Lab3.ipynb

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    "colab": {
      "name": "3η_Ομαδική_Άσκηση.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LpnEVMCYYlnD"
      },
      "source": [
        "#3η ομαδική άσκηση στη Τεχνολογία και Ανάλυση Εικόνων και Βίντεο"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsMnCpIT8dXV"
      },
      "source": [
        "* Ανδρέας Βεζάκης ΑΜ: 03117186\n",
        "* Σταύρος Κινικλής ΑΜ: 03117608\n",
        "\n",
        "\n",
        "## Θεωρητικό Μέρος\n",
        "\n",
        "1. <b>Γιατί και πώς τα συνελικτικά δίκτυα άλλαξαν το τοπίο της παραδοσιακής επεξεργασίας\n",
        "εικόνας;</b>\n",
        "\n",
        "Πριν τα συνελικτικά νευρωνικά δίκτυα (convolutional neural networks, CNN) η επεξεργασία των εικόνων ήταν πολύ πιο χρονοβόρα και υπολογιστικά ακριβή, αφού χρησιμοποιούνται βάρη για κάθε πίξελ της εικόνας. Για παράδειγμα μία εικόνα υψηλής ανάλυσης(512 x 762 RGB) η επεξεργασία της είναι πολύ χρονοβόρα και υπολογιστικά ακριβή. Ωστόσο, τα τελευταία χρόνια βρέθηκε ότι υπάρχει δυνατότητα \"απλοποίησης\" της εικόνας, ανιχνεύοντας  χαρακτηριστικά και υφές της, δηλαδή σημεία ενδιαφέροντος, και εφαρμόζοντας τα βάρη μόνο στις συγκεκριμένες περιοχές. Επομένως, τα CNN με αυτό το τρόπο μπορούν σε ογκώδη δεδομένα να εξάγουν χαρακτηριστικά για τις εικόνες και να τις κατηγοριοποιήσουν. Αρχικά, τα δίκτυα μας, χρησιμοποιούν συνέλιξη για να \"μαθαίνουν\" μόνα τους τα βάρη των φίλτρων, έπειτα μέσω ενός pooling layer  μειώνουν τη διάσταση της εισόδου, στη συνέχεια επιλέγεται η συνάρτηση ενεργοποίησης (ReLu) και τέλος ο τελευταίος τανυστής εξόδου τροφοδοτείται από τη συνελικτική βάση σε ένα ή πολλά dense networks. \n",
        "\n",
        "2. <b> Περιγράψτε τη λειτουργία της Rectified Linear Unit (ReLU).</b>\n",
        "\n",
        "Η Rectified Linear Unit (ReLU) είναι μία γραμμική συνάρτηση, η οποία εκφράζεται από τον τύπο: <br>\n",
        "<center>  <b> <i> f(x)=max⁡(0,x) </i> </b> </center>\n",
        "Χρησιμοποιείται στα νευρωνικά δίκτυα λόγω της απλότητάς της και της συμβολής της στην υψηλή απόδοση τους (επιτυγχάνει γραμμική αύξηση του υπολογιστικού κόστους του CNN μας). Η είσοδός της συγκρίνεται με το μηδέν και σε περίπτωση που δεν είναι θετική η τιμή της εισόδου, τότε στην έξοδό της έχει το μηδέν, διαφορετικά, η συνάρτηση παίρνει την τιμή της εισόδου.\n",
        "\n",
        "3. <b> Παρουσιάστε ένα συγκριτικό πίνακα με τα επίπεδα, το μέγεθος των φίλτρων, συνάρτηση\n",
        "ενεργοποίησης, πλήθος παραμέτρων και τεχνικές pooling και dropout που χρησιμοποιούν\n",
        "τα 3 διαφορετικά δίκτυα που παρουσιάζονται στα προαναφερθέντα άρθρα και σχολιάστε\n",
        "επαρκώς τις διαφορές τους. Ποια χαρακτηριστικά αυτών των τριών δικτύων είναι κατά τη\n",
        "γνώμη σας αυτά που συνέβαλαν στη σταδιακή αύξηση της ακρίβειας ταξινόμησης.\n",
        "</b>\n",
        "\n",
        "Για τη διαμόρφωση του παρακάτω πίνακα βασιστήκαμε στα μοντέλα που υλοποιήσαμε στα επόμενα ερωτήματα. \n"
      ]
    },
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      "metadata": {
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      "source": [
        "Συγκριτικός Πίνακας| LeNet              | AlexNet          | VGG\n",
        "-------------------|------------------  |------------------|------------------\n",
        "Επίπεδα            |7 (8 με είσοδο)    | 11 (12 με είσοδο)| 8 convolutional layers και 3 fully-connected layers\n",
        "Μέγεθος Φίλτρων    |convolution: 5x5, average pooling:2×2 |convolution: 11x11,5x5,3x3, maximum pooling: 3x3| convolution: 3x3, maximum pooling: 2x2 \n",
        "Συνάρτηση Ενεργοποίησης| sigmoid                   |   ReLu | ReLu\n",
        "Συνολικές Παράμετροι|     94,312          |21,990,884       |28,512,740\n",
        "Εκπαιδευόμενες Παράμετροι|  94,312             |21,990,884  |28,512,740\n",
        "Μη εκπαιδευόμενες Παράμετροι| 0           |2,752            |0\n",
        "Τεχνικές Pooling    | average pooling              |maximum pooling                   |maximum pooling\n",
        "Dropout             |       2x2 average pooling με stride 2            | 3x3 maximum pooling με stride 2, dropout rate                   |Dropout(0.5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5z7rH0MSXcb"
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      "source": [
        "Αρχικά, παρατηρούμε ότι τα μοντέλα AlexNet και VGG χρησιμοιποιούν για υποδειγματοληψία την τεχνική maximum pooling και την τεχνική Dropout, και για συνάρτηση εναργοποίησης την ReLU, ενώ το LeNet χρησιμοποιεί average pooling και sigmoid συνάρτηση ενεργοποίησης. Ακόμη, ο αριθμός των fully-connected επιπέδων παραμένει σταθερός σε όλα τα CNN. \n",
        "\n",
        "Ο παράγοντας που συνέβαλε περισσότερο στη σταδιακή αύξηση της ακρίβειας ταξινόμησης είναι η συνάρτηση ενεργοποίησης ReLU. Καθώς, όπως είπαμε και στο παραπάνω ερώτημα, επιτυγχάνει γραμμική αύξηση του υπολογιστικού κόστους και όχι εκθετική (συγκρίνει την είσοδο με το 0). Επίσης, το μοντέλο θα εκπαιδεύεται ταχύτερα αφού η παράγωγος θα είναι σταθερή και συγκεκριμένα ίση με 1. Τέλος, σε σύκριση με την sigmoid συνάρτηση, αναπαρηστά την είσοδο με πιο ωφέλιμη μορφή, αφού \"κόβει\" τις αρνητικές τιμές, ενώ η σιγμοειδής εμφανίζει μη μηδενικά αποτελέσματα και συνεπώς υπάρχει πιο πυκνή αναπαράσταση της εισόδου."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DfEMjsB4Yurm"
      },
      "source": [
        "## Εισαγωγή και επισκόπηση του συνόλου δεδομένων"
      ]
    },
    {
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87295e2a-0640-47aa-cc3c-0fe5e92af01b"
      },
      "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": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n",
            "169009152/169001437 [==============================] - 2s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGKYHffEE1do",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f91b6eb-1a0f-4eb0-8e34-91116be581a1"
      },
      "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\n",
        "team_seed = 25"
      ],
      "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\""
      ],
      "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": {
        "id": "_B4-tvVOQq3j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca8ada7b-eeae-4124-c6df-00e4dcac106a"
      },
      "source": [
        "# print our classes\n",
        "print(our_classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[' aquarium_fish', ' baby', ' bee', ' bicycle', ' bottle', ' camel', ' cup', ' elephant', ' lamp', ' lion', ' man', ' maple_tree', ' orange', ' pear', ' rabbit', ' rose', ' sunflower', ' telephone', ' train', ' wolf']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bk5wy0u7GGMj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2847d48-83d2-4f78-f29c-bbb6c0a7ca9f"
      },
      "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": {
        "id": "QpGXgTs_5ZCk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "458d8b6b-b3a3-49b1-aa05-ecc47766dc9a"
      },
      "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": "markdown",
      "metadata": {
        "id": "iAgowxR8oJH7"
      },
      "source": [
        "# Ερώτημα 1\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OB0rBWITncHT"
      },
      "source": [
        "## Αξιολόγηση των αποτελεσμάτων:\n",
        "* Για όλα τα μοντέλα, αυξήσαμε το πλήθος των δεδομένων/κλάσεων από 10 σε 100. Όπως ήταν αναμενόμενο με το cifar100 υπάρχουν πολλές περισσότερες κατηγορίες και επομένως τα μοντέλα μας δυσκολεύονται (περισσότερο από τη χρήση του cifar10) στην κατηγοριοποίηση των εικόνων. Αυτό επίσης να το επαληθεύσουμε συγκρίνοντας τα αποτελέσματα μας με του εργαστηρίου όπου χρησιμοποιήθηκε ένα απλό CNN με cifar10.\n",
        "* Οι optimizers που χρησιμοποιήσαμε ήταν οι SGD (μόνο στο LeNet), Adam (σε όλα τα CNN) και Adamax (σε όλα εκτός του LeNet). Όπως ήταν αναμενόμενο καλύτερα αποτελέσματα είχαμε με τους optimizers Adam και Adamax.  Στο CNN LeNet που χρησιμοποιούσαμε μόνο SGD και Adam, ο δεύτερος optimizer είχε το καλύτερο accuracy καθώς και το μικρότερο loss. Στα υπόλοιπα μοντέλα που χρησιμοποίθηκαν  οι Adam και Adamax, ο Adamax υπερτερούσε σε όλες τις περιπτώσεις του Adam, όπως και περιμέναμε αφού αποτελεί επέκταση του.\n",
        "* Για την παράμετρο batch size δκομίσαμε τις τιμές 64,128 και 256. Με αυτές τις δοκιμές παρατηρήσαμε ότι είναι δύσκολο να εξάγουμε ένα συμπέρασμα για την ιδανική τιμή του batch size, καθώς διαφέρει ανάλογα με το μοντέλο και το dataset που χρησιμοποιούμε. Ωστόσο, όπως περιμενάμε για μεγαλύτερες τιμές της παραμέτρου, μειώνεται ο χρόνος εκπαίδευσης του κάθε epoch, και συγκλίνει πιο αργά στο training loss (το οποίο παρουσίαζει μεγαλύτερες τιμες)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPucfvGimyvz"
      },
      "source": [
        "Βασιζόμενοι στο εργαστήριο 9.3 του μαθήματος αρχικά δημιουργούμε το συνελικτικό μας και στη συνέχεια το μεταγλωτίζουμε και το εκπαιδεύουμε.\n",
        "## LeNet\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3C-HaWlw2qRF"
      },
      "source": [
        "def LeNet():\n",
        "    return tf.keras.models.Sequential([\n",
        "        tf.keras.layers.Conv2D(filters=6, kernel_size=5, activation='sigmoid',\n",
        "                               padding='same'),\n",
        "        tf.keras.layers.AvgPool2D(pool_size=2, strides=2),\n",
        "        tf.keras.layers.Conv2D(filters=16, kernel_size=5,\n",
        "                               activation='sigmoid'),\n",
        "        tf.keras.layers.AvgPool2D(pool_size=2, strides=2),\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(120, activation='sigmoid'),\n",
        "        tf.keras.layers.Dense(100, activation='sigmoid'),\n",
        "        tf.keras.layers.Dense(100)])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGGKNUOy21A6",
        "outputId": "3f8416d6-289f-4dc9-e573-af4faadb4368"
      },
      "source": [
        "lenet = LeNet()\n",
        "lenet.build((None,32,32,3))\n",
        "lenet.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 32, 6)         456       \n",
            "_________________________________________________________________\n",
            "average_pooling2d (AveragePo (None, 16, 16, 6)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 12, 12, 16)        2416      \n",
            "_________________________________________________________________\n",
