img_Lab3.ipynb

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      "name": "img_Lab3.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/d-dimos/image_video_analysis_ntua/blob/main/img_Lab3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LpnEVMCYYlnD"
      },
      "source": [
        "# 3η ομαδική άσκηση στη Τεχνολογία και Ανάλυση Εικόνων και Βίντεο"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-5UaTREeLBx"
      },
      "source": [
        "## <p style=\"text-align: center;\"> Τεχνολογία και Ανάλυση Εικόνων και Βίντεο - Ροή Υ </p>\n",
        "### 3η Εργαστηριακή Άσκηση, Άνοιξη 2021\n",
        "\n",
        "    'Αμπατζή Ναυσικά (03117198) - nafsika.abatzi@gmail.com'\n",
        "    'Δήμος Δημήτρης (03117165) - dimitris.dimos647@gmail.com'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Rr4syQOebYP"
      },
      "source": [
        "## 1. Θεωρητικό  Μέρος"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MgJK85oGemzT"
      },
      "source": [
        "### Ερώτηση 1: Γιατί και πώς τα συνελικτικά δίκτυα άλλαξαν το τοπίο της παραδοσιακής επεξεργασίας εικόνας;\n",
        "\n",
        "    Απάντηση\n",
        "\n",
        "Η παραδοσιακή επεξεργασία εικόνας χρησιμοποιούσε κατά κόρον τεχνικές εξαγωγής χαρακτηριστικών (interest feature extraction) που παρουσίαζαν κάποιο ενδιαφέρον κι, εν γένει, αναλυτικές προσεγγίσεις προκειμένου να αναπτυχθούν αλγόριθμοι μηχανικής μάθησης. Με την ανάδυση των συνελικτικών νευρωνικών δικτύων, οι προσεγγίσεις αυτές αντικαθίσταται από περισσότερο προσιτές που συμπεριλάμβανουν τροφοδότηση ολόκληρων εικόνων (με κατάλληλη προεπεξεργασία) σε αυτά (τα CNNs). Αυτή η αλλαγή σήμαινε ότι μπορούσαν να διαμορφωθούν αρχιτεκτονικές ικανές να επεξεργαστούν μεγάλο όγκο πληροφορίας χαμηλού επιπέδου, χωρίς να χρειάζεται επιπλέον εξειδίκευση στους αλγορίθμους που χρησιμοποιούνται ανάλογα με το είδος του προβλήματος και των εικόνων εισόδου. Στη συνέχεια, τα CNNs επιτυγχάνουν καλύτερη γενίκευση σε προβλήματα όρασης, μειώνοντας δραματικά τον αριθμό των ελεύθερων παραμέτρων που εκμαθαίνονται, ελαττώνοντας με αυτόν τον τρόπο τις απαιτήσεις σε μνήμη για το \"τρέξιμο\" του δικτύου κι επιτρέποντας την εκπαίδευση μεγαλύτερων, πιο ισχυρών δικτύων. Σε απευθείας σύγκριση με τα απλά feedforward neural networks (που αποτελούνται από περίπου ίσο αριθμό επιπέδων), σημειώνουμε ότι τα συνελικτικά δίκτυα έχουν σημαντικά λιγότερες συνδέσεις (άρα και ελέυθερες παραμέτρους), με συνέπεια να εκπαιδεύονται γρηγορότερα, χωρίς σημαντική πτώση της απόδοσής τους. Τέλος, τα CNNs παρέχουν σημαντική διευκόλυνση ως προς την άντληση, χρήση κι επεξεργασία εικόνων που \"κατεβάζονται\" από το διαδίκτυο ως μαζικό σύνολο δεδομένων. Κι αυτό, διότι μπορούν και αξιοποιούν τις GPU τεχνολογίες και τις τεχνικές γρήγορης υλοποίησης της συνέλιξης δύο διαστάσεων, με αποτέλεσμα να μπορούν να εκπαιδεύονται γρήγορα και αποτελεσματικά πάνω σε μεγάλα databases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fLmZWVOAPic9"
      },
      "source": [
        "### Ερώτηση 2: Περιγράψτε τη λειτουργία της Rectified Linear Unit (ReLU).\n",
        "\n",
        "    Απάντηση\n",
        "    \n",
        "Η ReLU ορίζεται ως:\n",
        "\n",
        "$$ReLU(x) = max(0,x)$$\n",
        "\n",
        "και χρησιμοποιείται ευρέως ως συνάρτηση ενεργοποίησης σε πολλά νευρωνικά δίκτυα. Πρόκειται για μη γραμμική συνάρτηση που πλεονεκτεί έναντι άλλων συναρτήσεων (επίσης μη γραμμικών) που χρησιμοποιούνται για την ενεργοποίηση των νευρώνων, καθώς επιφέρει γρηγορότερη σύγκλιση των αλγορίθμων εκπαίδευσης, μειώνει την πιθανότητα να συμβεί το φαινόμενο του \"vanishing gradient\" και πετυχαίνει αραιή ενεργοποίηση των νευρώνων. Άλλα χαρακτηριστικά του δικτύου περιλαμβάνουν την - συγκριτικά με άλλες -  εύκολότερη υλοποίηση, το scale invariance και το γεγονός ότι πρόκειται για αναλυτική συνάρτηση που περιλαμβάνει προσθέσεις, συγκρίσεις και πολλαπλασιασμούς."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FXUHIH3kPoj7"
      },
      "source": [
        "### Ερώτηση 3: Παρουσιάστε έναν συγκριτικό πίνακα με τα επίπεδα, το μέγεθος των φίλτρων, συνάρτηση ενεργοποίησης, πλήθος παραμέτρων και τεχνικές pooling και dropout που χρησιμοποιούν τα 3 διαφορετικά δίκτυα που παρουσιάζονται στα προαναφερθέντα άρθρα και σχολιάστε επαρκώς τις διαφορές τους. Ποιά χαρακτηριστικά αυτών των τριών δικτύων είναι κατά τη γνώμη σας αυτά που συνέβαλαν στη σταδιακή αύξηση της ακρίβειας ταξινόμησης;\n",
        "\n",
        "    Απάντηση\n",
        "\n",
        "$$\n",
        "    \\begin{array}\n",
        "                          && VGG & AlexNet                & LeNet \\\\\\hline\n",
        "    \\text{Επίπεδα}              & 16    & 8                     & 7\\\\\n",
        "    \\text{Μέγεθος φίλτρων}        & \\text{3x3x3} \\text{ kernel-sized filters} & \\text{3x3x3, 5x5x3, 11x11x3} \\text{ kernel-sized filters} & \\text{5x5x3} \\text{ kernel-sized filters}\\\\\n",
        "    \\text{Συνάρτηση ενεργοποίησης}   & ReLU, Softmax & ReLU & Sigmoid \\\\\n",
        "    \\text{Πλήθος παραμέτρων}      & 138 million & 62 million & 3246 \\\\\n",
        "    \\text{Τεχνικές pooling}        & \\text{max-pooling of size 2x2 with strides of 2} & \\text{max-pooling of size 3x3 with a stride of 2} & \\text{average-pooling of size 2x2 with a stride of 2} \\\\\n",
        "    \\text{Τεχνικές dropout}        & - & Yes (p=50\\%) & -\n",
        "    \\end{array}\n",
        "$$\n",
        "\n",
        "Στη συνέχεια σχολιάζουμε τις διαφορές των δικτύων:\n",
        "\n",
        "Αρχίζοντας από το Lenet δίκτυο, παρατηρούμε ότι έχει τον μικρότερο αριθμό επιπέδων και τις λιγότερες (με διαφορά) παραμέτρους. Είναι το μόνο εκ των τριών που χρησιμοποιεί την σιγμοειδή συνάρτηση έναντι της ReLU και χρησιμοποιεί την τεχνική average-polling αντί για max-pooling. Η δομή του είναι εν γένει πιο απλή σε σχέση με τα άλλα δύο δίκτυα. Συνεχίζοντας στο AlexNet δίκτυο, βλέπουμε ότι χρησιμοποιεί τα μεγαλύτερα σε διάσταση φίλτρα και είναι το μόνο που χρησιμοποιεί τριών διαστάσεων φίλτρα ανα επίπεδο. Επιπλέον, ανάμεσα στα τρία CNNs, μόνο αυτό χρησιμοποιεί dropout για αποφυγή του overfitting. Όσον αφορά το VGG δίκτυο, αυτό έχει το μεγαλύτερο βάθος, έχοντας πολλά επίπεδα και εκατομμύρια παραμέτρους. Φυσικά, ως άμεση συνέπεια απορρέει η βραδεία εκπαίδευσή του (όπως βλέπουμε και κατά τη διάρκεια της εκπαίδευσής του στο εργαστηριακό μέρος). Τέλος, ανάμεσα στα τρία CNNs είναι το μόνο που χρησιμοποιεί στο τελευταίο του επίπεδο τη συνάρτηση ενεργοποίησης Softmax.\n",
        "\n",
        "Η σταδιακή αύξηση της ακρίβειας ταξινόμησης οφείλεται, καταρχάς, στη χρήση συναρτήσεων ενεργοποίησης (εξ αιτίας των οποίων η εκπαίδευση παύει να είναι γραμμική διαδικασία). Επιπλέον, η αξιοποίηση πολλαπλών επιπέδων εκπαίδευσης καθιστά πιο εύκολη τη διαδικασία εκπαίδευσης, καθώς μπορούμε και εισάγουμε απευθείας ολόκληρες εικόνες, οι οποίες \"αποσυντίθενται\" εντός του νευρωνικού σε επιμέρους features και εξυπηρετούν classification tasks. Όπως επισημάνθηκε και σε προηγούμενη ερώτηση, τα CNNs έχουν χαμηλό αριθμό παραμέτρων προς εκμάθηση και μπορούν και αξιοποιούν τις τεχνολογίες GPU. Τέλος, η σταδιακή αύξηση της ακρίβειας ταξινόμησης οφείλεται και σε ένα επιπλέον χαρακτηριστικό των τριών εν λόγω δικτύων: η εύκολη αλλαγή των δομικών παραμέτρων τους (επίπεδα, αριθμός νευρώνων) και η χρήση τεχνικών pooling και dropout που αποσκοπούν στην αντιμετώπιση του overfitting και του vanishing gradient."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Pgj4APrenCu"
      },
      "source": [
        "## 2. Εργαστηρικό Μέρος"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DfEMjsB4Yurm"
      },
      "source": [
        "## Εισαγωγή και επισκόπηση του συνόλου δεδομένων\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STXQMBuN3nZ6"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals # legacy compatibility\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WSEGPI9Q7tW"
      },
      "source": [
        "# helper functions\n",
        "\n",
        "# select from from_list elements with index in index_list\n",
        "def select_from_list(from_list, index_list):\n",
        "  filtered_list= [from_list[i] for i in index_list]\n",
        "  return(filtered_list)\n",
        "\n",
        "# append in filtered_list the index of each element of unfilterd_list if it exists in in target_list\n",
        "def get_ds_index(unfiliterd_list, target_list):\n",
        "  index = 0\n",
        "  filtered_list=[]\n",
        "  for i_ in unfiliterd_list:\n",
        "    if i_[0] in target_list:\n",
        "      filtered_list.append(index)\n",
        "    index += 1\n",
        "  return(filtered_list)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCW71UaGzz0Q"
      },
      "source": [
        "# load the entire dataset\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "\n",
        "(x_train_all, y_train_all), (x_test_all, y_test_all) = tf.keras.datasets.cifar100.load_data(label_mode='fine')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGKYHffEE1do",
        "outputId": "4c468b21-deb2-49a2-a14d-5b1cb1638d80"
      },
      "source": [
        "print(x_train_all.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000, 32, 32, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PO9wIwYUf0R"
      },
      "source": [
        "Η κάθε ομάδα θα δουλέψει με διαφορετικό υποσύνολο του dataset.\n",
        "Στο επόμενο κελί, αντικαταστήστε την τιμή της μεταβλητής `team_seed` με τον αριθμό που αντιστοιχεί στην ομάδας σας. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NN0kEze7uZ_G"
      },
      "source": [
        "# REPLACE WITH YOUR TEAM NUMBER (16)\n",
        "team_seed = 16"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhbZJW6PxUDJ"
      },
      "source": [
        "# select from CIFAR100 20 classes\n",
        "cifar100_classes_url = \"https://pastebin.com/raw/nzE1n98V\"\n",
        "classes_num = 20"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yO9f6wb0Wi0H"
      },
      "source": [
        "Δημιουργούμε το μοναδικό dataset της ομάδας μας:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgIN2h_KuCp_"
      },
      "source": [
        "team_classes = pd.read_csv(cifar100_classes_url, sep=',', header=None)\n",
        "CIFAR100_LABELS_LIST = pd.read_csv('https://pastebin.com/raw/qgDaNggt', sep=',', header=None).astype(str).values.tolist()[0]\n",
        "\n",
        "our_index = team_classes.iloc[team_seed,:].values.tolist()\n",
        "our_classes = select_from_list(CIFAR100_LABELS_LIST, our_index)\n",
        "train_index = get_ds_index(y_train_all, our_index)\n",
        "test_index = get_ds_index(y_test_all, our_index)\n",
        "\n",
        "x_train_ds = np.asarray(select_from_list(x_train_all, train_index))\n",
        "y_train_ds = np.asarray(select_from_list(y_train_all, train_index))\n",
        "x_test_ds = np.asarray(select_from_list(x_test_all, test_index))\n",
        "y_test_ds = np.asarray(select_from_list(y_test_all, test_index))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_B4-tvVOQq3j",
        "outputId": "3967112e-3dd7-4b98-c009-2a4b96de4734"
      },
      "source": [
        "# print our classes\n",
        "print(our_classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[' aquarium_fish', ' beaver', ' bed', ' bottle', ' boy', ' bridge', ' bus', ' camel', ' caterpillar', ' cockroach', ' crab', ' fox', ' lawn_mower', ' mountain', ' oak_tree', ' poppy', ' porcupine', ' possum', ' raccoon', ' rocket']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bk5wy0u7GGMj",
        "outputId": "7b27e6b8-08f5-41ca-803a-4ae97e1924f5"
      },
      "source": [
        "print(x_train_ds[1].shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 32, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "QpGXgTs_5ZCk",
        "outputId": "402515f0-e667-4975-b209-214f10e475ff"
      },
      "source": [
        "# get (train) dataset dimensions\n",
        "data_size, img_rows, img_cols, img_channels = x_train_ds.shape\n",
        "\n",
        "# set validation set percentage (wrt the training set size)\n",
        "validation_percentage = 0.15\n",
        "val_size = round(validation_percentage * data_size)\n",
        "\n",
        "# Reserve val_size samples for validation and normalize all values\n",
        "x_val = x_train_ds[-val_size:]/255\n",
        "y_val = y_train_ds[-val_size:]\n",
        "x_train = x_train_ds[:-val_size]/255\n",
        "y_train = y_train_ds[:-val_size]\n",
        "x_test = x_test_ds/255\n",
        "y_test = y_test_ds\n",
        "\n",
        "# summarize loaded dataset\n",
        "print('Train: X=%s, y=%s' % (x_train.shape, y_train.shape))\n",
        "print('Validation: X=%s, y=%s' % (x_val.shape, y_val.shape))\n",
        "print('Test: X=%s, y=%s' % (x_test.shape, y_test.shape))\n",
        "\n",
        "# get class label from class index\n",
        "def class_label_from_index(fine_category):\n",
        "  return(CIFAR100_LABELS_LIST[fine_category.item(0)])\n",
        "\n",
        "# plot first few images\n",
        "plt.figure(figsize=(6, 6))\n",
        "for i in range(9):\n",
        "\t# define subplot\n",
        "  plt.subplot(330 + 1 + i).set_title(class_label_from_index(y_train[i]))\n",
        "\t# plot raw pixel data\n",
        "  plt.imshow(x_train[i], cmap=plt.get_cmap('gray'))\n",
        "  #show the figure\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train: X=(8500, 32, 32, 3), y=(8500, 1)\n",
            "Validation: X=(1500, 32, 32, 3), y=(1500, 1)\n",
            "Test: X=(2000, 32, 32, 3), y=(2000, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUwyFE7tF_gs"
      },
      "source": [
        "# We have to map our classes [0, num_classes-1] -> [0,19]\n",
        "def mapping(array,di): \n",
        "    return [di[x] for x in array]\n",
        "\n",
        "def dict_mapp(array):\n",
        "    dictionary = {}\n",
        "    i = 0\n",
        "    for label in array:\n",
        "        if label not in dictionary.keys():\n",
        "            dictionary[label] = i\n",
        "            i+=1\n",
        "    return dictionary\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qx_QXYBrF_gs"
      },
      "source": [
        "label_dictionary = dict_mapp(y_train.reshape(1,-1)[0])\n",
        "# Apply Mapping\n",
        "y_train = mapping(y_train.reshape(1,-1)[0], label_dictionary)\n",
        "y_test = mapping(y_test.reshape(1,-1)[0], label_dictionary)\n",
        "y_val = mapping(y_val.reshape(1,-1)[0], label_dictionary)\n",
        "\n",
        "\n",
        "y_train = np.array([[i] for i in y_train])\n",
        "y_test = np.array([[i] for i in y_test])\n",
        "y_val = np.array([[i] for i in y_val])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EO_hqB4eF_gt"
      },
      "source": [
        "## Ερώτημα 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZD_dI8bF_gt"
      },
      "source": [
        "### Σχεδίαση, μεταγλώττιση και εκπαίδευση των μοντέλων"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmc4Ev3-F_gt"
      },
      "source": [
        " 1. Σχεδίαστε, μεταγλωττίστε και εκπαιδεύστε τα μοντέλα  **LeNet, AlexNet και  VGG**, καθώς και ένα δικό σας μοντέλο (ονομάστε το π.χ. **MyCNN**) χρησιμοποιώντας διαφορετικούς συνδυασμούς (τουλάχιστον 2 ανά μοντέλο) για [optimizer](https://keras.io/api/optimizers/), [loss](https://keras.io/api/losses/), batch size και epoch.  Για [metrics](https://keras.io/api/metrics/) να χρησιμοποιήσετε την ορθότητα (όπως στο Lab9.1)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5XJfJgdF_gt"
      },
      "source": [
        "####**Μοντέλο1: LeNet** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7ZyzczXF_gt"
      },
      "source": [
        "# CONV(32, 3), SIGMOID, POOL(2), CONV(32,3), SIGMOID, POOL(2), FC(512, SIGMOID, FC(200),SOFTMAX\n",
        "LeNet_1 = models.Sequential()\n",
        "LeNet_1.add(layers.Conv2D(32, (3, 3), activation='sigmoid', input_shape=(32, 32, 3)))\n",
        "LeNet_1.add(layers.MaxPooling2D((2, 2)))\n",
        "LeNet_1.add(layers.Conv2D(32, (3, 3), activation='sigmoid'))\n",
        "LeNet_1.add(layers.MaxPooling2D((2, 2)))\n",
        "\n",
        "LeNet_1.add(layers.Flatten())\n",
        "\n",
        "LeNet_1.add(layers.Dense(512, activation='sigmoid'))\n",
        "LeNet_1.add(layers.Dense(200, activation='sigmoid'))\n",
        "LeNet_1.add(layers.Dense(20, activation='softmax'))\n",
        "LeNet_1.compile(optimizer='adam', loss= tf.keras.losses.sparse_categorical_crossentropy, metrics=[\"accuracy\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AzsaPptUF_gu",
        "outputId": "f5a3107a-0410-4a3f-9030-1cdd9056300a"
      },
      "source": [
        "# LeNet using Adam Optimizer \n",
        "LeNet_1.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_30 (Conv2D)           (None, 30, 30, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_31 (Conv2D)           (None, 13, 13, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling (None, 6, 6, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 1152)              0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 512)               590336    \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 200)               102600    \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 20)                4020      \n",
            "=================================================================\n",
            "Total params: 707,100\n",
            "Trainable params: 707,100\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MR1d7NGtF_gu",
        "outputId": "19a68eaf-fe9d-40bb-d170-69126bc466d2"
      },
      "source": [
        "# 20 epochs, batch_size = 64\n",
        "history1 = LeNet_1.fit(x_train, y_train, epochs=20, batch_size=64, validation_data=(x_val, y_val))\n",
        "LeNet_1.save('LeNet_1.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "133/133 [==============================] - 1s 7ms/step - loss: 3.0287 - accuracy: 0.0525 - val_loss: 3.0014 - val_accuracy: 0.0427\n",
            "Epoch 2/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 2.8494 - accuracy: 0.1189 - val_loss: 2.6513 - val_accuracy: 0.2040\n",
            "Epoch 3/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 2.5639 - accuracy: 0.2140 - val_loss: 2.5685 - val_accuracy: 0.2140\n",
            "Epoch 4/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 2.4792 - accuracy: 0.2394 - val_loss: 2.4737 - val_accuracy: 0.2500\n",
            "Epoch 5/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 2.4259 - accuracy: 0.2605 - val_loss: 2.4431 - val_accuracy: 0.2513\n",
