theanets-1.1-Copy0.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:3c9e1c1df3e343a539529382ee73b965e788f7be2362e206232033d1e0b4cae9"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import climate\n",
      "import theanets\n",
      "import logging\n",
      "from sklearn.datasets import make_classification\n",
      "from sklearn.metrics import confusion_matrix\n",
      "import matplotlib.pyplot as plt\n",
      "import time\n",
      "import numpy as np\n",
      "from theanets.trainer import SupervisedPretrainer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nlayers = 4\n",
      "module_size=20 #cm\n",
      "module_cell = 5 #per edge\n",
      "distance_between_layers = 5 #cm\n",
      "ncells=module_cell*module_cell*nlayers\n",
      "nhidden=ncells\n",
      "nhidden_layers = 10\n",
      "layers = (ncells,) + tuple([nhidden for i in range(nhidden_layers)])\n",
      "#net = theanets.Regressor((ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,(ncells,'logistic')))\n",
      "trainer = theanets.Experiment(theanets.feedforward.Regressor(layers=(ncells,ncells,ncells,ncells,(ncells,'logistic'))),'layerwise')\n",
      "maximum_hight = nlayers * distance_between_layers\n",
      "theta_max = math.atan(0.5 * module_size / maximum_hight)\n",
      "theta_min = -1 * theta_max\n",
      "y = []\n",
      "y.append(0)\n",
      "y_prime = []\n",
      "y_prime.append(0)\n",
      "z = []\n",
      "z.append(0)\n",
      "x = []\n",
      "x.append(0)\n",
      "for i in range (nlayers) :\n",
      "    y.append((i+1) * distance_between_layers)\n",
      "    y_prime.append((i+1) * distance_between_layers)\n",
      "    z.append(0)\n",
      "    x.append(0)\n",
      "spent = []\n",
      "list_error = []\n",
      "color_list = ['bo','yo','ro','co','mo','go','ko','wo']\n",
      "min_ntracks = 4\n",
      "max_ntracks = 4\n",
      "nsamples = 100000\n",
      "#nsamples = 2 * ncells * ncells * (nhidden_layers + 1)\n",
      "#print nsamples\n",
      "training_ratio = 0.8\n",
      "ds_input = np.zeros((nsamples,ncells))\n",
      "ds_output = np.zeros((nsamples,ncells))\n",
      "cut = int(nsamples * training_ratio)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def addSample (sample_number = 1,sampleInput = np.zeros (ncells),sampleOutput = np.zeros (ncells)) :\n",
      "    for i in range (ncells) :\n",
      "        ds_input[sample_number][i] = sampleInput[i]\n",
      "        ds_output[sample_number][i] = sampleOutput[i]\n",
      "    \n",
      "def sample_creater(sample_number = 1,display = False) :                  # creates a sample with a random number of tracks\n",
      "    sampleInput = np.zeros (ncells)\n",
      "    sampleOutput = np.zeros (ncells)\n",
      "    ntracks = int( uniform(min_ntracks, max_ntracks ) ) #should be a better model than flat\n",
      "    sample_matrix = [[0 for t in range(nlayers + 1)] for t in range(ntracks)]\n",
      "# first argument of each row is track's radius and the other \"nlayers\" ones are hit indices of that track\n",
      "# then we would be able to sort these hits by their radius\n",
      "    \n",
      "# theta is the angle between the circle plane and xy plane. we randomly choose two points in the second and 4th layer and then fit a circle through these two points and also the coordinate origin\n",
      "# as y is fixed for each layer and z can be derived easily by y*tan(theta) , to specify a point in a layer we only need x of it\n",
      "    track = 0\n",
