{ "metadata": { "name": "", "signature": "sha256:5545a0920b5da633f7332bf80a6ecccb31b4d9dd85ef090c9a927aa4e66b64d6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Probl\u00e8me facile: deux lunes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On va s'attaquer au probl\u00e8me bien simple, mais qui n'est pas lin\u00e9airement s\u00e9parable, afin de se familiariser avec les r\u00e9seaux de neurones feedforward, aussi appel\u00e9 MLP (multilayer perceptron). Vous devez t\u00e9l\u00e9charger les donn\u00e9es [ici](http://www.iro.umontreal.ca/~memisevr/teaching/ift3395_2014/demos/2moons.txt)." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "R\u00e9gression logistique" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2\n", "%pylab inline\n", "%aimport numpy\n", "np=numpy\n", "import time\n", "import utilitaires7 as utilitaires" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Using gpu device 0: GeForce GTX TITAN Black\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Regardez dans le fichier utilitaires7 et lisez la documentation des classes MulticlassLogisticRegression puis SGDTraining. Vous devrez initialiser la class MultiClassLogisticRegression et l'entra\u00eener. Vous pouvez ex\u00e9cuter la cellule suivante pour visualiser les r\u00e9sultats et comprendre pourquoi l'ensemble se nomme 2moons. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "[train_x, train_y], [test_x, test_y] = utilitaires.load_2moons()\n", "\n", "dim = train_x.shape[1]\n", "n_classes = np.unique(train_y).shape[0]\n", "learning_rate = 0.1\n", "\n", "modele = utilitaires.MultiClassLogisticRegression(dim, n_classes)\n", "modele.train(train_data=train_x, train_labels=train_y, learning_rate=learning_rate, max_epoch=100)\n", "\n", "print modele.compute_cost(test_x, test_y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 6% : \u00e9poque 6 : perde = 0.441156 \r", " 12% : \u00e9poque 12 : perde = 0.428876 \r", " 18% : \u00e9poque 18 : perde = 0.427547 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 24% : \u00e9poque 24 : perde = 0.427370 \r", " 30% : \u00e9poque 30 : perde = 0.427417 \r", " 36% : \u00e9poque 36 : perde = 0.427485 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 42% : \u00e9poque 42 : perde = 0.427537 \r", " 48% : \u00e9poque 48 : perde = 0.427570 \r", " 54% : \u00e9poque 54 : perde = 0.427591 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 60% : \u00e9poque 60 : perde = 0.427602 \r", " 66% : \u00e9poque 66 : perde = 0.427609 \r", " 72% : \u00e9poque 72 : perde = 0.427613 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 78% : \u00e9poque 78 : perde = 0.427616 \r", " 84% : \u00e9poque 84 : perde = 0.427617 \r", " 90% : \u00e9poque 90 : perde = 0.427618 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 96 : perde = 0.427618 \n", "0.22\n" ] } ], "prompt_number": 271 }, { "cell_type": "code", "collapsed": false, "input": [ "utilitaires.plot_training_curves(modele.epochs, modele.loss_curves, \n", " title=u\"Courbe d'apprentissage d'un\\n mod\u00e8le de r\u00e9gression logistique - Fonction de perte\",\n", " ylabel=\"Perte\")\n", "utilitaires.plot_training_curves(modele.epochs, modele.cost_curves, \n", " title=u\"Courbe d'apprentissage d'un\\n mod\u00e8le de r\u00e9gression logistique - Erreur de classification\",\n", " ylabel=\"Taux d'erreur\")\n", "utilitaires.plot_decision_frontiers(modele, train_x, train_y, test_x, test_y, n_points=50)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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4oMV26ZxzOedNiO2Q38jhnHMtywNYjngAc865luUBLEc8gDnnXMvyAJYjHsCc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\nNdgXYrEqZF+IANu2Qdeu8P7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ca5S3ACapPXANMA4YBRwlaWRastnA3mY2GrgYuDHOrwLONbOdgLHADySNiMt+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\nUJYsWZLxeM455xx+9KMf1Zq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8V0PpBXsdTsbUUU69cjxIim17A4m6VcgFciYyU+xDhgVn9Z1MtLxAIaTog+7E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"text": [ "" ] } ], "prompt_number": 272 }, { "cell_type": "markdown", "metadata": {}, "source": [ "C'est tout pour la r\u00e9gression logistique pour l'instant. Nous y reviendrons apr\u00e8s pour le comparer avec les r\u00e9seaux de neurones sur les donn\u00e9es MNIST" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "R\u00e9seaux de neurones" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lisez maintenant la documentation des classes FeedForwardNeuralNet et FeedForwardNeuralNetLayer pour comprendre leur fonctionnement. Initialisez le mod\u00e8le avec une seule couche cach\u00e9es de 2 unit\u00e9s cach\u00e9es et lancez son entra\u00eenement. Vous pouvez encore une fois ex\u00e9cuter la cellule suivante pour visualiser les r\u00e9sultats. \n", "\n", "Une fois cela fait pour 2 unit\u00e9s cach\u00e9es, r\u00e9essayez avec 3, 4 puis 5 unit\u00e9s cach\u00e9es. Qu'observez vous comme diff\u00e9rence? Comment est-ce que la fonti\u00e8re de d\u00e9cision est fragment\u00e9? Vous pouvez vous amuser et augmenter le nombre d'unit\u00e9 d'un ordre de grandeur \u00e0 50 ou plus. Qu'est-ce qu'il se passe, c'est mieux ou pas? " ] }, { "cell_type": "code", "collapsed": false, "input": [ "[train_x, train_y], [test_x, test_y] = utilitaires.load_2moons()\n", "\n", "n_in = train_x.shape[1]\n", "n_classes = np.unique(train_y).shape[0]\n", "learning_rate = 0.5\n", "\n", "modele = utilitaires.FeedForwardNeuralNet(n_in, n_hids=[10], n_out=n_classes, non_linearities=\"sigmoid\")\n", "modele.train(train_data=train_x, train_labels=train_y, learning_rate=learning_rate, max_epoch=1000)\n", "\n", "print modele.compute_cost(test_x, test_y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 5% : \u00e9poque 51 : perte = 0.407588 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 10% : \u00e9poque 102 : perte = 0.243349 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 153 : perte = 0.180381 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 204 : perte = 0.151244 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 255 : perte = 0.134466 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 306 : perte = 0.123460 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 357 : perte = 0.115602 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 408 : perte = 0.109643 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 459 : perte = 0.104912 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 510 : perte = 0.101008 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.097675 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 612 : perte = 0.094727 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 663 : perte = 0.092008 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 714 : perte = 0.089351 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 76% : \u00e9poque 765 : perte = 0.086543 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 816 : perte = 0.083316 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 867 : perte = 0.079473 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 918 : perte = 0.075130 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 969 : perte = 0.070705 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.01625\n" ] } ], "prompt_number": 466 }, { "cell_type": "code", "collapsed": false, "input": [ "utilitaires.plot_training_curves(modele.epochs, modele.loss_curves, \n", " title=u\"Courbe d'apprentissage d'un\\n r\u00e9seau feedforward - Fonction de perte\",\n", " ylabel=\"Perte\")\n", "utilitaires.plot_training_curves(modele.epochs, modele.cost_curves, \n", " title=u\"Courbe d'apprentissage d'un\\n r\u00e9seau feedforward - Erreur de classification\",\n", " ylabel=\"Taux d'erreur\")\n", "utilitaires.plot_decision_frontiers(modele, train_x, train_y, test_x, test_y, n_points=50)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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OhBJXSX371HPsycA9seXTgVvr2f+/gbtjywuB2cAs4Nw6jvFsqqhw79zZfePG\nrCZD8lC2P7t1qays9K985St+9dVXe0VFhS9cuNCHDRvmzz77rLu7X3nlld6xY0d/5plnvKqqyqdM\nmeLjx493d/fS0lIfPHiwl5eXu7v74sWL/eOPP3Z396lTp/pBBx3kZWVlvnXrVj///PN90qRJ7u7+\nySefuJn5hRde6Fu2bPHnnnvO27dv7yeeeKKvXLnSy8rKvG/fvv7KK6+4u/t9993nbdu29alTp/q2\nbdv8T3/6k3fr1s3Xrl3r7u5FRUV+7733urv7k08+6cOHD/fS0lKvrKz0a665xg8++OCU1z537lzf\ndddd/bXXXvMtW7b4pZde6m3btvUXX3xxu33ffvtt79u3r8+YMcOrqqr8/vvv98LCQt+6dau7uxcW\nFvqBBx7o5eXlvmbNGh81apTfeeed7u5eUlLigwYNqnW+K6+80tu1a+d/+9vf3N39iy++8Lfeesvf\nfPNNr6ys9EWLFvmoUaN86tSp1ceYWXX+nnnmmd6rVy+fOXOmb9u2zU877TQ/5ZRT3N1948aNPmjQ\nIJ82bZpXVlb67NmzvXfv3v7BBx9UH9u7d29/++23/csvv/QjjzzShw4d6g8++KBXVVX5L3/5Sz/i\niCN2+PORyJdU+ZkQ/V9s/12faqXXDgYvAt0b2i/FcSc1NlABRwAfAD1i6/pHf/sA7wCHpjjOr7zy\nyurp5ZdfrjMD0uWAA9zfeCPjLyt5rqFAFdqV7vjUVP/5z398yJAhtdZdd911fvbZZ7t7+CI66qij\nqrfNnTvXO3Xq5O7u8+fP9759+/oLL7xQ/YWdMGrUqFpfUMuXL/d27dp5ZWVldaBavnx59fZevXr5\nY489Vr180kknVX9J33fffT5gwIBa5x83bpw/+OCD7l47UE2YMKF63j180Xbu3NmXLFmy3bVfddVV\n1cHT3X3Tpk3evn37lF+sF1xwgV9xxRW11u21117+6quvunv4Qn744Yert/385z/3Cy64wN3dX375\n5ZSB6vDDD9/udeJ+97vf+Xe/+93q5XigOuuss/zcc8+t3vbPf/7TR44c6e7u06dP90MPPbTWuc47\n7zy/6qqr3D0EqvPOO69626233uqjR4+uXp4zZ453797d3Xfs85HIl+YEqsb09bcJeM/Mno/mEyWZ\nhlr+lQHxO4aDgWXJO0UNKO4BJrj72sR6dy+P/q40s78SqhJfSz6+uLi4EZeQPonqv/HjG95XpLGy\n1XJ98eKGTtk0AAAZ5ElEQVTFLF++nB49elSvq6ys5LDDDqte3m233arnO3fuzJdffklVVRXDhw9n\n6tSpFBcXM3fuXI455hh++9vf0r9/fxYtWlRdXZXQtm1bVqxYkfK8nTp12m5506ZN1csDBw6sle6h\nQ4dSXl6e8nouueQSfvazn9VaX1ZWtl2DhvLycgYNqrn70LlzZ3r16pUil8J5H3jggVrVXhUVFSxf\nvrx6uV+/frXSH9+WSvy1AT766CMuvfRS3nrrLTZv3sy2bdv46le/Wufxyfm1cePG6rS++eabtd7T\nbdu28cMf/hAI1Zl9+9Z0NtSxY8day8nnau7nI/7eN1VjjnwCuAJ4hVAN91Y0NWQWMMLMCs2sPTAR\nqNX0wMyGROc/3d0XxNZ3NrMu0fwuhGe43iMH6T6VtCRDhgxh9913Z+3atdXT559/ztNPPw003KBg\n0qRJvPbaayxevBgzY/LkydXn/de//lXrvJs3b6Z///7NSmdZWVmt5cWLFzMgxQONQ4YM4e677671\nups2bWJ8il+W/fv3Z+nSpdXLmzdvZvXq1O3GhgwZwuWXX17rvBs3bmTixIkNpj1VHqZqqXjhhRcy\nevRoFixYwPr167n22mupqqpq8Pyp0nr44YfXSuuGDRu4/fbbm3yuwYMH79Dno7kNUhoMVO4+DXgM\neNPd73f3ae5+fyOO2wZcRHhA+APgT+4+z8zON7Pzo91+BfQA7khqht4PeM3M3iE0snja3Z9r6sVl\nwv77w1uNCdsieWDcuHF06dKF3/zmN3zxxRdUVlby/vvvM2vWLKD+jnQ/+ugjXnrpJbZs2UKHDh3o\n2LFjdcu1Cy64gMsuu6y6IcPKlSt5qolNZuOv/dlnn3HLLbdQUVHBn//8Z0pLSzn22GO3O+aCCy7g\nuuuu44MPPgBg/fr1tRoSxJ188sk8/fTTvP7662zdupVf/epXdQaGc889lzvvvJMZM2bg7mzatIl/\n/OMf1SWP+uy2226sXr2azz+vGYAiVb5u3LiRLl260LlzZ0pLS7njjjvqPGd978txxx3HRx99xEMP\nPURFRQUVFRXMnDmT0tLSBo9NtiOfDwjX/vHHHzf69RIaDFRm9h1Co4Z/RctjzaxRnzB3f8bd93L3\n4e5+fbTuLne/K5r/L3fv5e5jo2lctH6hu4+Jpn0Sx+aiceNg+XJ4++1sp0Rkx7Vp04ann36ad955\nh2HDhtGnTx/OO++86i/VVL/8E8tbtmxhypQp9OnTh/79+7Nq1Squvz78615yySV85zvf4eijj6Zr\n164cdNBBzJgxY7tz1Cexj5kxfvx45s+fT58+fbjiiit4/PHHa1VHJZx44olMnjyZU045hW7durHv\nvvvy7LPPpjz/6NGjuf322zn11FMZMGAAPXv2rPN5pwMOOIB77rmHiy66iJ49ezJixAgeeOCBOq8j\nnm8jR45k0qRJDBs2jJ49e1JeXp4yX2+66SYeeeQRunbtynnnnccpp5xSa5/k+brely5duvDcc88x\nffp0Bg4cSP/+/ZkyZUr181fJx9Z3roKCgmZ/PgCmTJnCNddcQ48ePfjtb3+bMq9S5l9DEdDM3gaO\nBF5297HRuvfdPesDXGSzC6W4qVPhtdfg8ceznRLJF+pCSWR7O9KFUoW7r0ta1/SK0hbsvPPg9ddh\n7txsp0REpOVpTKCaa2anAW3NbISZ3Up4GFginTvD//2/cO212U6JiEjL05iqv87AL6nde/rV7v5l\nmtPWoFyp+gPYsAGGDQslqz33zHZqJNep6k9ke3VV/dUZqMysE3ABMByYA/zRQ1dIOSOXAhXAr38N\nn3wC992X7ZRIrlOgEtlecwLVY8BW4P8BE4DF7n5JWlPZRLkWqNauheHDQ3P1wsJsp0ZymQKVyPaa\nE6jec/d9o/m2wMxEq79ckWuBCuCyy2DNGrjzzmynRHKZApXI9poTqGbHA1Pyci7IxUC1ciXstRfM\nmQODkvuCF4lkcsgIkXzS1EBVCWyOreoEfFFzLu+601PYRLkYqAB+9jOorAzPV4mI5Jq6Si65qsFW\nf7ksVwNVeTnsvTfMmwex/hlFRHJCvgWq5ndnK3Xq3x9OPRWa0EOIiIjUQSWqNFmyBMaOhY8+gjpG\nChARyQqVqASAIUPge9+D3/8+2ykREclvKlGl0ccfw4EHhr/dumU7NSIigUpUUm2PPeDYY+G227Kd\nEhGR/KUSVZqVlsJhh8HChbDrrtlOjYiISlSSZORIOOII9VQhItJcKlFlwJw5cMwxoVTVqVO2UyMi\nrZ1KVLKd/fYLjSr+8Idsp0REJP+kNVCZ2QQzKzWz+WY2OcX208zsXTObY2avm9l+jT0231x+Ofzm\nN7BlS7ZTIiKSX9IWqMysALiNMETIaGCSmY1K2m0hcJi77wdcDdzdhGPzyte+FrpVuv/+bKdERCS/\npLNENQ5Y4O6LogEXpwMnxHdw9zfcfX20+CYwqLHH5qMrroAbboCKnBp+UkQkt6UzUA0ElsaWl0Xr\n6nIO8M9mHpsXDjkEhg6FRx/NdkpERPJH2zSeu9HN8czsCOBHwCFNPba4uLh6vqioiKKiosYemhVX\nXAEXXginnQYFBdlOjYi0BiUlJZSUlGQ7Gc2WtubpZjYeKHb3CdHyFKDK3W9M2m8/4AlggrsvaOKx\nedE8Pc49lKwuuQQmTsx2akSkNVLz9BqzgBFmVmhm7YGJwFPxHcxsCCFInZ4IUo09Nl+ZhVLVNddA\nVVW2UyMikvvSFqjcfRtwEfAs8AHwJ3efZ2bnm9n50W6/AnoAd5jZbDObUd+x6Uprpk2YAO3bw1Mt\nIvSKiKSXeqbIkr/+NZSqZs0KpSwRkUxR1Z80ygknwNat8K9/ZTslIiK5TYEqS9q0Cb1VXH11aGAh\nIiKpKVBl0fe/D6tXw8svZzslIiK5S4EqiwoK4LLLwr0qERFJTYEqy049FRYtgtdfz3ZKRERykwJV\nlrVrB7/4BRQX67kqEZFUFKhywJlnwubNcM45UFmZ7dSIiOQWBaoc0KEDPPssLFkSqgLVu7qISA0F\nqhyx667wj3+EktVJJ8GXX2Y7RSIiuUGBKod07AhPPAGdOsHxx8PGjdlOkYhI9ilQ5Zh27eCRR2DI\nEDjmGFi3LtspEhHJLgWqHFRQAH/4AxxwABx5JKxale0UiYhkjwJVjmrTBn7/+9DT+uGHQ3l5tlMk\nIpId6RzhV3aQGVx3XWhocdhh8MILYSh7EZHWRIEqD1x2GeyySwhWzz8Pe+6Z7RSJiGSOAlWeuOSS\nULI64ogwNMi++2Y7RSIimaFAlUfOOQc6d4ajjoKnn4avfjXbKRIRST8FqjwzaVIIVsceC48/Doce\nmu0UiYikl1r95aETToCHH4bvfS/csxIRacnSGqjMbIKZlZrZfDObnGL7SDN7w8y+NLOfJW1bZGZz\nzGy2mc1IZzrz0VFHwV//CqedBn/7W7ZTIyKSPmmr+jOzAuA24JtAGTDTzJ5y93mx3VYDFwMnpjiF\nA0XuviZdacx3X/86PPMMHHdc6CNw0qRsp0hEZOdLZ4lqHLDA3Re5ewUwHTghvoO7r3T3WUBd/YVb\nGtPXIhxwQHi+6r//G+65J9u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88sn8/Oc/58ADD6Rjx47svvvufPrpp5xzzjl07dqVoUOHMnny5A3+fJxwwgnM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\nzWwX4E/uPiKumwns5O5f1HG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3daXP93i1wlANKC1+DUoV/wek/n7u8V9YmzeB/6QB+D+UWAn8iiKXUuR14L/p\n5DI0/XkzRS6jwD8R7nY7KEPX6RK0gNzvYNficr9HsxDLUYmoT6Db0R9RHM/LKLXmMjTleZfH70aB\ngC8iy/pytObNfSiW6E2UgjUMuVC62LHj1pSrk2NvIneP1AFOPOkkXkCr0xyA/npDUIr6ix538rGe\n/ACPj7U2f+F9PhqtYgO1VeQhe5ZC9V9pVvi+KB75Ims3FCwBpAAU6KDADykQUeCfKPA6Kjn7Epr4\n2w9Fh4xCIX8ve7wahlEuYzvU+FjjrxjfB6nWA4ABNNinA5Ez5lUUjDkQKcRx6NnhT5T4B3TVLwf+\nBjmuLgP+hhKDkTI+AMWerEQWc6PxQ9Et5Q3KyeiOB0gBH25n4XiEbjOHo29iNUp87zI+Ft3OJ9u4\nXmTixBMyXKUcfY1cadcBGpqamI2UbwC7OGjRV1dP5HHjgcmXGn/U47NQgNdW9Pd260ZCWWmn+aRd\nlb0wZXt3EcZeBaTqAmQPvoZUWAmlgUCJEqXd+P32E78X2bUgD+/rCbwaOmnkDzTwBlA0/ibQScMu\nXqSB22lgFRBQQpaui7q4A13VCFnDq/bgBW4G/hV9A7/x+M0U+Lr1czvl5STS+O+Ri6MSvw1N0Tq+\nPqFNhH41j9HcnD+YvxOQfwt1gKfmz+dspHA3oQVaZxr/GKqjvQnFD9yFbKmB6K8Z2Ot266sJKfGe\nLN3lx2SntanWh48CMJ3yuUxH49+IMjvvQMtsVRptkZC3bN/bUSz6dHTucV4Nm+kCzucXPECRduBc\nfsGDxs8x3gGcw895kOKuaI5bkCf+HFQcdyf6lm7x+M1Ae4br3oYqifwaRYicAfwKfaunefwDKGre\nRYI4PsXjkz0+CU3DRsBJxgcgn3YLcDYLFszLcJVy9DVyS7sO0NzUxEgUXTsAuQA+b3yUx8cgFdGG\n/LtF9Jdss3dXO9lNnlVC2OtRV++hEU2PfcH4IR5/D/BFyjl7lVBAU3d/YfzwGP8iWf8IBeAoityA\nbnVHGy8Ax8T4X1qbY4A/8/jnra8JxgPjrk0WHI++bVDlbMcnxvh1xk9EUes+D1BB3ms8frW1mQTc\ngG6NV9g496OpKUvqVY6+Rq606wCTTz2V+eivNxZ24/M87iJwQ6r7lLOsNB5PAKm0zX8VMuVd6iaz\nAP1Ik3iD8WoYhlw+Dcgf7vgoFOncaLw69kOxNc3IC77I+Ajj/ZDX/Wl0BRzvj5JVnkG3z2EeH4pW\ncxyIfMfAvaryAAAgAElEQVTVMBitCjkY+ckXI0/6QBQPtI/1+7z119/4MOMvGO9nfLjH97XzedGu\nymg7twBYwNSpkzJdpRx9i9w9UgdYvXIlr6J4gE3IZXC3vW9GfttNZHMBdAf+ZGHI7oo7aUEC9yrx\n95n634pUYYTGvhDdTLYiZdtlXEivcrKeAo/RQAdFNtLIo2C8gVkEtNHJRhopUEy1/ws0UGIrmhlo\ntyM/asfcimYJ2hLkM9HzzVbkqNqBnmdmePJHULrNtgxXZTvlyPrtaEnfrcbvtyu1A3n9fb7Z2tyL\nfg07KP8yfL4T/Xq2UE5jXwAsZ+XKARnGl6OvkSvtOsBLS5dyKbKn1gKf8fhnkS22Dq3Q/eNu9Bt6\nvFLhf59X669A8y4rvkAzYUph0wLQSj9GE/E0Ujf7Ayvo2FVq9VFgI43I0VO5jFSRL/AEM+liMfAX\nzGUGXSwBJjKblchhNBFNS7plbtuQ1XkaJZahqIrTUCTHDrTu/Rpr93k0q9CBXBRxXvRG7fhMdBv6\nnMez4Eqk9AP0Dc+wK5bEG5CL4xH0d3fc1WR0/FPGm1HI32x0g2pC0fuXsWzZHRnHl6MvkbtH6gCj\nRo5kJ5qSGgG7+L7GP+rx7iD0XrVKT/etbHH/KLo5NFu2ILSzmg62UwQ6WEPHrtomP8EVdsqq6FbR\nxYV2hLV0cRJSYs+gW1oJ2fXrPe7H17hID58/hNwKTcganY6UZDNwgfF+xgt2bMf7W/vA41kQIbfI\ndBvnYOR7LiHXyHT0DOLzodbG50Xj040PN3mn8Y+j0MMb0C9oJyNGZAmMzNHXyC3tOsDREycy46WX\nGIf+bjPQBN5wZDuN83hvENp7Wu2NSnU3sqwyAy4NPB5DEVboOayy3eF25EpoQEsdJ40mPko/lTut\nxmBo7RaiRd6Gom/gfOOPGB9i8vM8fq7xmSiyZAjlELw0DEKW+1lIYT+KFpRzcscfRcp2YAofgFw6\nZ3j8I+gG8jh6inAoAjM4/vh8ubF3AnKlXQdYsnAhzcj67ESq4scpPAlp3uCQZLfIjey5GELocX+f\n0Nv2zxToqKhcC6aw+wLOG+4Uc3wc3V1kLL4tpLyMRIDyJFs9vsH4Lzzu5BjfSIGAkOSyDwUCSmxG\nEeq/RH7qV41vifHlyHWzFXa5dny+DT1JOP4SeoLYjp4e1lD2aS8EBvL00y9UuAY59hZypV0HWLt+\nPZeg+OXbkC23ESU8n49Ux23owfyHlNWPv7xWSDLCWPuklWscnLL29/Gt8r+nRIjy/G4F/hdKeLkV\nPYgvprSrLFIyerOkglPWaaPvTr/xcYT23mrHGYGUXiOy7LcbbzQOugqLrd3lu3iJv0VKcia6Qk8h\nC/qvKbEQWcrXmvwt464u9rXITbMOhfstQO6e61BMkePzjF/vcSdvNfkSdBNowjnYWltv6cY1ytFX\nyJV2HeCoI49kxZw5nICCtFag6N0xxo/35H+HbLHjUbWKLOVUw5T3tHY+4rZrM0qW3g9N+R3pcSHN\nEg5JXsUxbTRx+H2696SbgNvmPy/cGNvmI+lZpBNZ3EWUAengeAH4lif/lidvRcktjyEr/GTjm2J8\nkvHNyD3ThZbzbbbjOt6ObuGT0eTiVpRgMwdZ2I5vR1Vr5qJJ1on2cniGww8/nBxvP3KlXQcYNXYs\nd6EKFwPQIq4HIu/kQzE+BqkpV9yzr+tfF+z1QeAlCmwENlOikQJ3A/tTosn4Z3ZZwmFKb2ly6JlH\nPas7pLtuk3VIcVbaJ2lbiJTtp9D53IkiOVpQ/uen0MSmkzvuFPViry+f/xg9y7j2lxl3xaSaTf4J\n43egglP9bP9W4GHGjj0z5Xxy7E30WmkHQfCfKFt6bRRFE3o/pBzdxfzZszkR+ClSTycZd3luP7F2\nxxjvInvMBZRtXP+zg29nJtnBTg0/DEQEBEzmuyygC4zPp4sIOIEf8DS7T/51B2mThA7x0cVHWkts\nqt4kFWOR5V1EhaB8fhO6PuM9+XiUnNNF5ZuEv+9N6Fs5FC0PHKGEm5us/VDr/1Bkqa8EJjF37oJe\nnFeOWqEWlvbP0XLO8eUBc+wldBaLTEAJyj9BlvVRqArFQcjD+kfkKXUIY++VEFZo11FhW3xCEpqI\neI0SI4G1REylxClIcSyyQk/dRTULu5ny80S8Xdp+vYUrBJDWf6VI2zORW+LnMX6W8V+gWiXHo2/4\nY8gZ9h9VxnQ5qnVyDqo0fiuKXjnO48dSnhU5DTnUGlFNxW0Ui8uqHCPH3kCvlXYURY8HQXBw74eS\no6eYNGUKz915J2tRLPZ/e9t+7XF/orAnSNovrNDfnvI2FK0AmqBbgnytY5GCqNRbGna/LdRWMfdm\nX4ekUMFK/f4Q3WgaUMHX8cjp5Xh/44ci98WrqBJLNRxgfa5At/JCjL9uHGRZH4Ssb4e5TJ6cp7G/\nE5D7tOsA7e3tPInKDvXEI9zT9RC7jzDGZyIllFz8v3bIGiUeR0jPFHcYe3UHLk67gJ6PzkcTm3d4\n3MmLaDr5/Az9vmLtf+/tm8ZvQ0k3ByOXyjPAAtrbp3XzXHL0BfaK0g7DcBefNm0a06ZN2xuH/R+D\npxcuZDoK6uou4ushhjUZUVZcjJJMVldr6CFMkVeKg+lp7HfasSq1K9C7VTBD7/1cVGekA2UrPoCU\n9nRUZ6SIAjnvz9DvffZ+FuVp6DONByis724b/xmoFskOO8YBwHQWLZrZ47PKUR2zZs1i1qxZVdvt\ndaWdo/YYOHAgA9av5wq6r3STAuH2HoYjJRSgybCQ6r5gXwG754A2kv3W1Z4Tqj1jOLfGVzO2c0hr\nW61NfLzvQeVSb4rx8SjP9VvGDwL+rcrxrkJTT4ejWY/voeDLA4EfxPhRKLFmG+WFf99gwIC8YFRf\nIm7QfuUrX0lsl7tH6gAnT53K7BUruLQXfVSLRO4bzEE+7btTjgx73lLCWNv456R90+Ar5UqOpbS4\n8Wr9Z4F7Qkjq6ym0yuc+Cfx9KI19IYq1bqgwnmaU2j4AlYo92fgzKNaoBUWgnGj8GeODbf8ImMOU\nKSf19CRz1BC1CPn7DVomY98gCFYCX46i6Oe9HlmOzHht2TLWoxiALEiyL6tFIve0spjrUzZyaNxV\n+lsOKVX+0lHJOq6EnkZz9BXC2HsSHkPJLjtRPZB1KJnmMZQBuRllSLpMzBYU2OnqfDvehFwo21B2\nZSvl8rDrUWLNI9a/4xtRdT9XmvUtli0b1LtTzlET1CJ65LJaDCRHz/Haa6/tWp/7xSptK6WhV0Iz\nyeqlkipML7HkFPUVwBMJLaplKoaxdkkjaPHe25AlGpAez5wk6y7S0ttDyq6dQmwbCW1g99mGAlKe\nT9vnTo8XKSvpzyEl3mR8Vow3x/g1aDK4H3K9+HwemvBsRK6S03n99dsyXIMcfY3cPVIHOGDMGDa2\ntjIV2UhhSrtm9oyrTmsbR7XAOr8/90pS2GU0oKrfixK2+Yo4rgj9bZVuGU7ughz9lKL4PrVyAFV7\nXvFfldokcf89ab8S+jufhQo+Of4iUtw+P9N4PxTzfVMCPyt2jKWMHj025fg59iZypV0HOGLCBB5Y\nvJiD0RTSGmRD3YUeoq9GScr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hFNmMJg5B7gkX0b7W5AWkWH0+Av2kk3gjcm3sixSc483A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\ngg==\n", "text": [ "" ] } ], "prompt_number": 467 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Probl\u00e8me difficile: MNIST (reconnaissance de chiffres manuscrits)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'ensemble MNIST est bien plus difficile \u00e0 classifier. Vous vous rappelez surement des r\u00e9sultats obtenus dans votre premier devoir, de 15 et 19% d'erreur de classification environ sur l'ensemble de test. On va commencer par am\u00e9liorer ces r\u00e9sultats avec la r\u00e9gression logisitique multiclasse, qui fait mieux que la r\u00e9gression avec erreur quadratique.\n", "\n", "Pour rendre les exp\u00e9riences plus rapide, trois version de MNIST sont disponibles pour vous avec les fonctions `load_mini_mnist`, `load_medium_mnist` et `load_full_mnist`. Vous devez t\u00e9l\u00e9charger [ici](http://deeplearning.net/data/mnist/mnist.pkl.gz) l'ensemble pour avoir acc\u00e8s au format moyen et complet de MNIST.\n", "\n", "Le mini MNIST se trouve ici en 4 fichiers\n", "\n", "- www.iro.umontreal.ca/~memisevr/teaching/ift3395_2014/devoirs/train_images.txt\n", "- www.iro.umontreal.ca/~memisevr/teaching/ift3395_2014/devoirs/test_images.txt\n", "- www.iro.umontreal.ca/~memisevr/teaching/ift3395_2014/devoirs/train_labels.txt\n", "- www.iro.umontreal.ca/~memisevr/teaching/ift3395_2014/devoirs/test_labels.txt" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "R\u00e9gression logistique" ] }, { "cell_type": "code", "collapsed": false, "input": [ "[train_x, train_y], [valid_x, valid_y], [test_x, test_y] = utilitaires.load_mini_mnist()\n", "\n", "dim = train_x.shape[1]\n", "n_classes = np.unique(train_y).shape[0]\n", "learning_rate = 0.13\n", "\n", "modele = utilitaires.MultiClassLogisticRegression(dim, n_classes)\n", "modele.train(train_data=train_x, train_labels=train_y, learning_rate=learning_rate, batch_size=600, max_epoch=1000,\n", " monitoring_data={\"ensemble de validation\": (valid_x, valid_y)})\n", "\n", "print modele.compute_cost(test_x, test_y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 1% : \u00e9poque 11 : perte = 0.904442 \r", " 2% : \u00e9poque 22 : perte = 0.658073 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 3% : \u00e9poque 33 : perte = 0.546947 \r", " 4% : \u00e9poque 44 : perte = 0.479642 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 5% : \u00e9poque 55 : perte = 0.432679 \r", " 6% : \u00e9poque 66 : perte = 0.397106 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 7% : \u00e9poque 77 : perte = 0.368692 \r", " 8% : \u00e9poque 88 : perte = 0.345145 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 9% : \u00e9poque 99 : perte = 0.325103 \r", " 11% : \u00e9poque 110 : perte = 0.307696 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 12% : \u00e9poque 121 : perte = 0.292342 \r", " 13% : \u00e9poque 132 : perte = 0.278630 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 14% : \u00e9poque 143 : perte = 0.266262 \r", " 15% : \u00e9poque 154 : perte = 0.255015 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 16% : \u00e9poque 165 : perte = 0.244720 \r", " 17% : \u00e9poque 176 : perte = 0.235240 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 18% : \u00e9poque 187 : perte = 0.226470 \r", " 19% : \u00e9poque 198 : perte = 0.218321 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 209 : perte = 0.210722 \r", " 22% : \u00e9poque 220 : perte = 0.203612 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 23% : \u00e9poque 231 : perte = 0.196940 \r", " 24% : \u00e9poque 242 : perte = 0.190663 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 253 : perte = 0.184743 \r", " 26% : \u00e9poque 264 : perte = 0.179148 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 27% : \u00e9poque 275 : perte = 0.173851 \r", " 28% : \u00e9poque 286 : perte = 0.168827 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 29% : \u00e9poque 297 : perte = 0.164054 \r", " 30% : \u00e9poque 308 : perte = 0.159514 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 31% : \u00e9poque 319 : perte = 0.155189 \r", " 33% : \u00e9poque 330 : perte = 0.151064 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 34% : \u00e9poque 341 : perte = 0.147127 \r", " 35% : \u00e9poque 352 : perte = 0.143364 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 36% : \u00e9poque 363 : perte = 0.139766 \r", " 37% : \u00e9poque 374 : perte = 0.136321 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 38% : \u00e9poque 385 : perte = 0.133021 \r", " 39% : \u00e9poque 396 : perte = 0.129857 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 407 : perte = 0.126822 \r", " 41% : \u00e9poque 418 : perte = 0.123908 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 42% : \u00e9poque 429 : perte = 0.121110 \r", " 44% : \u00e9poque 440 : perte = 0.118420 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 451 : perte = 0.115834 \r", " 46% : \u00e9poque 462 : perte = 0.113346 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 47% : \u00e9poque 473 : perte = 0.110951 \r", " 48% : \u00e9poque 484 : perte = 0.108644 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 49% : \u00e9poque 495 : perte = 0.106421 \r", " 50% : \u00e9poque 506 : perte = 0.104279 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 517 : perte = 0.102212 \r", " 52% : \u00e9poque 528 : perte = 0.100219 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 53% : \u00e9poque 539 : perte = 0.098294 \r", " 55% : \u00e9poque 550 : perte = 0.096436 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.094641 \r", " 57% : \u00e9poque 572 : perte = 0.092906 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 58% : \u00e9poque 583 : perte = 0.091228 \r", " 59% : \u00e9poque 594 : perte = 0.089606 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 60% : \u00e9poque 605 : perte = 0.088035 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 616 : perte = 0.086516 \r", " 62% : \u00e9poque 627 : perte = 0.085044 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 63% : \u00e9poque 638 : perte = 0.083618 \r", " 64% : \u00e9poque 649 : perte = 0.082236 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 660 : perte = 0.080896 \r", " 67% : \u00e9poque 671 : perte = 0.079596 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 68% : \u00e9poque 682 : perte = 0.078336 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 69% : \u00e9poque 693 : perte = 0.077112 \r", " 70% : \u00e9poque 704 : perte = 0.075924 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 715 : perte = 0.074770 \r", " 72% : \u00e9poque 726 : perte = 0.073649 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 73% : \u00e9poque 737 : perte = 0.072559 \r", " 74% : \u00e9poque 748 : perte = 0.071499 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 75% : \u00e9poque 759 : perte = 0.070469 \r", " 77% : \u00e9poque 770 : perte = 0.069466 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 78% : \u00e9poque 781 : perte = 0.068490 \r", " 79% : \u00e9poque 792 : perte = 0.067540 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 80% : \u00e9poque 803 : perte = 0.066615 \r", " 81% : \u00e9poque 814 : perte = 0.065714 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 82% : \u00e9poque 825 : perte = 0.064835 \r", " 83% : \u00e9poque 836 : perte = 0.063979 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 84% : \u00e9poque 847 : perte = 0.063145 \r", " 85% : \u00e9poque 858 : perte = 0.062331 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 869 : perte = 0.061536 \r", " 88% : \u00e9poque 880 : perte = 0.060761 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 89% : \u00e9poque 891 : perte = 0.060005 \r", " 90% : \u00e9poque 902 : perte = 0.059267 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 913 : perte = 0.058545 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 92% : \u00e9poque 924 : perte = 0.057841 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 93% : \u00e9poque 935 : perte = 0.057152 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 94% : \u00e9poque 946 : perte = 0.056480 \r", " 95% : \u00e9poque 957 : perte = 0.055822 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 968 : perte = 0.055179 \r", " 97% : \u00e9poque 979 : perte = 0.054550 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 99% : \u00e9poque 990 : perte = 0.053935 \n", "0.13\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "utilitaires.plot_training_curves(modele.epochs, modele.loss_curves, title=u\"Courbe d'apprentissage d'un\\n r\u00e9seau feedforward - Fonction de perte\", ylabel=\"Perte\")\n", "utilitaires.plot_training_curves(modele.epochs, modele.cost_curves, title=u\"Courbe d'apprentissage d'un\\n r\u00e9seau feedforward - Erreur de classification\", ylabel=\"Taux d'erreur\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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2hRL42gCrV6/mjjvu4Msvv2Tv3r0UFxdz8sknV/r84PIqKCjw53XRokXl3tPi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\nBaX58yEpKdI5MsaYxsUCUi3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hCzIXcMPMG6wfQFNpZGZmcumll5KSkkK7du2YMGFC/rwxY8Zw+eWXM2jQIOrV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\nk6gm4xJVCtADCAz4chauqXr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"text": [ "" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "On va utiliser l'ensemble de validation pour optimiser les hyper-param\u00e8tres et v\u00e9rifier le r\u00e9sultat final sur l'ensemble de test. Il ne faut jamais optimier les hyper-param\u00e8tres sur l'ensemble de test lui-m\u00eame, car on peut se retrouver \u00e0 \"over-fitter\" l'ensemble de test. Les hyper-param\u00e8tres donnant les meilleurs r\u00e9sultats sur l'ensemble de validation ne sont pas n\u00e9cessairement ceux qui donneront les meilleurs r\u00e9sultats du l'ensemble de test. Il faut donc prendre bien soin de garder un ensemble de test bien \u00e0 l'abris du mod\u00e8le pendant l'entra\u00eenement et l'optimisation des hyper-param\u00e8tres afin d'avoir une \u00e9valuation non biais\u00e9. \n", "\n", "Choisissez un hyper-param\u00e8tres que vous d\u00e9sirez optimiser. Je vous conseille le taux d'apprentissage, mais \u00e7a peut \u00eatre aussi une p\u00e9nalit\u00e9 sur les poids comme la p\u00e9nalit\u00e9 de norme L1 ou L2. Cr\u00e9ez une liste de valeur que vous voudriez tester et entra\u00eenez le mod\u00e8le sur chacune. Apr\u00e8s chaque entra\u00eenement, enregistrez dans deux listes `train_results` et `valid_results` les r\u00e9sultats de `compute_cost` sur l'ensemble d'entra\u00eenement et l'ensemble de validation. Gardez aussi les mod\u00e8les dans une liste `modeles`. