AlexNet-Baseline-Ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mini AlexNet Baseline Results\n",
    "\n",
    "Expects:\n",
    "\n",
    "- weight.h5 files in ./weights \n",
    "- CIFAR10 data in ../data/cifar-10-batches-py/\n",
    "\n",
    "and while the keras code needs py27, the notebooks can use py3.6 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:22:41.839852Z",
     "start_time": "2018-09-02T20:22:39.160291Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlesmartin14/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n",
      "/Users/charlesmartin14/.local/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing notebook from RMT_Util.ipynb\n",
      "3.6.5 |Anaconda custom (64-bit)| (default, Apr 26 2018, 08:42:37) \n",
      "[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version 1.14.2\n",
      "scipy version 1.0.1\n",
      "tensforflow version 1.5.0-dev20171116\n",
      "keras version 2.1.5\n",
      "sklearn version 0.19.1\n",
      "powerlaw version 1.4.3\n",
      "3.6.5 |Anaconda custom (64-bit)| (default, Apr 26 2018, 08:42:37) \n",
      "[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version 1.14.2\n",
      "tensforflow version 1.5.0-dev20171116\n",
      "keras version 2.1.5\n",
      "sklearn version 0.19.1\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import pickle, time\n",
    "from copy import deepcopy\n",
    "from shutil import copy\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.callbacks import TensorBoard, EarlyStopping\n",
    "from keras.optimizers import SGD\n",
    "from keras.initializers import Constant\n",
    "import keras.backend as K\n",
    "\n",
    "import import_ipynb\n",
    "import RMT_Util\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import sklearn\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.random_projection import sparse_random_matrix\n",
    "\n",
    "print(sys.version)\n",
    "print(\"numpy version {}\".format(np.__version__))\n",
    "print(\"tensforflow version {}\".format(tf.__version__))\n",
    "print(\"keras version {}\".format(keras.__version__))\n",
    "print(\"sklearn version {}\".format(sklearn.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:22:41.848890Z",
     "start_time": "2018-09-02T20:22:41.841555Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [5, 5]\n",
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:22:41.923817Z",
     "start_time": "2018-09-02T20:22:41.850574Z"
    }
   },
   "outputs": [],
   "source": [
    "BASELINE_outfile = 'results/cifar10_alexnet.out'\n",
    "\n",
    "BASELINE_final_weightfile = 'weights/alexnet.b16.final.h5'\n",
    "BASELINE_per_epoch_weightfile = \"weights/alexnet.b16.e{}.h5\"\n",
    "NUM_BASELINE_EPOCHS = 100\n",
    "\n",
    "BASELINE_init_weighfile = 'weights/alexnet.b16.e0.h5'\n",
    "\n",
    "REGULARIZED_final_weightfile = 'weights/alexnet.b16.wd.final.h5'\n",
    "RANDOMIZED_final_weightfile = 'weights/alexnet.b16.rand100.final.h5'\n",
    "\n",
    "CIFAR_TEST_BATCH = \"./data/cifar-10-batches-py/test_batch\"\n",
    "\n",
    "                                          \n",
    "BASELINE_ensemble_weightfile = \"./weights/alexnet.b16.id{}.final.h5\"\n",
    "NUM_ENSEMBLE_RUNS = 10 \n",
    "\n",
    "N7, M7 = 4096, 384\n",
    "N8, M8 = 384, 192\n",
    "\n",
    "Q7 = float(N7)/float(M7)\n",
    "Q8 = float(N8)/float(M8)\n",
    "\n",
    "Q_FC1 = Q7\n",
    "Q_FC2 = Q8\n",
    "\n",
    "LAYER_FC1 = 7\n",
    "LAYER_FC2 = 8\n",
    "\n",
    "ENTROPY_FILE = \"img/mini-alexnet-baseline-entropy-per-epoch.png\"\n",
    "RANK_FILE = \"img/mini-alexnet-baseline-rank-per-epoch.png\"\n",
    "\n",
    "ACCURACY_FILE = \"img/mini-alexnet-baseline-accuracy-per-epoch.png\"\n",
    "\n",
    "FC1_ESD_PER_EPOCH_FILE = \"img/mini-alexnet-fc1-esd-per-epoch.png\"\n",
    "FC2_ESD_PER_EPOCH_FILE = \"img/mini-alexnet-fc2-esd-per-epoch.png\"\n",
    "\n",
    "ALPHA = 0.25 # not passing through\n",
    "\n",
    "XLABEL_FC1 = r\"Eigenvalues $\\lambda$ of $\\mathbf{X}=\\mathbf{W}_{FC1}^{T}\\mathbf{W}_{FC1}$\"\n",
    "XLABEL_FC2 = r\"Eigenvalues $\\lambda$ of $\\mathbf{X}=\\mathbf{W}_{FC2}^{T}\\mathbf{W}_{FC2}$\"\n",
    "\n",
    "XLABEL = r\"Eigenvalues $\\lambda$ of $\\mathbf{X}=\\mathbf{W}}^{T}\\mathbf{W}$\"\n",
    "YLABEL = \"Spectral Density\"\n",
    "\n",
    "ESD_FC1_0_EPOCH = \"img/mini-alexnet-fc1-esd-epoch-0.png\"\n",
    "ESD_FC2_0_EPOCH = \"img/mini-alexnet-fc2-esd-epoch-0.png\"\n",
    "ESD_FC1_FINAL_EPOCH = \"img/mini-alexnet-fc1-esd-epoch-final.png\"\n",
    "ESD_FC2_FINAL_EPOCH = \"img/mini-alexnet-fc2-esd-epoch-final.png\"\n",
    "\n",
    "\n",
    "ENSEMBLE_ESD_FC1_EPOCH0 = \"img/mini-alexnet-fc1-ensemble-esd-epoch0.png\"\n",
