notebooks/STC Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: GeForce GT 750M (CNMeM is disabled)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from os.path import expanduser\n",
    "import os\n",
    "import json\n",
    "import theano\n",
    "import pyret.filtertools as ft\n",
    "import pyret.visualizations as pyviz\n",
    "import deepretina.visualizations as viz\n",
    "from deepretina.preprocessing import datagen, loadexpt\n",
    "from deepretina.utils import rolling_window\n",
    "from keras.models import model_from_json\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import pearsonr\n",
    "%matplotlib inline\n",
    "#%matplotlib qt\n",
    "\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "rcParams['image.interpolation'] = 'nearest'\n",
    "rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load natural scenes CNN and white noise LN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "architecture_filename = 'architecture.json'\n",
    "naturalscenes_data_dir = expanduser('~/Dropbox/deep-retina/saved/lenna.salamander/2015-11-07 16.52.44 convnet/')\n",
    "naturalscenes_weight_filename = 'epoch038_iter02700_weights.h5' # .53 cc on held-out\n",
    "ln_data_dir = expanduser('~/Dropbox/deep-retina/saved/lenna.nirum/2015-11-08 04.41.18 LN/')\n",
    "ln_weight_filename = 'epoch010_iter00750_weights.h5' # .468 cc on held-out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "naturalscenes_architecture_data = open(naturalscenes_data_dir + architecture_filename, 'r')\n",
    "naturalscenes_architecture_string = naturalscenes_architecture_data.read()\n",
    "naturalscenes_model = model_from_json(naturalscenes_architecture_string)\n",
    "naturalscenes_model.load_weights(naturalscenes_data_dir + naturalscenes_weight_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ln_architecture_data = open(ln_data_dir + architecture_filename, 'r')\n",
    "ln_architecture_string = ln_architecture_data.read()\n",
    "ln_model = model_from_json(ln_architecture_string)\n",
    "ln_model.load_weights(ln_data_dir + ln_weight_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nsamples = 1000\n",
    "batch_size = 50\n",
    "cnn_sta = np.zeros((40,50,50), dtype='float32')\n",
    "for iteration in range(nsamples):\n",
    "    sample = np.random.randn(batch_size, 40, 50, 50).astype('float32')\n",
    "    \n",
    "    cnn_response = naturalscenes_model.predict(sample)\n",
    "    \n",
    "    cnn_sta_subset = np.tensordot(cnn_response.T, sample, axes=1)\n",
    "    cnn_sta += cnn_sta_subset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11c85a4d0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "/eecj8s5r845Hw9sBd5A9W+/rN79NJ8kDfZ1qt9Yr6sfr22w9nrgCxHR+098bYNnxaD6RBXAO4F1\n",
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       "JEnSEAxTkiRJQzBMSZIkDeG/J5fu6stEvPcAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1224e9610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp, tm = ft.decompose(cnn_sta)\n",
    "plt.subplot(121)\n",
    "plt.imshow(sp, clim=[-np.max(abs(sp)), np.max(abs(sp))])\n",
    "plt.subplot(122)\n",
    "plt.plot(tm, 'k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 50, 50)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_sta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnn_stc = np.outer(cnn_sta, cnn_sta.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cnn_stc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x12742df90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1224f6310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cnn_stc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "architecture_filename = 'architecture.json'\n",
    "whitenoise_data_dir = expanduser('~/Dropbox/deep-retina/saved/lenna.salamander/2015-11-08 15.42.33 convnet/')\n",
    "whitenoise_weight_filename = 'epoch018_iter01300_weights.h5' # .63 cc on held-out\n",
    "naturalscenes_data_dir = expanduser('~/Dropbox/deep-retina/saved/lenna.salamander/2015-11-07 16.52.44 convnet/')\n",
    "naturalscenes_weight_filename = 'epoch038_iter02700_weights.h5' # .53 cc on held-out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "whitenoise_architecture_data = open(whitenoise_data_dir + architecture_filename, 'r')\n",
    "whitenoise_architecture_string = whitenoise_architecture_data.read()\n",
