ICLR Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproduction des résultats d'une publication de ICLR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conçu par Laurence Liang, le jeudi 7 juin 2018, dans le contexte de reproduire la publication \"When is a Convolutional Filter Easy to Learn\", https://openreview.net/forum?id=SkA-IE06W des chercheurs **Simon S. Du(CMU), Jason D. Lee (USC), Yuandong Tian (Facebook AI)**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### But"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "J'ai conçu une classe qui utilise un réseau neuronal convolutif (CNN), **iclr_conv** (inclus dans ce dossier GitHub), afin d'étudier l'évolution de l'apprentissage d'un CNN en temps polynomial sous forme de **\"relative loss\"**.\n",
    "\n",
    "Je varie l'initialisaiton des filtres du CNN en modifiant le seed pour l'initialisateur **glorot** en utilisant 3 seed différents.\n",
    "\n",
    "J'utilise la même tâche que les chercheurs, la tâche de **MNIST**. Je maintiens les mêmes fonctions d'activation, l'architecture et la durée de l'entraînement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Résumé des résultats obtenus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour 2 des 3 initialisateurs utilisés, je constate que la courbe du \"loss relative\" suit une relation prévisible qui se ressemble à celle de la fonction logarithmique. **Cela complète les observations de la publication d'ICLR.** Toutefois, la courbe du seed=3 se différencie des 2 autres.\n",
    "\n",
    "Mes résultats reproduits semblent affirmer que l'apprentissage par CNN suit une tendance en temps polynomial, mais qu'on a trouvé un exemple qui oppose ce constat, dont la raison n'est pas connue.\n",
    "\n",
    "Il reste ainsi à faire davantage d'essais pour observer cette tendance en temps polynomial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importer librairies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "J'utilise la libraire Keras pour créer des réseaux de neurones. J'ai conçu un module, iclr_conv (dont le code se retrouve sur ce dossier GitHub), qui sert à identifier des images de l'ensemble MNIST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.utils import np_utils\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import math\n",
    "import iclr_conv\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape to be [samples][pixels][width][height]\n",
    "X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')\n",
    "X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')\n",
    "\n",
    "# Labels\n",
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyser les données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Je calcule la moyenne des valeurs numériques des données MNIST afin d'en servir pour calculer le \"relative loss\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_sum = 0\n",
    "for image in X_test:\n",
    "    N = len(X_test)\n",
    "    for container in image:\n",
    "        sum_of_datapoint = 0.0\n",