      "    while track < ntracks :\n",
      "        theta = uniform (theta_min,theta_max)\n",
      "        x[2] = uniform (-1*module_size/2.0,module_size/2.0)\n",
      "        x[4] = uniform (-1*module_size/2.0,module_size/2.0)\n",
      "        for i in range (4) :\n",
      "            y_prime[i+1] = y[i+1] / cos(theta)\n",
      "            z[i+1] = y[i+1] * tan(theta)\n",
      "        # center point of our circle\n",
      "        x_c = 0.5 * (((x[2]**2 + y_prime[2]**2) * y_prime[4]/y_prime[2] ) - x[4]**2 - y_prime[4]**2 )/ (x[2] * y_prime[4]/y_prime[2] - x[4])\n",
      "        y_c = 0.5 * (x[2]**2 + y_prime[2]**2 - 2 * x[2] * x_c) / y_prime[2]\n",
      "        R = sqrt(x_c**2 + y_c**2)\n",
      "# y_prime is the y component when we place our coordinate on the circle plane and as the layers are parallel, it would be y/cos(theta) for each layer\n",
      "        if x[2] > 0.5 * x[4] :\n",
      "            x[1] = x_c + sqrt(R**2 - (y_prime[1] - y_c)**2)\n",
      "            x[3] = x_c + sqrt(R**2 - (y_prime[3] - y_c)**2)\n",
      "        else :\n",
      "            x[1] = x_c - sqrt(R**2 - (y_prime[1] - y_c)**2)\n",
      "            x[3] = x_c - sqrt(R**2 - (y_prime[3] - y_c)**2)\n",
      "        sample_matrix [track][0] = R\n",
      "        x_edge = (-1) * module_size / 2.0\n",
      "        z_edge = (-1) * module_size / 2.0\n",
      "        \n",
      "        track_hits = np.zeros(nlayers)\n",
      "        no_good = False\n",
      "        for layer in range (1,nlayers+1) :\n",
      "            # local indices on the module\n",
      "            index_hit1 = int( (x[layer] - x_edge ) / module_size * module_cell )\n",
      "            index_hit2 = int( (z[layer] - z_edge ) / module_size * module_cell )\n",
      "            # transform into the global index\n",
      "            hit = module_cell**2 * (layer - 1) + index_hit2 * module_cell + index_hit1\n",
      "            track_hits[layer-1] = hit\n",
      "            if sampleInput[hit] == 1:\n",
      "                    #discard this track\n",
      "                    no_good = True\n",
      "        if no_good:\n",
      "            #print \"discarding an overlaping track\"\n",
      "            continue\n",
      "        else:\n",
      "            for (layer,hit) in enumerate(track_hits):\n",
      "                sampleInput[int(hit)] = 1\n",
      "                sample_matrix [track][layer+1] = int(hit)\n",
      "        if display:\n",
      "            for i in range (0,5) :\n",
      "                plt.figure(1)\n",
      "                plt.plot(x[i],y_prime[i],color_list[track])\n",
      "                axis([-module_size/2. + 1 , module_size/2. + 1, 0, maximum_hight/cos(theta) + 1])\n",
      "                plt.suptitle('x - y_prime (perpendicular view of the circle plane)')\n",
      "                plt.xlabel('x')\n",
      "                plt.ylabel('y_prime')\n",
      "                plt.figure(2)\n",
      "                plt.plot(x[i],z[i],color_list[track])\n",
      "                axis([-module_size/2. + 1, module_size/2. + 1,-module_size/2. + 1, module_size/2. + 1])\n",
      "                plt.suptitle('x - z')\n",
      "                plt.xlabel('x')\n",
      "                plt.ylabel('z')\n",
      "                plt.figure(3)\n",
      "                plt.plot(z[i],y[i],color_list[track])\n",
      "                axis([-module_size/2. + 1, module_size/2. + 1, 0, maximum_hight/cos(theta) + 1])\n",
