Cifar100 Collages.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from os import listdir\n",
    "from os.path import join, exists\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_layer_saturation(df, excluded = 'classifier-6', epoch=19):\n",
    "    cols = list(df.columns)\n",
    "    #print(cols)\n",
    "    train_cols = [col for col in cols if 'train' in col and not excluded in col and not 'accuracy' in col and not 'loss' in col]\n",
    "    epoch_df = df[df.index.values == epoch]\n",
    "    epoch_df = epoch_df[train_cols]\n",
    "    return epoch_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_saturation_level(df, title=''):\n",
    "    cols = list(df.columns)\n",
    "    col_names = ['Layer {}'.format(i+1) for i in range(len(df.columns))]\n",
    "    plt.figure(figsize=(7,5))\n",
    "    plt.grid()\n",
    "    plt.bar(list(range(len(cols))), df.values[0])\n",
    "    plt.xticks(list(range(len(cols))), col_names, rotation='vertical', fontsize=16)\n",
    "    plt.ylim((0,100))\n",
    "    plt.yticks(fontsize=16)\n",
    "    plt.xlabel('Layers', fontsize=16)\n",
    "    plt.title(title, fontsize=16)\n",
    "    plt.ylabel('Saturation in %', rotation='vertical', fontsize=16)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_saturation_level_ax(df, ax):\n",
    "    cols = list(df.columns)\n",
    "    col_names = [i+1 for i in range(len(df.columns))]\n",
    "    ax.grid()\n",
    "    ax.bar(list(range(len(cols))), df.values[0])\n",
    "    ax.set_xticks([])\n",
    "    ax.set_ylim((0,100))\n",
    "    #plt.yticks(fontsize=16)\n",
    "    #plt.xlabel('Layers', fontsize=16)\n",
    "    #plt.title(title, fontsize=16)\n",
    "    #plt.ylabel('Saturation in %', rotation='vertical', fontsize=16)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ADAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "File b'.\\\\logs\\\\VGG11_XXXS\\\\Cifar100\\\\VGG11_XXXS_bs8_e50_idADAM.csv' does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-a951e524fcac>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'logs'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'VGG{}{}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterstack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Cifar100'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'VGG{}{}_bs{}_e50_id{}.csv'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterstack\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midentifieer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m         \u001b[1;31m#print(filename)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m         \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_layer_saturation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m';'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m49\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m         \u001b[0mplot_saturation_level_ax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 678\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    679\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    442\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    785\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    786\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 787\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    788\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    789\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1012\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1013\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1014\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1015\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1016\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1706\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'usecols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1707\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1708\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1709\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1710\u001b[0m         \u001b[0mpassed_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnames\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: File b'.\\\\logs\\\\VGG11_XXXS\\\\Cifar100\\\\VGG11_XXXS_bs8_e50_idADAM.csv' does not exist"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 8\n",
    "identifieer = 'ADAM'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 32\n",
    "identifieer = 'ADAM'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 128\n",
    "identifieer = 'ADAM'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "File b'.\\\\logs\\\\VGG11_XXXS\\\\Cifar100\\\\VGG11_XXXS_bs512_50_idADAM.csv' does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-72e8e1791af5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'logs'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'VGG{}{}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterstack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Cifar100'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'VGG{}{}_bs{}_50_id{}.csv'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterstack\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midentifieer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m         \u001b[1;31m#print(filename)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m         \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_layer_saturation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m';'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m49\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m         \u001b[0mplot_saturation_level_ax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 678\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    679\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    442\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    785\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    786\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 