challenge.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {
    "id": "OreWa8IV4iPJ"
   },
   "source": [
    "# Challenge Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2239,
     "status": "ok",
     "timestamp": 1704919091702,
     "user": {
      "displayName": "Stephanie Siewe",
      "userId": "05727558530592519713"
     },
     "user_tz": -60
    },
    "id": "Xt6GjyIJ5G7I",
    "outputId": "92a774b2-32dc-43d8-8ebd-46677786c5b0"
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l-TUngAm4iPQ"
   },
   "source": [
    "L'objectif de ce challenge est de proposer une méthode de classification, basée sur des réseaux de neurones, permettant de classer des images issues de Google Quickdraw (https://quickdraw.withgoogle.com/data).\n",
    "Le jeu de données proposé comprend 5 classes balancées, avec 15000 exemples d'apprentissage et 5000 exemples de validation : des paniers, des yeux, des lunettes, des lapins et des mains.\n",
    "\n",
    "Vous pouvez utiliser tout algorithme qui vous semble pertinent (PMC, CNN,), en faisant varier les données d'entrée (normalisation, augmentation de données,...), les paramètres des réseaux considérés (fonction objectif, optimiseur, dropout, learning rate, taille des batchs...)\n",
    "\n",
    "Bien sûr, pour valider votre travail, nous ferons tourner le code en local, sur la base originale que nous conservons, pour voir si les résultats que vous proposez sont reproductibles.\n",
    "\n",
    "Le compte-rendu sera effectué sur un notebook jupyter, dans lequel vous reporterez votre méthodologie en markdown, et vos codes en Python. Vous expliquerez votre démarche, justifierez vos choix, commenterez vos expérimentations et vos résultats.\n",
    "\n",
    "La notation sera construite de la manière suivante :\n",
    "- 12 points sur le compte-rendu\n",
    "- 8 points seront accordés sur un classement par le taux de reconnsaissance des objets de la base de test. Les binômes seront classés par taux de reconnaissance décroissant, les premiers ayant 8 points accordés, les autres binômes ayant une note suivant une décroissance linéaire.\n",
    "\n",
    "Le notebook sera déposé sur la plateforme moodle du cours."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Notre objetif est de concevoir un modèle de réseaux de neurones convolutionels afin de d'éffectuer la classification des images en 5 catégories<br>\n",
    "Notre travail s'articulera autour des points suivants : <br>\n",
    "* Tester une variétée d'architecture, principalement - des architectures de types VGG, Resnet et Inception - pour en choisir la meilleur architecture repondant à notre besoin<br>\n",
    "* Augmentation de données (data augmentation) pour en avoir plus de données d'entraînement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dOfuYRrf4iPS"
   },
   "source": [
    "## Imports\n",
    "* Import de toutes les couche necessaires pour la conception d'un modèl\n",
    "* Autres import pour sauvegarder le meilleur modèle lors de l'entraînement, changer le taux d'entraînement lors de l'entraînement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "executionInfo": {
     "elapsed": 239,
     "status": "ok",
     "timestamp": 1704927428569,
     "user": {
      "displayName": "Stephanie Siewe",
      "userId": "05727558530592519713"
     },
     "user_tz": -60
    },
    "id": "9lgnJRwF4iPS"
   },
   "outputs": [],
   "source": [
    "#Import de toutes les couches keras\n",
    "import tensorflow as tf # Tensoflow\n",
    "from tensorflow import keras as keras # Keras\n",
    "from tensorflow.keras.models import Sequential # Pour la concetion d'un modèle séquentiel ou des couches séquentielles\n",
    "from tensorflow.keras import Model # Pour concevoir un modèle de type Keras\n",
    "from tensorflow.keras.layers import Conv2D # Couche convolutionnelle\n",
    "from tensorflow.keras.layers import MaxPooling2D # Couche de pooling\n",
    "from tensorflow.keras.layers import Flatten # Couche permettant de transformer une donnée 2D (image) en un donnée 1D (vecteur)\n",
    "from tensorflow.keras.layers import Dense # Pour la conception de couches complètement connectées \n",
    "from tensorflow.keras.layers import Dropout # Couche de regularization\n",
    "from tensorflow.keras.layers import Input # Couche d'entrée\n",
    "from tensorflow.keras.layers import BatchNormalization # Couche de normalization ou standardisation\n",
    "from tensorflow.keras.layers import ReLU # Couche d'activation\n",
    "from tensorflow.keras.layers import GlobalAveragePooling2D # Autre couhe de pooling\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from keras.models import load_model\n",
    "\n",
    "#Pretraitement des données\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator # Pour charger et prétraiter les données \n",
    "\n",
    "# Autres import \n",
    "from tensorflow.keras.callbacks import ModelCheckpoint # Pour sauvegarder le modèle ayant la meilleur précision sur le jeu de données\n",
    "                                                        # de validation lors de l'entraînement\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler # Pour changer le learning rate de manière dynamique lors de l'entraînement\n",
    "\n",
    "import pandas as pd # Pour gérer le dataFrame\n",
    "import numpy as np # Pour la manipoulation de tableaux numpy\n",
    "\n",
    "# from PIL import Image\n",
    "# import glob\n",
    "import matplotlib.pyplot as plt # Pour les affichages graphique\n",
    "%matplotlib inline\n",
    "\n",
    "#Data Augmentation\n",
    "import cv2 # Pour les transorformation géométriques des données\n",
    "import os "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Augmentation\n",
    "### Definition\n",
    "L'augmentation de données, data augmentation en anglais, est une techniques permettant d'appliquer des transformations sur les données originales<br> afin d'avoir plus de données pour l'entraînement de notre modèle. \n",
    "Ces transformations sont diverses. \n",
    "### Différents types de data Augmentation\n",
    "#### Les transformations simples\n",
    "Les plus simples d'entres elles permettent simplement d'effectuer des transformations géométriques, le filtrage ou encores le changement d'espace de couleur. Les plus connus d'entre ces techniques sont :\n",
    "* Les rotions selon divers angles de rotation.\n",
    "* Les retournement(flip) horizontaux, vericaux, ..\n",
    "* Le filtrage à travers divers filtres.\n",
    "* L'ajout de brouille(gaussien, uniforme).\n",
    "* Les coups aléatoires (Random crop).\n",
    "* Le changement de couleur.\n",
    "* Cutout, consistant à masquer une partir de l'image. <a href=\"https://arxiv.org/pdf/1708.04552.pdf\"> Terrance DeVries et al.</a> <br>\n",
    "#### Données synthétiques\n",
    "Les transformations basiques ne sont pas souvent assez robuste pour apporter une grande varité. Se faisant il y'a une variété de transformations plus sofistiquées utilisées pour générer des données synthétiques. \n",
    "Ce sont : <br>\n",
    "* L'usage des GANs pour la génération de données <a href=\"https://arxiv.org/pdf/2306.02019.pdf\" > Angona Biswas et al. </a><br>\n",
    "Cette approche necéssite un entraînement à l'avance avec les données d'entraînement afin d'en générer de nouvelles images sur la base de la distribution de des données d'entraînement apprise.\n",
    "* L'usage de transfert de style (Neural Style Transfer) <a href=\"https://arxiv.org/pdf/1909.01056.pdf\"> Xu Zheng et al.</a> Cette permet de créer des nouvelles données en changeant la texture , la couleur et la luminosité via une transformation non linéare.\n",
    "\n",
    "L'objectif de la data augmentation est de générer plus de données pour l'entraînement afin d'éviter les sur-entraînements surtout quand l'on dispose d'une petite basse de données d'entraînement, car la capacité des modèles de réseaux de neurones profonds résident de la capacité de généralisation après être entraîné avec une grosse base de données <a href=\"https://arxiv.org/pdf/1909.01056.pdf\"> Xu Zheng et al</a>, <a href=\"https://arxiv.org/pdf/2306.02019.pdf\" > Angona Biswas et al</a>. Le problème est que la collection d'une grosse base de données d'entraînement peut être parfois très coûteuse. Parfois il est dificile voire impossible d'avoir accès à une grosse base données d'entraînement même si l'on en a les ressources nécessaires. Ces limites font la data augmentation une solution intéressante dans diverses domaines. \n",
    "\n",
    "#### Limites de la data augmentation\n",
    "* Les transformations simples peuvent ne pas apporter un grand changement quant à la capacité du modèle à généraliser.\n",
    "* La génération de données de qualité avec les GANs est un autre défit.\n",
    "* Les biais existant dans les données originelles sont présent dans les données générées via les GANs.\n",
    "\n",
    "#### Notre cas\n",
    "Dans notre travail nous avons utilisé que les transformations simples comme la rotation, les retournement en raison des dificultés suivantes:<br>\n",
    "* Les données sont en niveau de grilles donc pas possible changer l'espace de couleur ni d'appliquer des modèles de Neural Style Transfer pour générer de nouvelles données.\n",
    "* La tailles des données 28x28 est un frein au random crop (Nos données ont une une taille un peu petite)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def data_augmentation(source_folder,destination_folder,dataframe):\n",
    "    \"\"\" Args:\n",
    "            source_folder : Repertoir source où sont les vraies images d'entraînement\n",
    "            \n",
    "            destination_folder : Repertoire de destination où les données augmentées seront sauvegarder y compris une copie des données réelles.\n",
    "            Une fois les données augmentées, seules les données de cette repertoire seront utilisées comme jeux de données d'entraînement de nos\n",
    "            différent modèle\n",
    "            \n",
    "            dataframe : DataFrame contenant les nom des images destinées à l'entraînement. Rappelons que le jeux de données d'entraînement de test ne\n",
    "            sont dans le même repertoire. \n",
    "    \"\"\"\n",
    "    dir=os.listdir(source_folder) \n",
    "    \n",
    "    for i in range(5): # Boucle pour créer les 5 sous répertoire representant les différentes classes du jeu d'entraînemet\n",
    "        subpath=os.path.join(source_folder,dir[i])\n",
    "        dirr=os.listdir(subpath)\n",
    "        new_sub_folder=os.path.join(destination_folder,dir[i])\n",
    "        os.makedirs(new_sub_folder) # Création d'un sous répertoire\n",
    "        for file in dirr: #Parcours du répertoire où sont staockées les images d'entrainément. C'est l'un des 5 sous répertoires représenatant les classes\n",
    "            for filename in dataframe: # Parcours du dataFrame contenant les noms des images d'entraînement\n",
    "                if filename==file: # Comparaison entre nom du fichier du repertoire courant et le nomù du fichier de la dataframe \n",
    "                    file_path = os.path.join(subpath, filename) # Obtension du chemin complet du fichier du répertoire courant\n",
    "                    image=cv2.imread(file_path,-1) #Ouverture du fichier via le dhemin obtenu précédemment. -1 indique ici que nous chargeons l'image\n",
    "                                                    # Sous son format original. Par défaut OpenCV ouvrira l'image avec 3 canaux. D'où l'intérêt de changer \n",
    "                                                    # Le flag à -1\n",
    "                    shutil.copy(file_path, new_sub_folder) # Copy de l'image dans notre nouvelle sous répertoire. On se contente de copier plutôt que \n",
    "                                                           # de déplacer dans l'image dans le nouveau répertoire. Cela nous permet de garder les images\n",
    "                                                           \n",
    "\n",
    "                    flip_types = [0, 1, -1] # Flags pour retourner nos images en 3 différents sens (retournement par erapport à l'axe x, y, ..)\n",
    "\n",
    "                    # Boucle pour le retourner de chaque image en trois différents formes\n",
    "                    for flip_type in flip_types:\n",
    "                        \n",
    "                        flipped_image = cv2.flip(image, flip_type)\n",
    "                        flip_label = \"_hflip\" if flip_type == 0 else \"_vflip\" if flip_type == 1 else \"_vhflip\"\n",
    "                        \n",
    "                        f = f\"{file_path[:-4]}{flip_label}.png\"\n",
    "                        cv2.imwrite(f,flipped_image)\n",
    "                        shutil.move(f, new_sub_folder)\n",
    "\n",
    "    \n",
    "                    w,h=image.shape\n",
    "                    centerX, centerY=(w//2,h//2)\n",
    "\n",
    "                    rotations = [30, 60, -30, -60]# Les différents angles de rotaion\n",
    "\n",
    "                    #Boucle pour effectuer diverses rotations de l'image de base\n",
    "                    for angle in rotations:\n",
    "                        M = cv2.getRotationMatrix2D((centerX, centerY), angle, 1.0)\n",
    "                        rotated = cv2.warpAffine(image, M, (w, h))\n",
    "                        f = f\"{file_path[:-4]}_rotated{angle}.png\"\n",
    "                        cv2.imwrite(f,rotated)\n",
    "                        shutil.move(f, new_sub_folder)\n",
    "                    break\n",
    "                    \n",
    "                    \n",
    "\n",
    "def validation_data_creation(source_folder,destination_folder,dataframe):\n",
    "    \"\"\" Args:\n",
    "            source_folder : Repertoir source où sont les vraies images de validation\n",
    "            \n",
    "            destination_folder : Repertoire de destination où les données de validation  seront sauvegarder.\n",
    "            \n",
    "            dataframe : DataFrame contenant les nom des images destinées à l'entraînement. Rappelons que le jeux de données d'entraînement de test ne\n",
    "            sont dans le même repertoire. \n",
    "    \"\"\"\n",
    "    dir=os.listdir(source_folder) \n",
    "    \n",
    "    for i in range(5): # Boucle pour créer les 5 sous répertoire representant les différentes classes du jeu d'entraînemet\n",
    "        subpath=os.path.join(source_folder,dir[i])\n",
    "        dirr=os.listdir(subpath)\n",
    "        new_sub_folder=os.path.join(destination_folder,dir[i])\n",
    "        os.makedirs(new_sub_folder) # Création d'un sous répertoire\n",
    "        for file in dirr: #Parcours du répertoire où sont staockées les images de validation. C'est l'un des 5 sous répertoires représenatant les classes\n",
    "            for filename in dataframe: # Parcours du dataFrame contenant les noms des images de validation\n",
    "                if filename==file: # Comparaison entre nom du fichier du repertoire courant et le nomù du fichier de la dataframe \n",
    "                    file_path = os.path.join(subpath, filename) # Obtension du chemin complet du fichier du répertoire courant\n",
    "                \n",
    "                    shutil.copy(file_path, new_sub_folder) # Copy de l'image dans notre nouvelle sous répertoire. On se contente de copier plutôt que \n",
    "                                                           # de déplacer dans l'image dans le nouveau répertoire. Cela nous permet de garder les images\n",
    "                                                           \n",
    "\n",
    "# # Génération des données augmentées\n",
    "\n",
    "# source_folder = '/home/dah/deep_learning_isima/challenge/images'\n",
    "# destination_folder = '/home/dah/deep_learning_isima/challenge/train'\n",
    "\n",
    "# train_csv_path = \"train.csv\"\n",
    "# train=pd.read_csv(train_csv_path)\n",
    "# dataframe=train['image_name']\n",
    "# data_augmentation(source_folder,destination_folder,dataframe)\n",
    "\n",
    "\n",
    "\n",
    "# # Création du répertoire contenant les données de validation\n",
    "\n",
    "# source_folder = '/home/dah/deep_learning_isima/challenge/images'\n",
    "# destination_folder = '/home/dah/deep_learning_isima/challenge/val'\n",
    "\n",
    "# train_csv_path = \"valid.csv\"\n",
    "# train=pd.read_csv(train_csv_path)\n",
    "# dataframe=train['image_name']\n",
    "# data_augmentation(source_folder,destination_folder,dataframe)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization des effets de la data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x1300 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image=cv2.imread(\"/home/dah/deep_learning_isima/challenge/images/basket/basket_000004.png\",-1)\n",
    "\n",
    "fig, ax=plt.subplots(3,3,figsize=(13,13))\n",
