diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,3 @@
-data
\ No newline at end of file
+data
+config
+test
\ No newline at end of file
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diff --git a/buildEmotionModelFromFer2013.ipynb b/buildEmotionModelFromFer2013.ipynb
index 0a910ec7a3cac5f029351fe158a80932962b50fd..f82baacc2f82d9a87f821dae64a8226e7b941390 100644
--- a/buildEmotionModelFromFer2013.ipynb
+++ b/buildEmotionModelFromFer2013.ipynb
@@ -1 +1 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"buildEmotionModelFromFer2013.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"12gj_RNUkhCcpPsrHCcnjM-aFz0bHG8Nk","authorship_tag":"ABX9TyP/j4/kXn/SBJc9poEkTWTi"},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.8.5"}},"cells":[{"cell_type":"code","metadata":{"id":"1321rUeQzURj","cellView":"form","executionInfo":{"status":"ok","timestamp":1616663382789,"user_tz":-60,"elapsed":3339,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}}},"source":["#@title Imports\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import datasets, layers, models, losses\n","import tensorflow_datasets as tfds\n","#from google.colab import files\n","\n","from matplotlib import image\n","import os\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import matplotlib\n","import random as rd\n","import cv2\n","import csv"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-6y_a77Xmx3X","executionInfo":{"status":"ok","timestamp":1616662751659,"user_tz":-60,"elapsed":5015,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"fa99ea9f-0f75-42a2-fba5-c09606d4198d"},"source":["#from google.colab import drive\n","#drive.mount('/content/drive')"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"id":"a4LizvrK0fes","cellView":"form","executionInfo":{"status":"ok","timestamp":1616664903890,"user_tz":-60,"elapsed":1577,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}}},"source":["#@title Hyperparamètres\n","classes = [\"Angry\", \"Disgust\", \"Fear\", \"Happy\", \"Sad\", \"Suprise\", \"Neutral\"]\n","Na = len(classes)\n","N = len(X)\n","maxNbrImagesForEachClasses = float('inf')\n","h = 48\n","l = 48\n","p = 1\n","input_shape = (h, l, p)\n","\n","epochs = 5\n","batch_size = 128\n","validation_size = 0.1"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"J26HwuSTpVQK","executionInfo":{"status":"ok","timestamp":1616663639205,"user_tz":-60,"elapsed":726,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}}},"source":["#@title Fonction utils\n","def afficher(image):\n","  if len(image.shape) == 3:\n","    if image.shape[2] == 3: # (h,l,3)\n","      plt.imshow(image)\n","    elif image.shape[2] == 1: # (h,l,1)->(h,l)\n","      image2 = image\n","      plt.imshow(tf.squeeze(image))\n","  elif len(image.shape)== 2:  # (h,l)\n","    plt.imshow(image)\n","\n","def predir():\n","  pass\n","\n","def normAndResize(image):\n","  #For an array image of shape (a,b,c) or (a,b), transform it into (h,l,p). Also normalize it.\n","\n","  image = cv2.resize(image, dsize=(h,l), interpolation=cv2.INTER_CUBIC) #resize for h and l                                       #\n","  if len(image.shape) == 3 and p==1 and image.shape[2] != 1 : #if we want (h,l,3) -> (h,l,1) , we first transform it in to (h,l) (grey the image)\n","    image = image.mean(2)\n","  image = np.reshape(image, (h,l,p))                                    #restore third dimension\n","  image = image.astype(\"float32\")\n","  image = image/255                                                     #normalisation\n","\n","  return image"],"execution_count":32,"outputs":[]},{"cell_type":"code","metadata":{"id":"33votd1Y0fcg","colab":{"base_uri":"https://localhost:8080/"},"cellView":"form","executionInfo":{"status":"ok","timestamp":1616664902296,"user_tz":-60,"elapsed":102287,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"ec40cf93-99e9-458f-b24b-a9d45934f7db"},"source":["#@title Load data as array\n","nbrImages = 35887\n","maxNbrImages = 10\n","emotions = [\"Angry\", \"Disgust\", \"Fear\", \"Happy\", \"Sad\", \"Suprise\", \"Neutral\"]\n","\n","def traitement(a,b,c):  #For testing\n","\tpass\n","\t# arr = strToArray(b)\n","\t# print(a)\n","\t# plt.imshow(arr)\n","\t# plt.show()\n","\t# pass\n","\n","def strToArray(string):  #Fer2013 provides images as string so it needs to be transformed\n","\tA = []\n","\tlenght = len(string)\n","\ti=0\n","\tnbr = \"\"\n","\n","\twhile i<lenght:\n","\t\tcar = string[i]\n","\n","\t\tif car != \" \":\n","\t\t\tnbr += car\n","\t\telse:\n","\t\t\tA.append(int(nbr))\n","\t\t\tnbr = \"\"\n","\t\ti+=1\n","\tA.append(int(nbr))\n","\t\n","\tA = np.array(A)\n","\tA = np.reshape(A, (48, 48))\n","\n","\treturn A\n","\n","\n","\n","#LOAD DATA AS ARRAY\n","X = []\n","Y = []\n","\n","filename = \"/content/drive/MyDrive/Colab Notebooks/facial emotion recognition/fer2013.csv\"\n","filename = \"data/fer2013.csv\"\n","\n","with open(filename,'r',encoding='utf-8') as file:\n","\t\n","\tcsv_reader = csv.reader(file, delimiter=\",\")\n","\tnext(csv_reader)  \t\t\t\t\t\t\t\t#Passe la ligne de titre\n","\t\n","\ti=0\n","\tfor row in csv_reader:\n","\n","\t\ti+=1\n","\t\tif i>maxNbrImages: break\n","\t\t\n","\t\temotionNbr, stringImage, typeImage = row\n","\t\ttraitement(emotionNbr, stringImage, typeImage)\n","\t\timage = normAndResize(strToArray(stringImage))\n","\n","\t\tX.append(image)\n","\t\tY.append(emotionNbr)\n","\n","\t\tprint(f\"Image {i} sur {nbrImages} chargée\", end='\\r')\n","\n","X = np.array(X)\n","Y = np.array(Y)\n","Y = keras.utils.to_categorical(Y)"],"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":[""]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":108},"cellView":"form","id":"9c7SsmqlpVUT","executionInfo":{"status":"ok","timestamp":1616664903889,"user_tz":-60,"elapsed":1586,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"6cd175f5-9d89-42cf-80b8-71d4d8eb82a6"},"source":["#@title Visualisation\n","N=5\n","plt.figure()\n","for i in range(N):\n","    k = rd.randrange(X.shape[0])\n","    plt.subplot(1, N, i+1)\n","    plt.xticks([])\n","    plt.yticks([])\n","    plt.grid(False)\n","    \n","    #plt.imshow(x[k])\n","    afficher(X[k])\n","    plt.title(classes[np.argmax(Y[k])])\n","plt.show()"],"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 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OmcjLaIKuRLH2bdVq140S4Em4smlboV7ttJ5NUvB12qxaByA4unZmmFeMhkF6oEBayAExAjhdIpsjSG16zvgoFVNFsYrIEB+4PGZoelBehpjEqtRQj0wlE9UPDvaZyeZkEvNpp0xMnNyqRlIvbx2264E8XmDRbFGScVjCcxCyjykZLZhMCyYbFumiSPbN9gyem7VQZNHJ8Ais0gt2EIRr6gzpMcNahKaXgzzWWCxaos/h2Pla8rpMePJOUwQF4ErnUzeKpO3Gz8PYQJunRO9HO3naGZjxDOgrl0VFiEqiZL2K/p5RZY05K5hkFTkrad6AUsgMw0Tn5OZmpGdk4jHqxAw5BJVzkKTVtUM1OqwEpj4HqH1cA9MCUBuahITYFRTbub4UQ5VHZ0r3iPzEskSNFl/V6PEmyIK0ijJjLhabyWYJr7Bpwa1UGwaBpfmDFy54hBqGEhFLtGOufDchtXFQMJyB2c04BF23ITjpkffVeRJQzGoqQ0U1pGcCnYukUQWfNZmARXxQeutlhCJZMuALQz1IL6v3Tgu7V4PomKeXcPjxZPz+gsee66sKZYLIZN3dQIGxDkkTVFr2pzpEMOIJJ5M3MobfKLUlWMuUDWO0jUUjeOkzOknFeO0IDWennX0FrEbCUvVIWDwarjtNyhDwsTn2FYNqdUy89G2VwaHEWVoS66nRwxtQWI9aa+mHufUmxmptdB6wakbpPZxUjiPK1faS25iTOMifnCRiQNxlTc+ZmtFx9C9d8WILskD4JHlTi20q7lFSYk7u8tuwiTNOar77CVDyl4FotReaIKNJPGyjKlUiUSSANl+tG8RFGkCbu5J5oa6ze4xeq/z4GE54R4rnpx3UXrcudLJZK3x83Am4DQF11qhQwCJF2DqmJ2itWCcojOD1iklKQhMbfRy08Z69scFW4M5O70pW9ls6a0MGOpgmIWMY9/jtWKL3fmY4yqnaByNt/ggqArOBgZpxTAt2UznlMExtHGHkySeoqfUA0tmDdoECAGta8T7mIix7mSzavMJMaVxkc8uLalsFbAV+NRC0txjgwwYKiy5NgSRZaossFSnlwkGgJFATs3IzLnsJhxnfQ57veVx31hCAO8tbirYKqr4bt6ebmCZqgkgPmDnipta5JI5W7UXeIgOuMeLJ+c0QXRc6WSygoczAWdptIu1K0KMlTwLTl4I2FYCheCmZ2EeCIQUUCg2E25fSWm2YpjQRlpQhiRWrlLLxOfcKjd54egqt49GlKcZMreYQiAI6pSQB442KsajGXXfElTYyU6pfTTQqIsB15goGW0axPvocDm3AEA0ksZ4kCZgmniTbBVwU48pPT7LsS5mYi08tZVaarVUYu5ZwRdYrvStiu6Rpdp8yZ5yM02Y9xOCCnUwlJWjagyhiNfrZuBTSI91ubiIh/SkaTO6FOpActpgK4fPVtSmlkwPbQd80XnSXsfjzJVzxQI/5jJ5V3fAYi04Fw3pIcTdjYsrgU+Fpg9NT2NokYKpBFfEuD7xZ2EdTR96u4YiZOyLMspKUuuZ+RSbBjyGUh175YDd4xHlXo/k2JJMordSfPRMVltCLSmnNgbs91zNHkNOioyyjJfrU1k63DQoNA0PEly/WK3Fg3hdPpKZ4k497rjEHk+ZX48qtF2EPrU3plZHrZaU6FyyKxHdqys6QEq0YVlp2Lany+IztVomdc4sTalmKaJxwnIzxU0hOwlt4Hj8HjuPBFIjmNpjZxVuntHkEkWzSqDz1j5YldHjxpNzyuRx50onk/XGz4NPwIkDZ5EyWp41S/CDjOlVR3kpDioM2LnQuyOMXvdkhzWm8CDQ9B3T6ynlliAe3KmhOsy40x+ylc3wGAyBVBqGtmDgKowJiMb3mxqSiZKexoFbTAzzylL5HruNpRg7ntk84MrwlFmRRr+PEHc2YpBEQB5Az9Z7V7moNgVsGVfx9LjCHk7Q6ZwmF8ZpcY8Kdb8ta/lau8J7lbOVfeksjirVQGoSO2EgFWMzJ6ghqDA56WEqiWX8FAZ3mmhjqwPGx+wd8YopK9RapAlI1ZCeeIpLDlY3ecqyFuqD4HHjybkWpQvAlU4m642f9Sbg+z2/K6X5tIrOEHE2FtFOQBro7wqDNwOjl2e4V+6gRYlkKToaoFmKnVZke3Ngg2JH0ALUGU43cu4OhjzZOwSgb0pGJmFgK+bTjOyOJd+D/t1A726FOyqR2iMhoM4wvzni8AM9Dp9PmRUZ77+8Rz+vOEr7UXVIE8J0BrVH0xTjQyz8vCZEY464qcHWijQKqpjSx1qj0xKKErGGJhN20imZqdsCMaFNElh4ac9+f1V1WpBoQSQrbRgVJu4WJTCyc75l+Bq1GvZOB0wOU3qFxALmCm4e1TjTBNRKlJVXwC93dcmkRoI7CyQPYM6jcV8Anug5dO0LwZU1h9CFkMnbYK0JWLIU4waE2exelb2s0Fm0Uosx2KJh/ErNxsuQ3p1jD07Qw2N8WSLWIv0eiKCJxQ8SfG4Zv1pgmiwWT7ZCdZIw3UrvEajHcGs+xr2eke/B4Lan/2aB2ztFqjpmK+XxM71bU9KjhNHrObufHHKnX1BUCeJbR5sIdmuD6lveRwiKVAHxYX0blkbvvWla1SkoeMVUHqkDVDWqihhDSKFnq3tqEqwiqBBEls4D324LVlWoWg37vs9r9Ta79QYTn1O3AYh9U7GZzHli45gXtnPq/R7JBJq+IZk0mCLaMaVNfkA1nmMbDmbnNabqxXjGxU/q+v6mC8GTc4QsXgiudDJZa/ysNQGrNUieIWWJNtHzLNbGUn3ex+4BgL2b0N87gbIiHJ/g212PGQ6QwQAd9AiDnNCPg6oeOcxhTX+3phlYfGpxE0tZO3zrRvQIx02f3dmI9ERwMyU7bLDT+N3qYoC8OoOmMdPKzBvGXzzEZ5fY7V9CaiE5MahRJMuYffQGu59IyY5g+Ian/+Z8HXHE64dlJSdp1Q0JAWowVYPUDep9PDcTkwZyiUVDAIqQYEwg1bZbA/4si6ddsWPSgGGqKQd+yMvVDnv1iKCCkZhiC8QUy2AwooiJTo1FlJYEjY4OH8N8xCtqTHQsQSRT6WNVqJ4sHQmLWMx10PHka6PjSieT+7GeCUJp00hXVn9rY2iRSBTUfI6/XaFlDOlZtpvZGOMvjQEIuaO4mlENzLLdB5IweOUUUyVkgxw3jT27gko0qgOzkHJaZMvybz4zFNeHSFDSw6haFtcGMYfcQHLqyV844tKvVDT59ViYOW/rDQx63PmOlOojM6o3eti5oXfHnMuOZVZsPLEGqCK1h6pue4tp60WPGVoew5HvU6slqCERz4adMbLzZT77IrMHoCKGVu37IS+WV3ml2AZgYEsyafBqKIPjqO5ztxhy62SMP0mxRdu+pVZCYjBWkJoYexsE7MJzEsArUjeRQP02/rEtKr12xNWF4Mn57OKPO1c6maw3ftYzQfioEiwD0yEONI12G7EWTKwWpcYiRrBXr1A+f43jZzLKLWHnN0vmlxN2fzdsv/8AEWX6c5fp3VUGrVqanAbczDCvHEENIzMnFR+LqSQNp1cCw9eFN/6Ao9mpcfsJN/+fmAr4yr8qkAT6X0m5/BsaC78cHHLlZxvK565w9P6MeiiEjT7ldsCXFguxBsH5/CrLQG1o/w8gVY3UTawl4D3qA9lJ9La+MLvGrfkGx1UPg9KoIbMNl7IZO9kp19Njnsnu8IQ7JGnrFHiEaUg5aAZ4FXbnY1493uToYIjZT5BaaC7X4AUztWSTGMKVHdSExFBuJTG8Z1IgwdBs9jF1tF+JSky79WFZyX+5IznHoLoIPFnJbu648gBcuegyWdMJRxRGUBCJdV2zFBofdzVGYq7/oA+nU7QoaZ7cYfc7c06fq0kOHHe/PYMAmjV8+5XX+BM7n+U/tj9A9le2AAipRVRJTpXTWTy9qHJ4ttyUS70Zh9UOdz7l+bHv+xt8pbrCX/7i93JwaxPTgPRKft8Hf5sv37jC7d5lnjraxO4dQJ7GSvdtrGJIDIPXDMXNmmpk8ak7v7e/9ZiraVXbxCJltZxwCIpWFf3bFb99eoXNdM6HRrtcT4+47E6Y+B57zYiDZsDcJ9ypRtxMD3jSzdgwluPg+dnpE/zPtz7BV964jJYWfIyhTfcsm1+GegD1fkrTg2aoVBuxrOL8csLWbx5QXx6gicFfGmD3JgCUOz2y/QKoI+l9iEkRK3PnIja348l9PDlvZN5jzpVOJuuNn7XD0HRh8xATM5usgcYj1mLGI/z1HeY3B/hUGL4yxb1yhydKz8mrI05vCraApgfJkeXnfvHj/MLsW+m/CbIZQ09iaqrElaQyOOPjrqYtvLGZzRGF3puOH/yZP4sd1fjSku5otPm9mfLL0w+Tv2m58vmG5OVdmt/1DCfP9NG2ilGsN2vJ95XKKFIa3EzjinaeXXDb+XShlkpoHTVhRfLek+5O+LWX38fGeIo1yqxMmd4eILUhmURC+L7iB4Efbz6BbFSkeUN4YUh6LJTbyrd86iW+ePsqH762S0D4rTeucTrvM7/hY+PK0sCoYWt7wvXRhC/mT1NublMPhOxQGb8i2GNHvZEyu+JwpxZX1isXw7Lq02oq6Noyecx5ErLzRXB2XOlksoq1bcBio8qItUiaRI/yIEWvbHF6c8DkCUuxE/s2nTw9ZuPlAclJQ3bcxnPmgpuDm8fQJGkU49uq90mbyudYBjwvPJ2L7JVr+Qn11Yr0hYyNf+4IiUNdLJKRnCrZAdjKkMzj5w7/wPvY/5hQ3yzRykIdSA8tTc/iytjmxs1iMeZzoTUDmSYKOxaV0eVDfUAGfeSZJzh934jky4762wqOjnLsGzkJ0AwD+V4sODJvHfqDVy3VKOfGp97g9Q8Iszf7SC0YCVzeOF12fLi5c8Sdb2+4Opizlc9JjWc7m3I9P2bDzXDfHjj4SJ9JkXH8whbJLCHdTykuWYotYZjZuHq37ZjE67IqVbTHnc8G/Ljz5Fy7vQvAlU4m642fNZfx9pusbe14Ma8/DHKKG30mNy2z61Btx9Wk3jLUY0t6bLHzGMDs82icDintblOWX+t70Qnmk1jwGKv0bUXflCQSGJk5T2UHPPvkXV4qrpEeWKSJthqfQtOPf5sSZomlHivljZrnn7nNB8Z3Oaj6fPXkErdlm6ZnmF0xNI0hPRaSU3/ujC9dtKlRUCsxeF812q+aBt0ccfSRDdTA8FXl+MMJqlHNbUYeO66ZX83jxLPVYPoNahzVjucHbvwzZtdSfnLrY7zy0hVeuHuFD1/ZJbcNRgLPjPd5/3gPZzzbyZSd5JShLcilImD47p0vUavljXKTn/Yf5vh4g/5uTpMJ9RjKLUdykiDHM3C2DTRvb8256wF3POm40snknchkfT3KtDa9tqMBqoTcUfdN2wK67eXlApI3lCOh8BJtLiH+L0Fis0VAasFODaYW6oHFNLHRXXBAGhjagpHUpBIoTMHT6R7fe/WLvDjaZ9Jk3J0POZz1KKoEFcXaQD+r2MrnXO+d8Pxgl+vJIQDHfsDAVewdjvBpzvHzHp2ljPeUdFLHjgfnwGod0JBEO9bCe4sGtJ9x+oRh67cbbKnsTTKGO1N0WLKdl1gTqDdPmVcJI9cQguHkSsYnv+VFfmD4RawIuTT8renv5fTNIVeeOo0e3Lb5YCKxFXtiGvqmxBJikRJ19E3JXjNiO5nyiRuv8kvz55h9NQOBciswu2zI9xKyvYaQJm14DXEwtKGw59rtdTy5kFzpZLLe+Fl/Ak5SJIHVflmmCbhCSaZKPRWaDQOZx7rYRtysFK9uGkPwltBINIa3Zyd+YTs5c4CIVfqmIpdAIjBo87eTvOG5bJdJyLnbjDhuYkjKhp2Tm3q5guUmOmW8xrCVRWm64IX0NKAjT7Kb0jsImNITEvuWy30n8FmMF9QQCRRSi10JU5KqIT9Qem/O2666Kb/rym0+PLxN2RqMTpoer882cSb2M/tcmXApnfFS08eiPJvt8mc/+Et87omb3MiO2HGTSJY2j70IscB5buplWcZUGip1ZKZmrx4xcgXXto85ePYapoaN5w7Qr2xjZ3Uke9uR4B4Prp6p+WvhcefJOcPQHneudDJZb/ysaQNWZNBDnY0B0scnsWB17WPlorLNaikE3xeMDfSymsRFofhgKBtLVSm1OjQJMZ5udhaKEh9tHJ1RZiFlppaReHLxjEwRbxrS9oOquWSny2yWWl2sbETMyQ5iyE1Nbmp26w12ixFhmiBBMUcJ6ZFgiyhwteeLQwtWaLK2h2St+J4jsQa8oD5gTufkh4GT9w/o3a0ZXTnlo6NbXElOmIWUMiT0bcXTvb2lLfN3b36Vm+n+Mph8ZAren95hc2OGV4kTBgZLIDc119zxUi5ezbIm7iTkzEiXBUx2elOmnzjg0mDGd26/yk/1fg8YQRO3vB5TwaJ6lPG6vnp5AXhyru4PF4Er5/AXPPYyeRuurF8LovEw6KFpAtMZ2nik9tjSYxqLaQRTCb60aF9iWxnrsaIEE/BB8DYQbBxUaqLRWk0MmjeNxgr5QdDGcOpzCrX09SzDBWLIESa2m8lNTRGStjRdrJIPMed7UTW/CEMOmz4nZY40QrFp6N8y9O4qpgrLJpFrQ6IBPqTQ0CYLKGiaID4geY5WFaMvHTB9bovXvifl49uvtN15hZEp6JuKMiTUapdlFe1KFf/Qlln0CNfcEYl4UhaZP4Jvuz9YdPm+Wt2yVF9QQx0stRr6ruJjV27xocEuX5jcaKuNKdrP0NShLtpHgyO22w5yT6B8x5MHKNx0AbjSyWS98bN2KjK+zVBJHDLoo9MZNLFQhSsVU8XKU+IF7w2NtwTnY6sXwJpof/M2QG0hxKpDtlTcPFZACs5Gn4sXZj5lqim5ngms0GTZkG8BL4ZK7VlLalEssVhHrY5j3+egGjCtEtQo0+uW5FTp323iYDZyrmI8y/hFA6A0uSEkAmkSOyj4Vl2dFfRfnSBPJzzdP1ie+6IdzgmQ6FnLnUKTVhWOBacLTZj4HrVx9KVkRgawzPpZrPRGAoZFTYRIrkiqOHFktuF6dszV5Ji/8+rv5sp+gCbg+ykYWTZGXPi81LD2DvhC8KTjytfmSieTt8rkYe6AddhvPZQB7edQlOA9pmywRcCWBlMKUguhsjSZwQdDaAeWrMZkaByApo7dSN20PdORxbfe77lPmYWMgVTLFWoaMgwhCl/Aq8FLg5WE2GjXRIOSnKkRu/WYu+WQWREFX1xWbCnYeVsFrQ0dORdamQcr+FTxPUNIHXZuzjLARDAHE7JszNjFVTuVhk07W37NNGTMQkatlqy1SxoJywaDlVpOmh7X3BGzkHESelgCl90Jm3YWJ5J21V+s4rVa/MrImPuEL59e4fVik+RzA3p7sa6BpobgYsrvQn1a3qp155oLwJPzxrw+9lzpZLLW+Fk7E658aot0b4YUNZolmCSJHQ5Kjy0CybwlbCHUhaXOLU1i8Dbm6htRQjDowpAX2lqt84CbNviewyeC74HNPGVwnIScS/aURAJeDdMQbTKpRgGn7WpYSLK08QUMQWOBjbvNiDvliLuzAVVbbLvZrqlOUtTFBo4Axvtz2bBWs13UQjUw9DIbbaAtedQaRDzTSQ7App3GpAEzW67Qr1Q7vFFu0qjlWwavtd1ePSmBQmqsKKdNjk0C05DxarnN3CdsJZs8n7/JppnFnZ3SyilbOhcWO4bb0zGvfPUyUhre/0/muKMiqk5G8IvdByxbxJxrYF0InpxDMB1XOpnch7Um4JAYDj6cke8n9O/UJEfFsqyfeI+b1aQnlnQoNH3BZ4Y6SyhcwNmAF6VqbBviZ1AvGB/7LmUHDaZqqDZT6gE0fWUwKLianZCKZyQ1myZgmfP50AOgLyX5SlK+RZddcQtNl3ahvXrEm7Mxh5M+YpT88hxrA/pSGgU3MNHZUp9vC7ywWy2EHpzQ9BPMvG3BA200QIDDlFlIKULCjIyA4cTn/PrpM/zC68/RS2u+8/Jry3J7uXgKzqIzLrlTACYh55KbUtvYgudXJ+/nI/1bjOx8qT4VIaHWeIuDCplpOCkydn7FxV5bs7MOD2qEkEjsArHwaCtI0LVVywvBk/MW43nMudLJZL3xs9YE7DOoRkAwmMZh5w6TuJgyaEzsEDrzZCeGaiz4nhDmltIliCip89Te4r1pdzWx/fPolYAtPfVmTrllKbcEf7XgA9t3uZ4ecc0eMzChjefzfGv+Cq/V28w01guwEqiJdj2rgUIzypBw0AzYrwfcLsa8drhJP6/40PYdPjC8w6/uv4/XdANbeiTEbqbnbkCpgEAwEg3vplWjeglmpYQd1jN43fBmMeaprEcZEqYh40495qe/8mGc8zy7sc+3DF/jt2Y3+L/2PsrHxrewEpj5lFOfkUgMGM9NzSV3ysgUWAl8YX6Tz89ucj09pm+qtgV7dCJYCSTGMy0zTk57PLHnaXoGnzuSWQW1b4kSCbSIsQWW7WI6njwEnlwErnQyWWv8rOeEa4knushWcjg4C8EAjA+4IpCctrub3BBSS2UTQohfIALWebxPSI+F4etzykspPpPYCBHQuWPWpLxc7JCI59VmCsT6n8+nu/RNyZHvUxnLwJTR7rdyrhOfs1uNuTXbYHc2pCwTtoczNtM5mTQ80T/mpVFreK9a5pzTBrwMX2s3AD4XmtyQJG0mWPAxRjBNGL8S+O2jy1zNJnGVNp79asigV/Kh7Tt8dHQLS+BadsypzzhpcjxmuQI/lR3wbLZLQqz6ZYnOo+/sf5XPTp/juOmBg6EtsCihLc2USUOtJlb1mvuYQbbYxWnsRLAIGDfLWFvuua53LI8LwJNzOWzpuNLJ5F6sWY6yra+aCnUf0txA3cRar4tdQeAs1rMCWwqaWhqrqMZwI2MDznmqzBMSh/hAOTZUIyE7UQa3QI3jK1s7+GCiV9qn7M5GHM9zvufJF/hw7xbTEFWQqFI2VMsYTxtrwjYp0yZlVrbdD1yNE0+tllFSxK67bQuUWNvznJ6E9mPSplP6RGh6sWaBWXROMKDO0N8t+erBmDc3NthM5tRqCGq4MT7h2cEeV5NjRnZOIg2jYcHIFng9sz/mUjPxPQqNq3/Q2Bn4Tj1m5lOeyA7ZtDNyqanUMmsdE6ENx0FjOca6L+QL4vsoAzXRBkfFWV4+rL2tuQg80fvbLnVcORdXLrpM1pqAbQluGvPz1bVb68kUSRPUtnYWjV1sTROdJqYCKQW1Fq+CtsUynAskeUO5lTC/ltP0hHJL6O8FensNxjvu7vR5zQYaNRxM+xwfDtC55dd6T3P9xhHQVsQnkEr8fY+h0IS5T6hCDLZXiA0aRZk2GXOf8urpFv3b2rYhkntK164LWQxIPUsUaHqCOlmmrS5UFHdcortj7l4bcjU7YV73GbiSZ4d79E3FgR+w14yo1cZMLa2jA0ADs5Cx6zc4aAZYwjKQHOD/O3yS58d3uJEcsmlmBMxy4pmFDK+GwjuoYhX/csOQHzrcocSmgm3pyHg93PN8XXQ8+frouNLJZBXrTcCzmsHt+OPJVMl2Z4T9A8zGCNr2LkAcXKqxAEojMTPEmDb0B0jAO0OSNvjLJXsf65HfVZq+RhtKFejtebL9hGIrg80YluTyGtOvMKLL3YxvBVSIkrbV7Y+bPqc+I6iQGE9iPYWmHMz7HJc5R6d9mpeGPP1SeXZxD7j7BVhUdhKlrXcQ21YLxIr5Epv5DV8z3H1uwJPXDujZmksuqs179ZAvHT3FK8dbAFzqxxCXrWyGEaVoEqZNSmYbnhvd5ansgKEtOGwGzOqUsSu4YmOK5SxkTNtYx4XHf78YkBxY3Kyk3M6YnzryOw4pC0wdWgdTe/+8IBLbsqyrVl4InjzATm/x+Y4rnUzWDkMb//yXY3sQ5yBN8E2DTOexd5czINL2dmo/orQ1W6P3IigEUerKxVrLtWV+o6EaW3p3hLoHhx/MmF8WissBlzU8O9rnxuWj2E4a4f3Z7rL9TLGSAVOLXaqVoTVEOhMY5yVHX7lE9U8GJKfK2EM6iXn9MbD+PpVhTZks1FGVGAO4MLqrkRUvLqBK6Kdc+mLNyx/YIDxreC7f5clkn0Q8t+otfuvkOseTPh954jb/yVP/J3f9KGZztfj2dELfJBgM/7xq+Mz8OV6cXeFPP/0Znk3vkBJV50UWz6qM9qd9bCEcPd9n/mRN/5YjpBZjBKkaTK1IKkuZnNu73fHk68rlcedKJ5P1xs96YWiZQ5uGMJlgBgNMvwfGopNTZHscv641SCfzgCsEn5+VDERBaoNaxZc2rhJNDAIMeaC8ZKKDpa80N0puXj8AYifU68kRl90EK4GTtpNp35TktqbQZNkjaubjqpWZhtzWVN7hTCA9MGz9dk16VFLs5Ox/1GGrlGy/vPdmn2NwGa8xA8acOaBiWM2K4ANI8DSjjP7n32Dn+tP82M1P8CMf/L+53WzyZLLPZXfCn7z+WV7avszP3fkQ/8vhv8D3bXyOZ90xVmAaDEch8FOza3xxfoODekDPVHxi/FU+kN5e/tQ0ZMw0o2qLjMxCiiVQzFOcwtGH4vt6B4GQWTR1yLzCFiGG0XDmGDlPbOdF4Ml5S1J2XOlksor1wtByQdrAZMkzwvYYO50SpnNMUWNSRxAbt+KV4ubQ5MTOAgmYOub0B+zZ1txASBUJsbRgM1SaYSAbVGSuYVYnTJuMvWYUO5hKRaHpMmtlFYuwkdBuURa7n6BCctruXIKCwPSDJWjGzZ8vWoGZcweSiwfbxvupiW2w1XEWiuJD7J4aQLxSP3WZ2XXhWn/OK+UOfRtL4Jl26b9kpzw72ufX958C4Hp6DESPvZXA7XKDsZvz4f6byyyehT2r0ISaSJxCUyYhZ9pkbKenXN6acHurx/A1w+C1hGRStgVtDBJipwlbmRi5sBgMBmTNHP+OJ18HF4ArnUzWGz/nCEOL23Lp95nfGNE/nqLHJ7GJXpNBYpftm9084HJDyKJBPXhFXdzJiI8nFVzc8ago6hTvQLNAkjRspHP2Tge8OR9zKZ2yYedxIKqQmrAsuuJXdMKwKCWILF8vGkdyqlF9bIupJL2aepyySHNcFIQ+jxq1cBxIzGoltI4nNXJGShGkrqk2R9z6LkfvI4dc75/whckNPjjcXQaOpxJrlD6d73PQ7zP1Gbv1uA2XsSTiuZEdcTU55rI7WebCh7aCU1y1UwpNKEPCYT1YEvOJ4TG3Ni8x+FXH4Ha7o1txFJjKY2obB0EiseIYrK0VXAienBOPPVc6maw1ftabgIWYkQJoL2O+beklba52WSG1R3KH+nZgFXF34zMIFozEFjRqY8UgbYUvtYmqp9BWvYq3ciMpCCoclzlTny0HUt+UpMtup2aZs31WtchSB0sTLI3GGgO2Wr2Q+F+7STu7wWF9F7e037dMpST+bcuvQUfvCZnwzCdf41++9gXerDb5zO6zzJqUj27cYsPN2rq2NVeTY75tHFus9021TIXsm5L3pXtLu9Zq5a9KbRtekzLxPY59j5OmR89WeDVcyU5JBxXZEaQv71Hf3L6H4NGRwNLRdK6GnFwQnpwj66vjSieT+7FeHLByRjyNPbo4jql9Op3BoIfkDjFtWbkykMwFn0anS3C0lY/iIEJAquj9Vte+bgWtDcU8ZdJk9NIoqKOqx14yihlPLsbpHfhhqyokBDUUIaEMjjI45j6h8I7SOwZpxcEwxu9J63m3Vil7YVmEXY3ETgxrYiHfe5wzAZKZnhnfV+b1/htzXjva4PRyTt9UnBQZd/7ZVYpPOj6+9QZXkxOsidW5ribHy0kkhlB5RiamSlbYpQMJIpFqYtziJPSW2V21Gi7bkoDgjCfLauphb+ncUCvgTMxQq2I1KePjucs5vU0XgifnqB3dcaWTyf1YLwytAA0FkqTIZMrmb+7j9/ZABH86xfZyTJrEttI+5sy7uZDa6BWMsX3t4HIaa8LWgMRdxsKxIUcGed3xmy8+T70Z0ES5xQ6ftc9h+w0b4ynfdeMlPtZ/nVnI2GuGzHzK3KfUapjUOYV3FD6h9q0ncxs2XmpvdqOknxkxPlBCZpdxfOfNblqUngOWMYCmAWmIQdoQV0VjsLcP6f/kk/yPv//3snnplPQnN3niH3yO6ff8Ln78j23z/R/8HM/nt2OZvLZ+rWlL5sXMnfh9KX4ZQuARjkKfIz/g9eoSe/Uw7gRV2E5iiI5XQ2YaxnlJsWnY8LHzrJi2yWU/wxQV0oS4e12YY4R7WsZ0PHkAnsDjz5XzzMOPu0zeZvysmYjhMZc3IIT4uHtwZhsLHp3NMXmGZEnsiIvBlAHnoqEahZAKkkJ6IKQnipsrTb7IfImhSIvcb58L2ZGlydub0waHFvT4GbvD/3H9O5CNiv6gZHsw46Nbby53NFVw+GCY1QnFz19m81bAzc/aDm283JDt1yur7MqNPgfUsLT1xDq3bx2oUjeEjSE7v3bIzq/HAc7dF5HNDYa//BWe3n+K/+37PsXv+e4v8P3b/xQrAWNiOcWBnKlRi6LRi6Ih05Cx3wz5SnmV2+WYJlh6tmYzmdG3FQZd1j/Y6U350lPC1RA9uHbWEJzBD1PMrMSWAd+zyxx2tSue6XeIi8ATac6fkfE4c6WTyXrjZ7044LKCpgFj0KoiHJ/cK8SqQosSUzXgDEFiNXjTKLaOHQwIsZ2LmynpJJBOAmpgdsXF3lBlTFEVVep+rBNgF97RFOpBVEvdXMhvW/ROj2KQ89r1jJ3eKc4ECp8QVJg3CfsHQ57+fIUaocnt8iYnEx8HkbAMeZF1Oz/AWbxfe46Lik621PY6aGMYzVJtEe+RaRVr5No2JjbLSF++y1P/+DK/fvwxen+45t+49P/Gljm0tiq1y799W5i2VsdR6PPV8gqvzi9hUJzxUV0yTds5IJ6cQdnKZtTPztHNUVTtmpik6a0QRjmmbGj60UFmPARZKTLd8eTcPLkoXFk7bf0CyOTtxs96TrgQCKdTRCQOoqa59w1B48CraiSxiI2dbaWJg0uCRNVCoyxipkssR2caxWdCsFHF9BlUG0I9UkICvhfQYUNvFL2P80kG89huHAuosF8MGCQVpY+XdVqm2Ddy0v0TfD/F9yxqorHcFv4sBbIdXOfd/Yq2G7zFpkjjNS0HqrQe0UU7m2VweftcFZyFsiJ98U1u1Ff4mSvfyrf+K69yzR1hCNQ4/H1llWJc64DXq212q3HsdWWbljjNmcolMUSn9paRK3jy6iH7336d/p0aOwOagDiDHyTYWXNPg8W4kq8nlwvBk44rX5Mr54oiesxl8tASMQDCZPLWF+8JmFZk0YrGGcQI1sTwIhRspTReloVajLfYMixP2OeCz6EaQ7XjYVjjUs/OeMZOf8o4LWiC4XCzHzsoqFA0jnmVMK1iAYHax3oCJ5M+45ejOmPKs9qd0e4Ys5vitknjpPAAtr3Ve3u/J1SNRDK5FR1NJD68j0QKCtaiRYF98Q2e/fEn+fSnPsj3bX+esS0o9KyguCG2zT7yfW43G7xaXuK0SRm6Cidx5V54fk2M5wFiUemerXlqdMAvfeoKl38twVYBd1Jiak/dd0hiomq3cm9Ns75hr+PJ18fjzJXzxkg/zjJ5u/Gzflv6bwQN4AMyK5DEYtqsIVMbbKWEUkgm7Y0SqAaCyWJQvi0gDBZ2E43ebm9AGnwQDuZ93jjeoPGGPK0Z5yWbWQxB2pMBZe2obAywP5724M2Mja/WhNwhtced+ui1NKYli6BtXOeDBNff/3lXxsIhakGdQb1iKo+m7sypAEvHAiGAs2jRpkw2De6ff5VfefH9PNk/5GZ6SGZqcjmLkZqEHnfqMXerEadNytiVZKbGiNK3FX1bLglUE+McE9PQ14qhq3jfB3Z5Ra8RXMbmi5DszbCppR4lUfULZ9d13kLbby+39zhPzmMCvgBc6WSy3vh5qBOwen/mzCorZBYLr4gzmCrgZnElMHWs5+oz8IMoaNPIPQZrNxPsLYdPlZAmHPZ66MCTj0sujaZsZAXOhKUNb7HLmRQZzgTmd/tsfUXI9ub4XoKt26IDIf4THTgaPdtK6zA6H4lMo6zmtMcb8FZboTqDSCy3SG2QNrBfGx+7CAcP3qNVjXqPlpZpk/GF+sbSMdCzFWVwTJuYSuuM51I6Y9gSJjd1u3q3ITHtBdVEU9rIFtSJ5YnBMW9sbzC/MmT4hiU7nYEVQmqpR47gYkSCmqWz+KHh8eDJ+Ralx50r5zFBPO4yebvx89B3wOr9kpxyOgNnMW2zOuMNrghIWKRzxsHUJFAPovPEZ9GWp4kS0hAb88W3goCIxtblKpRNjN+sg8FZT9XELgqT/QEbX3Rsf2GOJhbTBKQdWPGmtSrBwqHyAANqFRJ0WQc3aZ8vvLkqAgE0sRACkibQxIgAmqYlTkWoauzWBif/4ge4enM/OgOomUtKGVyMRxTPTna6zP5JxC9X7ET8sssv0MY/xthXT2TDTjIh9IRf8c8wu9mw5xPUXmPwwh5p7VE7othOCK4dDA8smbfiPc+TBzZDPJ5c6WSy3vh5uBOwhtZOFk9eG4/MS4wxqDOERsEJpo5539ZBqMG4GFoUnMb4z0TRRMEpftDqelYxicfagDUBVaEKti0lGN9TiFKVCePPpVz5pzOSl26jW2NCP73vPKMdTzCorLQZP4dzRRb/LJwI2s4BGsNkTNkgba858VG1lSZEQqUJzOZo06BVhd3axD97jb3n+xx9CJ7rzZakMKLUxi7tU7mply3XY++rmkSaZV2DBWLdUwUTYxlrtQQMifFLuU6faZjdMIyfuca1z56Q7s3w+ZBi09LkhnR6/pCrr4nHgCfniphZ/PMYc6WTyXrj5+HbgBfpvNZGtaAoEWMwzmBt2zVUYxynaWKo0dIALys2PQCjSOaxSeyOkCSeXlqT2SaqlQiuHVRBhSYY/GHGpRdq3Iu38PsHmBAQc+ms48UyHjWeq7gVT+oDzDMxOSDaLE0TK31JEyKBmhCJU9Rx4NbNWUhNmiIihGee4OiDGxw9Z5k93ZBuFQyTMk4c1pPREBAyaZardSxAEuvb5lIvc9ajPMzyuSHEQiMkHPgBL86ucFj1Mdaj04ww9Gy874jJTo/bMmbrhQoC2BrqPjTZu7AHfq/z5AF2wI8zV9aNGb8IMnm78fPwJ2CIhnFnkcagdY3M2pRpEUJq8KmJRbi9YvxK7OCC09ouiQZc6tkczxikFanxiCibWSy0nJqGRi2Vt8zrjGKekt2x9F47IhwcoU2Dv7uP6/cI4z5nxVQ05vOrnvWjOqcZQom2q1hAJKbW2jI6DUzVRJW2buKqXtVQ1bG7a+LQxBE2hjSbOXe/rc/xx2qGlydcTmu28jmp8W28IuQmVvaPzoT6PhUp2q4W3l0A3waPL96DwH495Eun1/nVW09T1xZrFZkKDRZ3I/Bd73+RL29f5vala4xfikVyxMfkh3cFF4gni9N+3LmybkW0iyCTtxs/D9kEodFbDGiaII0HH9A2MN+o4pyBUWw3bZq2FgAx7jMWY44DTi2Y1DMcFFwZnDJOCno27mq8CtMmIxih8ZbSO47nOc1RihNaD/ZiwHj06ARGvXsHjwg0/iyj6QFiO5vY/RxXgCsDpgrYosEUTdzdqSJ1/Ft7GTrso5nF9xKqrZRb32WRJ6eMehXzWcZ8lvHx527RtNsJQ2yj3jclfVMyMBUeWeayL1QoAE/At6u3lViK0bRb+5eLHX7ljadpvjSm2m7L+z1VkvZqrAmkpuHj27eYf99dPvPVZ7FfGrD52+GeEKGHgseBJ+dswfO4c+U8WuTjLpO3Gz+ia0w6InIXeGV9Eb+n8LSqXn6nb74gMoE15NLJ5Gvjgsilk8nXxteUy1oTcIcOHTp0eHg4p8m8Q4cOHTo8KLoJuEOHDh0eEboJuEOHDh0eEboJuEOHDh0eEboJuEOHDh0eEboJuEOHDh0eEboJuEOHDh0eEboJuEOHDh0eEboJuEOHDh0eEf5/914rb67rWcQAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"oSf4medy0fgr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1616664903891,"user_tz":-60,"elapsed":1573,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"c6a7c032-e6b9-4057-cb3e-b5a5e82b3e27"},"source":["#Images et labels\n","print('X:', X.shape)\n","print('Y:', Y.shape)"],"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["X: (10, 48, 48, 1)\nY: (10, 7)\n"]}]},{"cell_type":"code","metadata":{"cellView":"form","id":"n4cvkzgQpVL7","executionInfo":{"status":"ok","timestamp":1616664903893,"user_tz":-60,"elapsed":1567,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}}},"source":["#@title Modèle\n","class MyModel(keras.Model):\n","\n","    def __init__(self, input_shape):\n","        super(MyModel, self).__init__()\n","        self.conv2D1 = keras.layers.Conv2D(32, kernel_size = (3, 3), activation = 'relu', input_shape = input_shape)\n","        self.conv2D2 = keras.layers.Conv2D(64, kernel_size = (3, 3), activation = 'relu')\n","        self.conv2D3 = keras.layers.Conv2D(128, kernel_size = (3, 3), activation = 'relu')\n","        self.maxPooling = keras.layers.MaxPooling2D(pool_size = 2)\n","        self.flatten = keras.layers.Flatten()\n","        self.Dense1 = keras.layers.Dense(64, activation = 'relu')\n","        self.Dense2 = keras.layers.Dense(Na, activation = 'softmax')\n","\n","\n","    def call(self, x):\n","        y = self.conv2D1(x)\n","        y = self.maxPooling(y)\n","        y = self.conv2D2(y)\n","        y = self.maxPooling(y)\n","        y = self.conv2D3(y)\n","        y = self.maxPooling(y)\n","        y = self.flatten(y)\n","        y = self.Dense1(y)\n","        y = self.Dense2(y)\n","        return y\n","    \n","    def predir(self, monImage):\n","        return self.predict(np.array([monImage]))[0,:]\n","\n","    def compile_o(self):\n","        self.compile(optimizer = 'adam', loss=losses.categorical_crossentropy, metrics = ['accuracy'])\n","\n","myModel = MyModel(input_shape)\n","myModel.compile_o()"],"execution_count":27,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AcIJ3LVYpVSK","executionInfo":{"status":"ok","timestamp":1616664903894,"user_tz":-60,"elapsed":1562,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"155e177e-7b01-4178-ea36-836325f55312"},"source":["theImage = X[0]\n","myModel(np.array([theImage]))"],"execution_count":33,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(1, 7), dtype=float32, numpy=\n","array([[2.96738029e-01, 1.24476401e-05, 2.17953697e-01, 1.91844806e-01,\n","        1.76624253e-01, 1.78373352e-06, 1.16824925e-01]], dtype=float32)>"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PfIugTuzpVOF","executionInfo":{"status":"ok","timestamp":1616665409321,"user_tz":-60,"elapsed":506981,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"6b48caa8-e249-4159-f26e-3b0624998944"},"source":["#Entrainement\n","\n","history = myModel.fit(X, Y, epochs=epochs, validation_split=0.05)\n","\n","myModel.save('modeleTest')"],"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","1/1 [==============================] - 0s 98ms/step - loss: 1.6300 - accuracy: 0.2222 - val_loss: 1.3779 - val_accuracy: 0.0000e+00\n","Epoch 2/5\n","1/1 [==============================] - 0s 58ms/step - loss: 1.5696 - accuracy: 0.2222 - val_loss: 1.4577 - val_accuracy: 0.0000e+00\n","Epoch 3/5\n","1/1 [==============================] - 0s 60ms/step - loss: 1.5309 - accuracy: 0.2222 - val_loss: 1.5352 - val_accuracy: 0.0000e+00\n","Epoch 4/5\n","1/1 [==============================] - 0s 70ms/step - loss: 1.5018 - accuracy: 0.5556 - val_loss: 1.5440 - val_accuracy: 0.0000e+00\n","Epoch 5/5\n","1/1 [==============================] - 0s 59ms/step - loss: 1.4829 - accuracy: 0.4444 - val_loss: 1.4377 - val_accuracy: 1.0000\n","INFO:tensorflow:Assets written to: firstModel/assets\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":287},"id":"Etye2vRNpVWY","executionInfo":{"status":"ok","timestamp":1616665440369,"user_tz":-60,"elapsed":1700,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"2499af31-7138-4953-8bc6-9152b0c43b8e"},"source":["#Affichage de l'historique de l'apprentissage\n","plt.plot(history.history['accuracy'], label='accuracy')\n","plt.plot(history.history['val_accuracy'], label='val_accuracy')\n","plt.legend()\n","plt.ylim([min(history.history['val_accuracy']+history.history['accuracy']), 