " [0.19349411 0.16493535]\n",
      " [0.06547949 0.09361374]\n",
      " [0.06995924 0.13657136]\n",
      " [0.06532154 0.10048264]\n",
      " [0.06653367 0.097041  ]\n",
      " [0.15244147 0.16852354]\n",
      " [0.0554226  0.09878539]\n",
      " [0.07215143 0.23746249]\n",
      " [0.0655532  0.09614539]\n",
      " [0.15609452 0.15777506]\n",
      " [0.15284337 0.16954988]\n",
      " [0.0686408  0.23893923]\n",
      " [0.06261083 0.10052145]\n",
      " [0.08670598 0.18786435]\n",
      " [0.15711595 0.14874493]\n",
      " [0.07563476 0.26075363]\n",
      " [0.28777152 0.14143272]\n",
      " [0.06012025 0.09839267]\n",
      " [0.15430251 0.15667781]\n",
      " [0.06661941 0.09564687]\n",
      " [0.15339565 0.16696621]\n",
      " [0.15708965 0.15041599]\n",
      " [0.06742363 0.09776525]\n",
      " [0.06515756 0.10102851]\n",
      " [0.06354605 0.09759856]\n",
      " [0.15302473 0.16041262]\n",
      " [0.06074417 0.09704353]\n",
      " [0.06556483 0.09801028]\n",
      " [0.06710188 0.09818721]\n",
      " [0.0634805  0.09617137]\n",
      " [0.1574492  0.14010966]\n",
      " [0.15728441 0.14431903]\n",
      " [0.06322721 0.09562991]\n",
      " [0.06693985 0.10180136]\n",
      " [0.2560338  0.14547168]\n",
      " [0.06266764 0.09957437]\n",
      " [0.0698439  0.11462801]\n",
      " [0.15451293 0.1621048 ]\n",
      " [0.0619344  0.09664205]\n",
      " [0.1563646  0.16038191]\n",
      " [0.23201126 0.13941379]\n",
      " [0.07370301 0.23924243]\n",
      " [0.06489705 0.10157213]\n",
      " [0.06485865 0.10064065]\n",
      " [0.06416214 0.09664758]\n",
      " [0.05717998 0.09932264]\n",
      " [0.15871161 0.15186927]\n",
      " [0.06672397 0.10001276]\n",
      " [0.14871137 0.17089748]\n",
      " [0.0656923  0.10094475]\n",
      " [0.15058069 0.16906002]\n",
      " [0.1549568  0.16310245]\n",
      " [0.06922429 0.09711372]\n",
      " [0.0658324  0.10920136]\n",
      " [0.0646063  0.09450329]\n",
      " [0.06444418 0.0995239 ]\n",
      " [0.06958297 0.10683144]\n",
      " [0.06615724 0.09603577]\n",
      " [0.06309103 0.10104354]\n",
      " [0.15049767 0.16277692]\n",
      " [0.08116403 0.20523849]\n",
      " [0.06918371 0.09796035]\n",
      " [0.06126681 0.09465964]\n",
      " [0.06412472 0.0970694 ]\n",
      " [0.06490628 0.09582826]\n",
      " [0.06032168 0.09901629]\n",
      " [0.06332839 0.10033702]\n",
      " [0.06213253 0.09860065]\n",
      " [0.15318023 0.16187389]\n",
      " [0.06288685 0.09892403]\n",
      " [0.06688242 0.0992182 ]\n",
      " [0.15925792 0.1686085 ]\n",
      " [0.15743557 0.15812813]\n",
      " [0.06169999 0.09777659]\n",
      " [0.07037994 0.26171428]\n",
      " [0.18850683 0.14142688]\n",
      " [0.0610086  0.10105312]\n",
      " [0.06807692 0.10025167]\n",
      " [0.15176827 0.15914102]\n",
      " [0.06486961 0.09623593]\n",
      " [0.15296982 0.15490015]\n",
      " [0.06399518 0.09855054]\n",
      " [0.15793742 0.15374301]\n",
      " [0.15106434 0.16634752]\n",
      " [0.16008754 0.15342952]\n",
      " [0.07159018 0.1665866 ]\n",
      " [0.07145858 0.1039891 ]\n",
      " [0.15791261 0.15209389]\n",
      " [0.17552747 0.1649533 ]\n",
      " [0.24805918 0.1668613 ]\n",
      " [0.0674595  0.10133208]\n",
      " [0.0623454  0.10157382]\n",
      " [0.1536445  0.15708277]\n",
      " [0.06299829 0.09972627]\n",
      " [0.14960562 0.17142588]\n",
      " [0.06817394 0.09743224]\n",
      " [0.06358325 0.09798586]\n",
      " [0.06968123 0.14996086]\n",
      " [0.15823638 0.1528224 ]\n",
      " [0.06189892 0.10028517]\n",
      " [0.08521265 0.1846686 ]\n",
      " [0.06639767 0.10604001]\n",
      " [0.15673856 0.16117315]\n",
      " [0.06408433 0.09638726]\n",
      " [0.0829239  0.20093398]\n",
      " [0.06417473 0.09558867]\n",
      " [0.05971077 0.0969267 ]\n",
      " [0.15102677 0.17368734]\n",
      " [0.15333754 0.1632551 ]\n",
      " [0.0650854  0.09595047]\n",
      " [0.06568166 0.0988779 ]\n",
      " [0.06120472 0.10023172]\n",
      " [0.06370875 0.09525127]\n",
      " [0.15243176 0.16628882]\n",
      " [0.06193952 0.09932895]\n",
      " [0.06418803 0.12761658]\n",
      " [0.06296807 0.09684133]\n",
      " [0.15420265 0.16314322]\n",
      " [0.07130703 0.09935284]\n",
      " [0.06351195 0.09792396]\n",
      " [0.06482282 0.09722844]\n",
      " [0.06493163 0.09971029]\n",
      " [0.06598312 0.10037744]\n",
      " [0.15270399 0.16668303]\n",
      " [0.07317276 0.19868557]\n",
      " [0.06419627 0.09603684]\n",
      " [0.0640094  0.09996863]\n",
      " [0.0587965  0.09967228]\n",
      " [0.07159328 0.11100914]\n",
      " [0.155838   0.15982835]\n",
      " [0.06530377 0.0950184 ]\n",
      " [0.07699393 0.19448136]\n",
      " [0.1543197  0.15345313]\n",
      " [0.15744495 0.16099216]\n",
      " [0.15202776 0.16630773]\n",
      " [0.15692444 0.15123238]]\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session() \n",
    "saver = tf.train.import_meta_graph('model/my_test_model.meta')\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "saver.restore(sess, tf.train.latest_checkpoint('model/'))\n",
    "\n",
    "graph = tf.get_default_graph()\n",
    "variable_names = [v.name for v in tf.trainable_variables()]\n",
    "values = sess.run(variable_names)\n",
    "print sess.run('fully_connected_2/weights:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('t_08/1', [1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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