            "Epoch 6/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 2.3516 - accuracy: 0.2734 - val_loss: 2.3337 - val_accuracy: 0.2800\n",
            "Epoch 7/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 2.2435 - accuracy: 0.3073 - val_loss: 2.2176 - val_accuracy: 0.3193\n",
            "Epoch 8/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 2.1399 - accuracy: 0.3404 - val_loss: 2.1424 - val_accuracy: 0.3420\n",
            "Epoch 9/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 2.0710 - accuracy: 0.3564 - val_loss: 2.1202 - val_accuracy: 0.3413\n",
            "Epoch 10/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 2.0215 - accuracy: 0.3707 - val_loss: 2.0753 - val_accuracy: 0.3593\n",
            "Epoch 11/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 1.9601 - accuracy: 0.3901 - val_loss: 2.0364 - val_accuracy: 0.3733\n",
            "Epoch 12/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 1.9330 - accuracy: 0.4021 - val_loss: 2.0269 - val_accuracy: 0.3860\n",
            "Epoch 13/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 1.8823 - accuracy: 0.4174 - val_loss: 2.0431 - val_accuracy: 0.3747\n",
            "Epoch 14/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 1.8449 - accuracy: 0.4227 - val_loss: 1.9819 - val_accuracy: 0.3867\n",
            "Epoch 15/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 1.8186 - accuracy: 0.4336 - val_loss: 1.9769 - val_accuracy: 0.3980\n",
            "Epoch 16/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 1.7804 - accuracy: 0.4444 - val_loss: 1.9205 - val_accuracy: 0.4047\n",
            "Epoch 17/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 1.7334 - accuracy: 0.4586 - val_loss: 1.8976 - val_accuracy: 0.4213\n",
            "Epoch 18/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 1.6847 - accuracy: 0.4700 - val_loss: 1.9147 - val_accuracy: 0.4140\n",
            "Epoch 19/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 1.6550 - accuracy: 0.4833 - val_loss: 1.8856 - val_accuracy: 0.4213\n",
            "Epoch 20/20\n",
            "133/133 [==============================] - 1s 4ms/step - loss: 1.6260 - accuracy: 0.4889 - val_loss: 1.8689 - val_accuracy: 0.4347\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTF-vFo4F_gu"
      },
      "source": [
        "# LeNet using Stochastic Gradient Descent Optimizer\n",
        "# CONV(32, 3), SIGMOID, POOL(2), CONV(32,3), SIGMOID, POOL(2), FC(512, SIGMOID, FC(200),SOFTMAX\n",
        "LeNet_2 = models.Sequential()\n",
        "LeNet_2.add(layers.Conv2D(32, (3, 3), activation='sigmoid', input_shape=(32, 32, 3)))\n",
        "LeNet_2.add(layers.MaxPooling2D((2, 2)))\n",
        "LeNet_2.add(layers.Conv2D(32, (3, 3), activation='sigmoid'))\n",
        "LeNet_2.add(layers.MaxPooling2D((2, 2)))\n",
        "\n",
        "LeNet_2.add(layers.Flatten())\n",
        "\n",
        "LeNet_2.add(layers.Dense(512, activation='sigmoid'))\n",
        "LeNet_2.add(layers.Dense(200, activation='sigmoid'))\n",
        "LeNet_2.add(layers.Dense(20, activation='softmax'))\n",
        "LeNet_2.compile(optimizer='sgd', loss= tf.keras.losses.sparse_categorical_crossentropy, metrics=[\"accuracy\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7awBkFsF_gu",
        "outputId": "c19abefa-80e4-47ef-8368-e8e79fb97d73"
      },
      "source": [
        "LeNet_2.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_32 (Conv2D)           (None, 30, 30, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_20 (MaxPooling (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_33 (Conv2D)           (None, 13, 13, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_21 (MaxPooling (None, 6, 6, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_7 (Flatten)          (None, 1152)              0         \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 512)               590336    \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 200)               102600    \n",
            "_________________________________________________________________\n",
            "dense_21 (Dense)             (None, 20)                4020      \n",
            "=================================================================\n",
            "Total params: 707,100\n",
            "Trainable params: 707,100\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8ss00a1F_gv",
        "outputId": "edbc9121-5197-4911-dc07-89d7f391fd58"
      },
      "source": [
        "# 40 epochs, batch_size = 128\n",
        "history2 = LeNet_2.fit(x_train, y_train, epochs=40, batch_size=128, validation_data=(x_val, y_val))\n",
        "LeNet_2.save('LeNet_2.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "67/67 [==============================] - 1s 10ms/step - loss: 3.0375 - accuracy: 0.0471 - val_loss: 2.9977 - val_accuracy: 0.0620\n",
            "Epoch 2/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9980 - accuracy: 0.0484 - val_loss: 2.9977 - val_accuracy: 0.0533\n",
            "Epoch 3/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9976 - accuracy: 0.0473 - val_loss: 2.9997 - val_accuracy: 0.0387\n",
            "Epoch 4/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9977 - accuracy: 0.0441 - val_loss: 2.9981 - val_accuracy: 0.0420\n",
            "Epoch 5/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9975 - accuracy: 0.0445 - val_loss: 2.9967 - val_accuracy: 0.0553\n",
            "Epoch 6/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9977 - accuracy: 0.0501 - val_loss: 2.9991 - val_accuracy: 0.0533\n",
            "Epoch 7/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9975 - accuracy: 0.0465 - val_loss: 2.9997 - val_accuracy: 0.0380\n",
            "Epoch 8/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9974 - accuracy: 0.0493 - val_loss: 2.9977 - val_accuracy: 0.0500\n",
            "Epoch 9/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9978 - accuracy: 0.0459 - val_loss: 2.9971 - val_accuracy: 0.0427\n",
            "Epoch 10/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9973 - accuracy: 0.0468 - val_loss: 2.9982 - val_accuracy: 0.0460\n",
            "Epoch 11/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9972 - accuracy: 0.0484 - val_loss: 2.9974 - val_accuracy: 0.0487\n",
            "Epoch 12/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9974 - accuracy: 0.0487 - val_loss: 2.9991 - val_accuracy: 0.0387\n",
            "Epoch 13/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9976 - accuracy: 0.0504 - val_loss: 2.9985 - val_accuracy: 0.0420\n",
            "Epoch 14/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9975 - accuracy: 0.0487 - val_loss: 2.9992 - val_accuracy: 0.0500\n",
            "Epoch 15/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9977 - accuracy: 0.0473 - val_loss: 2.9985 - val_accuracy: 0.0500\n",
            "Epoch 16/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9972 - accuracy: 0.0496 - val_loss: 2.9979 - val_accuracy: 0.0493\n",
            "Epoch 17/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9973 - accuracy: 0.0473 - val_loss: 2.9974 - val_accuracy: 0.0433\n",
            "Epoch 18/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9974 - accuracy: 0.0499 - val_loss: 2.9978 - val_accuracy: 0.0500\n",
            "Epoch 19/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9973 - accuracy: 0.0484 - val_loss: 2.9998 - val_accuracy: 0.0427\n",
            "Epoch 20/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9977 - accuracy: 0.0491 - val_loss: 2.9990 - val_accuracy: 0.0460\n",
            "Epoch 21/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9974 - accuracy: 0.0414 - val_loss: 3.0001 - val_accuracy: 0.0387\n",
            "Epoch 22/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9975 - accuracy: 0.0481 - val_loss: 2.9989 - val_accuracy: 0.0433\n",
            "Epoch 23/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9972 - accuracy: 0.0496 - val_loss: 2.9976 - val_accuracy: 0.0433\n",
            "Epoch 24/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9974 - accuracy: 0.0487 - val_loss: 2.9985 - val_accuracy: 0.0433\n",
            "Epoch 25/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9972 - accuracy: 0.0475 - val_loss: 3.0008 - val_accuracy: 0.0427\n",
            "Epoch 26/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9975 - accuracy: 0.0509 - val_loss: 2.9975 - val_accuracy: 0.0500\n",
            "Epoch 27/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9972 - accuracy: 0.0493 - val_loss: 2.9985 - val_accuracy: 0.0420\n",
            "Epoch 28/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9974 - accuracy: 0.0502 - val_loss: 2.9990 - val_accuracy: 0.0387\n",
            "Epoch 29/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9974 - accuracy: 0.0461 - val_loss: 2.9985 - val_accuracy: 0.0420\n",
            "Epoch 30/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9973 - accuracy: 0.0493 - val_loss: 2.9990 - val_accuracy: 0.0387\n",
            "Epoch 31/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9974 - accuracy: 0.0438 - val_loss: 2.9986 - val_accuracy: 0.0420\n",
            "Epoch 32/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9972 - accuracy: 0.0485 - val_loss: 2.9988 - val_accuracy: 0.0387\n",
            "Epoch 33/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9975 - accuracy: 0.0484 - val_loss: 2.9977 - val_accuracy: 0.0513\n",
            "Epoch 34/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9973 - accuracy: 0.0507 - val_loss: 2.9975 - val_accuracy: 0.0420\n",
            "Epoch 35/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9972 - accuracy: 0.0485 - val_loss: 2.9983 - val_accuracy: 0.0460\n",
            "Epoch 36/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9975 - accuracy: 0.0494 - val_loss: 2.9974 - val_accuracy: 0.0473\n",
            "Epoch 37/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9973 - accuracy: 0.0485 - val_loss: 2.9980 - val_accuracy: 0.0473\n",
            "Epoch 38/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9973 - accuracy: 0.0456 - val_loss: 2.9976 - val_accuracy: 0.0420\n",
            "Epoch 39/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9973 - accuracy: 0.0467 - val_loss: 2.9988 - val_accuracy: 0.0387\n",
            "Epoch 40/40\n",
            "67/67 [==============================] - 0s 6ms/step - loss: 2.9973 - accuracy: 0.0441 - val_loss: 2.9996 - val_accuracy: 0.0513\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YP3tD-vJF_gv"
      },
      "source": [
        "Από τις παραπάνω διαδικασίες, παρατηρούμε ότι ο Adam Optimizer έχει καλύτερα αποτελέσματα σε σχέση με τον Stochastic Gradient Descent Optimizer. Αυτό φαίνεται καθώς ο πρώτος επιφέρει καλύτερα αποτελέσματα χρησιμοποιώντας λιγότερες εποχές εκπαίδευσης (τις μισές εδώ)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-Jamf3UF_gv"
      },
      "source": [
        "####**Μοντέλο2: AlexNet**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EIucE56hF_gv"
      },
      "source": [
        "# AlexNet using Adam Optimizer \n",
        "\n",
        "AlexNet_1 = models.Sequential()\n",
        "AlexNet_1.add(layers.experimental.preprocessing.Resizing(224, 224, interpolation=\"bilinear\", input_shape=x_train.shape[1:]))\n",
        "AlexNet_1.add(layers.Conv2D(96, 11, strides=4, padding='same'))\n",
        "AlexNet_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "AlexNet_1.add(layers.Lambda(tf.nn.local_response_normalization))\n",
        "AlexNet_1.add(layers.Activation('relu'))\n",
        "AlexNet_1.add(layers.MaxPooling2D(3, strides=2))\n",
        "AlexNet_1.add(layers.Conv2D(256, 5, strides=4, padding='same'))\n",
        "AlexNet_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "AlexNet_1.add(layers.Lambda(tf.nn.local_response_normalization))\n",
        "AlexNet_1.add(layers.Activation('relu'))\n",
        "AlexNet_1.add(layers.MaxPooling2D(3, strides=2))\n",
        "AlexNet_1.add(layers.Conv2D(384, 3, strides=4, padding='same'))\n",
        "AlexNet_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "AlexNet_1.add(layers.Activation('relu'))\n",
        "AlexNet_1.add(layers.Conv2D(384, 3, strides=4, padding='same'))\n",
        "AlexNet_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "AlexNet_1.add(layers.Activation('relu'))\n",
        "AlexNet_1.add(layers.Conv2D(256, 3, strides=4, padding='same'))\n",
        "AlexNet_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "AlexNet_1.add(layers.Activation('relu'))\n",
        "AlexNet_1.add(layers.Flatten())\n",
        "AlexNet_1.add(layers.Dense(4096, activation='relu'))\n",
        "AlexNet_1.add(layers.Dropout(0.5))\n",
        "AlexNet_1.add(layers.Dense(4096, activation='relu'))\n",
        "AlexNet_1.add(layers.Dropout(0.5))\n",
        "AlexNet_1.add(layers.Dense(classes_num, activation='softmax'))\n",
        "\n",
        "AlexNet_1.compile(optimizer='adam', loss= tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wMDoXRpF_gw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e84b6f6b-2f28-4de1-9dc1-0a4e3e29e526"
      },
      "source": [
        "AlexNet_1.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "resizing_3 (Resizing)        (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_47 (Conv2D)           (None, 56, 56, 96)        34944     \n",
            "_________________________________________________________________\n",
            "batch_normalization_31 (Batc (None, 56, 56, 96)        384       \n",
            "_________________________________________________________________\n",
            "lambda_2 (Lambda)            (None, 56, 56, 96)        0         \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 56, 56, 96)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_28 (MaxPooling (None, 27, 27, 96)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_48 (Conv2D)           (None, 7, 7, 256)         614656    \n",
            "_________________________________________________________________\n",
            "batch_normalization_32 (Batc (None, 7, 7, 256)         1024      \n",
            "_________________________________________________________________\n",
            "lambda_3 (Lambda)            (None, 7, 7, 256)         0         \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 7, 7, 256)         0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_29 (MaxPooling (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_49 (Conv2D)           (None, 1, 1, 384)         885120    \n",
            "_________________________________________________________________\n",
            "batch_normalization_33 (Batc (None, 1, 1, 384)         1536      \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 1, 1, 384)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_50 (Conv2D)           (None, 1, 1, 384)         1327488   \n",
            "_________________________________________________________________\n",
            "batch_normalization_34 (Batc (None, 1, 1, 384)         1536      \n",
            "_________________________________________________________________\n",
            "activation_8 (Activation)    (None, 1, 1, 384)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_51 (Conv2D)           (None, 1, 1, 256)         884992    \n",
            "_________________________________________________________________\n",
            "batch_normalization_35 (Batc (None, 1, 1, 256)         1024      \n",
            "_________________________________________________________________\n",
            "activation_9 (Activation)    (None, 1, 1, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_9 (Flatten)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_25 (Dense)             (None, 4096)              1052672   \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_26 (Dense)             (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_27 (Dense)             (None, 20)                81940     \n",
            "=================================================================\n",
            "Total params: 21,668,628\n",
            "Trainable params: 21,665,876\n",
            "Non-trainable params: 2,752\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdbVWJ95F_gw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ac33f7d-848f-4c35-e05f-8e54e0efcd37"
      },
      "source": [
        "# 13 epochs\n",
        "history3 = AlexNet_1.fit(x_train, y_train,  epochs=13, validation_data=(x_val, y_val)) \n",
        "AlexNet_1.save('AlexNet_1.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/13\n",
            "266/266 [==============================] - 35s 127ms/step - loss: 2.3980 - accuracy: 0.2828 - val_loss: 2.1872 - val_accuracy: 0.3433\n",
            "Epoch 2/13\n",
            "266/266 [==============================] - 33s 126ms/step - loss: 2.0450 - accuracy: 0.3918 - val_loss: 2.0345 - val_accuracy: 0.3947\n",
            "Epoch 3/13\n",
            "266/266 [==============================] - 33s 125ms/step - loss: 1.8044 - accuracy: 0.4629 - val_loss: 1.8372 - val_accuracy: 0.4693\n",
            "Epoch 4/13\n",
            "266/266 [==============================] - 33s 125ms/step - loss: 1.6593 - accuracy: 0.5113 - val_loss: 1.6586 - val_accuracy: 0.5100\n",
            "Epoch 5/13\n",
            "266/266 [==============================] - 33s 126ms/step - loss: 1.4957 - accuracy: 0.5627 - val_loss: 1.6360 - val_accuracy: 0.5187\n",
            "Epoch 6/13\n",
            "266/266 [==============================] - 33s 126ms/step - loss: 1.3551 - accuracy: 0.6021 - val_loss: 1.4534 - val_accuracy: 0.5840\n",
            "Epoch 7/13\n",
            "266/266 [==============================] - 33s 125ms/step - loss: 1.2405 - accuracy: 0.6362 - val_loss: 1.3965 - val_accuracy: 0.5940\n",
            "Epoch 8/13\n",
            "266/266 [==============================] - 33s 125ms/step - loss: 1.1266 - accuracy: 0.6645 - val_loss: 1.4221 - val_accuracy: 0.5787\n",
            "Epoch 9/13\n",
            "266/266 [==============================] - 33s 126ms/step - loss: 1.0160 - accuracy: 0.7029 - val_loss: 1.4495 - val_accuracy: 0.5947\n",
            "Epoch 10/13\n",
            "266/266 [==============================] - 33s 125ms/step - loss: 0.9005 - accuracy: 0.7421 - val_loss: 1.3463 - val_accuracy: 0.6287\n",
            "Epoch 11/13\n",
            "266/266 [==============================] - 33s 125ms/step - loss: 0.8160 - accuracy: 0.7622 - val_loss: 1.4035 - val_accuracy: 0.6180\n",
