visualize_interval_bound_propagation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import chainer\n",
    "mnist = chainer.datasets.get_mnist(ndim=3)[-1] # test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 7\n",
    "\n",
    "def visualize_bounds(samples, merge_as_color=True):\n",
    "    plt.close('all')\n",
    "    print(samples.keys())\n",
    "    for k, vs in samples.items():\n",
    "        if 'l' in k and k.replace('l', 'a') in samples:\n",
    "            continue\n",
    "\n",
    "        if merge_as_color:\n",
    "            if vs['normal'].ndim == 1:\n",
    "                vmin, vmax = np.min(vs['lower']), np.max(vs['upper'])\n",
    "            else:\n",
    "                vmin, vmax = 0., 1.\n",
    "                up_diff = vs['upper']-vs['normal']\n",
    "                low_diff = vs['normal']-vs['lower']\n",
    "                merged_v = np.stack([1. - low_diff,\n",
    "                                     np.ones_like(vs['normal']) - low_diff - up_diff,\n",
    "                                     1. - up_diff], axis=-1)\n",
    "\n",
    "                row_size = merged_v.shape[0] // N + 1\n",
    "                display_activation(merged_v, N, row_size, vmin=vmin, vmax=vmax, label=k + ':range')\n",
    "\n",
    "            for subk, v in vs.items():\n",
    "                if v.ndim == 1:\n",
    "                    v = v.reshape(1, -1, v.shape[0])\n",
    "                row_size = v.shape[0] // N + 1\n",
    "                display_activation(v, N, row_size, vmin=vmin, vmax=vmax, label=k + '.' + subk)\n",
    "        else:\n",
    "            _vmin, _vmax = np.min(vs['lower']), np.max(vs['upper'])\n",
    "            for subk, v in vs.items():\n",
    "                if v.ndim == 1:\n",
    "                    v = v.reshape(1, -1, v.shape[0])\n",
    "                    vmin, vmax = _vmin, _vmax\n",
    "                else:\n",
    "                    vmin, vmax = 0., 1.\n",
    "                row_size = v.shape[0] // N + 1\n",
    "                display_activation(v, N, row_size, vmin=vmin, vmax=vmax, label=k + '.' + subk)\n",
    "    plt.tight_layout()\n",
    "    plt.margins(0, 0)\n",
    "\n",
    "def display_activation(activations, col_size, row_size, vmin=-1, vmax=2, label=None):\n",
    "    index = 0\n",
    "    scale = 1.2\n",
    "\n",
    "    fig = plt.figure(figsize=(row_size*10 * scale, col_size*3.5 * scale * (4./N) ** 3),\n",
    "                     tight_layout=dict(pad=0))\n",
    "\n",
    "    fig.subplots_adjust(bottom=-5, left=-5, top=1, right=1, wspace=0)\n",
    "    for row in range(0, row_size):\n",
    "        for col in range(0, col_size):\n",
    "            if index >= activations.shape[0]:\n",
    "                break\n",
    "            sub_ax = fig.add_subplot(row_size, col_size, index + 1)\n",
    "            if index == 0 and label:\n",
    "                sub_ax.set_title(label)\n",
    "            if activations[index].ndim == 2:\n",
    "                sub_ax.imshow(activations[index, :, :], cmap='Greys', interpolation='nearest',\n",
    "                              vmin=vmin, vmax=vmax)\n",
    "            else:\n",
    "                sub_ax.imshow(activations[index, :, :], interpolation='nearest',\n",
    "                              vmin=vmin, vmax=vmax)\n",
    "            sub_ax.xaxis.set_ticklabels([]), sub_ax.axes.get_xaxis().set_ticks([])\n",
    "            sub_ax.yaxis.set_ticklabels([]), sub_ax.axes.get_yaxis().set_ticks([])\n",
    "            plt.axis('off')\n",
    "            index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and loss weight is linearly decayed [1.0, 1.0] between [1000000000, 1000000000] iterations,\n",
      "and epsilon is linearly increased [0.0, 0.1] between [1000000000, 1000000000].\n"
     ]
    }
   ],
   "source": [
    "# load a model\n",
    "from models import SmallerCNN4Visualization\n",
    "model = SmallerCNN4Visualization(\n",
    "                   n_class=10,\n",
    "                   verify=True,\n",
    "                   warmup_steps=1000000000,\n",
    "                   rampup_steps=1000000000,\n",
    "                   normal_loss_weight=1.,\n",
    "                   epsilon=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['x', 'l1', 'a1', 'l2', 'a2', 'l3', 'a3', 'y'])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sos/.pyenv/versions/anaconda3-5.0.0/lib/python3.6/site-packages/matplotlib/figure.py:1743: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
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      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI0AAACZCAYAAAASGbkoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105ba1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAACWCAYAAABevXdQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10685ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1187610f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118a5aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118dc4898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAACWCAYAAADTyo+wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nggAAgiMIACC4/wFd+eP/oTELcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ff7668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAACWCAYAAADTyo+wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njBq+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119239be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119582a90>"
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     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119690a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI0AAAAnCAYAAAAy2cstAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119724eb8>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI0AAAAnCAYAAAAy2cstAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11977c5c0>"
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1197abc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11990dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI0AAAAtCAYAAACTQuhLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11998e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "use_ibp_model = True\n",
    "if use_ibp_model:\n",
    "    resume_path = 'sample_data/model_files/vissmall_eps01_10000.npz'\n",
    "else:\n",
    "    resume_path = 'sample_data/model_files/vissmall_baseline_10000.npz'\n",
    "chainer.serializers.load_npz(resume_path, model)\n",
    "EPSILON = 0.1\n",
    "model.epsilon = EPSILON\n",
    "\n",
    "# pick an image, predict, and visualize bounds\n",
    "i = 0 # data index\n",
    "x, t = mnist[i][0][None], mnist[i][1][None]\n",
    "samples = model.forward_for_visualization_samples(x, t)\n",
    "visualize_bounds(samples, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['x', 'l1', 'a1', 'l2', 'a2', 'l3', 'a3', 'y'])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sos/.pyenv/versions/anaconda3-5.0.0/lib/python3.6/site-packages/matplotlib/figure.py:1743: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI0AAACZCAYAAAASGbkoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCA3CCA3CCA3CCA3CCA3CCA3C/gVBs4R8J5HZzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1193a3748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI0AAACZCAYAAAASGbkoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhAZuhAZu/wHFUbcG0vZhHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c0e6d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c173eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI0AAACZCAYAAAASGbkoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c1f5cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c29e7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106836550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118dbe208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd15c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ef3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194fbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAACWCAYAAADTyo+wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118b68b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAACWCAYAAADTyo+wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAMkRAgBIjhAAQHL/A2hnggmzZBRSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c23b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI0AAAAnCAYAAAAy2cstAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c53048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e75400>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ce2da0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118b7f438>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ee6cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "use_ibp_model = False\n",
    "if use_ibp_model:\n",
    "    resume_path = 'sample_data/model_files/vissmall_eps01_10000.npz'\n",
    "else:\n",
    "    resume_path = 'sample_data/model_files/vissmall_baseline_10000.npz'\n",
    "chainer.serializers.load_npz(resume_path, model)\n",
    "EPSILON = 0.1\n",
    "model.epsilon = EPSILON\n",
    "\n",
    "# pick an image, predict, and visualize bounds\n",
    "i = 0 # data index\n",
    "x, t = mnist[i][0][None], mnist[i][1][None]\n",
    "samples = model.forward_for_visualization_samples(x, t)\n",
    "visualize_bounds(samples, True)"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}