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "[train_x, train_y], [valid_x, valid_y], [test_x, test_y] = utilitaires.load_mini_mnist()\n", "\n", "n_in = train_x.shape[1]\n", "n_classes = np.unique(train_y).shape[0]\n", "learning_rate = 0.13\n", "\n", "train_results = []\n", "valid_results = []\n", "modeles = []\n", "\n", "learning_rates = [0.01, 0.05, 0.1, 0.5, 1.0, 10.]\n", "\n", "for learning_rate in learning_rates:\n", "\n", " modele = utilitaires.MultiClassLogisticRegression(n_in=n_in, n_out=n_classes)\n", " modele.train(train_data=train_x, train_labels=train_y, learning_rate=learning_rate, max_epoch=1000, batch_size=600)\n", "\n", " train_results.append(modele.compute_cost(train_x, train_y))\n", " valid_results.append(modele.compute_cost(valid_x, valid_y))\n", " modeles.append(modele)\n", "\n", "print learning_rates\n", "print train_results\n", "print test_results" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 1% : \u00e9poque 11 : perte = 2.130574 \r", " 2% : \u00e9poque 22 : perte = 1.919891 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 3% : \u00e9poque 33 : perte = 1.743123 \r", " 4% : \u00e9poque 44 : perte = 1.594787 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 5% : \u00e9poque 55 : perte = 1.470363 \r", " 6% : \u00e9poque 66 : perte = 1.365705 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 7% : \u00e9poque 77 : perte = 1.277176 \r", " 8% : \u00e9poque 88 : perte = 1.201731 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 9% : \u00e9poque 99 : perte = 1.136904 \r", " 11% : \u00e9poque 110 : perte = 1.080726 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 12% : \u00e9poque 121 : perte = 1.031636 \r", " 13% : \u00e9poque 132 : perte = 0.988400 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 14% : \u00e9poque 143 : perte = 0.950034 \r", " 15% : \u00e9poque 154 : perte = 0.915755 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 16% : \u00e9poque 165 : perte = 0.884932 \r", " 17% : \u00e9poque 176 : perte = 0.857053 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 18% : \u00e9poque 187 : perte = 0.831701 \r", " 19% : \u00e9poque 198 : perte = 0.808532 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 209 : perte = 0.787262 \r", " 22% : \u00e9poque 220 : perte = 0.767653 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 23% : \u00e9poque 231 : perte = 0.749506 \r", " 24% : \u00e9poque 242 : perte = 0.732652 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 253 : perte = 0.716946 \r", " 26% : \u00e9poque 264 : perte = 0.702267 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 27% : \u00e9poque 275 : perte = 0.688508 \r", " 28% : \u00e9poque 286 : perte = 0.675577 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 29% : \u00e9poque 297 : perte = 0.663395 \r", " 30% : \u00e9poque 308 : perte = 0.651892 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 31% : \u00e9poque 319 : perte = 0.641007 \r", " 33% : \u00e9poque 330 : perte = 0.630687 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 34% : \u00e9poque 341 : perte = 0.620882 \r", " 35% : \u00e9poque 352 : perte = 0.611552 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 36% : \u00e9poque 363 : perte = 0.602659 \r", " 37% : \u00e9poque 374 : perte = 0.594168 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 38% : \u00e9poque 385 : perte = 0.586050 \r", " 39% : \u00e9poque 396 : perte = 0.578278 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 407 : perte = 0.570826 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 41% : \u00e9poque 418 : perte = 0.563673 \r", " 42% : \u00e9poque 429 : perte = 0.556799 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 44% : \u00e9poque 440 : perte = 0.550185 \r", " 45% : \u00e9poque 451 : perte = 0.543815 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 46% : \u00e9poque 462 : perte = 0.537673 \r", " 47% : \u00e9poque 473 : perte = 0.531746 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 48% : \u00e9poque 484 : perte = 0.526020 \r", " 49% : \u00e9poque 495 : perte = 0.520485 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 50% : \u00e9poque 506 : perte = 0.515129 \r", " 51% : \u00e9poque 517 : perte = 0.509942 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 52% : \u00e9poque 528 : perte = 0.504914 \r", " 53% : \u00e9poque 539 : perte = 0.500039 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 55% : \u00e9poque 550 : perte = 0.495307 \r", " 56% : \u00e9poque 561 : perte = 0.490711 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 57% : \u00e9poque 572 : perte = 0.486245 \r", " 58% : \u00e9poque 583 : perte = 0.481901 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 59% : \u00e9poque 594 : perte = 0.477675 \r", " 60% : \u00e9poque 605 : perte = 0.473561 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 616 : perte = 0.469552 \r", " 62% : \u00e9poque 627 : perte = 0.465646 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 63% : \u00e9poque 638 : perte = 0.461836 \r", " 64% : \u00e9poque 649 : perte = 0.458119 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 660 : perte = 0.454491 \r", " 67% : \u00e9poque 671 : perte = 0.450948 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 68% : \u00e9poque 682 : perte = 0.447486 \r", " 69% : \u00e9poque 693 : perte = 0.444103 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 70% : \u00e9poque 704 : perte = 0.440794 \r", " 71% : \u00e9poque 715 : perte = 0.437558 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 72% : \u00e9poque 726 : perte = 0.434390 \r", " 73% : \u00e9poque 737 : perte = 0.431289 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 74% : \u00e9poque 748 : perte = 0.428253 \r", " 75% : \u00e9poque 759 : perte = 0.425278 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 77% : \u00e9poque 770 : perte = 0.422362 \r", " 78% : \u00e9poque 781 : perte = 0.419504 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 79% : \u00e9poque 792 : perte = 0.416700 \r", " 80% : \u00e9poque 803 : perte = 0.413951 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 814 : perte = 0.411252 \r", " 82% : \u00e9poque 825 : perte = 0.408604 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 83% : \u00e9poque 836 : perte = 0.406003 \r", " 84% : \u00e9poque 847 : perte = 0.403449 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 85% : \u00e9poque 858 : perte = 0.400940 \r", " 86% : \u00e9poque 869 : perte = 0.398474 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 88% : \u00e9poque 880 : perte = 0.396050 \r", " 89% : \u00e9poque 891 : perte = 0.393668 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 90% : \u00e9poque 902 : perte = 0.391325 \r", " 91% : \u00e9poque 913 : perte = 0.389020 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 92% : \u00e9poque 924 : perte = 0.386752 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 93% : \u00e9poque 935 : perte = 0.384521 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 94% : \u00e9poque 946 : perte = 0.382324 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 95% : \u00e9poque 957 : perte = 0.380162 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 968 : perte = 0.378032 \r", " 97% : \u00e9poque 979 : perte = 0.375935 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 99% : \u00e9poque 990 : perte = 0.373869 \n", "\r", " 1% : \u00e9poque 11 : perte = 1.425971" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", " 2% : \u00e9poque 22 : perte = 1.058589 \r", " 3% : \u00e9poque 33 : perte = 0.871878 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 4% : \u00e9poque 44 : perte = 0.758937 \r", " 5% : \u00e9poque 55 : perte = 0.682184 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 6% : \u00e9poque 66 : perte = 0.625826 \r", " 7% : \u00e9poque 77 : perte = 0.582156 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 8% : \u00e9poque 88 : perte = 0.546967 \r", " 9% : \u00e9poque 99 : perte = 0.517761 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 11% : \u00e9poque 110 : perte = 0.492957 \r", " 12% : \u00e9poque 121 : perte = 0.471501 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 13% : \u00e9poque 132 : perte = 0.452663 \r", " 14% : \u00e9poque 143 : perte = 0.435918 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 154 : perte = 0.420878 \r", " 16% : \u00e9poque 165 : perte = 0.407249 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 17% : \u00e9poque 176 : perte = 0.394807 \r", " 18% : \u00e9poque 187 : perte = 0.383371 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 19% : \u00e9poque 198 : perte = 0.372802 \r", " 20% : \u00e9poque 209 : perte = 0.362983 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 22% : \u00e9poque 220 : perte = 0.353821 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 23% : \u00e9poque 231 : perte = 0.345238 \r", " 24% : \u00e9poque 242 : perte = 0.337168 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 253 : perte = 0.329557 \r", " 26% : \u00e9poque 264 : perte = 0.322358 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 27% : \u00e9poque 275 : perte = 0.315531 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 28% : \u00e9poque 286 : perte = 0.309041 \r", " 29% : \u00e9poque 297 : perte = 0.302858 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 308 : perte = 0.296957 \r", " 31% : \u00e9poque 319 : perte = 0.291314 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 33% : \u00e9poque 330 : perte = 0.285909 \r", " 34% : \u00e9poque 341 : perte = 0.280724 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 352 : perte = 0.275743 \r", " 36% : \u00e9poque 363 : perte = 0.270952 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 37% : \u00e9poque 374 : perte = 0.266338 \r", " 38% : \u00e9poque 385 : perte = 0.261890 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 39% : \u00e9poque 396 : perte = 0.257596 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 407 : perte = 0.253448 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 41% : \u00e9poque 418 : perte = 0.249437 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 42% : \u00e9poque 429 : perte = 0.245555 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 44% : \u00e9poque 440 : perte = 0.241795 \r", " 45% : \u00e9poque 451 : perte = 0.238150 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 46% : \u00e9poque 462 : perte = 0.234615 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 47% : \u00e9poque 473 : perte = 0.231183 \r", " 48% : \u00e9poque 484 : perte = 0.227849 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 49% : \u00e9poque 495 : perte = 0.224609 \r", " 50% : \u00e9poque 506 : perte = 0.221459 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 517 : perte = 0.218393 \r", " 52% : \u00e9poque 528 : perte = 0.215408 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 53% : \u00e9poque 539 : perte = 0.212502 \r", " 55% : \u00e9poque 550 : perte = 0.209669 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.206907 \r", " 57% : \u00e9poque 572 : perte = 0.204213 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 58% : \u00e9poque 583 : perte = 0.201585 \r", " 59% : \u00e9poque 594 : perte = 0.199019 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 60% : \u00e9poque 605 : perte = 0.196514 \r", " 61% : \u00e9poque 616 : perte = 0.194066 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 62% : \u00e9poque 627 : perte = 0.191674 \r", " 63% : \u00e9poque 638 : perte = 0.189336 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 64% : \u00e9poque 649 : perte = 0.187049 \r", " 66% : \u00e9poque 660 : perte = 0.184812 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 67% : \u00e9poque 671 : perte = 0.182623 \r", " 68% : \u00e9poque 682 : perte = 0.180481 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 69% : \u00e9poque 693 : perte = 0.178384 \r", " 70% : \u00e9poque 704 : perte = 0.176330 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 715 : perte = 0.174317 \r", " 72% : \u00e9poque 726 : perte = 0.172346 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 73% : \u00e9poque 737 : perte = 0.170414 \r", " 74% : \u00e9poque 748 : perte = 0.168520 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 75% : \u00e9poque 759 : perte = 0.166662 \r", " 77% : \u00e9poque 770 : perte = 0.164841 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 78% : \u00e9poque 781 : perte = 0.163054 \r", " 79% : \u00e9poque 792 : perte = 0.161302 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 80% : \u00e9poque 803 : perte = 0.159582 \r", " 81% : \u00e9poque 814 : perte = 0.157893 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 82% : \u00e9poque 825 : perte = 0.156236 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 83% : \u00e9poque 836 : perte = 0.154609 \r", " 84% : \u00e9poque 847 : perte = 0.153011 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 85% : \u00e9poque 858 : perte = 0.151442 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 869 : perte = 0.149901 \r", " 88% : \u00e9poque 880 : perte = 0.148386 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 89% : \u00e9poque 891 : perte = 0.146899 \r", " 90% : \u00e9poque 902 : perte = 0.145436 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 913 : perte = 0.143999 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 92% : \u00e9poque 924 : perte = 0.142587 \r", " 93% : \u00e9poque 935 : perte = 0.141198 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 