            "Epoch 12/13\n",
            "266/266 [==============================] - 33s 125ms/step - loss: 0.7523 - accuracy: 0.7766 - val_loss: 1.3702 - val_accuracy: 0.6333\n",
            "Epoch 13/13\n",
            "266/266 [==============================] - 33s 126ms/step - loss: 0.6723 - accuracy: 0.8066 - val_loss: 1.3647 - val_accuracy: 0.6327\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8r23yNogF_gx"
      },
      "source": [
        "# AlexNet using Stochastic Gradient Descent Optimizer\n",
        "\n",
        "AlexNet_2 = models.Sequential()\n",
        "AlexNet_2.add(layers.experimental.preprocessing.Resizing(224, 224, interpolation=\"bilinear\", input_shape=x_train.shape[1:]))\n",
        "AlexNet_2.add(layers.Conv2D(96, 11, strides=4, padding='same'))\n",
        "AlexNet_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "AlexNet_2.add(layers.Lambda(tf.nn.local_response_normalization))\n",
        "AlexNet_2.add(layers.Activation('relu'))\n",
        "AlexNet_2.add(layers.MaxPooling2D(3, strides=2))\n",
        "AlexNet_2.add(layers.Conv2D(256, 5, strides=4, padding='same'))\n",
        "AlexNet_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "AlexNet_2.add(layers.Lambda(tf.nn.local_response_normalization))\n",
        "AlexNet_2.add(layers.Activation('relu'))\n",
        "AlexNet_2.add(layers.MaxPooling2D(3, strides=2))\n",
        "AlexNet_2.add(layers.Conv2D(384, 3, strides=4, padding='same'))\n",
        "AlexNet_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "AlexNet_2.add(layers.Activation('relu'))\n",
        "AlexNet_2.add(layers.Conv2D(384, 3, strides=4, padding='same'))\n",
        "AlexNet_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "AlexNet_2.add(layers.Activation('relu'))\n",
        "AlexNet_2.add(layers.Conv2D(256, 3, strides=4, padding='same'))\n",
        "AlexNet_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "AlexNet_2.add(layers.Activation('relu'))\n",
        "AlexNet_2.add(layers.Flatten())\n",
        "AlexNet_2.add(layers.Dense(4096, activation='relu'))\n",
        "AlexNet_2.add(layers.Dropout(0.5))\n",
        "AlexNet_2.add(layers.Dense(4096, activation='relu'))\n",
        "AlexNet_2.add(layers.Dropout(0.5))\n",
        "AlexNet_2.add(layers.Dense(classes_num, activation='softmax'))\n",
        "\n",
        "AlexNet_2.compile(optimizer='sgd', loss= tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnoqaPU5F_gx",
        "outputId": "cdb78dd3-fea2-4f99-ce9a-4d127ab3e9f1"
      },
      "source": [
        "AlexNet_2.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "resizing (Resizing)          (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 56, 56, 96)        34944     \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 56, 56, 96)        384       \n",
            "_________________________________________________________________\n",
            "lambda (Lambda)              (None, 56, 56, 96)        0         \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 56, 56, 96)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 27, 27, 96)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 7, 7, 256)         614656    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 7, 7, 256)         1024      \n",
            "_________________________________________________________________\n",
            "lambda_1 (Lambda)            (None, 7, 7, 256)         0         \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 7, 7, 256)         0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 1, 1, 384)         885120    \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 1, 1, 384)         1536      \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 1, 1, 384)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 1, 1, 384)         1327488   \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 1, 1, 384)         1536      \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 1, 1, 384)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 1, 1, 256)         884992    \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 1, 1, 256)         1024      \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 1, 1, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 4096)              1052672   \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 20)                81940     \n",
            "=================================================================\n",
            "Total params: 21,668,628\n",
            "Trainable params: 21,665,876\n",
            "Non-trainable params: 2,752\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKiuMUzFHU5Y",
        "outputId": "991d7517-a430-4957-fee9-ce605a806b63"
      },
      "source": [
        "# 25 epochs\n",
        "history4 = AlexNet_2.fit(x_train, y_train,  epochs=25, validation_data=(x_val, y_val)) \n",
        "AlexNet_2.save('AlexNet_2.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "266/266 [==============================] - 63s 125ms/step - loss: 2.5766 - accuracy: 0.2222 - val_loss: 2.2083 - val_accuracy: 0.3440\n",
            "Epoch 2/25\n",
            "266/266 [==============================] - 33s 123ms/step - loss: 2.0573 - accuracy: 0.3753 - val_loss: 2.0222 - val_accuracy: 0.3713\n",
            "Epoch 3/25\n",
            "266/266 [==============================] - 33s 123ms/step - loss: 1.7726 - accuracy: 0.4588 - val_loss: 2.1781 - val_accuracy: 0.3687\n",
            "Epoch 4/25\n",
            "266/266 [==============================] - 32s 122ms/step - loss: 1.5335 - accuracy: 0.5236 - val_loss: 1.7352 - val_accuracy: 0.4900\n",
            "Epoch 5/25\n",
            "266/266 [==============================] - 32s 122ms/step - loss: 1.3516 - accuracy: 0.5869 - val_loss: 1.5353 - val_accuracy: 0.5433\n",
            "Epoch 6/25\n",
            "266/266 [==============================] - 33s 124ms/step - loss: 1.1862 - accuracy: 0.6361 - val_loss: 1.5304 - val_accuracy: 0.5540\n",
            "Epoch 7/25\n",
            "266/266 [==============================] - 32s 122ms/step - loss: 1.0264 - accuracy: 0.6800 - val_loss: 1.8056 - val_accuracy: 0.5220\n",
            "Epoch 8/25\n",
            "266/266 [==============================] - 32s 122ms/step - loss: 0.8658 - accuracy: 0.7260 - val_loss: 2.2145 - val_accuracy: 0.4720\n",
            "Epoch 9/25\n",
            "266/266 [==============================] - 33s 123ms/step - loss: 0.7469 - accuracy: 0.7629 - val_loss: 1.8225 - val_accuracy: 0.5387\n",
            "Epoch 10/25\n",
            "266/266 [==============================] - 33s 122ms/step - loss: 0.6151 - accuracy: 0.8044 - val_loss: 1.6418 - val_accuracy: 0.5793\n",
            "Epoch 11/25\n",
            "266/266 [==============================] - 32s 122ms/step - loss: 0.5074 - accuracy: 0.8408 - val_loss: 2.6772 - val_accuracy: 0.4420\n",
            "Epoch 12/25\n",
            "266/266 [==============================] - 33s 122ms/step - loss: 0.4534 - accuracy: 0.8551 - val_loss: 2.0285 - val_accuracy: 0.5347\n",
            "Epoch 13/25\n",
            "266/266 [==============================] - 33s 125ms/step - loss: 0.3705 - accuracy: 0.8800 - val_loss: 1.9795 - val_accuracy: 0.5573\n",
            "Epoch 14/25\n",
            "266/266 [==============================] - 33s 122ms/step - loss: 0.3217 - accuracy: 0.8942 - val_loss: 2.0050 - val_accuracy: 0.5507\n",
            "Epoch 15/25\n",
            "266/266 [==============================] - 32s 121ms/step - loss: 0.2622 - accuracy: 0.9158 - val_loss: 2.2183 - val_accuracy: 0.5440\n",
            "Epoch 16/25\n",
            "266/266 [==============================] - 32s 121ms/step - loss: 0.2372 - accuracy: 0.9255 - val_loss: 1.9816 - val_accuracy: 0.5780\n",
            "Epoch 17/25\n",
            "266/266 [==============================] - 33s 123ms/step - loss: 0.1935 - accuracy: 0.9398 - val_loss: 1.8724 - val_accuracy: 0.5940\n",
            "Epoch 18/25\n",
            "266/266 [==============================] - 33s 123ms/step - loss: 0.1708 - accuracy: 0.9446 - val_loss: 3.1881 - val_accuracy: 0.4507\n",
            "Epoch 19/25\n",
            "266/266 [==============================] - 32s 122ms/step - loss: 0.1602 - accuracy: 0.9468 - val_loss: 1.8842 - val_accuracy: 0.6153\n",
            "Epoch 20/25\n",
            "266/266 [==============================] - 32s 122ms/step - loss: 0.1451 - accuracy: 0.9516 - val_loss: 2.0094 - val_accuracy: 0.5900\n",
            "Epoch 21/25\n",
            "266/266 [==============================] - 33s 123ms/step - loss: 0.1222 - accuracy: 0.9608 - val_loss: 2.5959 - val_accuracy: 0.5420\n",
            "Epoch 22/25\n",
            "266/266 [==============================] - 32s 122ms/step - loss: 0.1035 - accuracy: 0.9701 - val_loss: 2.0384 - val_accuracy: 0.5933\n",
            "Epoch 23/25\n",
            "266/266 [==============================] - 32s 121ms/step - loss: 0.0997 - accuracy: 0.9695 - val_loss: 2.0853 - val_accuracy: 0.5933\n",
            "Epoch 24/25\n",
            "266/266 [==============================] - 32s 122ms/step - loss: 0.0657 - accuracy: 0.9807 - val_loss: 2.5221 - val_accuracy: 0.5547\n",
            "Epoch 25/25\n",
            "266/266 [==============================] - 32s 122ms/step - loss: 0.0797 - accuracy: 0.9745 - val_loss: 2.4363 - val_accuracy: 0.5747\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_giQUqBPSCv"
      },
      "source": [
        "####**Μοντέλο3: VGG**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCUftRH-HVbS"
      },
      "source": [
        "# VGG Model using Adam Optimizer\n",
        "\n",
        "VGG_1 = models.Sequential()\n",
        "VGG_1.add(layers.experimental.preprocessing.Resizing(224, 224, interpolation=\"bilinear\", input_shape=x_train.shape[1:]))\n",
        "VGG_1.add(layers.Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.Conv2D(filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "VGG_1.add(layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "VGG_1.add(layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "VGG_1.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "VGG_1.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_1.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_1.add(layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "VGG_1.add(layers.Flatten())\n",
        "VGG_1.add(layers.Dense(units=4096,activation=\"relu\"))\n",
        "VGG_1.add(layers.Dense(units=4096,activation=\"relu\"))\n",
        "VGG_1.add(layers.Dense(classes_num, activation=\"softmax\"))\n",
        "\n",
        "VGG_1.compile(optimizer='adam', loss=tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1bqcUaqHVg3",
        "outputId": "dd3308e2-8875-40fa-911d-1ce0419a61aa"
      },
      "source": [
        "VGG_1.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "resizing_1 (Resizing)        (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 224, 224, 64)      256       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 224, 224, 64)      256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 112, 112, 128)     512       \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 112, 112, 128)     512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 56, 56, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 56, 56, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 56, 56, 256)       1024      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "batch_normalization_12 (Batc (None, 28, 28, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_13 (Batc (None, 28, 28, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_14 (Batc (None, 28, 28, 512)       2048      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_15 (Batc (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_16 (Batc (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_17 (Batc (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 20)                81940     \n",
            "=================================================================\n",
            "Total params: 134,359,380\n",
            "Trainable params: 134,350,932\n",
            "Non-trainable params: 8,448\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_t1TOfNQ-5-",
        "outputId": "7505d8ff-20dd-447c-e3a8-338fb76b5ef7"
      },
      "source": [
        "# 18 epochs, batch_size = 64\n",
        "model_history5 = VGG_1.fit(x_train, y_train, epochs=18, batch_size=64, validation_data=(x_val, y_val))\n",
        "VGG_1.save('VGG_1.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/18\n",
            "133/133 [==============================] - 319s 2s/step - loss: 8.8495 - accuracy: 0.1895 - val_loss: 2.5093 - val_accuracy: 0.2687\n",
            "Epoch 2/18\n",
            "133/133 [==============================] - 265s 2s/step - loss: 2.3442 - accuracy: 0.3014 - val_loss: 2.3097 - val_accuracy: 0.3027\n",
            "Epoch 3/18\n",
            "133/133 [==============================] - 265s 2s/step - loss: 2.1639 - accuracy: 0.3467 - val_loss: 2.1838 - val_accuracy: 0.3420\n",
            "Epoch 4/18\n",
            "133/133 [==============================] - 265s 2s/step - loss: 2.0023 - accuracy: 0.3829 - val_loss: 2.0718 - val_accuracy: 0.3707\n",
            "Epoch 5/18\n",
            "133/133 [==============================] - 265s 2s/step - loss: 1.8996 - accuracy: 0.4154 - val_loss: 2.0364 - val_accuracy: 0.4040\n",
            "Epoch 6/18\n",
            "133/133 [==============================] - 265s 2s/step - loss: 1.7885 - accuracy: 0.4479 - val_loss: 1.9258 - val_accuracy: 0.4207\n",
            "Epoch 7/18\n",
            "133/133 [==============================] - 265s 2s/step - loss: 1.6835 - accuracy: 0.4779 - val_loss: 1.9150 - val_accuracy: 0.4480\n",
            "Epoch 8/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 1.6014 - accuracy: 0.5060 - val_loss: 2.1005 - val_accuracy: 0.4040\n",
            "Epoch 9/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 1.5024 - accuracy: 0.5395 - val_loss: 1.9665 - val_accuracy: 0.4480\n",
            "Epoch 10/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 1.3888 - accuracy: 0.5704 - val_loss: 1.9201 - val_accuracy: 0.4480\n",
            "Epoch 11/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 1.2336 - accuracy: 0.6132 - val_loss: 1.9932 - val_accuracy: 0.4660\n",
            "Epoch 12/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 1.1441 - accuracy: 0.6436 - val_loss: 2.0100 - val_accuracy: 0.4787\n",
            "Epoch 13/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 1.0290 - accuracy: 0.6792 - val_loss: 2.1419 - val_accuracy: 0.4647\n",
            "Epoch 14/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 0.9123 - accuracy: 0.7184 - val_loss: 2.2024 - val_accuracy: 0.4640\n",
            "Epoch 15/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 0.8125 - accuracy: 0.7499 - val_loss: 2.3482 - val_accuracy: 0.4893\n",
            "Epoch 16/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 0.7246 - accuracy: 0.7793 - val_loss: 2.5425 - val_accuracy: 0.4853\n",
            "Epoch 17/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 0.6834 - accuracy: 0.7938 - val_loss: 2.6173 - val_accuracy: 0.4753\n",
            "Epoch 18/18\n",
            "133/133 [==============================] - 266s 2s/step - loss: 0.5571 - accuracy: 0.8292 - val_loss: 2.6337 - val_accuracy: 0.4913\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cg2zpSaOQ_CY"
      },
      "source": [
        "# VGG using Stochastic Gradient Descent Optimizer\n",
        "# VGG Model using Adam Optimizer\n",
        "\n",
        "VGG_2 = models.Sequential()\n",
        "VGG_2.add(layers.experimental.preprocessing.Resizing(224, 224, interpolation=\"bilinear\", input_shape=x_train.shape[1:]))\n",
        "VGG_2.add(layers.Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.Conv2D(filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "VGG_2.add(layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "VGG_2.add(layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "VGG_2.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "VGG_2.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "VGG_2.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
        "VGG_2.add(layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "VGG_2.add(layers.Flatten())\n",
        "VGG_2.add(layers.Dense(units=4096,activation=\"relu\"))\n",
        "VGG_2.add(layers.Dense(units=4096,activation=\"relu\"))\n",
        "VGG_2.add(layers.Dense(classes_num, activation=\"softmax\"))\n",
        "\n",
        "VGG_2.compile(optimizer='sgd', loss=tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kd_hIdVYQ_Ie",
        "outputId": "56056967-7943-450f-b705-d35061afc1d9"
      },
      "source": [
        "VGG_2.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "resizing_2 (Resizing)        (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "batch_normalization_18 (Batc (None, 224, 224, 64)      256       \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_19 (Batc (None, 224, 224, 64)      256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_20 (Batc (None, 112, 112, 128)     512       \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_21 (Batc (None, 112, 112, 128)     512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 56, 56, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_23 (Batc (None, 56, 56, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv2d_24 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_24 (Batc (None, 56, 56, 256)       1024      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_25 (Conv2D)           (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "batch_normalization_25 (Batc (None, 28, 28, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv2d_26 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_26 (Batc (None, 28, 28, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv2d_27 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_27 (Batc (None, 28, 28, 512)       2048      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_28 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_28 (Batc (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv2d_29 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_29 (Batc (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv2d_30 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_30 (Batc (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 20)                81940     \n",
