PJT_partie_Joris.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLn9E0E6-mjJ",
        "colab_type": "text"
      },
      "source": [
        "#Configuration de l'environnement de travail"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhDcqt5vcbYT",
        "colab_type": "code",
        "outputId": "ac65dca5-9e2c-4c68-ab9f-7b595e030790",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        }
      },
      "source": [
        "#!rm -rf /content/melspectrogram/ #supprime le dossier melspectrogram si problèmes\n",
        "!pip install librosa # installation de la librairie librosa\n",
        "!git clone https://github.com/Jakobovski/free-spoken-digit-dataset #téléchargement du jeu de données\n",
        "!mkdir /content/melspectrogram #création du dossier qui contiendra les mel-spectrogrammes\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: librosa in /usr/local/lib/python3.6/dist-packages (0.6.3)\n",
            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.14.1)\n",
            "Requirement already satisfied: numba>=0.38.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.47.0)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (4.4.1)\n",
            "Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.12.0)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.22.1)\n",
            "Requirement already satisfied: numpy>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.17.5)\n",
            "Requirement already satisfied: resampy>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.2.2)\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (2.1.8)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (42.0.2)\n",
            "Requirement already satisfied: llvmlite>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (0.31.0)\n",
            "Cloning into 'free-spoken-digit-dataset'...\n",
            "remote: Enumerating objects: 9, done.\u001b[K\n",
            "remote: Counting objects: 100% (9/9), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 3166 (delta 3), reused 5 (delta 0), pack-reused 3157\n",
            "Receiving objects: 100% (3166/3166), 23.92 MiB | 27.36 MiB/s, done.\n",
            "Resolving deltas: 100% (72/72), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PvlWzHT9V5k",
        "colab_type": "text"
      },
      "source": [
        "###Chargement des chemins de fichiers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SRgc4IHu-8TZ",
        "colab_type": "text"
      },
      "source": [
        "Pour éviter d'avoir à recalculer constamment les mel-spectrogrammes, on les calcule une fois et on les sauvegarde dans le dossier melspectrogram. On commence par charger les chemins de fichiers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vNFgcDVeiWO",
        "colab_type": "code",
        "outputId": "4835fc24-f9b2-443c-ae5a-da8f7dca5424",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "import glob # librairie spécialisée dans la recherche de chemin\n",
        "list_of_files = glob.glob(\"free-spoken-digit-dataset/recordings/*\") #permet d'obtenir le chemin relatif de tous les fichiers du dossier recordings\n",
        "print(list_of_files[:2])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['free-spoken-digit-dataset/recordings/6_yweweler_21.wav', 'free-spoken-digit-dataset/recordings/6_yweweler_13.wav']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGZ4AmO9eiae",
        "colab_type": "code",
        "outputId": "59f958a4-2b92-49bd-e4f6-bb8a1169be11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "n_files = len(list_of_files) # nombres de fichiers audios\n",
        "print(n_files)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KtS6Ar-69Dgl",
        "colab_type": "text"
      },
      "source": [
        "###Exemple librairie librosa"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e2FY5StZLIk8",
        "colab_type": "text"
      },
      "source": [
        "On peut charger les fichiers audio au format .wav"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xRAsGODaeidd",
        "colab_type": "code",
        "outputId": "3f10214a-2063-4c81-c8c9-dfab74a53512",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import librosa\n",
        "y, sr = librosa.load(list_of_files[3], sr= 8000) #y est le signal sous forme d'array numpy, sr est le taux d'échantillonage (vaut 8khz  d'après les indications github)\n",
        "print(y, sr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-0.015625  -0.0078125 -0.015625  ... -0.015625  -0.0078125 -0.0078125] 8000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JgRBQn9eePaB",
        "colab_type": "text"
      },
      "source": [
        "On peut calculer certaines caractéristiques des signaux audios, ici le melspectrogramme"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pp1HYj4ycecM",
        "colab_type": "code",
        "outputId": "2e337995-d3bf-467f-dd8a-69a8c3b334e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "sp = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128) # calcul du melspectrogramme\n",
        "s_dB = librosa.power_to_db(sp, ref=np.max) #conversion de l'amplitude en dB pour une meilleure vi\n",
        "\n",
        "print(type(s_dB))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZVYPnQYhx6k6",
        "colab_type": "text"
      },
      "source": [
        "Vous pouvez écouter un signal audio via la commande suivante"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWHra8VYsmtZ",
        "colab_type": "code",
        "outputId": "ec28ce51-b157-4c61-d554-9a6f907fde36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "from IPython.display import Audio\n",
        "Audio(y,rate=sr) # permet de lire un fichier audio sous forme d'array numpy dans un notebook jupyter ou dans colab"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQkh6EDM_fyE",
        "colab_type": "text"
      },
      "source": [
        "###Exemple d'affichage de mel-spectrogramme"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5qBo1tEc_tu",
        "colab_type": "code",
        "outputId": "9d245958-0e0c-44f6-9b34-f8ac0ae7b0ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import librosa.display\n",
        "import numpy as np\n",
        "\n",
        "def plot_spec(S_dB):\n",
        "  plt.figure(figsize=(10, 4))\n",
        "  librosa.display.specshow(S_dB, x_axis='time',\n",
        "                         y_axis='mel', sr=sr)\n",
        "  plt.colorbar(format='%+2.0f dB')\n",
        "  plt.title('Mel-frequency spectrogram')\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "plot_spec(s_dB)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DoUMoxGIWhg6hEKmMup5I5HelbXRwLWUjWtZcbmY/By4Gfkk2fVEJOE/SeyWtJetveYuk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\nWk1jAOL28WjTgrY0iymMOpJ/m5xzzjn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            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbmTEuFbATvR",
        "colab_type": "text"
      },
      "source": [
        "###Prétraitement et enregistrement des fichiers audios en mel-spectrogramme\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3XENNvEX9LS",
        "colab_type": "text"
      },
      "source": [
        "Définition des fonctions utilisées pour le prétraitement"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlvUAVCBe4ge",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_mels=128\n",
        "sr = 8000\n",
        "\n",
        "\n",
        "def compute_spectrogram(path):\n",
        "    \"Calcule le melspectrogramme du fichier audio situé à path dans le repertoire de fichiers\"\n",
        "    file_name = os.path.basename(path) \n",
        "    s , _ = librosa.load(path, sr= sr) # sr est le taux d'échantillonnage du signal.\n",
        "    \n",
        "    melspec = librosa.feature.melspectrogram(y=s, sr=sr, n_mels=n_mels)\n",
        "    melspec_db = librosa.power_to_db(melspec, ref=np.max)\n",
        "    melspec_db_image = melspec_db.reshape(melspec_db.shape[0],melspec_db.shape[1],1) # on rajoute la dimension 1 à la fin pour forcer la représentation en tant qu'image (hauteur, largeur, nombre de couleurs ici égale à 1)\n",
        "    return (path,melspec_db_image)\n",
        "\n",
        "def get_duration(spec):\n",
        "  \"Renvoie la dimension correspondant à l'axe temporel d'un melspectrogramme\"\n",
        "  return spec.shape[1]\n",
        "\n",
        "def get_longest_duration(list_path_spec):\n",
        "  \"Renvoie la dimension temporelle du melspectrogramme le plus long\"\n",
        "  list_time = [get_duration(spec) for path, spec in list_path_spec ]\n",
        "  return max(list_time)\n",
        "\n",
        "def save_melspec(path_melspec): \n",
        "    \"\"\"\n",
        "    Permet d'enregistrer les spectrogrammes dans le dossier melspectrogram\n",
        "    \"\"\"\n",
        "    path, melspec = path_melspec\n",
        "    file_name = os.path.basename(path)\n",
        "    melspec_path = os.path.join(\"/content/melspectrogram\", os.path.splitext(file_name)[0])\n",
        "    np.save(melspec_path, melspec)\n",
        "    #print(\"{} saved\".format(melspec_path))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqhQ-AyGYNkR",
        "colab_type": "text"
      },
      "source": [
        "Le prétraitement et l'enregistrement des melspectrogrammes prend quelques minutes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9N5okW2_vbgc",
        "colab_type": "code",