94% : \u00e9poque 946 : perte = 0.139832 \r", " 95% : \u00e9poque 957 : perte = 0.138489 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 968 : perte = 0.137169 \r", " 97% : \u00e9poque 979 : perte = 0.135870 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 99% : \u00e9poque 990 : perte = 0.134593 \n", "\r", " 1% : \u00e9poque 11 : perte = 1.032382" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", " 2% : \u00e9poque 22 : perte = 0.748411 \r", " 3% : \u00e9poque 33 : perte = 0.619892 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 4% : \u00e9poque 44 : perte = 0.543014 \r", " 5% : \u00e9poque 55 : perte = 0.490058 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 6% : \u00e9poque 66 : perte = 0.450402 \r", " 7% : \u00e9poque 77 : perte = 0.419038 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 8% : \u00e9poque 88 : perte = 0.393262 \r", " 9% : \u00e9poque 99 : perte = 0.371475 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 11% : \u00e9poque 110 : perte = 0.352660 \r", " 12% : \u00e9poque 121 : perte = 0.336137 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 13% : \u00e9poque 132 : perte = 0.321431 \r", " 14% : \u00e9poque 143 : perte = 0.308200 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 154 : perte = 0.296188 \r", " 16% : \u00e9poque 165 : perte = 0.285200 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 17% : \u00e9poque 176 : perte = 0.275086 \r", " 18% : \u00e9poque 187 : perte = 0.265726 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 19% : \u00e9poque 198 : perte = 0.257024 \r", " 20% : \u00e9poque 209 : perte = 0.248899 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 22% : \u00e9poque 220 : perte = 0.241288 \r", " 23% : \u00e9poque 231 : perte = 0.234135 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 24% : \u00e9poque 242 : perte = 0.227395 \r", " 25% : \u00e9poque 253 : perte = 0.221027 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 26% : \u00e9poque 264 : perte = 0.214997 \r", " 27% : \u00e9poque 275 : perte = 0.209276 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 28% : \u00e9poque 286 : perte = 0.203838 \r", " 29% : \u00e9poque 297 : perte = 0.198659 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 308 : perte = 0.193721 \r", " 31% : \u00e9poque 319 : perte = 0.189004 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 33% : \u00e9poque 330 : perte = 0.184493 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 34% : \u00e9poque 341 : perte = 0.180174 \r", " 35% : \u00e9poque 352 : perte = 0.176033 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 36% : \u00e9poque 363 : perte = 0.172060 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 37% : \u00e9poque 374 : perte = 0.168243 \r", " 38% : \u00e9poque 385 : perte = 0.164574 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 39% : \u00e9poque 396 : perte = 0.161043 \r", " 40% : \u00e9poque 407 : perte = 0.157643 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 41% : \u00e9poque 418 : perte = 0.154366 \r", " 42% : \u00e9poque 429 : perte = 0.151206 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 44% : \u00e9poque 440 : perte = 0.148157 \r", " 45% : \u00e9poque 451 : perte = 0.145214 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 46% : \u00e9poque 462 : perte = 0.142370 \r", " 47% : \u00e9poque 473 : perte = 0.139622 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 48% : \u00e9poque 484 : perte = 0.136964 \r", " 49% : \u00e9poque 495 : perte = 0.134393 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 50% : \u00e9poque 506 : perte = 0.131905 \r", " 51% : \u00e9poque 517 : perte = 0.129495 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 52% : \u00e9poque 528 : perte = 0.127162 \r", " 53% : \u00e9poque 539 : perte = 0.124900 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 55% : \u00e9poque 550 : perte = 0.122708 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.120582 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 57% : \u00e9poque 572 : perte = 0.118521 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 58% : \u00e9poque 583 : perte = 0.116520 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 59% : \u00e9poque 594 : perte = 0.114578 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 60% : \u00e9poque 605 : perte = 0.112693 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 616 : perte = 0.110862 \r", " 62% : \u00e9poque 627 : perte = 0.109083 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 63% : \u00e9poque 638 : perte = 0.107354 \r", " 64% : \u00e9poque 649 : perte = 0.105674 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 660 : perte = 0.104040 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 67% : \u00e9poque 671 : perte = 0.102451 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 68% : \u00e9poque 682 : perte = 0.100905 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 69% : \u00e9poque 693 : perte = 0.099401 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 70% : \u00e9poque 704 : perte = 0.097937 \r", " 71% : \u00e9poque 715 : perte = 0.096512 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 72% : \u00e9poque 726 : perte = 0.095124 \r", " 73% : \u00e9poque 737 : perte = 0.093772 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 74% : \u00e9poque 748 : perte = 0.092455 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 75% : \u00e9poque 759 : perte = 0.091171 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 77% : \u00e9poque 770 : perte = 0.089920 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 78% : \u00e9poque 781 : perte = 0.088700 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 79% : \u00e9poque 792 : perte = 0.087511 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 80% : \u00e9poque 803 : perte = 0.086350 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 814 : perte = 0.085218 \r", " 82% : \u00e9poque 825 : perte = 0.084114 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 83% : \u00e9poque 836 : perte = 0.083035 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 84% : \u00e9poque 847 : perte = 0.081983 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 85% : \u00e9poque 858 : perte = 0.080955 \r", " 86% : \u00e9poque 869 : perte = 0.079950 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 88% : \u00e9poque 880 : perte = 0.078969 \r", " 89% : \u00e9poque 891 : perte = 0.078011 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 90% : \u00e9poque 902 : perte = 0.077074 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 913 : perte = 0.076158 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 92% : \u00e9poque 924 : perte = 0.075263 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 93% : \u00e9poque 935 : perte = 0.074387 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 94% : \u00e9poque 946 : perte = 0.073530 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 95% : \u00e9poque 957 : perte = 0.072692 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 968 : perte = 0.071871 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 97% : \u00e9poque 979 : perte = 0.071069 \r", " 99% : \u00e9poque 990 : perte = 0.070283 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\r", " 1% : \u00e9poque 11 : perte = 0.467020" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", " 2% : \u00e9poque 22 : perte = 0.343145 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 3% : \u00e9poque 33 : perte = 0.279344 \r", " 4% : \u00e9poque 44 : perte = 0.237085 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 5% : \u00e9poque 55 : perte = 0.206017 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 6% : \u00e9poque 66 : perte = 0.181846 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 7% : \u00e9poque 77 : perte = 0.162355 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 8% : \u00e9poque 88 : perte = 0.146256 \r", " 9% : \u00e9poque 99 : perte = 0.132739 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 11% : \u00e9poque 110 : perte = 0.121252 \r", " 12% : \u00e9poque 121 : perte = 0.111400 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 13% : \u00e9poque 132 : perte = 0.102884 \r", " 14% : \u00e9poque 143 : perte = 0.095472 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 154 : perte = 0.088981 \r", " 16% : \u00e9poque 165 : perte = 0.083261 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 17% : \u00e9poque 176 : perte = 0.078191 \r", " 18% : \u00e9poque 187 : perte = 0.073674 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 19% : \u00e9poque 198 : perte = 0.069627 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 209 : perte = 0.065984 \r", " 22% : \u00e9poque 220 : perte = 0.062689 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 23% : \u00e9poque 231 : perte = 0.059696 \r", " 24% : \u00e9poque 242 : perte = 0.056966 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 253 : perte = 0.054468 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 26% : \u00e9poque 264 : perte = 0.052173 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 27% : \u00e9poque 275 : perte = 0.050058 \r", " 28% : \u00e9poque 286 : perte = 0.048104 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 29% : \u00e9poque 297 : perte = 0.046294 \r", " 30% : \u00e9poque 308 : perte = 0.044611 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 31% : \u00e9poque 319 : perte = 0.043044 \r", " 33% : \u00e9poque 330 : perte = 0.041581 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 34% : \u00e9poque 341 : perte = 0.040213 \r", " 35% : \u00e9poque 352 : perte = 0.038930 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 36% : \u00e9poque 363 : perte = 0.037725 \r", " 37% : \u00e9poque 374 : perte = 0.036591 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 38% : \u00e9poque 385 : perte = 0.035522 \r", " 39% : \u00e9poque 396 : perte = 0.034514 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 407 : perte = 0.033560 \r", " 41% : \u00e9poque 418 : perte = 0.032656 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 42% : \u00e9poque 429 : perte = 0.031800 \r", " 44% : \u00e9poque 440 : perte = 0.030987 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 451 : perte = 0.030214 \r", " 46% : \u00e9poque 462 : perte = 0.029478 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 47% : \u00e9poque 473 : perte = 0.028777 \r", " 48% : \u00e9poque 484 : perte = 0.028108 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 49% : \u00e9poque 495 : perte = 0.027469 \r", " 50% : \u00e9poque 506 : perte = 0.026858 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 517 : perte = 0.026274 \r", " 52% : \u00e9poque 528 : perte = 0.025714 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 53% : \u00e9poque 539 : perte = 0.025178 \r", " 55% : \u00e9poque 550 : perte = 0.024663 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.024169 \r", " 57% : \u00e9poque 572 : perte = 0.023694 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 58% : \u00e9poque 583 : perte = 0.023238 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 59% : \u00e9poque 594 : perte = 0.022798 \r", " 60% : \u00e9poque 605 : perte = 0.022375 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 616 : perte = 0.021967 \r", " 62% : \u00e9poque 627 : perte = 0.021574 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 63% : \u00e9poque 638 : perte = 0.021194 \r", " 64% : \u00e9poque 649 : perte = 0.020828 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 660 : perte = 0.020474 \r", " 67% : \u00e9poque 671 : perte = 0.020132 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 68% : \u00e9poque 682 : perte = 0.019801 \r", " 69% : \u00e9poque 693 : perte = 0.019480 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 70% : \u00e9poque 704 : perte = 0.019170 \r", " 71% : \u00e9poque 715 : perte = 0.018870 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 72% : \u00e9poque 726 : perte = 0.018578 \r", " 73% : \u00e9poque 737 : perte = 0.018296 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 74% : \u00e9poque 748 : perte = 0.018022 \r", " 75% : \u00e9poque 759 : perte = 0.017756 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 77% : \u00e9poque 770 : perte = 0.017498 \r", " 78% : \u00e9poque 781 : perte = 0.017247 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 79% : \u00e9poque 792 : perte = 0.017004 \r", " 80% : \u00e9poque 803 : perte = 0.016767 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 814 : perte = 0.016536 \r", " 82% : \u00e9poque 825 : perte = 0.016312 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 83% : \u00e9poque 836 : perte = 0.016094 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 84% : \u00e9poque 847 : perte = 0.015882 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 85% : \u00e9poque 858 : perte = 0.015675 \r", " 86% : \u00e9poque 869 : perte = 0.015473 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 88% : \u00e9poque 880 : perte = 0.015277 \r", " 89% : \u00e9poque 891 : perte = 0.015085 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 90% : \u00e9poque 902 : perte = 0.014899 \r", " 91% : \u00e9poque 913 : perte = 0.014716 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 92% : \u00e9poque 924 : perte = 0.014539 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 93% : \u00e9poque 935 : perte = 0.014365 \r", " 94% : \u00e9poque 946 : perte = 0.014196 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 95% : \u00e9poque 957 : perte = 0.014030 \r", " 96% : \u00e9poque 968 : perte = 0.013869 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 97% : \u00e9poque 979 : perte = 0.013711 \r", " 99% : \u00e9poque 990 : perte = 0.013556 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\r", " 1% : \u00e9poque 11 : perte = 0.329639" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", " 2% : \u00e9poque 22 : perte = 0.228104 \r", " 3% : \u00e9poque 33 : perte = 0.176352 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 4% : \u00e9poque 44 : perte = 0.142408 \r", " 5% : \u00e9poque 55 : perte = 0.118371 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 