            "average_pooling2d_1 (Average (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 120)               69240     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               12100     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 100)               10100     \n",
            "=================================================================\n",
            "Total params: 94,312\n",
            "Trainable params: 94,312\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUABxyAA85Ux"
      },
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  lenet = LeNet()\n",
        "  lenet.build((None, 32, 32, 3))\n",
        "\n",
        "  lenet.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.9),  ##lr = 0.9 standard value\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  history = lenet.fit(x_train, y_train, epochs=100, batch_size=128,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTILBhU-HlTg"
      },
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  lenet = LeNet()\n",
        "  lenet.build((None, 32, 32, 3))\n",
        "\n",
        "  lenet.compile(optimizer=tf.keras.optimizers.Adam(),  \n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  history2 = lenet.fit(x_train, y_train, epochs=100, batch_size=128,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fhdo2__RLuDu"
      },
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  lenet = LeNet()\n",
        "  lenet.build((None, 32, 32, 3))\n",
        "\n",
        "  lenet.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.9),  ##lr = 0.9 standard value\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  history3 = lenet.fit(x_train, y_train, epochs=130, batch_size=64,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pBWGOKbmMCMw"
      },
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  lenet = LeNet()\n",
        "  lenet.build((None, 32, 32, 3))\n",
        "\n",
        "  lenet.compile(optimizer=tf.keras.optimizers.Adam(),  \n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  history4 = lenet.fit(x_train, y_train, epochs=130, batch_size=64,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wynitUBcHG48"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = SGD, loss = crossentropy, epochs = 100, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "Oc_HpislAuV3",
        "outputId": "3f3f0fc5-6f50-41f9-cce1-566cc1bec727"
      },
      "source": [
        "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.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = lenet.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 1.9878 - accuracy: 0.4585\n",
            "test_los =  1.9877524375915527\n",
            "test_acc =  0.4584999978542328\n"
          ],
          "name": "stdout"
        },
        {
          "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": "FMrbbLgzLkYM"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adam, loss = crossentropy,epochs = 100, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "UkUSSib8Lnu5",
        "outputId": "79bc3bf0-0b6c-4f3b-cc13-1c5a12792c84"
      },
      "source": [
        "plt.plot(history2.history['accuracy'], label='accuracy')\n",
        "plt.plot(history2.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = lenet.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 1.7077 - accuracy: 0.4850\n",
            "test_los =  1.7077155113220215\n",
            "test_acc =  0.48500001430511475\n"
          ],
          "name": "stdout"
        },
        {
          "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": "d5zl9p3HMUUz"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = SGD, loss = crossentropy,  epochs = 130, batchsize = 64"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "O-aPDZbqMcMl",
        "outputId": "eab45c20-b7f3-4b5a-a188-506f0f9751d7"
      },
      "source": [
        "plt.plot(history3.history['accuracy'], label='accuracy')\n",
        "plt.plot(history3.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = lenet.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 3.4353 - accuracy: 0.4755\n",
            "test_los =  3.4353435039520264\n",
            "test_acc =  0.4754999876022339\n"
          ],
          "name": "stdout"
        },
        {
          "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": "YIYu6YIEMiuK"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adam, loss = crossentropy,epochs = 130, batchsize = 64"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "hnSPndfAMoo8",
        "outputId": "09d90af9-5921-4d92-fbcd-9860a59d99a0"
      },
      "source": [
        "plt.plot(history3.history['accuracy'], label='accuracy')\n",
        "plt.plot(history3.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = lenet.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 2.2328 - accuracy: 0.4745\n",
            "test_los =  2.2328381538391113\n",
            "test_acc =  0.47450000047683716\n"
          ],
          "name": "stdout"
        },
        {
          "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": "UtG6EUmiNGuk"
      },
      "source": [
        "## AlexNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hqs0xiwsNJbn"
      },
      "source": [
        "def AlexNet():\n",
        "    return tf.keras.models.Sequential([\n",
        "        # Here, we use a larger 11 x 11 window to capture objects. At the same\n",
        "        # time, we use a stride of 4 to greatly reduce the height and width of\n",
        "        # the output. Here, the number of output channels is much larger than\n",
        "        # that in LeNet\n",
        "        tf.keras.layers.Conv2D(filters=96, kernel_size=11, strides=4,\n",
        "                               activation='relu'),\n",
        "        tf.keras.layers.MaxPool2D(pool_size=3, strides=2),\n",
        "        # Make the convolution window smaller, set padding to 2 for consistent\n",
        "        # height and width across the input and output, and increase the\n",
        "        # number of output channels\n",
        "        tf.keras.layers.Conv2D(filters=256, kernel_size=5, padding='same',\n",
        "                               activation='relu'),\n",
        "        tf.keras.layers.MaxPool2D(pool_size=3, strides=2),\n",
        "        # Use three successive convolutional layers and a smaller convolution\n",
        "        # window. Except for the final convolutional layer, the number of\n",
        "        # output channels is further increased. Pooling layers are not used to\n",
        "        # reduce the height and width of input after the first two\n",
        "        # convolutional layers\n",
        "        tf.keras.layers.Conv2D(filters=384, kernel_size=3, padding='same',\n",
        "                               activation='relu'),\n",
        "        tf.keras.layers.Conv2D(filters=384, kernel_size=3, padding='same',\n",
        "                               activation='relu'),\n",
        "        tf.keras.layers.Conv2D(filters=256, kernel_size=3, padding='same',\n",
        "                               activation='relu'),\n",
        "        tf.keras.layers.MaxPool2D(pool_size=3, strides=2),\n",
        "        tf.keras.layers.Flatten(),\n",
        "        # Here, the number of outputs of the fully-connected layer is several\n",
        "        # times larger than that in LeNet. Use the dropout layer to mitigate\n",
        "        # overfitting\n",
        "        tf.keras.layers.Dense(4096, activation='relu'),\n",
        "        tf.keras.layers.Dropout(0.5),\n",
        "        tf.keras.layers.Dense(4096, activation='relu'),\n",
        "        tf.keras.layers.Dropout(0.5),\n",
        "        tf.keras.layers.Dense(100)])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHsPQElhNcmx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94a95d4d-998a-4eb6-c0b5-c1414c439684"
      },
      "source": [
        "alexnet = AlexNet()\n",
        "alexnet.build((None, 68,68,3))\n",
        "alexnet.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 15, 15, 96)        34944     \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 7, 7, 96)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 7, 7, 256)         614656    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 3, 3, 384)         885120    \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 3, 3, 384)         1327488   \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 3, 3, 256)         884992    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (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, 100)               409700    \n",
            "=================================================================\n",
            "Total params: 21,990,884\n",
            "Trainable params: 21,990,884\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2b4x7Ag6f_Ju"
      },
      "source": [
        "Στο συγκεκρίμενο σημείο φέρνουμε τα δεδομένα μας στη μορφή που τα θέλει το AlexNet CNN. Αρχικά, κάνουμε normalize τα x_test και x_train και στη συνέχεια τα κάνουμε rescale σε 227x277x3 που απαιτεί το AlexNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eu6UPQ4tyNSt"
      },
      "source": [
        "train = []\n",
        "test = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBLAWMZOwZEx"
      },
      "source": [
        "for i in range(x_train.shape[0]):\n",
        "  temp = x_train[i]\n",
        "  temp = tf.image.per_image_standardization(temp)\n",
        "  temp = tf.image.resize(temp,(227,227))\n",
        "  train.append(temp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gTFPJbfMNJF"
      },
      "source": [
        "for i in range(x_test.shape[0]):\n",
        "  temp = x_test[i]\n",
        "  temp = tf.image.per_image_standardization(temp)\n",
        "  temp = tf.image.resize(temp,(227,227))\n",
        "  test.append(temp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGeO1AMCNU2y"
      },
      "source": [
        "train = np.array(train)\n",
        "test = np.array(test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0C6yadPGyJa"
      },
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  alexnet = AlexNet()\n",
        "  alexnet.build((None, 227,227,3))\n",
        "\n",
        "  alexnet.compile(optimizer=tf.keras.optimizers.Adam(),  \n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  history6 = alexnet.fit(train, y_train, epochs=100, batch_size=64,\n",