      "                plt.suptitle('z - y')\n",
      "                plt.xlabel('z')\n",
      "                plt.ylabel('y')\n",
      "        track += 1\n",
      "    sample_matrix.sort(key=lambda x: x[0])\n",
      "    for i in range (ntracks) :\n",
      "        for j in range(1,nlayers+1) :\n",
      "            sampleOutput[sample_matrix[i][j]] = (i+1)/float(ntracks)\n",
      "    if display == False :\n",
      "        addSample(sample_number,sampleInput,sampleOutput)\n",
      "        \n",
      "def new_samples() :\n",
      "    ds_input = np.zeros((nsamples,ncells))\n",
      "    ds_output = np.zeros((nsamples,ncells))\n",
      "    for i in range (nsamples) :\n",
      "        sample_creater(i,False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_samples()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we try to create some training samples and train on them and then check the mean error in some testing samples\n",
      "# a function of iteration and train per iterations\n",
      "e = []\n",
      "for i in range(10) :\n",
      "    e.append(0)\n",
      "train = ds_input[:cut], ds_output[:cut]\n",
      "valid = ds_input[cut:], ds_output[cut:]\n",
      "niter=4\n",
      "ntrain=2\n",
      "for i in range (1,niter+1) :\n",
      "    # training on training samples\n",
      "    for j in range (ntrain) :\n",
      "        start = time.mktime(time.gmtime())\n",
      "        print \"training started ... \"\n",
      "        e.append(trainer.train(train,valid, algo='rprop',learning_rate= (1.0/(j+1)) * 10**(-1*i),batch_size=100))\n",
      "        print \"\\ttraining ended\"\n",
      "        stop = time.mktime (time.gmtime())\n",
      "        spent.append (stop - start)\n",
      "        print \"training time (seconds) \\t\" , stop - start"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainer.save(\"100K3L\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "[(OrderedDict([('loss', 0.062339705282386887), ('err', 0.062339705282386887)]),\n",
        "  OrderedDict([('loss', 0.052765326509105127), ('err', 0.052765326509105127)])),\n",
        " (OrderedDict([('loss', 0.049857340331685061), ('err', 0.049857340331685061)]),\n",
        "  OrderedDict([('loss', 0.034154127887018687), ('err', 0.034154127887018687)])),\n",
        " (OrderedDict([('loss', 0.014733522075740106), ('err', 0.014733522075740106)]),\n",
        "  OrderedDict([('loss', 0.015385224715122351), ('err', 0.015385224715122351)])),\n",
        " (OrderedDict([('loss', 0.022433299550778024), ('err', 0.022433299550778024)]),\n",
        "  OrderedDict([('loss', 0.020167370776469861), ('err', 0.020167370776469861)])),\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(trainer.network.predict(valid[0])[5])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.text.Text at 0x7ff29cc1a0d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEPCAYAAABIut/fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH2leZYyjyZysAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff29d0e1390>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(ds_output[8005])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.text.Text at 