787\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    788\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    789\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1012\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1013\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1014\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1015\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1016\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1706\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'usecols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1707\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1708\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1709\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1710\u001b[0m         \u001b[0mpassed_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnames\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: File b'.\\\\logs\\\\VGG11_XXXS\\\\Cifar100\\\\VGG11_XXXS_bs512_50_idADAM.csv' does not exist"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 512\n",
    "identifieer = 'ADAM'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RADAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 8\n",
    "identifieer = 'RADAM'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w+RnwY7p/iD+E+su1nUNvBtD5MTKZ+R/AW6leq5uB/YDFHP1Z6L3oPcBnqb78/HXmfAdhQLrt206kej/5alRnL/r/qD4czWefepkfUz0P/5Bbnzd5T6rvP/zpnP3xLy/hvWyi1PuvV1LtyzdRdQgM5Tz3NTMwV2b6448/Df5QfSC7CVg16rb4s6jX7WVUH9Z/SlU0rwN275j/UKqe+7uAK4A/AzZ2zH8zcDtVcf42qp7wl9fzXgpcNmd721IdDbmdaoz47Db+hupLk3dRjWl/dT3voM7tdaznYKqx1z+i6sX+KPDIet408A2qjonz6583zfP4t2pjx7y1s/eb2w6qTonv1dt/TT3tafXjv53qw8engV+u562bfV7q2yvqZe+s17EOeNzcNtVtuJfqzXj25x/reTsA76IqMO6sH/NLRp0pf/zxZ/n9RGZbhghLk6Pudfwa1aH//001fOSx2eVLb2q/iDiGqpf7GVmdCUiSpJ6GMnQkIs6JiB9GxFUd03aOiEuiuijLJfWYsdkLFrw9Iq6LiCuiOl2VlokJysp/ozq9261UwzMOs8genGHnJDPPoRoa9fTBPQoNwwTtU9Qgc6KmDGuM9lqqw5mdTgIuzeqMB5fywBczDqEan7MP1XlV3zWkNqod1jIBWcnMUzLz0Zn5yMx8WmZ+bdRtmjBrGXJOMvP9+cDpKTU+1jIB+xQ1bi3mRA0YSqGdmV+kGpvX6VCqiwpQ/z6sY/r7svJVYMfoclUsTSazohLmRKXMikqYEzVllGcdmcrq2+HUv2dPqr8HW57vdGM9TcuXWVEJc6JSZkUlzIn61sYL1nQ7MX/Xb2xGxBqqwzZsv/32T95rr726LaYxs3LlSm644YatTufVhVlZxsyJSpkVlTAnWoxrr7321szseTGgURbaN0fEiszcXB9y+WE9fSNbXr1qT6pTo20lM88CzgKYnp7OmZmZJturIdmwYQMrV668t2OSWdFWzIlKmRWVMCdajIjodWVdYLRDRy4Cjqr/Pgq4sGP6kfW3eg8A7pw9dKNly6yohDlRKbOiEuZEfRtKj3ZEfJjqoga7RMRG4A1UVxX8SES8jOriBi+qF78YeB7VlXzuobpqo5aJ1atXs27dOoDtzIrmY05UyqyohDlRUybmgjUekpksEbE+M6ebWLdZmRzmRKXMikqYE5Uqzcooh45IkiRJE8tCW5IkSWqAhbYkSZLUAAttSZIkqQEW2pIkSVIDLLQlSZKkBlhoS5IkSQ2w0JYkSZIaYKEtSZIkNcBCW5IkSWqAhbYkSZLUAAttSZIkqQEW2pIkSVIDLLQlSZKkBlhoS5IkSQ14yCg3HhH7Aud3THos8HpgR+BY4JZ6+smZefGQm6cWMSsqYU5UyqyohDlRv0ZaaGfmNcD+ABHxYGATcAFwNHBGZr5lhM1Ti5gVlTAnKmVWVMKcqF9tGjryLOD6zLxx1A1R65kVlTAnKmVWVMKcaNHaVGi/BPhwx+3jI+KKiDgnInYaVaPUSmZFJcyJSpkVlTAnWrSRDh2ZFRHbAr8L/GU96V3AqUDWv98KHNPlfmuANQBTU1OsW7eur3ZcuenOouX222OHvrajpWtLVtRu5kSlzIpKmBMtVWTmqNtARBwK/ElmPrfLvL2BT2Xm4xdax/T0dM7MzPTVjr1P+nTRchtOe35f21FvEbE+M6e7TG9FVtQO5kSlzIpKmBOVmi8rc7Vl6MhqOg7HRMSKjnmHA1cNvUVqK7OiEuZEpcyKSpgTLcnIh45ExMOA5wDHdUw+PSL2pzoks2HOvGXDHvYtmZX5lWTFnJgTbcmsqIQ5UT9GXmhn5j3Ao+dMO2JEzVGLmRWVMCcqZVZUwpyoH0seOhIR20bEdwfZGEmSJGlS9DNGO4C9B9QOSZIkaaIsOHQkIu5faDbV2CRJkiRJc/Qao3071Xkh/6PLvO2AKwfeIkmSJGkC9Cq01wO7ZOb1c2dExHZUvdqSpDHgmYwkabh6FdonAPd2m5GZP4uIlYNvkiRJkjT+Fiy0M/NbPebfONjmTDZ7kyZH0+et9rzYKuE+RZLarZ/T+0VEPHOQjZEkSZImRT+n99sW+PygGiJJkiRNkl6n9ztygdnbDrgtkiRJ0sTo9WXI91KdeeRnXeZ5xhFJkiRpHr0K7e8AJ2bmVkNEIuKhwD2NtEqSJEkac70K7S8Av073sdj31/OXLb/xL0mSpPn0Or3fcQvMuxdYNfAWSZIkSROgV4924yJiA3A3VQ/5fZk5HRE7A+cDewMbgBdn5h2jaqPawayohDlRKbOiEuZE/eh11pF9gWdn5jvr259hy7ON/HFmXjOAdqzKzFs7bp8EXJqZp0XESfXtEwewnYm2TC5yYlZUwpyolFlRCXOiJel1Hu0TgR933H468MH651tUwWrCocC59d/nAoc1tB2NP7OiEuZEpcyKSpgTFek1dOSZwJ923L4/M88GiIhHAl8fQBsS+FxEJPDuzDwLmMrMzQCZuTkidhvAdjT+zIpKmBOVMivzWCZHSEuZEy1Zr0J7t8y8q+P2Ly5gk5l3R8TUANrwjMy8qQ7pJRHx7dI7RsQaYA3A1NQU69at66shJ+x3X9Fys9sZx+X7fY5GbKyy0s82Frv+ZfDaL0ZrctK0pvcpy8CyycpiuU/ZgjnRkkVmzj8zYhNVwDZ0mfdY4LLM3H1gjYk4hWqoyrHAQfWnxBXAuszcd6H7Tk9P58zMTF/bX+zp+sZx+XHpgYiI9Zk5vcD8U2h5Vvp5rhe7/kl67Rej7TlpWtP7lEmy3LOyWO5T5p1/CuZE9M7KrF492p8GTgWO6DLvjfX8JYuIhwMPqnvHHw48F/hr4CLgKOC0+veF/WxH48+sqIQ5USmzohJty8ly/rA8rnoV2q8HvhwR3wAuAH4ArKAa9L8TcECf258CLoiI2bZ8KDM/ExGXAx+JiJcB3wNe1Od2NP7MikqYE5VqVVaWaw/yGGhVTjR+el2w5gcRMQ38OXAIsAtwG3Ax8LbMvK2fjWfmd4EndJl+G/CsftatyWJWVMKcqNRyy4qF/NIst5xo8HpesCYzbwdeV/9IkiRJKtDrgjU7AE/IzC/Wt0+ec593tPlKSI5lGi17UB6wnJ4Lv6D3gEl+bJKk3nr1aL8GuA/4Yn37ZKqx2gArgW2Av2qmaZIkSdL46lVoHw4c3HH73sw8AiAi9qQaq22hLUmSJM3Rq9DePTM3dtw+a/aPzNxYF9uSJC3achpSJWl5elCvBSJi19m/M/PEbtMlSZIkbalXj/aXgKOB07vMOxr4ysBbJElSF/aAL09+qVjjrFeh/Ubg8xGxO/BxHrhgzQuBY4DfarZ5ahPf5CRJksr1KrS/TnW50dOB46mGmvwc+BpwcGZe3mzzJA2avUOSJA1Hr0J7E/B+4JXA9VSXXb8jM+9pumGSJEnSOOtVaL8C+CPg34CrgbXAhwALbU0Uh8WoDTzaIEmTZcGzjmTmhZn5Iqpx2e8GXgxsjIiLIuL3ImKbYTRSkiRJGje9erQByMwfURXa746IlcARwBn1tF2aa54kSZKGYbFH1TwK11tRoT0rIrYDngI8DZgCvtxEoyRJGmcORxsdiz+1SVGhHREHAkdSDR35IfUXJDPzxgbbJkmSJBVp44esBcdoR8QpEXE98Ml60vMz89cy89RBFNkRsVdEfD4iro6Ib0XE/+rY7qaI+Gb987x+t6XxZlZUwpyolFlRCXOifvXq0T4AeC3wicz8aQPbvw84ITO/HhGPBNZHxCX1vDMy8y0NbFO1MTu0aVZUotGctLG3REs21vuUMdt/j7OxzolGb8FCOzMPbnLjmbkZ2Fz/fXdEXA3s0eQ2NZ7MikqYE5UyKyphTtSvBYeODFNE7A08keqqkwDHR8QVEXFOROw0soapdcyKSpgTlTIrKmFOtBSRmaNuAxHxCOALwJsz8+MRMQXcCiRwKrAiM4/pcr81wBqAqampJ5933nlbzL9y051F299vjx2WzfKzy7Zx+U6rVq1an5nTc6ePMittfu4Ws/xic7VYTee8Uxtz0tnWcV++ae5T2rmPWMryTXKfMpn77yYMsz3zZWWukRfa9UVvPgV8NjPf1mX+3sCnMvPxC61neno6Z2ZmtpjW9Pkgx3H5zjF7bVu+U0RsFeBRZ6XNz91ilm96nPEwz8Paxpx0tnXcl2+a+5R27iOWsnyT3KdM5v67CU3XTZ26ZaWbkQ4diYgAzgau7gxvRKzoWOxw4Kpht03tYlZUwpyolFlRCXOifi3qgjUNeAbVVSavjIhv1tNOBlZHxP5Uh2Q2AMeNpnlqEbOiEuZEpcyKSpgT9WWkhXZmXgZEl1kXD7stajezMjptOzS4EHOiUmZFJcyJ+jXqHm1JkiQ1YJw6SiZVa07vJ0mSJE0SC21JkiSpAQ4dkSQNRJtOCSdJbWChLUnqyvGdktQfh45IkiRJDbDQliRJkhpgoS1JkiQ1wEJbkiRJaoCFtiRJktQAzzoiSZKGxrPZaDmx0JYkSX3xHOpSdw4dkSRJkhpgj7YkSZJaZxKGGdmjLUmSJDWgtT3aEXEwcCbwYOCfMvO0ETdJLWVWVMqsqIQ5USmzsjiT0EO9WK3s0Y6IBwPvBA4BHgesjojHjbZVaiOzolJmRSXMiUqZFZVoZaENPBW4LjO/m5n/BZwHHDriNqmdzIpKmRWVMCcqZVbUU2TmqNuwlYj4feDgzHx5ffsI4GmZefyc5dYAa+qb+wLXDLWhatJjMnPXXguZlWWvKCdQlhVzMtHcp6iE+xSVKspKW8doR5dpW30iyMyzgLOab45azKyoVM+smBPhPkXl3Keop7YOHdkI7NVxe0/gphG1Re1mVlTKrKiEOVEps6Ke2lpoXw7sExErI2Jb4CXARSNuk9rJrKiUWVEJc6JSZkU9tXLoSGbeFxHHA5+lOmXOOZn5rRE3Sy1kVlTKrKiEOVEps6ISrfwypCRJkjTu2jp0RJIkSRprFtqSJElSAyy0JUmSpAZYaEuSJEkNsNCWJEmSGmChLUmSJDXAQluSJElqgIW2JEmS1AALbUmSJKkBFtqSJElSA4ZSaEfEORHxw4i4qmPazhFxSUR8p/69Uz09IuLtEXFdRFwREU8aRhvVDmZFJcyJSpkVlTAnasqwerTXAgfPmXYScGlm7gNcWt8GOATYp/5ZA7xrSG1UO6zFrKi3tZgTlVmLWVFvazEnasBQCu3M/CJw+5zJhwLn1n+fCxzWMf19WfkqsGNErBhGOzV6ZkUlzIlKmRWVMCdqykNGuO2pzNwMkJmbI2K3evoewPc7lttYT9s8dwURsYbq0yTbb7/9k/faa69mW6yhWLlyJTfccMP9HZPMirZiTlTKrKiEOdFiXHvttbdm5q69lhtloT2f6DItuy2YmWcBZwFMT0/nzMxMk+3SkGzYsIGVK1feW7CoWVnGzIlKmRWVMCdajIi4sWS5UZ515ObZQy317x/W0zcCnR/59gRuGnLb1C5mRSXMiUqZFZUwJ+rbKAvti4Cj6r+PAi7smH5k/a3eA4A7Zw/daNkyKyphTlTKrKiEOVHfhjJ0JCI+DBwE7BIRG4E3AKcBH4mIlwHfA15UL34x8DzgOuAe4OhhtFHtsHr1atatWwewnVnRfMyJSpkVlTAnakpkdh1WNHYc+zRZImJ9Zk43sW6zMjnMiUqZFZUwJypVmhWvDClJkiQ1wEJbkiRJaoCFtiRJktQAC21JkiSpARbakiRJUgMstCVJkqQGWGhLkiRJDbDQliRJkhpgoS1JkiQ1wEJbkiRJaoCFtiRJktQAC21JkiSpARbakiRJUgMstCVJkqQGPGSUG4+IfYHzOyY9Fng9sCNwLHBLPf3kzLx4yM1Ti5gVlTAnKmVWVMKcqF8jLbQz8xpgf4CIeDCwCbgAOBo4IzPfMsLmqUXMikqYE5UyKyphTtSvNg0deRZwfWbeOOqGqPXMikqYE5UyKyphTrRobSq0XwJ8uOP28RFxRUScExE7japRaiWzohLmRKXMikqYEy3aSIeOzIqIbYHfBf6ynvQu4FQg699vBY7pcr81wBqAqakp1q1bt6TtX7npzgXn77fHDkta7zAt9BjGof2l2pyVSXqex92oc6LxYVZUwpxoqSIzR90GIuJtl2rLAAAgAElEQVRQ4E8y87ld5u0NfCozH7/QOqanp3NmZmZJ29/7pE8vOH/Dac9f0nqHaaHHMA7tnysi1mfmdJfprc3KODzPk5D1Tm3NidrHrKiEOVGp+bIyV1uGjqym43BMRKzomHc4cNXQW6S2MisqYU5UyqyohDnRkox86EhEPAx4DnBcx+TTI2J/qkMyG+bM0zJlVlTCnKiUWVEJc6J+jLzQzsx7gEfPmXbEiJqjFjMrKmFOVMqsqIQ5UT/aMnREkiRJmigLFtoR8YiI+LuI+FREPCsi9omI9RFxR0R81NPZSJIkSd31GjryDmAb4DbgE8D/AV5ZT3sT8DfAHzfZQEm9TdrZRDR6436GHUlqg16F9iHAY6l6vo8A3pOZmwEi4ijgX5ttniRJkjSeehXaD62/BEBE3DVbZANk5o0OHZEkSZK66/VlyB9ExM7131scK4yIvYAfNdIqSZIkacz1KrTfAGwPkJlfmjPvvwPnNtEoSZIkadwtOHQkM89fYN6HBt+c5aP0y2t+yU1t5hfmuvP/VpIEfZxHOyrPHGRjJEmSpEnRzwVrtgU+P6iGSJIkSZNkwaEjEXHkArO3HXBbJEmSpInR6/R+7wXWAz/rMi8G3xxJkiRpMvQqtL8DnJiZWw0RiYiHAvc00ipJUiP8oqYkDU+vQvsLwK/TfSz2/fV8SeqbBaAkadL0Or3fcQvMuxdYNfAWSZIk6RfsiBhfvXq0GxcRG4C7qXrI78vM6fpqlOcDewMbgBdn5h2jaqPawayohDlRKbOiEuZE/eh11pF9gWdn5jvr259hy7ON/HFmXjOAdqzKzFs7bp8EXJqZp0XESfXtEwewHY0/s6IS5mSCDfhCSSPNihd9GhvuU7QkvXq0T2TLcdhPB/6s/nt/qmAd3UC7DgUOqv8+F1iHAVZ3ZkUlzIlKmZVFaPOQhobbZk5UpNcFa54JXNBx+/7MPDszzwZOBg4cQBsS+FxErI+INfW0qczcDFD/3m0A29H4MysqYU5UyqyohDnRkkVmzj8z4q7MfFTH7Rdk5ifnm7+kBkTsnpk3RcRuwCXAq4CLMnPHjmXuyMydutx3DbAGYGpq6snnnXfektpw5aY7F5y/3x47LGq5UWxzoeUGua7O5UqVtG2uVatWrc/M6c5pbc9KU89fiXF6bZeyrvnWN445gWYyUGqQ+5RRWW77lDabhKx3GsecwHhkZdJ0y0o3vQrtTcAzMnNDl3mPBS7LzN37aeicdZ4C/Bg4FjgoMzdHxApgXWbuu9B9p6enc2ZmZknbLT28NMjDUIPeZsk4v1E8ztK2zRURCwa4jVlp6vkrMU6v7VLWNd/6xjEnMB6H09s8dni57VPabBKy3mkccwLjkZVJ0ysrs3oNHfk0cOo8895Yz1+yiHh4RDxy9m/gucBVwEXAUfViRwEX9rMdjT+zohLmRKXMikqYE/Wr15chXw98OSK+QTVW+wfACuAwYCfggD63PwVcEBGzbflQZn4mIi4HPhIRLwO+B7yoz+1o/C3brNiTsSgTmZNx7/VsqYnMyiCZO8CcqE+9Lljzg4iYBv4cOATYBbgNuBh4W2be1s/GM/O7wBO6TL8NeFY/69ZkMSsqYU5UyqyohDlRv3pesCYzbwdeV/9IkiRJKtDrgjU7AE/IzC/Wt0+ec593eCWkLXmYX6XMipYLsy5puerVo/0a4D7gi/Xtk3ngvNorgW2Av2qmaZIkSdL46lVoHw4c3HH73sw8AiAi9qQaq22hLUmSpLHU5Bd/exXau2fmxo7bZ83+kZkb62JbkiSNmEN0pPbp+WXIiNg1M28ByMwTO6c32TBJUjmLLElqn16F9peAo4HTu8w7GvjKwFs0QL7xSGoz91HSljx3tyZNr0L7jcDnI2J34OM8cMGaFwLHAL/VbPMkSZKk8dTrEuxfp7rc6JOB/wt8G7gUmAYOzszLm22eJEmSNJ569WhvAt4PvBK4nuqy63dk5j1NN0waJg/hS5KkQevVo/0KqvNl/xtwGfB7wMOabpQkSZI07hbs0c7MC4ELI2JH4A+AI4DTI+JzwLnARZl5b/PNlCRpefKImzS+evVoA5CZP8rMd2fmgcBvADPAGcDmJhsnSZIkjaue59HuFBHbAU8BngZMAV9uolGSJElqxihOo7hcj8wU9WhHxIERcRZwM/Am4KvAr2XmqiYbJ0mSJI2rBXu0I+IUqnHZOwMfBZ6fmV8a1MYjYi/gfcAvAT8HzsrMM+vtHgvcUi96cmZePKjtavyYFZVoOifLtUdmEpkVlfC9pzezvrBeQ0cOAF4LfCIzf9rA9u8DTsjMr0fEI4H1EXFJPe+MzHxLA9vUeDIrKmFOVGrZZsXCaFGWbU40GL3OOnJwkxvPzM3UX6jMzLsj4mpgjya3qfFkVlTCnKiUWVEJc9JO4/RhMTJz1G0AICL2Br4IPB74c+ClwF1UZzg5ITPv6HKfNcAagKmpqSefd955W8y/ctOdC25zvz12GPhyo9hmr+VGsc3Ftm2uVatWrc/M6W7z2pqVUT7Pg95mqaW8tktZ13zrG8ecDHq5Nv3fzqcNuRvHrIzDvrsNr+1S1jXf+oadk1KT8DwP8jG04flYKCudWlFoR8QjgC8Ab87Mj0fEFHArkMCpwIrMPGahdUxPT+fMzMwW00o/8QxyuVFss9dyo9jmYts2V0R0DXCbszLK53nQ2yw1yG+uL6Vt45iTQS/Xpv/b+bQhd+OYlXHYd7fhtV3KuuZb3zjmpHO5UqN4ngf5GNrwfMyXlbmKzjrSpIjYBvhn4IOZ+XGAzLw5M+/PzJ8D7wGeOso2qh3MikqYE5UyKyphTtSPRZ1He9AiIoCzgasz820d01fU46IADgeuGkX71B5mRSXMiUqZFZUYt5yM09jl5WKkhTbwDKrTB14ZEd+sp50MrI6I/akOyWwAjhtN89QiZkUlzIlKmRWVMCfqy0gL7cy8DIgus5bluSg1P7MyXOPaK2JOVMqsqMRyzsm4vg+0zcjHaEuSJEmTyEJbkiRJasCox2hLmnAefpQkLVf2aEuSJEkNsNCWJEmSGuDQEUlSKzjMSNKksUdbkiRJaoA92pKkJbEHWpIWZo+2JEmS1AALbUmSJKkBDh2RJEmNGuQwI4csaZzYoy1JkiQ1wEJbkiRJaoBDRyRJkjRx2jDMqLWFdkQcDJwJPBj4p8w8bcRNUkuZFZUyKyphTlTKrAxWGwrjQWvl0JGIeDDwTuAQ4HHA6oh43GhbpTYyKyplVlTCnKiUWVGJVhbawFOB6zLzu5n5X8B5wKEjbpPayayolFlRCXOiUmZFPbW10N4D+H7H7Y31NGkus6JSZkUlzIlKmRX1FJk56jZsJSJeBPx2Zr68vn0E8NTMfNWc5dYAa+qb+wLXDLWhatJjMnPXXguZlWWvKCdQlhVzMtHcp6iE+xSVKspKW78MuRHYq+P2nsBNcxfKzLOAs4bVKLWSWVGpnlkxJ8J9isq5T1FPbR06cjmwT0SsjIhtgZcAF424TWons6JSZkUlzIlKmRX11Moe7cy8LyKOBz5LdcqcczLzWyNullrIrKiUWVEJc6JSZkUlWjlGW5IkSRp3bR06IkmSJI01C21JkiSpARbakiRJUgMstCVJkqQGWGhLkiRJDbDQliRJkhpgoS1JkiQ1wEJbkiRJaoCFtiRJktQAC21JkiSpAUMptCPinIj4YURc1TFt54i4JCK+U//eqZ4eEfH2iLguIq6IiCcNo41qB7OiEuZEpcyKSpgTNWVYPdprgYPnTDsJuDQz9wEurW8DHALsU/+sAd41pDaqHdZiVtTbWsyJyqzFrKi3tZgTNWAohXZmfhG4fc7kQ4Fz67/PBQ7rmP6+rHwV2DEiVgyjnRo9s6IS5kSlzIpKmBM1ZZRjtKcyczNA/Xu3evoewPc7lttYT9PyZVZUwpyolFlRCXOivj1k1A3oIrpMy64LRqyhOmzD9ttv/+S99tqryXZpSFauXMkNN9xwf8GiZmUZMycqZVZUwpxoMa699tpbM3PXXsuNstC+OSJWZObm+pDLD+vpG4HOJO4J3NRtBZl5FnAWwPT0dM7MzDTZXg3Jhg0bWLly5b0dk8yKtmJOVMqsqIQ50WJExI0ly41y6MhFwFH130cBF3ZMP7L+Vu8BwJ2zh260bJkVlTAnKmVWVMKcqG9D6dGOiA8DBwG7RMRG4A3AacBHIuJlwPeAF9WLXww8D7gOuAc4ehhtVDusXr2adevWAWxnVjQfc6JSZkUlzImaEpldhxWNHQ/JTJaIWJ+Z002s26xMDnOiUmZFJcyJSpVmxStDSpIkSQ2w0JYkSZIaYKEtSZIkNcBCW5IkSWqAhbYkSZLUAAttSZIkqQEW2pIkSVIDLLQlSZKkBlhoS5IkSQ2w0JYkSZIaYKEtSZIkNcBCW5IkSWqAhbYkSZLUAAttSZIkqQEW2pIkSVIDHjLKjUfEvsD5HZMeC7we2BE4Frilnn5yZl485OapRcyKSpgTlTIrKmFO1K+RFtqZeQ2wP0BEPBjYBFwAHA2ckZlvGWHz1CJmRSXMiUqZFZUwJ+pXm4aOPAu4PjNvHHVD1HpmRSXMiUqZFZUwJ1q0kfZoz/ES4MMdt4+PiCOBGeCEzLxj7h0iYg2wBmBqaop169YtaoNXbrqz6/T99thhUevR0A01K/PlBMxKyw19n6KxZVZUwpxo0SIzR90GImJb4CbgNzPz5oiYAm4FEjgVWJGZxyy0junp6ZyZmVnUdvc+6dNdp2847fmLWo8GLyLWZ+Z0l+lDz8p8OQGzMmptyonazayohDlRqfmyMldbho4cAnw9M28GyMybM/P+zPw58B7gqSNtndrErKiEOVEps6IS5kRL0pahI6vpOBwTESsyc3N983DgqpG0qqWWeU+8WVEJc6JSZkUlzImWZOSFdkQ8DHgOcFzH5NMjYn+qQzIb5szTMmVWVMKcqJRZUQlzon4sqtCOiIcAv1HfvDoz7+u3AZl5D/DoOdOO6He9mjxmRSXMiUqZFZUwJ+rHgoV2RJwGfCAzr4qIxwMX8kDYbomI383Mq5tupAZnmQ87kSRJGppeX4Y8Bri2/vvtwLsyc8fM3BH4B+CdTTZOkiRJGle9Cu2HdiyzH/B3HfPOBJ7URKMkSZKkcddrjPYXqAb4nwlcDvwW8Ll63kHALY21TJJDfaQJ5f+2tDz0KrRfDVwSES8EbgQ+GRGXUX3L9gDgjxpunyRJkjSWFhw6kpk3UJ1l5APAvcA64G7gy8CTMvMTTTdQkiRJGkc9T++XmfdSXfXoPc03p136ObTnYUGNWq8MmtHB8zmVNEjuU8bfki/BHpVnDrIxkiRJ0qRYcqENbAt8flANkSRJkiZJrwvWHLnA7G0H3BbVPFSkUTODkiT1r9cY7fcC64GfdZkXg2+OJEmSNBl6FdrfAU7MzK2GiETEQ4F7GmmVpJ7m63UGe54lSWqDkgvW/Drdx2LfX8/XInlYfnnx9ZYkaXlasNDOzOMWmHcvsGrgLZIkSZImQM/zaDctIjZQXQTnfuC+zJyOiJ2B84G9gQ3AizPzjlG1Ue1gVlTCnGzNoyrdmRWVMCfqR6+zjuwLPDsz31nf/gxbnm3kjzPzmgG0Y1Vm3tpx+yTg0sw8LSJOqm+fOIDtaPyZFZUwJyplVibcgD5omhMtSa/zaJ8I/Ljj9tOBD9Y/36IKVhMOBc6t/z4XOKyh7Wj8mRWVMCcqZVZUwpyoSK9C+5nABR2378/MszPzbOBk4MABtCGBz0XE+ohYU0+byszNAPXv3QawHY0/s6IS5kSlzIpKmBMtWWTm/DMj7srMR3XcfkFmfnK++UtqQMTumXlTROwGXAK8CrgoM3fsWOaOzNypy33XAGsApqamnnzeeectattXbrqz6/T99tihaP5S193vdptcd1MWu91Vq1atz8zpzmmjysp8bV+o/SX3bypHveYPIkdNWsy225STxbZ9mJrK4CiN8z5lKe3X0rlPURO6ZaWbXoX2JuAZmbmhy7zHApdl5u79NHTOOk+hGqpyLHBQZm6OiBXAuszcd6H7Tk9P58zMzKK212vcVj/juha6b7/bbXLdTVnsdiNiwQAPMyv9nq962DnqNX8QOWrSYrbdppxAe7902FQGR2mc9ynQ3ud1ErlPURN6ZWVWr7OOfBo4FTiiy7w31vOXLCIeDjwoM++u/34u8NfARcBRwGn17wv72Y7Gn1lRieWaEy9etHjLNStaHHOifvUqtF8PfDkivkE1VvsHwAqqQf87AQf0uf0p4IKImG3LhzLzMxFxOfCRiHgZ8D3gRX1uR+PPrKhEq3Ni71SrtDorag1zor70umDNDyJiGvhz4BBgF+A24GLgbZl5Wz8bz8zvAk/oMv024Fn9rFuTxayohDlRKbOiEuZE/ep5wZrMvB14Xf0jSZIkqUCvC9bsADwhM79Y3z55zn3e0eYrITV5mNZDwJplFiRJUje9erRfA9wHfLG+fTIPnFd7JbAN8FfNNE2SNAx+WJSkZvQqtA8HDu64fW9mHgEQEXtSjdW20JakZcoiXZLm16vQ3j0zN3bcPmv2j8zcWBfbmhC+YQ6fz7lmmQWVMivS+Oj5ZciI2DUzbwHIzBM7pzfZMEmSJGkYmvoA+6Ae878EHD3PvKOBr/S1dUmSJGlC9erRfiPw+YjYHfg4D1yw5oXAMcBvNds8SZIkaTz1KrS/TnW50dOB46l6wH8OfA04ODMvb7Z5kiRpUBzfLQ1Xr0J7E/B+4JXA9VSXXb8jM+9pumGSJEnSOOtVaL8C+CPg34CrgbXAhwALbU0Ue3kkqb99oftRaWsLFtqZeSFwYUTsCPwBcARwekR8DjgXuCgz722+mZK0vFnESNL46XXWEQAy80eZ+e7MPBD4DWAGOAPY3GTjJEmSpHFVVGjPiojtgKcATwOmgCubaJQkSZI07npesAYgIg4EjgReDPyQ+guSmXljg22TJGnZcZiQNDkWLLQj4hSqcdk7Ax8Fnp+ZXxrUxiNiL+B9wC9RnTbwrMw8s97uscAt9aInZ+bFg9quxo9ZUQlzolJmZfGW4wcAc6J+9erRPgB4LfCJzPxpA9u/DzghM78eEY8E1kfEJfW8MzLzLQ1sU+PJrKhEozlZjoXGBHOfohLmRH3pddaRg5vceGZupv5CZWbeHRFXA3s0uU2NJ7OiEuZEpSY1K239MNjWdvUyzjkZ1+d80izqy5BNioi9gSdSXXUS4PiIuCIizomInUbWMLWOWVEJc6JSZkUlzImWIjJz1G0gIh4BfAF4c2Z+PCKmgFuBBE4FVmTmMV3utwZYAzA1NfXk8847b4v5V266s+v29ttjh77nj+q+bW7XQhZ731WrVq3PzOm500eRlfnm9Zo/qtdrGO1q0mK23aac9Dt/XLMwqqy0fZ/ST/uX4/tLk9q8T+m37U0+56N8zUZhUPuUuUZeaEfENsCngM9m5tu6zN8b+FRmPn6h9UxPT+fMzMwW03odNuln/qju2+Z2LWSx942IrQI8qqzMN6/X/FG9XsNoV5MWs+025aTf+eOahVFlZZz3Kb3mL8f3lya1eZ/Syyif8+U29GQQ+5RuRjp0JCICOBu4ujO8EbGiY7HDgauG3Ta1i1lRCXOiUmZFJcyJ+lV0Hu0GPYPq9IFXRsQ362knA6sjYn+qQzIbgONG0zwtRsOffs2KSpgTlTIrKmFO1JeRFtqZeRkQXWZ5LkptwayohDlRKbOiEuZE/Rp1j7YkSZLGyHIbv90PC21JkiQNRVu/YN2U1pxHW5IkSZok9mhLkiQtM5PWc9xWFtqSFs0dtCRJvVloS5IkjUCvi01p/FloS5IkaWA86vkAvwwpSZIkNcBCW5IkSWqAQ0ckDdxChw09pLh8OP5U0nJnj7YkSZLUAAttSZIkqQEOHZEktZLDjCSNO3u0JUmSpAbYoy1Jkkaqn6MXHvlQm7W20I6Ig4EzgQcD/5SZp424SWops6JSZqVd2logmROVMivqpZWFdkQ8GHgn8BxgI3B5RFyUmf8x2papbcyKSpkVlTAn7dTGD2VmZTTG7ehHKwtt4KnAdZn5XYCIOA84FDC8msusqJRZUQlzolJmpWVa+YEsM0e28flExO8DB2fmy+vbRwBPy8zj5yy3BlhT39wXuGaoDVWTHpOZu/ZayKwse0U5gbKsmJOJ5j5FJdynqFRRVtraox1dpm31iSAzzwLOar45ajGzolI9s2JOhPsUlXOfop7aenq/jcBeHbf3BG4aUVvUbmZFpcyKSpgTlTIr6qmthfblwD4RsTIitgVeAlw04japncyKSpkVlTAnKmVW1FMrh45k5n0RcTzwWapT5pyTmd8acbPUQmZFpcyKSpgTlTIrKtHKL0NKkiRJ466tQ0ckSZKksWahLUmSJDXAQluSJElqgIW2JEmS1AALbUmSJKkBFtqSJElSAyy0JUmSpAZYaEuSJEkNsNCWJEmSGmChLUmSJDVgKIV2RJwTET+MiKs6pu0cEZdExHfq3zvV0yMi3h4R10XEFRHxpGG0Ue1gVlTCnKiUWVEJc6KmDKtHey1w8JxpJwGXZuY+wKX1bYBDgH3qnzXAu4bURrXDWsyKeluLOVGZtZgV9bYWc6IGDKXQzswvArfPmXwocG7997nAYR3T35eVrwI7RsSKYbRTo2dWVMKcqJRZUQlzoqY8ZITbnsrMzQCZuTkidqun7wF8v2O5jfW0zXNXEBFrqD5Nsv322z95r732arbFGoqVK1dyww033N8xyaxoK+ZEpcyKSpgTLca11157a2bu2mu5URba84ku07Lbgpl5FnAWwPT0dM7MzDTZLg3Jhg0bWLly5b0Fi5qVZcycqJRZUQlzosWIiBtLlhvlWUdunj3UUv/+YT19I9D5kW9P4KYht03tYlZUwpyolFlRCXOivo2y0L4IOKr++yjgwo7pR9bf6j0AuHP20I2WLbOiEuZEpcyKSpgT9W0oQ0ci4sPAQcAuEbEReANwGvCRiHgZ8D3gRfXiFwPPA64D7gGOHkYb1Q6rV69m3bp1ANuZFc3HnKiUWVEJc6KmRGbXYUVjx7FPkyUi1mfmdBPrNiuTw5yolFlRCXOiUqVZ8cqQkiRJUgMstCVJkqQGWGhLkiRJDbDQliRJkhpgoS1JkiQ1wEJbkiRJaoCFtiRJktQAC21JkiSpARbakiRJUgMstCVJkqQGWGhLkiRJDbDQliRJkhpgoS1JkiQ1wEJbkiRJasBDRrnxiNgXOL9j0mOB1wM7AscCt9TTT87Mi4fcPLWIWVEJc6JSZkUlzIn6NdJCOzOvAfYHiIgHA5uAC4CjgTMy8y0jbJ5axKyohDlRKbOiEuZE/WrT0JFnAddn5o2jbohaz6yohDlRKbOiEuZEi9amQvslwIc7bh8fEVdExDkRsdOoGqVWMisqYU5UyqyohDnRoo106MisiNgW+F3gL+tJ7wJOBbL+/VbgmC73WwOsAZiammLdunXF27xy051bTdtvjx0W13AN3SiyovFjTlTKrKiEOdFSRWaOug1ExKHAn2Tmc7vM2xv4VGY+fqF1TE9P58zMTPE29z7p01tN23Da84vvr2ZFxPrMnO4yfehZUXuZE5UyKyphTlRqvqzM1ZahI6vpOBwTESs65h0OXDX0FqmtzIpKmBOVMisqYU60JCMfOhIRDwOeAxzXMfn0iNif6pDMhjnztEyZFZUwJyplVlTCnKgfIy+0M/Me4NFzph0xouaoxcyKSpgTlTIrKmFO1I/ioSMR8ciImIqIaLJBkiRJ0iRYsEe7Pjn766i+SbtnPfneiFgPvD4zL224fWNtGF+49EudUjv4vyhJmqvX0JEzgScDJ1H1fr+K6opIdwDvjYi/yMzzmm2iJEmSNH56FdqrgV/LzNsAIuJS4LLM/NWIuAJ4L2ChLUmSJM3Rq9D+KXBfx+37gIcCZOZXI2Kvphqm/ngYW5IkabR6FdofAS6KiLcDAfwv4OPwi3NI3tJs8yRJkqTx1KvQPpHqcqMnURXa/wK8uZ4XwJHNNU2SJEkaXwsW2pn5X8Ab65+5824CbmqoXWNjKUM05ruPwz3aZ9xek3Frr7Tc+D+qUmZlMiz5EuxReeYgGyNJkiRNiiUX2sC2wOcH1RBJkiRpkvS6YM1CY7C3HXBbNAQeimofL2wkSdJk6vVlyPcC64GfdZnnpdglSZKkefQqtL8DnJiZWw0RiYiHAvc00ipJkiRpzPUqtL8A/Drdx2LfX8+XNGSTPhRk0h+fJGl56HV6v+MWmHcvsGrgLZIkSZImQK8e7cZFxAbgbqoe8vsyczoidgbOB/YGNgAvzsw7RtVGtYNZUQlzolJmRSXMifrR66wj+wLPzsx31rc/w5ZnG/njzLxmAO1YlZm3dtw+Cbg0M0+LiJPq2ycOYDsaf2ObFYdDDNXY5kTlBvQ/ZVZUwpxoSUouwd45DvvpwJ/Vf+9PFayjG2jXocBB9d/nAuswwOrOrKiEOVEpszKBGujoMCcq0uuCNc8ELui4fX9mnp2ZZwMnAwcOoA0JfC4i1kfEmnraVGZuBqh/7zaA7Wj8mRWVMCcqZVZUwpxoySIz558ZcVdmPqrj9gsy85PzzV9SAyJ2z8ybImI34BLgVcBFmbljxzJ3ZOZOXe67BlgDMDU19eTzzjuveLtXbrpzq2n77bHDotu/0Hrmmzeo6YPcxjAsZturVq1an5nTndNGkZWlPF/DeE3G6XVfitL2tiUni2lzP4ax3xony2Wfov65T9GgdctKN70K7U3AMzJzQ5d5jwUuy8zd+2nonHWeAvwYOBY4KDM3R8QKYF1m7rvQfaenp3NmZqZ4W4M6jLTQeuabN6jpg9zGMCxm2xGxYICHlZWlPF/DeE3G6XVfitL2tiUni2lzP4ax3xony2Wfov65T9Gg9crKrF5DRz4NnDrPvDfW85csIh4eEY+c/Rt4LnAVcBFwVL3YUcCF/397d8tLM4UAAAcKSURBVBdqWVnHcfz7R0yIxBTlNM5IMxfSTWHRSQW7mSKa6KIs7OViAq2miOmmgmR6oYhuhlAiunAgoYI4FFgOKlkXkWhRM1o0mggmvYwp1fRCEIFD/y72Hjyvez37rLX2etnfDwyctdY+53mG9dvP+u/nPGetOu1o+JYxK/tvv3/LP822jDnR7pgVlTAnqqvqjyE/D/wsIn7FZK3288Ae4J3A5cCNNdtfAb4fERf68p3M/GFEnAK+GxEfBP4I3FKzHQ2fWVEJc6JSZkUlzIlqqXpgzfMRsQp8AngbcCVwDngAuCMzz9VpPDOfAa7bZv854M11frbGxayoxBhy0uRSIu1sDFlR+8yJ6qp8YE1m/h347PSfJMZT2Izl/6HteX4lqVtVD6y5DLguMx+abh/b9D1f80lIkiRJGro2JieqZrQ/BZwHHppuH+PF+2ofAC4GPlerB5K0hJxtlqTxqyq0bwYOrdt+ITMPA0TEPiZrtS20JWkHFtSStLyqbu93dWaeXbd94sIX0/37WumVJEmSNHBVhTYRcdWFrzPz09vtlyRJkrRR1dKRR4BbgePbHLsV+HnjPerYWJ6sJ0m74VjXP54TabiqCu0vAj+JiKuBe3jxgTXvAm4D3tRu9yRJkqRhqiq0H2PyuNHjwFEmS03+B/wCOJSZp9rtnqSuOIsmLY7vN2mcqgrtZ4FvAx8Dfsfksev/yMz/tN0xSZIkaciq/hjyo0zul/1L4GHg3cBL2+6UJEmSNHQzZ7Qz817g3oh4OfBe4DBwPCJ+BHwTOJmZL7TfTUlSl1zaIEnzq1o6AkBm/hO4C7grIg4wKbjvnO67sr3u1eOFQZK0bJq89nkdleqpvI/2ehFxCfAG4AZgBTjTRqckSZKkoSua0Y6INwIfAN4D/IXpH0hm5h/qNB4R1wDfAl7B5G4mJzLzqxHxBeDDwF+nLz2WmQ/UaUvDZlZUwpyo1BiyMu9ss7PT8xtDTtStmYX2NEiHgSuA7wFvz8xHGmz/PPDJzHwsIi4FHo2IH0+P3ZmZX2mwLQ1bq1nxAjQajikqZVZUYjA58TrWT1Uz2jcCnwF+kJn/bbrxzHwOeG769b8j4klgb9PtaPjMikqYk8Ub6sXdrKiEOVFdM9doZ+ahzFxro8jeLCL2A69j8jAcgKMR8ZuIuDsiLm+7fQ2HWVEJc6JSZkUlzIl2IzKz6z4QES8Dfgp8OTPviYgV4G9AAl8C9mTmbdt83xHgCMDKysrr19bWNhw/8+y/trT1mr2XzTzW1P4xtdGUedo4ePDgo5m5unn/orPS13PSZRuLUNr2onOymz6P5bx3OXbspM9jyrx97vq8z9vfoRnTmNLU67VVE2PKZp0X2hFxMXAf8GBm3rHN8f3AfZn56lk/Z3V1NU+fPr1h36xfae50rKn9Y2qjKfO0ERFbAtxFVvp6TrpsYxFK2150TnbT57Gc9y7Hjp30eUyZt89DO+9DM6YxpanXa6u6Y8p2iu460paICOAbwJPrwxsRe6brogBuBh7von/qD7OiEuZEpcyKSvQxJxbUw9JpoQ3cxOSuJmci4tfTfceA90fEa5n8Sub3wEe66Z5mWfCb3ayoRKs58QI3Ko4pKmFOVEunhXZmPgzENoe8F6U2MCsqYU5UyqyohDlRXXM9GVKSJElSma6XjmgA/HW5JEnD1LebHSwbZ7QlSZKkFjijLUmS5uIM5jh4Httnoa3G+caVJEmy0JYkSdI6Tpg1x0JbkqSGWahIAgttSXOygJC0E8cHaSMLbUmSJFXyg9T8LLQlSVLrLNK0jCy0JXXGC6+kZeYYOH4W2pKkXrMYkYZr2d+/PhlSkiRJaoEz2pIasdOsxbLPZkiStlqWa4OFtiSpcctyEVW/mLvhG9s57O3SkYg4FBFPRcTTEXF71/1Rf5kVlTIrKmFOVMqsqEovZ7Qj4iLg68BbgLPAqYg4mZm/7bZn6huzolJmpT/6vMzInKiUWVGJXhbawPXA05n5DEBErAHvAAyvNjMrKmVWVMKcLNi8H7z68IFsyqwM1CIz1NdCey/wp3XbZ4EbOuqL+s2sqJRZUQlzolJmZYFmFcd9/lAWmbnQBktExC3AWzPzQ9Ptw8D1mfnxTa87AhyZbr4KeGqhHVWbXpmZV1W9yKwsvaKcQFlWzMmoOaaohGOKShVlpa8z2meBa9Zt7wP+vPlFmXkCOLGoTqmXzIpKVWbFnAjHFJVzTFGlvt515BRwbUQciIiXAO8DTnbcJ/WTWVEps6IS5kSlzIoq9XJGOzPPR8RR4EHgIuDuzHyi426ph8yKSpkVlTAnKmVWVKKXa7QlSZKkoevr0hFJkiRp0Cy0JUmSpBZYaEuSJEktsNCWJEmSWmChLUmSJLXAQluSJElqgYW2JEmS1AILbUmSJKkF/wfaUMtKE34gMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 32\n",
    "identifieer = 'RADAM'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 128\n",
    "identifieer = 'RADAM'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 512\n",
    "identifieer = 'RADAM'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Decent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 8\n",
    "identifieer = '0'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 32\n",
    "identifieer = '0'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 128\n",
    "identifieer = '0'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 512\n",
    "identifieer = '0'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decaying Learning Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0/xB/BPWXazuH3gyg82NkMvM/gLdQvVe3AwcAizn6s9C+6D3AJ6m+/PxV5nwHYUC6bdtOodqffDmqsxf9/1QfjuazX73M96leh7/Lbc+bvDfV9x/+eM72+GeXsC+bKPX26w+otuWbqToEhnKe+5oZmCszvXjx0uCF6gPZrcCho26Ll0W9b6+k+rD+I6qieR2wZ8f8x1D13N8PXA38L2BTx/w3AfdQFedvpeoJ/7163iuAz89Z3/ZUR0PuoRojPrOOv6L60uT9VGPaX1PPO6RzfR2PczjV2OvvUfVifwjYqZ43DXyNqmPiwvryl/M8/23a2DFv7cz95raDqlPiO/X6X1dPe1b9/O+h+vBxGfCz9bx1M69LfXtFvex99WOsA54yt011Gx6i2hnPXP6+nrcz8C6qAuO++jm/bNSZ8uLFy/K7RGZbhghLk6PudfwK1aH//001fGTf7PKlN7VfRJxA1cv9nKzOBCRJUk9DGToSEedFxB0RcW3HtCdExBVR/SjLFfWYsZkfLHh7RNwYEVdHdboqLRMTlJX/RnV6t7uohmccbZE9OMPOSWaeRzU06tmDexYahgnapqhB5kRNGdYY7bVUhzM7nQpcmdUZD67kkS9mHEE1Pmc/qvOqvmtIbVQ7rGUCspKZp2fmEzNzp8x8VmZ+ZdRtmjBrGXJOMvN9+cjpKTU+1jIB2xQ1bi3mRA0YSqGdmZ+jGpvX6SiqHxWg/nt0x/R/zMqXgV2iy69iaTKZFZUwJyplVlTCnKgpozzryFRW3w6n/jtzUv29mH2+0031NC1fZkUlzIlKmRWVMCfqWxt/sKbbifm7fmMzItZQHbZhxx13fPo+++zTbTGNmVWrVnHzzTdvczqvLszKMmZOVMqsqIQ50WLccMMNd2Vmzx8DGmWhfXtErMjMLfUhlzvq6ZuY/etVe1OdGm0bmXkOcA7A9PR0rl+/vsn2akg2btzIqlWrHuqYZFa0DXOiUmZFJcyJFiMiev2yLjDaoSOXAsfV148DLumYfmz9rd6DgPtmDt1o2TIrKmFOVMqsqIQ5Ud+G0qMdER+k+lGD3SJiE/AGql8VvCgiXkn14wYvqRe/HHgB1S/5PEj1q41aJlavXs26desAdjArmo85USmzohLmRE2ZmB+s8ZDMZImIDZk53cRjm5XJYU5UyqyohDlRqdKsjHLoiCRJkjSxLLQlSZKkBlhoS5IkSQ2w0JYkSZIaYKEtSZIkNcBCW5IkSWqAhbYkSZLUAAttSZIkqQEW2pIkSVIDLLQlSZKkBlhoS5IkSQ2w0JYkSZIaYKEtSZIkNcBCW5IkSWqAhbYkSZLUgEeNcuURsT9wYcekfYHXA7sArwLurKeflpmXD7l5ahGzohLmRKXMikqYE/VrpIV2Zn4TOBAgIrYDNgMXA8cDZ2fmm0fYPLWIWVEJc6JSZkUlzIn61aahI4cBN2XmLaNuiFrPrKiEOVEps6IS5kSL1qZC+2XABztunxQRV0fEeRGx66gapVYyKyphTlTKrKiEOdGijXToyIyI2B74TeBP60nvAs4Asv77FuCELvdbA6wBmJqaYt26dbPmX7P5vqL1H7DXzktruIauqaxospgTlTIrKmFOtFSRmaNuAxFxFPDqzHx+l3krgY9n5lMXeozp6elcv379rGkrT72saP0bzzyytKkakojYkJnTXaY3khWNJ3OiUmZFJcyJSs2XlbnaMnRkNR2HYyJiRce8FwHXDr1FaiuzohLmRKXMikqYEy3JyIeORMRjgecBJ3ZMPisiDqQ6JLNxzjwtU2ZFJcyJSpkVlTAn6sfIC+3MfBB44pxpx4yoOWoxs6IS5kSlzIpKmBP1Y8lDRyJi+4j49iAbI0mSJE2KfsZoB7ByQO2QJEmSJsqCQ0ci4uGFZlONTZIkSZI0R68x2vdQnRfyP7rM2wG4ZuAtkiRJkiZAr0J7A7BbZt40d0ZE7EDVqy1JkiRpjl6F9snAQ91mZOaPI2LV4JskSZIkjb8FC+3MvK7H/FsG2xxJkiRpMvRzer+IiOcOsjGSJEnSpOjn9H7bA58ZVEMkSZKkSdLr9H7HLjB7+wG3RZIkSZoYvb4M+V6qM4/8uMs8zzgiSZIkzaNXof0t4JTM3GaISEQ8BniwkVZJkiRJY67XGO3PAr84z7yH6/mSJEmS5uh1er8TF5j3EHDowFskSZIkTYBeQ0caFxEbgQeoesi3ZuZ0RDwBuBBYCWwEXpqZ946qjWoHs6IS5kSlzIpKmBP1Y8GhIxGxf0S8uuP2JyLi0x2X/QfUjkMz88DMnK5vnwpcmZn7AVfWtyUwKypjTlTKrKiEOdGS9BqjfQrw/Y7bzwbeX1+uo7lgHQWcX18/Hzi6ofVo/JkVlTAnKmVWVMKcqEivQvu5wMUdtx/OzHMz81zgNODgAbQhgU9FxIaIWFNPm8rMLQD13z0GsB6NP7OiEuZEpcyKSpgTLVmvMdp7ZOb9Hbd/8gM2mflAREwNoA3PycxbI2IP4IqI+EbpHevArwGYmppi3bp1s+affMDWoseZez+1VmNZuWbzfT0f44C9dl5UYzUyjeVEE8esqIQ50ZL1KrQfiIiVmbkRIDM/NjMjIvZl9rCSJcnMW+u/d0TExcAzgdsjYkVmbomIFcAd89z3HOAcgOnp6TzkkENmzX/FqZcVtWHj7x7ScxmN3qizYk7GQ5M50WQxKyphTtSPXkNHLgPOmGfeG+v5SxYRj4uInWauA88HrgUuBY6rFzsOuKSf9Wj8mRWVMCcqZVZUwpyoX716tF8PfDEivkY1Vvs2YAXVoP9dgYP6XP8UcHFEzLTlA5n5iYi4CrgoIl4JfAd4SZ/rWRZWlvTKnnnkEFrSCLOiEuZEpcyKSpgT9aXXD9bcFhHTwGuBI4DdgLuBy4G3Zubd/aw8M78NPK3L9LuBw/p5bE0Ws6IS5kSlzIpKmBP1q+cP1mTmPcCf1xdJY67kyAeM9dEPSZJaYcFCOyJ2Bp6WmZ+rb5825z7v8JeQNJ8JH8oiSZK0oF492q8DtgKfq2+fxiPn1V4FPBr4i2aaJkmSJI2vXoX2i4DDO24/lJnHAETE3lRjtS20JWkCOcxIkvrTq9DeMzM3ddw+Z+ZKZm6qi21JWjKLOUnSpOp1Hm0iYveZ65l5SrfpkiRJkmbrVWh/ATh+nnnHA18abHMkSZKkydBr6Mgbgc9ExJ7AR3jkB2teDJwA/FqzzRuuxR7Cbnp5Se3i//BojdOZjMaprZKa06tH+6tUPzf6dODTwDeAK4Fp4PDMvKrZ5kmSJEnjqVeP9mbgfcAfADdR/ez6vZn5YNMNkyRJksZZr0L794GXA/8GXA+sBT4AWGgPgYepJUmSxteChXZmXgJcEhG7AL8DHAOcFRGfAs4HLs3Mh5pvpqSFNDke1A98kiQtTc/T+wFk5vcy892ZeTDwZGA9cDawpcnGSZIkSeOq19CRWSJiB+AZwLOAKeCLTTRKw+G34iVJkppTVGhHxMHAscBLgTuovyCZmbc02DZJkjQG2tRx43A3tcmCQ0ci4vSIuAn4WD3pyMz8hcw8YxBFdkTsExGfiYjrI+K6iPijjvVujoiv15cX9LsujTezohLmRKXMikqYE/WrV4/2QcCfAR/NzB81sP6twMmZ+dWI2AnYEBFX1PPOzsw3N7BOjadllZU29Q6NmWWVk8Wyp28Ws6IS5kR96XXWkcObXHlmbqH+QmVmPhAR1wN7NblOjSezohLmRKXMiko0nRM//E6+orOODENErAR+GfhKPemkiLg6Is6LiF1H1jC1jllRCXOiUmZFJcyJliIyc9RtICIeD3wWeFNmfiQipoC7gATOAFZk5gld7rcGWAMwNTX19AsuuGDW/Gs231e0/gP22nnZLD+zbBuX73TooYduyMzpudNHmZX52lqiTa910zlcrH4ev405ma+tbdC2936x3Ka0V5uer9sUDcN8WZlr5IV2RDwa+Djwycx8a5f5K4GPZ+ZTF3qc6enpXL9+/axpiz0ksxyW7zz81LblO0XENgEedVb6OXTXpte6DTnsZ/lObczJfG1tg2G+N01wm9JebXq+blM0DN2y0s2izqM9aBERwLnA9Z3hjYgV9bgogBcB146ifWoPs6IS5kSlzMrC2lQ4j5I5Ub9GWmgDz6H6WfdrIuLr9bTTgNURcSDVIZmNwImjaZ5axKyohDlRKbOiEuZEfRlpoZ2Znweiy6zLh90WtZtZUQlzolJmRSXMyWC1bajMMI7ctOasI5IkSdIkGfXQEWksOX5RJZruvWlb75AkaTYLbUnSSPiBVdKkc+iIJEmS1AB7tCWpJRwKIkmTxR5tSZIkqQH2aEuSBsIx1+3le6MmeBSuNwttSZJGbLGFsIWzNB4stCVJUmvZa6px5hhtSZIkqQH2aEuSpKGxh1rLiYW2JEnSGFjshxQ/1IyeQ0ckSZKkBtijLUmSpNb1gLetPUthj7YkSZLUgNb2aEfE4cDbgO2Af8jMM0fcJLWUWVEps6IS5kSlzEq7tLEHvJU92hGxHfBO4AjgKcDqiHjKaFulNjIrKmVWVMKcqJRZUYlWFtrAM4EbM/PbmfmfwAXAUSNuk9rJrKiUWVEJc6JSZkU9RWaOug3biIjfBg7PzN+rbx8DPCszT5qz3BpgTX1zf+CbQ22omvSkzNy910JmZdkrygmUZcWcTDS3KSrhNkWlirLS1jHa0WXaNp8IMvMc4Jzmm6MWMysq1TMr5kS4TVE5tynqqa1DRzYB+3Tc3hu4dURtUbuZFZUyKyphTlTKrKinthbaVwH7RcSqiNgeeBlw6YjbpHYyKyplVlTCnKiUWVFPrRw6kplbI+Ik4JNUp8w5LzOvG3Gz1EJmRaXMikqYE5UyKyrRyi9DSpIkSeOurUNHJEmSpLFmoS1JkiQ1wEJbkiRJaoCFtiRJktQAC21JkiSpARbakiRJUgMstCVJkqQGWGhLkiRJDbDQliRJkhpgoS1JkiQ1YCiFdkScFxF3RMS1HdOeEBFXRMS36r+71tMjIt4eETdGxNUR8SvDaKPawayohDlRKbOiEuZETRlWj/Za4PA5004FrszM/YAr69sARwD71Zc1wLuG1Ea1w1rMinpbizlRmbWYFfW2FnOiBgyl0M7MzwH3zJl8FHB+ff184OiO6f+YlS8Du0TEimG0U6NnVlTCnKiUWVEJc6KmPGqE657KzC0AmbklIvaop+8FfLdjuU31tC1zHyAi1lB9mmTHHXd8+j777NNsizUUq1at4uabb364Y5JZ0TbMiUqZFZUwJ1qMG2644a7M3L3XcqMstOcTXaZltwUz8xzgHIDp6elcv359k+3SkGzcuJFVq1Y9VLCoWVnGzIlKmRWVMCdajIi4pWS5UZ515PaZQy313zvq6ZuAzo98ewO3DrltahezohLmRKXMikqYE/VtlIX2pcBx9fXjgEs6ph9bf6v3IOC+mUM3WrbMikqYE5UyKyphTtS3oQwdiYgPAocAu0XEJuANwJnARRHxSuA7wEvqxS8HXgDcCDwIHD+MNqodVq9ezbp16wB2MCuajzlRKbOiEuZETYnMrsOKxo5jnyZLRGzIzOkmHtusTA5zolJmRSXMiUqVZsVfhpQkSZIaYKEtSZIkNcBCW5IkSWqAhbYkSZLUAAttSZIkqQEW2pIkSVIDLLQlSZKkBlhoS5IkSQ2w0JYkSZIaYKEtSZIkNcBCW5IkSWqAhbYkSZLUAAttSZIkqQEW2pIkSVIDHjXKlUfE/sCFHZP2BV4P7AK8Criznn5aZl4+5OapRcyKSpgTlTIrKmFO1K+RFtqZ+U3gQICI2A7YDFwMHA+cnZlvHmHz1CJmRSXMiUqZFZUwJ+pXm4aOHAbclJm3jLohaj2zohLmRKXMikqYEy1amwrtlwEf7Lh9UkRcHRHnRcSuo2qUWsmsqIQ5USmzohLmRIs20qEjMyJie+A3gT+tJ70LOAPI+u9bgBO63G8NsAZgamqKdevWzZp/zeb7FlzvAXvt3F/DNXRNZUWTxZyolFlRCXOipYrMHHUbiIijgFdn5vO7zFsJfDwzn7rQY0xPT+f69etnTVt56mULrnfjmUcutqkakojYkJnTXaY3khWNJ3OiUmZFJcyJSs2XlbnaMnRkNR2HYyLzlh2UAAAgAElEQVRiRce8FwHXDr1FaiuzohLmRKXMikqYEy3JyIeORMRjgecBJ3ZMPisiDqQ6JLNxzjwtU2ZFJcyJSpkVlTAn6sfIC+3MfBB44pxpx4yoOWoxs6IS5kSlzIpKmBP1oy1DRyRJkqSJsmChHRGPj4i/iYiPR8RhEbFfRGyIiHsj4kOezkaSJEnqrleP9juA3YC7gY9SfRngD4AXArsDf9Vo6yRJkqQx1WuM9hHAvlQF+THAezJzC0BEHAf8a7PNkyRJksZTr0L7MfWXAIiI+2eKbIDMvMWhI5IkSVJ3vYaO3BYRT6ivz/p1l4jYB/heI62SJEmSxlyvQvsNwI4AmfmFOfN+FTi/iUZJkiRJ427BoSOZeeEC8z4w+OZIo7Hy1MsWnL/xzCMXnC9JkjTXks+jHZXnDrIxkiRJ0qTo5wdrtgc+M6iGSJIkSZNkwaEjEXHsArO3H3BbJEmSpInR6/R+7wU2AD/uMi8G3xxJkiRpMvQqtL8FnJKZ2wwRiYjHAA820ipJkiRpzPUao/1Z4BfnmfdwPV+SJEnSHL1O73fiAvMeAg4deIskSZKkCdBr6EjjImIj8ABVD/nWzJyuf43yQmAlsBF4aWbeO6o2qh3MikqYE5UyKyphTtSPBYeORMT+EfHqjtufiIhPd1z2H1A7Ds3MAzNzur59KnBlZu4HXFnflsCsqIw5USmzohLmREvSa4z2KcD3O24/G3h/fbmO5oJ1FI/8vPv5wNENrUfjz6yohDlRKbOiEuZERXoV2s8FLu64/XBmnpuZ5wKnAQcPoA0JfCoiNkTEmnraVGZuAaj/7jGA9Wj8mRWVMCcqZVZUwpxoyXqN0d4jM+/vuP2TH7DJzAciYmoAbXhOZt4aEXsAV0TEN0rvWAd+DcDU1BTr1q2bNf/kA7YueP+Z5a/ZfN+Cyx2w186LWk6NaXVWJun9H/OsN5YTTRyzohLmREsWmTn/zIjNVAHb2GXevsDnM3PPgTUm4nSqoSqvAg7JzC0RsQJYl5kLjgefnp7O9evXz5q28tTLFlzfxjOPbGQ59S8iNnSMhes2/3RalpVJev/HJevDzonGl1lRCXOiUr2yMqPX0JHLgDPmmffGev6SRcTjImKnmevA84FrgUuB4+rFjgMu6Wc9Gn9mRSXMiUqZFZUwJ+pXr6Ejrwe+GBFfoxqrfRuwgmrQ/67AQX2ufwq4OCJm2vKBzPxERFwFXBQRrwS+A7ykz/Vo/JmVCTegXnRzolJmRSXMifrS6wdrbouIaeC1wBHAbsDdwOXAWzPz7n5WnpnfBp7WZfrdwGH9PLYmi1lRCXOiUmZFJcyJ+tXzB2sy8x7gz+uLJEmSpAILFtoRsTPwtMz8XH37tDn3eYe/hNQOJV/SG9UX3JbLFwg1+cblS6KSpHbo1aP9OmAr8Ln69mk8cl7tVcCjgb9opmmSpDbzg4ckLaxXof0i4PCO2w9l5jEAEbE31VhtC21JkiRpjl6n99szMzd13D5n5ko9fe9GWiVJkiSNuV6FNhGx+8z1zDyl23RJkiRJs/UaOvIF4HjgrC7zjge+NPAWjblBj1l0DKQkjR+33ZKgd6H9RuAzEbEn8BEe+cGaFwMnAL/WbPMkSZKk8dSr0P4q1c+NngWcRDXU5L+ArwCHZ+ZVzTZPkqTZPGWopHHRq9DeDLwP+APgJqqfXb83Mx9sumEaHQ95SpIk9a9Xof37wMuBfwOuB9YCHwAstCVpgtlrLEn9W7DQzsxLgEsiYhfgd4BjgLMi4lPA+cClmflQ882UNAgerZAkaXh69WgDkJnfA94NvDsiVlEV3GfX03ZrrnmTy4JncpW+t2ZAkqTJ1vM82p0iYgfgGcCzgCngmiYaJUmSJI27oh7tiDgYOBZ4KXAH9RckM/OWBtsmqeUcxyuprTxqqDZYsNCOiNOphok8AfgQcGRmfmFQK4+IfYB/BH6G6rSB52Tm2+r1vgq4s170tMy8fFDr1fgxKwtzh1IxJyplVlTCnKhfvXq0DwL+DPhoZv6ogfVvBU7OzK9GxE7Ahoi4op53dma+uYF1ajyZFZUwJyplVoZojDsDzIn60uusI4c3ufLM3AJsqa8/EBHXA3s1uU6NJ7OiEuZEpcyKSjSdkzH+AKJCkZmjbgMAEbES+BzwVOC1wCuA+4H1VJ8m7+1ynzXAGoCpqamnX3DBBbPmX7P5vgXXecBeOw98uVGss9dyTa2zVEnb5jr00EM3