CatDogClassifier_Part_1.ipynb

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    "colab": {
      "name": "CI_HW3_CatDogClassifier_Part_1.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/farkoo/Cat-Dog-Classifier/blob/master/CatDogClassifier_Part_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1g4Li6ngEF9d"
      },
      "source": [
        "# Prepare train and test data (Data Preprocessing)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "38ScwC7bewNw",
        "outputId": "d3c5ddf5-71b9-4c00-e9eb-7a5122e6b474"
      },
      "source": [
        "%%bash\n",
        "git clone \"https://github.com/MKasaei00/IUT-CI-HW3-cat-dog-classifier.git\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'IUT-CI-HW3-cat-dog-classifier' already exists and is not an empty directory.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvNMNj1whfmG",
        "outputId": "35a67aec-228d-4fa7-d349-87d3e48749eb"
      },
      "source": [
        "%cd IUT-CI-HW3-cat-dog-classifier"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/IUT-CI-HW3-cat-dog-classifier\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nb69RhJ5iDrW",
        "outputId": "c44f1805-c4a4-4e98-b691-608d18a44d51"
      },
      "source": [
        "%%bash\n",
        "7z x dataset/test_set.zip -odataset\n",
        "7z x dataset/training_set.zip -odataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "7-Zip [64] 16.02 : Copyright (c) 1999-2016 Igor Pavlov : 2016-05-21\n",
            "p7zip Version 16.02 (locale=en_US.UTF-8,Utf16=on,HugeFiles=on,64 bits,2 CPUs Intel(R) Xeon(R) CPU @ 2.20GHz (406F0),ASM,AES-NI)\n",
            "\n",
            "Scanning the drive for archives:\n",
            "1 file, 3711630 bytes (3625 KiB)\n",
            "\n",
            "Extracting archive: dataset/test_set.zip\n",
            "--\n",
            "Path = dataset/test_set.zip\n",
            "Type = zip\n",
            "Physical Size = 3711630\n",
            "Embedded Stub Size = 4\n",
            "Total Physical Size = 45654670\n",
            "Multivolume = +\n",
            "Volume Index = 2\n",
            "Volumes = 3\n",
            "\n",
            "\n",
            "Would you like to replace the existing file:\n",
            "  Path:     dataset/test_set/cats/cat.4001.jpg\n",
            "  Size:     46992 bytes (46 KiB)\n",
            "  Modified: 2019-09-30 02:52:14\n",
            "with the file from archive:\n",
            "  Path:     test_set/cats/cat.4001.jpg\n",
            "  Size:     46992 bytes (46 KiB)\n",
            "  Modified: 2019-09-30 02:52:14\n",
            "? (Y)es / (N)o / (A)lways / (S)kip all / A(u)to rename all / (Q)uit? (Y)es / (N)o / (A)lways / (S)kip all / A(u)to rename all / (Q)uit? "
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "ERROR:\n",
            "Unexpected end of input stream\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hTd3JReGlT0r"
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "from keras.utils import np_utils\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import random"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NA3x7ObDlopB"
      },
      "source": [
        "test_dir = 'dataset/test_set'\n",
        "training_dir = 'dataset/training_set'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5lNX7WfLln7S"
      },
      "source": [
        "def load_data_from_directory(dir,width,height,classes):\n",
        "  img_data = []\n",
        "  img_label = []\n",
        "  categories = os.listdir(dir)\n",
        "  for category in categories:\n",
        "    for file in os.listdir(os.path.join(dir,category)):\n",
        "      img_path = os.path.join(dir,category,file)\n",
        "      img = cv2.imread(img_path)\n",
        "      img = cv2.resize(img,(width,height))\n",
        "      img = np.array(img).astype('float32') / 255\n",
        "      \n",
        "      img_data.append(img)\n",
        "      img_label.append(category)\n",
        "\n",
        "  img_onehot = np_utils.to_categorical([categories.index(label) for label in img_label],classes,dtype=np.ubyte)\n",
        "  img_data = np.array(img_data)\n",
        "  img_onehot = np.array(img_onehot)\n",
        "\n",
        "  zip_list = list(zip(img_data, img_label, img_onehot))\n",
        "  random.shuffle(zip_list)\n",
        "  img_data, img_label, img_onehot = zip(*zip_list)\n",
        "  return img_data , img_label, img_onehot"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVb181xCZZbJ"
      },
      "source": [
        "classes = 2\n",
        "w = 128\n",
        "h = 128"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kpvfOQ3mDqq"
      },
      "source": [
        "train_x , train_label, train_onehot = load_data_from_directory(training_dir,w,h,classes)\n",
        "test_x , test_label, test_onehot = load_data_from_directory(test_dir,w,h,classes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TV8o9GRwGPi"
      },
      "source": [
        "test_label2 = []\n",
        "for i, label in enumerate(test_label):\n",
        "     test_label2.append(1) if (label == 'dogs') else test_label2.append(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v7XgBhICe2Y6"
      },
      "source": [
        "train_x = np.asarray(train_x)\n",
        "train_label = np.asarray(train_label)\n",
        "train_onehot = np.asarray(train_onehot)\n",
        "\n",
        "test_x = np.asarray(test_x)\n",
        "test_label = np.asarray(test_label2)\n",
        "test_onehot = np.asarray(test_onehot)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYnCpcoumHxk"
      },
      "source": [
        "!rm -rf cleanData\n",
        "!mkdir cleanData\n",
        "np.save('cleanData/train_x.npy',train_x)\n",
        "np.save('cleanData/train_label.npy',train_label)\n",
        "np.save('cleanData/train_onehot.npy',train_onehot)\n",
        "\n",
        "np.save('cleanData/test_x.npy',test_x)\n",
        "np.save('cleanData/test_label.npy',test_label)\n",
        "np.save('cleanData/test_onehot.npy',test_onehot)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mpcwCHVPmI1z"
      },
      "source": [
        "train_x = np.load('cleanData/train_x.npy')\n",
        "train_label = np.load('cleanData/train_onehot.npy')\n",
        "\n",
        "test_x = np.load('cleanData/test_x.npy')\n",
        "test_label = np.load('cleanData/test_onehot.npy')\n",
        "test_vec = np.load('cleanData/test_label.npy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FLzp06qGu1b"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_X, valid_X, train_Label, valid_Label = train_test_split(train_x, train_label, test_size = 0.2, random_state = 13)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_knM4FV1EPdM"
      },
      "source": [
        "# Generate basic Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKZBz2xEmUby"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout,Activation,Flatten,LeakyReLU\n",
        "from keras.optimizers import Adam\n",
        "from keras.losses import categorical_crossentropy\n",
        "from keras.initializers import RandomNormal"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSYVxFTDHd2E"
      },
      "source": [
        "# توضیح کلی در مورد عملکرد این تابع"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZeBLOZwqVghE"
      },
      "source": [
        "<div dir='rtl'>\n",
        "\n",
        "در این تابع متغیر \n",
        "config\n",
        "که در واقع تمام تنظیمات لازم برای ساخت شکبه‌ی عصبی ، داده‌های آموزشی ، منظم‌سازی و هم‌چنین آگمنتیشن داده‌ها است را دریافت می‌کند.\n",
        "\n",
        "مدل براساس مجموعه‌ی لایه‌های شبکه‌ی عصبی مطرح شده در ورودی ساخته می‌شود هر مجموعه‌ لایه می‌توانند توابع فعال‌سازی متناسب با کاربرد و متفاوتی داشته باشند و هم‌چنین ضرایب متفاوتی برای \n",
        "dropout\n",
        "در نظرگرفت\n",
        "\n",
        "تعداد \n",
        "epoch\n",
        "های آموزش مدل براساس پارامتر مشخص شده و پس از اموزش مدل نمودار صحت مدل روی داده‌های آموزش و تست رسم می‌شود.\n",
        "\n",
        " برای اجرای های بعدی می‌تواینم از همین تابع کلی استفاده کنیم و در مواقع نیاز به پارامترها مقادیر غیرصفر بدهیم.\n",
        "\n",
        " در نهایت داده‌های تست را به مدل نهایی می‌دهیم و مدل را با روش های مختلف مانند \n",
        " accuracy , recall , F1-measure\n",
        " ارزیابی کرده و گزارش را چاپ می‌کنیم.\n",
        "\n",
        "لازم به ذکر است که منظور از کلاس 0 کلاس مربوط به تصاویر\n",
        "Cats \n",
        "است و کلاس 1 کلاس مربوط به تصاویر \n",
        "‌Dogs\n",
        " می باشد.\n",
        "\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfKUyZ6VzqZB"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "def train_model(config):\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Flatten(input_shape=(h,w,3)))\n",
        "  model.add(Dropout(config['in_drop_out']))\n",
        "\n",
        "  for act ,drop_out, layers in config['layers']:\n",
        "    for layer in layers:\n",
        "      model.add(Dense(layer))\n",
        "      model.add(act())\n",
        "      model.add(Dropout(drop_out))\n",
        "\n",
        "  model.compile(\n",
        "    optimizer=Adam(config['learning_rate']),\n",
        "    loss=categorical_crossentropy,\n",
        "    metrics=['accuracy']\n",
        "  )\n",
        "\n",
        "  train_datagen = ImageDataGenerator(\n",
        "        rotation_range=config['aug_rotation'],\n",
        "        width_shift_range=config['aug_shift_w'],\n",
        "        height_shift_range=config['aug_shift_h'],\n",
        "        shear_range=config['aug_shear'],\n",
        "        zoom_range=config['aug_zoom'],\n",
        "        horizontal_flip=config['aug_horizontal_flip'],\n",
        "        fill_mode='nearest'\n",
        "  )\n",
        "\n",
        "  train_generator = train_datagen.flow(train_X, train_Label, batch_size = config['batch'])\n",
        "\n",
        "  validation_datagen = ImageDataGenerator()\n",
        "  validation_generator = validation_datagen.flow(valid_X,valid_Label,batch_size=256)\n",
        "\n",
        "  history = model.fit(train_generator,\n",
        "                      validation_data=validation_generator,\n",
        "                      epochs = config['epochs'],\n",
        "                    )\n",
        "  \n",
        "  plt.plot(history.history['accuracy'])\n",
        "  plt.plot(history.history['val_accuracy'])\n",
        "  plt.title('Model Accuracy')\n",
        "  plt.ylabel('accuracy')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train','validation'],loc='lower right')\n",
        "  plt.show()\n",
        "\n",
        "  test_eval = model.evaluate(test_x, test_label, verbose = 0)\n",
        "  print('Test loss: ', test_eval[0])\n",
        "  print('Test accuracy: ', test_eval[1])\n",
        "  print('\\n\\n')\n",
        "  \n",
        "  predictes_classes = model.predict(test_x)\n",
        "  predicted_classes = np.argmax(np.round(predictes_classes), axis = 1)\n",
        "\n",
        "  from sklearn.metrics import classification_report\n",
        "  target_names = [\"Class {}\".format(i) for i in range(classes)]\n",
        "  print(classification_report(test_vec, predicted_classes, target_names = target_names))\n",
        "\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4Kquo41EqtQ"
      },
      "source": [
        "# 1- Initial model, Without any dropout and data augmentation\n",
        "<div dir='rtl'>\n",
        "به کمک دراپ‌اوت بصورت تصادفی تعدادی از نرون ها در هر لایه غیرفعال شده و با استفاده از این راه از بیش‌برازش شدن مدل جلوگیری می‌کنیم.\n",
        "\n",
        "</div>\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_ZCp78UkQtGz",
        "outputId": "64aae708-0662-4eb6-fff3-8f0c74e8e1f5"
      },
      "source": [
        "model = train_model({\n",
        "    'in_drop_out':0,\n",
        "    'layers' : [\n",
        "          (lambda : Activation('relu'),0,[2000,1600,800,600,500]+[400]*3+[400]*5+[300,250,200,150,120]), \n",
        "          (lambda : Activation('softmax'),0,[2])\n",
        "    ],\n",
        "    'learning_rate':3e-4,\n",
        "    'batch':256,\n",
        "    'epochs':100,\n",
        "    'aug_rotation':0,\n",
        "    'aug_shift_w':0,\n",
        "    'aug_shift_h':0,\n",
        "    'aug_shear':0,\n",
        "    'aug_zoom':0,\n",
        "    'aug_horizontal_flip':False\n",