0x7ff29d7c1450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEPCAYAAABIut/fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnX2QJVd53n/v7MeMdrVoZYQESAuoAkREMiA5CdgmZsDY\npVhIJCUiggnlUKZMFKPCBSYiIUG7tlP4owJVmDIkgOyKHLAMCNsEhJwQTwFGMoJVgbQLm8LFh3Zj\nJCS0Wq1WM3Nn7skffXu7p7fv7Y97+vv5VU3t3Du9d3ree/q5bz/n6dPmnEMIIUT/WGh6B4QQQlSD\nBF4IIXqKBF4IIXqKBF4IIXqKBF4IIXqKBF4IIXpKpsCb2c1m9oCZ3Ttjm/eZ2SEzO2hml/vdRSGE\nEGXI08H/IXDltB+a2bXAM5xzlwK/PNleCCFEw2QKvHPui8AjMzb5BeCWybb3ANvN7CI/uyeEEKIs\nPjz4i4D7Y4+PTp4TQgjRIL4mWS3xWOsfCCFEw2z38BpHgX3A30weXzR5bgtmJtEXQogSOOeSTXQu\nfHTwnwVeB2BmVwCbzrljaRs651r59eu/7njHO+r7fTfddFPjf/O0rxtucPzGb6gWvr4uvthxzz2q\nhXOO8893HDmiWhT9mofMDt7MPga8FDjPzO4HbgJ2TAT7vzrnPmlmLzOzQ8Aa8Ia59qgBRqOm96A9\nbGwEX8IPqmeEalE/mQLvnHttjm3e7Gd3mmE0Ait1AtQ/dBD6RfWMUC3qx4cH33lGI1io8Zre5eXl\n+n5ZQeo+CNtcCx8UqadqEdH3WtSFBJ5A4Ldtq+/3tXnwSuD9IlGLUC3qR2vREAi8Th0DdBrtF9Uz\nQrWoHwk8gcBvbja9F+1gc1O18InqGeAcjMeqRd1I4FEHH0ddll9Uz4BQ2FWLepHAA+vrGnghEiS/\nqJ4BYQ1Ui3qRwKMOPo4EyR/jcfClekrgm0ICjwQ+jgTeH7IlIiTwzSCBRwIfRwLvD4lahGrRDBJ4\nlKKJo9SHP8I6qp6qRVNI4FEHH0cdvD/UtUaoFs0ggUcCH0cC7w+JWoRq0QwSeCTwcSTw/pCoRagW\nzSCBRzn4OBJ4f0jUIlSLZpDAow4+jgTeHxK1CNWiGSTwKEUTRykafyg5EqFaNIMEHnXwcdTB+0Nd\na4Rq0QwSeCTwcSTw/pCoRagWzSCBRwIfRwLvD4lahGrRDBJ4JPBxJPD+kKhFqBbNMHiBd06iFke1\n8IdELUK1aIbBC/xoFPyr2f0ApWj8EYqZ6qkUTVNI4CcCr84iQB28P7RccIQ6+GaQwEvgtyCB94dE\nLUK1aAYJ/AgWFjTwQiTw/pCoRagWzSCBH8FZZ2nghUjg/SFRi1AtmkECL4E/zXgcpYrE/EjUIlSL\nZpDAj2BpSbP7oElB3yhFE6FaNIMEXh38aXQQ+kUfmBGqRTNI4EewuBh8Px43uy9Ns7EB27frIPSF\n6hmhWjTD4AV+fR127NDgg+DvX1xUHXyhekaoFs0weIEfjWDnTgk8BH//zp3B6bRzTe9N95GoRagW\nzSCBHwUd/LZtGnyhwC8syIf3wcZGMIE/9HEFqkVTSOBHkUUzdFHb3AzqoLMZP4SiNvRxBapFU0jg\nR/LgQ8KJMH3Y+WFzU11riGrRDBJ4Cfxp4gI/9Fr4QLZEhGrRDJkCb2ZXmtm9ZnbYzG5M+fklZvY3\nZnbfZJtXVbOr1SCBj9jYCOYiNB/hB00sRqgWzTBT4M1sEfgAcCXwfODVZnZ5YrP/CNzsnLsMuBZ4\nfxU7WhUS+Ah18H6RqEWoFs2Q1cG/CDjknDvmnNsAbgWuSmxzP3DO5Pu9wPf87mK1hDl4da0SeN/I\nlohQLZphe8bPLyIQ8JCjwHJim3cDd5rZDcBu4Ge97V0NxHPwQ59YVIrGL0qORIS1OH686T0ZFlkC\nn+dyl/cAH3bOvdfMXgz8MXBp2ob79+8//f3y8jLLy8v59rJCZNFEKEXjF3WtEUrR5GdlZYWVlRUv\nr5Ul8EeBfbHH+9ja0QO8BLgJwDl3l5ktmdn5zrkHky8WF/i2IIGPkEXjF4lahD7s8pNsfg8cOFD6\ntbI8+LuBy8zsQjPbAVwH3J7Y5m+BVwCY2fMIbJqHS+9RzUjgI5Si8YsmFiNUi2aYKfDOuVXgeuAO\n4OvAbc65g2Z2wMyunmz2VuDfmNkh4JPAG51znTnBl8BHqIP3i0QtQrVohiyLBufc7SS6dufcTbHv\njwA/6X/X6kFr0URI4P2ysQG7dqmWIIumKXQlqzr400jg/SJRi1AtmmHwAh9fD37oyZF4THLotfCB\nYpIR4YSzalEvgxd4rQcfoQ7eL0rRRKiDbwYJvCya00jg/SJRi1AtmkECL4E/jWKSflFyJEK1aAYJ\nfA0pmtGomtf1TRc6+PG4vfuWZCiilmd8D6UWZdjcDMZ1FUjgK+7gT56E5zzH/+tWQRcE/lOfgl/9\n1ab3Ih+hqPX9HreXXJK9xowsmum8851w883VvLYEvuJb9j3+ODx4xqIN7aQLKZpHHunOglXxe9xW\n1aG1gQcfhIcemr2NUjTTqXJMS+Ar7uBHI1k0PulSPYeyOudopA5+Hqoc0xL4GgR+Y6Mbp+gSeL90\noZ4+GI3g0UdnbxOezYzH/T6bKYMEvkLiFzpVJfDQjQO8Cymargl82+s5L6Fg5xH47duDWsim2YoE\nvkLCC52qOgjDN64LotSFjrNrAt/2es5L+F7ksWj6XouySOArpA6LJv5vm+nCQSiBbxfhe5G3g+9z\nLcoiga+QqlM0XRL4LkwKdlXg+2pLlBH4vtaiLBL4ClEHH9GFg7CrAt/WD8x5yWvRdKF5aAoJfIVU\nLfDr61v/bTNdEKT19W7UEoYhauF7IYumPFWOaQm8OvjTdCH10bUOvu31nJcik6x9r0VZ1MFXiAQ+\nogtdVtcEvu31nBdNss6PBL5Cwhy8YpLdOAgl8O1CAj8/EvgKid/wQyma9h+EXRX4tk5az8toBLt3\nF8vB97UWZZHAV4gsmoguHIRdFfi2fmDOy2gE552X3cF3oXloCgl8hUjgI7ogSF0S+CGIWlzgZ623\n1IWx1RQS+AqRwEd0IenQJYHvQj3nZTSCXbuCY+jUqenbDaEWZZHAV0hdOfguiFIXuqzRqDs5+C7U\nc17W14M5rL17Z9s0Q6hFWZSDr5Cqb9kXCnsXRKkLB+H6upZfbhPh8XPOObMnWodQi7Kog68I54LB\nprVoArrgGXdt+eW2T1rPS1zgp3Xw43FwrC0s9LsWZZHAV8RoFHTuZvLgoRuC1NV6duEDqQyhwM+y\naMLGocrjrMtI4CsizMCDBB66IUhdqufmZtBAtLme85LHognHFfS7FmVwrlqB317Ny3aDcHCCBB4k\n8D4Zj4OONbQl2lrPeQmPobPOmt7BS+CnE54pq4OvAAn8VroQZetKPcNaQrvrOS95LJqh1KIMVY9n\nCfxE4LUWjTp4nwyla5VFMx8S+ApJdvBVTCyurwen6m0XJOhOisas/bHTpKi1ddJ6XsLF+malaIZS\nizJUrQ8S+Bosml272i9I0I0Uzfp6UM+2f2AOpWsNgwp7907v4MPGAfpdizJUrQ8S+JoEvu2CBN2x\naLpQzyEJfNEOvq+1KEPV41kCL4E/jQTeH0PpWiXw89G4wJvZlWZ2r5kdNrMbp2xznZndY2bfMLOP\n+t/Nagj9Q5DAQ3dSNF2o51CSI/EUzaxJ1iHUogyjESwuBt9XYYvOzMGb2SLwAeAlwAPAnWb2l865\ne2LbvAB4G/AS