            "=================================================================\n",
            "Total params: 134,359,380\n",
            "Trainable params: 134,350,932\n",
            "Non-trainable params: 8,448\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TUpz3bUNnMWR",
        "outputId": "7e695d72-c0ab-47f9-8af4-2a66a144509a"
      },
      "source": [
        "# 25 epochs\n",
        "model_history6 = VGG_2.fit(x_train, y_train, epochs=25, batch_size=64, validation_data=(x_val, y_val)) # 20 epochs\n",
        "VGG_2.save('VGG_2.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "133/133 [==============================] - 266s 2s/step - loss: 2.3608 - accuracy: 0.3598 - val_loss: 1.7600 - val_accuracy: 0.4667\n",
            "Epoch 2/25\n",
            "133/133 [==============================] - 264s 2s/step - loss: 1.4530 - accuracy: 0.5526 - val_loss: 1.5777 - val_accuracy: 0.5200\n",
            "Epoch 3/25\n",
            "133/133 [==============================] - 264s 2s/step - loss: 1.0213 - accuracy: 0.6873 - val_loss: 1.4825 - val_accuracy: 0.5580\n",
            "Epoch 4/25\n",
            "133/133 [==============================] - 264s 2s/step - loss: 0.6422 - accuracy: 0.8104 - val_loss: 1.4825 - val_accuracy: 0.5747\n",
            "Epoch 5/25\n",
            "133/133 [==============================] - 264s 2s/step - loss: 0.3528 - accuracy: 0.9133 - val_loss: 1.4230 - val_accuracy: 0.5900\n",
            "Epoch 6/25\n",
            "133/133 [==============================] - 263s 2s/step - loss: 0.1513 - accuracy: 0.9774 - val_loss: 1.3693 - val_accuracy: 0.6233\n",
            "Epoch 7/25\n",
            "133/133 [==============================] - 263s 2s/step - loss: 0.0599 - accuracy: 0.9959 - val_loss: 1.3804 - val_accuracy: 0.6260\n",
            "Epoch 8/25\n",
            "133/133 [==============================] - 263s 2s/step - loss: 0.0373 - accuracy: 0.9969 - val_loss: 1.3932 - val_accuracy: 0.6373\n",
            "Epoch 9/25\n",
            "133/133 [==============================] - 263s 2s/step - loss: 0.0252 - accuracy: 0.9985 - val_loss: 1.3879 - val_accuracy: 0.6413\n",
            "Epoch 10/25\n",
            "133/133 [==============================] - 263s 2s/step - loss: 0.0193 - accuracy: 0.9989 - val_loss: 1.4115 - val_accuracy: 0.6413\n",
            "Epoch 11/25\n",
            "133/133 [==============================] - 263s 2s/step - loss: 0.0179 - accuracy: 0.9987 - val_loss: 1.4027 - val_accuracy: 0.6333\n",
            "Epoch 12/25\n",
            "133/133 [==============================] - 263s 2s/step - loss: 0.0142 - accuracy: 0.9989 - val_loss: 1.4601 - val_accuracy: 0.6320\n",
            "Epoch 13/25\n",
            "133/133 [==============================] - 264s 2s/step - loss: 0.0117 - accuracy: 0.9996 - val_loss: 1.4569 - val_accuracy: 0.6353\n",
            "Epoch 14/25\n",
            "133/133 [==============================] - 266s 2s/step - loss: 0.0107 - accuracy: 0.9994 - val_loss: 1.4649 - val_accuracy: 0.6393\n",
            "Epoch 15/25\n",
            "133/133 [==============================] - 266s 2s/step - loss: 0.0082 - accuracy: 0.9996 - val_loss: 1.4628 - val_accuracy: 0.6393\n",
            "Epoch 16/25\n",
            "133/133 [==============================] - 265s 2s/step - loss: 0.0108 - accuracy: 0.9994 - val_loss: 1.4590 - val_accuracy: 0.6447\n",
            "Epoch 17/25\n",
            "133/133 [==============================] - 266s 2s/step - loss: 0.0078 - accuracy: 0.9995 - val_loss: 1.4662 - val_accuracy: 0.6440\n",
            "Epoch 18/25\n",
            "133/133 [==============================] - 265s 2s/step - loss: 0.0073 - accuracy: 0.9996 - val_loss: 1.4819 - val_accuracy: 0.6420\n",
            "Epoch 19/25\n",
            "133/133 [==============================] - 265s 2s/step - loss: 0.0052 - accuracy: 0.9999 - val_loss: 1.4896 - val_accuracy: 0.6380\n",
            "Epoch 20/25\n",
            "133/133 [==============================] - 266s 2s/step - loss: 0.0061 - accuracy: 0.9995 - val_loss: 1.5322 - val_accuracy: 0.6260\n",
            "Epoch 21/25\n",
            "133/133 [==============================] - 265s 2s/step - loss: 0.0052 - accuracy: 0.9998 - val_loss: 1.4930 - val_accuracy: 0.6407\n",
            "Epoch 22/25\n",
            "133/133 [==============================] - 265s 2s/step - loss: 0.0073 - accuracy: 0.9993 - val_loss: 1.5101 - val_accuracy: 0.6433\n",
            "Epoch 23/25\n",
            "133/133 [==============================] - 265s 2s/step - loss: 0.0045 - accuracy: 0.9999 - val_loss: 1.5010 - val_accuracy: 0.6433\n",
            "Epoch 24/25\n",
            "133/133 [==============================] - 265s 2s/step - loss: 0.0038 - accuracy: 0.9999 - val_loss: 1.5150 - val_accuracy: 0.6447\n",
            "Epoch 25/25\n",
            "133/133 [==============================] - 265s 2s/step - loss: 0.0031 - accuracy: 0.9998 - val_loss: 1.5153 - val_accuracy: 0.6387\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3v2mugZTE4xw"
      },
      "source": [
        "Στο VGG μοντέλο ο Stochastic Gradient Descent Optimizer έχει καλύτερα αποτελέσματα σε σχέση με τα LeNet και AlexNet. Αυτό οφείλεται στα αυξημένα επίπεδα του δικτύου."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rory_d26FXHW"
      },
      "source": [
        "####**Μοντέλο4: MyCNN**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTeB4rsLIRLm"
      },
      "source": [
        "Στο σημείο αυτό δημιουργούμε ένα συνελικτικό μοντέλο το MyCNN. Για την υλοποίησή του επιλέγουμε μία ισχυρότερη συνελικτική βάση. Η σχεδίαση της κεφαλής της ταξινόμησης έγινε με τέτοιο τρόπο ώστε να έχουμε αρκατά καλά εποτελέσματα και να μην επιβαρύνουμε το νευρωνικό."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orK7ab_eMpce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93d43534-efd9-4789-8c85-c68f4e47bc2a"
      },
      "source": [
        "# MyCNN Using Adam Optimizer\n",
        "MyCNN1 = models.Sequential()\n",
        "MyCNN1.add(layers.Conv2D(128, (3,3), padding=\"same\",input_shape=(32, 32, 3)))\n",
        "MyCNN1.add(tf.keras.layers.LeakyReLU())\n",
        "MyCNN1.add(layers.Conv2D(128, (3, 3), padding='same'))\n",
        "MyCNN1.add(layers.MaxPooling2D((2, 2)))\n",
        "MyCNN1.add(tf.keras.layers.LeakyReLU())\n",
        "MyCNN1.add(layers.Conv2D(256, (3, 3), padding='same'))\n",
        "MyCNN1.add(tf.keras.layers.LeakyReLU())\n",
        "MyCNN1.add(layers.MaxPooling2D((2, 2)))\n",
        "MyCNN1.add(layers.Conv2D(512, (3, 3), padding='same'))\n",
        "MyCNN1.add(tf.keras.layers.LeakyReLU())\n",
        "MyCNN1.add(layers.Conv2D(256, (3, 3)))\n",
        "MyCNN1.add(tf.keras.layers.LeakyReLU())\n",
        "MyCNN1.add(layers.MaxPooling2D((2, 2)))\n",
        "MyCNN1.add(layers.Flatten())\n",
        "MyCNN1.add(layers.Dense(1024))\n",
        "MyCNN1.add(tf.keras.layers.LeakyReLU())\n",
        "MyCNN1.add(layers.Dense(200))\n",
        "MyCNN1.add(layers.Dense(classes_num, activation='softmax'))\n",
        "MyCNN1.compile(optimizer='adam', loss= tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])\n",
        "MyCNN1.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_20 (Conv2D)           (None, 32, 32, 128)       3584      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_24 (LeakyReLU)   (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 32, 32, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_25 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 16, 16, 256)       295168    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_26 (LeakyReLU)   (None, 16, 16, 256)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 8, 8, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_27 (LeakyReLU)   (None, 8, 8, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_24 (Conv2D)           (None, 6, 6, 256)         1179904   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_28 (LeakyReLU)   (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 1024)              2360320   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_29 (LeakyReLU)   (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 200)               205000    \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 20)                4020      \n",
            "=================================================================\n",
            "Total params: 5,375,740\n",
            "Trainable params: 5,375,740\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prkIY0SYIQmB",
        "outputId": "a83ac26f-2541-45cd-ab7d-b1e57209cf11"
      },
      "source": [
        "# 15 epochs, batch_size = 128\n",
        "history7 = MyCNN1.fit(x_train, y_train, epochs=15, batch_size=128, validation_data=(x_val, y_val))\n",
        "MyCNN1.save('MyCNN.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "67/67 [==============================] - 37s 64ms/step - loss: 2.6641 - accuracy: 0.1915 - val_loss: 2.1525 - val_accuracy: 0.3267\n",
            "Epoch 2/15\n",
            "67/67 [==============================] - 3s 47ms/step - loss: 1.9183 - accuracy: 0.4141 - val_loss: 1.7950 - val_accuracy: 0.4533\n",
            "Epoch 3/15\n",
            "67/67 [==============================] - 3s 47ms/step - loss: 1.5745 - accuracy: 0.5172 - val_loss: 1.6512 - val_accuracy: 0.5053\n",
            "Epoch 4/15\n",
            "67/67 [==============================] - 3s 47ms/step - loss: 1.3768 - accuracy: 0.5780 - val_loss: 1.4884 - val_accuracy: 0.5393\n",
            "Epoch 5/15\n",
            "67/67 [==============================] - 3s 47ms/step - loss: 1.1657 - accuracy: 0.6342 - val_loss: 1.3342 - val_accuracy: 0.6007\n",
            "Epoch 6/15\n",
            "67/67 [==============================] - 3s 47ms/step - loss: 0.9436 - accuracy: 0.7040 - val_loss: 1.3757 - val_accuracy: 0.6173\n",
            "Epoch 7/15\n",
            "67/67 [==============================] - 3s 47ms/step - loss: 0.7740 - accuracy: 0.7582 - val_loss: 1.3240 - val_accuracy: 0.6213\n",
            "Epoch 8/15\n",
            "67/67 [==============================] - 3s 47ms/step - loss: 0.6024 - accuracy: 0.8053 - val_loss: 1.3539 - val_accuracy: 0.6273\n",
            "Epoch 9/15\n",
            "67/67 [==============================] - 3s 47ms/step - loss: 0.5058 - accuracy: 0.8365 - val_loss: 1.5750 - val_accuracy: 0.6167\n",
            "Epoch 10/15\n",
            "67/67 [==============================] - 3s 48ms/step - loss: 0.3747 - accuracy: 0.8818 - val_loss: 1.7747 - val_accuracy: 0.6100\n",
            "Epoch 11/15\n",
            "67/67 [==============================] - 3s 48ms/step - loss: 0.2644 - accuracy: 0.9125 - val_loss: 2.0180 - val_accuracy: 0.6260\n",
            "Epoch 12/15\n",
            "67/67 [==============================] - 3s 48ms/step - loss: 0.2966 - accuracy: 0.9080 - val_loss: 2.0617 - val_accuracy: 0.6053\n",
            "Epoch 13/15\n",
            "67/67 [==============================] - 3s 48ms/step - loss: 0.2825 - accuracy: 0.9107 - val_loss: 2.1550 - val_accuracy: 0.6107\n",
            "Epoch 14/15\n",
            "67/67 [==============================] - 3s 48ms/step - loss: 0.2611 - accuracy: 0.9211 - val_loss: 2.3879 - val_accuracy: 0.6213\n",
            "Epoch 15/15\n",
            "67/67 [==============================] - 3s 48ms/step - loss: 0.2293 - accuracy: 0.9316 - val_loss: 2.4232 - val_accuracy: 0.6227\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FM8Bo66BQBIT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34fc6b6a-c5fc-496a-8cd8-df77ade5489f"
      },
      "source": [
        "# MyCNN Using Adagrad Optimizer\n",
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(128, (3,3), padding=\"same\",input_shape=(32, 32, 3)))\n",
        "model.add(tf.keras.layers.LeakyReLU())\n",
        "model.add(layers.Conv2D(128, (3, 3), padding='same'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(tf.keras.layers.LeakyReLU())\n",
        "model.add(layers.Conv2D(256, (3, 3), padding='same'))\n",
        "model.add(tf.keras.layers.LeakyReLU())\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(512, (3, 3), padding='same'))\n",
        "model.add(tf.keras.layers.LeakyReLU())\n",
        "model.add(layers.Conv2D(256, (3, 3)))\n",
        "model.add(tf.keras.layers.LeakyReLU())\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(1024))\n",
        "model.add(tf.keras.layers.LeakyReLU())\n",
        "model.add(layers.Dense(200))\n",
        "model.add(layers.Dense(classes_num, activation='softmax'))\n",
        "model.compile(optimizer='adagrad', loss= tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_25 (Conv2D)           (None, 32, 32, 128)       3584      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_30 (LeakyReLU)   (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_26 (Conv2D)           (None, 32, 32, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_15 (MaxPooling (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_31 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_27 (Conv2D)           (None, 16, 16, 256)       295168    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_32 (LeakyReLU)   (None, 16, 16, 256)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_16 (MaxPooling (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_28 (Conv2D)           (None, 8, 8, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_33 (LeakyReLU)   (None, 8, 8, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_29 (Conv2D)           (None, 6, 6, 256)         1179904   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_34 (LeakyReLU)   (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 1024)              2360320   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_35 (LeakyReLU)   (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 200)               205000    \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 20)                4020      \n",
            "=================================================================\n",
            "Total params: 5,375,740\n",
            "Trainable params: 5,375,740\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Y8Ex4kpxaDc",
        "outputId": "1eeed4aa-0199-4c79-fc30-86e5bc9df3fa"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_25 (Conv2D)           (None, 32, 32, 128)       3584      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_30 (LeakyReLU)   (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_26 (Conv2D)           (None, 32, 32, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_15 (MaxPooling (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_31 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_27 (Conv2D)           (None, 16, 16, 256)       295168    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_32 (LeakyReLU)   (None, 16, 16, 256)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_16 (MaxPooling (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_28 (Conv2D)           (None, 8, 8, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_33 (LeakyReLU)   (None, 8, 8, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_29 (Conv2D)           (None, 6, 6, 256)         1179904   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_34 (LeakyReLU)   (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 1024)              2360320   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_35 (LeakyReLU)   (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 200)               205000    \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 20)                4020      \n",
            "=================================================================\n",
            "Total params: 5,375,740\n",
            "Trainable params: 5,375,740\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9udHGSoQBO1",
        "outputId": "a06baa46-121d-47bf-918b-54dab687c8c1"
      },
      "source": [
        "# 10 epochs, batch_size = 64\n",
        "history8 = model.fit(x_train, y_train, epochs=15, batch_size=64, validation_data=(x_val, y_val))\n",
        "model.save('MyCNN2.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "133/133 [==============================] - 5s 30ms/step - loss: 2.9900 - accuracy: 0.0666 - val_loss: 2.9851 - val_accuracy: 0.0613\n",
            "Epoch 2/15\n",
            "133/133 [==============================] - 4s 27ms/step - loss: 2.9795 - accuracy: 0.0928 - val_loss: 2.9751 - val_accuracy: 0.1053\n",
            "Epoch 3/15\n",
            "133/133 [==============================] - 4s 27ms/step - loss: 2.9658 - accuracy: 0.0946 - val_loss: 2.9591 - val_accuracy: 0.1100\n",
            "Epoch 4/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.9437 - accuracy: 0.1125 - val_loss: 2.9319 - val_accuracy: 0.1187\n",
            "Epoch 5/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.9018 - accuracy: 0.1434 - val_loss: 2.8739 - val_accuracy: 0.1967\n",
            "Epoch 6/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.8019 - accuracy: 0.2075 - val_loss: 2.7303 - val_accuracy: 0.2220\n",
            "Epoch 7/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.6071 - accuracy: 0.2334 - val_loss: 2.5435 - val_accuracy: 0.2340\n",
            "Epoch 8/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.4666 - accuracy: 0.2531 - val_loss: 2.4671 - val_accuracy: 0.2393\n",
            "Epoch 9/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.3931 - accuracy: 0.2745 - val_loss: 2.4054 - val_accuracy: 0.2800\n",
            "Epoch 10/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.3446 - accuracy: 0.2904 - val_loss: 2.3820 - val_accuracy: 0.2560\n",
            "Epoch 11/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.2961 - accuracy: 0.3061 - val_loss: 2.3398 - val_accuracy: 0.2760\n",
            "Epoch 12/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.2526 - accuracy: 0.3218 - val_loss: 2.2791 - val_accuracy: 0.2820\n",
            "Epoch 13/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.2118 - accuracy: 0.3312 - val_loss: 2.2803 - val_accuracy: 0.2940\n",
            "Epoch 14/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.1758 - accuracy: 0.3434 - val_loss: 2.2257 - val_accuracy: 0.3227\n",
            "Epoch 15/15\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.1462 - accuracy: 0.3539 - val_loss: 2.1806 - val_accuracy: 0.3480\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "All8voQTRH27"
      },
      "source": [
        "#### **Βήμα 2: Αξιολόγηση των μοντέλων**\n",