    "ENSEMBLE_ESD_FC2_EPOCH0 = \"img/mini-alexnet-fc2-ensemble-esd-epoch0.png\"\n",
    "\n",
    "ENSEMBLE_ESD_FC1_FINAL_EPOCH_9SPIKES = \"img/mini-alexnet-fc1-ensemble-esd-final-9spikes.png\"\n",
    "ENSEMBLE_ESD_FC1_FINAL_EPOCH_18SPIKES = \"img/mini-alexnet-fc1-ensemble-esd-final-18spikes.png\"\n",
    "\n",
    "ENSEMBLE_ESD_FC2_FINAL_EPOCH_9SPIKES = \"img/mini-alexnet-fc2-ensemble-esd-final-9spikes.png\"\n",
    "ENSEMBLE_ESD_FC2_FINAL_EPOCH_18SPIKES = \"img/mini-alexnet-fc2-ensemble-esd-final-18spikes.png\"\n",
    "\n",
    "ENSEMBLE_ESD_FC1_FINAL_RAND = \"img/mini-alexnet-fc1-ensemble-esd-rand-final.png\"\n",
    "ENSEMBLE_ESD_FC2_FINAL_EPOCH = \"img/mini-alexnet-fc2-ensemble-esd-rand-final.png\"\n",
    "\n",
    "ENSEMBLE_FC1_3COLOR_ESD = \"img/mini-alexnet-fc1-ensemble-3color-ssd.png\"\n",
    "\n",
    "ENSEMBLE_FC1_LOCS =  \"img/mini-alexnet-fc1-ensemble-locs.png\"\n",
    "ENSEMBLE_FC1_PARTS =  \"img/mini-alexnet-fc1-ensemble-parts.png\"\n",
    "ENSEMBLE_FC1_VENTROPIES =  \"img/mini-alexnet-fc1-ensemble-ventropies.png\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:22:42.178810Z",
     "start_time": "2018-09-02T20:22:41.925688Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln: alexnet.b16.e100.h5: File exists\n",
      "lrwxr-xr-x  1 charlesmartin14  staff  20 Apr 10 23:41 \u001b[35malexnet.b16.e100.h5\u001b[m\u001b[m -> alexnet.b16.final.h5\n"
     ]
    }
   ],
   "source": [
    "!cd weights; ln -s alexnet.b16.final.h5 alexnet.b16.e100.h5\n",
    "!cd weights; ls -l alexnet.b16.e100.h5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:22:42.585381Z",
     "start_time": "2018-09-02T20:22:42.181317Z"
    }
   },
   "outputs": [],
   "source": [
    "model = RMT_Util.Alexnet_Model()\n",
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:22:42.666533Z",
     "start_time": "2018-09-02T20:22:42.587639Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 96)        7296      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 10, 10, 96)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 10, 10, 96)        384       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 10, 10, 256)       614656    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 384)               1573248   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 192)               73920     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                1930      \n",
      "=================================================================\n",
      "Total params: 2,272,458\n",
      "Trainable params: 2,271,754\n",
      "Non-trainable params: 704\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model.load_weights(BASELINE_final_weightfile)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check baseline shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:22:42.713003Z",
     "start_time": "2018-09-02T20:22:42.668888Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer  0 :  (5, 5, 3, 96)\n",
      "layer  2 :  (96,)\n",
      "layer  3 :  (5, 5, 96, 256)\n",
      "layer  5 :  (256,)\n",
      "layer  7 :  (4096, 384)\n",
      "layer  8 :  (384, 192)\n",
      "layer  9 :  (192, 10)\n"
     ]
    }
   ],
   "source": [
    "for il, layer in enumerate(model.layers):\n",
    "    weights = layer.get_weights() \n",
    "    if len(weights)>0 :\n",
    "        W = weights[0]\n",
    "        print(\"layer \",il,\": \", W.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Baseline Ensemble Fits\n",
    "### <font color='red'>IDs 1 and 2 were corrupted<font>\n",
    "\n",
    "### FC1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:22:44.816590Z",
     "start_time": "2018-09-02T20:22:42.714628Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "eigenvalues, _ = RMT_Util.keras_ensemble_spectrum(model, \n",
    "                        BASELINE_ensemble_weightfile, LAYER_FC1,num_runs=NUM_ENSEMBLE_RUNS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:22:44.822100Z",
     "start_time": "2018-09-02T20:22:44.818512Z"
    }
   },
   "outputs": [],
   "source": [
    "assert(len(eigenvalues)==NUM_ENSEMBLE_RUNS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:22:44.853724Z",
     "start_time": "2018-09-02T20:22:44.824225Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# all_evals=np.concatenate(eigenvalues)\n",
    "all_evals = []\n",
    "for i in tqdm(range(len(eigenvalues))):\n",
    "    all_evals.extend(eigenvalues[i-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First get Baseline Shuffled sigma to select best $\\lambda_{max}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:24:50.502437Z",
     "start_time": "2018-09-02T20:22:44.856034Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "get_shuffled_eigenvalues\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RMT_Util.ipynb:7: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \"## RMT Util\\n\",\n",
