datasets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import cv2\n",
    "import numpy as np\n",
    "import nolearn.lasagne\n",
    "import vizlib\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (15.0, 12.0)\n",
    "plt.rcParams['image.interpolation'] = 'none'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = vizlib.data.triangle_and_circle()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01d3205c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_sample()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "nw = vizlib.networks.triangle.build_network(data.X.shape[1:])\n",
    "\n",
    "filters = []\n",
    "def track_filters(nn, train_history):\n",
    "    # the convolution layer is the second last layer\n",
    "    conv = nn.get_all_layers()[-2]\n",
    "    F = conv.W.get_value().T.squeeze()\n",
    "    filters.append(F)\n",
    "\n",
    "nn = nolearn.lasagne.NeuralNet(\n",
    "    nw,\n",
    "    verbose=0,\n",
    "    update_learning_rate = 1e-2,\n",
    "    update_momentum = 0.9,\n",
    "    \n",
    "    on_epoch_finished = [track_filters]\n",
    ")\n",
    "nn.initialize()\n",
    "nn.fit(data.X, data.y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3QAAAByCAYAAAAbIZM7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA50kSZIkNer/Ae7PqGRjg6ipAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01d77f1c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.linspace(0, len(filters) - 1, 10).astype(np.int)\n",
    "for i, x in enumerate(xs, 1):\n",
    "    plt.subplot(1,10,i)\n",
    "    plt.imshow(filters[x])\n",
    "    plt.axis('off')\n",
    "    plt.title(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gQvOLGQAAQJgwAwAACBNmAAAAYcIMAAAgTJgBAACECTMAAIAwYQYAABAmzAAAAMKEGQAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01f1c16790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vizlib.data.plot_imgs(filters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AsH7GewAAIJzQDwAA4YR+AAAIJ/QDAEC4yNDfWvvl1tr/aK19t7X2W621nx69JkijzmAZ\nag3mt4U6izy9p7X2uar6r73391trf7qq/m1V/fXe+68PXhrEUGewDLUG89tCne1GL2AOvfdvv3Rr\nX1W/N2ItkEqdwTLUGsxvC3UWOd5TVdVa+2pr7Q+q6ttV9XO992+OXhOkUWewDLUG80uvs8jxnhut\ntVZVn6+qf1FVP957/43BS4I46gyWodZgfsl1Fh36b7TWvlZV7/fer0evBVKpM1iGWoP5JdZZ7HjP\nSz5SVX8wehEQTp3BMtQazC+uzuJCf2vtrdbaT7bWvq+1dtFa+0JV/eWq+pej1wYp1BksQ63B/LZS\nZ4mn9/Sq+lJVfa2qWlX9ZlX9td77vx+6KsiizmAZag3mt4k628RMPwAAbFnceA8AAPAioR8AAMIJ\n/QAAEE7oBwCAcPee3tNa8ynfwXrvbfQamJ9aG0udbYM6G0+t5VNn491VZzr9Z+rq6mr0EgDgSVxf\nxzzUFM7W5eXlvV8X+gEAIJzQDwAA4YR+AAAIJ/QDAEA4oR8AAMIJ/QAAEE7oBwCAcEI/AACEE/oB\nACCc0A8AAOF2oxcAAKxPa+10vd/vT9cXFxcjlgM8QKcfAADCCf0AABDOeA8A8CjTkZ7D4fDgfaM+\ncD50+gEAIFxEp9+HiQBgfr330/Vudxshpu+90/vA+dDpBwCAcEI/AACEW+0enA8TAcA4x+PxdD19\nj52OAAHnQ6cfAADCCf0AABButeM9ThAAgPNgpAfOn04/AACEE/oBACBcxPyLEwQAAHiZB7je0ukH\nAIBwQj8AAISLGO+ZMtIDALBdHuD6wXT6AQAgnNAPAADh4sZ7INXNtuQ5nz7glAQAzsn0fekx95Pp\n9AMAQDihHwAAwhnvgTP2QScNnNvpA05JAOCcTE9ynL7nbH3sVKcfAADCCf0AABDOeA+csekW5W73\nrFyn25M390b6oDVWnd86Adie4/F4up6O9GzxYa46/QAAEE7oBwCAcPbcYSVutijPeXvSNioA52rr\n70U6/QAAEE7oBwCAcMZ7YGXWsj25lnUCwBbo9AMAQDihHwAAwgn9AAAQTugHAIBwQj8AAIRzeg8A\nADyh1trper/fn66nD65cmk4/AACEE/oBACCc8R4AAHhN05Gew+Hw4P2lR310+gEAIJzQDwAA4Yz3\nAADAa+q9n653u9uIPT29Z3p/aTr9AAAQTugHAIBwxnsAAOAJHY/H0/X0lJ7pCNDSdPoBACCc0A8A\nAOGM9wDAc9OH6ExP3Fj6ITpAjpEjPVM6/QAAEE7oBwCAcMZ7ANi06UjP4XB48L5RH2CNdPoBACCc\n0A8AAOGM9wCwadOTNXa727fF6ek90/sAa6TTDwAA4YR+AAAIZ78SAJ47Ho+n6+kpPefycB2AD0un\nHwAAwgn9AAAQzngPAHwAIz1AEp1+AAAIJ/QDAEA44z0TrbXT9fShLNMTHGANvJYBgCmdfgAACCf0\nAwBAuM2P90zHIA6Hw4P3jUdwrryWIYPxPGAOOv0AABBO6AcAgHCbH++ZPnxlt7v9v2M6BvHGG298\n4H1brZyTu17L0/GA6X3gfBjPA+am0w8AAOGEfgAACGevf+J4PJ6up1unxiNYm7tey9MRIOB8GM8D\n5qbTDwAA4YR+AAAIZ6/wDsYjSOE1C+vi/QeYg04/AACEE/oBACCc8Z5HsKUKwAjef4CnotMPAADh\nhH4AAAjX7ts6bK3ZVxys995Gr4H5qbWx1Nk2qLPx1Fo+dTbeXXV2b+gHAADWz3gPAACEE/oBACCc\n0A8AAOGiQ39r7U+11t5vrf2z0WuBVOoMlqHWYH7JdRYd+qvqF6vqN6rKp5VhPuoMlqHWYH6xdRYb\n+ltrP1lV/6eq/nVVOSIMZqDOYBlqDeaXXmeRob+19omq+kdVdV2B3zQ4B+oMlqHWYH5bqLPI0F9V\nX66qf9x7/90K3J6BM6HOYBlqDeYXX2e70Qt4aq21H6mqv1BVP3pza+ByIJI6g2WoNZjfVuosLvRX\n1eer6k9W1e+01qqqPlZVF621H+q9/9jIhUEQdQbLUGswv03UWes9awejtfbRqvr4zX9W1d+rZ9/I\nL/Xe/9eodUESdQbLUGswv63UWVynv/f+XlW9d/PfrbXvVdV7Sd80GE2dwTLUGsxvK3UW1+kHAABe\nlHp6DwAA8JzQDwAA4YR+AAAIJ/QDAEC4e0/vaa35lO9gvffIB0TwIrU2ljrbBnU2nlrLp87Gu6vO\ndPrP1NXV1eglAMCTuL6+Hr0EiHd5eXnv14V+AAAIF/dwLgAA+DBau52M2e/3p+uLi4sRy3lSOv0A\nABBO6AcAgHDGewAA2KzpSM/hcHjw/lpHfXT6AQAgnNAPAADhjPcAALBZvd8+T2y3u43G09N7pvfX\nSqcfAADCCf0AABBu/XsVAADwBI7H4+l6ekrPdARorXT6AQAgnNAPAADhjPcAAMBLEkZ6pnT6AQAg\nnNAPAADhhH4AAAgn9AMAQDihHwAAwjm9BwAe0Fo7XR8Oh9P19HSP6YN8AM6NTj8AAIQT+gEAIJzx\nHuCF0YX9fn+6Nq7Alt010jO9f9fvmbt21CychzXVok4/AACEE/oBACCc8R7YqMeMLiw5rjC1pu1S\nck1P5tntbt8u7zq9Z/p75nDONQuvYu0/49daizr9AAAQTugHAIBwxntgo+4aS5hutc49rjC11u1S\ntuF4PJ6u33jjg/tl05qaw7nVLLyKpJ/xa61FnX4AAAgn9AMAQLjz23sAFjcdXZhuqc49rjC11u1S\ntmf6Wh01mnAONQuvIvVn/JpqUacfAADCCf0AABBuffsoZ2rtD5qAG+ewJbmm7VK259xOIVEXrE3q\nz/hzX79OPwAAhBP6AQAgnPGe13BuW7wwp1EjbOe+Xcr2pJ5CAiP4Gb8cnX4AAAgn9AMAQDj7j6/B\nFi/plhxhcwIWa5R6CgmQR6cfAADCCf0AABBu8dmT1C18W7wkmnuEzQlYJPHzHjhnOv0AABBO6AcA\ngHCLjPdsbQvfFi+J5hhhcwIWACxDpx8AAMIJ/QAAEG6RfXNb+JBljhE2J2ABwHx0+gEAIJzQDwAA\n4RafqZl7Cz/14V+wJUZ6AOBp6fQDAEA4oR8AAMIt/nCuOUZutvbwLwAAeBU6/QAAEE7oBwCAcLON\n9yw5cuPhXwAAcDedfgAACCf0AwBAuNlmXkaN3Mz98C8AAFgbnX4AAAgn9AMAQLhFjrQZNXJjpAcA\nAHT6AQAgntAPAADhFn9ilZEbAABYlk4/AACEE/oBACCc0A8AAOGEfgAACCf0AwBAOKEfAADCCf0A\nABBO6AcAgHBCPwAAhBP6AQAgnNAPAADhhH4AAAgn9AMAQDihHwAAwu1GLwAAANaotXa63u/3p+uL\ni4sRy7mXTj8AAIQT+gEAIJzxHgAAeKTpSM/hcHjw/rmM+uj0AwBAOKEfAADCGe8BAIBH6r2frne7\n2yg9Pb1nev9c6PQDAEA4oR8AAMKd394DAACswPF4PF1PT+mZjgCdC51+AAAIJ/QDAEC4dt/2Q2vt\n/PYmNqb33h7+XaydWhtLnW2DOhtPreVTZ+PdVWf3hn4AAGD9jPcAAEA4oR8AAMIJ/QAAEC4y9LfW\n/k1r7b3W2u8///Wd0WuCRGoN5qfOYH5bqLPI0F9Vvar+Tu/9489//dDoBUEotQbzU2cwv/g6Sw39\nVVWOBYNlqDWYnzqD+UXXWXLo//nW2v9srf271trnRy8Ggqk1mJ86g/lF11nkOf2ttT9TVd+uqv9f\nVT9VVb9QVT/Se/+toQuDMGoN5qfOYH5bqLPI0P+y1tqvVdWv9t5/YfRaIJlag/mpM5hfYp0lj/cA\nAAAVGPpba9/fWvtCa+2PtdZ2rbW/UlV/rqp+ffTaIIlag/mpM5jfVupsN3oBM/hIVX25qn6wqg5V\n9Z2q+one+ztDVwV51BrMT53B/DZRZ5uY6QcAgC2LG+8BAABeJPQDAEA4oR8AAMIJ/QAAEO7e03