{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "def to_bits(*l):\n",
    "    return reduce(lambda r,i: r |(1<<i), l, 0)\n",
    "\n",
    "def winning_patterns():\n",
    "    v1 = to_bits(0,1,2)\n",
    "    h1 = to_bits(0,3,6)\n",
    "    return [v1, v1<<3, v1<<6, h1, h1<<1, h1<<2, to_bits(0,4,8), to_bits(2,4,6)]\n",
    "WINNING_PATTERNS = winning_patterns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: GeForce GTX 965M (CNMeM is disabled)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'float32'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_input_data = np.zeros((2**9, 1, 3, 3), dtype=floatX)\n",
    "all_target_data = np.zeros(2**9, dtype=floatX)\n",
    "for board in range(2**9):\n",
    "    b = 1\n",
    "    for i in range(3):\n",
    "        for j in range(3):\n",
    "            all_input_data[board, 0, i, j] = bool(board & b)\n",
    "            b <<= 1\n",
    "    all_target_data[board] = any(p&board == p for p in WINNING_PATTERNS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#theano.config.exception_verbosity = \"high\"\n",
    "#theano.config.optimizer= 'None'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_var = T.tensor4('inputs')\n",
    "target_var = T.vector('targets')\n",
    "l_in = lasagne.layers.InputLayer(shape=(None, 1, 3, 3), input_var=input_var)\n",
    "#l_in_drop = lasagne.layers.DropoutLayer(l_in, p=0.2)\n",
    "l_hidden = lasagne.layers.DenseLayer(l_in, num_units=64, nonlinearity=lasagne.nonlinearities.tanh, W=lasagne.init.GlorotUniform())\n",
    "#l_hidden_drop = lasagne.layers.DropoutLayer(l_hidden, p=0.5)\n",
    "l_out = lasagne.layers.DenseLayer(l_hidden, num_units=1, nonlinearity=lasagne.nonlinearities.sigmoid, W=lasagne.init.GlorotUniform())\n",
    "\n",
    "prediction = lasagne.layers.get_output(l_out).flatten()\n",
    "#loss = lasagne.objectives.binary_crossentropy(prediction, target_var)\n",
    "loss = lasagne.objectives.squared_error(prediction, target_var*0.99)\n",
    "\n",
    "loss = loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[W, b, W, b]\n"
     ]
    }
   ],
   "source": [
    "params = lasagne.layers.get_all_params(l_out, trainable=True)\n",
    "print(params)\n",
    "#updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)\n",
    "updates = lasagne.updates.adam(loss, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_prediction = lasagne.layers.get_output(l_out, deterministic=True).flatten()\n",
    "test_loss = lasagne.objectives.binary_crossentropy(test_prediction, target_var)\n",
    "test_loss = test_loss.mean()\n",
    "test_acc = lasagne.objectives.binary_accuracy(T.gt(test_prediction, 0.5), target_var).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_fn = theano.function([input_var, target_var], loss, updates=updates)\n",
    "\n",
    "prediction_fn = theano.function([input_var], T.gt(test_prediction, 0.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val_fn = theano.function([input_var, target_var], [test_loss, test_acc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 accuracy 0.5859375 0.6583781838417053\n",
      "100 accuracy 0.708984375 0.5453886985778809\n",
      "200 accuracy 0.828125 0.46027469635009766\n",
      "300 accuracy 0.8359375 0.40126198530197144\n",
      "400 accuracy 0.849609375 0.3687146306037903\n",
      "500 accuracy 0.84765625 0.3505007028579712\n",
      "600 accuracy 0.84375 0.33722493052482605\n",
      "700 accuracy 0.859375 0.3303086757659912\n",