        "outputId": "bef90bca-c43c-40de-caf9-65540628d302",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349,
          "referenced_widgets": [
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            "8ddad765c2e2477a915c6377656268cf",
            "5269b55dea3a4deb906c2d2bdf51f2f7",
            "22e5c397c3ff45e98addcd03a561f381",
            "a9999fb0fcdd45a8820d3128cdd7050b",
            "45ba752f8b2d43769c6150cb1140a448"
          ]
        }
      },
      "source": [
        "import os\n",
        "from tqdm.autonotebook import tqdm # bar de remplissage pour visualiser l'évolution du calcul et de l'enregistrement des spectrogrammes\n",
        "import sys\n",
        "import tensorflow as tf\n",
        "n=n_files\n",
        "\n",
        "\n",
        "\n",
        "print(\"Conversion des fichiers audio en melspectrogrammes\")\n",
        "list_spec = []\n",
        "for path, spec in tqdm(map(compute_spectrogram, list_of_files[:n]), total=n):\n",
        "   list_spec.append((path, spec))\n",
        "   \n",
        "\n",
        "max_time = get_longest_duration(list_spec)\n",
        "print(\"La plus longue dimension temporelle est {}\".format(max_time))\n",
        "def spec_padding(path_data):\n",
        "  path, spec = path_data\n",
        "  min_spec = np.min(spec)\n",
        "  spec_padded = np.pad(spec, ((0,0),(0,max_time-spec.shape[1]),(0,0)),\"constant\",constant_values=((min_spec,min_spec),(min_spec,min_spec),(min_spec,min_spec)))\n",
        "  return (path, spec_padded)\n",
        "\n",
        "\n",
        "\n",
        "print(\"Traitement des melspectrogrammes pour qu'ils aient la même durée\")\n",
        "list_padded_spec = []\n",
        "for path, spec_padded in tqdm(map(spec_padding, list_spec[:n]), total=n):\n",
        "  list_padded_spec.append((path,spec_padded))\n",
        "  \n",
        "\n",
        "\n",
        "print(\"Traitement des melspectrogrammes pour qu'ils aient la même durée\")\n",
        "for spec in tqdm(map(save_melspec, list_padded_spec[:n]), total=n):\n",
        "  pass\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Conversion des fichiers audio en melspectrogrammes\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "16a4a29377bc4fbda0d758d94224f028",
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              "HBox(children=(IntProgress(value=0, max=2000), HTML(value='')))"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "La plus longue dimension temporelle est 36\n",
            "Traitement des melspectrogrammes pour qu'ils aient la même durée\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "Traitement des melspectrogrammes pour qu'ils aient la même durée\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(IntProgress(value=0, max=2000), HTML(value='')))"
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U1pp6ZKLBh_j",
        "colab_type": "text"
      },
      "source": [
        "# Votre travail"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83VkdgZRqaEf",
        "colab_type": "text"
      },
      "source": [
        "## Importation des fichiers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyFnpEWXKVyO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import random\n",
        "noms_fichiers = os.listdir('melspectrogram/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMCauAXhgXeX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def importer(nom):\n",
        "  data = np.load('melspectrogram/'+nom)\n",
        "  infos = nom[0:-4]\n",
        "  infos = infos.split(\"_\")\n",
        "  infos[0] = int(infos[0]) # Conversion des strings en nombres\n",
        "  infos[2] = int(infos[2])\n",
        "  if infos[1] == 'jackson':\n",
        "    infos[1] = [1,0,0,0]\n",
        "  elif infos[1] == 'nicolas':\n",
        "    infos[1] = [0,1,0,0]\n",
        "  elif infos[1] == 'theo':\n",
        "    infos[1] = [0,0,1,0]\n",
        "  elif infos[1] == 'yweweler':\n",
        "    infos[1] = [0,0,0,1]\n",
        "  return data, infos"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9meUFUmIunhP",
        "colab_type": "text"
      },
      "source": [
        "## Création du modèle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2FsRWqMmxleH"
      },
      "source": [
        "### Fonctions de calcul appelées dans la fonction d'optimisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHx54NmerzBt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fonction calculant la moyenne de la dérivée de la variation de précision (vitesse d'évolution moyenne)\n",
        "\n",
        "def atteinte_90(history):\n",
        "  if history.history['acc'][-1] < 0.9:\n",
        "    return\n",
        "  i = len(history.history['acc'])-1\n",
        "  while history.history['acc'][i] > 0.9:\n",
        "    i -= 1\n",
        "  return i\n",
        "\n",
        "# Fonction qui affiche le nombre d'éléments dans chaque classe (on veut 1/4 des données)\n",
        "# Inutilisée par la suite (mais peut être utile)\n",
        "\n",
        "def verif_repartition(y_train):\n",
        "    un = 0\n",
        "    deux = 0\n",
        "    trois = 0\n",
        "    quatre = 0\n",
        "    for i in range(len(y_train)):\n",
        "      if y_train[i][0] == 1:\n",
        "        un += 1\n",
        "      elif y_train[i][1] == 1:\n",
        "        deux += 1\n",
        "      elif y_train[i][2] == 1:\n",
        "        trois += 1\n",
        "      elif y_train[i][3] == 1:\n",
        "        quatre += 1\n",
        "    print('REPARTITION DES CLASSES')\n",
        "    print ('1 :'+str(un)+'\\n2 :'+str(deux)+'\\n3 :'+str(trois)+'\\n4 :'+str(quatre))\n",
        "\n",
        "# Création des données d'entrainement\n",
        "def creer_X_Y(noms_fichiers):\n",
        "  X,Y = [],[]\n",
        "  for i in range(len(noms_fichiers)):\n",
        "    data,infos = importer(noms_fichiers[i])\n",
        "    X.append(data)\n",
        "    Y.append(infos[1])\n",
        "  return np.asarray(X),np.asarray(Y)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RAY_kiqEHbkB",
        "colab_type": "text"
      },
      "source": [
        "### Fonction qui crée un modèle avec des paramètres"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4YOc9Z0ALJPs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout\n",
        "from keras.optimizers import SGD\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "def nouveau_modele(nb_conv2D,epoques,BS,pourcentage_test):\n",
        "  \n",
        "  X,Y = creer_X_Y(noms_fichiers)\n",
        "  x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=pourcentage_test)\n",
        "\n",
        "  # create model\n",
        "  model = Sequential()\n",
        "  model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(128, 36, 1)))\n",
        "  model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "  model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "  model.add(Dropout(0.25))\n",
        "\n",
        "  # Convolutions\n",
        "  for i in range(nb_conv2D):\n",
        "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "  model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "  model.add(Dropout(0.25))\n",
        "\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(256, activation='relu'))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(Dense(256, activation='relu'))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(Dense(4, activation='softmax'))\n",
        "\n",
        "  model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.SGD(lr=0.01), metrics=['accuracy'])\n",
        "\n",
        "  history = model.fit(np.asarray(x_train), np.asarray(y_train), epochs=epoques, batch_size=BS, verbose = 1)\n",
        "\n",
        "  evaluate = model.evaluate(np.asarray(x_test),np.asarray(y_test))\n",
        "\n",
        "  prediction = model.predict(x_test)\n",
        "\n",
        "  dernier_atteinte_90 = atteinte_90(history)\n",
        "\n",
        "  # Dans le return il y a un _ à la place de xtest, ytest et prediction, version précédente qui était trop gourmande\n",
        "  # je n'ai pas eu le temps de changer la suite donc cette solution de fortune est moins gourmande mais inutile (histoire d'index appelées dans la suite)\n",
        "  return _, _, model, history.history, evaluate, _, dernier_atteinte_90"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KetgTeN1QT2f",
        "colab_type": "text"
      },
      "source": [
        "## Test itératif des critères"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CeQC6SpQTL52",
        "outputId": "19e04745-1b4a-49ff-d88f-e9a3eb82c697",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "from google.colab import files\n",
        "\n",
        "def opti_gourmand(iteration):\n",
        "  max_conv2D = 4 # doit rester inférieur à 8 (experience)\n",
        "  max_epoques = 300\n",
        "  max_BS = 0.9\n",
        "  max_pourcentage_test = 0.8 # Valeurs sur le site de M.Gibaru : [0.6,0.8]\n",
        "\n",
        "  resultats = []\n",
        "  i = -1\n",
        "  compteur = 0\n",
        "  for nb_conv2D in np.arange(2,max_conv2D,2):\n",
        "    i += 1\n",
        "    j = -1\n",
        "    resultats.append([])\n",
        "    for epoques in np.arange(50,max_epoques,(max_epoques-50)//3):\n",
        "      j += 1\n",
        "      k = -1\n",
        "      resultats[i].append([])\n",
        "      for BS in np.arange(0.04,max_BS,(max_BS-0.01)/4):\n",
        "        k += 1\n",