ta\naz7lO1jvvY1eA/NTa2Ops21QZ+OptXzqbLy76iyu099aO/06Ho+nX2tzdXU1egkA8CSur69HLwHi\nXV5e3vv1uNAPAAC8KOLhXK3d7mIcDocH719cXCyzMACY2fS9br/fn66918F6LFHHOv0AABBO6AcA\ngHAR4z29335QfLe7/StNt0em9wFgzYy1wvotXcc6/QAAEE7oBwCAcHEzL9Mz+afbINMRIABYM2Ot\nsH5L17FOPwAAhBP6AQAgXPTen5EeANIZa4X1W6KOdfoBACCc0A8AAOFWO94zfXDB9FPOHkACwFYZ\n6YH1m6uOdfoBACCc0A8AAOFWNd4zHek5HA4P3jfqAwAAOv0AABBP6AcAgHCrGu+Zfpp5t7td+vT0\nnul9AABApx8AAOIJ/QAAEG61szDH4/F0PT2lx4NJAGB+HpIJ66LTDwAA4YR+AAAIt9rxnikjPQAw\nPw/JhPXS6QcAgHBCPwAAhIsY7wEA5uchmbBeOv0AABBO6AcAgHD24ADgA3j41P08JBPWRacfAADC\nCf0AABDOeA/wWoxAkMTDpz4cIz1w/nT6AQAgnNAPAADhjPcAr8wIBGv3mLE0D58Ckuj0AwBAOKEf\nAADC2aMEXtn0pA4jEKzJzVjPq46lefgUsHY6/QAAEE7oBwCAcPbfJx5zmoMHEcGLjsfj6doIBOfu\n5nX5qmNpXs8wD7lqOTr9AAAQTugHAIBwmx/vecxDhqbjC4/539qSYquMQLAWxtJgHA94HEOnHwAA\nwgn9AAAQbvPjPY95yNBjTu/xICKAdTLSA8vygMcxdPoBACCc0A8AAOHsnUw85jSH6bUTH+BFHrIC\nwKtwktZydPoBACCc0A8AAOGM99zhMdtKtp7AQ1YAeBpy1bx0+gEAIJzQDwAA4Yz3AK/lHB6y4tQg\ngG2Y4+f9Vt5DdPoBACCc0A8AAOGM9wBPZsmHrDg1CGAb5vh5P/efeY5jQjr9AAAQTugHAIBwxnuA\nWcz9kJVzODWI7Tj3bXtINsfP+6f6M9c0aqrTDwAA4YR+AAAIZ+8bWL0lTw1iO9a0bQ9bMcfP+9f5\nM9c0aqrTDwAA4YR+AAAIdx77DSvlNAc4P0Z6eCpr2raHLZrj5/2oMaEl6PQDAEA4oR8AAMLZl3xF\nTnMA2J5z37YHzss5/mzQ6QcAgHBCPwAAhDPe84qc5sBcbkbEnAQF5+0ct+0BHqLTDwAA4YR+AAAI\nZw7lNTjNgad0c+rTQydBeSgcAPCqdPoBACCc0A8AAOGM9zwRIz28rptTn+46CepmrMdD4QCAV6XT\nDwAA4YR+AAAIZ7wHzsTNaVAPnQTloXAAwKvS6QcAgHBCPwAAhDMPAGfmoZOgPBQOAHhVOv0AABBO\n6AcAgHDGe2DFjPQAAI+h0w8AAOEe7PS//fbbS6yDl3zmM5+pd955Z/QyWJBaG+Ob3/zm6CWwIHU2\nxqc//enRS2BB6myMN998s9599907v97uGw9orZkdGKz33kavgfmptbHU2Taos/HUWj51Nt5ddXZv\n6AcAANbPTD8AAIQT+gEAIJzQDwAA4YR+AAAIJ/QDAEC4PwSLsygxjGpyFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01d3205b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counting = vizlib.data.counting_2d().show_sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Not sure what is causing the distortions above. To be assured: all dots are the same shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
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