    "        for row in container:\n",
    "            for value in row:\n",
    "                sum_of_datapoint += value\n",
    "    avg = sum_of_datapoint/784\n",
    "    avg_sum += avg\n",
    "\n",
    "final_avg = avg_sum/10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Lire données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Je lis les données de l'erreur de chaque itération d'un réseau neuronal Keras (ma classe **ConvNet** de mon module **iclr_conv**. Je calcule le \"relative loss\" par le logarithme du taux d'erreur au carré divisé par le moyen terme des valeurs de l'ensemble **MNIST**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "relative_loss = []\n",
    "for value in np.load(\"data/cnn1_val_acc.npy\"):\n",
    "    relative_loss.append(math.log(((1-value)**2)/(final_avg**2), 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "relative_loss_2 = []\n",
    "for value in np.load(\"data/cnn2_val_acc.npy\"):\n",
    "    relative_loss_2.append(math.log(((1-value)**2)/(final_avg**2), 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "relative_loss_3 = []\n",
    "for value in np.load(\"data/cnn3_val_acc.npy\"):\n",
    "    relative_loss_3.append(math.log(((1-value)**2)/(final_avg**2), 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualiser les résultats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_axis = [x for x in range(50)]\n",
    "\n",
    "plt.plot(x_axis, relative_loss, label='Seed=0', color='black')\n",
    "plt.plot(x_axis, relative_loss_2, label='Seed=1', color='blue')\n",
    "plt.plot(x_axis, relative_loss_3, label='Seed=3', color='red')\n",
    "\n",
    "plt.xlim(0, 49)\n",
    "plt.ylim(-8, 0)\n",
    "\n",
    "plt.xlabel(\"Époque\")\n",
    "plt.ylabel(\"Relative loss\")\n",
    "\n",
    "plt.title(\"Observation du \\\"loss relative\\\" d'un CNN sur MNIST en modifiant ses filtres\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.savefig(\"Résultats/Observation.png\", dpi=1000)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a7972d588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Visualiser résultats\n",
    "publication = mpimg.imread('Résultats/Publication.png')\n",
    "reproduction = mpimg.imread('Résultats/Observation.png')\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(publication)\n",
    "plt.title(\"Résultats publiés\")\n",
    "plt.axis('off')\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(reproduction)\n",
    "plt.title(\"Résultats reproduits\")\n",
    "plt.axis('off')\n",
    "plt.savefig(\"Résultats/Comparaison\", dpi=500)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque que pour les essais quand le seed vaut 0 ou 1, la courbe suit une relation presque logarithmique. Les valeurs du \"loss relative\" ne sont pas les mêmes possiblement à cause d'un nombre différent de neurones dans les 2 réseaux, ou à cause d'une initialisation différente des images.\n",
    "\n",
    "Le cas quand seed vaut 3 contredit les 2 autres courbes. Celui-ci ne semble pas suivre une courbe logarithmique comme les résultats publiés ou les 2 autres courbes.\n",