        "        resultats[i][j].append([])\n",
        "        for pourcentage_test in np.arange(0.6,max_pourcentage_test,(max_pourcentage_test-0.1)/3):\n",
        "          resultats[i][j][k].append(nouveau_modele(int(nb_conv2D),int(epoques),int(BS*pourcentage_test*2000),pourcentage_test))\n",
        "  return resultats\n",
        "\n",
        "resultats = opti_gourmand(3)\n",
        "\n",
        "def opti_simple():\n",
        "  ntest = 4\n",
        "  max_conv2D = 7\n",
        "  max_epoques = 200\n",
        "  max_BS = 0.9\n",
        "  max_pourcentage_test = 0.95\n",
        "  res_conv2D = []\n",
        "  res_epoques = []\n",
        "  res_BS =[]\n",
        "  res_pourcentage_test = []\n",
        "  for i in np.arange(1,max_epoques,200//6):\n",
        "    res_epoques.append(nouveau_modele(4,i,32,0.8))\n",
        "    print(str(i))\n",
        "  return res_epoques\n",
        "\n",
        "# resultats = opti_simple()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "800/800 [==============================] - 3s 4ms/step - loss: 2.8306 - acc: 0.2463\n",
            "Epoch 2/50\n",
            "800/800 [==============================] - 0s 366us/step - loss: 1.3702 - acc: 0.3150\n",
            "Epoch 3/50\n",
            "800/800 [==============================] - 0s 358us/step - loss: 1.3749 - acc: 0.2862\n",
            "Epoch 4/50\n",
            "800/800 [==============================] - 0s 352us/step - loss: 1.3319 - acc: 0.3375\n",
            "Epoch 5/50\n",
            "800/800 [==============================] - 0s 357us/step - loss: 1.2825 - acc: 0.3975\n",
            "Epoch 6/50\n",
            "800/800 [==============================] - 0s 371us/step - loss: 1.2482 - acc: 0.4037\n",
            "Epoch 7/50\n",
            "800/800 [==============================] - 0s 364us/step - loss: 1.1645 - acc: 0.4475\n",
            "Epoch 8/50\n",
            "800/800 [==============================] - 0s 361us/step - loss: 1.1458 - acc: 0.4700\n",
            "Epoch 9/50\n",
            "800/800 [==============================] - 0s 350us/step - loss: 1.1012 - acc: 0.5025\n",
            "Epoch 10/50\n",
            "800/800 [==============================] - 0s 363us/step - loss: 1.0818 - acc: 0.5437\n",
            "Epoch 11/50\n",
            "800/800 [==============================] - 0s 345us/step - loss: 1.0403 - acc: 0.5450\n",
            "Epoch 12/50\n",
            "800/800 [==============================] - 0s 347us/step - loss: 1.0254 - acc: 0.5725\n",
            "Epoch 13/50\n",
            "800/800 [==============================] - 0s 355us/step - loss: 0.9736 - acc: 0.5700\n",
            "Epoch 14/50\n",
            "800/800 [==============================] - 0s 344us/step - loss: 0.9570 - acc: 0.6000\n",
            "Epoch 15/50\n",
            "800/800 [==============================] - 0s 357us/step - loss: 0.9318 - acc: 0.6088\n",
            "Epoch 16/50\n",
            "800/800 [==============================] - 0s 369us/step - loss: 0.8671 - acc: 0.6475\n",
            "Epoch 17/50\n",
            "800/800 [==============================] - 0s 375us/step - loss: 0.8637 - acc: 0.6575\n",
            "Epoch 18/50\n",
            "800/800 [==============================] - 0s 369us/step - loss: 0.8076 - acc: 0.6988\n",
            "Epoch 19/50\n",
            "800/800 [==============================] - 0s 360us/step - loss: 0.8027 - acc: 0.6938\n",
            "Epoch 20/50\n",
            "800/800 [==============================] - 0s 368us/step - loss: 0.6793 - acc: 0.7387\n",
            "Epoch 21/50\n",
            "800/800 [==============================] - 0s 362us/step - loss: 0.7118 - acc: 0.7275\n",
            "Epoch 22/50\n",
            "800/800 [==============================] - 0s 352us/step - loss: 0.6090 - acc: 0.7738\n",
            "Epoch 23/50\n",
            "800/800 [==============================] - 0s 354us/step - loss: 0.6295 - acc: 0.7725\n",
            "Epoch 24/50\n",
            "800/800 [==============================] - 0s 365us/step - loss: 0.5969 - acc: 0.7887\n",
            "Epoch 25/50\n",
            "800/800 [==============================] - 0s 348us/step - loss: 0.6906 - acc: 0.7462\n",
            "Epoch 26/50\n",
            "800/800 [==============================] - 0s 349us/step - loss: 0.5173 - acc: 0.8038\n",
            "Epoch 27/50\n",
            "800/800 [==============================] - 0s 370us/step - loss: 0.4419 - acc: 0.8325\n",
            "Epoch 28/50\n",
            "800/800 [==============================] - 0s 360us/step - loss: 0.4949 - acc: 0.8037\n",
            "Epoch 29/50\n",
            "800/800 [==============================] - 0s 359us/step - loss: 0.4309 - acc: 0.8462\n",
            "Epoch 30/50\n",
            "800/800 [==============================] - 0s 356us/step - loss: 0.4383 - acc: 0.8500\n",
            "Epoch 31/50\n",
            "800/800 [==============================] - 0s 359us/step - loss: 0.4027 - acc: 0.8575\n",
            "Epoch 32/50\n",
            "800/800 [==============================] - 0s 350us/step - loss: 0.3714 - acc: 0.8737\n",
            "Epoch 33/50\n",
            "800/800 [==============================] - 0s 350us/step - loss: 0.3469 - acc: 0.8800\n",
            "Epoch 34/50\n",
            "800/800 [==============================] - 0s 348us/step - loss: 0.3325 - acc: 0.8813\n",
            "Epoch 35/50\n",
            "800/800 [==============================] - 0s 362us/step - loss: 0.3681 - acc: 0.8688\n",
            "Epoch 36/50\n",
            "800/800 [==============================] - 0s 358us/step - loss: 0.3722 - acc: 0.8750\n",
            "Epoch 37/50\n",
            "800/800 [==============================] - 0s 356us/step - loss: 0.2665 - acc: 0.9162\n",
            "Epoch 38/50\n",
            "800/800 [==============================] - 0s 354us/step - loss: 0.2697 - acc: 0.9100\n",
            "Epoch 39/50\n",
            "800/800 [==============================] - 0s 351us/step - loss: 0.2455 - acc: 0.9075\n",
            "Epoch 40/50\n",
            "800/800 [==============================] - 0s 347us/step - loss: 0.3373 - acc: 0.8900\n",
            "Epoch 41/50\n",
            "800/800 [==============================] - 0s 354us/step - loss: 0.2105 - acc: 0.9250\n",
            "Epoch 42/50\n",
            "800/800 [==============================] - 0s 359us/step - loss: 0.2435 - acc: 0.9113\n",
            "Epoch 43/50\n",
            "800/800 [==============================] - 0s 354us/step - loss: 0.1954 - acc: 0.9363\n",
            "Epoch 44/50\n",
            "800/800 [==============================] - 0s 350us/step - loss: 0.2076 - acc: 0.9337\n",
            "Epoch 45/50\n",
            "800/800 [==============================] - 0s 361us/step - loss: 0.2233 - acc: 0.9250\n",
            "Epoch 46/50\n",
            "800/800 [==============================] - 0s 350us/step - loss: 0.2302 - acc: 0.9325\n",
            "Epoch 47/50\n",
            "800/800 [==============================] - 0s 351us/step - loss: 0.2036 - acc: 0.9400\n",
            "Epoch 48/50\n",
            "800/800 [==============================] - 0s 342us/step - loss: 0.1926 - acc: 0.9475\n",
            "Epoch 49/50\n",
            "800/800 [==============================] - 0s 360us/step - loss: 0.1908 - acc: 0.9375\n",
            "Epoch 50/50\n",
            "800/800 [==============================] - 0s 340us/step - loss: 0.2117 - acc: 0.9325\n",
            "1200/1200 [==============================] - 1s 843us/step\n",
            "Epoch 1/50\n",
            "800/800 [==============================] - 5s 6ms/step - loss: 5.7347 - acc: 0.2488\n",
            "Epoch 2/50\n",
            "800/800 [==============================] - 0s 269us/step - loss: 1.5627 - acc: 0.2438\n",
            "Epoch 3/50\n",
            "800/800 [==============================] - 0s 267us/step - loss: 1.3974 - acc: 0.2338\n",
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            "800/800 [==============================] - 0s 278us/step - loss: 0.9074 - acc: 0.6263\n",
            "1200/1200 [==============================] - 1s 909us/step\n",
            "Epoch 1/50\n",
            "800/800 [==============================] - 6s 8ms/step - loss: 6.1175 - acc: 0.2450\n",
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            "800/800 [==============================] - 0s 265us/step - loss: 1.3157 - acc: 0.3700\n",
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            "800/800 [==============================] - 0s 266us/step - loss: 1.1894 - acc: 0.4387\n",
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            "Epoch 30/50\n",
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            "800/800 [==============================] - 0s 264us/step - loss: 1.1133 - acc: 0.5237\n",
            "Epoch 37/50\n",
            "800/800 [==============================] - 0s 276us/step - loss: 1.1770 - acc: 0.4462\n",
            "Epoch 38/50\n",
            "800/800 [==============================] - 0s 274us/step - loss: 1.1070 - acc: 0.5188\n",
            "Epoch 39/50\n",
            "800/800 [==============================] - 0s 270us/step - loss: 1.0996 - acc: 0.5262\n",
            "Epoch 40/50\n",
            "800/800 [==============================] - 0s 269us/step - loss: 1.0922 - acc: 0.5263\n",
            "Epoch 41/50\n",