    "whitenoise_model = model_from_json(whitenoise_architecture_string)\n",
    "whitenoise_model.load_weights(whitenoise_data_dir + whitenoise_weight_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "naturalscenes_architecture_data = open(naturalscenes_data_dir + architecture_filename, 'r')\n",
    "naturalscenes_architecture_string = naturalscenes_architecture_data.read()\n",
    "naturalscenes_model = model_from_json(naturalscenes_architecture_string)\n",
    "naturalscenes_model.load_weights(naturalscenes_data_dir + naturalscenes_weight_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nsamples = 1000\n",
    "batch_size = 50\n",
    "whitenoise_cnn_sta = np.zeros((40,50,50), dtype='float32')\n",
    "naturalscenes_cnn_sta = np.zeros((40,50,50), dtype='float32')\n",
    "for iteration in range(nsamples):\n",
    "    sample = np.random.randn(batch_size, 40, 50, 50).astype('float32')\n",
    "    \n",
    "    whitenoise_cnn_response = whitenoise_model.predict(sample)\n",
    "    naturalscenes_cnn_response = naturalscenes_model.predict(sample)\n",
    "    \n",
    "    whitenoise_cnn_sta_subset = np.tensordot(whitenoise_cnn_response.T, sample, axes=1)\n",
    "    naturalscenes_cnn_sta_subset = np.tensordot(naturalscenes_cnn_response.T, sample, axes=1)\n",
    "    whitenoise_cnn_sta += whitenoise_cnn_sta_subset[0]\n",
    "    naturalscenes_cnn_sta += naturalscenes_cnn_sta_subset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sp_whitenoise, tm_whitenoise = ft.decompose(whitenoise_cnn_sta)\n",
    "sp_naturalscenes, tm_naturalscenes = ft.decompose(naturalscenes_cnn_sta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "whitenoise_abs_max = np.max(abs(sp_whitenoise))\n",
    "naturalscenes_abs_max = np.max(abs(sp_naturalscenes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "RsFDiDEixnglgKcAOInKROjXdnu6sommLUKIInTnIYQoQsFDCFGEgocQoggFDyFEEQoeQogiFDyE\n",
       "EEUoeAghilDwEEIUoeAhhChCwUMIUYSChxCiCAUPIUQRCh5CiCIUPIQQRSh4CCGKUPAQQhSh4CGE\n",
       "KELBQwhRhIKHEKIIBQ8hRBEKHkKIIhQ8hBBF/D9dHHcjfe46xwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132e65610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.gcf()\n",
    "fig.set_size_inches((4,5))\n",
    "\n",
    "ax = plt.subplot2grid((4, 1), (0,0), rowspan=3)\n",
    "ax.imshow(sp_whitenoise, interpolation='nearest', cmap='seismic', clim=[-whitenoise_abs_max, whitenoise_abs_max]) #, clim=[np.min(W0), np.max(W0)])\n",
    "plt.grid('off')\n",
    "plt.axis('off')\n",
    "\n",
    "ax = plt.subplot2grid((4, 1), (3, 0), rowspan=1)\n",
    "ax.plot(np.linspace(0,400,40), tm_whitenoise, 'k', linewidth=4)\n",
    "plt.grid('off')\n",
    "plt.axis('off')\n",
    "plt.savefig('Whitenoise_CNN_Total_RF_seismic.png', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "BCHjIYQIQsZDCBGEjIcQIoj/DylnsuzNSr3PAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132e2a310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.gcf()\n",
    "fig.set_size_inches((4,5))\n",
    "\n",
    "ax = plt.subplot2grid((4, 1), (0,0), rowspan=3)\n",
    "ax.imshow(sp_naturalscenes, interpolation='nearest', cmap='seismic', clim=[-naturalscenes_abs_max, naturalscenes_abs_max]) #, clim=[np.min(W0), np.max(W0)])\n",
    "plt.grid('off')\n",
    "plt.axis('off')\n",
    "\n",
    "ax = plt.subplot2grid((4, 1), (3, 0), rowspan=1)\n",
    "ax.plot(np.linspace(0,400,40), tm_naturalscenes, 'k', linewidth=4)\n",
    "plt.grid('off')\n",
    "plt.axis('off')\n",
    "plt.savefig('Naturalscenes_CNN_Total_RF_seismic.png', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'layer_0', u'layer_1', u'layer_2', u'layer_3', u'layer_4']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naturalscenes_weights = h5py.File(naturalscenes_data_dir + naturalscenes_weight_filename, 'r')\n",
    "list(naturalscenes_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'layer_0', u'layer_1', u'layer_2', u'layer_3', u'layer_4']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whitenoise_weights = h5py.File(whitenoise_data_dir + whitenoise_weight_filename, 'r')\n",
    "list(whitenoise_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "whitenoise_W0 = whitenoise_weights['layer_0']['param_0']\n",
    "naturalscenes_W0 = naturalscenes_weights['layer_0']['param_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "whitenoise_spatial_profiles = [ft.decompose(i)[0] for i in whitenoise_W0]\n",