ZOZ0t3ltzcoo3tuZ5dqcu1JLyd045mQpy43CILM+aJO0TVkuRvF6TNI2Re2zUFY6taLQjojHA58F3pSZH4mIKeAuIIEzgBWZecJCjzE9PZ3r16+fNW3Qp1krWW4U6+y1XFPrLLWUL8xFRNcAtzkro3hvZ5Zrc+5KLSV345iTpSw3CoPM+qBN0jZluRjF6zFJ2xS1z3xZmavorCNNiohHA/8MvD8zPwKQmbd3zH8P8PERNU8tYlbG07B3JOZEpcyKSpgT9WOkhXZEBHAucH1mvrVj+op6XBTAi4BrR9E+LWyYBZRZUYlJzYmnURy8Sc2KBsucqF+j7tF+DtXpA6+JiK/X004DVkfEgVSHZDYCJ46meWoRs6IS5kSlzMoALIOhD+ZEfRlpoZ2ZnweiyyzPRalZzIpKmJN2amMxNk5ZaePrt1yMU060dE0eNRx1j7YkaYgs2iRpeH5q1A2QJEmSJpE92pI0AeyplqT2sdCWJGlERn0ueqlNJjHDFtqSpFaYxJ1sG/k6S8NjoS1JkjQB2vwhqs1ta5KFtiRJ2oY/lCT1z7OOSJIkSQ2wR1uSJEmzLNehHoNmj7YkSZLUAHu0JUmSlhFPKzk8FtqSJEktZjE7viy0JUlSK5T0tFp0apw4RluSJElqQGt7tCPicOBtwHbAP2TmmSNuklrKrKiUWVEJc6JSZmU0xumoRisL7YjYDngn8DxgE3BVRFyamf8x2papbcyKSpkVlTAnKmVW2q8NBXlbh448E7gxM7+dmf8JXAAcNeI2qZ3MikqZFZUwJyplVtRTWwvtvYDvdtzeVE+T5jIrKmVWVMKcqJRZUU+RmaNuwzYi4iXAr2fm79W3jwGemZl/OGe5NcCa+ub+wDeH2lA16UmZuXuvhczKsleUEyjLijmZaG5TVMJtikoVZaWVY7SpPhXu03F7b+DWuQtl5jnAOcNqlFrJrKhUz6yYE+E2ReXcpqintg4duQrYLyJWRcT2wMuAS0fcJrWTWVEps6IS5kSlzIp6amWPdmZujYiTgE9SnTLnvMy8bsTNUguZFZUyKyphTlTKrKhEK8doS5IkSeOurUNHJEmSpLFmoS1JkiQ1wEJbkiRJaoCFtiRJktQAC21JkiSpARbakiRJUgMstCVJkqQGWGhLkiRJDbDQliRJkhpgoS1JkiQ1YCiFdkScFxF3RMS1HdOeEBFXRMS36r+71tMjIt4eETdGxNUR8SvDaKPawayohDlRKbOiEuZETRlWj/Za4PA5004FrszM/YAr69sARwD71Zc1wLuG1Ea1w1rMinpbizlRmbWYFfW2FnOiBgyl0M7MzwH3zJl8FHB+ff184OiO6f+YlS8Du0TEimG0U6NnVlTCnKiUWVEJc6KmjHKM9lRmbgGo/+5RT98L+G7HcpvqaVq+zIpKmBOVMisqYU7Ut0eNugFdRJdp2XXBiDVUh23Ycccdn77PPvs02S4NyapVq7j55psfLljUrCxj5kSlzIpKmBMtxg033HBXZu7ea7lRFtq3R8SKzNxSH3K5o56+CehM4t7Ard0eIDPPAc4BmJ6ezvXr1zfZXg3Jxo0bWbVq1UMdk8yKtmFOVMqsqIQ50WJExC0ly41y6MilwHH19eOASzqmH1t/q/cg4L6ZQzdatsyKSpgTlTIrKmFO1Leh9GhHxAeBQ4DdImIT8AbgTOCiiHgl8B3gJfXilwMvAG4EHgSOH0Yb1Q6rV69m3bp1ADuYFc3HnKiUWVEJc6KmRGbXYUVjx0MykyUiNmTmdBOPbVYmhzlRKbOiEuZEpUqz4i9DSpIkSQ2w0JYkSZIaYKEtSZIkNcBCW5IkSWqAhbYkSZLUAAttSZIkqQEW2pIkSVIDLLQlSZKkBlhoS5IkSQ2w0JYkSZIaYKEtSZIkNcBCW5IkSWqAhbYkSZLUAAttSZIkqQEW2pIkSVIDHjXKlUfE/sCFHZP2BV4P7AK8Criznn5aZl4+5OapRcyKSpgTlTIrKmFO1K+RFtqZ+U3gQICI2A7YDFwMHA+cnZlvHmHz1CJmRSXMiUqZFZUwJ+pXm4aOHAbclJm3jLohaj2zohLmRKXMikqYEy3aSHu053gZ8MGO2ydFxLHAeuDkzLx37h0iYg2wBmBqaop169bNmn/N5vu6ruiAvXYeTIs1KgPPiiaSOVEps6IS5kSLFpk56jYQEdsDtwK/lJm3R8QUcBeQwBnAisw8YaHHmJ6ezvXr18+atvLUy7ouu/HMIwfRbDUoIjZk5nSX6Y1kRePJnKiUWVEJc6JS82VlrrYMHTkC+Gpm3g6Qmbdn5sOZ+V/Ae4BnjrR1ahOzohLmRKXMikqYEy1JWwrt1XQcjomIFR3zXgRcO/QWqa3MikqYE5UyKyphTrQkIx+jHRGPBZ4HnNgx+ayIOJDqkMzGOfO0TJkVlTAnKmVWVMKcqB+LKrQj4lHAk+ub12fm1n4bkJkPAk+cM+2Yfh9Xk8esqIQ5USmzohLmRP1YcOhIRJwZEU+trz8V+Cbwr/Xl+oh48kL3lyRJkparXmO0TwBuqK+/HXhXZu6SmbsAfwe8s8nGSZIkSeOqV6H9mI5lDgD+pmPe24BfaaJRkiRJ0rjrVWh/lkcG+F8F/FrHvEOAOxtokyRJkjT2en0Z8jXAFRHxYuAW4GMR8Xmqb9keBLy84fZJkiRJY2nBHu3MvJnqLCP/BDwErAMeAL4I/EpmfrTpBkqSJEnjqOfp/TLzIapfPXpP882RRmPlqZd1nb7xzCOH3BJJkjQplvzLkFF57iAbI0mSJE2Kfn6CfXvgM4NqiCRJkjRJFhw6EhHHLjB7+wG3RZIkSZoYvcZovxfYAPy4y7wYfHMkSZKkydCr0P4WcEpmbjNEJCIeAzzYSKskSZKkMVfygzW/OM+8h+v5kiRJkuZYsEc7M09cYN5DwKEDb5EkSZI0AXqeR7tpEbGR6kdwHga2ZuZ0RDwBuBBYCWwEXpqZ946qjWoHs6IS5kSlzIpKmBP1Y8GhIxGxf0S8uuP2JyLi0x2X/QfUjkMz88DMnK5vnwpcmZn7AVfWtyUwKypjTlTKrKiEOdGS9BqjfQrw/Y7bzwbeX1+uo7lgHQWcX18/Hzi6ofVo/JkVlTAnKmVWVMKcqEivQvu5wMUdtx/OzHMz81zgNODgAbQhgU9FxIaIWFNPm8rMLQD13z0GsB6NP7OiEuZEpcyKSpgTLVmvMdp7ZOb9Hbd/8gM2mflAREwNoA3PycxbI2IP4IqI+EbpHevArwGYmppi3bp1s+affMDWrvebWe6azfd1nX/AXjuXNqF1JvE5dRhJVuZ7TWFiXtdJ01hONHHMikqYEy1ZZOb8MyM2UwVsY5d5+wKfz8w9B9aYiNOphqq8CjgkM7dExApgXWYuOB58eno6169fP2vaylMv67rsxjOPLJo/jiblOUXEho6xcN3mn86QsjLfvJn5Gp1h50Tjy6yohDlRqV5ZmdFr6MhlwBnzzHtjPX/JIuJxEbHTzHXg+cC1wKXAcfVixwGX9LMejT+zMngrT72s62WcmROVMisqYU7Ur15DR14PfDEivkY1Vvs2YAXVoP9dgYP6XP8UcHFEzLTlA5n5iYi4CrgoIl4JfAd4SZ/r0fgzKyphTlTKrKiEOVFfev1gzW0RMQ28FjgC2A24G7gceGtm3t3PyjPz28DTuky/Gzisn8fWZDErKmFOVMqsqIQ5Ub96/mBNZt4D/Hl9kSRJklRgwUI7InYGnpaZn6tvnzbnPu/wl5AkLReT8mVjSdJw9OrRfh2wFfhcffs0Hjmv9irg0cBfNNM0SZIkaXz1KrRfBBzecfuhzDwGICL2phqrbaEtSZIkzdGr0N4zMzd13D5n5kpmbqqLbUkaGIdnSJImRa/zaBMRu89cz8xTuk2XJEmSNFuvQvsLwPHzzDse+NJgmyNJkiRNhl5DR94IfCYi9gQ+wiM/WPNi4ATg15pt3mgt5We5Sw5vL8dD48vxOUuSpOWtV6H9VaqfGz0LOImqB/y/gK8Ah2fmVc02T5I0Sn5IlqSl61VobwbeB/wBcBPVz67fm5kPNt0wSZIkaZz1KrR/H3g58G/A9cBa4AOAhbY0BEsZvjQzv6n1SpKkMgt+GTIzL8nMl1CNy3438FJgU0RcGhG/FRGPHkYjJUmSpHHT8/R+AJn5vcx8d2YeDDwZWA+cDWxpsnGSJEnSuOo1dGSWiNgBeAbwLGAK+GITjZoEbT0riUMChs/XfPnwvdYMsyAJCgvtiDgYOJZq6Mgd1F+QzMxbGmybJEldWchKGgcLDh2JiNMj4ibgY/WkIzPzFzLzjEEU2RGxT0R8JiKuj4jrIuKPOta7OSK+Xl9e0O+6NN7MikqYE5UyKyphTtSvXj3aBwF/Bnw0M3/UwPq3Aidn5lcjYidgQ0RcUc87OzPf3MA6x94y7ckxK4tkTtqXk2X6nrRVq7Oi1jAn6suChXZmHt7kyjNzC/UXKjPzgYi4HtiryXVqPJkVlTAn3Vngb8usqIQ5Ub+KzjoyDBGxEvhlql+dBDgpIq6OiPMiYteRNUytY1ZUwpyolFlRCXOipYjMHHUbiIjHA58F3pSZH4mIKeAuIIEzgBWZeUKX+60B1gBMTU09/YILLpg1/5rN93Vd3wF77dz3/FHdt+nHbspi13vooYduyMzpudNHkZX55vWaP6ocDaNdTVrMutuUk37nN/mat7Vd/RjnbYraa9g50fiaLytzjbzQrn/05uPAJzPzrV3mrwQ+nplPXehxpqenc/369bOm9Tpc2s/8Ud236cduymLXGxHbBHhUWen1C4xty9Ew2tWkxay7TTnpd/4oT+vZ1iwsZJy3KWqvYedE46tbVrpZ1Hm0By0iAjgXuL4zvBGxoh4XBfAi4NpRtE/tYVZUwpwM37gWlGZFJcyJ+jXSQht4DnAMcE1EfL2edhqwOiIOpDoksxE4cTTNU4uYFZVYljnpdXRDXS3LrGjRzIn6MtJCOzM/D0SXWZcPuy1qN7OiEuZEpcyKSpgT9as1Zx2RJEmSJsmoh45IkgqM61hojZdJzFmbn5PDviafhbYkSWOkrWePanNBK42KQ0ckSZKkBtijrZ9o8hzBmiy+11J/xvHc5ZIWz0JbkqRlwiJeGi4LbUmSpDHkB6f2s9CWJGlCtLXwamu7pBlNZdRCW9JQucOVJC0XFtqSJEkt1E/HhJ0a7WChLUmSpFks1AfDQlvSwLmBlqR2a7K33NNXPsIfrJEkSZIaYI+2JKkR8/VcgWNMJS0PFtqSJElqhUn7gN3aQjsiDgfeBmwH/ENmnjniJqmlzIpKmZV2aesO1ZyMl1HmyKyol1YW2hGxHfBO4HnAJuCqiLg0M/9jtC1T25gVlTIrKmFORqOfL9eNilkZP6PIUSsLbeCZwI2Z+W2AiLgAOAowvJrLrKiUWVEJc6JSZmUExu2MJpGZI1v5fCLit4HDM/P36tvHAM/KzJPmLLcGWFPf3B/45lAbqiY9KTN377WQWVn2inICZVkxJxPNbYpKuE1RqaKstLVHO7pM2+YTQWaeA5zTfHPUYmZFpXpmxZwItykq5zZFPbX1PNqbgH06bu8N3DqitqjdzIpKmRWVMCcqZVbUU1sL7auA/SJiVURsD7wMuHTEbVI7mRWVMisqYU5Uyqyop1YOHcnMrRFxEvBJqlPmnJeZ1424WWohs6JSZkUlzIlKmRWVaOWXISVJkqRx19ahI5IkSdJYs9CWJEmSGmChLUmSJDXAQluSJElqgIW2JEmS1AALbUmSJKkBFtqSJElSAyy0JUmSpAZYaEuSJEkNsNCWJEmSGjCUQjsizouIOyLi2o5pT4iIKyLiW/XfXevpERFvj4gbI+LqiPiVYbRR7WBWVMKcqJRZUQlzoqYMq0d7LXD4nGmnAldm5n7AlfVtgCOA/erLGuBdQ2qj2mEtZkW9rcWcqMxazIp6W4s5UQOGUmhn5ueAe+ZMPgo4v75+PnB0x/R/zMqXgV0iYsUw2qnRMysqYU5