Skin-Cancer-ISIC-CNN-87.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Skin Cancer ISIC CNN 87%.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AhmedHatemS/Skin-cancer-detiction-CNN/blob/master/Skin-Cancer-ISIC-CNN-87.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **SKIN CANCER MULTICLASS IMAGE CLASSIFICATION VIA FINE-TUNING WITH EFFICIENTNET**"
      ],
      "metadata": {
        "id": "05VSdbeoUDsx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Table of Contents:\n",
        "**1** - Introduction\n",
        "\n",
        "**2** - Import Libraries\n",
        "\n",
        "**3** - Read and Overview Data\n",
        "\n",
        "**4** - Load Data\n",
        "\n",
        "**5** - Data Augmentation\n",
        "\n",
        "**6** - Prepare Inputs\n",
        "\n",
        "**7** - Training A Model From Scratch\n",
        "\n",
        "**8** - Validation Accuracy & Loss Curve\n",
        "\n",
        "**9** - Transfer Learning From Pre-Trained Weights\n",
        "\n",
        "**10** - Unfreezing Model\n",
        "\n",
        "**11** - Model Prediction\n",
        "\n",
        "**12** - Test Accuracy\n",
        "\n",
        "**13** - Confusion Matrix\n",
        "\n",
        "**14** - Training Loss Curve\n",
        "\n",
        "**15** - ROC Curve\n",
        "\n",
        "**16** - Conclusion"
      ],
      "metadata": {
        "id": "1Hg8PPMQXfb3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1 - INTRODUCTION**\n",
        "\n",
        "The various deep learning methods use data to train neural network algorithms to do a variety of machine learning tasks, such as the classification of different classes of objects. Convolutional neural networks are deep learning algorithms that are very powerful for the analysis of images. Convolutional Neural Networks specialized for applications in image & video recognition. It is mainly used in image analysis tasks like Image recognition, Object detection & Segmentation.\n",
        "\n",
        "**Abstract**\n",
        "\n",
        "Skin cancer is one of the most prevalent and deadly types of cancer. Dermatologists diagnose this disease primarily visually. Multiclass skin cancer classification is challenging due to the fine-grained variability in the appearance of its various diagnostic categories. On the other hand, recent studies have demonstrated that convolutional neural networks outperform dermatologists in multiclass skin cancer classification. We developed a preprocessing image pipeline for this work. We removed hairs from the images, augmented the dataset, and resized the imageries to meet the requirements of our model. By performing transfer learning on pre-trained ImageNet weights and fine-tuning the Convolutional Neural Networks, we trained the EfficientNet B0 on the Skin Cancer dataset. Our model, the EfficientNet B0, achieved a final Accuaracy of 87%. The classification score resulted from many factors such as resolution scaling, data enhancement, noise removal, successful transfer learning of ImageNet weights, and fine-tuning.\n"
      ],
      "metadata": {
        "id": "wfm_PbXtXvhs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2 - IMPORT LIBRARIES**"
      ],
      "metadata": {
        "id": "mh8cQBw-cNTl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LG0Hb_Nbxzyr"
      },
      "outputs": [],
      "source": [
        "#import libraries to use it in our project\n",
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow.keras.layers\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import drive\n",
        "from IPython.display import Image\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.python.keras import layers\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.python.keras.models import Model\n",
        "from tensorflow.keras.applications import imagenet_utils\n",
        "from tensorflow.keras.metrics import categorical_crossentropy\n",
        "from tensorflow.keras.layers import Dense,GlobalAveragePooling2D\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.callbacks import EarlyStopping"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "try:\n",
        "    tpu = tf.distribute.cluster_resolver.TPUClusterResolver.connect()\n",
        "    print(\"Device:\", tpu.master())\n",
        "    strategy = tf.distribute.TPUStrategy(tpu)\n",
        "except ValueError:\n",
        "    print(\"Not connected to a TPU runtime. Using CPU/GPU strategy\")\n",
        "    strategy = tf.distribute.MirroredStrategy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPuhL5MOLBDg",
        "outputId": "010f0e79-39cc-4f9c-99cd-4a858ba87371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Not connected to a TPU runtime. Using CPU/GPU strategy\n",
            "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')  # access my drive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8S18CctapJc",
        "outputId": "3d4d88cc-3dd1-4dd2-ac99-bc4e2d2ca681"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3 - READ AND OVERVIEW DATA**"
      ],
      "metadata": {
        "id": "57GurDU2cbZF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Showing one Vascular lesion and one Actinic keratosis to see how it looks*"
      ],
      "metadata": {
        "id": "NN-GNUN-cgHz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data1 = cv2.imread(\"/content/drive/MyDrive/Skin Cancer/vascular lesion/vascular lesion_original_ISIC_0034214.jpg_1365466a-27e1-4977-bad2-c4a4bb443bf7.jpg\") #Melanoma path and call one image\n",
        "data1 = cv2.resize(data1,(160,160)) \n",
        "plt.subplot(1,2,1)\n",
        "plt.imshow(data1)\n",
        "plt.axis(\"off\")\n",
        "plt.title(\"Vascular lesion\")\n",
        "data1 = cv2.imread(\"/content/drive/MyDrive/Skin Cancer/actinic keratosis/ISIC_0025605.jpg\") #Nevus path and call one image\n",
        "data1 = cv2.resize(data1,(160,160))\n",
        "plt.subplot(1,2,2)\n",
        "plt.imshow(data1)\n",
        "plt.axis(\"off\")\n",
        "plt.title(\"Actinic keratosis\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Im6nhfSsfJgn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "24592ddd-4dc3-4c2a-9867-9294e7a61fcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4 - LOAD DATA**"
      ],
      "metadata": {
        "id": "J3pBeUpbir0l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "All data is read and resized per image to 224px, 224px to lower processing power. images and labels datas are created. Images will be in \"images\" data and \"labels\" data for its correspond."
      ],
      "metadata": {
        "id": "h5CeJUkDiwsy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "images = list()\n",
        "labels = list()\n",
        "\n",
        "for i in os.listdir(\"/content/drive/MyDrive/Skin Cancer/actinic keratosis/\"): #read all Actinic Keratosis data \n",
        "    if \".jpg\" in i: #this if block for only read .jpg files\n",
        "        path = \"/content/drive/MyDrive/Skin Cancer/actinic keratosis/\"+i # create path\n",
        "        img = plt.imread(path) # read created path\n",
        "        img = cv2.resize(img,(224,224)) # resize image for lower processing power\n",
        "        images.append(img) # append image to images data\n",
        "        labels.append(0)\n",
        "        \n",
        "for i in os.listdir(\"/content/drive/MyDrive/Skin Cancer/basal cell carcinoma/\"): #read all Basal Cell Carcinoma data \n",
        "    if \".jpg\" in i: #this if block for only read .jpg files\n",
        "        path = \"/content/drive/MyDrive/Skin Cancer/basal cell carcinoma/\"+i # create path\n",
        "        img = plt.imread(path) # read created path\n",
        "        img = cv2.resize(img,(224,224)) # resize image for lower processing power\n",
        "        images.append(img) # append image to images data\n",
        "        labels.append(1)\n",
        "for i in os.listdir(\"/content/drive/MyDrive/Skin Cancer/dermatofibroma/\"): #read all Dermatofibroma data \n",
        "    if \".jpg\" in i: #this if block for only read .jpg files\n",
        "        path = \"/content/drive/MyDrive/Skin Cancer/dermatofibroma/\"+i # create path\n",
        "        img = plt.imread(path) # read created path\n",
        "        img = cv2.resize(img,(224,224)) # resize image for lower processing power\n",
        "        images.append(img) # append image to images data\n",
        "        labels.append(2)\n",
        "for i in os.listdir(\"/content/drive/MyDrive/Skin Cancer/pigmented benign keratosis/\"): #read all Pigmented Benign Keratosis data \n",
        "    if \".jpg\" in i: #this if block for only read .jpg files\n",
        "        path = \"/content/drive/MyDrive/Skin Cancer/pigmented benign keratosis/\"+i # create path\n",
        "        img = plt.imread(path) # read created path\n",
        "        img = cv2.resize(img,(224,224)) # resize image for lower processing power\n",
        "        images.append(img) # append image to images data\n",
        "        labels.append(3)\n",
        "for i in os.listdir(\"/content/drive/MyDrive/Skin Cancer/vascular lesion/\"): #read all Vascular Lesion data \n",
        "    if \".jpg\" in i: #this if block for only read .jpg files\n",
        "        path = \"/content/drive/MyDrive/Skin Cancer/vascular lesion/\"+i # create path\n",
        "        img = plt.imread(path) # read created path\n",
        "        img = cv2.resize(img,(224,224)) # resize image for lower processing power\n",
        "        images.append(img) # append image to images data\n",
        "        labels.append(4)\n",
        "images = np.array(images)\n",
        "\n",
        "images.shape[0] #array length"
      ],
      "metadata": {
        "id": "djoNVyCHyGAs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b61224a6-b7c4-4dce-d8d2-0f3c6d8490e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5067"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Show 4 data randomly and their status**"
      ],
      "metadata": {
        "id": "LQC4bqZvltCt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,4,1)\n",
        "plt.imshow(images[20]) # image 1\n",
        "plt.title(labels[20])\n",
        "plt.axis(\"off\")\n",
        "plt.subplot(1,4,2) # image 2\n",
        "plt.imshow(images[300])\n",
        "plt.title(labels[300])\n",
        "plt.axis(\"off\")\n",
        "plt.subplot(1,4,3) #image 3\n",
        "plt.imshow(images[2000])\n",
        "plt.title(labels[2000])\n",
        "plt.axis(\"off\")\n",
        "plt.subplot(1,4,4) #image 4 \n",
        "plt.imshow(images[200])\n",
        "plt.title(labels[200])\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "pW7_kQhtfcLo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "outputId": "bbfdd4c6-5b2e-4004-d7a3-e940a871f021"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5 - DATA AUGMENTATION**"
      ],
      "metadata": {
        "id": "Gl4puNwknYLO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "img_augmentation = Sequential(\n",
        "    [\n",
        "        layers.RandomRotation(factor=0.15),\n",
        "        layers.RandomTranslation(height_factor=0.1, width_factor=0.1),\n",
        "        layers.RandomFlip(),\n",
        "        layers.RandomContrast(factor=0.1),\n",
        "    ],\n",
        "    name=\"img_augmentation\",\n",
        ")"
      ],
      "metadata": {
        "id": "XHragVnLftw4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The labels are put into One-Hot (a.k.a. categorical) encoding."
      ],
      "metadata": {
        "id": "3Mx4ZXBr4frJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def input_preprocess(images, labels):\n",
        "    label = tf.one_hot(labels, 5)\n",
        "    return images, labels"
      ],
      "metadata": {
        "id": "aUWkevwtf1pe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6 - PREPARE INPUTS**"
      ],
      "metadata": {
        "id": "lB2x3W58qEbB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Split dataset Train split and Test split*"
      ],
      "metadata": {
        "id": "d3vDYBpC5WXx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,Y_train,Y_test = train_test_split(images,labels,train_size = 0.8, random_state = 0)"
      ],
      "metadata": {
        "id": "JRVuF40ilS6K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Validation and train split*"
      ],
      "metadata": {
        "id": "7ziDAjc95dIn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_val,Y_train,Y_val = train_test_split(x_train, Y_train, test_size = 0.1, random_state = 42)"
      ],
      "metadata": {
        "id": "vBxxGhb75TcF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Convert the fields into a numpy array*"
      ],
      "metadata": {
        "id": "lRI05hxS5kQV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.array(x_train)\n",
        "x_test = np.array(x_test)\n",
        "x_val = np.array(x_val)\n",
        "Y_train = np.array(Y_train)\n",
        "Y_test = np.array(Y_test)\n",
        "Y_val = np.array(Y_val)\n"
      ],
      "metadata": {
        "id": "omg_IDK8HB9K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Show number of samples and dimensions on each split*"
      ],
      "metadata": {
        "id": "oK8MVdrp5sxy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Train Split: \", x_train.shape)\n",
        "print(\"Test Split: \", x_test.shape)\n",
        "print(\"Validation Split: \", x_val.shape)"
      ],
      "metadata": {
        "id": "dOWJO3gO-n5a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea6f7091-e965-41da-cf9a-c6de55deb372"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Split:  (3647, 224, 224, 3)\n",
            "Test Split:  (1014, 224, 224, 3)\n",
            "Validation Split:  (406, 224, 224, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **7 - TRAINING A MODEL FROM SCRATCH**"
      ],
      "metadata": {
        "id": "S9KUPs_e92dP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We build an EfficientNetB0 with 5 output classes, that is initialized from scratch."
      ],
      "metadata": {
        "id": "9R3lzfeR-JhW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.applications import EfficientNetB0\n",
        "\n",
        "with strategy.scope():\n",
        "    inputs = layers.Input(shape=(224, 224, 3))\n",
        "    x = img_augmentation(inputs)\n",
        "    outputs = EfficientNetB0(include_top=True, weights=None, classes=5)(x)\n",
        "\n",
        "    model = tf.keras.Model(inputs, outputs)\n",
        "    model.compile(\n",
        "        optimizer=\"adam\", loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=[\"accuracy\"]\n",
        "    )\n",
        "\n",
        "model.summary()\n",
        "\n",
        "epochs = 40  # @param {type: \"slider\", min:10, max:100}"
      ],
      "metadata": {
        "id": "5DsVbF62Kcq2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "927a7d6a-3dbf-4ceb-d560-1c2aaf6054b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_4 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " img_augmentation (Sequentia  (None, 224, 224, 3)      0         \n",
            " l)                                                              \n",
            "                                                                 \n",
            " efficientnetb0 (Functional)  (None, 5)                4055976   \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,055,976\n",
            "Trainable params: 4,013,953\n",
            "Non-trainable params: 42,023\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Model Fitting with Batch Size of 32 & 40 Epochs.*"
      ],
      "metadata": {
        "id": "BWGf7eh5QgHx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit(x_train,Y_train ,epochs=epochs , validation_data=(x_val,Y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29hkNRvC_aK2",
        "outputId": "fc855bd7-6e32-4138-cb4f-f4e2ae83a814"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
            "  return dispatch_target(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "114/114 [==============================] - 62s 373ms/step - loss: 2.6520 - accuracy: 0.2852 - val_loss: 1.7064 - val_accuracy: 0.1995\n",
            "Epoch 2/40\n",
            "114/114 [==============================] - 39s 339ms/step - loss: 1.4943 - accuracy: 0.3951 - val_loss: 2.0596 - val_accuracy: 0.2143\n",
            "Epoch 3/40\n",
            "114/114 [==============================] - 39s 339ms/step - loss: 1.3701 - accuracy: 0.4332 - val_loss: 1.9409 - val_accuracy: 0.2118\n",
            "Epoch 4/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 1.3261 - accuracy: 0.4724 - val_loss: 1.5128 - val_accuracy: 0.3128\n",
            "Epoch 5/40\n",
            "114/114 [==============================] - 38s 336ms/step - loss: 1.1639 - accuracy: 0.5141 - val_loss: 1.3008 - val_accuracy: 0.4409\n",
            "Epoch 6/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 1.1009 - accuracy: 0.5396 - val_loss: 1.3140 - val_accuracy: 0.4458\n",
            "Epoch 7/40\n",
            "114/114 [==============================] - 38s 335ms/step - loss: 1.0898 - accuracy: 0.5629 - val_loss: 1.7612 - val_accuracy: 0.3719\n",
            "Epoch 8/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 1.1157 - accuracy: 0.5462 - val_loss: 2.1240 - val_accuracy: 0.3818\n",
            "Epoch 9/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 1.0861 - accuracy: 0.5662 - val_loss: 1.1296 - val_accuracy: 0.5616\n",
            "Epoch 10/40\n",
            "114/114 [==============================] - 39s 339ms/step - loss: 0.9959 - accuracy: 0.5934 - val_loss: 1.0902 - val_accuracy: 0.5542\n",
            "Epoch 11/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 0.9324 - accuracy: 0.6298 - val_loss: 1.3065 - val_accuracy: 0.5271\n",
            "Epoch 12/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 0.9006 - accuracy: 0.6394 - val_loss: 1.2489 - val_accuracy: 0.5074\n",
            "Epoch 13/40\n",
            "114/114 [==============================] - 38s 336ms/step - loss: 0.9008 - accuracy: 0.6435 - val_loss: 2.5587 - val_accuracy: 0.3941\n",
            "Epoch 14/40\n",
            "114/114 [==============================] - 39s 339ms/step - loss: 0.8715 - accuracy: 0.6699 - val_loss: 1.3988 - val_accuracy: 0.4483\n",
            "Epoch 15/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 0.8935 - accuracy: 0.6625 - val_loss: 1.2411 - val_accuracy: 0.5394\n",
            "Epoch 16/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 0.7988 - accuracy: 0.6888 - val_loss: 0.8809 - val_accuracy: 0.6700\n",
            "Epoch 17/40\n",
            "114/114 [==============================] - 38s 336ms/step - loss: 0.7586 - accuracy: 0.7157 - val_loss: 1.3461 - val_accuracy: 0.5271\n",
            "Epoch 18/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 0.7699 - accuracy: 0.7055 - val_loss: 0.9612 - val_accuracy: 0.6207\n",
            "Epoch 19/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 0.7659 - accuracy: 0.7080 - val_loss: 0.8929 - val_accuracy: 0.6527\n",
            "Epoch 20/40\n",
            "114/114 [==============================] - 38s 338ms/step - loss: 0.7369 - accuracy: 0.7239 - val_loss: 1.0901 - val_accuracy: 0.5887\n",
            "Epoch 21/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 0.6869 - accuracy: 0.7318 - val_loss: 1.5618 - val_accuracy: 0.5074\n",
            "Epoch 22/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 0.6864 - accuracy: 0.7373 - val_loss: 1.3323 - val_accuracy: 0.6034\n",
            "Epoch 23/40\n",
            "114/114 [==============================] - 39s 340ms/step - loss: 0.7031 - accuracy: 0.7343 - val_loss: 1.4538 - val_accuracy: 0.5320\n",
            "Epoch 24/40\n",
            "114/114 [==============================] - 39s 339ms/step - loss: 0.6262 - accuracy: 0.7642 - val_loss: 1.8266 - val_accuracy: 0.4557\n",
            "Epoch 25/40\n",
            "114/114 [==============================] - 39s 339ms/step - loss: 0.6507 - accuracy: 0.7546 - val_loss: 0.7955 - val_accuracy: 0.6970\n",
            "Epoch 26/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 0.6228 - accuracy: 0.7595 - val_loss: 0.9330 - val_accuracy: 0.6281\n",
            "Epoch 27/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 0.6018 - accuracy: 0.7727 - val_loss: 1.0042 - val_accuracy: 0.6429\n",
            "Epoch 28/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 0.5952 - accuracy: 0.7741 - val_loss: 0.8937 - val_accuracy: 0.6946\n",
            "Epoch 29/40\n",
            "114/114 [==============================] - 38s 336ms/step - loss: 0.5696 - accuracy: 0.7891 - val_loss: 0.6672 - val_accuracy: 0.7463\n",
            "Epoch 30/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 0.5795 - accuracy: 0.7935 - val_loss: 0.7900 - val_accuracy: 0.7291\n",
            "Epoch 31/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 0.5578 - accuracy: 0.7941 - val_loss: 0.6803 - val_accuracy: 0.7586\n",
            "Epoch 32/40\n",
            "114/114 [==============================] - 39s 339ms/step - loss: 0.5867 - accuracy: 0.7886 - val_loss: 1.0427 - val_accuracy: 0.6379\n",
            "Epoch 33/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 0.6177 - accuracy: 0.7834 - val_loss: 12.8897 - val_accuracy: 0.2882\n",
            "Epoch 34/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 0.6206 - accuracy: 0.7809 - val_loss: 5.9496 - val_accuracy: 0.1700\n",
            "Epoch 35/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 0.6774 - accuracy: 0.7573 - val_loss: 1.1216 - val_accuracy: 0.5788\n",
            "Epoch 36/40\n",
            "114/114 [==============================] - 38s 337ms/step - loss: 0.5410 - accuracy: 0.7944 - val_loss: 0.7108 - val_accuracy: 0.7143\n",
            "Epoch 37/40\n",
            "114/114 [==============================] - 38s 336ms/step - loss: 0.5174 - accuracy: 0.8023 - val_loss: 0.9815 - val_accuracy: 0.6749\n",
            "Epoch 38/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 0.4780 - accuracy: 0.8190 - val_loss: 1.7483 - val_accuracy: 0.5493\n",
            "Epoch 39/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 0.4741 - accuracy: 0.8168 - val_loss: 0.9976 - val_accuracy: 0.6355\n",
            "Epoch 40/40\n",
            "114/114 [==============================] - 39s 338ms/step - loss: 0.4596 - accuracy: 0.8330 - val_loss: 0.6923 - val_accuracy: 0.7660\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Plotting the training and validation accuracy.*"
      ],
      "metadata": {
        "id": "HXVV7CZoQyGu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_hist(hist):\n",
        "    plt.plot(hist.history[\"accuracy\"])\n",
        "    plt.plot(hist.history[\"val_accuracy\"])\n",
        "    plt.title(\"model accuracy\")\n",
        "    plt.ylabel(\"accuracy\")\n",
        "    plt.xlabel(\"epoch\")\n",
        "    plt.legend([\"train\", \"validation\"], loc=\"upper left\")\n",
        "    plt.show()\n",
        "\n",
        "plot_hist(hist)"
      ],
      "metadata": {
        "id": "68lfqMdr_I4-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "a800f038-402c-4254-8b12-0dd7ec15ff1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top 1 Accuracy Test Result**"
      ],
      "metadata": {
        "id": "4zvLUrgNRNzP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(x_test, Y_test)\n",
        "print('Test Accuracy: ', test_acc)"
      ],
      "metadata": {
        "id": "J9jo7BffCTJs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d679f1df-b49d-4179-b46c-584f9e54222e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 2s 66ms/step - loss: 0.5843 - accuracy: 0.7899\n",
            "Test Accuracy:  0.7899408340454102\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **8 - TRANSFER LEARNING FROM PRE-TRAINED WEIGHTS**"
      ],
      "metadata": {
        "id": "xJ4JqfpHR4Me"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Here we initialize the model with pre-trained ImageNet weights, and we fine-tune it on our own dataset.*"
      ],
      "metadata": {
        "id": "EjKE_bjwSClD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first step to transfer learning is to freeze all layers and train only the top layers. For this step, a relatively large learning rate (1e-2) can be used. Note that validation accuracy and loss will usually be better than training accuracy and loss. This is because the regularization is strong, which only suppresses training-time metrics."