        "\n",
        "1. Για κάθε ένα από τα μοντέλα που εκπαιδεύσατε, απεικονίστε σε κοινό διάγραμμα την ορθότητα εκπαίδευσης και την ορθότητα επικύρωσης στο σύνολο των εποχών, για κάθε διαφορετικό συνδυασμό του βήματος 1 και επιλέξτε αυτό με την καλύτερη ορθότητα από κάθε μοντέλο (ένα από κάθε αρχιτεκτονική, συνολικά 4).\n",
        "2. Για κάθε ένα από τα μοντέλα με την καλύτερη ορθότητα (συνολικά **4**), απεικονίστε σε κοινό διάγραμμα την ορθότητα εκπαίδευσης και την ορθότητα επικύρωσης στο σύνολο των εποχών.\n",
        "3. Αξιολογήστε, αναλυτικά, τα αποτελέσματά σας ως προς τα εξής:\n",
        " - Επίδραση του πλήθους των δεδομένων/κλάσεων στην απόδοση του μοντέλου\n",
        " - Επίδραση του αλγόριθμου βελτιστοποίησης (optimizer)\n",
        " - Επίδραση του μεγέθους δέσμης (batch size)\n",
        " \n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vhxop73ISsUB"
      },
      "source": [
        "# Function for plotting\n",
        "def plot_acc(history):\n",
        "  fig = plt.figure()\n",
        "  plt.plot(history.history['accuracy'], label='accuracy')\n",
        "  plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.legend(loc='lower right')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qHJ2gfwRtGV"
      },
      "source": [
        "####**Μοντέλο1: LeNet**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Y23wvIpHRd5D",
        "outputId": "55c1a74e-3b90-4819-a6a4-8c3e27b69fb2"
      },
      "source": [
        "# Adam Optimizer\n",
        "plot_acc(history1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "xO6_KWlOXOsW",
        "outputId": "caaf7e08-bb2c-4903-fd6e-dc336e6f475a"
      },
      "source": [
        "# Stochastic Gradient Descent Optimizer\n",
        "plot_acc(history2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0NuangkYGhU"
      },
      "source": [
        "#### **Μοντέλο2: AlexNet**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZKOmNc4XOx5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "5411c226-2f56-47b3-9ed7-587b0823844a"
      },
      "source": [
        "# Adam Optimizer\n",
        "plot_acc(history3)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UAqL37WXO1X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "b5439d7b-7111-49c6-e597-2ec8da26a0f3"
      },
      "source": [
        "# Stochastic Gradient Descent Optimizer\n",
        "plot_acc(history4)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WrsKYKsaSggmKfRlZXP3K96zfc5B7zunGraefQESENRcZU9VYUjDltjvtKFe+8h27044y9dpBDOvWPNQhGWOOkyUFUy7b9h3mipe/4+DRHP57wxAGdWgc6pCMMeVgScEctw17DnHlK9+Rm+fjzYkn2vwDY6oBL9doRkRGisgGEdksIvcV8/zTIrLS/7VRRNK8jMcEz6qdaYybsoQIgbdvPskSgjHVhGd3CiISCTwHjACSgKUiMtu/BCcAqnp3of3vAPp7FY8Jnu+27OeG1xNoVDeKN244kXZN6oQ6JGNMkHh5pzAY2KyqW1Q1G5gJjDrG/hOANz2MxwTBog3JXD31e1o0qMU7N59sCcGYasbLpNAG2FnocZJ/26+ISHugI7CghOcnikiCiCSkpKQEPVATmE9W/8RN0xLo3Lweb998Ei1jokMdkjEmyDztUyiD8cC7qppX3JOqOkVV41U1vlmzZhUcmgF4J2Enk2Ysp29sQ2bcdCJN6tUKdUjGGA94mRR2AW0LPY71byvOeKzpqNJ67eut3PPuDwzt3JRpNwy2onbGVGNeJoWlQBcR6SgiNXEf/LOL7iQi3YFGwBIPYzHHQVV5buFmHv5oLWf3bMHL18RTp6aNYjamOvPsf7iq5orIJGAuEAlMVdVEEXkESFDV/AQxHpipqupVLKbsVJXH56znxS+2cHG/1vxjTF+iIitLa6MxxiueXvap6ifAJ0W2PVjk8cNexmDKLjfPxwMfruHN73dyxZB2PDqqt9UxMiZMlHrpJyIXiohdIoaJzJw8bpm+nDe/38ntZ5zAYxdbQjAmnATyYT8O2CQif/e3/5tqKu1INle+/B3z1+/lTxf14p5zutvCOMaEmVKbj1T1ShFpgJtc9pqIKPAq8KaqHvI6QFMxdqcd5Zqp37N9/xH+PWEA5/dpFeqQjDEhEFCzkKoeBN7FzUpuBVwCLPeXpjBV3Ka9h7j0hW/Yk57Ja9cPsoRgTBgLpE/hIhH5AFgERAGDVfVcoC/wW2/DM15L2JbKZf9ZQq5Peevmkzj5hKahDskYE0KBjD66FHhaVRcX3qiqR0TkBm/CMhVh3tq9TJqxnNYNazPt+sG0bWx1jIwJd4EkhYeBn/IfiEhtoIWqblPV+V4FZrw18/sd3P/BauLaxDD12kFWtsIYAwTWp/AO4Cv0OM+/zVRBqsqz8zdx3/urObVLM6tjZIz5hUDuFGr4S18DoKrZ/rIVporJ8ykPzV7D9G93MHpAG/52aR+bpWyM+YVAPhFSROSi/AciMgrY511IxguZOXlMmrGc6d/u4ObTO/Gkla0wxhQjkDuFW4A3ROTfgODWSLja06hMUGVk5XLDa0v5bmsqD1zQkxtO6RjqkIwxlVQgk9d+BE4UkXr+xxmeR2WC5nBWLte/upRlOw7wzPh+jOpX7DpHxhgDBFgQT0TOB3oB0fllD1T1EQ/jMkFwJDuX61/7OSFc0Kd1qEMyxlRygUxe+w+u/tEduOajMUB7j+My5XQ0O48bXktg6bZUnh5nCcEYE5hAehpPVtWrgQOq+ifgJKCrt2GZ8sjMyePGaUv5but+nhrbj4v6WkIwxgQmkKSQ6f9+RERaAzm4+kemEsrMyeOmaQl88+N+nhjTl4v7Wx+CqaT2JkJudun7mQoVSFL4SEQaAv8AlgPbgBleBmWOT35C+GrzPv5xWV9GD4gNdUjGFC9pGbxwMvx7IKx4A/JyQx2R8TtmUvAvrjNfVdNU9T1cX0L3oqunHeP4kSKyQUQ2i8h9JewzVkTWikiiiFiyOU5ZuXncMn0ZX27ax99G9+GygZYQTCW2/mOQSKjdCD68DZ4/Eda8Dz5f6ccaTx0zKaiqD3iu0OMsVU0P5IVFJNJ/7LlAT2CCiPQssk8X4PfAUFXtBdxVtvANuIRw6/TlLNqQwuOj4xg7qG2oQzLm2DbOhfYnw8QvYOw0kAh49zqYcpp7zpZsD5lAmo/mi8ilUvYluAYDm1V1i79MxkxgVJF9bgKeU9UDAKqaXMbfEfayc33c/sZyFqxP5i+XxDF+cLtQh2TMsaXthORE6HoOiEDPUXDbErjkRcg6BDPGwitnw9YvQx1pWAokKdyMK4CXJSIHReSQiBwM4Lg2uNnP+ZL82wrrCnQVka9F5FsRGVncC4nIRBFJEJGElJSUAH51eMjO9XH7jOV8vi6ZRy/uzeVDLCGYKmDTXPe9yzk/b4uIhL7jYVICXPA0pO+E1y+AaaNc/4OpMIHMaK7v8e/vAgwDYoHFIhKnqmlFYpgCTAGIj4+3+0ogJ8/HHW8uZ97avTwyqhdXnWhTR0w5qcLuFbD2Q3cFf9bD3vyejXOhUUdo2uXXz0VGQfz10HcCLH0FvnoKXh4O3c6DM/4ALXt7E1O+nEz49jk4klq24+q1gCE3Q42qX3G41KQgIqcVt73oojvF2AUUbtyO9W8rLAn4TlVzgK0ishGXJJaWFlc4y8nzcefMFcxN3MvDF/bk6pM6hDokU1X5fLBrGaydBWtnQ/qOn5/rezk0C/KUpOwjsHUxDLzOJZ6SRNWGkyfBwGvg2//AN8/Cf05xyeKCpyEqOrhxAeTlwDvXwsZPoWa9sh2bnQHrZsPY/0KDqj1iP5AyF/cU+jka11ewDBheynFLgS4i0hGXDMYDlxfZZxYwAXhVRJrimpO2BBBT2FJV7n3vBz5ZvYcHLujJtUOtuJ0pI58Pkr6HxFnug+zgLoiIghOGw7D7oHV/N1w08X33OJi2LobcTOh6dmD716oPp98Dg26Ar552ySFjL4x/wyWOYPHlwQc3u4Rw3hMw+KayHZ84C2bdBi+e5jrO258UvNgqWCDNRxcWfiwibYF/BnBcrohMAuYCkcBUVU0UkUeABFWd7X/ubBFZi1u85x5V3X8c5xE2pn+7nfeX7+Lus7patVMTOF8e7FjimobWzoaMPRBZCzqfCWc+5Dp9azf8ef/2J7shoqffe+wr+rLaNNddhbcfWrbj6jSGsx+Fpl1h9h3w5ngY/ybUDMISsqrw8V2w5j04609lTwgAvS6GZt1g5hWuL2Tk4zDoxuD+7SpIQAXxikgCegSyo6p+AnxSZNuDhX5WYLL/y5Ri5c40Hvl4LcO7N+eO4Z1DHU5o/PA2LH3Z3abXbxHqaCq/Q3th8d9dMjicAjWiocsI6HkxdDkbohsUf1yvS+CT/4PktdCiV3BiUXX9CSeccfxt7wOucp3Ss26DN8fBhJlQs275Ypr7B1g+DU79PzilHKPim/eAmxbA+xPd3273Cjj/KW+aujwUSJ/Cv4D8zt0IoB9uZrOpQAcOZ3P7G8tp0SCap8b2JSKi6l2BlIvPBwsfgy+fdI9/mAlD7wxtTJXduo/ho99AVgZ0O9ddzXYeAbUCaC/vOQo+/Z27WwhWUti7xjVVDft9+V6n3+Vu4tusW+CNsXD5W4GdU3EW/dV1LA+5BYb/sXxxgbvbmjATvngcvvibK+Uxbjo0LMfcIZ8PtiyEhKlw0iTPm6YCGZKagOtDWAYsAe5V1Ss9jcr8gs+n3PXWSlIOZfH8FQNoWCfMVkPNPgxvX+USwoBrXJv3alsmvERZh+DD2+GtK6BBG7h5MYx93V39B/rhWa85dDjV9SsEayLZxvyhqAH2JxxL33Ew+iXY8Q28Mcadc1l9/az74O5/JZzz1+A19UREwBn3u+at/T/ClNNdX0pZHUl1Mf5rAEwfDTu+hUO7gxPjMQSSFN4Fpqvq66r6BvCtiAShIc8E6l8LNvPFxhQeuqgnfWIbln5AdZKeBFPPgQ2fuP+4Fz4DfcbDntWQvD7U0VU+O76FF4bCyhlwymS4cT407358r9V7NKRugZ9WBSe2jXOh9YDgNfvFXQaXvgI7v4Ppl0JmINOn/BKmwrwHXKK88Fn3QR5s3c+DiQuhThOYdjF88+/SE6wq7FwKH9wCT3Z3MdZvCaNfhslrofelwY+ziIBmNAOFu/lrA597E44p6stNKfxz/kZG92/D5eE2WzkpAV4aDqnbYMJbcNJt7mqu1yWuLMLqt0MdYeWRlwPzH4VXzwUUrv0EznoIapTjrrL7ha6ZJvGD8sd3eB8kLXUd2sHUezSMedUNq50+GjIDqMKz6i34eLKbPHfJFNdH4ZWmXVxi7nYufPYHeO9GNyy3qKwMSHgVXjwVXjkL1n3k+k9u/QaunwN9xlTYHIhAkkJ04SU4/T/bnUIF2J12lDtnrqRL83o8dklvyl5ppApb/S68ep7rGL1x3i+HMNZvAZ2GuSakylIjRxXSdrimroqWsgFePgu+fMLNLbjl6+C0O9dt4v7OwWhC2vw5oMFPCuD6P8a85jp2/3sJHE0red91H8OsW6HDKa5JrTxJM1DRDdzAiOEPuBFOr4yA1K3uueT18Mk98FQPNwJK1XVO/3Y9nP9k8PpzyiCQ0UeHRWSAqi4HEJGBwFFvwzL5JSyyc328cOVA6tQ8noFifkfTIHkdtIw7/g65iuLz/dxJ1+5k10lXt8mv94sb6zoad34P7YZUfJz5fHnuqu7rf7oPJYB6LaFxRzdrt+j3Oo2D13atCt+/5JoYouq4D56eFwXntfP1Hu36J3Yth9iBx/86G+e4v0vLvsGLrbAeF7rzf/tqVxrjqg/c37qwHxe4onut+8OEN4M7z6E0ERFw2v9Bq77w3g0wZRg07+n6RCJrutFgg26AtkNCPow1kE+au4B3RGQ3bjnOlrjlOY2H/vLJOlbsSOP5KwZwQrMyfJD78lwCSFrqml+SlsK+De65mHZw0TNuklJllH3EfdCv/RD6XQkXPFXyLXOPC+DjaNeEFIqkkJMJq96Eb/4FqT9C405w9mNuYlbqNjiwFbYsglVFqsHXagCNOhRKFp1+/rlB68CbMg7+5D6sf5zvRhSN+rdrew627ufDR3e5u4XjTQp5ObB5vrui96LtPl/389yktreudInh6g9/Tgzbl8Cbl0PTbnDlu25SXCh0GQETF8F7N7m5Imf9yXV0120amniKIRrAbaGIRAHd/A83+MtShER8fLwmJCSE6tdXiI9/2M2kGSu4fmhHHryw57F3zkiBXf4P/6Sl7oou29/aV7sxxA5yX43au6vv/ZthwNXuAyw6xvuTCdTB3fDmBNepOeIROPmO0q+Y3rnWjer47QZXM6ciZKa7TspvX3Aza1v3h6F3uSvV4j7Qc47Cge0uSaRu/fl76hbX3OQr9F8psiY0bF/8XUbD9j+Pd0+c5ZoacjLdhC6vJ0nNGOc69u9ac3wf6lu/dBO6xr3hkrnXNs1zk8iadnWJIX0HvH6Rq0903adQr5n3MVRCIrJMVeNL2y+QeQq3A2+o6hr/40YiMkFVnw9CnKaIzckZ3PvuDwxo15D7zi1h1MiWRbBiuksCB7a5bRE1oEVvVxsmdhDExrur0MIfFj0udOOyv/kXbPocLvynN228ZbVruUsI2Rnutr7buYEdFzfWdYL+uDDwsgnH69AelwgSpkLWQeh0hhsS2fG00mv4NO9e/AggX54bXVU0YRzY6q5sswsPsxR3J1G3qUucrfu7319cUblg6zXaNf8kfQ/tTiz78RvnuITXaViwIytelxHu39HMy+G1813yjm4IV88K24RQFqXeKYjISlXtV2TbClXt72lkJajOdwpHsnO5+Lmv2ZeRzf9+cwqtYopp8/xxgZuwU7uh+w8aO9glgVZ9A5/yv2sZzLodUtZBn3FuSn7R9teKkviBG35XtzlcPrNsHWu52fBEF/chcOnL3sS3/0f4+hnXVOTLdW2/Q++E1v1KP7Y8VOHI/uLvLjqdDqf+tgLvjg7CPzrDwGvhvL+X/fh/xbvJW1cFYRRTWWxZBDPGuzvi6z91F0lhLGh3CkCkiIi/JEX+imphNnvKe6rK/e+vZlNyBv+9fkjxCWHXMph5pauxcu3/flmrpizaDISbv3CTwb580l1pn/9k8DspS7P9G3j3emgTD+NnlP0qrkZNN0v3h7fdkL5gdqLvWQ2L/+HqBEXWdO2+J99RcR8sIu6uoG5TaDuoYn5nSaIbuMS7dhaM/GvZhnDu/xH2bzq+ekLl1WkY3Pq1K4PhRX9LNRVIA+Ec4C0ROVNEzgTeBD71NqzwM/27HcxauZvJZ3XllC7FdDrt2+RmbttMp1IAAB7FSURBVNZtAle+d/wJIV+NWm7W5U0L3X+Yt6+Ct69xfRQV4UiqG7PdsD1c9f7x39bHjYWcI7AhiP8kD/7kVv76cRGcOhnuXuPKNYfzlWbv0a4ZZvs3ZTtu02fuezBmMR+PJidYQiijQJLCvcAC4Bb/12p+OZnNlNOqnWk8+tFahnVrxu1nFFPo7uBuN/4agatmBfcfeas+rojX8D+6WcPPDXZzBLwc/6/qKl1mJMNlU8s3EqTdSdAgNrgT2Rb/A/Ky4eZFcOaDruRDuOs60g17TXy/bMdtnONG/DS2ir5VRalJQVV9wHfANtxaCsOBdd6GFT7SjmRz2xvLaVa/Fk+P7ffrQndHUuG/o91cgyvfc1c+wRYZBafd42rkNO7oxlHPvMJdMXth6cuw/mM347bNgPK9VkSEK3eweb6bNVteqVtg+euu/Tyc7wyKqlnXDUpYOxvycgM7JusQbPu6cgxmMAErMSmISFcReUhE1gP/AnYAqOoZqvrvigqwOvP5lMlvryL5UCbPXzGARnWLdNVkH3F141N/dOOvve7cbN4Drv8MRjzqxr8/f6LrxwimPWtcqeLOZ8GJtwfnNePGgOYFpxzDosfdgjOn3VP6vuGm12g4sg+2fRnY/j8udENuuxa79LqppI51p7Aed1dwgaqeoqr/wi2EY4Lk5a+2sGB9Mn88vyd92xbpI8hfGnDn927oYafTKyaoyBow9DeuVEJ0jGu2ClZBtOzDrmM5OgYu/k/wJjK17O1mh5a3cureRNdpPWSitUMXp8sIt0BOoE1IG+e697ptCGecmzI71v/K0cBPwEIRecnfyRxGxXe8tWx7Kn+bs4Fze7fk6pPa//JJn8+1uW+a60YF9bq44gNs2hmu+Qhq1ncVHvcmlv815/zeza4e/WLwx4vHjXHVMvPnbRyPBX92/RtDy7HQSnUWVdvNIVn3kbtoORafz3Uydz7LXWiYKqPEpKCqs1R1PNAdWIgrd9FcRF4QkYCGEojISBHZICKbReRXi72KyLUikiIiK/1fNx7viVQlBw5nc8eMFbRpWJu/Xdbn14XuPn/QjYsfdr+rhxIqjdrDNbPdSKVpoyBl4/G/VuIHrq1+6F3elNmIu8x9P967haQE2PA/OPk3oZuzURX0Gg1HD7g5AMfy0wo4nGxNR1VQIB3Nh1V1hn+t5lhgBW5E0jH55zM8B5wL9AQmiEhxNRveUtV+/i+PZiBVHj6f8tt3VrEvI5vnLh9Ag+giE5C+fsbNOB50E5z+u9AEWViTE+Dq2YDA6xe6cedldWA7zL7TzUcIxupWxWnYzo1E+uE4K6fO/xPUaQon3hr82KqTzmdCrRi3ItuxbPzMlTfvfFbFxGWCpkyNuqp6QFWnqOqZAew+GNisqltUNRuYCYw6niCrk5e+9PcjXNCDuNgitYdWzoB5D7qrsXP/HvJqiQWadXV3DL4clxjK0kSTl+NGM6Fw2SvezsKNG+Oap/asLttxWxa5Gkqn/V/lryIbajVquSJ56/8HuVkl77dxjpttb3ddVY6HJQtpA+ws9DjJv62oS0XkBxF5V0TKsZBp5bdseyp/n7uB8+JactWJRfoRNsyBDye5WZiXBLETNlia93DFxbIPu8SQtrP0Y8DVWkpa6uosNergaYj0usTVgCrLnAVVmP+Im+sQf713sVUnvUdDVrobBlycQ3vgp5U2FLWKCvUnz0dAB1XtA8wDXi9uJxGZKCIJIpKQklJBM26D7MDhbCb5+xEev7RIP8L2JfDONW4i2bjpFbbCUpm1jHP1a46mucRQ2jyGLV/Al09B/6sqZBlB6jR2zRWr33PF5gKx/n9u2O2w+yrv372y6TQMajcqeRRS/ixmSwpVkpdJYRdQ+Mo/1r+tgKruV9X8e9CXgWILtvubrOJVNb5Zs6pX5TC/H2F/RjbPX1GkH2HfJnhzHMTEwhUhrPMeqDYD4Mr34XCKSwwZycXvd3gfvD/RVfE8928VF1/cGLe4eSDlGHx5sOAxaNLFVZc1gYmMchV3N3zqSoMXtXEuxLR1w4RNleNlUlgKdBGRjiJSExgPzC68g4i0KvTwIqrpTOkp/n6EBy7oQe82RfoRvnrafThd+X6lWmjjmNoOgivegYO7XJ36ojOJfT635OHRA66MRc26FRdbt/PcWPpAmpBWv+MqxQ7/gw2bLKteo12p8/y7gny5Wf5S5udUnj4xUyaeJQVVzQUmAXNxH/Zvq2qiiDwiIvnlOH8jIokisgr4DXCtV/GESsK2VP4xdwPnx7XiyqL9CEfT3CiOuMvc8M+qpP3JMGGmK+s87WJXjiPfdy+4D4uzH3NNThWpZh3ofoFbve1YHaG52bDwL67keI+wH/9Qdh1OdaO1io5C2vYV5ByGLtZ0VFV52qegqp+oaldVPUFV/+zf9qCqzvb//HtV7aWqff3lM9Z7GU9FSz2czR1vriC2UW3+emncr+cj/PAW5B6FgdeFJsDy6nS6K7+xbwNMH+1WJdu9EuY9BN3OD025ZHBNSJnpbgWukix/HdK2w/AHK1+nflUQWcMtr7lxrht8kG/jXKhRGzqeGrrYTLnY/waP+HzKb99eyf6S5iOowrLX3ApaXtc08lLns9yC6XvWwPRLXRmLes3dmsGhaj7oNAzqNiu5CSn7iKuE2n6oG3dvjk/v0e6iZuMc91jV/dxpmJv9bKokSwoemfLlFhZuSCm+HwFcTaPkta4aZ1XXbSSMedUtq3lgq6vVFMrx6ZE1XJv3hjnujqGo7190awMMf8Davcuj3UlQr+XPTUj7Nrq7L6+XRjWesqTggaX5/Qh9iulHyLfsVVdXqPdlFRucV3pc6Ep7j3kNOgwNdTTQZyzkZbk6PYUdTYOv/ukWfWl/Umhiqy4iIl1drk3z3JKd+XcM1p9QpVlSCLJUf12j2Ea1eXx0Mf0I4EblJH4AfcZUrxm0J5zh2pkrgzYDoVHHX9dCWvJvyExzdwmm/Hpd4pLvhk9df0LLOIgpbo6qqSosKQSRqjL57ZWkHnb9CPWL9iPkW/UW5GZWj6ajykrEdThvXexm2IKbU7Hkede01KpPaOOrLmIHQ4M27s53x7d2l1ANWFIIolkrd7FoQwp/OL+EfgTwdzC/Cq0HuOGQxjt9xoL6YM177vGXT7lkfMYfQhtXdRIR4e4WdixxCx1ZVdQqz5JCkBzOyuXxT9fTJzbm13WNCtv5HaSsh/gqOgy1KmnaBVr1c01IaTsg4RXof4VbK8IET6/R7nudpuVfXtWEnCWFIHlu4Wb2Hszi4Yt6/Xqd5cIS/B3M+f+RjLfixsDuFTDrNkDg9FKrvpuyajMAmvVwnc4RkaGOxpSTJYUg2L7/MC9/uZXR/dswoF2jknc8kurvYB5bvTqYK7PelwLi1hUedKOrMWWCSwQmLoKRFVjjynjGkkIQPPa/ddSIFO49t/uxd/zhLTdSw5qOKk6DVm7mdc16cOrkUEdTfUVFW/2oasLexXL6clMK89bu5Xcju9GiQXTJO6q6pqM2Ayu+HlC4u+jfcDS16hQcNCaE7E6hHHLyfPzpo7W0b1KHG07peOydd3zragRV1TpHVVnDtjbSy5gAWVIoh/8u2c7m5Az+eH5PatUopYNt2atQq4GrF2OMMZWUJYXjtD8ji6c/38ipXZpyVo/mx975SCokznIdzBW5toAxxpSRJYXj9MRnGziancdDF/YsvpRFYatmug5mazoyxlRylhSOw5pd6cxcupNrTu5A5+alLJ+ZP4M5dhC07F0xARpjzHGypFBGqsrDsxNpXKcmvzmzS+kHbP/GlRS2OkfGmCrA06QgIiNFZIOIbBaR+46x36UioiIS72U8wTB71W4Sth/gnnO6EVO7hIJ3hS17DWrF2AxmY0yV4FlSEJFI4DngXKAnMEFEehazX33gTuA7r2IJliPZufz1k/X0btOAMfFtAzgg1a0V3HecWzvYGGMqOS/vFAYDm1V1i6pmAzOB4ortPwr8Dcj0MJageGHRj+w5mMnDF/Yi8lj1jfKtnOHvYL7W89iMMSYYvEwKbYCdhR4n+bcVEJEBQFtV/Z+HcQTFztQjvLh4C6P6tSa+QwBLTeavwRw7GFr08jw+Y4wJhpB1NItIBPAU8NsA9p0oIgkikpCSkuJ9cMX48//WESnCfaXVN8q3/WvYv8nqHBljqhQvk8IuoHDDe6x/W776QG9gkYhsA04EZhfX2ayqU1Q1XlXjmzVr5mHIxftm8z7mJO7h9jNOoFVM7cAOSngVomPcAiTGGFNFeJkUlgJdRKSjiNQExgOz859U1XRVbaqqHVS1A/AtcJGqJngYU5nl5vl4+KNE2jauzY2ndgrsoMP7Yd1s6DMeogJMIsYYUwl4lhRUNReYBMwF1gFvq2qiiDwiIhd59XuDbfq329m419U3io4KcAGRVTMgL9uajowxVY6npbNV9RPgkyLbHixh32FexnI8Ug9n89S8jZzSuSln92wR2EH5HcxtT4TmPTyNzxhjgs1mNB/DPz/fyOFA6xvl2/YV7N9sw1CNMVWSLbJTguyMA7RZ/iTvN0mly7f/c2WvazWAWvULfRV9XB8SpkJ0Q7derTHGVDGWFIrKy3UF7Ob/mZtI4ygdYPNGyDoI2RmBvcaQW62D2RhTJVlSyKcKm+bBZ3+EfRvYWbc/9zGO//5mIuR3MPvyXGLIOlTo6+AvH+cchQHXhPZcjDHmOFlSANizxiWDLQuh8QnkjXuDce/W5MRuTX854igi0s09iI4JXazGGOOh8E4Kh/bCwsdgxXTXPzDybxB/PcuTMth3eAnn9GoZ6giNMaZChWdSyD4CS56Dr5528wmG3Aqn/R/UcTWN5qzZQ83ICM7oXsoym8YYU82EV1Lw+WD1OzD/T3BwF/S4EM76EzQ5oWAXVWVu4h5O6dKUerXC689jjDHh86m383v49HewewW06gejX4IOQ3+1W+LugyQdOModwzuHIEhjjAmt8EkKyetcH8IlL0LcWIgoft7eZ4l7iBA4q0eAM5iNMaYaCZ+k0P9KiBtT6gpocxP3MqhDY5rUq1VBgRljTOURPmUuIiJLTQhb9x1mw95DjOxto46MMeEpfO4UAjA3cQ8AZ9tQVGMClpOTQ1JSEpmZlX5F3bAQHR1NbGwsUVFRx3W8JYVC5qzZQ1ybGNo0tBIVxgQqKSmJ+vXr06FDh8ALRxpPqCr79+8nKSmJjh07HtdrhE/zUSn2pGeycmeaNR0ZU0aZmZk0adLEEkIlICI0adKkXHdtlhT85q11TUfn9LJRR8aUlSWEyqO874UlBb85iXs4oVldOjevH+pQjDEmZDxNCiIyUkQ2iMhmEbmvmOdvEZHVIrJSRL4SkZ5exlOStCPZfLsl1WodGWPCnmdJQUQigeeAc4GewIRiPvRnqGqcqvYD/g485VU8x/L5umTyfGpJwRhzTLm5uaEOwXNejj4aDGxW1S0AIjITGAWszd9BVQ8W2r8uoB7GU6K5iXtoFRNNn1griW1Mefzpo0TW7j5Y+o5l0LN1Ax66sFep+1188cXs3LmTzMxM7rzzTiZOnMicOXO4//77ycvLo2nTpsyfP5+MjAzuuOMOEhISEBEeeughLr30UurVq0dGhltI69133+Xjjz/mtdde49prryU6OpoVK1YwdOhQxo8fz5133klmZia1a9fm1VdfpVu3buTl5XHvvfcyZ84cIiIiuOmmm+jVqxfPPvsss2bNAmDevHk8//zzfPDBB0H9GwWTl0mhDbCz0OMkYEjRnUTkdmAyUBMY7mE8xTqSncvijSlMGNzOOsuMqcKmTp1K48aNOXr0KIMGDWLUqFHcdNNNLF68mI4dO5KamgrAo48+SkxMDKtXrwbgwIEDpb52UlIS33zzDZGRkRw8eJAvv/ySGjVq8Pnnn3P//ffz3nvvMWXKFLZt28bKlSupUaMGqampNGrUiNtuu42UlBSaNWvGq6++yvXXX+/p36G8Qj5PQVWfA54TkcuBPwK/WrZMRCYCEwHatWsX1N//xYYUsnJ9nG2jjowpt0Cu6L3y7LPPFlyB79y5kylTpnDaaacVjNdv3NiVxv/888+ZOXNmwXGNGjUq9bXHjBlDZKRbcCs9PZ1rrrmGTZs2ISLk5OQUvO4tt9xCjRo1fvH7rrrqKqZPn851113HkiVLmDZtWpDO2BtedjTvAtoWehzr31aSmUCxq92r6hRVjVfV+GbNmgUxRNd01KhOFIM7NA7q6xpjKs6iRYv4/PPPWbJkCatWraJ///7069evTK9RuKWg6Dj/unXrFvz8wAMPcMYZZ7BmzRo++uijUucEXHfddUyfPp0333yTMWPGFCSNysrLpLAU6CIiHUWkJjAemF14BxHpUujh+cAmD+P5lexcH/PXJ3NWjxbUiLTRucZUVenp6TRq1Ig6deqwfv16vv32WzIzM1m8eDFbt24FKGg+GjFiBM8991zBsfnNRy1atGDdunX4fL5jtvmnp6fTpk0bAF577bWC7SNGjODFF18s6IzO/32tW7emdevWPPbYY1x33XXBO2mPePZJqKq5wCRgLrAOeFtVE0XkERG5yL/bJBFJFJGVuH6FCl3xfsmW/RzKzLVZzMZUcSNHjiQ3N5cePXpw3333ceKJJ9KsWTOmTJnC6NGj6du3L+PGjQPgj3/8IwcOHKB379707duXhQsXAvD4449zwQUXcPLJJ9OqVasSf9fvfvc7fv/739O/f/9fjEa68cYbadeuHX369KFv377MmDGj4LkrrriCtm3b0qNHD4/+AsEjqiEZ8HPc4uPjNSEhISivdf8Hq/lwxS6WPTCC6KjIoLymMeFm3bp1VeLDLpQmTZpE//79ueGGGyrk9xX3nojIMlWNL+3Yyt245aE8n/JZ4l6GdWtuCcEY45mBAwdSt25dnnzyyVCHEpCwTQordhxgX0YW51jTkTHGQ8uWLQt1CGUStr2rc9bsoWZkBGd0C+5oJmOMqcrCMimoKnPX7uHkzk2oH318C1EYY0x1FJZJYd1Ph9iZepSRVuvIGGN+ISyTwpzEPUQInNXTZjEbY0xhYZkUPkvcQ3yHxjStVyvUoRhjTKUSdklh277DrN9zyMpkGxOm6tWrF+oQKrWwG5I6N9Etu3m2NR0ZE3yf3gd7Vgf3NVvGwbmPB/c1K4Hc3NxKWQcp7O4U5ibuoXebBrRtXCfUoRhjguC+++77RS2jhx9+mMcee4wzzzyTAQMGEBcXx4cffhjQa2VkZJR43LRp0wpKWFx11VUA7N27l0suuYS+ffvSt29fvvnmG7Zt20bv3r0LjnviiSd4+OGHARg2bBh33XUX8fHxPPPMM3z00UcMGTKE/v37c9ZZZ7F3796COK677jri4uLo06cP7733HlOnTuWuu+4qeN2XXnqJu++++7j/biVS1Sr1NXDgQD1ee9KPavt7P9ZnP9943K9hjPmltWvXhvT3L1++XE877bSCxz169NAdO3Zoenq6qqqmpKToCSecoD6fT1VV69atW+Jr5eTkFHvcmjVrtEuXLpqSkqKqqvv371dV1bFjx+rTTz+tqqq5ubmalpamW7du1V69ehW85j/+8Q996KGHVFX19NNP11tvvbXgudTU1IK4XnrpJZ08ebKqqv7ud7/TO++88xf7HTp0SDt16qTZ2dmqqnrSSSfpDz/8UOx5FPeeAAkawGds5bt38dBna10WtlnMxlQf/fv3Jzk5md27d5OSkkKjRo1o2bIld999N4sXLyYiIoJdu3axd+9eWrY89v99VeX+++//1XELFixgzJgxNG3aFPh5rYQFCxYUrI8QGRlJTExMqYv25BfmA7d4z7hx4/jpp5/Izs4uWPuhpDUfhg8fzscff0yPHj3IyckhLi6ujH+t0oVVUpi7Zg+dmtalS3PraDKmOhkzZgzvvvsue/bsYdy4cbzxxhukpKSwbNkyoqKi6NChQ6nrHgDHfVxhNWrUwOfzFTw+1toMd9xxB5MnT+aiiy5i0aJFBc1MJbnxxhv5y1/+Qvfu3T0rwx02fQrpR3L4dst+zu7V0pbdNKaaGTduHDNnzuTdd99lzJgxpKen07x5c6Kioli4cCHbt28P6HVKOm748OG888477N+/H/h5rYQzzzyTF154AYC8vDzS09Np0aIFycnJ7N+/n6ysLD7++ONj/r78tRlef/31gu0lrfkwZMgQdu7cyYwZM5gwYUKgf54yCZukMH/9XnJ9amsnGFMN9erVi0OHDtGmTRtatWrFFVdcQUJCAnFxcUybNo3u3bsH9DolHderVy/+8Ic/cPrpp9O3b18mT54MwDPPPMPChQuJi4tj4MCBrF27lqioKB588EEGDx7MiBEjjvm7H374YcaMGcPAgQMLmqag5DUfAMaOHcvQoUMDWkb0eITNegrz1u7l7YSdvHjlQCIi7E7BmGCx9RQq1gUXXMDdd9/NmWeeWeI+5VlPIWzuFEb0bMFLV8dbQjDGVElpaWl07dqV2rVrHzMhlFdYdTQbYwzA6tWrC+Ya5KtVqxbfffddiCIqXcOGDdm4caPnv8fTpCAiI4FngEjgZVV9vMjzk4EbgVwgBbheVQPrETLGVBqqWqUGcMTFxbFy5cpQh+GJ8nYJeNZ8JCKRwHPAuUBPYIKI9Cyy2wogXlX7AO8Cf/cqHmOMN6Kjo9m/f3+5P4xM+akq+/fvJzo6+rhfw8s7hcHAZlXdAiAiM4FRwNr8HVR1YaH9vwWu9DAeY4wHYmNjSUpKIiUlJdShGFySjo2NPe7jvUwKbYCdhR4nAUOOsf8NwKfFPSEiE4GJAO3atQtWfMaYIIiKiiqYiWuqvkox+khErgTigX8U97yqTlHVeFWNb9bM1lQ2xhiveHmnsAtoW+hxrH/bL4jIWcAfgNNVNcvDeIwxxpTCyzuFpUAXEekoIjWB8cDswjuISH/gReAiVU32MBZjjDEB8HRGs4icB/wTNyR1qqr+WUQewZVwnS0inwNxwE/+Q3ao6kWlvGYKcLzDVpsC+47z2OognM8/nM8dwvv87dyd9qpaavt7lStzUR4ikhDINO/qKpzPP5zPHcL7/O3cy3bulaKj2RhjTOVgScEYY0yBcEsKU0IdQIiF8/mH87lDeJ+/nXsZhFWfgjHGmGMLtzsFY4wxx2BJwRhjTIGwSQoiMlJENojIZhG5L9TxVCQR2SYiq0VkpYiUfdm6KkZEpopIsoisKbStsYjME5FN/u/erGUYYiWc+8Missv//q/0zx+qdkSkrYgsFJG1IpIoInf6t4fLe1/S+Zfp/Q+LPgV/Ge+NwAhcYb6lwARVXXvMA6sJEdmGK1EeFhN4ROQ0IAOYpqq9/dv+DqSq6uP+i4JGqnpvKOP0Qgnn/jCQoapPhDI2r4lIK6CVqi4XkfrAMuBi4FrC470v6fzHUob3P1zuFArKeKtqNpBfxttUQ6q6GEgtsnkU8Lr/59dx/1mqnRLOPSyo6k+qutz/8yFgHa5ac7i89yWdf5mES1Iorox3mf9YVZgCn4nIMn8Z8nDUQlXzy6nsAVqEMpgQmCQiP/ibl6pl80lhItIB6A98Rxi+90XOH8rw/odLUgh3p6jqANwqeLf7mxjClro20+rfbvqzF4ATgH64OmNPhjYcb4lIPeA94C5VPVj4uXB474s5/zK9/+GSFAIq411dqeou//dk4ANcc1q42etvc81vew2bqryquldV81TVB7xENX7/RSQK94H4hqq+798cNu99cedf1vc/XJJCqWW8qysRqevvdEJE6gJnA2uOfVS1NBu4xv/zNcCHIYylQuV/IPpdQjV9/0VEgFeAdar6VKGnwuK9L+n8y/r+h8XoIyi+jHeIQ6oQItIJd3cAblGlGdX93EXkTWAYrmzwXuAhYBbwNtAOV3p9rKpWuw7ZEs59GK7pQIFtwM2F2tirDRE5BfgSWA34/Jvvx7Wrh8N7X9L5T6AM73/YJAVjjDGlC5fmI2OMMQGwpGCMMaaAJQVjjDEFLCkYY4wpYEnBGGNMAUsKxhQhInmFKkquDGZVXRHpULiCqTGVTY1QB2BMJXRUVfuFOghjQsHuFIwJkH9dir/716b4XkQ6+7d3EJEF/oJj80WknX97CxH5QERW+b9O9r9UpIi85K95/5mI1A7ZSRlThCUFY36tdpHmo3GFnktX1Tjg37gZ8gD/Al5X1T7AG8Cz/u3PAl+oal9gAJDo394FeE5VewFpwKUen48xAbMZzcYUISIZqlqvmO3bgOGqusVfeGyPqjYRkX24xU1y/Nt/UtWmIpICxKpqVqHX6ADMU9Uu/sf3AlGq+pj3Z2ZM6exOwZiy0RJ+LousQj/nYX17phKxpGBM2Ywr9H2J/+dvcJV3Aa7AFSUDmA/cCm5JWBGJqaggjTledoVizK/VFpGVhR7PUdX8YamNROQH3NX+BP+2O4BXReQeIAW4zr/9TmCKiNyAuyO4FbfIiTGVlvUpGBMgf59CvKruC3UsxnjFmo+MMcYUsDsFY4wxBexOwRhjTAFLCsYYYwpYUjDGGFPAkoIxxpgClhSMMcYU+H/0gn35CighcwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Potpb_y6eO3m"