59zjZvZj/nezGuIXOlWZoumCIIE6eJ8MpWsdjWBpSR18WcIPyB07oivrfZLVwb8I\nOOScO+ac2wBuBa5KbPMG4P3OuccBnHM/8ruL1VFHiqYrggTdSdF0oZ5DSY7kzcEPoRZlSAq8b7IE\n/iLg/tjjo5Pn4vx94IVm9lUz+5qZXeNzB6tEHvxW2p6iGY+Dr6Wl9tdzKF1reAzt2QMnT6aPm6HM\nR5ShaYHPsyjrAvAsgm7/WuCDXbFp6hD4rsT6oP0WTfxgaHvsdCgCH85jLSzA2WfDY4+duc1QalGG\nsH5VCXzWWjRHgX2xx/vY2tEzefwl59wm8F0zOww8F7gr+WL79+8//f3y8jLLy8vF99gjdXXwu3dL\n4H0Qzpns3Nn+eg6la43PY4UTrXv3bt1GAj+d+JgOm5aVlRVWVla8vH6WwN8NXGZmFwIPAtcBb0ps\n8xngVcAfmdl5wPOAv017sbjAt4E6Bf7kSf+v7Zu2p2iq7nZ8kkyOrK42uz9VET+Gpk20KkUznTSL\nJtn8HjhwoPTrz7RonHOrwPXAHcDXgduccwfN7ICZXT3Z5lPAw2Z2CPgS8A7n3A9L71GN1LUWjSwa\nP1TtV/pkKF1rXoEfQi3KUPWYzlwu2Dl3O3B74rmbEo/fRhCV7BTKwW9FAu+PoYhaXOCnZeGTtVhb\nq2//2k7Tk6y9JnnDD8UkJfC+GEo0sEwH39dalEECXyGKSW6l7THJLgt8Gz8wfZBH4Icy4VwGCXyF\nSOC3IovGH0MRtTIWTV9rUQYJfIUoBx/hXPtTNF3LwQ8hORKfx1KKpjhVJ8Mk8DWkaLqQg+/CPUS7\nlIMfStcan8eadlenodSiDGk5eJ9I4GvKwXeh42z7Qdi1HHzb6+mDpEWjmGQxZNFUSLKD39wMrArf\nv6MLFk0XPOMuefBDSY4oRTMfEvgKifuHCwvB13js93d0ReC7cBB2WeDb+IHpgzyTrF1oHppCAl8h\ncf8Qqhl8XRX4Nh6EEvj2oStZ50MCXyHxwQn+B19Xl7cNz2R8n83MS5cEfihdqwR+PiTwFVK1wHdJ\nkOJRNrNoTqJNdLWefY4G5s3BD6EWZZDAV0hS4H0Pvq6mPqCdnVbXcvBD6Frj81hnnRX8ncm1ZoZS\nizIoB18haR28z661S7ntuKUA7TwQu1TPLkxa+yB+DJml2zRDqUUZqh7TEnhZNEB6B9+2A7Gr9Wzj\nh6UvkkGFtCz8UOYjylD1WakEviaB75KlAO08ELtqebWxlr5IHkN5Ovi+1qIM8uArpE6B39jwfxGV\nT7og8F3q4IfStaYJfHKiVQI/HQl8hayvV5uDD9+8MJXS5oEdTzpAO/e3SwI/hORIGKWNj5s0i2YI\ntSiLBL4gR47k37bqFE389dsuSnk6+CNHmj0LaVLgNzbg298utn3fu9Z4AxMii6YYEviCvPzlcPRo\nvm3rSNF0ReDzpGiuuQYOH653v+I0KfB33QW/9Ev5tx9CciR5/ADs2QMnTmx9bgi1KIsEviCnTgVf\neajLg4f2C3yeFM2pU/DII/XuV5wmBb7IuIJhdK1pAr+0dGYOfijzEWWQwBdkbS3/TX2rFvj4RSBd\nFPhkLdbW0q9UrIsmU0lFxhUMQ+Dj4zskTeCHUIuy6EKnAjgHq6vBVx7q6ODDSdy2X5yTR+BXV9PX\nGqmLJi90KjKuYBhdazIDD4HAJ+skgZ+ObvhRgNEoEvm828uiCciTommDwDdl0RQV+CEkR9IsmsXF\ndIHvey3KIoumAOGpYVmLpuoUTZsvdsrq4Mfj4O9p0qJp8kInWTRnMs2DVwefHwl8AcKBlbfTSnqI\nStFEj5MHYihu6uDzMYTkSN5J1iHUoiwS+AIUFfiqb/jRJYHP6uDDmg5d4PNeBzCErrWIRdP3WpRF\nAl+AMgIvDz4gKyYZdmVtSdE0IfDjcf7xMQRRy2vRDGHCuSwS+ALM68FL4KPH6uC3UnRsDUHUylo0\nfaxFWSTwBWhbB9+1HPysFE3bBL7uCeuiY2sIyZG0HLxSNMVQDr4A8wp8FSmavuTgV1eD55q2aJrM\nwcf/zSJez23bAnunzauJlkE5+PnRDT8K4MOiUYomIC1Fc/757eng615+uejYiotaW+9xOy9K0cyP\nbvhRgCJdlnPBwKvTg+9yDn51FS64oB05+CaWX56ng4d+dq5K0cyPPPgCFDkIR6NAJOJLnWqSNXqc\n7LRWV+HJTw66s6b+jibrKYE/kzIpmoWJ4ozH1e9fF5DAF6DIaXSafyiBjx6nWTRLS/CkJ525HGxd\nNFnPeSwaGJbAz7JooJ+1KINzEvhCFO3gk4Nz6AKflaJZWgru2NOUTdOlDn5zs/13yJqXMmvRQD9r\nUYbNzeCMZmFBAp+LUITKCrzu6BQ9TvPgl5bS79hTF00LfN6xBcPoWsusRQP9rEUZ6hjPmQJvZlea\n2b1mdtjMbpyx3bVmNjazK/zuYn7W1oIOM69Fk9bB+5zh71IOPk+KZnGxXQJf56R1kbEF+W6g0nXK\nrAcP/axFGerQh5kCb2aLwAeAK4HnA682s8tTttsDvAW4y/8u5md1NRCgNlk0fcrBt8GiaaqeRcYW\nDKNrTTuGwnXN45OoQ6hFGeoYz1kd/IuAQ865Y865DeBW4KqU7X4T+G1gDbCUn9dCGwW+Kx18nhTN\n0C0aCfxW0oIKZsGZXryLz3O/3yFSxxlplsBfBNwfe3x08txpJpbMhc65z06eaux6PR8WjQQ+YFqK\nZu/eYQq8D4umb6KWdgzBmTbNEGpRhjrG8/aMn88UazNbAN4DxO8332gHv3cvfP/72dum+Ye60Cl6\nnGbRLC4GM/5NWTRNzmmEY6tIiqbvojZN4JNJGgl8Om0Q+KPAvtjjfWzt6PcAlwIrFlwx9FTgL8zs\naufcweSL7d+///T3y8vLLC8vl9rpaRS1aJKnl9u2FbupQ57fEX8Dn3jC32v7Jk9M8pxzYNcuOHas\n/v2D7lk0fY8GzurgkwLf91qUIV6/sD6bm/DFL66wsrLi5XdkCfzdwGVmdiHwIHAd8Kbwh865R4Gn\nhI/N7K+At6WJO2wV+CpYW4PzzmuvRdPUBUJ5yGvR7NoFhw/Xv3/QvEVzzjmyaOKMRsGYSCKLJh9J\nDQrHdLL5PXDgQOnfMdODd86tAtcDdwBfB25zzh00swNmdnXp31oRRU6j64hJdsmDz7IUQovmnHOG\nd6FTeCP3eSdZ+xYNnMei6VstyjBN4H2S1cHjnLsduD3x3E1Ttn2Zp/0qRdtSNF3KwacdhKdORY+b\nTtGMx8FXeCpb55xGuG7Rrl3w8MP5/s8Quta0eSw406IZwnxEGZL1q0IjenUla5HTaKVotlIkRdNE\nBx/WMlwcrs56hn972kU80xiCwCtFMx91dPC9EngfFo0udAqYZdE00cEnJ8XrrGd49lJkqYIhdK1p\nQQVQiiYvaWPa91lp7wS+TRZN1