      ],
      "metadata": {
        "id": "R3txDBllSTDr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "IMG_SIZE = 224\n",
        "\n",
        "def build_model(num_classes):\n",
        "    inputs = layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))\n",
        "    x = img_augmentation(inputs)\n",
        "    model = EfficientNetB0(include_top=False, input_tensor=x, weights=\"imagenet\")\n",
        "\n",
        "    # Freeze the pretrained weights\n",
        "    model.trainable = False\n",
        "\n",
        "    # Rebuild top\n",
        "    x = layers.GlobalAveragePooling2D(name=\"avg_pool\")(model.output)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "\n",
        "    top_dropout_rate = 0.2\n",
        "    x = layers.Dropout(top_dropout_rate, name=\"top_dropout\")(x)\n",
        "    outputs = layers.Dense(NUM_CLASSES, activation=\"softmax\", name=\"pred\")(x)\n",
        "\n",
        "    # Compile\n",
        "    model = tf.keras.Model(inputs, outputs, name=\"EfficientNet\")\n",
        "    optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)\n",
        "    model.compile(\n",
        "        optimizer=optimizer, loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=[\"accuracy\"]\n",
        "    )\n",
        "    model.summary()\n",
        "    return model\n",
        "\n",
        "  "
      ],
      "metadata": {
        "id": "b79RhrJh-Tb_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Model Fitting with Batch Size of 32 & 25 Epochs.*"
      ],
      "metadata": {
        "id": "KcslzJ8DSpyB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NUM_CLASSES = 5\n",
        "\n",
        "with strategy.scope():\n",
        "    model = build_model(num_classes=NUM_CLASSES)\n",
        "\n",
        "epochs = 25  # @param {type: \"slider\", min:8, max:80}\n",
        "hist = model.fit(x_train,Y_train ,epochs=epochs , validation_data=(x_val,Y_val), verbose=2)"
      ],
      "metadata": {
        "id": "vqY6FMw2-4N2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03afce89-f715-4df7-9bca-6fbee3600aaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"EfficientNet\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_6 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " img_augmentation (Sequential)  (None, 224, 224, 3)  0           ['input_6[0][0]']                \n",
            "                                                                                                  \n",
            " rescaling_3 (Rescaling)        (None, 224, 224, 3)  0           ['img_augmentation[1][0]']       \n",
            "                                                                                                  \n",
            " normalization_3 (Normalization  (None, 224, 224, 3)  7          ['rescaling_3[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " stem_conv_pad (ZeroPadding2D)  (None, 225, 225, 3)  0           ['normalization_3[0][0]']        \n",
            "                                                                                                  \n",
            " stem_conv (Conv2D)             (None, 112, 112, 32  864         ['stem_conv_pad[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " stem_bn (BatchNormalization)   (None, 112, 112, 32  128         ['stem_conv[0][0]']              \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " stem_activation (Activation)   (None, 112, 112, 32  0           ['stem_bn[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block1a_dwconv (DepthwiseConv2  (None, 112, 112, 32  288        ['stem_activation[0][0]']        \n",
            " D)                             )                                                                 \n",
            "                                                                                                  \n",
            " block1a_bn (BatchNormalization  (None, 112, 112, 32  128        ['block1a_dwconv[0][0]']         \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " block1a_activation (Activation  (None, 112, 112, 32  0          ['block1a_bn[0][0]']             \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " block1a_se_squeeze (GlobalAver  (None, 32)          0           ['block1a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block1a_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block1a_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block1a_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block1a_se_excite (Multiply)   (None, 112, 112, 32  0           ['block1a_activation[0][0]',     \n",
            "                                )                                 'block1a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block1a_project_conv (Conv2D)  (None, 112, 112, 16  512         ['block1a_se_excite[0][0]']      \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block1a_project_bn (BatchNorma  (None, 112, 112, 16  64         ['block1a_project_conv[0][0]']   \n",
            " lization)                      )                                                                 \n",
            "                                                                                                  \n",
            " block2a_expand_conv (Conv2D)   (None, 112, 112, 96  1536        ['block1a_project_bn[0][0]']     \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block2a_expand_bn (BatchNormal  (None, 112, 112, 96  384        ['block2a_expand_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " block2a_expand_activation (Act  (None, 112, 112, 96  0          ['block2a_expand_bn[0][0]']      \n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " block2a_dwconv_pad (ZeroPaddin  (None, 113, 113, 96  0          ['block2a_expand_activation[0][0]\n",
            " g2D)                           )                                ']                               \n",
            "                                                                                                  \n",
            " block2a_dwconv (DepthwiseConv2  (None, 56, 56, 96)  864         ['block2a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2a_bn (BatchNormalization  (None, 56, 56, 96)  384         ['block2a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2a_activation (Activation  (None, 56, 56, 96)  0           ['block2a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2a_se_squeeze (GlobalAver  (None, 96)          0           ['block2a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block2a_se_reshape (Reshape)   (None, 1, 1, 96)     0           ['block2a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block2a_se_reduce (Conv2D)     (None, 1, 1, 4)      388         ['block2a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block2a_se_expand (Conv2D)     (None, 1, 1, 96)     480         ['block2a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_se_excite (Multiply)   (None, 56, 56, 96)   0           ['block2a_activation[0][0]',     \n",
            "                                                                  'block2a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_project_conv (Conv2D)  (None, 56, 56, 24)   2304        ['block2a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_project_bn (BatchNorma  (None, 56, 56, 24)  96          ['block2a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block2b_expand_conv (Conv2D)   (None, 56, 56, 144)  3456        ['block2a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_expand_bn (BatchNormal  (None, 56, 56, 144)  576        ['block2b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block2b_expand_activation (Act  (None, 56, 56, 144)  0          ['block2b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block2b_dwconv (DepthwiseConv2  (None, 56, 56, 144)  1296       ['block2b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block2b_bn (BatchNormalization  (None, 56, 56, 144)  576        ['block2b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2b_activation (Activation  (None, 56, 56, 144)  0          ['block2b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2b_se_squeeze (GlobalAver  (None, 144)         0           ['block2b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block2b_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_se_excite (Multiply)   (None, 56, 56, 144)  0           ['block2b_activation[0][0]',     \n",
            "                                                                  'block2b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_project_conv (Conv2D)  (None, 56, 56, 24)   3456        ['block2b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_project_bn (BatchNorma  (None, 56, 56, 24)  96          ['block2b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block2b_drop (Dropout)         (None, 56, 56, 24)   0           ['block2b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_add (Add)              (None, 56, 56, 24)   0           ['block2b_drop[0][0]',           \n",
            "                                                                  'block2a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_expand_conv (Conv2D)   (None, 56, 56, 144)  3456        ['block2b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block3a_expand_bn (BatchNormal  (None, 56, 56, 144)  576        ['block3a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block3a_expand_activation (Act  (None, 56, 56, 144)  0          ['block3a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block3a_dwconv_pad (ZeroPaddin  (None, 59, 59, 144)  0          ['block3a_expand_activation[0][0]\n",
            " g2D)                                                            ']                               \n",
            "                                                                                                  \n",
            " block3a_dwconv (DepthwiseConv2  (None, 28, 28, 144)  3600       ['block3a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3a_bn (BatchNormalization  (None, 28, 28, 144)  576        ['block3a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3a_activation (Activation  (None, 28, 28, 144)  0          ['block3a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3a_se_squeeze (GlobalAver  (None, 144)         0           ['block3a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block3a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block3a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block3a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block3a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_se_excite (Multiply)   (None, 28, 28, 144)  0           ['block3a_activation[0][0]',     \n",
            "                                                                  'block3a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_project_conv (Conv2D)  (None, 28, 28, 40)   5760        ['block3a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_project_bn (BatchNorma  (None, 28, 28, 40)  160         ['block3a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block3b_expand_conv (Conv2D)   (None, 28, 28, 240)  9600        ['block3a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_expand_bn (BatchNormal  (None, 28, 28, 240)  960        ['block3b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block3b_expand_activation (Act  (None, 28, 28, 240)  0          ['block3b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block3b_dwconv (DepthwiseConv2  (None, 28, 28, 240)  6000       ['block3b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block3b_bn (BatchNormalization  (None, 28, 28, 240)  960        ['block3b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3b_activation (Activation  (None, 28, 28, 240)  0          ['block3b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3b_se_squeeze (GlobalAver  (None, 240)         0           ['block3b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block3b_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_se_excite (Multiply)   (None, 28, 28, 240)  0           ['block3b_activation[0][0]',     \n",
            "                                                                  'block3b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_project_conv (Conv2D)  (None, 28, 28, 40)   9600        ['block3b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_project_bn (BatchNorma  (None, 28, 28, 40)  160         ['block3b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block3b_drop (Dropout)         (None, 28, 28, 40)   0           ['block3b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_add (Add)              (None, 28, 28, 40)   0           ['block3b_drop[0][0]',           \n",
            "                                                                  'block3a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_expand_conv (Conv2D)   (None, 28, 28, 240)  9600        ['block3b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4a_expand_bn (BatchNormal  (None, 28, 28, 240)  960        ['block4a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4a_expand_activation (Act  (None, 28, 28, 240)  0          ['block4a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4a_dwconv_pad (ZeroPaddin  (None, 29, 29, 240)  0          ['block4a_expand_activation[0][0]\n",
            " g2D)                                                            ']                               \n",
            "                                                                                                  \n",
            " block4a_dwconv (DepthwiseConv2  (None, 14, 14, 240)  2160       ['block4a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4a_bn (BatchNormalization  (None, 14, 14, 240)  960        ['block4a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4a_activation (Activation  (None, 14, 14, 240)  0          ['block4a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4a_se_squeeze (GlobalAver  (None, 240)         0           ['block4a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4a_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block4a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block4a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block4a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_se_excite (Multiply)   (None, 14, 14, 240)  0           ['block4a_activation[0][0]',     \n",
            "                                                                  'block4a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_project_conv (Conv2D)  (None, 14, 14, 80)   19200       ['block4a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4b_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block4b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4b_expand_activation (Act  (None, 14, 14, 480)  0          ['block4b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4b_dwconv (DepthwiseConv2  (None, 14, 14, 480)  4320       ['block4b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block4b_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block4b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4b_activation (Activation  (None, 14, 14, 480)  0          ['block4b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4b_se_squeeze (GlobalAver  (None, 480)         0           ['block4b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block4b_activation[0][0]',     \n",
            "                                                                  'block4b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_project_conv (Conv2D)  (None, 14, 14, 80)   38400       ['block4b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4b_drop (Dropout)         (None, 14, 14, 80)   0           ['block4b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_add (Add)              (None, 14, 14, 80)   0           ['block4b_drop[0][0]',           \n",
