Neural Network Pruning and Sparsification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "import time\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "#!pip freeze | grep 'matplotlib\\|^numpy\\|pandas\\|seaborn\\|tensorflow\\|tqdm\\|ipython-autotime\\|watermark' > requirements.txt\n",
    "\n",
    "sns.set()\n",
    "tf.enable_eager_execution()\n",
    "tf.set_random_seed(1867)\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autotime\n",
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare MNIST Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 4.16 s\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "dataset_train = tf.data.Dataset.from_tensor_slices((\n",
    "    tf.cast(x_train/255, tf.float32),\n",
    "    tf.cast(y_train, tf.int64)\n",
    ")).shuffle(1000).batch(64)\n",
    "\n",
    "dataset_test = tf.data.Dataset.from_tensor_slices((\n",
    "    tf.cast(x_test/255, tf.float32),\n",
    "    tf.cast(y_test, tf.int64)\n",
    ")).batch(64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 21 ms\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(1000, activation=tf.nn.relu, use_bias=False),\n",
    "    tf.keras.layers.Dense(1000, activation=tf.nn.relu, use_bias=False),\n",
    "    tf.keras.layers.Dense(500, activation=tf.nn.relu, use_bias=False),\n",
    "    tf.keras.layers.Dense(200, activation=tf.nn.relu, use_bias=False),\n",
    "    tf.keras.layers.Dense(10, use_bias=False)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 938/938 [00:13<00:00, 71.75it/s]\n",
      "100%|██████████| 938/938 [00:13<00:00, 74.26it/s]\n",
      "100%|██████████| 938/938 [00:13<00:00, 71.11it/s]\n",
      "100%|██████████| 938/938 [00:13<00:00, 71.48it/s]\n",
      "100%|██████████| 938/938 [00:12<00:00, 72.48it/s]\n",
      "100%|██████████| 938/938 [00:12<00:00, 73.09it/s]\n",
      "100%|██████████| 938/938 [00:13<00:00, 71.08it/s]\n",
      "100%|██████████| 938/938 [00:12<00:00, 72.52it/s]\n",
      "100%|██████████| 938/938 [00:13<00:00, 71.75it/s]\n",
      "100%|██████████| 938/938 [00:12<00:00, 72.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2min 15s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "optimizer = tf.train.AdamOptimizer()\n",
    "global_step = tf.train.get_or_create_global_step()\n",
    "\n",
    "training_losses = []\n",
    "training_accuracies = []\n",
    "\n",
    "for epoch in range(10):\n",
    "    epoch_loss_avg = tf.contrib.eager.metrics.Mean()\n",
    "    epoch_accuracy = tf.contrib.eager.metrics.Accuracy()\n",
    "    for x, y in tqdm(dataset_train, total=round(len(x_train)/64)):\n",
    "        with tf.GradientTape() as tape:\n",
    "            outputs = model(x)\n",
    "            loss = tf.losses.softmax_cross_entropy(tf.one_hot(y, 10), outputs)\n",
    "        grads = tape.gradient(loss, model.trainable_weights)\n",
    "        optimizer.apply_gradients(zip(grads, model.trainable_weights), global_step)\n",
    "        epoch_loss_avg(loss)\n",
    "        epoch_accuracy(tf.argmax(outputs, axis=1, output_type=tf.int64), y)\n",
    "    training_losses.append(epoch_loss_avg.result())\n",
    "    training_accuracies.append(epoch_accuracy.result())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f994c13acf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 273 ms\n"
     ]
    }
   ],
   "source": [
    "# Plot training curves\n",
    "plt.figure(1, figsize=(15,3))\n",
    "plt.subplot(121)\n",
    "plt.plot(training_losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Train Loss')\n",
    "plt.subplot(122)\n",
    "plt.plot(training_accuracies)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Train Accuracy');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.10831883247318595, 0.9796)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.25 s\n"
     ]
    }
   ],
   "source": [
    "def test(model, dataset):\n",
    "    epoch_loss_avg = tf.contrib.eager.metrics.Mean()\n",
    "    epoch_accuracy = tf.contrib.eager.metrics.Accuracy()\n",
    "    for x, y in dataset:\n",
    "        outputs = model(x)\n",
    "        loss = tf.losses.softmax_cross_entropy(tf.one_hot(y, 10), outputs)\n",
    "        epoch_loss_avg(loss)\n",
    "        epoch_accuracy(tf.argmax(outputs, axis=1, output_type=tf.int64), y)\n",
    "    return epoch_loss_avg.result().numpy(), epoch_accuracy.result().numpy()\n",
    "\n",
    "test(model, dataset_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f993c537470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.39 s\n"
     ]
    }
   ],
   "source": [
    "# Plot histogram of each layer's weights (for weight pruning)\n",
    "plt.figure(1, figsize=(15,6))\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "for i, weights in enumerate(model.trainable_weights):\n",
    "    plt.subplot(3,2,i+1)\n",
    "    plt.title(\"Layer {}\".format(i+1))\n",
    "    plt.hist(weights.numpy().reshape(-1), bins=100)\n",
    "    plt.xlim(-0.5, 0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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frY/SpKmrJn1Xb57KUqpimGGe7WSuWeUTpcGGmTfVstS0OOH3bOt8AidJkjRLfKIvaRATuDUMag4x6RnvJ719Se2x0dyVHv/SfKprkmdJs8kEbg2DmhJOesb7SW9fUntsNHelx7/qNIn+V/bpktrDJ7fzY24TuN5Kp/eu9zAGze80qFKrY7AQD1Kpm+qYg26YC2UvsOdbf31T9w2Crvfp8im4ZknVvtj93/utD96y5vUreEy01dwmcP2VzihNFQbN7zSoUqtjsBCbV0jdVNccdFUvlLt+ga3xODjVYD4F1zwaNMCJx0Q3jJ3ARcTRwMXANmAn8JLMvGHc7UqS1GVtrh+rtiCpa5ujqtqaZdSn0oP070/vk4iqLXdsLaO28Ls4W+p4Anch8J7MvCwiTgUuAn66hu3Wavd9S+y37+JEv7zrHRyjHjTTONg8oKV2GnRsDrqw7G0KM8igOYKcP6g2ra0fq7YgWVk27jZHNUxrllGfSq9nrf1Zq+XOsNuQmuBUN7NlrAQuIg4GjgeeXr70YeCCiNiemTvGDa5O0ziJrndwjPrZTcYsqVmDjv+NLiyrXMgO24TGZjPD6VL9OA3D3DBY7+lW1Zsa27dv7US/HW+gqusm3ce6rX242xDXuE/gHgnckplLAJm5FBHfKV/fqIJaBFhY2DRmCHDwQVvW/LlNy9oalzHPd1xdjLmtcbUp5v7z6nrL+rdRddmwet47/GhR3TWV+rHKOlW/l4P+/lW/X+ttc//N+/Dy//LXD7z+x29+Bj8YsGzl9z9+8zMe2OZ++y7utd6gZb3b+MEQMU/rGB+0P3XFVXfM8xxXF2MeZhujHP+9x+5ax9m4Jr399Wx0Xq07rlHqyE3Ly8sjf2BEnABckpnH9Lz2NeDUzPzSBm8/Cbhm5A+XJHXNycC1TQcxDdaPkqQhVa4jx30C9y3g0IhYLO8uLgKPKF/fyOcpAv0usDRmHJKk9loEDqE4788L60dJUhVD15FjJXCZeVtEfBl4MXBZ+f/1Fdv372JO7sRKkvhG0wFMk/WjJGkIQ9WRYzWhBIiIH6MYJvkg4HsUwyTnWBuVJKnjrB8lSZMwdgInSZIkSZqOhaYDkCRJkiRVYwInSZIkSR1hAidJkiRJHWECJ0mSJEkdMe48cK0SEecBzwOOAI7LzK+ssc4icD7wLGAZODcz3z/NONuoYtmdDfw68J3ypc9m5qumFWMbRcQ24FLgMcBu4Abg9P6hwiPiAOADwAnA/cCZmXnVlMNtjSHK7YPA04Dby5cuz8xzphhq60TER4FHA3uAu4FXZ+aX+9bxPDcjrNdWs65azTpob9Yve7PeWK1ieZxNi88jM5XAAR8F/hC4ZsA6vwwcCRwFbAOuj4irM/OmyYfXalXKDuCSzDxzCvF0xTLwtsz8NEBEvB04F3h533pnAndm5pERcRRwTUQcmZl3TzXa9qhablBUIhdMMba2e2lmfh8gIp4N/AlwfN86nudmh/XaatZVq1kH7c36ZW/WG6tVKQ9o8XlkpppQZua1mfmtDVZ7IfC+zNxT3o35KPD8yUfXbhXLTn0y846VSqJ0HfCoNVZ9IXBR+Z4bgC8APzvxAFtqiHJTn5VKp/QjFHcQ+3memxHWa6tZV61mHbQ365e9WW+sVrE8Wm3WnsBVcTjwzZ7fbwYe2VAsXfSiiHgGcCtwVmb+bdMBtUVELACvBK5cY7Hfu3VsUG4Ar4uI04FvAG/MzH+cWnAtFRHvB54BbKJo7tLP79t88e