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            "800/800 [==============================] - 0s 265us/step - loss: 1.0945 - acc: 0.5300\n",
            "Epoch 50/50\n",
            "800/800 [==============================] - 0s 267us/step - loss: 1.0232 - acc: 0.5812\n",
            "1200/1200 [==============================] - 1s 1ms/step\n",
            "Epoch 1/50\n",
            "800/800 [==============================] - 6s 8ms/step - loss: 6.6226 - acc: 0.2550\n",
            "Epoch 2/50\n",
            "800/800 [==============================] - 0s 270us/step - loss: 5.0921 - acc: 0.2262\n",
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            "800/800 [==============================] - 0s 265us/step - loss: 1.3854 - acc: 0.2500\n",
            "Epoch 11/50\n",
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            "800/800 [==============================] - 0s 263us/step - loss: 1.3557 - acc: 0.2825\n",
            "1200/1200 [==============================] - 1s 1ms/step\n",
            "Epoch 1/133\n",
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            "1200/1200 [==============================] - 1s 1ms/step\n",
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            "800/800 [==============================] - 0s 342us/step - loss: 0.0327 - acc: 0.9888\n",
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            "Epoch 129/299\n",
            "800/800 [==============================] - 0s 377us/step - loss: 0.0521 - acc: 0.9775\n",
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            "Epoch 133/299\n",
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            "Epoch 162/299\n",
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            "800/800 [==============================] - 0s 351us/step - loss: 0.0114 - acc: 0.9950\n",
            "Epoch 274/299\n",
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            "Epoch 275/299\n",
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            "Epoch 279/299\n",
            "800/800 [==============================] - 0s 366us/step - loss: 0.0171 - acc: 0.9963\n",
            "Epoch 280/299\n",
            "800/800 [==============================] - 0s 359us/step - loss: 0.0069 - acc: 0.9988\n",
            "Epoch 281/299\n",
            "800/800 [==============================] - 0s 354us/step - loss: 0.0078 - acc: 0.9975\n",
            "Epoch 282/299\n",
            "800/800 [==============================] - 0s 358us/step - loss: 0.0038 - acc: 1.0000\n",
            "Epoch 283/299\n",
            "800/800 [==============================] - 0s 347us/step - loss: 0.0149 - acc: 0.9950\n",
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            "800/800 [==============================] - 0s 350us/step - loss: 0.0187 - acc: 0.9938\n",
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            "800/800 [==============================] - 0s 353us/step - loss: 0.0183 - acc: 0.9913\n",
            "Epoch 286/299\n",
            "800/800 [==============================] - 0s 365us/step - loss: 0.0077 - acc: 0.9975\n",
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            "800/800 [==============================] - 0s 344us/step - loss: 0.0150 - acc: 0.9950\n",
            "Epoch 288/299\n",
            "800/800 [==============================] - 0s 344us/step - loss: 0.0083 - acc: 0.9975\n",
            "Epoch 289/299\n",
            "800/800 [==============================] - 0s 360us/step - loss: 0.0069 - acc: 0.9988\n",
            "Epoch 290/299\n",
            "800/800 [==============================] - 0s 347us/step - loss: 0.0177 - acc: 0.9950\n",
            "Epoch 291/299\n",
            "800/800 [==============================] - 0s 344us/step - loss: 0.0126 - acc: 0.9963\n",
            "Epoch 292/299\n",
            "800/800 [==============================] - 0s 357us/step - loss: 0.0088 - acc: 0.9950\n",
            "Epoch 293/299\n",
            "800/800 [==============================] - 0s 350us/step - loss: 0.0117 - acc: 0.9963\n",
            "Epoch 294/299\n",
            "800/800 [==============================] - 0s 342us/step - loss: 0.0062 - acc: 0.9988\n",
            "Epoch 295/299\n",
            "800/800 [==============================] - 0s 346us/step - loss: 0.0280 - acc: 0.9925\n",
            "Epoch 296/299\n",
            "800/800 [==============================] - 0s 354us/step - loss: 0.0279 - acc: 0.9938\n",
            "Epoch 297/299\n",
            "800/800 [==============================] - 0s 357us/step - loss: 0.0109 - acc: 0.9963\n",
            "Epoch 298/299\n",
            "800/800 [==============================] - 0s 352us/step - loss: 0.0070 - acc: 0.9988\n",
            "Epoch 299/299\n",
            "800/800 [==============================] - 0s 342us/step - loss: 0.0092 - acc: 0.9975\n",
            "1200/1200 [==============================] - 1s 1ms/step\n",
            "Epoch 1/299\n",
            "800/800 [==============================] - 4s 4ms/step - loss: 6.9110 - acc: 0.2313\n",
            "Epoch 2/299\n",
            "800/800 [==============================] - 0s 269us/step - loss: 1.5490 - acc: 0.2563\n",
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            "800/800 [==============================] - 0s 267us/step - loss: 1.4044 - acc: 0.2413\n",
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            "800/800 [==============================] - 0s 266us/step - loss: 1.3817 - acc: 0.2675\n",
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            "800/800 [==============================] - 0s 266us/step - loss: 1.3780 - acc: 0.2600\n",
            "Epoch 6/299\n",
            "800/800 [==============================] - 0s 269us/step - loss: 1.3722 - acc: 0.2725\n",
            "Epoch 7/299\n",
            "800/800 [==============================] - 0s 271us/step - loss: 1.3720 - acc: 0.2800\n",
            "Epoch 8/299\n",
            "800/800 [==============================] - 0s 273us/step - loss: 1.3568 - acc: 0.2850\n",
            "Epoch 9/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 1.3690 - acc: 0.2725\n",
            "Epoch 10/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 1.3669 - acc: 0.2938\n",
            "Epoch 11/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 1.3400 - acc: 0.3175\n",
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            "800/800 [==============================] - 0s 263us/step - loss: 1.3412 - acc: 0.2788\n",
            "Epoch 13/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 1.3731 - acc: 0.2900\n",
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            "800/800 [==============================] - 0s 268us/step - loss: 1.3374 - acc: 0.3038\n",
            "Epoch 15/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 1.3336 - acc: 0.3225\n",
            "Epoch 16/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 1.3220 - acc: 0.3125\n",
            "Epoch 17/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 1.3093 - acc: 0.3300\n",
            "Epoch 18/299\n",
            "800/800 [==============================] - 0s 265us/step - loss: 1.3041 - acc: 0.3213\n",
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            "800/800 [==============================] - 0s 268us/step - loss: 1.3032 - acc: 0.3538\n",
            "Epoch 20/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 1.3005 - acc: 0.3400\n",
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            "800/800 [==============================] - 0s 269us/step - loss: 1.2444 - acc: 0.3938\n",
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            "800/800 [==============================] - 0s 277us/step - loss: 1.2391 - acc: 0.4050\n",
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            "Epoch 29/299\n",
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            "Epoch 30/299\n",
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            "Epoch 31/299\n",
            "800/800 [==============================] - 0s 271us/step - loss: 1.1677 - acc: 0.4913\n",
            "Epoch 32/299\n",
            "800/800 [==============================] - 0s 269us/step - loss: 1.1571 - acc: 0.4763\n",
            "Epoch 33/299\n",
            "800/800 [==============================] - 0s 263us/step - loss: 1.1247 - acc: 0.5050\n",
            "Epoch 34/299\n",
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            "Epoch 35/299\n",
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            "800/800 [==============================] - 0s 268us/step - loss: 1.1619 - acc: 0.5063\n",
            "Epoch 37/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 1.1337 - acc: 0.5088\n",
            "Epoch 38/299\n",
            "800/800 [==============================] - 0s 274us/step - loss: 1.1546 - acc: 0.4963\n",
            "Epoch 39/299\n",
            "800/800 [==============================] - 0s 265us/step - loss: 1.1084 - acc: 0.5138\n",
            "Epoch 40/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 1.0910 - acc: 0.5075\n",
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            "800/800 [==============================] - 0s 269us/step - loss: 1.1211 - acc: 0.5213\n",
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            "800/800 [==============================] - 0s 266us/step - loss: 1.0673 - acc: 0.5288\n",
            "Epoch 43/299\n",
            "800/800 [==============================] - 0s 269us/step - loss: 1.0544 - acc: 0.5600\n",
