script.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partie 1 : conversion des audio en spectrogramme + labellisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "\n",
    "\n",
    "#for loading and visualizing audio files\n",
    "import librosa\n",
    "import librosa.display\n",
    "\n",
    "#image processing\n",
    "import cv2\n",
    "\n",
    "#to play audio\n",
    "import IPython.display as ipd\n",
    "\n",
    "#ML\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras import Sequential\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Input, MaxPooling2D\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#variables défaut\n",
    "savelocation = \"./Images/\"\n",
    "main_folder =\"20200413\"\n",
    "file_meta = \"metadata.json\"\n",
    "complete = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ouvre les metadonnées pour récupérer le statut du patient lorsqu'on a le chemin d'accès.\n",
    "def open_metadata(audio_fpath,file_meta):\n",
    "    f = open(audio_fpath + file_meta,)\n",
    "\n",
    "    # returns JSON object as a dictionary\n",
    "    meta = json.load(f)\n",
    "    patient_status =  meta[\"covid_status\"]\n",
    "    print(patient_status)\n",
    "    return patient_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Doss = os.listdir(\"./\" + main_folder)\n",
    "#print(Doss)\n",
    "for subfolder in Doss:\n",
    "    #print(\"./\" + main_folder + \"/\" + subfolder)\n",
    "    #print(os.listdir(\"./\" + main_folder + \"/\" + subfolder))\n",
    "    files = os.listdir(\"./\" + main_folder + \"/\" + subfolder)\n",
    "    audio_fpath = \"./\" + main_folder + \"/\" + subfolder + \"/\"\n",
    "    name = main_folder + \"/\" + subfolder\n",
    "    #print(audio_fpath)\n",
    "    #print(type(audio_fpath))\n",
    "    for file in os.listdir(audio_fpath):\n",
    "        audio_clips = []\n",
    "        if file != file_meta:\n",
    "            SR = librosa.get_samplerate(audio_fpath+\"/\"+file)  #récupère la valeur de sample rate pour optimiser le load\n",
    "            x, sr = librosa.load(audio_fpath+\"/\"+file, sr=SR) #ouvre et récupère l'audio dans deux variables\n",
    "    \n",
    "            print(type(x), type(sr))\n",
    "            print(x.shape, sr)\n",
    "\n",
    "            #plt.figure(figsize=(14, 5))\n",
    "            #librosa.display.waveplot(x, sr=sr)\n",
    "\n",
    "            X = librosa.stft(x)\n",
    "            Xdb = librosa.amplitude_to_db(abs(X))\n",
    "            #plt.figure(figsize=(14, 5))\n",
    "            #librosa.display.specshow(Xdb, sr=sr, x_axis='time', y_axis='hz')\n",
    "            #plt.colorbar()\n",
    "\n",
    "            plt.figure(figsize=(14, 5))\n",
    "            librosa.display.specshow(Xdb, sr=sr, x_axis='time', y_axis='log')\n",
    "            plt.colorbar()\n",
    "            #pour le nom du fichier png\n",
    "            save_namefile = main_folder + subfolder + file + \".png\"\n",
    "            plt.savefig(savelocation + save_namefile) # on enregistre l'image dans le bon dossier avec le bon nom\n",
    "            print(save_namefile)\n",
    "        \n",
    "            audio_clips.append(file)\n",
    "            getmeta = open_metadata(audio_fpath, file_meta)\n",
    "            complete[save_namefile] = getmeta\n",
    "    #print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(complete)\n",
    "def convert_csv(dictio):\n",
    "    a_file = open(\"complete.csv\", \"w\")\n",
    "    writer = csv.writer(a_file)\n",
    "    writer.writerow([\"name\", \"label\"])\n",
    "    for i, v in dictio.items():\n",
    "        writer.writerow([i, v])\n",
    "    a_file.close()\n",
    "        \n",
    "#convert_csv(complete)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Commencer à éxécuter le code ci dessous si vous avez déjà les audios convertis en images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def open_csv():\n",
    "    df = pd.read_csv(\"complete.csv\")\n",
    "    length = df[\"name\"].size\n",
    "    i = 0\n",
    "    new_dict = {}\n",
    "    while i < length : \n",
    "        temp = df[\"name\"]\n",
    "        temp= temp[i]\n",
    "        temp2 = df[\"label\"]\n",
    "        temp2 =temp2[i]\n",
    "        new_dict[temp]=temp2\n",
    "        i += 1\n",
    "    return new_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       " '20200413tiKv850hJFTmpCAvfJOmOOqkeUs1breathing-shallow.wav.png': 'healthy',\n",