    "naturalscenes_spatial_profiles = [ft.decompose(i)[0] for i in naturalscenes_W0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "whitenoise_abs_max = np.max(abs(np.array(whitenoise_spatial_profiles)))\n",
    "naturalscenes_abs_max = np.max(abs(np.array(naturalscenes_spatial_profiles)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "kSxKHiKSRclDRLIoeYhIFiUPEcmi5CEiWf4/7W12DmLQLQkAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126331b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nfilters = whitenoise_W0.shape[0]\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches((4,10))\n",
    "plt.title('Whitenoise Affine STAs', fontsize=20)\n",
    "num_cols = int(np.sqrt(nfilters))\n",
    "num_rows = int(np.ceil(nfilters/num_cols))\n",
    "idxs = range(num_cols)\n",
    "for x in range(num_cols):\n",
    "    for y in range(num_rows):\n",
    "        plt_idx = y * num_cols + x + 1\n",
    "        spatial,temporal = ft.decompose(whitenoise_W0[plt_idx-1])\n",
    "        #plt.subplot(num_rows, num_cols, plt_idx)\n",
    "        ax = plt.subplot2grid((num_rows*4, num_cols), (4*y, x), rowspan=3)\n",
    "        ax.imshow(spatial, interpolation='nearest', cmap='seismic', clim=[-whitenoise_abs_max, whitenoise_abs_max]) #, clim=[np.min(W0), np.max(W0)])\n",
    "        plt.grid('off')\n",
    "        plt.axis('off')\n",
    "\n",
    "        ax = plt.subplot2grid((num_rows*4, num_cols), (4*y+3, x), rowspan=1)\n",
    "        ax.plot(np.linspace(0,400,40), temporal, 'k', linewidth=2)\n",
    "        plt.grid('off')\n",
    "        plt.axis('off')\n",
    "plt.savefig('Whitenoise_Conv_filters_seismic.png', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "5ESJl4iIiEhO/h9KksO7TSIEpwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13343db90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nfilters = whitenoise_W0.shape[0]\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches((10,4))\n",
    "num_rows = int(np.sqrt(nfilters))\n",
    "num_cols = int(np.ceil(nfilters/num_cols))\n",
    "idxs = range(num_cols)\n",
    "for x in range(num_cols):\n",
    "    for y in range(num_rows):\n",
    "        plt_idx = y * num_cols + x + 1\n",
    "        spatial,temporal = ft.decompose(whitenoise_W0[plt_idx-1])\n",
    "        #plt.subplot(num_rows, num_cols, plt_idx)\n",
    "        ax = plt.subplot2grid((num_rows*4, num_cols), (4*y, x), rowspan=3)\n",
    "        ax.imshow(spatial, interpolation='nearest', cmap='seismic', clim=[-whitenoise_abs_max, whitenoise_abs_max]) #, clim=[np.min(W0), np.max(W0)])\n",
    "        plt.grid('off')\n",
    "        plt.axis('off')\n",
    "\n",
    "        ax = plt.subplot2grid((num_rows*4, num_cols), (4*y+3, x), rowspan=1)\n",
    "        ax.plot(np.linspace(0,400,40), temporal, 'k', linewidth=2)\n",
    "        plt.grid('off')\n",
    "        plt.axis('off')\n",
    "plt.savefig('Whitenoise_Conv_filters_seismic.png', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12636e190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nfilters = whitenoise_W0.shape[0]\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches((10,4))\n",
    "num_rows = int(np.sqrt(nfilters))\n",
    "num_cols = int(np.ceil(nfilters/num_cols))\n",
    "idxs = range(num_cols)\n",
    "for x in range(num_cols):\n",
    "    for y in range(num_rows):\n",
    "        plt_idx = y * num_cols + x + 1\n",
    "        spatial,temporal = ft.decompose(naturalscenes_W0[plt_idx-1])\n",
    "        #plt.subplot(num_rows, num_cols, plt_idx)\n",
    "        ax = plt.subplot2grid((num_rows*4, num_cols), (4*y, x), rowspan=3)\n",
    "        ax.imshow(spatial, interpolation='nearest', cmap='seismic', clim=[-naturalscenes_abs_max, naturalscenes_abs_max]) #, clim=[np.min(W0), np.max(W0)])\n",
    "        plt.grid('off')\n",
    "        plt.axis('off')\n",
    "\n",
    "        ax = plt.subplot2grid((num_rows*4, num_cols), (4*y+3, x), rowspan=1)\n",
    "        ax.plot(np.linspace(0,400,40), temporal, 'k', linewidth=2)\n",
    "        plt.grid('off')\n",
    "        plt.axis('off')\n",
    "\n",
    "        \n",
    "plt.savefig('Naturalscenes_Conv_filters_seismic.png', dpi=500)"
   ]
  },
  {
   "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": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}