            "                                                                  'block4a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4c_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block4c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4c_expand_activation (Act  (None, 14, 14, 480)  0          ['block4c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4c_dwconv (DepthwiseConv2  (None, 14, 14, 480)  4320       ['block4c_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block4c_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block4c_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4c_activation (Activation  (None, 14, 14, 480)  0          ['block4c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4c_se_squeeze (GlobalAver  (None, 480)         0           ['block4c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4c_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block4c_activation[0][0]',     \n",
            "                                                                  'block4c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_project_conv (Conv2D)  (None, 14, 14, 80)   38400       ['block4c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4c_drop (Dropout)         (None, 14, 14, 80)   0           ['block4c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_add (Add)              (None, 14, 14, 80)   0           ['block4c_drop[0][0]',           \n",
            "                                                                  'block4b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5a_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5a_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block5a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5a_expand_activation (Act  (None, 14, 14, 480)  0          ['block5a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5a_dwconv (DepthwiseConv2  (None, 14, 14, 480)  12000      ['block5a_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block5a_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block5a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5a_activation (Activation  (None, 14, 14, 480)  0          ['block5a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5a_se_squeeze (GlobalAver  (None, 480)         0           ['block5a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5a_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block5a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5a_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block5a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5a_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block5a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block5a_activation[0][0]',     \n",
            "                                                                  'block5a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_project_conv (Conv2D)  (None, 14, 14, 112)  53760       ['block5a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5b_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block5b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5b_expand_activation (Act  (None, 14, 14, 672)  0          ['block5b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5b_dwconv (DepthwiseConv2  (None, 14, 14, 672)  16800      ['block5b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block5b_bn (BatchNormalization  (None, 14, 14, 672)  2688       ['block5b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5b_activation (Activation  (None, 14, 14, 672)  0          ['block5b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5b_se_squeeze (GlobalAver  (None, 672)         0           ['block5b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_se_excite (Multiply)   (None, 14, 14, 672)  0           ['block5b_activation[0][0]',     \n",
            "                                                                  'block5b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_project_conv (Conv2D)  (None, 14, 14, 112)  75264       ['block5b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5b_drop (Dropout)         (None, 14, 14, 112)  0           ['block5b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_add (Add)              (None, 14, 14, 112)  0           ['block5b_drop[0][0]',           \n",
            "                                                                  'block5a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5c_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block5c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5c_expand_activation (Act  (None, 14, 14, 672)  0          ['block5c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5c_dwconv (DepthwiseConv2  (None, 14, 14, 672)  16800      ['block5c_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block5c_bn (BatchNormalization  (None, 14, 14, 672)  2688       ['block5c_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5c_activation (Activation  (None, 14, 14, 672)  0          ['block5c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5c_se_squeeze (GlobalAver  (None, 672)         0           ['block5c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_se_excite (Multiply)   (None, 14, 14, 672)  0           ['block5c_activation[0][0]',     \n",
            "                                                                  'block5c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_project_conv (Conv2D)  (None, 14, 14, 112)  75264       ['block5c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5c_drop (Dropout)         (None, 14, 14, 112)  0           ['block5c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_add (Add)              (None, 14, 14, 112)  0           ['block5c_drop[0][0]',           \n",
            "                                                                  'block5b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6a_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6a_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block6a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6a_expand_activation (Act  (None, 14, 14, 672)  0          ['block6a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6a_dwconv_pad (ZeroPaddin  (None, 17, 17, 672)  0          ['block6a_expand_activation[0][0]\n",
            " g2D)                                                            ']                               \n",
            "                                                                                                  \n",
            " block6a_dwconv (DepthwiseConv2  (None, 7, 7, 672)   16800       ['block6a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6a_bn (BatchNormalization  (None, 7, 7, 672)   2688        ['block6a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6a_activation (Activation  (None, 7, 7, 672)   0           ['block6a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6a_se_squeeze (GlobalAver  (None, 672)         0           ['block6a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block6a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block6a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block6a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_se_excite (Multiply)   (None, 7, 7, 672)    0           ['block6a_activation[0][0]',     \n",
            "                                                                  'block6a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_project_conv (Conv2D)  (None, 7, 7, 192)    129024      ['block6a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6b_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6b_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6b_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block6b_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6b_activation (Activation  (None, 7, 7, 1152)  0           ['block6b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6b_se_squeeze (GlobalAver  (None, 1152)        0           ['block6b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6b_activation[0][0]',     \n",
            "                                                                  'block6b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6b_drop (Dropout)         (None, 7, 7, 192)    0           ['block6b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_add (Add)              (None, 7, 7, 192)    0           ['block6b_drop[0][0]',           \n",
            "                                                                  'block6a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6c_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6c_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6c_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6c_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block6c_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6c_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6c_activation (Activation  (None, 7, 7, 1152)  0           ['block6c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6c_se_squeeze (GlobalAver  (None, 1152)        0           ['block6c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6c_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6c_activation[0][0]',     \n",
            "                                                                  'block6c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6c_drop (Dropout)         (None, 7, 7, 192)    0           ['block6c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_add (Add)              (None, 7, 7, 192)    0           ['block6c_drop[0][0]',           \n",
            "                                                                  'block6b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6d_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6d_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6d_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6d_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6d_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6d_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6d_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block6d_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6d_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6d_activation (Activation  (None, 7, 7, 1152)  0           ['block6d_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6d_se_squeeze (GlobalAver  (None, 1152)        0           ['block6d_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6d_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6d_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6d_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6d_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6d_activation[0][0]',     \n",
            "                                                                  'block6d_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6d_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6d_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6d_drop (Dropout)         (None, 7, 7, 192)    0           ['block6d_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_add (Add)              (None, 7, 7, 192)    0           ['block6d_drop[0][0]',           \n",
            "                                                                  'block6c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block7a_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6d_add[0][0]']            \n",
            "                                                                                                  \n",
            " block7a_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block7a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block7a_expand_activation (Act  (None, 7, 7, 1152)  0           ['block7a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block7a_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  10368       ['block7a_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block7a_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block7a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block7a_activation (Activation  (None, 7, 7, 1152)  0           ['block7a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block7a_se_squeeze (GlobalAver  (None, 1152)        0           ['block7a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block7a_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block7a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block7a_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block7a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block7a_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block7a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block7a_activation[0][0]',     \n",
            "                                                                  'block7a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_project_conv (Conv2D)  (None, 7, 7, 320)    368640      ['block7a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_project_bn (BatchNorma  (None, 7, 7, 320)   1280        ['block7a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " top_conv (Conv2D)              (None, 7, 7, 1280)   409600      ['block7a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " top_bn (BatchNormalization)    (None, 7, 7, 1280)   5120        ['top_conv[0][0]']               \n",
            "                                                                                                  \n",
            " top_activation (Activation)    (None, 7, 7, 1280)   0           ['top_bn[0][0]']                 \n",
            "                                                                                                  \n",
            " avg_pool (GlobalAveragePooling  (None, 1280)        0           ['top_activation[0][0]']         \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 1280)        5120        ['avg_pool[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " top_dropout (Dropout)          (None, 1280)         0           ['batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " pred (Dense)                   (None, 5)            6405        ['top_dropout[0][0]']            \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 4,061,096\n",
            "Trainable params: 8,965\n",
            "Non-trainable params: 4,052,131\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/25\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
            "  return dispatch_target(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "114/114 - 19s - loss: 1.6091 - accuracy: 0.5920 - val_loss: 1.5053 - val_accuracy: 0.4778 - 19s/epoch - 167ms/step\n",
            "Epoch 2/25\n",
            "114/114 - 9s - loss: 1.2713 - accuracy: 0.6556 - val_loss: 1.0910 - val_accuracy: 0.6379 - 9s/epoch - 75ms/step\n",
            "Epoch 3/25\n",
            "114/114 - 9s - loss: 1.2260 - accuracy: 0.6704 - val_loss: 1.1151 - val_accuracy: 0.6601 - 9s/epoch - 75ms/step\n",
            "Epoch 4/25\n",
            "114/114 - 9s - loss: 1.0625 - accuracy: 0.6926 - val_loss: 1.0786 - val_accuracy: 0.6749 - 9s/epoch - 76ms/step\n",
            "Epoch 5/25\n",
            "114/114 - 9s - loss: 1.1069 - accuracy: 0.6915 - val_loss: 1.2690 - val_accuracy: 0.6527 - 9s/epoch - 75ms/step\n",
            "Epoch 6/25\n",
            "114/114 - 9s - loss: 1.0937 - accuracy: 0.6874 - val_loss: 1.2166 - val_accuracy: 0.6626 - 9s/epoch - 75ms/step\n",