      "RMT_Util.ipynb:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \"## RMT Util\\n\",\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% spikes outside bulk 0.00\n",
      "% sigma 1.2456\n",
      "1.2456492738440388\n",
      "saved figure img/mini-alexnet-fc1-ensemble-esd-rand-final.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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77oDzXnjhBd566y3mzJlT7MTWkaKP+R6rsMGuBrru33d5HUrcCdfAlFenAXOf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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "W = model.layers[LAYER_FC1].get_weights()[0]\n",
    "shuffled_evals = RMT_Util.get_shuffled_eigenvalues(W, layer=LAYER_FC1, num=100)\n",
    "sigma = RMT_Util.plot_ESD_and_fit(eigenvalues=shuffled_evals, Q=Q_FC1, \n",
    "                                  layer=LAYER_FC1, num_spikes=0)\n",
    "print(sigma)\n",
    "plt.title(\"Mini AlexNet FC1: ESD\\nShuffled 100X\")\n",
    "plt.xlabel(XLABEL_FC1)\n",
    "plt.ylabel(YLABEL)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "filename=ENSEMBLE_ESD_FC1_FINAL_RAND\n",
    "plt.savefig(filename)\n",
    "print(\"saved figure {}\".format(filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now select # spikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:24:51.025765Z",
     "start_time": "2018-09-02T20:24:50.505278Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RMT_Util.ipynb:7: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \"## RMT Util\\n\",\n",
      "RMT_Util.ipynb:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \"## RMT Util\\n\",\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% spikes outside bulk 2.40\n",
      "% sigma 1.2714\n",
      "1.2713706557882887\n",
      "saved figure img/mini-alexnet-fc1-ensemble-esd-final-9spikes.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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mdxr0ORhOPpmxK/ZOGD5k7oLUMX36dNq3t53nawoVfZ8iMllVu1R1HHMXpInK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\nQQsJJRsEQdBCQskGQRC0kFCyQRAELeT/olP+sdYKWmoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigma = RMT_Util.plot_ESD_and_fit(eigenvalues=all_evals, Q=Q_FC1,  \n",
    "                                  layer=LAYER_FC1, num_spikes=NUM_ENSEMBLE_RUNS*9)\n",
    "print(sigma)\n",
    "\n",
    "plt.title(\"Mini AlexNet FC1:  ESD\\nEnsemble of 10 runs, 9 Spikes\")\n",
    "plt.xlabel(XLABEL_FC1)\n",
    "plt.ylabel(YLABEL)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "filename=ENSEMBLE_ESD_FC1_FINAL_EPOCH_9SPIKES\n",
    "plt.savefig(filename)\n",
    "print(\"saved figure {}\".format(filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:24:51.929745Z",
     "start_time": "2018-09-02T20:24:51.029163Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RMT_Util.ipynb:7: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \"## RMT Util\\n\",\n",
      "RMT_Util.ipynb:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \"## RMT Util\\n\",\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% spikes outside bulk 4.92\n",
      "% sigma 1.2039\n",
      "1.2039065911893958\n",
      "saved figure img/mini-alexnet-fc1-ensemble-esd-final-18spikes.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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7ln//+98J99nkzRcsW7aMJ554gsmTJ3P11VfH7d+tWzeaNWtGUVER3bp1i9tv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\nRCJRI5LCTSQSiRrxf6ZBO+x6y768AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_spikes = NUM_ENSEMBLE_RUNS*18\n",
    "sigma = RMT_Util.plot_ESD_and_fit(eigenvalues=all_evals, \n",
    "                                  Q=Q_FC1, layer=LAYER_FC1, num_spikes=num_spikes)\n",
    "print(sigma)\n",
    "\n",
    "plt.title(\"Mini AlexNet FC1: ESD\\nEnsemble of 10 runs, 18 Spikes\")\n",
    "plt.xlabel(XLABEL_FC1)\n",
    "plt.ylabel(YLABEL)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "filename=ENSEMBLE_ESD_FC1_FINAL_EPOCH_18SPIKES\n",
    "plt.savefig(filename)\n",
    "print(\"saved figure {}\".format(filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Repear for FC2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:24:52.646491Z",
     "start_time": "2018-09-02T20:24:51.932622Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "eigenvalues, eigenvectors = RMT_Util.keras_ensemble_spectrum(model, \n",
    "                        BASELINE_ensemble_weightfile, LAYER_FC2,num_runs=NUM_ENSEMBLE_RUNS)\n",
    "all_evals = np.concatenate(eigenvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:24:53.468592Z",
     "start_time": "2018-09-02T20:24:52.648598Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% spikes outside bulk 4.92\n",