    "hflip=cv2.flip(image,0)\n",
    "vflip=cv2.flip(image,1)\n",
    "vhflip=cv2.flip(image,-1)\n",
    "\n",
    "w,h=image.shape\n",
    "centerX, centerY=(w//2,h//2)\n",
    "M1=cv2.getRotationMatrix2D((centerX,centerY),30,1.0)\n",
    "rotated1=cv2.warpAffine(image,M1,(w,h))\n",
    "\n",
    "M2=cv2.getRotationMatrix2D((centerX,centerY),60,1.0)\n",
    "rotated2=cv2.warpAffine(image,M2,(w,h))\n",
    "\n",
    "M3=cv2.getRotationMatrix2D((centerX,centerY),-30,1.0)\n",
    "rotated3=cv2.warpAffine(image,M3,(w,h))\n",
    "\n",
    "M4=cv2.getRotationMatrix2D((centerX,centerY),-60,1.0)\n",
    "rotated4=cv2.warpAffine(image,M4,(w,h))\n",
    "\n",
    "\n",
    "ax[0,0].imshow(image)\n",
    "ax[0,0].set_title(label='Vraie image')\n",
    "ax[0,1].imshow(vflip)\n",
    "ax[0,1].set_title(label='vertical flip')\n",
    "ax[0,2].imshow(hflip)\n",
    "ax[0,2].set_title(label='horizontal flip')\n",
    "ax[1,0].imshow(vhflip)\n",
    "ax[1,0].set_title(label='horizontal and vertical flip')\n",
    "ax[1,1].imshow(rotated1)\n",
    "ax[1,1].set_title(label='Rotation de 30 °')\n",
    "ax[1,2].imshow(rotated2)\n",
    "ax[1,2].set_title(label='Rotation de 60 °')\n",
    "ax[2,0].imshow(rotated3)\n",
    "ax[2,0].set_title(label='Rotation de -30 °')\n",
    "ax[2,1].imshow(rotated4)\n",
    "ax[2,1].set_title(label='Rotation de -60 °')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chargement des données\n",
    "* Nous avons dans une prémier temps sauvegardé nos données dans le répertoire **train** et **val** suivant que la donnée est une donnée d'entraînement ou de validation.\n",
    "* Nous avons ensuite utilisé un objet de la classe **ImageDataGenerator**<a href=\"https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator\"> ImageDataGenerator </a> pour charger nos différents jeux de donnéees. Les données en entrée de notre modèle sont normalisée via le paramètre **rescale** dans l'objet ImageDataGenerator.\n",
    "* Une chose très importante est le **batch size** d'entraînement. Si les modèles entraînés possèdent une couche de **Batch Normalization**, il faut un batch size élevé de sorte à avoir une quantité de données assez representative de la distribution du jeu de données d'entraînement afin que les **moyennes** et **écart-types** calculés dans ces couches soient aussi représentatives.Pour les mini-batch de petite taille ou les mini-batch qui ne contiennent pas une distribution représentative des exemples de l'ensemble de données d'apprentissage, les différences dans les entrées normalisées entre l'apprentissage et l'inférence (utilisation du modèle après l'apprentissage) peuvent entraîner des différences notables en termes de performances.<a href=\"https://machinelearningmastery.com/batch-normalization-for-training-of-deep-neural-networks/\"> Batch size </a>\n",
    "Aussi les petit-lot de batch ne garantissent par une covergence **rapide** et **stable** des algorithmes d'optimisation utilisant la méthode de descente de gradient stochastiques car les poids sont mis à jour en s'appuyant sur des données qui ne sont pas répresentatives. Ce problème de taille de batch à d'ailleurs l'objet de plusieurs études dont en voici quelques unes <a href =\"https://arxiv.org/pdf/1910.08222.pdf\" >Scott Sievert et al</a>, <a href =\"https://arxiv.org/pdf/2206.01029.pdf\" >Kiwon Lee et al</a> <br>\n",
    "Bien que le gros lots de batch presentent de nombreuses avantages, ils exigent plus deressources mémoires. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1286,
     "status": "ok",
     "timestamp": 1704928373546,
     "user": {
      "displayName": "Stephanie Siewe",
      "userId": "05727558530592519713"
     },
     "user_tz": -60
    },
    "id": "KtwmvJsGGwrf",
    "outputId": "74cda798-f687-4bc2-9fe4-103dad4d761a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 600000 images belonging to 5 classes.\n",
      "Found 25000 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "batch_size=256\n",
    "\n",
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_data = datagen.flow_from_directory(\n",
    "    directory=\"train/\",\n",
    "    target_size=(28, 28),\n",
    "    color_mode='grayscale',\n",
    "    # classes=None,\n",
    "    class_mode='categorical',\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "\n",
    "valid_data = datagen.flow_from_directory(\n",
    "    directory=\"val/\",\n",
    "    target_size=(28, 28),\n",
    "    color_mode='grayscale',\n",
    "    # classes=None,\n",
    "    class_mode='categorical',\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tf0XboQ692Xt"
   },
   "source": [
    "En agissant comme le montre le code ci-dessus nous avons determinee que la dimension des images est de 28x28. Nous avons jugee cette taille suffisante et nous avons decidee de ne pas redimensionner les images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DWwdDwJ2hpoa"
   },
   "source": [
    "## Mise en place des différents modèles\n",
    "### Objectifs\n",
    "La conception de ces différents modèles repond à différents objectifs:\n",
    "* Découvrir comment mettre en place un modèle perfomant de réseaux de neurones profonds en partant de zéro.\n",
    "* Découvrir les raisons d'utiliser certaines couches par rapport à d'autres.\n",
    "* Découvrir différentes architectures de réseaux convolutionnels.\n",
    "### Théorie des couches constituant l'architecture d'un modèle CNN (Convolutional Neural network)\n",
    "Dans les lignes qui suivent nous allons présenter les différentes couches couramment présentes dans l'architecture de CNNs.<br>\n",
    "Etant donné que ces couches seront utilisées dans nos différents modèles nous allons  expliquer brièvement pourquoi nous les avons utilisées\n",
    "#### Couche de convoltion\n",
    "* Les couches de convolution sont préférables aux couches complètement connectées car elles ont l'avantage d'extraire des caractéristiques localement. Ce qui n'est pas le cas pour les couches complètement connectées qui traitent un pixel indépendamment de ses pixels voisins. Avec les couches complètement connectées on perd la notion de spacialité.\n",
    "* Les couches de convolution sont le plus souvent utilisées avec des kernel de petit taille (3x3) car il est pertinent de traiter un pixel est tenant compte de ses plus proches voisins que des voisins qui sont très éloignés possédant ainsi des caratéristiques pouvant être très différents du pixel traité.\n",
    "* Le **ConvBlock** ci-dessus sera utilisé avec des **kernel size** 3x3 ou 1x1\n",
    "#### Couche de Batch Normalization\n",
    "Cette couche à pour objectif de normalizer données en utilisant la **moyenne** et **l'écart-type** du **lot de batch** traité. La normalisation permet d'accélérer l'entraînement de notre modèle. En fait elle assure une convergence **stable** et **rapide** de la fonction de perte lors de la descente du gradient. Lorsque les activations à l'entrée ou à la sortie d'une couche ne sont pas normalisées, elles peuvent prendre des valeurs très grandes en valeur absolue. Cela entraîne la disparition du gradient pour des fonctions d'activation de type sigmoid car la dérivé de celle-ci tend vers zéros( $sigmoid(x)=g(x)=\\frac{1}{1+ exp(-x)} $ et $g'(x)=g(x)(1-g(x))$)  ) comme expliqué par <a href=\"https://arxiv.org/pdf/1502.03167.pdf\" >  Sergey Ioffe et al</a>. Le problème de disparition de gradient(Vanishing gradient problem) a pour conséquence de rendre l'apprentissage très lent car les couches d'entrées reçoivent des gradients quasiment nuls lors de la phase de rétropropagation(back propagation) entraînant ainsi la non la non mise à jour des poids de ces dernieres. Aussi <a href=\"https://arxiv.org/pdf/1502.03167.pdf\" >  Sergey Ioffe et al</a> expique que la **batch normalization** nous permet d'entraîner plus aisément un modèle de deep learning sans trop se soucier de la valeur du taux d'apprentissage qui est un hyper paramètre très important devant être choisi avec délicatesse. Il a été ainsi démontré par les auteurs ayant mise en oeuvre cette approche qu'il est **14** plus rapide d'entraîner un modèle de réseaux de neurones ayant de couches de batch normalization que des réseaux qui n'en possèdent pas. Referez vous à<a href=\"https://arxiv.org/pdf/2209.14778.pdf\"> Randall Balestriero </a> pour plus de détails sur le fonctionnement de **batch normalization** et ses avantages.\n",
    "\n",
    "#### Intiatialisation des paramètres du modèle\n",
    "* L'initialisation éfficace des paramètres d'un modèle de deep learning est crucial pour avoir une bonne précision.\n",
    "* L'initialisation non appropriée des paramètres d'un modèle peut lourdement affecté sa performance car en effet chaque couche ou chaque neurones (chaque filtre dans le cas des réseaux convolutifs) aura tendance à après les mêmes caractéristiques que les autres couches ou neurones.<br>\n",
    "\n",
    "Les différentes approches pour initialiser les paramètres sont :\n",
    "##### Initialisation des paramètres avec une valeur constant :\n",
    "L'apporche la plus courantes est d'initialiser tous poids à zéros. Avec cette méthode, les neurones sont succeptible d'apprentres les mêmes caractéristiques. Aussi des valeurs très grandes ou très petites pourront être la cause de l'explosion ou de la disparition du gradient\n",
    "##### Initialisation aléatoire suivant une distribution de probabilité. \n",
    "Les distribuitions les plus couramment utilisées sont la distribution normale et la distribution. Cependant l'initialition aléatoire n'empêche pas de rencontrer le problème de disparition ou d'explosion du gradient.\n",
    "##### Initialisation de Xavier Glorot\n",
    "Cette appproche est l'initialisation par **défaut** avec **Keras**. <br>\n",
    "L'initialisation de faire aussi de manière **aléatoire** suivant une distribution normale ou uniforme avec des **paramètres bien déterminés** permettant d'éviter le problème de disparition ou d'explosion du gradient. Cette iniatilisation suppose plusieurs hypothèses :\n",
    "* La fonction d'activation doit être impair (**sigmoid** ou **tanh**)\n",
    "* La variance des activations de la couche $z^i$ est égale à la variation de la couche $z^{i+1}$, c'est-à-dire $ \\mathrm{Var}(z^i)=\\mathrm{Var}(z^{i+1})$, lors de la phase de propagation\n",
    "* La variance du gradient de la couche $z^i$ est égale à la variance du gradient de la couche $z^{i+1}$, c'est-à-dire $\\mathrm{Var}($$\\frac{\\partial L}{\\partial z^i}$$)=\\mathrm{Var}($$\\frac{\\partial L}{\\partial z^{i+1}}$$)$, lors de la phase de rétropropagation.<br>\n",
    "Ces deux précedentes hypothèse permet d'assurer la non disparition ou la non explosion du gradient. <br>\n",
    "Vous pouvez vous referez <a href =\"https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf\" > Xavier Glorot et al </a>(2010)à pour plus de détails quant à cette méthode d'initiamlisation. \n",
    "\n",
    "##### Initialisation Kaiming He\n",
    "Elle adapte le même processus d'initialisation que l'initialisation de **Xavier Glorot** à la seule différence qu'elle parvient à l'étendre et la rendre valide avec des fonctions d'activation non impair, notamment la fonction **ReLU**(Rectified Linear Unit, $ReLU(x)=max(0,x)$)<a href=\"https://arxiv.org/pdf/1502.01852.pdf\"> Kaiming He et al. </a>(2015)\n",
    "\n",
    "#### Foncrion d'activation ReLU\n",
    "**ReLU**(Rectified Linear Unit, $ReLU(x)=max(0,x)$ est une fonction d'activation très couramment utilisé dans les modèles de réseaux de neurones profonds du fait de sa capacité à introduire la linéarité lors de l'apprentissage. Elle a pour avantage d'être une solution fiable quant aux problèmes d'explosion ou de disparition du gradient.\n",
    "\n",
    "#### Regualarisarion Dropout\n",
    "<a href=\"https://arxiv.org/pdf/1207.0580.pdf\"> G. E. Hinton et al </a> (2012) ont expliqué dans leur papier que les réseaux de neurones complètement connectés (feedforward neural network) sont sujets aux sur-entraînements(overfitting) lorsque ces derniers sont entraînés sur de petites bases de données. Pour augmenter la capacité de généralisation de ces réseaux ils ont mis en oeuvre la technique de **Random Dropout** qui consiste à omettre certains neurones lors de la phase de propagation. Cette technique s'avère promeuteuse sur diverses tâches notamment dans des tâches de classifications. Le **dropout** est l'une des meilleures techniques de régularisation permettant de réduire l'overfittinhg.<br>\n",
    "L**overfitting** a lieu lorsque nous expérimentons une **très bonne précision**(accuracy) **sur le jeu de données d'entraînement** et une **faible précision sur le jeu de données de test**.On peut par exemple atteindre **99%** de précison lors de l'enttraînement et **90%** lors de la validation <br>\n",
    "\n",
    "\n",
    "#### Sunthèse\n",
    "Plusieurs techniques ont été mises en place afin de lutter contre l'explosion et la disparition du gradient notamment avec la techniques d'initialisation de **Xavier Glorot** et **Kaiming He**, l'introduction de la fonction d'activation **ReLU**.\n",
    "* D'un côté, éviter l'exposion de gradient permet d'assurer une **convergence stable** de la fonction  de perte.\n",
    "* D'un autre côté, éviter le problème de disparution du gradient permet d'assurer une **Convergence rapide** de la fonction de perte\n",
    "* Cependant  <a href=\"https://arxiv.org/pdf/1502.03167.pdf\" >  Sergey Ioffe et al</a> a démontré que la technique du **batch normalization** permettait d'entraîner efficacemment un modèle de réseau de neurones profonds sans trop s'attarder sur les techniques d'initialisation des poids car la technique **batch normalization** est très souvent robuste pour éviter la disparution et l'explosion du gradient en assurant une convergence rapide et stable du modèle.\n",
    "\n",
    "#### Notre cas \n",
    "Au vu de tout ce qui précède voici comment nous avons conçu nos différents modèles:\n",
    "* Couche de convolution constituée de **kernel size** vallant 3x3 ou 1x1\n",
    "* Couche de batch Normalization\n",
    "* Fonction d'activation : **ReLU**\n",
    "* Initialisation des poids : Initiatialiation **Xavier Glorot**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ConvBlock\n",
    "Cette couhe est utilisé pour soucis de **factorisation**\n",
    "de code dans la mise en place des différents modèles. <br>\n",
    "Elle est constitué d'une couhe de convolution, de batch normalization et d'avtivation ReLU que nous choissons d'ajouter ou pas en fonction du besoin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvBlock(keras.layers.Layer):\n",
    "\n",
    "    def __init__(self,num_filters,kernel_size,strides,padding,activation=None):\n",
    "\n",
    "        super(ConvBlock,self).__init__()\n",
    "        self.layer=Sequential([\n",
    "            Conv2D(filters=num_filters,kernel_size=kernel_size,strides=strides,padding=padding),\n",
    "            BatchNormalization()\n",
    "        ])\n",
    "        \n",
    "        self.activation=activation\n",
    "        \n",
    "        if self.activation: #On decide d'ajouter une activation ou pas. Cela serait utile lors de la mise en oeuvre de Residual connection.\n",
    "            self.relu=ReLU()\n",
    "\n",
    "    def call(self,inputs):\n",
    "        x=self.layer(inputs)\n",
    "        \n",
    "        return self.relu(x) if self.activation else x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation de ConvBlolck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input=Input(shape=(28,28,1))\n",
    "\n",
    "convb=ConvBlock(num_filters=128,kernel_size=(3,3),strides=(1,1),padding='same',activation=\"relu\")(input)\n",
    "model=keras.Model(inputs=input, outputs=convb)\n",
    "plot_model(model, to_file='convb_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Couche MLPs (Multi layers Perceptrons)\n",
    "Cette couche est constitué de couche de neurones complètement connecté afin d'effectuer la tâche de classification une fois que les caractéristiques ont été extraient par les couches convolutionelles.<br>\n",
    "Cette couche a été créer par soucis de **factorisation** de code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLPs(tf.keras.layers.Layer):\n",
    "\n",
    "    def __init__(self,units=1042,dropout=0.4):\n",
    "\n",
    "        super(MLPs,self).__init__()\n",
    "        self.mlp=Sequential([\n",
    "            Dense(units=units,activation='relu',name='fc1'),\n",
    "            Dropout(dropout,name='dropout1'),\n",
    "            Dense(units=5,activation='softmax')\n",
    "        ])\n",
    "\n",
    "    def call(self,inputs):\n",
    "        return self.mlp(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation de MLPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input=Input(shape=(28*28,))\n",
    "\n",
    "mlps=MLPs(units=1042,dropout=0.4)(input)\n",