    "\n",
    "Un deuxième essai avec davantage de contrôle sur les seed sera nécessaire pour vérifier ce résultat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exemples de réseaux neuronaux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ici sont des exemples de l'utilisation de mon réseau neuronal de ma classe **ConvNet** du module **iclr_conv**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      " - 59s - loss: 5.7384 - acc: 0.4702 - val_loss: 0.9692 - val_acc: 0.7270\n",
      "Epoch 2/50\n",
      " - 53s - loss: 0.9608 - acc: 0.6871 - val_loss: 0.9012 - val_acc: 0.6318\n",
      "Epoch 3/50\n",
      " - 70s - loss: 0.6978 - acc: 0.7530 - val_loss: 0.4588 - val_acc: 0.8723\n",
      "Epoch 4/50\n",
      " - 65s - loss: 0.4091 - acc: 0.8743 - val_loss: 0.7208 - val_acc: 0.8637\n",
      "Epoch 5/50\n",
      " - 63s - loss: 0.4486 - acc: 0.8764 - val_loss: 0.2644 - val_acc: 0.9301\n",
      "Epoch 6/50\n",
      " - 60s - loss: 0.2608 - acc: 0.9320 - val_loss: 0.2113 - val_acc: 0.9434\n",
      "Epoch 7/50\n",
      " - 72s - loss: 0.2179 - acc: 0.9419 - val_loss: 0.1888 - val_acc: 0.9486\n",
      "Epoch 8/50\n",
      " - 70s - loss: 0.1884 - acc: 0.9481 - val_loss: 0.1823 - val_acc: 0.9494\n",
      "Epoch 9/50\n",
      " - 67s - loss: 0.1778 - acc: 0.9504 - val_loss: 0.1564 - val_acc: 0.9539\n",
      "Epoch 10/50\n",
      " - 60s - loss: 0.1619 - acc: 0.9546 - val_loss: 0.1510 - val_acc: 0.9590\n",
      "Epoch 11/50\n",
      " - 59s - loss: 0.1501 - acc: 0.9574 - val_loss: 0.1597 - val_acc: 0.9544\n",
      "Epoch 12/50\n",
      " - 58s - loss: 0.1434 - acc: 0.9587 - val_loss: 0.1323 - val_acc: 0.9617\n",
      "Epoch 13/50\n",
      " - 58s - loss: 0.1353 - acc: 0.9610 - val_loss: 0.1422 - val_acc: 0.9598\n",
      "Epoch 14/50\n",
      " - 59s - loss: 0.1307 - acc: 0.9628 - val_loss: 0.1437 - val_acc: 0.9611\n",
      "Epoch 15/50\n",
      " - 58s - loss: 0.1232 - acc: 0.9654 - val_loss: 0.1324 - val_acc: 0.9639\n",
      "Epoch 16/50\n",
      " - 59s - loss: 0.1203 - acc: 0.9657 - val_loss: 0.1258 - val_acc: 0.9636\n",
      "Epoch 17/50\n",
      " - 58s - loss: 0.1153 - acc: 0.9671 - val_loss: 0.1482 - val_acc: 0.9593\n",
      "Epoch 18/50\n",
      " - 59s - loss: 0.1109 - acc: 0.9678 - val_loss: 0.1193 - val_acc: 0.9675\n",
      "Epoch 19/50\n",
      " - 67s - loss: 0.1062 - acc: 0.9693 - val_loss: 0.1183 - val_acc: 0.9658\n",
      "Epoch 20/50\n",
      " - 61s - loss: 0.1067 - acc: 0.9692 - val_loss: 0.1130 - val_acc: 0.9682\n",
      "Epoch 21/50\n",
      " - 60s - loss: 0.1002 - acc: 0.9699 - val_loss: 0.1388 - val_acc: 0.9615\n",
      "Epoch 22/50\n",
      " - 58s - loss: 0.1009 - acc: 0.9704 - val_loss: 0.1135 - val_acc: 0.9672\n",
      "Epoch 23/50\n",
      " - 59s - loss: 0.0976 - acc: 0.9712 - val_loss: 0.1053 - val_acc: 0.9703\n",
      "Epoch 24/50\n",
      " - 59s - loss: 0.0921 - acc: 0.9738 - val_loss: 0.1091 - val_acc: 0.9682\n",
      "Epoch 25/50\n",