        "                      validation_data=(test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LawLoLFUEd_t"
      },
      "source": [
        "with tf.device('/device:GPU:0'):  \n",
        "  alexnet = AlexNet()\n",
        "  alexnet.build((None, 227,227,3))\n",
        "\n",
        "  alexnet.compile(optimizer=tf.keras.optimizers.Adamax(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "  history7 = alexnet.fit(train, y_train, epochs=60, batch_size=128,\n",
        "                      validation_data=(test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PWg6JI5rF3o5"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adam, loss = crossentropy, epochs = 100, batchsize = 64"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qoXbBiLPF4D7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "51a12a62-8994-4dcd-9c52-3b2ba9f1d950"
      },
      "source": [
        "plt.plot(history6.history['accuracy'], label='accuracy')\n",
        "plt.plot(history6.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = alexnet.evaluate(test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 2s - loss: 2.8706 - accuracy: 0.5165\n",
            "test_los =  2.8705849647521973\n",
            "test_acc =  0.5164999961853027\n"
          ],
          "name": "stdout"
        },
        {
          "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": "9aayGaaDfW3o"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adamax, loss = crossentropy, epochs = 60, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "umOVZrKLfY8_",
        "outputId": "dffbc4ee-83bb-49d9-f8fc-9ccbba7d6ddc"
      },
      "source": [
        "plt.plot(history7.history['accuracy'], label='accuracy')\n",
        "plt.plot(history7.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = alexnet.evaluate(test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 3.4377 - accuracy: 0.6150\n",
            "test_los =  3.4377171993255615\n",
            "test_acc =  0.6150000095367432\n"
          ],
          "name": "stdout"
        },
        {
          "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": "fcbIMKQqRDsc"
      },
      "source": [
        "## VGG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGC-hiMGRF-G"
      },
      "source": [
        "def vgg_block(num_convs, num_channels):\n",
        "    blk = tf.keras.models.Sequential()\n",
        "    for _ in range(num_convs):\n",
        "        blk.add(\n",
        "            tf.keras.layers.Conv2D(num_channels, kernel_size=3,\n",
        "                                   padding='same', activation='relu'))\n",
        "    blk.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n",
        "    return blk\n",
        "\n",
        "conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))\n",
        "\n",
        "def VGG(conv_arch):\n",
        "    net = tf.keras.models.Sequential()\n",
        "    # The convulational part\n",
        "    for (num_convs, num_channels) in conv_arch:\n",
        "        net.add(vgg_block(num_convs, num_channels))\n",
        "    # The fully-connected part\n",
        "    net.add(\n",
        "        tf.keras.models.Sequential([\n",
        "            tf.keras.layers.Flatten(),\n",
        "            tf.keras.layers.Dense(4096, activation='relu'),\n",
        "            tf.keras.layers.Dropout(0.5),\n",
        "            tf.keras.layers.Dense(4096, activation='relu'),\n",
        "            tf.keras.layers.Dropout(0.5),\n",
        "            tf.keras.layers.Dense(100)]))\n",
        "    return net"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdfiBzj7RdOF",
        "outputId": "e6481006-d07a-439a-ee2d-5cb5f8f74c9d"
      },
      "source": [
        "vgg = VGG(conv_arch)\n",
        "vgg.build((None, 32, 32, 3))\n",
        "vgg.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "sequential_1 (Sequential)    (None, 16, 16, 64)        1792      \n",
            "_________________________________________________________________\n",
            "sequential_2 (Sequential)    (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "sequential_3 (Sequential)    (None, 4, 4, 256)         885248    \n",
            "_________________________________________________________________\n",
            "sequential_4 (Sequential)    (None, 2, 2, 512)         3539968   \n",
            "_________________________________________________________________\n",
            "sequential_5 (Sequential)    (None, 1, 1, 512)         4719616   \n",
            "_________________________________________________________________\n",
            "sequential_6 (Sequential)    (None, 100)               19292260  \n",
            "=================================================================\n",
            "Total params: 28,512,740\n",
            "Trainable params: 28,512,740\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQY3VHFySvHI"
      },
      "source": [
        "with tf.device('/device:GPU:0'):  \n",
        "  vgg2 = VGG(conv_arch)\n",
        "  vgg2.build((None, 32, 32, 3))\n",
        "\n",
        "  vgg2.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  history = vgg2.fit(x_train, y_train, epochs=100, batch_size=128,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIfAEyQWUwKw"
      },
      "source": [
        "with tf.device('/device:GPU:0'):  \n",
        "  vgg = VGG(conv_arch)\n",
        "  vgg.build((None, 32, 32, 3))\n",
        "\n",
        "  vgg.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  history2 = vgg.fit(x_train, y_train, epochs=130, batch_size=64,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLeT84WNVYuj"
      },
      "source": [
        "conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))\n",
        "with tf.device('/device:GPU:0'):  \n",
        "  vgg4 = VGG(conv_arch)\n",
        "  vgg4.build((None, 32, 32, 3))\n",
        "\n",
        "  vgg4.compile(optimizer=tf.keras.optimizers.Adamax(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  history3 = vgg4.fit(x_train, y_train, epochs=80, batch_size=128,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDu_0EuIqjY8"
      },
      "source": [
        "conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))\n",
        "with tf.device('/device:GPU:0'):  \n",
        "  vgg4 = VGG(conv_arch)\n",
        "  vgg4.build((None, 32, 32, 3))\n",
        "\n",
        "  vgg4.compile(optimizer=tf.keras.optimizers.Adamax(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  history4 = vgg4.fit(x_train, y_train, epochs=50, batch_size=256,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4mrs44yra_gQ"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adam, loss = crossentropy, epochs = 100, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "TKqNZ5r4a_O8",
        "outputId": "723ae8a3-409e-4f57-b4ad-ab36569fde9a"
      },
      "source": [
        "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.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = vgg2.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 2.9992 - accuracy: 0.5570\n",
            "test_los =  2.9991753101348877\n",
            "test_acc =  0.5569999814033508\n"
          ],
          "name": "stdout"
        },
        {
          "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": "L0z5Ma83aVPJ"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adam, loss = crossentropy, epochs = 130, batchsize = 64"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "ATDU9pOMUQWe",
        "outputId": "6c7840fe-0909-4775-9173-261792412861"
      },
      "source": [
        "plt.plot(history2.history['accuracy'], label='accuracy')\n",
        "plt.plot(history2.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = vgg.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 3.1453 - accuracy: 0.5075\n",
            "test_los =  3.145305871963501\n",
            "test_acc =  0.5074999928474426\n"
          ],
          "name": "stdout"
        },
        {
          "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": "nrytCFuJeh6c"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adamax, loss = crossentropy, epochs = 80, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "vRDzpGWYeiS2",
        "outputId": "283b5279-506a-4f11-beb2-4b01fd14fa7f"
      },
      "source": [
        "plt.plot(history3.history['accuracy'], label='accuracy')\n",
        "plt.plot(history3.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = vgg4.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 3.3180 - accuracy: 0.5730\n",
            "test_los =  3.3179988861083984\n",
            "test_acc =  0.5730000138282776\n"
          ],
          "name": "stdout"
        },
        {
          "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": "VsCYeeo3rPN-"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adamax, loss = crossentropy, epochs = 50, batchsize = 256"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "KHvRyeARrPkv",
        "outputId": "9cf29287-1efc-44c3-aca8-41f9f12b56fe"
      },
      "source": [
        "plt.plot(history4.history['accuracy'], label='accuracy')\n",
        "plt.plot(history4.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = vgg4.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 3.7660 - accuracy: 0.4905\n",
            "test_los =  3.766007900238037\n",
            "test_acc =  0.49050000309944153\n"
          ],
          "name": "stdout"
        },
        {
          "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": "ZN3V7Tw0uWuf"
      },
      "source": [
        "## MyCNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4asHnzavucFB",
        "outputId": "19640e8b-99be-45ec-e222-5133bd3032d8"
      },
      "source": [
        "def MyCNN():\n",
        "  MyCNN = models.Sequential()\n",
        "  MyCNN.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) \n",
        "  MyCNN.add(layers.MaxPooling2D((2, 2)))\n",
        "  MyCNN.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "  MyCNN.add(layers.MaxPooling2D((2, 2)))\n",
        "  MyCNN.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
        "  MyCNN.add(layers.AvgPool2D(pool_size=2, strides=2)) \n",
        "  MyCNN.add(layers.Flatten())\n",
        "  MyCNN.add(layers.Dense(256, activation='relu'))\n",
        "  MyCNN.add(layers.Dropout(0.5))\n",