            "Epoch 7/25\n",
            "114/114 - 8s - loss: 0.9560 - accuracy: 0.7022 - val_loss: 1.1290 - val_accuracy: 0.6675 - 8s/epoch - 74ms/step\n",
            "Epoch 8/25\n",
            "114/114 - 8s - loss: 0.9410 - accuracy: 0.6962 - val_loss: 1.0848 - val_accuracy: 0.6847 - 8s/epoch - 74ms/step\n",
            "Epoch 9/25\n",
            "114/114 - 8s - loss: 0.8752 - accuracy: 0.7088 - val_loss: 1.0626 - val_accuracy: 0.6626 - 8s/epoch - 74ms/step\n",
            "Epoch 10/25\n",
            "114/114 - 8s - loss: 0.8523 - accuracy: 0.7124 - val_loss: 1.1102 - val_accuracy: 0.6700 - 8s/epoch - 74ms/step\n",
            "Epoch 11/25\n",
            "114/114 - 8s - loss: 0.8876 - accuracy: 0.7036 - val_loss: 0.9267 - val_accuracy: 0.7020 - 8s/epoch - 74ms/step\n",
            "Epoch 12/25\n",
            "114/114 - 9s - loss: 0.7945 - accuracy: 0.7264 - val_loss: 0.9595 - val_accuracy: 0.6749 - 9s/epoch - 75ms/step\n",
            "Epoch 13/25\n",
            "114/114 - 9s - loss: 0.7740 - accuracy: 0.7176 - val_loss: 0.9027 - val_accuracy: 0.7069 - 9s/epoch - 75ms/step\n",
            "Epoch 14/25\n",
            "114/114 - 9s - loss: 0.8148 - accuracy: 0.7110 - val_loss: 0.9144 - val_accuracy: 0.6897 - 9s/epoch - 75ms/step\n",
            "Epoch 15/25\n",
            "114/114 - 9s - loss: 0.8019 - accuracy: 0.7170 - val_loss: 0.9967 - val_accuracy: 0.6749 - 9s/epoch - 75ms/step\n",
            "Epoch 16/25\n",
            "114/114 - 9s - loss: 0.7700 - accuracy: 0.7184 - val_loss: 0.9398 - val_accuracy: 0.6749 - 9s/epoch - 75ms/step\n",
            "Epoch 17/25\n",
            "114/114 - 8s - loss: 0.7735 - accuracy: 0.7159 - val_loss: 1.0030 - val_accuracy: 0.6478 - 8s/epoch - 75ms/step\n",
            "Epoch 18/25\n",
            "114/114 - 9s - loss: 0.8084 - accuracy: 0.7074 - val_loss: 0.9831 - val_accuracy: 0.6576 - 9s/epoch - 75ms/step\n",
            "Epoch 19/25\n",
            "114/114 - 9s - loss: 0.7472 - accuracy: 0.7176 - val_loss: 0.8729 - val_accuracy: 0.6650 - 9s/epoch - 75ms/step\n",
            "Epoch 20/25\n",
            "114/114 - 9s - loss: 0.7723 - accuracy: 0.7209 - val_loss: 0.9824 - val_accuracy: 0.6502 - 9s/epoch - 75ms/step\n",
            "Epoch 21/25\n",
            "114/114 - 8s - loss: 0.7600 - accuracy: 0.7217 - val_loss: 0.9625 - val_accuracy: 0.6650 - 8s/epoch - 75ms/step\n",
            "Epoch 22/25\n",
            "114/114 - 8s - loss: 0.7553 - accuracy: 0.7225 - val_loss: 0.8858 - val_accuracy: 0.6897 - 8s/epoch - 74ms/step\n",
            "Epoch 23/25\n",
            "114/114 - 9s - loss: 0.7854 - accuracy: 0.7132 - val_loss: 0.9323 - val_accuracy: 0.6724 - 9s/epoch - 75ms/step\n",
            "Epoch 24/25\n",
            "114/114 - 8s - loss: 0.7815 - accuracy: 0.7113 - val_loss: 0.9361 - val_accuracy: 0.6453 - 8s/epoch - 74ms/step\n",
            "Epoch 25/25\n",
            "114/114 - 8s - loss: 0.7741 - accuracy: 0.7115 - val_loss: 0.7638 - val_accuracy: 0.7192 - 8s/epoch - 74ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Plotting the training and validation accuracy.*"
      ],
      "metadata": {
        "id": "RXvOyI7lTMjs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_hist(hist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ZLfJiZRPTNWb",
        "outputId": "c5f63743-51b9-458a-b324-60031c735a52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top 2 Accuracy Test Result**"
      ],
      "metadata": {
        "id": "KTWjdHUDS2Uy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(x_test, Y_test)\n",
        "print('Test Accuracy: ', test_acc)"
      ],
      "metadata": {
        "id": "CdJXtGvlDg2t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3548621e-6256-4422-8555-61ab9acee84d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 2s 65ms/step - loss: 0.6320 - accuracy: 0.7732\n",
            "Test Accuracy:  0.7731755375862122\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **9 - UNFREEZING MODEL**"
      ],
      "metadata": {
        "id": "p4kJ-wKaUd3t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The second step is to unfreeze a number of layers and fit the model using smaller learning rate. \n",
        "\n",
        "*Here we show unfreezing all layers, but depending on specific dataset it may be desireble to only unfreeze a fraction of all layers.*"
      ],
      "metadata": {
        "id": "w7o9OvSsVKnu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def unfreeze_model(model):\n",
        "    # We unfreeze the top 20 layers while leaving BatchNorm layers frozen\n",
        "    for layer in model.layers[-20:]:\n",
        "        if not isinstance(layer, layers.BatchNormalization):\n",
        "            layer.trainable = True\n",
        "\n",
        "    optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4)\n",
        "    model.compile(\n",
        "        optimizer=optimizer, loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=[\"accuracy\"]\n",
        "    )\n",
        "    model.summary()\n",
        "    \n",
        "unfreeze_model(model)"
      ],
      "metadata": {
        "id": "cnbAyDwY--M2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2debd4c-449d-4755-cdc9-1d4c5644f68b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"EfficientNet\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_6 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " img_augmentation (Sequential)  (None, 224, 224, 3)  0           ['input_6[0][0]']                \n",
            "                                                                                                  \n",
            " rescaling_3 (Rescaling)        (None, 224, 224, 3)  0           ['img_augmentation[1][0]']       \n",
            "                                                                                                  \n",
            " normalization_3 (Normalization  (None, 224, 224, 3)  7          ['rescaling_3[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " stem_conv_pad (ZeroPadding2D)  (None, 225, 225, 3)  0           ['normalization_3[0][0]']        \n",
            "                                                                                                  \n",
            " stem_conv (Conv2D)             (None, 112, 112, 32  864         ['stem_conv_pad[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " stem_bn (BatchNormalization)   (None, 112, 112, 32  128         ['stem_conv[0][0]']              \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " stem_activation (Activation)   (None, 112, 112, 32  0           ['stem_bn[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block1a_dwconv (DepthwiseConv2  (None, 112, 112, 32  288        ['stem_activation[0][0]']        \n",
            " D)                             )                                                                 \n",
            "                                                                                                  \n",
            " block1a_bn (BatchNormalization  (None, 112, 112, 32  128        ['block1a_dwconv[0][0]']         \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " block1a_activation (Activation  (None, 112, 112, 32  0          ['block1a_bn[0][0]']             \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " block1a_se_squeeze (GlobalAver  (None, 32)          0           ['block1a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block1a_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block1a_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block1a_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block1a_se_excite (Multiply)   (None, 112, 112, 32  0           ['block1a_activation[0][0]',     \n",
            "                                )                                 'block1a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block1a_project_conv (Conv2D)  (None, 112, 112, 16  512         ['block1a_se_excite[0][0]']      \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block1a_project_bn (BatchNorma  (None, 112, 112, 16  64         ['block1a_project_conv[0][0]']   \n",
            " lization)                      )                                                                 \n",
            "                                                                                                  \n",
            " block2a_expand_conv (Conv2D)   (None, 112, 112, 96  1536        ['block1a_project_bn[0][0]']     \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block2a_expand_bn (BatchNormal  (None, 112, 112, 96  384        ['block2a_expand_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " block2a_expand_activation (Act  (None, 112, 112, 96  0          ['block2a_expand_bn[0][0]']      \n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " block2a_dwconv_pad (ZeroPaddin  (None, 113, 113, 96  0          ['block2a_expand_activation[0][0]\n",
            " g2D)                           )                                ']                               \n",
            "                                                                                                  \n",
            " block2a_dwconv (DepthwiseConv2  (None, 56, 56, 96)  864         ['block2a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2a_bn (BatchNormalization  (None, 56, 56, 96)  384         ['block2a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2a_activation (Activation  (None, 56, 56, 96)  0           ['block2a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2a_se_squeeze (GlobalAver  (None, 96)          0           ['block2a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block2a_se_reshape (Reshape)   (None, 1, 1, 96)     0           ['block2a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block2a_se_reduce (Conv2D)     (None, 1, 1, 4)      388         ['block2a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block2a_se_expand (Conv2D)     (None, 1, 1, 96)     480         ['block2a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_se_excite (Multiply)   (None, 56, 56, 96)   0           ['block2a_activation[0][0]',     \n",
            "                                                                  'block2a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_project_conv (Conv2D)  (None, 56, 56, 24)   2304        ['block2a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_project_bn (BatchNorma  (None, 56, 56, 24)  96          ['block2a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block2b_expand_conv (Conv2D)   (None, 56, 56, 144)  3456        ['block2a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_expand_bn (BatchNormal  (None, 56, 56, 144)  576        ['block2b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block2b_expand_activation (Act  (None, 56, 56, 144)  0          ['block2b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block2b_dwconv (DepthwiseConv2  (None, 56, 56, 144)  1296       ['block2b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block2b_bn (BatchNormalization  (None, 56, 56, 144)  576        ['block2b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2b_activation (Activation  (None, 56, 56, 144)  0          ['block2b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2b_se_squeeze (GlobalAver  (None, 144)         0           ['block2b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block2b_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_se_excite (Multiply)   (None, 56, 56, 144)  0           ['block2b_activation[0][0]',     \n",
            "                                                                  'block2b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_project_conv (Conv2D)  (None, 56, 56, 24)   3456        ['block2b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_project_bn (BatchNorma  (None, 56, 56, 24)  96          ['block2b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block2b_drop (Dropout)         (None, 56, 56, 24)   0           ['block2b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_add (Add)              (None, 56, 56, 24)   0           ['block2b_drop[0][0]',           \n",
            "                                                                  'block2a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_expand_conv (Conv2D)   (None, 56, 56, 144)  3456        ['block2b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block3a_expand_bn (BatchNormal  (None, 56, 56, 144)  576        ['block3a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block3a_expand_activation (Act  (None, 56, 56, 144)  0          ['block3a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block3a_dwconv_pad (ZeroPaddin  (None, 59, 59, 144)  0          ['block3a_expand_activation[0][0]\n",
            " g2D)                                                            ']                               \n",
            "                                                                                                  \n",
            " block3a_dwconv (DepthwiseConv2  (None, 28, 28, 144)  3600       ['block3a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3a_bn (BatchNormalization  (None, 28, 28, 144)  576        ['block3a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3a_activation (Activation  (None, 28, 28, 144)  0          ['block3a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3a_se_squeeze (GlobalAver  (None, 144)         0           ['block3a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block3a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block3a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block3a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block3a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_se_excite (Multiply)   (None, 28, 28, 144)  0           ['block3a_activation[0][0]',     \n",
            "                                                                  'block3a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_project_conv (Conv2D)  (None, 28, 28, 40)   5760        ['block3a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_project_bn (BatchNorma  (None, 28, 28, 40)  160         ['block3a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block3b_expand_conv (Conv2D)   (None, 28, 28, 240)  9600        ['block3a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_expand_bn (BatchNormal  (None, 28, 28, 240)  960        ['block3b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block3b_expand_activation (Act  (None, 28, 28, 240)  0          ['block3b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block3b_dwconv (DepthwiseConv2  (None, 28, 28, 240)  6000       ['block3b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block3b_bn (BatchNormalization  (None, 28, 28, 240)  960        ['block3b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3b_activation (Activation  (None, 28, 28, 240)  0          ['block3b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3b_se_squeeze (GlobalAver  (None, 240)         0           ['block3b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block3b_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_se_excite (Multiply)   (None, 28, 28, 240)  0           ['block3b_activation[0][0]',     \n",
            "                                                                  'block3b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_project_conv (Conv2D)  (None, 28, 28, 40)   9600        ['block3b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_project_bn (BatchNorma  (None, 28, 28, 40)  160         ['block3b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block3b_drop (Dropout)         (None, 28, 28, 40)   0           ['block3b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_add (Add)              (None, 28, 28, 40)   0           ['block3b_drop[0][0]',           \n",