      "800 accuracy 0.853515625 0.32213646173477173\n",
      "900 accuracy 0.84765625 0.3188232183456421\n",
      "1000 accuracy 0.861328125 0.3121160864830017\n",
      "1100 accuracy 0.853515625 0.3075151741504669\n",
      "1200 accuracy 0.857421875 0.30120742321014404\n",
      "1300 accuracy 0.87109375 0.2947361171245575\n",
      "1400 accuracy 0.8828125 0.2909179925918579\n",
      "1500 accuracy 0.880859375 0.2801564931869507\n",
      "1600 accuracy 0.884765625 0.2742465138435364\n",
      "1700 accuracy 0.88671875 0.26584911346435547\n",
      "1800 accuracy 0.892578125 0.25704246759414673\n",
      "1900 accuracy 0.90234375 0.25057801604270935\n",
      "2000 accuracy 0.908203125 0.24204590916633606\n",
      "2100 accuracy 0.916015625 0.2357223927974701\n",
      "2200 accuracy 0.91796875 0.2258404940366745\n",
      "2300 accuracy 0.9375 0.22131146490573883\n",
      "2400 accuracy 0.947265625 0.21085698902606964\n",
      "2500 accuracy 0.951171875 0.20293736457824707\n",
      "2600 accuracy 0.9609375 0.19260381162166595\n",
      "2700 accuracy 0.96875 0.18439024686813354\n",
      "2800 accuracy 0.97265625 0.17622195184230804\n",
      "2900 accuracy 0.9765625 0.16739627718925476\n",
      "3000 accuracy 0.9765625 0.15894639492034912\n",
      "3100 accuracy 0.9765625 0.14979851245880127\n",
      "3200 accuracy 0.986328125 0.141909658908844\n",
      "3300 accuracy 0.986328125 0.13496193289756775\n",
      "3400 accuracy 0.990234375 0.12670326232910156\n",
      "3500 accuracy 0.98828125 0.11964216083288193\n",
      "3600 accuracy 0.994140625 0.11327987909317017\n",
      "3700 accuracy 0.9921875 0.10799919068813324\n",
      "3800 accuracy 0.99609375 0.10304692387580872\n",
      "3900 accuracy 0.994140625 0.0961313247680664\n",
      "4000 accuracy 0.99609375 0.09087066352367401\n",
      "4100 accuracy 0.994140625 0.08672349154949188\n",
      "4200 accuracy 0.998046875 0.08285956084728241\n",
      "4235 accuracy 1.0\n"
     ]
    }
   ],
   "source": [
    "from random import randint\n",
    "N = 100\n",
    "for epoch in range(100000):\n",
    "    idx = np.random.randint(2**9,size=N)\n",
    "    input_data = all_input_data[idx, :]\n",
    "    target_data = all_target_data[idx]\n",
    "    if epoch%1 == 0:\n",
    "        aloss, accuracy = val_fn(all_input_data, all_target_data)\n",
    "        if epoch%100==0:\n",
    "            print(epoch, \"accuracy\", accuracy, aloss)\n",
    "        if accuracy == 1.0:\n",
    "            break\n",
    "    loss = train_fn(input_data, target_data)\n",
    "print(epoch, \"accuracy\", val_fn(all_input_data, all_target_data)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  4.18483526e-01   2.69759119e-01  -7.59260170e-03   5.12706757e-01\n",
      "    4.55930084e-01   2.79328287e-01   8.04584146e-01   3.88474204e-02\n",
      "    4.17414069e-01  -5.56433916e-01   4.44707304e-01   6.61927164e-01\n",
      "   -3.49529803e-01   6.29592240e-01  -2.40869656e-01   1.90079331e-01\n",
      "    1.18805397e+00  -2.00313553e-01  -3.39206010e-01   4.57586423e-02\n",
      "    8.72410119e-01  -6.92570031e-01   5.20162821e-01  -1.19220361e-01\n",
      "   -2.57124305e-01   3.60738337e-01  -2.52967954e-01   2.16879901e-02\n",
      "    2.45237380e-01  -3.28956217e-01  -2.83724010e-01  -2.21403107e-01\n",
      "   -6.73699141e-01  -1.87377378e-01  -9.52876881e-02   2.64916360e-01\n",
      "   -2.08849087e-01  -1.00696731e+00  -6.75794065e-01  -4.93810743e-01\n",