        "})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 7s 127ms/step - loss: 0.6943 - accuracy: 0.4985 - val_loss: 0.6932 - val_accuracy: 0.4969\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 2s 72ms/step - loss: 0.6933 - accuracy: 0.5019 - val_loss: 0.6932 - val_accuracy: 0.5031\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 2s 73ms/step - loss: 0.6933 - accuracy: 0.5030 - val_loss: 0.6932 - val_accuracy: 0.5031\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 2s 74ms/step - loss: 0.6924 - accuracy: 0.5183 - val_loss: 0.6932 - val_accuracy: 0.5031\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 2s 71ms/step - loss: 0.6906 - accuracy: 0.5277 - val_loss: 0.6925 - val_accuracy: 0.5031\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6909 - accuracy: 0.5297 - val_loss: 0.6889 - val_accuracy: 0.5225\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 2s 70ms/step - loss: 0.6880 - accuracy: 0.5482 - val_loss: 0.6864 - val_accuracy: 0.5675\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6835 - accuracy: 0.5717 - val_loss: 0.6785 - val_accuracy: 0.5537\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.6816 - accuracy: 0.5491 - val_loss: 0.6882 - val_accuracy: 0.5763\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6614 - accuracy: 0.6070 - val_loss: 0.6712 - val_accuracy: 0.5800\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6640 - accuracy: 0.6059 - val_loss: 0.6556 - val_accuracy: 0.6025\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 2s 70ms/step - loss: 0.6479 - accuracy: 0.6218 - val_loss: 0.6652 - val_accuracy: 0.6162\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6533 - accuracy: 0.6231 - val_loss: 0.6525 - val_accuracy: 0.6306\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6379 - accuracy: 0.6379 - val_loss: 0.6467 - val_accuracy: 0.6250\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6379 - accuracy: 0.6370 - val_loss: 0.6279 - val_accuracy: 0.6394\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6263 - accuracy: 0.6445 - val_loss: 0.6318 - val_accuracy: 0.6519\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6191 - accuracy: 0.6637 - val_loss: 0.6883 - val_accuracy: 0.5844\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6355 - accuracy: 0.6408 - val_loss: 0.6143 - val_accuracy: 0.6725\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6217 - accuracy: 0.6605 - val_loss: 0.6148 - val_accuracy: 0.6850\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6231 - accuracy: 0.6476 - val_loss: 0.6001 - val_accuracy: 0.6812\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6008 - accuracy: 0.6667 - val_loss: 0.6106 - val_accuracy: 0.6712\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5960 - accuracy: 0.6809 - val_loss: 0.5939 - val_accuracy: 0.6787\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5962 - accuracy: 0.6731 - val_loss: 0.6060 - val_accuracy: 0.6719\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5889 - accuracy: 0.6826 - val_loss: 0.6210 - val_accuracy: 0.6562\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5893 - accuracy: 0.6799 - val_loss: 0.5676 - val_accuracy: 0.7000\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5860 - accuracy: 0.6856 - val_loss: 0.5690 - val_accuracy: 0.7050\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5657 - accuracy: 0.7047 - val_loss: 0.5501 - val_accuracy: 0.7212\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5395 - accuracy: 0.7202 - val_loss: 0.5557 - val_accuracy: 0.7225\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5395 - accuracy: 0.7143 - val_loss: 0.5506 - val_accuracy: 0.7025\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5468 - accuracy: 0.7110 - val_loss: 0.5882 - val_accuracy: 0.7006\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.5560 - accuracy: 0.7158 - val_loss: 0.5283 - val_accuracy: 0.7394\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.5264 - accuracy: 0.7320 - val_loss: 0.5390 - val_accuracy: 0.7119\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5342 - accuracy: 0.7331 - val_loss: 0.6141 - val_accuracy: 0.7088\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5377 - accuracy: 0.7235 - val_loss: 0.5018 - val_accuracy: 0.7500\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 2s 65ms/step - loss: 0.5127 - accuracy: 0.7487 - val_loss: 0.5772 - val_accuracy: 0.6775\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5408 - accuracy: 0.7127 - val_loss: 0.5021 - val_accuracy: 0.7600\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5265 - accuracy: 0.7375 - val_loss: 0.5235 - val_accuracy: 0.7319\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.4875 - accuracy: 0.7532 - val_loss: 0.4976 - val_accuracy: 0.7369\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.4962 - accuracy: 0.7584 - val_loss: 0.4891 - val_accuracy: 0.7494\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.4727 - accuracy: 0.7680 - val_loss: 0.4318 - val_accuracy: 0.7806\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.4427 - accuracy: 0.7828 - val_loss: 1.0706 - val_accuracy: 0.6219\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6602 - accuracy: 0.6766 - val_loss: 0.5491 - val_accuracy: 0.7412\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.6105 - accuracy: 0.6639 - val_loss: 0.5059 - val_accuracy: 0.7212\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6228 - accuracy: 0.6341 - val_loss: 0.4963 - val_accuracy: 0.7606\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.4981 - accuracy: 0.7483 - val_loss: 0.4635 - val_accuracy: 0.7962\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.4778 - accuracy: 0.7730 - val_loss: 0.4770 - val_accuracy: 0.7812\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.4770 - accuracy: 0.7703 - val_loss: 0.5472 - val_accuracy: 0.7362\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 2s 70ms/step - loss: 0.4910 - accuracy: 0.7530 - val_loss: 0.4200 - val_accuracy: 0.8119\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.4400 - accuracy: 0.7955 - val_loss: 0.4222 - val_accuracy: 0.8006\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.4255 - accuracy: 0.7932 - val_loss: 0.5298 - val_accuracy: 0.7100\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.4590 - accuracy: 0.7805 - val_loss: 0.4446 - val_accuracy: 0.7919\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.4586 - accuracy: 0.7853 - val_loss: 0.3946 - val_accuracy: 0.8288\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.4025 - accuracy: 0.8120 - val_loss: 0.3647 - val_accuracy: 0.8281\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.3717 - accuracy: 0.8280 - val_loss: 0.4205 - val_accuracy: 0.8156\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.4572 - accuracy: 0.7688 - val_loss: 0.3808 - val_accuracy: 0.8375\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.3839 - accuracy: 0.8293 - val_loss: 0.4064 - val_accuracy: 0.7912\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.3766 - accuracy: 0.8241 - val_loss: 0.4375 - val_accuracy: 0.8019\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.3916 - accuracy: 0.8127 - val_loss: 0.3094 - val_accuracy: 0.8650\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.3587 - accuracy: 0.8379 - val_loss: 0.3265 - val_accuracy: 0.8519\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.3321 - accuracy: 0.8482 - val_loss: 0.4846 - val_accuracy: 0.7688\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.3976 - accuracy: 0.8131 - val_loss: 0.3565 - val_accuracy: 0.8356\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.3625 - accuracy: 0.8341 - val_loss: 0.3156 - val_accuracy: 0.8644\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.3095 - accuracy: 0.8664 - val_loss: 0.3118 - val_accuracy: 0.8669\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.3540 - accuracy: 0.8364 - val_loss: 0.3467 - val_accuracy: 0.8413\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.3467 - accuracy: 0.8394 - val_loss: 0.3487 - val_accuracy: 0.8562\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.3115 - accuracy: 0.8656 - val_loss: 0.3272 - val_accuracy: 0.8675\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.4270 - accuracy: 0.8059 - val_loss: 0.3237 - val_accuracy: 0.8712\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.2988 - accuracy: 0.8766 - val_loss: 0.3793 - val_accuracy: 0.8681\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.4253 - accuracy: 0.7943 - val_loss: 0.5625 - val_accuracy: 0.6750\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.4918 - accuracy: 0.7793 - val_loss: 0.4017 - val_accuracy: 0.8200\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.3505 - accuracy: 0.8514 - val_loss: 0.2431 - val_accuracy: 0.9025\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.2700 - accuracy: 0.8834 - val_loss: 0.2387 - val_accuracy: 0.9050\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.2719 - accuracy: 0.8842 - val_loss: 0.3200 - val_accuracy: 0.8612\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.2641 - accuracy: 0.8838 - val_loss: 0.2859 - val_accuracy: 0.8712\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 2s 70ms/step - loss: 0.2661 - accuracy: 0.8826 - val_loss: 0.2290 - val_accuracy: 0.9050\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.2261 - accuracy: 0.9063 - val_loss: 0.3579 - val_accuracy: 0.8350\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.2657 - accuracy: 0.8868 - val_loss: 0.2372 - val_accuracy: 0.8988\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.2698 - accuracy: 0.8839 - val_loss: 0.1893 - val_accuracy: 0.9125\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.2305 - accuracy: 0.8960 - val_loss: 0.1784 - val_accuracy: 0.9306\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.2209 - accuracy: 0.9086 - val_loss: 0.1774 - val_accuracy: 0.9306\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.1889 - accuracy: 0.9189 - val_loss: 0.1573 - val_accuracy: 0.9394\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.3117 - accuracy: 0.8649 - val_loss: 0.2534 - val_accuracy: 0.8888\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.3147 - accuracy: 0.8584 - val_loss: 0.1605 - val_accuracy: 0.9394\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.2053 - accuracy: 0.9137 - val_loss: 0.2342 - val_accuracy: 0.8988\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.2032 - accuracy: 0.9117 - val_loss: 0.1840 - val_accuracy: 0.9287\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.2060 - accuracy: 0.9154 - val_loss: 0.1455 - val_accuracy: 0.9375\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.1951 - accuracy: 0.9192 - val_loss: 0.1400 - val_accuracy: 0.9469\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.1822 - accuracy: 0.9238 - val_loss: 0.1450 - val_accuracy: 0.9531\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 2s 70ms/step - loss: 0.2409 - accuracy: 0.8958 - val_loss: 0.1880 - val_accuracy: 0.9169\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.1691 - accuracy: 0.9259 - val_loss: 0.1591 - val_accuracy: 0.9350\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.1417 - accuracy: 0.9459 - val_loss: 0.1559 - val_accuracy: 0.9375\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.1414 - accuracy: 0.9455 - val_loss: 0.0824 - val_accuracy: 0.9719\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.1536 - accuracy: 0.9343 - val_loss: 0.1633 - val_accuracy: 0.9344\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.1305 - accuracy: 0.9476 - val_loss: 0.0952 - val_accuracy: 0.9656\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.1529 - accuracy: 0.9410 - val_loss: 0.1258 - val_accuracy: 0.9513\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.1063 - accuracy: 0.9620 - val_loss: 0.3490 - val_accuracy: 0.8544\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.1665 - accuracy: 0.9291 - val_loss: 0.1175 - val_accuracy: 0.9469\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.1333 - accuracy: 0.9491 - val_loss: 0.0949 - val_accuracy: 0.9631\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.0965 - accuracy: 0.9615 - val_loss: 0.0575 - val_accuracy: 0.9781\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.1187 - accuracy: 0.9512 - val_loss: 0.2681 - val_accuracy: 