        "  MyCNN.add(layers.Dense(128, activation='relu'))\n",
        "  MyCNN.add(layers.Dropout(0.5))\n",
        "  MyCNN.add(layers.Dense(100))\n",
        "  return MyCNN\n",
        "\n",
        "mycnn = MyCNN()\n",
        "mycnn.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_29 (Conv2D)           (None, 30, 30, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_30 (Conv2D)           (None, 13, 13, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_31 (Conv2D)           (None, 4, 4, 128)         73856     \n",
            "_________________________________________________________________\n",
            "average_pooling2d_10 (Averag (None, 2, 2, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 100)               12900     \n",
            "=================================================================\n",
            "Total params: 270,372\n",
            "Trainable params: 270,372\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlok0zDowFiN"
      },
      "source": [
        "with tf.device('/device:GPU:0'):  \n",
        "  mycnn = MyCNN()\n",
        "\n",
        "  mycnn.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "  mycnn_history= mycnn.fit(x_train, y_train, epochs=100, batch_size=128,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tGlEzk9s9i0"
      },
      "source": [
        "with tf.device('/device:GPU:0'):  \n",
        "  mycnn = MyCNN()\n",
        "\n",
        "  mycnn.compile(optimizer=tf.keras.optimizers.Adamax(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "  mycnn_history2= mycnn.fit(x_train, y_train, epochs=100, batch_size=128,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-ioepD23We0"
      },
      "source": [
        "with tf.device('/device:GPU:0'):  \n",
        "  mycnn = MyCNN()\n",
        "\n",
        "  mycnn.compile(optimizer=tf.keras.optimizers.Adamax(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "  mycnn_history3= mycnn.fit(x_train, y_train, epochs=150, batch_size=128,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5nA9CWs3cIh"
      },
      "source": [
        "with tf.device('/device:GPU:0'):  \n",
        "  mycnn = MyCNN()\n",
        "\n",
        "  mycnn.compile(optimizer=tf.keras.optimizers.Adamax(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "  mycnn_history4= mycnn.fit(x_train, y_train, epochs=100, batch_size=64,\n",
        "                      validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mmaeY4kkuW3c"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adam, loss = crossentropy, epochs = 100, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "3mo98ZWTstE-",
        "outputId": "4ce6eda3-3a04-44aa-d8d1-f01e6d7985ff"
      },
      "source": [
        "plt.plot(mycnn_history.history['accuracy'], label='accuracy')\n",
        "plt.plot(mycnn_history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = mycnn.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 2.3163 - accuracy: 0.6380\n",
            "test_los =  2.3163492679595947\n",
            "test_acc =  0.6380000114440918\n"
          ],
          "name": "stdout"
        },
        {
          "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": "undEiq_-ugG5"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adamax, loss = crossentropy, epochs = 100, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "DkYOaZietXHt",
        "outputId": "3bad4960-cfd8-4a95-e318-8da3cb60beea"
      },
      "source": [
        "plt.plot(mycnn_history2.history['accuracy'], label='accuracy')\n",
        "plt.plot(mycnn_history2.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = mycnn.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 1.2463 - accuracy: 0.6195\n",
            "test_los =  1.2462785243988037\n",
            "test_acc =  0.6194999814033508\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-dWj0xER3Ova"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adamax, loss = crossentropy, epochs = 150, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "lLybVe3C3T9J",
        "outputId": "875f818d-f068-4d89-aa59-94b4a712ae78"
      },
      "source": [
        "plt.plot(mycnn_history3.history['accuracy'], label='accuracy')\n",
        "plt.plot(mycnn_history3.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = mycnn.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 1.2979 - accuracy: 0.6415\n",
            "test_los =  1.2979307174682617\n",
            "test_acc =  0.6414999961853027\n"
          ],
          "name": "stdout"
        },
        {
          "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": "rWaIYuiH3ifE"
      },
      "source": [
        "### Διάγραμμα ορθότητας για optimizer = adamax, loss = crossentropy, epochs = 100, batchsize = 64"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "JeeQDFwm3mYc",
        "outputId": "358bda47-89b6-4c87-94fc-d3645a34d0b9"
      },
      "source": [
        "plt.plot(mycnn_history4.history['accuracy'], label='accuracy')\n",
        "plt.plot(mycnn_history4.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = mycnn.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 1.3247 - accuracy: 0.6600\n",
            "test_los =  1.3247326612472534\n",
            "test_acc =  0.6600000262260437\n"
          ],
          "name": "stdout"
        },
        {
          "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": "4ZOROiWEjS_j"
      },
      "source": [
        "## Οι καλύτερες ορθότητες από κάθε μοντέλο."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EeLqV1mwjmBu"
      },
      "source": [
        "### LeNet\n",
        "Διάγραμμα ορθότητας για optimizer = adam, loss = crossentropy,epochs = 100, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "EvGbQhewloKU",
        "outputId": "79bc3bf0-0b6c-4f3b-cc13-1c5a12792c84"
      },
      "source": [
        "plt.plot(history2.history['accuracy'], label='accuracy')\n",
        "plt.plot(history2.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = lenet.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 1.7077 - accuracy: 0.4850\n",
            "test_los =  1.7077155113220215\n",
            "test_acc =  0.48500001430511475\n"
          ],
          "name": "stdout"
        },
        {
          "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": "PlKBQupFjbDo"
      },
      "source": [
        "### AlexNet\n",
        "Διάγραμμα ορθότητας για optimizer = adamax, loss = crossentropy, epochs = 60, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "c-AydTqFmieN",
        "outputId": "dffbc4ee-83bb-49d9-f8fc-9ccbba7d6ddc"
      },
      "source": [
        "plt.plot(history7.history['accuracy'], label='accuracy')\n",
        "plt.plot(history7.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = alexnet.evaluate(test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 3.4377 - accuracy: 0.6150\n",
            "test_los =  3.4377171993255615\n",
            "test_acc =  0.6150000095367432\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HOhRjTBSxRBAmdlc28MH6Yq47rR+JcbGhDscYE0UsEYSJ5z7bhV+Vm6YOCHUoxpgoY4kgDDR5fTz/+S5mjsyhX4Z1EhtjTi5LBGHgnTV7KKtr5ptnWG3AGHPyWSIIA09/spPB2SmcaVcSG2NCwBJBiK0qqGRlQSXfOH0AMXYlsTEmBCwRhNgzS3eSEh/L1TbvgDEmRCwRhFBZbRNvrd7NVafmkpZow00bY0LDEkEIvZhXQLPXzzemWSexMWGnuT7UEZw0nlAHEK28Pj9//3QXZwzJZFiOzUl8QloaQP0Qn3Ly37t6t3vfxPST/96RQBUaKqBmD1TvgZrd7m9cEkz9NngSQh3hlzXXw4Jfwmd/hSlz4CsPgCf+2Lfja4HKXVC2Fcq2uFtcEpzxH5CW0/lxnwBLBCHy4fpiiiob+Pklo0/uG3ub4PPHYdsi+Op/Qfbwk/v+namxCj59DJY+Cs210Gss9J8G/adCv9OhW5CH6ljxf/CPH7gkcMVjMHRmcN/veC3+b9jwFoy9GsZdH9zPpb4cir6AouUHbvX72l933Wtw7TPQ/QTH1irbCm/dAQndYObPoeeow69bvt2tP/BMd1A+1K7P4PXvQPk2GHg2fP5X2LMSZj8F3focPRZV2LYY/vk/sPMT8HsPPJeY7pLM8qfg7B/A6d+DuMRj3NngEFUNdQzHZPLkyZqXlxfqME6IqnLJn/5JXZOX+Xefiyf2JLTQ+f2w7lVX0qncBZ4kiPHAVXNh5EWd+14tDbBlvvuh1xTDgGkw8CzoN7X9H9+xaqx2pbWlf3LJYOQlkD3C/YiLloO3wa3XY6B734HnwKCzO/ZD7ojmenjnP2Hl32HAWe5AV7oRpt0GM+9rv5Sr6g4KsSe5L2j1S/DqrZDeH6p2gcTA0PNh4k0w/MLjK+nupwr7NsOuT6HgM/e3fGvgSXH/k76ToOdo99l36wNpvSGtF+R/CK9/130Hr/lfGDLj+N5/+ZPw/k/d56rqCgQTb4Lp9x5IeKqwayksfQQ2/gNQiEuBERfCmCvd5wGw6AH45M8uMV3+CAw6B9a+Cm/cBvHJcM2T7nt0uFg2vw9LHoSiPLef465zn0HmUMgYAskZLsF8eB9sfBu694dZv4LRV7htVBVCyQYo3eB+N6d+/chJ7RiJyHJVndzuc5YITr4FG4q55ek8HrxmHLMnn4SRRrcvgQ9+7ko2vcbCrF9D1jB48SbYvQLOvQfO/THEnEBC8jbBlgUu2Wx61/0gkzMhvR/sXQPqg9h4yD0N+kx099uKS4IxV0HW0MO/R3055P2v+0E3VMCIi2D6PdB7fJs4mt377VrqSmQ7/+mSBbgf44gLYcbPjj8h7cuHl77hfrDn/NC9v68ZPvgZLPub+3yvfsLVtJrrYftH7gCR/wHUlrgD48Cz3AHleBOj3+8ONpvfc5/niAvbX69oOTx5EfSdDN94HSp2uuS16gXXRBOf6g5Srbch7nvRaxzEHGa8K1W3T3lPur8NFW55Ugb0P93FkzsZek+AxG5H+Sy3uO9g6Ub3Pznr7o5/B2uK4c3b3Oc6+Dy44lGITYCPf+9qvLFxLjFnDoVPH3Xf/aQeMOlmF+emd2D9m9BQDvFp7rmqXe75r/waEto015ZsdHGWb4Pz74eRF0NjpfteNVRC3T5Y8Yz73nXvD2fdBRNuPHKz17aP4P17oXitK7DUlUFzzYHnYzyuuXPSzXDevZBy4tcYWSIII6rKFY9+QlltE4v+czpxwaoNqMLWhfCvP7pEkN4PZvwcxs4+8GNraYR/3O0ODiMugiv/cqCt2+9zB666EncATUht/31qS9wBcNn/upJxUg8Ydak7qA88G2I9rgS/61PYsQR2/BOK17n42tpfhR7+VZj2ffdaCVxXsS/f/ZhXPu9K+8O+6g7AfU89+ufg97kf2/aP3eeQ/4E7WN3w/OF/XC0NsGae+xsbF7jFQ30ZLHzA/cCvmnugJLnfpnfhje+7BNB/qttnb6M74A45D7oPcMt2rzg4MQ4+z5WI+0w48gG46AuXaNe9DtWFB56bdhuc/0v3We9XsxfmTnex37ro4H31+2DrIsh//0DbdWUBEPifpPR0yWXUpa5U7EmAphqXQD5/HPZtcgf+ERe5/ew/zR1w5Tiug2mugzf/A9bOgyEzXfNaWu8DtYfUHPcZNlYduJVvhfm/hJZ6V6g57VsHJ5DybbDg1+6zAsgcBqd/F8bf4Er2+/la3Hdi3WvuO3bujw7fvNdYDW98zzWxtSdjiGvuGXdtx2t9fh+seNbVUnoMhOyRrvbUc6T7fy/+rfttxae62KbMOaEanCWCMLJkcynfeOJzfnPlWL42tX/nv4HPC+tfdwlg7xpI7QVn3Aan3dp+e6Sq+3G//xNI6wOp2a4zr3avK5EAxMS5UtSQwAGr13goWe8Ozmtedj+o4RfAabfA4OnH1/xRU+xK+/sTSs5YmHjjgQNWbAKMm+3aVXPGHP/ns/4NeHWOO8jcOO/LNZCti+Dtu6Bie/uv73c6XPMEpPc9zH7sda8v2+ISxbCvwIAzD/4BN1a7ppTtS1ypes8qtzyph/v8BpzpDnJtO1crd0JtsftfDJ3pEu3Q82Hxf8Gyx10T1TVPuE7IlkZ46mJXa7nlA+h1ytE/l5ZGqNgBe1e70nL+h65Wl9DN/e93LnUl1j4TYcq3XZNKZ7Vvq7qmvoUPHFwqPpLeE+Cqx4/cx7VnlauxDDznxGq7bePc9A401boCU9tbWu/OeY9DlW5yTV9bPoSMwXDJH9x35DiELBGIyAXAQ0As8DdV/W0761wL3I8rjqxS1a8daZuRnAhUldl/WUpRZQOLfzidBM8hpb89q+C177hqYff+rpTQvb+7peZASrYr2e1vTlB1zSX7S3Vl+bD2FdcHkDkMzrzDlVA6cmbGjn/Bwl+DJ/FAaaxbb1fy27PS1S72rnHrJqRDUxXEJcOEr8HU7x65SedYtDTCmpdc80/pRkjOgim3wuRbXJLqDAXL4PnrXan8+udgwBmuev/+T2H1C650d/H/55p5fC2u6cfvdSW4zCGHL7Ufr7p9roNx60J3q9njlsenuf/B/hLyoHNcKTyp+8GvX/Wi6yxNTIdrn4blT8Oq5+DaZ2H0ZccXU0ujS1Ib3oIdH7sEOGUO5E46oV09ovbOMKotcd/31oNud/e35+iDa0BdXf5815R0wW++XBPtoJAkAhGJBTYDs4BCYBlwg6qub7POMOAlYIaqVohIT1UtOdJ2IzkRfLJ1H197/DN+dfkYvjFt4MFPVhXB3wLV0l5jXXtu5a4DHZ9txae69vfGKtdWuV+Mx7UHn3G7O2B0dgmltsQdsLYvcQfEU7/pOsCCQdWVaDMGB+fMivLt8Ny1rhQ89duw4u+u+eOsu1wVP1Rnc6hCdZE72CUcw2nFe