    "model=keras.Model(inputs=input, outputs=mlps)\n",
    "plot_model(model, to_file='mlps_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conception de Mini VGG\n",
    "Nous avons adopté une architecture de type VGG qui est en effet une empilement de couche de **convolution** suivit de couche de d'**activation** et de **Maxpooling**\n",
    "* Les couches de **convolution** sont des **ConvBlock**.\n",
    "* L'architecture du papier original ne contient pas de couche de **Batch Normalization** mais nous les avons ajoutées afin d'éviter tous les problèmes que nous avons énuméré précédemment.\n",
    "* La couche de **Maxpooling** permet d'extraire les caractéristiques les plus importantes sans s'attarder au détails qui rendraient l'apprentissage **lent** du fait de la **dimensionalité** des **cartes caractéristiques**(feature map)<br>\n",
    "\n",
    "Pour plus de détails quand à l'architecture de **VGG** vous pouvez vous referez à <a href =\"https://arxiv.org/pdf/1409.1556.pdf\"> Karen Simonyan  et al</a> (2015)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Définition et implémentation du modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MiniVGG(keras.Model):\n",
    "\n",
    "    def __init__(self,units=1024,dropout=0.4):\n",
    "\n",
    "        super(MiniVGG,self).__init__()\n",
    "\n",
    "        self.layer1=Sequential([\n",
    "            ConvBlock(num_filters=64,kernel_size=(3,3),strides=(1,1),padding='same',activation=\"relu\"),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "\n",
    "        self.layer2=Sequential([\n",
    "            ConvBlock(num_filters=64,kernel_size=(3,3),strides=(1,1),padding='same',activation=\"relu\"),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "\n",
    "        self.layer3=Sequential([\n",
    "            ConvBlock(num_filters=128,kernel_size=(3,3),strides=(1,1),padding='same',activation=\"relu\"),\n",
    "            ConvBlock(num_filters=128,kernel_size=(3,3),strides=(1,1),padding='same',activation=\"relu\"),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "        self.layer4=Sequential([\n",
    "            ConvBlock(num_filters=256,kernel_size=(3,3),strides=(1,1),padding='same',activation=\"relu\"),\n",
    "            ConvBlock(num_filters=256,kernel_size=(3,3),strides=(1,1),padding='same',activation=\"relu\"),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "        \n",
    "        self.gap=GlobalAveragePooling2D(name=\"GlobalAveragePooling2D\")\n",
    "        self.flatten=Flatten()\n",
    "        \n",
    "        self.mlp=MLPs(units=units,dropout=dropout)\n",
    "        \n",
    "    def call(self,inputs):\n",
    "        layer1=self.layer1(inputs)\n",
    "        layer2=self.layer2(layer1)\n",
    "        layer3=self.layer3(layer2)\n",
    "        layer4=self.layer4(layer3)\n",
    "        flatten=self.flatten(self.gap(layer4))\n",
    "\n",
    "        return self.mlp(flatten)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisation de Mini VGG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input=Input(shape=(28,28,1))\n",
    "\n",
    "miniVGG=MiniVGG(dropout=0.6)(input)\n",
    "model=keras.Model(inputs=input, outputs=miniVGG)\n",
    "plot_model(model, to_file='miniVGG_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entraînement de Mini VGG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instanciation du modèle\n",
    "Lors de l'entraînement, modèle ayant le aynt le **meilleiur accuracy** sur le jeu de **données de validation** sera sauvegarder <br>\n",
    "Le sauvegarde se faire via le **callback** **ModelCheckpoint** de Keras."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### scheduler\n",
    "Pour permettre de changer progressivement le **learning rate** durant l'entraînement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scheduler(epoch, lr):\n",
    "    if epoch % 10 !=0:\n",
    "        return lr\n",
    "    else:\n",
    "        return lr * np.exp(-0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_5 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
      "                                                                 \n",
      " mini_vgg_2 (MiniVGG)        (None, 5)                 1416133   \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1416133 (5.40 MB)\n",
      "Trainable params: 1414341 (5.40 MB)\n",
      "Non-trainable params: 1792 (7.00 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "data=Input(shape=(28,28,1))\n",
    "miniVGG=MiniVGG(dropout=0.6)\n",
    "conv=miniVGG(data)\n",
    "\n",
    "checkpoint_filepath = 'best_mini_vgg.h5'\n",
    "lr_scheduler = LearningRateScheduler(scheduler, verbose=1)\n",
    "model_checkpoint = ModelCheckpoint(checkpoint_filepath, save_best_only=True, monitor='val_accuracy', mode='max', verbose=1)\n",
    "\n",
    "miniVGG=keras.Model(inputs=data,outputs=conv)\n",
    "miniVGG.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Entraînement proprement dit\n",
    "* Optimiseur : **Adam**\n",
    "Cette optimiseur est celui qui garantit la convergence rapide du modèle comparé à l'optimizeur **RMSprop**<br>\n",
    "Nous avons aussi mise place un scheduler afin de diminuer progressivement le **learning rate** durant l'entraînement mais cela ne semble pas apporter d'amélioration particulière quant comparé au learning rate par défaut.\n",
    "* Fonctiond de perte : **Entropie croisée**\n",
    "* Métrique d'évaluation : **Acuracy**\n",
    "* Epochs : **100** pour s'assurer de la convergence du modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.2252 - accuracy: 0.9232\n",
      "Epoch 1: val_accuracy improved from -inf to 0.94324, saving model to best_mini_vgg.h5\n",
      "2344/2344 [==============================] - 249s 106ms/step - loss: 0.2252 - accuracy: 0.9232 - val_loss: 0.1673 - val_accuracy: 0.9432\n",
      "Epoch 2/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.1330 - accuracy: 0.9550\n",
      "Epoch 2: val_accuracy improved from 0.94324 to 0.95196, saving model to best_mini_vgg.h5\n",
      "2344/2344 [==============================] - 246s 105ms/step - loss: 0.1330 - accuracy: 0.9550 - val_loss: 0.1459 - val_accuracy: 0.9520\n",
      "Epoch 3/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.1120 - accuracy: 0.9616\n",
      "Epoch 3: val_accuracy improved from 0.95196 to 0.95920, saving model to best_mini_vgg.h5\n",
      "2344/2344 [==============================] - 246s 105ms/step - loss: 0.1120 - accuracy: 0.9616 - val_loss: 0.1228 - val_accuracy: 0.9592\n",
      "Epoch 4/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0969 - accuracy: 0.9665\n",
      "Epoch 4: val_accuracy did not improve from 0.95920\n",
      "2344/2344 [==============================] - 245s 105ms/step - loss: 0.0969 - accuracy: 0.9665 - val_loss: 0.1293 - val_accuracy: 0.9589\n",
      "Epoch 5/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0851 - accuracy: 0.9706\n",
      "Epoch 5: val_accuracy improved from 0.95920 to 0.96312, saving model to best_mini_vgg.h5\n",
      "2344/2344 [==============================] - 246s 105ms/step - loss: 0.0851 - accuracy: 0.9706 - val_loss: 0.1226 - val_accuracy: 0.9631\n",
      "Epoch 6/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0741 - accuracy: 0.9739\n",
      "Epoch 6: val_accuracy improved from 0.96312 to 0.96404, saving model to best_mini_vgg.h5\n",
      "2344/2344 [==============================] - 246s 105ms/step - loss: 0.0741 - accuracy: 0.9739 - val_loss: 0.1204 - val_accuracy: 0.9640\n",
      "Epoch 7/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0644 - accuracy: 0.9771\n",
      "Epoch 7: val_accuracy did not improve from 0.96404\n",
      "2344/2344 [==============================] - 246s 105ms/step - loss: 0.0644 - accuracy: 0.9771 - val_loss: 0.1287 - val_accuracy: 0.9623\n",
      "Epoch 8/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0563 - accuracy: 0.9798\n",
      "Epoch 8: val_accuracy did not improve from 0.96404\n",
      "2344/2344 [==============================] - 246s 105ms/step - loss: 0.0563 - accuracy: 0.9798 - val_loss: 0.1455 - val_accuracy: 0.9581\n",
      "Epoch 9/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0493 - accuracy: 0.9823\n",
      "Epoch 9: val_accuracy did not improve from 0.96404\n",
      "2344/2344 [==============================] - 247s 105ms/step - loss: 0.0493 - accuracy: 0.9823 - val_loss: 0.1525 - val_accuracy: 0.9629\n",
      "Epoch 10/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0428 - accuracy: 0.9846\n",
      "Epoch 10: val_accuracy did not improve from 0.96404\n",
      "2344/2344 [==============================] - 247s 105ms/step - loss: 0.0428 - accuracy: 0.9846 - val_loss: 0.1434 - val_accuracy: 0.9620\n",
      "Epoch 11/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0378 - accuracy: 0.9864\n",
      "Epoch 11: val_accuracy did not improve from 0.96404\n",
      "2344/2344 [==============================] - 247s 105ms/step - loss: 0.0378 - accuracy: 0.9864 - val_loss: 0.1612 - val_accuracy: 0.9626\n",
      "Epoch 12/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0332 - accuracy: 0.9881\n",
      "Epoch 12: val_accuracy did not improve from 0.96404\n",
      "2344/2344 [==============================] - 247s 105ms/step - loss: 0.0332 - accuracy: 0.9881 - val_loss: 0.2150 - val_accuracy: 0.9570\n",
      "Epoch 13/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0300 - accuracy: 0.9892\n",
      "Epoch 13: val_accuracy did not improve from 0.96404\n",
      "2344/2344 [==============================] - 247s 105ms/step - loss: 0.0300 - accuracy: 0.9892 - val_loss: 0.1903 - val_accuracy: 0.9608\n",
      "Epoch 14/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0267 - accuracy: 0.9905\n",
      "Epoch 14: val_accuracy did not improve from 0.96404\n",
      "2344/2344 [==============================] - 247s 105ms/step - loss: 0.0267 - accuracy: 0.9905 - val_loss: 0.1994 - val_accuracy: 0.9619\n",
      "Epoch 15/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0241 - accuracy: 0.9914\n",
      "Epoch 15: val_accuracy improved from 0.96404 to 0.96460, saving model to best_mini_vgg.h5\n",
      "2344/2344 [==============================] - 247s 105ms/step - loss: 0.0241 - accuracy: 0.9914 - val_loss: 0.2116 - val_accuracy: 0.9646\n",
      "Epoch 16/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0224 - accuracy: 0.9919\n",
      "Epoch 16: val_accuracy did not improve from 0.96460\n",
      "2344/2344 [==============================] - 247s 105ms/step - loss: 0.0224 - accuracy: 0.9919 - val_loss: 0.2232 - val_accuracy: 0.9623\n",
      "Epoch 17/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0204 - accuracy: 0.9928\n",
      "Epoch 17: val_accuracy improved from 0.96460 to 0.96532, saving model to best_mini_vgg.h5\n",
      "2344/2344 [==============================] - 247s 105ms/step - loss: 0.0204 - accuracy: 0.9928 - val_loss: 0.2250 - val_accuracy: 0.9653\n",
      "Epoch 18/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0188 - accuracy: 0.9934\n",
      "Epoch 18: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 247s 106ms/step - loss: 0.0188 - accuracy: 0.9934 - val_loss: 0.2159 - val_accuracy: 0.9634\n",
      "Epoch 19/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0177 - accuracy: 0.9938\n",
      "Epoch 19: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 248s 106ms/step - loss: 0.0177 - accuracy: 0.9938 - val_loss: 0.2316 - val_accuracy: 0.9626\n",
      "Epoch 20/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0164 - accuracy: 0.9942\n",
      "Epoch 20: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 248s 106ms/step - loss: 0.0164 - accuracy: 0.9942 - val_loss: 0.2691 - val_accuracy: 0.9632\n",
      "Epoch 21/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0151 - accuracy: 0.9946\n",
      "Epoch 21: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 248s 106ms/step - loss: 0.0151 - accuracy: 0.9946 - val_loss: 0.2526 - val_accuracy: 0.9649\n",
      "Epoch 22/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0147 - accuracy: 0.9948\n",
      "Epoch 22: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 248s 106ms/step - loss: 0.0147 - accuracy: 0.9948 - val_loss: 0.2629 - val_accuracy: 0.9627\n",
      "Epoch 23/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0140 - accuracy: 0.9950\n",
      "Epoch 23: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 248s 106ms/step - loss: 0.0140 - accuracy: 0.9950 - val_loss: 0.2648 - val_accuracy: 0.9634\n",
      "Epoch 24/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0132 - accuracy: 0.9954\n",
      "Epoch 24: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 248s 106ms/step - loss: 0.0132 - accuracy: 0.9954 - val_loss: 0.2921 - val_accuracy: 0.9619\n",
      "Epoch 25/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0126 - accuracy: 0.9957\n",
      "Epoch 25: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 249s 106ms/step - loss: 0.0126 - accuracy: 0.9957 - val_loss: 0.3053 - val_accuracy: 0.9635\n",
      "Epoch 26/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0122 - accuracy: 0.9958\n",
      "Epoch 26: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 249s 106ms/step - loss: 0.0122 - accuracy: 0.9958 - val_loss: 0.2715 - val_accuracy: 0.9617\n",
      "Epoch 27/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0114 - accuracy: 0.9961\n",
      "Epoch 27: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 249s 106ms/step - loss: 0.0114 - accuracy: 0.9961 - val_loss: 0.3016 - val_accuracy: 0.9608\n",
      "Epoch 28/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0112 - accuracy: 0.9962\n",
      "Epoch 28: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 249s 106ms/step - loss: 0.0112 - accuracy: 0.9962 - val_loss: 0.2893 - val_accuracy: 0.9637\n",
      "Epoch 29/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0109 - accuracy: 0.9963\n",
      "Epoch 29: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 249s 106ms/step - loss: 0.0109 - accuracy: 0.9963 - val_loss: 0.2973 - val_accuracy: 0.9651\n",
      "Epoch 30/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0104 - accuracy: 0.9964\n",
      "Epoch 30: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 249s 106ms/step - loss: 0.0104 - accuracy: 0.9964 - val_loss: 0.2770 - val_accuracy: 0.9648\n",
      "Epoch 31/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0100 - accuracy: 0.9967\n",
      "Epoch 31: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 249s 106ms/step - loss: 0.0100 - accuracy: 0.9967 - val_loss: 0.2976 - val_accuracy: 0.9651\n",
      "Epoch 32/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0097 - accuracy: 0.9967\n",
      "Epoch 32: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 249s 106ms/step - loss: 0.0097 - accuracy: 0.9967 - val_loss: 0.3111 - val_accuracy: 0.9640\n",
      "Epoch 33/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0097 - accuracy: 0.9967\n",
      "Epoch 33: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 249s 106ms/step - loss: 0.0097 - accuracy: 0.9967 - val_loss: 0.3282 - val_accuracy: 0.9616\n",
      "Epoch 34/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0093 - accuracy: 0.9969\n",
      "Epoch 34: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 250s 106ms/step - loss: 0.0093 - accuracy: 0.9969 - val_loss: 0.3089 - val_accuracy: 0.9624\n",
      "Epoch 35/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0086 - accuracy: 0.9971\n",
      "Epoch 35: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 250s 106ms/step - loss: 0.0086 - accuracy: 0.9971 - val_loss: 0.3510 - val_accuracy: 0.9622\n",
      "Epoch 36/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0087 - accuracy: 0.9971\n",
      "Epoch 36: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 250s 107ms/step - loss: 0.0087 - accuracy: 0.9971 - val_loss: 0.3456 - val_accuracy: 0.9645\n",
      "Epoch 37/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0086 - accuracy: 0.9971\n",
      "Epoch 37: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 250s 107ms/step - loss: 0.0086 - accuracy: 0.9971 - val_loss: 0.3188 - val_accuracy: 0.9643\n",
      "Epoch 38/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0080 - accuracy: 0.9973\n",
      "Epoch 38: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 250s 107ms/step - loss: 0.0080 - accuracy: 0.9973 - val_loss: 0.3378 - val_accuracy: 0.9644\n",
      "Epoch 39/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0082 - accuracy: 0.9972\n",
      "Epoch 39: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 250s 107ms/step - loss: 0.0082 - accuracy: 0.9972 - val_loss: 0.3061 - val_accuracy: 0.9646\n",
      "Epoch 40/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0082 - accuracy: 0.9973\n",
      "Epoch 40: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0082 - accuracy: 0.9973 - val_loss: 0.3316 - val_accuracy: 0.9640\n",