0.8781\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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77zFNQ6bQMtfckyBwJiGQxOHiSJj8BNo0pNFoBi5b/qo2ycnXdBlqbfPxwb5SZvsMp2xVrjKxVOUqm3tsOiSP8zuMi3eovsIBgqC1zcfB0np2FtTy9/UnSI9z8K1LxuOTEiFgU3MOkwFsUbziWcS4NCvxzoi+/x5nshIsLQ3giPOHv/ZoGlJCIkk0cKSsgZkjgoS69hMtCDQazcCkrggOvQcL7oMIR5fh9UcrqW9u45gw6vZU5UL2ucpMlDxWHUufiizYzAsbTzC/YDkTgYrkOazbUcgHe8tYc7iCJqNlZFqsncc/N4MYu9oWx6XF8ElZJF9KGo0cczHrtni4YnoPG3YoOA2zkrtKCQKXIQh6cBab2cV3zYlnUkZc/+7fDVoQaDSagcn2fyin79w7gw4v360auhTJFHwiAotpEqrK9YeFDpuK2PsGj7+1mT9EfAxiBJf9RvULSIu1c93sTM4bk8zMEQlkJkS1V/0EmDUikQ/2lyIf+ITDVR7qP9nQpShcrzFNQO5qSBoVomlImaI+Py0WMuP7d/9u0IJAo9EMPNzV8OmfYfxlkJjTZdjj9fHh/jIunpjGxwfLqY/KIqEqV5lcGksh2YgSSp8GwCzbcRbZc8nLvo7/zZnEnJxEZmYlYAms+dOJmdkJvLq1gBMNgq0FqoTF3JH98A+Af8NvMkJIQxEEUaYWUd39nH6iBYFGoxl4rHtC1Q+66EdBh9fnVlLX5OHm+dmsP1pJeUQmCVVH/aGZpmlo2FQA7oxah8XTxKi5n+GeyaNDWsKsbGWL35Ffw7YTNSRHRzIy2dm/3xVoGgJlGrLHgy3y5Oc0hU8QhDVqSAhxmRDikBAiVwjxgyDjI4UQHwshdgshVgshutZh1Wg0Zw8Vh2DbP8DT1PO8oyuVDyAYtfnw6V9g5q0qozcIy/eUEGO3sXhcChnxUeRbhishUHlETTAFQWw61cSx2LNefR+5MOSfMi4tluhIKzvya9mWX8OckYkdTEd9ot00ZAgCd2W3oazt2OPAYgurRhA2QSCEsAJ/BJYBk4FbhBCTO017HHheSjkdeAT4ebjWo9FowkRbK6x9HJ46D/44H/77Ddj3ZvfzW13wz8/Bht8FH1/5qAr7DCwLHYBpFlo6KQ1HhJVhcXZy24ZBW7MKN4X2BLJKVyv7vNlY8ULa5J6jczphtQhmjEhg5cFy8qrczM3pp1kIAjZ1UxBU9WwWAhBCJaKdpRrBfCBXSnlMStkKvAJ0jgGbDKw0Pq8KMq7RaAY6eeth5U9UuYfLHgOrHcr3dT+/eCf42qCuax/eitytyN2vwjn3QnxwA8GGo1XUuj1cPk1FC6XHOdjXYmT6Hv5QlZyIUKWgD5TUc0CqnIDu8gd6YlZ2AkW1Srvpt6MYjE09KcA0VNVzxJBJsBpFp5BwCoJMoCDge6FxLJBdwHXG52uBWCFEF/EohPiyEGKrEGJrRUVFWBar0Wj6SKPRPeu6Z+Dc/1FN3MsPdj+/cLN6r/f34T1e6eI7r+1k+z8eoFZG8+Pay8ivcreP+3yST49V8Z3XdnLvC1uJj4rg/PGqvn96fBTbXcYm3ViqSk4Y7C+u54AvW33piyAYobSASJuFqZmnKHQzsN5QKKYhMIRHzcnn9ZEz7Sy+H/iDEOIOYC1QBHRpKSSlfBp4GmDu3LnydC5Qo9GchMZy9R6tNmbSJkHehu7nF6jOXjSo8M+fv3eAZ9YeI9Jm4SFHPsdiFvHPnXU8v2M1Y1KjqXF7qHa14vVJYu02rpudxR0LcnBEqEYy6XF2irwJSKcT4XH7/QMojWBPzCJYmAzjPtPrnzbTcBhPz4zHbrOeZHaIBGYXn6TgnP+cpJ77J/eTcAqCImBEwPcs41g7UspiDI1ACBEDXC+lrA3jmjQazammsQxsDrDHqu9pk2DPa9BcB45Oce9S+jWCxjIOFlfzlzXHuHrGcH60bCzxT1Yya9p01s65kGfXH+d4pYvZ2ZEkRUcyflgsn5mSTlRkxw05Pd4BCJrjRhFVtQ+SAjSCknpyhg+DC67s009LibFz2ZT0du3jlOBMhEoj1NXnObmPAJSP4CwNH90CjBNCjEIJgJuBWwMnCCFSgGoppQ94EHg2jOvRaDThwFUBMWnK/g3KKQvKPJR9Tse5tXlqfvo0KN3Dq6u24oy08sg1U0hoLVUJZPFZpMc7+OHl3dfoD2RYnMo6rnNmK0FgaATNHi9HK1x8ZkqQvgC94M9fnNOv87vgTAb3p6GVl2g/J0k5i6X0/51PIWHzEUgp24CvAx8AB4DXpJT7hBCPCCGuNqYtAQ4JIQ4Dw4BHw7UejUYTJhrLITqgLLPZZKV8f9e5plnIqB20e/9Bbpw7ggRnpN953I2TuDsy4pVjuCLSMEAYPoIjZY14fZLJYSrL0GecyWpTD6W8hElUEnhbVcRVGAirj0BKuRxY3unY/wV8fh14PZxr0Gg0YcZVAQnZ/u/xIyAyBsoPdJ1buBkiolUJiJU/ZRhV3LVolBprFwQjup7XAykxkVgEbI1dyrQFdkhU19tfUgcQtvo8fcaZrKKmqo/7v4dyDqjIIXuQPgf9RJeh1mg0/aOxrN1RXOf2cM+L2ymz5+Ap9WsELW1edhbU0pb/KWTOpsGhzDUXZXoZkWRk69YZQYbxnYMLe8ZmtZAaa2efJx0u/SlY1LZ2oKSB6Egr2Un9zAY+1ZibeuVh9R5K1FCYs4vPdNSQRqM5m/F51VNqjGrL+N/dxazYX8ZSWwoX1+/g56/torKxhc3Hq5EeN3vse1mZditr1lbyQ2njgnSP/1p1hWqTjIzu9TLS46Moq2/ucGx/cT0TM+J6rCd0RjBrB1UeUu+hmoYgbA5jrRFoNJq+465SDt4Y5SN4Z3cxo1OjWbL4fFJEHRt2H6Cwxs1N80bw10tsRAgv/ypN5x+b8qmzpZAqA5Kk6gp77R8wSY+zU1rnFwRSSg6U1A88/wAEaARHVLRVKIKvXSMITy6B1gg0Gk3fCcghKKtv5tPj1XzjonEMG+2DjbDhSxmI0ReoOetWAPDYN+/i/GMe4nZkQ32J/1q1BR2SwXpDepyDDbl+oVJY00RDSxuThw9EQWBs6lVHO0Zb9YTWCDQaTbhpaPZQUneSQnHBMLOKY9JYvqcEKeGqGRntIaSiIiDDuHALJI0mLiWDW+Zn40geAQ1GdrGUykfQV40gPoqGlrb2nr57igaooxj8GkGoOQTmOd87CnO/FJYlaUGg0Wj43r92c+sznyqb/57XYfUv1BPryXAZJV9ihvHO7hImpscyNi1W+QyiEv2RQz4fFGyGrHn+c2MzVJkJKVXyWWtjPwSBHYBSw0/w0YEy4hy2gWkasseqwnMQehE8i0XNtYbHiKMFgUYzxClvaObjAyVMrVmB76lz4Y27YPXP4fez4cXr4djq7k82TEMlbbFsy6vhyulG20ghlFZgCoJ1vwZXuWo0YxI3XFUMbarpcw6BiZlUVlrXTEublxX7y7h0SjqRtgG4xQnh1wRCcRSfBgbgX0mj0XRB9qHElrsatj8PL90M21/odtp/thfxmPVP/D7iD7T5BHzu7/Dt/bDkQZoLdtL2/LV4WroxG7nKwebgnYMNAFw5fbh/LHWiEgTHP4FVP4Npn4Mp1/rH44y59cV9ziEwMZPKSuuaWZ9bSUNzG5dP619GcVhpFwQhmobCjBYEGs1Ap+YEPDYSjq0J/Zzl34PHx8Hb98GRD2H9b4NOk1JSsvFVrrOu449tV7Ph0rfVZh2fCUt+wIrkW7Hho7Sym2gVI6v4nT0lTM2MIyclIAImbRK01MFrX1T9Aa78TUfHaKwhCBpKAnII+ho1ZGgE9c0s31NKrMPGorGnsD7QqcYUAKHkEJwGtCDQaAY6pXuUDf2974O37eTzG8th89MwYRl8eTW1F/xYNXQPYvPfdfgYX2v6E+UxE/lN2w0U1HSMxS92qY27rKqbaJXGclocKewqrOOqQG0A/DWHWt1KyzCL0pl01ggsER1LVfSCqEgrcQ4bhTVuPtxXyiWThw1Ms5CJGTmkTUMajSYkao2n5YqDsO25k88/vla9L/oODJ/FVz41NpsjH3adu/z7JOAi+sa/YLVFkl/t7jBc1Kjey6u7KQrsqqDWoko1LxzbaVNLnwbx2XDlE+1N5DsQmw4IvyCIz2zPCu4LGfFRLN9TSn1zG5dPzejzdU4LZjhoLzqmhRMtCDSagU5dIUQ4IWexsrWfLKno+BpV/jljBqV1zWysiSNXDqf1wHsdprXseZOZdR+xMu0OorNnkp3k7CAIXC1tlDWpLaKypnvTUI1QgiArMarjmCMOvr0HZn0h+LnWCFWaosEUBH3zD5gMi3dQ1+Qh1m5j8fiBscF2i/YRaDSaXlGXrzbJy36uhMCaX/U8/9gaJTQsVrbnqw18lXcm1vwN0GI84ns9eN77Xw74skle9gCAIQj8TuHjlS6aiASgurau6318XnBXUuaLI8ZuIz4qove/LW64SirrR1axSXqcCiFdOnnYqWsiEy501JBGo+kV5iaZPg1m3wab/9J9jH/NCVXzf9T5AGzLqyHSZuFg7HlYpac9FNS382Vi3IU877ydOaOUXX5EkpOCajfSiFA6UeWiSarNta4+iCAwyksUemLJTIhC9KVOftxwqM1XWkF/BYERObRs6gCOFjIZcyFMuhoSc870SgAtCDSagU9dISQYZpOF31QljA0/gMfr6zjX9A+MUmUdtufXMCMrnrFzL6FBRtG4bzl4PTR9/Bi7fKOZd8lN7Rt4dpKTxpY2atyqENzxCr9G0NjQ0HVdRg7BiebormahUInNUFU4jYY0/eGC8aksnZR2aruJhYu0SXDTC2CLPNMrAbQg0GgGNp4mlb1rbpLmu6uCT45UMPPHH/L6tkL//GNrVFZv6gSaPV72FtUxOzuRK2eN5BPfNDi8grYdLxPtLuSN2C9wzSz/5muWazb9BMerXMTEqMzcpqZGWts6CR2XEgSHXVFk9lUQxA0HZMff1kfmjEzkr7fPa+9lrAkdLQg0moFMndHm23Sk2uzgiKekKJ+7/7EVV6uXf36ap8akVBrBqPNBCPYV1+HxSmaPTGREkpNjCQuIaS3H+/4P2eUbzZIrP481oERzdnInQVDpIjVR9RyOoqVrLSJDI8hviem7RhAXEHIan939PE1Y0YJAoxnItCda+SNqmiKT2XXoMCOTndyzeBQ78mvJr3Kr8FJXebtZaFuechTPzk4EIHWWauBub2vgvaTbuXDisA63MjfzAkMQnKh0MSxFhTk6aKWwJrggqJDxZCb0sflLbECYZy8b0mhOHVoQaDQDkCNlDVz31HpqS46pA4bZpKSuib11dobbGvjn3edy+4IcQDWEac88NhzF2/NqyU5ykhqrHL4XzZvOVt94dvrGcMlnb+vi3HVG2kiJsZNf5abW3UqN20OmIQiiaGkXEO24yvFa7TQS1Q+NwNj8o5L61JBGc2rQgkCjOZOU7Ib/fkuFYgbw+5W5bM+vZe/+fSAs7SaUf27Kp9wXx6TYZlJj7WQlOpk7MpG3dhYps1BiDiSORErJtvwa5oxMbL9maqydf096gn9N/gNzcpKCLic7KYqCGjfHK1WT9Kw0Nc8pgmkEFbgjkgDRDx+BoRH00z+g6R9aEGg0Z5L1v1XZwmYjc6Cwxs27e0qItFqoKs7FF5MO1gha2ry8vDkfR2IGEU3+JizXzBzO4bIGvMc/adcGCmuaqGhoYXaAIAD42S2LefTmBd0ux0wqMwXBqLQ4sNpJtvsorOmkETSWUW9NxBFhITm6j9Ev9liIjO13Mpmmf2hBoNGcKVpdcMjI9g1o4PLsuhMI4Fefm06qt4Iqm4rzX76nhCpXK2NyRqlibh5VF+jyaRmkWhqxttZD2hSA9kSy2dkJvVpSdpKT4tomjpQ3YhFGJFFEFCkOb1eNwFVBpUzoew6ByYL7YOYtfT9f02+0INBowoXXo+oEtbqDjx9+HzzGmNHIvM7t4ZUt+Vw1YzhXzxhOTkQNexvjkFLyjw15jE6NJjs7R51jNIVJjrGzLFuZlqRhQtqWV0N0pJUJwzoVejsJI5Kc+CSsz60kK9GpCrdFOEmKCCIIGssp8caSldhHR7HJkgdg0lX9u4amX+Wqcd0AACAASURBVOiexRpNuPjvt2Dni+pzRDSMXgI3Pu/vMrX33xCTrkozVyhB8NLmfNytXu5ZPBohJcOo5K2muRxee4ydBbU8fNVkLDGG8HCVtyeaLcv2Qin884CX8sJDrNhfxszsBGzW3j3rmbkEe4rqWDzOSMyKiCLe2kZZg2r6YrdZ28tLFMiYvvsHNAMGLQg0mnCRtw4y58LEK1RJiJ0vwtZn4Zwvq7LSR1aoHrQVB6HiEC1tXp5bf5zF41JU0/WGUqw+DzURw3jm/YNER1q5fk4WVBr+gcaK9lvNTlDC4cktbqpELikxdq6b1XsH7AhDEEgJo83eAhFOYvEgJRTXNjMqJbq9vESBpx85BJoBgxYEGk04cFeruj9z7oRF31I7a30hrPypavyS+xF4W2DqdbD3Ddj+AqsOlFLe0MIvb5iurmGUnx4/fhJyF1w3O4tYh1GxE9ozewHsrhKkJYJ3H7yWpBhHrzUBk2FxDiKtFlq9PnKMBDMiooj2tgLKkT0qJToghyCBOQlaEJztaB+BRhMOSnaq9+Ez1bsQsOxXyifw0cNq84/PVs3cUyeAx0V5ocoZOHe0UZnSSCa7cP5slkxI5Z7Fo9XxGKN5S6NfEFBfjIjLIC3e2WchAGC1iPYn/PZuYxFRRNEC4PcTGEKoUsb330egOeNojUCjCQfFO9R7xoz2Qy2JYygYewdjdz6DFwuWBV9X0TYpEwDwlh8kJSbHXyvH6OObnDmGv98Z7792RJQKuXT5TUPUF0HcqYnFH5Hk5Fili9EpMcb9nEQ21WC1CH8IqVt1LKsmVpuGBgFaI9BowkHxDtWnN0rF8a88WMa5P/uYq3efR4lMwoqP2jFXq7mpEwGIrDlMZuDTdV0B2ONUk5nOxKR21AjMDl+ngFEp0dhtFoYnqD7ARDgQniYy4h1+jaBFVSNttUSTGmM/JffVnDm0INBoQqW+RIWEhkLxThg+q/3r02uP4Yy08acvnU/xkt/wXNtnOG4bowajk8GZTHzj8Y5P1z117YoZ5tcIfD7VAD5uePC5veSrF47hxbvP8ZuYIpzgaWJEotMvCFpVg5vY+AQsln7kEGgGBFoQaDShICX8eSG8cZf63BONFepp3hAEzR4v2/NrWTY1nQvGpxI/ZSk/bru9QzcwmTKBDE9eJ0FQ0H3phegAjcBdCd7WU2YaSot1MC+wBEVEFHjcZCVG+U1DRqez5MTgpSo0ZxdaEGg0odBcp0Im978F+/7T89x2R7ESBDvya2lt87U7gbMSoxAC8qr8iWbNCWMZQxFZ8Q7/dWp7EAQxaf6oIcOXELbqnRFR0NZMVqKTsvoWXC1t0NqIGwfDE3WhuMFAWAWBEOIyIcQhIUSuEOIHQcazhRCrhBA7hBC7hRCXh3M9Gk2faShV71Y7LL+/Qwx/F4p3AALSVRjoxmNVWATMH22UdI6wkh7nIK/a1X5KRdQoEoSL0VHmE3cDNNf6O5N1JjpN9S/2eqC+WB07RaahLkQ4weNmfk4iQsCdz22hqbGGRunQjuJBQtgEgRDCCvwRWAZMBm4RQkzuNO1HwGtSylnAzcBT4VqPRtMvGg1B8JlH1Sa9/P7u5xbvgJRx4FDdvTYdq2JqZjxxDn9z9+wkp+ohYFBoVRv+SGn0H2h/yu/OR2DmElSoiCE4ZaahLkSozf68kdH87uZZ7CioYf2+PBpkPzqTaQYU4dQI5gO5UspjUspW4BXgmk5zJBBnfI4HisO4Ho2m7zSUqffRF8KSH8D+N2H/28HnFu/o4B/YmV/rzw0wMKt8mhyRyqyT1mJ0G8vboN4TuunaFR2QS1BXCNZIiE7p/e8KhQgjksnTxFUzhvO32+cR4XXhwqFzCAYJ4RQEmUBBwPdC41ggDwNfEEIUAsuB+4JdSAjxZSHEViHE1oqKHlRyjSZcNJSo99hhsOCbqrPWgSCCoL5EzTUEwba8Glq9Ps7rJAhGJjspb2ihqVUVizvkiqGRKCKrj6iM5BUPQfYCyJwTfD1mUpmpEcQNV0lr4cDQCMwCeeePT2VOegRRMfFMzYzr4UTN2cKZdhbfAvxdSpkFXA68IITosiYp5dNSyrlSyrmpqamnfZGaQU5TLbx8C1Qc7n5OYxlExqj6+Vab6qzlquw6r5OjeNOxKqwWwdycjn0BspOVk9XUCgprmymyZUP5Afj3vWpTv+4vYOmmEbtZZqKxXPkIwmUWgg4agUkMTYzLysAZqXNSBwPhFARFQKCBM8s4FshdwGsAUsqNgAMIk36r0XTD8bVwaDl8/OPu5zSUqth9gzpLPK0NXbXTT9aswIeF1hTVF2DjUeUfiA3wDwCMNIq75VUph3FRjZvqqBxVqK5gE1zx6+7NQhCgEZSrBvfh7PfbSSMAoLUB7DHhu6fmtBJOQbAFGCeEGCWEiEQ5gzvr0vnAxQBCiEkoQaBtP5rTS/F29X7wHSjdE3xOQynEpgPQ5vWxssBLQ1VJx8vUNtFauIMjvuHc/1YujS1t7Cqs7WIWAn+55/xqN1JKCmuacMePVYNTr4dpn+t5zZHRqrR1Qyk0FIcvYggCBEFAP4KWRqUdaQYFYRMEUso24OvAB8ABVHTQPiHEI0III7ee7wL3CCF2AS8Dd0h5smwdjeYUU7QNksepcg5rHgs+p9EvCPYU1VHaFkuMt45tJ/wtI1/clEcGVZCYw9u7irnj2c14vJLzxnQVBAnOCGIdNvKr3VQ2ttLS5qN+5KUw/SalDYRi749JhbJ94GvzN4EPB+2moUCNoFGZyjSDgrAa+KSUy1FO4MBj/xfweT+wMJxr0Gh6xOeDoh0w4yZwJitBULoH0qd1nNdQpprIABuOVlEj47CLNv6xei9z7riAZo/qJ3xvRANxY8bw5QmjeXrtMWwWwdxOfYMBhBCMTHaSV+Vuz9aNy5oEk54Ofe3RaVCyS30OZ/N3m5HkZmoEXg+0NWuNYBARkkYghPi3EOKKYI5cjeaspuqIsncPnw3nfiW4VtDSAB6XihgCNhytxB6vbPS7D+WSW97I27uKqXO3EOerRcSk8eCyidyzeBS3npNNtD3489bIpGjyq90U1aoNttehmDFp0FKvPofVNNTJWWwUnNMaweAh1I39KeBW4IgQ4hdCiAlhXJNG03tcVe2ln/OqXLy2teAkJxgUGf6BzDmqUui5X4ED/4XSvf45ZlZxbAbNHi9bT9SQOVzFQQyzNvL02qP8Y8MJ5qZKhPRBdBpCCP73isk8cs3Ubm89IslJYY27vdREr5OzogMi6MIaNdTJWWwUnNPO4sFDSIJASvmRlPLzwGzgBPCREGKDEOJOIUREz2drNKeBj38ML1xHQ7OHO57bwvdf361q4pyMom2qtn/KOPV9/pfVe+4K/xxTEMQMY3t+DS1tPsaOGgnAVeMi+de2QvYV13PHdGPDNCN6TsLIZCcer2TLiWoSnBHEdKM5dIt5H5sDnGEs/tauETSr9xZTEGjT0GAhZFOPECIZuAO4G9gBPIkSDCt6OE2jOT2cWIdsquEHr+/keKUKyaxsbDn5eUXbVBcxM14/OkUlixnN5AGVQwAQm87GoyovYPJY1S3sslE2BBDnsHGRGe0ZqiAwIoc2H6/uW80eUyMIZzIZdK8RRGpBMFgI1UfwH+ATwAlcJaW8Wkr5qpTyPkDrh5ozS0MZVB9FIFm5N5/F41QqSkXDSQRBW4tyDGfO7ng8daJK7Gq/vplVnM763EqmZcYTk6gcxymige9eOoEfXTEZR7Pq2hWYb9AT2UZPYHerl8y+9P01BU44I4agq7PY9BFo09CgIVSN4HdSyslSyp9LKTsET0sp54ZhXRpN6ORvaP+4bHwsD1ymOn6dVCMo2ws+T5cyDq74cfgqDlFc46LW3apMQ7YoGnGyq7COhWOTIdKp4vjdVXztwrHcOG+EX3OIDi37PSM+igirepLvU80es95QOCOGACwWsEX5NQLtLB50hCoIJgshEswvQohEIcRXw7QmjaZXtB33C4IfX5ZDWqxqnXhSjSDQUWxQUO3mZ1t8WNqauPGXrzHzkRVs3XcQX8wwNp+oxuuTLBhjJL9HJ3fsG+wqV0/PIdrOVaN4JQD6ZBpq1wjCGDFkEhHl1wi0s3jQEaoguEdKWWt+kVLWAPeEZ0kaTe+o3L8ar1RP1rGWFpKiIxECKhpbez6xaJsy4wSYVv67u5gDXvWE/bMFNu5eNApPbTEHGqN5c0cxkTYLc8y8AGdKx3pDjeVqc+6Fvd7MMO6TRhCXCamTYOSC3p/bW4x2lUCAs1gXnBsshCoIrEL4/3UbvQYiw7MkjaZ7Wtt87RU7Afbk5pHmOkJBtBGm2erCZrWQ5Iw8uWmoaJvKHwjYuN/ZVUJUpmqbcX5iFT+6cjLT45so8MTy9q5i5mQn4ogIcCy7OwmC6NAcxSamIOiTjyDCAV/bBGOX9v7cXt8rwDTUqk1Dg41QBcH7wKtCiIuFEBejykG8H75laTTB+c5rO5nz0xX8vzf3crC0ntf+8wYWIUmfa1QtMcwWKTH2nk1DzXVQeaSDWehoRSP7S+q5aOZ4I3LoIADRrVWcM30yM0ckcOO8AHu8M0XlL5i4KkJ2FJtMzYzDGWltdxwPWAJNQy2Nqv+BTT8LDhZCDVx+ALgX+IrxfQXw17CsSKMB+O83IWUCnOd3RbW2+Vh5sJzkmEhe3VLAC5vy+L5tB74IG47xF8PaR6FVhY6mxtp71ghKdgESMme1H3pnVwlCwBXTMuCYETnU6oKWehKHZfPm9Z2qoUQnK41ASqVVNJZBVu9iJ26YM4Klk4b1PofgdGO0qwSUs1hrA4OKkP71SSl9wJ+Ml0YTfva/rZKkAgTBzoJa3K1enrh8MnNzEnn503yu3ZWHJW6WvzuXIQhSYiI5kecKdmWFWaMnY2b7oXd2FzNvZBLp8Q4VQrr9HwHJZOldrxGdqmrutLrUE7O7qtemIatFkBxj79U5Z4QIB7QG5BFoR/GgItQ8gnFCiNeFEPuFEMfMV7gXpxmieD3QVA1VuVDrLxWxLrcSi4DzRieTEmPnvvOzyGjcD9nn+Z9QO2kE3RazLd0DscPbBcih0gaOlDdy5YwMNZ42UT0BF2xW32ODmHychvBxVSinsfSFnEx21tHZWawdxYOKUH0Ez6G0gTbgQuB54MVwLUozxAmMxDm+pv3j+txKpmUlEO80qpoUbVN5ACMXqvr80MFH0Ozx4QpwLHegZDdkTG//+s7uYiwClk01BEHqJPV+bLV6j83oeg1TC3FXqdBRGMSCoJOzWJuGBhWhCoIoKeXHgJBS5kkpHwauCN+yNEMCKeGtr8OJ9R2Pm4lZ0L4RNzR72FlQy6KxAbX98zYAArLPAZsdhLU9tDElpodcAk8TVB6G9OnGMiTv7C7h3NHJpBo5CKRO6HD/oE7gdo2gUkUMdTdvMBARpcxgoHwE2jQ0qAhVELQYJaiPCCG+LoS4Fl1aQtNf6gphxwtw8N2Ox80krcQctRFLyafHVDLXorFG1m5LI2x9DkbMV1VDhVBPqQGmIegmu7hsP0hve8+BY5Uujle6uHxawFN/VILSAhpLwWpX9+hMtCGU3AGCIMSs4rOODs5i3ZRmsBGqIPgmqs7QN4A5wBeA28O1KM0QoWyfem8o7njc3FSnfU4JhfL9rMutxBFhYfZII8F97S/VeZf+1H9eZHQH0xBAZTCNoHS3ejdMQ+X1as7olOiO81JVqQpihwVPEgvUCIaEaSggs1hrBIOKkwoCI3nsJillo5SyUEp5p5TyeinlptOwPs1gpszoD2xG5piYm+q0G9X70VWsy61k/qhk7DYrVByGjX+EmV9QGoFJZLQ/aihWxbhXNrhVUbpASneDPR4SVCnpuiYPAHFRnSqqm4IgWMSQeT+bw68RRDgH75NyhFOZhnw+7SwehJxUEEgpvcCi07AWzWDC54N9/wFvDz0BTI2gvrNGUKE2ntTxkDyOlsMryS1vVP4BKeG976lNeOnDHc8LEATJ0XYsApKOvwu/nQrVx/3zSnYrs5DxlF/XpEpRJDg7CYK0AI0gGEIoU5DpI4hODW856DNJeylql+5XPAgJ1TS0QwjxthDii0KI68xXWFemObs5vhr+dYcSBt1hdgFrKFUbvEljmd/EMnoJ1oINRNDGwrEpsPcN5Te46P+p5u2BBPgIrBZBUnQk1oYC8LbCrpfVHJ9XCaCAiCFTI4jvTiMIFjFk4kz2m4YGq6MY/M1pXJWA1KahQUaogsABVAEXAVcZryvDtSjNIMBoG8nRlbS2+XhqdS6HShv8461uqD6qnLDeFmiq8Y+5Amr2jLkQm7eJS5yHmbz31/Dve1QS2Nwvdb1ngI8AlJ9ANtWpL7teVlpKVS60NbVHDIESBFaL6JrdmzoRLBGQkE23mPWGzIJzgxVTIzAd+VojGFSEmll8Z7gXohlklBgO2aMreWdXEb98/xC/XXGEby4dx73nj8ZWcUAlYI25SD3lN5T42y02VkDyGAB82QuRWHhSPobY4IHZtysHsdlRLJAA0xCoyCFLhdHcvTYf8tb7/REBGkGt20N8VASis1knKgHuWdm+lqA4U5TPwuOGEef06k90VmEKAtORr9tUDipCEgRCiOeALimaUsogj2UaDaqEg9UOjaV8vHYNo1OzmJQex68+OMQH+0p5KHMbcwA5dili7xvknchlS2E8541JJtNVDtnnAnCgVlDlncLc6AoibngKxl7c/T07CYKUGDu2onrV2L25Dna+pEI+rXZIGd8+r67J09UsZBIgMIISnaI0mLaWIWIa0oJgMBKqaegd4F3j9TEQBzT2eIZmSCKlVJtuzXGYeSsA6ZUbuXNBDn/8/Gz+eOtsSuqa2b1tPY3SwWf/6wPgD2+v4/5/7eKnb+0Cd3X7prrmcAV3e+6n8X+29SwEoIOPAJRGENnWgIxNh6nXwv63IH8TpE0Cq3/jr2vydI0YChVnspFoJbv6LAYTZrvKRm0aGoyEahp6I/C7EOJlYF1YVqQ5a9mWV8O9L2zlrxe0MBNg4hWU7fmIC+VeZs1W5ZuvmJ7BsqnpND/zK1xNE5iQPgEOwpemO/CJLD7dtRci/JvqmkMVjM1IJi0hhI3H9BEY1UBTYiKJxYU3MgXbzM/D9uehcAvMvq3DaXVNHhKdfSypbJaZgCGmEWhBMJgIVSPozDhgEHvGNEFprlNF2Lb9Q22qnQq6vbalgMrGVj746EMAKmIm8H7TZM6xHCDa6q/5YxHgrDlA6pjZ/PLmeRCVxKToRr543kjifUYjvOg0Gpo9bMur4YIJIT5pR0arjOE2lSCWGmsnFjfN1hhlv08arealdzT39GgaOhmBmcS9rDx6VtHZRxCpTUODiVB9BA109BGUonoUaIYKW/4G736n47H0aTBc1fP3eH18sL+URWNTmFp0gkoS+d2n9RT7pnG7/ECZZEZfoM6rL1JCJd3oKhY3HBpKmZEVz+S4ZmgBYtLYcLSKNp/kgvEhCgLTbt3aCBEOUmLsxAkXbksMMUIoU9XKnwYVBF1yCELFGagRDGLTULtGYJiGtEYwqAhJI5BSxkop4wJe4zubizSDGE8TrP45ZM2HW15h67L/4hEReLb5C9BuPFpFrdvDbeeN5KKEUvZ6c3hhUx72MeeDxQZHV/qvZ+YPDDMEQWw61BcjhOCiEepQlUhgzeEKYuw2ZmcHqfMTjM4VSKMjicNNPcbxc74CV/0Osua1n+LzyX5qBAFF8IaSRqCdxYOKUPsRXCuEiA/4niCE+Gz4lqUZUOx8ST0JLn2IplGXct9KD++3zcG761/tZph3d5cQY7dx/qgYompzyZx0LjF2G7ctmarMMsdW+a9XZgiCNNUbmNiM9rDOeSkquevdo17WHKpgwZhkIm0hWjDbBYFReC5KYhdt1EljE7PHwJzbweK/XkNLG1IGSSYLFVMjiIge3E/JgXkEwuLXEDSDglB9BA9JKevML1LKWuCh8CxJM6DweWHD71Vv35EL+cvao5TUNbMu+jM42upo3r+83Sy0dFIajupDIL2Mm7GAHf93CeeOToYxF6pwUrPPQNleVefHYdSric1QTkhvGynU04ydP20spai2KXT/AHQRBIkWVSSt2tt9Y/j67rKKQ8Ueq/r3DuZkMvBv/C31KmJosJbSGKKEKgiCzRvgTVY1p4QD/1WhoAu/SXFdM39ec5Qrpmdw8823USoTKVnzbLtZ6PJpGVBqtoCcQYTV+Gczdql6f+5y2PyMEgqmWQiUaUj6lDBwldPqSKGkTtW+P39cbwSB2aVMmYasrSqZrMLj6PaUWnc/BYEQSisY7ILAZgeMzV+Hjg46QhUEW4UQTwghxhivJ4Bt4VyYZgAgJax/UkXbTLySX7x3ECnhwWUTmZWTwvb4SxhRtY73Nu1SZqHxqWqTdyR0LMswfBZc/zdlXlh+P1Qf8zuKQTmLQWUXN5bjSFDVPsekRjMiqRcmiE4aAc1KiS1r7V4QdFtnqDekTYJhU/p+/tmAEH6tYDCbwIYooQqC+4BW4FXgFaAZ+NrJThJCXCaEOCSEyBVC/CDI+G+EEDuN12EhRG1vFq8JMyc+geLtsOA+tubX8fauYu49fzRZiWpDGHPJPdjw4Tz0by6elIYjwtqlsmc7026AL6+Guz+GBfe1J5sBSiMAqC8BVwWRCRncsSCHuxaN7t16uxEExS3dN4c3BUFCX/MIAG59DS5/vO/nny2YfgLtKB50hJpQ5gK6bOQ9YfQx+CNwCVAIbBFCvC2l3B9w3W8HzL8PmNWbe2jChJTKQfz+g6oW/4xb+NWzO0mLtfM/S/x1dyZMm8+xdybyOd9aCid+RzWdL9sH8+8Jfl0hIGuuegUSG6gRlMGIc3j4qj48YXcyDZmCoNDd/SZfa5Sg7pdGYB0iVlJTI9CmoUFHqFFDK4QQCQHfE4UQH5zktPlArpTymJSyFaVJXNPD/FuAl0NZjyaMNJTBSzfBW1+FYZPhzuVszHfz6fFqvrpkDM7Ijpte3MJ7mGgp4OK35sFfzleVRDNm9O6e0Smq33BdgVFeoo/29i4agVIwT7htqvRFEE6JaWiooDWCQUuojzIpRqQQAFLKGiHEyf5vzQQKAr4XAkHLMwohRgKjgJXdjH8Z+DJAdnYPJYE1/ef9B+D4GrjsFzD/XrBY+O3rG0mLtXPz/K5/+5TFd8GICYjja+HEOojLhJELe3dPi1WZh0r3ArLvfX9tUYDoYhqqbnNQ6/aQGN1VM6hr8hBps+CI6GuS/RAiwvC1aI1g0BGqIPAJIbKllPkAQogcglQj7Qc3A68b3dC6IKV8GngaYO7cuafyvprOFO+ECcvg3K8AKlHs0+PVPHTVZOUD6IwQMGqxevWH2HTlaIa+1+yxWDpWIG2uw2e100IkHx8s54Y5WV1OqeuuBLWmK9pZPGgJ9THof4F1QogXhBAvAmuAB09yThEwIuB7lnEsGDejzUJnnlY31JyA1Enth578+DBpsXZuCaINnFJiM1SDF+hfKGZgc5rmOkRUAjnJTv61tSDo9H5lFQ81tGlo0BJqiYn3gbnAIdSG/V2g6SSnbQHGCSFGCSEiUZv9250nCSEmAonAxl6sWxMOKg8BUoVDorSBTceq+cqSMcG1gVNJYDvIvpqGQAmClgBB4IjnhjlZfHq8mvwqd5fpdU0eErQgCA3tLB60hOosvhvVh+C7wP3AC8DDPZ0jpWwDvg58ABwAXpNS7hNCPCKEuDpg6s3AK7I7b57m9FF+QL2nTaa1zcdP3tnPsLjToA2AP4QUToFGYJiGmmrBEc91s7MQAt7YXthlutmdTBMCWiMYtIRqGvomMA/Ik1JeiArzPGnMv5RyuVGgboyU8lHj2P9JKd8OmPOwlLJXoamaIHiaupSF7jXl+1X3rqRR/HFVLvtL6vnJNVPDrw2AP6nMFtW/J87ImI7ho454hidEsWhsCq9vK8Tn6/g3qmvyEN/XyqNDDVMQaI1g0BGqIGiWUjYDCCHsUsqDwITwLUvTK8r2wWOj4LllcGx1twKhoqEFd2tb99cpPwCp49lb0sgfV+Vy3axMLp2S3v38U4mpEcSk9a+OTWCXMkMQANwwJ4ui2iY2Ha/qML1e+whCRzuLBy2hCoJCI4/gTWCFEOItIC98y9KEjJTw7v2qFkxNHjx/jarpU5nbYVpRbRMX/3o1l/5mLXsK6zqMldQ1sbeoDl/5Abwpk/jua7tIjonkob4kdfUVM6msvzV7OkUNmYLgM1PSibXbeH2b3zzU5vXR0NKmBUGoaNPQoCXUzOJrjY8PCyFWAfHA+2FblSZ09vwL8jfAVU/C9Jthxwuw6lF45Ra4ZxXYY/D5JPe/tovLfGupbsvi+j+18PDVU5gyPI5nPjnGe3tLcfpc7HEU8du9ERxqbuC5O+adXpOJqRH0t6a/qRGYvZMNQeCIsHLljOG8uaOIR65pI8Zuo75ZaUfaWRwi7c5iLQgGG73OjZdSrgnHQjR9oLkePvyRKhE96zYVRz//HkgZB89/Ft79Llz7Z55df5z5eU/z7Yg3kD47T2Y8yA//o5rGx9pt3L1oFBc4j8FqiBkxnQfGTOTCiae5mqYjXm3isf00RZnho54m8HnaBQHAsqnpvLw5n10FtSwcm+LPKtY+gtCICOjroBlUDJEiKYOU1b9QHaNueaVDsxVGL4ELHoA1v6AkaR7ujzcqITD9RkT1cb5Z9AjnzH2IgxnXcMOcLGIdEbB1EwB3X395x8qhpwsh4OaX/H2F+4ppGjKyigMFweThqv/BgZJ6Fo5NodZ9CuoMDSVMJ3HA31QzONCC4Gylrgg+/bPquJU5u+v4Bd/Hl7eetNXf4xtWH81Tbsbx2aegrRnx6hc5b+9DnJcTB44vqfkVB9X/6PEjul7rdGH2NO4PkdFKEzB76wZsWikxdlJi7BwsbQAC6wz1o/LoUGLKtco/YEZ4aQYNusDK2UrVEZBemHpD8HGLlf/kiD3zhgAAGYdJREFUPEyRL5n8nM/huP4pVdMnMlppEKOXwEcPK/MSqNDR1Ilnf+cp86m1vli9d3p6nZQRy4ES9Zt1wbleEpWgyolrBh1aEJytuHoux1DX5OHRT2r4fubzjLj9GSUETGyRcPFDynyy9W/qWPmB9ozisxqzAmm9Uc3EkdBheFJGHEfKGmnz+rQg0GgMtCA4WzEFgdE8fePRKuqMtosAf1h5hBp3K//vqinBC6plzoYxF8HGP0JtgTKlmM3kz2baBYGpEXQWBLG0en0cq3S1/720INAMdbQgOFtxVYCwQFQiG3IrueWZTVz8xBre3V3C8UoXf99wghvnjGDK8B4ce4u/q67zwQ/V90GhEfRsGpqY7ncY1zV5cEZaibTp/w00QxvtLD5bcVUobcBi4anVR0mJsZMeb+drL20nwRlBpNXCdz8zvudrjFwII86FA0bFj0EhCEyNwEgcc8R1GB6TGkOEVXCgpEFXHtVoDPSj0FlEfpWbG/60gUOlDeCuguhUdhfWsi63knsWj+LNry7kR1dMorXNx3cunUBabPdN2wHlGD7/fvU5KrHvfQAGEoGmIVuUyrgOHLZZGJMaw8HSemq1INBoAK0RnFX8Z0cRW/Nq+MqL21gRX441OpmnVh0lzmHj1nOysVkt3L14NHcuHIXVEmL0z9ilMHw2OJPO/ogh6Gga6ibefXJGHOuPVjIyOVoLAo0GrREMfJrrYPvzICUfHShjeLyDvGo3VeVFNFgT+WB/Kbedl6OSwgxCFgKgNv/b34bP/f3Ur/1MYGoEHne3gmBiRixl9S3kVbm0INBo0BrBwOf9H8LOF6mIncSeojoeuGwiQoBjZTXvH2/DbrNw58Kc/t1jMBURMwUBdCsIJmUov0FZfQsJuryERqMFwYCmYDPsfBGAvQf2AylcMjmN0QkRWFY1caI5mpvPySY5xt7zdYYSIQgCM3IIdOioRgPaNDRw8XlV0TgjDr7gxBFykp2MSY3B0qRq6s+cMIb7Lhp7Jlc58LBY/VUyuxEEqbF2UmJUWYkEpy4vodFoQTBQ2foslO6GK36NtNhwV+SzdNIwlRxm1NG5ZN5UrQ0Ew9QKeiiOZpqH4rRGoNFoQTAgcVXCyp/AqPNh6vU0OdJIpYqlk4f5x6F/Td4HMyEIgonpyi+iTUMajfYRDEzWPKZKKV/+OAhBqUwm21rDrJGJatxtCoKUM7fGgUwI5ZJNjUA3pdFotEYw8KjNh63PwawvQOoE2rw+jjTFkhNRi81q/OcySyxrQRCcEDSCCyekcd2sTGZmJ3Q7R6MZKmiN4AzT5vWxs6CW1YcqOFBSz+0Vv+JcH3yz4GLK/7QBd6uXz7YlstSyTbVfNH0E1kiwx538BkOREARBYnQkT9w08zQtSKMZ2GhBcAZZ9dbfqdvxJo8030StiOOilDoWulbwnvNqqqypOKyCaLuNtMjRWMtawF0N0cnKRxCdOjgygcNBCIJAo9H40YLgDPH6pkMs3v4Qw0Qtl/3/9u49ruvybOD45xIQROQgeARNMlM8o2Yuy5lby5p52Gy2fFr6rGw+Nqu1PQ9ttWWvtvUs18E9HWbL1iorsyzXdDYdYSdN8ICcwkOaoAiSIpYoyPX88f1CgGACvx8//f2u9+vFC75Hrtsbufje9/297+hsqqY9S0TmcsjvwKT/WsikiDodwTlFsAxnjv2aRBAe67PYz3m1fQTW7GPM2bA+Ah/4R+YBdrz9KN3kCFUTHyYsPIKIV6ZC9hswZi5ENBgNFBnvfK5ZbOWLEhsxdCb2RGBMs1giaGOpecXc++oH3B7yNqcunEDwmDkwJ81ZDza6N1z209MvqlkjtjYRHLJEcCaWCIxpFmsaakPLMwpIeT2T+6PW0el4OXz7186BsEiY/uxXncENRXSFdsFfLbbyRYmNGDqTjl2dKagtERhzViwRtAFV5f/+vZM//iufqxNDmFm6EpKug57J9U9sqvO3XRB06gFlhc77BVXHLRGcyaj/hH5XOWszG2O+ljUNtYE/rPmEP/4rn2nJ8TzZZz1y8gu48lfNu0lkvNM0VPsOgTUNNal9OHTp7+sojDlvWCLwsu0FZTydtosfjErgkas7E/TxYhh2Q/OXhYzs6TQN2fQSxhgPs0TgRaeqlV+9uZ24iFDunTQQSf29c6C5TwPgJoJCe6vYGONxlgi8aOnHn5FZUMa9300i8kgebHsZLr0Nons1/2ZRCVBVAYfyne1wSwTGGM/waiIQkYki8omI7BSRlCbO+YGI5IhItogs9WY8bamk/AR/+GceYy+KZfKwnrD2fmcUyxU/a9kNa4aQHtjmfLYnAmOMh3ht1JCIBAFPAFcBBcAmEVmpqjl1zukH3AOMVdXDItLVW/G0tYVrPuFEZTUPTBmMfJoGO9fCdx6EDjEtu2HNS2UHtkFIx/orcRljTCt484lgNLBTVXer6kngFWBKg3NuBZ5Q1cMAqlrsxXjajLoLzU8a2oO+XSJg/UKITIBLbm35TWsSQelOZ5oJY4zxEG8mgnhgX53tAndfXRcDF4vIByKyQUQmejGeNlN0tILSL04yrFc0lB+EPe9D8kwICWv5TWteKgMbMWSM8ShfdxYHA/2A8cAPgWdE5LSZwkRkjoiki0h6SUlJG4fYfFmFRwEYHB8JuSsBhYFTW3fTmpfKwBKBMcajvJkICoG6w2MS3H11FQArVbVSVT8F8nESQz2qulhVR6nqqC5dzv1fgtsLy2gnMLBHFOS8BXEXN/+9gcbUdBhbR7ExxoO8mQg2Af1EJFFE2gM3ACsbnPMmztMAIhKH01S024sxtYmswjIu6hpBh5OlsPcD52nAE2sH1CQCGzpqjPEgryUCVa0CbgfWALnAMlXNFpEHRGSye9oaoFREcoBU4BeqWuqtmNpKVmEZg3tGOc1CWg2DWtksVKOmw9iahowxHuTVSedUdRWwqsG+X9f5WoGfuR9+ofhoBcXlJxgcHwXZb0JsP+g60DM3t0RgjPECX3cW+52s/WUADO980mkWGjTNc0tKWh+BMcYLLBG00tqcg+w59EXt9vaCo4jAwLI0zzYLAVz4TRjxI+g12nP3NMYEPEsErXDky5Pc9mIGd7+2DaeVC7IKDjMragthGxZ5tlkInLeSJ/8JQjt57p7GmIBnC9O0wtrcYk5VKxl7D/PRrlIuk+2k7P0pfXUfRPSHSY96rlnIGGO8xBJBK6zJLqJ7ZBiK8uS6bMaU3kS7U+GsG/J7vvX925yXwIwx5hxnTUMt9OXJKtbnl3D1oG7cNq4voXvTaHeynPurZhE+YoYlAWPMecOeCFpofX4JJ6qquXpQd5J7x9Bt3SaOVHfkg+pB/Ck+0tfhGWPMWbNE0FzV1fDazRSUjyE6vB+jEzsTrJVc1W4zb1aOIiE2ksiwEF9HaYwxZ80SQXMdLYDclUziA3YMeIXgoHbwSSrtTx3j/fZjGdG7hesNGGOMj1giaK7iPAC6U8qPQ9YAoyHnTQiL4p45PyG8Q7hv4zPmPFNZWUlBQQEVFRW+DsUvhIWFkZCQQEjI2bdMWCJorhInEWRof0bkL4byWyHvH5B0HT06R/k4OGPOPwUFBXTq1Ik+ffogNty6VVSV0tJSCgoKSExMPOvrbNRQM2lxLoeIZlXvXyAnymHpD+DE0davN2BMgKqoqCA2NtaSgAeICLGxsc1+urJE0EwV+7PJOxVP/2FjYPhMZw3hsCi4cLyvQzPmvGVJwHNa8m9piaA5VAn+PJ8dmsDYi+Lgyl9CcAdIug6C2/s6OmOMaRFLBM1Rto+QU8f5PPxC4qM7QFQ8/OQ9uPp3vo7MGNNCR44c4cknn2z2dddeey1HjhzxQkRtzxJBM5w6mAtAx16Dv9oZ189pGjLGnJeaSgRVVVVnvG7VqlVER5+2xPp5yUYNNUPRrq3EA4lJI3wdijF+acHfs8nZf9Sj9xzYM5LfXDeoyeMpKSns2rWL4cOHExISQlhYGDExMeTl5ZGfn8/UqVPZt28fFRUV3HHHHcyZMweAPn36kJ6ezrFjx7jmmmu4/PLL+fDDD4mPj+ett96iQ4cOHi2HN9kTQRNOVSsvfLSHsuOVtfuO7t1OsUYzckBf3wVmjPGohx56iL59+7J161YefvhhNm/ezOOPP05+fj4AS5YsISMjg/T0dBYtWkRp6emr6e7YsYN58+aRnZ1NdHQ0r7/+elsXo1XsiaAJ6/NLuO+tbHKLyvndtCEAhHyeT2FIb5IjQn0cnTH+6Ux/ubeV0aNH1xuDv2jRIlasWAHAvn372LFjB7GxsfWuSUxMZPjw4QCMHDmSPXv2tFm8nmBPBE1Ytf0AAK9u2sfO4nJOVFbR4+ReTsb093Fkxhhv6tixY+3X7777LmvXruWjjz5i27ZtJCcnNzpGPzT0qz8Og4KCvrZ/4VxjiaARlaeqeSfnIOP7dyE8JIiHVueRlZtDR6kgsm5HsTHmvNepUyfKy8sbPVZWVkZMTAzh4eHk5eWxYcOGNo6ubVjTUCM27C6l7HglN47uzSV9OvPwmk/od3Q3I4ELrKPYGL8SGxvL2LFjGTx4MB06dKBbt261xyZOnMjTTz9NUlIS/fv3Z8yYMT6M1HssETRi1fYiOrYPYtzFXRh3cRde3LCXqqIcCIHweHsiMMbfLF26tNH9oaGhrF69utFjNf0AcXFxZGVl1e7/+c9/7vH4vM2ahhqoOlXNO9lFTEjqRlhIEGEhQdz9nf70k0KOhXSG8M6+DtEYYzzKngga+HjP55R+cZJrBnev3TctOZ7StFJCopJ8GJkxxniHPRE08M+sIsJC2jG+f5fafUECXSv2ENrD90PbjDHG0ywR1FFdrazOKuLK/l0Jb1/nYaloO5w8Bt2sf8AY43+saQjYuLuUNdkH+XfeQUrKT3DtkB71T9jyIgSFOrOMGmOMnwn4RLBy237mv7yF9sHtuKxvLHPH92XS0DqJoLICMl+FpEnWUWyM8UsB3TSkqjyZupOLu0Ww5b6r+Ovs0cy4pHf9hR3y3oaKI5B8k+8CNcacMyIiIgDYv38/06dPb/Sc8ePHk56efsb7PPbYY3z55Ze1276c1jqgE8H6HYfIKyrn1isupGNoEw9HW16A6N6Q+M22Dc4Yc07r2bMny5cvb/H1DROBL6e1DuimocXrd9EtMpQpw+MbP+HwXtj9Loz/JbQL6JxpTNtYneIMzvCk7kPgmoeaPJySkkKvXr2YN28eAPfffz/BwcGkpqZy+PBhKisrefDBB5kyZUq96/bs2cOkSZPIysri+PHjzJ49m23btjFgwACOHz9ee97cuXPZtGkTx48fZ/r06SxYsIBFixaxf/9+rrzySuLi4khNTa2d1jouLo5HHnmEJUuWAHDLLbdw5513smfPHq9Nd+3V324iMlFEPhGRnSKS0sjxWSJSIiJb3Y9bvBlPXVmFZXyws5TZYxNpH9zEP8PWpYDA8BvbKixjTBubMWMGy5Ytq91etmwZN998MytWrGDz5s2kpqZy9913o6pN3uOpp54iPDyc3NxcFixYQEZGRu2x3/72t6Snp5OZmUlaWhqZmZnMnz+fnj17kpqaSmpqar17ZWRk8Nxzz7Fx40Y2bNjAM888w5YtWwDvTXfttScCEQkCngCuAgqATSKyUlVzGpz6qqre7q04ahQeOc7BoxUMiY8i5ORRtv39SWaHfs7N7fdDelrjF215AfpeCdG9vB2eMQbO+Je7tyQnJ1NcXMz+/fspKSkhJiaG7t27c9ddd7F+/XratWtHYWEhBw8epHv37o3eY/369cyfPx+AoUOHMnTo0Npjy5YtY/HixVRVVXHgwAFycnLqHW/o/fffZ9q0abWzoH7ve9/jvffeY/LkyV6b7tqbTUOjgZ2quhtARF4BpgANE0GbWLG5gIXv5JPUvphn2y9kZlUBCLDmay689uG2CM8Y40PXX389y5cvp6ioiBkzZvDSSy9RUlJCRkYGISEh9OnTp9Hpp7/Op59+ysKFC9m0aRMxMTHMmjWrRfep0XC667pNUK3hzUQQD+yrs10AXNrIed8XkXFAPnCXqu5r5JxWu/HSC7hEsxjy4X1UnoKfyL0suOV6ukWGNX1RUHsbMmpMAJgxYwa33norhw4dIi0tjWXLltG1a1dCQkJITU1l7969Z7x+3LhxLF26lAkTJpCVlUVmZiYAR48epWPHjkRFRXHw4EFWr17N+PHjga+mv46Li6t3ryuuuIJZs2aRkpKCqrJixQpeeOEFr5S7hq87i/8OvKyqJ0TkNuB5YELDk0RkDjAHoHfv3i36Rp13LOfSD34KsRfBD1/h6c6JX3+RMSYgDBo0iPLycuLj4+nRowczZ87kuuuuY8iQIYwaNYoBAwac8fq5c+cye/ZskpKSSEpKYuTIkQAMGzaM5ORkBgwYQK9evRg7dmztNXPmzGHixIm1fQU1RowYwaxZsxg9ejTgdBYnJyd7ddUzOVMHSKtuLPIN4H5VvdrdvgdAVX/fxPlBwOeqGnWm+44aNUq/bnxuoz7bCB8ugqlPQVhk8683xnhFbm4uSUk2oaMnNfZvKiIZqjqqsfO9+USwCegnIolAIXADUG/4jYj0UNUD7uZkINdr0fS+FHq/5LXbG2PM+cpriUBVq0Tkdpzu2CBgiapmi8gDQLqqrgTmi8hkoAr4HJjlrXiMMcY0zqt9BKq6CljVYN+v63x9D3CPN2Mwxpz7VLX+1C6mxVrS3G+vyxpjfCosLIzS0tIW/QIz9akqpaWlhIWdYTRkI3w9asgYE+ASEhIoKCigpKTE16H4hbCwMBISEpp1jSUCY4xPhYSEkJhow7l9yZqGjDEmwFkiMMaYAGeJwBhjApzX3iz2FhEpAc488UfT4oBDHgznfBGI5Q7EMkNgljsQywzNL/cFqtqlsQPnXSJoDRFJb+oVa38WiOUOxDJDYJY7EMsMni23NQ0ZY0yAs0RgjDEBLtASwWJfB+AjgVjuQCwzBGa5A7HM4MFyB1QfgTHGmNMF2hOBMcaYBiwRGGNMgAuYRCAiE0XkExHZKSIpvo7HG0Skl4ikikiOiGSLyB3u/s4i8i8R2eF+jvF1rJ4mIkEiskVE3na3E0Vko1vfr4pIe1/H6GkiEi0iy0UkT0RyReQbAVLXd7k/31ki8rKIhPlbfYvIEhEpFpGsOvsarVtxLHLLnikiI5r7/QIiEbjLYD4BXAMMBH4oIgN9G5VXVAF3q+pAYAwwzy1nCrBOVfsB69xtf3MH9Ve4+1/gUVW9CDgM/NgnUXnX48A/VXUAMAyn/H5d1yISD8wHRqnqYJxFr27A/+r7r8DEBvuaqttrgH7uxxzgqeZ+s4BIBMBoYKeq7lbVk8ArwBQfx+RxqnpAVTe7X5fj/GKIxynr8+5pzwNTfROhd4hIAvBd4C/utgATgOXuKf5Y5ihgHPAsgKqeVNUj+Hldu4KBDiISDIQDB/Cz+lbV9TirNtbVVN1OAf6mjg1AtIj0aM73C5REEA/sq7Nd4O7zWyLSB0gGNgLd6qwNXQR081FY3vIY8N9AtbsdCxxR1Sp32x/rOxEoAZ5zm8T+IiId8fO6VtVCYCHwGU4CKAMy8P/6hqbrttW/3wIlEQQUEYkAXgfuVNWjdY+pM17Yb8YMi8gkoFhVM3wdSxsLBkYAT6lqMvAFDZqB/K2uAdx28Sk4ibAn0JHTm1D8nqfrNlASQSHQq852grvP74hICE4SeElV33B3H6x5VHQ/F/sqPi8YC0wWkT04TX4TcNrOo92mA/DP+i4AClR1o7u9HCcx+HNdA3wb+FRVS1S1EngD52fA3+sbmq7bVv9+C5REsAno544saI/TubTSxzF5nNs2/iyQq6qP1Dm0ErjZ/fpm4K22js1bVPUeVU1Q1T449fpvVZ0JpALT3dP8qswAqloE7BOR/u6ubwE5+HFduz4DxohIuPvzXlNuv65vV1N1uxL4kTt6aAxQVqcJ6eyoakB8ANcC+cAu4Fe+jsdLZbwc53ExE9jqflyL02a+DtgBrAU6+zpWL5V/PPC2+/WFwMfATuA1INTX8XmhvMOBdLe+3wRiAqGugQVAHpAFvACE+lt9Ay/j9IFU4jz9/bipugUEZ1TkLmA7zoiqZn0/m2LCGGMCXKA0DRljjGmCJQJjjAlwlgiMMSbAWSIwxpgAZ4nAGGMCnCUCY9qQiIyvmSHVmHOFJQJjjAlwlgiMaYSI/IeIfCwiW0Xkz+56B8dE5FF3Lvx1ItLFPXe4iGxw54JfUWee+ItEZK2IbBORzSLS1719RJ11BF5y35A1xmcsERjTgIgkATOAsao6HDgFzMSZ4CxdVQcBacBv3Ev+BvyPqg7FebOzZv9LwBOqOgy4DOdNUXBmhb0TZ22MC3HmyjHGZ4K//hRjAs63gJHAJveP9Q44E3xVA6+657wIvOGuCxCtqmnu/ueB10SkExCvqisAVLUCwL3fx6pa4G5vBfoA73u/WMY0zhKBMacT4HlVvafeTpH7GpzX0vlZTtT5+hT2/9D4mDUNGXO6dcB0EekKtWvFXoDz/6VmhssbgfdVtQw4LCJXuPtvAtLUWSGuQESmuvcIFZHwNi2FMWfJ/hIxpgFVzRGRe4F3RKQdzgyQ83AWfxntHivG6UcAZ0rgp91f9LuB2e7+m4A/i8gD7j2ub8NiGHPWbPZRY86SiBxT1Qhfx2GMp1nTkDHGBDh7IjDGmABnTwTGGBPgLBEYY0yAs0RgjDEBzhKBMcYEOEsExhgT4P4fcuhgITcjayUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test loss:  1.0615322589874268\n",
            "Test accuracy:  0.6209999918937683\n",
            "\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0       0.67      0.48      0.56      1000\n",
            "     Class 1       0.59      0.77      0.67      1000\n",
            "\n",
            "    accuracy                           0.62      2000\n",
            "   macro avg       0.63      0.62      0.61      2000\n",
            "weighted avg       0.63      0.62      0.61      2000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "690MmD_Ik20Z"
      },
      "source": [
        "# تحلیل مدل اولیه\n",
        "\n",
        "<div dir='rtl'>\n",
        "\n",