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            "800/800 [==============================] - 0s 270us/step - loss: 1.0482 - acc: 0.5500\n",
            "Epoch 45/299\n",
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            "800/800 [==============================] - 0s 270us/step - loss: 1.0730 - acc: 0.5463\n",
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            "800/800 [==============================] - 0s 265us/step - loss: 1.0474 - acc: 0.5500\n",
            "Epoch 48/299\n",
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            "800/800 [==============================] - 0s 272us/step - loss: 1.0403 - acc: 0.5813\n",
            "Epoch 51/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 1.0155 - acc: 0.5800\n",
            "Epoch 52/299\n",
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            "Epoch 53/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 0.9669 - acc: 0.6163\n",
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            "Epoch 55/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 0.9853 - acc: 0.5975\n",
            "Epoch 56/299\n",
            "800/800 [==============================] - 0s 272us/step - loss: 0.9188 - acc: 0.6338\n",
            "Epoch 57/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 1.0999 - acc: 0.5188\n",
            "Epoch 58/299\n",
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            "Epoch 59/299\n",
            "800/800 [==============================] - 0s 270us/step - loss: 0.9948 - acc: 0.5663\n",
            "Epoch 60/299\n",
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            "Epoch 61/299\n",
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            "Epoch 62/299\n",
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            "Epoch 63/299\n",
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            "Epoch 64/299\n",
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            "Epoch 65/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 0.9191 - acc: 0.6475\n",
            "Epoch 66/299\n",
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            "Epoch 67/299\n",
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            "Epoch 68/299\n",
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            "Epoch 69/299\n",
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            "Epoch 70/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 0.7929 - acc: 0.6975\n",
            "Epoch 71/299\n",
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            "Epoch 72/299\n",
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            "Epoch 73/299\n",
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            "Epoch 74/299\n",
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            "Epoch 75/299\n",
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            "Epoch 76/299\n",
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            "Epoch 77/299\n",
            "800/800 [==============================] - 0s 268us/step - loss: 0.8236 - acc: 0.6700\n",
            "Epoch 78/299\n",
            "800/800 [==============================] - 0s 270us/step - loss: 0.8365 - acc: 0.6575\n",
            "Epoch 79/299\n",
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            "Epoch 80/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 0.9003 - acc: 0.6138\n",
            "Epoch 81/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 0.7375 - acc: 0.7125\n",
            "Epoch 82/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 0.8192 - acc: 0.6475\n",
            "Epoch 83/299\n",
            "800/800 [==============================] - 0s 268us/step - loss: 0.9225 - acc: 0.6238\n",
            "Epoch 84/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 0.7508 - acc: 0.7200\n",
            "Epoch 85/299\n",
            "800/800 [==============================] - 0s 270us/step - loss: 0.7900 - acc: 0.6600\n",
            "Epoch 86/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 0.7883 - acc: 0.6875\n",
            "Epoch 87/299\n",
            "800/800 [==============================] - 0s 269us/step - loss: 0.7490 - acc: 0.6963\n",
            "Epoch 88/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 0.7094 - acc: 0.7175\n",
            "Epoch 89/299\n",
            "800/800 [==============================] - 0s 274us/step - loss: 0.7361 - acc: 0.7013\n",
            "Epoch 90/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 0.8527 - acc: 0.6475\n",
            "Epoch 91/299\n",
            "800/800 [==============================] - 0s 271us/step - loss: 0.7114 - acc: 0.7213\n",
            "Epoch 92/299\n",
            "800/800 [==============================] - 0s 275us/step - loss: 0.6820 - acc: 0.7438\n",
            "Epoch 93/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 0.7327 - acc: 0.7338\n",
            "Epoch 94/299\n",
            "800/800 [==============================] - 0s 272us/step - loss: 1.1836 - acc: 0.5075\n",
            "Epoch 95/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 0.9604 - acc: 0.6175\n",
            "Epoch 96/299\n",
            "800/800 [==============================] - 0s 270us/step - loss: 0.7854 - acc: 0.6813\n",
            "Epoch 97/299\n",
            "800/800 [==============================] - 0s 268us/step - loss: 0.7335 - acc: 0.7288\n",
            "Epoch 98/299\n",
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            "1200/1200 [==============================] - 1s 1ms/step\n",
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            "Epoch 97/299\n",
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            "Epoch 111/299\n",
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            "Epoch 137/299\n",
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            "Epoch 153/299\n",
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            "Epoch 215/299\n",
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            "Epoch 218/299\n",
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            "Epoch 220/299\n",
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            "Epoch 222/299\n",
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            "Epoch 223/299\n",
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            "Epoch 224/299\n",
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            "Epoch 225/299\n",
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            "Epoch 226/299\n",
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            "Epoch 227/299\n",
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            "Epoch 228/299\n",
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            "Epoch 229/299\n",
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            "Epoch 230/299\n",
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            "Epoch 231/299\n",
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            "Epoch 232/299\n",
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            "Epoch 233/299\n",
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            "Epoch 234/299\n",
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            "Epoch 235/299\n",
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            "Epoch 236/299\n",
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            "Epoch 237/299\n",
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            "Epoch 238/299\n",
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            "Epoch 239/299\n",
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            "Epoch 240/299\n",
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            "Epoch 241/299\n",
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            "Epoch 242/299\n",
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            "Epoch 243/299\n",
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            "Epoch 244/299\n",
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            "Epoch 245/299\n",
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            "Epoch 246/299\n",
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            "Epoch 247/299\n",
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            "Epoch 248/299\n",
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            "Epoch 249/299\n",
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            "Epoch 250/299\n",
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            "Epoch 252/299\n",
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            "Epoch 253/299\n",
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            "Epoch 254/299\n",
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            "Epoch 255/299\n",
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            "Epoch 256/299\n",
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            "Epoch 257/299\n",
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            "Epoch 258/299\n",
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            "Epoch 259/299\n",
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            "Epoch 260/299\n",
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            "Epoch 261/299\n",