+9t7mrq6yD9mb98kPWG6tVKA9o8Xlkpp7AaeIuBB6dmY8D3g5cUbY1V+HdFG2p56E5Rp0GldubgCMz8zjgL4FPlO3051pm/mpmHg78DsWxKOmH5rWusg7am/VLyXpjtQrl0erzyDwmcDez+lH64YDNMSrIzFsz877y509RlNuxzUbVDmXH+qOAF2bmWo/i/d6tYaNyy8xbVl7PzEuAA4HDphtle2XmpcBT16hU/L7NF//ePeaxrrIO2pv1y9qsN1Zbrzzafh6ZxwTucuDXImIhIrYDzwH+ouGYOiEiDu35+fEUo4BlYwG1RES8lWJkr+dk5q51VrscOL1c/yjgROAT04mwnaqUW9937pnAEnDLdCJsn4g4MCIe2fP7KcAd5b9enufmi3/vHvNWV1kH7c365YesN1arWh5tP49sWl5ebjqG2kTE+cAvAA+nGBZ2Z2YeExEfB96SmV8oH49fQNHuFeC/ZuZ7m4m4PSqW3cUUJ8QliqF5z8rMjzcWdAtExDHAV4CvA/eUL9+Ymc+NiC8D/yEzvxMRDwI+CDyBovx+KzOvaCLmNhii3K4GHkbRwfhO4D9n5nWNBN0CEfEw4ArgQRTfozsohgP/kue52WS9tpp11WrWQXuzflnNemO1Icqj1eeRmUrgJEmSJGmWzWMTSkmSJEnqJBM4SZIkSeoIEzhJkiRJ6ggTOEmSJEnqCBM4SZIkSeoIEzhJkiRJ6ggTOEmSJEnqCBM4SZIkSeqIfZoOQGqriLgJ+NXMvLrhUB4QEU8FzgceCSwBnwF+IzNvaTQwSdJcaWMd2Ssi/gT4FeCozPznpuOR6uQTOKmlImKtGyxfA56ZmT8KPAK4Afi/pxqYJEkNW6eOXFl2EvCYKYYjTZVP4KQhRcRBwKXAkyiOoc8CZ2TmtyPi+cAbMvOEnvVfBzwlM58dEZuBc4AXAJuBvwJem5n3RMRPAZcB7wZeC3wKOK33szPzX/rCWQKOrH8vJUkaXpN1ZLm9fcp1Xgr83cR2VGqQT+Ck4S0AHwAeBRwO3ANcUC67Enh0RDy2Z/3TgEvKn88FjgYeT5F4HQq8pWfdhwMPKbf9irU+PCIOj4h/LT/3TOBt4++SJEm1aLSOpEjuPpOZfz/2nkgt5RM4aUiZuRP4yMrvEXEO8Dflsl0R8WfAqcCbIuIY4AjgqojYRFHhPC4z7yjf+1bgQ8Aby83tAc7KzF0DPv9m4Ecj4iHArwH/VO8eSpI0mibryIh4JHA6cMJay6VZYQInDSkiDgDeCTwLOKh8eWtELGbmEnAx8OGIeDPFncU/Lyutg4EDgC9GxMrmNgGLPZvfkZn3VokjM++IiIuBv4uIQzPz/rF3TpKkMTRcR74L+L3M/H59eyS1jwmcNLzXAwE8KTNvjYjHA9dTVDRk5nURsRs4Gfil8h/A7RRNSY4ZMGrk8pCx7AMcDDwYuGPI90qSVLcm68ifAU6KiN6uBX8bEb+ZmR8abXek9jGBkwbbNyL27/n9fmArRSXzr2UzxrPWeN8lFG3+78vMawEyc09EvA94Z0T8RmbeFhGHAsdm5ierBBMRvwB8lWL0yW3AHwDXrzQ3kSRpilpVR1L0n+sd3+G7wCk4mIlmjIOYSIN9nKIiWvl3NkUTjS0UdwuvAz6xxvsuBY6lGDGr128D/wxcFxF3AldT3Kms6tDy8+4C/oGiP8Bzh3i/JEl1aVUdmZm3ZeatK//Kl2/PzHsq75HUAZuWl4dtsSVpIxGxBbgNOD4zb2g6HkmS2sI6UhrPhk0oI2IbxZ2SxwC7KZpunZ6ZOyLiycBFFHdabgJOzczbJheu1BmvBD5vxSRJ0l6sI6UxVGlCuQy8LTMjM48DvgGcGxELFI++X5WZRwOfoZi/Q5prEXET8JsUHbklSVLJOlIa39BNKCPieRR3Tt4IfCAzjy1ffyhwU2YeWHuUkiRJkqThErjyqdtfA1cCtwAvy8yf61n+b8BhFUfE2wycSDFC0NIwQUuSOmUROAT4PLDuJPV6gPWjJM2PoevIYacReDdwN8XQr+OOfHcicM2Y25AkdcfJwLVNB9EB1o+SNH8q15GVE7iIOA84CjilnKvjZuBRPcsfCuwZYj6q7wJ873s/YM+eyY2EuW3bgezceffEtj8Jxjx5XYsXuhdz1+KF7sXclXgXFjZx0EEPgvK8rw09UE5d+Ps2rSvHQdMsp2osp+osq2o2KqdR6shKCVxEvBU4Afi5zFx5tPdFYEtEnFROwngGcHnVD6ZsFrJnz/JEE7iVz+gaY568rsUL3Yu5a/FC92LuWLw2B6zmgXLq2N+3MZZTNZZTNZZTdZZVNRXLqXIdWWUagWMoBiz5OvC5iAC4MTOfGxGnARdFxP6U0whU/WBJkiRJ0nA2TOAy86vApnWWfQ44ru6gJLXL1gdvYf/Nxeni3l33c9ed9zQckSRpEnrP9zD4nG/dIDVj2EFMJM2h/TfvwymvvwKAj73j2dzVcDySpMnoPd/D4HO+dYPUjCoTeUuSJEmSWsAETpIkSZI6wiaU0hyz/4IkSVK3mMBJc8z+C5IkSd1iE0pJkiRJ6ggTOEmSJEnqCBM4SZIkSeoI+8BJLdU/meru+5YajEaSpMkaZhJxaZ6ZwEkttdZkqpIkzaphJhGX5plNKCVJkiSpI0zgJEmSJKkjbEIpjaitbfXbGpckSZLGZwInjaitbfXbGpckSZLGZxNKSZIkSeoIEzhJkiRJ6gibUEozbvd9S2zfvhWopz9c7/bq2qYkSZKqMYGTZtx++y4+0Ceujv5wvdura5uSpG4b5uZe72Bb3gSUhmcCJ0mSpLEMc3Ovd7AtbwJKw7MPnCRJkiR1hE/gJAGj95Wro4+dc9dJkiRVYwInCRi9r1wdfeycu06SJKkam1BKkiRJUkeYwEmSJElSR9iEUpoj/cM8S5IkqVtM4KQ5stYwz5JUlQMO1acrZdl742/X7iU277fYcER760pZSnUxgZMkSZU44FB9ulKW/QNVtfEmYFfKUqqLfeAkSZIkqSN8AidNWW9Tj/7mKDb7kCRJ0iAmcNKU9Tb1WKs5is0+JEmStB4TOEmSJiQibgLuLf8B/HZmfrKxgCRJnWcCJ0nSZP1iZn6l6SAkSbNhwwQuIs4DngccARy3UglFxNHAxcA2YCfwksy8YXKhSlrRP2RyW4d2liRJUr2qPIH7KPCHwDV9r18IvCczL4uIU4GLgJ+uOT5Ja1hryOTefnWSWuVPI2ITcC3wO5n5r00HJEnqrg0TuMy8FiAiHngtIg4GjgeeXr70YeCCiNiemTsmEKckSV10cmZ+KyI2A+8CLgBOrfrmlQmU26wNMbYhhjpMej+mXU5VPq93ovBRtzFom7vvW2K/fYdroTIr36dpsKyqqbucRu0D90jglsxcAsjMpYj4Tvm6CZwkSUBmfqv8f1dE/BFw5TDv37GjXePSrnUR0nSM27dvbTyGUUy7LKuWU50Xmut9Xu9n9E8UXnUb621vrW0OU65d/T41wbKqZqNyWljYxLZtBw61zcYHMRk24FF08e6AMU/eqHf1Bt3JW+uO37B3/+q+EzmKje6IDtL7vt6f+8uhallWNez26/rcpnUt3nkSEQ8C9snM75dNKF8EfLnhsCRJHTdqAvct4NCIWCyfvi0CjyhfH8rOnXezZ8/yiGFsrIt3B4x58kaNd/v2rZXuFPav1/tZG11w925jmGV16r17CcP1q+uNsX+/+7dZZV+rGrT9YbYxD9/jaRvl7uKMeBjwkbKOXAS+Bvx6syFJkrpupAQuM2+LiC8DLwYuK/+/3v5vkiQVMvN/A09oOg5J0mxZ2GiFiDg/Ir4NHAZcHRFfLRedAbw6Ir4OvLr8XZIkSZI0IVVGoXwN8Jo1Xv8n4EmTCEpqUu8ca/fuup+77ryn0vvG6TfWhu1PQv98dVX17mvvHHfD/D0kSZJmUeODmEht0zvH2sfe8Wyq9jDaaCStcU16+5PQX5ZV9e/rKH8PSZKkWWQCJ0mSWmnUFhGzrL9lQx3lMmpriUnrbY3Rv5+TjnkS5SzVpX1HqyRJEqO3iJhlvWUC9ZTLqK0lJq2/NUbvfk465kmUs1QXEzi1gne6JEmSpI2ZwKkVvNMlSZIkbWzDaQQkSZIkSe1gAidJkiRJHWETSqlFujjXW69h4q9jX9s6cpokSdKkeOUjtUgX53rr1Rs/DN6HOva1rSOnSZIkTYpNKCVJkiSpI3wCJ0mSajfNSbiHmYqmjriqbmPUKXKGeV9vc/Rdu5fYvN/iA8t6f981YL1Z1t9cf6O/F7Bh834nmFfTTOA0swadYHtP6FUrRklSddOchHuYqWjqiKvqNkadImeY9/U3R+9/31rL1lpvVq3VtL/qd2M9TjB7DYGcAAAZuklEQVSvppnAaWYNOsH2V3hVKsaVdSVJkqSm2AdOkiRJkjrCBE6SJEmSOsIETpIkSZI6wj5wDZjX0YtGHY1r1O1X5UAl3eHfSpIkzTsTuAbM6+hFo47GNc72q3Cgku7wbyVJkuadTSglSZIkqSN8AidJ0pypq0l71Tk1JxHLoGbzo8Y1qIvDqM3069D15uN1lF3TZbDe5w87Ufgku9C0qYtOm2KZRZ1P4Eb9gkz7faqPfwNJGk9dTdqrzqk5iVgGTbo8alyDujhUneR5Evr3p2tG7eLQq+km9Ov9DcaZKLzuLjRt6qLTplhmUecTuFG/INN+n+rj30CSJEnzyj5wkiRJktQRJnCSJEmS1BGdb0I5yNYHbwFg+/at7Nq9xOb9FkfaxiQ7La/V+XVck55vbZxY6tZ0p+YquhDjvJlEP8rebfaeb+ynKUmS6jTTCVx/X6lROr/W0fF2kEl0yp30fGujxjKJDsdd6NjddMdr7W0S/SjXO9/YT1OSJNXJJpSSJEmS1BEz/QROkqR5Nkpz4f6m71W7INTVXLzqfFvjbm+cbQ7axqBynnQsbdLW/alj7sKq2x907PSXz6Cm96PMTzjoe9mmeSB7takL0CBtmM7KBI7BB9Ew1usDM8z2dt+3xH77Lq7Zb2+aX5JBB9FalTsUfQ2HibFqRTbq36PqZ2k2Va1Y+r8bWx+8ZexJhCehDRWGumeU5sJrNcOv0lS9rubiw8y3Nez26trmRttfr5wHNfvvQpeAYbR1f+qYu3CY7Q/z3as6B2GV+QkHfS/bNA9krzZ1ARqkDdNZmcCx8UFU1aA+MFUriI0O/Gl9SQYdRBtV7lVjHKYi6/oEoJq+qhXLMBdfvSbdP3bQ57W1UpMkSZNnHzhJkiRJ6ggTOEmSJEnqiLGbUEbE0cDFwDZgJ/CSzLxh3O2OYtb7OQ3qF1a1o+yg99XRr2bW/waafaMeZ1Wt11cWBvc1naaudCRvuzbVj5Kk2VHH1cGFwHsy87KIOBW4CPjpGrY7tFnv57RRv7BR+5PV2a9m1v8Gmn2jHmdVbTQ/5aC+ptPSlY7kHdCa+lGSNDvGakIZEQcDxwMfLl/6MHB8RGwfNzBJkrrK+lGSNCnjPoF7JHBLZi4BZOZSRHynfH3HBu9dBFhY2DRmCHDwQVvW/Hkay/rjX+99046rjv3pX2/Qsrbsj3HNfsyTPlYnsWyYz656nK2sV8c5dC2D4hpWz3vHa3/aLWPXj1B/HdnkOX7QvrT1vDRqHW992Z246qgbph3XJL6Xo55rRo25yvbGiWvSht23QeuMUkduWl5errruXiLiBOCSzDym57WvAadm5pc2ePtJwDUjf7gkqWtOBq5tOohpsH6UJA2pch057hO4bwGHRsRieXdxEXhE+fpGPk8R6HeBpTHjkCS11yJwCMV5f15YP0qSqhi6jhwrgcvM2yLiy8CLgcvK/6/PzI2ahwDsYk7uxEqS+EbTAUyT9aMkaQhD1ZFjNaEEiIgfoxgm+SDgexTDJOdYG5UkqeOsHyVJkzB2AidJkiRJmo6xphGQJEmSJE2PCZwkSZIkdYQJnCRJkiR1hAmcJEmSJHWECZwkSZIkdcS4E3k3JiLOA54HHAEcl5lfWWOd3wVeRDER6n3A72TmJ8tlHwSeBtxern55Zp7TcLxnA78OfKd86bOZ+apy2QHAB4ATgPuBMzPzqknFO0TMlwCP63npccBzMvPKQfszoXi3AZcCjwF2AzcAp/fPuzSoLKddzkPE/B7gZyjmh7ob+M3M/EK57NPA4cCd5ep/mJkfaDjeD7LO8RURDyu3cQRwD/CKzPz/JhHvkDFfDTy0/HUf4BjgxzPz7xs4X3wUeDSwh+Lv/erM/HLfOovA+cCzgGXg3Mx8/0bL1B7j1mPzpEpZ9awbwPXAH2XmmdOJsB2qllNEvAD4XWATxTniaZn5L9OKs2kVj72DKa4HHgnsC/wN8JrMvH+KoTaqjuuqeVDHtdywuvwE7qPATwLfHLDO/wJOzMzHAS8D/iwitvQsPzczH1/+m9jFWKlKvACX9MTUm+ycCdyZmUcCpwDvj4gDJxTrig1jzsyXrMQLvJRirqPei4v19mcSloG3ZWZk5nEUkyKeu8Z6g8py2uVcNeb/QVHJ/Djw+8Cf9S1/TU85TyR5GzJeWP/4+n3gM5l5NPAq4LKI2NR0zJn5tJ7v8puBr2bm3/esMs3zxUsz88cz8wnAecCfrLHOLwNHAkcBPwGcHRFHVFim9qijHpsXlerQ8ubFReX682jDcoqIJwJnA0/PzGOBk4DvTyW69qjyffod4B/LY+9xFMnJL0whtjap47pqHtR1LVdZZxO4zLw2M7+1wTqfzMx/K3/9e4o7TdsmHtzasWwY7wZeSFEpkZk3AF8AfraO2NYzQswvB/40M3dNKqZBMvOOzPx0z0vXAY9aY9VBZTnVcq4ac2ZelZn3lb/+LXBYREz9+B2ijAd5AXBhub1rKe5EPbGWANcwYswvY+2kaSoys/di6kconsT1eyHwvszcU97l+yjw/ArL1BJdq8eaNER99AbgKuDrEw6plSqW02uB8zLz1vI938/MeycfXXtULKdlYGtZ124G9gNumXhwLVLTddXMa+JarrNNKEfwEuAbmfntntdeFxGnU2TKb8zMf2wmtFVeFBHPAG4FzsrMvy1fP5zVd4pupnis3woRsR/wSxTNzHqttz+TjmcBeCVw5RqLB5VlY+W8Qcy9fgP475nZe1H/9oj4feDvgN/OzIlXMhXi3ev4KpsZbMrM23vWWynjz080YKqVcUQ8nOJ7/PK+RVM9X0TE+4FnUFywP2uNVVr5PdZErVWPqRQRPw48E3gqRfNAre3fATdGxGeAA4G/BM7JzOVmw2qd/wv4CPBd4EHABZn52WZDas4Y11VzZcxruco6+wRuGBHxFIoD8cU9L78JOLJ81PmXwCfKphdNuhB4dPm4/u3AFeUFbxc8B7g5V/fTaXJ/3k3RvviCKX1eHTaMOSJeRJEov7Ln5dMy87HA44F/YoxH8kMaFG8bjy+o9r14CfCJvrbrU9+fzPzVzDycohnP2yf5WWq/deoxlSJiX+C9wBmZudR0PC23SNEk8OnAUyielJzWaETt9HyKp96HAIcCPxkRv9hsSI3q4nVVE0a9lhvKzCdwEfETwGUUA2vkyuuZectK1puZl1DchTqsmSgfiOnWlUermfkp4FvAseXim1n9OPbwcnlb7NXkbIP9mZiyc/JRwAvXubMxqCwbKecKMRMRzwXOAZ6ZPZ3NV5qBlBctfwg8edLNKzeKd73jKzN3lu9/aM/qrSnj0q+w93e5sfNFZl4KPHWNmx+t+x5rMtarx7TKIRQDCHw8Im4C/hPwaxHx3iaDaqmbgb/IzF2ZeRdwBfDvG46pjV5N0S1kT9ms/QqKp7tzZ8zrqrkxzrXcsGY6gYuIEymeRvxiZn6pb9mhPT8/k2KEr0bbNvfF9HiK0ZFWKuvLgdPLZUcBJwKfmHKIa4qIw4CTgT/te33Q/kwqlrdSdDR+zoC+eIPKcurlXCXmiPh54A8oDvibel7fpxzVccWLgX8Y9ZF8jfEOOr4uB84ol50EbAG+OKl4q8Zcrvd/UPQ5+x99r0/tfBERB0bEI3t+PwW4o/zX63KKC9SFiNhO8RT8LyosU4cMqsf0Q5l5c2Y+NDOPyMwjgHdR9AN9RcOhtdGHgGdExKbyyeXPUDS/12o3UjZfL7uJPA1Yd/TTWVXDddVcGOdabhSblpe72eQ5Is6nGA3o4RRDe+/MzGMi4uPAWzLzCxHxeYqkofdC67TM/IdyyPCHUQwOcCfwnzPzuobjvZjij79EMQzpWZn58fL9DwI+CDyhXP5bmXnFpOKtGnO53psoRtV5Ud/7192fCcV7DMXJ9esUw9MD3JiZz42ILwP/ITO/M6gsp13OQ8S8g6IMe5v1/QxwL/D/UnSu3kTxXf/NSd2lHyLedY+vso/ZZRR36+6haPL0uUnEO0zM5brvo/iev6FvG1M7X5QJ+RUUfS6WKBK3MzPzS33ni0WKJhrPKN/6XzPzveU21l2m9hi3Hpt6wA2qWh/1rH82cGDO3zQCVb5TCxSj2/4sxTntkxTnmInd+GubiuX0GIquIA+naHb6NxT16zxNIzD2ddU8GPdabqV10jA6m8BJkiRJ0ryZ6SaUkiRJkjRLTOAkSZIkqSNM4CRJkiSpI0zgJEmSJKkjTOAkSZIkqSNM4CRJkiSpI0zgJEmSJKkj9mk6AKmtIuIm4Fcz8+qGQ3lARPwU8D+Bf+t5+VWZeXEzEUmSJGmaTOCkloqIfTLz/jUWfSczD5t6QJIkSWqcCZw0pIg4CLgUeBLFMfRZ4IzM/HZEPB94Q2ae0LP+64CnZOazI2IzcA7wAmAz8FfAazPznvLp2mXAu4HXAp8CTpvenkmSJKnt7AMnDW8B+ADwKOBw4B7ggnLZlcCjI+KxPeufBlxS/nwucDTweOBI4FDgLT3rPhx4SLntV6zz+QdHxL9ExI0R8c6IeND4uyRJkqQuMIGThpSZOzPzI5n5b5l5F8UTtaeUy3YBfwacChARxwBHAFdFxCaKpOy1mXlH+d63Ai/q2fwe4KzM3JWZ96zx8f9EkfwdAvw0cALwBxPYTUmSJLWQTSilIUXEAcA7gWcBB5Uvb42IxcxcAi4GPhwRb6Z4+vbnmbkrIg4GDgC+GBErm9sELPZsfkdm3rveZ2fmrcCt5a83RsRvAVcBp9ezd5IkSWozEzhpeK8HAnhSZt4aEY8HrqdIxsjM6yJiN3Ay8EvlP4DbKZpbHpOZt6yz7eUhY1nGJ+mSJElzwwROGmzfiNi/5/f7ga0Uidi/RsRDgLPWeN8lFP3i7svMawEyc09EvA94Z0T8RmbeFhGHAsdm5ierBBMRTwX+N3AzcBhFn7orRtw3SZIkdYx37qXBPk6RrK38Oxt4F7CF4onadcAn1njfpcCxFKNK9vpt4J+B6yLiTuBqiqd5VT0B+Bzwg/L/fwBeM8T7JUmS1GGblpeHbbElaSMRsQW4DTg+M29oOh5JkiTNBp/ASZPxSuDzJm+SJEmqk33gpJpFxE0UA5o8p9lIJEmSNGtsQilJkiRJHWETSkmSJEnqiCabUG4GTgS+Cyw1GIckabIWgUOAzwO7Go5FkqROGyuBi4htFMOlPwbYDdwAnJ6ZOyq8/UTgmnE+X5LUKScD1zYdhCRJXTbuE7hl4G2Z+WmAiHg7xcTCL6/w3u8CfO97P2DPnu70w9u27UB27ry76TAq61q8YMzTYszT0bWYJxHvwsImDjroQVCe9yVJ0ujGSuAy8w7g0z0vXUcxfHoVSwB79ix3KoEDjHcKjHk6jHk6uhbzBOO1ubwkSWOqbRCTiFigSN6urGubkiRJkqQfqm0agYh4D3Ao8AuZuafCW44Abqzlw9VJu+9bYr99F/f6WdLMejRwU9NBSJLUZbWMQhkR5wFHAadUTN4esHPn3Z1qXrR9+1Z27Lir6TAqa3O827dv5ZTXXwHAx97x7AfibHPM6zHm6TDmyZtEvAsLm9i27cBatylJ0rwaO4GLiLcCJwA/l5kODy1JkiRJEzLuNALHAG8Evg58LiIAbszM59YQmyRJkiSpx7ijUH4V2FRTLJIkSZKkAWobhVKSJEmSNFkmcJIkSZLUESZwkiRJktQRJnCSJEmS1BEmcJIkSZLUESZwkiRJktQRJnCSJEmS1BEmcJIkSZLUESZwkiRJktQRJnCSJEmS1BEmcJIkSZLUESZwkiRJktQRJnCSJEmS1BEmcJIkSZLUESZwkiRJktQRJnCSJEmS1BEmcJIkSZLUESZwkiRJktQRJnCSJEmS1BEmcJIkSZLUESZwkiRJktQRJnCSJEmS1BEmcJIkSZLUESZwkiRJktQRJnCSJEmS1BEmcJIkSZLUESZwkiRJktQRJnCSJEmS1BEmcJIkSZLUESZwkiRJktQR+4zz5og4D3gecARwXGZ+pY6gJEmSJEl7G/cJ3EeBnwS+WUMskiRJkqQBxnoCl5nXAkREPdFIkiRJktY1VgJXh23bDhzr/bvvW2K/fRf3+nmStm/fOvHPqFPVeJsoy97P7o3zR370gLE/f9r70xt/k2U5jC58l/vLsgsx96s75v7vVN3fsf7jr83fYUmS5k3jCdzOnXezZ8/yyO/fvn0rp7z+CgA+9o5ns2PHXXWFtu7nTfoz6jRMvE2U5Yr99l184LPr+vxp7k9/OU+7LEfRle9yF8pykEmUc2+ZQL3lsn371tqPx4WFTWPfrJMkSQVHoZQkSZKkjjCBkyRJkqSOGCuBi4jzI+LbwGHA1RHx1XrCkiRJkiT1G3cUytcAr6kpFkmSJEnSADahlCRJkqSOMIGTJEmSpI4wgZMkSZKkjjCBkyRJkqSOMIGTJEmSpI4wgZMkSZKkjjCBkyRJkqSOMIGTJEmSpI4wgZMkSZKkjjCBkyRJkqSOMIGTJEmSpI4wgZMkSZKkjjCBkyRJkqSOMIGTJEmSpI4wgZMkSZKkjjCBkyRJkqSOMIGTJEmSpI4wgZMkSZKkjjCBkyRJkqSOMIGTJEmSpI4wgZMkSZKkjjCBkyRJkqSOMIGTJEmSpI4wgZMkSZKkjjCBkyRJkqSOMIGTJEmSpI4wgZMkSZKkjjCBkyRJkqSOMIGTJEmSpI7YZ9wNRMTRwMXANmAn8JLMvGHc7UqSJEmSVqvjCdyFwHsy82jgPcBFNWxTkiRJktRnrAQuIg4Gjgc+XL70YeD4iNg+bmCSJEmSpNXGbUL5SOCWzFwCyMyliPhO+fqODd67CLCwsGnMEODgg7Y88HMd29vIND6jTsPEO+2y7P283p/r+vxp7k//9qddlqNoa1z9ulCWg0wi5kkcL5Pafs97F0ePSJIkAWxaXl4e+c0RcQJwSWYe0/Pa14BTM/NLG7z9JOCakT9cktQ1JwPXNh2EJEldNu4TuG8Bh0bEYvn0bRF4RPn6Rj5PUZl/F1gaMw5JUnstAodQnPclSdIYxkrgMvO2iPgy8GLgsvL/6zNzo+aTALvwTqwkzYtvNB2AJEmzYKwmlAAR8WMU0wgcBHyPYhqBrCE2SZIkSVKPsRM4SZIkSdJ01DEPnCRJkiRpCkzgJEmSJKkjTOAkSZIkqSNM4CRJkiSpI0zgJEmSJKkjxp3Iey8RcR7wPOAI4LjM/Moa6/wK8FpgD8UEr+/LzPPLZYvA+cCzgGXg3Mx8f91x1hzz2cCvA98pV/9sZr6q6Zh71g3geuCPMvPM8rUDgA8AJwD3A2dm5lUtjveDwNOA28vVLs/McyYVb9WYB/3tp13GNcX8QVpYzuV6LwB+F9hEcW54Wmb+S1vPGRvEfDYtPGdExCXA43peehzwnMy8solyliRJe6s9gQM+CvwhcM2AdT4CfDAzlyNiK/CViPh0Zv498MvAkcBRwDbg+oi4OjNvmkCsdcUMcMlKsjElVWJeSYgvKtfvdSZwZ2YeGRFHAddExJGZefdEoh0/XiguGC+YQGzrqRQz6//tp13GMH7M0MJyjognAmcDP52Zt0bEjwC7ysWtPGdsEDO08JyRmS9Z+Tkifhz4n8Any5eaKGdJktSn9gQuM68FKB6irLvOnT2/HgDsS3FHF+CFFE+39gA7IuKjwPOBt9cda08848Y8dVViLr0BuAo4sPy34oXAS8tt3RARXwB+Fri89mCpJd6pGyLm9Uy1jMvPGTfmqasY82uB8zLz1vI93+9Z1spzBoNjnroRvhsvB/40M1eSzqmXsyRJ2tsknsBVEhH/Efh94DHAGzPzH8pFhwPf7Fn1ZuCRUw5vTQNiBnhRRDwDuBU4KzP/tokYe5V30J8JPJWiGVev1pXzBvECvC4iTge+QVH+/zjN+AZY72/fujLuMej72sZy/nfAjRHxGYrE/i+BczJzmfaW86CYoYXnjBURsR/wSxTNaVe0tZwlSZorjQ1ikplXZuYxwNHAadGBRwYDYr4QeHRmPo7ibvQVEbGtqTgBImJf4L3AGZm51GQsVVSI903AkZl5HMWF8CfK5pZNa93fvoJBMbe1nBcp+mM9HXgKxZPM0xqNaGODYm779+Y5wM2Z+eWmA5EkSas1PgplZt4M/C/g58uXbgYe1bPK4cC3ph3XIP0xZ+atmXlf+fOnKOI9trkIATiE4knhxyPiJuA/Ab8WEe8tl7etnAfGm5m3lE23yMxLKJ5oHNZMqD+0wd++bWUMDI65reVMUZZ/kZm7MvMu4Arg3/csa105MyDmlp4zer0M+JO+19pazpIkzZVGEriIeGzPzw+laDK30hzxcooL94WI2E5xJ/gvph/laoNijohDe5Y9nmKUt5xyiKtk5s2Z+dDMPCIzjwDeRdF/5RXlKpcDpwOUA2ycCHyikWDZON6+Mn4msATc0kiwPTb427eqjFcMirmt5Qx8CHhGRGwqn9b+DPB35bJWnjMYEHMbzxkrIuIw4GTgT/sWtbWcJUmaK5OYRuB84BeAhwNXR8TOzDwmIj4OvCUzvwC8ouz7cR/F8NoXZOZfl5u4FHgScEP5++9l5o11x1lzzG+NiBMoLnZ3A6etDFzQcMyDvB34YET8cxn3K8qnBG2N9+KIeBjFNA53Av8xM++fVLxDxDzobz/VMq4p5raW838Dngh8rYztk8Afl5to6zljUMxtPme8FPhYZn6vbxNTL2dJkrS3TcvLjQ2kKEmSJEkaQuN94CRJkiRJ1ZjASZIkSVJHmMBJkiRJUkeYwEmSJElSR5jASZIkSVJHmMBJkiRJUkeYwEmSJElSR5jASZIkSVJH/P9ncjXuuSIXQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9939f632b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.7 s\n"
     ]
    }
   ],
   "source": [
    "# Plot histogram of each layer's coloumn norms (for unit pruning)\n",
    "plt.figure(2, figsize=(15,6))\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "for i, weights in enumerate(model.trainable_weights):\n",
    "    column_norms = np.linalg.norm(weights.numpy(), ord=2, axis=0)\n",
    "    plt.subplot(3,2,i+1)\n",