      " - 58s - loss: 0.0929 - acc: 0.9724 - val_loss: 0.1149 - val_acc: 0.9677\n",
      "Epoch 26/50\n",
      " - 58s - loss: 0.0912 - acc: 0.9732 - val_loss: 0.1259 - val_acc: 0.9653\n",
      "Epoch 27/50\n",
      " - 58s - loss: 0.0898 - acc: 0.9732 - val_loss: 0.1139 - val_acc: 0.9676\n",
      "Epoch 28/50\n",
      " - 58s - loss: 0.0867 - acc: 0.9746 - val_loss: 0.1015 - val_acc: 0.9716\n",
      "Epoch 29/50\n",
      " - 59s - loss: 0.0843 - acc: 0.9751 - val_loss: 0.0957 - val_acc: 0.9727\n",
      "Epoch 30/50\n",
      " - 61s - loss: 0.0837 - acc: 0.9752 - val_loss: 0.1025 - val_acc: 0.9713\n",
      "Epoch 31/50\n",
      " - 62s - loss: 0.0839 - acc: 0.9748 - val_loss: 0.0923 - val_acc: 0.9739\n",
      "Epoch 32/50\n",
      " - 59s - loss: 0.0810 - acc: 0.9762 - val_loss: 0.1058 - val_acc: 0.9670\n",
      "Epoch 33/50\n",
      " - 59s - loss: 0.0787 - acc: 0.9763 - val_loss: 0.1006 - val_acc: 0.9720\n",
      "Epoch 34/50\n",
      " - 59s - loss: 0.0782 - acc: 0.9767 - val_loss: 0.0990 - val_acc: 0.9726\n",
      "Epoch 35/50\n",
      " - 59s - loss: 0.0776 - acc: 0.9769 - val_loss: 0.0986 - val_acc: 0.9723\n",
      "Epoch 36/50\n",
      " - 59s - loss: 0.0783 - acc: 0.9759 - val_loss: 0.0948 - val_acc: 0.9732\n",
      "Epoch 37/50\n",
      " - 58s - loss: 0.0756 - acc: 0.9778 - val_loss: 0.0957 - val_acc: 0.9734\n",
      "Epoch 38/50\n",
      " - 58s - loss: 0.0735 - acc: 0.9779 - val_loss: 0.0860 - val_acc: 0.9738\n",
      "Epoch 39/50\n",
      " - 59s - loss: 0.0757 - acc: 0.9769 - val_loss: 0.0853 - val_acc: 0.9732\n",
      "Epoch 40/50\n",
      " - 58s - loss: 0.0733 - acc: 0.9778 - val_loss: 0.0904 - val_acc: 0.9724\n",
      "Epoch 41/50\n",
      " - 58s - loss: 0.0718 - acc: 0.9787 - val_loss: 0.0926 - val_acc: 0.9726\n",
      "Epoch 42/50\n",
      " - 64s - loss: 0.0695 - acc: 0.9788 - val_loss: 0.1072 - val_acc: 0.9684\n",
      "Epoch 43/50\n",
      " - 55s - loss: 0.0710 - acc: 0.9781 - val_loss: 0.0845 - val_acc: 0.9755\n",
      "Epoch 44/50\n",
      " - 58s - loss: 0.0696 - acc: 0.9794 - val_loss: 0.0986 - val_acc: 0.9725\n",
      "Epoch 45/50\n",
      " - 60s - loss: 0.0713 - acc: 0.9783 - val_loss: 0.0843 - val_acc: 0.9746\n",
      "Epoch 46/50\n",
      " - 61s - loss: 0.0705 - acc: 0.9780 - val_loss: 0.0868 - val_acc: 0.9739\n",
      "Epoch 47/50\n",
      " - 68s - loss: 0.0680 - acc: 0.9795 - val_loss: 0.1049 - val_acc: 0.9718\n",
      "Epoch 48/50\n",
      " - 68s - loss: 0.0677 - acc: 0.9798 - val_loss: 0.0798 - val_acc: 0.9749\n",
      "Epoch 49/50\n",
      " - 67s - loss: 0.0666 - acc: 0.9798 - val_loss: 0.0814 - val_acc: 0.9764\n",
      "Epoch 50/50\n",
      " - 73s - loss: 0.0656 - acc: 0.9800 - val_loss: 0.0922 - val_acc: 0.9746\n"
     ]
    }
   ],
   "source": [
    "cnn2 = iclr_conv.ConvNet(None, None, 1)\n",
    "cnn2.run_conv_net(X_train, X_test, y_train, y_test, epoch_num=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      " - 61s - loss: 12.8169 - acc: 0.2035 - val_loss: 12.6789 - val_acc: 0.2125\n",
      "Epoch 2/50\n",