            "                                                                  'block3a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_expand_conv (Conv2D)   (None, 28, 28, 240)  9600        ['block3b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4a_expand_bn (BatchNormal  (None, 28, 28, 240)  960        ['block4a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4a_expand_activation (Act  (None, 28, 28, 240)  0          ['block4a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4a_dwconv_pad (ZeroPaddin  (None, 29, 29, 240)  0          ['block4a_expand_activation[0][0]\n",
            " g2D)                                                            ']                               \n",
            "                                                                                                  \n",
            " block4a_dwconv (DepthwiseConv2  (None, 14, 14, 240)  2160       ['block4a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4a_bn (BatchNormalization  (None, 14, 14, 240)  960        ['block4a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4a_activation (Activation  (None, 14, 14, 240)  0          ['block4a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4a_se_squeeze (GlobalAver  (None, 240)         0           ['block4a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4a_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block4a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block4a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block4a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_se_excite (Multiply)   (None, 14, 14, 240)  0           ['block4a_activation[0][0]',     \n",
            "                                                                  'block4a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_project_conv (Conv2D)  (None, 14, 14, 80)   19200       ['block4a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4b_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block4b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4b_expand_activation (Act  (None, 14, 14, 480)  0          ['block4b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4b_dwconv (DepthwiseConv2  (None, 14, 14, 480)  4320       ['block4b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block4b_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block4b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4b_activation (Activation  (None, 14, 14, 480)  0          ['block4b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4b_se_squeeze (GlobalAver  (None, 480)         0           ['block4b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block4b_activation[0][0]',     \n",
            "                                                                  'block4b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_project_conv (Conv2D)  (None, 14, 14, 80)   38400       ['block4b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4b_drop (Dropout)         (None, 14, 14, 80)   0           ['block4b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_add (Add)              (None, 14, 14, 80)   0           ['block4b_drop[0][0]',           \n",
            "                                                                  'block4a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4c_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block4c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4c_expand_activation (Act  (None, 14, 14, 480)  0          ['block4c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4c_dwconv (DepthwiseConv2  (None, 14, 14, 480)  4320       ['block4c_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block4c_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block4c_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4c_activation (Activation  (None, 14, 14, 480)  0          ['block4c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4c_se_squeeze (GlobalAver  (None, 480)         0           ['block4c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4c_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block4c_activation[0][0]',     \n",
            "                                                                  'block4c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_project_conv (Conv2D)  (None, 14, 14, 80)   38400       ['block4c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4c_drop (Dropout)         (None, 14, 14, 80)   0           ['block4c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_add (Add)              (None, 14, 14, 80)   0           ['block4c_drop[0][0]',           \n",
            "                                                                  'block4b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5a_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5a_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block5a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5a_expand_activation (Act  (None, 14, 14, 480)  0          ['block5a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5a_dwconv (DepthwiseConv2  (None, 14, 14, 480)  12000      ['block5a_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block5a_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block5a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5a_activation (Activation  (None, 14, 14, 480)  0          ['block5a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5a_se_squeeze (GlobalAver  (None, 480)         0           ['block5a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5a_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block5a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5a_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block5a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5a_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block5a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block5a_activation[0][0]',     \n",
            "                                                                  'block5a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_project_conv (Conv2D)  (None, 14, 14, 112)  53760       ['block5a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5b_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block5b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5b_expand_activation (Act  (None, 14, 14, 672)  0          ['block5b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5b_dwconv (DepthwiseConv2  (None, 14, 14, 672)  16800      ['block5b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block5b_bn (BatchNormalization  (None, 14, 14, 672)  2688       ['block5b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5b_activation (Activation  (None, 14, 14, 672)  0          ['block5b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5b_se_squeeze (GlobalAver  (None, 672)         0           ['block5b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_se_excite (Multiply)   (None, 14, 14, 672)  0           ['block5b_activation[0][0]',     \n",
            "                                                                  'block5b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_project_conv (Conv2D)  (None, 14, 14, 112)  75264       ['block5b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5b_drop (Dropout)         (None, 14, 14, 112)  0           ['block5b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_add (Add)              (None, 14, 14, 112)  0           ['block5b_drop[0][0]',           \n",
            "                                                                  'block5a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5c_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block5c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5c_expand_activation (Act  (None, 14, 14, 672)  0          ['block5c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5c_dwconv (DepthwiseConv2  (None, 14, 14, 672)  16800      ['block5c_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block5c_bn (BatchNormalization  (None, 14, 14, 672)  2688       ['block5c_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5c_activation (Activation  (None, 14, 14, 672)  0          ['block5c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5c_se_squeeze (GlobalAver  (None, 672)         0           ['block5c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_se_excite (Multiply)   (None, 14, 14, 672)  0           ['block5c_activation[0][0]',     \n",
            "                                                                  'block5c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_project_conv (Conv2D)  (None, 14, 14, 112)  75264       ['block5c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5c_drop (Dropout)         (None, 14, 14, 112)  0           ['block5c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_add (Add)              (None, 14, 14, 112)  0           ['block5c_drop[0][0]',           \n",
            "                                                                  'block5b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6a_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6a_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block6a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6a_expand_activation (Act  (None, 14, 14, 672)  0          ['block6a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6a_dwconv_pad (ZeroPaddin  (None, 17, 17, 672)  0          ['block6a_expand_activation[0][0]\n",
            " g2D)                                                            ']                               \n",
            "                                                                                                  \n",
            " block6a_dwconv (DepthwiseConv2  (None, 7, 7, 672)   16800       ['block6a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6a_bn (BatchNormalization  (None, 7, 7, 672)   2688        ['block6a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6a_activation (Activation  (None, 7, 7, 672)   0           ['block6a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6a_se_squeeze (GlobalAver  (None, 672)         0           ['block6a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block6a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block6a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block6a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_se_excite (Multiply)   (None, 7, 7, 672)    0           ['block6a_activation[0][0]',     \n",
            "                                                                  'block6a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_project_conv (Conv2D)  (None, 7, 7, 192)    129024      ['block6a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6b_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6b_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6b_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block6b_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6b_activation (Activation  (None, 7, 7, 1152)  0           ['block6b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6b_se_squeeze (GlobalAver  (None, 1152)        0           ['block6b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6b_activation[0][0]',     \n",
            "                                                                  'block6b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6b_drop (Dropout)         (None, 7, 7, 192)    0           ['block6b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_add (Add)              (None, 7, 7, 192)    0           ['block6b_drop[0][0]',           \n",
            "                                                                  'block6a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6c_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6c_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6c_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6c_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block6c_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6c_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6c_activation (Activation  (None, 7, 7, 1152)  0           ['block6c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6c_se_squeeze (GlobalAver  (None, 1152)        0           ['block6c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6c_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6c_activation[0][0]',     \n",
            "                                                                  'block6c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6c_drop (Dropout)         (None, 7, 7, 192)    0           ['block6c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_add (Add)              (None, 7, 7, 192)    0           ['block6c_drop[0][0]',           \n",
            "                                                                  'block6b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6d_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6d_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6d_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6d_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6d_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6d_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6d_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block6d_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6d_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6d_activation (Activation  (None, 7, 7, 1152)  0           ['block6d_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6d_se_squeeze (GlobalAver  (None, 1152)        0           ['block6d_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6d_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6d_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6d_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6d_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6d_activation[0][0]',     \n",
            "                                                                  'block6d_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6d_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6d_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6d_drop (Dropout)         (None, 7, 7, 192)    0           ['block6d_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_add (Add)              (None, 7, 7, 192)    0           ['block6d_drop[0][0]',           \n",
            "                                                                  'block6c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block7a_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6d_add[0][0]']            \n",
            "                                                                                                  \n",
            " block7a_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block7a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block7a_expand_activation (Act  (None, 7, 7, 1152)  0           ['block7a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block7a_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  10368       ['block7a_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block7a_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block7a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block7a_activation (Activation  (None, 7, 7, 1152)  0           ['block7a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block7a_se_squeeze (GlobalAver  (None, 1152)        0           ['block7a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block7a_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block7a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block7a_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block7a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block7a_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block7a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block7a_activation[0][0]',     \n",