6% : \u00e9poque 66 : perte = 0.100645 \r", " 7% : \u00e9poque 77 : perte = 0.087196 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 8% : \u00e9poque 88 : perte = 0.076738 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 9% : \u00e9poque 99 : perte = 0.068422 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 11% : \u00e9poque 110 : perte = 0.061672 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 12% : \u00e9poque 121 : perte = 0.056097 \r", " 13% : \u00e9poque 132 : perte = 0.051420 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 14% : \u00e9poque 143 : perte = 0.047446 \r", " 15% : \u00e9poque 154 : perte = 0.044030 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 16% : \u00e9poque 165 : perte = 0.041064 \r", " 17% : \u00e9poque 176 : perte = 0.038467 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 18% : \u00e9poque 187 : perte = 0.036174 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 19% : \u00e9poque 198 : perte = 0.034135 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 209 : perte = 0.032312 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 22% : \u00e9poque 220 : perte = 0.030671 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 23% : \u00e9poque 231 : perte = 0.029188 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 24% : \u00e9poque 242 : perte = 0.027840 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 253 : perte = 0.026611 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 26% : \u00e9poque 264 : perte = 0.025485 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 27% : \u00e9poque 275 : perte = 0.024449 \r", " 28% : \u00e9poque 286 : perte = 0.023495 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 29% : \u00e9poque 297 : perte = 0.022611 \r", " 30% : \u00e9poque 308 : perte = 0.021792 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 31% : \u00e9poque 319 : perte = 0.021029 \r", " 33% : \u00e9poque 330 : perte = 0.020318 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 34% : \u00e9poque 341 : perte = 0.019654 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 352 : perte = 0.019031 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 36% : \u00e9poque 363 : perte = 0.018447 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 37% : \u00e9poque 374 : perte = 0.017897 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 38% : \u00e9poque 385 : perte = 0.017379 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 39% : \u00e9poque 396 : perte = 0.016891 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 407 : perte = 0.016429 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 41% : \u00e9poque 418 : perte = 0.015991 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 42% : \u00e9poque 429 : perte = 0.015577 \r", " 44% : \u00e9poque 440 : perte = 0.015183 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 451 : perte = 0.014809 \r", " 46% : \u00e9poque 462 : perte = 0.014452 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 47% : \u00e9poque 473 : perte = 0.014113 \r", " 48% : \u00e9poque 484 : perte = 0.013789 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 49% : \u00e9poque 495 : perte = 0.013480 \r", " 50% : \u00e9poque 506 : perte = 0.013184 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 517 : perte = 0.012901 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 52% : \u00e9poque 528 : perte = 0.012630 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 53% : \u00e9poque 539 : perte = 0.012370 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 55% : \u00e9poque 550 : perte = 0.012121 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.011881 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 57% : \u00e9poque 572 : perte = 0.011651 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 58% : \u00e9poque 583 : perte = 0.011430 \r", " 59% : \u00e9poque 594 : perte = 0.011217 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 60% : \u00e9poque 605 : perte = 0.011011 \r", " 61% : \u00e9poque 616 : perte = 0.010814 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 62% : \u00e9poque 627 : perte = 0.010623 \r", " 63% : \u00e9poque 638 : perte = 0.010438 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 64% : \u00e9poque 649 : perte = 0.010261 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 660 : perte = 0.010089 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 67% : \u00e9poque 671 : perte = 0.009922 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 68% : \u00e9poque 682 : perte = 0.009762 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 69% : \u00e9poque 693 : perte = 0.009606 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 70% : \u00e9poque 704 : perte = 0.009455 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 715 : perte = 0.009309 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 72% : \u00e9poque 726 : perte = 0.009167 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 73% : \u00e9poque 737 : perte = 0.009030 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 74% : \u00e9poque 748 : perte = 0.008897 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 75% : \u00e9poque 759 : perte = 0.008767 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 77% : \u00e9poque 770 : perte = 0.008642 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 78% : \u00e9poque 781 : perte = 0.008520 \r", " 79% : \u00e9poque 792 : perte = 0.008401 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 80% : \u00e9poque 803 : perte = 0.008286 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 814 : perte = 0.008173 \r", " 82% : \u00e9poque 825 : perte = 0.008064 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 83% : \u00e9poque 836 : perte = 0.007958 \r", " 84% : \u00e9poque 847 : perte = 0.007854 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 85% : \u00e9poque 858 : perte = 0.007753 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 869 : perte = 0.007655 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 88% : \u00e9poque 880 : perte = 0.007559 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 89% : \u00e9poque 891 : perte = 0.007466 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 90% : \u00e9poque 902 : perte = 0.007374 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 913 : perte = 0.007285 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 92% : \u00e9poque 924 : perte = 0.007199 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 93% : \u00e9poque 935 : perte = 0.007114 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 94% : \u00e9poque 946 : perte = 0.007031 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 95% : \u00e9poque 957 : perte = 0.006950 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 968 : perte = 0.006871 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 97% : \u00e9poque 979 : perte = 0.006794 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 99% : \u00e9poque 990 : perte = 0.006718 \n", "\r", " 1% : \u00e9poque 11 : perte = 0.923381" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", " 2% : \u00e9poque 22 : perte = 0.143070 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 3% : \u00e9poque 33 : perte = 0.011099 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 4% : \u00e9poque 44 : perte = 0.005214 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 5% : \u00e9poque 55 : perte = 0.003656 \r", " 6% : \u00e9poque 66 : perte = 0.002868 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 7% : \u00e9poque 77 : perte = 0.002383 \r", " 8% : \u00e9poque 88 : perte = 0.002050 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 9% : \u00e9poque 99 : perte = 0.001806 \r", " 11% : \u00e9poque 110 : perte = 0.001619 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 12% : \u00e9poque 121 : perte = 0.001470 \r", " 13% : \u00e9poque 132 : perte = 0.001349 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 14% : \u00e9poque 143 : perte = 0.001248 \r", " 15% : \u00e9poque 154 : perte = 0.001163 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 16% : \u00e9poque 165 : perte = 0.001089 \r", " 17% : \u00e9poque 176 : perte = 0.001025 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 18% : \u00e9poque 187 : perte = 0.000969 \r", " 19% : \u00e9poque 198 : perte = 0.000920 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 209 : perte = 0.000876 \r", " 22% : \u00e9poque 220 : perte = 0.000836 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 23% : \u00e9poque 231 : perte = 0.000800 \r", " 24% : \u00e9poque 242 : perte = 0.000767 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 253 : perte = 0.000738 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 26% : \u00e9poque 264 : perte = 0.000710 \r", " 27% : \u00e9poque 275 : perte = 0.000685 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 28% : \u00e9poque 286 : perte = 0.000662 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 29% : \u00e9poque 297 : perte = 0.000640 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 308 : perte = 0.000620 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 31% : \u00e9poque 319 : perte = 0.000601 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 33% : \u00e9poque 330 : perte = 0.000584 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 34% : \u00e9poque 341 : perte = 0.000568 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 352 : perte = 0.000552 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 36% : \u00e9poque 363 : perte = 0.000538 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 37% : \u00e9poque 374 : perte = 0.000524 \r", " 38% : \u00e9poque 385 : perte = 0.000511 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 39% : \u00e9poque 396 : perte = 0.000499 \r", " 40% : \u00e9poque 407 : perte = 0.000487 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 41% : \u00e9poque 418 : perte = 0.000476 \r", " 42% : \u00e9poque 429 : perte = 0.000466 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 44% : \u00e9poque 440 : perte = 0.000456 \r", " 45% : \u00e9poque 451 : perte = 0.000446 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 46% : \u00e9poque 462 : perte = 0.000437 \r", " 47% : \u00e9poque 473 : perte = 0.000429 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 48% : \u00e9poque 484 : perte = 0.000420 \r", " 49% : \u00e9poque 495 : perte = 0.000412 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 50% : \u00e9poque 506 : perte = 0.000405 \r", " 51% : \u00e9poque 517 : perte = 0.000398 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 52% : \u00e9poque 528 : perte = 0.000391 \r", " 53% : \u00e9poque 539 : perte = 0.000384 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 55% : \u00e9poque 550 : perte = 0.000377 \r", " 56% : \u00e9poque 561 : perte = 0.000371 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 57% : \u00e9poque 572 : perte = 0.000365 \r", " 58% : \u00e9poque 583 : perte = 0.000359 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 59% : \u00e9poque 594 : perte = 0.000354 \r", " 60% : \u00e9poque 605 : perte = 0.000348 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 616 : perte = 0.000343 \r", " 62% : \u00e9poque 627 : perte = 0.000338 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 63% : \u00e9poque 638 : perte = 0.000333 \r", " 64% : \u00e9poque 649 : perte = 0.000328 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 660 : perte = 0.000324 \r", " 67% : \u00e9poque 671 : perte = 0.000319 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 68% : \u00e9poque 682 : perte = 0.000315 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 69% : \u00e9poque 693 : perte = 0.000311 \r", " 70% : \u00e9poque 704 : perte = 0.000307 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 715 : perte = 0.000303 \r", " 72% : \u00e9poque 726 : perte = 0.000299 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 73% : \u00e9poque 737 : perte = 0.000295 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 74% : \u00e9poque 748 : perte = 0.000292 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 75% : \u00e9poque 759 : perte = 0.000288 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 77% : \u00e9poque 770 : perte = 0.000285 \r", " 78% : \u00e9poque 781 : perte = 0.000281 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 79% : \u00e9poque 792 : perte = 0.000278 \r", " 80% : \u00e9poque 803 : perte = 0.000275 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 814 : perte = 0.000272 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 82% : \u00e9poque 825 : perte = 0.000269 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 83% : \u00e9poque 836 : perte = 0.000266 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 84% : \u00e9poque 847 : perte = 0.000263 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 85% : \u00e9poque 858 : perte = 0.000260 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 869 : perte = 0.000257 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 88% : \u00e9poque 880 : perte = 0.000255 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 89% : \u00e9poque 891 : perte = 0.000252 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 90% : \u00e9poque 902 : perte = 0.000249 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 913 : perte = 0.000247 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 92% : \u00e9poque 924 : perte = 0.000245 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 93% : \u00e9poque 935 : perte = 0.000242 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 94% : \u00e9poque 946 : perte = 0.000240 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 95% : \u00e9poque 957 : perte = 0.000237 \r", " 96% : \u00e9poque 968 : perte = 0.000235 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 97% : \u00e9poque 979 : perte = 0.000233 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 99% : \u00e9poque 990 : perte = 0.000231 \n", "[0.01, 0.05, 0.1, 0.5, 1.0, 10.0]\n", "[0.072, 0.02, 0.001, 0.0, 0.0, 0.0]\n", "[0.14, 0.122, 0.126, 0.126, 0.126, 0.138]\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "utilitaires.plot_training_curves(hyper_parametres, {u\"ensemble d'entra\u00eenement\":train_results, u\"ensemble de validation\":valid_results}, \n", " title=u\"Taux de classification en\\n fonction d'un hyper-param\u00e8tre x\",\n", " xlabel=u\"Valeurs de l'hyper-param\u00e8tre x\",\n", " ylabel=u\"Taux d'erreur\",\n", " xlog=True)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'hyper_parametres' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m utilitaires.plot_training_curves(hyper_parametres, {u\"ensemble d'entra\u00eenement\":train_results, u\"ensemble de validation\":valid_results}, \n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34mu\"Taux de classification en\\n fonction d'un hyper-param\u00e8tre x\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mxlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34mu\"Valeurs de l'hyper-param\u00e8tre x\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mylabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34mu\"Taux d'erreur\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m xlog=True)\n", "\u001b[1;31mNameError\u001b[0m: name 'hyper_parametres' is not defined" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Regardez maintenant pour quelle(s) valeur(s) vous avez obtenus le(s) meilleur(s) r\u00e9sultat(s) sur l'ensemble de validation. S\u00e9lectionnez le premier mod\u00e8le correspondant dans la liste `modeles` et testez le maintenant sur l'ensemble de test. \n", "\n", "Qu'est-ce que cela vous donne? Est-ce moins bon ou meilleur? Est-ce que ce serait une bonne id\u00e9e de refaire la s\u00e9lection de l'hyper-param\u00e8tre et rev\u00e9rifier sur l'ensemble de test?