      },
      "source": [
        "####**Μοντέλο3: VGG**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-cHq7NwXHXu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "898a5a67-830e-4c99-da6f-767c0e278241"
      },
      "source": [
        "# Adam Optimizer\n",
        "plot_acc(model_history5)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "e5VgCivde0AM",
        "outputId": "253bff5f-03db-46ac-8667-7e0ce9b41734"
      },
      "source": [
        "# Stochastic Gradient Descent Optimizer\n",
        "plot_acc(model_history6)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnUgh1L2e-Xz"
      },
      "source": [
        "####**Μοντέλο4: MyCNN**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "chealB7re0Cq",
        "outputId": "31f33dab-9130-4772-b3d7-9259d5e7bd44"
      },
      "source": [
        "# Adam\n",
        "plot_acc(history7)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "NREoDKdOe0GV",
        "outputId": "a3e10105-429e-4767-e823-faec4debd95d"
      },
      "source": [
        "# Adagrad\n",
        "plot_acc(history8)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBySaToBfqz3"
      },
      "source": [
        "Από τις γραφικές παραστάσεις του μοντέλου MyCNN είναι εμφανές ότι ο πρώτος συνδυασμός επιφέρει καλύτερη ευστοχία."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3CsflEhf-i9"
      },
      "source": [
        "Στη συνέχεια απεικονίζονται σε κοινό γράφημα η ακρίβεια εκπαίδευσης και η ακρίβεια επικύρωσης από τα 4 μοντέλα που εκπαιδεύσαμε σε κάθε εποχή. Επιλέγουμε τις περιπτώσεις με την καλύτερη ευστοχία."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "pha1xUomfdDM",
        "outputId": "5880d110-d571-4373-b782-69df74143861"
      },
      "source": [
        "def all_models(h, arr):\n",
        "  fig = plt.figure(figsize=(12,12))\n",
        "  plt.title(\"Accuracy and Validation Accuracy of different Neural Network models\")\n",
        "  i=0\n",
        "  color_arr = ['r','b','m','g','y']\n",
        "  for history in h:\n",
        "    plt.plot(history.history['accuracy'], label=arr[i]+': accuracy', color=color_arr[i%5])\n",
        "    plt.plot(history.history['val_accuracy'], linestyle='dashed', label = arr[i] + ': val_accuracy',color=color_arr[i%5])\n",
        "    i+=1\n",
        "\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.legend(loc='lower right')\n",
        "\n",
        "all_models([history1, history3, model_history5, history7,], ['LeNet', 'AlexNet', 'VGG', 'MyCNN'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jd_dxAD_isYI"
      },
      "source": [
        "###**Αξιολόγηση Αποτελεσμάτων**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GP8z7zK2iy-3"
      },
      "source": [
        "**1. Επίδραση του πλήθους των δεδομένων/κλάσεων στην απόδοση του μοντέλου**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnBhQ7efj1jd"
      },
      "source": [
        "Από τα παραπάνω παρατηρούμε ότι η απόδοση της εκπαίδευσης του CNN, μειώνεται όσο μεγαλώνουμε το πλήθος δεδομένων του CIFAR100, καθώς η εισαγωγή νέων εικόνων αυξάνει τις κλάσεις. Επίσης, ο χρόνος ολοκλήρωσης σε κάθε εποχή είναι αυξημένος αφού πρέπει να εισάγουμε μεγαλύτερο training set. Έτσι η εκπαίδευση του μοντέλου γίνεται πιο δύσκολη."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PEGoHszJlD_b"
      },
      "source": [
        "**2. Επίδραση του αλγόριθμου βελτιστοποίησης (optimizer)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qf1jtmtplEEh"
      },
      "source": [
        "Με βάσει τις δοκιμές, παρατηρούμε ότι ο Adam Optimizer συγκλίνει γρήγορα σε αρκετές δοκιμές, ενώ ο SGD Optimizer συγκλίνει γρήγορα σε μεγάλα accurancies. Επίσης ο Adam Optimizer έχει καλύτερη ευστοχία σε σχέση με τον SGD(με εξαίρεση το μοντέλο VGG). Τέλος έγινε και εφαρμογή του Adagrad Optimize, ο οποίος φαίνεται να πετυχαίνει μεγαλύτερο robustness σε σχέση με τον SGD.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r78jFiMtlEKq"
      },
      "source": [
        "**3. Επίδραση του μεγέθους δέσμης (batch size)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X25BJZe6nC2o"
      },
      "source": [
        "Γενικά, όσο μεγαλύτερο είναι το batch size, τόσο μικρότερο variance θα έχουμε, αφού χρησιμοποιούνται πολλές εικόνες σαν input. Από την άλλη, τα μικρά batch size έχουν μεγάλο θόρυβο και διακυμάνσεις αλλα διευκολύνουν την τοποθέτηση δεδομένων εκπαίδευσης στη μνήμη. Στην εκπαίδευση των μοντέλων παρατηρήθηκε ότι, με μεγαλύτερο batch size οι κύκλοι εποχής ολοκληρώνονται ταχύτερα, ενώ έχουμε πιο αργή σύγκλιση σε μεγάλες τιμες accurancies."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpxXrM_vnC51"
      },
      "source": [
        "\n",
        "#### **Βήμα 3: Αξιολόγηση ορθότητας**\n",
        "\n",
        "Αξιολογήστε την ορθότητα για το test set σας."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7NEb6R_yt4vX"
      },
      "source": [
        "Φορτώνουμε τις ιστορίες των μοντέλων που αποθηκεύτηκαν."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNZ2zhpTMT_P"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZtIW3aTgpXXs"
      },
      "source": [
        "####**Μοντέλο1: LeNet**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tbv7HZn5e0If",
        "outputId": "2e8a29d9-17e0-42b1-9eab-3b2cd8530fa6"
      },
      "source": [
        "LeNet1 =  tf.keras.models.load_model('LeNet_1.h5')\n",
        "\n",
        "print(\"LeNet Model1\")\n",
        "LeNet1.evaluate(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LeNet Model1\n",
            "63/63 [==============================] - 0s 3ms/step - loss: 1.8548 - accuracy: 0.4275\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.854777216911316, 0.42750000953674316]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQMHQcGAe0LX",
        "outputId": "2b77c83c-d725-4acc-b621-790fc991260c"
      },
      "source": [
        "LeNet2 =  tf.keras.models.load_model('LeNet_2.h5')\n",
        "\n",
        "print(\"LeNet Model2\")\n",
        "LeNet2.evaluate(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LeNet Model2\n",
            "63/63 [==============================] - 0s 3ms/step - loss: 2.9964 - accuracy: 0.0500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.9964487552642822, 0.05000000074505806]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXkYpVpsrk0j"
      },
      "source": [
        "####**Μοντέλο2: AlexNet**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMtZjJOMe0Oi",
        "outputId": "572f9d14-71c7-4cfa-d64f-a25109287f63"
      },
      "source": [
        "AlexNet1 =  tf.keras.models.load_model('AlexNet_1.h5')\n",
        "\n",
        "print(\"AlexNet Model1\")\n",
        "AlexNet1.evaluate(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AlexNet Model1\n",
            "63/63 [==============================] - 3s 46ms/step - loss: 1.3283 - accuracy: 0.6505\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.3282601833343506, 0.6504999995231628]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tPpJcm3r6GN",
        "outputId": "bd3114ae-ea19-46af-bf68-e01b32584e80"
      },
      "source": [
        "AlexNet2 =  tf.keras.models.load_model('AlexNet_2.h5')\n",
        "\n",
        "print(\"AlexNet Model2\")\n",
        "AlexNet2.evaluate(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AlexNet Model2\n",
            "63/63 [==============================] - 3s 48ms/step - loss: 2.3250 - accuracy: 0.5835\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.324951171875, 0.5835000276565552]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zJMgiTxFsC7R"
      },
      "source": [
        "####**Μοντέλο3:VGG**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJbuggTzr6Ir",
        "outputId": "cea9ecd1-53dd-4d8e-d131-cf2d25487029"
      },
      "source": [
        "VGG1 =  tf.keras.models.load_model('VGG_1.h5')\n",
        "\n",
        "print(\"VGG Model1\")\n",
        "VGG1.evaluate(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "VGG Model1\n",
            "63/63 [==============================] - 34s 363ms/step - loss: 2.6966 - accuracy: 0.4870\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.6966395378112793, 0.4869999885559082]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8k_1-OOr6L4",
        "outputId": "4f2d0917-a105-4c54-9161-a314162fae29"
      },
      "source": [
        "VGG2 =  tf.keras.models.load_model('VGG_2.h5')\n",
        "\n",
        "print(\"VGG Model2\")\n",
        "VGG2.evaluate(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "VGG Model2\n",
            "63/63 [==============================] - 18s 273ms/step - loss: 1.4895 - accuracy: 0.6480\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.4895334243774414, 0.6480000019073486]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gs4H2O4EtH6m"
      },
      "source": [
        "####**Μοντέλο4:MyCNN**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kCEjtnH5r6Pk",
        "outputId": "dc145e34-962b-4e96-8ac0-0b568dfe41e9"
      },
      "source": [
        "MyCNN1 =  tf.keras.models.load_model('MyCNN.h5')\n",
        "\n",
        "print(\"MyCNN Model1\")\n",
        "MyCNN1.evaluate(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MyCNN Model1\n",
            "63/63 [==============================] - 1s 9ms/step - loss: 2.3306 - accuracy: 0.6440\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.3306000232696533, 0.6439999938011169]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1s8AhzitlNg",
        "outputId": "52a4dea8-6205-4bcd-d7ea-4db02c34f0bf"
      },
      "source": [
        "MyCNN2 =  tf.keras.models.load_model('MyCNN2.h5')\n",
        "\n",
        "print(\"MyCNN Model2\")\n",
        "MyCNN2.evaluate(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MyCNN Model2\n",
            "63/63 [==============================] - 1s 8ms/step - loss: 2.1785 - accuracy: 0.3590\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.178544759750366, 0.35899999737739563]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbdWrnEvuiZk"
      },
      "source": [
        "Παρατηρούμε ότι το AlexNet παρουσιάζει την μεγαλύτερη ακρίβεια σε σχέση με τα υπόλοιπα μοντέλα. Αυτό διαπιστώνεται και από τα παραπάνω αποτελέσματα κατά την εκπαίδευση. Επίσης, το πιο υψηλό loss το έχει το δικό μας μοντέλο και ακολουθεί το LeNet(λίγα convolutional layers). Παράλληλα τα accurancies τους, έχουν ικανοποιητικές τιμές."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8rhgSGOV5pm"
      },
      "source": [
        "\n",
        "## Ερώτημα 2\n",
        "---\n",
        "### Βήμα 1: Έλεγχος υπερεκπαίδευσης\n",
        "\n",
        "Για τον καλύτερο συνδυασμό που λάβατε από το Ερώτημα 1 για μοντέλο σας  (**MyCNN**) και μόνο, δοκιμάστε διάφορους συνδυασμούς των ακόλουθων τεχνικών για τον έλεγχο της υπερεκπαίδευσης (overfitting), όπως: \n",
        "- Πρόωρος τερματισμός (early stopping [tf.keras.callbacks.EarlyStopping](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStoppinghttps://)) \n",
        "\n",
        "- Dropout ([Dropout](https://www.tensorflow.org/tutorials/images/classification#dropout))\n",
        "\n",
        "- Επαύξηση δεδομένων ([Data augmentation](https://www.tensorflow.org/tutorials/images/classification#data_augmentation), [ImageDataGenerator](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator#class_imagedatagenerator)) \n",
        "\n",
        "\\\\\n",
        ", ώστε το μοντέλο σας να γενικεύει καλύτερα.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "By3FoPxO3jvQ"
      },
      "source": [
        "**2. Dropout**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DID_UXu93zQN"
      },
      "source": [
        "Μέσω της τεχνικής Dropout, αγνοούνται κάποιες μονάδες του νευρωνικού κατά την εκπαίδευση με τυχαίο τρόπο. Επιβάλλεται στα βάρη του νευρωνικού η μηδενική τιμή, ώστε η ολική κατανομή των βαρών να πλησιάζει την κανονική. Έτσι μειώνεται το overfitting. Θα χρησιμοποιήσουμε το μοντέλο MyCNN με τα καλύτερα αποτελέσματα και θα το εκπαιδεύσουμε εξ'αρχής."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqMFn-wX56k3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ada1941b-8a0b-462c-8c6d-1dbec0944398"
      },
      "source": [
        "# MyCNN Using Adam Optimizer\n",
        "mycnn_1 = models.Sequential()\n",
        "mycnn_1.add(layers.Conv2D(128, (3,3), padding=\"same\",input_shape=(32, 32, 3)))\n",
        "mycnn_1.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_1.add(layers.Conv2D(128, (3, 3), padding='same'))\n",
        "mycnn_1.add(layers.MaxPooling2D((2, 2)))\n",
        "mycnn_1.add(layers.Dropout(0.05))\n",
        "mycnn_1.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_1.add(layers.Conv2D(256, (3, 3), padding='same'))\n",
        "mycnn_1.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_1.add(layers.MaxPooling2D((2, 2)))\n",
        "mycnn_1.add(layers.Dropout(0.05))\n",
        "mycnn_1.add(layers.Conv2D(512, (3, 3), padding='same'))\n",
        "mycnn_1.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_1.add(layers.Conv2D(256, (3, 3)))\n",
        "mycnn_1.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_1.add(layers.MaxPooling2D((2, 2)))\n",
        "mycnn_1.add(layers.Dropout(0.05))\n",
        "mycnn_1.add(layers.Flatten())\n",
        "mycnn_1.add(layers.Dense(1024))\n",
        "mycnn_1.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_1.add(layers.Dropout(0.05))\n",
        "mycnn_1.add(layers.Dense(200))\n",
        "mycnn_1.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_1.add(layers.Dropout(0.05))\n",
        "mycnn_1.add(layers.Dense(classes_num, activation='softmax'))\n",
        "mycnn_1.add(layers.Dropout(0.05))\n",
        "\n",
        "\n",
        "mycnn_1.compile(optimizer='adam', loss=tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])\n",
        "mycnn_1.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_10\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_44 (Conv2D)           (None, 32, 32, 128)       3584      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_50 (LeakyReLU)   (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_45 (Conv2D)           (None, 32, 32, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_28 (MaxPooling (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_12 (Dropout)         (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_51 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_46 (Conv2D)           (None, 16, 16, 256)       295168    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_52 (LeakyReLU)   (None, 16, 16, 256)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_29 (MaxPooling (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_47 (Conv2D)           (None, 8, 8, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_53 (LeakyReLU)   (None, 8, 8, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_48 (Conv2D)           (None, 6, 6, 256)         1179904   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_54 (LeakyReLU)   (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_30 (MaxPooling (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_14 (Dropout)         (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_10 (Flatten)         (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense_28 (Dense)             (None, 1024)              2360320   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_55 (LeakyReLU)   (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_29 (Dense)             (None, 200)               205000    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_56 (LeakyReLU)   (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_30 (Dense)             (None, 20)                4020      \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 20)                0         \n",
            "=================================================================\n",
            "Total params: 5,375,740\n",
            "Trainable params: 5,375,740\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-AgH1hS28WnW",
        "outputId": "d1b9c0bb-8a1b-47b1-a200-38e004ef39a5"
      },
      "source": [
        "# 10 epochs, batch_size = 64\n",
        "history9 = mycnn_1.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_val, y_val))\n",
        "mycnn_1.save('MyCNN_dropout.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "133/133 [==============================] - 5s 29ms/step - loss: 3.1804 - accuracy: 0.2358 - val_loss: 1.9888 - val_accuracy: 0.3893\n",
            "Epoch 2/10\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.4780 - accuracy: 0.4182 - val_loss: 1.7411 - val_accuracy: 0.4593\n",
            "Epoch 3/10\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.2006 - accuracy: 0.5026 - val_loss: 1.4789 - val_accuracy: 0.5413\n",
            "Epoch 4/10\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 2.0426 - accuracy: 0.5594 - val_loss: 1.6122 - val_accuracy: 0.5347\n",
            "Epoch 5/10\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 1.8548 - accuracy: 0.6051 - val_loss: 1.4832 - val_accuracy: 0.5800\n",
            "Epoch 6/10\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 1.6938 - accuracy: 0.6456 - val_loss: 1.4199 - val_accuracy: 0.6107\n",
            "Epoch 7/10\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 1.4523 - accuracy: 0.6982 - val_loss: 1.7179 - val_accuracy: 0.5860\n",
            "Epoch 8/10\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 1.4007 - accuracy: 0.7191 - val_loss: 1.9132 - val_accuracy: 0.5807\n",
            "Epoch 9/10\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 1.3411 - accuracy: 0.7382 - val_loss: 2.0630 - val_accuracy: 0.5900\n",
            "Epoch 10/10\n",
            "133/133 [==============================] - 4s 28ms/step - loss: 1.3277 - accuracy: 0.7429 - val_loss: 2.0620 - val_accuracy: 0.6007\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9td8hDjGwRLh"
      },
      "source": [
        "**1. Early Stopping**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvrp3t2tw003"
      },