    "    plt.title(\"Layer {}\".format(i+1))\n",
    "    plt.hist(column_norms, bins=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weight Pruning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.06 ms\n"
     ]
    }
   ],
   "source": [
    "percentiles = [0, 25, 50, 60, 70, 80, 90, 95, 97, 99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7768576069242635, 0.9608)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.44 s\n"
     ]
    }
   ],
   "source": [
    "class WeightSparseModel():\n",
    "    def __init__(self, sparsify=True):\n",
    "        self.sparsify = sparsify\n",
    "        self.flatten = tf.layers.Flatten()\n",
    "        self.weights = []\n",
    "        \n",
    "    def add_layer(self, weights):\n",
    "        if self.sparsify:\n",
    "            weights = tf.contrib.layers.dense_to_sparse(weights)\n",
    "        self.weights.append(weights)\n",
    "        \n",
    "    def __call__(self, dense_x):\n",
    "        dense_x = self.flatten(dense_x)\n",
    "        # Sparse matrix multiplication requires the sparse matrix\n",
    "        # to be the left operand. As such, we reorder operations\n",
    "        # when sparsified:\n",
    "        # relu(X x sL1) x sL2 == (sL2^T x relu(sL1^T x X^T))^T\n",
    "        if self.sparsify:\n",
    "            dense_x = tf.transpose(dense_x) # X^T\n",
    "        for i, weights in enumerate(self.weights):\n",
    "            if self.sparsify:\n",
    "                dense_x = tf.sparse.matmul(weights, dense_x, adjoint_a=True) # sL^T x X^T\n",
    "            else:\n",
    "                dense_x = tf.matmul(dense_x, weights)\n",
    "            # Apply ReLU to hidden outputs only\n",
    "            if i < len(self.weights)-1:\n",
    "                dense_x = tf.nn.relu(dense_x)\n",
    "        if self.sparsify:\n",
    "            dense_x = tf.transpose(dense_x) # X == (X^T)^T\n",
    "        return dense_x\n",
    "\n",
    "def prune_weights(dense_model, percentile, sparsify=True):\n",
    "    sparse_model = WeightSparseModel(sparsify)\n",
    "    num_layers = len(dense_model.trainable_weights)\n",
    "\n",
    "    for i_layer, weights in enumerate(dense_model.trainable_weights):\n",
    "        weights_np = weights.numpy()\n",
    "        \n",
    "        # Zero out pruned weights\n",
    "        if i_layer < num_layers-1: # Do not prune last layer\n",
    "            critical_value = np.percentile(np.abs(weights_np), percentile)\n",
    "            zero_mask = np.abs(weights_np) < critical_value\n",
    "            weights_np[zero_mask] = 0\n",
    "        # Add new layer to sparse model\n",
    "        sparse_model.add_layer(tf.convert_to_tensor(weights_np))\n",
    "\n",
    "    return sparse_model\n",
    "\n",
    "test(prune_weights(model, 90), dataset_test) # Sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pruning p0 -- Test Loss: 0.1083, Test Accuracy: 0.9796, Sparse Timing Ratio: 0.99 (1.60s/1.61s)\n",
      "Pruning p25 -- Test Loss: 0.1064, Test Accuracy: 0.9803, Sparse Timing Ratio: 0.99 (1.59s/1.60s)\n",
      "Pruning p50 -- Test Loss: 0.0939, Test Accuracy: 0.9802, Sparse Timing Ratio: 0.87 (1.46s/1.68s)\n",
      "Pruning p60 -- Test Loss: 0.0859, Test Accuracy: 0.9803, Sparse Timing Ratio: 0.87 (1.44s/1.65s)\n",
      "Pruning p70 -- Test Loss: 0.0872, Test Accuracy: 0.9793, Sparse Timing Ratio: 0.91 (1.45s/1.59s)\n",
      "Pruning p80 -- Test Loss: 0.1485, Test Accuracy: 0.9766, Sparse Timing Ratio: 0.86 (1.41s/1.63s)\n",
      "Pruning p90 -- Test Loss: 0.7769, Test Accuracy: 0.9608, Sparse Timing Ratio: 0.83 (1.34s/1.62s)\n",
      "Pruning p95 -- Test Loss: 1.8271, Test Accuracy: 0.8494, Sparse Timing Ratio: 0.76 (1.35s/1.77s)\n",
      "Pruning p97 -- Test Loss: 2.1518, Test Accuracy: 0.6411, Sparse Timing Ratio: 0.81 (1.35s/1.67s)\n",
      "Pruning p99 -- Test Loss: 2.2948, Test Accuracy: 0.1271, Sparse Timing Ratio: 0.79 (1.32s/1.66s)\n",
      "time: 33.2 s\n"
     ]
    }
   ],
   "source": [
    "pruned_weight_test_losses = []\n",
    "pruned_weight_test_accuracies = []\n",
    "pruned_weight_sparse_timings = []\n",
    "pruned_weight_timings = []\n",
    "\n",
    "for percentile in percentiles:\n",
    "    sparsified_model = prune_weights(model, percentile, True)\n",
    "    t1 = time.clock()\n",
    "    l, a = test(sparsified_model, dataset_test)\n",
    "    t1 = time.clock() - t1\n",
    "\n",
    "    pruned_model = prune_weights(model, percentile, False)\n",
    "    t2 = time.clock()\n",
    "    l2, a2 = test(pruned_model, dataset_test)\n",
    "    t2 = time.clock() - t2\n",
    "    \n",
    "    # Sanity check - both weight pruning methods return same results\n",
    "    np.testing.assert_almost_equal(l, l2)\n",
    "    np.testing.assert_almost_equal(a, a2)\n",
    "    \n",
    "    print(f\"Pruning p{percentile} -- Test Loss: {l:.4f}, Test Accuracy: {a:.4f}, Sparse Timing Ratio: {(t1/t2):.2f} ({t1:.2f}s/{t2:.2f}s)\")\n",
    "    pruned_weight_test_losses.append(l)\n",
    "    pruned_weight_test_accuracies.append(a)\n",
    "    pruned_weight_sparse_timings.append(t1)\n",
    "    pruned_weight_timings.append(t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9939d6e438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 303 ms\n"
     ]
    }
   ],
   "source": [
    "plt.figure(3, figsize=(15,3))\n",
    "plt.subplot(121)\n",
    "plt.plot(percentiles, pruned_weight_test_accuracies)\n",
    "plt.xlabel('Sparsity (%)')\n",
    "plt.ylabel('Test Accuracy')\n",
    "plt.subplot(122)\n",
    "plt.plot(percentiles, pruned_weight_test_losses)\n",
    "plt.xlabel('Sparsity (%)')\n",
    "plt.ylabel('Test Loss');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unit Pruning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.2797745731985493, 0.3715)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.18 s\n"
     ]
    }
   ],
   "source": [
    "def unit_prune(dense_model, percentile):\n",
    "    prev_kept_columns = None\n",
    "    pruned_model = tf.keras.models.Sequential()\n",
    "    pruned_model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))\n",
    "    num_layers = len(dense_model.trainable_weights)\n",
    "\n",
    "    for i_layer, weights in enumerate(dense_model.trainable_weights):\n",
    "        weights_np = weights.numpy()\n",
    "        \n",
    "        # Remove pruned columns\n",
    "        if i_layer < num_layers-1: # Do not prune last layer\n",
    "            column_norms = np.linalg.norm(weights_np, ord=2, axis=0)\n",
    "            critical_value = np.percentile(column_norms, percentile)\n",
    "            keep_mask = column_norms >= critical_value\n",
    "            weights_np = weights_np[:, keep_mask]\n",
    "            \n",