        "با مقایسه صحت مدل در زمان آموزش و در زمان تست درمی‌یابیم که مدل دچار پیش‌برازش شده است، چرا که در زمان آموزش صحت مدل تا ۹۶ درصد افزایش یافته و در ۸۸ درصد متوقف شده است اما در زمان تست به ۶۲ درصد کاهش یافته است.\n",
        "\n",
        "برای حل این مشکل میتوانیم:\n",
        "- از مدل پیچیده تری استفاده کنیم\n",
        "- در تکنیک منظم سازی دراپ‌اوت بهره بگیریم\n",
        "- به کمک آگمنتیشن تغییراتی در داده های زمان آموزش ایجاد کنیم\n",
        "\n",
        "در ادامه بوسیله‌ی دو راه حل آخر سعی شده که عملکرد مدل را بهبود دهیم.\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhb7FL87IGXz"
      },
      "source": [
        "# 2- Train model with Dropout Regularization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oNn6m5UnIPA9",
        "outputId": "53d07b99-30f3-4b92-c850-083f1f6a052f"
      },
      "source": [
        "model = train_model({\n",
        "    'in_drop_out':0,\n",
        "    'layers' : [\n",
        "          (lambda : Activation('relu'),0.1,[2000,1600,800,600,500]+[400]*3+[400]*5+[300,250,200,150,120]), \n",
        "          (lambda : Activation('softmax'),0,[2])\n",
        "    ],\n",
        "    'learning_rate':3e-4,\n",
        "    'batch':256,\n",
        "    'epochs':100,\n",
        "    'aug_rotation':0,\n",
        "    'aug_shift_w':0,\n",
        "    'aug_shift_h':0,\n",
        "    'aug_shear':0,\n",
        "    'aug_zoom':0,\n",
        "    'aug_horizontal_flip':False\n",
        "})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 4s 75ms/step - loss: 0.6979 - accuracy: 0.5043 - val_loss: 0.6951 - val_accuracy: 0.5031\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6966 - accuracy: 0.5029 - val_loss: 0.6934 - val_accuracy: 0.4969\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6935 - accuracy: 0.4937 - val_loss: 0.6932 - val_accuracy: 0.5031\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.6938 - accuracy: 0.4987 - val_loss: 0.6934 - val_accuracy: 0.4969\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6942 - accuracy: 0.4894 - val_loss: 0.6929 - val_accuracy: 0.5200\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6929 - accuracy: 0.5182 - val_loss: 0.6927 - val_accuracy: 0.4956\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6915 - accuracy: 0.5270 - val_loss: 0.6849 - val_accuracy: 0.5813\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6863 - accuracy: 0.5701 - val_loss: 0.6846 - val_accuracy: 0.5688\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6839 - accuracy: 0.5635 - val_loss: 0.6905 - val_accuracy: 0.5325\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6770 - accuracy: 0.5834 - val_loss: 0.6592 - val_accuracy: 0.6062\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6670 - accuracy: 0.6028 - val_loss: 0.6844 - val_accuracy: 0.5763\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6665 - accuracy: 0.6028 - val_loss: 0.6688 - val_accuracy: 0.6144\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6603 - accuracy: 0.6151 - val_loss: 0.6463 - val_accuracy: 0.6281\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6459 - accuracy: 0.6336 - val_loss: 0.6644 - val_accuracy: 0.6250\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6551 - accuracy: 0.6218 - val_loss: 0.6593 - val_accuracy: 0.6256\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6436 - accuracy: 0.6363 - val_loss: 0.6422 - val_accuracy: 0.6162\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6464 - accuracy: 0.6315 - val_loss: 0.6316 - val_accuracy: 0.6481\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6436 - accuracy: 0.6336 - val_loss: 0.6402 - val_accuracy: 0.6225\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6380 - accuracy: 0.6383 - val_loss: 0.6288 - val_accuracy: 0.6544\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6245 - accuracy: 0.6595 - val_loss: 0.6207 - val_accuracy: 0.6662\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6264 - accuracy: 0.6598 - val_loss: 0.6314 - val_accuracy: 0.6425\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6266 - accuracy: 0.6582 - val_loss: 0.6342 - val_accuracy: 0.6144\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.6246 - accuracy: 0.6475 - val_loss: 0.6137 - val_accuracy: 0.6631\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6132 - accuracy: 0.6648 - val_loss: 0.6056 - val_accuracy: 0.6731\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6080 - accuracy: 0.6687 - val_loss: 0.6099 - val_accuracy: 0.6731\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6137 - accuracy: 0.6657 - val_loss: 0.7080 - val_accuracy: 0.6000\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.6585 - accuracy: 0.6289 - val_loss: 0.5991 - val_accuracy: 0.6900\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6079 - accuracy: 0.6777 - val_loss: 0.6045 - val_accuracy: 0.6837\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5885 - accuracy: 0.6870 - val_loss: 0.6055 - val_accuracy: 0.6731\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6064 - accuracy: 0.6750 - val_loss: 0.5941 - val_accuracy: 0.6900\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.5925 - accuracy: 0.6937 - val_loss: 0.5909 - val_accuracy: 0.7063\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.6004 - accuracy: 0.6804 - val_loss: 0.5797 - val_accuracy: 0.7031\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5851 - accuracy: 0.6983 - val_loss: 0.6095 - val_accuracy: 0.6719\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5981 - accuracy: 0.6810 - val_loss: 0.5885 - val_accuracy: 0.7063\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5804 - accuracy: 0.6928 - val_loss: 0.5703 - val_accuracy: 0.7188\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5760 - accuracy: 0.6992 - val_loss: 0.5912 - val_accuracy: 0.6856\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6097 - accuracy: 0.6688 - val_loss: 0.6070 - val_accuracy: 0.6950\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.6108 - accuracy: 0.6572 - val_loss: 0.5766 - val_accuracy: 0.7075\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.6020 - accuracy: 0.6688 - val_loss: 0.5784 - val_accuracy: 0.7075\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5869 - accuracy: 0.6928 - val_loss: 0.5717 - val_accuracy: 0.7156\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5866 - accuracy: 0.6903 - val_loss: 0.5807 - val_accuracy: 0.6969\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.6190 - accuracy: 0.6616 - val_loss: 0.5974 - val_accuracy: 0.6919\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.6073 - accuracy: 0.6763 - val_loss: 0.6131 - val_accuracy: 0.6606\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5994 - accuracy: 0.6760 - val_loss: 0.6025 - val_accuracy: 0.6756\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5978 - accuracy: 0.6767 - val_loss: 0.5769 - val_accuracy: 0.7069\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5904 - accuracy: 0.6786 - val_loss: 0.5772 - val_accuracy: 0.7106\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5664 - accuracy: 0.6952 - val_loss: 0.6022 - val_accuracy: 0.6819\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.6047 - accuracy: 0.6707 - val_loss: 0.5643 - val_accuracy: 0.6881\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5696 - accuracy: 0.6907 - val_loss: 0.5440 - val_accuracy: 0.7156\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5815 - accuracy: 0.6909 - val_loss: 0.5875 - val_accuracy: 0.6819\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5881 - accuracy: 0.6734 - val_loss: 0.6144 - val_accuracy: 0.7050\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.6173 - accuracy: 0.6786 - val_loss: 0.5668 - val_accuracy: 0.7088\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5663 - accuracy: 0.7046 - val_loss: 0.5703 - val_accuracy: 0.7119\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5719 - accuracy: 0.7020 - val_loss: 0.5667 - val_accuracy: 0.7206\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5590 - accuracy: 0.7122 - val_loss: 0.5123 - val_accuracy: 0.7500\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5671 - accuracy: 0.7054 - val_loss: 0.5142 - val_accuracy: 0.7456\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5378 - accuracy: 0.7222 - val_loss: 0.5139 - val_accuracy: 0.7487\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5416 - accuracy: 0.7185 - val_loss: 0.5094 - val_accuracy: 0.7506\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5380 - accuracy: 0.7209 - val_loss: 0.5726 - val_accuracy: 0.7125\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5654 - accuracy: 0.6879 - val_loss: 0.5979 - val_accuracy: 0.6888\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5583 - accuracy: 0.7060 - val_loss: 0.5050 - val_accuracy: 0.7381\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5257 - accuracy: 0.7320 - val_loss: 0.5247 - val_accuracy: 0.7588\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5342 - accuracy: 0.7127 - val_loss: 0.4879 - val_accuracy: 0.7681\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.5181 - accuracy: 0.7290 - val_loss: 0.5047 - val_accuracy: 0.7500\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.5164 - accuracy: 0.7318 - val_loss: 0.5099 - val_accuracy: 0.7500\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.5232 - accuracy: 0.7053 - val_loss: 0.5436 - val_accuracy: 0.7462\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.5274 - accuracy: 0.7297 - val_loss: 0.4787 - val_accuracy: 0.7638\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.4996 - accuracy: 0.7457 - val_loss: 0.4730 - val_accuracy: 0.7681\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.4918 - accuracy: 0.7585 - val_loss: 0.4782 - val_accuracy: 0.7800\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5029 - accuracy: 0.7389 - val_loss: 0.4883 - val_accuracy: 0.7513\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5356 - accuracy: 0.7115 - val_loss: 0.4909 - val_accuracy: 0.7531\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5378 - accuracy: 0.7129 - val_loss: 0.4880 - val_accuracy: 0.7631\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.4945 - accuracy: 0.7366 - val_loss: 0.5604 - val_accuracy: 0.7300\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5422 - accuracy: 0.7251 - val_loss: 0.5065 - val_accuracy: 0.7606\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5190 - accuracy: 0.7457 - val_loss: 0.4987 - val_accuracy: 0.7681\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5124 - accuracy: 0.7472 - val_loss: 0.4907 - val_accuracy: 0.7725\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5124 - accuracy: 0.7448 - val_loss: 0.5239 - val_accuracy: 0.7538\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5198 - accuracy: 0.7509 - val_loss: 0.4886 - val_accuracy: 0.7906\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5327 - accuracy: 0.7323 - val_loss: 0.5117 - val_accuracy: 0.7544\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 2s 69ms/step - loss: 0.5177 - accuracy: 0.7498 - val_loss: 0.4796 - val_accuracy: 0.7925\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5174 - accuracy: 0.7512 - val_loss: 0.5130 - val_accuracy: 0.7375\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5192 - accuracy: 0.7498 - val_loss: 0.5720 - val_accuracy: 0.7038\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5622 - accuracy: 0.7203 - val_loss: 0.4863 - val_accuracy: 0.7794\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5236 - accuracy: 0.7411 - val_loss: 0.5129 - val_accuracy: 0.7462\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5283 - accuracy: 0.7276 - val_loss: 0.5007 - val_accuracy: 0.7650\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5395 - accuracy: 0.7516 - val_loss: 0.5414 - val_accuracy: 0.7344\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5575 - accuracy: 0.7306 - val_loss: 0.5287 - val_accuracy: 0.7544\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5599 - accuracy: 0.7266 - val_loss: 0.5104 - val_accuracy: 0.7625\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5359 - accuracy: 0.7456 - val_loss: 0.5167 - val_accuracy: 0.7625\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5327 - accuracy: 0.7538 - val_loss: 0.5191 - val_accuracy: 0.7619\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 2s 68ms/step - loss: 0.5573 - accuracy: 0.7309 - val_loss: 0.5593 - val_accuracy: 0.7237\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5609 - accuracy: 0.7270 - val_loss: 0.5654 - val_accuracy: 0.7175\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5866 - accuracy: 0.6949 - val_loss: 0.6080 - val_accuracy: 0.6719\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5777 - accuracy: 0.7091 - val_loss: 0.5299 - val_accuracy: 0.7481\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5585 - accuracy: 0.7315 - val_loss: 0.5312 - val_accuracy: 0.7513\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5540 - accuracy: 0.7311 - val_loss: 0.4983 - val_accuracy: 0.7800\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5482 - accuracy: 0.7331 - val_loss: 0.5503 - val_accuracy: 0.7400\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5384 - accuracy: 0.7454 - val_loss: 0.5055 - val_accuracy: 0.7937\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.5276 - accuracy: 0.7566 - val_loss: 0.5739 - val_accuracy: 0.7081\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5609 - accuracy: 0.7144 - val_loss: 0.4712 - val_accuracy: 0.7994\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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test loss:  0.7183194756507874\n",