       " '20200413tiKv850hJFTmpCAvfJOmOOqkeUs1cough-heavy.wav.png': 'healthy',\n",
       " '20200413tiKv850hJFTmpCAvfJOmOOqkeUs1cough-shallow.wav.png': 'healthy',\n",
       " '20200413tiKv850hJFTmpCAvfJOmOOqkeUs1counting-fast.wav.png': 'healthy',\n",
       " '20200413tiKv850hJFTmpCAvfJOmOOqkeUs1counting-normal.wav.png': 'healthy',\n",
       " '20200413tiKv850hJFTmpCAvfJOmOOqkeUs1vowel-a.wav.png': 'healthy',\n",
       " '20200413tiKv850hJFTmpCAvfJOmOOqkeUs1vowel-e.wav.png': 'healthy',\n",
       " '20200413tiKv850hJFTmpCAvfJOmOOqkeUs1vowel-o.wav.png': 'healthy',\n",
       " '20200413tOlOwYDEHCRx1QegeMOPaydVcgv1breathing-deep.wav.png': 'healthy',\n",
       " '20200413tOlOwYDEHCRx1QegeMOPaydVcgv1breathing-shallow.wav.png': 'healthy',\n",
       " '20200413tOlOwYDEHCRx1QegeMOPaydVcgv1cough-heavy.wav.png': 'healthy',\n",
       " '20200413tOlOwYDEHCRx1QegeMOPaydVcgv1cough-shallow.wav.png': 'healthy',\n",
       " '20200413tOlOwYDEHCRx1QegeMOPaydVcgv1counting-fast.wav.png': 'healthy',\n",
       " '20200413tOlOwYDEHCRx1QegeMOPaydVcgv1counting-normal.wav.png': 'healthy',\n",
       " '20200413tOlOwYDEHCRx1QegeMOPaydVcgv1vowel-a.wav.png': 'healthy',\n",
       " '20200413tOlOwYDEHCRx1QegeMOPaydVcgv1vowel-e.wav.png': 'healthy',\n",
       " '20200413tOlOwYDEHCRx1QegeMOPaydVcgv1vowel-o.wav.png': 'healthy'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = open_csv()\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "access = \"./Images/\"\n",
    "\n",
    "def open_images(dico):\n",
    "    liste_images = []\n",
    "    liste_labels = []\n",
    "    a_file = open(\"data_loaded.csv\", \"w\")\n",
    "    writer = csv.writer(a_file)\n",
    "    writer.writerow([\"Image\", \"label\"])\n",
    "    for file, status in dico.items():\n",
    "        #print(file)\n",
    "        image = cv2.imread(access + file, cv2.IMREAD_GRAYSCALE)\n",
    "        print(image)\n",
    "        #print(image.shape)\n",
    "        #writer.writerow([[image], status])\n",
    "        liste_images.append(image)\n",
    "        liste_labels.append(status)\n",
    "    liste_images = np.array(liste_images)    \n",
    "    liste_labels = np.array(liste_labels)    \n",
    "\n",
    "    return liste_images, liste_labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " [255 255 255 ... 255 255 255]\n",
      " ...\n",
      " [255 255 255 ... 255 255 255]\n",
      " [255 255 255 ... 255 255 255]\n",
      " [255 255 255 ... 255 255 255]]\n"
     ]
    }
   ],
   "source": [
    "data = open_images(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = data\n",
    "\n",
    "#x, y = train_test_split(data, test_size = 0.2)\n",
    "x = np.array(x)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train = \n",
      "(432, 360, 1008)\n",
      "test = \n",
      "(432,)\n"
     ]
    }
   ],
   "source": [
    "print('train = ')\n",
    "print(x.shape)\n",
    "print('test = ')\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2\n",
      " 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 2 2 2 2 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "x = x.reshape(432, 360, 1008, 1)\n",
    "y = y.reshape(432)\n",
    "label_encoder = LabelEncoder()\n",
    "y = label_encoder.fit_transform(y)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test = train_test_split(x, test_size = 0.2, shuffle=False)\n",
    "y_train, y_test = train_test_split(y, test_size = 0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87, 360, 1008, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(345, 362880, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test2 = x_test.reshape(87, 360,1008, 1)\n",
    "x_test2 = x_test2/255\n",
    "x_test2.shape\n",
    "\n",
    "x_train2 = x_train.reshape(345, 360,1008, 1)\n",
    "x_train2 = x_train2/255\n",
    "x_train2.shape\n",
    "\n",
    "\n",
    "x_test = x_test.reshape(87, 360*1008, 1)\n",
    "x_test = x_test/255\n",
    "x_test.shape\n",
    "\n",
    "x_train = x_train.reshape(345, 360*1008, 1)\n",
    "x_train = x_train/255\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(345, 362880, 1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOF while scanning triple-quoted string literal (<ipython-input-16-e0ca5cb4a5c1>, line 20)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-16-e0ca5cb4a5c1>\"\u001b[1;36m, line \u001b[1;32m20\u001b[0m\n\u001b[1;33m    \"\"\"\"\"\"\u001b[0m\n\u001b[1;37m          \n^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOF while scanning triple-quoted string literal\n"