1])\n","plt.show()"],"execution_count":69,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3yU5Z338c+PJGTCIQdIICEHAQU5BQQinrqK+rCL1oK1pWhdV2it9WnRrX1aH7VudRV3XatttWtdqVVr1arVx31RxVoP+GirWKG1opyMHCQhEgjJhEBCTtf+cU/CZMhhIpPM5Ob7fr3mlZn7vmbuX26YLxfXfc015pxDREQGvkHxLkBERGJDgS4i4hMKdBERn1Cgi4j4hAJdRMQnFOgiIj7RY6Cb2UNmVmlmH3Sx38zsXjMrNbP3zWxW7MsUEZGeRNNDfwSY383+84AJoduVwP1HX5aIiPRWj4HunHsD2NdNk4XAo86zBsg0s7xYFSgiItFJjsFr5AM7wx6XhbZVRDY0syvxevEMHTp09qRJk2JweBGRY8e6dev2OudyOtsXi0CPmnNuBbACoKSkxK1du7Y/Dy8iMuCZ2Y6u9sVilks5UBj2uCC0TURE+lEsAn0l8E+h2S6nAkHn3BHDLSIi0rd6HHIxs98Ac4FsMysDbgZSAJxz/wWsAs4HSoGDwNK+KlZERLrWY6A75y7pYb8Dvh2zikQkLpqamigrK6OhoSHepQgQCAQoKCggJSUl6uf060VREUlcZWVlDB8+nLFjx2Jm8S7nmOaco6qqirKyMsaNGxf18/TRfxEBoKGhgZEjRyrME4CZMXLkyF7/b0mBLiLtFOaJ47P8WSjQRUR8QoEuIuITCnQROeY0NzfHu4Q+oUAXkYRy4YUXMnv2bKZOncqKFSsA+P3vf8+sWbOYMWMG5557LgB1dXUsXbqU4uJipk+fzrPPPgvAsGHD2l/rmWeeYcmSJQAsWbKEq666ilNOOYXrrruOP//5z5x22mnMnDmT008/nc2bNwPQ0tLC9773PaZNm8b06dP52c9+xmuvvcaFF17Y/rovv/wyX/ziF/vjdPSKpi2KyBH+9XcfsmFXbUxfc8qYdG7+wtQe2z300EOMGDGC+vp6Tj75ZBYuXMg3vvEN3njjDcaNG8e+fd7ir7fddhsZGRmsX78egOrq6h5fu6ysjLfeeoukpCRqa2t58803SU5O5pVXXuHGG2/k2WefZcWKFWzfvp333nuP5ORk9u3bR1ZWFt/61rfYs2cPOTk5PPzww3zta187uhPSBxToIpJQ7r33Xp577jkAdu7cyYoVKzjzzDPb52OPGDECgFdeeYUnn3yy/XlZWVk9vvaiRYtISkoCIBgMcvnll/PRRx9hZjQ1NbW/7lVXXUVycnKH41122WU89thjLF26lLfffptHH300Rr9x7CjQReQI0fSk+8Lrr7/OK6+8wttvv82QIUOYO3cuJ510Eps2bYr6NcKn+0XO4x46dGj7/X/5l3/h7LPP5rnnnmP79u3MnTu329ddunQpX/jCFwgEAixatKg98BOJxtBFJGEEg0GysrIYMmQImzZtYs2aNTQ0NPDGG2+wbds2gPYhl3nz5nHfffe1P7dtyGX06NFs3LiR1tbW9p5+V8fKz88H4JFHHmnfPm/ePB544IH2C6dtxxszZgxjxoxh+fLlLF2amEtWKdBFJGHMnz+f5uZmJk+ezPXXX8+pp55KTk4OK1as4KKLLmLGjBksXrwYgJtuuonq6mqmTZvGjBkzWL16NQB33HEHF1xwAaeffjp5eV1/edp1113HDTfcwMyZMzvMerniiisoKipi+vTpzJgxgyeeeKJ936WXXkphYSGTJ0/uozNwdMxbW6v/6QsuRBLLxo0bEzaoEsWyZcuYOXMmX//61/vleJ39mZjZOudcSWftE28QSEQkAc2ePZuhQ4dy9913x7uULinQRUSisG7duniX0CONoYuI+IQCXUTEJxToIiI+oUAXEfEJBbqIiE8o0EVkQApfVVE8CnQRkaOQSGurax66iBzpxevh0/Wxfc3cYjjvji53X3/99RQWFvLtb38bgFtuuYXk5GRWr15NdXU1TU1NLF++nIULF/Z4qLq6OhYuXNjp8x599FHuuusuzIzp06fz61//mt27d3PVVVexdetWAO6//37GjBnDBRdcwAcffADAXXfdRV1dHbfcckv7omF//OMfueSSS5g4cSLLly+nsbGRkSNH8vjjjzN69Gjq6uq4+uqrWbt2LWbGzTffTDAY5P333+enP/0pAL/4xS/YsGEDP/nJT47q9IICXUQSxOLFi/nOd77THuhPP/00L730Etdccw3p6ens3buXU089lQULFvT4BcqBQIDnnnvuiOdt2LCB5cuX89Zbb5Gdnd2+8NY111zDWWedxXPPPUdLSwt1dXU9rq/e2NhI2/Il1dXVrFmzBjPjwQcf5M477+Tuu+/udM32lJQUbr/9dn70ox+RkpLCww8/zAMPPHC0pw9QoItIZ7rpSfeVmTNnUllZya5du9izZw9ZWVnk5uZy7bXX8sYbbzBo0CDKy8vZvXs3ubm53b6Wc44bb7zxiOe99tprLFq0iOzsbODwWuevvfZa+/rmSUlJZGRk9BjobYuEgffFGYsXL6aiooLGxsb2tdu7WrP9nHPO4fnnn2fy5Mk0NTVRXFzcy7PVuajG0M1svpltNrNSM7u+k/3HmdmrZva+mb1uZgUxqU5EjimLFi3imWee4amnnmLx4sU8/vjj7Nmzh3Xr1vHee+8xevToI9Y478xnfV645ORkWltb2x93t7b61VdfzbJly1i/fj0PPPBAj8e64ooreOSRR3j44YdjuhRvj4FuZknAfcB5wBTgEjObEtHsLuBR59x04Fbg32NWoYgcMxYvXsyTTz7JM888w6JFiwgGg4waNYqUlBRWr17Njh07onqdrp53zjnn8Nvf/paqqirg8Frn5557Lvfffz/gfadoMBhk9OjRVFZWUlVVxaFDh3j++ee7PV7b2uq/+tWv2rd3tWb7Kaecws6dO3niiSe45JJLoj09PYqmhz4HKHXObXXONQJPApFXJaYAr4Xur+5kv4hIj6ZOncr+/fvJz88nLy+PSy+9lLVr11JcXMyjjz7KpEmTonqdrp43depUfvCDH3DWWWcxY8YMvvvd7wJwzz33sHr1aoqLi5k9ezYbNmwgJSWFH/7wh8yZM4d58+Z1e+xbbrmFRYsWMXv27PbhHOh6zXaAr3zlK5xxxhlRfXVetHpcD93MvgzMd85dEXp8GXCKc25ZWJsngHecc/eY2UXAs0C2c64q4rWuBK4EKCoqmh3tv7Yi0ve0Hnr/uuCCC7j22ms599xzu2zT2/XQYzUP/XvAWWb2V+AsoBxoiWzknFvhnCtxzpXk5OTE6NAiIgNHTU0NEydOJC0trdsw/yyimeVSDhSGPS4IbWvnnNsFXARgZsOALznnamJVpIhIZ9avX89ll13WYVtqairvvPNOnCrqWWZmJlu2bOmT144m0N8FJpjZOLwgvxj4angDM8sG9jnnWoEbgIdiXaiI9D3nXI9zvBNJcXEx7733XrzL6BOf5etBexxycc41A8uAl4CNwNPOuQ/N7FYzWxBqNhfYbGZbgNHA7b2uRETiKhAIUFVV9ZmCRGLLOUdVVRWBQKBXz9OXRIsIAE1NTZSVlfV6vrb0jUAgQEFBASkpKR2260uiRaRHKSkp7Z9wlIFJqy2KiPiEAl1ExCcU6CIiPqFAFxHxCQW6iIhPKNBFRHxCgS4i4hMKdBERn1Cgi4j4hAJdRMQnFOgiIj6hQBcR8QkFuoiITyjQRUR8QoEuIuITCnQREZ9QoIuI+IQCXUTEJxToIiI+oUAXEfEJBbqIiE8o0EVEfEKBLiLiEwp0ERGfiCrQzWy+mW02s1Izu76T/UVmttrM/mpm75vZ+bEvVUREutNjoJtZEnAfcB4wBbjEzKZENLsJeNo5NxO4GPh5rAsVEZHuRdNDnwOUOue2OucagSeBhRFtHJAeup8B7IpdiSIiEo1oAj0f2Bn2uCy0LdwtwD+aWRmwCri6sxcysyvNbK2Zrd2zZ89nKFdERLoSq4uilwCPOOcKgPOBX5vZEa/tnFvhnCtxzpXk5OTE6NAiIgLRBXo5UBj2uCC0LdzXgacBnHNvAwEgOxYFiohIdKIJ9HeBCWY2zswG4130XBnR5hPgXAAzm4wX6BpTERHpRz0GunOuGVgGvARsxJvN8qGZ3WpmC0LN/g/wDTP7G/AbYIlzzvVV0SIicqTkaBo551bhXewM3/bDsPsbgDNiW5qIiPSGPikqIuITCnQREZ9QoIuI+IQCXUTEJxToIiI+oUAXEfEJBbqIiE8o0EVEfEKBLiLiEwp0ERGfUKCLiPiEAl1ExCcU6CIiPqFAFxHxCQW6iIhPKNBFRHxCgS4i4hMKdBERn1Cgi4j4hAJdRMQnFOgiIj6hQBcR8QkFuoiITyjQRUR8QoEuIuITUQW6mc03s81mVmpm13ey/ydm9l7otsXMamJfqoiIdCe5pwZmlgTcB8wDyoB3zWylc25DWxvn3LVh7a8GZvZBrSIi0o1oeuhzgFLn3FbnXCPwJLCwm/aXAL+JRXEiIhK9aAI9H9gZ9rgstO0IZnYcMA54rYv9V5rZWjNbu2fPnt7WKiIi3Yj1RdGLgWeccy2d7XTOrXDOlTjnSnJycmJ8aBGRY1s0gV4OFIY9Lght68zFaLhFRCQuogn0d4EJZjbOzAbjhfbKyEZmNgnIAt6ObYkiIhKNHgPdOdcMLANeAjYCTzvnPjSzW81sQVjTi4EnnXOub0oVEZHu9DhtEcA5twpYFbHthxGPb4ldWSIi0lv6pKiIiE8o0EVEfEKBLiLiEwp0ERGfUKCLiPiEAl1ExCcU6CIiPqFAFxHxCQW6iIhPKNBFRHxCgS4i4hMKdBERn1Cgi4j4hAJdRMQnFOgiIj6hQBcR8QkFuoiITyjQRUR8QoEuIuITCnQREZ9QoIuI+IQCXUTEJxToIiI+oUAXEfGJqALdzOab2WYzKzWz67to8xUz22BmH5rZE7EtU0REepLcUwMzSwLuA+YBZcC7ZrbSObchrM0E4AbgDOdctZmN6quCRUSkc9H00OcApc65rc65RuBJYGFEm28A9znnqgGcc5WxLVNERHoSTaDnAzvDHpeFtoWbCEw0sz+Z2Rozmx+rAkVEJDo9Drn04nUmAHOBAuANMyt2ztWENzKzK4ErAYqKimJ0aBERgeh66OVAYdjjgtC2cGXASudck3NuG7AFL+A7cM6tcM6VOOdKcnJyPmvNIiLSiWgC/V1ggpmNM7PBwMXAyog2/43XO8fMsvGGYLbGsE4REelBj4HunGsGlgEvARuBp51zH5rZrWa2INTsJaDKzDYAq4HvO+eq+qpoERE5kjnn4nLgkpISt3bt2rgcW0RkoDKzdc65ks72xeqiqIiIdOPAoWYqgg18GmxgXM5Q8jPTYn4MBbqIyFFwzlFb30xFbT0VwQZ2Bxvag7uitoFPg972/Q3N7c+5beFULjttbMxrUaCLiHTBOce+A42dBvSnbduCDdQ3tXR4nhnkDEslNyPA2JFDOW38SHIz0sjLCJCbEWDCqGF9Uq8CXUSOSS2tjr11h0LhfDikD4d3PbuDh2hsae3wvKRBxujhXlhPzkvn7Emj2oPa+5nGqOGppCT1/9qHCnQR8Z3G5lYq90cEdLCBT2vr23vWu/cfoqW146SQwUmDyA2F86yiLC+k072Qbgvs7GGpJA2yOP1m3VOgi8iA0tDUEhHQhw73sGu97XvrDhE5gS8tJYm8TC+UTzs+m7yMAKPbA9vbPmLoYMwSM6yjoUAXkYRRd6g5bGy6PmzcuqF9aKT6YNMRz0sPJJOXkcbojACTc9PDhj8C5IV61+mB5AEd1tFQoItIn4ucCRIe0OEXGPcfaj7iuSOGDiY3PcCYjACzijLbx6nbAjs3PcDQVEUZKNBF5Ci1tjr2HWzsNKQrgg3sru1+JkheRoDxOUM544Tswz3rdK9nPSo9lUBKUpx+s4FHgS4i3drf0MSumgZ2BevZVVNPRU0Du2rqQ4+94I6cCZI8yBgdGpuePCadcyaNar/YGO+ZIHHX2gquFZJiH78KdJFj2KFm7wLjrhpvzNoL6oYOwR05DNI2bW9MZhozCjM5b1qgwzBIXkaAkQk8E+SoOAeNddBQC4dqw34GvdsR2zppd2g/fOEemH15zMtToIv4VGtonnV5jTcEsqvG61HvqqmnIlhPeY03GyTSiKGDGZMZoGjkEE47fiR5GQHGZKYxJtP7mTMsleSB2LN2DprqI8I22HMAR7Zzrd0fZ1AypKZDIB0CGd79EeM6bsst7pNfUYEuMgA556htaD7cq24P6oZQgHszRJpaOs7dGzI4qT2gJ+WmMyYzjbzMAPmZbb3rNNIGJ+iYdVNDWNgGewjg2rAec9i21iMvunZggw4Hb2qG9zOzEFKnhrZFBHV4u7ZtKWneBYI4UKCLJKC2udZt49QVYWPWbcFdFzEU0jZunZ+ZxqyiLPIy0sjP9EK6rYedkZYSn6l7zY0RIdtVr7iboG5p7OEgBqnDO4btsFzInhgRwKHwPWJbOgweFrcwjgUFukg/a2117GkbCulwgfHw0MjeuiPDK3vYYPIy0tpnhOSHetdjMtMYk5FGzvA+HLdubfFCtb4aGmq8n/VhPxtqug/q5vqejzF4WMce8JBsGDG+k15xRuc95cHDYdAAHAqKIQW6SAy1zbfedcQFxvr2mSKfBhtojvjI+dDBSaHhjzSmjkk/3KsODY/kZgRiM32vqSEilKsjwrmLfQ1BoJvvTkgZcmQPOLOo62GJzoYvBiXoUM8AokAX6YWGphYqgt4QSIeLjWHBfaCx43zrlCRr/8TiyWNHeGPVmR2HQ3r1KcbW1lDPt6dQrjlyX3c9ZRsEaVkQyPR+DsmGkRMgLfS47RaIfJwByYOP4qxKrCjQRcIcONTMlt37Ka858mLjrpp6qg50NhSSSn5mgBNyhnHmhJz22SB5Gd54dvawVAZ1NhTS3OgFb10F7IkI5O560Q013c+0SE7rGLgjxncM5chAbtunIYsBT4Eux6yDjc1s2FXL+2VBPigPsr48SOmeug6LOg1PTW4fp56Wn3HERcbc9FRSW+vDQrfycPDuqIbNkb3ksMdNB7qpzryeb3jgZo3tPpDb9qUE+vrUSYJSoMsxob6xhQ0VQd4v84J7fVmQj/fU0TaUPWp4KtPz01k0KYUZw+vIH3yQkUkHSGve37GnXFENWyOGNrqbCpc0uGP4ZhZ6c5CPCOOIUA5kaExZek2BLr7TFt7ry4KsL69lfXkNpZVeeKfSSPGwIBeM2M/0STUcn7KX0S0VpNZ+AuU7YPvBzl80Nb1j6KaP6byHHNmDjuOcZDn2KNBlQPPCu5YPyr3e9wdlNVTvKaeA3RRaJZMC+/hi2j7G5uwhu2kXgfrd0AxUhl4gZYg3lDFiHBx/tnc/s8i7IBh+0a8P1t0QiTX9LZUBo6HJC+8NO/dQvm0zdRUfkRTcQQG7KbJKTk/aQ6FVEkhtOPykVmBQHmSOhaxzvcAOvw0bpR60+IYCXRKPczQEd7Oj9EMqd2zmwO5SrGYHmQ1lFFolJ1HNIAsNfidDS1KA1szjSB45Bcv6/OEed1tvOyUtnr+NSL9RoEt8NB+Cmp1QvZ2mvR9TXb6FhsqPSa79hMyGXQyhnhOBE0PNq5NGcDCjEDfiTA7kncCw3BOwUGgnDRtNknrZItEFupnNB+4BkoAHnXN3ROxfAvwIKA9t+k/n3IMxrFMGGufgYBVUbw/dtkH1dlqrttFctY2UAxVY6JOHKUC6S6HGjeLTQbk0DCsmOXs8mfkTKBg/hZzCCWQNHkpWPH8fkQGgx0A3syTgPmAeUAa8a2YrnXMbIpo+5Zxb1gc1SqJqboTgzvawZl/oZ/UO72fj/g7N91kW21py2OHG84k7laqUMaTmHM+IwgmMH3s8xYVZ/F1GwPff+yjSV6Lpoc8BSp1zWwHM7ElgIRAZ6OI3znlzrfdtOxza4bfa8g6fWGxNSmV/IJ9dg0bzkc1lfUsm21pGscONpi6QxwkFoynOz2B6QQZfzs8gPzNN4S0SQ9EEej6wM+xxGXBKJ+2+ZGZnAluAa51zOyMbmNmVwJUARUVFva9WYq+9l739iOERqnd4a4aEGzqK1qzjCGaXUJZ9PpsOjWRtbTp/qhpOeUMG7sAgMoekUJyfQXF+Bl/Kz2BafgYFWQpvkb4Wq4uivwN+45w7ZGbfBH4FnBPZyDm3AlgBUFJS0s3SbRJTB/dFBHXYLVjWcV2QpMGQeZw3S6ToNJozjqOc0XxQn8Wfq4fzl0+b2LSttv2LEzLSvPD+wpSM9hBXeIvERzSBXg4Uhj0u4PDFTwCcc1VhDx8E7jz60qRXGmph38dQFbq13y/1Pp4ebmiON6Wv8BSYvhiyvNkijelFbDk4jPW79rO+PMgHW4Nsqtgf+gLgQ6QHWphekMnXPze+fehE4S2SOKIJ9HeBCWY2Di/ILwa+Gt7AzPKccxWhhwuAjTGtUjxN9bBvqxfSHUL7YzhQ2bFtegGMHA9Tvwgjj28PbbKOg9ThNLW0svnT/d6iVNuDrP9TkE0VH7R/e3t6IJniggyWfm4s0/MzKc7PoHCEwlskkfUY6M65ZjNbBryEN23xIefch2Z2K7DWObcSuMbMFuB9qHofsKQPa/a35sbQjJGwHva+j6FqK9SWdWw7dJQX1hP/HkYc790feYIX3oOHtDdramlly+79fLAzyPo121lfFmTjp/tpbPbCe3ggmeL8DJaeMZbiAm/YpGjEEIW3yABjzsVnKLukpMStXbs2LseOu9YWqPmkkyGSUm97+Jh2INML6bawHjHeuz/ieO/bXrpRc7CRe18t5Yk/76Ch6XB4TxvjDZdMC415HzdS4S0yUJjZOudcSWf79EnRvtLaCvsrwnrYYcG9bxu0Nh1uO3iYF9RjZkHxolBvOxTiQ0b0+tBNLa08tmYHP33lI/Y3NHHRrALOnJjjhfeIIZ1/2YKIDHgK9KPhHBzY07GHXfVxaJz7445f95WU6gV09kQ48bxQbzsU3DFaIMo5x6sbK/m3VRvZuvcAnzshm5sumMyk3O578iLiDwr0aNRXe2PYHXrbpV5wh8/THpQcWhjqeBh3lndRsi200/P79Ou9Nuyq5fZVG/hTaRXjc4by0JISzj5xlIZSRI4hCvQ2h+oOh3X4EElVKdTvC2to3rfOjDwBCk4+PDQyYrw3f7uf182u3N/Aj/+whafW7iQjLYV/XTCVr55SREqSvhtS5FhzbAV6U4P34ZrOpv3Vfdqx7fAxXlBPWRAxg2QsJKfGpfxwDU0t/PKP2/j56lIaW1r52hnjuOacCWQMSYl3aSISJ/4L9JYmb6ZIW2iHT/sL7gTCZvUMyfZC+oRzQ7NHwnrbg4fG7VfojnOO371fwX+8uInymnr+fspobjh/MuOyE7NeEek/AzPQW1u8j6x3Nu2vege4lsNtUzO8kC46BUZeGupth8a20zLj9zt8Bn/5pJrbnt/AXz+pYUpeOj9aNJ3Tj8+Od1kikiAGXqCvuR9evhlaDh3eljLEC+jc6aFPRp5weJhkyMgB/xVjZdUHufP3m1n5t13kDE/lzi9P50uzCkjS9EMRCTPwAn30VDjlm4c/XDPyBBieO+BDuzN1h5q5//VSHnxzGwDXnHMC3zzreIamDrw/NhHpewMvGcad6d18rKXV8du1O7nrD1vYW3eIC08aw3XzJzEmU9+NKSJdG3iB7nNvle7lthc2srGiltnHZfHg5SWcVDiwxvpFJD4U6Ani4z11/PuqjbyysZKCrDT+86sz+Xxxnj4YJCJRU6DHWc3BRn76ykc8tmYHgZQk/u/8SSw9YyyBlKR4lyYiA4wCPU4am1v59Zod3Puqt4DWxXOKuPZ/TSRnePw/tCQiA5MCvZ8553gltIDWtr0H+LsJ2fzg81pAS0SOngK9H324K8jtL2zkrY+rOD5nKA8vOZm5J+ZonFxEYkKB3g8qaxu4+w9beHrdTjLTUrh14VQumaMFtEQkthTofaihqYUH39zKz1//mKaWVr5+xjiu1gJaItJHFOh9wDnHyr/t4j9e3MSuYAP/MHU0N5w3mbFaQEtE+pACPcbW7fAW0HpvZw1Tx6Rz91dO4rTjR8a7LBE5BijQY6Ss+iB3vLiJ59+vYNTwVH4UWkBL398pIv1FgX6U9jc0cf/rH/PgH7cxyOCacyfwzTPHawEtEel3Sp3PqKXV8fTandz9h83srWvkizPz+f4/nKgFtEQkbhTon8EfP9rL8hc2sOnT/ZQcl8UvLz+ZGVpAS0TiTIHeC6WV3gJar27yFtC676uzOL84Vx8MEpGEEFWgm9l84B4gCXjQOXdHF+2+BDwDnOycWxuzKuOs+kAj97x6eAGt68+bxJLTtYCWiCSWHgPdzJKA+4B5QBnwrpmtdM5tiGg3HPhn4J2+KDQeGptbefTt7dz76kfUHWrmkjlFXDtvItnDtICWiCSeaHroc4BS59xWADN7ElgIbIhodxvwH8D3Y1phHDjneHnDbv79xU3tC2jd9PkpnJg7PN6liYh0KZpAzwd2hj0uA04Jb2Bms4BC59wLZtZloJvZlcCVAEVFRb2vth98uCvI8uc38vbWKk4YNYyHl57M3IlaQEtEEt9RXxQ1s0HAj4ElPbV1zq0AVgCUlJS4oz12LFXWNnDXHzbz23VlWkBLRAakaAK9HCgMe1wQ2tZmODANeD3Ui80FVprZgoFwYbShqYVfvLGV+/+/t4DWFZ8bx7JzJpCRpgW0RGRgiSbQ3wUmmNk4vCC/GPhq207nXBDIbntsZq8D30v0MG9t9RbQuvP33gJa86fmcv15k7SAlogMWD0GunOu2cyWAS/hTVt8yDn3oZndCqx1zq3s6yJjbd2Ofdz6/Eb+trOGafnp/HjxSZw6XgtoicjAFtUYunNuFbAqYtsPu2g79+jL6hs79x3kjt9v4oX3Kxidnspdi2Zw0cx8LaAlIr5wTHxSdH9DEz9//WN+GbaA1lVnjfJcqd0AAAUvSURBVGfI4GPi1xeRY4SvE62l1fHUuzv58cveAloXzczn+/NPJC9DC2iJiP/4NtDf/GgPt7+wkU2f7ufksVpAS0T8z3eBXlq5n39btYnXNlVSOCKNn186i/OmaQEtEfE/3wT6vgON3PPKFh575xOGpCRxw3mTuFwLaInIMWTAB7oW0BIR8QzYQHfO8dKHu7njxY1srzrImRNzuOnzk5k4WgtoicixaUAG+gflQZa/sIE1W/e1L6B19omj4l2WiEhcDbhAf/DNrdy+aiOZaSncFlpAK1kLaImIYM7FZ9FDM9sD7PiMT88G9sawnFhRXb2junovUWtTXb1zNHUd55zL6WxH3AL9aJjZWudcSbzriKS6ekd19V6i1qa6eqev6tJYhYiITyjQRUR8YqAG+op4F9AF1dU7qqv3ErU21dU7fVLXgBxDFxGRIw3UHrqIiERQoIuI+ERCB7qZzTezzWZWambXd7I/1cyeCu1/x8zGJkhdS8xsj5m9F7pd0U91PWRmlWb2QRf7zczuDdX9vpnNSpC65ppZMOx8dfptWDGuqdDMVpvZBjP70Mz+uZM2/X6+oqwrHucrYGZ/NrO/her6107a9Pv7Mcq64vJ+DB07ycz+ambPd7Iv9ufLOZeQN7zvL/0YGA8MBv4GTIlo8y3gv0L3LwaeSpC6lgD/GYdzdiYwC/igi/3nAy8CBpwKvJMgdc0Fnu/nc5UHzArdHw5s6eTPsd/PV5R1xeN8GTAsdD8FeAc4NaJNPN6P0dQVl/dj6NjfBZ7o7M+rL85XIvfQ5wClzrmtzrlG4ElgYUSbhcCvQvefAc61vl/4PJq64sI59wawr5smC4FHnWcNkGlmeQlQV79zzlU45/4Sur8f2AjkRzTr9/MVZV39LnQO6kIPU0K3yBkV/f5+jLKuuDCzAuDzwINdNIn5+UrkQM8HdoY9LuPIv9jtbZxzzUAQGJkAdQF8KfTf9GfMrLCPa4pWtLXHw2mh/za/aGZT+/PAof/qzsTr3YWL6/nqpi6Iw/kKDR+8B1QCLzvnujxf/fh+jKYuiM/78afAdUBrF/tjfr4SOdAHst8BY51z04GXOfyvsHTuL3jrU8wAfgb8d38d2MyGAc8C33HO1fbXcXvSQ11xOV/OuRbn3ElAATDHzKb1x3F7EkVd/f5+NLMLgErn3Lq+Pla4RA70ciD8X9KC0LZO25hZMpABVMW7LudclXPuUOjhg8DsPq4pWtGc037nnKtt+2+zc24VkGJm2X19XDNLwQvNx51z/6+TJnE5Xz3VFa/zFXb8GmA1MD9iVzzejz3WFaf34xnAAjPbjjcse46ZPRbRJubnK5ED/V1ggpmNM7PBeBcNVka0WQlcHrr/ZeA1F7rCEM+6IsZZF+CNgyaClcA/hWZvnAoEnXMV8S7KzHLbxg7NbA7e38s+DYLQ8X4JbHTO/biLZv1+vqKpK07nK8fMMkP304B5wKaIZv3+foymrni8H51zNzjnCpxzY/Ey4jXn3D9GNIv5+UrY9dCdc81mtgx4CW9myUPOuQ/N7FZgrXNuJd5f/F+bWSneRbeLE6Sua8xsAdAcqmtJX9cFYGa/wZsBkW1mZcDNeBeJcM79F7AKb+ZGKXAQWJogdX0Z+N9m1gzUAxf3wz/MZwCXAetD468ANwJFYXXF43xFU1c8zlce8CszS8L7B+Rp59zz8X4/RllXXN6Pnenr86WP/ouI+EQiD7mIiEgvKNBFRHxCgS4i4hMKdBERn1Cgi4j4hAJdRMQnFOgiIj7xP5MGwsxpBi6CAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["INFO:tensorflow:Assets written to: modeleEntraine/assets\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"U5S7JROCpVYh"},"source":["# myModel = keras.models.load_model(\"modeleTest\")\r\n","# print(myModel.predict(np.array([theImage]))[0,:])"],"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["[2.96738029e-01 1.24476401e-05 2.17953697e-01 1.91844806e-01\n"," 1.76624253e-01 1.78373352e-06 1.16824925e-01]\n","[6.6510475e-01 8.1453304e-04 5.7720199e-02 1.3529294e-04 2.1474548e-01\n"," 2.6716415e-03 5.8808159e-02]\n"]}]},{"cell_type":"code","metadata":{"id":"aY5kLCgIpVa_"},"source":[],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"buildEmotionModelFromFer2013.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"12gj_RNUkhCcpPsrHCcnjM-aFz0bHG8Nk","authorship_tag":"ABX9TyP/j4/kXn/SBJc9poEkTWTi"},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.8.5"}},"cells":[{"cell_type":"code","metadata":{"id":"1321rUeQzURj","cellView":"form","executionInfo":{"status":"ok","timestamp":1616663382789,"user_tz":-60,"elapsed":3339,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}}},"source":["#@title Imports\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import datasets, layers, models, losses\n","import tensorflow_datasets as tfds\n","#from google.colab import files\n","\n","from matplotlib import image\n","import os\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import matplotlib\n","import random as rd\n","import cv2\n","import csv"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-6y_a77Xmx3X","executionInfo":{"status":"ok","timestamp":1616662751659,"user_tz":-60,"elapsed":5015,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"fa99ea9f-0f75-42a2-fba5-c09606d4198d"},"source":["#from google.colab import drive\n","#drive.mount('/content/drive')"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"a4LizvrK0fes","cellView":"form","executionInfo":{"status":"ok","timestamp":1616664903890,"user_tz":-60,"elapsed":1577,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}}},"source":["#@title Hyperparamètres\n","classes = [\"Angry\", \"Disgust\", \"Fear\", \"Happy\", \"Sad\", \"Suprise\", \"Neutral\"]\n","Na = len(classes)\n","maxNbrImagesForEachClasses = float('inf')\n","h = 48\n","l = 48\n","p = 1\n","input_shape = (h, l, p)\n","\n","epochs = 5\n","batch_size = 128\n","validation_size = 0.1"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"J26HwuSTpVQK","executionInfo":{"status":"ok","timestamp":1616663639205,"user_tz":-60,"elapsed":726,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}}},"source":["#@title Fonction utils\n","import numpy as np\n","import cv2\n","import matplotlib.pyplot as plt\n","\n","def afficher(image):\n","    if len(image.shape) == 3:\n","        if image.shape[2] == 3:  # (h,l,3)\n","            plt.imshow(image)\n","        elif image.shape[2] == 1:  # (h,l,1)->(h,l)\n","            image2 = image\n","            plt.imshow(tf.squeeze(image))\n","    elif len(image.shape) == 2:  # (h,l)\n","        plt.imshow(image)\n","\n","\n","def predir(modele, image):\n","    # Return output of image from modele\n","    return modele.predict(np.array([image]))[0, :]\n","\n","\n","def normAndResize(image, input_shape):\n","    # For an array image of shape (a,b,c) or (a,b), transform it into (h,l,p). Also normalize it.\n","\n","    h, l, p = input_shape\n","    # resize for h and l                                       #\n","    image = cv2.resize(image, dsize=(h, l), interpolation=cv2.INTER_CUBIC)\n","    # if we want (h,l,3) -> (h,l,1) , we first transform it in to (h,l) (grey the image)\n","    if len(image.shape) == 3 and p == 1 and image.shape[2] != 1:\n","        image = image.mean(2)\n","    image = np.reshape(image, (h, l, p))  # restore third dimension\n","    image = image.astype(\"float32\")\n","    image = image/255  # normalisation\n","\n","    return image\n","\n","\n","def selectFace(image):\n","    #Return a face identified on an colored image\n","\n","    #Import cv2 face detector\n","    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml')\n","\n","    #Face detection is made on gray images\n","    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n","\n","    faces = face_cascade.detectMultiScale(gray, 1.3, 5) #This return a list of tuple locating faces on image\n","    \n","    #The face returned is the first face detected on the image (if exists)\n","    if faces != []:\n","        x,y,w,h = faces[0]\n","        face = image[y:y+h, x:x+w]\n","        return face"],"execution_count":40,"outputs":[]},{"cell_type":"code","metadata":{"id":"33votd1Y0fcg","colab":{"base_uri":"https://localhost:8080/"},"cellView":"form","executionInfo":{"status":"ok","timestamp":1616664902296,"user_tz":-60,"elapsed":102287,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"ec40cf93-99e9-458f-b24b-a9d45934f7db","tags":["outputPrepend"]},"source":["#@title Load data as array\n","nbrImages = 35887\n","maxNbrImages = 10000000000\n","emotions = [\"Angry\", \"Disgust\", \"Fear\", \"Happy\", \"Sad\", \"Suprise\", \"Neutral\"]\n","\n","def traitement(a,b,c):  #For testing\n","\tpass\n","\t# arr = strToArray(b)\n","\t# print(a)\n","\t# plt.imshow(arr)\n","\t# plt.show()\n","\t# pass\n","\n","def strToArray(string):  #Fer2013 provides images as string so it needs to be transformed\n","\tA = []\n","\tlenght = len(string)\n","\ti=0\n","\tnbr = \"\"\n","\n","\twhile i<lenght:\n","\t\tcar = string[i]\n","\n","\t\tif car != \" \":\n","\t\t\tnbr += car\n","\t\telse:\n","\t\t\tA.append(int(nbr))\n","\t\t\tnbr = \"\"\n","\t\ti+=1\n","\tA.append(int(nbr))\n","\t\n","\tA = np.array(A)\n","\tA = np.reshape(A, (48, 48))\n","\n","\treturn A\n","\n","\n","\n","#LOAD DATA AS ARRAY\n","X = []\n","Y = []\n","\n","filename = \"/content/drive/MyDrive/Colab Notebooks/facial emotion recognition/fer2013.csv\"\n","filename = \"data/fer2013.csv\"\n","\n","with open(filename,'r',encoding='utf-8') as file:\n","\t\n","\tcsv_reader = csv.reader(file, delimiter=\",\")\n","\tnext(csv_reader)  \t\t\t\t\t\t\t\t#Passe la ligne de titre\n","\t\n","\ti=0\n","\tfor