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            "Epoch 262/299\n",
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            "Epoch 263/299\n",
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            "Epoch 264/299\n",
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            "Epoch 265/299\n",
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            "Epoch 266/299\n",
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            "Epoch 267/299\n",
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            "Epoch 268/299\n",
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            "Epoch 269/299\n",
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            "Epoch 270/299\n",
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            "Epoch 271/299\n",
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            "Epoch 272/299\n",
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            "Epoch 273/299\n",
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            "Epoch 274/299\n",
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            "Epoch 275/299\n",
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            "Epoch 276/299\n",
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            "Epoch 277/299\n",
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            "Epoch 278/299\n",
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            "Epoch 279/299\n",
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            "Epoch 280/299\n",
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            "Epoch 281/299\n",
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            "Epoch 282/299\n",
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            "Epoch 283/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 0.2430 - acc: 0.9175\n",
            "Epoch 284/299\n",
            "800/800 [==============================] - 0s 259us/step - loss: 0.2861 - acc: 0.8887\n",
            "Epoch 285/299\n",
            "800/800 [==============================] - 0s 268us/step - loss: 0.2198 - acc: 0.9275\n",
            "Epoch 286/299\n",
            "800/800 [==============================] - 0s 269us/step - loss: 0.2051 - acc: 0.9338\n",
            "Epoch 287/299\n",
            "800/800 [==============================] - 0s 263us/step - loss: 0.1905 - acc: 0.9313\n",
            "Epoch 288/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 0.2270 - acc: 0.9262\n",
            "Epoch 289/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.2311 - acc: 0.9200\n",
            "Epoch 290/299\n",
            "800/800 [==============================] - 0s 265us/step - loss: 0.2569 - acc: 0.9050\n",
            "Epoch 291/299\n",
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            "Epoch 292/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 0.2009 - acc: 0.9275\n",
            "Epoch 293/299\n",
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            "Epoch 294/299\n",
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            "Epoch 295/299\n",
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            "Epoch 297/299\n",
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            "Epoch 298/299\n",
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            "Epoch 299/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 0.2181 - acc: 0.9225\n",
            "1200/1200 [==============================] - 1s 1ms/step\n",
            "Epoch 1/299\n",
            "800/800 [==============================] - 4s 5ms/step - loss: 9.4182 - acc: 0.2475\n",
            "Epoch 2/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 6.1554 - acc: 0.2512\n",
            "Epoch 3/299\n",
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            "Epoch 8/299\n",
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            "Epoch 9/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 1.3842 - acc: 0.2600\n",
            "Epoch 10/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 1.3987 - acc: 0.2575\n",
            "Epoch 11/299\n",
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            "Epoch 12/299\n",
            "800/800 [==============================] - 0s 254us/step - loss: 1.3808 - acc: 0.2788\n",
            "Epoch 13/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 1.3971 - acc: 0.2387\n",
            "Epoch 14/299\n",
            "800/800 [==============================] - 0s 261us/step - loss: 1.3832 - acc: 0.2612\n",
            "Epoch 15/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 1.3811 - acc: 0.2625\n",
            "Epoch 16/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 1.3829 - acc: 0.2600\n",
            "Epoch 17/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 1.3768 - acc: 0.2862\n",
            "Epoch 18/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 1.3796 - acc: 0.2600\n",
            "Epoch 19/299\n",
            "800/800 [==============================] - 0s 259us/step - loss: 1.3791 - acc: 0.2612\n",
            "Epoch 20/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 1.3758 - acc: 0.2763\n",
            "Epoch 21/299\n",
            "800/800 [==============================] - 0s 266us/step - loss: 1.3741 - acc: 0.2725\n",
            "Epoch 22/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 1.3657 - acc: 0.2775\n",
            "Epoch 23/299\n",
            "800/800 [==============================] - 0s 254us/step - loss: 1.3729 - acc: 0.2688\n",
            "Epoch 24/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 1.3743 - acc: 0.2675\n",
            "Epoch 25/299\n",
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            "Epoch 26/299\n",
            "800/800 [==============================] - 0s 259us/step - loss: 1.3727 - acc: 0.2637\n",
            "Epoch 27/299\n",
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            "Epoch 28/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 1.3716 - acc: 0.2713\n",
            "Epoch 29/299\n",
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            "Epoch 30/299\n",
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            "800/800 [==============================] - 0s 259us/step - loss: 0.8582 - acc: 0.6562\n",
            "Epoch 208/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.9161 - acc: 0.6325\n",
            "Epoch 209/299\n",
            "800/800 [==============================] - 0s 263us/step - loss: 0.8531 - acc: 0.6700\n",
            "Epoch 210/299\n",
            "800/800 [==============================] - 0s 263us/step - loss: 0.8322 - acc: 0.6837\n",
            "Epoch 211/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.7644 - acc: 0.6888\n",
            "Epoch 212/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 0.8150 - acc: 0.6913\n",
            "Epoch 213/299\n",
            "800/800 [==============================] - 0s 255us/step - loss: 0.8701 - acc: 0.6550\n",
            "Epoch 214/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 0.9721 - acc: 0.5900\n",
            "Epoch 215/299\n",
            "800/800 [==============================] - 0s 261us/step - loss: 0.8585 - acc: 0.6475\n",
            "Epoch 216/299\n",
            "800/800 [==============================] - 0s 262us/step - loss: 0.7824 - acc: 0.7088\n",
            "Epoch 217/299\n",
            "800/800 [==============================] - 0s 255us/step - loss: 0.7918 - acc: 0.6787\n",
            "Epoch 218/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 0.8349 - acc: 0.6750\n",
            "Epoch 219/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 0.8072 - acc: 0.6550\n",
            "Epoch 220/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.8812 - acc: 0.6513\n",
            "Epoch 221/299\n",
            "800/800 [==============================] - 0s 255us/step - loss: 0.7957 - acc: 0.6963\n",
            "Epoch 222/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.7639 - acc: 0.7013\n",
            "Epoch 223/299\n",
            "800/800 [==============================] - 0s 263us/step - loss: 0.8202 - acc: 0.6725\n",
            "Epoch 224/299\n",
            "800/800 [==============================] - 0s 255us/step - loss: 0.8951 - acc: 0.6413\n",
            "Epoch 225/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 0.7629 - acc: 0.6762\n",
            "Epoch 226/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.8008 - acc: 0.7075\n",
            "Epoch 227/299\n",
            "800/800 [==============================] - 0s 263us/step - loss: 0.8807 - acc: 0.6338\n",
            "Epoch 228/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.9431 - acc: 0.5888\n",
            "Epoch 229/299\n",
            "800/800 [==============================] - 0s 259us/step - loss: 0.8144 - acc: 0.6913\n",
            "Epoch 230/299\n",
            "800/800 [==============================] - 0s 267us/step - loss: 0.8018 - acc: 0.6862\n",
            "Epoch 231/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 0.7770 - acc: 0.7113\n",
            "Epoch 232/299\n",
            "800/800 [==============================] - 0s 254us/step - loss: 0.7851 - acc: 0.6975\n",
            "Epoch 233/299\n",
            "800/800 [==============================] - 0s 259us/step - loss: 0.7913 - acc: 0.6825\n",