     ]
    }
   ],
   "source": [
    "#Model test\n",
    "\"\"\"\n",
    "model = Sequential()\n",
    "\n",
    "#partie convolutionnelle = feature engineering automatique\n",
    "model.add(Conv2D(32, kernel_size=(4, 4), activation=\"relu\", input_shape=(360, 1008,1,)))\n",
    "model.add(Conv2D(64, kernel_size=(4, 4), activation=\"relu\"))\n",
    "\n",
    "#applatir la sortie de la couche convolutionnelle pour que les Dense puis les manger\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "#partie de classification \n",
    "model.add(Dense(300, activation=\"relu\"))\n",
    "model.add(Dense(3, activation=\"softmax\"))\n",
    "\n",
    "\n",
    "model.compile(\"sgd\", \"sparse_categorical_entropy\")\n",
    "model.summary()\n",
    "\"\"\"\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input(shape=(360* 1008)) #objet qui représente mes entrées\n",
    "output_layer_1 = Dense(1000, activation=\"relu\")(inputs)\n",
    "output_layer_2 = Dense(700, activation=\"relu\")(output_layer_1)\n",
    "output_layer_3 = Dense(300, activation=\"relu\")(output_layer_2)\n",
    "output_layer_3 = Dense(100, activation=\"relu\")(output_layer_2)\n",
    "output_layer_4 = Dense(50, activation=\"relu\")(output_layer_3)\n",
    "\n",
    "output = Dense(3, activation=\"softmax\")(output_layer_4)\n",
    "\n",
    "\n",
    " \n",
    "\n",
    "model = Model(inputs=inputs, outputs= output)\n",
    "model.compile(loss='mse', optimizer='sgd', metrics=['accuracy'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "58/58 [==============================] - 40s 684ms/step - loss: 0.3567 - accuracy: 0.4338\n",
      "Epoch 2/4\n",
      "58/58 [==============================] - 39s 681ms/step - loss: 0.3165 - accuracy: 0.3121\n",
      "Epoch 3/4\n",
      "58/58 [==============================] - 43s 748ms/step - loss: 0.2711 - accuracy: 0.3143\n",
      "Epoch 4/4\n",
      "58/58 [==============================] - 39s 676ms/step - loss: 0.2778 - accuracy: 0.2481\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2a5a46ec5b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs = 4, batch_size = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 2s 125ms/step - loss: 0.2764 - accuracy: 0.0522\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.2764405310153961, 0.052173912525177]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 119ms/step - loss: 0.1112 - accuracy: 0.0000e+00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.11121898889541626, 0.0]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creation du modele\n",
    "model2 = Sequential()\n",
    "model2.add(Conv2D(128, kernel_size=(3, 3), activation=\"relu\", input_shape=(360, 1008, 1 )))\n",
    "model2.add(Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\n",
    "\n",
    "model2.add(MaxPooling2D(pool_size =(3,3)))\n",
    "model2.add(Flatten())\n",
    "model2.add(Dense(1, activation=\"softmax\")) #soucis technique, code crash si couche softmax > 1\n",
    "\n",
    "#compilation du modèle\n",
    "model2.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-98f987e2bea5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\antoi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1098\u001b[0m                 _r=1):\n\u001b[0;32m   1099\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1100\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1101\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1102\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\antoi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    826\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    827\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 828\u001b[1;33m       \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"xla\"\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m\"nonXla\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\antoi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    886\u001b[0m         \u001b[1;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    887\u001b[0m         \u001b[1;31m# stateless function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 888\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    889\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    