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Meilleur r\u00e9sultat sur l'ensemble de validation\"\n", "print modeles[1].compute_cost(valid_x, valid_y)\n", "print \"R\u00e9sultat du mod\u00e8le correspondant sur l'ensemble de test\"\n", "print modeles[1].compute_cost(test_x, test_y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.132\n" ] } ], "prompt_number": 13 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "R\u00e9seaux de neurones" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les r\u00e9seaux de neurones ne sont d\u00e9marqueront pas tellement sur le mini ensemble de MNIST. Commencez cependant par vous familiariser avec eux et MNIST avant de charger une version moyenne ou compl\u00e8te de MNIST. Le temps de calcul sera beaucoup plus long. Sur le plus gros ensemble, la r\u00e9gression logistique saturera vers 7% alors qu'une r\u00e9seau de neurones \u00e0 une seule couche cach\u00e9e peut descendre jusqu'\u00e0 1.6% d'erreur sur l'ensemble de test.\n", "\n", "Commencez par instancier un r\u00e9seau \u00e0 une seule couche cach\u00e9e. Le nombre d'unit\u00e9s cach\u00e9es devra \u00eatre beaucoup plus \u00e9lev\u00e9 pour MNIST que ce n'\u00e9tait le cas pour 2moons. Essayez diff\u00e9rentes valeurs entre 10 et 1000 et observez les diff\u00e9rences en affichant les courbes d'apprentissage. Pas besoin de faire une recherche exhaustif tout de suite, c'est ce qu'on se fera tout de suite apr\u00e8s." ] }, { "cell_type": "code", "collapsed": false, "input": [ "[train_x, train_y], [valid_x, valid_y], [test_x, test_y] = utilitaires.load_mini_mnist()\n", "\n", "n_in = train_x.shape[1]\n", "n_classes = np.unique(train_y).shape[0]\n", "learning_rate = 0.1\n", "\n", "modele = utilitaires.FeedForwardNeuralNet(n_in, n_hids=[500], n_out=n_classes, non_linearities=\"sigmoid\")\n", "modele.train(train_data=train_x, train_labels=train_y, learning_rate=learning_rate, max_epoch=1000, batch_size=600,\n", " monitoring_data={\"ensemble de validation\": (valid_x, valid_y)})\n", "\n", "print modele.compute_cost(test_x, test_y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 1% : \u00e9poque 11 : perte = 1.884895 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 2% : \u00e9poque 22 : perte = 1.524423 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 3% : \u00e9poque 33 : perte = 1.244026 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 4% : \u00e9poque 44 : perte = 1.040439 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 5% : \u00e9poque 55 : perte = 0.895044 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 6% : \u00e9poque 66 : perte = 0.789271 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 7% : \u00e9poque 77 : perte = 0.709865 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 8% : \u00e9poque 88 : perte = 0.648231 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 9% : \u00e9poque 99 : perte = 0.598906 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 11% : \u00e9poque 110 : perte = 0.558379 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 12% : \u00e9poque 121 : perte = 0.524339 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 13% : \u00e9poque 132 : perte = 0.495217 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 14% : \u00e9poque 143 : perte = 0.469915 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 154 : perte = 0.447644 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 16% : \u00e9poque 165 : perte = 0.427823 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 17% : \u00e9poque 176 : perte = 0.410013 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 18% : \u00e9poque 187 : perte = 0.393875 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 19% : \u00e9poque 198 : perte = 0.379144 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 209 : perte = 0.365610 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 22% : \u00e9poque 220 : perte = 0.353104 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 23% : \u00e9poque 231 : perte = 0.341486 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 24% : \u00e9poque 242 : perte = 0.330642 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 253 : perte = 0.320478 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 26% : \u00e9poque 264 : perte = 0.310915 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 27% : \u00e9poque 275 : perte = 0.301885 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 28% : \u00e9poque 286 : perte = 0.293332 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 29% : \u00e9poque 297 : perte = 0.285207 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 308 : perte = 0.277469 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 31% : \u00e9poque 319 : perte = 0.270082 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 33% : \u00e9poque 330 : perte = 0.263014 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 34% : \u00e9poque 341 : perte = 0.256239 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 352 : perte = 0.249732 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 36% : \u00e9poque 363 : perte = 0.243474 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 37% : \u00e9poque 374 : perte = 0.237445 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 38% : \u00e9poque 385 : perte = 0.231631 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 39% : \u00e9poque 396 : perte = 0.226016 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 407 : perte = 0.220587 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 41% : \u00e9poque 418 : perte = 0.215335 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 42% : \u00e9poque 429 : perte = 0.210247 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 44% : \u00e9poque 440 : perte = 0.205316 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 451 : perte = 0.200533 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 46% : \u00e9poque 462 : perte = 0.195891 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 47% : \u00e9poque 473 : perte = 0.191382 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 48% : \u00e9poque 484 : perte = 0.187002 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 49% : \u00e9poque 495 : perte = 0.182743 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 50% : \u00e9poque 506 : perte = 0.178602 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 517 : perte = 0.174574 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 52% : \u00e9poque 528 : perte = 0.170653 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 53% : \u00e9poque 539 : perte = 0.166837 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 55% : \u00e9poque 550 : perte = 0.163121 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.159502 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 57% : \u00e9poque 572 : perte = 0.155977 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 58% : \u00e9poque 583 : perte = 0.152543 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 59% : \u00e9poque 594 : perte = 0.149197 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 60% : \u00e9poque 605 : perte = 0.145936 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 616 : perte = 0.142759 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 62% : \u00e9poque 627 : perte = 0.139662 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 63% : \u00e9poque 638 : perte = 0.136644 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 64% : \u00e9poque 649 : perte = 0.133703 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 660 : perte = 0.130836 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 67% : \u00e9poque 671 : perte = 0.128042 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 68% : \u00e9poque 682 : perte = 0.125319 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 69% : \u00e9poque 693 : perte = 0.122666 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 70% : \u00e9poque 704 : perte = 0.120080 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 715 : perte = 0.117561 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 72% : \u00e9poque 726 : perte = 0.115107 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 73% : \u00e9poque 737 : perte = 0.112716 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 74% : \u00e9poque 748 : perte = 0.110387 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 75% : \u00e9poque 759 : perte = 0.108119 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 77% : \u00e9poque 770 : perte = 0.105910 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 78% : \u00e9poque 781 : perte = 0.103758 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 79% : \u00e9poque 792 : perte = 0.101664 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 80% : \u00e9poque 803 : perte = 0.099625 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 814 : perte = 0.097639 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 82% : \u00e9poque 825 : perte = 0.095706 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 83% : \u00e9poque 836 : perte = 0.093825 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 84% : \u00e9poque 847 : perte = 0.091993 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 85% : \u00e9poque 858 : perte = 0.090211 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 869 : perte = 0.088475 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 88% : \u00e9poque 880 : perte = 0.086786 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 89% : \u00e9poque 891 : perte = 0.085142 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 90% : \u00e9poque 902 : perte = 0.083541 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 913 : perte = 0.081983 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 92% : \u00e9poque 924 : perte = 0.080467 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 93% : \u00e9poque 935 : perte = 0.078990 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 94% : \u00e9poque 946 : perte = 0.077553 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 95% : \u00e9poque 957 : perte = 0.076153 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 968 : perte = 0.074790 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 97% : \u00e9poque 979 : perte = 0.073463 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 99% : \u00e9poque 990 : perte = 0.072170 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.13\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "utilitaires.plot_training_curves(modele.epochs, modele.loss_curves, title=u\"Courbe d'apprentissage d'un\\n r\u00e9seau feedforward - Fonction de perte\", ylabel=\"Perte\")\n", "utilitaires.plot_training_curves(modele.epochs, modele.cost_curves, title=u\"Courbe d'apprentissage d'un\\n r\u00e9seau feedforward - Erreur de classification\", ylabel=\"Taux