CNN_Hyperparameter_Tuning.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import cv2\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import PIL.Image as Image\n",
        "import tensorflow as tf\n",
        "import os\n",
        "import seaborn as sb\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D,MaxPooling2D,Flatten,Dense,Dropout\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5DMxPItvA7OL",
        "outputId": "b98858cb-9c43-484d-f30a-477aa29bf214"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HV0SYvzt_T_E",
        "outputId": "41fe463c-0a8c-41c9-fecd-1b8992385427"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Could not read image /content/drive/MyDrive/Dataset/hole/hole. Skipping this file.\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import cv2\n",
        "\n",
        "dataset_path = '/content/drive/MyDrive/Dataset'\n",
        "sub_folders = os.listdir(dataset_path)\n",
        "IMAGE_SHAPE = (224, 224)\n",
        "i = 0\n",
        "images = []\n",
        "labels = []\n",
        "path_lists = []\n",
        "temp = sub_folders\n",
        "\n",
        "for sub_folder in sub_folders:\n",
        "    sub_folder_index = temp.index(sub_folder)\n",
        "    label = sub_folder_index\n",
        "\n",
        "    # Skipping folder with label 2 and adjusting label 3 to 2\n",
        "    if label == 2:\n",
        "        continue\n",
        "    if label == 3:\n",
        "        label = 2\n",
        "\n",
        "    path = dataset_path + '/' + sub_folder\n",
        "    sub_folder_images = os.listdir(path)\n",
        "\n",
        "    # Reading images from the subfolder one at a time\n",
        "    for image in sub_folder_images:\n",
        "        image_path = path + '/' + image\n",
        "        image = cv2.imread(image_path)\n",
        "\n",
        "        # Check if the image was successfully loaded\n",
        "        if image is None:\n",
        "            print(f\"Warning: Could not read image {image_path}. Skipping this file.\")\n",
        "            continue\n",
        "\n",
        "        resized_image = cv2.resize(image, IMAGE_SHAPE)\n",
        "        path_lists.append(image_path)\n",
        "        images.append(resized_image)\n",
        "        labels.append(label)\n",
        "        i += 1\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "images = np.array(images)\n",
        "labels = np.array(labels)\n",
        "defect_classes = ['Holes','Horizontal','Vertical']\n",
        "num_of_classes = 3\n",
        "classes_encoded = to_categorical(labels,num_classes = num_of_classes)"
      ],
      "metadata": {
        "id": "Xp3gmT8SBg-R"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "images = images/255.0"
      ],
      "metadata": {
        "id": "NthdAun3CDTa"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(images,classes_encoded,test_size=0.2,random_state=10)"
      ],
      "metadata": {
        "id": "_wsh1BrnCFF4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5whCZsgCGKo",
        "outputId": "31523b60-e44b-4648-f059-86b6703f102a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48, 224, 224, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CNN Model 1"
      ],
      "metadata": {
        "id": "7CBeTUzFEfL0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "defect_classifier1 = Sequential()\n",
        "\n",
        "#Adding the CNN layers\n",
        "defect_classifier1.add(Conv2D(16,(3,3),1,activation='relu',input_shape=(224,224,3)))\n",
        "defect_classifier1.add(MaxPooling2D(2,2))\n",
        "defect_classifier1.add(Conv2D(32,(3,3),1,activation='relu'))\n",
        "defect_classifier1.add(MaxPooling2D(2,2))\n",
        "defect_classifier1.add(Conv2D(64,(3,3),1,activation='relu'))\n",
        "defect_classifier1.add(MaxPooling2D(2,2))\n",
        "defect_classifier1.add(Conv2D(32,(3,3),1,activation='relu'))\n",
        "defect_classifier1.add(MaxPooling2D())\n",
        "defect_classifier1.add(Conv2D(16,(3,3),1,activation='relu'))\n",
        "defect_classifier1.add(MaxPooling2D())\n",
        "\n",
        "#Flattening the output of Conv\n",
        "defect_classifier1.add(Flatten())\n",
        "\n",
        "#Adding Hidden layers along with Dropout\n",
        "defect_classifier1.add(Dense(128,activation='relu'))\n",
        "defect_classifier1.add(Dropout(0.25))\n",
        "defect_classifier1.add(Dense(64,activation='relu'))\n",
        "defect_classifier1.add(Dropout(0.2))\n",
        "\n",
        "#Output layer\n",
        "defect_classifier1.add(Dense(3,activation='softmax'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTcwsy25CHw6",
        "outputId": "888da001-3194-4edd-fa54-dd94a304b647"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "pxwo2wVdEc-a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "defect_classifier1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "1l6tXr30DLtq",
        "outputId": "ec5c063f-9ed6-40dc-d01e-dc4abebde413"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m16\u001b[0m)        │             \u001b[38;5;34m448\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m16\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │           \u001b[38;5;34m4,640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │          \u001b[38;5;34m18,464\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │           \u001b[38;5;34m4,624\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m16\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m400\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m51,328\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m8,256\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │             \u001b[38;5;34m195\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,464</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,624</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">400</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">51,328</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m106,451\u001b[0m (415.82 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">106,451</span> (415.82 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
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          "output_type": "display_data",
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            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m106,451\u001b[0m (415.82 KB)\n"
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">106,451</span> (415.82 KB)\n",
              "</pre>\n"
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          },
          "metadata": {}
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          "data": {
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              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "defect_classifier1.compile(optimizer='adam',loss=tf.keras.losses.CategoricalCrossentropy(),metrics=['accuracy'])\n",
        "filepath = '/content/model1.keras'\n",
        "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath = filepath,monitor='val_loss',verbose=1,save_best_only=True,mode='min')\n",
        "callbacks = [checkpoint]"
      ],
      "metadata": {
        "id": "yekw61lcCOTY"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history1 = defect_classifier1.fit(X_train,y_train,epochs=30,batch_size=32,validation_split=0.2,callbacks=[callbacks])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jf2tYG8UCWDY",
        "outputId": "76092c76-f9ee-4be9-8dc3-0c1ceed5707e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4s/step - accuracy: 0.4712 - loss: 1.0762\n",
            "Epoch 1: val_loss improved from inf to 1.00365, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 5s/step - accuracy: 0.4720 - loss: 1.0745 - val_accuracy: 0.6000 - val_loss: 1.0037\n",
            "Epoch 2/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.6250 - loss: 0.9290\n",
            "Epoch 2: val_loss did not improve from 1.00365\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - accuracy: 0.6294 - loss: 0.9213 - val_accuracy: 0.6000 - val_loss: 1.2084\n",
            "Epoch 3/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - accuracy: 0.5938 - loss: 0.8991\n",
            "Epoch 3: val_loss did not improve from 1.00365\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 0.6190 - loss: 0.8989 - val_accuracy: 0.6000 - val_loss: 1.1580\n",
            "Epoch 4/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.6875 - loss: 0.6454\n",
            "Epoch 4: val_loss improved from 1.00365 to 0.92859, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - accuracy: 0.6502 - loss: 0.7526 - val_accuracy: 0.6000 - val_loss: 0.9286\n",
            "Epoch 5/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.6562 - loss: 0.7656\n",
            "Epoch 5: val_loss improved from 0.92859 to 0.87377, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.6573 - loss: 0.7646 - val_accuracy: 0.7000 - val_loss: 0.8738\n",
            "Epoch 6/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - accuracy: 0.8125 - loss: 0.7704\n",
            "Epoch 6: val_loss improved from 0.87377 to 0.84981, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - accuracy: 0.8147 - loss: 0.7558 - val_accuracy: 0.7000 - val_loss: 0.8498\n",
            "Epoch 7/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - accuracy: 0.7812 - loss: 0.8412\n",
            "Epoch 7: val_loss improved from 0.84981 to 0.79281, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - accuracy: 0.7867 - loss: 0.7991 - val_accuracy: 0.7000 - val_loss: 0.7928\n",
            "Epoch 8/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 196ms/step - accuracy: 0.7188 - loss: 0.7018\n",
            "Epoch 8: val_loss did not improve from 0.79281\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 0.7133 - loss: 0.6824 - val_accuracy: 0.7000 - val_loss: 0.8262\n",
            "Epoch 9/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.7812 - loss: 0.6161\n",
            "Epoch 9: val_loss did not improve from 0.79281\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.7692 - loss: 0.6086 - val_accuracy: 0.7000 - val_loss: 0.9863\n",
            "Epoch 10/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.6562 - loss: 0.5645\n",
            "Epoch 10: val_loss did not improve from 0.79281\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.6573 - loss: 0.5994 - val_accuracy: 0.7000 - val_loss: 0.8976\n",
            "Epoch 11/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.7812 - loss: 0.5108\n",
            "Epoch 11: val_loss improved from 0.79281 to 0.69180, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - accuracy: 0.8043 - loss: 0.4866 - val_accuracy: 0.7000 - val_loss: 0.6918\n",
            "Epoch 12/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.7500 - loss: 0.4753\n",
            "Epoch 12: val_loss improved from 0.69180 to 0.63000, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - accuracy: 0.7588 - loss: 0.4661 - val_accuracy: 0.8000 - val_loss: 0.6300\n",
            "Epoch 13/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.8438 - loss: 0.4187\n",
            "Epoch 13: val_loss improved from 0.63000 to 0.59961, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - accuracy: 0.8251 - loss: 0.4326 - val_accuracy: 0.8000 - val_loss: 0.5996\n",
            "Epoch 14/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - accuracy: 0.9062 - loss: 0.3638\n",
            "Epoch 14: val_loss improved from 0.59961 to 0.56750, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - accuracy: 0.8635 - loss: 0.3873 - val_accuracy: 0.8000 - val_loss: 0.5675\n",
            "Epoch 15/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - accuracy: 0.7812 - loss: 0.3894\n",
            "Epoch 15: val_loss improved from 0.56750 to 0.55717, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - accuracy: 0.8043 - loss: 0.3774 - val_accuracy: 0.8000 - val_loss: 0.5572\n",
            "Epoch 16/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 204ms/step - accuracy: 0.8125 - loss: 0.3767\n",