      " - 59s - loss: 12.7230 - acc: 0.2101 - val_loss: 12.6685 - val_acc: 0.2137\n",
      "Epoch 3/50\n",
      " - 59s - loss: 5.5903 - acc: 0.2109 - val_loss: 2.0538 - val_acc: 0.2076\n",
      "Epoch 4/50\n",
      " - 57s - loss: 2.0434 - acc: 0.2096 - val_loss: 2.0264 - val_acc: 0.2131\n",
      "Epoch 5/50\n",
      " - 56s - loss: 2.0259 - acc: 0.2133 - val_loss: 2.0149 - val_acc: 0.2135\n",
      "Epoch 6/50\n",
      " - 57s - loss: 2.0159 - acc: 0.2134 - val_loss: 2.0053 - val_acc: 0.2137\n",
      "Epoch 7/50\n",
      " - 57s - loss: 2.0078 - acc: 0.2142 - val_loss: 2.0080 - val_acc: 0.2118\n",
      "Epoch 8/50\n",
      " - 57s - loss: 2.0023 - acc: 0.2138 - val_loss: 1.9948 - val_acc: 0.2139\n",
      "Epoch 9/50\n",
      " - 57s - loss: 1.8724 - acc: 0.2615 - val_loss: 1.7299 - val_acc: 0.2618\n",
      "Epoch 10/50\n",
      " - 59s - loss: 1.7817 - acc: 0.2940 - val_loss: 1.6830 - val_acc: 0.3157\n",
      "Epoch 11/50\n",
      " - 57s - loss: 1.6131 - acc: 0.3718 - val_loss: 1.3029 - val_acc: 0.5611\n",
      "Epoch 12/50\n",
      " - 57s - loss: 0.9399 - acc: 0.6812 - val_loss: 0.6505 - val_acc: 0.8385\n",
      "Epoch 13/50\n",
      " - 57s - loss: 0.5158 - acc: 0.8566 - val_loss: 0.3937 - val_acc: 0.8897\n",
      "Epoch 14/50\n",
      " - 56s - loss: 0.3487 - acc: 0.9061 - val_loss: 0.2974 - val_acc: 0.9223\n",
      "Epoch 15/50\n",
      " - 57s - loss: 0.2550 - acc: 0.9304 - val_loss: 0.2119 - val_acc: 0.9412\n",
      "Epoch 16/50\n",
      " - 59s - loss: 0.2210 - acc: 0.9392 - val_loss: 0.2154 - val_acc: 0.9414\n",
      "Epoch 17/50\n",
      " - 57s - loss: 0.2051 - acc: 0.9422 - val_loss: 0.1935 - val_acc: 0.9470\n",
      "Epoch 18/50\n",
      " - 56s - loss: 0.1808 - acc: 0.9489 - val_loss: 0.1713 - val_acc: 0.9515\n",
      "Epoch 19/50\n",
      " - 56s - loss: 0.1621 - acc: 0.9542 - val_loss: 0.1596 - val_acc: 0.9543\n",
      "Epoch 20/50\n",
      " - 56s - loss: 0.1527 - acc: 0.9568 - val_loss: 0.1814 - val_acc: 0.9516\n",
      "Epoch 21/50\n",
      " - 56s - loss: 0.1437 - acc: 0.9600 - val_loss: 0.1503 - val_acc: 0.9568\n",
      "Epoch 22/50\n",
      " - 58s - loss: 0.1390 - acc: 0.9602 - val_loss: 0.1390 - val_acc: 0.9609\n",
      "Epoch 23/50\n",
      " - 62s - loss: 0.1359 - acc: 0.9612 - val_loss: 0.1662 - val_acc: 0.9557\n",
      "Epoch 24/50\n",
      " - 57s - loss: 0.1266 - acc: 0.9637 - val_loss: 0.1434 - val_acc: 0.9592\n",
      "Epoch 25/50\n",
      " - 57s - loss: 0.1223 - acc: 0.9652 - val_loss: 0.1311 - val_acc: 0.9644\n",
      "Epoch 26/50\n",
      " - 67s - loss: 0.1199 - acc: 0.9652 - val_loss: 0.1608 - val_acc: 0.9535\n",
      "Epoch 27/50\n",
      " - 69s - loss: 0.1169 - acc: 0.9659 - val_loss: 0.1417 - val_acc: 0.9628\n",
      "Epoch 28/50\n",
      " - 67s - loss: 0.1153 - acc: 0.9665 - val_loss: 0.1419 - val_acc: 0.9599\n",
      "Epoch 29/50\n",
      " - 65s - loss: 0.1106 - acc: 0.9678 - val_loss: 0.1178 - val_acc: 0.9669\n",
      "Epoch 30/50\n",
      " - 67s - loss: 0.1103 - acc: 0.9678 - val_loss: 0.1301 - val_acc: 0.9655\n",