            "Test accuracy:  0.6259999871253967\n",
            "\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0       0.64      0.59      0.61      1000\n",
            "     Class 1       0.62      0.67      0.64      1000\n",
            "\n",
            "    accuracy                           0.63      2000\n",
            "   macro avg       0.63      0.63      0.63      2000\n",
            "weighted avg       0.63      0.63      0.63      2000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4lD8HZ9mbYd"
      },
      "source": [
        "# تحلیل عملکرد مدل دوم\n",
        "\n",
        "<div dir='rtl'>\n",
        "\n",
        "در این مدل از شبکه ای مشابه با شبکه مدل قبل استفاده کردیم با این تفاوت که با استفاده از دراپ‌اوت ۱۰ درصد در لایه‌های درونی، بصورت تصادفی ۱۰ درصد از نرون ها غیرفعال شده اند تا از بیش‌بردازش مدل جلوگیری شود.\n",
        "\n",
        "\n",
        "با وجود اینکه صحت مدل در زمان تست نسبت به حالت اول افزایش یافته است، اما همچنان شاهد آورفیت شدن مدل هستیم چرا که صحت مدل در زمان آموزش و تست فاصله‌ی قابل توجهی دارد.\n",
        "\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0ctm-4PULNj"
      },
      "source": [
        "\n",
        "# 3- Add Data Augmentation(Without dropuot)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9TpjPKIIztbV",
        "outputId": "3a3b1bc0-f906-4119-8690-e4d4f3e2c5cc"
      },
      "source": [
        "model = train_model({\n",
        "    'in_drop_out':0,\n",
        "    'layers' : [\n",
        "          (lambda : Activation('relu'),0,[2000,1600,800,600,500]+[400]*3+[400]*5+[300,250,200,150,120]), \n",
        "          (lambda : Activation('softmax'),0,[2])\n",
        "    ],\n",
        "    'learning_rate':3e-4,\n",
        "    'batch':256,\n",
        "    'epochs':100,\n",
        "    'aug_rotation':10,\n",
        "    'aug_shift_w':0.04,\n",
        "    'aug_shift_h':0.04,\n",
        "    'aug_shear':0.04,\n",
        "    'aug_zoom':0.04,\n",
        "    'aug_horizontal_flip':True\n",
        "})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "25/25 [==============================] - 20s 749ms/step - loss: 0.6948 - accuracy: 0.5031 - val_loss: 0.6937 - val_accuracy: 0.4969\n",
            "Epoch 2/100\n",
            "25/25 [==============================] - 19s 750ms/step - loss: 0.6942 - accuracy: 0.5003 - val_loss: 0.6930 - val_accuracy: 0.5275\n",
            "Epoch 3/100\n",
            "25/25 [==============================] - 19s 766ms/step - loss: 0.6931 - accuracy: 0.5111 - val_loss: 0.6928 - val_accuracy: 0.5350\n",
            "Epoch 4/100\n",
            "25/25 [==============================] - 19s 769ms/step - loss: 0.6923 - accuracy: 0.5344 - val_loss: 0.6951 - val_accuracy: 0.5194\n",
            "Epoch 5/100\n",
            "25/25 [==============================] - 19s 753ms/step - loss: 0.6880 - accuracy: 0.5560 - val_loss: 0.6831 - val_accuracy: 0.5663\n",
            "Epoch 6/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6774 - accuracy: 0.5797 - val_loss: 0.6803 - val_accuracy: 0.5750\n",
            "Epoch 7/100\n",
            "25/25 [==============================] - 18s 733ms/step - loss: 0.6713 - accuracy: 0.5895 - val_loss: 0.6712 - val_accuracy: 0.6006\n",
            "Epoch 8/100\n",
            "25/25 [==============================] - 18s 733ms/step - loss: 0.6612 - accuracy: 0.6113 - val_loss: 0.6608 - val_accuracy: 0.6019\n",
            "Epoch 9/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6714 - accuracy: 0.5918 - val_loss: 0.6844 - val_accuracy: 0.5644\n",
            "Epoch 10/100\n",
            "25/25 [==============================] - 18s 734ms/step - loss: 0.6799 - accuracy: 0.5569 - val_loss: 0.6714 - val_accuracy: 0.5950\n",
            "Epoch 11/100\n",
            "25/25 [==============================] - 18s 734ms/step - loss: 0.6697 - accuracy: 0.6040 - val_loss: 0.6748 - val_accuracy: 0.5794\n",
            "Epoch 12/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.6678 - accuracy: 0.5957 - val_loss: 0.6681 - val_accuracy: 0.5962\n",
            "Epoch 13/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6592 - accuracy: 0.6106 - val_loss: 0.6617 - val_accuracy: 0.6050\n",
            "Epoch 14/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6545 - accuracy: 0.6232 - val_loss: 0.6692 - val_accuracy: 0.5938\n",
            "Epoch 15/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6600 - accuracy: 0.6061 - val_loss: 0.6550 - val_accuracy: 0.6137\n",
            "Epoch 16/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6506 - accuracy: 0.6183 - val_loss: 0.6600 - val_accuracy: 0.6106\n",
            "Epoch 17/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6548 - accuracy: 0.6218 - val_loss: 0.6598 - val_accuracy: 0.6137\n",
            "Epoch 18/100\n",
            "25/25 [==============================] - 19s 747ms/step - loss: 0.6438 - accuracy: 0.6310 - val_loss: 0.6757 - val_accuracy: 0.5556\n",
            "Epoch 19/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6660 - accuracy: 0.5809 - val_loss: 0.6534 - val_accuracy: 0.6275\n",
            "Epoch 20/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6484 - accuracy: 0.6227 - val_loss: 0.6478 - val_accuracy: 0.6231\n",
            "Epoch 21/100\n",
            "25/25 [==============================] - 19s 743ms/step - loss: 0.6361 - accuracy: 0.6284 - val_loss: 0.6632 - val_accuracy: 0.6037\n",
            "Epoch 22/100\n",
            "25/25 [==============================] - 19s 745ms/step - loss: 0.6462 - accuracy: 0.6383 - val_loss: 0.6451 - val_accuracy: 0.6256\n",
            "Epoch 23/100\n",
            "25/25 [==============================] - 19s 748ms/step - loss: 0.6351 - accuracy: 0.6378 - val_loss: 0.6574 - val_accuracy: 0.6062\n",
            "Epoch 24/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6416 - accuracy: 0.6372 - val_loss: 0.6527 - val_accuracy: 0.6131\n",
            "Epoch 25/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6393 - accuracy: 0.6265 - val_loss: 0.6530 - val_accuracy: 0.6112\n",
            "Epoch 26/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6293 - accuracy: 0.6356 - val_loss: 0.6383 - val_accuracy: 0.6394\n",
            "Epoch 27/100\n",
            "25/25 [==============================] - 19s 746ms/step - loss: 0.6362 - accuracy: 0.6261 - val_loss: 0.6462 - val_accuracy: 0.6313\n",
            "Epoch 28/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6381 - accuracy: 0.6373 - val_loss: 0.6466 - val_accuracy: 0.6225\n",
            "Epoch 29/100\n",
            "25/25 [==============================] - 19s 747ms/step - loss: 0.6483 - accuracy: 0.6195 - val_loss: 0.6387 - val_accuracy: 0.6206\n",
            "Epoch 30/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6305 - accuracy: 0.6403 - val_loss: 0.6479 - val_accuracy: 0.6131\n",
            "Epoch 31/100\n",
            "25/25 [==============================] - 19s 746ms/step - loss: 0.6192 - accuracy: 0.6527 - val_loss: 0.6386 - val_accuracy: 0.6400\n",
            "Epoch 32/100\n",
            "25/25 [==============================] - 19s 747ms/step - loss: 0.6298 - accuracy: 0.6429 - val_loss: 0.6438 - val_accuracy: 0.6294\n",
            "Epoch 33/100\n",
            "25/25 [==============================] - 19s 745ms/step - loss: 0.6225 - accuracy: 0.6435 - val_loss: 0.6472 - val_accuracy: 0.6375\n",
            "Epoch 34/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6193 - accuracy: 0.6517 - val_loss: 0.6343 - val_accuracy: 0.6481\n",
            "Epoch 35/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6221 - accuracy: 0.6381 - val_loss: 0.6442 - val_accuracy: 0.6294\n",
            "Epoch 36/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6281 - accuracy: 0.6341 - val_loss: 0.6465 - val_accuracy: 0.6056\n",
            "Epoch 37/100\n",
            "25/25 [==============================] - 18s 739ms/step - loss: 0.6353 - accuracy: 0.6307 - val_loss: 0.6381 - val_accuracy: 0.6481\n",
            "Epoch 38/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.6119 - accuracy: 0.6585 - val_loss: 0.6614 - val_accuracy: 0.6425\n",
            "Epoch 39/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6267 - accuracy: 0.6464 - val_loss: 0.6378 - val_accuracy: 0.6431\n",
            "Epoch 40/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6203 - accuracy: 0.6466 - val_loss: 0.6468 - val_accuracy: 0.6294\n",
            "Epoch 41/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6239 - accuracy: 0.6580 - val_loss: 0.6468 - val_accuracy: 0.6375\n",
            "Epoch 42/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.6316 - accuracy: 0.6331 - val_loss: 0.6369 - val_accuracy: 0.6350\n",
            "Epoch 43/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.6155 - accuracy: 0.6518 - val_loss: 0.6420 - val_accuracy: 0.6550\n",
            "Epoch 44/100\n",
            "25/25 [==============================] - 19s 743ms/step - loss: 0.6109 - accuracy: 0.6532 - val_loss: 0.6339 - val_accuracy: 0.6319\n",
            "Epoch 45/100\n",
            "25/25 [==============================] - 19s 738ms/step - loss: 0.6170 - accuracy: 0.6482 - val_loss: 0.6356 - val_accuracy: 0.6556\n",
            "Epoch 46/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6074 - accuracy: 0.6610 - val_loss: 0.6293 - val_accuracy: 0.6450\n",
            "Epoch 47/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6115 - accuracy: 0.6559 - val_loss: 0.6547 - val_accuracy: 0.6481\n",
            "Epoch 48/100\n",
            "25/25 [==============================] - 19s 745ms/step - loss: 0.6216 - accuracy: 0.6527 - val_loss: 0.6484 - val_accuracy: 0.6488\n",
            "Epoch 49/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.6193 - accuracy: 0.6485 - val_loss: 0.6330 - val_accuracy: 0.6400\n",
            "Epoch 50/100\n",
            "25/25 [==============================] - 19s 738ms/step - loss: 0.5996 - accuracy: 0.6672 - val_loss: 0.6354 - val_accuracy: 0.6331\n",
            "Epoch 51/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6053 - accuracy: 0.6657 - val_loss: 0.6424 - val_accuracy: 0.6406\n",
            "Epoch 52/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.5996 - accuracy: 0.6661 - val_loss: 0.6409 - val_accuracy: 0.6531\n",
            "Epoch 53/100\n",
            "25/25 [==============================] - 19s 745ms/step - loss: 0.5970 - accuracy: 0.6668 - val_loss: 0.6518 - val_accuracy: 0.6488\n",
            "Epoch 54/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6061 - accuracy: 0.6576 - val_loss: 0.6306 - val_accuracy: 0.6475\n",
            "Epoch 55/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6045 - accuracy: 0.6636 - val_loss: 0.6406 - val_accuracy: 0.6488\n",
            "Epoch 56/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6087 - accuracy: 0.6669 - val_loss: 0.6354 - val_accuracy: 0.6475\n",
            "Epoch 57/100\n",
            "25/25 [==============================] - 19s 743ms/step - loss: 0.6047 - accuracy: 0.6633 - val_loss: 0.6316 - val_accuracy: 0.6438\n",
            "Epoch 58/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.5966 - accuracy: 0.6669 - val_loss: 0.6293 - val_accuracy: 0.6575\n",
            "Epoch 59/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6011 - accuracy: 0.6630 - val_loss: 0.6358 - val_accuracy: 0.6538\n",
            "Epoch 60/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.6071 - accuracy: 0.6549 - val_loss: 0.6635 - val_accuracy: 0.6525\n",
            "Epoch 61/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6087 - accuracy: 0.6676 - val_loss: 0.6671 - val_accuracy: 0.6519\n",
            "Epoch 62/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6040 - accuracy: 0.6653 - val_loss: 0.6233 - val_accuracy: 0.6669\n",
            "Epoch 63/100\n",
            "25/25 [==============================] - 19s 745ms/step - loss: 0.5949 - accuracy: 0.6679 - val_loss: 0.6498 - val_accuracy: 0.6450\n",