            "Epoch 16: val_loss improved from 0.55717 to 0.54747, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - accuracy: 0.8147 - loss: 0.3638 - val_accuracy: 0.8000 - val_loss: 0.5475\n",
            "Epoch 17/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 197ms/step - accuracy: 0.8438 - loss: 0.2762\n",
            "Epoch 17: val_loss improved from 0.54747 to 0.50262, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - accuracy: 0.8251 - loss: 0.3424 - val_accuracy: 0.8000 - val_loss: 0.5026\n",
            "Epoch 18/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 206ms/step - accuracy: 0.8125 - loss: 0.3086\n",
            "Epoch 18: val_loss improved from 0.50262 to 0.43493, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - accuracy: 0.7971 - loss: 0.3495 - val_accuracy: 0.8000 - val_loss: 0.4349\n",
            "Epoch 19/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.9375 - loss: 0.3009\n",
            "Epoch 19: val_loss improved from 0.43493 to 0.38769, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - accuracy: 0.9265 - loss: 0.3082 - val_accuracy: 0.8000 - val_loss: 0.3877\n",
            "Epoch 20/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 205ms/step - accuracy: 0.8750 - loss: 0.3436\n",
            "Epoch 20: val_loss did not improve from 0.38769\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.8882 - loss: 0.3173 - val_accuracy: 0.8000 - val_loss: 0.3913\n",
            "Epoch 21/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.8125 - loss: 0.3705\n",
            "Epoch 21: val_loss did not improve from 0.38769\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 0.8322 - loss: 0.3354 - val_accuracy: 0.8000 - val_loss: 0.4028\n",
            "Epoch 22/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - accuracy: 0.9062 - loss: 0.2194\n",
            "Epoch 22: val_loss did not improve from 0.38769\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.9161 - loss: 0.2276 - val_accuracy: 0.8000 - val_loss: 0.4296\n",
            "Epoch 23/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - accuracy: 0.9375 - loss: 0.2366\n",
            "Epoch 23: val_loss did not improve from 0.38769\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.9265 - loss: 0.2509 - val_accuracy: 0.8000 - val_loss: 0.4457\n",
            "Epoch 24/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - accuracy: 0.9062 - loss: 0.3223\n",
            "Epoch 24: val_loss improved from 0.38769 to 0.31862, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.8986 - loss: 0.3184 - val_accuracy: 0.9000 - val_loss: 0.3186\n",
            "Epoch 25/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - accuracy: 0.8750 - loss: 0.2353\n",
            "Epoch 25: val_loss improved from 0.31862 to 0.23051, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - accuracy: 0.8706 - loss: 0.2372 - val_accuracy: 0.9000 - val_loss: 0.2305\n",
            "Epoch 26/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.9062 - loss: 0.2855\n",
            "Epoch 26: val_loss improved from 0.23051 to 0.21245, saving model to /content/model1.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - accuracy: 0.9161 - loss: 0.2558 - val_accuracy: 0.9000 - val_loss: 0.2125\n",
            "Epoch 27/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.9375 - loss: 0.1908\n",
            "Epoch 27: val_loss did not improve from 0.21245\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.8914 - loss: 0.2255 - val_accuracy: 0.9000 - val_loss: 0.2393\n",
            "Epoch 28/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.9688 - loss: 0.1815\n",
            "Epoch 28: val_loss did not improve from 0.21245\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.9370 - loss: 0.2045 - val_accuracy: 0.9000 - val_loss: 0.2531\n",
            "Epoch 29/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.9062 - loss: 0.1941\n",
            "Epoch 29: val_loss did not improve from 0.21245\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9161 - loss: 0.1811 - val_accuracy: 0.9000 - val_loss: 0.2404\n",
            "Epoch 30/30\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.9375 - loss: 0.1716\n",
            "Epoch 30: val_loss did not improve from 0.21245\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.9441 - loss: 0.1718 - val_accuracy: 0.9000 - val_loss: 0.2747\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CNN Model 2"
      ],
      "metadata": {
        "id": "x7dGxdcqEreE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Define the model\n",
        "defect_classifier2 = Sequential()\n",
        "\n",
        "# Adding the CNN layers\n",
        "defect_classifier2.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))\n",
        "defect_classifier2.add(BatchNormalization())\n",
        "defect_classifier2.add(MaxPooling2D(2, 2))\n",
        "\n",
        "defect_classifier2.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "defect_classifier2.add(BatchNormalization())\n",
        "defect_classifier2.add(MaxPooling2D(2, 2))\n",
        "\n",
        "defect_classifier2.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "defect_classifier2.add(BatchNormalization())\n",
        "defect_classifier2.add(MaxPooling2D(2, 2))\n",
        "\n",
        "defect_classifier2.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "defect_classifier2.add(BatchNormalization())\n",
        "defect_classifier2.add(MaxPooling2D(2, 2))\n",
        "\n",
        "# Flatten the output\n",
        "defect_classifier2.add(Flatten())\n",
        "\n",
        "# Adding Dense layers with Dropout and Batch Normalization\n",
        "defect_classifier2.add(Dense(256, activation='relu'))\n",
        "defect_classifier2.add(BatchNormalization())\n",
        "defect_classifier2.add(Dropout(0.4))\n",
        "\n",
        "defect_classifier2.add(Dense(128, activation='relu'))\n",
        "defect_classifier2.add(BatchNormalization())\n",
        "defect_classifier2.add(Dropout(0.3))\n",
        "\n",
        "# Output layer\n",
        "defect_classifier2.add(Dense(3, activation='softmax'))\n",
        "\n",
        "# Compile the model with a lower learning rate\n",
        "optimizer = Adam(learning_rate=0.0001)\n",
        "defect_classifier2.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Model checkpoint\n",
        "filepath = '/content/model2.keras'\n",
        "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath=filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
        "callbacks = [checkpoint]\n",
        "\n",
        "# Train the model\n",
        "history2 = defect_classifier2.fit(X_train, y_train, epochs=50, batch_size=64, validation_split=0.2, callbacks=callbacks)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_G16atkDRBE",
        "outputId": "ca6bec60-4a62-4308-c2e4-2e3f9d1245f0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12s/step - accuracy: 0.3158 - loss: 2.0157\n",
            "Epoch 1: val_loss improved from inf to 1.08570, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 14s/step - accuracy: 0.3158 - loss: 2.0157 - val_accuracy: 0.4000 - val_loss: 1.0857\n",
            "Epoch 2/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10s/step - accuracy: 0.5000 - loss: 1.3428\n",
            "Epoch 2: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 10s/step - accuracy: 0.5000 - loss: 1.3428 - val_accuracy: 0.3000 - val_loss: 1.0864\n",
            "Epoch 3/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - accuracy: 0.6842 - loss: 0.8011\n",
            "Epoch 3: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - accuracy: 0.6842 - loss: 0.8011 - val_accuracy: 0.3000 - val_loss: 1.0886\n",
            "Epoch 4/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step - accuracy: 0.6316 - loss: 0.8678\n",
            "Epoch 4: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 288ms/step - accuracy: 0.6316 - loss: 0.8678 - val_accuracy: 0.5000 - val_loss: 1.0900\n",
            "Epoch 5/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 270ms/step - accuracy: 0.7105 - loss: 0.7049\n",
            "Epoch 5: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 329ms/step - accuracy: 0.7105 - loss: 0.7049 - val_accuracy: 0.5000 - val_loss: 1.0906\n",
            "Epoch 6/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - accuracy: 0.7368 - loss: 0.6632\n",
            "Epoch 6: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - accuracy: 0.7368 - loss: 0.6632 - val_accuracy: 0.5000 - val_loss: 1.0909\n",
            "Epoch 7/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - accuracy: 0.7895 - loss: 0.5462\n",
            "Epoch 7: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.7895 - loss: 0.5462 - val_accuracy: 0.5000 - val_loss: 1.0908\n",
            "Epoch 8/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - accuracy: 0.8158 - loss: 0.4992\n",
            "Epoch 8: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 294ms/step - accuracy: 0.8158 - loss: 0.4992 - val_accuracy: 0.5000 - val_loss: 1.0916\n",
            "Epoch 9/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.9474 - loss: 0.3405\n",
            "Epoch 9: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 301ms/step - accuracy: 0.9474 - loss: 0.3405 - val_accuracy: 0.5000 - val_loss: 1.0921\n",
            "Epoch 10/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - accuracy: 0.8421 - loss: 0.3549\n",
            "Epoch 10: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 182ms/step - accuracy: 0.8421 - loss: 0.3549 - val_accuracy: 0.5000 - val_loss: 1.0916\n",
            "Epoch 11/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 247ms/step - accuracy: 0.8947 - loss: 0.2611\n",
            "Epoch 11: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317ms/step - accuracy: 0.8947 - loss: 0.2611 - val_accuracy: 0.5000 - val_loss: 1.0910\n",
            "Epoch 12/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 222ms/step - accuracy: 0.7895 - loss: 0.3843\n",
            "Epoch 12: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 276ms/step - accuracy: 0.7895 - loss: 0.3843 - val_accuracy: 0.4000 - val_loss: 1.0901\n",
            "Epoch 13/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - accuracy: 0.8158 - loss: 0.4119\n",
            "Epoch 13: val_loss did not improve from 1.08570\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - accuracy: 0.8158 - loss: 0.4119 - val_accuracy: 0.4000 - val_loss: 1.0869\n",
            "Epoch 14/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - accuracy: 0.9211 - loss: 0.3583\n",
            "Epoch 14: val_loss improved from 1.08570 to 1.08340, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 487ms/step - accuracy: 0.9211 - loss: 0.3583 - val_accuracy: 0.4000 - val_loss: 1.0834\n",
            "Epoch 15/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358ms/step - accuracy: 0.9474 - loss: 0.1950\n",
            "Epoch 15: val_loss improved from 1.08340 to 1.07968, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 601ms/step - accuracy: 0.9474 - loss: 0.1950 - val_accuracy: 0.4000 - val_loss: 1.0797\n",
            "Epoch 16/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - accuracy: 0.9211 - loss: 0.2744\n",
            "Epoch 16: val_loss improved from 1.07968 to 1.07429, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 357ms/step - accuracy: 0.9211 - loss: 0.2744 - val_accuracy: 0.4000 - val_loss: 1.0743\n",
            "Epoch 17/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 370ms/step - accuracy: 0.8947 - loss: 0.3077\n",
            "Epoch 17: val_loss improved from 1.07429 to 1.06856, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 631ms/step - accuracy: 0.8947 - loss: 0.3077 - val_accuracy: 0.3000 - val_loss: 1.0686\n",
            "Epoch 18/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 354ms/step - accuracy: 0.8947 - loss: 0.2957\n",
            "Epoch 18: val_loss improved from 1.06856 to 1.06221, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 632ms/step - accuracy: 0.8947 - loss: 0.2957 - val_accuracy: 0.3000 - val_loss: 1.0622\n",