890\u001b[0m       \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfiltered_flat_args\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\antoi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2940\u001b[0m       (graph_function,\n\u001b[0;32m   2941\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2942\u001b[1;33m     return graph_function._call_flat(\n\u001b[0m\u001b[0;32m   2943\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0;32m   2944\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\antoi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1916\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1917\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1918\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1919\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1920\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[1;32mc:\\users\\antoi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    553\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    554\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 555\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    556\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\antoi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     57\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model2.fit(x_train2, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.evaluate(x_train2, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfert learning\n",
    "Pour pouvoir utiliser le modèle MobileNet, il faut une image de taille a*b*3. Nous allons donc ci dessous adapter le code précédent pour recharger des images à trois canaux (donc en couleur)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def open_images2(dico):\n",
    "    liste_images = []\n",
    "    liste_labels = []\n",
    "    a_file = open(\"data_loaded.csv\", \"w\")\n",
    "    writer = csv.writer(a_file)\n",
    "    writer.writerow([\"Image\", \"label\"])\n",
    "    for file, status in dico.items():\n",
    "        #print(file)\n",
    "        image = cv2.imread(access + file, 3)\n",
    "        print(image)\n",
    "        #print(image.shape)\n",
    "        #writer.writerow([[image], status])\n",
    "        liste_images.append(image)\n",
    "        liste_labels.append(status)\n",
    "    liste_images = np.array(liste_images)    \n",
    "    liste_labels = np.array(liste_labels)    \n",
    "\n",
    "    return liste_images, liste_labels\n",
    "\n",
    "betatest = open_images2(test)\n",
    "X2, y2 = betatest\n",
    "X2 = np.array(X2)\n",
    "y2 = np.array(y2)\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "X2 = X2.reshape(432, 360, 1008, 3)\n",
    "y2 = y2.reshape(432)\n",
    "label_encoder = LabelEncoder()\n",
    "y2 = label_encoder.fit_transform(y2)\n",
    "print(y2)\n",
    "\n",
    "x_2train, x2_test = train_test_split(X2, test_size = 0.2, shuffle=False)\n",
    "y_2train, y_2test = train_test_split(y2, test_size = 0.2, shuffle=False)\n",
    "\n",
    "x_2test = x_2test.reshape(87, 360,1008, 1)\n",
    "x_2test = x_2test/255\n",
    "x_2test.shape\n",
    "\n",
    "x_2train = x_2train.reshape(345, 360,1008, 1)\n",
    "x_2train = x_2train/255\n",
    "x_2train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import MobileNetV2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrained_model = MobileNetV2(\n",
    "    weights=\"imagenet\",\n",
    "    input_shape=(360, 1008, 3),\n",
    "    include_top=False\n",
    ")\n",
    "\n",
    "pretrained_model.trainable = False #les theta de mobilenet ne doivent pas varier pendant l'entrainement\n",
    "#uniquement les theta de la couche dense\n",
    "inputs = Input(shape=(360, 1008, 3))\n",
    "\n",
    "output = pretrained_model(inputs)\n",
    "\n",
    "\n",
    "\n",
    "output = Flatten()(output)\n",
    "\n",
    "output = Dense(200, activation=\"relu\")(output)\n",
    "output = Dense(50, activation=\"relu\")(output)\n",
    "output_final = Dense(10, activation=\"softmax\")(output)\n",
    "\n",
    "\n",
    "model = Model(inputs=inputs, outputs= output_final)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='mse', optimizer='sgd', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(x_2train, y_2train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.evaluate(x_2train, y_2train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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