            "Epoch 64/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6003 - accuracy: 0.6632 - val_loss: 0.7054 - val_accuracy: 0.6538\n",
            "Epoch 65/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.5921 - accuracy: 0.6757 - val_loss: 0.6348 - val_accuracy: 0.6656\n",
            "Epoch 66/100\n",
            "25/25 [==============================] - 19s 747ms/step - loss: 0.5871 - accuracy: 0.6788 - val_loss: 0.6400 - val_accuracy: 0.6669\n",
            "Epoch 67/100\n",
            "25/25 [==============================] - 18s 739ms/step - loss: 0.5921 - accuracy: 0.6844 - val_loss: 0.6293 - val_accuracy: 0.6600\n",
            "Epoch 68/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.5970 - accuracy: 0.6676 - val_loss: 0.6684 - val_accuracy: 0.6662\n",
            "Epoch 69/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.5977 - accuracy: 0.6699 - val_loss: 0.6440 - val_accuracy: 0.6338\n",
            "Epoch 70/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.5916 - accuracy: 0.6704 - val_loss: 0.6307 - val_accuracy: 0.6700\n",
            "Epoch 71/100\n",
            "25/25 [==============================] - 19s 738ms/step - loss: 0.5692 - accuracy: 0.6918 - val_loss: 0.6488 - val_accuracy: 0.6106\n",
            "Epoch 72/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.5932 - accuracy: 0.6747 - val_loss: 0.6278 - val_accuracy: 0.6513\n",
            "Epoch 73/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.5839 - accuracy: 0.6773 - val_loss: 0.6450 - val_accuracy: 0.6475\n",
            "Epoch 74/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.5991 - accuracy: 0.6682 - val_loss: 0.6359 - val_accuracy: 0.6562\n",
            "Epoch 75/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6105 - accuracy: 0.6581 - val_loss: 0.6239 - val_accuracy: 0.6687\n",
            "Epoch 76/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.5827 - accuracy: 0.6786 - val_loss: 0.6378 - val_accuracy: 0.6694\n",
            "Epoch 77/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.5784 - accuracy: 0.6800 - val_loss: 0.6318 - val_accuracy: 0.6687\n",
            "Epoch 78/100\n",
            "25/25 [==============================] - 18s 739ms/step - loss: 0.5881 - accuracy: 0.6759 - val_loss: 0.6281 - val_accuracy: 0.6637\n",
            "Epoch 79/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.5760 - accuracy: 0.6918 - val_loss: 0.6420 - val_accuracy: 0.6569\n",
            "Epoch 80/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.5725 - accuracy: 0.6836 - val_loss: 0.6323 - val_accuracy: 0.6581\n",
            "Epoch 81/100\n",
            "25/25 [==============================] - 18s 739ms/step - loss: 0.5851 - accuracy: 0.6724 - val_loss: 0.6454 - val_accuracy: 0.6644\n",
            "Epoch 82/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.5621 - accuracy: 0.6979 - val_loss: 0.6342 - val_accuracy: 0.6637\n",
            "Epoch 83/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.5616 - accuracy: 0.6957 - val_loss: 0.6332 - val_accuracy: 0.6781\n",
            "Epoch 84/100\n",
            "25/25 [==============================] - 19s 743ms/step - loss: 0.5649 - accuracy: 0.6974 - val_loss: 0.6583 - val_accuracy: 0.6619\n",
            "Epoch 85/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.5696 - accuracy: 0.6928 - val_loss: 0.6352 - val_accuracy: 0.6769\n",
            "Epoch 86/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.5588 - accuracy: 0.6945 - val_loss: 0.6426 - val_accuracy: 0.6625\n",
            "Epoch 87/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.5533 - accuracy: 0.7081 - val_loss: 0.6582 - val_accuracy: 0.6463\n",
            "Epoch 88/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.5854 - accuracy: 0.6823 - val_loss: 0.6422 - val_accuracy: 0.6538\n",
            "Epoch 89/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.5608 - accuracy: 0.6957 - val_loss: 0.6533 - val_accuracy: 0.6644\n",
            "Epoch 90/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.5581 - accuracy: 0.7020 - val_loss: 0.6629 - val_accuracy: 0.6481\n",
            "Epoch 91/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.5577 - accuracy: 0.6979 - val_loss: 0.6421 - val_accuracy: 0.6237\n",
            "Epoch 92/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.5783 - accuracy: 0.6800 - val_loss: 0.6420 - val_accuracy: 0.6438\n",
            "Epoch 93/100\n",
            "25/25 [==============================] - 19s 747ms/step - loss: 0.5790 - accuracy: 0.6826 - val_loss: 0.6403 - val_accuracy: 0.6506\n",
            "Epoch 94/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.5624 - accuracy: 0.6965 - val_loss: 0.6407 - val_accuracy: 0.6644\n",
            "Epoch 95/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.5620 - accuracy: 0.6969 - val_loss: 0.6529 - val_accuracy: 0.6219\n",
            "Epoch 96/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.5862 - accuracy: 0.6734 - val_loss: 0.6431 - val_accuracy: 0.6531\n",
            "Epoch 97/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.5474 - accuracy: 0.7106 - val_loss: 0.6459 - val_accuracy: 0.6550\n",
            "Epoch 98/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.5505 - accuracy: 0.7114 - val_loss: 0.6321 - val_accuracy: 0.6644\n",
            "Epoch 99/100\n",
            "25/25 [==============================] - 18s 733ms/step - loss: 0.5468 - accuracy: 0.7097 - val_loss: 0.6502 - val_accuracy: 0.6606\n",
            "Epoch 100/100\n",
            "25/25 [==============================] - 18s 733ms/step - loss: 0.5482 - accuracy: 0.7147 - val_loss: 0.6818 - val_accuracy: 0.6231\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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test loss:  0.6410242319107056\n",
            "Test accuracy:  0.6345000267028809\n",
            "\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0       0.68      0.51      0.58      1000\n",
            "     Class 1       0.61      0.76      0.68      1000\n",
            "\n",
            "    accuracy                           0.63      2000\n",
            "   macro avg       0.64      0.63      0.63      2000\n",
            "weighted avg       0.64      0.63      0.63      2000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSdrkIHBoQH9"
      },
      "source": [
        "# تحلیل عملکرد مدل سوم\n",
        "<div dir='rtl'>\n",
        "مدل سوم و مدل اولیه ساختار شبکه عصبی یکسانی دارند و هر دو از دراپ‌اوت استفاده نکردند،\n",
        " اما مدل سوم برخلاف اولین مدل از دادهای آگمنت شده استفاده کرده است. \n",
        "\n",
        " طبق انتظار، این مدل نسبت به مدل اولیه در زمان تست عملکرد بهتری داشته است و به صحت ۶۳.۵ درصد رسیده است، همچنین در این مدل دیگر شاهد آورفیت شدن مدل نیستیم و بین صحت مدل در زمان آموزش و تست تفاوت چشمگیری دیده نمیشود.\n",
        "\n",
        "\n",
        " لازم به ذکر است که مدل سوم از دومین مدل عملکرد بهتری دارد."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCFYu6YiZkhf"
      },
      "source": [
        "# 4- Use data augmentation and dropout regularization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K4yJen86a6gq",
        "outputId": "216f30fe-d49f-4ea2-cd9c-b757b2448bdc"
      },
      "source": [
        "model = train_model({\n",
        "    'in_drop_out':0,\n",
        "    'layers' : [\n",
        "          (lambda : Activation('relu'),0.03,[2000,1600,800,600,500]+[400]*3+[400]*5+[300,250,200,150,120]), \n",
        "          (lambda : Activation('softmax'),0,[2])\n",
        "    ],\n",
        "    'learning_rate':3e-4,\n",
        "    'batch':256,\n",
        "    'epochs':100,\n",
        "    'aug_rotation':10,\n",
        "    'aug_shift_w':0.04,\n",
        "    'aug_shift_h':0.04,\n",
        "    'aug_shear':0.04,\n",
        "    'aug_zoom':0.04,\n",
        "    'aug_horizontal_flip':True\n",
        "})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "25/25 [==============================] - 20s 749ms/step - loss: 0.6954 - accuracy: 0.4997 - val_loss: 0.6931 - val_accuracy: 0.4981\n",
            "Epoch 2/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.6935 - accuracy: 0.5167 - val_loss: 0.6931 - val_accuracy: 0.4963\n",
            "Epoch 3/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6931 - accuracy: 0.5045 - val_loss: 0.6931 - val_accuracy: 0.5031\n",
            "Epoch 4/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6931 - accuracy: 0.5009 - val_loss: 0.6922 - val_accuracy: 0.5556\n",
            "Epoch 5/100\n",
            "25/25 [==============================] - 18s 739ms/step - loss: 0.6931 - accuracy: 0.5085 - val_loss: 0.6934 - val_accuracy: 0.5031\n",
            "Epoch 6/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.6935 - accuracy: 0.4987 - val_loss: 0.6919 - val_accuracy: 0.5194\n",
            "Epoch 7/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.6909 - accuracy: 0.5442 - val_loss: 0.6905 - val_accuracy: 0.5163\n",
            "Epoch 8/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6898 - accuracy: 0.5430 - val_loss: 0.6918 - val_accuracy: 0.5131\n",
            "Epoch 9/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6905 - accuracy: 0.5344 - val_loss: 0.6894 - val_accuracy: 0.5406\n",
            "Epoch 10/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6834 - accuracy: 0.5702 - val_loss: 0.6913 - val_accuracy: 0.5587\n",
            "Epoch 11/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6693 - accuracy: 0.5972 - val_loss: 0.6708 - val_accuracy: 0.5900\n",
            "Epoch 12/100\n",
            "25/25 [==============================] - 19s 745ms/step - loss: 0.6695 - accuracy: 0.6024 - val_loss: 0.6660 - val_accuracy: 0.6012\n",
            "Epoch 13/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6664 - accuracy: 0.5998 - val_loss: 0.6664 - val_accuracy: 0.6094\n",
            "Epoch 14/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6656 - accuracy: 0.6071 - val_loss: 0.6667 - val_accuracy: 0.5938\n",
            "Epoch 15/100\n",
            "25/25 [==============================] - 19s 743ms/step - loss: 0.6583 - accuracy: 0.6153 - val_loss: 0.6590 - val_accuracy: 0.6006\n",
            "Epoch 16/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6558 - accuracy: 0.6188 - val_loss: 0.6655 - val_accuracy: 0.5813\n",
            "Epoch 17/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.6523 - accuracy: 0.6173 - val_loss: 0.6546 - val_accuracy: 0.6094\n",
            "Epoch 18/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6467 - accuracy: 0.6215 - val_loss: 0.6572 - val_accuracy: 0.6206\n",
            "Epoch 19/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6396 - accuracy: 0.6261 - val_loss: 0.6526 - val_accuracy: 0.6144\n",
            "Epoch 20/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.6395 - accuracy: 0.6271 - val_loss: 0.6557 - val_accuracy: 0.6081\n",
            "Epoch 21/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6423 - accuracy: 0.6158 - val_loss: 0.6529 - val_accuracy: 0.6169\n",
            "Epoch 22/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.6356 - accuracy: 0.6303 - val_loss: 0.6502 - val_accuracy: 0.6131\n",
            "Epoch 23/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6479 - accuracy: 0.6125 - val_loss: 0.6588 - val_accuracy: 0.6162\n",
            "Epoch 24/100\n",
            "25/25 [==============================] - 18s 734ms/step - loss: 0.6515 - accuracy: 0.6235 - val_loss: 0.6543 - val_accuracy: 0.6275\n",
            "Epoch 25/100\n",
            "25/25 [==============================] - 19s 738ms/step - loss: 0.6333 - accuracy: 0.6407 - val_loss: 0.6580 - val_accuracy: 0.6169\n",
            "Epoch 26/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6419 - accuracy: 0.6301 - val_loss: 0.6475 - val_accuracy: 0.6156\n",
            "Epoch 27/100\n",
            "25/25 [==============================] - 19s 749ms/step - loss: 0.6317 - accuracy: 0.6362 - val_loss: 0.6561 - val_accuracy: 0.6250\n",
            "Epoch 28/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.6415 - accuracy: 0.6281 - val_loss: 0.6525 - val_accuracy: 0.6094\n",
            "Epoch 29/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6369 - accuracy: 0.6351 - val_loss: 0.6476 - val_accuracy: 0.6256\n",
            "Epoch 30/100\n",
            "25/25 [==============================] - 19s 748ms/step - loss: 0.6353 - accuracy: 0.6300 - val_loss: 0.6470 - val_accuracy: 0.6275\n",
            "Epoch 31/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.6311 - accuracy: 0.6364 - val_loss: 0.6508 - val_accuracy: 0.6256\n",
            "Epoch 32/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6270 - accuracy: 0.6460 - val_loss: 0.6409 - val_accuracy: 0.6306\n",