            "Epoch 19/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 346ms/step - accuracy: 1.0000 - loss: 0.1580\n",
            "Epoch 19: val_loss improved from 1.06221 to 1.05558, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 605ms/step - accuracy: 1.0000 - loss: 0.1580 - val_accuracy: 0.4000 - val_loss: 1.0556\n",
            "Epoch 20/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - accuracy: 0.8421 - loss: 0.3047\n",
            "Epoch 20: val_loss improved from 1.05558 to 1.04959, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 393ms/step - accuracy: 0.8421 - loss: 0.3047 - val_accuracy: 0.4000 - val_loss: 1.0496\n",
            "Epoch 21/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 331ms/step - accuracy: 0.9211 - loss: 0.1600\n",
            "Epoch 21: val_loss improved from 1.04959 to 1.04475, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 601ms/step - accuracy: 0.9211 - loss: 0.1600 - val_accuracy: 0.4000 - val_loss: 1.0447\n",
            "Epoch 22/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - accuracy: 0.9211 - loss: 0.2308\n",
            "Epoch 22: val_loss improved from 1.04475 to 1.03819, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 907ms/step - accuracy: 0.9211 - loss: 0.2308 - val_accuracy: 0.4000 - val_loss: 1.0382\n",
            "Epoch 23/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - accuracy: 0.9737 - loss: 0.1322\n",
            "Epoch 23: val_loss improved from 1.03819 to 1.03226, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.9737 - loss: 0.1322 - val_accuracy: 0.4000 - val_loss: 1.0323\n",
            "Epoch 24/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 338ms/step - accuracy: 0.8947 - loss: 0.2948\n",
            "Epoch 24: val_loss improved from 1.03226 to 1.02501, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 588ms/step - accuracy: 0.8947 - loss: 0.2948 - val_accuracy: 0.4000 - val_loss: 1.0250\n",
            "Epoch 25/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - accuracy: 0.9211 - loss: 0.1994\n",
            "Epoch 25: val_loss improved from 1.02501 to 1.01596, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 368ms/step - accuracy: 0.9211 - loss: 0.1994 - val_accuracy: 0.4000 - val_loss: 1.0160\n",
            "Epoch 26/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - accuracy: 0.9211 - loss: 0.2244\n",
            "Epoch 26: val_loss improved from 1.01596 to 1.00524, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 390ms/step - accuracy: 0.9211 - loss: 0.2244 - val_accuracy: 0.4000 - val_loss: 1.0052\n",
            "Epoch 27/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - accuracy: 0.9474 - loss: 0.2237\n",
            "Epoch 27: val_loss improved from 1.00524 to 0.99589, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 383ms/step - accuracy: 0.9474 - loss: 0.2237 - val_accuracy: 0.4000 - val_loss: 0.9959\n",
            "Epoch 28/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 339ms/step - accuracy: 1.0000 - loss: 0.1077\n",
            "Epoch 28: val_loss improved from 0.99589 to 0.98707, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 580ms/step - accuracy: 1.0000 - loss: 0.1077 - val_accuracy: 0.6000 - val_loss: 0.9871\n",
            "Epoch 29/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - accuracy: 1.0000 - loss: 0.1290\n",
            "Epoch 29: val_loss improved from 0.98707 to 0.97779, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 357ms/step - accuracy: 1.0000 - loss: 0.1290 - val_accuracy: 0.6000 - val_loss: 0.9778\n",
            "Epoch 30/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - accuracy: 0.9474 - loss: 0.1512\n",
            "Epoch 30: val_loss improved from 0.97779 to 0.96967, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365ms/step - accuracy: 0.9474 - loss: 0.1512 - val_accuracy: 0.8000 - val_loss: 0.9697\n",
            "Epoch 31/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 369ms/step - accuracy: 0.9737 - loss: 0.1489\n",
            "Epoch 31: val_loss improved from 0.96967 to 0.96088, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 650ms/step - accuracy: 0.9737 - loss: 0.1489 - val_accuracy: 0.7000 - val_loss: 0.9609\n",
            "Epoch 32/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - accuracy: 0.9737 - loss: 0.0852\n",
            "Epoch 32: val_loss improved from 0.96088 to 0.95245, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 466ms/step - accuracy: 0.9737 - loss: 0.0852 - val_accuracy: 0.7000 - val_loss: 0.9525\n",
            "Epoch 33/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 264ms/step - accuracy: 0.9737 - loss: 0.1194\n",
            "Epoch 33: val_loss improved from 0.95245 to 0.94297, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 587ms/step - accuracy: 0.9737 - loss: 0.1194 - val_accuracy: 0.6000 - val_loss: 0.9430\n",
            "Epoch 34/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 301ms/step - accuracy: 1.0000 - loss: 0.0820\n",
            "Epoch 34: val_loss improved from 0.94297 to 0.93472, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 623ms/step - accuracy: 1.0000 - loss: 0.0820 - val_accuracy: 0.6000 - val_loss: 0.9347\n",
            "Epoch 35/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 287ms/step - accuracy: 0.9737 - loss: 0.1128\n",
            "Epoch 35: val_loss improved from 0.93472 to 0.92772, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 661ms/step - accuracy: 0.9737 - loss: 0.1128 - val_accuracy: 0.6000 - val_loss: 0.9277\n",
            "Epoch 36/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - accuracy: 0.9737 - loss: 0.1510\n",
            "Epoch 36: val_loss improved from 0.92772 to 0.92164, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 528ms/step - accuracy: 0.9737 - loss: 0.1510 - val_accuracy: 0.6000 - val_loss: 0.9216\n",
            "Epoch 37/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - accuracy: 1.0000 - loss: 0.0898\n",
            "Epoch 37: val_loss improved from 0.92164 to 0.91618, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 472ms/step - accuracy: 1.0000 - loss: 0.0898 - val_accuracy: 0.6000 - val_loss: 0.9162\n",
            "Epoch 38/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - accuracy: 0.9737 - loss: 0.1377\n",
            "Epoch 38: val_loss improved from 0.91618 to 0.91066, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 373ms/step - accuracy: 0.9737 - loss: 0.1377 - val_accuracy: 0.6000 - val_loss: 0.9107\n",
            "Epoch 39/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 372ms/step - accuracy: 1.0000 - loss: 0.0845\n",
            "Epoch 39: val_loss improved from 0.91066 to 0.90584, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 637ms/step - accuracy: 1.0000 - loss: 0.0845 - val_accuracy: 0.6000 - val_loss: 0.9058\n",
            "Epoch 40/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 342ms/step - accuracy: 0.9211 - loss: 0.1366\n",
            "Epoch 40: val_loss improved from 0.90584 to 0.90072, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 622ms/step - accuracy: 0.9211 - loss: 0.1366 - val_accuracy: 0.6000 - val_loss: 0.9007\n",
            "Epoch 41/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - accuracy: 1.0000 - loss: 0.0795\n",
            "Epoch 41: val_loss improved from 0.90072 to 0.89567, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 390ms/step - accuracy: 1.0000 - loss: 0.0795 - val_accuracy: 0.6000 - val_loss: 0.8957\n",
            "Epoch 42/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - accuracy: 1.0000 - loss: 0.0803\n",
            "Epoch 42: val_loss improved from 0.89567 to 0.89101, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 492ms/step - accuracy: 1.0000 - loss: 0.0803 - val_accuracy: 0.6000 - val_loss: 0.8910\n",
            "Epoch 43/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 1.0000 - loss: 0.0625\n",
            "Epoch 43: val_loss improved from 0.89101 to 0.88640, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 495ms/step - accuracy: 1.0000 - loss: 0.0625 - val_accuracy: 0.6000 - val_loss: 0.8864\n",
            "Epoch 44/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - accuracy: 1.0000 - loss: 0.0704\n",
            "Epoch 44: val_loss improved from 0.88640 to 0.88274, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 376ms/step - accuracy: 1.0000 - loss: 0.0704 - val_accuracy: 0.6000 - val_loss: 0.8827\n",
            "Epoch 45/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - accuracy: 0.9737 - loss: 0.1307\n",
            "Epoch 45: val_loss improved from 0.88274 to 0.87940, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - accuracy: 0.9737 - loss: 0.1307 - val_accuracy: 0.6000 - val_loss: 0.8794\n",
            "Epoch 46/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 371ms/step - accuracy: 0.9211 - loss: 0.1417\n",
            "Epoch 46: val_loss improved from 0.87940 to 0.87606, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 699ms/step - accuracy: 0.9211 - loss: 0.1417 - val_accuracy: 0.6000 - val_loss: 0.8761\n",
            "Epoch 47/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 282ms/step - accuracy: 1.0000 - loss: 0.0876\n",
            "Epoch 47: val_loss improved from 0.87606 to 0.87302, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 524ms/step - accuracy: 1.0000 - loss: 0.0876 - val_accuracy: 0.6000 - val_loss: 0.8730\n",
            "Epoch 48/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - accuracy: 0.9737 - loss: 0.0778\n",
            "Epoch 48: val_loss improved from 0.87302 to 0.86977, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 390ms/step - accuracy: 0.9737 - loss: 0.0778 - val_accuracy: 0.6000 - val_loss: 0.8698\n",
            "Epoch 49/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 343ms/step - accuracy: 1.0000 - loss: 0.0864\n",
            "Epoch 49: val_loss improved from 0.86977 to 0.86717, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 645ms/step - accuracy: 1.0000 - loss: 0.0864 - val_accuracy: 0.6000 - val_loss: 0.8672\n",
            "Epoch 50/50\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - accuracy: 1.0000 - loss: 0.0639\n",
            "Epoch 50: val_loss improved from 0.86717 to 0.86463, saving model to /content/model2.keras\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 820ms/step - accuracy: 1.0000 - loss: 0.0639 - val_accuracy: 0.6000 - val_loss: 0.8646\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "defect_classifier2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "ZpYr6hn6EYtO",
        "outputId": "932fb967-85bc-4542-94ef-84f90fac9ff1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m896\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m18,496\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_1                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │             \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_2                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m73,792\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_3                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9216\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │       \u001b[38;5;34m2,359,552\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_4                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │           \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m32,896\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_5                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │             \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │             \u001b[38;5;34m387\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_1                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_2                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,792</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_3                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9216</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,552</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_4                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_5                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">387</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,685,003\u001b[0m (29.32 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,685,003</span> (29.32 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,561,219\u001b[0m (9.77 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,561,219</span> (9.77 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,344\u001b[0m (5.25 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,344</span> (5.25 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m5,122,440\u001b[0m (19.54 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">5,122,440</span> (19.54 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CNN Model 3"