      "% sigma 1.0109\n",
      "1.0109147957317495\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RMT_Util.ipynb:7: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \"## RMT Util\\n\",\n",
      "RMT_Util.ipynb:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \"## RMT Util\\n\",\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved figure img/mini-alexnet-fc2-ensemble-esd-final-9spikes.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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LaBhG+SbdSvYg4Guf8m+ANnGOnYqb8d4rIm1EZHcRORYYCjwey9RQKiKUbDE6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\nEokKSUo2kUgkKuT/AT3r34YVH40DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_evals = np.array(eigenvalues).flatten()\n",
    "num_spikes = NUM_ENSEMBLE_RUNS*9\n",
    "sigma = RMT_Util.plot_ESD_and_fit(eigenvalues=all_evals, Q=Q_FC2, \n",
    "                                  layer=LAYER_FC2, num_spikes=num_spikes)\n",
    "print(sigma)\n",
    "plt.title(\"Mini AlexNet FC2: ESD\\nEnsemble of 10 runs, 9 Spikes\")\n",
    "plt.xlabel(XLABEL_FC2)\n",
    "plt.ylabel(YLABEL)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "filename=ENSEMBLE_ESD_FC2_FINAL_EPOCH_9SPIKES\n",
    "plt.savefig(filename)\n",
    "print(\"saved figure {}\".format(filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Localization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:04.751932Z",
     "start_time": "2018-09-02T20:24:53.471715Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "svals, svecs = RMT_Util.keras_singular_spectrum(model, \n",
    "                        BASELINE_ensemble_weightfile, LAYER_FC1,num_runs=NUM_ENSEMBLE_RUNS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:04.781259Z",
     "start_time": "2018-09-02T20:25:04.757662Z"
    }
   },
   "outputs": [],
   "source": [
    "num_spikes = 9\n",
    "num_bleeding_out = 9\n",
    "bulk_svals, bleeding_svals, spiked_svals = [], [], []\n",
    "bulk_svecs, bleeding_svecs, spiked_svecs = [], [], []\n",
    "\n",
    "for i in range(len(eigenvalues)):\n",
    "    spiked_svals.extend(svals[i][0:num_spikes])\n",
    "    bleeding_svals.extend(svals[i][num_spikes:num_spikes+num_bleeding_out])\n",
    "    bulk_svals.extend(svals[i][num_spikes+num_bleeding_out:])\n",
    "    \n",
    "    spiked_svecs.extend(svecs[i][0:num_spikes])\n",
    "    bleeding_svecs.extend(svecs[i][num_spikes:num_spikes+num_bleeding_out])\n",
    "    bulk_svecs.extend(svecs[i][num_spikes+num_bleeding_out:])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:05.970522Z",
     "start_time": "2018-09-02T20:25:04.784138Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved figure img/mini-alexnet-fc1-ensemble-3color-ssd.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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BN/Hwww9PM7NqM+l6JH1WAY8YMYKHHnqo1WIEQdBNSHq241I9i/CCCIIgaBGh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\nHARB0CJCAQdBELSIUMBBEAQt4v8BpMQ45do9yYkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [bulk_svals,bleeding_svals,spiked_svals]\n",
    "plt.hist(x, bins=100, color=['blue', 'red', 'purple']);\n",
    "plt.title(\"Mini AlexNet FC1:  Singular Values\\n10 runs, 9 Bleeding out, 9 Spikes\")\n",
    "plt.ylabel(\"Number of Singular Values\")\n",
    "plt.xlabel(r\"Singular Values of Weight Matrix $\\mathbf{W}_{FC1}$\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "filename=ENSEMBLE_FC1_3COLOR_ESD\n",
    "plt.savefig(filename)\n",
    "print(\"saved figure {}\".format(filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:06.096827Z",
     "start_time": "2018-09-02T20:25:05.973845Z"
    }
   },
   "outputs": [],
   "source": [
    "bulk_loc = [RMT_Util.localization_ratio(v) for v in bulk_svecs]\n",
    "bleeding_loc = [RMT_Util.localization_ratio(v) for v in bleeding_svecs]\n",
    "spike_loc = [RMT_Util.localization_ratio(v) for v in spiked_svecs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:46:16.648339Z",
     "start_time": "2018-09-02T20:46:15.347990Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.7284 82.91064\n",
      "saved figure img/mini-alexnet-fc1-ensemble-locs.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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fPp0+ffqw//77vxn45syZw3HHHccRRxzBk08+ydlnn81+++1X6OYT1F/i+aij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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bulk_loc, bins=20, density=True, alpha =0.50, color='blue', label='bulk');\n",
    "#plt.hist(bleeding_loc, bins=100, density=True, alpha = 0.50, color='red');\n",
    "plt.hist(spike_loc, bins=20, density=True, alpha = 0.50, color='purple', label='spike');\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "print(np.mean(bulk_loc), np.mean(spike_loc))\n",