9e6duyKHYDC9J+45jPTdfh9J1QICVUimAbcr6pfDTz+CYCq/lebdX4HbFbVv3V0u5GcCG6Y+ylbS2tZ8qPzDp58prEanrzQHfxvef9A04cq1JW6hFBb4u7Xlbq26rpSV23f38mXOdTVHE72WSmRrKECXvx6oMQ7FS59qFPP0jjpGird2UxxyXDJH4PTVGEi1pESQTDrVn2BgjaPC4Gph6wzHEBE/oVrPrpfVd87dEMiMgeYA9C/fxDa1U+CZTvKWbqtjJ9dPOrgJODzwrybXen3xpcPbv8WgdSe7mY6X1IPuOlVdwZOv9Mj/8CZ1B2u7nCZyphWof7me4BhwHTgBuBxEel+6EqqOldVJ6vq5OzsTmonPskeXpBPZko8N7adgUzVleC2zHedQOF6QVJX5ol3fQSRngSMOQFH/faLyKUicjy/kiKg7UnyuYFlbRUCb6pqi6pux/UpDDuO9wprKwsq+Th/H7eeM5ik+Da1gU8edhfEnHUXTPpm6AI0xkS1jhzgrwPyReR3IjLyGLa9DBgmIoNEJB64HnjzkHVex9UGEJEsXFPRtmN4j4jw+JJtdEv0cNPpbWoDeU+6KwzHXAkz7gtdcMaYqHfURKCqNwETga3AUyKyVETmiMgRT2lQVS9wG/A+sAF4SVXXicivRGT/OW3vA2Uish5YBPxQVctOYH/Czt6qRt5bt5frp/QnNcHjrgr98D54+04YOsuNUWPNEsaYEOpQZ7GqVovIPCAJuBO4EvihiDysqn86wuveAd45ZNl9be4rcHfg1iU999lO/KrcNHWAu1L1te+4C74m3wIX/i66zoU2xoSlox6FAqX3m4GhwDPAFFUtEZFkYD1w2EQQ7Zq8Pp77fBczRvSkf2I9PH0DFC5zw9pOu+34Lsk3xphO1pHi6NXAH1R1SduFqlovIrcEJ6yu4b21e9lX28yccTHuYrGave7Kz9GXhzo0Y4xp1ZFEcD+wZ/8DEUkCclR1h6ouCFZgXcHTn+xgUFYKUzb9Huor4JtvQ7/TQh2WMcYcpCO9lC8D/jaPfYFl5gjWFFbxxa5Kvj9WkU3vumEMLAkYY8JQRxKBR1Wb9z8I3D+B2SyiwzNLd5AcH8tl9a+44Yan3BrqkIwxpl0dSQSlbU73REQuBw4z95wBqKhr5o1Vu7nplCTi174EE26wYSKMMWGrI30E3wH+LiJ/BgQ3ftA3ghpVhHsxr4Bmr585iQvcEMbTbg91SMYYc1hHTQSquhU4XURSA49rgx5VBPP5lWeX7uTcgclkbXjGTWvXWWP1G2NMEHToaiYRuRgYAyRK4Nx3Vf1VEOOKWAs3llBU2cDjI9fB3go44z9CHZIxxhxRRwad+wtuvKHbcU1Ds4EBR3xRFHv2053kdotj1I5nIXeKm9PVGGPCWEc6i89Q1W8AFar6S2AagXkEzMF2VzbwcX4p9wzKRyp3wZlWGzDGhL+OJILGwN96EekDtAC9gxdS5HpleSGqylcqXnTz3o64KNQhGWPMUXUkEbwVmCzmQeALYAfwXDCDikR+v/JmXj6399lMfMkqOOO2zp/k3BhjguCIncWBCWkWqGol8IqIvA0kqmrVSYku3C19BNbMg7p9aG0JH/oaoQFIyYbxN4Q6OmOM6ZAjJgJV9YvII7j5CFDVJqDpZAQW9ip2wAc/g55jYMAZLCn0s7Lcw20Xn07c0HMhLinUERpjTId05PTRBSJyNfBqYP4AA/DpX0Bi4GsvUhXfk+/8v/nMnpxL3GljQx2ZMcYck470EXwbN8hck4hUi0iNiFQHOa7w1lAJK56FU66G9L68tWo3TV4/107ud/TXGmNMmOnIlcVHnJIyKn3xNDTXwrTvA/ByXgEje6Uxtm96iAMzxphj15EZys5pb/mhE9VEDV8LfPZXGHg29B7Pxr3VrCqs4r5LRiM245gxJgJ1pI/gh23uJwJTgOXAjKBEFO7WvQ7VRXDJHwB4aVkhcbHCFRP7hjgwY4w5Ph1pGrq07WMR6Qf8MWgRhTNVWPonyBoOQ2fR7PXz2opCZo3OISPFpmgwxkSmjnQWH6oQGNXZgUSEnf+CPavg9O9BTAzzNxRTUd9incTGmIjWkT6CPwH7TxuNASbgrjCOPp/8GZIzYfz1ALyUV0CvbomcPSw7xIEZY8zx60gfQV6b+17geVX9V5DiCV/78mHzu3DujyEuibLaJj7O38eccwYTG2OdxMaYyNWRRDAPaFRVH4CIxIpIsqrWBze0MPPpoxCbAKe5uYffWbMHn1+5bHyfEAdmjDEnpiN9BAuAtuMlJAHzgxNOmGqqhZXPw7hrIdU1A725ajfDeqYyspddZmGMiWwdSQSJbaenDNxPDl5IYSj/ffA2wISvAVBU2cCyHRVcNr6PXTtgjIl4HUkEdSJy6v4HIjIJN8Zm9Fj/BqTmQD8329jbq3YDcKk1CxljuoCO9BHcCbwsIrtxU1X2wk1dGR2a6yD/Q1cbCMwv8Oaq3YzPTWdgVkqIgzPGmBPXkQvKlonISGBEYNEmVW0JblhhJP9DaKmH0VcAsLW0lnW7q/nZxdF5KYUxpuvpyOT13wdSVHWtqq4FUkXke8EPLUysfwOSs2DAGQC8uXI3ItYsZIzpOjrSR3BrYIYyAFS1Arg1eCGFkZYG2Pw+jLoUYmJRVd5atZupgzLI6ZYY6uiMMaZTdCQRxEqbU2NEJBaIjoF1tsyHljoYfeQeXI0AABa4SURBVDkA63ZXs21fHZeNtwHmjDFdR0c6i98DXhSRvwYefxt4N3ghhZH1b0BShhtyGtdJ7IkRLjylV4gDM8aYztORRPBjYA7wncDj1bgzh7q2lkbY9B6cciXEevD7XbPQOcOz6WEjjRpjupCjNg2pqh/4DNiBm4tgBrChIxsXkQtEZJOIbBGRe46w3tUioiIyuWNhnwRbF0JzTWuzUN7OCvZUNdqQEsaYLuewNQIRGQ7cELjtA14EUNXzOrLhQF/CI8As3NDVy0TkTVVdf8h6acAduGQTPta/DondYdC5ALy5qojEuBhmjc4JcWDGGNO5jlQj2Igr/V+iqmep6p8A3zFsewqwRVW3qWoz8AJweTvr/Rr4b6DxGLYdXN4m2PQujLwEYuPw+vy8s2YvM0flkJLQkdY0Y4yJHEdKBFcBe4BFIvK4iMzEXVncUX2BgjaPCwPLWgWGruinqv840oZEZI6I5IlIXmlp6TGEcJy2LYam6tZmoRUFlZTXNVsnsTGmSzpsIlDV11X1emAksAg31ERPEXlMRL5yom8sIjHA/wA/ONq6qjpXVSer6uTs7JMwCcy61yEhHQZPB2D+hmI8McI5w20CGmNM19ORzuI6VX0uMHdxLrACdybR0RQBbedwzA0s2y8NOAVYLCI7gNOBN0PeYexthk3/gJEXgcedHbRwQwlTBmXQLTEupKEZY0wwHNOcxapaESidz+zA6suAYSIySETigeuBN9tsq0pVs1R1oKoOBD4FLlPVvPY3d5IUfAaNVTDyYgB2ldWTX1LLzFHWSWyM6ZqOZ/L6DlFVL3Ab8D7udNOXVHWdiPxKRC4L1vuesO0fgcS0XkS2YGMxAOeP6hnKqIwxJmiCegqMqr4DvHPIsvsOs+70YMbSYds+gj6nQlJ3ABZsKGFIdgoDMm3IaWNM1xS0GkFEaqyGouUw2F07UNPYwmfbyzjfmoWMMV2YJYK2dn4C6mu9iOzj/H20+NT6B4wxXZolgra2fwSexNYpKedvKCY9KY5T+3cPcWDGGBM8lgja2rbYJYG4RHx+ZfGmUs4bkY0n1j4mY0zXZUe4/WpLoGR9a//AyoIKyuuarVnIGNPlWSLYb/sS93fQdADmbyixq4mNMVHBEsF+2xa7YSX6TADc1cSnDcwgPcmuJjbGdG2WCPbb/hEMOhtiYikor2dTcQ0z7SIyY0wUsEQAUL4dKne1nja6cGMJgPUPGGOigiUCcLUBaO0onr+hmMHZKQzKsquJjTFdnyUCcMNKpPaCrOHUNXn5bFs5M0das5AxJjpYIvD7XY1g8LkgwopdlTT7/Jw9zM4WMsZEB0sEJeugvqy1f2DFrgpEYIJdTWyMiRKWCLYd3D+wsqCSIdmpNgmNMSZqWCLY/hFkDoX0XFSVFQWVTOxntQFjTPSI7kTga3EjjgaahXaV11Ne18zE/j1CHJgxxpw80Z0IipZDc21rs9CKXZUATLAagTEmikR3Itgy/6BpKVcWVJIcH8vwnNQQB2aMMSePJYLc0yA5A3BnDI3LTbdhp40xUSV6j3i1pbB7BQydBUBji4/1e6qZ0M/6B4wx0SV6E8HWBe7vsPMBWLe7mhafMtGuHzDGRJnoTQT5H0JKNvQaD7hmIcBOHTXGRJ3oTAR+H2xdCENmQoz7CFYUVNK3exI9uyWGODhjjDm5ojMR7F4BDeUwbFbropW7Km1YCWNMVIrORJD/ISAwZAYAJdWNFFU2WLOQMSYqRWci2DIf+k46cNpogbuQzDqKjTHRKPoSQV2Zu6K4bbNQQSVxscKYPukhDMwYY0Ij+hLB1oWAtl4/AO6ModG9u5EYFxu6uIwxJkSiLxFs+RCSM6HPRAB8fmV1YZUNNGeMiVrRlQj8ftiywHUSB04b3VxcQ32zzwaaM8ZErehKBHtWQv2+Q5qFrKPYGBPdoisRbJkPCAyd2bpoxa4KMlLi6Z+RHLq4jDEmhKIvEfSZCClZrYtWFlQyoV93RCSEgRljTOhETyKoL4fCZTD0/NZFVQ0t5JfU2oVkxpioFtREICIXiMgmEdkiIve08/zdIrJeRFaLyAIRGRC0YLYtAvUfdP3AmsIqABtawhgT1YKWCEQkFngEuBAYDdwgIqMPWW0FMFlVxwHzgN8FKx58LdB7vLuiOGDj3moARvfuFrS3NcaYcBfMGsEUYIuqblPVZuAF4PK2K6jqIlWtDzz8FMgNWjTjr4dvL4GYAxeNbS6uISs1nszUhKC9rTHGhLtgJoK+QEGbx4WBZYdzC/Bue0+IyBwRyRORvNLS0k4LcFNxLcNz0jpte8YYE4nCorNYRG4CJgMPtve8qs5V1cmqOjk7O7tT3tPvV7YU11giMMZEPU8Qt10E9GvzODew7CAicj7wU+BcVW0KYjwHKapsoK7ZZ4nAGBP1glkjWAYME5FBIhIPXA+82XYFEZkI/BW4TFVLghjLl+SX1AAwolfqyXxbY4wJO0FLBKrqBW4D3gc2AC+p6joR+ZWIXBZY7UEgFXhZRFaKyJuH2Vyn27S3FoChPa1GYIyJbsFsGkJV3wHeOWTZfW3un/+lF50km4tr6J2eSHpSXKhCMKZLaGlpobCwkMbGxlCHYoDExERyc3OJi+v4sS2oiSCcbS6uYZj1DxhzwgoLC0lLS2PgwIE2VEuIqSplZWUUFhYyaNCgDr8uLM4aOtl8fmVLSS0jcqx/wJgT1djYSGZmpiWBMCAiZGZmHnPtLCoTwa7yepq8fjtjyJhOYkkgfBzP/yIqE8Gmve6MIUsExhgTpYlgc7FLBMOsacgYY6I3EfTLSCI5Pmr7yo0xx8Hr9YY6hKCIyiPh5uIaRlizkDGd7pdvrWP97upO3eboPt34xaVjjrreFVdcQUFBAY2Njdxxxx3MmTOH9957j3vvvRefz0dWVhYLFiygtraW22+/nby8PESEX/ziF1x99dWkpqZSW+uuL5o3bx5vv/02Tz31FP/2b/9GYmIiK1as4Mwzz+T666/njjvuoLGxkaSkJJ588klGjBiBz+fjxz/+Me+99x4xMTHceuutjBkzhocffpjXX38dgA8//JBHH32U1157rVM/oxMVdYmg2etnW2kd54/KCXUoxphO9MQTT5CRkUFDQwOnnXYal19+ObfeeitLlixh0KBBlJeXA/DrX/+a9PR01qxZA0BFRcVRt11YWMgnn3xCbGws1dXVfPzxx3g8HubPn8+9997LK6+8wty5c9mxYwcrV67E4/FQXl5Ojx49+N73vkdpaSnZ2dk8+eST/Pu//3tQP4fjEXWJYEdZHV6/WkexMUHQkZJ7sDz88MOtJe2CggLmzp3LOeec03o+fUZGBgDz58/nhRdeaH1djx49jrrt2bNnExvrhrCvqqrim9/8Jvn5+YgILS0trdv9zne+g8fjOej9vv71r/N///d/3HzzzSxdupRnnnmmk/a480RdItjfUWyJwJiuY/HixcyfP5+lS5eSnJzM9OnTmTBhAhs3buzwNtqednnoefgpKSmt93/+859z3nnn8dprr7Fjxw6mT59+xO3efPPNXHrppSQmJjJ79uzWRBFOoq6zePPeGmIEBmenHH1lY0xEqKqqokePHiQnJ7Nx40Y+/fRTGhsbWbJkCdu3bwdobRqaNWsWjzzySOtr9zcN5eTksGHDBvx+/xHb8Kuqqujb102t8tRTT7UunzVrFn/9619bO5T3v1+fPn3o06cPDzzwADfffHPn7XQnirpEsKm4hoFZKSTGxR59ZWNMRLjgggvwer2MGjWKe+65h9NPP53s7Gzmzp3LVVddxfjx47nuuusA+NnPfkZFRQWnnHIK48ePZ9GiRQD89re/5ZJLLuGMM86gd+/eh32vH/3oR/zkJz9h4sSJB51F9K1vfYv+/fszbtw4xo8fz3PPPdf63I033ki/fv0YNWpUkD6BEyOqGuoYjsnkyZM1Ly/vuF8/4/eLGdErjcdumnT0lY0xR7Vhw4awPcCFi9tuu42JEydyyy23nJT3a+9/IiLLVXVye+tHVY2gscXHjrI6G2zOGHPSTJo0idWrV3PTTTeFOpTDCr9eiyDaWlqLX7FrCIwxJ83y5ctDHcJRRVWN4MAZQza0hDHG7BdViWDT3lriYoWBWXbGkDHG7BdViSC/uIYh2anExUbVbhtjzBFF1RFxk81KZowxXxI1iaCuyUthRYPNSmaMMYeImkSQX+JGFbQagTHRLTXVCoOHiprTRzcHZiWzU0eNCaJ374G9azp3m73GwoW/7dxthgGv1xs24w5FTY0g3hPD+Nx0+mUkhzoUY0wnuueeew4aO+j+++/ngQceYObMmZx66qmMHTuWN954o0Pbqq2tPezrnnnmmdbhI77+9a8DUFxczJVXXsn48eMZP348n3zyCTt27OCUU05pfd3vf/977r//fgCmT5/OnXfeyeTJk3nooYd46623mDp1KhMnTuT888+nuLi4NY6bb76ZsWPHMm7cOF555RWeeOIJ7rzzztbtPv7449x1113H/bkdRFUj6jZp0iQ1xoSP9evXh/T9v/jiCz3nnHNaH48aNUp37dqlVVVVqqpaWlqqQ4YMUb/fr6qqKSkph91WS0tLu69bu3atDhs2TEtLS1VVtaysTFVVr732Wv3DH/6gqqper1crKyt1+/btOmbMmNZtPvjgg/qLX/xCVVXPPfdc/e53v9v6XHl5eWtcjz/+uN59992qqvqjH/1I77jjjoPWq6mp0cGDB2tzc7Oqqk6bNk1Xr17d7n609z8B8vQwx9XwqJcYY8xxmjhxIiUlJezevZvS0lJ69OhBr169uOuuu1iyZAkxMTEUFRVRXFxMr169jrgtVeXee+/90usWLlzI7NmzycrKAg7MNbBw4cLW+QViY2NJT08/6kQ3+we/AzfhzXXXXceePXtobm5unTvhcHMmzJgxg7fffptRo0bR0tLC2LFjj/HTap8lAmNMxJs9ezbz5s1j7969XHfddfz973+ntLSU5cuXExcXx8CBA780x0B7jvd1bXk8Hvx+f+vjI81tcPvtt3P33Xdz2WWXsXjx4tYmpMP51re+xW9+8xtGjhzZqUNaR00fgTGm67ruuut44YUXmDdvHrNnz6aqqoqePXsSFxfHokWL2LlzZ4e2c7jXzZgxg5dffpmysjLgwFwDM2fO5LHHHgPA5/NRVVVFTk4OJSUllJWV0dTUxNtvv33E99s/t8HTTz/duvxwcyZMnTqVgoICnnvuOW644YaOfjxHZYnAGBPxxowZQ01NDX379qV3797ceOON5OXlMXbsWJ555hlGjhzZoe0c7nVjxozhpz/9Keeeey7jx4/n7rvvBuChhx5i0aJFjB07lkmTJrF+/Xri4uK47777mDJlCrNmzTrie99///3Mnj2bSZMmtTY7weHnTAC49tprOfPMMzs0xWZHRd18BMaYzmXzEZxcl1xyCXfddRczZ8487Do2H4ExxnRBlZWVDB8+nKSkpCMmgeNhncXGmKizZs2a1msB9ktISOCzzz4LUURH1717dzZv3hyUbVsiMMacMFVFREIdRoeNHTuWlStXhjqMoDie5n5rGjLGnJDExETKysqO6wBkOpeqUlZWRmJi4jG9zmoExpgTkpubS2FhIaWlpaEOxeASc25u7jG9xhKBMeaExMXFtV4RayJTUJuGROQCEdkkIltE5J52nk8QkRcDz38mIgODGY8xxpgvC1oiEJFY4BHgQmA0cIOIjD5ktVuAClUdCvwB+O9gxWOMMaZ9wawRTAG2qOo2VW0GXgAuP2Sdy4H911XPA2ZKJJ16YIwxXUAw+wj6AgVtHhcCUw+3jqp6RaQKyAT2tV1JROYAcwIPa0Vk03HGlHXotiNcV9qfrrQvYPsTzrrSvkDH92fA4Z6IiM5iVZ0LzD3R7YhI3uEusY5EXWl/utK+gO1POOtK+wKdsz/BbBoqAvq1eZwbWNbuOiLiAdKBsiDGZIwx5hDBTATLgGEiMkhE4oHrgTcPWedN4JuB+9cAC9WuSjHGmJMqaE1DgTb/24D3gVjgCVVdJyK/wk2Z9ibwv8CzIrIFKMcli2A64ealMNOV9qcr7QvY/oSzrrQv0BnN5lYAN8aY6GZjDRljTJSzRGCMMVEuahLB0Ya7CHci8oSIlIjI2jbLMkTkQxHJD/ztvLnrgkhE+onIIhFZLyLrROSOwPJI3Z9EEflcRFYF9ueXgeWDAkOnbAkMpRIf6lg7SkRiRWSFiLwdeBzJ+7JDRNaIyEoRyQssi9TvWncRmSciG0Vkg4hM64x9iYpE0MHhLsLdU8AFhyy7B1igqsOABYHHkcAL/EBVRwOnA98P/D8idX+agBmqOh6YAFwgIqfjhkz5Q2AIlQrckCqR4g5gQ5vHkbwvAOep6oQ259tH6nftIeA9VR0JjMf9j058X1S1y9+AacD7bR7/BPhJqOM6jv0YCKxt83gT0DtwvzewKdQxHud+vQHM6gr7AyQDX+Cuot8HeALLD/oOhvMNd83PAmAG8DYgkbovgXh3AFmHLIu47xruOqvtBE7y6cx9iYoaAe0Pd9E3RLF0phxV3RO4vxfICWUwxyMw4uxE4DMieH8CTSkrgRLgQ2ArUKmq3sAqkfSd+yPwI8AfeJxJ5O4LgAIfiMjywHA1EJnftUFAKfBkoNnubyKSQifsS7Qkgi5PXXEgos4FFpFU4BXgTlWtbvtcpO2PqvpUdQKuND0FGBnikI6LiFwClKjq8lDH0onOUtVTcU3D3xeRc9o+GUHfNQ9wKvCYqk4E6jikGeh49yVaEkFHhruIRMUi0hsg8LckxPF0mIjE4ZLA31X11cDiiN2f/VS1EliEaz7pHhg6BSLnO3cmcJmI7MCNGDwD1y4difsCgKoWBf6WAK/hEnUkftcKgUJV/SzweB4uMZzwvkRLIujIcBeRqO0QHd/EtbWHvcBQ4/8LbFDV/2nzVKTuT7aIdA/cT8L1d2zAJYRrAqtFxP6o6k9UNVdVB+J+JwtV9UYicF8ARCRFRNL23we+AqwlAr9rqroXKBCREYFFM4H1dMa+hLoD5CR2tFwEbMa13f401PEcR/zPA3uAFlzJ4BZc2+0CIB+YD2SEOs4O7stZuOrramBl4HZRBO/POGBFYH/WAvcFlg8GPge2AC8DCaGO9Rj3azrwdiTvSyDuVYHbuv2//Qj+rk0A8gLftdeBHp2xLzbEhDHGRLloaRoyxhhzGJYIjDEmylkiMMaYKGeJwBhjopwlAmOMiXKWCIw5hIj4AiNV7r912oBkIjKw7QiyxoSDoE1VaUwEa1A3XIQxUcFqBMZ0UGBc+98Fxrb/XESGBpYPFJGFIrJaRBaISP/A8hwReS0wT8EqETkjsKlYEXk8MHfBB4GrkY0JGUsExnxZ0iFNQ9e1ea5KVccCf8aN0gnwJ+BpVR0H/B14OLD8YeAjdfMUnIq7shVgGPCIqo4BKoGrg7w/xhyRXVlszCFEpFZVU9tZvgM3Ac22wKB5e1U1U0T24caDbwks36OqWSJSCuSqalObbQwEPlQ3iQgi8mMgTlUfCP6eGdM+qxEYc2z0MPePRVOb+z6sr86EmCUCY47NdW3+Lg3c/wQ3UifAjcDHgfsLgO9C68Q16ScrSGOOhZVEjPmypMBsY/u9p6r7TyHtISKrcaX6GwLLbsfNGvVD3AxSNweW3wHMFZFbcCX/7+JGkDUmrFgfgTEdFOgjmKyq+0IdizGdyZqGjDEmylmNwBhjopzVCIwxJspZIjDGmChnicAYY6KcJQJjjIlylgiMMSbK/f9n+rJzqa9iCgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ArEIEMZzmQDt"