    "        # Remove rows corresponding to previous layer's pruned columns\n",
    "        if prev_kept_columns is not None:\n",
    "            weights_np = weights_np[prev_kept_columns, :]\n",
    "        \n",
    "        # Record which columns were kept\n",
    "        if i_layer < num_layers-1: # No pruned columns in last layer\n",
    "            prev_kept_columns = np.argwhere(keep_mask).reshape(-1)\n",
    "        \n",
    "        # Add new layer to sparse model\n",
    "        new_layer = tf.keras.layers.Dense(weights_np.shape[1], activation=tf.nn.relu, use_bias=False)\n",
    "        pruned_model.add(new_layer)\n",
    "        new_layer.set_weights([weights_np])\n",
    "\n",
    "    return pruned_model\n",
    "\n",
    "test(unit_prune(model, 90), dataset_test) # Sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pruning p0 -- Test Loss: 0.1050, Test Accuracy: 0.9796, Timing: 1.46s\n",
      "Pruning p25 -- Test Loss: 0.0950, Test Accuracy: 0.9805, Timing: 1.43s\n",
      "Pruning p50 -- Test Loss: 0.1123, Test Accuracy: 0.9790, Timing: 1.49s\n",
      "Pruning p60 -- Test Loss: 0.2246, Test Accuracy: 0.9763, Timing: 1.41s\n",
      "Pruning p70 -- Test Loss: 0.5606, Test Accuracy: 0.9688, Timing: 1.37s\n",
      "Pruning p80 -- Test Loss: 1.7034, Test Accuracy: 0.6920, Timing: 1.43s\n",
      "Pruning p90 -- Test Loss: 2.2798, Test Accuracy: 0.3715, Timing: 1.40s\n",
      "Pruning p95 -- Test Loss: 2.3002, Test Accuracy: 0.1235, Timing: 1.44s\n",
      "Pruning p97 -- Test Loss: 2.3025, Test Accuracy: 0.1133, Timing: 1.45s\n",
      "Pruning p99 -- Test Loss: 2.3026, Test Accuracy: 0.0980, Timing: 1.39s\n",
      "time: 11 s\n"
     ]
    }
   ],
   "source": [
    "pruned_unit_test_losses = []\n",
    "pruned_unit_test_accuracies = []\n",
    "pruned_unit_timings = []\n",
    "\n",
    "for percentile in percentiles:\n",
    "    sparse_model = unit_prune(model, percentile)\n",
    "    t = time.clock()\n",
    "    l, a = test(sparse_model, dataset_test)\n",
    "    t = time.clock() - t\n",
    "    print(f\"Pruning p{percentile} -- Test Loss: {l:.4f}, Test Accuracy: {a:.4f}, Timing: {t:.2f}s\")\n",
    "    pruned_unit_test_losses.append(l)\n",
    "    pruned_unit_test_accuracies.append(a)\n",
    "    pruned_unit_timings.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fzaxHPa91I/AVM1tC8ongjQBmNsPMJgT7fBWYbGbvk0wilwB/ruf1RESaTI/OBVz3qZGs2ljBgy8uPfIBIiIickRV1XF+/+j7rP1oO1++4GiGlHQOO6SckGnH3J7A+cCVwC/M7AXgHuAJd9+TyQncfTFwXJryc1NeLwPOzDAmEZFQHTusiLMm9uPZOaux/l2ZWIfxhSIiInKgeCLBX578gEWrtnDdJ0cyZkj3sEPKGRk9KXT33e7+kLufBwwAngVuBjaY2Z9SnvSJiOSUC08dwpCSTtw5YxEbNb5QRESkXhKJBA88v5Q5izdx0WlD+MTRvcMOKafUaaIZM2tLcmbQs4G+wFMkl5R43Mz+p/HDExFp3vJiUW44fzSxaCRYv7Am7JBERERanBlvreL5t9dw1sR+nD2pf9jh5JxMJ5o53czuAjYCNwCPAH3d/Up3/y/gGOCLTRaliEgz1qNzAdd9chSlGyt54MUPww5HRESkRXl+dil/e2U5x4/qycVThhKJaHH6bMt0TOFtwN3AWHdfVftNdy8zs/9s1MhERFqQY4b14OxJ/Zk5uxTr14VJI3uGHZKIiEiz9+Harfzvw/MZPbAr135yJFElhKHIKCl092EZ7HNrw8MREWm5PnvKYJau/Zi7nl7MgJ6F9OzWPuyQREREmrV/vLGSwvb5fPkzR5MXy9YS6lJbpt1Hp5vZSbXKJpvZ/U0TlohIy5MXi3Ljp4/S+EIREZEMrCmr5P3l5XzqpMEUtM20A6M0hUzT8bOAt2qVvQVMbdxwRERatu6d23Hdp0ZRuqmS6S9ofKGIiMihPDO7lDZ5Uc49cVDYoeS8TJPCPUBBrbICQLfBRURqOWZoD84+rj8vz1vLrA82hh2OiIhIs7OlYjdvLdzISWN606lDm7DDyXmZJoXPAr83s/YAweffAM81VWAiIi3ZZ08ezNA+nblr5mI2btb6hSIiIqmef3s18USCs7T8RLOQaVL4dZLrEm42s1Jgc7D9b00VmIhIS5YXi3Lj+aPJi0b4w2ML2FOljhUiIiIAO3dX8/K8dYy3Yoq71O6MKGHIKCl093J3Px0YCVwOjHD3s9y9vEmjExFpwbp1asf1541i9aZKHnhhadjhiIiINAuvvbuOnburtUh9M1KnaX7cfQWwooliERFpdcYM6cE5x/fn6bdKmXjUGkb27Rx2SCIiIqGpronz7NzVDO/XhcElncIORwIZJYVm1hP4NXAK0APYt6qku2tkqIjIYXxm8mCWlH7MbY++x4+uO47OGlDfqpjZJ4A17r7KzIqBHwNx4Hvu/lG40YmINC9zF29i87bdXHGmhR2KpMh0TOHtJGcb/RywC5gMPAPc1ERxiYi0GnmxKNecO5Kdu2v463NLwg5HGt+f2H+z9P8B3YGOQbmIiAQSiQQzZ5XSu3t7xgztHnY4kiLTpPAk4Cp3fwtIuPss4BqUFIqIZKSkRwcuPWs4cxZv4p0lZWGHI42rr7uvNLMYcA5wLfAF4ORwwxIRaV4WrdpC6aZKpk7qTzQSOfIBkjWZjimMA7uD11vNrAewDejXJFGJiLRCnzttGC/PXcO9zzoj+nehfbv8sEOSxrE9aBePAtzdt5lZGyCjb7CZ3UKyJ85A4Gh3X5Bmn5uBLwPrgqI33P1fGiF2EZGsmTmrlE4d2nDC6J5hhyK1ZPqkcC4wNXj9PHAf8AAwvymCEhFpjfJiUa795Ai2bd/DQy99GHY40nj+CMwC7gduC8qOBzLtK/wYyaeKq46w3z3ufkzwoYRQRFqU1ZsqWbBiM6eP70t+XizscKSWTJPCq0g2eJBcm3AusBq4rCmCEhFprQb26sTZk/rz6rvr+WDl5rDDkUbg7j8GPgOc6e73BsVlwA0ZHv+6u69uqvhERJqDZ2aX0jY/xmnH9gk7FEnjiN1HgzESPyUYP+julcB3mzguEZFW6/yTBvHOkjLunrmYH117HG3b6I5pS+fu7+19bWYnAPFg/H1jutTMzgI2AD9w9zfreoLu3Ts2OIiiosIGn6M1Ur2kp3pJL9fq5aOPdzLrg42c+4lBDOrfLe0+uVYnmcpWvRwxKXT3GjM7H1BXFRGRRtAmP8bV54zgF3+dx99fW86lpw8LOyRpADN7HrjZ3V83s6+RvHFabWa/cvdbGukytwE/dfcqMzsTeNzMRrp7eV1OUl5eSTyeqHcQRUWFlJVV1Pv41kr1kp7qJb1crJeHXvqQeCLBSaN7pv3ac7FOMlGfeolGI/W6AZhp99HfA/8VPDUUEZEGsv5dOe3YPjw3dzXL1m0NOxxpmLHAW8HrLwGnAscB/9pYF3D3De5eFbx+juQQjqMa6/wiIk1l5+5qXpm/lokjiinqUhB2OHIImSaFnyd55/NjM1tqZkv2fjRhbCIirdqFpw6hS8e23DljMVXV8bDDkfrLA2rMbBDQxt3fd/eVQPo+UvVgZn1SXh9DcqZSb6zzi4g0lVfmr2Pn7hqmTuofdihyGJkuSdFodztFRCSpoG0eV001fvvIezz15koumDw47JCkft4C/gcoITmTKEGCmNFMQmb2O+CzQC/geTMrd/fRZjYD+L67zwV+ZmbjgRpgD3Clu29o/C9FRKTxVNfEeW7uakb078Kg3p3CDkcOI6Ok0N2faepARERy0dihPTh+dE+eenMVE6yYvsUNnwhEsu5q4D+BlSQnZoNk184/ZHKwu99EMJlbrfJzU15/vqFBiohk25xFm9hSsZurplrYocgRZJQUmtl3DvWeu/+s8cIREck9004fxoLlm7nz6UX815UTiEYjYYckdeDuG4Gv1yr7B/CPcCISEQlfIpHg6VmllPTowNFDuocdjhxBpt1Hj6613Qs4gTo0eGY2HLgb6A6UA1e5+9I0+10MfA+IAAngjKDBFRFplQrbt+HyM4dz+xMLeW7uao27aGGCSdj+A7gS6AOsBe4FfuXu1WHGJiISlg9WbmFNWSXXnDOCaEQ3O5u7TLuPTqtdZmafBj5dh2vdBtzq7veZ2RXA7cCUWuecANwMTHH3DWbWGdhdh2uIiLRIk0YWM+uDjfz91eUcO6wHxV3bhx2SZO6/Sc44+i1gFTAA+A7JiWb+PbywRETCM3PWKjp3aMPxo3uFHYpkINPZR9N5Ergwkx3NrBgYB0wPiqYD48ysqNauXwNu2Tt43t23uvuuBsQoItIiRCIRrpxqxGIR7np6MYlE/deSk6y7FPikuz/h7u+6+xMkb5oedENVRCQXlG6sYOHKLZwxoS/5eQ1JNyRbMh1TWFKrqD1wObA+w+v0A9a6ew2Au9eY2bqgvCxlv1HACjN7FegIPEpysd6M/zqqz2KNtRUVFTb4HK2R6iU91Ut6qpf0DlcvRUWFXPfpo/j9w+8yb/kWph4/IIuRhauF/7zkAbW7iVYDWttXRHLSM7NLaZsf49Rj+xx5Z2kWMh1TuIbk+L69HYKrgPeAaxo5nhgwBjgTaAPMBEqBezI9QXl5JfF4/e+wFxUVUlZWUe/jWyvVS3qql/RUL+llUi/HDu7GiP5duOOJ9xlU3IGuhW2zFF146vPzEo1GGuUmYCP5O/CYmX2fZJs1APhBUC4iklM2b9vF7EWbmDKuLx3a5YcdjmQo0+e5BSSfDhYEH+3cfaK7v5Xh8auBPsFg/L2D8kuC8lSlwCPuvtvdK4DHgUkZXkNEpMWLRCJ8/pwR1NQkuPcZVzfSluEbwCzgfmApyUlm5lJrRlIRkVzw3NzVJBJw5sS+YYcidZDpk8KhwBZ3X7e3IOhS2sXdPzjSwe6+yczmkxxfcV/weZ67l9Xa9a/AuWZ2bxDb6cAjGcYoItIq9OzangsmD+ahlz5kzuJNTBrZM+yQ5DCCse//EXzsY2YnAv8MJSgRkRDs2FXNK/PXMXFkMT06F4QdjtRBpk8K/wp0qFXWISjP1I3AV8xsCfCVYBszmxHMOgrwALAJ+ACYDywE7qjDNUREWoUzJ/ZlUO9C7n9uCRU79oQdjtSRmbUFXgs7DhGRbHrl3bXs2lPD2VpaqcXJ9EnhoNprCrr7UjMbnOmF3H0xcFya8nNTXsdJdrdRlxsRyWmxaJRrzhnJD++awwMvLOX680aHHZLUnRbmEpGcUV0T5/m5axg5oCsDerXoycNyUqZPCtea2ZjUgmB7Q+OHJCIiAH2LO/LJEwbw5sKNvLfso7DDkbrTgFARyRmzPtjIlordTNVTwhYp06Twf4HHzewLZjbFzK4nOavab5suNBER+eQJAynp0YF7nnF27q696oGIiEj4EokEz8wupU9RB44e3C3scKQeMuo+6u5/MLMK4DqSawuuBn7g7vc1ZXAiIrkuPy/KNeeO4Gf3vM0jryzjyrMs7JAkYGZVHPppoLqOikjOWLhiM2vKtnPtuSOJRPTfX0uU6ZhC3P1ektNsi4hIFg0p6cyZE/vx7JzVHDeyJ8P7dQk7JEk6KuwARESag6dnldKlYxuOH63ZsluqjJJCM/sV8LfUdQnN7ATgAnf/z6YKTkREkj4zeTDvLCnjzhmL+OG1k2iTHws7pJzn7h52DCIiYVu1oYJFq7Zw0alDyItlOjJNmptMv3OfB+bVKpsHXN2o0YiISFpt28S4+pwRbNyyk8ffWBF2OCIiIgA8M7uUtm1inHJMSdihSANkmhRGOHh8RIQ6dD8VEZGGGTWwG5PH9OaZWatZuWFb2OGIiEiO+2jrTmYv2sQpY0to3y4/7HCkATJNCt8Avler7DvAPxs3HBEROZxLpgylsEM+d85YTHVNPOxwREQkhz0/dw0AZ07oF3Ik0lCZJoX/BnzGzFab2atmVgp8FvjXpgtNRERqa98unyvPMlZvqmTmrNKwwxH2jbtPV/7zbMciIpItO3ZV8cq765g0qpjunduFHY40UEZJobuvAsYAVwJ/Bq4CxgblIiKSReOGFzFhRDFPvLGC9eXbww5H4IZDlF+f1ShERLLo5fnr2L2nhrO1WH2rUJclKaqBl/dum9npZvZ5d7+qKQITEZFDu/zM4SxauZk7ZyzmW1eMI6p1obLOzC4LXuaZ2TQOHHs/GNic/ahERJpeVXWc5+auZtTArvTvWRh2ONII6jRRjJkZyZlIrwC6A9ObIigRETm8zh3aMO2MYfzlyUW89M5aTh/fN+yQctFXgs9tgJtSyhPARuC6rEckIpIFsz7YyNbKPVx37siwQ5FGcsSk0My6AtNIJoMTgLeArsAYd1/WtOGJiMihnDC6F299sJFHXl7G2KHd6dG5IOyQcoq7nwBgZre4+zfDjkdEJBsSiQTPzC6lb1EHRg/qFnY40kgOO6bQzP4GrAO+DDwODHH3TwCVQEXThyciIocSiUS4aqpBBO6Z6SQSibBDylU/NrMCADOLmNk0M7s47KBERJrC+8s3s/aj7Uyd1J+Ihi60GkeaaOZcYDvJbqLT3X1lk0ckIiIZ69G5gAtPGcKCFZv554INYYeTq2YCo4LXPwF+CNxsZr/M5GAzu8XMVphZwsyOOsQ+MTO71cyWmdmHZvaFxgldRKRuZs5aRdfCthw3qmfYoUgjOlJS2IvkeoTnAkvN7J9m9i8ku53qlrSISDNw2rg+DO3bmQdeWMrWyt1hh5OLRgDzgtdXAmcBJ5Mcf5+Jx4L9Dzej9+XAUGAYcALJpHNgfYIVEamvlRu2sbj0Y86c0I+8WKYr20lLcNjvprtvdfc/BV1GRwEvAv9OcpKZv5jZlCzEKCIihxGNRLjmnBHsropz/3NLwg4nF8VJzkA6Gtge9KopBzKaks/dX3f31UfY7RLgz+4ed/cykonkRQ2IWUSkzmbOKqVdmxgnjy0JOxRpZHVZkmIJ8F3gu2Z2GnA1yUapU9OEJiIimerdvQPnnzSQv72ynLd9E+OtOOyQcsmzwH1AEfBQUDYCWN+I1+jPgU8SS4F+dT1J9+4dGxxIUZGmn09H9ZKe6iW9llgvGzfvYK6Xcf7JQxjQr2ujn78l1kk2ZKte6rQkxV7u/hLwkpl9uZHjERGRepo6qT9zFm3ivmeXMGJAVzq0yw87pFxxHfAFoAq4IygrITm+sFkpL68kHq//6I+iokLKyjTPXG2ql/RUL+m11Hp54PklRIBPjCpu9Phbap00tfrUSzQaqdcNwAZ1Bnb37Q05XkQPMWafAAAeRElEQVREGk9eLMo1546kYkcVD774Ydjh5Ax33+Huv3P3PwIdg7IX3P2eRrxMKTAgZbs/cKQupyIijWL7ripee3c9k0b2pFundmGHI02gXk8KRUSkeRrQq5Bzju/PU2+u4rhRPRk9UGtINTUz6wT8Grg0KOpgZp8CjnX3HzfSZR4GrjezR0mO678AmNxI5xYROayX561ld1UNUyfVude6tBCaNkhEpJX59CcG0rNbe+5+ejG799SEHU4uuBWIAEcBe4Ky2SRnIj0iM/udma0B+gLPm9nCoHyGmU0IdrsXWA4sBd4CfuTuKxrvSxARSa+qOs7zc9cwelA3+vfUuL/WKqMnhWb2K3f/9zTlP3f3bzV+WCIiUl/5eTGuOWcEP7//HR59dTnTzhgWdkit3VSgr7vvMbMEgLtvMrOMFvFy95uAm9KUn5vyugb4UiPFKyKSsbcWbmDr9j18YVL/sEORJpTpk8IbDlF+fWMFIiIijWd4vy5MGdeH5+eu5sO1W8MOp7WrAA6Yis/M+gIbwwlHRKRxxBMJZs4upX9xR0YNbPwZR6X5OOyTQjO7bO9+ZjaNZPeYvQYDmzO9kJkNB+4mORaiHLjK3ZceYl8juRDwH9z9m5leQ0RE9vvcKUOY/+FH3DljETdfM4n8PI0YaCJ3AQ+Z2beAiJkdC/wC+HOoUYmINND7y8pZX76D688bRSQSOfIB0mId6S+ErwQfbUh2bdm7/a/AeJLTcGfqNuBWdx9OcvzF7el2MrNY8N5jdTi3iIjUUtA2j6umjmB9+Q6e/OfKsMNpzX4KzADuJzn76KPAi8AtYQYlItJQM2eV0q1TWyaO0Nq3rd1hnxS6+wkAZnZLQ57YmVkxMA44MyiaDvzezIrcvazW7t8CniTZsDZ8lV0RkRw2Zkh3ThjdixlvrWLCiGL6Feu/1cZiZtPcfbq7x0k+GfxF2DGJiDSWFeu34as/5pIpQ8mLqadJa5fpkhQ/NrMCd99pZhGS027XuPtDGR7fD1gbDJTH3WvMbF1Qvi8pNLOxJAfsnwZ8L9MvIlV9FmusrahIMyulo3pJT/WSnuolvTDq5V8vOZYv//IF7n1uCbd8ZTKxZti4t9Cfl9tJ3uQUEWl1Zs4qpaBtjJPHloQdimRBpknhTJJdRt8GfgJcBFSb2QR3/4/GCMTM8oE/AdcESWO9zlNeXkk8nqh3HEVFhZSVVdT7+NZK9ZKe6iU91Ut6YdbLtNOHcdvjC/nr04s4+7jmNYNcfeolGo00yk3ABtIAGxFplco+3slc38TZk/pT0FbLmueCTL/LI0hO/ALJdZdOBiqB94BMksLVQB8ziwUJXwwoCcr36g0MAWYECWEXkgP2O7n7FzOMU0RE0pg4ophZH2zk768t59jhPejZtX3YIbUGMTM7jcMkh+7+YhbjERFpFM/OWU00EuGMCVqsPldkmhTGSc5AOgzY7u4rg26kGfX3CdZrmg9MA+4LPs9LHU/o7qVAj73bZnYz0FGzj4qINFwkEuGKs4zv/mUWd81YzL9fdixRzSTXUG2BOzh0UpggOVO3iEiLUbmzitfeW8fxo3rStbBt2OFIlmSaFD5LMpkrAvaOIxwBrK/DtW4E7jaz7wNbgKsAzGwG8H13n1uHc4mISB11LWzLJVOGctfTi3njvfVM1jiRhtru7kr6RKRVeWneWvZUxZmqxepzSqZJ4XXAF4AqkndFIdn98yeZXsjdFwPHpSk/9xD735zpuUVEJDOTx/Tm5XlrmTm7lJPG9Na6UyIisk9VdQ0vvL2GowZ3o69mq84pGSWF7r4D+B2AmXUDNrv7C00ZmIiINL5IJMLp4/tyx1OLWLxqCyMHdgs7pJZMGbWItCpvLtzItu17OFtPCXNORvOSm1knM7vDzLYTTA5jZp8ys3otGyEiIuGZNLKYjgX5vPDO2rBDadHcvUWuoyEikk48kWDmrFL69+zIyAFdww5HsizTxapuJXlH9ChgT1A2m+RMpCIi0oLk58WYPLY385aWsXnbrrDDERGRZuC9D8vZsHkHZx/XX0MLclCmSeFU4EZ3X0FyNjXcfRPQs6kCExGRpnPaMX0AeHm+nhaKiAjMnLWK7p3aMsGKww5FQpBpUlgBHPAc2cz6AhsbPSIREWlyPboUMHZID16Zv46q6njY4YiISIiWrdvKkjVbOXNif/JimaYH0ppk+l2/C3jIzE4guaD8scD/AX9uqsBERKRpTRnfh4odVcz1TWGHIiIiIXpmVikFbfOYPKZ32KFISDJNCn8KzADuBzoCjwIvArc0UVwiItLERg3sRs9u7XnxnTVhhyIiIiHZtGUHby8p47Rj+1DQNtPV6qS1Oex33symuft0d48Dvwg+RESkFYhGIkw5tg/TX1jKqg0VDOilyTRFRHLNs3NWEw2WK5LcdaQnhbdnJQoREQnFJ47uRZv8KC/oaaGISM6p3FnF6++t54TRveha2DbscCRER0oKNR+tiEgr1r5dPieO7sWsDzZSubMq7HBERCSLXnxnDXuq40yd1C/sUCRkR+o4HDOz0zhMcujuLzZuSCIikk1TxvXl5fnreP299Zx9XP+wwxERkSzYU1XDC2+vYcyQ7vQp6hh2OBKyIyWFbYE7OHRSmAAGN2pEIiKSVX2LOzK8b2demreGsyb1I6pFi0VEWr1/LtxAxY4qzp6km4Fy5KRwu7sr6RMRaeWmjO/LbY8vZMHycsYM6RF2OCIi0oTiiQTPzF7NgF6FWP8uYYcjzYBWpxQREcYNL6Jzxza88PbasEMREZEmtHX7Hm57fCEbN+/gnOP6E1HvEOHITwr1UyIikgPyYlFOGVvCP95YyaYtOyju2j7skEREpBElEgn+uWADD7ywlN1VNXzm5MFMHFEcdljSTBw2KXR3LVolIpIjTjmmD0+9uYqX5q3lkinDwg4np5jZcOBuoDtQDlzl7ktr7XMz8GVgXVD0hrv/SzbjFJGW6aOPd3L3M87CFZsZ2qczV58zgpIeHcIOS5qRIz0pFBGRHNG1sC3jhhfx2rvruWDyYNrmx8IOKZfcBtzq7veZ2RUk1wmekma/e9z9m9kNTURaqng8wQvvrOHRV5ZDBC4/czinjeujCcXkIBpTKCIi+0wZ14cdu6uZ9cHGsEPJGWZWDIwDpgdF04FxZlYUXlQi0tKt/Wg7/33f20x/finD+3XhJ9cdx+nj+yohlLT0pFBERPYZ3q8LfYs68OLba5g8prcmIMiOfsBad68BcPcaM1sXlJfV2vdSMzsL2AD8wN3frMuFundv+FpkRUUaWZKO6iU91Ut6TVkvVdVxHnlxKQ897xS0zefrl43j1HF9m/3/5/pZSS9b9aKkUERE9olEIkwZ15d7nnGWrd3G0L6dww5J9rsN+Km7V5nZmcDjZjbS3cszPUF5eSXxeKLeARQVFVJWVlHv41sr1Ut6qpf0mrJelq/bxp1PL2Jt2XYmjSzmsjOG06lDGz76qLJJrtdY9LOSXn3qJRqN1OsGoLqPiojIAY4f3ZOCtjFefGdN2KHkitVAHzOLAQSfS4Lyfdx9g7tXBa+fC94/KsuxikgztHtPDQ+8sJSf3juXHbuquelzY7jx/KPo1KFN2KFJC6GkUEREDtCuTR6fOLo3cxZvYuv2PWGH0+q5+yZgPjAtKJoGzHP3A7qOmlmflNfHAAMBz1KYItJMLVy5me/dMYtn56zmlGP68OPrjuOYYT3CDktaGHUfFRGRg0wZ15fn567h1flrOe8Tg8IOJxfcCNxtZt8HtgBXAZjZDOD77j4X+JmZjQdqgD3Ale6+IayARSRc23dV8eALH/L6++vp2bWA/7zsWKx/17DDkhZKSaGIiBykV7f2jB7YlZfnr+PcEwYQi6pjSVNy98XAcWnKz015/fmsBiUizdbcxZu4/7klVOyo4tzjB/DpTwykjZYRkgZQKy8iImlNGd+XLRW7mbfko7BDERER4OPK3fz+0ff5w2ML6NyxDd/7/AQuPHWIEkJpsKw9KTSz4cDdQHegHLjK3ZfW2ud7wKUku8ZUAd9x92eyFaOIiOw3dkgPundqx4vvrGHCiOKwwxERyVmJRILX3lvPgy9+SHVNnAtPHcLUSf3Ui0MaTTZ/km4DbnX34cCtwO1p9pkNTHT3McC1wINmVpDFGEVEJBCNRjj12BIWl37M2o+2hx2OiEhO2rRlB7c8MJ+7nl5Mv+KO/PDaSZx7vLr1S+PKyk+TmRUD44DpQdF0YJyZFaXu5+7PuPuOYPM9IELyyaKIiIRg8tgS8mJRLU8hIpJlNfE4M2eV8v07ZrNi/Taummr8x2XH0qtb+7BDk1YoW91H+wFr3b0GwN1rzGxdUF52iGOuApa5e53+EqnPYo21FRUVNvgcrZHqJT3VS3qql/RaWr0UAScf24c331/HjZ8bS/t2+U1znRZWLyIiTWn1pkrunLGIlRsqOGZoD644azjdOrULOyxpxZrl7KNmdgrwY+DMuh5bXl5JPJ6o97WLigopK6uo9/GtleolPdVLeqqX9FpqvZw4qicvzl3NEy9/yOnj+zb6+etTL9FopFFuAoqINCdV1XH+8c+VPP3WKtq3y+PG80czcUQxkUgk7NCklctWUrga6GNmseApYQwoCcoPYGYnAPcB57u7FuUVEQnZ4JJODOpdyIvvrGHKuD7640REpAl8uGYrdz69iPXlOzhhdC+mnTGMjgVN0ztDpLasjCl0903AfGBaUDQNmOfuB3QdNbOJwIPAhe7+TjZiExGRI5syri/ry3eweNWWsEMREWlVdu6u5v5nl/Df973NnqoavnbxWK4/b5QSQsmqbHYfvRG428y+D2whOWYQM5sBfN/d5wJ/AAqA281s73FXuvv7WYxTRERqmTSymAdf/JAX31nLyIHdwg5HRKRVeH95OffMXMzmbbuZMr4vnz15MAVtm+XoLmnlsvZT5+6LgePSlJ+b8npituIREZHM5efFmDy2NzNnlbJ52y5NeCAi0gCVO6uY/vxS3ly4gd7d2/PtK8YztG/nsMOSHKYFTkREJCOnHdMHEvDy/LVhhyIi0iIlEglenbeG//rzW8xetJHzThzIzddMUkIoodPzaRERyUiPLgWMHdqDV+av47wTB5Gfp/uKIiJHEk8k2Lh5B8vXbWPO4k28t6ycQb0LufrSkfQr1izK0jwoKRQRkYxNGd+H+R9+xFzfxAmje4UdjohIs1O5s4rl67ayfN02lq3bxop129ixuxqAgrZ5XPfp0ZwwophoVDM5S/OhpFBERDI2amA3enYt4MV31igpFJGcV10Tp3RjZTIJXL+N5Wu3senjnQBEItCnR0cmjChmcEknhpR0onf3DvTs2alFrlkrrZuSQhERyVg0EuG0cX154IWlrNpQwYBehWGHJCKSFYlEgrKtu/Y9BVy+bhulGyuorkkA0LljG4aUdObkY0oY3LsTA3sX0q6N/tSWlkE/qSIiUicnHd2LR19dxgtvr+HaT44MOxwRkSaxY1c1K9Zv258Ert9GxY4qANrkRRnYq5AzxvdjcEknBpd0omthWyIRdQmVlklJoYiI1En7dvmcOLoXL89fx+LSLVj/Lgzv1wXr35Wizu30R5GItDg18ThrNm1PdgENksD15Tv2vd+7e3vGDOnO4JLODO7diT5FHciLabItaT2UFIqISJ1dMmUYvbt3wFd/zLsflvPG+xsA6FrYFuvfhZEDujJ+eDHt26mZEZHmJZFIsKVi974uoMvXbWXlhgr2VMcB6FiQz+CSThw/qieDSzozqHch7dvlhxy1SNNSay0iInXWtk2MMyf248yJ/YgnEqz/aDu++mO89GM+WLmFtxZu5L5nl3DssB6ceFQvRg/qRiyqu+oikj3xRIJ4PMGeqjilGyuCp4DJJPDjyj0A5MUi9O9ZmBwHWNKJwSWd1eNBcpKSQhERaZBoJEKfoo70KerIlHF9SSQSrFhfwZsLNjBr0UZmL9pEpw5tOH5UT048qhf9e2pympZowfJyVs0qZefOKiIRiEQiRIPPe7cjJGdcjEYiB5bvK6u1zRHer3X+2teLUvv9xjhHra8pAhEOH2M2JRKJfclOPA418WB7X1nwkUgE70Einti/X7zWvgkOfK/WfjXxBIm9+6S+n7L/3uvUPnfbdvls3757fyzxZPy1r1eTSJA4VCz7rpd874Cv5aCYg32C8kSa+ivuUsCI/l0ZVNKJISWd6VfcUWuuiqCkUEREGlkkEtk38cIlpw/l/WXlvLFgAy+8vYZn56zmmKE9uOnCMWGHKXX0ztKP+Of764knkknC3s+SdNgklMMkpcFnIKNkrbnWeQSIRiP7PyIR8mJB4h+NEAvKogd95qD38mNRovnJ17Fosq5i0XTH7n3NvrKD94O8WJSSHh0YVNKJTu3bhF1VIs2SkkIREWkyebEoxw4v4tjhRVTurGLWBxvDDknq6aqpxjeumHDQ+mqJWkliIpF8QpNI2Y4njrB9hP0POv++7cY4x/6Y071P6vlIf/6CgjZUbt995OuRul07RtImSNE0SU6sVvKVut8BSdQhzwOxtOc+MNGKHTKGvcnX/mQsXXfLoqJCrccn0kIoKRQRkazoWJDP6eP7hh2GNLJ9XSnJ3TFYSn5EpKVTJ2oREREREZEcpieFIiIiITKz4cDdQHegHLjK3ZfW2icG/A44G0gAP3f3v2Q7VhERaZ30pFBERCRctwG3uvtw4Fbg9jT7XA4MBYYBJwA3m9nArEUoIiKtmpJCERGRkJhZMTAOmB4UTQfGmVlRrV0vAf7s7nF3LwMeAy7KXqQiItKatabuozFIzoLVUI1xjtZI9ZKe6iU91Ut6qpf06lovKfvHGj2Y7OoHrHX3GgB3rzGzdUF5Wcp+/YFVKdulwT6ZigF0796xYdGSnFRFDqZ6SU/1kp7q5WCqk/QaUC91ah9bU1LYG6Br1w4NPlFjNJqtkeolPdVLeqqX9FQv6TWgXnoDyxoxlNaqd9gBiIhIVtWpfWxNSeEcYDKwHqgJORYREWlaMZIN3pywA2mg1UAfM4sFTwljQElQnqoUGMD+r7f2k8MjURspIpIb6tU+tqakcDfwethBiIhI1rT4J4TuvsnM5gPTgPuCz/OCcYOpHgauN7NHSc5SegHJJC9TaiNFRHJHndtHTTQjIiISrhuBr5jZEuArwTZmNsPMJgT73AssB5YCbwE/cvcVYQQrIiKtTySRSIQdg4iIiIiIiIRETwpFRERERERymJJCERERERGRHKakUEREREREJIcpKRQREREREclhSgpFRERERERymJJCERERERGRHNaaFq9vMDMbDtxNcmHgcuAqd18ablTZZWbdSa6HNQTYQ3JNrBvcvczMjgduBwqAlcAV7r4prFjDYmY/AG4Gjnb3BbleL2bWDvg1cAawC3jT3b+Y679PZvYp4MdAJPj4obs/mkv1Yma3AJ8DBhL8vgTlh6yDXKqflkTflyS1kYen9vFAah/TU/uY1NzaSD0pPNBtwK3uPhy4leR/ZLkmAfzS3c3djwaWAT83syhwH/AvQf28Cvw8xDhDYWbjgOOBVcG26gV+SbKxGx78zHwvKM/Z3yczi5D8w/FKdz8GuBK4O/h5yaV6eQw4meD3JcXh6iCX6qcl0fclSW3kIah9TEvtYy1qHw/QrNpIJYUBMysGxgHTg6LpwDgzKwovquxz983u/nJK0VvAAGA8sMvdXw/KbwMuznJ4oTKztiR/Ab+UUpzT9WJmHYGrgO+5ewLA3Tfq9wmAONA5eN0FWA/0IIfqxd1fd/fVqWWH+9nQz03zpO/Lfmoj01P7eDC1j4eV8+0jNL82Uknhfv2Ate5eAxB8XheU56Tgrs2XgCeA/qTcyXD3j4ComXULKbww/Ai4z91XppTler0MIdl94QdmNtfMXjazk8jx36fgD4CLgcfNbBXJu4FXkeP1EjhcHah+mid9X9JQG3kAtY8HU/uYhtrHIwqtjVRSKIfzv0Al8PuwAwmbmZ0ATAD+EHYszUwMGAzMc/cJwH8CjwIdQ40qZGaWB3wbON/dBwDnAQ+R4/Ui0sqojUTt42GofUxD7WPzpaRwv9VAHzOLAQSfS4LynBMMfh0GXOLucaCUZBeZve/3AOLuvjmkELPtFGAksMLMVgJ9gWeAoeR2vZQC1QRdGdx9FvARsJPc/n06Bihx9zcAgs/bSY4tyeV6gcP/X6v/h5snfV9qURt5ALWP6al9TE/t4+GF1kYqKQwEs2HNB6YFRdNI3t0pCy+qcJjZz0iOBbjA3XcHxW8DBUHXB4AbgYfDiC8M7v5zdy9x94HuPhBYA0wFfkVu18tHwEvAmbBvVqxiYAm5/fu0BuhrZgZgZiOBniRnKszlejns/7X6f7h50vflQGojD6T2MT21j4ek9vEwwmwjI4lEojHO0yqY2QiS07x2BbaQnObVw40qu8xsNLCA5H9aO4PiFe7+GTM7keQsR+3YP7X0xlACDVlwN/RTwZTbOV0vZjYY+D+S0yNXAf/l7k/n+u+TmV0OfIvkgHqAH7j7Y7lUL2b2O+CzQC+Sd8jL3X304eogl+qnJdH3JUlt5JGpfdxP7WN6ah+TmlsbqaRQREREREQkh6n7qIiIiIiISA5TUigiIiIiIpLDlBSKiIiIiIjkMCWFIiIiIiIiOUxJoYiIiIiISA5TUijSQpnZQjM7tRHPd4OZ/aaex/7NzM5prFhEREQaQm2kSN3khR2ASEsTLMT7S2A0UAMsAr7q7nOyGYe7j06J6WZgqLtfUZ9zmVkb4LvA8cF2Z+Ah4DjgKZLr4NQE7/0JmOnuj6ac4hfAH4Gn63N9ERFpHdRGqo2UlklPCkXqwMw6AU8C/wt0A/oAPwR2N8G1snnT5nxgsbuvDbZvAOYBPYGBwGeCmE4ASmo1drj7bKCTmU3IWsQiItKsqI1UGyktl54UitTNcAB3nx5s7wSe3fummV0NXE+ysbgSWA/8i7u/ELx/DfAfQF+gDPiFu98evHcqcB/JxvRrwHNm9jXgLuAkIA4sBE5x97iZrQS+QPL3+DtAxMwuAJYBPwG+5e7jU2L7enDs+Wm+rnOAV1K2BwGPuftuM3sNGGxmMeDXwGWHqJuXgU8Ccw/xvoiItG5qI9VGSgulJ4UidbMEqDGzu83sHDPrmmaf40g2Oj2AHwCPmlm34L1NwKeATsA1wK/NbFzKsb1I3l0dAHwR+AawBigieUfyO0Ai9WLuPhP4GfCgu3d097HAE8AgMxuZsuuVwD2H+LqOBjxlewFwhpkVAJNJNrQ3AU+7+/JDnGMRMPYQ74mISOunNlJtpLRQSgpF6sDdt5G8I5kA/gyUmdkTZtYzZbdNwG/cvcrdHyTZkHwyOP4pd1/m7gl3f4XkHdTJKcfGgR+4+2533wlUAb2BAcH5XnP3Axq8Q8S5G3gQuALAzEaT7OLy5CEO6QJUpGzfAXQGZgGvAe+SbDB/Y2a3mdmrZvaTWueoCM4jIiI5SG2k2khpudR9VKSO3H0RcDWAmY0g2Z3lN8C0YJe1tRqlVUBJsP85JO+MDid5U6Y98H7KvmXuvitl+1fAzcCzZgbwJ3f/eYah3g1MN7PvkmysHgoawnS2AIUpX+MukndhCeJ+mOQd2MuDuE8JYjo7uAtLcPzHGcYmIiKtkNpItZHSMulJoUgDuPtikuMZjkop7mNmkZTt/sA6M2sL/A24Bejp7l2AGUDqvrW7vVS4+zfcfTDwaeDrZnZ6mlAOujPq7m8Be0jeZb0MuPcwX8p7BGNBajOzs4FI0LAdDcwNGvS5wJiUXUeSvFsqIiKiNlJtpLQgSgpF6sDMRpjZN8ysb7Ddj+Tdz7dSdisGbjKzfDO7iGRDMANoA7QlOXi+OrgjetYRrvcpMxsaNKBbSU7vHU+z60ZgoJnV/p2+B/g9UOXurx/mUjNI3tmsff12wM+BrwZFK4BTg+m5PwGkjp04BU23LSKSs9RGqo2UlktJoUjdVJAcJD/LzLaTbOgWkBzsvtcsYBjwEfBT4EJ3L3f3CpID0R8i2RXlMpKD3Q9nGPA8UAm8CfzB3V9Ks9/DwedyM3snpfxekndo7zvCdf4BjDCzklrl3wHud/c1wfbtJCcHKCM5uP/vAGY2EagMpt0WEZHcpDZSbaS0UJFE4ojjcUUkQ8F0219w95PCjgUgmBltEzDO3ZceYd8vAqPc/auH2+8Qx/4NuMPdZ9QvUhERae3URqqNlOZLE82ItG5fAuYcqbEDcPc/1fci7v65+h4rIiISErWRIgElhSKtVLBwbwS4INxI/n87d3ACAADCQGz/rX13A+GSJeSgCAC/uJGwzEcBAADCPJoBAAAIE4UAAABhohAAACBMFAIAAISJQgAAgLAD7O9DNWxI6uYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f993a3887f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 307 ms\n"
     ]
    }
   ],
   "source": [
    "plt.figure(4, figsize=(15,3))\n",
    "plt.subplot(121)\n",
    "plt.plot(percentiles, pruned_unit_test_accuracies)\n",
    "plt.xlabel('Sparsity (%)')\n",
    "plt.ylabel('Test Accuracy')\n",
    "plt.subplot(122)\n",
    "plt.plot(percentiles, pruned_unit_test_losses)\n",
    "plt.xlabel('Sparsity (%)')\n",
    "plt.ylabel('Test Loss');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary of Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Percent Sparsity</th>\n",
       "      <th>Test Accuracy (Unit Pruned)</th>\n",
       "      <th>Test Accuracy (Weight Pruned)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.9796</td>\n",
       "      <td>0.9796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0.9805</td>\n",
       "      <td>0.9803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>0.9790</td>\n",
       "      <td>0.9802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>0.9763</td>\n",
       "      <td>0.9803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>0.9688</td>\n",
       "      <td>0.9793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>80</td>\n",
       "      <td>0.6920</td>\n",
       "      <td>0.9766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>90</td>\n",
       "      <td>0.3715</td>\n",
       "      <td>0.9608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>95</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>0.8494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>97</td>\n",
       "      <td>0.1133</td>\n",