      "source": [
        "Η τεχνική αυτή χρησιμοποιείται για την αποφυγή του overfitting, ενώ εκπαιδεύεται κάποιο μοντέλο με επαναληπτική μέθοδο. Καθορίζει έναν επαρκή αριθμό εποχών εκπαίδευσης, εφόσον η διαδικασία της εκπαίδευσης διακόπτεται όταν η απόδοση του μοντέλου σταματά να παρουσιάζει βελτίωση. Στο παρακάτω πείραμα θα σταματάμε την εξαγωγή του όταν η απόδοση θα παρουσιάσει 5 διαδοχικές φορές μείωση."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aAmPhl6RnKq6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "386ddd7d-fb95-45af-a86f-fbd40b282776"
      },
      "source": [
        "# 60 epochs\n",
        "early = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=5)\n",
        "history10 = mycnn_1.fit(x_train, y_train, epochs=60, batch_size=128, validation_data=(x_val, y_val)\n",
        "                           ,callbacks = early) \n",
        "mycnn_1.save('mycnn1_early.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/60\n",
            "67/67 [==============================] - 3s 52ms/step - loss: 0.7252 - accuracy: 0.8560 - val_loss: 2.2252 - val_accuracy: 0.6660\n",
            "Epoch 2/60\n",
            "67/67 [==============================] - 3s 50ms/step - loss: 0.5261 - accuracy: 0.8902 - val_loss: 2.9359 - val_accuracy: 0.6500\n",
            "Epoch 3/60\n",
            "67/67 [==============================] - 3s 50ms/step - loss: 0.4852 - accuracy: 0.8998 - val_loss: 2.7867 - val_accuracy: 0.6667\n",
            "Epoch 4/60\n",
            "67/67 [==============================] - 3s 50ms/step - loss: 0.5136 - accuracy: 0.8951 - val_loss: 3.0899 - val_accuracy: 0.6360\n",
            "Epoch 5/60\n",
            "67/67 [==============================] - 3s 50ms/step - loss: 0.5989 - accuracy: 0.8876 - val_loss: 3.2655 - val_accuracy: 0.6453\n",
            "Epoch 6/60\n",
            "67/67 [==============================] - 3s 50ms/step - loss: 0.6431 - accuracy: 0.8831 - val_loss: 3.6001 - val_accuracy: 0.6307\n",
            "Epoch 7/60\n",
            "67/67 [==============================] - 3s 50ms/step - loss: 0.5968 - accuracy: 0.8899 - val_loss: 3.3290 - val_accuracy: 0.6380\n",
            "Epoch 8/60\n",
            "67/67 [==============================] - 3s 50ms/step - loss: 0.6196 - accuracy: 0.8856 - val_loss: 3.4620 - val_accuracy: 0.6347\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKXouFjxFgXc"
      },
      "source": [
        "**Data Augmentation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zaUTVWx6FonA"
      },
      "source": [
        "Με την τεχνική αυτή δημιουργούνται νέα δεδομένα εκπαίδευσης με χρήση τυχαίων μετασχηματισμών σε αυτά που ήδη υπάρχουν. Ο στόχος είναι το μοντέλο να εκπαιδευτεί σε περισσότερες εικόνες, ορισμένες εκ των οποίων θα προκύπτουν από τους μετασχηματισμούς, αλλά θα αντιστοιχούν στις ίδιες κλάσεις."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3T41j30QHIFQ"
      },
      "source": [
        "from keras.utils import np_utils"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PAFnd8EpFo_q"
      },
      "source": [
        "# data augmentation\n",
        "y_train_categ = np_utils.to_categorical(y_train, classes_num)\n",
        "y_test_categ = np_utils.to_categorical(y_test, classes_num)\n",
        "y_val_categ = np_utils.to_categorical(y_val, classes_num)\n",
        "\n",
        "# define a  data generator for train and validation set\n",
        "data_gen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    featurewise_center=True,\n",
        "    featurewise_std_normalization=True,\n",
        "    rotation_range=10,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    zoom_range=0.2)\n",
        "\n",
        "# define a data generator for test set\n",
        "test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "# compute quantities required for featurewise normalization\n",
        "# (std, mean, and principal components if ZCA whitening is applied)\n",
        "data_gen.fit(x_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4grzEqZID6g"
      },
      "source": [
        "Με τη χρήση των δύο παραπάνω τεχνικών εκπαιδεύουμε το μοντέλο MyCNN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQWgWf0qFpCD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48fe842e-1e0a-4b9b-9e41-a2da0c63871f"
      },
      "source": [
        "# MyCNN Using Adam Optimizer\n",
        "mycnn_2 = models.Sequential()\n",
        "mycnn_2.add(layers.Conv2D(128, (3,3), padding=\"same\",input_shape=(32, 32, 3)))\n",
        "mycnn_2.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_2.add(layers.Conv2D(128, (3, 3), padding='same'))\n",
        "mycnn_2.add(layers.MaxPooling2D((2, 2)))\n",
        "mycnn_2.add(layers.Dropout(0.05))\n",
        "mycnn_2.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_2.add(layers.Conv2D(256, (3, 3), padding='same'))\n",
        "mycnn_2.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_2.add(layers.MaxPooling2D((2, 2)))\n",
        "mycnn_2.add(layers.Dropout(0.05))\n",
        "mycnn_2.add(layers.Conv2D(512, (3, 3), padding='same'))\n",
        "mycnn_2.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_2.add(layers.Conv2D(256, (3, 3)))\n",
        "mycnn_2.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_2.add(layers.MaxPooling2D((2, 2)))\n",
        "mycnn_2.add(layers.Dropout(0.05))\n",
        "mycnn_2.add(layers.Flatten())\n",
        "mycnn_2.add(layers.Dense(1024))\n",
        "mycnn_2.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_2.add(layers.Dropout(0.05))\n",
        "mycnn_2.add(layers.Dense(200))\n",
        "mycnn_2.add(tf.keras.layers.LeakyReLU())\n",
        "mycnn_2.add(layers.Dropout(0.05))\n",
        "mycnn_2.add(layers.Dense(classes_num, activation='softmax'))\n",
        "mycnn_2.add(layers.Dropout(0.05))\n",
        "\n",
        "\n",
        "mycnn_2.compile(optimizer='adam', loss=tf.keras.losses.categorical_crossentropy, metrics=['accuracy'])\n",
        "mycnn_2.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_49 (Conv2D)           (None, 32, 32, 128)       3584      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_57 (LeakyReLU)   (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_50 (Conv2D)           (None, 32, 32, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_31 (MaxPooling (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_18 (Dropout)         (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_58 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_51 (Conv2D)           (None, 16, 16, 256)       295168    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_59 (LeakyReLU)   (None, 16, 16, 256)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_32 (MaxPooling (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_19 (Dropout)         (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_52 (Conv2D)           (None, 8, 8, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_60 (LeakyReLU)   (None, 8, 8, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_53 (Conv2D)           (None, 6, 6, 256)         1179904   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_61 (LeakyReLU)   (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_33 (MaxPooling (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_20 (Dropout)         (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_11 (Flatten)         (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense_31 (Dense)             (None, 1024)              2360320   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_62 (LeakyReLU)   (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dropout_21 (Dropout)         (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_32 (Dense)             (None, 200)               205000    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_63 (LeakyReLU)   (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dropout_22 (Dropout)         (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_33 (Dense)             (None, 20)                4020      \n",
            "_________________________________________________________________\n",
            "dropout_23 (Dropout)         (None, 20)                0         \n",
            "=================================================================\n",
            "Total params: 5,375,740\n",
            "Trainable params: 5,375,740\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfevJPGgJQ9j",
        "outputId": "02486bfc-1320-476f-9cd0-a52766147d69"
      },
      "source": [
        "#define early stopping after 5 consecutive epochs with increasing loss  \n",
        "callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=5)\n",
        "\n",
        "# fits the model on batches with real-time data augmentation\n",
        "mycnn_2.compile(optimizer='adam',\n",
        "                  loss='categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "history11 = mycnn_2.fit(data_gen.flow(x_train, y_train_categ, batch_size=64),\n",
        "                    steps_per_epoch=len(x_train)/64, epochs=50,\n",
        "                     validation_data=data_gen.flow(x_val, y_val_categ, batch_size=64),\n",
        "                    validation_steps = len(x_val)/64, callbacks=callback)\n",
        "\n",
        "mycnn_2.save('MyCNN_2.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "132/132 [==============================] - 7s 50ms/step - loss: 3.0887 - accuracy: 0.2614 - val_loss: 2.0880 - val_accuracy: 0.3560\n",
            "Epoch 2/50\n",
            "132/132 [==============================] - 7s 49ms/step - loss: 2.6072 - accuracy: 0.4033 - val_loss: 1.7949 - val_accuracy: 0.4507\n",
            "Epoch 3/50\n",
            "132/132 [==============================] - 7s 50ms/step - loss: 2.3881 - accuracy: 0.4565 - val_loss: 1.6666 - val_accuracy: 0.4887\n",
            "Epoch 4/50\n",
            "132/132 [==============================] - 7s 54ms/step - loss: 2.3368 - accuracy: 0.4896 - val_loss: 1.5223 - val_accuracy: 0.5347\n",
            "Epoch 5/50\n",
            "132/132 [==============================] - 7s 54ms/step - loss: 2.2158 - accuracy: 0.5340 - val_loss: 1.4810 - val_accuracy: 0.5547\n",
            "Epoch 6/50\n",
            "132/132 [==============================] - 7s 54ms/step - loss: 2.0148 - accuracy: 0.5634 - val_loss: 1.5104 - val_accuracy: 0.5400\n",
            "Epoch 7/50\n",
            "132/132 [==============================] - 7s 49ms/step - loss: 2.0289 - accuracy: 0.5776 - val_loss: 1.3888 - val_accuracy: 0.5940\n",
            "Epoch 8/50\n",
            "132/132 [==============================] - 7s 49ms/step - loss: 1.9574 - accuracy: 0.5981 - val_loss: 1.2218 - val_accuracy: 0.6273\n",
            "Epoch 9/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.8778 - accuracy: 0.6168 - val_loss: 1.3748 - val_accuracy: 0.5873\n",
            "Epoch 10/50\n",
            "132/132 [==============================] - 7s 49ms/step - loss: 1.8421 - accuracy: 0.6206 - val_loss: 1.4018 - val_accuracy: 0.6240\n",
            "Epoch 11/50\n",
            "132/132 [==============================] - 6s 49ms/step - loss: 1.7601 - accuracy: 0.6472 - val_loss: 1.3109 - val_accuracy: 0.6240\n",
            "Epoch 12/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.7337 - accuracy: 0.6556 - val_loss: 1.3700 - val_accuracy: 0.6013\n",
            "Epoch 13/50\n",
            "132/132 [==============================] - 7s 54ms/step - loss: 1.7537 - accuracy: 0.6626 - val_loss: 1.4547 - val_accuracy: 0.6127\n",
            "Epoch 14/50\n",
            "132/132 [==============================] - 7s 54ms/step - loss: 1.7061 - accuracy: 0.6692 - val_loss: 1.1813 - val_accuracy: 0.6600\n",
            "Epoch 15/50\n",
            "132/132 [==============================] - 8s 57ms/step - loss: 1.6225 - accuracy: 0.6758 - val_loss: 1.1457 - val_accuracy: 0.6640\n",
            "Epoch 16/50\n",
            "132/132 [==============================] - 7s 52ms/step - loss: 1.6612 - accuracy: 0.6839 - val_loss: 1.1313 - val_accuracy: 0.6567\n",
            "Epoch 17/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.6253 - accuracy: 0.6960 - val_loss: 1.1896 - val_accuracy: 0.6607\n",
            "Epoch 18/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.5809 - accuracy: 0.7013 - val_loss: 1.3136 - val_accuracy: 0.6453\n",
            "Epoch 19/50\n",
            "132/132 [==============================] - 7s 50ms/step - loss: 1.6617 - accuracy: 0.7007 - val_loss: 1.1250 - val_accuracy: 0.6780\n",
            "Epoch 20/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.5849 - accuracy: 0.7120 - val_loss: 1.3746 - val_accuracy: 0.6467\n",
            "Epoch 21/50\n",
            "132/132 [==============================] - 6s 47ms/step - loss: 1.5816 - accuracy: 0.7114 - val_loss: 1.1662 - val_accuracy: 0.6833\n",
            "Epoch 22/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.5514 - accuracy: 0.7219 - val_loss: 1.1482 - val_accuracy: 0.6813\n",
            "Epoch 23/50\n",
            "132/132 [==============================] - 7s 54ms/step - loss: 1.5357 - accuracy: 0.7156 - val_loss: 1.2005 - val_accuracy: 0.6607\n",
            "Epoch 24/50\n",
            "132/132 [==============================] - 7s 54ms/step - loss: 1.4984 - accuracy: 0.7293 - val_loss: 1.0921 - val_accuracy: 0.6993\n",
            "Epoch 25/50\n",
            "132/132 [==============================] - 7s 54ms/step - loss: 1.4964 - accuracy: 0.7391 - val_loss: 1.1146 - val_accuracy: 0.6927\n",
            "Epoch 26/50\n",
            "132/132 [==============================] - 7s 51ms/step - loss: 1.5206 - accuracy: 0.7368 - val_loss: 1.0710 - val_accuracy: 0.6947\n",
            "Epoch 27/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.5031 - accuracy: 0.7364 - val_loss: 1.2172 - val_accuracy: 0.6833\n",
            "Epoch 28/50\n",
            "132/132 [==============================] - 7s 50ms/step - loss: 1.4584 - accuracy: 0.7449 - val_loss: 1.0642 - val_accuracy: 0.7167\n",
            "Epoch 29/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.4465 - accuracy: 0.7507 - val_loss: 1.2702 - val_accuracy: 0.6947\n",
            "Epoch 30/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.4698 - accuracy: 0.7464 - val_loss: 1.1579 - val_accuracy: 0.6927\n",
            "Epoch 31/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.4310 - accuracy: 0.7584 - val_loss: 1.1365 - val_accuracy: 0.7113\n",
            "Epoch 32/50\n",
            "132/132 [==============================] - 7s 49ms/step - loss: 1.4294 - accuracy: 0.7561 - val_loss: 1.1765 - val_accuracy: 0.6727\n",
            "Epoch 33/50\n",
            "132/132 [==============================] - 7s 54ms/step - loss: 1.4112 - accuracy: 0.7628 - val_loss: 1.0435 - val_accuracy: 0.7227\n",
            "Epoch 34/50\n",
            "132/132 [==============================] - 7s 53ms/step - loss: 1.3798 - accuracy: 0.7687 - val_loss: 1.1974 - val_accuracy: 0.7127\n",
            "Epoch 35/50\n",
            "132/132 [==============================] - 7s 53ms/step - loss: 1.3725 - accuracy: 0.7691 - val_loss: 1.2019 - val_accuracy: 0.7020\n",
            "Epoch 36/50\n",
            "132/132 [==============================] - 7s 50ms/step - loss: 1.3914 - accuracy: 0.7649 - val_loss: 1.2057 - val_accuracy: 0.6960\n",
            "Epoch 37/50\n",
            "132/132 [==============================] - 7s 51ms/step - loss: 1.4231 - accuracy: 0.7666 - val_loss: 1.2388 - val_accuracy: 0.7073\n",
            "Epoch 38/50\n",
            "132/132 [==============================] - 7s 53ms/step - loss: 1.3158 - accuracy: 0.7745 - val_loss: 1.2307 - val_accuracy: 0.6893\n",
            "Epoch 39/50\n",
            "132/132 [==============================] - 7s 53ms/step - loss: 1.4065 - accuracy: 0.7705 - val_loss: 1.0736 - val_accuracy: 0.7227\n",
            "Epoch 40/50\n",
            "132/132 [==============================] - 7s 54ms/step - loss: 1.4128 - accuracy: 0.7739 - val_loss: 1.1788 - val_accuracy: 0.7107\n",
            "Epoch 41/50\n",
            "132/132 [==============================] - 7s 49ms/step - loss: 1.3282 - accuracy: 0.7911 - val_loss: 1.1281 - val_accuracy: 0.7207\n",
            "Epoch 42/50\n",
            "132/132 [==============================] - 6s 49ms/step - loss: 1.3296 - accuracy: 0.7795 - val_loss: 1.2932 - val_accuracy: 0.7047\n",
            "Epoch 43/50\n",
            "132/132 [==============================] - 6s 48ms/step - loss: 1.3629 - accuracy: 0.7742 - val_loss: 1.2780 - val_accuracy: 0.6767\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsAjwus5L_ru"
      },
      "source": [
        "---\n",
        "### Βήμα 2: Αξιολόγηση\n",
        "Αξιολογήστε της ορθότητας για το validation και το test set σας."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ffWWpYMlMBAQ",
        "outputId": "258040f3-f86b-4863-c8ea-23d06c69ba21"
      },
      "source": [
        "# Plot Implementation in MyCNN of Dropout and Early Stopping\n",
        "plot_acc(history10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VJp3WPU5NbmR",
        "outputId": "eeac50bb-c98a-4d08-9d9a-be6a09843a8c"
      },
      "source": [
        "mycnn1 =  tf.keras.models.load_model('mycnn1_early.h5')\n",
        "\n",
        "print(\"MyCNN using Dropout and Early Stopping\")\n",
        "\n",
        "print('Validation Analytics')\n",
        "loss, accuracy = mycnn1.evaluate(x_val, y_val)\n",
        "print(\"Loss: {:.2f}\".format(loss))\n",
        "print(\"Accuracy: {:.2f}\".format(accuracy))\n",
        "print('\\nTest set Analytics')\n",
        "loss, accuracy = mycnn1.evaluate(x_test, y_test)\n",
        "print(\"Loss: {:.2f}\".format(loss))\n",
        "print(\"Accuracy: {:.2f}\".format(accuracy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MyCNN using Dropout and Early Stopping\n",
            "Validation Analytics\n",
            "47/47 [==============================] - 1s 8ms/step - loss: 3.4620 - accuracy: 0.6347\n",
            "Loss: 3.46\n",
            "Accuracy: 0.63\n",
            "\n",
            "Test set Analytics\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 3.5701 - accuracy: 0.6345\n",
            "Loss: 3.57\n",
            "Accuracy: 0.63\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-f8fRU3OGih"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "TDezGLZYMo7d",
        "outputId": "55889635-4184-4753-8b66-13f28a0b3155"
      },
      "source": [
        "# Plot Implementation in MyCNN of Dropout,Early Stopping and Augmentation\n",