      },
      "source": [
        "### VGG\n",
        "Διάγραμμα ορθότητας για optimizer = adamax, loss = crossentropy, epochs = 80, batchsize = 128"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "MtYIAzjWmr4l",
        "outputId": "283b5279-506a-4f11-beb2-4b01fd14fa7f"
      },
      "source": [
        "plt.plot(history3.history['accuracy'], label='accuracy')\n",
        "plt.plot(history3.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = vgg4.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 3.3180 - accuracy: 0.5730\n",
            "test_los =  3.3179988861083984\n",
            "test_acc =  0.5730000138282776\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ir9/uIC2/lLvHdHF3KEqpM9BEoOrdij2ZvLdiPzePjGNYfKS7w1FKnYEmAlWvCksr+O1nm4iLCuLhSd3dHY5SqgZcOuican6e/XYHB3OK+eSOEQT56cdLqcZArwhUvVmemMG/V+5n+sh4hsZpSUipxkITgao3T329lfjoYH57oZaElGpMNBGoepGYVsCu1AKmj4oj0E9HGFWqMdFEoOrF/O2pAIzvqUNJKNXYaCJQ9eLHban0bhtKu/BAd4eilKolTQSqztLzS1l/IFsHllOqkdJEoOps0Y40jIEJWhZSqlHSRKDq7IdtqbQLD6R321B3h6KUOguaCFSdFJdV8nNiOhN6tkJE3B2OUuosaCJQdfJzYgYl5Q4maPuAUo2WJgJVJz9uO0ILfx+Gx0e5OxSl1FnSRKDOWqXDsGB7GmN6tMLPRz9KSjVW+t+rztrG5GwyC8uY0LOVu0NRStWBJgJ11n7YloqPlzCmuyYCpRozTQTqrBhj+GbzYUZ0jiIs0Nfd4Sil6kATgTor6w9kk5xVzOUD2rk7FKVUHWkiUGflqw2H8Pfx4sLe2m1UqcZOE4GqtfJKB/MSDjGxVwwtArQspFRjp4lA1dqSXelkF5VzhZaFlGoSNBGoWvtq4yHCg3w5v1tLd4eilKoHmghUrRSUVvDjtiNc0reN3kSmVBPh0v9kEZkkIjtFJFFEHjnFNteIyDYR2SoiH7oyHlV3P2w9Qkm5gysGallIqabCx1U7FhFv4DVgIpACrBGRucaYbVW26Qo8CowyxmSLiN6Z5OG+2niIduGBDO4Q4e5QlFL1xJVXBMOARGPMXmNMGTAHuPyEbW4HXjPGZAMYY9JcGI+qo/T8Un7enc7lA9ri5aVDTivVVLgyEbQDkqsspzjXVdUN6CYiy0RkpYhMqm5HIjJDRNaKyNr09HQXhavOZF7CIRwGrtSykFJNirtb+3yArsAY4FrgLREJP3EjY8wsY8wQY8yQli21p4q7zN+eStdWIXSNaeHuUJRS9eiMiUBELhWRs0kYB4H2VZZjneuqSgHmGmPKjTH7gF3YxKA8TEFpBav3ZTGuhzbjKNXU1OQAPxXYLSJ/E5Eetdj3GqCriMSLiB8wDZh7wjZfYa8GEJFobKloby1eQzWQZYkZlFcaHWlUqSbojL2GjDE3iEgotnTzrogY4B3gI2NM/mmeVyEi9wLfA97AbGPMVhH5I7DWGDPX+dgFIrINqAR+a4zJrPuvperb4p1phPj7MCROewupRqa0APyCoTHNqV2YCavfBN9A6DIBYvq4NH4xxtRsQ5Eo4EZgJrAd6AK8bIx5xWXRVWPIkCFm7dq1DfmSzZ4xhhF/WcjADuG8ccNgd4ej1OkVZsC+JZC0FPYthczd4B8KLbtDq57QZgAMvBF8/OrvNSvLYfkrsPxlu+zfwr5mYAREdITITvarTX/7/VQqymD1LPjpb1CaBziPzyGtoct4GDwd2g89qxBFZJ0xZkh1j53xikBELgOmYw/87wPDjDFpIhIEbAMaNBGohrfjSD5H8koYq2Whps2Ymp11GgM//wOS18DUf4O3hww8eDgBVrwKWz4HRwX4hUDHkdB3ChSmQdoO2D4P1r9vt7nmfQiOPn4fFWWQvh2y9v7yJd42gbTqCS17Qkir49+nAyvh65n2eV0vhPAOUJpvD+RFmbD7RyhItduKF4x6AMY8Cj7+v+zD4YDt/4X5T0P2PnsVcMEzEBAOexZC4o+wYx50GnvWieB0anJD2dXAC8aYJVVXGmOKROTWeo9IeZxFO+3tHaO7a4+tepV3GPYusgeYtgPrf/9Hr/ZrcnBPWgaf3gwj7oZzHzz9tj/9FRb/xf688g0Ydf/J25Tmg5cv+Aac+bV3fAPr3oFJz0JU5zNvf6L9K+CnZ2HvYvANhqG3Q99f2TN/7xMOccbA5k9h7n0wayxc+yG07gtlRbD+PVj2MuQf+mX74FbgKLePHeUbBKHtICzWHsx3fQdh7eHaOdD9oupjLC2wSWX1LPj5Bdj1PVz5T2jVyyalpf+AjJ3Qsgdc/zl0nfDLcwdeb78qK8A4av/+1MAZS0MiEg8cNsaUOJcDgRhjTJJLIjoDLQ01vGv+uYLCsgr+d/957g6l8ctJhjVvQeICSN1i14V3gPs3gpf3yds7HOB1ij4d6TvtgXjcHyA46vjHKkphzvW2LDL5Beg87tQxHVgJ/74KMFBeBGMfh9G/rX7bJc/BwmdgwPW2BLN/Gdy7BkLb/rJN3mF4axyUFUL/qTDoJmjd5+R9lRXC94/BunftclRXuO1HW06pqdSt9oAeFAnD74DBN9fs+QfX2fenJBcG/Ro2fwZFGdBxFAy5xZaRIuLBP8Qmj4I0SNsG6Tsg5wDkpkDeQShMh16X2zN8v+Caxbzre5h7v329kNaQlwKtesN5D0HvK6v/HNSD05WGapII1gIjnXcH4+wBtMwYU//XJzWgiaBh5RaVM+iZH7lrdGd+c2F3d4fjGSpKwduv9o13xtgD5JEE6DDCXv57ecMPj8PUD6Dn5OO3z0mGf02A8/4Phs84/jFHJfxrPBzaADF94aa59mAINnl8fits/QJCY+2BZuANttRw4kEyeTX8+0po0Rpu+tqWJhLm2APbmBOGB/v5BZj/FPSbCle8YQ+Irw23cf9qtt2mvBjeuRgydkHXC2DH/6CyFNoNhrjzfqmVGwfMe9CeJY+6HzqNgQ+ugbhRcP1nNSs3lZfY97MwDe5aASG1vGLNPwIf3wApa6DLRPs+dxxRu32craIs+PEJ+x6ecxd0m+Tyxuw6tREAPkeTAIAxpsyZDFQzsDQxnUqHYWyPZlAWyk+FPQtsTTa8I4x++Pg6LsCWL+C/99hywtjHIH50zf+B9y6GQ+th8oswZLpdV1kBq2bZM/sTE8FPf4WCI/DD7+0BMqb3L4+t+ZdNAsPvhLXvwPuX22QQEA7fPWKTwISn7eM//RWWvWRr1UNuhfD29gy+oswmjJBWNgmEtoUrXrfJafFfbHknLBYOb4JDG20NvM+vbBLw8obIeFtG+ulZeyYed54tuRzaANM+gB6X2ANewsew8UNY+TpUlv3yO4S2szHHn2+XL33RvrffPgyX/OPM7+uCpyFtq00ctU0CYJPfzd/YUlBEXO2fXxdBkXD5qw37mqdRkyuCH4FXnN09EZHLgfuNMeMbIL6T6BVBw/q/TzaxYEcq6x6fiHdTHV9oxzew+M9wZLNdDoqyjXyt+8Gv3oHoLvYse9H/g6V/t7XnwnRbGug4yiaEuHPP/DrvXQrpu2BmwvEJZvkr9qrgjiW2VwlA5h54daitde9ZaEsIty+wz8s7BK8Og/bD4IbP7QH+4+ttoug8DpY+DyPutVcARw+mhzfZBs1D64+PKSLOHgzDqgwb4nDA1/fDhn/b5ZDWNq64UXDOPcfX3cuL7VWBT4CNddH/s6Wq839z8u/vqLQllay9ttTS7YKTr1B+fBKWvWj30ftK25jrH3pyUkhcAP+5CobNgIufO/N7r+pcGuoMfAC0BQQ7ftCvjTGJ9R1oTWgiaDgOh2HYn+czqks0L01zQWPm6eQk2xp41UYzVzi8Cd6+wF4B9J/q7LPdF3Z/D1/dbctAk/4Mu36Anf+z3Q4ved6Weda/bw+6BUfslcHYx6DDOdW/Tso6+Nc4mPinkxtXi3PgH71srfnKN+y6z26Fnd/AA5vg4Hr4aCqMmgkTn4ZPfm3rzHevtGflADu/hY9vtA2b/abCFf+svm2hvMQmsLxDtidL/Ojqz6YdDjiyCVq0sWfOp7PzOxsfQJ+r4eq3z77M4XDAJzfaHjJHefvZv0/cKHvVEdPHXgEFhsOMxbavvTqjOiWCKjsJATDGFNRjbLWmiaDhJKTkcNmry3hx6oCGn3/g05th65e24fL837imflqUBbNG2/LMHUtOPiDmHoQvbrcNouINk/5iz0CrxlJebEszP79ga9Wdx8HY30PsCf9vc66HpJ/hwS22j/mJvvmtbTSducVebfxzFJz7EEx40j4+936beM57yCaf6s66d8+HfT/B+CcavkvnFzPs+3X9p+AXVLd9VZbbewDyU22DamG6PSnYv9zZtx6bHG5faEt0qkbqnAhE5BKgN3CsL5gx5o/1FmEtaCJoGBWVDm54exUbk3NY/sh4IoMbsFnIGHiuiz0bL8u3dejxT559MijJhdRttsHy6E1Ejkr4YIo94Ez/9uQD91GOSlg723bzixt16tcoK7J1+2Uv2rLSqJn2YO3tY/uvvz4czn8Yxv2++udn7oFXBsHoR2xjctIymLnpl9JJaYFNDtlJtovhHUvr94aouqpNV9WzVVlhr1KSfrY9jHpc7LrXaoLqekPZP4EgYCzwL+BXwOp6jVB5nOd+2MnKvVk8P6V/wyYBgLTt9kzwsldsw+PPL9gz7wv/cuqulCcqzbflkq1fQuJ820gZFAX9r7UNm5vm2IbhyS+eOgmAbRQddvuZX88vyJZ8htxiG3eXvWh7o/xqtv3ZJ9A23J5KVGfbc2TFq1BWAOMeP75+7h8CV70FX90Fl77sWUkAGmb4Bm8fm8zb6d3t9a0mvYZGGmP6iUiCMeZpEXke+NbVgSn3+W7LEd78aS/XD+/A1YNjGz6ApKX2e/xoW5P3DbIHyORVtnEzuKX96noBtB1w8vMTPrENo+WF0KKtvcGo3SDY9hWs+qfdF9h9D765fmP3D4FLX4IOI2HeTPjnuVCcDUNvO7mv/4nOucvenBQUDcPvOvnx9sPgvnX1G69S1CwRlDi/F4lIWyATaOO6kJQ77U0v4DefbqJ/bBhPXNqr/nacexC2/dfW0Uvy7Bm7CFz0N9voV9W+JfYmq4iOdvmCZ2yD5Y7/2Z49hem23LP4LzDiHhjzmD0jryyHH/4Aq96wvXnGPwGxw365iuj7K1t33vgB5OyHSX913Zls/6m2p80nv7a/74h7z/yc+NH2iqXLBJtQlGogNUkEXzsni3kOWI8dBektl0al3KKkvJK7/rMeX2/h9RsG4+9TxzscKyts75t179mxUozDDjsQEGobTLOT7GX+8Dt+eY7DYRtnu1/yyzoRGHmf/TqqOMfe3LT8FTt+zMQ/2r74B5bDOXfb5eoaTFvE2AbXhtCqB9zxk01c4e3PvL2IHXZAqQZ22kTgnJBmgTEmB/hcROYBAcaY3AaJTjWo77ceYWdqPm/9egjtwuvYJS/pZ1vPzjlg+6Gf+6AtxRzt7gjwz/PsjUZVE0HaVltKiT/DcBaB4fYGpD5X25uYPrnR1uGvegv6XVO32OuTb6C9ulHKg502ERhjHCLyGjDQuVwKlDZEYKrhLUvMICzQt26zkFWU2ZuKlr1kD/pTP7CNoCcO/gW2DPL9o7ZxuFVPu26fs32gJjdogU0Ydy23XS87jT7+7lulVI3UpAvGAhG5WqQxzeqgassYw8+7MxjZOers7yBO32nHv1n2oh3I646ldtiE6pIA2OGBxRs2ffTLuqSldrCvsFo0UvsF2VEzNQkodVZqkgjuAD4FSkUkT0TyRSTPxXGpBrYvo5BDuSWM6hJ95o1PVFpg6/X/PNfetTrtQ7js5TM3eIa0hK4TbS8fR6X92r/szGUhpVS9qslUldXcBqmammWJGQCcW5tEYIwdS/2HP9iBu/pfZ4dACKlFaan/tbbL5N7Ftp9/SS7EnV+74JVSdVKTG8qq/a88caIa1bj9nJhBbEQgHaNqMDxAab4dv33dO3asnjb94Zr3bD/32uo2CQLC7A1ebfrZdTVtH1BK1YuadB+tOkNFADAMWAecZqYL1ZhUVDpYvieTS/q24bimIEelHX64OBtKc20CSFlrh2IuL7TDLlz6sh3r/mwn0/ANsD1/Nn5ky0pRXSBUb1NRqiHVpDR0adVlEWkPvOiyiFSD23wwl/ySCs7tekJZaOXrdnjkqnyDoM9VMOhmOzRDffQh6H+tHc9n/zI7ObdSqkHV5IrgRClAz/oORLnP0faBkZ2rJILKcnuDVvtz4II/2RvA/ENtHb8m89DWRuxQiOwMWXu0oVgpN6hJG8Er2LuJwfYyGoC9w1g1EUt3Z9C7bejxg8tt+68t1Vzyj7Or/deGCAy6ERb92Y43r5RqUDW5Iqg65nMF8JExZvj2ttAAABvXSURBVJmL4lENrKisgvUHsrnl3Cp3/BoDK16z9fquFzRMICPvtzNS1abHkVKqXtQkEXwGlBhjKgFExFtEgowxRa4NTTWE1fuyKK80x3cbTV5lpzS85PmaD/tcV17eDT9vrFIKqOGdxUDVgWcCgfmuCUc1tE1btxHoYxgaF/nLyhWv2knQ+1/rvsCUUg2mJlcEAVWnpzTGFIhIHeeiUx4h5wB3J1zNJUFdCMjraSdHydpnh3se9QD4Bbs7QqVUA6jJFUGhiAw6uiAig4Fi14WkGkrW5h/wpYKOFfvtSKDr34dVb4J42bl5lVLNQk2uCGYCn4rIIUCA1sBUl0alGsSBdd9TacJwTF9AzIIH7XDOiB0MLrStu8NTSjWQmtxQtkZEegDdnat2GmPKXRuWcrVD2UW0yV7NoYgh9G/fFX49F1a+Bqtmwbkz3R2eUqoBnbE0JCL3AMHGmC3GmC1AiIjc7frQlCt99sMiYiSHDoMutCu8vOwMYA9u1uGclWpmatJGcLtzhjIAjDHZwO2uC0m5WlpeCRlbFgAQ0XuCm6NRSrlbTRKBd9VJaUTEG/A7zfbKw725ZC/D2UpFcBuI7OTucJRSblaTRPAd8LGIjBeR8cBHwLeuDUu5SkZBKR+sSuJ8vx34dB5dP4PGKaUatZr0GvodMAO407mcgO05pBqht5buJa7yAC0qc3SAN6UUUIMrAmOMA1gFJGHnIhgHbK/JzkVkkojsFJFEEXnkNNtdLSJGRIbULGx1NgpKK/jPiv3cGnvQrtAB3pRSnOaKQES6Adc6vzKAjwGMMWNrsmNnW8JrwETs0NVrRGSuMWbbCdu1AB7AJhvlQnM3HqKwrJIJATsgvANEdHR3SEopD3C6K4Id2LP/ycaYc40xrwCVtdj3MCDRGLPXGFMGzAEur2a7PwF/BUpqsW91FuasOUD3VkGEp62GeJ0XWCllnS4RXAUcBhaJyFvOhuLatCy2A5KrLKc41x3jHLqivTHmf6fbkYjMEJG1IrI2PT29FiEoVr8FS59nW3I6CSm53NWjBCnJ0QnilVLHnLI0ZIz5CvhKRIKxZ/IzgVYi8gbwpTHmh7q8sIh4Af8Abj7TtsaYWcAsgCFDhpgzbK6OMsZO9lKcRUTgR/TxuZ0Lg50XXtpQrJRyqkljcaEx5kPn3MWxwAZsT6IzOQi0r7Ic61x3VAugD7BYRJKAc4C52mBcj3JToDiLip5X4Fecxlc+jxG4bpadFlLHElJKOdVq1hFjTLYxZpYxZnwNNl8DdBWReBHxA6YBc6vsK9cYE22MiTPGxAErgcuMMWur352qtSMJACyJnMKEkr+S0/ECyEuBTmPcGpZSyrOczeT1NWKMqRCRe4HvAW9gtjFmq4j8EVhrjJl7+j2oOjucAOLF27uDCI8OIermD+3sY9Hd3B2ZUsqDuCwRABhjvgG+OWHdE6fYdowrY2mWDm+iLLwzyw4U88hFPRAR6HCOu6NSSnmYBpqQVrnFkQR2STw+XsLVg2LdHY1SykNpImiqCjMg7yDzc9owvmcrWrbwd3dESikPpYmgqTq8CYBVJbFc1KeNm4NRSnkyTQRNlbPH0C7iGNu9lZuDUUp5Mk0ETdXhBI5IK3p06kBYkK+7o1FKeTBNBE1U2cGNbKzoyAW9dMRwpdTpaSJoikrz8cvZy1ZHRyb0inF3NEopD6eJoCk6sgWAgshetAsPdHMwSilPp4mgCcpPWg9Aux4j3ByJUqoxcOmdxco90netosSEMmpgb3eHopRqBPSKoAnySdvCXu/O9GgT6u5QlFKNgCaCJqawsJA2ZUmUt+pjxxZSSqkz0ETQxGxctxxfqaRVt+HuDkUp1UhoImhiUravAqBTX20oVkrVjCaCJsQYQ2DqOoq9gvCJ6uTucJRSjYQmgibkyNqvmFy5iMNtLwAv/dMqpWpGjxZNRdp2or67h62mIz6T/+7uaJRSjYgmgqagKAs+mkYxfjwR+Hvax0S7OyKlVCOiiaCxqyyHT36NyTvEvY7f0KVLd+02qpSqFU0Ejd3K1yFpKQfPfZalxfGM7BLl7oiUUo2MJoLGbvvX0G4w33qPBWBkZy0LKaVqRxNBY1aUBQfXQZeJLN+TQeeWwcSEBrg7KqVUI6OJoDHbuwiMg4pOY1m9L0uvBpRSZ0UTQWOWuAACwtnk6ERhWSUjO2v7gFKq9jQRNFbGQOJ86DyWZXtzEYFzOmkiUErVniaCxip1CxSkQpcJLN+TQa82oUQE+7k7KqVUI6SJoLFKnA9ASccxrN+fo2UhpdRZ00TQWCUugJg+rM0MoKzSwcgu2lCslDo7mggao9J8OLACuozn58QMfLyEoXGR7o5KKdVIaSJojPYtAUcFdJnAoh1pDIuPJMRfp59WSp0dTQSNUeIC8A0mOaQfO1PzGdejlbsjUko1YpoIGhtjIPFH6DSahbtzABjfM8bNQSmlGjNNBI1N5h7IOQBdxrNgRxqdooOJjw52d1RKqUZME0Fjs2MeAEUdxrJyT6aWhZRSdebSRCAik0Rkp4gkisgj1Tz+kIhsE5EEEVkgIh1dGU+TkPAJxA5laUYwZZUOxvXURKCUqhuXJQIR8QZeAy4CegHXikivEzbbAAwxxvQDPgP+5qp4moQjmyFtK/SbyoLtqbQI8NFuo0qpOnPlFcEwINEYs9cYUwbMAS6vuoExZpExpsi5uBKIdWE8jV/Cx+Dlg6PXlSzckc7obi3x9dbqnlKqblx5FGkHJFdZTnGuO5VbgW+re0BEZojIWhFZm56eXo8hNiKOStj8GXS9gM3ZPmQUlDJey0JKqXrgEaeTInIDMAR4rrrHjTGzjDFDjDFDWrZs2bDBeYp9SyD/MPS7hgU70vASGN1NE4FSqu5cmQgOAu2rLMc61x1HRCYAvwcuM8aUujCexi3hY/APhW4XsXBHKoM6RBCpo40qpeqBKxPBGqCriMSLiB8wDZhbdQMRGQi8iU0CaS6MpXErK7RzE/e6nCNFsOVgnvYWUkrVG5clAmNMBXAv8D2wHfjEGLNVRP4oIpc5N3sOCAE+FZGNIjL3FLtr3nZ8A2UF0H8ac9YcAGCC3k2slKonLh2pzBjzDfDNCeueqPLzBFe+fpOR8DGEtWdvUD9eX7yMyf3a0C2mhbujUgqA8vJyUlJSKCkpcXcoCggICCA2NhZfX98aP0eHrPR0OcmwZyFm1AM8/t9t+Pt48cTkE2/HUMp9UlJSaNGiBXFxcYiIu8Np1owxZGZmkpKSQnx8fI2f5xG9htQplBXBxzeAbyDf+1/A8j2Z/G5SD1qFBrg7MqWOKSkpISoqSpOABxARoqKian11ponAUzkc8OUdcCSB/Mlv8tjiAgZ2COe6YR3cHZlSJ9Ek4DnO5m+hicBTLXoGts+FC/4ff9rVnrzicv5yVV+8vPQfTilVvzQReKKNH8HS56kcdDMv5I/nk7Up3HZeJ3q0DnV3ZEqpJkgbiz3Npo9h7n0Ux57