            "Epoch 234/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.8900 - acc: 0.6350\n",
            "Epoch 235/299\n",
            "800/800 [==============================] - 0s 255us/step - loss: 0.7901 - acc: 0.6837\n",
            "Epoch 236/299\n",
            "800/800 [==============================] - 0s 255us/step - loss: 0.7946 - acc: 0.6862\n",
            "Epoch 237/299\n",
            "800/800 [==============================] - 0s 254us/step - loss: 0.7454 - acc: 0.7100\n",
            "Epoch 238/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.7957 - acc: 0.6888\n",
            "Epoch 239/299\n",
            "800/800 [==============================] - 0s 262us/step - loss: 0.7619 - acc: 0.6938\n",
            "Epoch 240/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.7863 - acc: 0.6800\n",
            "Epoch 241/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 0.7759 - acc: 0.6913\n",
            "Epoch 242/299\n",
            "800/800 [==============================] - 0s 255us/step - loss: 0.8171 - acc: 0.6825\n",
            "Epoch 243/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 0.7871 - acc: 0.7063\n",
            "Epoch 244/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.7693 - acc: 0.7138\n",
            "Epoch 245/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.7530 - acc: 0.6837\n",
            "Epoch 246/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.7694 - acc: 0.7063\n",
            "Epoch 247/299\n",
            "800/800 [==============================] - 0s 261us/step - loss: 0.7408 - acc: 0.6975\n",
            "Epoch 248/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.7820 - acc: 0.7075\n",
            "Epoch 249/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.7405 - acc: 0.6888\n",
            "Epoch 250/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.7420 - acc: 0.7275\n",
            "Epoch 251/299\n",
            "800/800 [==============================] - 0s 261us/step - loss: 0.7604 - acc: 0.6950\n",
            "Epoch 252/299\n",
            "800/800 [==============================] - 0s 261us/step - loss: 0.8098 - acc: 0.6900\n",
            "Epoch 253/299\n",
            "800/800 [==============================] - 0s 259us/step - loss: 0.7367 - acc: 0.7075\n",
            "Epoch 254/299\n",
            "800/800 [==============================] - 0s 262us/step - loss: 0.7418 - acc: 0.7150\n",
            "Epoch 255/299\n",
            "800/800 [==============================] - 0s 264us/step - loss: 0.7454 - acc: 0.7138\n",
            "Epoch 256/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.7522 - acc: 0.7025\n",
            "Epoch 257/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.7005 - acc: 0.7262\n",
            "Epoch 258/299\n",
            "800/800 [==============================] - 0s 268us/step - loss: 0.6941 - acc: 0.7475\n",
            "Epoch 259/299\n",
            "800/800 [==============================] - 0s 261us/step - loss: 0.7164 - acc: 0.7250\n",
            "Epoch 260/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.7324 - acc: 0.7275\n",
            "Epoch 261/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.7070 - acc: 0.7075\n",
            "Epoch 262/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.7812 - acc: 0.6925\n",
            "Epoch 263/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.6588 - acc: 0.7437\n",
            "Epoch 264/299\n",
            "800/800 [==============================] - 0s 285us/step - loss: 0.7073 - acc: 0.7412\n",
            "Epoch 265/299\n",
            "800/800 [==============================] - 0s 289us/step - loss: 0.6977 - acc: 0.7325\n",
            "Epoch 266/299\n",
            "800/800 [==============================] - 0s 288us/step - loss: 0.7417 - acc: 0.7075\n",
            "Epoch 267/299\n",
            "800/800 [==============================] - 0s 293us/step - loss: 0.7016 - acc: 0.7088\n",
            "Epoch 268/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.7377 - acc: 0.7250\n",
            "Epoch 269/299\n",
            "800/800 [==============================] - 0s 261us/step - loss: 0.6479 - acc: 0.7575\n",
            "Epoch 270/299\n",
            "800/800 [==============================] - 0s 262us/step - loss: 0.7045 - acc: 0.7525\n",
            "Epoch 271/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.6651 - acc: 0.7325\n",
            "Epoch 272/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 0.6607 - acc: 0.7412\n",
            "Epoch 273/299\n",
            "800/800 [==============================] - 0s 254us/step - loss: 0.6604 - acc: 0.7525\n",
            "Epoch 274/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.7227 - acc: 0.7325\n",
            "Epoch 275/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.6794 - acc: 0.7425\n",
            "Epoch 276/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.7037 - acc: 0.7088\n",
            "Epoch 277/299\n",
            "800/800 [==============================] - 0s 254us/step - loss: 0.6341 - acc: 0.7550\n",
            "Epoch 278/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.6331 - acc: 0.7600\n",
            "Epoch 279/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.6329 - acc: 0.7600\n",
            "Epoch 280/299\n",
            "800/800 [==============================] - 0s 259us/step - loss: 0.6809 - acc: 0.7412\n",
            "Epoch 281/299\n",
            "800/800 [==============================] - 0s 255us/step - loss: 0.6101 - acc: 0.7450\n",
            "Epoch 282/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.6683 - acc: 0.7325\n",
            "Epoch 283/299\n",
            "800/800 [==============================] - 0s 259us/step - loss: 0.6375 - acc: 0.7638\n",
            "Epoch 284/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.6860 - acc: 0.7325\n",
            "Epoch 285/299\n",
            "800/800 [==============================] - 0s 255us/step - loss: 0.5902 - acc: 0.7725\n",
            "Epoch 286/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.6098 - acc: 0.7800\n",
            "Epoch 287/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.6218 - acc: 0.7538\n",
            "Epoch 288/299\n",
            "800/800 [==============================] - 0s 260us/step - loss: 0.6550 - acc: 0.7650\n",
            "Epoch 289/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.6600 - acc: 0.7337\n",
            "Epoch 290/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.6977 - acc: 0.7287\n",
            "Epoch 291/299\n",
            "800/800 [==============================] - 0s 258us/step - loss: 0.6060 - acc: 0.7525\n",
            "Epoch 292/299\n",
            "800/800 [==============================] - 0s 259us/step - loss: 0.6353 - acc: 0.7850\n",
            "Epoch 293/299\n",
            "800/800 [==============================] - 0s 255us/step - loss: 0.5767 - acc: 0.7800\n",
            "Epoch 294/299\n",
            "800/800 [==============================] - 0s 271us/step - loss: 0.6685 - acc: 0.7563\n",
            "Epoch 295/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.6326 - acc: 0.7513\n",
            "Epoch 296/299\n",
            "800/800 [==============================] - 0s 257us/step - loss: 0.6876 - acc: 0.7350\n",
            "Epoch 297/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 0.5697 - acc: 0.7987\n",
            "Epoch 298/299\n",
            "800/800 [==============================] - 0s 261us/step - loss: 0.6246 - acc: 0.7575\n",
            "Epoch 299/299\n",
            "800/800 [==============================] - 0s 256us/step - loss: 0.5886 - acc: 0.7912\n",
            "1200/1200 [==============================] - 2s 1ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gs_aaUviZZl6",
        "colab_type": "text"
      },
      "source": [
        "# Recherche d'un optimum en fonction de différents critères"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YVdMFKmZY6B",
        "colab_type": "code",
        "outputId": "81d7a105-9019-4ce7-f400-55dc9e313a39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        }
      },
      "source": [
        "def convergence_la_plus_rapide_gour():\n",
        "  index = []\n",
        "  min_dernier_atteinte_90 = resultats[0][0][0][0][6]\n",
        "  for a in range(len(resultats)):\n",
        "    for b in range(len(resultats[a])):\n",
        "      for c in range(len(resultats[a][b])):\n",
        "        for d in range(len(resultats[a][b][c])):\n",
        "          if resultats[a][b][c][d][6] < min_dernier_atteinte_90: # Si la valeur de dernier_atteinte_90 pour le jeu de paramètres choisis est la plus basse, on conserve l'index correspondant de la liste resultats (il indique le jeu optimal pour ce critère)\n",
        "            index = [(a,b,c,d)]\n",
        "            min_dernier_atteinte_90 = resultats[a][b][c][d][6]\n",
        "          elif resultats[a][b][c][d][6] == min_dernier_atteinte_90: # On peut trouver plusieurs possibilités\n",
        "            index.append((a,b,c,d))\n",
        "  print('On obtient la convergence la plus rapide avec le(s) jeu(x) de paramètres : '+str(index))\n",
        "  print('Avec la dernière atteinte de 90% de précision au bout de '+str(min_dernier_atteinte_90)+' époques !')\n",
        "  return\n",
        "\n",
        "\n",
        "# convergence_la_plus_rapide_gour()\n",
        "\n",
        "print('')\n",
        "\n",