        "plot_acc(history11)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gsJ5AkQT3U0q",
        "outputId": "07ec6d31-ee13-4822-ecd0-094304eec829"
      },
      "source": [
        "print(\"MyCNN Model using Dropout, Early Stopping, Data Augmentation\")\n",
        "\n",
        "print('Validation Analytics')\n",
        "validation_loss, validation_accuracy = mycnn_2.evaluate(x_val, y_val_categ)\n",
        "\n",
        "print(\"Loss: {:.2f}\".format(validation_loss))\n",
        "print(\"Accuracy: {:.2f}\".format(validation_accuracy))\n",
        "\n",
        "print('Test set Analytics')\n",
        "test_loss, test_accuracy = mycnn_2.evaluate(x_test, y_test_categ)\n",
        "print(\"Loss: {:.2f}\".format(test_loss))\n",
        "print(\"Accuracy: {:.2f}\".format(test_accuracy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MyCNN Model using Dropout, Early Stopping, Data Augmentation\n",
            "Validation Analytics\n",
            "47/47 [==============================] - 0s 8ms/step - loss: 2.9077 - accuracy: 0.2713\n",
            "Loss: 2.91\n",
            "Accuracy: 0.27\n",
            "Test set Analytics\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 2.8156 - accuracy: 0.2915\n",
            "Loss: 2.82\n",
            "Accuracy: 0.29\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAPz4ivrWamW"
      },
      "source": [
        "Παρατηρούμε μείωση στο φαινόμενο του overfit. Επίσης ο υπολογισμός του  validation accuracy γίνεται με απενεργοποιημένα τα dropout layers και επομένως παράγει πιο ομαλά και καλύτερα αποτελέσματα από αυτά στο test. Επιπλέον, τα αποτελέσματα από την εκπαίδευση με χρήση των μεθόδων του ερωτήματος αυτού φαίνεται να έχει καλύτερα αποτελέσματα σε σχέση με το 1ο (όπου δεν τα χρησιμοποιήσαμε)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTxKRNEHWfGs"
      },
      "source": [
        "## Ερώτημα 3\n",
        "---\n",
        "### Βήμα 1: Μεταφορά γνώσης\n",
        "Εφαρμόστε μεταφορά γνώσης (transfer learning) στο δικό σας μοντέλο (**MyCNN**), που αξιολογήσατε ως καλύτερο προς την ακρίβεια στην αντιμετώπιση της υπερεκπαίδεσης. \n",
        "\n",
        "Για το transfer learning επιλέξτε το [VGG16](https://www.tensorflow.org/api_docs/python/tf/keras/applications/vgg16) για μεταφορά μάθησης.\n",
        "\n",
        "1. \"Παγώστε\" τη συνελικτική βάση και εκπαιδεύστε την κεφαλή ταξινόμησης (classification head - σημαία trainable = False).  \n",
        "\n",
        "2. Εκπαιδέστε μόνο ένα ποσοστό των επιπέδων, το οποίο βρίσκεται προς την έξοδο του δικτύου. Οι σημαίες trainable εδώ θα πρέπει να οριστούν ανά επίπεδο.\n",
        "\n",
        "3. Αξιολογήστε τα αποτελέσματά σας, βάσει της ορθότητας του validation set και του test set σας."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMAtb8hspOto"
      },
      "source": [
        "**Βήμα 1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkI-D-hLOxvj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "044054a8-6449-4510-c457-71aedc01b852"
      },
      "source": [
        "# Pretrained Model and Freeze Weights\n",
        "pretr_model = tf.keras.applications.VGG16(\n",
        "    include_top=False, weights='imagenet', input_tensor=None, input_shape=(32,32,3),\n",
        "    pooling=max, classes=classes_num\n",
        ")\n",
        "pretr_model.trainable=False "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2pnFNtbOno9",
        "outputId": "0f03b84f-0192-4aad-ad2b-7491c36f87a4"
      },
      "source": [
        "# Using the above pretrained model and CNN2, create full sequential model\n",
        "transf_model = models.Sequential([\n",
        "    pretr_model\n",
        "])\n",
        "transf_model.add(layers.Flatten()),\n",
        "transf_model.add(layers.Dense(1024)),\n",
        "transf_model.add(tf.keras.layers.LeakyReLU()),\n",
        "transf_model.add(layers.Dropout(0.15)),\n",
        "transf_model.add(layers.Dense(200))\n",
        "transf_model.add(tf.keras.layers.LeakyReLU())\n",
        "transf_model.add(layers.Dropout(0.18))\n",
        "transf_model.add(layers.Dense(classes_num, activation='softmax'))\n",
        "transf_model.add(layers.Dropout(0.2))\n",
        "transf_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg16 (Functional)           (None, 1, 1, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1024)              525312    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)    (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 200)               205000    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)    (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 20)                4020      \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 20)                0         \n",
            "=================================================================\n",
            "Total params: 15,449,020\n",
            "Trainable params: 734,332\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gz_UK2UGOntC",
        "outputId": "f2a0c170-7c6d-446e-f594-a1aeefc72a27"
      },
      "source": [
        "# early Stopping = 3, 20 epochs \n",
        "c = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n",
        "\n",
        "# define batch size\n",
        "bs = 32\n",
        "\n",
        "# fits the model on batches with real-time data augmentation\n",
        "transf_model.compile(optimizer='adam',\n",
        "                  loss='categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "history12 = transf_model.fit(data_gen.flow(x_train, y_train_categ, batch_size=bs),\n",
        "                    steps_per_epoch=len(x_train)/bs, epochs=20,\n",
        "                     validation_data=data_gen.flow(x_val, y_val_categ, batch_size=bs),\n",
        "                    validation_steps = len(x_val)/bs, callbacks=c)\n",
        "transf_model.save('transf.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "265/265 [==============================] - 37s 26ms/step - loss: 4.5946 - accuracy: 0.3860 - val_loss: 1.4973 - val_accuracy: 0.5373\n",
            "Epoch 2/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.1702 - accuracy: 0.4747 - val_loss: 1.4503 - val_accuracy: 0.5467\n",
            "Epoch 3/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.2789 - accuracy: 0.4865 - val_loss: 1.4079 - val_accuracy: 0.5680\n",
            "Epoch 4/20\n",
            "265/265 [==============================] - 6s 24ms/step - loss: 4.2349 - accuracy: 0.4959 - val_loss: 1.4057 - val_accuracy: 0.5793\n",
            "Epoch 5/20\n",
            "265/265 [==============================] - 6s 24ms/step - loss: 4.0429 - accuracy: 0.5196 - val_loss: 1.3945 - val_accuracy: 0.5687\n",
            "Epoch 6/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.1417 - accuracy: 0.5195 - val_loss: 1.4592 - val_accuracy: 0.5667\n",
            "Epoch 7/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.0123 - accuracy: 0.5399 - val_loss: 1.3764 - val_accuracy: 0.5767\n",
            "Epoch 8/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.0316 - accuracy: 0.5345 - val_loss: 1.4308 - val_accuracy: 0.5800\n",
            "Epoch 9/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.0214 - accuracy: 0.5387 - val_loss: 1.4435 - val_accuracy: 0.5647\n",
            "Epoch 10/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.0811 - accuracy: 0.5335 - val_loss: 1.4215 - val_accuracy: 0.5733\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-wZH0dBUQz8"
      },
      "source": [
        "Παρατηρούμε ότι το αποτέλεσμα συγκλίνει γρήγορα, κάτι το οποίο είναι και λογικό αφού εφαρμόσαμε ένα εκπαιδευμένο νευρωνικό δίκτυο. Επίσης σε σχέση με το μέγεθος του δικτύου αυτού, η εκπαίδευση τελειώνει γρήγορα αφού δεν εκπαιδεύονται τα layers της συνελικτικής βάσης."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jw1RgB6DpSCF"
      },
      "source": [
        "**Βήμα 2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v2ozryvYVY4K"
      },
      "source": [
        "Έπειτα εκπαιδεύουμε μόνο ένα ποσοστό των επιπέδων, το οποίο βρίσκεται προς την έξοδο του δικτύου. Οι σημαίες trainable εδώ θα πρέπει να οριστούν ανά επίπεδο."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54RZzHaJOnwN",
        "outputId": "435f9135-c37c-461f-c4dc-3b77da5e6479"
      },
      "source": [
        "# Full sequential model \n",
        "transf_model2 = models.Sequential([pretr_model])\n",
        "transf_model2.add(layers.Flatten()),\n",
        "transf_model2.add(layers.Dense(1024)),\n",
        "transf_model2.add(tf.keras.layers.LeakyReLU()),\n",
        "transf_model2.add(layers.Dropout(0.15)),\n",
        "transf_model2.add(layers.Dense(200, trainable=False))\n",
        "transf_model2.add(tf.keras.layers.LeakyReLU())\n",
        "transf_model2.add(layers.Dropout(0.18))\n",
        "transf_model2.add(layers.Dense(classes_num, activation='softmax'))\n",
        "transf_model2.add(layers.Dropout(0.2))\n",
        "transf_model2.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg16 (Functional)           (None, 1, 1, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1024)              525312    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_4 (LeakyReLU)    (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 200)               205000    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_5 (LeakyReLU)    (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 20)                4020      \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 20)                0         \n",
            "=================================================================\n",
            "Total params: 15,449,020\n",
            "Trainable params: 529,332\n",
            "Non-trainable params: 14,919,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sKZAMLuWSXS"
      },
      "source": [
        "# with pretrained weights \n",
        "w = transf_model.layers[-5].get_weights()  \n",
        "transf_model2.layers[5].set_weights(w)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ap854YZhWSaL",
        "outputId": "82fca19f-2205-4f91-83b7-a8ea4ced6202"
      },
      "source": [
        "# Eearly stopping: 3, increasing loss, 20 epochs\n",
        "c = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n",
        "\n",
        "# fits the model on batches with real-time data augmentation\n",
        "transf_model2.compile(optimizer='adam',\n",
        "                  loss='categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "history13 = transf_model2.fit(data_gen.flow(x_train, y_train_categ, batch_size=32),\n",
        "                    steps_per_epoch=len(x_train)/32, epochs=20,\n",
        "                     validation_data=data_gen.flow(x_val, y_val_categ, batch_size=32),\n",
        "                    validation_steps = len(x_val)/32, callbacks=c)\n",
        "\n",
        "transf_model2.save('transf_v2.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "265/265 [==============================] - 7s 24ms/step - loss: 4.5589 - accuracy: 0.3844 - val_loss: 1.5617 - val_accuracy: 0.5080\n",
            "Epoch 2/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.2107 - accuracy: 0.4694 - val_loss: 1.4094 - val_accuracy: 0.5773\n",
            "Epoch 3/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.1253 - accuracy: 0.4984 - val_loss: 1.3479 - val_accuracy: 0.5793\n",
            "Epoch 4/20\n",
            "265/265 [==============================] - 6s 24ms/step - loss: 4.1293 - accuracy: 0.5007 - val_loss: 1.3166 - val_accuracy: 0.5947\n",
            "Epoch 5/20\n",
            "265/265 [==============================] - 6s 24ms/step - loss: 4.1673 - accuracy: 0.5065 - val_loss: 1.3025 - val_accuracy: 0.5927\n",
            "Epoch 6/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.0339 - accuracy: 0.5212 - val_loss: 1.3494 - val_accuracy: 0.5793\n",
            "Epoch 7/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 4.0349 - accuracy: 0.5300 - val_loss: 1.3220 - val_accuracy: 0.5940\n",
            "Epoch 8/20\n",
            "265/265 [==============================] - 6s 24ms/step - loss: 3.9600 - accuracy: 0.5404 - val_loss: 1.3371 - val_accuracy: 0.5940\n",
            "Epoch 9/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 3.9151 - accuracy: 0.5475 - val_loss: 1.3120 - val_accuracy: 0.6047\n",
            "Epoch 10/20\n",
            "265/265 [==============================] - 6s 24ms/step - loss: 4.0057 - accuracy: 0.5484 - val_loss: 1.3033 - val_accuracy: 0.5880\n",
            "Epoch 11/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 3.9292 - accuracy: 0.5496 - val_loss: 1.3242 - val_accuracy: 0.5993\n",
            "Epoch 12/20\n",
            "265/265 [==============================] - 6s 23ms/step - loss: 3.9164 - accuracy: 0.5588 - val_loss: 1.3220 - val_accuracy: 0.6000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9EarqKrXX5-"
      },
      "source": [
        "Σε σχέση με το προηγούμενο βήμα, παρατηρούμε ότι η σύγκλιση είναι πιο γρήγορη. Επίσης τα αποτελέσματα φαίνονται βελτιωμένα. Αυτό οφείλεται στο γεγονός ότι εκπαιδεύεται μόνο το τελευταίο επίπεδο της ταξινόμησης, που ορίζει και την κατηγοριοποίηση στον ζητούμενο αριθμό κλάσσεων. Τέλος, παρατηρούμε σημαντική επίδραση των dropout layers στο αποτέλεσμα, αλλά και τον τυχαίο τρόπο μηδενισμού ποσοστού των νευρώνων στο layer που εφαρμόζονται."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWyalqgipUyC"
      },
      "source": [
        "**Βήμα 3**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GlR_vS9TpaZ5"
      },
      "source": [
        "Αξιολόγηση αποτελεσμάτων:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "rTRWL-xvpZl-",
        "outputId": "6890a697-8f94-45bf-ef4a-00ed04a7cf49"
      },
      "source": [
        "# Plot Implementation in MyCNN using transfer learning and freeze weights of VGG16\n",
        "plot_acc(history12)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GfF8vzTMqGZ",
        "outputId": "a0da8db3-a72c-4850-e5c9-e9b862edf0ed"
      },
      "source": [
        "print(\"Trander Learning - First Attempt\")\n",
        "test_loss, test_accuracy = transf_model.evaluate(x_test, y_test_categ)\n",
        "validation_loss, validation_accuracy = transf_model.evaluate(x_val, y_val_categ)\n",
        "print(\"Test loss:\", test_loss)\n",
        "print(\"Test accuracy:\", test_accuracy)\n",
        "print(\"Validation loss:\", validation_loss)\n",
        "print(\"Validation accuracy:\",validation_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Trander Learning - First Attempt\n",
            "63/63 [==============================] - 1s 15ms/step - loss: 2.6366 - accuracy: 0.3345\n",
            "47/47 [==============================] - 0s 9ms/step - loss: 2.7125 - accuracy: 0.3247\n",
            "Test loss: 2.6365771293640137\n",
            "Test accuracy: 0.3345000147819519\n",
            "Validation loss: 2.7124595642089844\n",
            "Validation accuracy: 0.32466667890548706\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "fjlXekIkWSdj",
        "outputId": "ce056a04-42d7-46d3-9f81-f4113bbff240"
      },
      "source": [
        "# Plot Implementation in MyCNN using transfer learning and freeze weights of VGG16 and third to last dense layer\n",
        "plot_acc(history13)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HqthLKKSM32s",
        "outputId": "761356eb-8dd7-4826-9aa5-89ac826c582b"
      },
      "source": [
        "print(\"Trander Learning - Second Attempt\")\n",
        "test_loss, test_accuracy = transf_model2.evaluate(x_test, y_test_categ)\n",
        "validation_loss, validation_accuracy = transf_model2.evaluate(x_val, y_val_categ)\n",
        "print(\"Test loss:\", test_loss)\n",
        "print(\"Test accuracy:\", test_accuracy)\n",
        "print(\"Validation loss:\", validation_loss)\n",
        "print(\"Validation accuracy:\",validation_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Trander Learning - Second Attempt\n",
            "63/63 [==============================] - 1s 12ms/step - loss: 2.1705 - accuracy: 0.3535\n",
            "47/47 [==============================] - 1s 11ms/step - loss: 2.2178 - accuracy: 0.3540\n",
            "Test loss: 2.170492649078369\n",
            "Test accuracy: 0.35350000858306885\n",
            "Validation loss: 2.217787504196167\n",
            "Validation accuracy: 0.3540000021457672\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ad0xJgG3NCTQ"
      },
      "source": [
        "Παρατηρούμε ότι τα test accurancy και validation accurancy έχουν παρόμοιες τιμές αναλογικά με τα προηγούμενα βήματα. Επίσης το evaluation accurancy που προκύπτει από την fit σε σχέση με αυτό που προκύπτει από την evaluate είναι διαφορετικό."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m09FA2dgOnyx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_T6Y9EJDuOu"
      },
      "source": [
        "## Διαχείριση μνήμης (TFRecord)\n",
        "Η φόρτωση δεδομένων με τον τρόπο που το κάναμε παραπάνω στο απλό παράδειγμα υλοποίησης είναι πολύ βολική αλλά δεν είναι αποτελεσματική ως προς τη διαχείριση της μνήμης. Συγκεκριμένα, με τον τρόπο αυτό, τα δεδομένα αποθηκεύονται απευθείας σε μεταβλητές, οι οποίες όλες μαζί καταλαμβάνουν τη RAM της CPU ή της GPU, κάτι που κάνει αδύνατη τη διαχείριση μεγάλων datasets ή τον μεταχηματισμό των δεδομένων όπως όταν κάνουμε αύξηση δεδομένων (data augmentation).\n",
        "\n",
        "Για να παρακαμφθεί αυτό το πρόβλημα, υπάρχει η δυνατότητα της σειριοποίησης των δεδομένων (serialization) και της αποθήκευσής τους σε αρχεία μεσαίου μεγέθους (κάποιων MB) τα οποία μπορούν να αναγνωστούν γραμμικά.\n",
        "\n",
        "Το φορμάτ TFRecord είναι ένα φορμάτ που επιτρέπει την αποθήκευση σειράς δυαδικών εγγραφών. Διαβάστε σχετικά για το [TFRecord and tf.Example](https://www.tensorflow.org/tutorials/load_data/tfrecord) και [tf.data: Build TensorFlow input pipelines](https://www.tensorflow.org/guide/data). \n",
        "\n",
        "Σημειώστε ότι με τη μέθοδο αυτή θα πρέπει να γίνει import η `tensorflow_datasets` και να χρησιμοποιήσουμε την `tfds.load` ώστε να αποθηκευθεί το σύνολο δεδομένων σε αρχεία tfrecord στο δίσκο (δείτε [εδώ](https://colab.research.google.com/github/tensorflow/datasets/blob/master/docs/overview.ipynb) ένα παράδειγμα). Φυσικά μπορούμε να μετατρέψουμε και τα πρωτογενή δεδομένα (raw data) του dataset όπως αρχεία jpg σε φορματ tfrecord όπως [εδώ](https://towardsdatascience.com/working-with-tfrecords-and-tf-train-example-36d111b3ff4d).\n"
      ]
    }
  ]
}