      "Epoch 41/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0076 - accuracy: 0.9975\n",
      "Epoch 41: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0076 - accuracy: 0.9975 - val_loss: 0.3353 - val_accuracy: 0.9646\n",
      "Epoch 42/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0079 - accuracy: 0.9974\n",
      "Epoch 42: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0079 - accuracy: 0.9974 - val_loss: 0.3587 - val_accuracy: 0.9638\n",
      "Epoch 43/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0073 - accuracy: 0.9976\n",
      "Epoch 43: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0073 - accuracy: 0.9976 - val_loss: 0.3256 - val_accuracy: 0.9640\n",
      "Epoch 44/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0071 - accuracy: 0.9976\n",
      "Epoch 44: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0071 - accuracy: 0.9976 - val_loss: 0.3286 - val_accuracy: 0.9648\n",
      "Epoch 45/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0074 - accuracy: 0.9975\n",
      "Epoch 45: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0074 - accuracy: 0.9975 - val_loss: 0.3579 - val_accuracy: 0.9645\n",
      "Epoch 46/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0070 - accuracy: 0.9977\n",
      "Epoch 46: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.3624 - val_accuracy: 0.9651\n",
      "Epoch 47/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0068 - accuracy: 0.9977\n",
      "Epoch 47: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0068 - accuracy: 0.9977 - val_loss: 0.3468 - val_accuracy: 0.9638\n",
      "Epoch 48/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0068 - accuracy: 0.9977\n",
      "Epoch 48: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0068 - accuracy: 0.9977 - val_loss: 0.3428 - val_accuracy: 0.9642\n",
      "Epoch 49/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0063 - accuracy: 0.9979\n",
      "Epoch 49: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0063 - accuracy: 0.9979 - val_loss: 0.3925 - val_accuracy: 0.9626\n",
      "Epoch 50/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0068 - accuracy: 0.9977\n",
      "Epoch 50: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0068 - accuracy: 0.9977 - val_loss: 0.3571 - val_accuracy: 0.9628\n",
      "Epoch 51/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0061 - accuracy: 0.9980\n",
      "Epoch 51: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0061 - accuracy: 0.9980 - val_loss: 0.3630 - val_accuracy: 0.9638\n",
      "Epoch 52/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0062 - accuracy: 0.9979\n",
      "Epoch 52: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0062 - accuracy: 0.9979 - val_loss: 0.3665 - val_accuracy: 0.9649\n",
      "Epoch 53/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0067 - accuracy: 0.9978\n",
      "Epoch 53: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0067 - accuracy: 0.9978 - val_loss: 0.3431 - val_accuracy: 0.9643\n",
      "Epoch 54/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0060 - accuracy: 0.9981\n",
      "Epoch 54: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.4112 - val_accuracy: 0.9636\n",
      "Epoch 55/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0060 - accuracy: 0.9980\n",
      "Epoch 55: val_accuracy did not improve from 0.96532\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0060 - accuracy: 0.9980 - val_loss: 0.3938 - val_accuracy: 0.9625\n",
      "Epoch 56/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0058 - accuracy: 0.9980\n",
      "Epoch 56: val_accuracy improved from 0.96532 to 0.96584, saving model to best_mini_vgg.h5\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0058 - accuracy: 0.9980 - val_loss: 0.3499 - val_accuracy: 0.9658\n",
      "Epoch 57/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0060 - accuracy: 0.9981\n",
      "Epoch 57: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.3721 - val_accuracy: 0.9644\n",
      "Epoch 58/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0059 - accuracy: 0.9981\n",
      "Epoch 58: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0059 - accuracy: 0.9981 - val_loss: 0.3970 - val_accuracy: 0.9630\n",
      "Epoch 59/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0055 - accuracy: 0.9983\n",
      "Epoch 59: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0055 - accuracy: 0.9983 - val_loss: 0.3890 - val_accuracy: 0.9635\n",
      "Epoch 60/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0055 - accuracy: 0.9982\n",
      "Epoch 60: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0055 - accuracy: 0.9982 - val_loss: 0.4527 - val_accuracy: 0.9619\n",
      "Epoch 61/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9982\n",
      "Epoch 61: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0056 - accuracy: 0.9982 - val_loss: 0.3892 - val_accuracy: 0.9649\n",
      "Epoch 62/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0058 - accuracy: 0.9981\n",
      "Epoch 62: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0058 - accuracy: 0.9981 - val_loss: 0.4008 - val_accuracy: 0.9645\n",
      "Epoch 63/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0050 - accuracy: 0.9984\n",
      "Epoch 63: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0050 - accuracy: 0.9984 - val_loss: 0.4009 - val_accuracy: 0.9653\n",
      "Epoch 64/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9983\n",
      "Epoch 64: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0056 - accuracy: 0.9983 - val_loss: 0.4108 - val_accuracy: 0.9657\n",
      "Epoch 65/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0052 - accuracy: 0.9983\n",
      "Epoch 65: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0052 - accuracy: 0.9983 - val_loss: 0.3842 - val_accuracy: 0.9643\n",
      "Epoch 66/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9984\n",
      "Epoch 66: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.3941 - val_accuracy: 0.9642\n",
      "Epoch 67/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0050 - accuracy: 0.9984\n",
      "Epoch 67: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0050 - accuracy: 0.9984 - val_loss: 0.4113 - val_accuracy: 0.9648\n",
      "Epoch 68/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.9983\n",
      "Epoch 68: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0053 - accuracy: 0.9983 - val_loss: 0.4024 - val_accuracy: 0.9651\n",
      "Epoch 69/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0046 - accuracy: 0.9985\n",
      "Epoch 69: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0046 - accuracy: 0.9985 - val_loss: 0.3872 - val_accuracy: 0.9653\n",
      "Epoch 70/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9985\n",
      "Epoch 70: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0047 - accuracy: 0.9985 - val_loss: 0.4159 - val_accuracy: 0.9640\n",
      "Epoch 71/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9985\n",
      "Epoch 71: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.4121 - val_accuracy: 0.9651\n",
      "Epoch 72/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9984\n",
      "Epoch 72: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.3817 - val_accuracy: 0.9646\n",
      "Epoch 73/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9985\n",
      "Epoch 73: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0047 - accuracy: 0.9985 - val_loss: 0.4283 - val_accuracy: 0.9650\n",
      "Epoch 74/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9985\n",
      "Epoch 74: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0047 - accuracy: 0.9985 - val_loss: 0.4107 - val_accuracy: 0.9654\n",
      "Epoch 75/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0046 - accuracy: 0.9986\n",
      "Epoch 75: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0046 - accuracy: 0.9986 - val_loss: 0.4378 - val_accuracy: 0.9651\n",
      "Epoch 76/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9985\n",
      "Epoch 76: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0047 - accuracy: 0.9985 - val_loss: 0.3940 - val_accuracy: 0.9640\n",
      "Epoch 77/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0043 - accuracy: 0.9986\n",
      "Epoch 77: val_accuracy did not improve from 0.96584\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.3905 - val_accuracy: 0.9648\n",
      "Epoch 78/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0042 - accuracy: 0.9987\n",
      "Epoch 78: val_accuracy improved from 0.96584 to 0.96636, saving model to best_mini_vgg.h5\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.4227 - val_accuracy: 0.9664\n",
      "Epoch 79/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0046 - accuracy: 0.9986\n",
      "Epoch 79: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0046 - accuracy: 0.9986 - val_loss: 0.4144 - val_accuracy: 0.9655\n",
      "Epoch 80/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0046 - accuracy: 0.9986\n",
      "Epoch 80: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0046 - accuracy: 0.9986 - val_loss: 0.3807 - val_accuracy: 0.9636\n",
      "Epoch 81/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0039 - accuracy: 0.9987\n",
      "Epoch 81: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.4423 - val_accuracy: 0.9640\n",
      "Epoch 82/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9986\n",
      "Epoch 82: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.4563 - val_accuracy: 0.9656\n",
      "Epoch 83/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0042 - accuracy: 0.9986\n",
      "Epoch 83: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0042 - accuracy: 0.9986 - val_loss: 0.4550 - val_accuracy: 0.9636\n",
      "Epoch 84/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0040 - accuracy: 0.9988\n",
      "Epoch 84: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.4674 - val_accuracy: 0.9648\n",
      "Epoch 85/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9986\n",
      "Epoch 85: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.4106 - val_accuracy: 0.9650\n",
      "Epoch 86/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0041 - accuracy: 0.9987\n",
      "Epoch 86: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0041 - accuracy: 0.9987 - val_loss: 0.4367 - val_accuracy: 0.9650\n",
      "Epoch 87/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9987\n",
      "Epoch 87: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0047 - accuracy: 0.9987 - val_loss: 0.4116 - val_accuracy: 0.9656\n",
      "Epoch 88/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 0.9988\n",
      "Epoch 88: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.4698 - val_accuracy: 0.9640\n",
      "Epoch 89/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0042 - accuracy: 0.9987\n",
      "Epoch 89: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.4368 - val_accuracy: 0.9651\n",
      "Epoch 90/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0040 - accuracy: 0.9988\n",
      "Epoch 90: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.4202 - val_accuracy: 0.9634\n",
      "Epoch 91/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0039 - accuracy: 0.9988\n",
      "Epoch 91: val_accuracy did not improve from 0.96636\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0039 - accuracy: 0.9988 - val_loss: 0.4181 - val_accuracy: 0.9640\n",
      "Epoch 92/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0037 - accuracy: 0.9988\n",
      "Epoch 92: val_accuracy improved from 0.96636 to 0.96644, saving model to best_mini_vgg.h5\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 0.4482 - val_accuracy: 0.9664\n",
      "Epoch 93/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0041 - accuracy: 0.9988\n",
      "Epoch 93: val_accuracy did not improve from 0.96644\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0041 - accuracy: 0.9988 - val_loss: 0.4177 - val_accuracy: 0.9660\n",
      "Epoch 94/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0039 - accuracy: 0.9988\n",
      "Epoch 94: val_accuracy did not improve from 0.96644\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0039 - accuracy: 0.9988 - val_loss: 0.4614 - val_accuracy: 0.9638\n",
      "Epoch 95/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0034 - accuracy: 0.9989\n",
      "Epoch 95: val_accuracy did not improve from 0.96644\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0034 - accuracy: 0.9989 - val_loss: 0.4582 - val_accuracy: 0.9657\n",
      "Epoch 96/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 0.9988\n",
      "Epoch 96: val_accuracy did not improve from 0.96644\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.4611 - val_accuracy: 0.9656\n",
      "Epoch 97/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0040 - accuracy: 0.9988\n",
      "Epoch 97: val_accuracy did not improve from 0.96644\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.4644 - val_accuracy: 0.9636\n",
      "Epoch 98/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0036 - accuracy: 0.9988\n",
      "Epoch 98: val_accuracy did not improve from 0.96644\n",
      "2344/2344 [==============================] - 252s 107ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.5083 - val_accuracy: 0.9656\n",
      "Epoch 99/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 0.9988\n",
      "Epoch 99: val_accuracy did not improve from 0.96644\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.5100 - val_accuracy: 0.9647\n",
      "Epoch 100/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0036 - accuracy: 0.9989\n",
      "Epoch 100: val_accuracy did not improve from 0.96644\n",
      "2344/2344 [==============================] - 251s 107ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 0.4105 - val_accuracy: 0.9637\n"
     ]
    }
   ],
   "source": [
    "# opt=keras.optimizers.RMSprop(learning_rate=0.001,momentum=0.9)\n",
    "miniVGG.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "history = miniVGG.fit(train_data, epochs=100, validation_data=valid_data,callbacks=[model_checkpoint])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### resultats\n",
    "* **96.64%** d'accuracy sur sur le jeux de données de validation avec un entraînement sur le jeu de données d'entrainement obtenu par **data augmentation**\n",
    "* Accuracy autour de **94%** sur sur le jeux de données de validation avec un entraînement sur le jeu de données d'entrainement obtenu par **sans data augmentation**\n",
    "* On constate néanmoins un **overfitting** malgré la régularisation **dropout**\n",
    "* Nous avons aussi constaté que le learning rate par défaut fonctionnaire plutôt bien de même que l'initialisation par défaut (**Xavier Glorot**). Cela est problément dû au différents couches de **Batch Normalization** que nous avons introduit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ResidualBlock\n",
    "Malgré les diverses solutions précédemment présentées pour resoudre le problème de **vanishing** ou **exploding** gradient presiste si le modèle devient de plus en profond comme l'explique <a href=\"https://arxiv.org/pdf/1512.03385.pdf\"> Kaiming He et al </a>(2015). Cependant, la profondeur d'un modèle joue une rôle crucial quant à sa capacité d'extraire des caractéristiques (feature map) <a href=\"https://arxiv.org/pdf/1512.03385.pdf\"> Kaiming He et al </a>. Plus le modèle devient profond plus sa performance se degrade (<a href=\"https://arxiv.org/pdf/1512.03385.pdf\"> Kaiming He et al </a>) alors qu'il a été empiriquement démontrer par <a href=\"https://arxiv.org/pdf/1409.4842.pdf\"> Christian Szegedy et al </a>, <a href=\"https://arxiv.org/pdf/1409.1556.pdf\"> Karen Simonyan et al </a> que la profondeur permet d'extraire plus caractéristiques et donc plus performance.\n",
    "* Comment permet à un modèle de réseaux profond d'augmenter sa profondeur sans **dégradation** de sa perfomance ?\n",
    "* Existe-il d'autres solutions de contourner le problème de **vanishing** ou **exploding** engendrer pas la profondeur du réseau <br>\n",
    "\n",
    "Une réponse à ses intérrogations a été élucidé par <a href=\"https://arxiv.org/pdf/1512.03385.pdf\"> Kaiming He et al </a>(2015)<br>\n",
    "Leur solution consistait à utiliser les sauts de connections(**skipping connection**) afin d'apporter plus d'informations aux couches plus profondes. <br>\n",
    "La figure ci-dessous illustre le mécanisme de skipping connection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "display.Image(\"https://blog.kakaocdn.net/dn/dBesUR/btqVyUjjEyV/gCqE4ukhiXR2J0yw70xpOK/img.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResidualBlock(keras.layers.Layer):\n",
    "\n",
    "    def __init__(self,filter1,filter2,kernel_size2,stride1,padding1,downsample_state=False):\n",
    "\n",