row in csv_reader:\n","\n","\t\ti+=1\n","\t\tif i>maxNbrImages: break\n","\t\t\n","\t\temotionNbr, stringImage, typeImage = row\n","\t\ttraitement(emotionNbr, stringImage, typeImage)\n","\t\timage = normAndResize(strToArray(stringImage), input_shape)\n","\n","\t\tX.append(image)\n","\t\tY.append(emotionNbr)\n","\n","\t\tprint(f\"Image {i} sur {nbrImages} chargée\", end='\\r')\n","\n","X = np.array(X)\n","N = len(X)\n","\n","Y = np.array(Y)\n","Y = keras.utils.to_categorical(Y)"],"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["1 sur 35887 chargée\n","Image 35222 sur 35887 chargée\n","Image 35223 sur 35887 chargée\n","Image 35224 sur 35887 chargée\n","Image 35225 sur 35887 chargée\n","Image 35226 sur 35887 chargée\n","Image 35227 sur 35887 chargée\n","Image 35228 sur 35887 chargée\n","Image 35229 sur 35887 chargée\n","Image 35230 sur 35887 chargée\n","Image 35231 sur 35887 chargée\n","Image 35232 sur 35887 chargée\n","Image 35233 sur 35887 chargée\n","Image 35234 sur 35887 chargée\n","Image 35235 sur 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range(N):\n","    k = rd.randrange(X.shape[0])\n","    plt.subplot(1, N, i+1)\n","    plt.xticks([])\n","    plt.yticks([])\n","    plt.grid(False)\n","    \n","    afficher(X[k])\n","    plt.title(classes[np.argmax(Y[k])])\n","plt.show()"],"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 5 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"90.742263pt\" version=\"1.1\" viewBox=\"0 0 352.7 90.742263\" width=\"352.7pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type 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tGs4vzvjkD7/Hs6dPefbsKSkljLEYYyuwKZn5NI4Mw0QMkV0Zud/eMw49P/3pX5NzQKnC1cUZLz94zvNnL3n9+jUhBJSyaBAznCO6aBpdmFRh6TUfPn/Cu7fvGIYB4y2xGKasjqXhlB9ESDXsVTXDUEdA5ZT8fVOxyu72B25uekBhnNyYMZqmaQT5tJocg5iqgoByCgkhlZotJFpJHdgY0ZaSs8T0WULAnKDEKFFI3zNMgXGK3G+2vH37jrdv37Hb7ogpctk+YXXRsj5bc7Zes1otaNuGtvNY64hxYnfYobTBWYu1DmMcyjhsa2ltQ4qZYbjDOU8IgRInYsyUktj3Pfd3G6wzGOuYpoBWFqMd3sgOTiniNDRW0TWWb334ks8++4z9bktSBWUMKZZjQvfbHoqCKrUIoIyYrwfFqIelWzvGxBAm2qZFW12FV2oZVGrTJUUpCKlaMq2CMLWoY7QWsE6bI0zy0DGVUgSrSYntbscw3bM/9Gy3e7a7Pf0wUtCszs9ZLJZcLp/ivT9WC401QKJYQyBzt9uyO+yhyGcaI5GX1haKqj4lElUEJbu3lKbmOBnvHWiDVgbljDhjE3GNq0hCgJhwqtBoRecUT85XvHx6xbvre+73I64tWKMJsZBTkhT4oVzK7MCP1ZhvFpY61dDtYQo0XcdqtaJbLrDOHjPWxjmc0RRdk6m6+CKIqhl6BvioNWweC6NCz6WarnEK3G923N7es9v3pFzwTcv67Jyz8wu6rsPRidkpsB0GUgrEONF2Hucsu+FArLA6KaNSxsSMMZKPjGNkmiacr1prDBZfNVfRtg7vBeLPKTFU9EFVjVc5U5Ikw1ZlvJWa+svnT/nNV9dc37/GpIj1C9I0knJEW3NEE44a8eB5Ml+n1zz0G6UUbB8ST87OOD87w3sPOVNSRisttenK0iBX7dD6KBh1/Do7osqyyHPNIR8/rG4WlDYYY/FNwxKDNo5uuWK1XtO0HVprUlTElIjTBGRCHEkpMJVA4x19mE6gZkqQwSpNLhowRGDKGaYo8IU2NSqTiND5mucYIywSY6VsK3G07OIUEbgu47RETc+eXnF1ccavP3tFDImm1UJdKgVtmweCeN9U1XU4ieXxusyagZUIyjcC9sWUyDlhlWSouRaLKPqRZszPWRBzkSXnTIqSSeeUUZXzZEyhYPDOcX5+wWJ1VvGhTC4CL4wh4pyDmgsYK2VtVxpSnig5MeRI1JLVTlnwJYVcrzcWrSApRVSKMgWaxop2aI11Buc9isIUIzpA6331jwVVsqAGACWRp4RSCmcVqiTO10ueXq5ZtpbNUGiKJLoq/3a/od8zT+9TfR5pxur8HCLkmYNU42ZdSWM5JmzjSUqhVREHzntQeX3OiKs8I7kkDMJ7MhRy0WhjcNqgLWgTUToREyhr6LoFi8WCPkgQkHIglihBhW1JecQUxeXZku1uRw6BRA0TswCXRWkikVigtRJQxBiIMVGKUHysNTjjWXQLFBnbanKMkj9V6g+5EINA69ZIeSGhef7sCR98+JLb//glMUa896jy/pJ/80Opr2vGI6Cw65aYwyS4kjFYbTBK0XkvjA1gsViw29x/45u/L+mchVRWUkUzFWIqUFA0H3/729ze77i5vSelgvctF6s1vu2IMTIcBrJuMVbErpXDOgkQDv3Ioe9RRqGNRhlNptRaQkSZiKJwGAYO/UCOPevVCucMTePpuobVakEhkeJ0hLud8yQKcUyCN4mOE+OE1RKgNM6yHyMX52d88PIFf/GLL4gh4rsOXTT9lDiyVvi6qfpdHtbfjzxvNGq/qxfmaJuGRoFRNfN9e4M3Fucc3gm3iFKIfWRIQUhkWqM66PtRNKOCaDkVMIVl1+JsQ5gOeD1xtbbklcVai3OQ8p7duKMcNij1DO083lpBkkMkhoBJijOzxgVH2Ey4YBh2mTQOGB9otHCspmnHSkeetAUVN1g0rWlpY8LuJgzQtC0r6xn6PSUkcazGkVUSqGexZFkz+s8++4L11RXWN6QxcdU6fu/Cc98HSBMog1ECzecarGQlLMeIwDmogorx6MBnHFsfIXewM3dWiGauAmuS1eb6IuccNdc/AVw1WnropEtKNUafnbfY2mSSFKgwNE0DaLQaSTO4qEUrV6sl1hr2gxenqjU5V7NpDVnDFAK7wxarNUMIDIee3W5LyZnt/b2YjRrO2qXFGkPXNSwXC9oKLuYQxGHzAM5Wj9kexhiom6Hr2hqyZ4xWtG1D1y14u7mj7RT5IQAHoB4Yo29Qk0c/PrAstm1bKAPaWKzTWFvZDjmQi1yUc444TuSsyFnqujPhONXFKkXyiJKTfK03mVDoEInaUggsuo7iFUoLelqKRFjaaBrnWS6WNAdPiIlxGIjTSExJggUNJUWGw4H9Yc80juSUpP5+vHmpk4Qx8PntKxrvOT9bU66EoOCde0SonjeSrmE55WR6TTWB65UholEq45xlvVpxfn5O/OoOaxRxDKCtwB+FB/5DwNL3iQi/7WGdc9gcj9TKozhLOdZ4Hz4kgcvknMglU0qmoKsTzRIeFlB5LjcWSsrkIKFinCJKaZy2WG2Pu0MpVaF1RUyGoT+wub9lv9+TZqaHUkxhoj8MvH39BmM0F+cXrGtYPtehtdIc+gNfvvoVChiHnlIjn/Pzc9oHSMHMSBHYH5SSexFUWTbjCsNuGLGp4J1lvVxyeXFB4VPZiDnLRqlacDJEv5sQjsKAUmuzUm0jpSMF3yhdq3/pkbN+v0ByUm99XNyiyrEwr4qipAIkxn7EWIux4i+0kpsued5ShXHYs7m75vbmHX3fQylobaQOHQMpgcoZ7z1nyxVPr57Std3R7DnnGPqeePcl+/0OoBIaJkp9zXxfxpiTMGpJ9aEwcs4sjGMKgWgNWSmWXcvF2RkWqsN/kNgxJ3b//wQBYLVRNI0jxUhOiVzV1BgjJOAarj7sn3ioMvONyE2VCokXiq5AYS2vzmyRaRgwztOgsNpinFT8Ys6C8MbI5vYt++0taTpglZRQSylM00AYJ7SxPLk4Z7FY8eTiiqv1OW3XUTIYa2mblpgiLn2fd+/est9tsNX+z4/Z33nnamKojwnsfM+lhvlOG5w1tGgKhcY5Ls5WrBpLClFymwp9yG6d1aO8567/FmEsFgusEfMi4N7DHc8R8p1DX6UV5JNmKKhlTYnHjxoj/x+pOMIC0ZKkqUTSkawNpfoLBaQQGIaB3f0tVmWeXp7TNC3WCJi32Ww4HAa0tpytL1it1lxeXnF2dk7jGyj6WMDyxvHxxx/TNJ7rd54YQ83ayzEoUErhvXvkMxRzfaSQs2i6jgVv5d5DjhitOVsueXK15subLdZ4chH0+lhS+k+Ib61ESqP4jGKP0VFKEY5qbwXhrCqdU/UVVYuslURKQlEoeVbaUsut5ehXjDIkijTe1ODOe4WWnJMSE9N44PLykqdPn7FYLCkFDvuBi/MzxjGQU8Faz8X5Jev1OcvlmrZt0doy9BP7/Z6coe1a1us1YRoZx+EYnYUQaJqGtm2l+qcflEmPZeQT+aykQGMNVlkOU0SRWbQNz58+5dOvbmjPl8SQSVUI6pEQHvjhv00Y2+2GrhEMynlHTpkQxVxkFE3TfC360FpRaoY+mzStNc66r/sVjlHxMTqbpokpJGJJaBWwR/q/lGTHfk/z/BkXZyu8bxj6EWdgvew4W66E9aEs5+cXdJW14X2D9a10G4UgptNPrFYrKJlh6AVKzwLVtG1L0zQ4+yBwKQL5f63dK0TRfGcxFFSOeG84O1vLPSkFZHQxZPXNy/67KIm9urrCTVuctXUBhQxmjBVzRMI6Lf6g1PyhCMLpqrYYrUDlY4RyouYcUUQxWzU6adtOmCCVUX44DIQUj1pZkrDGp6GHnIkhQkk4I+zFUhTnZ1dMUyTFgG47rDXkGJiGHmc0i8WKL9/+hplGJI4/CUeq6+i67uSoj7mTwDhSDTwFLU3jyPX6L9YrMA69G/nWRx+QgdViQR+3CKFXv2eeOWrb3yqMUk45wVGKR5T1VASZnWg5hrynJLAcv1fVz0geMYesCmGnC+QudltjMCpRVMJQhFUYBTF9cnmOt7LDsQ5nDEULhbKoRNN0kBOafNyphAmyghwpKRLGfb3WU13l4f3NuNDD3z9ahXkxlULnRKoI7oxcN9aw7loWCrbbDSULeFkJrXCsg/9t5acHwpDkLVNsqTv+Ad708IKqvdVaU8hfE3bOmZSToLRqrggqSeiq2hdVG1pAbFaSBLIog9UJpWVjPHv2XPKNWIkHxqEUpBRQKLquk++VRuki8PpUKFmRSyDlSN9PjygxMyqg34uqHjH7HjEDH3wtBZUSOmdBh7X0n1ycnfPs6QWfXm/wbYtGid+Yyc5lDnF/t1DXSgiXyCVVNPZEVJ53+rG4PpN9j5Faqal/ZgaL9ZEGIguNrvxaZSiVwCZ3mCk1H7Emk5NGZdG+1dkFw9ATU2acAsmKsENKFCJTjFjAOQ+6EEsgTpJUSlONsMTfF8TDjaVqieARQ+PBwsw01FIKRks7gqJglJbGH2c5X6/58IPn/PLt3dcW9pRrPPz6twkjx8paEBriXIQ5CUMd13b2FUXNwldHGr+uZmmmc5ZKSFVagzYoXcPiB0V5SfgyFC0E5VwLLb4hRqHcTCEQYmIOGXMpjNNAwWOspdSKn/gyuc5cJqbQg85HYcwCUZyy7oc0n4eaAbVHcAbwtNRHpK9EiM+N0iw7zUcffkj7F7/gGM8qqXk/yDreE9LfJIwkBLCcs8T7j+oTp11SHuyi+XfHhG/m1j54qoc/Vw2ZWdnHy6rlWlXKCXsqhaQ0znlyLoQQKqtDY4wDpQhhwlhNKVJ7LkV6MmQRNahEJkCRjqJYnXK98MfCKDP0c8qij5yvWiuxRhpIlRbHbI3CKYNzmg8/+ICnF+fcHMYjFHQM6Y/3Wt/3b9EO/XDXzDv/mJHqk2bkGY0t+ZFtNbWvwzmBOIypSVzl0halyUoo/7lea0Z8rXSqalBGWl2NRhlDQWOdxzctxnpQhoLCWIN1VmCKUkglkXKkkEBllM6gM8oUpDO5mqn4QDPeh3UeOPfyQBiKk0lztgKMRmO0tLrNrRMvX7zgu9/9bkV1OZm7oyR+d1jECiwQKSUhpaSTqZqbV6AQUzjegK4RktYyasJaEUix9pTxqKMtk90OCNuulujnDL2+VnyRwAjG2GpOLEpb7GSlFFzxLFvNU86RXFTVzAe9dlbhvCMepmNgMQcfs5maH7OAZOdysiOKY1uDsRZUJmIwRVqbVZbE9cnVJd/7g0/4f3/681NC9Z/4sL/NgT1+qEetUqVCk/POsdaijezuoh79WX39/IeStZ+aSHi0i+Ydqo0DCsbKxANjDSFGIKPqLu37PbFkdCmCKWnxO4Us4b59uPu/bl6V+i3Z2fHS63sVMEaBspQk+JrSGkjEEFicd3z44YdYJbHUw7hprnTKfq5QyTdA4fP+tUs6tClkvLRa1d1dSkFZLwNTtCWhcNZKpa/C2bpGHFk7rGtIzH0KM6nxhEuJ1oFzcrkCwSdykU5U0cwMJaHcoqL4BaXEZhvvjqXcnCOLphUtLaBLteG1OLbf77m+uYaxIY3gyoKuO6NtGpwx7O8nco6gC8tlJ8GDUShDZcQnQorEGAg5sqi7RhkxyyudETKk4ze3n2FNJOnEoApJO7Iy5JIoKdJahBg3jYTkZTWOxuNx2drmlNAP/Wp9kWAsp8Rt7kZ6tLMe7CPZ4JpTGFd7+kr99xkSSVJwEY1QKOxRdKJxhsM4StHpwWc8bD2TN3q8w44Y2IMgI6ZwjKagMCooxZ3ut35GiolpmogpiEkrol26Om2FYR6HobTwyObPN0Uir3EMKO0wZmbJzN1SIkn9aL2+/lAK7PuJ3vH7B9Kbvx5bxKrtVQ/+vZRy6u6sAhDAUEsNuC5+UnNWb44CE1s9V8UghIFSTiGoqdHY8bOVIo7To8V/38yWUggxHKHyOWJUCtrWY7Qj5cBcEigxE0Mi5kChoI2MYHJaAgbhXsk15VrBU9RKqNXCnTqaMF0FXVAqHwtb9aMerfNDGVnFKck7PquhKw8EJVHSKZs+2cEHC6DM0SFLxUieeXYOpcbhnKIVVXvelFJQs/NjPyD1379BG98XxPy7fAQnK94kbyQEiTw39Ris0xBhHAMpB1IU/6CLIAwkyJMgvKqxohkPupJOzBddTa86CksXISZoNCjxWadN/WjFHm14q0iPBXGMsR8LyNQWsYdTAciPMZEyR0VlDvNEAGUuA+fC7EH0nOkjzYoodazje+e/5nAfCuAhrvRNz2MjSsWHeHBfVD+ntEGpOaSXBNRqS0Yz9+pRWxYnnXClgFUYVaqgJRw01tB4Uxc5i8Yo87VFn++8HO/p0dLNZioh/TqPVedIKXmQJGlzqmlQHjZPzmZKQMLH9lu6jkgcI4o54tCIg54BNlWlIQjyHF19fbHn23v4Xy5f/z4fbYJEW8K1UnUWSSGlXE2HohRdS7+izWoOtZE+8VQsDXX6AhDnbiOjhZPlDLuYyTGCqYBpzlWwqvYcvsfHVQ8Eo4oIQyn7Ne04htv1gh5qxrwzv26mKt40KwIc2wJOwqC6eVmsCnKgSgZV8SM7+5jHux1O0whSVXHpHy9opC9QVY1IyPyQUuvqM0JQtGKMkZIn0iTN+eRyhM1LhdKPAYNS7M2Azw2lKJyV2nhK0hRqjaVxjsvzBYfrHWOYmMvMKSUUGWc1MSTA1TWd161uyyoU+9AvnGzyKVp+31QdhZGzELYeCkRlioaSi6DZx4U8pd5qJiqUAiWjShKyNamWcxPZPKZBfuNDP37N1xw4ME3jkUzhvccqqWQe+p4wTcQYOez3qDIH41UjagRWar2lXFm6I+4l1cyYReu1VjTO8uTJJV9cbwjTJLV9a8kxgS5o7QgpYUx5L6ytAERNrO37mc9RbR6Elg9N1eOQ9nH4iYo8SOXEec1hLCAjWXz141UIOcssqCwFpFJgGOKjzz1GUb/lGqqbYv60+ROnaaqdqHVxSiHkVFvOhgrvQOMcbdPROo/VhpIyUz+w3+5IcaQfE9qESmoQSCRlEb7RGqsV5+slWilSCpiSKEVLSaHehwyWebhw5Zjylxp5Wdc25Jzpuo62bclZRlWQy5HA1jTSXptTQqt5AoI6TtAEAeRKHurizJo12+5KdEhilsI0UVKqXU9igpyRhY9hxCpHjJGb21v6YaBddDx9+hSA3X4vrW0Lqdah5vkkQk2V0ReZbrmgbbcMw4HVeolvPdv9jpu7W4z1RwLd5eUlTbMg5cJmP5CDmChdQOuWxXJBaHsSsDv07Pc7aS1oW3y3IuXEfrvl6dUTjLa0vlqSAstuUeH3SWas+IeVUGFt9n0PZD766CPsOEy0XXNS9dmu14hpmiZKEmHN4WlKqYJ96tFrR6S1y2grfX9aUVQm5cAwTIRRKP8pJGIM1UbPlJiC0cIEs6Uhl1Jr2x6Aze0dsWSatj3O7XDOSWNkJUFrcyp5FqXYb++PuYUxmtV6SbNccBhGphi4uHpCDIkxJA77nvEwUEKq2bzFKIstisMQKCVidabRCmsVaX9gPwSa5QVn589oG4d3YOJcXYwVClGcAmJo25bdbscwDDhnWa0WTNPE8+dPsdqJ2YgxHuvBczIzV/dSEpCuVAR0RnjnwPboSzJQYtUSRQyZaQgM/cShD4TaUXRsTnmgRzFOjNMIFJZ6gdGG1XrNcrkECpvdljhMmBDY7/cY7/Btg1XCDrTOoa090k3nndc4K2MmKlnOOsP29pZcCk99Q0gjzXLBYn0hid8QmYZJ2g1CIeTM25tb+n6HLYll61i00nAac6Y9G7h0HcuupfWWw5SYqL4GUEZLBUFrGcva98K2sUuZvTv0PH/+jH/8j/8x1rcdOstA3RlmNlqjncZ7Lwt/TFoeJ12PgEKtCUneI8QoCz9GGUsUCzECRabbNE135GCZmpmO00je74BM6iNFCypKKfimYbWU956miVwKrmQWKcGcA9WkNJdyrC52jQclzJdMZpoiUSkOQ89iteYQApu+Z799R38Y0EWzXp7z/OoZZ6s1t2+v+flf/5z7tGO7vUPnwLTugBWNd6SUGIae7eaermmkjcJGhD+R6swpQEu52XlPjLH6HtHWrrvgH/2jf8Q/+Sf/BGucxyOUyIcLPZsfrTWqcNSG2ebJ2CB9JLAZY+j7QgqRMAaGMRBCIkWFUoK0Gt0IBdM8yNQr2qqtRlnRKlt6aUeYJoZhwDpH17bs93vut1uWxhwTO63Vcdzq7MxB8orzszPuN1umcSAAhxBIStO0DU9ePGPbj/zq88+4u71nc7cjxcLF+oJPPg78wSd/gOkWBKVFMw5bGgONNyfurzFkpRn7nsZbqXloXQvQEvqXB/O1msZjjK7WwfDixQt+/OM/5p//8/+a3//k97HGOrw5acGcP8zmSSlpdEl1gs5DjXiYdyilUEmjokGlgsmQi0zQUdpiTIM2DSolxhCIOYFW2DrxJmOJxbM9bDmfpjpLSsxaCgGjJYg4HA6s1mvZLOr0+QC5SOfVbB4uzs65vb3l/v6OMReGlFDe056dcf7kil//+V/x+atX7HY9u+2Bfj/w9vqOMWaaxYrOt2yHkVdv3qFVxq46VIVElFJYY0nGkHOqk3nmNRFg8JRtS4Y/j9ZIKXJ1dcWPf/zH/It/8d/w4x//GN84bDGWGKV4b+qOy9VcTdMEcJyEoMrjMBc4vq6UgsHjlKHoBDqCysRSSFGL6qoExrDd7SWk7Fps02G9DBWOxXF9t2e335NipFsujgHDbrfj7v6eYRgIIdAuZaz33DyZc6ak2ldSk7zlaolWmvvNPftpYsgZfMO5c9xvd/zHX/+aPkyMKREoTCUzjQOvr2/41WefU1LhJ3/6p0Tdc7ZesVgu6RbLR1ZE1xC1UI7Irsx3NzUiPc1R2W63pJT54IMX/MN/+J/zz/7Zf8WPfvQjuq5jCiP2+vaWvHnHs6dPWK1W1f7aY4SUs3TkpJQw6oRLlVJkJMU0CfwcIyvVEEOiHyeGIYi/SJCUQWkHWtEuW7b3I7Fk1njaZUu3OOfp0yuUNbhmzfWfX3N3dyfE5Gpn311fc3N3y2K9EiK2czgvan8kKmcZxz1vLKMTl2dn7PsD20GGXW7v73m72/LV3R2/+vQ3vH73Dt8uWS7PaZZL7m/u+dl//CXvru+4vLhgiJE//MH3WC9bLlcdF8uGZWsxKktH7kx4iIGSJK+wWuOMJHSylpIkr1YrXrwQZ/1P/+k/5fvf/z7eezbbe6lg/uxnP8P0d8Qw8fLlS9brNY1vsFZmgGsto7EfJodz7ThME4e+Z19Dte1omMbAoR8ZxkjKoG3DYrlmebGi7db88tNP+cl/+FNut/d0yyXPnj/jo2/9Hj/+kx/z8Xe/w8XVU7rf+xZ/9qf/gevra4ZpZBgnPvv8M1zj+fG3fo/lcomzrpZbjSR6WRxmTPHo86Zp4urqiiFFkjWM3PJuu+X29hZ7twGt6JYr9v3EYbgm1ejPtY4Pv/V7/Mkf/2cs2pZc7onjHpUmmRiqJK/a7/ds9j37bFCLi1NlT2sZRlaofS+yen/yJz/mX/2rf8UPfvADlsvlERnQRnE4HLC/+D/+jPvLjF6/4OKDBSu3JGvBfpwuWFXoGonrkYl+hAx9SOyHifv9wN124DAMfDgNKDTX9+/46vqG20NPNJarl7/H71/+EZv7W/7PP/93jESWL9YY3/Bpf8OnP73GXC55+cnHfPbqK778xVdEd0Xf93z+2R3jOOD9Jc+ePyOyJKsF1q3p+0QIfTVX0uZFjBASKUaC6xix+KcvuFqssWdXLC+esdls2OwPXPglh24k5oxxjtX6jKsnT3n6/Bnn5+cobQkh8OVWU1xDo1uyhv00srk98OrNLXebnmA8wUT2GKJ1hBgZwsA49XQYvvXhS/67f/nf86//9f9Uh5eZ4zPlCSi0rcf+7MtXdKblzZu3vH3+DGc1q9b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7g8lreoe4jh61c8Dfy6zTn4/sLsEf3zV403Qwx6q7T+JkziJNx9vuXBYZmj8fZLC3E+q6t9+SzfoUIjIPwc+QFJQvAr818C/An4GeBvwCklpbjsn0/8O+KPABPiLqvrJt2CzARCR7wf+PfBZFuPOf4uEuz/0238SJ3ESbz7e8uR+EidxEidxEvc/3mpY5iRO4iRO4iQeQJwk95M4iZM4ia/DOEnuJ3ESJ3ESX4dxktxP4iRO4iS+DuMkuZ/ESZzESXwdxklyP4mTOImT+DqMk+R+EidxEifxdRgnyf0kTuIkTuLrMP7/oBDFEnXZ8asAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}],"source":["#Comparison on Cagnol test image for test\n","cagnol = plt.imread(\"cagnol.jpg\")\n","cagnolFace = selectFace(cagnol)\n","cagnolFaceForInput = normAndResize(cagnolFace, input_shape)\n","\n","plt.subplot(131)\n","plt.title(\"Originale\")\n","afficher(cagnol)\n","\n","plt.subplot(132)\n","plt.title(\"Visage trouvé\")\n","afficher(cagnolFace)\n","\n","plt.subplot(133)\n","plt.title(\"Resize, gray\")\n","afficher(cagnolFaceForInput)\n"]},{"cell_type":"code","metadata":{"id":"oSf4medy0fgr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1616664903891,"user_tz":-60,"elapsed":1573,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"c6a7c032-e6b9-4057-cb3e-b5a5e82b3e27"},"source":["#Images et labels\n","print('X:', X.shape)\n","print('Y:', Y.shape)"],"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["X: (35887, 48, 48, 1)\nY: (35887, 7)\n"]}]},{"cell_type":"code","metadata":{"cellView":"form","id":"n4cvkzgQpVL7","executionInfo":{"status":"ok","timestamp":1616664903893,"user_tz":-60,"elapsed":1567,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}}},"source":["#@title Modèle\n","class MyModel(keras.Model):\n","\n","    def __init__(self, input_shape):\n","        super(MyModel, self).__init__()\n","        self.conv2D1 = keras.layers.Conv2D(32, kernel_size = (3, 3), activation = 'relu', input_shape = input_shape)\n","        self.conv2D2 = keras.layers.Conv2D(64, kernel_size = (3, 3), activation = 'relu')\n","        self.conv2D3 = keras.layers.Conv2D(128, kernel_size = (3, 3), activation = 'relu')\n","        self.maxPooling = keras.layers.MaxPooling2D(pool_size = 2)\n","        self.flatten = keras.layers.Flatten()\n","        self.Dense1 = keras.layers.Dense(64, activation = 'relu')\n","        self.Dense2 = keras.layers.Dense(Na, activation = 'softmax')\n","\n","\n","    def call(self, x):\n","        y = self.conv2D1(x)\n","        y = self.maxPooling(y)\n","        y = self.conv2D2(y)\n","        y = self.maxPooling(y)\n","        y = self.conv2D3(y)\n","        y = self.maxPooling(y)\n","        y = self.flatten(y)\n","        y = self.Dense1(y)\n","        y = self.Dense2(y)\n","        return y\n","    \n","    def predir(self, monImage):\n","        return self.predict(np.array([monImage]))[0,:]\n","\n","    def compile_o(self):\n","        self.compile(optimizer = 'adam', loss=losses.categorical_crossentropy, metrics = ['accuracy'])\n","\n","myModel = MyModel(input_shape)\n","myModel.compile_o()"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AcIJ3LVYpVSK","executionInfo":{"status":"ok","timestamp":1616664903894,"user_tz":-60,"elapsed":1562,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"155e177e-7b01-4178-ea36-836325f55312"},"source":["theImage = X[1]\n","afficher(theImage)\n","print(predir(myModel, theImage))\n","print(predir(myModel, cagnolFaceForInput))"],"execution_count":43,"outputs":[{"output_type":"stream","name":"stdout","text":["[0.12694494 0.00185027 0.16615286 0.2684518  0.18009424 0.07906247\n 0.17744346]\n[0.05335368 0.00411757 0.06580067 0.1750659  0.11889929 0.00182784\n 0.58093494]\n"]},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" 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Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"6b48caa8-e249-4159-f26e-3b0624998944"},"source":["#Entrainement\n","\n","history = myModel.fit(X, Y, epochs=epochs, validation_split=0.05)\n","\n","myModel.save('modelBadFast')"],"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","1066/1066 [==============================] - 23s 21ms/step - loss: 1.6812 - accuracy: 0.3342 - val_loss: 1.4630 - val_accuracy: 0.4373\n","Epoch 2/5\n","1066/1066 [==============================] - 23s 21ms/step - loss: 1.4233 - accuracy: 0.4578 - val_loss: 1.4197 - val_accuracy: 0.4557\n","Epoch 3/5\n","1066/1066 [==============================] - 23s 21ms/step - loss: 1.3201 - accuracy: 0.5002 - val_loss: 1.3840 - val_accuracy: 0.4674\n","Epoch 4/5\n","1066/1066 [==============================] - 23s 21ms/step - loss: 1.2424 - accuracy: 0.5356 - val_loss: 1.3459 - val_accuracy: 0.4858\n","Epoch 5/5\n","1066/1066 [==============================] - 23s 22ms/step - loss: 1.1637 - accuracy: 0.5634 - val_loss: 1.3297 - val_accuracy: 0.5025\n","WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f7044d1d880>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f7044d1dd90>, because it is not built.\n","INFO:tensorflow:Assets written to: modelBadFast/assets\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":287},"id":"Etye2vRNpVWY","executionInfo":{"status":"ok","timestamp":1616665440369,"user_tz":-60,"elapsed":1700,"user":{"displayName":"Boulet Timothé","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiayvINTDL_0Iuf52iuumxhg4psHVMYz59ow2vJ=s64","userId":"06174172927805952140"}},"outputId":"2499af31-7138-4953-8bc6-9152b0c43b8e"},"source":["#Affichage de l'historique de l'apprentissage\n","plt.plot(history.history['accuracy'], label='accuracy')\n","plt.plot(history.history['val_accuracy'], label='val_accuracy')\n","plt.legend()\n","plt.ylim([min(history.history['val_accuracy']+history.history['accuracy']), 1])\n","plt.show()"],"execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"252.317344pt\" version=\"1.1\" viewBox=\"0 0 372.103125 252.317344\" width=\"372.103125pt\" 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"U5S7JROCpVYh"},"source":["# myModel = keras.models.load_model(\"modeleTest\")\r\n","# print(myModel.predict(np.array([theImage]))[0,:])"],"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["[2.96738029e-01 1.24476401e-05 2.17953697e-01 1.91844806e-01\n"," 1.76624253e-01 1.78373352e-06 1.16824925e-01]\n","[6.6510475e-01 8.1453304e-04 5.7720199e-02 1.3529294e-04 2.1474548e-01\n"," 2.6716415e-03 5.8808159e-02]\n"]}]},{"cell_type":"code","metadata":{"id":"aY5kLCgIpVa_"},"source":[],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/faceAnalysis.py b/faceAnalysis.py
index 7ac476fe3049073b6944f802b7475c216ee558eb..8b1993a1f2275c405ee6806585875ffc8481d3cc 100644
--- a/faceAnalysis.py
+++ b/faceAnalysis.py
@@ -5,14 +5,15 @@ import cv2
 from utils import *
 from config import emotions, input_shape, modelName
 