      ],
      "metadata": {
        "id": "mcZHRjphFn6e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization, LeakyReLU\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "\n",
        "# Define an alternative CNN architecture\n",
        "defect_classifier3 = Sequential()\n",
        "\n",
        "# Adding the CNN layers with LeakyReLU activation and Batch Normalization\n",
        "defect_classifier3.add(Conv2D(32, (3, 3), padding='same', input_shape=(224, 224, 3)))\n",
        "defect_classifier3.add(BatchNormalization())\n",
        "defect_classifier3.add(LeakyReLU(alpha=0.1))\n",
        "defect_classifier3.add(MaxPooling2D(2, 2))\n",
        "\n",
        "defect_classifier3.add(Conv2D(64, (3, 3), padding='same'))\n",
        "defect_classifier3.add(BatchNormalization())\n",
        "defect_classifier3.add(LeakyReLU(alpha=0.1))\n",
        "defect_classifier3.add(MaxPooling2D(2, 2))\n",
        "\n",
        "defect_classifier3.add(Conv2D(128, (3, 3), padding='same'))\n",
        "defect_classifier3.add(BatchNormalization())\n",
        "defect_classifier3.add(LeakyReLU(alpha=0.1))\n",
        "defect_classifier3.add(MaxPooling2D(2, 2))\n",
        "\n",
        "defect_classifier3.add(Conv2D(256, (3, 3), padding='same'))\n",
        "defect_classifier3.add(BatchNormalization())\n",
        "defect_classifier3.add(LeakyReLU(alpha=0.1))\n",
        "defect_classifier3.add(MaxPooling2D(2, 2))\n",
        "\n",
        "# Flattening the output of Conv layers\n",
        "defect_classifier3.add(Flatten())\n",
        "\n",
        "# Adding Dense layers with Dropout and a higher number of neurons\n",
        "defect_classifier3.add(Dense(512))\n",
        "defect_classifier3.add(BatchNormalization())\n",
        "defect_classifier3.add(LeakyReLU(alpha=0.1))\n",
        "defect_classifier3.add(Dropout(0.5))\n",
        "\n",
        "defect_classifier3.add(Dense(256))\n",
        "defect_classifier3.add(BatchNormalization())\n",
        "defect_classifier3.add(LeakyReLU(alpha=0.1))\n",
        "defect_classifier3.add(Dropout(0.4))\n",
        "\n",
        "# Output layer\n",
        "defect_classifier3.add(Dense(3, activation='softmax'))\n",
        "\n",
        "# Compile the model with the RMSprop optimizer and a different learning rate\n",
        "learning_rate = 0.00005\n",
        "defect_classifier3.compile(optimizer=RMSprop(learning_rate=learning_rate),\n",
        "                           loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                           metrics=['accuracy'])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqcVQrxEEpXY",
        "outputId": "69ab471b-f59c-42d3-f60d-a932ab8cffe7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/activations/leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "defect_classifier3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9v-xZiKgFkw4",
        "outputId": "737e8146-4492-4d2c-8fca-9ccb2f1ebc4c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m896\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_6                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu (\u001b[38;5;33mLeakyReLU\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m18,496\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_7                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │             \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_1 (\u001b[38;5;33mLeakyReLU\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_10 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_8                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_2 (\u001b[38;5;33mLeakyReLU\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_11 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_9                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_3 (\u001b[38;5;33mLeakyReLU\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_12 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50176\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │      \u001b[38;5;34m25,690,624\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_10               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_4 (\u001b[38;5;33mLeakyReLU\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │         \u001b[38;5;34m131,328\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_11               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │           \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_5 (\u001b[38;5;33mLeakyReLU\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │             \u001b[38;5;34m771\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_6                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_7                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_8                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_9                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50176</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │      <span style=\"color: #00af00; text-decoration-color: #00af00\">25,690,624</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_10               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">131,328</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_11               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">771</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m26,216,131\u001b[0m (100.01 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">26,216,131</span> (100.01 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m26,213,635\u001b[0m (100.00 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">26,213,635</span> (100.00 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,496\u001b[0m (9.75 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,496</span> (9.75 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up callbacks: ModelCheckpoint and EarlyStopping\n",
        "filepath = '/content/model3.keras'\n",
        "checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=5, verbose=1, restore_best_weights=True)\n",
        "callbacks = [checkpoint, early_stop]\n",
        "\n",
        "# Train the model with an adjusted number of epochs\n",
        "history3 = defect_classifier3.fit(X_train, y_train, epochs=40, batch_size=32, validation_split=0.2, callbacks=callbacks)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tF-Si6laFlM3",
        "outputId": "acfd27da-81f0-481b-ca4a-f180fe6e9153"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7s/step - accuracy: 0.2697 - loss: 1.4522  \n",
            "Epoch 1: val_loss improved from inf to 1.05299, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 13s/step - accuracy: 0.2763 - loss: 1.4393 - val_accuracy: 0.8000 - val_loss: 1.0530\n",
            "Epoch 2/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - accuracy: 0.6875 - loss: 0.8251\n",
            "Epoch 2: val_loss improved from 1.05299 to 1.04878, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.6678 - loss: 0.8394 - val_accuracy: 0.7000 - val_loss: 1.0488\n",
            "Epoch 3/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - accuracy: 0.9375 - loss: 0.4506\n",
            "Epoch 3: val_loss improved from 1.04878 to 1.04701, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.9090 - loss: 0.4811 - val_accuracy: 0.4000 - val_loss: 1.0470\n",
            "Epoch 4/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 445ms/step - accuracy: 0.7500 - loss: 0.5457\n",
            "Epoch 4: val_loss improved from 1.04701 to 1.04193, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2s/step - accuracy: 0.7763 - loss: 0.5143 - val_accuracy: 0.4000 - val_loss: 1.0419\n",
            "Epoch 5/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - accuracy: 0.8438 - loss: 0.4973\n",
            "Epoch 5: val_loss improved from 1.04193 to 1.02931, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.8251 - loss: 0.4901 - val_accuracy: 0.7000 - val_loss: 1.0293\n",
            "Epoch 6/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 602ms/step - accuracy: 0.8125 - loss: 0.4564\n",
            "Epoch 6: val_loss did not improve from 1.02931\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 72ms/step - accuracy: 0.8322 - loss: 0.4455 - val_accuracy: 0.7000 - val_loss: 1.0296\n",
            "Epoch 7/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 222ms/step - accuracy: 0.8125 - loss: 0.4666\n",
            "Epoch 7: val_loss did not improve from 1.02931\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.7971 - loss: 0.5090 - val_accuracy: 0.7000 - val_loss: 1.0296\n",
            "Epoch 8/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.9062 - loss: 0.3251\n",
            "Epoch 8: val_loss did not improve from 1.02931\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.8986 - loss: 0.3276 - val_accuracy: 0.8000 - val_loss: 1.0303\n",
            "Epoch 9/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - accuracy: 0.8750 - loss: 0.4001\n",
            "Epoch 9: val_loss improved from 1.02931 to 1.02089, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5s/step - accuracy: 0.8706 - loss: 0.3969 - val_accuracy: 0.9000 - val_loss: 1.0209\n",
            "Epoch 10/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - accuracy: 0.9062 - loss: 0.3668\n",
            "Epoch 10: val_loss did not improve from 1.02089\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 0.8986 - loss: 0.3729 - val_accuracy: 0.6000 - val_loss: 1.0261\n",
            "Epoch 11/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 208ms/step - accuracy: 0.9375 - loss: 0.2895\n",
            "Epoch 11: val_loss improved from 1.02089 to 1.01689, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5s/step - accuracy: 0.9265 - loss: 0.2914 - val_accuracy: 0.7000 - val_loss: 1.0169\n",
            "Epoch 12/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - accuracy: 0.7500 - loss: 0.4734\n",
            "Epoch 12: val_loss improved from 1.01689 to 1.00538, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.7412 - loss: 0.4977 - val_accuracy: 0.7000 - val_loss: 1.0054\n",
            "Epoch 13/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - accuracy: 0.8750 - loss: 0.3454\n",
            "Epoch 13: val_loss did not improve from 1.00538\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.8706 - loss: 0.3527 - val_accuracy: 0.8000 - val_loss: 1.0194\n",
            "Epoch 14/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 0.8125 - loss: 0.3985\n",
            "Epoch 14: val_loss improved from 1.00538 to 1.00100, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.8147 - loss: 0.4276 - val_accuracy: 0.6000 - val_loss: 1.0010\n",
            "Epoch 15/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 723ms/step - accuracy: 0.8125 - loss: 0.4591\n",
            "Epoch 15: val_loss improved from 1.00100 to 0.99439, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4s/step - accuracy: 0.8322 - loss: 0.4294 - val_accuracy: 0.6000 - val_loss: 0.9944\n",
            "Epoch 16/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - accuracy: 0.8438 - loss: 0.3266\n",