LDQeuYl1KIlcNbMfMCV3dHZlSzV5FRQU+Pk3vsNn0fqPGyhhY+ndY+AypkUO59MCtlHiV8Op1A5ncr627o1OqRp7+eivbDuXV6z57tQ3lyUt7n3G7K664guTkZEpKSnjggQeYMWMG3333HY899hiVlZVER0ezYMECCgoKuO+++1i7di0iwpNPPsnVV19NSEgIBQUFAHz22WfMmzePd999l5tvvpmAgAA2bNjAqFGjmDZtGg888AAlJSUEBgbyzjvv0L17dyorK/nd737Hd999h5eXF7fffju9e/fm5Zdf5quvvgLgxx9/5PXXX+fLL7+s1/eorjQReILKcvjfQ7D+fdaFTeTaQzcwpHNrnr+mP23CAt0dnVKNwuzZs4mMjKS4uJihQ4dy+eWXc/vtt7NkyRLi4+PJysoC4E9/+hNhYWFs3rwZgOzs7DPuOyUlheXLl+Pt7U1eXh5Lly7Fx8eH+fPn89hjj/H5558za9YskpKS2LhxIz4+PmRlZREREcHdd99Neno6LVu25J133uGWW25x6ftwNjQRuFt5CXxyI+z+gTkBU3kk9TIeGN+NB8Z31YZh1ejU5MzdVV5++eVjZ9rJycnMmjWL888//1h/+shIO3fH/PnzmTNnzrHnRUREnHHfU6ZMwdvbG4Dc3Fxuuukmdu/ejYhQXl5+bL933nnnsdLR0de78cYb+c9//sP06dNZsWIF77//fj39xvVHE4E7VZZTNufX+O35gWdkBh+XTODtmwboZPRK1dLixYuZP38+K1asICgoiDFjxjBgwAB27NhR431U7XZ5Yj/84OBf5gX/wx/+wNixY/nyyy9JSkpizJgxp93v9OnTufTSSwkICGDKlCke2cagvYYamMNhmP3zPq6ftYwfnrkMvz3f83j5dJaETmbuvedqElDqLOTm5hIREUFQUBA7duxg5cqVlJSUsGTJEvbt2wdwrDQ0ceJEXnvttWPPPVoaiomJYfv27TgcjtPW8HNzc2nXzk6t8u677x5bP3HiRN58800qKiqOe722bdvStm1bnnnmGaZPn15/v3Q90kTQgDIKSrnpndX8ad4Wbs74BxeY5azq8iAX3vx75t57LvHRwWfeiVLqJJMmTaKiooKePXvyyCOPcM4559CyZUtmzZrFVVddRf/+/Zk6dSoAjz/+ONnZ2fTp04f+/fuzaNEiAJ599lkmT57MyJEjadOmzSlf6+GHH+bRRx9l4MCBxw76ALfddhsdOnSgX79+9O/fnw8//PDYY9dffz3t27enZ8+eLnoH6kaMMe6OoVaGDBli1q5d6+4was4Y2PI56evnkpCUilSW0T+ijKjcLTDmURjziLsjVKpOtm/f7rEHOE9x7733MnDgQG699dYGeb3q/iYiss4YM6S67T2vWNWUFGVh5j2EbPuSShNBZ+8QWrcKIyAgEIY+DaMecHeESikXGzx4MMHBwTz//PPuDuWUNBG4yt6fcHxxB46CNJ4vn8aRPrfzzFUDCPDXt1yp5mTdunXuDuGM9KjkCps/g89vJVnacX/5H7l68mQePqejDsyllPJImgjqmzEUzX+W/aYjd/r+mX/cPJLBHSPdHZVSSp2SJoL6lrSUoNzdfOF3H5/eP4FWLXTuAKWUZ9NEUM+yF7+GMSF0OO96TQJKqUZB7yOoTznJhO3/gS8Zz5XDdbRQpVTjoImgHhUsfwtjDEX9biJEewcp5ZFCQkLcHYLH0aNVfSkvwWv9eyxwDOKKsSPcHY1S7vHtI3Bkc/3us3VfuOjZ+t2nB/CkuQ30iqCelCV8TlBFDtvbT6N9ZJC7w1Gq2XjkkUeOGzvoqaee4plnnmH8+PEMGjSIvn378t///rdG+yooKDjl895///1jw0fceOONAKSmpnLllVfSv39/+vfvz/Lly0lKSqJPnz7Hnvf3v/+dp556CoAxY8Ywc+ZMhgwZwksvvcTXX3/N8OHDGThwIBMmTCA1NfVYHNOnT6dv377069ePzz//nNmzZzNz5sxj+33rrbd48MEHz/p9O44xplF9DR482Hii9H+MNIl/6G5W781wdyhKNaht27a59fXXr19vzj///GPLPXv2NAcOHDC5ubnGGGPS09NN586djcPhMMYYExwcfMp9lZeXV/u8LVu2mK5du5r09HRjjDGZmZnGGGOuueYa88ILLxhjjKmoqDA5OTlm3759pnfv3sf2+dxzz5knn3zSGGPM6NGjzV133XXssaysrGNxvfXWW+ahhx4yxhjz8MMPmwceeOC47fLz802nTp1MWVmZMcaYESNGmISEhGp/j+r+JsBac4rjqmdclzRmFaU4Vr9FdO4WPgu5kzvi9J4BpRrSwIEDSUtL49ChQ6SnpxMREUHr1q158MEHWbJkCV5eXhw8eJDU1FRat2592n0ZY3jsscdOet7ChQuZMmUK0dHRwC9zDSxcuPDY/ALe3t6EhYWdcaKbo4PfgZ3wZurUqRw+fJiysrJjcyecas6EcePGMW/ePHr27El5eTl9+/at5btVvWaTCHYcyWPbwRx6kETH7BUEHViElORCpzHQZTx0HEUJfmxMzmHd/mzW78/G19uLLq1C6NwqmC4tWxDfMviXRuDKcsrX/ZuKxc8RWHSIlY6etB17i949rJQbTJkyhc8++4wjR44wdepUPvjgA9LT01m3bh2+vr7ExcWdNMdAdc72eVX5+PjgcDiOLZ9uboP77ruPhx56iMsuu4zFixcfKyGdym233caf//xnevToUa9DWjebRJCyYBbn7XqZlpILwFYTT5F3KP3S3sJ/5euU4McWRzzJjhiKTGviWnTAn1KCdiXjy2GMpJFMBT5egp+PF+Emj7DKLLY4uvAvnz8QO+xifjNIu4wq5Q5Tp07l9ttvJyMjg59++olPPvmEVq1a4evry6JFi9i/f3+N9pObm1vt88aNG8eVV17JQw89RFRUFFlZWURGRjJ+/HjeeOMNZs6cSWVlJQUFBcTExJCWlkZmZiYhISHMmzePSZMmnfL1js5t8N577x1bf3TOhBdffBGwcyZEREQwfPhwkpOTWb9+PQkJCXV5y47TbBLB6MF9KGEc2yJHssl/EDsLAskqLMPflNC1OIEehauIK99L3/Id+JcsAWcSNz7elIe2Jy+gHXmVfhSWVZBVWkliRSz7OlxK93Ov4qXO0fh4a7u7Uu7Su3dv8vPzadeuHW3atOH666/n0ksvpW/fvgwZMoQePXrUaD+nel7v3r35/e9/z+jRo/H29mbgwIG8++67vPTSS8yYMYO3334bb29v3njjDUaMGMETTzzBsGHDaNeu3Wlf+6mnnmLKlClEREQwbty4Y5PoPP7449xzzz306dMHb29vnnzySa666ioArrnmGjZu3FijKTZrSucjqE5ZIWQngW8ghLUHb1/Xvp5SjZjOR9CwJk+ezIMPPsj48eNPuU1t5yPQ09jq+AVDTG+I7KRJQCnlEXJycujWrRuBgYGnTQJno9mUhpRS6qjNmzcfuxfgKH9/f1atWuWmiM4sPDycXbt2uWTfmgiUUnVmjGlUPeb69u3Lxo0b3R2GS5xNuV9LQ0qpOgkICCAzM/OsDkCqfhljyMzMJCCgdiMf6xWBUqpOYmNjSUlJIT093d2hKGxijo2NrdVzNBEoperE19f32B2xqnFyaWlIRCaJyE4RSRSRR6p53F9EPnY+vkpE4lwZj1JKqZO5LBGIiDfwGnAR0Au4VkR6nbDZrUC2MaYL8ALwV1fFo5RSqnquvCIYBiQaY/YaY8qAOcDlJ2xzOXD0vurPgPHSmLoeKKVUE+DKNoJ2QHKV5RRg+Km2McZUiEguEAVkVN1IRGYAM5yLBSKy8yxjij5x3x7EU2Pz1LjAc2Pz1LjAc2Pz1Lig6cTW8VQPNIrGYmPMLGBWXfcjImtPdYu1u3lqbJ4aF3hubJ4aF3hubJ4aFzSP2FxZGjoItK+yHOtcV+02IuIDhAGZLoxJKaXUCVyZCNYAXUUkXkT8gGnA3BO2mQvc5Pz5V8BCo3elKKVUg3JZachZ878X+B7wBmYbY7aKyB+xU6bNBd4G/i0iiUAWNlm4Up3LSy7kqbF5alzgubF5alzgubF5alzQDGJrdMNQK6WUql861pBSSjVzmgiUUqqZazaJ4EzDXTRwLLNFJE1EtlRZFykiP4rIbuf3+puHruZxtReRRSKyTUS2isgDnhCbiASIyGoR2eSM62nn+njn0CSJzqFK/BoyrhNi9BaRDSIyz1NiE5EkEdksIhtFZK1znds/Z844wkXkMxHZISLbRWSEu2MTke7O9+roV56IzHR3XFXie9D5+d8iIh85/y/q5XPWLBJBDYe7aEjvAifOZv0IsMAY0xVY4FxuaBXA/xljegHnAPc43yd3x1YKjDPG9AcGAJNE5BzskCQvOIcoycYOWeIuDwDbqyx7SmxjjTEDqvQ1d/ff8qiXgO+MMT2A/tj3zq2xGWN2Ot+rAcBgoAj40t1xAYhIO+B+YIgxpg+2A8406utzZoxp8l/ACOD7KsuPAo+6OaY4YEuV5Z1AG+fPbYCdHvC+/ReY6EmxAUHAeuxd6hmAT3V/4waOKRZ7gBgHzAPEE2IDkoDoE9a5/W+JvV9oH87OKp4UW5VYLgCWeUpc/DIKQyS2t+c84ML6+pw1iysCqh/uop2bYjmVGGPMYefPR4AYdwbjHAl2ILAKD4jNWXrZCKQBPwJ7gBxjTIVzE3f+TV8EHgYczuUoPCM2A/wgIuucw7SAB/wtgXggHXjHWU77l4gEe0hsR00DPnL+7Pa4jDEHgb8DB4DDQC6wjnr6nDWXRNCoGJve3davV0RCgM+BmcaYvKqPuSs2Y0ylsZfssdgBDXs0dAzVEZHJQJoxZp27Y6nGucaYQdiS6D0icn7VB934OfMBBgFvGGMGAoWcUG5x5/+As85+GfDpiY+5Ky5nu8Tl2CTaFgjm5PLyWWsuiaAmw124W6qItAFwfk9zRxAi4otNAh8YY77wpNgAjDE5wCLsZXC4c2gScN/fdBRwmYgkYUfYHYetf7s9NudZJMaYNGytexie8bdMAVKMMUdniv8Mmxg8ITawiXO9MSbVuewJcU0A9hlj0o0x5cAX2M9evXzOmksiqMlwF+5WdbiNm7D1+QYlIoK923u7MeYfnhKbiLQUkXDnz4HYdovt2ITwK3fFBWCMedQYE2uMicN+rhYaY653d2wiEiwiLY7+jK15b8EDPmfGmCNAsoh0d64aD2zzhNicruWXshB4RlwHgHNEJMj5f3r0Paufz5m7GmPc0NhyMbALW1v+vZtj+Qhb5yvHnh3diq0rLwB2A/OBSDfEdS72sjcB2Oj8utjdsQH9gA3OuLYATzjXdwJWA4nYy3h/N/9dxwDzPCE25+tvcn5tPfqZd/ffskp8A4C1zr/pV0CEJ8SGLblkAmFV1rk9LmccTwM7nP8D/wb86+tzpkNMKKVUM9dcSkNKKaVOQROBUko1c5oIlFKqmdNEoJRSzZwmAqWUauY0ESh1AhGpPGEUynobZExE4qTKqLNKeQKXTVWpVCNWbOxwFko1C3pFoFQNOcf3/5tzjP/VItLFuT5ORBaKSIKILBCRDs71MSLypXMehU0iMtK5K28Recs5tvwPzrullXIbTQRKnSzwhNLQ1CqP5Rpj+gKvYkcdBXgFeM8Y0w/4AHjZuf5l4Cdj51EYhL3DF6Ar8JoxpjeQA1zt4t9HqdPSO4uVOoGIFBhjQqpZn4SdIGevc3C+I8aYKBHJwI5XX+5cf9gYEy0i6UCsMaa0yj7igB+NneQEEfkd4GuMecb1v5lS1dMrAqVqx5zi59oorfJzJdpWp9xME4FStTO1yvcVzp+XY0ceBbgeWOr8eQFwFxybWCesoYJUqjb0TESpkwU6Z0M76jtjzNEupBEikoA9q7/Wue4+7Gxbv8XOvDXduf4BYJaI3Io9878LO+qsUh5F2wiUqiFnG8EQY0yGu2NRqj5paUgppZo5vSJQSqlmTq8IlFKqmdNEoJRSzZwmAqWUauY0ESilVDOniUAppZq5/w/4pqBB9dhwvwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFNUOGNimSNy"
      },
      "source": [
        "### MyCNN\n",
        "optimizer = adamax, loss = crossentropy, epochs = 100, batchsize = 64"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "H-xW_Btd4fah",
        "outputId": "358bda47-89b6-4c87-94fc-d3645a34d0b9"
      },
      "source": [
        "plt.plot(mycnn_history4.history['accuracy'], label='accuracy')\n",
        "plt.plot(mycnn_history4.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = mycnn.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 1.3247 - accuracy: 0.6600\n",
            "test_los =  1.3247326612472534\n",
            "test_acc =  0.6600000262260437\n"
          ],
          "name": "stdout"
        },
        {
          "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": "v8rhgSGOV5pm"
      },
      "source": [
        "\n",
        "# Ερώτημα 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uIa3xsSG7dv6"
      },
      "source": [
        "* Στο δικό μας CNN χρησιμοποιούσαμε ήδη Dropout με στόχο να ελέγξουμε το overfitting όπως ζητάει και το ερώτημα 2.\n",
        "* Προσθέτουμε στην αρχή του μοντέλου μας τις 3 παρακάτω εντολές, ώστε να υλοποιήσουμε το Data augmentation που μας ζητάει το ερώτημα:\n",
        "  * MyCNN.add(layers.experimental.preprocessing.RandomFlip(\"horizontal\",input_shape=(32, 32, 3)))\n",
        "  * MyCNN.add(layers.experimental.preprocessing.RandomRotation(0.1))\n",
        "  * MyCNN.add(layers.experimental.preprocessing.RandomZoom(0.1))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJxRK3F65OQB",
        "outputId": "86e0ef1f-c853-4417-9931-6b084788045c"
      },
      "source": [
        "def MyCNN():\n",
        "  MyCNN = models.Sequential()\n",
        "\n",
        "  ####################### Data augmentation #########################\n",
        "  MyCNN.add(layers.experimental.preprocessing.RandomFlip(\"horizontal\", input_shape=(32, 32, 3)))\n",
        "  MyCNN.add(layers.experimental.preprocessing.RandomRotation(0.1))\n",
        "  MyCNN.add(layers.experimental.preprocessing.RandomZoom(0.1))\n",
        "  ###################################################################\n",
        "\n",
        "  MyCNN.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) \n",
        "  MyCNN.add(layers.MaxPooling2D((2, 2)))\n",
        "  MyCNN.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "  MyCNN.add(layers.MaxPooling2D((2, 2)))\n",
        "  MyCNN.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
        "  MyCNN.add(layers.AvgPool2D(pool_size=2, strides=2)) \n",
        "  MyCNN.add(layers.Flatten())\n",
        "  MyCNN.add(layers.Dense(256, activation='relu'))\n",
        "  ##### Dropout, as previously used #####\n",
        "  MyCNN.add(layers.Dropout(0.5))\n",
        "  MyCNN.add(layers.Dense(128, activation='relu'))\n",
        "  MyCNN.add(layers.Dropout(0.5))\n",
        "  MyCNN.add(layers.Dense(100))\n",
        "  return MyCNN\n",
        "\n",
        "mycnn = MyCNN()\n",
        "mycnn.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "random_flip_11 (RandomFlip)  (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "random_rotation_11 (RandomRo (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "random_zoom (RandomZoom)     (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 30, 30, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 13, 13, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 4, 4, 128)         73856     \n",
            "_________________________________________________________________\n",
            "average_pooling2d_5 (Average (None, 2, 2, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_10 (Flatten)         (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_24 (Dense)             (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_25 (Dense)             (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_26 (Dense)             (None, 100)               12900     \n",
            "=================================================================\n",
            "Total params: 270,372\n",
            "Trainable params: 270,372\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCFtwE5m6hzA"
      },
      "source": [
        "callback = tf.keras.callbacks.EarlyStopping( \n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0.001,\n",