       "      <td>0.6411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99</td>\n",
       "      <td>0.0980</td>\n",
       "      <td>0.1271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Percent Sparsity  Test Accuracy (Unit Pruned)  \\\n",
       "0                 0                       0.9796   \n",
       "1                25                       0.9805   \n",
       "2                50                       0.9790   \n",
       "3                60                       0.9763   \n",
       "4                70                       0.9688   \n",
       "5                80                       0.6920   \n",
       "6                90                       0.3715   \n",
       "7                95                       0.1235   \n",
       "8                97                       0.1133   \n",
       "9                99                       0.0980   \n",
       "\n",
       "   Test Accuracy (Weight Pruned)  \n",
       "0                         0.9796  \n",
       "1                         0.9803  \n",
       "2                         0.9802  \n",
       "3                         0.9803  \n",
       "4                         0.9793  \n",
       "5                         0.9766  \n",
       "6                         0.9608  \n",
       "7                         0.8494  \n",
       "8                         0.6411  \n",
       "9                         0.1271  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 27.6 ms\n"
     ]
    }
   ],
   "source": [
    "pd.options.display.float_format = '{:.4f}'.format\n",
    "pd.DataFrame(data={\n",
    "    'Percent Sparsity':percentiles,\n",
    "    'Test Accuracy (Unit Pruned)': pruned_unit_test_accuracies,\n",
    "    'Test Accuracy (Weight Pruned)': pruned_weight_test_accuracies    \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f993c585eb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 514 ms\n"
     ]
    }
   ],
   "source": [
    "plt.figure(5, figsize=(15,6))\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "plt.subplot(221)\n",
    "plt.plot(percentiles, pruned_unit_test_accuracies, label='Unit Pruned')\n",
    "plt.plot(percentiles, pruned_weight_test_accuracies, label='Weight Pruned')\n",
    "plt.xlabel('Sparsity (%)')\n",
    "plt.ylabel('Test Accuracy')\n",
    "plt.legend(loc='lower left')\n",
    "plt.subplot(222)\n",
    "plt.plot(percentiles, pruned_unit_test_losses, label='Unit Pruned')\n",
    "plt.plot(percentiles, pruned_weight_test_losses, label='Weight Pruned')\n",
    "plt.xlabel('Sparsity (%)')\n",
    "plt.ylabel('Test Loss')\n",
    "plt.legend(loc='upper left')\n",
    "plt.subplot(223)\n",
    "plt.plot(percentiles, pruned_unit_timings, label='Unit Pruned')\n",
    "plt.plot(percentiles, pruned_weight_timings, label='Weight Pruned')\n",
    "plt.plot(percentiles, pruned_weight_sparse_timings, label='Weight Pruned (Sparsified)')\n",
    "plt.xlabel('Sparsity (%)')\n",
    "plt.ylabel('Test Timing (s)')\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis\n",
    "We can observe that both pruning strategies are able to remove a large proportion of the network weights with minimal impact to classification performance. We only start to see performance impact at 70% sparsity for unit pruning, and 90% sparsity for weight pruning. Interestingly, unit pruning suffers a serious decline in performance between 70% and 95% sparsity, whereas weight pruning only runs into a similar decline in performance between 90% and 99% sparsity. At 99% sparsity (the maximum tested), both unit pruning and weight pruning perform at the level of random selection (~10% accuracy).\n",
    "\n",
    "For lower sparsity percentages (< 50%) unit pruning appears to be more computationally efficient, as we always deal with dense matrices (and smaller ones with more pruning). At higher sparsity percentages howevever, weight pruning with sparsification (using sparse matrices and operations for weights) seems to be roughly in line with unit pruning performance, if not better at the highest sparsities. Both methods provide upto a 20% speedup over just using dense matrix operations on the full model.\n",
    "\n",
    "In Figure 1 we see that the weights of the model (barring those in the final layer) are normally distributed, with most of the weights being centered around 0. Thus when weight pruning, the vast majority of weights are of low magnitude and can be removed with minimal impact to performance. In contrast, Figure 2 shows that the column norms of the weights are far more spread out, and not centered around 0 (by definition of the L2 norm). If we view the individual weights in a column as being independent draws from the normal distribution for that layer (maybe not a valid assumption), then the L2 column norms would follow a chi-distribution (which seems in-line with the histograms actually). When pruning columns, it's unlikely that a column is filled with many high-magnitude weights; it's more common for there to be a mixture of low and high magnitude weights. Thus, when unit pruning we risk deleting columns with individually important weights, because the rest of the column is low-magnitude and deemed unimportant. It is possible that this is what causes unit pruning's earlier decline in performance.\n",
    "\n",
    "##### Future Directions\n",
    "One recent paper in the area of model compression which I found very convincing was [The Lottery Ticket Hypothesis](https://arxiv.org/abs/1803.03635), which postulated that most of the predictive performance of large neural networks was attributable to smaller subnetworks which were initialized with good starting weights. These subnetworks could be recovered after training and weight pruning the large network; re-initialization and re-training the preserved subnetworks then leads to much faster convergence and possibly better test accuracies than the originally trained model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Notebook Runtime Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy       1.15.4\n",
      "pandas      0.23.4\n",
      "seaborn     0.9.0\n",
      "tensorflow  1.12.0\n",
      "last updated: 2018-11-14T02:59:41+00:00\n",
      "\n",
      "CPython 3.6.7\n",
      "IPython 7.1.1\n",
      "\n",
      "matplotlib 2.1.0\n",
      "tqdm 4.28.1\n",
      "\n",
      "compiler   : GCC 7.3.0\n",
      "system     : Linux\n",
      "release    : 4.4.0-137-generic\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 4\n",
      "interpreter: 64bit\n",
      "watermark 1.7.0\n",
      "time: 44.1 ms\n"
     ]
    }
   ],
   "source": [
    "%watermark -i -u -v -iv -w -m -p matplotlib,tqdm"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (User)",
   "language": "python",
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