      "Epoch 31/50\n",
      " - 61s - loss: 0.1061 - acc: 0.9702 - val_loss: 0.1297 - val_acc: 0.9634\n",
      "Epoch 32/50\n",
      " - 58s - loss: 0.1047 - acc: 0.9691 - val_loss: 0.1216 - val_acc: 0.9673\n",
      "Epoch 33/50\n",
      " - 57s - loss: 0.1528 - acc: 0.9577 - val_loss: 0.1364 - val_acc: 0.9627\n",
      "Epoch 34/50\n",
      " - 57s - loss: 0.1084 - acc: 0.9684 - val_loss: 0.1196 - val_acc: 0.9662\n",
      "Epoch 35/50\n",
      " - 57s - loss: 0.1052 - acc: 0.9692 - val_loss: 0.1327 - val_acc: 0.9613\n",
      "Epoch 36/50\n",
      " - 57s - loss: 0.1046 - acc: 0.9693 - val_loss: 0.1125 - val_acc: 0.9682\n",
      "Epoch 37/50\n",
      " - 57s - loss: 0.1024 - acc: 0.9693 - val_loss: 0.1140 - val_acc: 0.9679\n",
      "Epoch 38/50\n",
      " - 57s - loss: 0.0994 - acc: 0.9702 - val_loss: 0.1148 - val_acc: 0.9705\n",
      "Epoch 39/50\n",
      " - 57s - loss: 0.0969 - acc: 0.9715 - val_loss: 0.1118 - val_acc: 0.9693\n",
      "Epoch 40/50\n",
      " - 56s - loss: 0.0954 - acc: 0.9713 - val_loss: 0.1274 - val_acc: 0.9652\n",
      "Epoch 41/50\n",
      " - 56s - loss: 0.0954 - acc: 0.9721 - val_loss: 0.1088 - val_acc: 0.9711\n",
      "Epoch 42/50\n",
      " - 57s - loss: 0.0915 - acc: 0.9730 - val_loss: 0.1114 - val_acc: 0.9679\n",
      "Epoch 43/50\n",
      " - 57s - loss: 0.0936 - acc: 0.9723 - val_loss: 0.1195 - val_acc: 0.9662\n",
      "Epoch 44/50\n",
      " - 56s - loss: 0.0889 - acc: 0.9735 - val_loss: 0.1091 - val_acc: 0.9720\n",
      "Epoch 45/50\n",
      " - 56s - loss: 0.0895 - acc: 0.9733 - val_loss: 0.1082 - val_acc: 0.9728\n",
      "Epoch 46/50\n",
      " - 57s - loss: 0.0877 - acc: 0.9740 - val_loss: 0.1091 - val_acc: 0.9716\n",
      "Epoch 47/50\n",
      " - 56s - loss: 0.0870 - acc: 0.9746 - val_loss: 0.1144 - val_acc: 0.9701\n",
      "Epoch 48/50\n",
      " - 56s - loss: 0.0866 - acc: 0.9742 - val_loss: 0.1200 - val_acc: 0.9676\n",
      "Epoch 49/50\n",
      " - 56s - loss: 0.0842 - acc: 0.9750 - val_loss: 0.1041 - val_acc: 0.9742\n",
      "Epoch 50/50\n",
      " - 56s - loss: 0.0838 - acc: 0.9753 - val_loss: 0.1019 - val_acc: 0.9733\n"
     ]
    }
   ],
   "source": [
    "cnn3 = iclr_conv.ConvNet(None, None, 3)\n",
    "cnn3.run_conv_net(X_train, X_test, y_train, y_test, epoch_num=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn1 = iclr_conv.ConvNet(None, None, 0)\n",
    "cnn1.run_conv_net(X_train, X_test, y_train, y_test, epoch_num=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sauvegarder les données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('data/cnn3_loss',cnn3.loss)\n",
    "np.save('data/cnn3_val_loss',cnn3.val_loss)\n",
    "np.save('data/cnn3_acc',cnn3.acc)\n",
    "np.save('data/cnn3_val_acc',cnn3.val_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('data/cnn2_loss',cnn2.loss)\n",
    "np.save('data/cnn2_val_loss',cnn2.val_loss)\n",
    "np.save('data/cnn2_acc',cnn2.acc)\n",
    "np.save('data/cnn2_val_acc',cnn2.val_acc)"
   ]
  }
 ],
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