    "        super(ResidualBlock,self).__init__()\n",
    "\n",
    "        self.layer=Sequential([\n",
    "            ConvBlock(num_filters=filter1,kernel_size=(3,3),strides=stride1,padding=padding1,activation=\"relu\"),\n",
    "            ConvBlock(num_filters=filter2,kernel_size=kernel_size2,strides=(1,1),padding=\"same\",activation=None)#La 2ième couche de notre de notre \n",
    "                                                                                                                # ResidualBlock ne possède pas d'actiavation\n",
    "                                                                                                                #L'activationn sera ajouter après avoir addictionner\n",
    "                                                                                                                # La sortie de cette couche et le skipping connection\n",
    "        ])\n",
    "        \n",
    "        self.downsample_state=downsample_state #Si le downsample_state=True alors le skipping connection est une couche de convolution\n",
    "                                               # Si downsample_state=False alors le skipping connection est une couche de convolution\n",
    "        \n",
    "        if self.downsample_state :\n",
    "            self.downsample=ConvBlock(num_filters=filter2,kernel_size=kernel_size2,strides=stride1,padding=padding1,activation=None)\n",
    "        self.relu=ReLU()\n",
    "\n",
    "    def call(self,inputs):\n",
    "\n",
    "        identity=inputs\n",
    "\n",
    "        if self.downsample_state :\n",
    "            identity=self.downsample(inputs)\n",
    "\n",
    "        out=self.relu(self.layer(inputs) + identity)\n",
    "\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conception de Mini Resnet\n",
    "Notre objectif est construire un modèle qui utilise les skipping connection comme **Resnet** sans être pour autant gros comme resnet. En effet le plus petit modèle resnet present sur keras est **Resnet50**, qui est lui d'être un petit modèle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Définition et implémentation du modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MiniResnet(keras.Model):\n",
    "\n",
    "    def __init__(self,units=1024,dropout=0.4):\n",
    "\n",
    "        super(MiniResnet,self).__init__()\n",
    "        self.layer1=Sequential([\n",
    "            ConvBlock(num_filters=64,kernel_size=(3,3),strides=(1,1),padding='same',activation='relu'),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "        \n",
    "        self.layer2=Sequential([\n",
    "            ResidualBlock(filter1=64,filter2=64,kernel_size2=(1,1),stride1=(1,1), padding1='same',downsample_state=False),\n",
    "            ResidualBlock(filter1=64,filter2=64,kernel_size2=(1,1),stride1=(1,1), padding1='same',downsample_state=False),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "\n",
    "        self.layer3=Sequential([\n",
    "            ResidualBlock(filter1=128,filter2=128,kernel_size2=(1,1),stride1=(1,1), padding1='same',downsample_state=True),\n",
    "            ResidualBlock(filter1=128,filter2=128,kernel_size2=(1,1),stride1=(1,1), padding1='same',downsample_state=True),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "\n",
    "        self.layer4=Sequential([\n",
    "            ResidualBlock(filter1=256,filter2=256,kernel_size2=(1,1),stride1=(1,1), padding1='same',downsample_state=True),\n",
    "            ResidualBlock(filter1=256,filter2=256,kernel_size2=(1,1),stride1=(1,1), padding1='same',downsample_state=True),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "\n",
    "        self.gap=GlobalAveragePooling2D(name=\"GlobalAveragePooling2D\")\n",
    "        self.flatten=Flatten()\n",
    "        \n",
    "        self.mlp=MLPs(units=units,dropout=dropout)\n",
    "\n",
    "    \n",
    "    def call(self,inputs):\n",
    "        layer=self.layer1(inputs)\n",
    "        layer=self.layer2(layer)\n",
    "        layer=self.layer3(layer)\n",
    "        layer=self.layer4(layer)\n",
    "        flatten=self.flatten(self.gap(layer))\n",
    "\n",
    "        return self.mlp(flatten)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entraînement de Mini Resnet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instanciation du modèle\n",
    "La même procedure d'entraînement que celle de **Mini VGG** est appliquée ici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_15\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_21 (InputLayer)       [(None, 28, 28, 1)]       0         \n",
      "                                                                 \n",
      " mini_resnet_5 (MiniResnet)  (None, 5)                 1756549   \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1756549 (6.70 MB)\n",
      "Trainable params: 1751301 (6.68 MB)\n",
      "Non-trainable params: 5248 (20.50 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "data=Input(shape=(28,28,1))\n",
    "miniResnet=MiniResnet(dropout=0.6)\n",
    "conv=miniResnet(data)\n",
    "\n",
    "checkpoint_filepath = 'best_mini_resnet.h5'\n",
    "lr_scheduler = LearningRateScheduler(scheduler, verbose=1)\n",
    "model_checkpoint = ModelCheckpoint(checkpoint_filepath, save_best_only=True, monitor='val_accuracy', mode='max', verbose=1)\n",
    "\n",
    "miniResnet=keras.Model(inputs=data,outputs=conv)\n",
    "miniResnet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.2314 - accuracy: 0.9218\n",
      "Epoch 1: val_accuracy improved from -inf to 0.93284, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 400s 169ms/step - loss: 0.2314 - accuracy: 0.9218 - val_loss: 0.1978 - val_accuracy: 0.9328\n",
      "Epoch 2/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.1339 - accuracy: 0.9548\n",
      "Epoch 2: val_accuracy improved from 0.93284 to 0.93964, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 399s 170ms/step - loss: 0.1339 - accuracy: 0.9548 - val_loss: 0.1773 - val_accuracy: 0.9396\n",
      "Epoch 3/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.1137 - accuracy: 0.9615\n",
      "Epoch 3: val_accuracy improved from 0.93964 to 0.95756, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 400s 171ms/step - loss: 0.1137 - accuracy: 0.9615 - val_loss: 0.1337 - val_accuracy: 0.9576\n",
      "Epoch 4/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0998 - accuracy: 0.9659\n",
      "Epoch 4: val_accuracy improved from 0.95756 to 0.96280, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 398s 170ms/step - loss: 0.0998 - accuracy: 0.9659 - val_loss: 0.1129 - val_accuracy: 0.9628\n",
      "Epoch 5/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0880 - accuracy: 0.9695\n",
      "Epoch 5: val_accuracy did not improve from 0.96280\n",
      "2344/2344 [==============================] - 397s 169ms/step - loss: 0.0880 - accuracy: 0.9695 - val_loss: 0.1278 - val_accuracy: 0.9611\n",
      "Epoch 6/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0779 - accuracy: 0.9728\n",
      "Epoch 6: val_accuracy did not improve from 0.96280\n",
      "2344/2344 [==============================] - 398s 170ms/step - loss: 0.0779 - accuracy: 0.9728 - val_loss: 0.1478 - val_accuracy: 0.9542\n",
      "Epoch 7/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0691 - accuracy: 0.9757\n",
      "Epoch 7: val_accuracy did not improve from 0.96280\n",
      "2344/2344 [==============================] - 400s 171ms/step - loss: 0.0691 - accuracy: 0.9757 - val_loss: 0.1307 - val_accuracy: 0.9611\n",
      "Epoch 8/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0616 - accuracy: 0.9783\n",
      "Epoch 8: val_accuracy improved from 0.96280 to 0.96480, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 402s 171ms/step - loss: 0.0616 - accuracy: 0.9783 - val_loss: 0.1223 - val_accuracy: 0.9648\n",
      "Epoch 9/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0553 - accuracy: 0.9802\n",
      "Epoch 9: val_accuracy did not improve from 0.96480\n",
      "2344/2344 [==============================] - 391s 167ms/step - loss: 0.0553 - accuracy: 0.9802 - val_loss: 0.1397 - val_accuracy: 0.9634\n",
      "Epoch 10/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0491 - accuracy: 0.9825\n",
      "Epoch 10: val_accuracy improved from 0.96480 to 0.96576, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 386s 165ms/step - loss: 0.0491 - accuracy: 0.9825 - val_loss: 0.1276 - val_accuracy: 0.9658\n",
      "Epoch 11/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0438 - accuracy: 0.9843\n",
      "Epoch 11: val_accuracy did not improve from 0.96576\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0438 - accuracy: 0.9843 - val_loss: 0.2237 - val_accuracy: 0.9522\n",
      "Epoch 12/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0390 - accuracy: 0.9860\n",
      "Epoch 12: val_accuracy did not improve from 0.96576\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0390 - accuracy: 0.9860 - val_loss: 0.1675 - val_accuracy: 0.9654\n",
      "Epoch 13/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0358 - accuracy: 0.9871\n",
      "Epoch 13: val_accuracy did not improve from 0.96576\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0358 - accuracy: 0.9871 - val_loss: 0.1688 - val_accuracy: 0.9658\n",
      "Epoch 14/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0323 - accuracy: 0.9884\n",
      "Epoch 14: val_accuracy did not improve from 0.96576\n",
      "2344/2344 [==============================] - 385s 164ms/step - loss: 0.0323 - accuracy: 0.9884 - val_loss: 0.1620 - val_accuracy: 0.9645\n",
      "Epoch 15/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0296 - accuracy: 0.9894\n",
      "Epoch 15: val_accuracy did not improve from 0.96576\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0296 - accuracy: 0.9894 - val_loss: 0.1957 - val_accuracy: 0.9636\n",
      "Epoch 16/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0272 - accuracy: 0.9902\n",
      "Epoch 16: val_accuracy improved from 0.96576 to 0.96656, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 383s 164ms/step - loss: 0.0272 - accuracy: 0.9902 - val_loss: 0.1768 - val_accuracy: 0.9666\n",
      "Epoch 17/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0255 - accuracy: 0.9908\n",
      "Epoch 17: val_accuracy did not improve from 0.96656\n",
      "2344/2344 [==============================] - 383s 164ms/step - loss: 0.0255 - accuracy: 0.9908 - val_loss: 0.1876 - val_accuracy: 0.9654\n",
      "Epoch 18/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0230 - accuracy: 0.9916\n",
      "Epoch 18: val_accuracy did not improve from 0.96656\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0230 - accuracy: 0.9916 - val_loss: 0.2115 - val_accuracy: 0.9646\n",
      "Epoch 19/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0219 - accuracy: 0.9921\n",
      "Epoch 19: val_accuracy did not improve from 0.96656\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0219 - accuracy: 0.9921 - val_loss: 0.1994 - val_accuracy: 0.9664\n",
      "Epoch 20/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0199 - accuracy: 0.9928\n",
      "Epoch 20: val_accuracy did not improve from 0.96656\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0199 - accuracy: 0.9928 - val_loss: 0.2081 - val_accuracy: 0.9661\n",
      "Epoch 21/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0198 - accuracy: 0.9930\n",
      "Epoch 21: val_accuracy did not improve from 0.96656\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0198 - accuracy: 0.9930 - val_loss: 0.2198 - val_accuracy: 0.9649\n",
      "Epoch 22/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0180 - accuracy: 0.9938\n",
      "Epoch 22: val_accuracy did not improve from 0.96656\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0180 - accuracy: 0.9938 - val_loss: 0.2473 - val_accuracy: 0.9618\n",
      "Epoch 23/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0171 - accuracy: 0.9939\n",
      "Epoch 23: val_accuracy improved from 0.96656 to 0.96684, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0171 - accuracy: 0.9939 - val_loss: 0.2029 - val_accuracy: 0.9668\n",
      "Epoch 24/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0169 - accuracy: 0.9940\n",
      "Epoch 24: val_accuracy improved from 0.96684 to 0.96720, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0169 - accuracy: 0.9940 - val_loss: 0.2020 - val_accuracy: 0.9672\n",
      "Epoch 25/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0152 - accuracy: 0.9945\n",
      "Epoch 25: val_accuracy did not improve from 0.96720\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0152 - accuracy: 0.9945 - val_loss: 0.2586 - val_accuracy: 0.9624\n",
      "Epoch 26/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0147 - accuracy: 0.9949\n",
      "Epoch 26: val_accuracy did not improve from 0.96720\n",
      "2344/2344 [==============================] - 384s 164ms/step - loss: 0.0147 - accuracy: 0.9949 - val_loss: 0.2295 - val_accuracy: 0.9643\n",
      "Epoch 27/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0143 - accuracy: 0.9951\n",
      "Epoch 27: val_accuracy did not improve from 0.96720\n",
      "2344/2344 [==============================] - 385s 164ms/step - loss: 0.0143 - accuracy: 0.9951 - val_loss: 0.2365 - val_accuracy: 0.9664\n",
      "Epoch 28/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0141 - accuracy: 0.9951\n",
      "Epoch 28: val_accuracy did not improve from 0.96720\n",
      "2344/2344 [==============================] - 385s 164ms/step - loss: 0.0141 - accuracy: 0.9951 - val_loss: 0.2638 - val_accuracy: 0.9632\n",
      "Epoch 29/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0127 - accuracy: 0.9955\n",
      "Epoch 29: val_accuracy did not improve from 0.96720\n",
      "2344/2344 [==============================] - 386s 164ms/step - loss: 0.0127 - accuracy: 0.9955 - val_loss: 0.2435 - val_accuracy: 0.9641\n",
      "Epoch 30/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0129 - accuracy: 0.9957\n",
      "Epoch 30: val_accuracy did not improve from 0.96720\n",
      "2344/2344 [==============================] - 386s 165ms/step - loss: 0.0129 - accuracy: 0.9957 - val_loss: 0.2468 - val_accuracy: 0.9662\n",
      "Epoch 31/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0123 - accuracy: 0.9958\n",
      "Epoch 31: val_accuracy did not improve from 0.96720\n",
      "2344/2344 [==============================] - 386s 165ms/step - loss: 0.0123 - accuracy: 0.9958 - val_loss: 0.2247 - val_accuracy: 0.9657\n",
      "Epoch 32/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0118 - accuracy: 0.9959\n",
      "Epoch 32: val_accuracy did not improve from 0.96720\n",
      "2344/2344 [==============================] - 387s 165ms/step - loss: 0.0118 - accuracy: 0.9959 - val_loss: 0.2516 - val_accuracy: 0.9666\n",
      "Epoch 33/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0117 - accuracy: 0.9960\n",
      "Epoch 33: val_accuracy improved from 0.96720 to 0.96776, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 387s 165ms/step - loss: 0.0117 - accuracy: 0.9960 - val_loss: 0.2744 - val_accuracy: 0.9678\n",
      "Epoch 34/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9961\n",
      "Epoch 34: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 387s 165ms/step - loss: 0.0113 - accuracy: 0.9961 - val_loss: 0.2618 - val_accuracy: 0.9653\n",
      "Epoch 35/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0108 - accuracy: 0.9962\n",
      "Epoch 35: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 387s 165ms/step - loss: 0.0108 - accuracy: 0.9962 - val_loss: 0.2759 - val_accuracy: 0.9652\n",
      "Epoch 36/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0105 - accuracy: 0.9963\n",
      "Epoch 36: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 387s 165ms/step - loss: 0.0105 - accuracy: 0.9963 - val_loss: 0.2637 - val_accuracy: 0.9654\n",
      "Epoch 37/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0101 - accuracy: 0.9966\n",
      "Epoch 37: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 387s 165ms/step - loss: 0.0101 - accuracy: 0.9966 - val_loss: 0.2640 - val_accuracy: 0.9660\n",
      "Epoch 38/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0101 - accuracy: 0.9966\n",
      "Epoch 38: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 387s 165ms/step - loss: 0.0101 - accuracy: 0.9966 - val_loss: 0.3434 - val_accuracy: 0.9644\n",
      "Epoch 39/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0100 - accuracy: 0.9967\n",
      "Epoch 39: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 387s 165ms/step - loss: 0.0100 - accuracy: 0.9967 - val_loss: 0.2722 - val_accuracy: 0.9669\n",
      "Epoch 40/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0096 - accuracy: 0.9968\n",