zr4PIuNNSnwVXc70wg/3NIW0prGEJIjeVI04X0X+l6LMqiDL8ja\nGuzeHZzGZw2gJhYb63IOPrQpwknWum8/V4dfOQ1ZNOnksWjG4+B4XIgpTR9rUQYJfEGKnEpPu9BJ\nKZqAaRbN0lJwsPq8XiAPTXfwRS2aISRH8nTwyTpAP2tRBgl8QYqcSuuGH1vJuxYNNLNcQdMCX8Si\nce7M9eD72LXmiUkmGwfoZy3KIIEvSJFTaXnwW8lr0UAzWfgmBb6oRROKe5W3g2wDeSyaaR1832pR\nBgl8QYqcSisHv5W8KRpoZqI17WCoa06jqEUzFFHLk4MfSi3KoBx8QYpaNOrgI/KmaEAWTRZJewb6\nmRzJY9EkxxX0sxZlUAdfkHktmipSNH3JwbfBomkqB1/UohlK1zotBy+LJh91jOneCPzGRhDJ2r59\nPotGKZqALlg0bU3RDCU5ohTNfNRhO/ZG4MN7hoZ3lJFFU4xZHbxzUX2hmeUKmhb4IhbNULrWvBbN\nEGpRBlk0BYhbCHlOpXXDj63MikmORsHj0EttooPv0oVOQxG1PCkaxSSnI4EvQHwSMK9Foxx8xKwO\nPm7PwDAnWZWiOZN5LJq+1aIMEvgCxEWoDRbN5uaZy9u2XeCnpWjiH54wvBx8OLZ27AhqkuUfK0Wj\nFE0eJPAFKNPBV5miaXJ52zLM6uDj9he0Z5K17hx8OL+TZdMMpWudlYNXiiYb5eALUNSDrzpF02TH\nWYZZKZpkBz80i6bo2BpKckQpmvlQB1+Atlk0XRP4Ih78UC0ayDe2htK1KkUzHxL4AviwaHwLfFMX\n5pRhVoqmLRZN0zf8gHxjayiiludCJ6VopqMbfhTAl0WjDj5AFk2EL4umb6KmFM18qIMvQNHT6Kpz\n8NPevLpvlJGXIhbNk54EJ04EKaG6aDIHX3RsDaVrlUUzHxL4AvjIwVeRogkxa3c8bFZMMmnRbNsG\nu3bByZP17V/THnxRi6bv0cDxeGsMOE4yRdP3WpRFAl+AtqdooN02TZEUDdRv0/TBoulTciQZA46j\nFE0+JPAFaOMka5cEvohFA/UnabrWwffdlphmz4AsmrxI4AvQtphkmsffNYGflqKBdnTwdV7opJjk\nVqZd5ARbPwSHMh9RBl3oVIC2p2igvQKfdQ/RNItmSB28UjRnktXBr60F42oItSiLOvgCtNGiSU7i\ntjULn3UP0WkWTd0dvHLw7WFaBh6CsbRtW7DNEGpRFt3wowA+LJoqUzTQ3g4+K+kw9EnWMjHJvidH\nZnXwEH0QKkUzHd3wowA+LJpt24Lol4+sepcEPssnTfPgZdFMp0xy5DOfgVOn5tvPOskj8Gtrw0vR\nfPzj+fVDFk0Bip5GP/54kOWOE2bVfQy+aQLfxpt+ZJ1Gt8GiafpCp6otmhtvhIMH59vPOinSwQ/F\nohmP4TWvyd/4SOALUPQ0+tFHA5shia/B16UOPqvLkkVTfYrm0UfrX/5hHrIEPqzTkFI0J08G3Xve\n91ECX4Aip9HjcfBm7Nlz5s8k8AELk5ExHsuiqSNFc/x4/St0zsO8Fk0fBT58/9TBV0CR0+gTJ+Ds\ns9Mvs/Y1+LqUg087CCGqRRssmqZy8BsbwYdcWJ8qLJrNzaDh6FIHPysHD8O0aML3L+/7qBx8AYqc\nRj/6aCBQafia4e9aB5/2YRfWYsgWzdpaMJ7CCGkVKZoTJ4J/uyTweS2aIaVoigq8OvgCFDmNPn58\nusD7muHvWg5+VgffFoumiRx88m+vIkVT9NS+DShFcyZlLBrl4HNSxKKZNsEK8uDjzLJo2tDBb2xU\nv/xy8uylCoumaOfXBmZd6ASyaPLQmhy8mV1pZvea2WEzuzHl5283s0Nmdp+ZfcHMLva7m9nERSg8\n+KetVz7LopHAR4SdVhuXKqhr+eXkh1sVKZo+dvBDTNF0cpLVzBaBDwBXAs8HXm1mlyc2uwu4wjl3\nGfBR4D1+dzOb+Km0WbQeRhpZFo0EPmCWRbN7d/B8XX9PU/X0adHM6uB37OheB68UzVaKvo+tEHjg\nRcAh59wx59wGcCtwVXwD59wXnXPhsP9r4EK/u5lNkVPpJi2arlzoBFstmqTAmwUfkuEEYdU0lUqq\ny6J5xjP6J/BDtGjyvo/OtUfgLwLujz0+OnluGm8C/nyenSpDkVNpWTRbyZOiSXrwUK9N01Q9y1g0\nRVM0x48HwtBHi2ZIKZoi7+PmZnCtyUJMgasYzyl92xnknsYys9cBVwAvTfv5/v37T3+/vLzM8vJy\n3pfOpMip9PHjcN556T/zNfjW14N7l8Zps8AXtWig3ix8U2dEZS2aZL2yOvhnPhOOHJlvX+skTw5+\niBbNM58Jx45lbzvrjHRlZYWVlRUv+5RH4I8C+2KP97G1owfAzF4BvBP4GedcqozFBd43RS2aZz87\n/Wc+Y5Jd6eCzYpJpFg3Um6RpsoP3ZdFMG1ehMAzJouljTPLRR+GSS+Dw4extZ43nZPN74MCB0vuU\nx6K5G7jMzC40sx3AdcDt8Q0mk64fBK52zj1Uem/mQBZNecrEJEEWzTTKpGie/nR44ol2jo80ilg0\nQ+ngi1g0dY3nTIF3zq0C1wN3AF8HbnPOHTSzA2b2yslmvwvsBj5hZveY2Z/53c2sfQwKEz8Qsyya\nOiZZu3KhU1ZMsi0WTRP1rCtFc+659U5az0ueHPza2rBikkXOxKaNZ9+WYx6LBufc7SS6dufcTbHv\nf87vbhVjbS0oTvyORFkWjTr4iDIpGpBFM40yAn/OOdEH5pOfPN8+14FSNGdSVOBb0cF3gTQLQRZN\nfmalaFZXgzOktA8AWTTpFE2OhNdl7N3bnSSNUjRncvw4PPWp0VnvLCTwBUizEMpaNFUvNta1HPzj\nj6d371CvRdNUDr6MRVPUlgivy6h7hc550IVOWxmNgjG6e3e+93HaHeXA7wR0LwQ+zUKYx6JRiiZg\n+/ZgGdtpAj9Eiya8QnrWGjhlUjRxi6YLKEWzlRMnglh0eAFgGYEH/2O6NwKf16JZXw8KmLxdX4jW\ng48IO/i0BA20w6Kp+owoObYWFoK6zPq9ZVI0XbNosnLwQ0vRxJc/yfM+TqufBD6FIh182C3FJ2Tj\nDNWDL2PR1NXBj8fBV9LLbaKDh+yJ1iKiFs5xLC31r4MfUoomvvyJOnjPFPHgZ9kzIIGPk2XR1NXB\nh7VMfig34cFDtg9fRODjDUfdK3TOg1I0W4l38HneRwl8Acp08NNQDj4iFPhZFk0dHee0zHUd9fTV\nwcfvcRsnPh7rXmN/HrJy8EOzaIq+j3WN6d4IfF4PflaCBnTLvjjbtrXDoqmr20mjaAQX0hcbg/Sx\nVfTUvi0USdEMISbp06LxOa/UC4H3bdEoRROQ16Kp+q5KTQq8L4sG0sdW0cm5tjCPRbNtW1CHqsdN\nnRR9H2XRFKCtFk1XBL5simZxMbAesi78mZemO