+model = keras.models.load_model(modelName)	#Load our model
+print('Model used:', modelName)
+
 def detectEmotion(face):
-	#Return the most likely emotion there is on a 48x48x1 gray face
+	#Return the most likely emotion there is on a face
 	
 	face = normAndResize(face, input_shape)		#Process our image for input of model
 
-	model = keras.models.load_model(modelName)	#Load our model
-
-	emotionVect = predir(model, face)	
+	emotionVect = predir(model, face)
 	emotionNbr = np.argmax(emotionVect)			 
 	emotion = emotions[emotionNbr]
 	return emotion
\ No newline at end of file
diff --git a/imageProcess.py b/imageProcess.py
index 13dc64af80d0d4c3e284a0d350db093eb2d1afb4..a773b4d5b448d284166a75b54707929f66637535 100644
--- a/imageProcess.py
+++ b/imageProcess.py
@@ -2,6 +2,7 @@
 import cv2
 import numpy as np
 import faceAnalysis as fa
+import timeit as ti
 
 def imageProcess(image):
     #Objectives : detect faces, identify emotion associated on it, modify the image by framing faces and writing their emotions associated
@@ -35,7 +36,6 @@ def imageProcess(image):
         cv2.putText(image, emotion, (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0), 2)
 
 
-
 def selectFace(image):
     #Return a face identified on an colored image
 
diff --git a/utils.py b/utils.py
index 18799bf3421789fdf58c2d5368c5443e322eba7a..d15c29486bab40e3238bc7318de61a3660a7ca66 100644
--- a/utils.py
+++ b/utils.py
@@ -1,29 +1,35 @@
 import numpy as np
 import cv2
+import matplotlib.pyplot as plt
 
 def afficher(image):
-  if len(image.shape) == 3:
-    if image.shape[2] == 3: # (h,l,3)
-      plt.imshow(image)
-    elif image.shape[2] == 1: # (h,l,1)->(h,l)
-      image2 = image
-      plt.imshow(tf.squeeze(image))
-  elif len(image.shape)== 2:  # (h,l)
-    plt.imshow(image)
+    if len(image.shape) == 3:
+        if image.shape[2] == 3:  # (h,l,3)
+            plt.imshow(image)
+        elif image.shape[2] == 1:  # (h,l,1)->(h,l)
+            image2 = image
+            plt.imshow(tf.squeeze(image))
+    elif len(image.shape) == 2:  # (h,l)
+        plt.imshow(image)
+
 
 def predir(modele, image):
-  #Return output of image from modele
-  modele.predict(np.array([image]))[0,:]
+    # Return output of image from modele
+    return modele.predict(np.array([image]))[0, :]
+
 
 def normAndResize(image, input_shape):
-  #For an array image of shape (a,b,c) or (a,b), transform it into (h,l,p). Also normalize it.
+    # For an array image of shape (a,b,c) or (a,b), transform it into (h,l,p). Also normalize it.
+
+    h, l, p = input_shape
+    # resize for h and l                                       #
+    image = cv2.resize(image, dsize=(h, l), interpolation=cv2.INTER_CUBIC)
+    # if we want (h,l,3) -> (h,l,1) , we first transform it in to (h,l) (grey the image)
+    if len(image.shape) == 3 and p == 1 and image.shape[2] != 1:
+        image = image.mean(2)
+    image = np.reshape(image, (h, l, p))  # restore third dimension
+    image = image.astype("float32")
+    image = image/255  # normalisation
 
-  h,l,p = input_shape
-  image = cv2.resize(image, dsize=(h,l), interpolation=cv2.INTER_CUBIC) #resize for h and l                                       #
-  if len(image.shape) == 3 and p==1 and image.shape[2] != 1 : #if we want (h,l,3) -> (h,l,1) , we first transform it in to (h,l) (grey the image)
-    image = image.mean(2)
-  image = np.reshape(image, (h,l,p))                                    #restore third dimension
-  image = image.astype("float32")
-  image = image/255                                                     #normalisation
+    return image
 
-  return image
\ No newline at end of file
diff --git a/videoCapture.py b/videoCapture.py
index 1f65de96ca5b87d2444a6e4c4dbe564f12effeaf..f2b8ce9a9dd19ee55efc62e6f13f2822ec01036d 100644
--- a/videoCapture.py
+++ b/videoCapture.py
@@ -4,16 +4,16 @@ import imageProcess as ip
 
 cap = cv2.VideoCapture(0)   #0 means we capture the first camera, your webcam probably
 
-while cap.isOpened():
+while cap.isOpened():		 #or while 1. cap.isOpened() is false if there is a problem
     ret, frame = cap.read()  #Read next video frame, stop if frame not well read
-    if not ret: break       
-    
+    if not ret: break
+
     ip.imageProcess(frame)                          #Process frame
-    
+
     cv2.imshow("Image traitée", frame)  			#Show processed image in a window
 
     if cv2.waitKey(1) & 0xFF == ord('q'):			#If you press Q, stop the while and so the capture
-        break          
+        break       
 
 cap.release()
 cv2.destroyAllWindows()
\ No newline at end of file