      "    7.17632532e-01  -6.64196849e-01  -1.80892199e-01   9.95973349e-02\n",
      "   -1.09519444e-01   6.33552790e-01  -7.73389399e-01  -3.06025475e-01\n",
      "    6.89434171e-01  -7.62161016e-01  -4.28372651e-01   1.30543355e-02\n",
      "   -8.49999189e-02  -3.06482241e-02  -3.22102845e-01   6.07555211e-01\n",
      "   -1.57011449e-01  -5.73117018e-01   3.62516999e-01  -9.39474702e-02\n",
      "    5.55969834e-01   7.56791532e-02  -3.94294292e-01   7.57431269e-01]\n",
      " [  1.67151675e-01   2.64278531e-01   4.43806425e-02   7.30237067e-01\n",
      "   -2.42764801e-01   4.88336027e-01   3.98325831e-01  -6.80895231e-04\n",
      "    2.52512712e-02   6.49237514e-01  -1.75191656e-01  -3.58412981e-01\n",
      "    3.88933301e-01  -7.26354897e-01   1.06493461e+00  -5.18472314e-01\n",
      "    6.28828406e-01  -1.62441358e-01  -8.17095712e-02  -1.36176705e+00\n",
      "    5.04715502e-01  -5.35405934e-01   1.07246172e+00   4.93461639e-02\n",
      "    1.30169719e-01  -3.96274596e-01  -1.72919810e-01  -3.06978017e-01\n",
      "    1.11386023e-01   4.60290313e-01   1.52396455e-01  -4.60059226e-01\n",
      "   -2.94177324e-01   2.26197109e-01  -6.36561096e-01  -3.13768178e-01\n",
      "   -4.45106387e-01  -1.83074459e-01   1.51198149e-01   7.04321980e-01\n",
      "   -2.01739281e-01   7.35423446e-01   2.35101432e-01  -7.77905136e-02\n",
      "   -8.36337268e-01  -3.02311331e-01  -2.21972689e-01   2.36513555e-01\n",
      "   -4.72001940e-01  -8.14709008e-01   4.10909027e-01   7.62284920e-02\n",
      "    2.69056231e-01   2.92792201e-01   2.56092489e-01   1.56151831e-01\n",
      "   -1.16285086e+00  -4.51067746e-01   1.16512388e-01  -4.44965690e-01\n",
      "   -2.85560459e-01   4.12789248e-02   5.58527671e-02   5.42474449e-01]\n",
      " [ -2.11870536e-01  -7.57893845e-02  -1.42873362e-01   6.98603690e-01\n",
      "   -6.04689240e-01  -6.35428727e-02  -2.45596200e-01   4.05158281e-01\n",
      "    3.29469234e-01  -6.35195136e-01  -2.67238766e-01  -6.20838761e-01\n",
      "    4.90247488e-01   7.39130259e-01   1.05368048e-01  -1.98600501e-01\n",
      "    3.35916489e-01  -2.32930124e-01   1.16074249e-01   2.47073650e-01\n",
      "   -8.34672868e-01   7.80489266e-01   5.62718511e-01   3.88561219e-01\n",
      "   -2.53121048e-01  -3.80851835e-01  -5.69320843e-02  -5.75732470e-01\n",
      "    4.17046845e-02  -2.23490030e-01  -1.87550351e-01   5.87576404e-02\n",
      "   -1.96262300e-01   5.77715218e-01   3.25957984e-01  -3.32458913e-01\n",
      "   -3.49753618e-01   3.43952775e-01  -6.56557918e-01   7.52728462e-01\n",
      "    1.12010576e-01  -6.25354826e-01  -9.91278216e-02   3.45303714e-01\n",
      "    2.38130942e-01   2.60378152e-01  -4.80126113e-01   3.13060164e-01\n",
      "   -5.62594950e-01  -9.11764383e-01   6.14916980e-01   6.63202286e-01\n",
      "   -1.12665221e-01  -2.81394832e-02   3.54103506e-01  -8.65743756e-01\n",
      "   -1.31995189e+00   6.09415770e-01   9.38001722e-02   8.71729136e-01\n",
      "    5.67386150e-01   3.52047056e-01  -6.77942216e-01  -2.54786730e-01]\n",
      " [  4.82647307e-03  -1.98780194e-01   5.82843870e-02  -1.22866899e-01\n",
      "   -2.27141783e-01   9.66657922e-02  -6.74539924e-01   6.55264929e-02\n",
      "    4.14957851e-01   9.20446292e-02   2.65304595e-01  -4.80816811e-01\n",
      "   -5.40773049e-02   2.98815966e-01   8.88912737e-01  -1.50266781e-01\n",
      "    5.15716493e-01   1.73503160e-01  -2.21236795e-01  -3.95721607e-02\n",