d'erreur\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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CFRQUMHjw4KDX86c//anRecvLy5kQ5BdpTk4OW7du9S9XVFSwd+/eoOfPy8vj\n5ptvbnTcsrIyLrjggjbTHiwPg7UMvPrqqxk9ejSbNm2ipKSEO++8k/r6+jaPHyytJ510UqO0Hjhw\ngPnz53f4WLm5uYf0+ehsw5D2BKTlInIzkCoi3wWeAV7s1Nm6kbW0M6Zl48aNIy0tjd/+9rccPHiQ\nuro6PvnkE9577z2g9T74Nm7cyOuvv05VVRU9evSgZ8+eJCYmAnDVVVdx0003+RsU7N69m8WLF3co\nbYHn3rVrFw888AA1NTU888wzbNiwgVNPPbXZa6666iruuusu1nv/9CUlJY1u6Ac699xzeemll3jr\nrbeorq7mV7/6VYsB4Cc/+QkPPfQQq1atQlUpLy/nn//8p78k0ZqBAweyd+9eSktLg16bT1lZGWlp\naaSmprJhwwYefPDBFo/Z2vvygx/8gI0bN/K3v/2NmpoaampqWL16NRs2bGjztU0dyucD3LV/8cUX\n7T6fT3sC0ixgN/AxcCXwMnBLh8/UzXx92hljmktISOCll15i7dq1DB8+nAEDBnDFFVf4vzyD/ZL3\nLVdVVTF79mwGDBhATk4Oe/bs4e677wZg5syZnHHGGXzve98jPT2diRMnsmrVqmbHaI1vHxFhwoQJ\nfP755wwYMIBbb72Vv//9742qkXzOOussZs2axbRp08jIyODoo49myZIlQY8/evRo5s+fz4UXXsjg\nwYPJyspq8Xmh448/nj//+c9cd911ZGVlcfjhh/Poo4+2eB2B+TZy5EimT5/O8OHDycrKYseOHUHz\n9d577+WJJ54gPT2dK664gmnTpjXap+l8S+9LWloaS5cu5cknn2TIkCHk5OQwe/Zs//NLTV/b2rES\nExM7/fkAmD17NnfccQd9+/bl97//fdC8Cpp/bUU6EZmpqn9oa10otXcI80B1dZCWBrt2uVFkjelu\nnX0Ww5hY19LQ6u0pIc0Isu6ydp70ryKyU0Q+bmWfB0TkcxH5UESOa89x2yMxEcaMAa+EaYwxJsK1\nGJBEZLqIvAgcJiIvBkzLgOB3AJtbAExt5RynAiNU9XBc8/KWK087YdIkeOutrjyiMcaYUGmt2ffb\nwA6gP3Av4CtelQIftefgqrpCRIa1sssZwCPevu+KSKaIDFTVna28pt0mTYIu6nvRGGNMiLUYkFS1\nQES245p9Lw/R+YcAWwOWtwFDgS4JSBMnwn/+J9TXwyE0jTfGGNMNWn0wVlVrRaRORDJVtThEaWh6\nYyvoXeDO1Ce+AAAfUUlEQVQ5c+b456dMmcKUKVPaPHBODmRmwmefuVZ3xhhjIld7emooBz4WkX95\n8+C6fbihC86/HQhsbznUW9dMYEDqiEmT4O23LSAZY0yka09F1nPArcBy4D3gfW/qCouBSwBEZAJQ\n3FX3j3xOPNEFJGOMMZGtzeeQAEQkFchT1Q0dOrjIIuAkXMOIncBtQDKAqj7s7fNHXEu8cuAyVf0g\nyHE6/BySz4cfwrRp1o2Q6X72HJIxwXX6OSQROQNYA7zqLR8nIu3qC0RVp6vqYFVNUdVcVf2rqj7s\nC0bePtep6ghVPTZYMDpURx0FRUXQQldVxpgIFetDmM+YMYNbb70VgBUrVjBy5Mh27dsZaWlpbNmy\npdOv7y7tqbKbA4wH9gOo6hpgeAjT1KUSE2HcOFi5MtwpMcZ0pVANYd5dAs85efJkf59zbe3blmCB\n+sCBAwwbNqzTae0u7QlINUFa2HW8K9ow8jVsMMaYSNKRKt327tvdgbUrtScgrRORi4AkETlcRObh\nHpqNGhaQjGnOhjAPPoR5a9fQ1KhRo/jnP//pX66trWXAgAH+ocDPO+88cnJyyMzM5KSTTvL3Rt7U\nsmXLGnXwumbNGsaOHUt6ejrTpk2jsrLSv23//v2cdtppZGdnk5WVxemnn+4fpuPmm29mxYoVXHfd\ndaSlpXHDDa4xdEJCgn+o85KSEi655BKys7MZNmwYd955pz/YLVy4kG9+85v84he/ICsri+HDh/Pq\nq6+2mI9drT0B6Trg60AVsAjXU8NPQ5morjZ+vOvTrqYm3CkxJjLU19dz+umnc9xxx1FUVMRrr73G\n/fffz9KlS/37vPjii0yfPp2SkhLOOOMMrrvuOgA+++wz5s+fz3vvvUdpaSlLly71Vwc98MADLF68\nmDfffJMdO3bQt29frr322kbnXrVqFZs2beLJJ59k5syZ3HXXXbz++uusW7eOp59+mjfffNO/77vv\nvsuIESPYu3cvc+fO5Yc//CHFxc0fiXzhhRe4++67+cc//sGePXuYPHky06dPD3rt69ev55prruHx\nxx+nqKiIvXv3sm3bNv/29lyDz4UXXsiiRYv8y0uWLCE7O5sxY8YAbkiITZs2sXv3bsaOHctFF13U\n2tsCQHV1NWeddRaXXnop+/fv57zzzuPvf/+7v+Sjqlx++eUUFhZSWFhIr169/O/NnXfeyeTJk5k/\nfz4HDhzggQceaHb866+/ngMHDrB582aWL1/Oo48+yoIFC/zbV61axciRI9m7dy+//OUvufzyy9tM\nc5cJNoysFy17AT8D5uOGnUhuad9QT3TBUNBHHaW6evUhH8aYduuKz22o2BDmLQ9h3to1NLVp0yZN\nS0vTgwcPqqrqhRdeqLfffnuz/VRV9+/fryKipaWlquqGI7/llltUtfFw58uXL2923ZMmTWo2lLrP\nmjVrtG/fvv7lKVOm6P/+7/822sc3DHptba2mpKTop59+6t/28MMP65QpU1TV5fmIESMa5Y2I6M6d\nO4Oeu7PoxBDmjwDVwP8BpwCjgZmhC42h5au2a2VkYGO6ncztmvp+va1jzcttCPOWhzBv7Rqajnz7\nta99jVGjRrF48WJOO+00XnzxRW6//XbA5efNN9/Ms88+y+7du/3H27NnD2lpac2uwaeoqCjodatX\nrVZRUcHPfvYzlixZ4q9KLCsrQ1UbjSUVzJ49e6ipqSE/P9+/Li8vr9HIvIFDsqempvqPHzjceai0\nFpBGqerRACLyv8DqkKcmhCZNgpdfhhu6on8JY7pIRwNJV/ENYb5x48ag29szhPn06dM5cOAAV155\nJbNmzeLRRx8lLy+PBQsWMHHixGav6Uyz42BDmJ955pnN9svLy+PWW29tsZouUE5ODp8GPJjYdAjz\n1q4hmOnTp7No0SLq6uoYPXo0w4e7RshPPPEEixcv5rXXXiM/P5/i4mKysrIaNU4Ils85OTlBr3vE\niBEA/O53v2Pjxo2sWrWK7Oxs1q5dy9ixY/0BqbX3rn///iQnJ7NlyxZGed3XFBYWNgrQ4dTaPaRa\n34yq1rayX1T4znfg3/+2+0jGgA1h3toQ5h29hmnTprFkyRIeeuihRveIysrK6NGjB1lZWZSXl3PT\nTTc1u85g+Txx4kSSkpL81/3cc8+xenVDeaCsrIxevXqRkZHBvn37mDt3bqPXtzZ8eGJiIueffz43\n33wzZWVlFBQUcN999/GjH/2oxevrTq0FpGNE5IBvAo4OWC5t5XURaehQGD4cVqwId0qMCT8bwrzl\nIczbuoamBg0axKRJk1i5ciUXXHCBf/0ll1xCfn4+Q4YM4aijjmLixImtDiHum09JSeG5555j4cKF\n9OvXj6effppzzjnHv99Pf/pTDh48SP/+/Zk0aRKnnHJKo+PMnDmTZ599lqysLH760+btz+bNm0fv\n3r0ZPnw4kydP5qKLLuKyyy4LmqbAdHWHdnUdFG6H0nVQoDvvhJ07IUjDE2O6nHUdZExwhzKEecw4\n6yx44QWw7whjjIk8cRWQRo+GlBTwnlkzxhgTQeIqIIm4UtLzz4c7JcYYY5qKq4AEFpCMMSZSxV1A\nmjABvvoKNm8Od0qMMcYEiruAlJgIp5/uGjcYY4yJHHEXkMCq7YwxJhLF1XNIPgcPwqBB8MUX0L9/\nlx3WmEaieVwaY0It2HNIrfVlF7N69YLvftdV23Vnz+omvkTDjz0T21p6ADVSxWWVHcCMGfCnP4U7\nFcYYY3ziNiCdcgrs2uUG7jPGGBN+cRuQEhPhqqvgf/4n3CkxxhgDcdqowWf3bjjiCNi0CQLG5zLG\nmJhg95CiyIAB7pmkhQvDnRJjjDFxXUICeOcd+NGPYONGSIjr8GyMiTVWQooy48dDRgYsXRrulBhj\nTHyL+4AkAtdcA/PnhzslxhgT3+K+yg6gogLy8uCtt+DII0N2GmOM6VZWZReFUlPh5z+HW24Jd0qM\nMSZ+WQnJU1EBhx/uOl39xjdCeipjjOkWVkKKUqmp8KtfwezZ4U6JMcbEJwtIAX78YygshH/9K9wp\nMcaY+GMBKUByMtx5J/zXf0F9fbhTY4wx8cUCUhPnnusekH3mmXCnxBhj4os1agji9dfdOEkffwx9\n+nTbaY0xpktFW6MGC0gtuOwyN5Cf9QZujIlWFpBCIBwBqbgYjj4aHnkETj65W09tjDFdItoCkt1D\nakFmphtR9vLL4cCBcKfGGGNin5WQ2nD55ZCSAg8+GJbTG2NMp0VbCckCUhtKSlzV3Z//DN//fliS\nYIwxnRJtASmqquzq6uv4x6f/6NZzZmTAo4/CpZfC5s3dempjjIkrURWQEiSBi567iLLqsm4975Qp\ncPPNcOaZUNa9pzbGmLgRVQFJRMjNyGVrydZuP/d118G4cXDJJdaLgzHGhEJUBSSAvIw8CksKu/28\nIm4Qv6++gttv7/bTG2NMzEsKdwI6Kjc9l62l3V9CAujRA557zpWU8vNhxoywJMMYY2JS1AWkcJWQ\nfAYNgqVL4TvfcQFq+vSwJcUYY2JKVFbZhauE5DNyJCxZAjfe6EpMxhhjDl1IA5KITBWRDSLyuYjM\nCrJ9ioiUiMgab2pzEPHc9NywlpB8jjoKXn4Zrr4aXnop3KkxxpjoF7KAJCKJwB+BqcBoYLqIjAqy\n63JVPc6b7mjruHkZeWFpZRfMccfBiy+63hz+9rdwp8YYY6JbKO8hjQM2qeoWABF5EjgT+LTJfh16\nijg3wzVqUFVEwv8A8rhx8MYbcOqpsHWrG9wvApJljDFRJ5RVdkOAwKLMNm9dIAUmiciHIvKyiIxu\n66Cpyan0Tu7N7ordXZjUQzN6NLz9Njz1FFxzDdTWhjtFxhgTfUJZQmpP53MfALmqWiEipwDPA0cE\n23HOnDn++czSTLaWbCW7d3ZXpLNLDB4Mb77pRpz9wQ/g8cehf/9wp8oYE0+WLVvGsmXLwp2MTgtZ\n56oiMgGYo6pTveXZQL2q3tPKazYDx6vqvibrG3WueuaTZzLj2BmcPerskKT9UNTWum6GnnwSnn4a\nxo8Pd4qMMfHKOldt8B5wuIgME5EU4AJgceAOIjJQvBtBIjIOFyD3NT9UY+F8OLYtSUlwzz0wbx6c\ncYb7GwUdqhtjTNiFLCCpai1wHbAEWA88paqfisiVInKlt9u5wMcisha4H5jWnmOH++HY9jjjDFi5\n0o04e/rpUFQU7hQZY0xkC+lzSKr6iqoeqaojVPVub93DqvqwNz9fVY9S1TGqOklV32nPcSPh4dj2\nGD7cBaUTTnBNxB9/3EpLxhjTkqjrqQEi5+HY9khOhjlz4JVX4De/gbPPhsLoSLoxxnSrqAxI0VBl\n19TYsfDee66kNHYs3H03VFWFO1XGGBM5ojIg5aTlsLt8NzV1NeFOSof06AG33QarVrmqvGOOcSUn\nq8YzxpgoDUhJCUkM6jOI7Qe2hzspnTJ8OCxeDL/7HfzsZ67n8NWrw50qY4wJr6gMSEDYRo7tSqed\nBp98AtOmwVlnwQUXwKdNO1Yyxpg4EbUBKRrvIwWTlARXXAEbN8KYMXDSSS4wffxxuFNmjDHdK2oD\nUiQ/HNsZvXvD7Nnw5Zeumfj3vuda5L31lt1jMsbEh6gNSLFSQmqqTx/4xS/giy/cvaVLL4WJE103\nRNZpqzEmlkV1QIqlElJTqalw3XXw2WduSIt58+Cww+D222HHjnCnzhhjul7UBqRoejj2UCQmugYP\nK1a4wQC3bXPDXZx3HixdCnV14U6hMcZ0jagNSLFaZdeaMWPg4YdhyxaYMsXdczrsMLjlFti0Kdyp\nM8aYQxO1ASmrVxZVtVUcqDoQ7qR0u4wMuPZaeP99eOklKC+HE09095rmzYOdO8OdQmOM6bioDUgi\nEvP3kdrjmGPgvvtcVd5tt7kHbEeOdK30/vQn2LUr3Ck0xpj2idqABLHxcGxXSU6GqVPh0Udh+3b3\nbNMbb8ARR8DJJ8Mf/2iduhpjIltUB6S89Pi7j9QeqaluKPVFi1yLvBtucB27Hn+869x1zhxXkqqv\nD3dKjTGmQVQHpNyM2Ho4NhR69XKt9BYudMHpD3+AsjL3fFNODsyYAU89BXv2hDulxph4F9UBKR5b\n2h2KpCT41rfg3nth/Xp45x0YNw7+9jf42tfgG9+Am292VX2VleFOrTEm3ohGQb80IqLB0vnxzo85\n9YlT2TxzM0kJSWFIWeyornZDYixZAq+/DuvWwfjx7v7TlCmuO6OUlHCn0hjTESKCqkq409FeUR2Q\nACYvmMzM8TM5d/S53Zyq2FZSAm++Ca+9BsuXu+ecxo93JazJk13JqnfvcKfSGNMaC0gh0FpAeuqT\np3jwvQdZNmNZ9yYqzuzf7zp6Xb7c/f3wQzjqqIbnnyZOhKFDw51KY0wgC0gh0FpAqqmrYdgfhvHq\nRa9y9MCjuzll8evgQddS7+233bRypWtAMX68m8aNc636rBRlTPhYQAqB1gISwK+X/5rtpdt5+PSH\nuzFVJpCqq9Z79103RPu777oxnUaMcPefvvEN9/foo6Fnz3Cn1pj4YAEpBNoKSF+VfcWo+aP48oYv\n6durbzemzLSmqsoFpffec6Wp9993AxEecQSMHeueiRozBo49FtLTw51aY2KPBaQQaCsgAVz03EWc\nkHMCP5v4s25KlemMykoXpN5/H9asgbVr3TDuOTkuMPmmY46B/HxIiOoHE4wJLwtIIdCegLRy60ou\n/sfFbLx+Iwli32LRpLbWlZw++sg1lvjwQzdfWgpf/7qr5jvqKDd9/euQnQ0SNf9ixoSPBaQQaE9A\nUlW+8edv8JOxP+HKE67sppSZUNq/35WmPv7YPRf1ySduSkx0Y0IFTiNHwuDBFqiMCWQBKQTaE5DA\nPSh75pNnctbIs7jnP+4hOTG5G1JnupOq6wLp009dbxO+6dNP3T2rkSMbT0ce6Xqh6NEj3Ck3pvtZ\nQAqB9gYkgH0H93HRcxdxsOYgT537FAP7DAxx6kyk2LsXNmxw02efNcwXFsKQIXD44a5BxeGHN8zn\n5bkulYyJRRaQQqAjAQmgrr6OucvnsmDtAuadMo8zjzwTsbqcuFVTA5s3u/tUGzfC5583TDt3uqA0\nYoSbvva1humww6yJuoluFpBCoKMByeeNzW9wzcvXMLzvcOadMo/hfYeHIHUmmlVWumC1aZObvvii\nYSoogAEDYPhwNx12WONp8GBrBWgimwWkEOhsQAKorqvmvpX38d9v/zdnHnkmfVL6+Lcd2f9ITj/i\ndHIzcrsqqSaG1NW5kXg3b3YBavPmxtP+/ZCbC8OGuSbqvr++acgQqw404WUBKQQOJSD5FJYU8sKG\nF6hXNyqdoqz5ag3/3PhPcjNy+cHhP+DrA75OfmY++Rn55KTlWPNx06qDB939qS1b3FRQ0PC3sNBV\nBw4a5IJTbq6rGgz8O3Qo9OtnLQNN6FhACoGuCEgtqa2vZeXWlby66VU+3/c5BSUFFJYUsu/gPoam\nDyU/I5/8zHwG9R5ERs8MMntmktHD++st5/TJsR4iTDM1NW44+YIC2LrVBSnftHWrK31VVbnA5Jty\nc13JKnDKznZN3Y3pKAtIIRDKgNSSytpKCksKKSguoKCkgF3luyipLKG4spjiqmL/fElVCUUHipgw\ndAKXHHMJZ408i94p1qOoaZ8DB1zQ2rbNTVu3umXftG0bFBfDwIHuntWQIe6vb8rJcdPgwVbaMs1Z\nQAqBcASkjqioqeCFDS/w2EeP8fbWtxkzaAx9e/X1l6QCS1VZvbLIzcglPyOfrF5Z1vrPtKm62j17\ntX07FBU1nnbsaPhbXu4C16BBDYFq0CA3+dYPHOgm64U9PlhACoFID0iBdpbt5JNdn1BSVdKoFFVc\nWUxxZTF7D+71l7xq62ubBaXs3tmumjAjnyHpQ1odCTc1OdUf6NJ7pLf7nleCJDA4bTAD+wy0+2Qx\npLISvvrKTTt2uGnnzoblnTsbpqQkF5iysxuClG8+O7thGjAAsrKsNWG0soAUAtEUkDrCF7B86rWe\nXeW7KCgpYEvxFooOFPkbYTSlqlTUVPiDXWlVKUr78qi2vpbtpdsprSolNyOXIWlDGu6J9cikR1LL\n3Rr0SOxBXkYeeRl55Gfmk9kzs9H2tJQ0UpNTreQXwVRdP4G7djUOUrt2NazbvdvN797t9s3KcsGp\n6dS/f8Nf39SvX/ie36quq3Y/AisbfgSWVZe1+/W19bWUVpX6/68qaytDmNoGvZJ6+e9Jp/dIJ1Ea\nbhomJSSR0TPD/+Mz8P9TEPqk9Gnxf84CUgjEakAKt4qaCgpLCik6UNSoNFddV93mawpKCigoLmj0\nz16v9ZRVl1FTX9Oo4YdvXlFKKksoqSqhtKq00T9hn5Q+3V5aU1XKa8r9115eU07v5N7+dPdO7t3u\nwNozsSe5GbkMyxxGfkY+SQlJjX4sBP6wqKmraShBVxW3mt+19bWN3puq2qpDvu5gkhOTGzfW6dHQ\naKdnYm92FZexs7iE3QeKKa04SFUVVFZBZaX7YVRRX0wlJdRIKXX1iggkJEJigmuQkeD9TUnoSZ+k\nTNJTMsjslUHPHomkpECPFEhOabkkVq/1HKg64M/T8upy/zZFqaytpKSyhJr6mmbV5H1S+rT7fUyQ\nhIZ86JFBz6SeIf9xpaocrD3o/zy0+nmpbPx5Cfyfy+yZyUn5J/Hs+c/6t1tACgELSNGluq660Zdo\nYEnQ94WXlpLmvkS8L5iy6jLC8R73Tunt/wLqndKb8upyf7oDv/Ta0ihQlxRQV1/X8Is3JZ3EhIZf\nvImS2ChY90xquTiRmJDYKFC0tu+haOk98703aT3S/OnoldwLoeE7LjU5tdH7miCJlJe757T2F0Px\nftcwY99+ZU9xJbtKi9lTVsL+8hLKKuopL4MDZXCwAnr2cve3+vR2fxsmIat3GgPSMxiYkUl2395k\npAvp6W4srQF9ezIgLZNeSb3isnTue/+q66oZkj7Ev94CUghYQDIm9tXXQ0kJ7Nvnpv37m0/FxY3n\nfcslJa6aMCMDMjPd5JvPyGg++QKZb973NznG+mO2gBQCFpCMMa1Rda0MfUGqpKT539JS99c3+ZZL\nSxumpKSGYJWeDmlpDX+bzgdOffo0/5uaGv5m+BaQQsACkjEm1FRd7xsHDjQOUoHLBw40n8rKgs9X\nV3vVj30apsBlX3Vk4HxLU2pq4/mUlPYFOwtIIWAByRgTberqXKnNF6R8877Jt1xe3jAFLldUtDxf\nX+8CU9Pp+OPhwQcb0hBtAcm6fjTGmBBITGyo+utqNTUNQergQTdfUeFKTtHMSkjGGBOjoq2EZM9f\nG2OMiQghDUgiMlVENojI5yIyq4V9HvC2fygix4UyPcYYYyJXyAKSiCQCfwSmAqOB6SIyqsk+pwIj\nVPVw4ArgwWYHMo0sW7Ys3EmIGJYXDSwvGlheRK9QlpDGAZtUdYuq1gBPAmc22ecM4BEAVX0XyBSR\ngSFMU9Szf7YGlhcNLC8aWF5Er1AGpCH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RRVxxxRV07dqVZ555hnnz5jFq1CgyMzPp3bs3N910E5WVlWRnZ3PBBRfQoUMH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\nkbZt20ZlZSX9+/cPrMvNza0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"text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans le m\u00eame ordre d'id\u00e9e que pour la r\u00e9gression logistique, essayez d'optimiser diff\u00e9rent hyper-param\u00e8tres, un seul \u00e0 la fois. L'id\u00e9al serait de fixer un nombre d'unit\u00e9s cach\u00e9es d\u00e9cent de l'ordre de 200, et d'optimiser le taux d'apprentissage. Vous pourrez ensuite optimiser le nombre d'unit\u00e9s cach\u00e9es, puis r\u00e9optimiser le taux d'apprentissage. Et oui, la valeur des hyper-param\u00e8tres influence les autres et peut changer leur valeur optimale." ] }, { "cell_type": "code", "collapsed": false, "input": [ "[train_x, train_y], [valid_x, valid_y], [test_x, test_y] = utilitaires.load_mini_mnist()\n", "\n", "n_in = train_x.shape[1]\n", "n_classes = np.unique(train_y).shape[0]\n", "learning_rate = 0.01\n", "\n", "train_results = []\n", "test_results = []\n", "\n", "l2s = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]\n", "\n", "for l2 in l2s:\n", "\n", " modele = utilitaires.FeedForwardNeuralNet(n_in, n_hids=[500], n_out=n_classes, non_linearities=\"rectified\", l2=[l2, l2])\n", " modele.train(train_data=train_x, train_labels=train_y, learning_rate=learning_rate, max_epoch=1000, batch_size=20)\n", "# stopping_rule=EarlyStopping(modele, patience=10))\n", "\n", " train_results.append(modele.compute_cost(train_x, train_y))\n", " test_results.append(modele.compute_cost(test_x, test_y))\n", "\n", "print l2s\n", "print train_results\n", "print test_results" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 5% : \u00e9poque 51 : perte = 0.125815 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 10% : \u00e9poque 102 : perte = 0.050864 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 153 : perte = 0.031067 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 204 : perte = 0.022975 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 255 : perte = 0.018763 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 306 : perte = 0.016236 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 357 : perte = 0.014574 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 408 : perte = 0.013409 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 459 : perte = 0.012551 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 510 : perte = 0.011897 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.011383 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 612 : perte = 0.010970 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 663 : perte = 0.010631 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 714 : perte = 0.010350 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 76% : \u00e9poque 765 : perte = 0.010112 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 816 : perte = 0.009908 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 867 : perte = 0.009733 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 918 : perte = 0.009579 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 969 : perte = 0.009445 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\r", " 5% : \u00e9poque 51 : perte = 0.186213" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", " 10% : \u00e9poque 102 : perte = 0.112239 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 153 : perte = 0.092727 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 204 : perte = 0.084656 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 255 : perte = 0.080317 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 306 : perte = 0.077570 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 357 : perte = 0.075628 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 408 : perte = 0.074138 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 459 : perte = 0.072924 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 510 : perte = 0.071890 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.070979 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 612 : perte = 0.070155 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 663 : perte = 0.069395 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 714 : perte = 0.068684 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 76% : \u00e9poque 765 : perte = 0.068011 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 816 : perte = 0.067367 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 867 : perte = 0.066749 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 918 : perte = 0.066150 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 969 : perte = 0.065569 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\r", " 5% : \u00e9poque 51 : perte = 0.734104" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", " 10% : \u00e9poque 102 : perte = 0.620611 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 153 : perte = 0.559703 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 204 : perte = 0.512272 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 255 : perte = 0.471424 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 306 : perte = 0.435197 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 357 : perte = 0.402743 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 408 : perte = 0.373558 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 459 : perte = 0.347265 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 510 : perte = 0.323554 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.302160 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 612 : perte = 0.282850 