      "Epoch 40: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 387s 165ms/step - loss: 0.0096 - accuracy: 0.9968 - val_loss: 0.3211 - val_accuracy: 0.9640\n",
      "Epoch 41/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0094 - accuracy: 0.9968\n",
      "Epoch 41: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 388s 165ms/step - loss: 0.0094 - accuracy: 0.9968 - val_loss: 0.2602 - val_accuracy: 0.9664\n",
      "Epoch 42/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0096 - accuracy: 0.9968\n",
      "Epoch 42: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 388s 165ms/step - loss: 0.0096 - accuracy: 0.9968 - val_loss: 0.2771 - val_accuracy: 0.9658\n",
      "Epoch 43/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0086 - accuracy: 0.9972\n",
      "Epoch 43: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 388s 165ms/step - loss: 0.0086 - accuracy: 0.9972 - val_loss: 0.2802 - val_accuracy: 0.9645\n",
      "Epoch 44/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0084 - accuracy: 0.9971\n",
      "Epoch 44: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 388s 165ms/step - loss: 0.0084 - accuracy: 0.9971 - val_loss: 0.3024 - val_accuracy: 0.9665\n",
      "Epoch 45/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0088 - accuracy: 0.9970\n",
      "Epoch 45: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 388s 165ms/step - loss: 0.0088 - accuracy: 0.9970 - val_loss: 0.2983 - val_accuracy: 0.9664\n",
      "Epoch 46/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0085 - accuracy: 0.9971\n",
      "Epoch 46: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 388s 165ms/step - loss: 0.0085 - accuracy: 0.9971 - val_loss: 0.2975 - val_accuracy: 0.9645\n",
      "Epoch 47/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0082 - accuracy: 0.9972\n",
      "Epoch 47: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 388s 165ms/step - loss: 0.0082 - accuracy: 0.9972 - val_loss: 0.2926 - val_accuracy: 0.9663\n",
      "Epoch 48/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0085 - accuracy: 0.9973\n",
      "Epoch 48: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0085 - accuracy: 0.9973 - val_loss: 0.3114 - val_accuracy: 0.9659\n",
      "Epoch 49/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0079 - accuracy: 0.9974\n",
      "Epoch 49: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0079 - accuracy: 0.9974 - val_loss: 0.2997 - val_accuracy: 0.9664\n",
      "Epoch 50/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0076 - accuracy: 0.9974\n",
      "Epoch 50: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 399s 170ms/step - loss: 0.0076 - accuracy: 0.9974 - val_loss: 0.3016 - val_accuracy: 0.9671\n",
      "Epoch 51/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0076 - accuracy: 0.9974\n",
      "Epoch 51: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 394s 168ms/step - loss: 0.0076 - accuracy: 0.9974 - val_loss: 0.3417 - val_accuracy: 0.9639\n",
      "Epoch 52/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0075 - accuracy: 0.9974\n",
      "Epoch 52: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 391s 167ms/step - loss: 0.0075 - accuracy: 0.9974 - val_loss: 0.3024 - val_accuracy: 0.9670\n",
      "Epoch 53/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0077 - accuracy: 0.9975\n",
      "Epoch 53: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 390s 166ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.2793 - val_accuracy: 0.9660\n",
      "Epoch 54/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0068 - accuracy: 0.9977\n",
      "Epoch 54: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 390s 166ms/step - loss: 0.0068 - accuracy: 0.9977 - val_loss: 0.2886 - val_accuracy: 0.9665\n",
      "Epoch 55/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0074 - accuracy: 0.9975\n",
      "Epoch 55: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 400s 171ms/step - loss: 0.0074 - accuracy: 0.9975 - val_loss: 0.3173 - val_accuracy: 0.9664\n",
      "Epoch 56/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0070 - accuracy: 0.9976\n",
      "Epoch 56: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 397s 169ms/step - loss: 0.0070 - accuracy: 0.9976 - val_loss: 0.3093 - val_accuracy: 0.9664\n",
      "Epoch 57/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0068 - accuracy: 0.9977\n",
      "Epoch 57: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 397s 169ms/step - loss: 0.0068 - accuracy: 0.9977 - val_loss: 0.3256 - val_accuracy: 0.9673\n",
      "Epoch 58/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0067 - accuracy: 0.9978\n",
      "Epoch 58: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0067 - accuracy: 0.9978 - val_loss: 0.3395 - val_accuracy: 0.9653\n",
      "Epoch 59/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0069 - accuracy: 0.9978\n",
      "Epoch 59: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 391s 167ms/step - loss: 0.0069 - accuracy: 0.9978 - val_loss: 0.3065 - val_accuracy: 0.9661\n",
      "Epoch 60/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0070 - accuracy: 0.9977\n",
      "Epoch 60: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 390s 166ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.3190 - val_accuracy: 0.9647\n",
      "Epoch 61/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0063 - accuracy: 0.9979\n",
      "Epoch 61: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 390s 166ms/step - loss: 0.0063 - accuracy: 0.9979 - val_loss: 0.3376 - val_accuracy: 0.9670\n",
      "Epoch 62/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0065 - accuracy: 0.9979\n",
      "Epoch 62: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 390s 166ms/step - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.3482 - val_accuracy: 0.9657\n",
      "Epoch 63/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0070 - accuracy: 0.9978\n",
      "Epoch 63: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 390s 166ms/step - loss: 0.0070 - accuracy: 0.9978 - val_loss: 0.3326 - val_accuracy: 0.9666\n",
      "Epoch 64/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0061 - accuracy: 0.9981\n",
      "Epoch 64: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 390s 167ms/step - loss: 0.0061 - accuracy: 0.9981 - val_loss: 0.3233 - val_accuracy: 0.9668\n",
      "Epoch 65/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 0.9980\n",
      "Epoch 65: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0064 - accuracy: 0.9980 - val_loss: 0.3379 - val_accuracy: 0.9664\n",
      "Epoch 66/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0062 - accuracy: 0.9979\n",
      "Epoch 66: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 391s 167ms/step - loss: 0.0062 - accuracy: 0.9979 - val_loss: 0.3019 - val_accuracy: 0.9672\n",
      "Epoch 67/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0061 - accuracy: 0.9980\n",
      "Epoch 67: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 391s 167ms/step - loss: 0.0061 - accuracy: 0.9980 - val_loss: 0.3399 - val_accuracy: 0.9667\n",
      "Epoch 68/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0060 - accuracy: 0.9981\n",
      "Epoch 68: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 391s 167ms/step - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.3492 - val_accuracy: 0.9659\n",
      "Epoch 69/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0059 - accuracy: 0.9980\n",
      "Epoch 69: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 391s 167ms/step - loss: 0.0059 - accuracy: 0.9980 - val_loss: 0.3368 - val_accuracy: 0.9674\n",
      "Epoch 70/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9982\n",
      "Epoch 70: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 391s 167ms/step - loss: 0.0056 - accuracy: 0.9982 - val_loss: 0.3368 - val_accuracy: 0.9660\n",
      "Epoch 71/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0059 - accuracy: 0.9981\n",
      "Epoch 71: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0059 - accuracy: 0.9981 - val_loss: 0.3296 - val_accuracy: 0.9654\n",
      "Epoch 72/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 0.9981\n",
      "Epoch 72: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0057 - accuracy: 0.9981 - val_loss: 0.3293 - val_accuracy: 0.9670\n",
      "Epoch 73/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9982\n",
      "Epoch 73: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0056 - accuracy: 0.9982 - val_loss: 0.3827 - val_accuracy: 0.9644\n",
      "Epoch 74/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0055 - accuracy: 0.9982\n",
      "Epoch 74: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0055 - accuracy: 0.9982 - val_loss: 0.3593 - val_accuracy: 0.9655\n",
      "Epoch 75/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0052 - accuracy: 0.9983\n",
      "Epoch 75: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0052 - accuracy: 0.9983 - val_loss: 0.3393 - val_accuracy: 0.9658\n",
      "Epoch 76/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 0.9982\n",
      "Epoch 76: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0057 - accuracy: 0.9982 - val_loss: 0.3368 - val_accuracy: 0.9637\n",
      "Epoch 77/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 0.9982\n",
      "Epoch 77: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0057 - accuracy: 0.9982 - val_loss: 0.3617 - val_accuracy: 0.9656\n",
      "Epoch 78/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0050 - accuracy: 0.9983\n",
      "Epoch 78: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0050 - accuracy: 0.9983 - val_loss: 0.3342 - val_accuracy: 0.9670\n",
      "Epoch 79/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.9983\n",
      "Epoch 79: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0053 - accuracy: 0.9983 - val_loss: 0.3622 - val_accuracy: 0.9656\n",
      "Epoch 80/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9982\n",
      "Epoch 80: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0056 - accuracy: 0.9982 - val_loss: 0.3649 - val_accuracy: 0.9669\n",
      "Epoch 81/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9984\n",
      "Epoch 81: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.3434 - val_accuracy: 0.9668\n",
      "Epoch 82/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0052 - accuracy: 0.9984\n",
      "Epoch 82: val_accuracy did not improve from 0.96776\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0052 - accuracy: 0.9984 - val_loss: 0.3565 - val_accuracy: 0.9663\n",
      "Epoch 83/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9983\n",
      "Epoch 83: val_accuracy improved from 0.96776 to 0.96828, saving model to best_mini_resnet.h5\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0051 - accuracy: 0.9983 - val_loss: 0.3116 - val_accuracy: 0.9683\n",
      "Epoch 84/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0054 - accuracy: 0.9983\n",
      "Epoch 84: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0054 - accuracy: 0.9983 - val_loss: 0.3743 - val_accuracy: 0.9675\n",
      "Epoch 85/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9984\n",
      "Epoch 85: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 394s 168ms/step - loss: 0.0047 - accuracy: 0.9984 - val_loss: 0.3672 - val_accuracy: 0.9671\n",
      "Epoch 86/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9984\n",
      "Epoch 86: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 392s 167ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.3629 - val_accuracy: 0.9672\n",
      "Epoch 87/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9984\n",
      "Epoch 87: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 167ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.3767 - val_accuracy: 0.9662\n",
      "Epoch 88/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0050 - accuracy: 0.9984\n",
      "Epoch 88: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0050 - accuracy: 0.9984 - val_loss: 0.3586 - val_accuracy: 0.9658\n",
      "Epoch 89/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.9984\n",
      "Epoch 89: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 0.3482 - val_accuracy: 0.9663\n",
      "Epoch 90/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0045 - accuracy: 0.9985\n",
      "Epoch 90: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0045 - accuracy: 0.9985 - val_loss: 0.3712 - val_accuracy: 0.9651\n",
      "Epoch 91/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9985\n",
      "Epoch 91: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0047 - accuracy: 0.9985 - val_loss: 0.3656 - val_accuracy: 0.9670\n",
      "Epoch 92/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9985\n",
      "Epoch 92: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 167ms/step - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.3448 - val_accuracy: 0.9668\n",
      "Epoch 93/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9984\n",
      "Epoch 93: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.3451 - val_accuracy: 0.9653\n",
      "Epoch 94/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9986\n",
      "Epoch 94: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 167ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.3993 - val_accuracy: 0.9656\n",
      "Epoch 95/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9986\n",
      "Epoch 95: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.3733 - val_accuracy: 0.9668\n",
      "Epoch 96/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9985\n",
      "Epoch 96: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0047 - accuracy: 0.9985 - val_loss: 0.3881 - val_accuracy: 0.9666\n",
      "Epoch 97/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0043 - accuracy: 0.9987\n",
      "Epoch 97: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.4048 - val_accuracy: 0.9666\n",
      "Epoch 98/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9985\n",
      "Epoch 98: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.3641 - val_accuracy: 0.9667\n",
      "Epoch 99/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0042 - accuracy: 0.9987\n",
      "Epoch 99: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 393s 168ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.4178 - val_accuracy: 0.9660\n",
      "Epoch 100/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0040 - accuracy: 0.9987\n",
      "Epoch 100: val_accuracy did not improve from 0.96828\n",
      "2344/2344 [==============================] - 394s 168ms/step - loss: 0.0040 - accuracy: 0.9987 - val_loss: 0.3713 - val_accuracy: 0.9665\n"
     ]
    }
   ],
   "source": [
    "opt=keras.optimizers.RMSprop(learning_rate=0.0001,momentum=0.9)\n",
    "miniResnet.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "history = miniResnet.fit(train_data, epochs=100, validation_data=valid_data,callbacks=[model_checkpoint])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### resultats\n",
    "* **96.82%** d'accuracy sur sur le jeux de données de validation avec un entraînement sur le jeu de données d'entrainement obtenu par **data augmentation**\n",
    "* L'accuracy s'est nettement amélioré comparé à **Mini VGG** ayant un accuracy de **96.64**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## InceptionBlock_V1\n",
    "Plutôt de descendre plus en profondeur pour extraire plus de caractéristiques, une autre approche consite à utiliser extracteurs de features concatener en parallèle mais avec des des kernel différents. L'objectif est de pouvoir extraire différents caractériques dans la même couche. Au lieu d'utiliser des **kernel 3x3** dans une couche et des **kernel 1x1** dans la couche suivante, les auteurs ayant inventé cette technoque uilise les **deux types de kernel** dans la même couche mais en **parallèle** plutôt qu'en **séquantiel** comme on le voir dans la majorité des achitectures. Leur approche permet d'eviter de descendre trop en profondeur (éviter exploding ou vanishing gradient problème) tout en étant capable d'extraire autant de caractéristique pertinentes que les réseaux ayant plus de profondeur. Vous pouvez vous referer à <a href=\"https://arxiv.org/pdf/1512.00567.pdf\"> Christian Szegedy et al. </a> pour plus de détails.<br>\n",
    "L'image ci-dessous illustre bien le mécanisme mise en jeu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "display.Image(\"https://i.stack.imgur.com/zTinD.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InceptionBlock_V1(keras.layers.Layer):\n",
    "\n",
    "    def __init__(self,num_filter1,num_filter21,num_filter22,num_filter31,num_filter32,num_filter4):\n",
    "\n",
    "        super(InceptionBlock_V1, self).__init__()\n",
    "        \n",
    "        self.branch1=ConvBlock(num_filters=num_filter1,kernel_size=(1,1),strides=(1,1),padding='same',activation=\"relu\")\n",
    "        \n",