      "    8.56035233e-01  -4.06634808e-01  -7.30633616e-01  -8.41819495e-02\n",
      "    1.01426411e-02  -3.98423910e-01   1.70232713e-01  -1.08258331e+00\n",
      "    1.01034291e-01  -1.26416326e-01   1.48776993e-02  -1.63162395e-01\n",
      "   -3.89225148e-02   7.11737216e-01   2.00615197e-01  -2.82574058e-01\n",
      "    2.61138886e-01   7.60514796e-01  -3.94940257e-01  -1.40347469e+00\n",
      "   -4.65573937e-01  -2.02380851e-01  -2.52319008e-01  -1.05546512e-01\n",
      "   -1.14958145e-01   1.19513303e-01   8.00218999e-01  -3.62284482e-01\n",
      "   -2.88515925e-01   2.15127110e-01   4.25384045e-02  -1.27148047e-01\n",
      "   -2.44119480e-01  -9.73497480e-02  -4.26746160e-01   3.93325895e-01\n",
      "    1.01967776e+00  -3.79121751e-01  -3.08109283e-01  -6.51261091e-01\n",
      "    4.66107219e-01   2.86760423e-02  -1.20721668e-01   4.50918049e-01]\n",
      " [ -3.13755870e-01   1.17489137e-01   3.66920114e-01  -9.89474177e-01\n",
      "    5.08355916e-01   2.87953526e-01   1.46398216e-01  -1.08244054e-01\n",
      "   -4.53598350e-02   7.54209161e-01  -3.69676352e-01   6.21540248e-01\n",
      "   -2.04147324e-02   5.83658397e-01   8.09098855e-02  -2.20327321e-02\n",
      "   -1.09467053e+00   4.64596540e-01  -1.61413893e-01  -3.20746273e-01\n",
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      "   -1.80363968e-01  -4.93950576e-01   1.77235797e-01  -1.02430187e-01\n",
      "    2.25718264e-02   3.95187974e-01  -1.86797604e-01  -7.25470260e-02\n",
      "    1.23207164e+00   4.31666642e-01  -6.73638523e-01   3.12578648e-01\n",
      "   -2.42272094e-02  -4.00122076e-01  -1.32399291e-01   7.36798346e-02\n",
      "    5.18847525e-01  -5.29653311e-01   9.93238389e-02  -1.35287806e-01\n",
      "   -1.69990391e-01   5.85826278e-01   6.79551423e-01   4.25367832e-01\n",
      "    7.58942068e-01   1.02630544e+00   5.37900329e-01   3.60927165e-01\n",
      "    3.12062204e-02  -3.51585537e-01   6.63518190e-01   1.08626986e+00\n",
      "   -3.64488661e-02   6.09239161e-01   2.33363081e-02  -5.63642621e-01\n",
      "    9.56821255e-03  -9.65002030e-02   2.15926662e-01   7.78436780e-01]\n",
      " [  1.52726471e-01  -3.81888121e-01   8.80553424e-02   3.50277349e-02\n",
      "   -4.80940849e-01  -2.11031109e-01   1.73385942e+00   3.42017651e-01\n",
      "    2.67591119e-01   1.08979732e-01  -6.31761968e-01  -3.45875055e-01\n",
      "    2.79467493e-01  -1.68567851e-01  -1.45836666e-01  -7.82942623e-02\n",
      "   -3.72703910e-01   8.75643566e-02   6.90157637e-02  -5.10598063e-01\n",
      "   -9.18696821e-01  -5.56833923e-01  -3.54434371e-01  -1.19453475e-01\n",
      "   -7.73116872e-02   4.25316334e-01   4.01346922e-01   4.89702702e-01\n",
      "   -3.40829715e-02  -1.08146667e-01   2.00812653e-01   3.54507744e-01\n",
      "    8.44726980e-01  -6.79010868e-01  -8.56161416e-02  -2.34694079e-01\n",
      "    3.16483468e-01  -1.18462145e+00  -4.65101004e-01  -2.46768996e-01\n",
      "    4.40040201e-01   1.10015057e-01  -2.46539321e-02   2.76523642e-02\n",
      "   -2.33010486e-01  -8.26398283e-02   8.89547825e-01   2.74895042e-01\n",
      "   -2.26332396e-01   3.22282791e-01  -5.13832629e-01   6.88645303e-01\n",
      "    9.97817218e-02  -7.87154064e-02   5.02774417e-01  -8.57327342e-01\n",
      "    4.72455949e-01  -2.52752721e-01   8.83315057e-02   1.56614989e-01\n",
      "    1.26951069e-01  -1.24301314e-01  -3.50111693e-01  -4.92299467e-01]\n",