    "\n",
    "plt.title(\"Mini AlexNet FC1:\\nLocalization Ratios, 10 runs\")\n",
    "filename=ENSEMBLE_FC1_LOCS\n",
    "plt.savefig(filename)\n",
    "print(\"saved figure {}\".format(filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:07.060855Z",
     "start_time": "2018-09-02T20:25:06.804562Z"
    }
   },
   "outputs": [],
   "source": [
    "bulk_part = [RMT_Util.participation_ratio(v) for v in bulk_svecs]\n",
    "bleeding_part = [RMT_Util.participation_ratio(v) for v in bleeding_svecs]\n",
    "spike_part = [RMT_Util.participation_ratio(v) for v in spiked_svecs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:45:55.814334Z",
     "start_time": "2018-09-02T20:45:54.648315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.370514254754385 3.209000675657671\n",
      "saved figure img/mini-alexnet-fc1-ensemble-parts.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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mmqmZDTYzL/D4uNIBiojUglJrpj8Ank/93xwjCHfHzGIsStpANWaR8pWaTGe7\ne9Sbu7t06cLSpUvV9rEDWLp0KXV1ddUOQ6QmVf0CVH19PQsXLmTRokWsXLlStaMqcHc+//xz3nnn\nHfUoJVKmUmumd5tZPfAxMA0Y7e7z2xLAZpttRteuXWlqauKjjz6iuTnKmQMpUV1dHVtvvTW9evWq\ndigiNSlrMv0EuA6YASwB9gcuAZ41s/3d/YN0YTM7BzgHoF+/fq0uvFu3bmy//fYlhC0i0rFkSqbu\n/kfgj6lJM8zsaeD3hItSl+WVnwBMABg4cKCO20Vkg1f2OVN3/wMwBxgULxwRkdrU1gtQBqjmKSIb\nvbLvgDKzgcCuQOFeiEU2IOogRFqTKZma2d3AW8AfCFfy9wcuBt4Bflax6EREakTWmukrwKnA94Hu\nwHvA/wBj3P3DCsUmIlIzsl7Nvxq4usKxiIjUrKrfASUisiFQMhURiUDJVEQkAiVTEZEIlExFRCJQ\nMhURiUDJVEQkAiVTEZEIlExFRCJQMhURiUDJVEQkAiVTEZEIlExFRCJQMhURiUDJVEQkAiVTEZEI\nlExFRCJQMhURiUDJVEQkgrKHehbJ19jQWO0QRKpGNVMRkQiUTEVEIigrmZrZVDNzM7sydkAiIrWo\n5GRqZqcC+1YgFhGRmlVSMjWzzYF/B/6lMuGIiNSmUmum1wKvuvvkSgQjIlKrMjeNMrNDgG+gQ3wR\nkfVkqpmaWR1wK/BTd/9zhvLnmNksM5vV1NTU1hhFRDq8rIf5o4BNgauyFHb3Ce4+0N0H9unTp+zg\nRERqRauH+WbWD7gUOBvoamZdU7O7JhelPnX3VRWKUUSkw8tSMx0AdAPuAhanHgAXJn/vXZHoRERq\nRJYLUH8ChhSY/hQhwf4n8EbMoEREak2rydTdPwYa86ebGcDb7r7ePBHpmBoa2uc1GyPdmy8iEkHZ\nXfC5u8UMRESklqlmKiISgZKpiEgESqYiIhFo2JKNmIYZEYlHNVMRkQiUTEVEIlAyFRGJQMlURCQC\nXYCSjZJukZTYVDMVEYlAyVREJAIlUxGRCJRMRUQiUDIVEYlAyVREJAIlUxGRCJRMRUQiUDIVEYlA\nyVREJAIlUxGRCJRMRUQiUDIVEYlAvUZ1EFmGEBncMLjicYhIeTLVTM3sSDN70szeM7PlZrbQzO41\nsy9VOkARkVqQtWa6BfACcAvQBPQDRgMzzWxvd3+7QvGJiNSETMnU3ScDk9PTzOz3wOvAicB18UMT\nEakdbbkA9VHyvDJGICIitaykZGpmm5hZFzPbBbgVeA+4pyKRiYjUkFJrps8By4E5wD7A4e7+QX4h\nMzvHzGaZ2aympqYIYYqIdGwBp8vCAAAJNElEQVSlJtN/BA4CTgOWAI+bWf/8Qu4+wd0HuvvAPn36\ntDlIEZGOrqRk6u6z3f255ILUEUAPwlV9EZGNWtkXoNz9Y+ANYOd44YiI1Kayk6mZbQ3sDrwZLxwR\nkdqUqZ2pmf0K+APwEuFc6a7AD4Fm1MZURCTzHVAzgZOAC4AuwAKgEbja3edVJDIRkRqS9Q6oHwM/\nrnAsIiI1S13wiYhEoGQqIhKBkqmISARKpiIiESiZiohEoGFLRNIaG7OVGzy4klFIDVLNVEQkAiVT\nEZEIlExFRCJQMhURiUDJVEQkAiVTEZEIlExFRCJQMhURiUDJVEQkAiVTEZEIlExFRCLQvflS87Lc\nTt/YUOkoZGOnmqmISARKpiIiESiZiohEoGQqIhKBkqmISAStJlMzO9HMHjCzt81sqZn92cyuNrOe\n7RGgiEgtyNI06kJgPnAJsBDYH2gAhpjZwe6+unLhdVyNDY2Zyg1uGFzROArJGpu0Aw2DstHIkkyP\ndfem1P8zzGwRcCcwGHiyEoGJSMfQ0NA+r6l1rR7m5yXSnOeT575xwxERqU3lXoA6LHmeHSsQEZFa\nVnIyNbO+wBXAE+4+q0iZc8xslpnNamoqVLEVEdmwlJRMzawHMAVoBkYWK+fuE9x9oLsP7NOnTxtD\nFBHp+DJ3dGJm3YCHgAHAYe6+sGJRiYjUmEzJ1MzqgAeAA4Gh7v5yRaMSEakxrSZTM+sE3A0cARzt\n7jMrHpWISI3JUjP9D2AEcBXwVzM7KDVvoQ73RUSyXYA6Knm+FHg273F2heISEakprdZM3b1/O8Qh\nIlLT1GuUiEgESqYiIhEomYqIRKBkKiISgZKpiEgESqYiIhEomYqIRJC5oxMpT8whRDQcSQeSdTgS\n2WioZioiEoGSqYhIBEqmIiIRKJmKiESgZCoiEoGSqYhIBEqmIiIRKJmKiESgZCoiEoGSqYhIBEqm\nIiIR6N586VDSt7w3NlQrCpHSqWYqIhKBkqmISARKpiIiEWRKpma2nZn9zMyeNbPPzczNrH9lQxMR\nqR1Za6Y7AycBi4HfVi4cEZHalDWZPu3uW7v73wL3VTIgEZFalKlplLuvrnQg+bIO0TG4YXBV1ivt\nIObQIB19mJGs8Q0eXMkopA10AUpEJIKKJFMzO8fMZpnZrKampkqsQkSkQ6lIMnX3Ce4+0N0H9unT\npxKrEBHpUHSYLyISgZKpiEgESqYiIhFk7jXKzE5M/jwgeT7KzJqAJnefET0yEalZDQ2VLd8RldIF\nX35j/VuS5xnA4CjRiIjUqMzJ1N2tkoGIiNQynTMVEYlAPe1LRXX0uzhFYlHNVEQkAiVTEZEIlExF\nRCJQMhURiUDJVEQkAiVTEZEIlExFRCLYaNqZajgSEakk1UxFRCJQMhURiUDJVEQkAiVTEZEIlExF\nRCLYaK7mS9upByiR4lQzFRGJQDVTEam6csaA6mjjRqlmKiISgZKpiEgESqYiIhHonOlGTFfnReJR\nzVREJIJMydTMtjez+83sEzNbYmb/Y2b9Kh2ciEitaPUw38y6A08Cy4EzAQeuBJ4ys33c/a+VDVGy\n0CG7bGxKbRpV6aZUWc6ZfgsYAOzm7m8AmNlLwFzg28D1lQtPRKQ2ZEmmxwEzc4kUwN3fMrPfAX+H\nkmlFqKYpUluyJNM9gSkFpr8KjIgbTvtT0hKRGLIk0y2AxQWmLwJ6F3qBmZ0DnJP8+5mZ/bm88DIY\nu+aveuDDiq1HitF2b08z1vyl7V6isWNbL1PEDlkKZW1n6gWmWdHC7hOACRmXHYWZzXL3ge25TtF2\nrxZt944nS9OoxYTaab7eFK6xiohsdLIk01cJ503zfQl4LW44IiK1KUsyfQg4yMwG5CaYWX/gb5J5\nHUW7nlaQNbTdq0PbvYMx90KnQ1MFzL4AvAgsBS4jnD8dB/QE9nH3zyodpIhIR9dqzTS5w+lwYA7w\nX8DdwFvA4UqkIiJBqzVTERFpXYfuNcrMjjSzJ83sPTNbbmYLzexeM/tShtf2NrPbzexDM/urmT1h\nZnu3R9y1rtztbmZHmNldZvammS1Nnseb2VbtFXsta8vnPW85t5qZm9ldlYpV1teha6ZmdirwZeA5\noAnoB4wGtgf2dve3i7zOgKeBHYGLCE24Lia0StjP3RdWPvra1Ybtfh/QA7gX+AuwC+G2iuXo/Hqr\nyt3uecs4GHgMWAX82t3PqFzEsg53r6kHsBvhItgFLZT5u6TMkNS0zQh3bd1U7fdQi4+M271PgWmH\nJq/7ZrXfQy0+smz3VNk64BVCxWEecFe149