UyqyohDlRUx41wnVPZeYWgMzcEhF71NP3Ar7bsdymetqWuQ8QEWuoPk2y4447Pn2fffZptsUailWrVnHzzTc/3DHJrGgb5kSlzIpKmBMtxg033HBXZu7ea7lRFtrziS7TstuCmXkOcA7A9PR0rl+/vsl2aUg2btzIqlWrHipY1KwsY+ZEpcyKSpgTLUZE3FKy3CjPOnL7zKGW+u8d9fRNQOdHvr2BW4fcNrWLWVEJc6JSZkUlzIn6NspC+1LguPr6ccAlHdOPrb/VexBw38yhGy1bZkUlzIlKmRWVMCfq21CGjkTEB4FDgN0iYhPwBuBM4KKIeCXwHeAl9eKXAy8AbgQeBI4fRhvVDqtXr2bdunUAO5gVzcecqJRZUQlzoqZEZtdhRWPHsU+TJSI2ZOZ0E49tViaHOVEps6IS5kSlSrPiL0NKkiRJDbDQliRJkhpgoS1JkiQ1wEJbkiRJaoCFtiRJktQAC21JkiSpARbakiRJUgMstCVJkqQGWGhLkiRJDbDQliRJkhpgoS1JkiQ1wEJbkiRJaoCFtiRJktQAC21JkiSpAY8a5cojYn/gwo5J+wKvB3YBXgXcWU8/LTMvH3Lz1CJmRSXMiUqZFZUwJ+rXSAvtzPwmcCBARGwHbAYuBo4Hzs7MN4+weWoRs6IS5kSlzIpKmBP1q01DRw4DbsrMW0bdELWeWVEJc6JSZkUlzIkWrU2F9suAD3bcPikiro6I8yJi11E1Sq1kVlTCnKiUWVEJc6JFG+nQkRkRsT3wm8Cf1pPeBZwBZP33LcAJXe63BlgDMDU1xbp162bNv2bzfdus64C9dh5cwzV0TWVFk8WcqJRZUQlzoqWKzBx1G4iIo4BXZ+bzu8xbCXw8M5+60GNMT0/n+vXrZ01beepl2yy38cwj+2mqhiQiNmTmdJfpjWRF48mcqJRZUQlzolLzZWWutgwdWU3H4ZiIWNEx70XAtUNvkdrKrKiEOVEps6IS5kRLMvKhIxHxWOB5wIkdk8+KiAOpDslsnDNPy5RZUQlzolJmRSXMifox8kI7Mx8Enjhn2jEjao5azKyohDlRKbOiEuZE/SgeOhIRO0XEVEREkw2SJEmSJsGChXZEbBcRb4iIW4DvAbcCP4yIL0TEYUNpoSRJkjSGeg0deRvwdOBUqqL8D6l+Eele4L0R8SeZeUGzTZQkSZLGT69CezXwC5l5N0BEXAl8PjN/PiKuBt4LWGhLkiRJc/Qao/0jYGvH7a3AYwAy88vAPg21S5IkSRprvXq0LwIujYi3AwH8EfAR+Mk5JO9stnmSJEnSeOpVaJ9C9XOjp1IV2v8CvKmeF8CxzTVNkiRJGl8LFtqZ+Z/AG+vL3Hm3Up2FRBp7K0+9bJtpG888cgQtkSRJk2LJP8EelecOsjGSJEnSpFhyoQ1sD3xmUA2RJEmSJsmCQ0ciYqEx2NsPuC2SJEnSxOj1Zcj3AhuAH3eZ50+xS5IkSfPoVWh/CzglM7cZIhIRjwEebKRVkiRJ0pjrVWh/FvhFuo/Ffrier3l4JgtJkqTlq9fp/U5cYN5DwKEDb5EkSZI0AXr1aDcuIjYCD1D1kG/NzOmIeAJwIbAS2Ai8NDPvHVUb1Q5mRSXMiUqZFZUwJ+rHgqf3i4j9I+LVHbc/ERGf7rjsP6B2HJqZB2bmdH37VODKzNwPuLK+LYFZURlzolJmRSXMiZak13m0TwG+33H72cD768t1NBeso4Dz6+vnA0c3tB6NP7OiEuZEpcyKSpgTFelVaD8XuLjj9sOZeW5mngucBhw8gDYk8KmI2BARa+ppU5m5BaD+u8cA1qPxZ1ZUwpyolFlRCXOiJes1RnuPzLy/4/ZPfsAmMx+IiKkBtOE5mXlrROwBXBER3yi9Yx34NQBTU1OsW7du1vyTD9i6zX1mlrlm833bzDtgr53LW11gofVrSUaSFY2dxnKiiWNWVMKcaMl6FdoPRMTKzNwIkJkfm5kREfsye1jJkmTmrfXfOyLiYuCZwO0RsSIzt0TECuCOee57DnAOwPT0dB5yyCGz5r+i2+n1fveQnvMGZRjrWE5GlRWNlyZzosliVlTCnKgfvYaOXAacMc+8N9bzlywiHhcRO81cB54PXAtcChxXL3YccEk/69H4MysqYU5UyqyohDlRv3r1aL8e+GJEfI1qrPZtwAqqQf+7Agf1uf4p4OKImGnLBzLzExFxFXBRRLwS+A7wkj7Xo/FnVlTCnKiUWVEJc6K+9PrBmtsiYhp4LXAEsBtwN3A58NbMvLuflWfmt4GndZl+N3BYP4+tyWJWVMKcqJRZUYn/2979hEhylnEc/z6GKIgSFMOQ7AZ3D+pFUcmYCHrZgLjoQWKI6GEPCbKKRDwoKKtCRAMikhCChyyai5cBIegShSAaDAn+2U0IrnEVVARnDaJERQhCFh4P3ZLZ2c7U213/3ur+fmBgqnqm36e7fvPO09XVVeZEbTVesCYznwe+NP9SBzb10uyb+ri1PsywJGkZBzbaEXEN8PbMfHy+fGrf7zzglZAkSZKkKzXt0f4ccAl4fL58ipfOq30UuBr4cj+lSZIkSdPV1GjfChzfs/xiZp4AiIjDzI7VttEekW9lry+3rSRJ09Z0er/rM3N3z/Lp/38zX3+4l6okSZKkiWv8MGREXJuZfwfIzM/vXd9nYZK6495xSZKG19RoPwncAXxjwW13AD/vvKJK2ahspnXf7uv++IbkcylJ2q+p0f4K8KdGTSwAAAVtSURBVFhEXA88zEsXrPkwcCdwS7/lSZIkSdPUdIz208wuN3oj8FPgd8BPgG3geGae7bc8SZIkaZqa9mhfBL4LfAr4I7PLrv8zM1/ouzC149vY07HKtlr37bvuj0+StBma9mh/ktn5sn8FPAHcBry676IkSZKkqTuw0c7MH2Tm7cyOy34Q+AiwGxFnIuK2iLh6iCIlSZKkqWk8vR9AZv6LWaP9YEQcBU4A983XvaG/8tQ135LvxkHPo8+xJOcBSdB86MhlIuJVwLuAm4Et4HwfRUmSJElTV9RoR8R7I+I08Dfga8AvgDdn5rE2g0fEDRHxWERciIhnI+Iz8/V3R8TFiHhm/vWBNuNo+syKSpiT9o584YdXfK0js6IS5kRtHXjoSETczewwkdcD3wM+mJlPdjj+JeCzmfl0RLwWeCoifjy/7b7M/GaHY02Wb0ECa5AVt+MgJp+TLpm5A5kVlTAnaqXpGO13A18Evp+Z/+168Mx8Dnhu/v1/IuICcKjrcTR9ZkUlzIlKmRWVMCdqq+msI8czc6ePJnu/iDgCvBP45XzVXRHx64h4KCJe1/f4mg6zohKblJNNOdyjL5uUFa3OnGgVkZlj10BEvAb4GXBPZj4cEVvAP4AEvgpcl5l3Lvi9k8BJgK2trRt3dnYuu/38xX9fMdbbDl1z4G1drZ/aGENYZuxjx449lZnb+9cPnZV13+5j5uEgpXXVkpNlai65r2V1uX1rzURbNWVF9Ro6J5qul8vKfqM32vNzcT8CPJqZ9y64/QjwSGa+9aD72d7eznPnzl22bpVTsHW1fmpjDGGZsSPiigCPkZV13+61HsNbWlctOVmm5pL7WtYQ2Zq6mrKieg2dE03XoqwsUnQe7b5ERADfAS7sDW9EXDc/LgrgVuA3Y9SnepgVlTAn/Vm3xtGsqIQ5UVujNtrAe5id1eR8RDwzX3cK+FhEvIPZWzJ/Bj4xTnmqiFlRiepyMsSe603V8vmoLiuqkjlRK6M22pn5BBALbvrR0LWobmZFJcyJSpkVlTAnamvsPdqSJKlDvvMh1cNGW5KkgdT6weSxx5fWVdEl2CVJkiQtxz3akhZyD9c4fN4laX3YaEuSVLEaX3zVWJNUIxttSdLKbLimw20lDc9GW5KkCbJx3kxu92mx0VajMS8tLUkaj/O51I6NtiRJWmub8IJhEx7jFNloS5KkTtjsTYvbq3822gJW+2PzD1RtmSFJWmzM+dG5uTs22pKW4gQsSZvJ+X95NtqSpM75D1nS1PQxb9loS+qEjZUkLWeIeXPsMTb9f4ONtqRebfokq/bMkKSpqrbRjojjwP3AVcC3M/PrI5ekSpkVlTIrdai9cTYn/ah9u6/CrKxuHfOwSJWNdkRcBXwLeB+wC5yNiDOZ+dtxK1NtzIpKmZX61fCP15yolFlRiSobbeAm4A+Z+SeAiNgBPgQYXu1nVlTKrKiEOanEBE47a1Z6UMML7i7V2mgfAv6yZ3kXuHmkWlQ3s6JSZkUlzMnAlm2sKmrEzMqAVvnA5bLr+xCZ2csdtxERtwPvz8yPz5dPADdl5qf3/dxJ4OR88S3A7wctVH16Y2Ze2/RDZmXjFeUEyrJiTtaac4pKOKeoVFFWat2jvQvcsGf5MPDX/T+UmaeB00MVpSqZFZVqzIo5Ec4pKuecokavGLuAl3EWeFNEHI2IVwIfBc6MXJPqZFZUyqyohDlRKbOiRlXu0c7MSxFxF/Aos1PmPJSZz45clipkVlTKrKiEOVEps6ISVR6jLUmSJE1drYeOSJIkSZNmoy1JkiT1wEZbkiRJ6oGNtiRJktQDG21JkiSpBzbakiRJUg9stCVJkqQe2GhLkiRJPfgf4Vu/4loi5qQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 8\n",
    "identifieer = 'LRS'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 32\n",
    "identifieer = 'LRS'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 128\n",
    "identifieer = 'LRS'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch_size = 512\n",
    "identifieer = 'LRS'\n",
    "cols = ['{} Filtersize'.format(col) for col in reversed(['Full','Halfed', 'Quartered', 'Eigth', 'Sixteenth'])]\n",
    "rows = ['{}'.format(row) for row in ['VGG11', 'VGG13', 'VGG16', 'VGG19']]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=5, figsize=(12, 8))\n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation='vertical', size='large')\n",
    "\n",
    "    \n",
    "\n",
    "#fig.tight_layout()\n",
    "for i,net in enumerate([11, 13, 16, 19]):\n",
    "    for j,filterstack in enumerate(reversed(['', '_S', '_XS', '_XXS', '_XXXS'])):\n",
    "        filename = join('.','logs','VGG{}{}'.format(net, filterstack),'Cifar100','VGG{}{}_bs{}_e50_id{}.csv'.format(net, filterstack, batch_size, identifieer))\n",
    "        #print(filename)\n",
    "        df = extract_layer_saturation(pd.read_csv(filename, sep=';'), epoch=49)\n",
    "        plot_saturation_level_ax(df, axes[i][j])\n",
    "plt.show()\n",
    "#plt.savefig('collage_cifar.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}