      " [ -1.28980607e-01   1.05951473e-01   3.23907912e-01  -4.52438027e-01\n",
      "   -5.96742272e-01  -4.65104759e-01   8.92923057e-01   3.95191640e-01\n",
      "   -3.74380261e-01  -7.34798968e-01   9.21874344e-01  -5.63195646e-01\n",
      "   -1.09255455e-01  -3.32228959e-01   7.10433945e-02  -3.84402663e-01\n",
      "    3.03599179e-01   4.01687384e-01   6.74579218e-02  -1.14292037e+00\n",
      "    1.76932681e+00   6.05398357e-01  -7.44244337e-01  -1.57134727e-01\n",
      "   -4.64350469e-02  -2.33833253e-01  -1.87384412e-01   2.33285144e-01\n",
      "   -1.51849926e-01  -3.64999741e-01  -1.28453672e-01  -3.19381744e-01\n",
      "    2.72750054e-02   5.90682290e-02  -6.83418810e-01  -2.92619765e-01\n",
      "   -8.90894905e-02  -5.47839642e-01   4.56304401e-01  -5.52570939e-01\n",
      "    3.14277798e-01   2.61917353e-01  -1.35128334e-01   1.15978122e-01\n",
      "   -3.78009379e-01  -7.69701898e-02  -7.31372178e-01  -4.09047335e-01\n",
      "   -7.35478103e-01   7.69780636e-01  -2.53710337e-03  -5.98885179e-01\n",
      "   -3.77774060e-01  -2.75469720e-02  -5.73520184e-01   7.85714269e-01\n",
      "   -2.19918340e-01   7.51124680e-01   4.74281818e-01   7.28578389e-01\n",
      "   -2.55773574e-01   1.84770033e-01  -2.44939327e-01  -3.09004933e-01]\n",
      " [  1.48891481e-02   1.05748512e-01  -2.30051339e-01  -6.46029413e-01\n",
      "   -4.96444315e-01  -3.34029168e-01   3.60235721e-01   5.45433819e-01\n",
      "   -9.29341733e-01   6.03839159e-01   3.19483757e-01  -3.91308606e-01\n",
      "    8.52180719e-02   8.11671734e-01  -2.11976290e-01   5.61531037e-02\n",
      "   -5.70635796e-01  -7.43554607e-02   7.02141821e-02   5.67699969e-01\n",
      "    1.18432796e+00  -4.73354727e-01   1.52944222e-01   8.08209255e-02\n",
      "   -4.09048557e-01   3.08905125e-01  -1.09348699e-01  -2.63088197e-01\n",
      "    2.37225816e-01   3.74674618e-01  -2.33153775e-01  -3.53675485e-01\n",
      "    9.16921079e-01  -3.73192243e-02   8.01338375e-01  -3.40588182e-01\n",
      "   -7.44539872e-02  -2.21374825e-01   1.10618222e+00   5.63558757e-01\n",
      "    1.00012816e-01  -9.13917184e-01   2.03752860e-01  -7.05113411e-02\n",
      "    3.56672466e-01   1.77385196e-01  -2.17976168e-01   9.52056646e-02\n",
      "   -6.25625730e-01   2.47311980e-01  -1.98170036e-01  -6.48819447e-01\n",
      "    2.94325799e-01  -3.11757624e-01  -5.08828647e-02   3.27321261e-01\n",
      "   -9.69880879e-01  -6.32486820e-01  -9.18154642e-02   2.97784060e-01\n",
      "    1.07144475e+00   1.32302389e-01   3.87274593e-01  -4.51003045e-01]\n",
      " [  5.10926664e-01  -9.50601101e-02   3.10929120e-01  -1.81989089e-01\n",
      "    7.15278149e-01  -5.36913276e-01  -2.37568095e-01  -4.39516902e-01\n",
      "   -4.89345670e-01  -6.46295369e-01   3.03571671e-01   5.90506494e-01\n",
      "   -2.95945495e-01   1.72119945e-01   1.14438450e+00  -1.36222184e-01\n",
      "   -9.08134580e-01   4.33219105e-01   4.96932939e-02  -4.83662337e-01\n",
      "   -5.82826972e-01  -6.06962025e-01  -4.54020977e-01   4.07646775e-01\n",
      "   -1.31258527e-02  -7.25396872e-01   6.95380270e-02  -7.87484288e-01\n",
      "    1.97102621e-01  -4.46066797e-01  -1.16135061e-01   1.94256753e-01\n",
      "    9.98753190e-01   7.39023924e-01  -6.09172106e-01   3.20188940e-01\n",
      "    2.16472834e-01  -5.32284342e-02   4.51938242e-01   4.75898802e-01\n",
      "   -2.49191895e-01  -1.45937920e-01  -2.51330972e-01   6.96447715e-02\n",