            "Epoch 33/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.6298 - accuracy: 0.6420 - val_loss: 0.6369 - val_accuracy: 0.6356\n",
            "Epoch 34/100\n",
            "25/25 [==============================] - 19s 738ms/step - loss: 0.6276 - accuracy: 0.6417 - val_loss: 0.6579 - val_accuracy: 0.6094\n",
            "Epoch 35/100\n",
            "25/25 [==============================] - 19s 745ms/step - loss: 0.6307 - accuracy: 0.6464 - val_loss: 0.6334 - val_accuracy: 0.6375\n",
            "Epoch 36/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.6233 - accuracy: 0.6504 - val_loss: 0.6456 - val_accuracy: 0.6450\n",
            "Epoch 37/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.6252 - accuracy: 0.6444 - val_loss: 0.6448 - val_accuracy: 0.6263\n",
            "Epoch 38/100\n",
            "25/25 [==============================] - 18s 734ms/step - loss: 0.6201 - accuracy: 0.6460 - val_loss: 0.6491 - val_accuracy: 0.6225\n",
            "Epoch 39/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6248 - accuracy: 0.6524 - val_loss: 0.6430 - val_accuracy: 0.6294\n",
            "Epoch 40/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6157 - accuracy: 0.6555 - val_loss: 0.6431 - val_accuracy: 0.6288\n",
            "Epoch 41/100\n",
            "25/25 [==============================] - 19s 738ms/step - loss: 0.6137 - accuracy: 0.6652 - val_loss: 0.6376 - val_accuracy: 0.6369\n",
            "Epoch 42/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.6141 - accuracy: 0.6545 - val_loss: 0.6363 - val_accuracy: 0.6463\n",
            "Epoch 43/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6274 - accuracy: 0.6491 - val_loss: 0.6450 - val_accuracy: 0.6313\n",
            "Epoch 44/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6215 - accuracy: 0.6492 - val_loss: 0.6429 - val_accuracy: 0.6438\n",
            "Epoch 45/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6174 - accuracy: 0.6489 - val_loss: 0.6369 - val_accuracy: 0.6488\n",
            "Epoch 46/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.6104 - accuracy: 0.6518 - val_loss: 0.6365 - val_accuracy: 0.6294\n",
            "Epoch 47/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6207 - accuracy: 0.6551 - val_loss: 0.6428 - val_accuracy: 0.6481\n",
            "Epoch 48/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6124 - accuracy: 0.6616 - val_loss: 0.6546 - val_accuracy: 0.6438\n",
            "Epoch 49/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6205 - accuracy: 0.6387 - val_loss: 0.6370 - val_accuracy: 0.6394\n",
            "Epoch 50/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.6225 - accuracy: 0.6529 - val_loss: 0.6351 - val_accuracy: 0.6419\n",
            "Epoch 51/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6168 - accuracy: 0.6547 - val_loss: 0.6357 - val_accuracy: 0.6475\n",
            "Epoch 52/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.6080 - accuracy: 0.6578 - val_loss: 0.6416 - val_accuracy: 0.6500\n",
            "Epoch 53/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6098 - accuracy: 0.6664 - val_loss: 0.6480 - val_accuracy: 0.6325\n",
            "Epoch 54/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6107 - accuracy: 0.6622 - val_loss: 0.6352 - val_accuracy: 0.6350\n",
            "Epoch 55/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.6121 - accuracy: 0.6646 - val_loss: 0.6345 - val_accuracy: 0.6413\n",
            "Epoch 56/100\n",
            "25/25 [==============================] - 18s 734ms/step - loss: 0.6088 - accuracy: 0.6573 - val_loss: 0.6445 - val_accuracy: 0.6431\n",
            "Epoch 57/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6091 - accuracy: 0.6580 - val_loss: 0.6519 - val_accuracy: 0.6044\n",
            "Epoch 58/100\n",
            "25/25 [==============================] - 19s 743ms/step - loss: 0.6132 - accuracy: 0.6499 - val_loss: 0.6445 - val_accuracy: 0.6200\n",
            "Epoch 59/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6087 - accuracy: 0.6613 - val_loss: 0.6419 - val_accuracy: 0.6381\n",
            "Epoch 60/100\n",
            "25/25 [==============================] - 19s 747ms/step - loss: 0.5987 - accuracy: 0.6736 - val_loss: 0.6686 - val_accuracy: 0.6350\n",
            "Epoch 61/100\n",
            "25/25 [==============================] - 19s 742ms/step - loss: 0.6166 - accuracy: 0.6509 - val_loss: 0.6492 - val_accuracy: 0.6381\n",
            "Epoch 62/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.5977 - accuracy: 0.6722 - val_loss: 0.6487 - val_accuracy: 0.6406\n",
            "Epoch 63/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6004 - accuracy: 0.6693 - val_loss: 0.6299 - val_accuracy: 0.6637\n",
            "Epoch 64/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.5999 - accuracy: 0.6744 - val_loss: 0.6378 - val_accuracy: 0.6425\n",
            "Epoch 65/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6052 - accuracy: 0.6689 - val_loss: 0.6406 - val_accuracy: 0.6494\n",
            "Epoch 66/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6041 - accuracy: 0.6724 - val_loss: 0.6321 - val_accuracy: 0.6381\n",
            "Epoch 67/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.6005 - accuracy: 0.6694 - val_loss: 0.6364 - val_accuracy: 0.6575\n",
            "Epoch 68/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.6055 - accuracy: 0.6647 - val_loss: 0.6358 - val_accuracy: 0.6612\n",
            "Epoch 69/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.5973 - accuracy: 0.6721 - val_loss: 0.6265 - val_accuracy: 0.6538\n",
            "Epoch 70/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.5963 - accuracy: 0.6812 - val_loss: 0.6344 - val_accuracy: 0.6438\n",
            "Epoch 71/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6008 - accuracy: 0.6663 - val_loss: 0.6326 - val_accuracy: 0.6481\n",
            "Epoch 72/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.6041 - accuracy: 0.6742 - val_loss: 0.6333 - val_accuracy: 0.6494\n",
            "Epoch 73/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.6036 - accuracy: 0.6641 - val_loss: 0.6293 - val_accuracy: 0.6513\n",
            "Epoch 74/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.5974 - accuracy: 0.6746 - val_loss: 0.6392 - val_accuracy: 0.6294\n",
            "Epoch 75/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6057 - accuracy: 0.6652 - val_loss: 0.6406 - val_accuracy: 0.6288\n",
            "Epoch 76/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.6058 - accuracy: 0.6594 - val_loss: 0.6462 - val_accuracy: 0.6338\n",
            "Epoch 77/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.6138 - accuracy: 0.6584 - val_loss: 0.6311 - val_accuracy: 0.6488\n",
            "Epoch 78/100\n",
            "25/25 [==============================] - 18s 739ms/step - loss: 0.5900 - accuracy: 0.6766 - val_loss: 0.6301 - val_accuracy: 0.6488\n",
            "Epoch 79/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.5907 - accuracy: 0.6807 - val_loss: 0.6268 - val_accuracy: 0.6556\n",
            "Epoch 80/100\n",
            "25/25 [==============================] - 19s 746ms/step - loss: 0.5880 - accuracy: 0.6730 - val_loss: 0.6327 - val_accuracy: 0.6406\n",
            "Epoch 81/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.5956 - accuracy: 0.6700 - val_loss: 0.6294 - val_accuracy: 0.6469\n",
            "Epoch 82/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.5863 - accuracy: 0.6859 - val_loss: 0.6273 - val_accuracy: 0.6587\n",
            "Epoch 83/100\n",
            "25/25 [==============================] - 19s 739ms/step - loss: 0.5790 - accuracy: 0.6888 - val_loss: 0.6407 - val_accuracy: 0.6463\n",
            "Epoch 84/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.5975 - accuracy: 0.6675 - val_loss: 0.6323 - val_accuracy: 0.6594\n",
            "Epoch 85/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.5926 - accuracy: 0.6791 - val_loss: 0.6311 - val_accuracy: 0.6425\n",
            "Epoch 86/100\n",
            "25/25 [==============================] - 18s 735ms/step - loss: 0.5958 - accuracy: 0.6672 - val_loss: 0.6265 - val_accuracy: 0.6600\n",
            "Epoch 87/100\n",
            "25/25 [==============================] - 19s 741ms/step - loss: 0.5886 - accuracy: 0.6811 - val_loss: 0.6422 - val_accuracy: 0.6394\n",
            "Epoch 88/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.5929 - accuracy: 0.6689 - val_loss: 0.6460 - val_accuracy: 0.6281\n",
            "Epoch 89/100\n",
            "25/25 [==============================] - 18s 737ms/step - loss: 0.6079 - accuracy: 0.6720 - val_loss: 0.6424 - val_accuracy: 0.6256\n",
            "Epoch 90/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.6029 - accuracy: 0.6735 - val_loss: 0.6224 - val_accuracy: 0.6525\n",
            "Epoch 91/100\n",
            "25/25 [==============================] - 18s 734ms/step - loss: 0.5803 - accuracy: 0.6994 - val_loss: 0.6295 - val_accuracy: 0.6600\n",
            "Epoch 92/100\n",
            "25/25 [==============================] - 19s 740ms/step - loss: 0.5793 - accuracy: 0.6880 - val_loss: 0.6350 - val_accuracy: 0.6475\n",
            "Epoch 93/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.5872 - accuracy: 0.6876 - val_loss: 0.6281 - val_accuracy: 0.6519\n",
            "Epoch 94/100\n",
            "25/25 [==============================] - 19s 738ms/step - loss: 0.5846 - accuracy: 0.6906 - val_loss: 0.6260 - val_accuracy: 0.6569\n",
            "Epoch 95/100\n",
            "25/25 [==============================] - 18s 734ms/step - loss: 0.5887 - accuracy: 0.6787 - val_loss: 0.6643 - val_accuracy: 0.6475\n",
            "Epoch 96/100\n",
            "25/25 [==============================] - 18s 733ms/step - loss: 0.5989 - accuracy: 0.6721 - val_loss: 0.6343 - val_accuracy: 0.6431\n",
            "Epoch 97/100\n",
            "25/25 [==============================] - 19s 744ms/step - loss: 0.5949 - accuracy: 0.6681 - val_loss: 0.6373 - val_accuracy: 0.6406\n",
            "Epoch 98/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.5914 - accuracy: 0.6778 - val_loss: 0.6317 - val_accuracy: 0.6500\n",
            "Epoch 99/100\n",
            "25/25 [==============================] - 18s 738ms/step - loss: 0.5850 - accuracy: 0.6798 - val_loss: 0.6334 - val_accuracy: 0.6538\n",
            "Epoch 100/100\n",
            "25/25 [==============================] - 18s 736ms/step - loss: 0.5843 - accuracy: 0.6785 - val_loss: 0.6305 - val_accuracy: 0.6556\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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test loss:  0.616276204586029\n",
            "Test accuracy:  0.6629999876022339\n",
            "\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0       0.67      0.63      0.65      1000\n",
            "     Class 1       0.65      0.69      0.67      1000\n",
            "\n",
            "    accuracy                           0.66      2000\n",
            "   macro avg       0.66      0.66      0.66      2000\n",
            "weighted avg       0.66      0.66      0.66      2000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bF3OOVaKopET"
      },
      "source": [
        "# تحلیل عملکرد چهارمین مدل\n",
        "\n",
        "<div dir='rtl'>\n",
        "در این قسمت همانند سه مدل قبلی شبکه یکسانی داریم، مشابه مدل دوم از دراپ‌اوت استفاده کرده ایم و مشابه مدل سوم از داده های آگمنت شده برای آموزش مدل استفاده کرده ایم.\n",
        "\n",
        "در این مدل نیز نسبت به سه مدل قبلی صحت مدل هنگام ارزیابی با داده های تست افزایش یافته است، اگرچه لازم است به این نکته توجه کنیم که درصورت استفاده بیش از انداره از دراپ‌اوت و یا آگمنتیشن مدل آندرفیت شده است و نمیتوانیم به خوبی از قابلیتهای شبکه استفاده کرده و مدل مناسبی آموزش دهیم.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQFJ9Jmq4VSs"
      },
      "source": [
        "# جمع بندی قسمت الف\n",
        "<div dir='rtl'>\n",
        "در این قسمت ابتدا مدل را با استفاده از یک شبکه عمیق ۱۹ لایه آموزش می‌دهیم.\n",
        "بعد از ۱۰۰ ایپاک آموزش و ارزیابی مدل با داده های تست دریافتیم که مدل دچار بیش‌برازش شده و عملکرد یکسانی بروی داده های آموزش و تست ندارد.\n",
        "\n",
        "برای رفع این مشکل از تکنیک منظم سازی دراپ‌اوت و آگمنت کردن داده ها استفاده کردیم و طی مراحل جداگانه رفتار مدل پس از استفاده از این موارد را نشان دادیم و نهایتا توانستیم با داشتن شبکه یکسان از صحت ۶۲ درصد به ۶۶ درصد برسیم.\n",
        "\n",
        "در هر یک از این مراحل برای مشخص کردن بهترین تنظیم پارامترها موارد گوناگونی بررسی شده است و هم اکنون پارامترهای مدل به گونه ای تنظیم شده اند که از آورفیت شدن یا آندرفیت شدن مدل جلوگیری شود.\n",
        "</div>"
      ]
    }
  ]
}