+YHh36ML+Ij5LnlS2UOQ54192fyk1w90+AXxMSrZSu\n1e3u7k0FJj+fPPeNHtHGIcvnPeciYBPgusqFI8XURDI1s03MrIuZ7QLcCrwH3NPCS/Yk/ELnexXo\nZ2Y9KhDmBqeM7V7IYcnz7KjBbcDK2e5mthOh6eJ33X1FO4QpeWoimRLOIS0nNM/ah9As64MWyrfU\nOQsU6aBF1lPqdl+HmfUEbiAk0gcrEuGGqZzt/nPgf9JHY9K+aiWZ/iNwEHAasAR4PLkLqxijxM5Z\npKBSt/saZtYZmEw4vD/F3ZsrFOOGqKTtbmZnAIOAC9sjOCmsJpKpu8929+fcfTJwBOGK8egWXrKI\n4p2zgDpoyaSM7Q6AmXUC7gSGAse7+0uVjXTDUsp2T05ZXQ/8GFhmZpub2eaE73Zd8n9de8W+MauJ\nZJrm7h8DbwA7t1Cspc5Z5rua6JQs43bP+TlwMqFGOr2igW3gMmz3eqAP8G+ESkLusT1wUvL30ZWP\nVGoumZrZ1sDuwJstFHsI6GtmuYsfmFkv4Fg6VucsNSPjdsfMrgPOBka6u86TtlGG7f4eMKTA433g\nieTvZyofqXT0Rvu/Av4AvEQ4d7Qr8ENgG+BAd5+TJMzphHaMk5LXdSJ8gLZn3Ub7+wD7uvuC9n4v\ntaQN230UcA3wC+C2vMU2uXuLiXhjV+52L7KsecAzrkb77SZrT/vVMpNwqHIB0AVYADQCV7v7vKSM\nEdrWrallu/tqMzsG+ClwC9ANeJbQiF+JtHVlbXfgqOT5m8kj7U7grIpEu+Eod7tLB9Cha6YiIrVC\nv24iIhEomYqIRKBkKiISgZKpiEgESqYiIhEomYqIRKBkKiISgZKpiEgE/w+Nlc+xZkOhtgAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bulk_part, bins=20, density=True, alpha =0.50, color='blue', label='bulk');\n",
    "#plt.hist(bleeding_loc, bins=100, density=True, alpha = 0.50, color='red');\n",
    "plt.hist(spike_part, bins=20, density=True, alpha = 0.50, color='purple', label='spike');\n",
    "plt.title(\"Mini AlexNet FC1:\\nParticipation Ratios, 10 runs\\n\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "print(np.mean(bulk_part), np.mean(spike_part))\n",
    "\n",
    "filename=ENSEMBLE_FC1_PARTS\n",
    "plt.savefig(filename)\n",
    "print(\"saved figure {}\".format(filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-26T00:33:12.553008Z",
     "start_time": "2018-05-26T00:33:12.550854Z"
    }
   },
   "source": [
    "### Entropy: need good histogram estimator\n",
    "\n",
    "https://stackoverflow.com/questions/39626432/how-to-calculate-entropy-from-np-histogram?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa\n",
    "\n",
    "https://github.com/rmcgibbo/information\n",
    "\n",
    "https://github.com/rmcgibbo/information\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:07.720621Z",
     "start_time": "2018-09-02T20:25:07.700156Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def discrete_entropy(vec, num_bins=100):\n",
    "    vec = vec - np.mean(vec)\n",
    "    h = np.histogram(vec, density=True, bins=num_bins)[0];\n",
    "    p = np.array(h)+0.0000000001\n",
    "    \n",
    "    p = p / np.sqrt(2*np.pi)\n",
    "    p = p / np.sum(p)\n",
    "\n",
    "    #p = p/(2*np.pi)\n",
    "    entropy = -np.sum(p*np.log(p))\n",
    "    entropy = entropy#/(2*np.pi)#/float(num_bins)\n",
    "    return entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compute maximum possible entropy for this vector length and bin sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:07.734491Z",
     "start_time": "2018-09-02T20:25:07.723679Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def max_discrete_entropy(len_vec,num_bins=100, sample_size = 100000):\n",
    "    \"\"\"compute maximum possible entropy for this vector length and bin sizes\"\"\"\n",
    "    entropies = []\n",
    "    for i in tqdm(range(sample_size)):\n",
    "        test_vec = np.random.normal(0,1,len_vec)\n",
    "        s = discrete_entropy(test_vec, num_bins=num_bins)\n",
    "        entropies.append(s)\n",
    "\n",
    "    return np.max(entropies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:36.615904Z",
     "start_time": "2018-09-02T20:25:07.737284Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4.365803034769936"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_discrete_entropy(len(bulk_svecs[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:37.705841Z",
     "start_time": "2018-09-02T20:25:36.618855Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bulk_entropy = [discrete_entropy(v) for v in tqdm(bulk_svecs)]\n",
    "#bleeding_entropy = [discrete_entropy(v) for v in bleeding_svecs]\n",
    "spike_entropy  = [discrete_entropy(v) for v in tqdm(spiked_svecs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:37.716186Z",
     "start_time": "2018-09-02T20:25:37.708443Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.1085782855251844, 3.9915675648641415, 0.1170107206610429)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bulk_entropy), np.mean(spike_entropy), np.mean(bulk_entropy)- np.mean(spike_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:25:38.131504Z",