      "   -6.25155687e-01  -2.99078405e-01  -4.73462403e-01   1.63482666e-01\n",
      "    8.16761017e-01   6.14592850e-01   7.80209661e-01  -2.63774723e-01\n",
      "    5.22259116e-01   2.40453910e-02   1.98942907e-02  -7.27659881e-01\n",
      "   -1.14654028e+00  -6.62159622e-01   9.54491347e-02   1.14061630e+00\n",
      "    2.96662506e-02   1.19721917e-02  -2.84518689e-01  -8.16897690e-01]]\n",
      "[-0.32571009 -0.48874131 -0.3270874   0.32692969  0.37803906 -0.30155432\n",
      " -0.16395621 -0.27514377 -0.21979423  0.35770056  0.23739098  0.32894292\n",
      " -0.42133906 -0.21086395 -0.04061511  0.27547225  0.27973431 -0.46671966\n",
      "  0.48462519  0.14245707  0.7242803   0.44150189  0.37441638 -0.34307024\n",
      "  0.32654846  0.17491664  0.45054659  0.1082921  -0.40649605  0.43048802\n",
      "  0.36877716 -0.51132172  0.56276733 -0.16996717  0.16795002  0.32153451\n",
      " -0.49326968  0.18044193  0.28776923 -0.40389299 -0.17252436  0.19697575\n",
      "  0.51116955 -0.44292337  0.17883576 -0.28902099  0.30460393 -0.39438644\n",
      "  0.23629031 -0.1571061  -0.2268829  -0.51737887 -0.38377669  0.34875521\n",
      " -0.41533169 -0.27694708 -0.5844999   0.34087992 -0.33050111  0.39185333\n",
      " -0.114302   -0.3569966  -0.49816149 -0.35555646]\n",
      "[[ 0.48723856]\n",
      " [ 0.60480052]\n",
      " [ 0.62330908]\n",
      " [-0.96644455]\n",
      " [-0.88113916]\n",
      " [ 0.45159534]\n",
      " [ 0.96177834]\n",
      " [ 0.7204473 ]\n",
      " [ 0.58423793]\n",
      " [-1.3539201 ]\n",
      " [-0.43407202]\n",
      " [-1.1494323 ]\n",
      " [ 0.43500751]\n",
      " [ 0.90524864]\n",
      " [ 0.83264339]\n",
      " [-0.59006268]\n",
      " [-1.11550426]\n",
      " [ 0.59520704]\n",
      " [-0.51298326]\n",
      " [-0.86559886]\n",
      " [-0.78313315]\n",
      " [-1.26345336]\n",
      " [-0.82620966]\n",
      " [ 0.69827366]\n",
      " [-0.48619375]\n",
      " [-0.82099462]\n",
      " [-0.60774225]\n",
      " [-0.84828478]\n",
      " [ 0.45489886]\n",
      " [-0.85440809]\n",
      " [-0.59685689]\n",
      " [ 0.69321775]\n",
      " [-0.59790462]\n",
      " [ 0.83870232]\n",
      " [-0.93293458]\n",
      " [-0.71416676]\n",
      " [ 0.52368629]\n",
      " [-0.86841691]\n",
      " [-0.95480931]\n",
      " [ 1.01682889]\n",
      " [ 0.81646997]\n",
      " [-0.85834318]\n",
      " [-0.60507244]\n",
      " [ 0.54665875]\n",
      " [-0.76002777]\n",
      " [ 0.87522888]\n",
      " [-1.35240412]\n",
      " [ 0.73926353]\n",
      " [-1.13527179]\n",
      " [ 0.86649674]\n",
      " [ 0.50064993]\n",
      " [ 0.7484867 ]\n",
      " [ 0.62787777]\n",
      " [-0.46786523]\n",
      " [ 0.68521672]\n",
      " [ 1.29471874]\n",
      " [ 0.7990222 ]\n",
      " [-1.1824789 ]\n",
      " [ 0.62338585]\n",
      " [-0.65906829]\n",
      " [ 0.85014784]\n",
      " [ 0.63636106]\n",
      " [ 0.67048615]\n",
      " [ 1.14917779]]\n",
      "[-0.3289195]\n"
     ]
    }
   ],
   "source": [
    "for x in params:\n",
    "    print(x.get_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.80458415,  0.39832583, -0.2455962 , -0.67453992,  0.14639822,\n",
       "        1.73385942,  0.89292306,  0.36023572, -0.2375681 ], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params[0].get_value()[:, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
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 },
 "nbformat": 4,
 "nbformat_minor": 0
}