            "Epoch 16: val_loss improved from 0.99439 to 0.98188, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 6s/step - accuracy: 0.8602 - loss: 0.3072 - val_accuracy: 0.7000 - val_loss: 0.9819\n",
            "Epoch 17/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - accuracy: 1.0000 - loss: 0.2427\n",
            "Epoch 17: val_loss improved from 0.98188 to 0.97991, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step - accuracy: 0.9825 - loss: 0.2543 - val_accuracy: 0.6000 - val_loss: 0.9799\n",
            "Epoch 18/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 2s/step - accuracy: 0.8750 - loss: 0.2717\n",
            "Epoch 18: val_loss did not improve from 0.97991\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 74ms/step - accuracy: 0.8706 - loss: 0.2790 - val_accuracy: 0.6000 - val_loss: 0.9806\n",
            "Epoch 19/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.9688 - loss: 0.2276\n",
            "Epoch 19: val_loss did not improve from 0.97991\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.9370 - loss: 0.2578 - val_accuracy: 0.7000 - val_loss: 0.9830\n",
            "Epoch 20/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 227ms/step - accuracy: 0.9375 - loss: 0.2154\n",
            "Epoch 20: val_loss improved from 0.97991 to 0.97859, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.9090 - loss: 0.2640 - val_accuracy: 0.7000 - val_loss: 0.9786\n",
            "Epoch 21/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - accuracy: 0.9062 - loss: 0.3948\n",
            "Epoch 21: val_loss improved from 0.97859 to 0.97518, saving model to /content/model3.keras\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2s/step - accuracy: 0.8810 - loss: 0.4235 - val_accuracy: 0.7000 - val_loss: 0.9752\n",
            "Epoch 22/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - accuracy: 0.9688 - loss: 0.2506\n",
            "Epoch 22: val_loss did not improve from 0.97518\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.9194 - loss: 0.3746 - val_accuracy: 0.6000 - val_loss: 0.9874\n",
            "Epoch 23/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - accuracy: 0.9688 - loss: 0.2483\n",
            "Epoch 23: val_loss did not improve from 0.97518\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - accuracy: 0.9019 - loss: 0.3256 - val_accuracy: 0.6000 - val_loss: 0.9885\n",
            "Epoch 24/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - accuracy: 0.9062 - loss: 0.2952\n",
            "Epoch 24: val_loss did not improve from 0.97518\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.9161 - loss: 0.2744 - val_accuracy: 0.5000 - val_loss: 0.9892\n",
            "Epoch 25/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - accuracy: 0.9375 - loss: 0.2088\n",
            "Epoch 25: val_loss did not improve from 0.97518\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - accuracy: 0.9441 - loss: 0.2059 - val_accuracy: 0.4000 - val_loss: 0.9892\n",
            "Epoch 26/40\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - accuracy: 0.8438 - loss: 0.3416\n",
            "Epoch 26: val_loss did not improve from 0.97518\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - accuracy: 0.8602 - loss: 0.3206 - val_accuracy: 0.4000 - val_loss: 0.9869\n",
            "Epoch 26: early stopping\n",
            "Restoring model weights from the end of the best epoch: 21.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluating the different Models"
      ],
      "metadata": {
        "id": "VkjQgg3JF6Md"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "defect_classifier1.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNm4_2mxFwRm",
        "outputId": "bfe6b9ba-4727-4ca0-d24c-76a31b645318"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.7500 - loss: 0.2390\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.23895245790481567, 0.75]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "defect_classifier2.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-Yr4jHNGAKR",
        "outputId": "56d1ae86-e040-484b-ef89-82e8bb226c76"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.8333 - loss: 0.8048\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8047957420349121, 0.8333333134651184]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "defect_classifier3.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qCCKT9EGD-z",
        "outputId": "8b286b28-8bd9-4f4b-ec6e-309a1043c0a2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.8333 - loss: 0.9417\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9416699409484863, 0.8333333134651184]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predictions1 = defect_classifier1.predict(X_test)\n",
        "y_predictions1 = [np.argmax(i) for i in y_predictions1]\n",
        "y_actual1 = [np.argmax(i) for i in y_test]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZkrkPN-GOsN",
        "outputId": "a5e92ef2-82aa-4c05-ec5e-ae18a0d1e14b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 887ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predictions2 = defect_classifier2.predict(X_test)\n",
        "y_predictions2 = [np.argmax(i) for i in y_predictions2]\n",
        "y_actual2 = [np.argmax(i) for i in y_test]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78Eso9u6GiME",
        "outputId": "b785c9a0-71b6-482b-ac56-1095511b49dd"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 476ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predictions3 = defect_classifier3.predict(X_test)\n",
        "y_predictions3 = [np.argmax(i) for i in y_predictions3]\n",
        "y_actual3 = [np.argmax(i) for i in y_test]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnT5tONiGqO5",
        "outputId": "29a026a6-8276-4b3b-923f-7f35e403a0fd"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 478ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm1 = confusion_matrix(y_actual1,y_predictions1)\n",
        "plt.figure(figsize=(10,7))\n",
        "sb.heatmap(cm1,annot=True,fmt='d')\n",
        "plt.xlabel('Prediction')\n",
        "plt.ylabel('True')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "mWN7neFNGuoe",
        "outputId": "f1a9bad3-a3d2-4b31-9ee2-26dd6b8a7d84"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm2 = confusion_matrix(y_actual2,y_predictions2)\n",
        "plt.figure(figsize=(10,7))\n",
        "sb.heatmap(cm2,annot=True,fmt='d')\n",
        "plt.xlabel('Prediction')\n",
        "plt.ylabel('True')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "SFXg4tv9G2PJ",
        "outputId": "0fe3a1d6-5803-4d3a-88b6-d512e9892d60"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm3 = confusion_matrix(y_actual3,y_predictions3)\n",
        "plt.figure(figsize=(10,7))\n",
        "sb.heatmap(cm3,annot=True,fmt='d')\n",
        "plt.xlabel('Prediction')\n",
        "plt.ylabel('True')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "0BZrN5vjG9vp",
        "outputId": "552a5430-5c47-4b1d-83b0-3e9fc60f0e42"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_actual1,y_predictions1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItIJcx60HCsG",
        "outputId": "f3ff34ed-1827-4a59-bceb-1ec47b0554f6"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         1\n",
            "           1       1.00      0.70      0.82        10\n",
            "           2       0.25      1.00      0.40         1\n",
            "\n",
            "    accuracy                           0.75        12\n",
            "   macro avg       0.75      0.90      0.74        12\n",
            "weighted avg       0.94      0.75      0.80        12\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_actual2,y_predictions2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RgdO0ia-HK5k",
        "outputId": "9a6705e3-4c49-4f20-b96a-8cf138e99b6e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         1\n",
            "           1       0.83      1.00      0.91        10\n",
            "           2       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.83        12\n",
            "   macro avg       0.28      0.33      0.30        12\n",
            "weighted avg       0.69      0.83      0.76        12\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_actual3,y_predictions3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKMIcLVDHMyj",
        "outputId": "ab6ab975-47d0-42e1-a437-ec40afa57797"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         1\n",
            "           1       0.83      1.00      0.91        10\n",
            "           2       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.83        12\n",
            "   macro avg       0.28      0.33      0.30        12\n",
            "weighted avg       0.69      0.83      0.76        12\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Performance plots"
      ],
      "metadata": {
        "id": "TfgvcmFQHQcs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history1.history['loss'],color='green',label='Training Loss')\n",
        "plt.plot(history1.history['val_loss'],color='blue',label='Validation loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Loss variation')\n",
        "plt.savefig('Model Training History')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "fXP4uiBHHPqX",
        "outputId": "8d97000b-2d42-49a0-b3dc-556edea3ecff"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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0xfPj4+OZN28eJUqUAKB///5MmDDB8PqsWbMYMWIErVu3BmD27NmGVpX0SAo3+/fvp1atWgAsWbIELy8v1q5dS/v27blx4wZt27alYsWKABQvXtzw/hs3blClShXeeustQG3NMjXZeuDx1KlTefr0Ke+9994rz4mNjSUiIiLZw9QcOKD+W7Ys9OoFej107AgZ+O4KIYRIp6Q/2kmePn3K0KFD8fb2Jk+ePDg4OBAUFMSNGzdee51KlSoZntvb2+Pk5MT9+/dfeb6dnZ0h4AB4eHgYzg8PD+fevXvUqFHD8Lq5uTnVqlVL0+/2oqCgICwsLPD19TUcy5s3L2XKlCEoKAiAgQMH8tVXX1G7dm3Gjh3LmTNnDOf27duXpUuXUrlyZb744gsOJP0xMyHZtiXnjz/+YPz48axbt478+fO/8rxJkyYxfvz4LKws7fbtU/+tWxfmzoWoKFi6VG3J2bwZGjTQtDwhhEgVO0s7no54qtm9jcXePnmL0NChQ9m+fTtTp06lZMmS2Nra0q5dO+Li4l57nf9uS6DT6dDr9Wk6X1GUNFZvXB9++CH+/v5s3LiRbdu2MWnSJKZNm8aAAQMICAjg+vXrbNq0ie3bt9OoUSP69evH1KlTNa35RdmyJWfp0qV8+OGHLF++HD8/v9eeO2LECMLDww2PmzdvZlGVqZcUcurUAXNzWLwYAgPh2TNo0QIOHdK2PiGESA2dToe9lb0mj8ycpbR//366d+9O69atqVixIgUKFODatWuZdr+UODs74+7uztGjRw3HEhMTOXHiRLqv6e3tTUJCAocPHzYce/ToEcHBwZQrV85wzMvLi48//pjVq1fz2WefMX/+fMNrbm5udOvWjd9//50ZM2bw008/pbuezJDtWnL+/PNPevbsydKlS2nevPkbz7e2tsba2joLKkufmBhI+s7WqaP+a2kJy5erAWfnTggIgF27oHJlzcoUQohcq1SpUqxevZrAwEB0Oh2jR49+bYtMZhkwYACTJk2iZMmSlC1bllmzZvHkyZNUBbyzZ8/i6Oho+Fmn0+Hj40PLli3p3bs3P/74I46OjgwfPpyCBQvSsmVLAD799FMCAgIoXbo0T548YdeuXXh7ewMwZswYqlWrRvny5YmNjWXDhg2G10yFpiHn6dOnXL582fBzSEgIp06dwtXVlcKFCzNixAhu377N4sWLAbWLqlu3bsycORNfX19CQ0MBsLW1zfQFlTLLsWMQHw8eHlCs2PPjNjawbh34+8P+/dCkCfzzjzpuRwghRNaZPn06PXv2pFatWuTLl49hw4ZpMr5z2LBhhIaG0rVrV8zNzenTpw/+/v6Ym5u/8b316tVL9rO5uTkJCQksXLiQQYMG0aJFC+Li4qhXrx6bNm0ydJ0lJibSr18/bt26hZOTE02bNuW7774D1LV+RowYwbVr17C1taVu3bosXbrU+L94Riga2rVrlwK89OjWrZuiKIrSrVs3pX79+obz69ev/9rzUyM8PFwBlPDwcOP+Muk0caKigKK0b5/y62FhilK1qnqOp6eiXLmStfUJIURKYmJilPPnzysxMTFal5JrJSYmKqVLl1ZGjRqldSlG87rvVXr+fmvaktOgQYPXDqpatGhRsp93796duQVp4MXxOClxdoatW6F+fXWauZ8f7N0LBQtmXY1CCCG0d/36dbZt20b9+vWJjY1l9uzZhISE0KlTJ61LM1nZcuBxTqHXq11R8OqQA5AvH+zYASVKQEiIGnReMwtRCCFEDmRmZsaiRYuoXr06tWvX5uzZs+zYscPkxsGYkmw38DgnOXcOwsPBwQFeWE4hRR4e6iDkunXhwgV1jM6uXeDikjW1CiGE0JaXlxf7k/7LWKSKtORoKKmrqmZNsEhF3CxSRG3RcXeH06fVWVeRkZlboxBCCJFdScjR0JvG46SkdGnYvh1cXeHwYXj3XXUauhBCCCGSk5CjofSEHICKFWHLFnB0hN271ZWR37DwphBCCJHrSMjRyI0b6sPcHF7YNiTVqleHjRvB1lbd+qFTJ3UXcyGEEEKoJORoJGnsWNWq6o7j6VG3LqxZA1ZWsGoVDBhgvPqEEEKI7E5CjkbS21X1X/7+6maeAD/9BE+12RtPCCGEMDkScjRirJAD0Lo1FCqkrruTgb3ahBBCpFKDBg349NNPDT8XLVqUGTNmvPY9Op2OtWvXZvjexrrO64wbN47KOWDDRAk5GggLg7Nn1ee1axvnmtWrq/8eOWKc6wkhRE4UGBhI06ZNU3xt79696HQ6zpw5k+brHj16lD59+mS0vGReFTTu3r1LQECAUe+VU0nI0cDBg6AoUKqUuuaNMdSoof6btKO5EEKIl/Xq1Yvt27dz69atl15buHAhb731FpXetDprCtzc3LCzszNGiW9UoEABrK2ts+Re2Z2EHA0Ys6sqibTkCCHEm7Vo0QI3N7eX9kZ8+vQpK1asoFevXjx69IiOHTtSsGBB7OzsqFixIn/++edrr/vf7qpLly5Rr149bGxsKFeuHNu3b3/pPcOGDaN06dLY2dlRvHhxRo8eTXx8PKDu3Th+/HhOnz6NTqdDp9MZav5vd9XZs2d55513sLW1JW/evPTp04enLwzQ7N69O61atWLq1Kl4eHiQN29e+vXrZ7hXauj1eiZMmEChQoWwtramcuXKbNmyxfB6XFwc/fv3x8PDAxsbG4oUKcKkSZMAUBSFcePGUbhwYaytrfH09GTgwIGpvndGyLYOGsiMkPPWW+q/167Bgwfg5ma8awshRGooCkRHa3NvOzvQ6d58noWFBV27dmXRokWMHDkS3f+/acWKFSQmJtKxY0eePn1KtWrVGDZsGE5OTmzcuJEuXbpQokQJaiQ1m7+GXq+nTZs2uLu7c/jwYcLDw5ON30ni6OjIokWL8PT05OzZs/Tu3RtHR0e++OILOnTowL///suWLVvYsWMHAM7Ozi9dIyoqCn9/f2rWrMnRo0e5f/8+H374If37908W5Hbt2oWHhwe7du3i8uXLdOjQgcqVK9O7d+83f2jAzJkzmTZtGj/++CNVqlRhwYIFvPvuu5w7d45SpUrx/fffs379epYvX07hwoW5efMmN2/eBGDVqlV89913LF26lPLlyxMaGsrp06dTdd8MM94G6dlDerZqN6ZnzxTFxkZRQFGCg4177TJl1Otu3Gjc6wohxH/FxMQo58+fV2JiYgzHnj5V/zdIi8fTp6mvPSgoSAGUXbt2GY7VrVtX+eCDD175nubNmyufffaZ4ef69esrgwYNMvxcpEgR5bvvvlMURVG2bt2qWFhYKLdv3za8vnnzZgVQ1qxZ88p7TJkyRalWrZrh57Fjxyo+Pj4vnffidX766SfFxcVFefrCB7Bx40bFzMxMCQ0NVRRFUbp166YUKVJESUhIMJzTvn17pUOHDq+s5b/39vT0VL7++utk51SvXl355JNPFEVRlAEDBijvvPOOotfrX7rWtGnTlNKlSytxcXGvvF+SlL5XSdLz91u6q7LYiRPw7Jna0lKqlHGvLeNyhBDizcqWLUutWrVYsGABAJcvX2bv3r306tULgMTERL788ksqVqyIq6srDg4ObN26lRs3bqTq+kFBQXh5eeHp6Wk4VrNmzZfOW7ZsGbVr16ZAgQI4ODgwatSoVN/jxXv5+Phg/8KCa7Vr10av1xMcHGw4Vr58eczNzQ0/e3h4cP/+/VTdIyIigjt37lD7PzNlateuTVBQEKB2iZ06dYoyZcowcOBAtm3bZjivffv2xMTEULx4cXr37s2aNWtIyKLVayXkZLEXu6pS07SaFjIuRwihJTs7da0uLR5pHfPbq1cvVq1aRWRkJAsXLqREiRLUr18fgClTpjBz5kyGDRvGrl27OHXqFP7+/sQZcf+cgwcP0rlzZ5o1a8aGDRs4efIkI0eONOo9XmRpaZnsZ51Oh16vN9r1q1atSkhICF9++SUxMTG89957tGvXDlB3Tw8ODuaHH37A1taWTz75hHr16qVpTFB6yZicLJYUcow1dfxFSS05R46oDbjGDlFCCPE6Ol36V3DPau+99x6DBg3ijz/+YPHixfTt29cwPmf//v20bNmSDz74AFDH2Fy8eJFy5cql6tre3t7cvHmTu3fv4uHhAcChQ4eSnXPgwAGKFCnCyJEjDceuX7+e7BwrKysSExPfeK9FixYRFRVlaM3Zv38/ZmZmlClTJlX1vomTkxOenp7s37/fEAST7vPiGCUnJyc6dOhAhw4daNeuHU2bNuXx48e4urpia2tLYGAggYGB9OvXj7Jly3L27FmqVq1qlBpfRUJOFtLrn2/nYMxBx0l8fMDCAh4+hOvXoWhR499DCCFyAgcHBzp06MCIESOIiIige/fuhtdKlSrFypUrOXDgAC4uLkyfPp179+6lOuT4+flRunRpunXrxpQpU4iIiEgWZpLucePGDZYuXUr16tXZuHEja9asSXZO0aJFCQkJ4dSpUxQqVAhHR8eXpo537tyZsWPH0q1bN8aNG8eDBw8YMGAAXbp0wd1Ya5QAn3/+OWPHjqVEiRJUrlyZhQsXcurUKZYsWQLA9OnT8fDwoEqVKpiZmbFixQoKFChAnjx5WLRoEYmJifj6+mJnZ8fvv/+Ora0tRYoUMVp9ryLdVVkoOBgePVI31axSxfjXt7FRgw5Il5UQQrxJr169ePLkCf7+/snGz4waNYqqVavi7+9PgwYNKFCgAK1atUr1dc3MzFizZg0xMTHUqFGDDz/8kK+//jrZOe+++y6DBw+mf//+VK5cmQMHDjB69Ohk57Rt25amTZvSsGFD3NzcUpzGbmdnx9atW3n8+DHVq1enXbt2NGrUiNmzZ6ftw3iDgQMHMmTIED777DMqVqzIli1bWL9+PaX+f3Cpo6Mj3377LW+99RbVq1fn2rVrbNq0CTMzM/LkycP8+fOpXbs2lSpVYseOHfz111/kzZvXqDWmRKcoipLpdzEhERERODs7Ex4ejpOTU5bee/586NMHGjSAXbsy5x59+8K8eTB0KEyZkjn3EEKIZ8+eERISQrFixbCxsdG6HJFDvO57lZ6/39KSk4UyY32c/3pxXI4QQgiRm0nIyUJZEXKSZlgdPw5vGK8mhBBC5GgScrLInTtw9SqYmUEKyyUYjbe3OrshKgr+f/kCIYQQIleSkJNFkmZVVaoEmTkUyNz8+RYPsiigEEKI3ExCThbJiq6qJLIooBAiq+SyuSsikxn7+yQhJ4tkZciR7R2EEJktaQXdaK125BQ5UtKKzy9uQZERshhgFoiMhFOn1OeZsdLxfyW15Jw+re6TJbM7hRDGZm5uTp48eQz7H9nZ2RlWDBYiPfR6PQ8ePMDOzg4LC+PEEwk5WeDQIXW146JFoVChzL9fkSLqBqAPHqhBx9c38+8phMh9ChQoAJDqjR6FeBMzMzMKFy5stMAsIScLZGVXFaj7x1SvDps2qeNyJOQIITKDTqfDw8OD/PnzZ8lmiyLns7KywszMeCNpJORkgawOOaCOy9m0ScblCCEyn7m5udHGUAhhTDLwOJPFx6vdVZC1IUdmWAkhhMjtJORkslOnIDoaXFzUhfqySlLICQ6G8PCsu68QQghhKiTkZLKkrqratdXVjrOKm5s60Bng2LGsu68QQghhKiTkZDItxuMkkfVyhBBC5GYScozkyuMrjNgxgnG7xxmOKYq2IUfG5QghhMjNJOQYyfXw60zeP5k5R+eQqFe3/758Ge7fB2vr5/tJZaWklhwJOUIIIXIjCTlGUrdwXVxsXHgY/ZADNw8Az1txqldXg05Wq1pVHQd0+7a6C7oQQgiRm0jIMRJLc0talG4BwNoLawFtu6oAHBygXDn1uYzLEUIIkdtIyDGiVmVbAbA2eC2KomgecuD5uBwJOUIIIXIbCTlG5F/CHxsLG64+ucqec0FcvKger1VLu5pkXI4QQojcSkKOEdlb2dO4eGMAflz7LwAVKqgLAWrlxWnkiqJdHUIIIURWk5BjZEldVn/viQO07aoCqFhRHfQcFqbO9hJCCCFyCwk5RhZYOhAznRn3g0oB2occS0uoUkV9LuNyhBBC5CYScozMzd6Nt/M3grtVAXU7B63JooBCCCFyIwk5mcAnoTfoLbF2eUCRIlpXI9s7CCGEyJ0k5GQC6zuNAIgr+Ddhz55oXM3zlpwTJyA+XttahBBCiKwiIScTnD/uCoDitZeNlzZqXA2UKgXOzvDsGZw7p3U1QgghRNaQkGNkCQlw4MD//1B4n2H1Yy2ZmT3fO0vG5QghhMgtJOQY2dmz8PQp2DskgvtZtlzeQkx8jNZlybgcIYQQuY6EHCMzbOVQ2wyvPAWJio9iZ8hObYtCZlgJIYTIfSTkGNnz/ap0z/eyMoEuq6SWnHPnICpK21qEEEKIrCAhx4gUJfnO40khZ33wehL1idoVBhQsCB4ekJgIJ09qWooQQgiRJSTkGNG1a3DnDlhYqC0ndQvXxcXGhQfRDzh466DW5cm4HCGEELmKhBwjSmrFqVYN7OzA0tySFqVbAKbRZSXjcoQQQuQmmoacf/75h8DAQDw9PdHpdKxdu/aN79m9ezdVq1bF2tqakiVLsmjRokyvM7Ve7KpK8uK4HEXjbcCTWnIk5AghhMgNNA05UVFR+Pj4MGfOnFSdHxISQvPmzWnYsCGnTp3i008/5cMPP2Tr1q2ZXGnqpBRy/Ev4Y2Nhw5UnVzj3QNuV+JLWyrl6FR490rQUIYQQItNZaHnzgIAAAgICUn3+vHnzKFasGNOmTQPA29ubffv28d133+Hv759ZZabKo0dw/rz6/MVNOe2t7GlcvDF/XfyLtRfWUiF/BW0KBFxc1NWPL11Sx+U0bapZKUIIIUSmy1Zjcg4ePIifn1+yY/7+/hw8+OpBvbGxsURERCR7ZIakVY7LlAE3t+SvmeJUchl8LIQQIqfLViEnNDQUd3f3ZMfc3d2JiIggJiblVYUnTZqEs7Oz4eHl5ZUptTVoAJs3w8SJL78WWDoQM50Zx+8e52b4zUy5f2rJ4GMhhBC5RbYKOekxYsQIwsPDDY+bNzMnZDg6qt0/bdq8/JqbvRu1vdQ+rHXB6zLl/qn1YkuOxuOghRBCiEyVrUJOgQIFuHfvXrJj9+7dw8nJCVtb2xTfY21tjZOTU7KHFkyly6pyZXUdn3v3IJPynhBCCGESslXIqVmzJjt3Jt8Havv27dSsWVOjilKvZZmWAOy+tpsnMU80q8PWFipWVJ/LuJyMSUyULTKEEMKUaRpynj59yqlTpzh16hSgThE/deoUN27cANSupq5duxrO//jjj7l69SpffPEFFy5c4IcffmD58uUMHjxYi/LTpIRrCSrmr0iiksjGSxs1rUXG5RhHx47qVhlnzmhdiRBCiJRoGnKOHTtGlSpVqFKlCgBDhgyhSpUqjBkzBoC7d+8aAg9AsWLF2LhxI9u3b8fHx4dp06bx888/az59PLVMpctKZlhl3OPHsGoVREbCF19oXY0QQoiU6BStl+HNYhERETg7OxMeHp7l43NO3D1BtZ+qYW9pz4PPH2BrmfI4osx25gz4+KiDpcPCwCxbdVqahiVL4IMPnv+8Ywc0aqRdPUIIkdOl5++3/HnLQlUKVMHLyYuo+Ch2hux88xsySbly6t5akZEQHKxZGdnaX3+p/zo7q/8OGwZ6vXb1CCGEeJmEnCyk0+lMosvKwgKqVlWfy7ictIuPhy1b1OeLF4ODAxw/DitWaFuXEEKI5CTkZLGkkLM+eD2J+kTN6pBxOem3bx+Eh6srWzdv/nxMzv/+B3Fx2tYmhBDiOQk5Waxu4bq42LjwIPoBB2+9ejuKzCYzrNIvqauqeXMwN4fBg8HdXd349KeftK1NCCHEcxJyspiluSUtSrcAtO2ySmrJOX0aYmM1KyPbUZTnIScwUP3XwQHGjlWfT5igjnUSQgihPQk5GnhxXI5Wk9uKFYO8edXuFVnnJfUuXoTLl8HKCho3fn78ww/VHd4fPICpU7WrTwghxHMScjTgX8IfGwsbrjy5wrkH5zSpQad73mUl43JSL6kVp0EDdQp+EkvL55uzTpsGoaFZXpoQQoj/kJCjAXsrexoXV5sBTKHLSsblpN5/u6pe1Lat+plGRcGXX2ZtXUIIIV4mIUcjpjCVXAYfp83jx7B/v/q8RYuXX9fp4Ntv1ec//QSXLmVdbUIIIV4mIUcjgaUDMdOZcfzucW6Ga7MdeFLIuXABIiI0KSFb2bJF3ZSzQgUoWjTlc+rXh2bNICEBRo7M0vKEEEL8h4QcjbjZu1HbqzYA64LXaVKDuzsULqzOGDp+XJMSspXXdVW9aPJktVVnxQppJRNCCC1JyNGQKXRZyaKAqfPiKsdvCjkVK0LXrurzL75QQ6QQQoisJyFHQy3LtARg97XdPIl5okkNMi4ndfbvVzczzZfveTB8nQkTwNoa9ux5Ho6EEEJkLQk5GirhWoKK+SuSqCSy8dJGTWqQlpzU+e8qx29SuDAMGKA+HzZMHcsjhBAia0nI0ZjWXVbVqqnjR27cgHv3NCkhW9iwQf33TV1VLxoxAvLkgbNnYcmSTClLCCHEa0jI0VhSyNlyeQsx8TFZfn9HR/D2Vp9La07KLl5UH5aWyVc5fhNXVzXoAIweDc+eZU59QgghUiYhR2NVClTBy8mLqPgodobs1KSGpC6rGTPUAbYiuRdXOXZyStt7BwyAQoXUlrI5c4xemhBCiNeQkKMxnU6neZfV4MFgZwc7d8JHH8lsoP9K7dTxlNjaqoOQAb7+Wh28LIQQImtIyDEBSSFnffB6EvVZP0K1UiVYvhzMzGDhQtmS4EVPnsC+ferzlFY5To2uXaF8efVakycbrzYhhBCvJyHHBNQtXBcXGxceRD9g3O5x3Hua9SOAmzeHH35Qn48dC7/+muUlmKSkVY7Ll1d3bk8Pc3OYNEl9PnMm3LplvPqEEEK8moQcE2BpbkmXSl0A+GrvVxT6rhBtlrVh48WNJOgTsqyOjz6C4cPV5x9+CDt2ZNmtTVZGuqpe1KIF1K2rDj4eNy7DZQkhhEgFnaLkrhEYERERODs7Ex4ejlNaR5FmovjEeBafXswvJ3/h4K2DhuMFHQvSo3IPelbpSTGXdDYlpIFeDx98AH/+qc682rdP7c7KjRISwM1NHUezbx/Urp2x6x08CLVqqd2CZ89CuXJGKVMIIXKF9Pz9lpBjgs7dP8cvJ39h8enFPIp5ZDjeqFgjelXpRWvv1thY2GTa/WNjwd9fXa23YEE4dEidIZTb7NmjzqjKlw9CQ1O3COCbtG0Lq1fDu+/COm22LBNCiGwpPX+/pbvKBJXPX57p/tO5PeQ2y9stp0mJJujQsTNkJ51Wd8JzmicDNw/kdOjpTLm/tTWsWaOun3P7tjpeJzfuUp7UVdWsmXECDsDEieq11q9/PqBZCCFE5pCWnGzieth1Fp5ayIKTC7gZcdNw/C3Pt/iwyoe8X+F9nG2cjXrPa9fg7bfVlZAbN4aNG9UF8XKLsmUhOFideda+vfGu+9FH8NNPULOmuieWTme8awshRE4l3VWpkF1DTpJEfSI7ru7g55M/s+7COuL16up9tha2jKgzgtH1Rxv1fsePQ716EB0NPXrAL7/kjj/Kly5B6dJqqHv4MO2LAL7O3btQsqT6ma5ZA61aGe/aQgiRU0l3VS5gbmaOf0l/VrRfwe0ht5nWZBre+byJSYhhzO4xHL512Kj3q1Ytd66hk9RVVb++cQMOgIeHugAjwDffGPfaQgghnpOQk4252bsxpOYQzn1yjq4+XQEYtWuU0e+TG9fQSdqQM70LAL5Jnz7qv0ePQlRU5txDCCFyOwk5OYBOp2N8g/FYmlmy4+oOdl/bbfR75KY1dMLCYO9e9XlG18d5lcKFwctLXWjwyJHMuYcQQuR2EnJyiKJ5itK7am8ARv09iswYavX119Cxo7p+TJs2cOaM0W9hErZsUX/HcuWgePHMu0/Sujsyy0oIITKHhJwcZGS9kdhY2LD/5n62Xtlq9OsnjcupXx8iI9Wp1Tlxi4Kk8TiZ1VWVpE4d9V8JOUIIkTkk5OQgno6e9KveD8i81pycvoZOQgJs3qw+z6yuqiRJIefgQbXbSgghhHFJyMlhhtUehoOVA8fvHmfNhTWZcg8XF9i0Cdzd1S6rdu0gPj5TbpXlDhxQdwvPm1ddxyYzVaigztyKjFS3eRBCCGFcEnJyGDd7Nz71/RSAMbvGkKjPnCaCokXVxQHt7GD7dujSBR48yJRbZanMWOX4VczNnwcp6bISQgjjk5CTA31W6zPy2OTh3INzLP13aabd58U1dJYtg2LF1BlY2TnsZPbU8f9K6rLavz9r7ieEELmJhJwcKI9NHj6v9TkA4/aMIz4x8/qSmjeHrVuhalV1vZdvvsm+YefyZbhwASws1A1Ks0LSDKu9eyF3rT0uhBCZT0JODjXQdyBudm5cfnyZX09n7up9fn5w7Ji66WR2DjsvrnLsbNxtwF6pRg01VN2+DTduZM09hRAit5CQk0M5WDkwos4IACbsmUBsQmym3k+nU2cjZeewk9VdVQD29upnBdJlJYQQxiYhJwfrW70vBR0LcjPiJj8d/ylL7vmmsDNsmGmGnfBw+Ocf9XlmTx3/L1kUUAghMoeEnBzMxsKGUfXUvay+3vs1UXFZt0nSq8LOt9+qM7NMLewkrXLs7Q0lSmTtvWVRQCGEyBwScnK4nlV6UixPMe5F3WPO0TlZfv8Xw85ff6kzsqKjTS/saNFVlSSpJefff9V9s4QQQhiHhJwczsrcirH1xwLwzf5vCH8WrkkdOp0aII4eTTns/PmnJmUBagvOpk3q86zuqgJ1UcWSJdXZVQcPZv39hRAip5KQkwt8UOkDyuYry+OYx8w4NEPTWl4Vdrp1e77zd1Y7eBAePwZX18xf5fhVpMtKCCGMT0JOLmBuZs6EBhMAmHZwGo+iH2lc0fOwc+TI820hWreGK1eyvpakrqqAAHU6txaSuqxkhpUQQhiPhJxcom25tvi4+xAZF8m3+7/VuhwDMzP49Ve1RefRI7W7KDyLe9SS1sfRoqsqSVJLzuHDEBenXR1CCJGTSMjJJcx0Znz1zlcAzDoyi9CnoRpX9JydnToDq2BBCAqC995Tx8lkhStX1Htm5SrHKSlTRt0U9NkzOHFCuzqEECInkZCTizQv1Rzfgr7EJMQwce9ErctJxtNTDTp2drBtG3z6adbcN6kVp25dyJMna+6ZEp1OuqyEEMLYJOTkIjqdjq/f+RqAH4//yI1w09pHoGpV+P139fmcOTB7dubfM2k8jpZdVUlk8LEQQhiXhJxc5p1i79CgaAPiEuP4cs+XWpfzktatYdIk9fmgQermn5klPBz27FGfm1LI2b9fNusUQghjkJCTy7zYmrPw1EIuPbqkcUUvGzYMunYFvV4dn3P+vPHv8eQJfPihOvanTBl1nRqtVa0K1tbq4oiXTO//LEIIke1IyMmFannVolmpZiQqiYzfM17rcl6i08FPP6ktGxER6lRzY66KvGMHVKwIK1eCuTmMHm28a2eEtbW6KzlIl5UQQhiDhJxc6suGalfVH2f/4N/7/2pczcusrWHNGnVTz5AQaNMGYjO4kXpMjDqguXFjuH0bSpWCAwegc2ejlGwUMi5HCCGMR0JOLlXVoyptvduioDB291ity0lRvnzqwGAnJ/WP/kcfpX+sysmT8NZbMHOm+nPfvuqxpJYTUyEzrIQQwngk5ORi4xuMR4eO1UGrOX7nuNblpKhcOVi+/Pmigd98k7b3JyaqA5l9fdWxPQUKqPtU/fAD2NtnTs0ZUauW+u/Fi3D/vra1CCFEdqd5yJkzZw5FixbFxsYGX19fjhw58trzZ8yYQZkyZbC1tcXLy4vBgwfz7NmzLKo2ZymfvzydK6l9NaN2jdK4mlfz94fvv1efjxgBq1en7n1Xr0L9+vC//z3fNuLsWXX7BlPl4gIVKqjPpTVHCCEyRtOQs2zZMoYMGcLYsWM5ceIEPj4++Pv7c/8V/wn7xx9/MHz4cMaOHUtQUBC//PILy5Yt43//+18WV55zjK0/FnOdOVsub2HblW1al/NK/fqpD4AuXV6/KrCiwIIF4OOjBgVHR1i0CFatUrvATJ10WQkhhHFoGnKmT59O79696dGjB+XKlWPevHnY2dmxYMGCFM8/cOAAtWvXplOnThQtWpQmTZrQsWPHN7b+iFcr6VqSD6t+CEDLpS1ZE7RG44pebcYMaNJE3bU8MBDu3Hn5nAcP1EHKvXrB06fqSsanT6u7nOt0WV5yusjgYyGEMA7NQk5cXBzHjx/Hz8/veTFmZvj5+XHw4MEU31OrVi2OHz9uCDVXr15l06ZNNGvW7JX3iY2NJSIiItlDJDetyTRalG7Bs4RntF3elu8Pf691SSmysIBly8DbWw04776rBp4kGzaoXT1r14KlpTp+Z9cudYZWdpIUco4fT/77CSGESBvNQs7Dhw9JTEzE3d092XF3d3dCQ1PePLJTp05MmDCBOnXqYGlpSYkSJWjQoMFru6smTZqEs7Oz4eHl5WXU3yMnsLeyZ02HNXxc7WMUFAZtGcSQrUPQK3qtS3tJnjzqflN586ohoGtXiIxUZ14FBqqDdcuXhyNH4Isv1HVwspsiRdS9vBIS4OhRrasRQojsS/OBx2mxe/duJk6cyA8//MCJEydYvXo1Gzdu5MsvX709wYgRIwgPDzc8bt68mYUVZx8WZhb80PwHJjVS91T47tB3vL/yfZ4lmN6g7hIl1DV0LC3VcTaFC6uLBwIMGQLHjkHlypqWmCE6nXRZCSGEMWgWcvLly4e5uTn37t1LdvzevXsUKFAgxfeMHj2aLl268OGHH1KxYkVat27NxIkTmTRpEnp9yq0O1tbWODk5JXuIlOl0OobXGc6SNkuwNLNkxfkVNP6tMY9jHmtd2kvq1oX589XnYWHg5QU7d8K0aWBjo2lpRpE0+FhCjhBCpJ9mIcfKyopq1aqxc+dOwzG9Xs/OnTupWbNmiu+Jjo7GzCx5yeb/3x+hyI6GRtOpYie2frAVZ2tn9t3YR61fahHyJETrsl7SrRv88gsMHw5nzsA772hdkfEkteQcPKiu9SOEECLtNO2uGjJkCPPnz+fXX38lKCiIvn37EhUVRY8ePQDo2rUrI0aMMJwfGBjI3LlzWbp0KSEhIWzfvp3Ro0cTGBhoCDvCOBoWa8i+nvvwcvIi+FEwb//yNsfuHNO6rJf07Kku9pcnj9aVGFelSuDgoO6Ufu6c1tUIIUT2ZKHlzTt06MCDBw8YM2YMoaGhVK5cmS1bthgGI9+4cSNZy82oUaPQ6XSMGjWK27dv4+bmRmBgIF9//bVWv0KOViF/BQ72OkjzP5pz+t5p6i+qz/J2y2leurnWpeV4Fhbw9tvqZqL79qmhRwghRNrolFzWzxMREYGzszPh4eEyPieVImIjaLe8HduvbsdMZ8bc5nPpU62P1mWRqE9k8enFRMdH80n1T9Bll4VwUmn8eBg3Djp1giVLtK5GCCG0lZ6/39lqdpXQhpO1Exs7baR75e7oFT0fbfiIkTtHajoO6tidY/j+7EvP9T3pv7k/my5t0qyWzCIzrIQQImMk5IhUsTS3ZMG7CxhbX92xfOK+iXRZ04W4xLgsrSPsWRj9N/WnxvwaHL/7fFPRqQenZmkdWcHXV13n58YNkJUPhBAi7STkiFTT6XSMazCOX979BXOdOUvOLiFgSQDhz8Iz/d6KovDn2T8pO7ssc47OQUHhg0ofcKz3MSzMLNh9bbdJDozOCAeH5+v9yD5WQgiRdhJyRJr1rNKTjZ024mDlwN8hf1NnYR1uhmdeU8PFRxdp/FtjOq3uxL2oe5TOW5qdXXfyW+vfqOZZjY4VOgIw9UDOa82RLishhEg/CTkiXfxL+vNP93/wcPDg3/v/4j3Hmw4rO7D83HKexj01yj2eJTxj7K6xVJxbkZ0hO7E2t+bLhl9y5uMzvFPs+aI4Q2sNBWDF+RUmuZ5PRsiigEIIkX4yu0pkyPWw67Rc2pLT904bjtlY2OBfwp+23m0JLBNIHps8ab7utivb6LepH5cfXwagacmmzA6YTQnXEime7/+7P9uubGNgjYHMDJiZrt/FFN25AwULgpkZPH4Mzs5aVySEENpIz99vCTkiw/SKnqO3j7I6aDWrglZx5ckVw2uWZpY0Kt6INmXb0KpsK9zs3V57rTuRdxiydQjLzi0DwNPRkxn+M2hXrt1rp4jvuLqDxr81xs7SjpuDb+Jq62qcX84ElCgBV6/Cli3g7691NUIIoQ2ZQi40YaYzw7eQL980/oZLAy5x6qNTjK43mnJu5YjXx7Pl8hb6bOhDgWkFaPhrQ2Yfmc3tiNvJrpGoT2TW4VmUnV2WZeeWYaYzY5DvIIL6BdG+fPs3roHTqFgjKheoTHR8NPOOzcvMXzfLSZeVEEKkj7TkiEx14eEFQwvPibsnkr32dqG3aevdlgr5KzDq71GGKeE1CtZgXvN5VPGokqZ7LTmzhA/WfIC7vTvXPr2GjUUO2KkTdYf1jz6Chg3h77+1rkYIIbQh3VWpICFHOyFPQlhzYQ2rglZx4OaBl153tnZmst9kelftjblZ2vcii0+Mp8T3JbgZcZOfA3+mV9Vexihbc+fPQ/nyYGur7mVlaal1RUIIkfUk5KSChBzTcCfyDmsvrGVV0CqO3zlOi9ItmNZkGu4O7hm67vSD0/ls22eUzVeWc5+cw0yX/Xtk9XrIlw+ePIHDh6FGDa0rEkKIrCchJxUk5ORsEbEReH3nRURsBH91/IsWpVtoXZJRBAbChg0wfToMHqx1NUIIkfVk4LHI9Zysnfi42scATDkwReNqjEcGHwshRNpJyBE5zkDfgViaWfLP9X84cvuI1uUYxYsrH+eutlchhEg/CTkixynoVJBOFTsBOWerh7feAisruH8frlx58/lCCCEk5Igc6rOanwGwKmgVV59c1biajLOxUYMOSJeVEEKkloQckSNVdK9I05JN0St6vjv4ndblGIVs1imEEGmTrpBz8+ZNbt26Zfj5yJEjfPrpp/z0009GK0yIjBpaU924c8GpBTyKfqRxNRmXFHL279e2DiGEyC7SFXI6derErl27AAgNDaVx48YcOXKEkSNHMmHCBKMWKER6vVPsHcNWD3OPzdW6nAyrVUv998IFePBA21qEECI7SFfI+ffff6nx/yuSLV++nAoVKnDgwAGWLFnCokWLjFmfEOmm0+n4vNbnAMw6MotnCc80rihj8uYFb2/1+YGXF4wWQgjxH+kKOfHx8VhbWwOwY8cO3n33XQDKli3L3bt3jVedEBnUvlx7vJy8uB91n99O/6Z1ORkmXVZCCJF66Qo55cuXZ968eezdu5ft27fTtGlTAO7cuUPevHmNWqAQGWFpbsngt9UlgqcdnIZe0WtcUcbIooBCCJF66Qo533zzDT/++CMNGjSgY8eO+Pj4ALB+/XpDN5YQpuLDqh/ibO1M8KNgNlzcoHU5GZLUknPsGMTEaFuLEEKYunTvXZWYmEhERAQuLi6GY9euXcPOzo78+fMbrUBjk72rcqfhO4bzzf5vqFu4Lv/0+EfrctJNUcDTE0JD4Z9/oG5drSsSQoiskWV7V8XExBAbG2sIONevX2fGjBkEBwebdMARuVfSVg97b+zl8K3DWpeTbjqddFkJIURqpSvktGzZksWLFwMQFhaGr68v06ZNo1WrVsydm/2n6oqcx9PRk86VOgMw9WD23upBFgUUQojUSVfIOXHiBHX/v5185cqVuLu7c/36dRYvXsz3339v1AKFMJakrR5WB63myuPsuwFUUsg5cAD02XsctRBCZKp0hZzo6GgcHR0B2LZtG23atMHMzIy3336b69evG7VAIYylQv4KBJQMULd6OJR9t3rw8QE7OwgLg3PntK5GCCFMV7pCTsmSJVm7di03b95k69atNGnSBID79+/LYF5h0obW+v+tHk4u4GH0Q42rSR9Ly+etOcOHS2uOEEK8SrpCzpgxYxg6dChFixalRo0a1KxZE1BbdapUqWLUAoUwpoZFG1LVoyoxCTHMPZp9x4998426M/mmTfDVV1pXI4QQpindU8hDQ0O5e/cuPj4+mJmpWenIkSM4OTlRtmxZoxZpTDKFXPx59k86re6Em50b1z+9jq2lrdYlpcuiRdCjhzrjavNm8PfXuiIhhMg8WTaFHKBAgQJUqVKFO3fuGHYkr1GjhkkHHCEA2pdvT2HnwjyIfsBvZ7LvVg/du0OfPuraOZ06gQyHE0KI5NIVcvR6PRMmTMDZ2ZkiRYpQpEgR8uTJw5dffoleBggIE2dhZpFjtnqYOROqVYPHj6FdO3iWvfcgFUIIo0pXyBk5ciSzZ89m8uTJnDx5kpMnTzJx4kRmzZrF6NGjjV2jEEbXq0ov8tjk4eKji/wV/JfW5aSbjQ2sWgWurupWD59+qnVFQghhOtI1JsfT05N58+YZdh9Psm7dOj755BNu375ttAKNTcbkiCQjdoxg8v7JlM1XluN9jmNnaad1Sem2dSsEBKhdV4sWQbduWlckhBDGlWVjch4/fpzi2JuyZcvy+PHj9FxSiCw3tNZQPBw8uPDwAp9v+1zrcjLE3x/GjVOff/wxnDqlZTVCCGEa0hVyfHx8mD179kvHZ8+eTaVKlTJclBBZIa9dXha1WgTAD8d+YNOlTdoWlEGjRqmtOc+eQdu28OSJ1hUJIYS20tVdtWfPHpo3b07hwoUNa+QcPHiQmzdvsmnTJsOWD6ZIuqvEf3265VNmHp5Jfvv8nO17lvz22XeT2ceP1YHI165BYCCsXQtm6Z5DKYQQpiPLuqvq16/PxYsXad26NWFhYYSFhdGmTRvOnTvHb79l3ym5Inea7DeZCvkrcD/qPr3W9yKdS0eZBFdXWLkSrK3hr79g8mStKxJCCO2kezHAlJw+fZqqVauSmJhorEsanbTkiJScuXeG6vOrE5cYx9zmc/n4rY+1LilDfv4ZevdWW3G2bgU/P60rEkKIjMnSxQCFyEkquVdiciO12WPI1iFceHhB44oy5sMPoWdPdV+rjh3h5k2tKxJCiKwnIUeI/zfo7UH4FfcjJiGGzqs7E5cYp3VJGTJ7NlSpAg8fQvv2EBurdUVCCJG1JOQI8f/MdGb82upXXG1dOXH3BGN3jdW6pAyxtVUXCsyTBw4fhiFDtK5ICCGyVprG5LRp0+a1r4eFhbFnzx4ZkyOytVXnV9FuRTt06NjVbRf1i9bXuqQM2bgRWrRQn//2G3zwgbb1CCFEemT6mBxnZ+fXPooUKULXrl3TVbwQpqJtubb0qNwDBYWua7sS9ixM65IypHlzSNptpU8fOHtW23qEECKrGHV2VXYgLTkiNSJjI6nyYxWuPLlCp4qdWNJmidYlZUhiIjRrBtu2QcmS6j5Xzs5aVyWEEKkns6uEMBJHa0d+b/M75jpz/jj7B3+c/UPrkjLE3ByWLIHCheHyZejeXd3nSgghcjIJOUK8wtuF3mZ0PbWfp+/GvlwPu65xRRmTL5+6UKCVlboS8tix6hRzIYTIqSTkCPEaI+uNpGahmkTERtBlTRcS9aY7qD41qleH779Xn3/5JdSrB+fPa1uTEEJkFgk5QryGhZkFv7f5HQcrB/be2Mu3+7/VuqQM69MH5swBBwfYvx8qV1ZbdZ4907oyIYQwLgk5QrxBcZfizAqYBcCY3WM4dueYxhVljE4Hn3yituAEBkJ8PEyYoIadf/7RujohhDAeCTlCpEI3n260K9eOBH0CnVd3JiouKkPXux91n82XNnPv6T0jVZh2Xl6wbh2sWAEFCkBwMNSvr+559eSJZmUJIYTRSMgRIhV0Oh0/tviRgo4FufjoIp9t+yxN738Q9YCV51fSf1N/KvxQAfep7jT7oxltlr9+gc3MptNBu3YQFAQffaQe+/ln8PaG5ctlBpYQInuTdXKESIOdV3fi95u6pff699cTWCYwxfMeRT9iz/U97L62m13XdvHv/X9fec3LAy5TwrVEptSbVnv3qmN2Lvz//qTNm8MPP6hTz4UQQkvZcp2cOXPmULRoUWxsbPD19eXIkSOvPT8sLIx+/frh4eGBtbU1pUuXZtOmTVlUrcjtGhVvxJC31U2geq3vZehuehzzmLUX1vLplk/xmedDvin5aLu8LbOOzDIEnAr5K9C/en9WvbeKB58/oHHxxgCsOL9Cm18mBXXrwqlTMG6cOtV840YoVw5mzlQXFBRCiOxE05acZcuW0bVrV+bNm4evry8zZsxgxYoVBAcHkz9//pfOj4uLo3bt2uTPn5///e9/FCxYkOvXr5MnTx58fHxSdU9pyREZFZsQS42fa3Dm3hl83H3Q6XScDj2NQvL/VyrnVo6GRRvSoGgD6hWpR3775N/pn0/8TO+/elOlQBVOfHQiK3+FVAkKUlt19u1Tf37rLZg/Xx2gLIQQWS09f781DTm+vr5Ur16d2bNnA6DX6/Hy8mLAgAEMHz78pfPnzZvHlClTuHDhApaWlum6p4QcYQzn7p+j2k/ViE2MNRzzzudNg6INaFi0IfWK1MPdwf2113gU/YgC0wqQoE/gYv+LlMpbKrPLTjO9Xh2j88UXEB6urpz82WfqlHM7O62rE0LkJtkq5MTFxWFnZ8fKlStp1aqV4Xi3bt0ICwtj3bp1L72nWbNmuLq6Ymdnx7p163Bzc6NTp04MGzYMc3PzFO8TGxtLbOzzP0QRERF4eXlJyBEZtunSJrZd2UbNQjWpX7Q+BRwKpPkaAUsC2HJ5C181/IqR9UZmQpXGcfcuDBqkzsQCKF5cDT8NG2pblxAi98hWY3IePnxIYmIi7u7J/2vX3d2d0NDQFN9z9epVVq5cSWJiIps2bWL06NFMmzaNr7766pX3mTRpUrKd0r28vIz6e4jcq1mpZsxoOoMOFTqkK+AAvFfuPQCWn19uzNKMzsNDnW21fj0UKgRXr8I770D//vD0qdbVCSFEyjQfeJwWer2e/Pnz89NPP1GtWjU6dOjAyJEjmTdv3ivfM2LECMLDww2PmzdvZmHFQrxeq7KtsDSz5My9M1x4eEHrct4oMFBdRPDjj9Wf58yBSpVgzx5t6xJCiJRoFnLy5cuHubk59+4lXwzt3r17FCiQ8n8Ve3h4ULp06WRdU97e3oSGhhIXF5fie6ytrXFyckr2EMJUuNi60LiEOstq+TnTbs1J4ugIc+fC9u3q1PKQEGjQAAYOhKiMrZEohBBGpVnIsbKyolq1auzcudNwTK/Xs3PnTmrWrJnie2rXrs3ly5fRv7B18sWLF/Hw8MDKyirTaxYiMxi6rLJJyEni5wdnz6ozsABmzQIfH3WtHSGEMAWadlcNGTKE+fPn8+uvvxIUFETfvn2JioqiR48eAHTt2pURI0YYzu/bty+PHz9m0KBBXLx4kY0bNzJx4kT69eun1a8gRIa1LNsSK3Mrzj04x7n757QuJ02cnODHH2HrVnWbiCtX1K0hPv0UoqO1rk4IkdtpGnI6dOjA1KlTGTNmDJUrV+bUqVNs2bLFMBj5xo0b3L1713C+l5cXW7du5ejRo1SqVImBAwcyaNCgFKebC5Fd5LHJg38Jf8C0FgZMiyZN1FadXr3UrSBmzlRbdZLW2BFCCC3Itg5CmIDfz/xOlzVdKJuvLOc/OY9Op9O6pHTbsgU+/BBu31b3xho8GL76Cmxtta5MCJGdZasp5EKI594t8y7W5tZceHjhtftcZQdNm8K//0KPHmqrzvTp6irJBw5oXZkQIreRkCOECXCydiKgVACQ/QYgpyRPHliwQN37ytMTLl6EOnXg888hJkbr6oQQuYWEHCFMxIsLA+aUXuRmzdRWnW7d1FadqVOhShXYsEHdJkIIITKThBwhTESL0i2wsbDh4qOLnLl3RutyjMbFBRYtgr/+UldODg5WFxV0cVF3OO/RA+bNg5MnISFB62qFEDmJhBwhTISjtSPNSjUDYNm5ZRpXY3wtWqitOp98AsWKqS07QUFqAOrbF6pWVaek160LQ4fCypVw86Z6nhBCpIfMrhLChCz7dxnvr3qfEi4luDTgUraeZfUm9+/DkSNw+DAcOqQ+j4h4+TwPD/D1ff546y111WUhRO6SrXYh14qEHGHKouKicJviRkxCDMf7HKeqR1WtS8oyer3alXX48PPHmTOQmJj8PJ0OvL3VsJP08PEBOztt6hZCZA0JOakgIUeYuvdWvMeK8ysYVnsYk/0ma12OpqKj4cSJ56Hn0CG1C+u/zM3V8T0vBp9KlcDGJutrFkJkDgk5qSAhR5i6ledX0n5Fe4rmKcrVgVdzdJdVeoSGwvHjcOyY+jh6FP6zzy8AFhZQocLz0FOtGlSsCNbWWV+zECLjJOSkgoQcYeqi46Nxm+JGdHw0Rz48QvWC1bUuyaQpCty5owaeF8PPgwcvn2tpCf7+8Ouv4Oqa9bUKIdIvPX+/LTK5JiFEGtlZ2hFYOpBl55ax/NxyCTlvoNNBwYLqo2VL9ZiiqN1a/w0+jx+ra/TUqwfbtqkLFQohci6ZQi6ECepQvgOQsxYGzEo6HRQuDG3awNdfq7ukP3yodm15eMC5c+pU9ZAQrSsVQmQmCTlCmKCmJZviYOXAjfAbHL59WOtycgSdTh2bs28fFC8OV69C7dpq4BFC5EwScoQwQbaWtrxb5l0gZ+xlZUqKF1eDTvnycPeu2nV19KjWVQkhMoOEHCFMVNJeVivOr0Cv6DWuJmfx8IA9e6BGDXWczjvvwK5dWlclhDA2CTlCmCj/kv44WTtxK+IWh24d0rqcHCdvXtixQw04T59CQACsX691VUIIY5KQI4SJsrGwoWUZdbqQdFllDkdH2LhRnZUVG6sOVP79d62rEkIYi4QcIUzYe+Wlyyqz2diom4F27apuIdGlC8yZo3VVQghjkJAjhAlrXLwxztbO3Im8w/4b+7UuJ8eysICFC2HAAPXn/v3Vqecye1+I7E1CjhAmzNrCmlZlWwHSZZXZzMxg5kwYPVr9edQo+OILCTpCZGcScoQwcUkLA64MWkmiPvENZ4uM0OlgwgSYPl39eepU6NPn5Z3QhRDZg4QcIUxco+KNcLFxIfRpKHtv7NW6nFxh8GD45Re1defnn6FjR4iL07oqIURaScgRwsRZmVvRumxrQLqsslLPnrB8ubqp54oV8O67EBWldVVCiLSQkCNENpA0y2pV0CoS9AkaV5N7tG2rbuhpZ6fuf+XvD2FhWlclhEgtCTlCZAPvFHuHvLZ5uR91n3+u/6N1OblKkyawfTs4O8P+/fD++6CX2fxCZAsScoTIBizNLWnj3QaAZf8u07ia3KdWLXXbBxsbtUVn9mytKxJCpIaEHCGyCemy0laVKjBtmvr8iy/g33+1rUcI8WYScoTIJhoUbUA+u3w8innErhDZTVILfftC8+bqFhCdO8OzZ1pXJIR4HQk5QmQTFmYWtPVuC8gsK63odOrUcjc3OHMG/vc/rSsSQryOhBwhspGkhQFXX1hNfGK8xtXkTu7u6hYQAN99pw5KFkKYJgk5QmQj9YrUI799fh7HPGZnyE6ty8m1mjeHTz5Rn3frBg8faluPECJlEnKEyEbMzcxp590OkC4rrU2ZAt7ecPeuuvWD7HElhOmRkCNENpM0y2rNhTXEJcpeA1qxs4M//lBXRF6zRh2rI4QwLRJyhMhm6hSuQwGHAoQ9C6PQ9EJ0WtWJRacWcTvittal5TqVK8PEierzQYPg0iVNyxFC/IdOUXJXI2tERATOzs6Eh4fj5OSkdTlCpMtPx39iyNYhRMUn30ypnFs5GhdvTJMSTahfpD72VvYaVZh76PXQuDH8/TdUr66uimxpqXVVQuQ86fn7LSFHiGwqLjGOQ7cOsf3KdrZd3caxO8fQK8/3G7A0s6R24dqG0FOlQBXMzcw1rDjnunULKlWCJ09g5Ej46iutKxIi55GQkwoSckRO9TjmMX+H/G0IPdfCriV73dXWFb/ifjQu3pjGxRtTJE8RbQrNoVasgPfeAzMz2L0b6tbVuiIhchYJOakgIUfkBoqicOXJFbZd2cb2q9v5O+RvImIjkp1T3bM6Q2sNpY13GyzMLDSqNGfp0QMWLYIiReD0aXVTTyGEcUjISQUJOSI3StAncOT2EUPoOXzrMIlKIgDF8hRjSM0h9KjcQ8bwZFBkpDoY+epVdduH33/XuiIhcg4JOakgIUcIeBD1gDlH5zD7yGwexTwC1O6sftX70b9Gf/Lb59e4wuzr0CGoUwcSE2HJEujUSeuKhMgZJOSkgoQcIZ6Ljo9m0alFTD84nStPrgBgY2FDN59ufFbzM0rlLaVxhdnThAkwdiw4Oal7XBWR4U9CZJiEnFSQkCPEyxL1iay9sJYpB6Zw+PZhAHToaFW2FZ/X+pyaXjU1rjB7SUiA+vXhwAF1APKuXWAuE9uEyJD0/P2WxQCFEJibmdO2XFsO9jrIP93/IbB0IAoKay6sodaCWtRZUId1F9Ylm6IuXs3CAn77DRwdYe9e+OYbrSvKfi5fhkmToFYt+OILrasR2ZW05AghUhT0IIhpB6fx25nfDNtHlM5bms9qfkZXn67YWNhoXKHpW7xY3cDTwkJt1aleXeuKTNvVq+pU/OXL4cSJ5K9dvgwlSmhTlzAN0l2VChJyhEibu5F3mXVkFnOPzSXsWRgA7vbuLGi5gGalmmlbnIlTFHj/ffWPdqlS6h9uBwetqzIt1649DzbHjj0/bm4OjRpBaKg6rmnUKPjyS83KFCZAQk4qSMgRIn0iYyP55eQvfHfoO26E38BMZ8Z3/t8xoMYAdDqd1uWZrCdP1NWQb92C3r3hp5+0rkh7N27AypVqsDl8+PlxMzNo2BA6dIDWrSFfPli2TA2KXl5qIDKTQRa5loScVJCQI0TGxCXG0W9jP34++TMAfd/qy8ymM7E0lw2bXmX3bnjnHbVlZ+JEGDFC64qy3q1bz4PNwYPPj5uZqYO033sP2rSB/P9ZveDZM/DwgLAw2L4d/PyytGxhQmTgsRAi01mZW/FT4E9MbTwVHTrmHptL8z+aG7qyxMsaNFAH0QL873+5p9tFUdSuqDp11JaYwYPVgKPTQb16MGcO3L6tbm768ccvBxwAGxvo2FF9vmhRlpYvcgBpyRFCpNu6C+votLoT0fHReOfzZkOnDRR3Ka51WSZr0iQ15IC6js7Yseof/Jzo4EH47LPkrTZ16qgtNm3bgqdn6q919CjUqKEGntBQ2S4jt5KWHCFElmpZtiX7euyjoGNBgh4GUWN+Dfbd2Kd1WSZrxAj49lv1+fjxMGaM2tqRk4SEqGNqatVSA46dnfp73rypTqcfMCBtAQfgrbegXDm162rZssypW+RMEnKEEBlSxaMKR3ofoZpHNR7FPKLR4kb8dvo3rcsyWZ9/DtOnq8+/+kpt2ckJQScsTP3dypZVx93odNCzJ1y6pAa6QoXSf22dTt38FKTLSqSNhBwhRIZ5OnryT49/aOPdhrjEOLqu7cqov0fJ4oGvMHgwfP+9+nzyZHWxu+wadOLjYdYsKFkSpk6FuDh1cPDJk/DLL2lvtXmVDz5Qp5UfPAgXLhjnmiLnk5AjhDAKO0s7VrRfwYg66tShr/d+zfsr3yc6PlrjykzTgAHqwFtQw8GQIdkr6CgKrFsHFSrAwIHw6BF4e8PGjbBtG/j4GPd+BQpAQID6/NdfjXttkXNJyBFCGI2ZzoyJjSayqOUiLM0sWXF+BQ0WNeBu5F2tSzNJn3wC8+apz2fMUMNCdgg6J06oU+JbtYKLF8HNDebOVRfta9Ys8wZTJ3VZLV6s7vIuxJuYRMiZM2cORYsWxcbGBl9fX44cOZKq9y1duhSdTkerVq0yt0AhRJp0q9yNHV134GrrytE7R/H92ZfToae1LsskffQR/PyzGgxmz4Z+/UBvor18N29C165QrZq69o+NjTqY+vJldQq4hUXm3r9FC8ibF+7cUVuLhHgTzUPOsmXLGDJkCGPHjuXEiRP4+Pjg7+/P/fv3X/u+a9euMXToUOrWrZtFlQoh0qJekXoc/vAwZfKW4WbETWovqM1fwX9pXZZJ6tULFixQg87cuWpgMKWgExmpbqtQurS68ShA584QHKwubphVq3FYWan3BRmALFJH83VyfH19qV69OrNnzwZAr9fj5eXFgAEDGD58eIrvSUxMpF69evTs2ZO9e/cSFhbG2rVrU3U/WSdHiKz1JOYJ7Ve0Z2fITnTomNpkKoPfHixbQaTg99/VDT31erVrZv58dbBtVktIUAcO790L//wDe/aos6cA6taFadO022z05EmoWlUNPHfvgqurNnWIrJft1smJi4vj+PHj+L2wTreZmRl+fn4cfHEFqf+YMGEC+fPnp1evXm+8R2xsLBEREckeQois42LrwubOm+lTtQ8KCp9t+4we63rwMPqh1qWZnA8+UIOOmRksXKhOwc6KsScxMWqQ+eoraNIEXFzUxfc++0wdXBwWps6eWr1aPU/L3dSrVFEHNcfFwdKl2tUhsgdNQ87Dhw9JTEzE3d092XF3d3dCQ0NTfM++ffv45ZdfmD9/fqruMWnSJJydnQ0PLy+vDNcthEgbS3NL5rWYx/Qm09Gh49fTv1JqVilmHJpBfGK81uWZlI4d4c8/1RacxYvVMTAJCca9R0QEbNmirtFTty7kyaNuPTF6tLo/1NOn6rHAQJgyBQ4dgqAgddNMU2iASxqAvHChtnUI05fJw8SMKzIyki5dujB//nzy5cuXqveMGDGCIUOGGH6OiIiQoCOEBnQ6HYNrDuYtz7cYuGUgp0JPMXjrYOYdm8d3/t8RUCpA6xJNxnvvqSHn/ffhjz/U1pzff0/7wN5nz9Sp3Y8eqYvy7d2rPk6dennMj4eHup9U3brqv+XLm+6O3506wdChcOwY/PuvOo1diJRoGnLy5cuHubk59+7dS3b83r17FChQ4KXzr1y5wrVr1wgMDDQc0////6daWFgQHBxMiRIlkr3H2toaa2vrTKheCJEedYvU5VjvYyw4uYCRf48k+FEwzf5oRkDJAKb7T6dsvrJal2gS2rZVd+1u317dyiAxUV048PHj58El6fHw4cvHHj2CqKhXX79EieeBpm5d9WdTaKVJDTc3tZVpzRp1APLUqVpXJEyVSQw8rlGjBrNmzQLU0FK4cGH69+//0sDjZ8+ecfny5WTHRo0aRWRkJDNnzqR06dJYWVm99n4y8FgI0xH+LJyv/vmKmYdnEq+Px8LMgn7V+zG2/lhcbF20Ls8kbNigBp64uPS938xMHZxbsCDUrq0Gmrp11Z+zs/XroWVLdefyW7fA0lLrikRmS8/fb81DzrJly+jWrRs//vgjNWrUYMaMGSxfvpwLFy7g7u5O165dKViwIJMmTUrx/d27d5fZVUJkc5ceXWLo9qGsD14PgKutKxMaTOCjtz7Cwixb9apnis2b1UHI4eHqOjH/feTLl/LxvHnVHbtNtdspI+Lj1f2w7t9XA88LDfwih0rP32/N/9ejQ4cOPHjwgDFjxhAaGkrlypXZsmWLYTDyjRs3MMuJ/x8qhDAolbcU695fx/Yr2xm8dTDnHpyj/+b+zD02lxlNZ+BX3O/NF8nBAgLUBfCyS3dSVrC0VGejTZ+uDkCWkCNSonlLTlaTlhwhTFuCPoGfjv/E6F2jeRzzGIB3y7zL1MZTKZW3lKa1PYx+iIOVAzYWNprWIVRnz0KlSuqA7Dt31LE6IufKlt1VWU1CjhDZw+OYx4zfPZ45R+eQqCRiaWbJIN9BjKo3Cmcb5yyrI/xZOEv/XcrCUws5fPswAPnt8+Pl5EVh58IvPbycvHB3cMdMJy3QWeGtt+D4cXXvr0GDtK5GZCYJOakgIUeI7CXoQRBDtg1hy+UtALjZudG5YmdalG5B3SJ1sTJ//WSD9NArenaF7GLhqYWsClrFs4RnaXq/pZklXs4vhCCnwng5e1HLqxYV8st8Z2OaMwf691cXCDx1SutqRGaSkJMKEnKEyJ42XdrEkK1DCH4UbDjmaOVIkxJNaFG6BQElA3B3cH/NFd7sWtg1Fp1axKJTi7geft1wvJxbOXpU7sEHlT7AytyKG+E3UnzcjLjJncg76JWUN57SoWPVe6to7d06Q3WK5x4/Vtf4iYtTt3yoXFnrikRmkZCTChJyhMi+4hPj+eviX2y4uIFNlzZxL+r5Gls6dFQvWJ0WpVrQvHRzqhSokqr9saLjo1kdtJqFpxbyd8jfhuNO1k50rNCRnlV6Ut2zeqr32opPjOdO5J2Xws+p0FMcvHUQe0t7DvQ6QCX3Smn/AESK3nsPVqyAgQNh5kytqxGZRUJOKkjIESJn0Ct6jt85zsZLG9lwcQPH7x5P9rqnoyfNSjajRekW+BX3w97K3vCaoigcvn2YBScXsOzcMiJi1T3tdOh4p9g79KzSk9ZlW2NraWu0ehP0CQQsCWDH1R0UcS7Ckd5HyG+f32jXz802b4ZmzdQp83fuqJt3ipxHQk4qSMgRIme6G3mXTZc2seHSBrZf2U5U/PPlfq3NrWlQtAEtSrcgJj6GBacWcOHhBcPrxfIUo3vl7nTz6UaRPEUyrcYnMU/w/dmXS48vUadwHXZ23ZkpY4pym4QEKFxY3ZV81Spo00brikRmkJCTChJyhMj5YhNi2XN9DxsubmDDxQ2EhIW8dI6thS3tyrWjR+Ue1C9aP8tmQ114eIG3f36b8NhwelXpxfzA+anuChOvNnw4fPMNtGgBf/2ldTUiM0jISQUJOULkLoqicOHhBTZc3MDmy5tRUOhUoRMdKnTAyVqb/w3YcnkLzf9ojl7RM8N/BoPelrnPGXXhAnh7qxub3roFKWx/KLI5CTmpICFHCGEKvjv4HUO2DcFMZ8amTpvwL+mvdUnZXs2acOgQTJmi7lIucpb0/P2W1aqEEEIDn779KT0q90Cv6OmwsgPBD4Pf/CbxWj16qP8uXAi56z/fxatIyBFCCA3odDrmNp9LLa9ahMeGE/hnIE9inmhdVrbWoQPY2MD583DsmNbVCFMgIUcIITRibWHN6vdWU9i5MJceX6LDyg4k6BO0LivbcnZ+PrNq4UJtaxGmQUKOEEJoyN3BnXXvr8PO0o7tV7czdJsMJsmI7t3Vf//8E56lbTcOkQNJyBFCCI1VLlCZ31r/BsDMwzP5+cTPGleUfb3zDnh5QVgYrFundTVCaxJyhBDCBLTxbsOEBhMA+GTjJ+y9vlfjirInc3Po2lV9Ll1WQkKOEEKYiFH1RvFe+feI18fTZnkbroVd07qkbCmpy2r7drh9W9NShMYk5AghhInQ6XQsbLmQqh5VeRj9kHf/fJfI2Eity8p2SpaEunVBr4fFi7WuRmhJQo4QQpgQO0s71nZYi7u9O2fvn6XLmi7oFb3WZWU7Sa05ixbJmjm5mYQcIYQwMV7OXqx9fy3W5tasC17HmF1jNKsl9GkoK8+vJOTJy/t/mbL27cHODi5ehIMHta5GaEVCjhBCmKC3C73N/MD5AHy992v+PPtnltw3QZ/Avhv7GLlzJFV/rIrHNA/ar2hPw18bEhMfkyU1GIOjI/i8cxGAz0Y/0LgaoRULrQsQQgiRsi4+XTh7/yxTDkyh5/qeRMZFUqVAFcrmK4ujtaPR7nMn8g5bLm9h8+XNbL+ynfDY8GSvW5tbcz38OjMPz2R4neFGu29m2n5lO4eKDQTdWQ797cbWnTH4N7LVuiyRxWSDTiGEMGGJ+kRaLm3Jxksbkx0v5FQI73ze6sPt+b9udm7odLrXXjM+MZ4DNw+w+fJmNl/ezJl7Z5K97mrrin8Jf5qWbIp/CX+2X91OlzVdcLRy5PLAy+S3z2/039OYLj26RI2faxD2LAw2/ADH+uJR9ia3z3vxho9GmDDZhTwVJOQIIbKbyNhIvt77NYduHeLCwwvci7r3ynNdbV0pm6/sSwHI3MycrZe3svnyZnZc3UFk3PNZWzp0VC9YnaYlmhJQKoDqntUxNzM3vK5X9NSYX4Pjd4/zyVufMKf5nEz9fTMi/Fk4b//yNhceXqBmoZr0KDmcPo3fgXgHpv98k8G9vLQuUaSThJxUkJAjhMjuHsc85sLDCwQ9CCLo4f8/HgRxLewaCqn7n3Q3Ozf8S/rTtERTmpRogpu922vP33NtDw1+bYC5zpyzfc/i7eZthN/EuBL1iQT+Gcjmy5sp5FSIo72PUsChAGXa/cnFVR2xzX+LsJsFsbKS5pzsSEJOKkjIEULkVNHx0Vx8dNEQfi48vEDQwyAuPrpIgj4B34K+BJQMoGnJplTzrIaZLm1zT1otbcW64HW0KN2Cvzr+lUm/Rfp9vu1zph6ciq2FLft67qOqR1UAzt28QYVy1vDUnS7DD7N4kq/GlYr0kJCTChJyhBC5TYI+gdiEWOyt7DN0neCHwVSYW4EEfQI7u+7knWLvGKnCjFt8ejHd1nYDYGnbpXSo0CHZ660/28ra6f7o7B9w9Yo5Rd1dtShTZEB6/n7LFHIhhMjhLMwsMhxwAMrkK8PH1T4GYOi2oSazSOGhW4fo/VdvAEbVHfVSwAH4/euGWOUPQYlyo90gWTgnt5CQI4QQItXGNhiLk7UTJ0NP8tvp37Quh1sRt2i9rDVxiXG0KtuK8Q3Hp3ievY0Vo8ZHA3B8dQM2nTiZlWUKjUjIEUIIkWr57PIxsu5IAEb+PZLo+GjNaomJj6HV0laEPg2lYv6K/Nb6t9eOMxr1UXncylyCeHu6fnqVBH1CFlYrtCAhRwghRJoM9B1IEeci3I68zfSD0zWpQVEUeq7vyfG7x8lnl49176/Dwcrhte/R6eCXWfkAeLSvJWOWZ80q0kI7EnKEEEKkiY2FDZP9JgMwed9kQp+GZnkNk/dNZum/S7Ews2Bl+5UUcymWqvcFNnbBp34IKBZMmeDCncg7mVyp0JKEHCGEEGnWoXwHahSsQVR8VJZvILo+eD0j/1a7zGYHzKZ+0fppev+SH4qALpGEoBZ0mTkvM0oUJkJCjhBCiDTT6XRMb6J2Vf1y8hf+vf9vltz33/v/0nl1ZxQU+lXvx0dvfZTma5QvZ0brTk8A+PvHpmy7vN3YZeZaCQkwZgzs26d1JSoJOUIIIdKlduHatPVui17R8/n2zzP9fg+jH/Lun+/yNO4pDYs25Dv/79J9rdnf5sPCOhZu1aL75HU8S3hmxEpzp6tXoW5d+PJL6NwZorUbk24gIUcIIUS6TfabjKWZJVsub2HblW2Zdp/4xHjar2hPSFgIxV2Ks6L9CizNLdN9PU9PGDxYfX537QAm7ZlqpEpzH0WB336DypXh0CFwdoZvvwU7O60rk5AjhBAiA0q6lqRf9X6AukBgoj4xU+4zaMsgdl/bjaOVI+vfX09eu7wZvuaoEdY4uT6DR2WYODOUy48vG6HS3CU8XG216doVIiOhTh04fRo6vLweoyYk5AghhMiQ0fVHk8cmD2fvn2XRqUVGv/7co3OZe2wuOnQsabOE8vnLG+W6Tk7w1ThrABL+HsXHq4eSy3Y6ypD9+9XWmz//BHNztZtq924oUkTryp6TkCOEECJDXG1dGV1vNACjdo3iadxTo11797XdDNwyEICJjSYSWCbQaNcG+OgjHYWLxUFUAXYu8WFV0CqjXj8nSkiAceOgXj24dg2KFVMHGo8apYYdUyIhRwghRIb1q96P4i7FCX0aytQDGR/fEpcYx/eHv6fV0lYk6BPoVLETw2oPM0KlyVlZwdRvrNQf9n/OgBVfERkbafT75BQhIVC/PowfD3o9dOkCp07B229rXVnKJOQIIYTIMGsLa77x+waAKQempHuRPUVRWHFuBeXmlGPQlkGEx4ZTy6sWPwf+jE6nM2bJBu3awVvV9RDvQOiGPozdPTZT7pNVLl+GX39VW1fi44133T/+ULunDhxQu/r++AMWL1afmyoJOUIIIYyirXdbannVIjo+mlF/j0rz+/de30vNX2ry3sr3uPLkCu727vzY4kf2dN+DraVtJlSs0ulg6pT//3N4vA8zN27mdOjpTLufsT17Blu3wqBBUKqU+ujeXZ3O7eoKLVrAjBnw77/qTKi0iohQW2w6d1af16qlDi7u2NHYv4nx6ZRcNsoqIiICZ2dnwsPDcTLl+CmEENnQoVuHqPlLTXToOPnRSXwK+LzxPRceXmD4juGsC14HgL2lPZ/X+pzPan32xv2ojCkwEDZsALxXUvOz6ezrue+1G35qKSQENm2CzZvh778hJub5a5aW8NZbcOkSPHyY/H3u7tCoEfj5qQ8vr9ff5+BBNdyEhICZmbrQ38iRYGFh/N/pTdLz91tCjhBCCKN6f+X7LDu3DL/ifmz7YNsru5nuPb3HuN3jmH9iPolKIuY6cz6s+iFj64/Fw9Eji6uGc+egUiUFvV4HvWoy/5NefFj1wyytQa/oeZbwjOj4aBytHLG2UGd/xcbC3r3Pg82FC8nfV6gQNGsGAQFqiHF0VMfMnD0LO3aoj3/+eXmBvtKlnweeBg3AxUU9npgIEyeqY28SE6FoUViyRG3F0YqEnFSQkCOEEJkr5EkIZeeUJS4xjo2dNtKsVLNkr0fFRTHt4DSmHJhimIn1bpl3mdxoMt5u3lqUbNCrFyxYABTei8snrbg4IJh8dvlSPDc+MZ7w2HDCn4Wn+G9EbARRcVFEx0erj4To58///5Hs9fhoYhKeN8k4xJSjceI04oMbs+tvc6Kint/b3FxdkyYgQA03FSqo3W6vExurLtaXFHqOHFGDUBIzM6hWTQ08e/c+35qhUyf44Qd1kT8tSchJBQk5QgiR+T7f9jlTD07FO583Z/qewcLMggR9AotOLWLMrjHcfXoXgOqe1ZnSeEqaN9nMLLduQenSCjExOnj/XWo0ukdh58IpBpkXA4lR3Xwbtn8LN+omO1yggEKzZjoCAtQgkidPxm4THq6ua7NjB+zcCUFByV93dFTDzQcfZOw+xiIhJxUk5AghROYLexZGye9L8ijmEXObz6WQUyGG7RjG+QfnASiWpxiTGk3ivfLvZdqsqfT63/9g0iQgXxD0rQjmr1/F2d7SHmcbZ5ytnZP962TlhIOVA3aWdq992FvZY2dpx6M7TkydkI/1q20A0JnpsSx8jLjia6HUZqpXs2K6/zTqFK6TKb/37dtq2NmxQ20VGjsWihfPlFuli4ScVJCQI4QQWWPW4VkM3DIQc505iYoaFJIWDuz7Vl/DeBNTEx4OJUrAo0fQcuhG/NqHvBRgDEHG2gkLs4yNwg0LU8e/zJwJcXFqwOjZEyZMgDxu0Uw/OJ3J+yYTFa/2V7XxbsM3ft9Q0rWkEX7b7ENCTipIyBFCiKwRlxhHhR8qcOnxJazNrRnkO4gRdUeQxyaP1qW90cyZ8Omn6iaT77+vTsmuU+fN417SIj4efvxRXT340SP1mJ8fTJ0KPv+ZlBb6NJQxu8bwy8lf0Ct6LM0s6V+jP6PqjcLV1tV4RZkwCTmpICFHCCGyTvDDYFYFreKDSh9Q2Lmw1uWkWmysGjiSBt+C2rrTvbu6GWXhDPwqigJ//QVffAHBweoxb2813AQEvD5Inb13ls+3f87WK1sBcLFxYUz9MXxS/ROszK3SX1Q2ICEnFSTkCCGESA1FUUPOokWwfDk8/f8tuXQ6dZp29+7QurXa2pNaJ07AZ5+pA34B3NzUbqkPP0zb2jNbL29l6Pah/Hv/XwBKuJTgG79vaOPdxuTGOBmLhJxUkJAjhBAiraKiYNUqNfDs2vX8uJMTdOigBp6aNV/dCnP7trqI3uLFaniytobBg2HEiPRvi5CgT2DhyYWM3jWae1H3AKhTuA7TmkyjRsEa6buoCZOQkwoScoQQQmRESIgaVhYtUnfhTlK6tBp2unRRF+cDtfXn22/VrqikVYk7dVIHGhcpYpx6ImMj+Xb/t0w7OM0wrb1jhY5MbTIVT0dP49zEBEjISQUJOUIIIYxBr1dXEV60CFaseL6asJkZNG6sDlSeMwdCQ9XjderAtGlQI5MaWW5F3GLk3yNZfHoxAJ6OnmzouIEqHlUy54ZZTEJOKkjIEUIIYWyRkbBypRp4/vkn+WslSqitOa1bG3d21qucuHuCLmu6cP7Beewt7VnWbhnNSzfP/BtnsvT8/TaJncfmzJlD0aJFsbGxwdfXlyNHjrzy3Pnz51O3bl1cXFxwcXHBz8/vtecLIYQQmc3REXr0gD174PJlGD1anZ01fTqcPw9t2mRNwAGo6lGV/T3341fcj6j4KN5d+i4/HP0ha25uYjQPOcuWLWPIkCGMHTuWEydO4OPjg7+/P/fv30/x/N27d9OxY0d27drFwYMH8fLyokmTJty+fTuLKxdCCCFeVqKEOmNq+3Z1cLGVBjO789jkYVOnTfSs3BO9oqffpn58tvUz9Ir+zW/OQTTvrvL19aV69erMnj0bAL1ej5eXFwMGDGD48OFvfH9iYiIuLi7Mnj2brl27vvF86a4SQgiRWyiKwsS9Exm1axQArcu25vc2v2NnmYZ57yYi23VXxcXFcfz4cfz8/AzHzMzM8PPz4+DBg6m6RnR0NPHx8bi6prziY2xsLBEREckeQgghRG6g0+kYWW8kf7T5AytzK9ZcWEPDXxty7+k9rUvLEpqGnIcPH5KYmIi7u3uy4+7u7oQmDUd/g2HDhuHp6ZksKL1o0qRJODs7Gx5eXl4ZrlsIIYTITjpW7MiOLjtwtXXlyO0jvP3L2wQ9CHrzG7M5zcfkZMTkyZNZunQpa9aswcbGJsVzRowYQXh4uOFx8+bNLK5SCCGE0F7dInU52OsgJVxKcC3sGrUW1GJXyK43vzEb0zTk5MuXD3Nzc+7dS95sdu/ePQoUKPDa906dOpXJkyezbds2KlWq9MrzrK2tcXJySvYQQgghcqPSeUtz6MND1PKqRdizMPx/9+fXU79qXVam0TTkWFlZUa1aNXbu3Gk4ptfr2blzJzVr1nzl+7799lu+/PJLtmzZwltvvZUVpQohhBA5Qj67fOzsupMO5TsQr4+n+7rujN01lpy4bJ7m3VVDhgxh/vz5/PrrrwQFBdG3b1+ioqLo0aMHAF27dmXEiBGG87/55htGjx7NggULKFq0KKGhoYSGhvI0aec0IYQQQryWjYUNf7T9g+G11VnME/6ZQLe13YhNiNW4MuNKw56nmaNDhw48ePCAMWPGEBoaSuXKldmyZYthMPKNGzcwM3uexebOnUtcXBzt2rVLdp2xY8cybty4rCxdCCGEyLbMdGZM8ptEcZfi9N3Yl9/O/MaN8Bus7rAaV9uUZyxnN5qvk5PVZJ0cIYQQIrltV7bRbnk7IuMiKZO3DJs6b6K4S3Gty0pG9q5KBQk5QgghxMvO3jtL8z+aczPiJhZmFrjYuOBk7YSzjTNO1k6Gh7P16392snbCxdbF6K1BEnJSQUKOEEIIkbI7kXdovaw1R25nbE/Iah7VONbnmJGqUqXn77fmY3KEEEIIYRo8HT051OsQN8JvEBEbYXiEx4Yn+/lNx51tnLX+VQAJOUIIIYR4gU6no0ieIhm6hql0Emk+hVwIIYQQOYtOp9O6BEBCjhBCCCFyKAk5QgghhMiRJOQIIYQQIkeSkCOEEEKIHElCjhBCCCFyJAk5QgghhMiRJOQIIYQQIkeSkCOEEEKIHElCjhBCCCFyJAk5QgghhMiRJOQIIYQQIkeSkCOEEEKIHElCjhBCCCFyJAutC8hqSdu/R0REaFyJEEIIIVIr6e920t/x1Mh1IScyMhIALy8vjSsRQgghRFpFRkbi7OycqnN1SloiUQ6g1+u5c+cOjo6O6HQ6o147IiICLy8vbt68iZOTk1GvnZPJ55Z28pmlj3xu6SOfW/rI55Z2r/vMFEUhMjIST09PzMxSN9om17XkmJmZUahQoUy9h5OTk3yh00E+t7STzyx95HNLH/nc0kc+t7R71WeW2hacJDLwWAghhBA5koQcIYQQQuRIEnKMyNramrFjx2Jtba11KdmKfG5pJ59Z+sjnlj7yuaWPfG5pZ+zPLNcNPBZCCCFE7iAtOUIIIYTIkSTkCCGEECJHkpAjhBBCiBxJQo4QQgghciQJOUYyZ84cihYtio2NDb6+vhw5ckTrkkzauHHj0Ol0yR5ly5bVuiyT888//xAYGIinpyc6nY61a9cme11RFMaMGYOHhwe2trb4+flx6dIlbYo1IW/63Lp37/7S969p06baFGsiJk2aRPXq1XF0dCR//vy0atWK4ODgZOc8e/aMfv36kTdvXhwcHGjbti337t3TqGLTkJrPrUGDBi993z7++GONKjYNc+fOpVKlSoZF/2rWrMnmzZsNrxvruyYhxwiWLVvGkCFDGDt2LCdOnMDHxwd/f3/u37+vdWkmrXz58ty9e9fw2Ldvn9YlmZyoqCh8fHyYM2dOiq9/++23fP/998ybN4/Dhw9jb2+Pv78/z549y+JKTcubPjeApk2bJvv+/fnnn1lYoenZs2cP/fr149ChQ2zfvp34+HiaNGlCVFSU4ZzBgwfz119/sWLFCvbs2cOdO3do06aNhlVrLzWfG0Dv3r2Tfd++/fZbjSo2DYUKFWLy5MkcP36cY8eO8c4779CyZUvOnTsHGPG7pogMq1GjhtKvXz/Dz4mJiYqnp6cyadIkDasybWPHjlV8fHy0LiNbAZQ1a9YYftbr9UqBAgWUKVOmGI6FhYUp1tbWyp9//qlBhabpv5+boihKt27dlJYtW2pST3Zx//59BVD27NmjKIr63bK0tFRWrFhhOCcoKEgBlIMHD2pVpsn57+emKIpSv359ZdCgQdoVlU24uLgoP//8s1G/a9KSk0FxcXEcP34cPz8/wzEzMzP8/Pw4ePCghpWZvkuXLuHp6Unx4sXp3LkzN27c0LqkbCUkJITQ0NBk3z1nZ2d8fX3lu5cKu3fvJn/+/JQpU4a+ffvy6NEjrUsyKeHh4QC4uroCcPz4ceLj45N938qWLUvhwoXl+/aC/35uSZYsWUK+fPmoUKECI0aMIDo6WovyTFJiYiJLly4lKiqKmjVrGvW7lus26DS2hw8fkpiYiLu7e7Lj7u7uXLhwQaOqTJ+vry+LFi2iTJky3L17l/Hjx1O3bl3+/fdfHB0dtS4vWwgNDQVI8buX9JpIWdOmTWnTpg3FihXjypUr/O9//yMgIICDBw9ibm6udXma0+v1fPrpp9SuXZsKFSoA6vfNysqKPHnyJDtXvm/PpfS5AXTq1IkiRYrg6enJmTNnGDZsGMHBwaxevVrDarV39uxZatasybNnz3BwcGDNmjWUK1eOU6dOGe27JiFHaCIgIMDwvFKlSvj6+lKkSBGWL19Or169NKxM5Abvv/++4XnFihWpVKkSJUqUYPfu3TRq1EjDykxDv379+Pfff2WcXBq96nPr06eP4XnFihXx8PCgUaNGXLlyhRIlSmR1mSajTJkynDp1ivDwcFauXEm3bt3Ys2ePUe8h3VUZlC9fPszNzV8a9X3v3j0KFCigUVXZT548eShdujSXL1/WupRsI+n7Jd+9jCtevDj58uWT7x/Qv39/NmzYwK5duyhUqJDheIECBYiLiyMsLCzZ+fJ9U73qc0uJr68vQK7/vllZWVGyZEmqVavGpEmT8PHxYebMmUb9rknIySArKyuqVavGzp07Dcf0ej07d+6kZs2aGlaWvTx9+pQrV67g4eGhdSnZRrFixShQoECy715ERASHDx+W714a3bp1i0ePHuXq75+iKPTv3581a9bw999/U6xYsWSvV6tWDUtLy2Tft+DgYG7cuJGrv29v+txScurUKYBc/X1LiV6vJzY21rjfNeOOjc6dli5dqlhbWyuLFi1Szp8/r/Tp00fJkyePEhoaqnVpJuuzzz5Tdu/erYSEhCj79+9X/Pz8lHz58in379/XujSTEhkZqZw8eVI5efKkAijTp09XTp48qVy/fl1RFEWZPHmykidPHmXdunXKmTNnlJYtWyrFihVTYmJiNK5cW6/73CIjI5WhQ4cqBw8eVEJCQpQdO3YoVatWVUqVKqU8e/ZM69I107dvX8XZ2VnZvXu3cvfuXcMjOjracM7HH3+sFC5cWPn777+VY8eOKTVr1lRq1qypYdXae9PndvnyZWXChAnKsWPHlJCQEGXdunVK8eLFlXr16mlcubaGDx+u7NmzRwkJCVHOnDmjDB8+XNHpdMq2bdsURTHed01CjpHMmjVLKVy4sGJlZaXUqFFDOXTokNYlmbQOHTooHh4eipWVlVKwYEGlQ4cOyuXLl7Uuy+Ts2rVLAV56dOvWTVEUdRr56NGjFXd3d8Xa2lpp1KiREhwcrG3RJuB1n1t0dLTSpEkTxc3NTbG0tFSKFCmi9O7dO9f/R0lKnxegLFy40HBOTEyM8sknnyguLi6KnZ2d0rp1a+Xu3bvaFW0C3vS53bhxQ6lXr57i6uqqWFtbKyVLllQ+//xzJTw8XNvCNdazZ0+lSJEiipWVleLm5qY0atTIEHAUxXjfNZ2iKEo6W5aEEEIIIUyWjMkRQgghRI4kIUcIIYQQOZKEHCGEEELkSBJyhBBCCJEjScgRQgghRI4kIUcIIYQQOZKEHCGEEELkSBJyhBC5kk6nY+3atVqXIYTIRBJyhBBZrnv37uh0upceTZs21bo0IUQOYqF1AUKI3Klp06YsXLgw2TFra2uNqhFC5ETSkiOE0IS1tTUFChRI9nBxcQHUrqS5c+cSEBCAra0txYsXZ+XKlcnef/bsWd555x1sbW3Jmzcvffr04enTp8nOWbBgAeXLl8fa2hoPDw/69++f7PWHDx/SunVr7OzsKFWqFOvXrze89uTJEzp37oybmxu2traUKlXqpVAmhDBtEnKEECZp9OjRtG3bltOnT9O5c2fef/99goKCAIiKisLf3x8XFxeOHj3KihUr2LFjR7IQM3fuXPr160efPn04e/Ys69evp2TJksnuMX78eN577z3OnDlDs2bN6Ny5M48fPzbc//z582zevJmgoCDmzp1Lvnz5su4DEEJknPH2FBVCiNTp1q2bYm5urtjb2yd7fP3114qiqDs7f/zxx8ne4+vrq/Tt21dRFEX56aefFBcXF+Xp06eG1zdu3KiYmZkZdhP39PRURo4c+coaAGXUqFGGn58+faoAyubNmxVFUZTAwEClR48exvmFhRCakDE5QghNNGzYkLlz5yY75urqanhes2bNZK/VrFmTU6dOARAUFISPjw/29vaG12vXro1eryc4OBidTsedO3do1KjRa2uoVKmS4bm9vT1OTk7cv38fgL59+9K2bVtOnDhBkyZNaNWqFbVq1UrX7yqE0IaEHCGEJuzt7V/qPjIWW1vbVJ1naWmZ7GedToderwcgICCA69evs2nTJrZv306jRo3o168fU6dONXq9QojMIWNyhBAm6dChQy/97O3tDYC3tzenT58mKirK8Pr+/fsxMzOjTJkyODo6UrRoUXbu3JmhGtzc3OjWrRu///47M2bM4KeffsrQ9YQQWUtacoQQmoiNjSU0NDTZMQsLC8Pg3hUrVvDWW29Rp04dlixZwpEjR/jll18A6Ny5M2PHjqVbt26MGzeOBw8eMGDAALp06YK7uzsA48aN4+OPPyZ//vwEBAQQGRnJ/v37GTBgQKrqGzNmDNWqVaN8+fLExsayYcMGQ8gSQmQPEnKEEJrYsmULHh4eyY6VKVOGCxcuAOrMp6VLl/LJJ5/g4eHBn3/+Sbly5QCws7Nj69atDBo0iOrVq2NnZ0fbtm2ZPn264VrdunXj2bNnfPfddwwdOpR8+fLRrl27VNdnZWXFiBEjuHbtGra2ttStW5elS5ca4TcXQmQVnaIoitZFCCHEi3Q6HWvWrKFVq1ZalyKEyMZkTI4QQgghciQJOUIIIYTIkWRMjhDC5EgvuhDCGKQlRwghhBA5koQcIYQQQuRIEnKEEEIIkSNJyBFCCCFEjiQhRwghhBA5koQcIYQQQuRIEnKEEEIIkSNJyBFCCCFEjiQhRwghhBA50v8BGPAQ+lz/FqgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history2.history['loss'],color='green',label='Training Loss')\n",
        "plt.plot(history2.history['val_loss'],color='blue',label='Validation loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Loss variation')\n",
        "plt.savefig('Model Training History')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "44cGKLx6HWMb",
        "outputId": "1b03a405-aa0a-4ce7-feb2-5976776f2b7e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history3.history['loss'],color='green',label='Training Loss')\n",
        "plt.plot(history3.history['val_loss'],color='blue',label='Validation loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Loss variation')\n",
        "plt.savefig('Model Training History')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "TljfikqBHepM",
        "outputId": "aaa27e40-9f83-4804-adc3-b87131f4b9b8"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SFbDc-X2HtGj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}