     "start_time": "2018-09-02T20:25:37.718310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7h7bfBnx9lX2fChwyYvttwP2T/t6m8Qb8EfAz4NkD244GHgfescq+z6deHuzsgW0HUF9x\nc/2kv7dZuo35PCyM2HYU8ATwvkl/b/u6zXNHdzLwFOCqoe1XASfsqx3PzIcz88cj7loGntleiUU5\nA/hS9pf6AsjM7wD/Bryqwb6PAf80sO/jwDXAyRFxUPvlFmvdz0Nm7hix7T5gB3Bky3W2ap6D7njq\njm770PZt/bfHreNzvgS4e5yiCnY8cNeI7dtY/Wd9PPCdEf9ctlH/s3r2+OXNjXGehz1ExHOpR0ff\nHLOuTs3zi+M8HXgg+/33gJ0D9zfWX8jghdTH7LSncVbB2de+K/ermdZWI4qIA4DLqDu6K8YvrTvF\ndHQRcVJEZINbtbIL9XGfPT7VOr72EvUB2isz8+p1fxPlW+/Pu7XnSkB7P8tLgN8E3piZIxf4mBYl\ndXRfAJ7b4HErw5+dwGEREUNd3WED968qIn6N+prf24E/aFjrPFrzKjgDdlLPdI/ad+V+NTPO87BL\nRHyIeim2N2fmzS3V1pligq5//GYtx8e2AQcBv8Lux+lWjlOsujJLRJwA3AR8FXhNZj62hq8/b8ZZ\nBWcb8LsRccjQcbrjgEfZ8zir9m7s1Ygi4kLqU0venplXtlhbZ4oZuq7DjdR/JGcNbX8jcFd/Jmqv\nIuJY4Bbg28BpmflIJ1WWY5xVcK4HDmRg6f7+8aHXATdn5k/bLrZgY61GFBFvp34VwAsz8286qrF9\nkz6/ZZI34M+BnwDvAJaAS6nPCTp96HG3AdsHPj6CekWXncArqSchBm8HTfp7m7Yb9bmH24FvUJ/G\ncAbwNep/FE8beNxR1Od0vWdo/2uoh1ZvBV4OXNt/7l4w6e9tlm7jPA/UJww/Adww4nf+uEl/b/u6\nFTN0XacLgYeoT6L8JeoTUH8vMz839Lj92X2Yfxz1LwLAP4/4vEdTB6H6MvPhiHgZ9So4V7L7KjgP\nDTw0qH/ew6ONs4GLqLuJQ6n/OE/JzK90XXtJxnweTulvP6V/G3QHdbMwlVy9RFLx5vkYnaQ5YdBJ\nKp5BJ6l4Bp2k4hl0kopn0EkqnkEnqXgGnaTi/T9NaUuzhFADOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.concatenate(bulk_svecs), bins=100, alpha=0.5, color='blue', density=True);\n",
    "#plt.hist(np.concatenate(bleeding_svecs), bins=100, alpha=0.5, color='red', density=True);\n",
    "plt.hist(np.concatenate(spiked_svecs), bins=100, alpha=0.5, color='purple', density=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:46:25.944994Z",
     "start_time": "2018-09-02T20:46:25.008586Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9432414711688868 0.9163783188498249\n",
      "saved figure img/mini-alexnet-fc1-ensemble-ventropies.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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durTipaOOyN1ZunQpCxcurOhoUmqAEilR9+7dAXjzzTdZtWpVhXPTMXXq1Imt\nt9567XtRCQqmIhF07969ol9kqTxd5ouIRKBgKiISQZPB1MyOM7NJZvaGmS0zs5fN7Eoz2yyVpp+Z\neYFli/I+BRGRyiumzvRnwDzgfGAB8CWgDhhsZvu7e3q6wCuBKZn9P0ZEpJ0rJpge6e6LUv9PNbPF\nwC1ALfBEattr7l6dE7SIiJRRk5f5mUCa89fksen5CUREOoCWNkANSh5fyqy/0swazOxDM5tiZruX\nkDcRkTaj2cHUzLYDLgMec/fcSM4rgBuA7wKDCfWsuwN/NrNGR801s9PMbIaZzVi0KF8hWESk+jUr\nmJpZN2Ay0ACMzK1393+7++nufq+7P+3uNwIHAg5c0Ngx3X2su9e4e03v3r2b/wxERKpA0XdAmVkX\nQkv9AGCQuy9oLL27zzezZ4C9S8uiiEj1KyqYmlknYBKwD3CIu/+jyOMboXQqItKuFdNpfwPgNuBg\n4Ohiuz6ZWR/gK8BzJeVQRKQNKKZk+r/AMOAK4BMz2y+1bYG7LzCzqwmBeRqwCNgFOA9YA/w8bpZF\nRKpPMQ1QhyWPFxCCZXoZlWybBRxAaNF/lHCH1LPAvu7+csT8iohUpSZLpu7er4g0NwM3x8iQiEhb\npFGjREQiUDAVEYlAwVREJAIFUxGRCBRMRUQiUDAVEYlAwVREJAIFUxGRCBRMRUQiUDAVEYlAwVRE\nJAIFUxGRCBRMRUQiKHraEpGm1NfVN5mmtq627PkQqQSVTEVEIlAwFRGJQJf5ImVQV9c6+0j1UMlU\nRCQCBVMRkQgUTEVEIlCdqXQoqsuUclHJVEQkAgVTEZEIFExFRCJQMBURiaDJYGpmx5nZJDN7w8yW\nmdnLZnalmW2WSdfDzG4ys3fN7BMze8zMdi9f1kVEqkcxrfk/A+YB5wMLgC8BdcBgM9vf3deYmQFT\ngP7AGcD7wHnAk2a2h7svKEfmRVqDWvOlGMUE0yPdfVHq/6lmthi4BagFngCOAg4ADnL3JwHMbBow\nF/gv4EcxMy0iUm2avMzPBNKcvyaP2yWPRwFv5gJpst+HwAPA0aVmUkSk2rW0AWpQ8vhS8jgQeDFP\nullAHzPr1sLziIi0Cc0Opma2HXAZ8Ji7z0hW9yTUk2YtTh57NHK808xshpnNWLQoXyFYRKT6NSuY\nJiXMyUADMDK9CfB8uzR1THcf6+417l7Tu3fv5mRHRKRqFH1vvpl1IbTYDwAGZVroFxNKp1m5Emm+\nUqt0QJraRNqrokqmZtYJmATsA3zN3f+RSTKLUG+atRswz92XlJRLEZEqV0yn/Q2A24CDgaPdfXqe\nZFOA7cxsUGq/7sCRyTYRkXb8MS7BAAAMXklEQVStmMv8/wWGAVcAn5jZfqltC5LL/SnANOBWMzub\nTzvtG/DLuFkWEak+xVzmH5Y8XkAImOllFIC7rwGOAB4FrgfuA1YDg919fuQ8i4hUnSZLpu7er5gD\nufti4DvJIiLSoWjUKBGRCBRMRUQiUDAVEYlAwVREJAIFUxGRCBRMRUQiUDAVEYlAwVREJAIFUxGR\nCBRMRUQiUDAVEYlAwVREJIKiR9oXkfKqq2udfaQ8FEyl3co7RUp2VW1t+TMiHYIu80VEIlAwFRGJ\nQMFURCQCBVMRkQjUACVtVpMt2fWtkAmRhEqmIiIRKJiKiESgYCoiEoGCqYhIBAqmIiIRKJiKiESg\nYCoiEkFRwdTMtjez35rZNDNbamZuZv3ypPMCyx6xMy4iUk2K7bS/I3A88DzwNDCkkbTjgRsy62Y3\nO2ciIm1IscH0KXffGsDMRtF4MF3o7tNLzpmISBtS1GW+u68pd0ZERNqycjRAfc/MViR1q0+Y2VfL\ncA4RkaoSO5jeCnwfOAQ4DdgSeMLMagvtYGanmdkMM5uxaNGiyNkREWkdUUeNcvdvpv592swmAy8C\nlwMHFNhnLDAWoKamxmPmR0SktZS1n6m7fwz8Edi7nOcREam01ui0b4BKnCLSrpU1mJpZd+Bw4Lly\nnkdEpNKKrjM1s+OSP/dKHg8zs0XAInefamY/A3YBngTeBPoCPwO2AU6Ol2URkerTnAaouzP/X588\nTgVqgZeBY5Nlc+Aj4FngP939L6VlU0SkuhUdTN3dmtj+APBAyTkSEWmDNGqUiEgECqYiIhEomIqI\nRKBgKiISgYKpiEgECqYiIhEomIqIRKBgKiISgYKpiEgECqYiIhFEHRxapBT19cljXSVzIdIyKpmK\niESgkmmF1NfVRzlObV1tlONUlVwRVaQNUclURCQCBVMRkQgUTEVEIlAwFRGJQMFURCQCteaLtGF1\ndeVNL8VTyVREJAIFUxGRCBRMRUQiUDAVEYlAwVREJAIFUxGRCIoKpma2vZn91symmdlSM3Mz65cn\nXRcz+5WZ/dvMliXpD4ydaRGRalNsyXRH4HjgfeDpRtL9HjgVuBg4Avg38IiZ7VFKJkVEql2xnfaf\ncvetAcxsFDAkm8DMvggMB77j7uOSdVOBWcBlwFFRciwiUoWKKpm6+5oikh0FrALuTO3XANwBHGpm\nG7cohyIibUDMBqiBwFx3X5pZPwvoTKgqEBFpl2IG056EOtWsxantIiLtUsyBTgzwAusL72R2GnAa\nQJ8+fSJmpzyKmW6kNacSiZWfWNOodFjFTLVSW1vuXEgFxSyZLiZ/6bNHavt63H2su9e4e03v3r0j\nZkdEpPXELJnOAo41s66ZetPdgJXAKxHPJVVOc+JJRxOzZDoF6AQMy60ws42AE4A/ufuKiOcSEakq\nRZdMzey45M+9ksfDzGwRsMjdp7r7/zOzO4Ffm1knYC7wPaA/cHLMTIuIVJvmXObfnfn/+uRxKlCb\n/D0SuAK4HNgCeAEY6u5/KyGPIiJVr+hg6u6NtsonaZYBZyWLiEiHoVGjREQiUDAVEYlAwVREJAIF\nUxGRCBRMRUQiUDAVEYlAwVREJAIFUxGRCBRMRUQiUDAVEYlAwVREJAIFUxGRCBRMRUQiiDnSviSq\nbT6lastPVammuZuqKS/SbCqZiohEoGAqIhKBgqmISAQKpiIiEagBSqQDqatrnX06IpVMRUQiUDAV\nEYlAwVREJAIFUxGRCBRMRUQiUDAVEYlAwVREJIKowdTMas3M8ywfxDyPiEi1KVen/R8Bf03931Cm\n80grKGYwI5GOrlzB9CV3n16mY4uIVB3VmYqIRFCuYHqbma02s/fM7HYz61Om84iIVIXYl/kfAlcD\nU4GPgC8B5wPTzOxL7v5OdgczOw04DaBPH8VcEWmbogZTd/878PfUqqlm9hTwF0Kj1IV59hkLjAWo\nqanxmPnJamr6jtq62nKeXtoqtcBJEcpeZ+rufwNmA3uX+1wiIpXSWg1QBpS11CkiUkllD6ZmVgPs\nDDxX7nOJiFRK1DpTM7sNmAv8DfiA0AB1HrAQ+G3Mc4mIVJPYrfkvAicBZwBdgbeAe4FL3P3dyOcS\nEakasVvzrwSujHlMEZG2QHdAiYhEoGAqIhKBgqmISAQKpiIiESiYiohEoGAqIhKBgqmISATlGmlf\nqpQGQJLmqqtrnX3aOpVMRUQiUDAVEYlAwVREJAIFUxGRCNQAJdJa1PrXrqlkKiISgYKpiEgECqYi\nIhEomIqIRKBgKiISgVrz2zA1DotUD5VMRUQiUDAVEYlAwVREJAIFUxGRCNQAJSLRdcQxUFUyFRGJ\nQMFURCSCqMHUzD5rZveY2Ydm9pGZ3WtmfWKeQ0SkGkWrMzWzrsATwArg24ADlwNPmtkX3P2TWOdq\nr9QJX6TtitkAdSowANjF3V8BMLOZwBzgu8A1Ec/VqtapGK8vbp/a2vj5EJHqFTOYHgVMzwVSAHef\na2bPAkfThoNpS6iUKdI8bb0HQMxgOhCYnGf9LGBYxPOs1ewXsr6Jzc09nohIImYw7Qm8n2f9YqBH\noZ3M7DTgtOTfJWb2csQ8Nc/Uip05tl7Au5XOhACx34v28xmN4tJLm5W8F9C3PDmJ32nf86yzRndw\nHwuMjZyPDs3MZrh7TaXzIXovqknyXvQr1/Fjdo16n1A6zepB/hKriEi7ETOYziLUm2btBvwz4nlE\nRKpOzGA6BdjPzAbkVphZP+AryTZpPao2qR56L6pHWd8Lc89XzdmCA5ltCrwALAMuJNSfjgY2A77g\n7kuinEhEpApFK5kmdzgdBMwG/gDcBswFDlIgFZH2LlrJVESkI9OoUVWolAFjzKx/su8HZvaJmT1p\nZut1zTGzDczsPDN73cyWm9kLZvaN+M+mbWul9+J1M/M8yzHxn1HbZWbbm9lvzWyamS1NXqN+Re5b\n9OfdzE41s3+Z2Qoze9nMTi/mHAqmVSY1YMznCAPGfBPYiTBgzKZN7Lsl8AzwecJ4CCcmm540s10z\nyUcDdcB1wGHAdOBuM/tanGfS9rXiewHwCPDlzKIu+uvaETie0NXy6WbuW9Tn3cxOBW4AJgFDgbuB\n683se02ewd21VNEC/BhYDeyYWtcfaADOamLfC5N06X03Bd4G7kqt24owutelmf0fB2ZW+jWolqU1\n3otk/evArZV+vtW+ABuk/h5FaOTuV8R+RX3eCTcxvQPckkl3M+Eutk6NnUcl0+qTd8AYIDdgTGP2\nA+Zk9v2E8Ct+hJnl7ng7FOgM3JrZ/1ZgdzPrX9pTaDda472QIrn7mhbuWuzn/ctA7zzp/gBsCRzQ\n2EkUTKvPQODFPOtnEW6AaMxqYGWe9SuATYAdUudYAbySSTcreWzqPB1Fa7wXOUcm9YArzGy66kuj\nKvbznrvpKPueF/W9UDCtPi0aMCbxMrBTUl8HhIp3YJ/UsXOPH3hyDZM5RzpdR9ca7wXAA8AZhBLU\nycBy4D4zO6WF+ZZ1Fft5zz1m3/OivhcKptWp2QPGJH5HeE8nmNkOZrYtcC2hng8gd5lkJZyjoyn3\ne4G7n+HuE9z9aXe/BzgYmAFcWVrWJVHs5z33f4v6iyqYVp8WDxjj7q8RSjZ7ES5p3iTUA/1PkuTf\nyeNioIeZZT9MPVLbpXXei3z7ria0Im+fBGEpTbGf90Il0J6Z7XkpmFafkgaMcfdJwHZJ+h3dfS+g\nGzDf3eelzrEx69fb5eqENDBN0BrvRSEllZJkHcV+3nN1o9n3vKjvhYJp9Sl5wBh3X+3uL7n7q2b2\nGeAEYEwqycOExpGTM7ueAryYtFhL67wX60la+ocB89z9rRbmXT5V7Od9GqELVL50iwm9OAqrdN8x\nLev1iduUcFn4D0L3m6MIA8i8BnRLpetL6Md4cWpdJ8Jl5DGEcRLOIFxePg10zpznKkJDx1lALeEL\nvgY4stKvQbUsrfFeACcBdwDfAgYTOvc/TSiRnljp16DaFuC4ZBmTvEbfS/4flErTAPw+s19Rn3fg\n9GT95Um6y5L/f9Bk3ir94mjJ+4HpQ7gD4yPgY+B+Mp2TgX7Jh6kutW4j4EFCx/AVwKvJh6JrnnNs\nSOhY/kaSdiZwXKWfe7Ut5X4vCP1Rn0jSrQI+BB4DDq30c6/GJXmd8y31mTTjM/sV/Xkn3LE2O0k3\nB/h+MXnTQCciIhGozlREJAIFUxGRCBRMRUQiUDAVEYlAwVREJAIFUxGRCBRMRUQiUDAVEYng/wNW\nVbdxmPxZVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.array(bulk_entropy), bins=20, density=True, alpha =0.50, color='blue', label='bulk');\n",
    "#plt.hist(bleeding_entropy, bins=100, density=True, alpha = 0.50, color='red');\n",
    "plt.hist(np.array(spike_entropy), bins=20, density=True, alpha = 0.50, color='purple', label='spike');\n",
    "plt.title(\"Mini AlexNet FC1:\\nVector Entropies, 10 runs\")\n",
    "plt.legend();\n",
    "\n",
    "print(np.mean(bulk_entropy), np.mean(spike_entropy))\n",
    "filename=ENSEMBLE_FC1_VENTROPIES\n",
    "plt.savefig(filename)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "print(\"saved figure {}\".format(filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:26:06.984781Z",
     "start_time": "2018-09-02T20:25:38.602808Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "max_entropy = max_discrete_entropy(len(bulk_svecs[0]))\n",
    "bulk_entropy = bulk_entropy/max_entropy\n",
    "spike_entropy = spike_entropy/max_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T20:46:39.055002Z",
     "start_time": "2018-09-02T20:46:37.780938Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9432414711688868 0.9163783188498249\n",
      "saved figure img/mini-alexnet-fc1-ensemble-ventropies.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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durTipaOOyN1ZunQpCxcurOhoUmqAEilR9+7dAXjzzTdZtWpVhXPTMXXq1Imt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\nVbdxmPxZVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.array(bulk_entropy), bins=20, density=True, alpha =0.50, color='blue', label='bulk');\n",
    "#plt.hist(bleeding_entropy, bins=100, density=True, alpha = 0.50, color='red');\n",
    "plt.hist(np.array(spike_entropy), bins=20, density=True, alpha = 0.50, color='purple', label='spike');\n",
    "plt.title(\"Mini AlexNet FC1:\\nVector Entropies, 10 runs\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "print(np.mean(bulk_entropy), np.mean(spike_entropy))\n",
    "filename=ENSEMBLE_FC1_VENTROPIES\n",
    "plt.savefig(filename)\n",
    "print(\"saved figure {}\".format(filename))"
   ]
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