    "        self.branch2=Sequential([\n",
    "            ConvBlock(num_filters=num_filter21,kernel_size=(1,1),strides=(1,1),padding='same',activation=\"relu\"),\n",
    "            ConvBlock(num_filters=num_filter22,kernel_size=(3,3),strides=(1,1),padding='same',activation=\"relu\")\n",
    "        ])\n",
    "        \n",
    "        self.branch3=Sequential([\n",
    "            ConvBlock(num_filters=num_filter31,kernel_size=(1,1),strides=(1,1),padding='same',activation=\"relu\"),\n",
    "            ConvBlock(num_filters=num_filter32,kernel_size=(5,5),strides=(1,1),padding='same',activation=\"relu\")\n",
    "        ])\n",
    "        \n",
    "        self.branch4=Sequential([\n",
    "            MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same'),\n",
    "            ConvBlock(num_filters=num_filter4,kernel_size=(1,1),strides=(1,1),padding='same',activation=\"relu\")\n",
    "        ])\n",
    "\n",
    "    def call(self, inputs):\n",
    "        branches=(self.branch1,self.branch2,self.branch3,self.branch4)\n",
    "        return tf.concat([branch(inputs) for branch in branches],axis=3)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conception de Mini Inception_V1\n",
    "Notre objectif est construire un modèle simple qui utilise les **Block d'Inception_V1**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Définition et implémentation du modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MiniInception_V1(keras.Model):\n",
    "\n",
    "    def __init__(self,units=1024,dropout=0.4):\n",
    "\n",
    "        super(MiniInception_V1,self).__init__()\n",
    "\n",
    "        self.layer1=Sequential([\n",
    "            ConvBlock(num_filters=64,kernel_size=(3,3),strides=(1,1),padding='same',activation='relu'),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "        \n",
    "        self.layer2=Sequential([\n",
    "            InceptionBlock_V1(num_filter1=64,num_filter21=48,num_filter22=64,num_filter31=32,num_filter32=64,num_filter4=64),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "\n",
    "        self.layer3=Sequential([\n",
    "            InceptionBlock_V1(num_filter1=128,num_filter21=96,num_filter22=128,num_filter31=64,num_filter32=128,num_filter4=128),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "\n",
    "        self.layer4=Sequential([\n",
    "            InceptionBlock_V1(num_filter1=256,num_filter21=192,num_filter22=256,num_filter31=128,num_filter32=256,num_filter4=256),\n",
    "            MaxPooling2D(pool_size=(2,2),strides=(2,2))\n",
    "        ])\n",
    "        \n",
    "        self.gap=GlobalAveragePooling2D(name=\"GlobalAveragePooling2D\")\n",
    "        self.flatten=Flatten()\n",
    "        \n",
    "        self.mlp=MLPs(units=units,dropout=dropout)\n",
    "\n",
    "    def call(self,inputs):\n",
    "        layer=self.layer1(inputs)\n",
    "        layer=self.layer2(layer)\n",
    "        layer=self.layer3(layer)\n",
    "        layer=self.layer4(layer)\n",
    "        flatten=self.flatten(self.gap(layer))\n",
    "\n",
    "        return self.mlp(flatten)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instanciation du modèle\n",
    "La même procedure d'entraînement que celle de **Mini VGG** est appliquée ici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_22 (InputLayer)       [(None, 28, 28, 1)]       0         \n",
      "                                                                 \n",
      " mini_inception_v1_1 (MiniI  (None, 5)                 3268981   \n",
      " nception_V1)                                                    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3268981 (12.47 MB)\n",
      "Trainable params: 3264149 (12.45 MB)\n",
      "Non-trainable params: 4832 (18.88 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "data=Input(shape=(28,28,1))\n",
    "miniInception_V1=MiniInception_V1(dropout=0.6)\n",
    "conv=miniInception_V1(data)\n",
    "\n",
    "checkpoint_filepath = 'best_mini_Inception_V1.h5'\n",
    "lr_scheduler = LearningRateScheduler(scheduler, verbose=1)\n",
    "model_checkpoint = ModelCheckpoint(checkpoint_filepath, save_best_only=True, monitor='val_accuracy', mode='max', verbose=1)\n",
    "\n",
    "miniInception_V1=keras.Model(inputs=data,outputs=conv)\n",
    "miniInception_V1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.2201 - accuracy: 0.9280\n",
      "Epoch 1: val_accuracy improved from -inf to 0.92836, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 538s 228ms/step - loss: 0.2201 - accuracy: 0.9280 - val_loss: 0.2245 - val_accuracy: 0.9284\n",
      "Epoch 2/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.1240 - accuracy: 0.9583\n",
      "Epoch 2: val_accuracy improved from 0.92836 to 0.93248, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.1240 - accuracy: 0.9583 - val_loss: 0.2104 - val_accuracy: 0.9325\n",
      "Epoch 3/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.1026 - accuracy: 0.9651\n",
      "Epoch 3: val_accuracy improved from 0.93248 to 0.95900, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.1026 - accuracy: 0.9651 - val_loss: 0.1237 - val_accuracy: 0.9590\n",
      "Epoch 4/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0880 - accuracy: 0.9693\n",
      "Epoch 4: val_accuracy did not improve from 0.95900\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.0880 - accuracy: 0.9693 - val_loss: 0.1687 - val_accuracy: 0.9435\n",
      "Epoch 5/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0750 - accuracy: 0.9736\n",
      "Epoch 5: val_accuracy did not improve from 0.95900\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.0750 - accuracy: 0.9736 - val_loss: 0.1562 - val_accuracy: 0.9514\n",
      "Epoch 6/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0643 - accuracy: 0.9772\n",
      "Epoch 6: val_accuracy improved from 0.95900 to 0.96124, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.0643 - accuracy: 0.9772 - val_loss: 0.1315 - val_accuracy: 0.9612\n",
      "Epoch 7/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0556 - accuracy: 0.9801\n",
      "Epoch 7: val_accuracy improved from 0.96124 to 0.96412, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.0556 - accuracy: 0.9801 - val_loss: 0.1282 - val_accuracy: 0.9641\n",
      "Epoch 8/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0478 - accuracy: 0.9826\n",
      "Epoch 8: val_accuracy did not improve from 0.96412\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.0478 - accuracy: 0.9826 - val_loss: 0.1410 - val_accuracy: 0.9641\n",
      "Epoch 9/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0416 - accuracy: 0.9848\n",
      "Epoch 9: val_accuracy did not improve from 0.96412\n",
      "2344/2344 [==============================] - 535s 228ms/step - loss: 0.0416 - accuracy: 0.9848 - val_loss: 0.1761 - val_accuracy: 0.9595\n",
      "Epoch 10/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0368 - accuracy: 0.9867\n",
      "Epoch 10: val_accuracy improved from 0.96412 to 0.96472, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 537s 229ms/step - loss: 0.0368 - accuracy: 0.9867 - val_loss: 0.1666 - val_accuracy: 0.9647\n",
      "Epoch 11/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0328 - accuracy: 0.9881\n",
      "Epoch 11: val_accuracy did not improve from 0.96472\n",
      "2344/2344 [==============================] - 536s 229ms/step - loss: 0.0328 - accuracy: 0.9881 - val_loss: 0.1867 - val_accuracy: 0.9605\n",
      "Epoch 12/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0286 - accuracy: 0.9897\n",
      "Epoch 12: val_accuracy did not improve from 0.96472\n",
      "2344/2344 [==============================] - 535s 228ms/step - loss: 0.0286 - accuracy: 0.9897 - val_loss: 0.2058 - val_accuracy: 0.9611\n",
      "Epoch 13/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0264 - accuracy: 0.9904\n",
      "Epoch 13: val_accuracy improved from 0.96472 to 0.96536, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 536s 229ms/step - loss: 0.0264 - accuracy: 0.9904 - val_loss: 0.2111 - val_accuracy: 0.9654\n",
      "Epoch 14/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0236 - accuracy: 0.9915\n",
      "Epoch 14: val_accuracy did not improve from 0.96536\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.0236 - accuracy: 0.9915 - val_loss: 0.2064 - val_accuracy: 0.9648\n",
      "Epoch 15/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0227 - accuracy: 0.9920\n",
      "Epoch 15: val_accuracy did not improve from 0.96536\n",
      "2344/2344 [==============================] - 533s 227ms/step - loss: 0.0227 - accuracy: 0.9920 - val_loss: 0.2022 - val_accuracy: 0.9648\n",
      "Epoch 16/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0204 - accuracy: 0.9927\n",
      "Epoch 16: val_accuracy did not improve from 0.96536\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.0204 - accuracy: 0.9927 - val_loss: 0.2385 - val_accuracy: 0.9622\n",
      "Epoch 17/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0191 - accuracy: 0.9933\n",
      "Epoch 17: val_accuracy did not improve from 0.96536\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.0191 - accuracy: 0.9933 - val_loss: 0.2179 - val_accuracy: 0.9641\n",
      "Epoch 18/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0175 - accuracy: 0.9938\n",
      "Epoch 18: val_accuracy did not improve from 0.96536\n",
      "2344/2344 [==============================] - 534s 228ms/step - loss: 0.0175 - accuracy: 0.9938 - val_loss: 0.2376 - val_accuracy: 0.9653\n",
      "Epoch 19/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0168 - accuracy: 0.9942\n",
      "Epoch 19: val_accuracy did not improve from 0.96536\n",
      "2344/2344 [==============================] - 537s 229ms/step - loss: 0.0168 - accuracy: 0.9942 - val_loss: 0.2962 - val_accuracy: 0.9604\n",
      "Epoch 20/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0162 - accuracy: 0.9943\n",
      "Epoch 20: val_accuracy did not improve from 0.96536\n",
      "2344/2344 [==============================] - 537s 229ms/step - loss: 0.0162 - accuracy: 0.9943 - val_loss: 0.2581 - val_accuracy: 0.9600\n",
      "Epoch 21/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0155 - accuracy: 0.9947\n",
      "Epoch 21: val_accuracy did not improve from 0.96536\n",
      "2344/2344 [==============================] - 535s 228ms/step - loss: 0.0155 - accuracy: 0.9947 - val_loss: 0.2306 - val_accuracy: 0.9648\n",
      "Epoch 22/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0143 - accuracy: 0.9951\n",
      "Epoch 22: val_accuracy improved from 0.96536 to 0.96612, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 535s 228ms/step - loss: 0.0143 - accuracy: 0.9951 - val_loss: 0.2391 - val_accuracy: 0.9661\n",
      "Epoch 23/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0135 - accuracy: 0.9953\n",
      "Epoch 23: val_accuracy did not improve from 0.96612\n",
      "2344/2344 [==============================] - 535s 228ms/step - loss: 0.0135 - accuracy: 0.9953 - val_loss: 0.3118 - val_accuracy: 0.9641\n",
      "Epoch 24/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0133 - accuracy: 0.9955\n",
      "Epoch 24: val_accuracy did not improve from 0.96612\n",
      "2344/2344 [==============================] - 538s 230ms/step - loss: 0.0133 - accuracy: 0.9955 - val_loss: 0.2780 - val_accuracy: 0.9639\n",
      "Epoch 25/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0125 - accuracy: 0.9957\n",
      "Epoch 25: val_accuracy did not improve from 0.96612\n",
      "2344/2344 [==============================] - 538s 229ms/step - loss: 0.0125 - accuracy: 0.9957 - val_loss: 0.2728 - val_accuracy: 0.9628\n",
      "Epoch 26/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0122 - accuracy: 0.9958\n",
      "Epoch 26: val_accuracy did not improve from 0.96612\n",
      "2344/2344 [==============================] - 537s 229ms/step - loss: 0.0122 - accuracy: 0.9958 - val_loss: 0.3021 - val_accuracy: 0.9611\n",
      "Epoch 27/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0116 - accuracy: 0.9961\n",
      "Epoch 27: val_accuracy did not improve from 0.96612\n",
      "2344/2344 [==============================] - 602s 257ms/step - loss: 0.0116 - accuracy: 0.9961 - val_loss: 0.2698 - val_accuracy: 0.9656\n",
      "Epoch 28/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0111 - accuracy: 0.9963\n",
      "Epoch 28: val_accuracy did not improve from 0.96612\n",
      "2344/2344 [==============================] - 731s 312ms/step - loss: 0.0111 - accuracy: 0.9963 - val_loss: 0.3397 - val_accuracy: 0.9629\n",
      "Epoch 29/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0117 - accuracy: 0.9962\n",
      "Epoch 29: val_accuracy did not improve from 0.96612\n",
      "2344/2344 [==============================] - 1003s 428ms/step - loss: 0.0117 - accuracy: 0.9962 - val_loss: 0.3134 - val_accuracy: 0.9647\n",
      "Epoch 30/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0104 - accuracy: 0.9965\n",
      "Epoch 30: val_accuracy did not improve from 0.96612\n",
      "2344/2344 [==============================] - 1015s 433ms/step - loss: 0.0104 - accuracy: 0.9965 - val_loss: 0.3202 - val_accuracy: 0.9644\n",
      "Epoch 31/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0106 - accuracy: 0.9964\n",
      "Epoch 31: val_accuracy improved from 0.96612 to 0.96628, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 551s 235ms/step - loss: 0.0106 - accuracy: 0.9964 - val_loss: 0.3545 - val_accuracy: 0.9663\n",
      "Epoch 32/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0103 - accuracy: 0.9966\n",
      "Epoch 32: val_accuracy improved from 0.96628 to 0.96660, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 568s 242ms/step - loss: 0.0103 - accuracy: 0.9966 - val_loss: 0.2650 - val_accuracy: 0.9666\n",
      "Epoch 33/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0095 - accuracy: 0.9968\n",
      "Epoch 33: val_accuracy did not improve from 0.96660\n",
      "2344/2344 [==============================] - 581s 248ms/step - loss: 0.0095 - accuracy: 0.9968 - val_loss: 0.4688 - val_accuracy: 0.9598\n",
      "Epoch 34/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0095 - accuracy: 0.9968\n",
      "Epoch 34: val_accuracy did not improve from 0.96660\n",
      "2344/2344 [==============================] - 582s 248ms/step - loss: 0.0095 - accuracy: 0.9968 - val_loss: 0.3482 - val_accuracy: 0.9648\n",
      "Epoch 35/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0094 - accuracy: 0.9969\n",
      "Epoch 35: val_accuracy did not improve from 0.96660\n",
      "2344/2344 [==============================] - 549s 234ms/step - loss: 0.0094 - accuracy: 0.9969 - val_loss: 0.3224 - val_accuracy: 0.9659\n",
      "Epoch 36/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0094 - accuracy: 0.9969\n",
      "Epoch 36: val_accuracy improved from 0.96660 to 0.96668, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 543s 231ms/step - loss: 0.0094 - accuracy: 0.9969 - val_loss: 0.3385 - val_accuracy: 0.9667\n",
      "Epoch 37/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0094 - accuracy: 0.9970\n",
      "Epoch 37: val_accuracy did not improve from 0.96668\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0094 - accuracy: 0.9970 - val_loss: 0.2893 - val_accuracy: 0.9642\n",
      "Epoch 38/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0088 - accuracy: 0.9971\n",
      "Epoch 38: val_accuracy did not improve from 0.96668\n",
      "2344/2344 [==============================] - 553s 236ms/step - loss: 0.0088 - accuracy: 0.9971 - val_loss: 0.3642 - val_accuracy: 0.9609\n",
      "Epoch 39/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0086 - accuracy: 0.9972\n",
      "Epoch 39: val_accuracy improved from 0.96668 to 0.96768, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 552s 235ms/step - loss: 0.0086 - accuracy: 0.9972 - val_loss: 0.3041 - val_accuracy: 0.9677\n",
      "Epoch 40/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0083 - accuracy: 0.9973\n",
      "Epoch 40: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 542s 231ms/step - loss: 0.0083 - accuracy: 0.9973 - val_loss: 0.3369 - val_accuracy: 0.9660\n",
      "Epoch 41/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0081 - accuracy: 0.9973\n",
      "Epoch 41: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0081 - accuracy: 0.9973 - val_loss: 0.3546 - val_accuracy: 0.9652\n",
      "Epoch 42/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0085 - accuracy: 0.9973\n",
      "Epoch 42: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 548s 234ms/step - loss: 0.0085 - accuracy: 0.9973 - val_loss: 0.3208 - val_accuracy: 0.9652\n",
      "Epoch 43/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0079 - accuracy: 0.9975\n",