3gfFg2kj60+d/DTBD5cJnfaHde6iCZZK8SnRaMb\nfkRkpWigHpumrkhZGmUsmqICH5+c64vAh9cLZK1U2heKvo8S+AK0MUXTlxz8LIsG6kl+1OVXpuHb\nokmOra5aNHnWg5/WwUP/BF45+AopchrddAffRYGfZtFAPV2nLJr2MY9FA/0TeFk0FVLkNDqrg1eK\nJiIrRQP1WDRNC3zVKZrkqX0XJh/zWjRZt4PsC7JoKkQpmvkom6KB5i2aPqVolpaCC56qnrT2QZbA\nb98eTKKuruavRZfxlaJRDj6Ftlo0fbjQqS0dfNcvdIJsiwa6Y9NkXehkFtTp1ClZNGlMq59y8Cn4\ntGjkwUdkxSSh/x181SmaeOcH3ZlozergIajT448PQ+CTSxVkWW2yaArQxhRNXwQ+j0XTZw++6hRN\ncjx2qYMObar7AAALiklEQVTPEvilpWD89F3gndv6Pu7YEX24TUMCX4C2WjR9yMFD8xZN0zl4WTRn\nklfgof8Cv7oaTBrHz/Sy3kcJfAHynkYnP2nT8DG7v7nZ3PK2ZZiVdIB2WzR1rwcP+SyavMmRrlo0\nWTl4iOrW9xRN8j2E7PdROfgC5D2NfuKJoHOYNTnkY3a/yeVtyzArRQPDtWicC14/KfBZFs2sesbH\nlnPRnYBChtTB9yVFk9Y0qoP3SN7T6Cx7BvycOjbpGZeh7RZNU/VcWwuageQHtS+LJkwoxf+2IQl8\nXzr4pM0GEniv5D2NzrJnQAIfJ3yuzRZNlfVMG1fgL0VT5tS+LeRN0UD/Bb7M+yiBL0Bei6ZOgW8q\nt12GeTr4uiyaJuqZNq7AX4qmzKl9W8jKwcOwOvgyFo1u+JGTNIsmvFQ6nkWVRZOOLJp00sYV+LNo\n0k7tu3JXpyIWTdoka98Evuj7qBt+FCDtVHphIRhE8WK1xaJp21oj86RonvSkYKKwyrW9u2jR5EmO\npJ3ad+W+rHktmoWF9Bvc9z1Fk/U+yqIpQN5T6bRP2iTh3WbmYdqbZ+bn9X0zT4pm2zbYtSu4oKUq\nmpxkLWPR5E3RlJmcawPjcXoMOMnSUnodoH8pGk2yVkjeU+m0T9okVXbw0M6LneaxaKD6icGmLnQq\nY9GE4reQcmT1ZZJ1Wgw4SZbA97mD1ySrR/KeStdl0cy6CKSNPvw8KRqovuts6kKnMhZN2L2niV9f\nJlnzXOQEQZ2GIPBl3kdd6FQAnxZNHR181wQ+TwfflMC3zaKZVkvIN8naBYHP47/DcDp4WTQV0zWL\npm8CX/XEYJdSNEUEPm081jFpPS8S+K3IoqkQ59pn0czKCLcxC98Fi6aJHHwZi6ZoB58cj9u3Vz9p\nPS95MvAgi0Y5eA+EkbS0Gf22pWignR38rFvMgSyaJLMsmmm1hDPH1rTx2PYsfJEOPm8tuoxy8BUy\n7TQaZNHkJauDz+rWZNFEzGvRQPuz8LJotpL2Pp59drDW0LQPMVk0OSki8E1f6ATdE/jFxew4XJMp\nmi4L/LTx2PaJVgl8xHgMjz22dUVQCGKye/YE8ylptEbgzexKM7vXzA6b2Y0pP180s1sn2/y1mT3T\n3+5ls7Y23SMOlysIUYomnVkCn2XPQL8tmmkefFUpGuiPRTMED/7kSdi9O92KmvU+tkLgzWwR+ABw\nJfB84NVmdnliszcDf+ec+3Hg94D3+du9bPJ28OEn7Z49s1+vjhz8XXetzPcLPDOvwM9jKaysrGRu\nMyszXHUOPu3vD2uVNk7msWjCWrTdosmbg5+ng88zLtrALNt31vvYlhz8i4BDzrljzrkN4FbgqsQ2\nvwDcMvn+L4CfMss6qfdHXoF/7LHpn7Rx6ujg7757Zb5f4BkfAl+248xzILfNooHpNk1egd/YgFOn\nAq82JC7wfejghyDws2zfWe9jKzp44CLg/tjjo5PnUrdxzo2Bh4Hzfe1gFtOibLA1zpZnJUnwswhS\nlsC3LeM8a3GsrIgkVH95fdsWG4PpUclptYStYyu8k1PakgZtX66giEWTpxZdZpauzHof6xrTUz5f\nT+N13cOrr/b5agGPPAJnnZX+s1274A/+AD73uaCDzyPwO3fCd787375+73vw0z+d/rPFRfj856up\nRVl+8IP0wba4GNQwi3PPhUOHyv1NR47A1742e5uvfAV+5VfOfH7nzsALr6qW990Hb31r+s927YLX\nvvbMDv/Eienit3Mn3HJL8Pc88cT08XjuufChD8Gdd5bf9yr54Q+zrU4IajTrepAPfximNep5xkUb\nePBBeMpT0n927rlw4EDwdyZ5+OH05sF3Dt7cjLVrzeyfADc65145efx2YKdz7j/Htvn8ZJuvmtkC\n8ABwwaSbj79WyxbJFUKIbuCcK2V7Z3XwdwOXmdmFwIPAdcCbEtt8FvhXwFeBVwF3JsV9nh0UQghR\njpkC75xbNbPrgTsI/PpbnHMHzewA8FXn3KeB9wO3mNm9wGPAL1a900IIIbKZadEIIYToLpVfyZp1\noVSfMbN9ZvaFyd9/xMz+3eT5HzOz/2Vm3zCzO8wsx/RvPzCzbWZ2j5l9evL4YjO7c1KjPzGzHPmM\n7mNme83s42b2dTP7ppm9eKjjwswOmNn/NbNvmdknzGzXUMaFmd1sZg9MHJDwuanjwMzeZ2aHzOxg\nyjVJZ1CpwOe8UKrPrAP/dnIR2E8AbzSzFwAHgM84554P3D55PBTeAhwmSmi9D/idSY1+QHDh3BD4\nEHCbc+4FwKUENRncuDCzZwOvBy5zzl0CbAKvZTjj4g8J9DFO6jgws2uBZzjnLgV+efJ/Z1J1B5/n\nQqne4px7wDl33+T7k8A3gAvZenHYHzOQmpjZRQR/+4eDh7YNeLFz7s8mmwyiFmb2ZOCFzrmPQXD9\niHPuBMMcFz8CRsBuM9sO7AK+z0DGhXPui8AjiaenjYOrwuedc/cA2yfH1FSqFvg8F0oNAjN7FvCP\ngC8BT3HOPQzgnHuIGi8Ma5j3Am8HwpTV+cBDsZ8fYxjj4znAD83sT83sPjP772a2hwGOC+fcj4D/\nQiDq/w84DtzHMMdFyLRxcCEF9bRqgdcMLmBmZwOfAN4y6dQGh5m9Enhw0nmEkdmhRmcXCD7sf885\ndxlBF/ufmt2lZjCzvwf8GvAs4OnA2cDPNblPLSd5zMzU2KoF/iiwL/Z4H1s/gXrPZHLok8D/iJ1y\n/tDMzpv8/CkE1xj0nZ8CrjGz7wAfA14O/A5wXmybiwjGTN+5HzjmnLt78vgTwAuBBwc4Lv4x8GXn\n3MMTG/c24GcY5rgImaYPST3NrEvVAn/6QqmJ0F1HMGkwCCaLrn0EOOyce2/sR+HFYUz+/Wzd+1Y3\nzrn/4Jzb55y7GPiXwP9xzr0euMvM/tlks6HU4n7gITN77uSpVwDfJDg2BjUugG8DLzazsybHyyuA\nbzHAcRFjmj58FngdgJldAWw6547NeqHKc/Bm9k8JlhEOL5R6d6