        "def meilleur_acc_gour(): # Fonction qui affiche l'index du jeu de paramètre idéal et la valeur maximale de précision atteinte\n",
        "  index = []\n",
        "  meilleur_acc = 0\n",
        "  for a in range(len(resultats)):\n",
        "    for b in range(len(resultats[a])):\n",
        "      for c in range(len(resultats[a][b])):\n",
        "        for d in range(len(resultats[a][b][c])):\n",
        "          if resultats[a][b][c][d][4][1] > meilleur_acc: \n",
        "            index = [(a,b,c,d)]\n",
        "            meilleur_acc = resultats[a][b][c][d][4][1]\n",
        "          elif resultats[a][b][c][d][4][1] == meilleur_acc: # On peut trouver plusieurs possibilités\n",
        "            index.append((a,b,c,d))\n",
        "  print('On obtient la meilleure précision avec le(s) jeu(x) de paramètres : '+str(index))\n",
        "  print('Avec une précision de '+str(meilleur_acc)+' !')\n",
        "  return\n",
        "\n",
        "  print('On obtient la meilleure précision avec le(s) jeu(x) de paramètres : '+str(index))\n",
        "  print('Avec une précision de '+str(meilleur_acc)+' !')\n",
        "  return\n",
        "\n",
        "meilleur_acc_sim()\n",
        "# meilleur_acc_gour()\n",
        "print('')\n",
        "\n",
        "def meilleur_loss(): \n",
        "  index = []\n",
        "  meilleur_loss = 100\n",
        "  for a in range(len(resultats)):\n",
        "    for b in range(len(resultats[a])):\n",
        "      for c in range(len(resultats[a][b])):\n",
        "        for d in range(len(resultats[a][b][c])):\n",
        "          if resultats[a][b][c][d][4][0] < meilleur_loss: \n",
        "            index = [(a,b,c,d)]\n",
        "            meilleur_loss = resultats[a][b][c][d][4][1]\n",
        "          elif resultats[a][b][c][d][4][1] == meilleur_loss: # On peut trouver plusieurs possibilités\n",
        "            index.append((a,b,c,d))\n",
        "  print('On obtient le meilleur loss avec le(s) jeu(x) de paramètres : '+str(index))\n",
        "  print('Avec un loss de '+str(meilleur_loss)+' !')\n",
        "  return\n",
        "\n",
        "# meilleur_loss_gour()\n",
        "print('')\n",
        "\n",
        "def le_plus_overfit_gour(): \n",
        "  index = []\n",
        "  max_overfit = 0\n",
        "  for a in range(len(resultats)):\n",
        "    for b in range(len(resultats[a])):\n",
        "      for c in range(len(resultats[a][b])):\n",
        "        for d in range(len(resultats[a][b][c])):\n",
        "          overfit = abs(resultats[a][b][c][d][3]['acc'][-1]-resultats[a][b][c][d][4][1])\n",
        "          if overfit > max_overfit: \n",
        "            index = [(a,b,c,d)]\n",
        "            max_overfit = overfit\n",
        "          elif overfit == max_overfit: # On peut trouver plusieurs possibilités\n",
        "            index.append((a,b,c,d))\n",
        "  print('On obtient le plus grand overfitting avec le(s) jeu(x) de paramètres : '+str(index))\n",
        "  print(\"Avec un critère d'overfitting de \"+str(max_overfit)+' !')\n",
        "  return\n",
        "\n",
        "# le_plus_overfit_gour()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-45-fc2613b0d6b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m   \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0mmeilleur_acc_sim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;31m# meilleur_acc_gour()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'meilleur_acc_sim' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mfj_KqID1FQ1",
        "colab_type": "text"
      },
      "source": [
        "### Graphiques"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHcKr1gWxqdv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Tracés des effets des différentes variations des 4 paramètres avec la meilleure config de précision\n",
        "\n",
        "# Variation du nombre d'époques (cf avant : C2D=2 ; epochs = 148 idéalement ? ; BS = 0,27 ; %test = 0.2)\n",
        "\n",
        "epoques = []\n",
        "accuracy = []\n",
        "for i in range(len(resultats[0])):\n",
        "  accuracy.append(resultats[0][i][1][0][4][1]) # Valeur de la précision\n",
        "  epoques.append(50+i*(200-1)//3)\n",
        "\n",
        "plt.plot(epoques,accuracy)\n",
        "plt.xlabel(\"nombre d'époques\")\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()\n",
        "\n",
        "# Variation du Batch Size (cf avant : C2D=2 ; epochs = 148 ; BS = 0,27 ? ; %test = 0.2)\n",
        "\n",
        "batchsize = []\n",
        "accuracy = []\n",
        "for i in range(len(resultats[0][2])):\n",
        "  accuracy.append(resultats[0][2][i][0][4][1]) # Valeur de la précision\n",
        "  batchsize.append((0.01+i*(0.8-0.01)/3)*0.2*2000)\n",
        "\n",
        "plt.plot(batchsize,accuracy)\n",
        "plt.xlabel('Batch Size')\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()\n",
        "# Variation du %test (cf avant : C2D=2 ; epochs = 148 ; BS = 0,27 ; %test = 0.2 ?)\n",
        "\n",
        "pourc_test = []\n",
        "accuracy = []\n",
        "for i in range(len(resultats[0][2][1])):\n",
        "  accuracy.append(resultats[0][2][1][i][4][1]) # Valeur de la précision\n",
        "  pourc_test.append(0.2+i*(0.9-0.2)/3)\n",
        "\n",
        "plt.plot(pourc_test,accuracy)\n",
        "plt.xlabel('pourcentage utilisé pour tester')\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r57wA8V2whRu",
        "colab_type": "text"
      },
      "source": [
        "## Matrice de confusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eoauMDzZrK3H",
        "colab_type": "text"
      },
      "source": [
        "### Fonction qui crée la matrice de confusion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fHgieUmNTHr2",
        "colab": {}
      },
      "source": [
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "def mat_conf(cm): \n",
        "  df_cm = pd.DataFrame(cm, index = [i for i in [\"jackson\",\"nicolas\",\"theo\",\"yweweler\"]],\n",
        "                       columns = [i for i in [\"jackson\",\"nicolas\",\"theo\",\"yweweler\"]])\n",
        "  plt.figure(figsize = (10,7))\n",
        "  return sn.heatmap(df_cm, annot=True,fmt='g')\n",
        "\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QTKmcvAqwTv9",
        "colab_type": "text"
      },
      "source": [
        "### Adaptation des arrays de prédiction retournés par le modèle au bon format pour la matrice de confusion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "le6Lz0TtKD_9",
        "colab_type": "code",
        "outputId": "5c0ae9cd-5105-44af-f0be-cc6eeefc8b47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "# Fonction qui prend la liste sous la forme [[0.000001,0.000001,0.9999,0.0001],...] et qui retourne [[0,0,1,0],...]\n",
        "def adapter_Y_pred(Y_pred):\n",
        "  for i in range(len(Y_pred)):\n",
        "    imax = list(Y_pred[i]).index(max(list(Y_pred[i])))\n",
        "    Y_pred[i][imax]= 1\n",
        "    for k in range(len(Y_pred[i])):\n",
        "      if Y_pred[i][k] != 1:\n",
        "        Y_pred[i][k]=0\n",
        "\n",
        "# Fonction qui prend un array de résultats prédits sous la forme [[0,0,1,0],...] et qui retourne [3,...]\n",
        "def adapter(L):\n",
        "  LS = []\n",
        "  for i in range(len(L)):\n",
        "    LS.append(list(L[i]).index(max(list(L[i]))))\n",
        "  return LS\n",
        "\n",
        "Y_test_mat = adapter(y_test)\n",
        "Y_pred_mat = adapter(prediction)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-da9390628f4e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mLS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mY_test_mat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0mY_pred_mat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'y_test' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0TOItGdwBPR",
        "colab_type": "text"
      },
      "source": [
        "### Création de la matrice de confusion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "go9yZAPyv8zp",
        "colab_type": "code",
        "outputId": "2100d0d8-3fb1-4910-c6e1-d0879801f745",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "cm = confusion_matrix(Y_test_mat,Y_pred_mat)\n",
        "mat_conf(cm)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-f50c94a8eaac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_test_mat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY_pred_mat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mmat_conf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'confusion_matrix' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8o1fntqvBjvP",
        "colab_type": "code",
        "outputId": "09c8023a-e5ae-407a-dbaa-8842fba36c08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "mat_conf(matrice)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f901b4c2ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HHvk7CxYsYNKki9hj912bHZZK8rOzQTrruDRZqVJORGwIzMzMVyNiR+AtwK8y0/9dqoRZ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\nQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MoLXpGjPq9L-",
        "colab_type": "text"
      },
      "source": [
        "## Schéma du modèle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ByAqcQSRcS5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.utils import plot_model\n",
        "plot_model(model, to_file='model.png',show_shapes=True)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}