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 663 : perte = 0.265418 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 714 : perte = 0.249683 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 76% : \u00e9poque 765 : perte = 0.235477 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 816 : perte = 0.222647 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 867 : perte = 0.211055 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 918 : perte = 0.200586 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 969 : perte = 0.191127 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\r", " 5% : \u00e9poque 51 : perte = 2.758305" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", " 10% : \u00e9poque 102 : perte = 1.360408 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 153 : perte = 0.858929 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 204 : perte = 0.676439 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 255 : perte = 0.608853 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 306 : perte = 0.582918 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 357 : perte = 0.572250 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 408 : perte = 0.567307 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 459 : perte = 0.564586 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 510 : perte = 0.562799 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 0.561427 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 612 : perte = 0.560293 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 663 : perte = 0.559311 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 714 : perte = 0.558457 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 76% : \u00e9poque 765 : perte = 0.557701 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 816 : perte = 0.557011 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 867 : perte = 0.556385 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 918 : perte = 0.555813 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 969 : perte = 0.555286 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\r", " 5% : \u00e9poque 51 : perte = 1.903590" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", " 10% : \u00e9poque 102 : perte = 1.882112 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 153 : perte = 1.873078 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 204 : perte = 1.867868 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 255 : perte = 1.864551 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 306 : perte = 1.862273 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 357 : perte = 1.860651 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 408 : perte = 1.859450 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 459 : perte = 1.858528 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 51% : \u00e9poque 510 : perte = 1.857810 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 56% : \u00e9poque 561 : perte = 1.857242 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 61% : \u00e9poque 612 : perte = 1.856778 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 66% : \u00e9poque 663 : perte = 1.856402 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 71% : \u00e9poque 714 : perte = 1.856085 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 76% : \u00e9poque 765 : perte = 1.855822 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 81% : \u00e9poque 816 : perte = 1.855600 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 86% : \u00e9poque 867 : perte = 1.855411 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 91% : \u00e9poque 918 : perte = 1.855249 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96% : \u00e9poque 969 : perte = 1.855111 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[1e-05, 0.0001, 0.001, 0.01, 0.1]\n", "[0.0, 0.0, 0.0, 0.014, 0.205]\n", "[0.114, 0.114, 0.116, 0.124, 0.224]\n" ] } ], "prompt_number": 487 }, { "cell_type": "code", "collapsed": false, "input": [ "utilitaires.plot_training_curves(hyper_parametres, {u\"ensemble d'entra\u00eenement\":train_results, u\"ensemble de validation\":valid_results}, \n", " title=u\"Taux de classification en\\n fonction d'un hyper-param\u00e8tre x\",\n", " xlabel=u\"Valeurs de l'hyper-param\u00e8tre x\",\n", " ylabel=u\"Taux d'erreur\",\n", " xlog=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ZZntkspoyZQo///nPad26NQceeCCffvpp+bZu3bpx2223sffee9OqVSsGDRrE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\nWBERqR+SufW3ga3zUjUGGgE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"text": [ "" ] } ], "prompt_number": 488 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Bonus: Early stopping" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une m\u00e9thode simple et efficace souvent utilis\u00e9 \u00e0 l'entra\u00eenement par descente de gradient est ce qu'on appelle l'\u00abearly stopping\u00bb. Le principe est simple, on observe le co\u00fbt pendant l'entra\u00eenement et on arr\u00eate l'entra\u00eenement s'il arr\u00eate de descendre pendant plusieurs \u00e9poque. On peut enregistrer les param\u00e8tres au moment o\u00f9 le co\u00fbt \u00e9tait minimal sur l'ensemble de validation. Regardez la classe suivante qui impl\u00e9mente ce principe." ] }, { "cell_type": "code", "collapsed": false, "input": [ "class EarlyStopping:\n", " \"\"\"\n", " R\u00e8gle d'arr\u00eat pour l'entra\u00eenement d'un mod\u00e8le d'apprentissage machine\n", " \n", " Attributes\n", " ----------\n", " model: object\n", " Mod\u00e8le d'apprentissage machine (ex: r\u00e9seau de neurones)\n", " patience: int\n", " Nombre de fois un co\u00fbt sup\u00e9rieur au meilleur peut \u00eatre \n", " vu avant d'arr\u00eater l'apprentissage\n", " \n", " Methods\n", " -------\n", " restore_best_model()\n", " R\u00e9injecte la valeur des meilleurs param\u00e8tres dans \n", " le mod\u00e8le apr\u00e8s l'arr\u00eat de l'apprentissage\n", " \"\"\"\n", "\n", " def __init__(self, model, patience=5):\n", " \"\"\"\n", " Parameters\n", " ----------\n", " model: object\n", " Mod\u00e8le d'apprentissage machine (ex: r\u00e9seau de neurones)\n", " patience: int\n", " Nombre de fois un co\u00fbt sup\u00e9rieur au meilleur peut \u00eatre \n", " vu avant d'arr\u00eater l'apprentissage\n", " \"\"\"\n", " self.model = model\n", " self.patience = patience\n", " self.best_cost = float('inf')\n", " self.patience_buffer = patience\n", "\n", " def __call__(self, train_data, train_labels):\n", " \"\"\"\n", " M\u00e9thode principale pour l'arr\u00eat (stopping_rule)\n", " \n", " Parameters\n", " ----------\n", " train_data: ndarray\n", " Matrice d'exemples de format (n,d) o\u00f9 n est le nombre d'exemple et \n", " d la dimension\n", " train_labels: ndarray\n", " Vecteur de cibles, de format n\n", " \n", " Returns\n", " -------\n", " bool\n", " True si la patience est vide, False sinon et l'entra\u00eenement continue\n", " \"\"\"\n", " cost = self.model.compute_cost(train_data, train_labels)\n", " if cost < self.best_cost:\n", " self.best_cost = cost\n", " self.patience_buffer = self.patience\n", " self.params = [param.get_value() for param in self.model.params]\n", " else:\n", " self.patience_buffer -= 1\n", "\n", " return self.patience_buffer < 0\n", " \n", " def restore_best_model(self):\n", " \"\"\"\n", " R\u00e9injecte la valeur des meilleurs param\u00e8tres dans \n", " le mod\u00e8le apr\u00e8s l'arr\u00eat de l'apprentissage\n", " \"\"\"\n", " for param, best_values in zip(self.model.params, self.params):\n", " param.set_value(best_values)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici comment utiliser la classe EarlyStopping. Vous pouvez tester diff\u00e9rentes valeurs de patience pour observer son effet. Et n'oubliez pas, le early stopping se calcule sur l'ensemble de validation, pas l'ensemble de test! On test \u00e0 la toute fin seulement!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "[train_x, train_y], [valid_x, valid_y], [test_x, test_y] = utilitaires.load_mini_mnist()\n", "\n", "n_in = train_x.shape[1]\n", "n_classes = np.unique(train_y).shape[0]\n", "learning_rate = 0.1\n", "max_epoch = 1000\n", "\n", "modele = utilitaires.FeedForwardNeuralNet(n_in, n_hids=[500], n_out=n_classes, non_linearities=\"sigmoid\")\n", "\n", "early_stopping = EarlyStopping(modele, patience=20 * max_epoch * modele.message_frequency)\n", "\n", "modele.train(train_data=train_x, train_labels=train_y, learning_rate=learning_rate, max_epoch=1000, batch_size=128,\n", " monitoring_data={\"ensemble de validation\": (valid_x, valid_y)},\n", " stopping_rule=early_stopping)\n", "\n", "print \"avant la r\u00e9injection\"\n", "print modele.compute_cost(test_x, test_y)\n", "\n", "# R\u00e9injecte les meilleurs valeurs dans le mod\u00e8le\n", "early_stopping.restore_best_model()\n", "\n", "print \"apr\u00e8s la r\u00e9injection\"\n", "print modele.compute_cost(test_x, test_y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 1% : \u00e9poque 11 : perte = 1.099339 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 2% : \u00e9poque 22 : perte = 0.696322 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 3% : \u00e9poque 33 : perte = 0.528799 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 4% : \u00e9poque 44 : perte = 0.436055 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 5% : \u00e9poque 55 : perte = 0.374977 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 6% : \u00e9poque 66 : perte = 0.330291 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 7% : \u00e9poque 77 : perte = 0.295255 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 8% : \u00e9poque 88 : perte = 0.266445 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 9% : \u00e9poque 99 : perte = 0.241958 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 11% : \u00e9poque 110 : perte = 0.220656 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 12% : \u00e9poque 121 : perte = 0.201822 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 13% : \u00e9poque 132 : perte = 0.184985 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 14% : \u00e9poque 143 : perte = 0.169823 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 15% : \u00e9poque 154 : perte = 0.156107 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 16% : \u00e9poque 165 : perte = 0.143669 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 17% : \u00e9poque 176 : perte = 0.132377 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 18% : \u00e9poque 187 : perte = 0.122124 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 19% : \u00e9poque 198 : perte = 0.112822 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 20% : \u00e9poque 209 : perte = 0.104391 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 22% : \u00e9poque 220 : perte = 0.096760 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 23% : \u00e9poque 231 : perte = 0.089859 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 24% : \u00e9poque 242 : perte = 0.083622 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 25% : \u00e9poque 253 : perte = 0.077986 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 26% : \u00e9poque 264 : perte = 0.072888 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 27% : \u00e9poque 275 : perte = 0.068273 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 28% : \u00e9poque 286 : perte = 0.064087 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 29% : \u00e9poque 297 : perte = 0.060282 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 30% : \u00e9poque 308 : perte = 0.056818 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 31% : \u00e9poque 319 : perte = 0.053657 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 33% : \u00e9poque 330 : perte = 0.050766 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 34% : \u00e9poque 341 : perte = 0.048117 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 35% : \u00e9poque 352 : perte = 0.045683 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 36% : \u00e9poque 363 : perte = 0.043443 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 37% : \u00e9poque 374 : perte = 0.041377 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 38% : \u00e9poque 385 : perte = 0.039467 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 39% : \u00e9poque 396 : perte = 0.037699 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 40% : \u00e9poque 407 : perte = 0.036058 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 41% : \u00e9poque 418 : perte = 0.034533 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 42% : \u00e9poque 429 : perte = 0.033112 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 44% : \u00e9poque 440 : perte = 0.031788 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 45% : \u00e9poque 451 : perte = 0.030550 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 46% : \u00e9poque 462 : perte = 0.029391 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 47% : \u00e9poque 473 : perte = 0.028306 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 48% : \u00e9poque 484 : perte = 0.027286 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "R\u00e8gle d'arr\u00eat atteinte\n", "\n", "avant la r\u00e9injection\n", "0.134\n", "apr\u00e8s la r\u00e9injection\n", "0.128\n" ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afficher les filters \u00e0 l'aide du code [ici](http://deeplearning.net/tutorial/code/utils.py)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import PIL.Image as Image\n", "from utils import tile_raster_images\n", "\n", "print modele.layers[0].W.get_value().shape\n", "\n", "image = Image.fromarray(tile_raster_images(X=modele.layers[0].W.get_value(borrow=True).T,\n", " img_shape=(28, 28), tile_shape=(10, 10),\n", " tile_spacing=(1, 1)))\n", "\n", "plt.imshow(image, cmap=\"gray\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(784, 500)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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JSQkAk5OTSCQSgWKvra3x4YcfMjY2Rjqd5vnnnxdc8gFAeYCFHKjYTCYTiUQC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\ngLm5OU6cOCHb8MuXL4s3xb179yguL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