W/sEWY2UuALxBMroaF/vfAVwgm\nnC8gSAhc55xr8fJSfjGzlwJvc85dY2YXAx8lODU/BLzeOdeyFfP9M0lTfZhgUvF7BAeuMcBxYWb7\nCf7+MXAP8K+BpzGAcWFmHwNeSnDG8gDwLuDPmTIOzOz9wMuANeCNzrmDM19fFzoJIUQ/6fwt+4QQ\nQqQjgRdCiJ4igRdCiJ4igRdCiJ4igRdCiJ4igRdCiJ4igRedwcyeFV9WNfGzA2b28sn3v2ZmqXfq\nNbMPmdnzCv7ek8X3VojmUQ5edIbJgm2fniwhO2u77wD/MFywycPvfcw5l+M200K0C3XwomtsM7MP\nTlZhXDGz3QBm9kdmdq2Z3UCwaNVfWXBD+C1M/s8Vk+9PmtlvTW5Aco+ZPW3y/HMnj79mZr+V+P/v\nmtyI4Ztm9u7Jc//czP735PunWXBzl96vBCnajwRedI3nAO+frML4APAvJs87wDnnfp9g2dll59zP\npvz/+CnrLuBLzrnLgb8kuqH87wO/65z7CYJlbAEws2uACyc3YrgU+HEze4Vz7lPA35nZm4H/BrzL\nOTeEhcJEy5HAi67xnfAmKsDX2Lq6XlHWnXOfS3mtnwT+dPL9n8S2/3ng583snsn2zyVY5hbgBoJ1\nhladc7fOsU9CeGN70zsgREHWYt9vMl+TEl+8apx4rWmTU7/pnLs55fl9k/25wMzMaXJLtAB18KKP\nPAHsnuP/f5lgaWuA18SevwN4g5ktAZjZBWZ23uRWcx8hWAb5W8Bb5/jdQnhDHbzoGsnOOK1T/gjB\nJOv3pvjwaf/XxR7fANxqZm8HPhM+75z7tJn9A+Cgma0Dq8AvAtcDX3DOfdnMvgHcbWb/0zl3pOgf\nJ4RPFJMUQoieIotGCCF6igReCCF6igReCCF6igReCCF6igReCCF6igReCCF6igReCCF6igReCCF6\nyv8HQaUNcK7XK0cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff29d7051d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist([val for val in trainer.network.predict(valid[0])[5] if val > 0.1 ], bins = 20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(array([ 2.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  2.,  1.,  2.,  0.,  0.,\n",
        "         1.,  0.,  0.,  0.,  2.,  0.,  3.]),\n",
        " array([ 0.13427415,  0.17756044,  0.22084674,  0.26413303,  0.30741932,\n",
        "         0.35070561,  0.39399191,  0.4372782 ,  0.48056449,  0.52385078,\n",
        "         0.56713708,  0.61042337,  0.65370966,  0.69699595,  0.74028225,\n",
        "         0.78356854,  0.82685483,  0.87014112,  0.91342742,  0.95671371,  1.        ]),\n",
        " <a list of 20 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEACAYAAACnJV25AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS1JjLHZJaozFLkmNsdglqTEWuyQ15v8BM6WixWkIVokAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff29d663690>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def max_finder (listt) :\n",
      "    m = 0\n",
      "    for i in range (len(listt)) :\n",
      "        if listt[i] > listt[m] :\n",
      "            m = i\n",
      "    return m\n",
      "def compare (output,learned,n = nlayers,m = 4) : # m is ntracks\n",
      "    counter = 0\n",
      "    for track in range (m) :\n",
      "        subcounter = 0\n",
      "        for layer in range (n) :\n",
      "            start = int(float(layer) * len(output) / n)\n",
      "            end = int(float(layer+1) * len(output) / n) + 1\n",
      "            subOutput = output[start:end]\n",
      "            subLearned = learned[start:end]\n",
      "            if max_finder(subOutput) == max_finder(subLearned) :\n",
      "                subcounter += 1\n",
      "                output[max_finder(subOutput)+start] = 0\n",
      "                learned[max_finder(subLearned)+start] = 0\n",
      "        if counter == n :\n",
      "            counter += 1\n",
      "    return counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counter = np.zeros(5 , dtype=float32)\n",
      "start = time.mktime(time.gmtime())\n",
      "for i in range (200) :\n",
      "    counter[compare(ds_output[cut+i],trainer.network.predict(valid[0])[i],4,4)] += 1\n",
      "stop = time.mktime (time.gmtime())\n",
      "spent.append (stop - start)\n",
      "print \"\\t\" , stop - start\n",
      "print counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}