        "    patience=10,\n",
        "    verbose=0,\n",
        "    mode=\"auto\", \n",
        "    baseline=None,\n",
        "    restore_best_weights=False,\n",
        ")\n",
        "# min_delta: Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.\n",
        "# patience: Number of epochs with no improvement after which training will be stopped.\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-NBWRln7KKc"
      },
      "source": [
        "with tf.device('/device:GPU:0'):  \n",
        "  mycnn = MyCNN()\n",
        "\n",
        "  mycnn.compile(optimizer=tf.keras.optimizers.Adamax(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "  mycnn_history= mycnn.fit(x_train, y_train, epochs=100, batch_size=128,\n",
        "                      validation_data=(x_test, y_test),callbacks=[callback])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "I-ZXUMk8-OO9",
        "outputId": "1d36e9d8-3647-4a37-e416-1ed0667c03f3"
      },
      "source": [
        "plt.plot(mycnn_history.history['accuracy'], label='accuracy')\n",
        "plt.plot(mycnn_history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = mycnn.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 1.3053 - accuracy: 0.6015\n",
            "test_los =  1.3053443431854248\n",
            "test_acc =  0.6014999747276306\n"
          ],
          "name": "stdout"
        },
        {
          "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": "VKZNd3Zz_8lx"
      },
      "source": [
        "with tf.device('/device:GPU:0'):  \n",
        "  mycnn = MyCNN()\n",
        "\n",
        "  mycnn.compile(optimizer=tf.keras.optimizers.Adamax(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "  mycnn_history2 = mycnn.fit(x_train, y_train, epochs=100, batch_size=64,\n",
        "                      validation_data=(x_test, y_test),callbacks=[callback])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "O3vDC19-AbQa",
        "outputId": "272e1c6a-080e-41f4-c92a-356fafabc745"
      },
      "source": [
        "plt.plot(mycnn_history2.history['accuracy'], label='accuracy')\n",
        "plt.plot(mycnn_history2.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = mycnn.evaluate(x_test,  y_test, verbose=2)\n",
        "print(\"test_los = \",test_loss)\n",
        "print(\"test_acc = \",test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 0s - loss: 1.1793 - accuracy: 0.6520\n",
            "test_los =  1.1793261766433716\n",
            "test_acc =  0.6520000100135803\n"
          ],
          "name": "stdout"
        },
        {
          "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": "DKm6N_L-BFne"
      },
      "source": [
        "## Αξιολόγηση ορθότητας\n",
        "Σε αρχικές δοκιμές του πρόωρου τερμαρισμού, παρατηρήσαμε ότι για μικρές τιμές του patience και μεγάλες του min_delta, το μοντέλο μας είχε σαφώς χειρότερες επιδόσεις ,όπως και περιμέναμε, αφού σταμάταγε πρόωρα η εκπαίδευση. Έπειτα, επιλέγοντας σωστές τιμές, πήραμε τα παραπάνω διαγράμματα.  \n",
        "Από τα διαγράμματα παρατηρούμε ότι τα αποτελέσματα είναι παρόμοια με αυτά του αρχικού μοντέλου μας, πριν προσθέσουμε εντολές για τον έλεγχο του overfitting. Η μόνη διαφορά είναι στο δεύτερο διάγραμμα όπου φαίνεται μία μικρή μείωση του loss. Αυτό είναι λογικό αφού ήδη χρησιμοποιούσαμε dropout στο CNN μας το οποίο μείωνε σημαντικά το loss. Η εγκυρότητα σε όλες τις περιπτώσεις παραμένει σχεδόν σταθερή. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTxKRNEHWfGs"
      },
      "source": [
        "# Ερώτημα 3\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9gwH0luYrYY"
      },
      "source": [
        "def MyCNN_tf():\n",
        "\n",
        "  base_model = tf.keras.applications.VGG16(weights='imagenet', input_shape=(32,32,3),include_top=False)\n",
        "\n",
        "  base_model.trainable = False #freeze convolutiional base\n",
        "  input = tf.keras.Input(shape=(32, 32, 3))\n",
        "  data_augmentation = tf.keras.Sequential(\n",
        "    [\n",
        "        layers.experimental.preprocessing.RandomFlip(\"horizontal\"),\n",
        "        layers.experimental.preprocessing.RandomRotation(0.1),\n",
        "    ]\n",
        "  )\n",
        "  x = data_augmentation(input) \n",
        "  norm_layer = tf.keras.layers.experimental.preprocessing.Normalization()\n",
        "  x = norm_layer(x)\n",
        "  x = base_model(x, training=False)\n",
        "  x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
        "  x = tf.keras.layers.Flatten()(x) \n",
        "  x = tf.keras.layers.Dropout(0.2)(x)  \n",
        "  output = tf.keras.layers.Dense(256, activation='relu')(x)\n",
        "  x = tf.keras.layers.Dropout(0.2)(x) \n",
        "  output = tf.keras.layers.Dense(128, activation='relu')(output)\n",
        "\n",
        "  model = tf.keras.Model(input, output)  \n",
        "\n",
        "  return model\n",
        "  # model = tf.keras.Sequential([\n",
        "  # tf.keras.layers.InputLayer((32, 32, 3)) ,\n",
        "  # tf.keras.layers.experimental.preprocessing.RandomFlip(\"horizontal_and_vertical\"),\n",
        "  # tf.keras.layers.experimental.preprocessing.RandomRotation(0.1),     \n",
        "  # tf.keras.layers.experimental.preprocessing.Normalization(),                    \n",
        "  # VGG16,\n",
        "  # tf.keras.layers.MaxPool2D(pool_size=1, strides=1),\n",
        "  # tf.keras.layers.MaxPool2D(pool_size=1, strides=1),\n",
        "  # tf.keras.layers.AvgPool2D(pool_size=1, strides=1),\n",
        "  # tf.keras.layers.Flatten(),\n",
        "  # tf.keras.layers.Dropout(0.2),\n",
        "  # tf.keras.layers.Dense(256,activation='relu'),\n",
        "  # tf.keras.layers.Dropout(0.2),       \n",
        "  # tf.keras.layers.Dense(128)               \n",
        "  # ])                               \n",
        "  # return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXD4amdcYtSh",
        "outputId": "e3f9c4ed-8338-424b-f2ed-07b3083bc237"
      },
      "source": [
        "mycnn = MyCNN_tf()\n",
        "mycnn.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_12\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_26 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "sequential_12 (Sequential)   (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "normalization_12 (Normalizat (None, 32, 32, 3)         7         \n",
            "_________________________________________________________________\n",
            "vgg16 (Functional)           (None, 1, 1, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_10  (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "flatten_12 (Flatten)         (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_21 (Dropout)         (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_27 (Dense)             (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dense_28 (Dense)             (None, 128)               32896     \n",
            "=================================================================\n",
            "Total params: 14,878,919\n",
            "Trainable params: 164,224\n",
            "Non-trainable params: 14,714,695\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Of-Jpu-Xp4nX",
        "outputId": "2fce80fe-1bae-487f-e5ec-ee8ece220cf3"
      },
      "source": [
        "for i, layer in enumerate(mycnn.layers):\n",
        "    print(i, layer.name, layer.trainable)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_26 True\n",
            "1 sequential_12 True\n",
            "2 normalization_12 True\n",
            "3 vgg16 False\n",
            "4 global_average_pooling2d_10 True\n",
            "5 flatten_12 True\n",
            "6 dropout_21 True\n",
            "7 dense_27 True\n",
            "8 dense_28 True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16VAIAiWYuy7"
      },
      "source": [
        "callback = [\n",
        "    tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss', \n",
        "    min_delta=0, \n",
        "    patience=10, \n",
        "    verbose=0,\n",
        "    mode='auto', \n",
        "    baseline=None, \n",
        "    restore_best_weights=False)\n",
        "]\n",
        "with tf.device('/device:GPU:0'):  \n",
        "  mycnn = MyCNN_tf()\n",
        "\n",
        "  mycnn.compile(optimizer=tf.keras.optimizers.Adamax(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "  history = mycnn.fit(x_train, y_train, epochs=100, batch_size=128,\n",
        "                      validation_data=(x_test, y_test), callbacks= callback)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "7ya1L768Y6wF",
        "outputId": "9d6ef7c9-4bba-4b31-e184-9f89dac64884"
      },
      "source": [
        "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.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = mycnn.evaluate(x_test,  y_test, verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 1.6305 - accuracy: 0.5430\n"
          ],
          "name": "stdout"
        },
        {
          "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": "UO53kKKuY-V6"
      },
      "source": [
        "for output_layer in mycnn.layers[0](mycnn).layers[-3:]:\n",
        "  output_layer.trainable = True\n",
        "\n",
        "with tf.device('/device:GPU:0'):  \n",
        "\n",
        "  mycnn.compile(optimizer=tf.keras.optimizers.Adamax(),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                metrics=['accuracy'])\n",
        "  history = mycnn.fit(x_train, y_train, epochs=100, batch_size=128,\n",
        "                      validation_data=(x_test, y_test), callbacks=callback)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "Gd_HnDz7bHCv",
        "outputId": "4cccdfbe-c1df-4eb8-b564-f8e528c0faf3"
      },
      "source": [
        "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.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = mycnn.evaluate(x_test,  y_test, verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 1.6289 - accuracy: 0.5475\n"
          ],
          "name": "stdout"
        },
        {
          "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": "lP089RP6-IWl"
      },
      "source": [
        "Εκπαιδεύοντας επιπλέον επίπεδα όπως περιμέναμε η γραφική κυμαίνεται μόνο σε υψηλές τιμές, αφού το μοντέλο μας ήταν ήδη εκαιδευμένο."
      ]
    },
    {
      "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"
      ]
    }
  ]
}