      "Epoch 43: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 546s 233ms/step - loss: 0.0079 - accuracy: 0.9975 - val_loss: 0.3268 - val_accuracy: 0.9655\n",
      "Epoch 44/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0080 - accuracy: 0.9974\n",
      "Epoch 44: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 559s 239ms/step - loss: 0.0080 - accuracy: 0.9974 - val_loss: 0.4042 - val_accuracy: 0.9618\n",
      "Epoch 45/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0079 - accuracy: 0.9974\n",
      "Epoch 45: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0079 - accuracy: 0.9974 - val_loss: 0.3810 - val_accuracy: 0.9650\n",
      "Epoch 46/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0075 - accuracy: 0.9976\n",
      "Epoch 46: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0075 - accuracy: 0.9976 - val_loss: 0.4026 - val_accuracy: 0.9622\n",
      "Epoch 47/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0073 - accuracy: 0.9976\n",
      "Epoch 47: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 540s 231ms/step - loss: 0.0073 - accuracy: 0.9976 - val_loss: 0.4804 - val_accuracy: 0.9589\n",
      "Epoch 48/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0071 - accuracy: 0.9977\n",
      "Epoch 48: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 542s 231ms/step - loss: 0.0071 - accuracy: 0.9977 - val_loss: 0.4781 - val_accuracy: 0.9595\n",
      "Epoch 49/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0072 - accuracy: 0.9978\n",
      "Epoch 49: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 540s 230ms/step - loss: 0.0072 - accuracy: 0.9978 - val_loss: 0.4824 - val_accuracy: 0.9582\n",
      "Epoch 50/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0070 - accuracy: 0.9979\n",
      "Epoch 50: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0070 - accuracy: 0.9979 - val_loss: 0.3897 - val_accuracy: 0.9616\n",
      "Epoch 51/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0073 - accuracy: 0.9977\n",
      "Epoch 51: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 541s 231ms/step - loss: 0.0073 - accuracy: 0.9977 - val_loss: 0.3666 - val_accuracy: 0.9665\n",
      "Epoch 52/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0070 - accuracy: 0.9978\n",
      "Epoch 52: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0070 - accuracy: 0.9978 - val_loss: 0.3615 - val_accuracy: 0.9648\n",
      "Epoch 53/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0067 - accuracy: 0.9979\n",
      "Epoch 53: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 541s 231ms/step - loss: 0.0067 - accuracy: 0.9979 - val_loss: 0.3856 - val_accuracy: 0.9676\n",
      "Epoch 54/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0072 - accuracy: 0.9978\n",
      "Epoch 54: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0072 - accuracy: 0.9978 - val_loss: 0.3977 - val_accuracy: 0.9671\n",
      "Epoch 55/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0065 - accuracy: 0.9979\n",
      "Epoch 55: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.3693 - val_accuracy: 0.9656\n",
      "Epoch 56/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0063 - accuracy: 0.9981\n",
      "Epoch 56: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0063 - accuracy: 0.9981 - val_loss: 0.3707 - val_accuracy: 0.9658\n",
      "Epoch 57/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0070 - accuracy: 0.9978\n",
      "Epoch 57: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0070 - accuracy: 0.9978 - val_loss: 0.4358 - val_accuracy: 0.9664\n",
      "Epoch 58/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0066 - accuracy: 0.9980\n",
      "Epoch 58: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0066 - accuracy: 0.9980 - val_loss: 0.3967 - val_accuracy: 0.9653\n",
      "Epoch 59/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0062 - accuracy: 0.9981\n",
      "Epoch 59: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0062 - accuracy: 0.9981 - val_loss: 0.4542 - val_accuracy: 0.9660\n",
      "Epoch 60/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0068 - accuracy: 0.9980\n",
      "Epoch 60: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0068 - accuracy: 0.9980 - val_loss: 0.5056 - val_accuracy: 0.9638\n",
      "Epoch 61/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0068 - accuracy: 0.9980\n",
      "Epoch 61: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 542s 231ms/step - loss: 0.0068 - accuracy: 0.9980 - val_loss: 0.4262 - val_accuracy: 0.9649\n",
      "Epoch 62/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0060 - accuracy: 0.9982\n",
      "Epoch 62: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0060 - accuracy: 0.9982 - val_loss: 0.4376 - val_accuracy: 0.9662\n",
      "Epoch 63/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0062 - accuracy: 0.9981\n",
      "Epoch 63: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 542s 231ms/step - loss: 0.0062 - accuracy: 0.9981 - val_loss: 0.4190 - val_accuracy: 0.9641\n",
      "Epoch 64/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0062 - accuracy: 0.9981\n",
      "Epoch 64: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 542s 231ms/step - loss: 0.0062 - accuracy: 0.9981 - val_loss: 0.4116 - val_accuracy: 0.9635\n",
      "Epoch 65/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0059 - accuracy: 0.9983\n",
      "Epoch 65: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 542s 231ms/step - loss: 0.0059 - accuracy: 0.9983 - val_loss: 0.4016 - val_accuracy: 0.9663\n",
      "Epoch 66/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0059 - accuracy: 0.9982\n",
      "Epoch 66: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 543s 232ms/step - loss: 0.0059 - accuracy: 0.9982 - val_loss: 0.4271 - val_accuracy: 0.9659\n",
      "Epoch 67/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 0.9983\n",
      "Epoch 67: val_accuracy did not improve from 0.96768\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0057 - accuracy: 0.9983 - val_loss: 0.4548 - val_accuracy: 0.9671\n",
      "Epoch 68/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9983\n",
      "Epoch 68: val_accuracy improved from 0.96768 to 0.96780, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0056 - accuracy: 0.9983 - val_loss: 0.4241 - val_accuracy: 0.9678\n",
      "Epoch 69/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0059 - accuracy: 0.9983\n",
      "Epoch 69: val_accuracy did not improve from 0.96780\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0059 - accuracy: 0.9983 - val_loss: 0.4112 - val_accuracy: 0.9656\n",
      "Epoch 70/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0061 - accuracy: 0.9982\n",
      "Epoch 70: val_accuracy did not improve from 0.96780\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0061 - accuracy: 0.9982 - val_loss: 0.4292 - val_accuracy: 0.9659\n",
      "Epoch 71/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9984\n",
      "Epoch 71: val_accuracy did not improve from 0.96780\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0056 - accuracy: 0.9984 - val_loss: 0.4546 - val_accuracy: 0.9664\n",
      "Epoch 72/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0054 - accuracy: 0.9984\n",
      "Epoch 72: val_accuracy improved from 0.96780 to 0.96784, saving model to best_mini_Inception_V1.h5\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0054 - accuracy: 0.9984 - val_loss: 0.4314 - val_accuracy: 0.9678\n",
      "Epoch 73/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0059 - accuracy: 0.9983\n",
      "Epoch 73: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 545s 232ms/step - loss: 0.0059 - accuracy: 0.9983 - val_loss: 0.5258 - val_accuracy: 0.9655\n",
      "Epoch 74/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9984\n",
      "Epoch 74: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0056 - accuracy: 0.9984 - val_loss: 0.4738 - val_accuracy: 0.9668\n",
      "Epoch 75/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0055 - accuracy: 0.9984\n",
      "Epoch 75: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 544s 232ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.4174 - val_accuracy: 0.9663\n",
      "Epoch 76/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 0.9983\n",
      "Epoch 76: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 545s 232ms/step - loss: 0.0057 - accuracy: 0.9983 - val_loss: 0.4715 - val_accuracy: 0.9653\n",
      "Epoch 77/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 0.9984\n",
      "Epoch 77: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 545s 232ms/step - loss: 0.0057 - accuracy: 0.9984 - val_loss: 0.4231 - val_accuracy: 0.9660\n",
      "Epoch 78/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9986\n",
      "Epoch 78: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 545s 232ms/step - loss: 0.0049 - accuracy: 0.9986 - val_loss: 0.4629 - val_accuracy: 0.9654\n",
      "Epoch 79/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9985\n",
      "Epoch 79: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 545s 232ms/step - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.4780 - val_accuracy: 0.9668\n",
      "Epoch 80/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0054 - accuracy: 0.9984\n",
      "Epoch 80: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 545s 233ms/step - loss: 0.0054 - accuracy: 0.9984 - val_loss: 0.5442 - val_accuracy: 0.9664\n",
      "Epoch 81/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0054 - accuracy: 0.9985\n",
      "Epoch 81: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 546s 233ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.4729 - val_accuracy: 0.9665\n",
      "Epoch 82/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0050 - accuracy: 0.9986\n",
      "Epoch 82: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 546s 233ms/step - loss: 0.0050 - accuracy: 0.9986 - val_loss: 0.4915 - val_accuracy: 0.9664\n",
      "Epoch 83/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0052 - accuracy: 0.9985\n",
      "Epoch 83: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 546s 233ms/step - loss: 0.0052 - accuracy: 0.9985 - val_loss: 0.4971 - val_accuracy: 0.9626\n",
      "Epoch 84/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9984\n",
      "Epoch 84: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 547s 233ms/step - loss: 0.0056 - accuracy: 0.9984 - val_loss: 0.4140 - val_accuracy: 0.9660\n",
      "Epoch 85/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9986\n",
      "Epoch 85: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 546s 233ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.4508 - val_accuracy: 0.9665\n",
      "Epoch 86/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0048 - accuracy: 0.9985\n",
      "Epoch 86: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 547s 233ms/step - loss: 0.0048 - accuracy: 0.9985 - val_loss: 0.4547 - val_accuracy: 0.9667\n",
      "Epoch 87/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0050 - accuracy: 0.9986\n",
      "Epoch 87: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 547s 233ms/step - loss: 0.0050 - accuracy: 0.9986 - val_loss: 0.4460 - val_accuracy: 0.9670\n",
      "Epoch 88/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0048 - accuracy: 0.9986\n",
      "Epoch 88: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 552s 235ms/step - loss: 0.0048 - accuracy: 0.9986 - val_loss: 0.4918 - val_accuracy: 0.9652\n",
      "Epoch 89/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0048 - accuracy: 0.9987\n",
      "Epoch 89: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 553s 236ms/step - loss: 0.0048 - accuracy: 0.9987 - val_loss: 0.5044 - val_accuracy: 0.9662\n",
      "Epoch 90/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.9984\n",
      "Epoch 90: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 549s 234ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 0.5401 - val_accuracy: 0.9666\n",
      "Epoch 91/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0046 - accuracy: 0.9987\n",
      "Epoch 91: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 548s 234ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.4800 - val_accuracy: 0.9650\n",
      "Epoch 92/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9986\n",
      "Epoch 92: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 547s 233ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.4646 - val_accuracy: 0.9668\n",
      "Epoch 93/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0046 - accuracy: 0.9987\n",
      "Epoch 93: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 546s 233ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.4989 - val_accuracy: 0.9651\n",
      "Epoch 94/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9986\n",
      "Epoch 94: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 546s 233ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.4521 - val_accuracy: 0.9664\n",
      "Epoch 95/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0043 - accuracy: 0.9988\n",
      "Epoch 95: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 546s 233ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 0.4585 - val_accuracy: 0.9658\n",
      "Epoch 96/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9986\n",
      "Epoch 96: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 545s 233ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.4580 - val_accuracy: 0.9662\n",
      "Epoch 97/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9988\n",
      "Epoch 97: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 545s 233ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.5121 - val_accuracy: 0.9637\n",
      "Epoch 98/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9987\n",
      "Epoch 98: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 546s 233ms/step - loss: 0.0047 - accuracy: 0.9987 - val_loss: 0.5339 - val_accuracy: 0.9668\n",
      "Epoch 99/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0048 - accuracy: 0.9987\n",
      "Epoch 99: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 545s 233ms/step - loss: 0.0048 - accuracy: 0.9987 - val_loss: 0.4726 - val_accuracy: 0.9671\n",
      "Epoch 100/100\n",
      "2344/2344 [==============================] - ETA: 0s - loss: 0.0052 - accuracy: 0.9986\n",
      "Epoch 100: val_accuracy did not improve from 0.96784\n",
      "2344/2344 [==============================] - 546s 233ms/step - loss: 0.0052 - accuracy: 0.9986 - val_loss: 0.4890 - val_accuracy: 0.9652\n"
     ]
    }
   ],
   "source": [
    "opt=keras.optimizers.RMSprop(learning_rate=0.0001,momentum=0.9)\n",
    "miniInception_V1.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "history = miniInception_V1.fit(train_data, epochs=100, validation_data=valid_data,callbacks=[model_checkpoint])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **96.78%** d'accuracy sur sur le jeux de données de validation avec un entraînement sur le jeu de données d'entrainement obtenu par **data augmentation**\n",
    "* L'accuracy s'est nettement amélioré comparé à **Mini VGG** ayant un accuracy de **96.64%** mais en baisse comparé à **Mini resnet** ayant un accuracy de **96.82%** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "* Le meilleur modèle d'entre les trois architectures et celui utilisant les skipping connection (Mini Resnet) avec une précision de **96.82%**.\n",
    "* La data augmentation a permis de gagner près de **2%** d'accuracy sur le jeu de données de validation.\n",
    "* Decouverte\n",
    "## Perspective d'amélioration\n",
    "* Data augmentation utilisant les **GANs** et la technique du**cutout** en plus de celle mise en place ici\n",
    "* Utiliser d'autres architecture de l'état de l'art comme les **Vision Transformer**\n",
    "* Utiliser du transfer learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion Personnelle\n",
    "* Comprehension plus approfondie des différences couches de réseaux de neurones.\n",
    "* Découverte et comprehension de architectures des modèles de l'état de l'art (**GoogleNet, Resnet, VGG**).\n",
    "* Mise en place des modèle de réseaux de neurones profond en partant de zéro\n",
    "J'ai fait de choix de mettre en place mes propres modèles afin de me familialiser et de plus apprendre quand à la mise place de réseaux de neurones profond. J'ai également tester le **Transfer Learning** afin de m'assurer qu'il ne surpasse pas mes modèles, cependant l'entraônement était très lent au vu de la taille de ces dernier. Je n'ai constater de résultat surpassant le mien au bout de quelques itérations alors j'ai décider de me focaliser sur la conceptioon de propre modèle moins lourds mais  aussi performant, ce qui pourrait me garantir **un temps d'inférence plus intéressant**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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