            "                                                                  'block7a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_project_conv (Conv2D)  (None, 7, 7, 320)    368640      ['block7a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_project_bn (BatchNorma  (None, 7, 7, 320)   1280        ['block7a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " top_conv (Conv2D)              (None, 7, 7, 1280)   409600      ['block7a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " top_bn (BatchNormalization)    (None, 7, 7, 1280)   5120        ['top_conv[0][0]']               \n",
            "                                                                                                  \n",
            " top_activation (Activation)    (None, 7, 7, 1280)   0           ['top_bn[0][0]']                 \n",
            "                                                                                                  \n",
            " avg_pool (GlobalAveragePooling  (None, 1280)        0           ['top_activation[0][0]']         \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 1280)        5120        ['avg_pool[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " top_dropout (Dropout)          (None, 1280)         0           ['batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " pred (Dense)                   (None, 5)            6405        ['top_dropout[0][0]']            \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 4,061,096\n",
            "Trainable params: 1,130,549\n",
            "Non-trainable params: 2,930,547\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Model Fitting with Batch Size of 32 & 15 Epochs.*"
      ],
      "metadata": {
        "id": "ou2X0jiLV8FW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss', patience=2)\n",
        "\n",
        "epochs = 15  # @param {type: \"slider\", min:8, max:50}\n",
        "\n",
        "hist = model.fit(x_train,Y_train ,epochs=epochs , validation_data=(x_val,Y_val), verbose=2, callbacks=[early_stop])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OadccbhnV7Qd",
        "outputId": "e83ccda1-2bc4-4e5d-d910-5922f14bc0bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
            "  return dispatch_target(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "114/114 - 21s - loss: 0.6264 - accuracy: 0.7617 - val_loss: 0.8312 - val_accuracy: 0.6897 - 21s/epoch - 186ms/step\n",
            "Epoch 2/15\n",
            "114/114 - 9s - loss: 0.5758 - accuracy: 0.7853 - val_loss: 0.7450 - val_accuracy: 0.7167 - 9s/epoch - 82ms/step\n",
            "Epoch 3/15\n",
            "114/114 - 9s - loss: 0.5283 - accuracy: 0.8031 - val_loss: 0.6633 - val_accuracy: 0.7611 - 9s/epoch - 82ms/step\n",
            "Epoch 4/15\n",
            "114/114 - 10s - loss: 0.4788 - accuracy: 0.8193 - val_loss: 0.6478 - val_accuracy: 0.7512 - 10s/epoch - 89ms/step\n",
            "Epoch 5/15\n",
            "114/114 - 9s - loss: 0.4395 - accuracy: 0.8399 - val_loss: 0.6038 - val_accuracy: 0.7734 - 9s/epoch - 82ms/step\n",
            "Epoch 6/15\n",
            "114/114 - 9s - loss: 0.4295 - accuracy: 0.8421 - val_loss: 0.6400 - val_accuracy: 0.7562 - 9s/epoch - 81ms/step\n",
            "Epoch 7/15\n",
            "114/114 - 9s - loss: 0.3978 - accuracy: 0.8486 - val_loss: 0.5651 - val_accuracy: 0.7857 - 9s/epoch - 81ms/step\n",
            "Epoch 8/15\n",
            "114/114 - 9s - loss: 0.3782 - accuracy: 0.8555 - val_loss: 0.5566 - val_accuracy: 0.7833 - 9s/epoch - 81ms/step\n",
            "Epoch 9/15\n",
            "114/114 - 9s - loss: 0.3659 - accuracy: 0.8613 - val_loss: 0.5418 - val_accuracy: 0.7906 - 9s/epoch - 81ms/step\n",
            "Epoch 10/15\n",
            "114/114 - 9s - loss: 0.3226 - accuracy: 0.8826 - val_loss: 0.5172 - val_accuracy: 0.7980 - 9s/epoch - 81ms/step\n",
            "Epoch 11/15\n",
            "114/114 - 9s - loss: 0.3222 - accuracy: 0.8741 - val_loss: 0.4764 - val_accuracy: 0.7882 - 9s/epoch - 81ms/step\n",
            "Epoch 12/15\n",
            "114/114 - 9s - loss: 0.3001 - accuracy: 0.8887 - val_loss: 0.4752 - val_accuracy: 0.7980 - 9s/epoch - 81ms/step\n",
            "Epoch 13/15\n",
            "114/114 - 9s - loss: 0.3058 - accuracy: 0.8892 - val_loss: 0.4870 - val_accuracy: 0.8030 - 9s/epoch - 81ms/step\n",
            "Epoch 14/15\n",
            "114/114 - 9s - loss: 0.2945 - accuracy: 0.8917 - val_loss: 0.4595 - val_accuracy: 0.8153 - 9s/epoch - 81ms/step\n",
            "Epoch 15/15\n",
            "114/114 - 9s - loss: 0.2599 - accuracy: 0.9021 - val_loss: 0.4250 - val_accuracy: 0.8177 - 9s/epoch - 81ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Plotting the training and validation accuracy.*"
      ],
      "metadata": {
        "id": "nFjnDchbVl4J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_hist(hist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "c1P3MaRkT85-",
        "outputId": "1bcdf629-d905-458c-94fa-844ede442859"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top 3 Accuracy Test Result**"
      ],
      "metadata": {
        "id": "lEi1y_K3VqT4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(x_test, Y_test)\n",
        "print('Test Accuracy: ', test_acc)"
      ],
      "metadata": {
        "id": "UJxY9VC2FWUX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "502173af-48ba-4936-d784-095e9dad3a97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 2s 65ms/step - loss: 0.3447 - accuracy: 0.8708\n",
            "Test Accuracy:  0.8708086609840393\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **10 - MODEL PREDICTING**"
      ],
      "metadata": {
        "id": "Flv0JTnwWPJp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(x_test)\n",
        "print(predictions)\n",
        "print(np.argmax(predictions[9]))\n",
        "print(Y_test[9])"
      ],
      "metadata": {
        "id": "m7h5HUo2u62r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60373bf5-03a3-4444-cf50-88ae65af7a97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.0659082e-09 3.5680609e-06 1.3669630e-08 5.4459561e-09 9.9999642e-01]\n",
            " [3.4123328e-01 2.8239021e-03 2.3564672e-02 6.3237786e-01 2.0187157e-07]\n",
            " [9.9419206e-01 7.6034652e-05 1.2253244e-05 5.7181660e-03 1.3911804e-06]\n",
            " ...\n",
            " [8.3538017e-11 1.0170634e-08 1.5991858e-09 3.1305046e-07 9.9999964e-01]\n",
            " [1.4428850e-04 3.5145434e-03 9.9313134e-01 2.9937287e-03 2.1603497e-04]\n",
            " [1.5825604e-04 4.0353328e-01 1.5620385e-01 3.6390895e-01 7.6195687e-02]]\n",
            "3\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **11 - TEST ACCURACY**"
      ],
      "metadata": {
        "id": "tvQm9Um5WcM_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(x_test, Y_test)\n",
        "print('Test Accuracy: ', test_acc)"
      ],
      "metadata": {
        "id": "1gedI0Hh0GMk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6fd5abc-763a-476a-8ec4-1f55acccecc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 2s 65ms/step - loss: 0.3447 - accuracy: 0.8708\n",
            "Test Accuracy:  0.8708086609840393\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=model.predict(x_test)"
      ],
      "metadata": {
        "id": "IaD4PxGD7Gni"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m = tf.keras.metrics.CategoricalAccuracy()\n",
        "m.update_state(Y_test,y_pred)\n",
        "print('Final validation score: ', m.result().numpy())"
      ],
      "metadata": {
        "id": "DcnOsukj7N3I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d756940e-67bb-4a1c-b7e9-814c1da941fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final validation score:  0.17948718\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.argmax(y_pred, axis=1)\n",
        "len(y_pred)"
      ],
      "metadata": {
        "id": "kvzw9EFO7bEE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57e268bf-ed69-426e-fba0-98e2ecf2bad9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1014"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **11 - CONFUSION MATRIX**"
      ],
      "metadata": {
        "id": "Y3SMPbNxWyt-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "matrix = confusion_matrix(Y_test, y_pred)\n",
        "print(matrix)"
      ],
      "metadata": {
        "id": "1jhmGFr95LA5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "714583d6-35ea-46be-fd19-12b06dc9b633"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[172   8   5  25   0]\n",
            " [  0 155  16  23  10]\n",
            " [  0   3 194   5   0]\n",
            " [ 10   8  15 160   1]\n",
            " [  0   1   1   0 202]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Plottin The Confusion Matrix*"
      ],
      "metadata": {
        "id": "o36ENwruXBao"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(7.5, 7.5))\n",
        "ax.matshow(matrix, cmap=plt.cm.Blues, alpha=0.3)\n",
        "for i in range(matrix.shape[0]):\n",
        "    for j in range(matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=matrix[i, j], va='center', ha='center', size='xx-large')\n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix', fontsize=18)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kASerMtE8FXN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "outputId": "d8e4774d-51f2-4e7c-9b2e-27d95cdba4d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x540 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **11 - TRAINING LOSS CURVE**"
      ],
      "metadata": {
        "id": "6qpdvapGXOM6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_dict = hist.history\n",
        "loss_values = classifier_dict['loss']\n",
        " \n",
        "epochs = range(1, len(loss_values) + 1)\n",
        "fig, ax = plt.subplots(figsize=(7, 7))\n",
        "\n",
        "# Plot the loss vs Epochs\n",
        "\n",
        "ax.plot(epochs, loss_values, '-', label='Training loss')\n",
        "ax.set_title('Training Loss', fontsize=16)\n",
        "ax.set_xlabel('Epochs', fontsize=16)\n",
        "ax.set_ylabel('Loss', fontsize=16)\n",
        "ax.legend()"
      ],
      "metadata": {
        "id": "S6eM-zxh8Zpi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "outputId": "04f2fcf3-6b78-4634-99c6-64d347512cb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f538417e590>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **12 - ROC CURVE**"
      ],
      "metadata": {
        "id": "I3WlzjQTXbM4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve,auc\n",
        "from sklearn.preprocessing import label_binarize\n",
        "\n",
        "y_t = label_binarize(Y_test, classes=[0, 1, 2, 3, 4])\n",
        "y_pr = label_binarize(y_pred, classes=[0, 1, 2, 3, 4])\n",
        "\n",
        "n_classes = 5\n",
        "\n",
        "# Compute ROC curve and ROC area for each class\n",
        "fpr = dict()\n",
        "tpr = dict()\n",
        "roc_auc = dict()\n",
        "for i in range(n_classes):\n",
        "    fpr[i], tpr[i], _ = roc_curve(y_t[:, i], y_pr[:, i])\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "# # Compute micro-average ROC curve and ROC area\n",
        "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_t.ravel(), y_pr.ravel())\n",
        "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
        "\n",
        "# Plot of a ROC curve for a specific class\n",
        "plt.figure()\n",
        "plt.plot(fpr[2], tpr[2], label='ROC curve (area = %0.2f)' % roc_auc[2])\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver operating characteristic example')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
        "         label='micro-average ROC curve (area = {0:0.2f})'\n",
        "               ''.format(roc_auc[\"micro\"]))\n",
        "for i in range(n_classes):\n",
        "    plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})'\n",
        "                                   ''.format(i, roc_auc[i]))\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "RtAIAAu78oHH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "0977ee3c-b227-430c-88ea-9475562b5c9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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ENovIm0OsFxH5kYi8LyKvi8je4yWLRqPRaCYv42kRrQKOGmb9p4EF9ucMrARMGo1Go9kGlFKYpoFpGBjpFKn+fno2b01u0olj3Aa0KqWeF5HwMEWOA35p5wh60Z5ZeIadEE2jmVIopUApVM7H+m8Os06hTCtfmDJNFAplKrC/8+sApUywvwvrHPht5teBAvvbqnOgjuw2RfaXu5/CfeTKXigzOftRCsiTfYhzMMxxDT5XA8dVuL+BOoY/V3nnNVNH7nLTRNnXhOz3wDkqrMM6xaZVzrS+i32ydWf2myODVd/A+c7cU9n9oKzjyflWQGaq0IG0uYPvyzdbN/JG6w6qiEpgFvk56FvsZYMUkYicgWU10dTUlLdu2IevpJsy98Fk4MYo+oCN8GAWeegGHqKMXPZNXlKDUVj/MDKrwXVmZYas7EPVOXyDUew8Yi0f5bkq3mAUK5e/31E3rkM0GHnnarjzUUwxDHNfacYOEQfiEEAQhyAI2N8iAiKIYC0Ha5n9nWmHc9tjUbnLVPY/KvNbIcpq4O0La//P/W2CrTzEViqYmWWmtczel2C7mmwtkZUp978akGeQjEphHaAD5RAUTtIuL4bTi+nwYDi92Y/prMBw+FDizh65IChMOqMbefClJ3i79SNm1hTLeTd5mBJT/Cil7gDuANh3332zyr+/N87d532dvmikbLJtL4g4QAYagbyHP7dREIfdEAx8Bv7n1CEFdeaVy91umEbHMVCnw+lEBLDrKtx3tq4i+yvcrzgc2f3l7lccYtdfKPtQ+x1uXc5+HQ6sYkX2W2R/uedsyOMc4RoJgMNRZN3gc5V3jQAMA5VOg2FA7nfagHQKZX+TSoORRqXS1vpUytoulULl/CY1sIxUCpVMWfUkrf8kk1b5VBKS9raZZfa3aW87pjgciMeDuN3Wd/a3G3EP/Hd4PPZ/e3mm/KDtBr7TDidxU4gpB1FDiKaFSBq609CdUnT2KzpS0NvXi7e/h1C6n1oxCAFVuHCbPtKqiqRRQ9oMDhI97u4h5u0i5uki7Y3irewjVO1mZmMdc8PzmT9nV044/HOs7d7MTTfdxHnnnYfb7R58DiYJ5VRErcCcnP+z7WUlE+/upi8aYZf9D6YhvNOoG51RP/yZssUe/szbWrGHP9NgjLKBLlQMuXXmN2RDNIBFG7nMebHlymyjmTCUUnYjazW+A9/Joo2wSg69zsxbl1vf4O3MEeqckMZ+UCPuRtxunJUV4A7ajX6+Iii+XeEyd/a/o9TtnM6Sr1dv0qAznqQ9nqQz3k9HLElnPElHPGn/7rf+d1vLe5MGXpI00clcM84MlaDONAkqB7OVh9lGJUkjSK8xHTOvGYSoo4+Yt5uIt4uYp4WYt4uEL0Ig6KS+IcTsWU3sWhdmXnB/5gbn4vf4s9u+8MILLJm3hEAgwN133U19fT1z5swpPKRJRzkV0cPAN0XkfmB/oGe0/UNm2npodjnwUBYeeMjYS6iZsgzd2I++0Tfz1hVp7EdQFoXKYMwbe5HBjWyxhtrnxREMFDT2BW/5JSiNXEuBjMUw1Hauyed0UUoR60/T2Z3IUyQd8SSdOQqmM56kI2Yt70/nu1/dpJlBJ/Mlzs6ufhZhEjTAnXZipnz0p/z0GjUk1by87fowiHt66PX10OVtYYvnNWKerqx1U1XrYXbtTMKhMHsGw4RDezMvOI/GqkYcMnRsWUdHB5dccgl33XUXV111FVdffTV77bXXuJy/8WDc7hIR+TWwHKgXkRbgKsANoJS6DXgcOBp4H+gFTh3tPgzDAMA5CW/2HQGlFKRSmEM29iU0+pkGesh1w2835LqJaOyLNdR2Yz/g0smUGe3bvVVfXmM/1HY7+P2vlCLSl6YjY5XYSqTTVjLZ5bGB5UmjeL9ehdtJfaWT+RVxDnBGmFnRS7WrH18yjSSEVMJFX7KKeCpErzmHTOBxEit1rtsVQ7xR+qvjRCreY4ungxbXBjrdW4h5u+hzR6n0VBIOhgmHwuwXDBMOfYx5wXk0BZuocFWM+th/+ctfcvHFF9PV1cWKFStYsWLFtp3QMjCeUXNfGmG9As7Zln2Y6TQADldpJvZUpHhjn2lwizT6o3Ll5Lp0Snirn6jGfqSG2ufFEfAXd8PkvuWP+HZf/C2/WHmcTu3CnCBMU9HTl8pRKP05lktG0Qy4x7p6k6SM4pmmqzxO6vxeaqs8zAj52G1GgNm+GLPMLmr7evD1xnHEEqTjBn1xB/GYj1hngJhRh0GQXqy3ZACXJPH7Yrj9/XirIqSrNtFZEaXNvZmPWMs69RGG03omHOJgln8W4WCY/UKLCQc/zbzQPMLBMPUV9WN2L33729/m+9//PgcddBC33XYbS5YsGZN6J5op/Spl2K45p3PbOuGGb+yLNPoluHIo6r8fvStnzBt7GGjch2uocxr7QW4Yd2FDXoprp6CxL6JsdGO/fWKYiu5eS2m0x/KVS1ax5FguXb0pDLO4Ygn4XNRVeait8jC7ppI9Z1dT6/dQV+WhrsrNNHeCBmMLvkg70tVDX0eUWFeC6CaI9bqJ9lcRS9exSQXYRFW2XsGkyhPDX5lkWr1iVihKImjS7U+wydfDOsd6Puxbw7rYOlLmwDMZ8oYIB8PsEVzMZ0OfZl5wHuFQmDmBOXicnnE5n319fcTjcerr6zn99NNZsGABp59+Og7H1J0oZ0orInMUrrm+115jwxXfweztndjGfri3e6/d2JfihinJtZP/ll/s7R6XSzf2mm0ibZh02orFUiCDLZVc91hXbzI73qWQUIU7q1jm1Vexz9za7P86v/VdW+WhrspLjbsfT2wD/ZtaiW5YR3RzD7G2XmLdaaIxF7FEJe+na3nNrEHhBwY68b3OPvwVfQTqTWaGDPx1KSoagvTWOGmviNHCOj6INtMcaaa5p5mu/i4rsjoKrpiLOcE5hENhDms6LKtswsEwNb6aCTnnGZ588knOOeccli5dyoMPPsjChQtZuHDhhMowHkxpRWSMwjUX//tL9K9ZQ/DYYxHvMG6YUbl2iisP3dhrphLJtElXb67rq3+QGyy376W7t/iLmwhUV7izrrAF0/y2ErEVit9LfZWHWlvB1FR6cDvtt/hUAiKtpDtaiLVtItrWSaw9TrQ7zaaogw/6vERT1cSMBtJ4gUb7A05J4/f14g+mmB1y4q9NEWioxD9zGlUzGklW9dGaWE9zpJnVPessZRNppmVzC8YmIyt/na+OcCjMJ5o+kXWjhUNhZvln4XKUt6lsa2vjggsu4Le//S0LFy7km9/8ZlnlGWu2C0XkdI3smjO6uhCfj1nfv3G8xdJoykp/2sjpqM/vTykWERZNpIvW4xCyFkltlYfF04NZS8VSLt48y6Wm0oPTUeQFzEhBdANmVzO9GzcQe6ODnvYorV1JolGIxT3EkkGiRj0JFQLq7Q+ASaWnj0Blkrppwtwak0CDi8D0OvwzpuNvCFARcJM0k6yNrKW5p5nXIs009/yV5k3NNL/XTDQVzYridXppCjaxS80ufGrup7IKZ25oLkHP4PE6k4H//d//5YQTTiCZTHLttdeyYsUKvF5vucUaU6a0IjKNjEU08mEYXV04aybWjNZoxoK+pJEfEZY3hmVwpFisv7hicTmEmhwLZcns6gFrJWe5pWi8hCrcOIopllxMA2KbUV0tJN9rpWtDO7EtPcQ6E0QjJrG4m2iiipjRQNysxaQGGHgOPc5+/JX9BKYpplW78dcJgem1+Gc0EpgWoqrai9NlWU1KKTb1brItmzdo7niE5g8t66Yt1mZPemPRWNlIOBTm6J2OzrNuZlTNGDYMejKRSqVwu93sueeeHH300Vx33XXMnz+/3GKNC1NaEWVdcyUMTDO6u3FWV4+3SBrNsOQOjizWp5I3ODI+MDiyGG6nUJdjlcytq8xRKNbyev9AH0uwYpQuY6Ug3g49LRidrcQ2bCK2pZtoey+xHoNozEEsUUnUqCNm1JNSQWDAqnCIgd+XwF9tMCPkJFDnxD8thH/GNAKNNfhrfXgrBjdBvalemiPNvNbzKs0tVp9Nxp3Wl+7LlqtwVViBAg17cNzOx2X7beYG51Lpriz9OCcZkUiE73znO/z973/nb3/7G/X19dx///3lFmtcmdKKyByla85VoxWRZmzJDo4sFhFW4uDIDF6Xw1IifkuR7NTgL+4Ks8sEvNvQF6kUJHog0orqbqV30wZimzqJtceIdqeIRYVYn49oupaYUUevWQuE86qo8CQIVKWoCTqYU+sl0FCFf3oD/um1BOoqqAx47FlABmOYBhviG2huzVE0Pc18FPmIzb2bs+UEYabfGuC5T+M+WcsmHAwzrXLadtUXq5Tid7/7Heeffz4bN27k7LPPpr+/n8rKqatUS2VKK6KBPqISXHPd3bhnzRpvkTRTnLEeHJlVJH4PuzQGCiLBBqyVOr+HSs8Yhq8n49DTCpEWklvaiG1qJ7olSqyrn1hEEe31EEtVEzXqiRn19jQzA1PBuJwpApVJ/DVQX+PBX+/DP72OwPR6/HUV+Gu8uNwjeyIiyUieommONPNRz0esi6wjaSaz5QKeAPOC8zhgxgF5yqYp2ITXuX31hxRjy5YtfPWrX+WJJ55gr7324n/+53/Yb7/9yi3WhDGlFZE5CkWU1q65HZJigyPbB3Xcb+XgyJnB7BiWWluZ5CqXCs84DbRO90OkFXpaMbpaiW/aTGxzD9HOBLEeg1jcRTQZJGbUETMa6FczgZnZzUVMqnz9BEImjdVu5tdX4p9Wg39GA4H6Svw1PryVpVtbKTNFa7R1kLJpjjTTmejMlnOKkzmBOYSDYQ6ZdUiewqn11W5X1s1oCQaDtLe388Mf/pBzzjkH1w42W8aUPtqBPqLhD0Ol05g9PTpYYTtgwgZH2u6x3M58XwkWwLYfYBqiG2yXWQuJLRuJbeom2tFLrCdFNOog1l9FzKgnatTTa9ahmJZXhc+dxO9PEwg5mVlbgX9aNYHpDfgb/PhrvFSFPDico+uwV0rR1d81yI3W3NNMS7SFtBoIkKj11RIOhlk+Z7mlbGyFMzswG7dj8s4APdE8//zzrFy5kgcffBC/38+LL744pQelbgtTXBHZMyuM8PZgRKw0EdoimnwMNziyWETYmA2OrHLjneipoUwT4lsg0gI9raQ624ht7LD7ZZLEIhDt82UtmZhRT5pd86pwOgz8VSkCdcKcGi/+hiCB6fUEGoL4a734a3y4vVt/XEkjybrIwFibjGXT3NNMJDmQbsXtcDM3OJf51fM5fO7hedZNyBva6v3vCLS3t7NixQpWrVpFOBymubmZ3XfffYdVQjDFFVFmZoWRwreNri4AbRFNAJNicGQ5UAr6uqCnBSKtmF0t9G7eYvfLJIj2mMR6PcTStXa/TAMJtUthJVRVJPFXQ321m3C9H39jHYHGavw1XgK1Pnx+9za7sJRSbOnbMsiN1tzTTFu8DTMn0d+0immEQ2GOCh+VVTThUJiZVTNxOrbfOR7HA6UUP//5z1mxYgWRSIRLL72UK664YocIRhiJKa2ISg3fHlBE2iIaLZNucGS5SESy/TKqp4X+9o3ENvcQ6+wl2mMQi7mIpkLEjAaiRj1xM4xi57wqPO40Ab+Bv9pFY22lFcLcWEOg1oe/xpc3ZmYs6Ev3ZQd5ZtxozZFm1kbWEk/Fs+UqXBXMDc5l9/rdOWbnY/Ksmyp31TB70IyWe++9l1133ZXbbruN3XbbrdziTBqmtCIy0ykczpE7VY3ubkC75mCSD44sF6m+bIQZPa2ku9qsUOaOOLFuu18mGbD7ZRqImQ2kVH7KeoeY+KvSBOoczKqtwD8tRKCxFn9tBf5aL4EaH54iY2a2FVOZbIxvHKRsmiPNbIxvzJYThBlVMwiHwizdeWlW0cwLzWNa5bQpM8hzqtHb28v111/PmWeeyezZs3nwwQcJhUI7tBuuGFNaERmGUVrEnG0RubYz11yxwZF5EWGjHByZG0o85oMjy0U6CdE2W9FYAQC9W9qJbYkS7e4nFhFiiQpLwRj1RM16+sx9BlVT4UsRqBZqarw01QfwT6/FX1tpWTO13mHHzIwFsWRskButOdLMusg6EkYiW87v9hMOhtm3cd88y2ZucC4+l2/c5E+WzQoAACAASURBVNMM5vHHH+ecc86hubmZWbNmcdZZZ1GznbVBY8WUVkRmOl3ShKdGl20RTfKboNTBkR3x/mzH/qQYHFkuTAOiG22XmdU3k2zfSLS9h1hHgmhEWf0yWUumnpixGyb5kVsul0EgoAhUu6mvryIwrQZ/vR9/rY9ArZeq6tLGzGwraTNNW6ytqMJp72vPlnOK08p1Ewpb425yrJs6X93Uu47bGS0tLVxwwQU8+OCDLF68mOeee47DDjus3GJNaqa0IjLSqdJmVejutlIuVIwu++G2MtzgyHbbDbY1gyPr/V4WNgYnbnBkOVDKijCzFQw9rRjdrcQ3dxHr7CPanbbGy6TrbAVTT9QIk1T5fncRhb/KwF/norGugvnTqvHXB2xLxoe/xjuqMTNjQXeiu7h1E11H2hxwhVZ7qweNuZkXnMecwBzc25iDSzN+rFy5kscee4zrr7+eiy66CI9nfPISbU9MaUVkGsaETng62sGRnfEk6SHGsEyawZHlIBNhZisYIi2o7lYSHe1E22PEulJEY0IsVUPUtFxmMaOeuDmfTGrmDD6vgT/kIFjjZea0EP76IIE6nx0A4KUy5C1L31TKSLE+uj6/38b+7u7vzpZzOVw0BZryxt1kJums9uk+zanCSy+9REVFBUuWLOG6665jxYoV7LTTTuUWa8owpRWRkU7jLHXC0yKKKDM4siPPKtmOBkeWi/5YnrvMGjOzgdiWCNGufmu8TDKYY8k0EDOWYZA/lYvTaRIIgL/Gw5z6AIFpIctdVmP1y/hrfbjLqKCVUnQkOgZZNs09zbTGWjHUQH9cfUU94WA4O+Ymo2xm+meWPdeNZuvp6enhsssu46c//SnHHHMMDz/8MHV1ddTV1ZVbtCnFlH4CjHQaxxCuuXUdvbzW0k1nPMmC5jYSDg/fv/eV7WdwZLmwE5jlWjNmVxvxji5iHdYUM9FEJTGzITuPWdTYlX61f0FFiqoqRaDWRUNdFfOmhQjUVeCv8WUDAHxV2z5mZixIpBNWGHSBsmmONBNLxbLlvE4vc4NzWVS7iKPmHZVVOHODcwl4AmU8As1Yo5TigQce4MILL2Tz5s2ce+65XHvtteUWa8oypRWRmU4PGTV3+i/+wZrNViNx1+YO1tXNYc3m2NQYHFku7ARmmQgzelpQPa30d7QTtUOZY71eorYlY42ZaSJuLkWRr4i9XhN/yIm/poLp04L46yrzlExVtRfnJDrHpjLZ3Lu5qHWzIb4hL9fN9KrphINhjtnpmGy/TTgUZnrVdB0GvYNw7733csopp7Dvvvvy6KOPss8+gyMtNaUztRWRkR5yMOuWWD8n7DWLyz+zmI5nrmX3QxZz2rc+NsESTiJME2KbBrnM0l0biLVHiXUniUadOe4yW9GYu5BW+WG/DofCHxQCNV5mNQQJ1Ffhr/Hmuc08vsl5a8VT8aKWzdrI2rxcN5WuSmvMzbSlHB86PqtsmgJNUzrXjWbr6e/v58MPP2Tx4sWceOKJpNNpTjnllJK6BzTDMzlbixIxhrCIlFJEE2lmVVdQ53OyJRLZvgezKgW9HXkKJhMA0Nthz8ocVXZumfrs6P+YuR995uB5wSqrFP5aD7X1fprq/dnpZTJRZuM9ZmZbMUyDtnjbIGXT3NPM5r6BXDcOcTCzysp1s2/jvnmZPBsqGiaFW1AzOXjmmWc466yz6O3tZc2aNXi9Xk499dRyi7XdMKUVkeWaG9xH1Js0MExFwOeyJjxVatKPIRqSnARmuaP/ibTS37HZCmXuMYmlQnmWTMxcTMw4BLPgErvdCn+1i0BdJQ31AQK1+ZaMv9qH0z013Es9/T1FrZvCXDdBT9AaczPzgAFlY+e68Th1aK1maDZv3szFF1/MPffcw0477cQdd9yB17v950eaaKa0IjLSaVxFYvQz850FfO7JP71PTgKz3L4Zo3sD8Y6YNV6mv2rAVWbUEzNnEzP3Jmnmj4tyOBRVAQf+2gqm1/vx11ZkFY3VP+PFM1VmRLBJmSlaoi15U9dkfufmunGJi9mB2YRDYQ6ddWjeBJ013popdcyaycH777/PsmXLiMViXH755Vx++eVUTPBYxB2FKa+IvEVmro0krBmdgxUujK4tQJlmVchJYJbbN6O6W+nr7CbW1W9P+59rydQRNXeh1wgB+Y2nrwICDV5C9X5m1VZkrZjMpJmVIc/knc9tGJRSdCY6i1o3Q+W6+ficj+dNYTMrMEvnutGMCZFIhGAwyM4778zpp5/OaaedxuLFi8st1nbNlFZEplE8fDtqK6KAz42xeZxm3jbSENtY1JpJdm4i1pUgFnPmjJOx5jGLmcuIGbUYqnCaGfBXuwnUVzG3tiI7vUwm0qyqxlvWMTNjQb/RP5DrpiDBWjQZzZbzODw0BZtYULOAI+YekWfdBD3BMh6BZnsmHo/z3e9+lzvvvJPXX3+d2bNn8/3vf7/cYu0QTGlFNNSA1ojtmgv6XFnXnGs0rrmCBGa51ozZ3Ua8M040YhJL19qRZQ3EjDqi5iJi5qH0G/lWmghUBZz46ypoqKtkp5qBjv9AraVovFVTy2U2FEopNvduzk8bbc8u0BZrywuDnlY5jXnBeRw97+g862ZG1Qyd60YzoTzyyCN885vfZN26dZx++uk6R9AEM6UVkTXp6eBDiPQNWETpwqR4BQnMciPNVE8r/V2dVr9Mqtruj8lYM7OJmfsQTwdRBdPMeCsEf52PQF0lM2oHppfx278rQ55JNWZmLOhN9eYN8swom7WRtfSme7PlKlwVhINh9qjfg8/u/Nk8haPDoDXlJp1Oc+KJJ/KHP/yB3Xbbjb/85S8ccsgh5RZrh2NKKyLDKBI1l4jg2PIOH3O8xrT32uh77UnE5UB+c9JAOoCc8SJKCf8bOY9NxieIpWtIm/nBDw4nBGo8+GsrmV2bb8lkfk/WMTPbiqlMNsQ3FM3kual3U7acIMz0zyQcDLN34955yqaxsnG7sPQ02xdKKUQEl8vFjBkzuOGGG7jwwgv1BKVlYkq3oGayH0frS3Dv5wdcaP0RjgWO9QBPQ+yDapzeCiTVC427wS5HQXAWhGZBcDZxNY13V66hcV6QuTuFsiP/M30zFX73pB4zMxZEk9HBysYOg+43+rPlAu4A4VCYZdOX5fXbNAWadK4bzZThxRdf5JxzzuHOO+9k77335tZbby23SDs8U1oRGf1xnB3rIe6H2p1g3qEQnMXDzcJ97xj8+qLPY1z6PZzeTfD1PxStI/K+1Ye07Jh5NO22/U5UmDbTtMZai1o3HYmObDmnOK0w6GCYg2YclKdwdK4bzVSmq6uLyy67jNtvv52ZM2fSZbvtNeVnXBWRiBwF3AI4gbuUUjcUrG8CfgFU22UuUUo9Xmr9pqmsYIVvPJ+3/KX2N3jPuxGpmYvRExk2Yi7abrnpAnXbxxt9V6JrUL9Nc6SZ9dH1ebluarw1hENhDpt9WJ6ymePXuW402x8PPPAA5513Hu3t7VxwwQVcc801BAJ6ItrJwrgpIhFxArcCRwAtwD9E5GGl1Ns5xa4AfqOU+qmI7Ao8DoRL3YdhqKK53yN9aYJ2v43R1YV38aIh64h0WGmWp5IiShpJ1kfXD1I2zZFmevp7suXcDjdNgSZ2Cu3EJ+Z8Ii+TZ8g7eGofjWZ75Z133iEcDvPkk0+y1157lVscTQHjaREtA95XSn0IICL3A8cBuYpIAZmBISGgbTQ7MEyF0znYVRRNpAj4rLd6o6sL1zCDWaMdCSpDnglJBT0alFK097UXzeTZGmvFVAOZXBsqGgiHwnxq7qfyMnnO9M/UYdCaHZJEIsF//ud/svfee3Psscdy2WWXccUVV+gJSicp46mIZgHrc/63AIVJaa4GnhKRc4Eq4PBiFYnIGcAZAE1NTQCYppV0rNjs29FEmoDPhTIMjBEmPI109BEsozXUl+5jXWRd0Uye8VQ8W87n9DE3OJdd63a1xt3YymZucC5+j79s8ms0k42nn36as88+mzVr1nDRRRdx7LHH4nZrd/NkptzBCl8CVimlbhKRA4F7RGR3pXJe9wGl1B3AHQD77ruvAmswK4CjyPicSCLFTvX+gQlPq4e3iBrnja+bylQmm+KbiiqbDfENeWVnVM0gHAznjbmZF5xHY1WjznWj0QzDpk2b+Na3vsV9993H/PnzeeqppzjiiCPKLZamBMZTEbUCc3L+z7aX5XI6cBSAUur/RMQH1AObGQHTVkTFTO2MRWQUDmYtrMMwiXb2s2DfsbGIYskYayNrBymctZG1JIxEtlyVu2rQmJt5wXk0BZuocOlJFTWareFPf/oTv/vd77jyyiu59NJL8fmmTr/vjs54KqJ/AAtEZB6WAvp/wJcLyqwDPgmsEpHFgA/YUkrlRlYRDbYSLEU08szbse5+lKkI1pfe+KfNNG2xtkF9N2sja9nSNyC6QxzM8s8iHAyzbMaybJBAOBimvqJeh0FrNGPAa6+9xpo1a/jCF77AySefzMEHH8y8efPKLZZmlIybIlJKpUXkm8AfsUKzf6aUektEvgu8rJR6GLgIuFNELsQKXPh3pZQautYBhrKIDFMR60/bM28PbxFF24eOmOtOdBcNFFgfXU/KTGXLhbwha8zNzIPy0kbPCczRuW40mnEiFotx1VVXccsttxAOhzn++ONxuVxaCU1RxrWPyB4T9HjBsitzfr8NHLw1dWf7iAqm+Inl5iLaZE94OsQ4oq4tVjDAm8l/8tQb+X033f3d2XIuh4s5gTmEg2E+NudjWWUTDoap8U3RhHsazRTloYce4txzz6WlpYUzzjiD733ve7iKzDmpmTpM2atnGsUtokg2BcSARdTtM2je+I9B6Qca396dvTmCS169GNNhUOerIxwK88mmT+aljZ7ln4XLMWVPlUaz3fDGG29wwgknsGTJEh544AEOOuigcoukGQOmbOs6YBENHELKSPH02qfx1D3LYxv+RPTlf7KfEz7xyNFWLgbA6/TSFGxiYe1C5lUswxWAe4+5h3AoTMCjR1prNJONVCrFX/7yFz7xiU+wZMkSHnvsMY444ggdkr0dsR0oooGb8dEPH+UHr1+Jdxp8FGvgMwlIBSu4ZP+LstbNjKoZ2TDoP7zyKqpRsaRhSVmOQaPRDM8LL7zAmWeeyVtvvcW7777L/PnzOfroo8stlmaMmbIDU7KuuRyLqDPRCUD0ve/wk8P+wIGVu1MzrYmTF5/MwbMOZpZ/Vt5YnEh7H8E6HS6t0Uw2Ojs7OeOMMzj44IPp7u7m97//PfPnzy+3WJpxYspbRLn5iOKpOIIDjMpsH9FQEXNG2iTe3U+gXo810GgmE4lEgqVLl9LW1sZFF13E1Vdfjd+vZw/Znpmyisgs0kcUT8XxOCoAIeBz09XdjXfhwqLbx7oSKEVZp/fRaDQDtLS0MHv2bHw+H9deey1Lly5lzz33LLdYmglg6rrmMhaRe2CsTiwVwy2Wq23AIioeup2ZdVu75jSa8tLX18eVV17JzjvvzCOPPALAV7/6Va2EdiCmrEVkZPuIBhRRb6oXJz4q3E5cKIyeniFn3h5uMKtGo5kYnnrqKc4++2w++OADvvKVr7Bs2bJyi6QpAyVbRCJSOZ6CjJZs1FxOCGcsFUNUhWUNZSc8Hcoi6kMcgr/GOyHyajSafM4991yOPPJIHA4HTz/9NPfccw+NjY3lFktTBka0iETkIOAuwA80iciewDeUUmePt3DDMaCIBhRJb6oXlJdghRujy55nbiiLqCOBv8ZbdPZujUYzPhiGlb7F6XRywAEHUF9fz7e//W09QekOTimt8M3AkUAHgFLqNeCw8RSqFMxUEsjvI4qn4ijDa1lE2QlPiyuiSHtCBypoNBPIq6++yoEHHshPfvITAE4++WSuuuoqrYQ0pbnmlFLrCxYZ4yDLqDCSVh+PoyBYwTC89szb9oSnQ7jmoh19BEYx67ZGo9k6otEoF154Ifvttx/r1q1jxowZ5RZJM8koJVhhve2eUyLiBs4HVo+vWCNj2orIWeCaM1IegqHhZ95OpwziPUltEWk048xTTz3FaaedRltbG2eeeSbXX3891cNkTNbsmJSiiM4EbsFK/d0KPAWUtX8IclxzHksRKaWIp+NIym1ZRF1Dz7wd6+wH9BgijWa88Xg8TJs2jQcffJD999+/3OJoJimlKKKFSqmTcxeIyMHA38ZHpNIwUpYycbgtZdKX7sNUJumUm6A9hkjcbqRycLBfpL0PgIAeQ6TRjCmpVIof/OAHRCIRVq5cyfLly3n55ZdxOHRQkGZoSrk7/rvEZRNKRhFlLKJ4ysotlE55CFa4SdvT+xTLhJodzKqn99Foxoy//vWv7LXXXlxyySWsWbMG0zQBtBLSjMiQFpGIHAgcBDSIyLdyVgWxMq6WFTNl5R1yeCxlklFEysxEzfUME6iQwOEUKkN6DJFGs610dHTw7W9/m7vvvpumpiYeeeQRjjnmmHKLpZlCDPeq4sEaO+QCAjmfCPCF8RdteIxUEgcm4sq3iLKKaJgJTyMdffhrfTgcg60ljUYzOjo6Orj//vv5j//4D95++22thDSjZkiLSCn1HPCciKxSSq2dQJlKwkglcYgCpxW+nVFEmF6CPjdGdzfeXXYpum20Q48h0mi2hdWrV/Ob3/yGq666il122YV169ZRW1tbbrE0U5RSnLe9IvJ9EXlcRP6c+Yy7ZCNgplM4xQSnNcVPLBUDQJk+O2quC2d1qOi2Vh4irYg0mtHS29vL5Zdfzp577sktt9xCS0sLgFZCmm2iFEX0K+AdYB5wDdAM/GMcZSoJM52xiApcc4aXgMeB0dNT1DWXShr0RVN6MKtGM0qefPJJdt99d66//nq+/OUv8+677zJ79uxyi6XZDiglfLtOKXW3iJyf464ruyIyUimcOa653lSvtcL0EkgniJpm0Zm3M7Nua4tIoymdWCzGv/3bv1FXV8czzzzD8uXLyy2SZjuiFIsoZX9vEJHPiMheQNntcNNID+Ga81KRiALFp/eJdFhjiILaItJohsUwDO69914Mw8Dv9/P000/z2muvaSWkGXNKsYiuE5EQcBHW+KEgcMG4SlUCRjo1KFhBcCC48cVsRVTMIurQeYg0mpF45ZVX+MY3vsErr7xCRUUFn//853WiOs24MaJFpJR6VCnVo5R6Uyn1caXUPkDnBMg2LEbasFxzOeHbLqnA73Vj9vQAxWfejnQkcLodVAY9g9ZpNDs6PT09nHfeeSxbtozW1lbuv/9+Pve5z5VbLM12znADWp3AiVhzzD2plHpTRI4BLgMqgL0mRsTimOk0jhzXXDwVt7Kz2hFzQNE04dGOPgK1vqIzLmg0Ozqf//zn+fOf/8w555zDddddRyhUPPJUoxlLhnPN3Q3MAV4CfiQibcC+wCVKqYcmQrjhMIw0Tgpcc8pnz6qQSQFRxCLSeYg0mjw+/PBDGhoaCAQCrFy5EofDwX777VdusTQ7EMO55vYFjlBKXQocDRwDHDwZlBCAmTYGD2jNGcyK242javCEp9GOhA7d1miAZDLJ9ddfz2677cZ1110HwP7776+VkGbCGc4iSiqlTAClVEJEPlRKdUyQXCNiGAYuMfMUUSY7a7qlC1d19SD3WzKRJhFPaYtIs8Pz/PPPc+aZZ7J69Wq+8IUvcN5555VbJM0OzHCKaJGIvG7/FmBn+78ASim1x7hLNwymMdgiMgw/wYBlEemIOY2mODfffDPf+ta3CIfDPPbYYxx99NHlFkmzgzOcIlo8YVJsBYZhR83ljCNKp2vtCU+7hxhDlBnMql1zmh0L0zSJx+MEAgE+85nPsGXLFq644goqi+Tr0mgmmuEmPZ10E53mYhomTgdgu996U70kU57szNveBQsGbZNJiKfzEGl2JN566y3OPPPMbKbUXXbZheuvv77cYmk0WcY1Y5WIHCUi74rI+yJyyRBlThSRt0XkLRG5r9S6DcPMpnHIpAk3jYFgheKh2wlcHgc+v3trD0mjmTL09vZy6aWXsnTpUlavXs0xxxyDUqrcYmk0gyhlZoWtwh6HdCtwBNAC/ENEHlZKvZ1TZgFwKVY0XpeITCu1fsM0cbotRZRJE47hJeBxWoqomGuuvY9AXYUeQ6TZ7vnnP//J5z73OZqbmzn11FO58cYbqa+vL7dYGk1RSlJEIlIBNCml3h1F3cuA95VSH9p13A8cB7ydU+brwK1KqS4ApdTmUis3TYXDaRl0vWlrwlNlegmZCRhqwtPOhHbLabZrlFKICE1NTTQ1NfGLX/yCww47rNxiaTTDMqJrTkSOBf4FPGn/XyoiD5dQ9yxgfc7/FntZLrsAu4jI30TkRRE5qjSxwTAUTts1F0sOTHgasmfhLhY1F2lPEKzVikiz/ZFOp/nhD3/IJz/5SQzDoK6ujueee04rIc2UoJQ+oquxrJtuAKXUv7ByE40FLmABsBz4EnCniAzyqYnIGSLysoi8vGXLFsC2iByW+PH0QJpwf8JSSoWuuf7eFMm+tB7MqtnueOmll1i2bBkXXnghPp+PSCRSbpE0mlFRUhoIpVRPwbJSejxbsaYIyjDbXpZLC/CwUiqllPoIeA9LMeXvTKk7lFL7KqX2bWhoACxF5LRdc/HkQJrwyj5bERVYRAOh29oi0mwfxGIxzjnnHA444AA2bdrEb3/7Wx577DFqingDNJrJTCmK6C0R+TLgFJEFIvLfwAslbPcPYIGIzBMRD/D/gEKX3kNY1hAiUo/lqvuwFMENE5wuJ5CTndX04usrbhFlE+Jpi0izneB2u3n22Wc599xzszMk6EAczVSkFEV0LrAb0A/cB/RQQj4ipVQa+CbwR2A18Bul1Fsi8l0R+axd7I9Ah4i8DTwDrChlGiHTNFCQdc1lk+IZPjwxyy0x2CKyxhDpWRU0U5n333+fU045hWg0itfr5ZVXXuGWW24hGAyWWzSNZqspJWpukVLqcuDy0VaulHoceLxg2ZU5vxXwLftTMmbaAMBhW0SZNOEu8SHRdnvC06q8bSIdCdw+J97KcYtY12jGjf7+fm688UZWrlyJx+Ph61//Ooceeig+n36x0kx9SrGIbhKR1SJyrYjsPu4SlYCRTgPgdFpKJROs4Pf4Mbu7cVaHBrkooh0JgnoMkWYK8swzz7Dnnnty5ZVXcvzxx/POO+9w6KGHllssjWbMGNE8UEp9XESmYyXJu11EgsADSqnrxl26ITDSKQAcTssissK3hYC3gvTGLlxF8xD16f4hzZRDKcXKlStJpVI8+eSTHHnkkeUWSaMZc0qa4kcptVEp9SPgTKwxRVeOsMm4YhqWay4TrNCb7sWpfIR8nqIzbyulLItID2bVTAFM0+TOO+9k/fr1iAj33HMPb775plZCmu2WUga0LhaRq0XkDSATMTd73CUbBjPjmnPZM28nYwPZWYvMvJ2Ip0j1G3rWbc2k5/XXX+eQQw7hjDPO4K677gJgxowZVFToe1ez/VJKz/3PgAeAI5VSbeMsT0lkXXMuS/zedC+Y3uzM24UWkc5DpJnsxGIxrrnmGm6++WZqampYtWoVp5xySrnF0mgmhFL6iA6cCEFGw4BrzhI/loxhml6CHidGT8+gmbcj2TFEWhFpJidXX301N910E1/72te44YYbqKurK7dIGs2EMaQiEpHfKKVOtF1yuTMplD1DayZqzmG75uLpOEbaSy0pMIxBrrmBMUTavaGZPKxfv554PM6iRYu45JJLOP744znkkEPKLZZGM+EMZxGdb38fMxGCjIaMa87pstKEx5Jx0ukKau1ZuAtn3o52JPBWuvBW6DFEmvKTTqf50Y9+xJVXXsk+++zDc889R319vVZCmh2WIYMVlFIb7J9nK6XW5n6AsydGvOIUWkSxZBxML9Vpy/IZZBG1J3T/kGZS8OKLL7Lvvvty0UUXsXz5cn7xi1+UWySNpuyUEr59RJFlnx5rQUZDNmrObbvmUjGU6SWYLD7habRDjyHSlJ/HHnuMgw46iPb2dn7/+9/zyCOPEA6Hyy2WRlN2hlREInKW3T+0UERez/l8BLw+cSIOxkz2A+BweVBK0ZvutVJA9FszLOQqoswYIm0RacqBUorWVmvS+cMPP5zvfve7rF69mhNOOEHP8qHR2AzXaXIf8ATwPeCSnOVRpVTnuEo1AkbSioJzejwkjAQKEwwflb2DZ97ui6ZIp0w9hkgz4bz33nucffbZvPfee7z99tv4/X6uuOKKcoul0Uw6hnPNKaVUM3AOEM35ICK14y/a0JhJuy/I7c1PAdEbBZcLh9+fLZuJmNN5iDQTRSKR4Oqrr2bJkiW8/PLLXHrppXpAqkYzDCNZRMcAr2CFb+f6ERSw0zjKNSxGynbNuT3EchSROx6Fmuo8l0cmD1FAjyHSTAAbN27ksMMOY82aNXzpS1/iBz/4AdOnTy+3WBrNpGZIRaSUOsb+Hqu04GOGYfcROd2+gVxEphdXbAMMNYaoVisizfiRSqVwu900NjZy2GGHceutt3LEEcXifDQaTSGlzDV3sIhU2b+/IiI/EJGm8RdtaMyMReTxZnMRYXqRSA/O6sEpwn1+Nx6fHkOkGXtM0+S2225j5513pqWlBRHhrrvu0kpIoxkFpYRv/xToFZE9gYuAD4B7xlWqEci45pxuj50CAjyOSsyewTNvW3mItDWkGXtee+01DjroIM466ywWLFhAKpUqt0gazZSkFEWUtjOpHgf8WCl1KxAYX7GGx0wlAXC4fdmkeFXuyqIzb0fa+/TUPpoxRSnFxRdfzD777MOHH37IPffcw9NPP828eZPOi63RTAlKUURREbkU+DfgMRFxAO7xFWt4DFsROb0VxJN2dlZX1aBcRMpURDt1HiLN2CIidHV1cfrpp/Puu+/yla98RY8J0mi2gVIU0UlAP3CaUmojVi6i74+rVCOQtYg83qxF1OhwDprwtDeSxEwr7ZrTbDNr167l+OOPetWXUwAAIABJREFU59VXXwXgzjvv5Pbbb6emZnA2YI1GMzpGVES28vkVEBKRY4CEUuqX4y7ZMGQtIneF1UekhEbTtJblpICItNsRc3p6H81WkkqluPHGG9l1113505/+xLvvvguAw1FScmONRlMCI4aSiciJWBbQs1hjif5bRFYopX43zrINiZlO4cBE3D56072I8lFvWOOFcmfejtgJ8bRFNHakUilaWlpIJBLlFmXc6e/vp6Ojgz322IPHH3+c2tpaXC4Xq1evLrdoGs2Q+Hw+Zs+ejdtd1h6UUVFKTPPlwH5Kqc0AItIAPA2UTREZ6RQOUeB0E0/FUaaX2iIzb0f1GKIxp6WlhUAgQDgc3u77Rdra2nA4HDQ1NVFdEASj0UxGlFJ0dHTQ0tIypYJnSlFEjowSsumgtL6lccNIJ3GKAqeHeCqOaXipTg2e8DTSkaAy6MHlcZZL1O2ORCKx3SqhzEPsdrsJhUJMnz6dxsZGnE59/2imBiJCXV0dW7ZsKbcoo6IURfSkiPwR+LX9/yTg8fETaWTMtIFDTHC6iSZjKNNDIFlEEek8ROPC9qiE+vr6WLduHdFolJqaGkKhkO4H0kxJpuLzOaIiUkqtEJHPAZn0kXcopf4wvmINj5FOWRaRy0ukP4YyfPgT8UETnkY7+micFyqjpJrJjmmabNiwgY0bN+JwOJg7dy719fXlFkuj2aEYLh/RAhH5HxF5E/gicJNS6lvlVkJgByvYrrlYMo4yPVT2RXFWD0x4apqKWGe/toh2UB5++GFuuOGGEct1d3ezYcMGampq2H333WloaJiSb5RDsWrVKhoaGli6dCmLFi3i5ptvzlt/xx13sGjRIhYtWsSyZcv461//ml2XSqW45JJLWLBgAXvvvTcHHnggTzzxxEQfwohccMEFPP/88+UWY0heeeUVlixZwvz58znvvPOw5gfIp6urixNOOIE99tiDZcuW8eabbwKwfv16Pv7xj7Prrruy2267ccstt2S3ufjii/nzn/88Yccxngzne/gZ8CjweawZuP97QiQqASNt4LRdc/FUDEwf3t4YrpzQ7Xh3P6apxxDtqHz2s5/lkksuKboulUrR09MDQE1NDYsWLSIcDpctyihtZxweL0466ST+9a9/8be//Y2VK1eyfv16AB599FFuv/12/vrXv/LOO+9w22238eUvf5mNGzcC8J3vfIcNGzbw5ptv8uqrr/LQQw8RjUbHVDbDMLZp+46ODl588UUOO+ywkrcZ7/NdyFlnncWdd97JmjVrWLNmDU8++eSgMtdffz1Lly7l9ddf55e//CXnn38+AC6Xi5tuuom3336bF198kVtvvZW3334bgHPPPbekl62pwHCuuYBS6k7797si8upECFQKppG2gxW82eys7lgEZ2hwxJxOiDd+XPPIW7zdFhnTOnedGeSqY3cbcn1zczNHHXUUBxxwAC+88AL77bcfp556KldddRWbN2/mV7/6FcuWLWPVqlW8/PLL/PjHP2bTpk2ceeaZfPjhh6TTaVasWMG0adO48MIL2X///XnllVd4/PHH+fGPf8wTTzyBiHDFFVdw0kknDdr/Sy+9xPnnn08ikaCiooKf//znLFy4kAMOOIC7776b3XazZF++fDn/9V//xeLFizn33HN58803SaVSXH311Rx33HGsWrWK3//+98RiMQzD4LHHHuO4446jq6uLVCrFddddx/9v77zDo6q2PvzuJCQhhACR8iEgnfRCCF26dAxVigUQLhZEuIIIelERuFcUFESwoCIWpIgiyAVBIIhgIaFXgUuTSAmhpZeZ9f1xJoeEFIKQTCbs93nOkzlz9tlnzZ7JWWfvvfb69ezZE4CpU6fy5ZdfUqlSJWrUqEGjRo14/vnn+d///sczzzxDbGwsHh4efPTRR/j6+ubZdvfccw/16tXj7Nmz1KhRgzfeeIMZM2aYQ5FhYWEMGTKEefPm8eKLL/LRRx9x4sQJ3NzcAKhSpQr9+/fPUW9UVBRjxowhMTERNzc3Nm7cyDfffGO2P0CPHj14/vnnadu2LZ6enjz55JNs2LCBhx56iD179vD1118DsHnzZmbOnMnq1atZv349r776KqmpqdStW5dPP/0UzyxD7wDffPMNXbp0MfenTJnC999/T3JyMi1atODDDz9EKUXbtm0JDQ1l69atDBo0iLZt2zJ27FgSEhKoWLEiCxcupGrVqnz00UfMnz+ftLQ06tWrxxdffIGHh0eebXozzp49y7Vr12jWrBkAgwcP5rvvvqNr167Zyh08eNB8cPL19eXkyZOcP3+eqlWrUrVqVQDKli2Ln58fMTEx+Pv7U7NmTeLi4jh37pzDS43k1yNyV0o1VEqFKaXCgNI37NsNS4YFJwRxKkWKxXBELvFXcwQqAHporgRy7Ngxxo0bx+HDhzl8+DBfffUVW7duZebMmfznP//JUX706NE0b96cJUuWsGDBAoKCgqhbty5Hjx5l5MiRHDhwgOjoaHbv3s2ePXvYsGED48eP5+zZsznq8vX15eeff2bXrl1MmTKFl156CTB6HcuWLQOMm8/Zs2cJDw/n3//+N+3bt2f79u1ERkYyfvx4EhONwJqdO3eyfPlyfvrpJ9zd3VmxYgU7d+4kMjKScePGISJERUXxzTffsGfPHtauXUt0dLRpyxNPPMG7777Ljh07mDlzJiNHjsy33U6fPk1KSgrBwcEAHDhwgEaNGmUrEx4ezoEDBzh27Bj33XcfXl5e+daZlpbGgAEDeOedd8y2u5kIYGJiIk2bNmXPnj1MnDiR33//3WyTpUuXMnDgQC5evMi0adPYsGEDO3fuJDw8nLfffjtHXdu2bcv2GUaNGkVUVBT79+8nOTmZ1atXZ7M1Ojqa0aNH8+yzz7J8+XJ27NjBsGHD+Ne//gVAnz59iIqKYs+ePfj5+fHJJ5/kuGZkZCShoaE5thYtWuQoGxMTQ/Xq1c396tWrm9LxWQkJCeHbb78FjIedU6dOcebMmWxlTp48ya5du2jatKn5XlhYGNu2bcu9oR2I/HpEZ4Gs3/y5LPsCtC8so26G1WIMzaVIuiETbnVDxV/LtoboWlwKKL2GqDDJr+dSmNSuXZugoCAAAgIC6NChA0opgoKCOHnyZI7ymzZtYsyYMeZTrre3N6dOnaJmzZrmk2rmk7KzszNVqlShTZs2REVFERERka2uq1evMmTIEI4ePYpSysy43b9/fzp16sRrr73GsmXL6NevHwDr169n1apVzJw5EzDC30+fPg1Ax44d8fY2xI5FhJdeeoktW7bg5ORETEwM58+fZ9u2bfTs2RN3d3fc3d158MEHAUhISOCXX37hoYceMm1LTU3Ntb2WLl3Kli1bOHz4MHPnzsXd/c79T/zxxx9UrVqVxo0bA9zUcQE4OzvTt29fwBh66tKlC99//z39+vXjv//9L2+++SY//fQTBw8epGXLloDhRJo3b56jrrNnz1KpUiVzPzIykjfffJOkpCQuXbpEQECA2WaZPdw//viD/fv3m1IdFovF7HXs37+fSZMmceXKFRISEujcuXOOa7Zr147du3cXuI0KwsSJExkzZgyhoaEEBQXRsGHDbMsGEhIS6Nu3L7Nnz87WxpUrV+avv/66o7bYg/yE8doVpSG3gsViwckJEjMMLSKxuCLXsveI4i8mU6acG86ldAhuSSNzqAiMVDuZ+05OTub4v4hkmwuoVasWlStXxsXl+k++TJkyN73WvHnz+OgjY4R6zZo1vPzyy7Rr144VK1Zw8uRJ2rZtC0C1atW455572Lt3L0uXLuWDDz4w7fjmm2/w8fHJVu/vv/+e7fqLFi0iNjaWHTt2UKpUKWrVqpVv9gqr1Ur58uULdEMcMGAAc+fOJTo6mk6dOhEREcH//d//4e/vz44dO2jf/voz5Y4dOwgICKBevXqcPn2aa9euFci53IiLiwtWW9otINtncXd3z3aTHThwIHPnzsXb25vw8HDKli2LiNCxY0cWL15MfpQuXdqsOyUlhZEjRxIdHU2NGjWYPHlytutmtreIEBAQwK+//pqjvqFDh/Ldd98REhLCwoUL2bx5c44ykZGRPPfcczne9/Dw4Jdffsn2XrVq1bL1bM6cOUO1atVynOvl5cWnn35q2le7dm3q1DFEsNPT0+nbty+PPPIIffr0yXZe5hCxo+OQd2mLxYqzkyLRtojVI9UZMjJyLGbVWbfvTlJTU7lw4QKXLl0iKSmJDh06sGzZMlxcXLBYLGagQlZatWrF0qVLsVgsxMbGsmXLFpo0acIzzzzD7t272b17N/feey9Xr141byQLFy7MVseAAQN48803uXr1qjn81blzZ959910zUmrXrl252nz16lUqV65MqVKliIyM5NSpUwC0bNmS77//npSUFBISEsyhJi8vL2rXrm3OrYgIe/bsybddwsPDeeyxx8zIqxdeeIEJEyYQFxcHwO7du1m4cCEjR47Ew8OD4cOHmz1JgNjYWPN6mfj4+HD27FmioqIAiI+PJyMjg1q1arF7926sVit//vkn27dvz9OuNm3asHPnTj766CMGDhwIQLNmzdi2bRvHjh0DjOG8I0eO5DjXz8/PLJPpdCpWrEhCQgLLl+ee/MXHx4fY2FjTEaWnp3PgwAHT/qpVq5Kens6iRYtyPT+zR3TjdqMTAqhatSpeXl789ttviAiff/65OfeXlStXrpjt/PHHH9O6dWu8vLwQEYYPH46fnx9jx47Ncd6RI0cIDAzM1U5HolAdkVKqi1LqD6XUMaVU7iFMRrm+SilRSoUXpF6rxYpTFkdUKd0It80+NJes54fuMqxWKxkZGRw4cIDk5GQ8PT0pXbo077zzDpGRkQQFBdGoUSMz6igrmaGzISEhtG/fnjfffDPXCeAXXniBF198kYYNG+aIvurXrx9LlizJNqH/8ssvk56eTnBwMAEBAbz88su52v7II48QHR1NUFAQn3/+uRl00LhxYyIiIggODqZr164EBQVRrpyxNm7RokV88sknhISEEBAQwMqVK2/aRhMmTODTTz8lPj6eiIgIhg0bRosWLfD19WXEiBF8+eWX5jDVtGnTqFSpEv7+/gQGBtKjR48cvSNXV1eWLl3Ks88+S0hICB07diQlJYWWLVtSu3Zt/P39GT16NGFheU8rOzs706NHD9auXUuPHj0AqFSpEgsXLmTQoEEEBwfTvHlzDh8+nOPc7t27m72W8uXLM2LECAIDA+ncubM5XHgjrq6uLF++nAkTJhASEkJoaKjpRKZOnUrTpk1p2bJlvoEft8J7773HP/7xD+rVq0fdunXNQIUPPvjA7DkfOnSIwMBAfHx8WLt2rfmwsG3bNr744gs2bdpkzkWtWWPkE0hPT+fYsWOEhxfotlm8EZFC2QBnDDXXOoArsAfwz6VcWWAL8BsQfrN6GzVqJJ892VtWPNFGtp/dLoELA2XghFfloI+vXIuMFBGRjAyLzHtqo/y28n+iubMcPHjQ3ibkitVqlQMHDkhUVJQcPXpUUlNT7W3SHSM+Pl5ERBITE6VRo0ayY8cOO1tUvGjZsqVcvnzZ3mYUOd9++61MmjQp12O5/Z8C0VJI9/vb3QqSfVsBjwB1RGSKUuo+4P9EJO++tkET4JiIHLfVswRD5fXGx9GpwBvA+AL4TcDWI3JxMntEFdONYY/MzNuJl1MR0RFzdwMWiwVnZ2eUUlSsWBFXV9cSl6D0iSee4ODBg6SkpDBkyJB8exd3I2+99RanT58ucd/7zcjIyGDcuHH2NuOOUJBcc+8BVowouSlAPPANkHu/9zrVgD+z7J8BmmYtYAsDryEi/1VK5emIlFJPAE8A3HfffVit1XB2ViSkJwBQMcNwRJlDc5k6RHoxa8lFsmQZrlWrFuXLl6dy5cr2NqtQ+Oqrr+xtQrEmazjz3UTWiElHpyCOqKmIhCmldgGIyGWllOvtXtgmOf42MPRmZUVkPjAfIDw8XCxWK85OTlxLN6LmvG0htJnBCqYOkRbEK5EkJydz6tQpEhIS8PT0zBZFp9FoHI+COKJ0pZQzxtqhTD0ia/6nABAD1MiyX932XiZlgUBgsy231/8Bq5RSESISTT5YLIKT0/UeUYW0DHB2xqlsWQDi41JQCspU0Deoksa5c+eIiYnB2dnZTFBaknLDaTR3IwVxRHOAFUBlpdS/gX7ApAKcFwXUV0rVxnBAA4GHMw+KyFXATHOslNoMPH8zJwRgFXBycTbmiEThlZaSLeHptbhkPCu44+zskNHpmlwQEZRSuLi44O3t7XAKlBqNJm8KIgOxSCm1A+iAIRXeS0RuqpUsIhlKqVHAOowIugUickApNQUjemPV3zXaYhWcnZ1JSEtArG54piTgnCXhabzWISoxpKWl8eeff+Lp6UmVKlWoWLGilmnQaEoYN+0y2KLkkoDvgVVAou29myIia0SkgYjUFZF/2957JTcnJCJtC9IbArBajVX011INmfDSKQk50vvoxayOjYhw/vx59u/fz5UrV7Idc3Z2JjQ0lMDAQB588MFsxw8cOED79u3x8fGhfv36TJ06NVva/bVr1xIeHo6/vz8NGzZ0qKijzDU1N0o55MWNCULvFCLC6NGjqVevHsHBwezcmXs+5OTkZNq0aXPbGbYLk9dff5169erh4+PDunXrci2zadMmwsLCCAwMZMiQIdmyd+TWDrGxsdkSsWpuTkHGrv6LIQfxX2AjcBywqyiJRcDZxZmrqUaPyC0x3gzdtqRbSbyaSlmdddthSUpK4tChQ2ZPKCAggCpVqpjHS5cuze7du9m/fz/e3t7MmzcPMG58mfIPf/zxB3v27OGXX37hvffeA4w8YqNGjeLLL7/k4MGDREdHU69evTtqe2FJDJw7d46oqCj27t2ba3qZomTt2rWmpMH8+fN5+umncy23YMEC+vTpU2CpdRHJlhaosDl48CBLlizhwIED/PDDD4wcOTKH07RarQwZMoQlS5awf/9+atasyWeffQbk3Q6VKlWiatWqJSIZaVFxU0ckIkEiEmz7Wx9jfVDOJE1FiIjC2dmZ+NR4sLpRKjEe5/KGI4q/lAKC7hEVBWsnwqfd7+y2diIWi4X09HTq1KlD/fr1803S2bx5czOb8VdffUXLli3p1KkTYOT+mjt3rqnZ8uabb/Kvf/3LXDHv7Oyc6000ISGBxx9/nKCgIIKDg/nmm2+A7D2M5cuXM3ToUMDIT/bUU0/RtGlTXnjhBWrVqpWtl1a/fn3Onz9PbGwsffv2pXHjxjRu3DjXG1VKSop57YYNGxIZGQlAp06diImJITQ0lJ9//jnbOefPn6d3796EhIQQEhKSI9VMQkICHTp0ICwsjKCgIDMDQ2JiIt27dyckJITAwECWLl0KGAk4/f39CQ4O5vnnn89h48qVKxk8eDBKKZo1a2aKC97IokWLzHQ2edlw8uRJfHx8GDx4MIGBgfz555/MmDGDxo0bExwczKuvvmrW16tXLxo1akRAQADz58/Pcb1bZeXKlQwcOBA3Nzdq165NvXr1cqQiiouLw9XVlQYNGgBGotrM30N+7dCrV688UwRpclKQYIVsiMhOpZTdAvczh1mcnF1ISE9EMlxxjr9qDs3FZ4Zu6zkih0EwEpRarVbcMHRXgoKCcHLK/znJYrGwceNGhg8fDuQua1C3bl0SEhK4du0a+/fvL9BQ3NSpUylXrhz79u0DDPXMm3HmzBl++eUXnJ2dsVgsrFixgscff5zff/+dmjVrUqVKFR5++GGee+457r//fk6fPk3nzp05dCj7dOu8efNQSrFv3z4OHz5Mp06dOHLkCKtWraJHjx65JjkdPXo0bdq0YcWKFVgsFhISErIdz5SY8PLy4uLFizRr1oyIiAh++OEH7r33Xv773/8CRr67uLg4VqxYweHDh1FK5RgWBUPaoEaN6wGxmdIGmamBwJjbO378OLVq1crXBoCjR4/y2Wef0axZM9avX8/Ro0fZvn07IkJERARbtmyhdevWLFiwAG9vb5KTk2ncuDF9+/blnnvuyWbbc889ZzrvrAwcODCHUGJMTIyZfT3r58hKxYoVycjIIDo6mvDwcJYvX24KC+bXDuHh4UyaVJCYLg0UwBEppbJm2nMCwgD75R23OSJnF2cS069ROsUdZbFkWUNkLGbVQ3NFQNfbV4fMlEW4du0aZcqUwcfHByfI1wklJycTGhpKTEwMfn5+Zjr/O8WGDRtYsmSJuV8hSzLdvHjooYfMIagBAwYwZcoUHn/8cZYsWWLKD2zYsCFbnrtr166Za6Ey2bp1K88++yxgaB/VrFmTI0eO5JsBe9OmTXz++eeA0cvLzEWXieQhMREUFMS4ceOYMGECPXr0oFWrVmRkZODu7s7w4cPp0aOHmfvtVrl48WK2TAd52QBkk+NYv34969evp2HDhoDRkzp69CitW7dmzpw5rFixAjAktI8ePZrDERV0/qygKKVYsmQJzz33HKmpqXTq1KlAQ40lRZ6hqChIj6hsltcZGHNF3xSOOTdHsPWIXEqRnJFE2WRDPTHrYlYnJ0WZ8noNUXHGarVy/vx5/vrrL5RS1KhRg8qVKxdoTVDmHFFSUhKdO3dm3rx5jB49Gn9/f7Zs2ZKt7PHjx/H09MTLy4uAgAB27NhBSEjI37I5q203SjRklXRo3rw5x44dIzY2lu+++858MrZarfz22293VA+oIOQlMdGgQQN27tzJmjVrmDRpEh06dOCVV15h+/btbNy4keXLlzN37lw2bdqUrb5q1aqZvQLIXdogqzxDfjZA9rYTEV588UWefPLJbPVt3ryZDRs28Ouvv+Lh4UHbtm1zlcm4lR5RQT4HGN9n5nDo+vXrzSzg+Z1fUuQZiop8xz5sC1nLishrtu3fIrJIRPIWSilszB6RC8mWRMomGR/BubzxFBh/MRlPbzecnPQix+JMWloaf/31F+XLlycwMJAqVarc8sJUDw8P5syZw1tvvUVGRgaPPPIIW7duZcOGDYDRcxo9ejQvvPACAOPHj+c///mPeSOxWq1m9uOsdOzY0QyAgOtDc1WqVOHQoUNYrVbzyTw3lFL07t2bsWPH4ufnZz61d+rUiXfffdcsl9swW6tWrcy5hSNHjnD69OkcWkY30qFDB95//32AXGUu8pKY+Ouvv/Dw8ODRRx9l/Pjx7Ny5k4SEBK5evUq3bt2YNWtWrtISERERfP7554gIv/32G+XKlcs2LAdGL9JisZjOIi8bbqRz584sWLDAHF6MiYnhwoULXL16lQoVKuDh4cHhw4f57bffcj1/1qxZuUo03OiEMj/HkiVLSE1N5cSJExw9epQmTZrkKHfhwgXAkBd54403eOqpp27aDiVFnqGoyNMRKaVcRMQCtCxCe25KZiSuk7MLadZkyhpZfsyoOSN0Wz+JFEfS09PNf2p3d3cCAwOpW7curq5/P2NUw4YNCQ4OZvHixZQuXZqVK1cybdo0fHx8CAoKonHjxowaNQqA4OBgZs+ezaBBg/Dz8yMwMJDjx4/nqHPSpElcvnyZwMBAQkJCzCfs6dOn06NHD1q0aJHjxnsjAwYM4MsvvzSH5QDmzJlDdHQ0wcHB+Pv75+oER44cidVqJSgoiAEDBrBw4cKbpjC6mcxFXhIT+/bto0mTJoSGhvLaa68xadIk4uPj6dGjB8HBwdx///25ynN369aNOnXqUK9ePUaMGGFGJd5Ip06d2Lp1a7425HbOww8/TPPmzQkKCqJfv37Ex8fTpUsXMjIy8PPzY+LEidnmdv4uAQEB9O/fH39/f7p06cK8efPMYbdu3bqZQ2szZszAz8+P4OBgHnzwQVNIML92iIyMpHv37rdt492CyrrGItsBpXbacsy9j5HA9GsgMfO4iHxbNCZmp2FIiDzqW4MObWryWJkttPglkH/+tJu6637AtWZNPn1hKzWD7qH9Y372MK/Ec+jQIfz8bq1tRYSLFy8SExODxWIhICCgyIenNEXPzp07mTVrFl988YW9TSlyWrduzcqVKws0v1gY5PZ/qpTaISLFUryoIHNE7kAcRvZtwciuIIBdHFGm40x3NoZxvA1RQ5zLlycjzULStTS8dKBCseHGBKU1a9bUTuguISwsjHbt2plSHXcLsbGxjB071m5OyBHJzxFVtkXM7ee6A8ok925UUSDGgrd0Wx65CmlWM+HplQuZEXP6RlccsFqtHDlyBBGhVq1a3HPPPTpB6V3GsGHD7G1CkVOpUiV69eplbzMcivwckTPgSXYHlIndHJHYHFGarUdUPtWCc7lyKCcnrl3Ua4iKA9euXaNs2bI4OTlRp04d3N3ddYJSjUaTJ/k5orMiMqXILCkotqG5VGcFAuVT083Q7XjbGiIdrGAf0tLSOH36NFeuXKFWrVpUrFiRsmXL3vxEjUZzV5OfIyqWYyhiy0WV5iRggbKpaThXNsJjr11MwdnFCQ+v29bt09wCIsKFCxfMVenVqlXD29vbzlZpNBpHIT9H1KHIrLgVbD2iFCfAAp4pKdclwuMM+Qel1xAVKSdOnODSpUuUK1eO++67TyumajSaWyLPdUQicqkoDSkomXNEqc6GQ/JITjbXEMXHJetAhSIiIyPDzFRcqVIlcz1FUTghLQNhXxmIw4cP07x5c9zc3Jg5c2ae5USE9u3bc+3atUKx407w2WefUb9+ferXr29m1b6R3bt306xZM0JDQwkPDzcTo86YMYPQ0FDzt+js7MylS5dIS0ujdevWhZaJvSTieBKmtptKElYQwT0pwcy8fS0uRQcqFAGXLl3iwIED5oK/smXL4u3tXWQRcVoGwr4yEN7e3syZMyfXzNxZWbNmDSEhIfnmybuRotQuunTpEq+99hq///4727dv57XXXss1we0LL7zAq6++yu7du5kyZUq2TB2ZmRtef/112rRpg7e3N66urnTo0MHMZq65ObecfdveZPaI4iUd91SFkyUD5/Joa6PRAAAgAElEQVTlSUvJICUhXfeICpFjx45x/vx5EhMT8fDw4LPTn/G/A/+7o9fw9fZlQpMJBS7fvHlz9u7dC+QtA9G2bVueeeaZW5KBePbZZ4mOjkYpxauvvkrfvn3x9PQ0U88sX76c1atXs3DhQoYOHYq7uzu7du2iZcuWfPvtt+zevdtM+lm/fn22bt2Kk5MTTz31FKdPnwZg9uzZtGyZPXFJSkoKTz/9NNHR0bi4uPD222/Trl27bDIQ7777Lq1atTLPOX/+PE899ZSZJeL999+nRYsW2T5Pz549uXz5Munp6UybNo2ePXuSmJhI//79OXPmDBaLhZdffpkBAwYwceJEVq1ahYuLC506dcrR66lcuTKVK1c2s3bnxaJFi3jiiSfM/V69evHnn3+SkpLCmDFjzGOenp48+eSTbNiwgXnz5nHy5EnmzJlDWloaTZs25b333jO/q6ioKJKTk+nXrx+vvfZavte/GevWraNjx47mfGbHjh354YcfGDRoULZySimzV3f16lXuvffeHHUtXrw423m9evXixRdf5JFHHrktG+8WHM4RZfaI4iWDsomlgAycK1TIIv+gI+YKg6+++ophw4axcuVK/P39qVSpEqWi7BuSrWUgDIpaBqKgbNu2jQ8//NDcz0vGITExkaZNm/LWW29x6NAh3njjDbZt20apUqUYOXIkixYtYvDgwfz73//G29sbi8VChw4d2Lt3L8HBwdmuOWPGjFx1gDKzd2clLxmHG5k9ezadO3fm+eefx2q15tB7SkpK4ocffmDu3Lnme4GBgURFRd1ag93FOJwjyuwRJUgGnomlgGScK5Tnis0RldWCeHeU9PR0SpUqRXh4OP369aNatWpUrlwZ4JZ6LncSLQORneIoAwHG0FfW8P28ZBycnZ3p27cvABs3bmTHjh00btwYML7rzN/bsmXLmD9/PhkZGZw9e5aDBw/mcETjx49n/Pjxf9vm3Hj//feZNWsWffv2ZdmyZQwfPtxMrAvw/fff07Jly2yRos7Ozri6uhIfH6+XMBQAh50jiremUTbJ8KPO5cubOkS6R3RnuHDhAo899ph5E23QoAFffvllsUjVkjlHdOrUKUTEnCPy9/dnx44d2crmJgPxd/m7MhB9+vQBrstAZM4rxMTEFFpAQVaySjDs3r2bKlWqZJOBCAoKYtKkSUyZMgUXFxe2b99Ov379WL16NV26dPnb13VxcTGlv7PKOOzZs4eGDRuabeju7m7+rkSEIUOGmG30xx9/MHnyZE6cOMHMmTPZuHEje/fupXv37rnKQGQNIMi6jR49OkfZgspAfPbZZ+Z3+NBDD+VQcV2yZEmO4TwwsnXrdFYFw+EcUWYEVAJpeNkkIFwqVOBaXAoupZwoXVav4L8drFYr8+fPx8fHh6VLlxIQEFCkE8i3gpaBMChqGYiC4uPjY85bFVTGoUOHDixfvtzM0n7p0iVOnTplCieWK1eO8+fPs3bt2lzPzxpAkHW7cVgODMmJ9evXc/nyZS5fvsz69evp3LlzjnL33nsvP/30E2D0PuvXr28eu3r1Kj/99JMpiZ5JXFwcFStW1BlFCojDOqJEayqephZReeIz1xDpXGZ/m+PHj3P//ffz5JNPEhoayt69e5k6dWqx6AXlhZaBKHoZiHPnzlG9enXefvttpk2bRvXq1XMN0e7evTubN28GKLCMg7+/P9OmTaNTp04EBwfTsWNHzp49S0hICA0bNsTX15eHH344R5DH38Hb25uXX36Zxo0b07hxY1555RVzeO0f//gH0dHRAHz00UeMGzeOkJAQXnrpJebPn2/WsWLFCjp16pStRwxaBuJWyVMGorjiV/Ne+UezMH7onkardak8tOsivvv3sez1aMqUc6PHqL+nvqkxnuJatWrFxIkTeeyxx3J16n9HBkJzd3L27FkGDx7Mjz/+aG9Tipw+ffowffp0GjRoYJfrO5oMhOP2iEihbDI4lS+PcnIye0SaW2PVqlX06dMHi8XCPffcw/79+xk8eLDuWWpum6pVqzJixIhivaC1MEhLS6NXr152c0KOiMM5IkRQCGmk4pVixaV8eVKTM0hNytCBCrfA6dOn6dWrFz179uTIkSOcPXsWACcnx/tJaIov/fv3v6UFrSUBV1dXBg8ebG8zHAqHu+uICM7KSpqkUC7ValtDpHWICkpGRgYzZ87Ez8+P9evX88Ybb7Br1y6qV69ub9M0Gs1disOtIwLBSQmCFa+UDJwrlL+uQ6TXEN0Ui8XCxx9/TPv27Xn33XepVauWvU3SaDR3OQ7ZI1LKmCcqm5JuRsyBXkOUF5cvX2bChAnEx8fj5ubGtm3bWLVqlXZCGo2mWOBwjgjBcEQieCanGmuILiZTys0ZtzIO2MErRESERYsW4evry1tvvWWGIWvJbo1GU5xwOEckIignwT0NXKxWnMsbi1m9Kuo1RFk5cuQIHTt25NFHH6VWrVpER0cTERFhb7PuCFoGwr4yEIsWLSI4OJigoCBatGiR56LXkiIDsWfPHpo3b05QUBAPPvhgts+zd+9emjdvTkBAAEFBQWa2hwceeKBAOQo1NkTEobZ6/+ct8x57QNrNDpCDPr5yefk3snjK77J63h7RXKdr165Srlw5ee+99yQjI+OO1Xvw4ME7VtffpUyZMubrwYMHy7Rp00REJCkpSerUqSPr1q0TEZHExETp0qWLzJ07V0RE9u3bJ3Xq1JFDhw6JiEhGRoa89957d9S29PT0O1pfJmfPnpW6deve0jlZ2+lOsm3bNrl06ZKIiKxZs0aaNGmSa7nVq1fLP//5z1uq+07+Vm9GXFyc1K5dW+Li4uTSpUtSu3Zt83NlJTw8XDZv3iwiIp988olMmjRJRIzvOigoSHbv3i0iIhcvXjTtX7hwofm7tAe5/Z8C0VIM7uG5bQ43liUC4iR4JRn7TrY8c9UalLevYcWAH3/8EV9fX2rUqMH777+Pm5sb//d//1do1zv3n/+QeujwHa3Tzc+X/3vppQKX1zIQRS8DkbXuZs2acebMmVy/m5IiA3HkyBFat25tluncuTNTp05l/fr1BAcHExJiLKLPTOUEEBERQatWrfjXv/51WzbeLRSqI1JKdQHeAZyBj0Vk+g3HxwL/ADKAWGCYiJzKv1ZBnMAz2RhusZQuR3rK1bs6dPvcuXOMHTuWxYsX88wzzzB37lxq1qxpb7MKHS0DYWBPGYhPPvmErl275nqspMhABAQEsHLlSnr16sXXX39tJko9cuQISik6d+5MbGwsAwcONPMaVqhQgdTUVOLi4rI5KE3uFJojUko5A/OAjsAZIEoptUpEsibC2gWEi0iSUupp4E1gQM7ariMiWJ3Ay1g6RJIqA1y9KyPmMhOUTpw4keTkZF599VUmTpxYZNe/lZ7LnUTLQGTHXjIQkZGRfPLJJ2zdujXX4yVFBmLBggWMHj2aqVOnEhERgaurK2Csydu6dStRUVF4eHjQoUMHGjVqRIcOHQBDQPCvv/7SjqgAFGawQhPgmIgcF5E0YAmQLUWtiESKiG2Qjd+Am6+qFLAq8Mx0RBlGQsi7UYfo9ddf5+mnn6ZRo0bs3buXyZMn3xVp57UMxK1RGDIQe/fu5R//+AcrV67M80ZbUmQgfH19Wb9+PTt27GDQoEHUrVsXMHpQrVu3pmLFinh4eNCtWzd27txpnpeSkkLp0nffA/LfoTAdUTXgzyz7Z2zv5cVwINfc7kqpJ5RS0UqpaKvVisUJyiaBODkRn2TcHLzukqG5+Ph4Tpw4AcBTTz3FokWL2LBhw02lAkoiWgbCoKhlIE6fPk2fPn344osv8s2nVlJkIDJtsVqtTJs2jaeeeso8f9++fSQlJZGRkcFPP/2Ev78/YDjUc+fO6bV6BaRYhG8rpR4FwoEZuR0XkfkiEi4i4UopLE7gleSEeHqRcCkVNw8X3DxKtu6HiLBixQr8/f0ZMGAAIsI999zDww8/fFeHrWsZiKKXgZgyZQpxcXGMHDmS0NBQwsNzT+hcUmQgFi9eTIMGDfD19eXee+/l8ccfB4wh27Fjx9K4cWNCQ0MJCwszpR927NhBs2bNcHFxuHgwu1BoMhBKqebAZBHpbNt/EUBEXr+h3APAu0AbEblws3prVyovI/qHU+XPWFqk3Mv/ek4n8UoqA/7VpBA+RfHg1KlTjBo1itWrVxMcHMyHH36Y5z9yYaNlIDQF5W6WgRgzZgwRERHmfFFR42gyEIXprqOA+kqp2kAMMBB4OGsBpVRD4EOgS0GcEAAC6U5C2WRwrlCOaxdTqFDF4w6bXnz49ddfeeCBBwCYOXMmY8aM0U9ZGocgqwzE3ZaBOzAw0G5OyBEptKE5EckARgHrgEPAMhE5oJSaopTKXOI/A/AEvlZK7VZKrbppvQgZCrySBRdvb+Ljkktk6Hbm6u2wsDCGDRvGoUOHGDdunHZCGofibpSBABgxYoS9TXAoCvWuJiJrgDU3vPdKltcP/J16050Ez2RBVahCxmVriXJEcXFxTJw4kfXr13PgwAE8PT2zTXBrNBpNSaNYBCvcCiKQ5iR4JVtILVMJAK+Kjh8iKSLmRPKnn37KgAED7uogBI1Gc/fgkOM8VhFKWYVkVyPCxdFDt69evUqvXr3YvHkzzZs354MPPsixUE+j0WhKKg7niATAtkguyclYDOioQ3MiglIKLy8vKlasyPz58xk+fLiW69ZoNHcVjnfHE1BWI+Q8STxwL1MKV3eH86esW7eOsLAwzpw5g1KKr7/+mhEjRmgnVAC0DIR9ZSBWrlxJcHCwuYYorxQ/ycnJtGnTBovFUih23Alef/116tWrh4+PD+vWrcu1zMaNGwkLCyM0NJT777+fY8eOAcbC3nbt2plr2dasMabD9+3bx9ChQ4vqI5QM7J3++1a36hW85PmnW8pBH19ZMfUnWfaf7eJI/PXXXzJgwAABpEGDBrJjxw57m3RLaBmI/LkbZCDi4+PFarWKiMiePXvEx8cn13Jz586V2bNnF7heq9UqFovljthYEA4cOCDBwcGSkpIix48flzp16uQqQ1G/fn3zdz9v3jwZMmSIiIiMGDHC/P0cOHBAatasaZ7ToUMHOXXqVKF/hrzQMhCFjqJUhvGEm5CkqFjLcYbl5s2bx0svvURqaiqvvfYaEyZMuOmq+eLMz8uOcPHPhJsXvAUq1vCkVf+808bciJaBKHoZiKw9rcTExDyDahYtWsRXX32Vrw0nT56kc+fONG3alB07drBmzRqWLVvGsmXLSE1NpXfv3qbcQ14yEn+XlStXMnDgQNzc3Khduzb16tVj+/btNG/ePFs5pZS5nOLq1avce++9+b4P8OCDD7JkyRIzvZQmfxzQEYFrhiAoEq5lUNuBsm7v2LGDpk2bMm/ePOrXr29vcxweLQNhYA8ZiBUrVvDiiy9y4cIF8/yspKWlcfz4cTPXWl42ABw9epTPPvuMZs2asX79eo4ePcr27dsRESIiItiyZQutW7fOU0YiK88995yZkikrAwcOzJGZPiYmJluGkrxkID7++GO6detG6dKl8fLyMvPkTZ482cwfmJiYaOY4BAgPD2f69OnaERUQh3REbulCipsXlgwp1hFz165d45VXXuGxxx6jUaNGvPfee7i5uZWYsOxb6bncSbQMRHbsIQPRu3dvevfuzZYtW3j55Zez3YQBLl68aPYI87MBoGbNmqZDWL9+PevXr6dhw4aA0ZM6evSoqSeUm4xEVgo6f3YrzJo1izVr1tC0aVNmzJjB2LFj+fjjj1m8eDFDhw5l3Lhx/Prrrzz22GPs378fJycnUwJCUzAccmbcLV2IL290g4tjxJyIsHz5cvz8/JgzZw4//fQTYDwVlhQnZE+0DMStURgyEJm0bt2a48ePc/HixWzvly5dOlsb5WUDZG87EeHFF1802+jYsWMMHz48XxmJrDz33HO5ykBMnz49R9mCyEDExsayZ88emjZtChgPGb/88gtgiAL2798fML7zlJQUsx20BMSt4ZCOqHSakORlZD8ubotZT5w4QY8ePXjooYeoXLkyv/76K2PHjrW3WSUSLQNhUNQyEMeOHTMjEXfu3ElqamqOnkmFChWwWCyms8jLhhvp3LkzCxYsMIcXY2JiuHDhQoFlJGbNmpWrDERugpEREREsWbKE1NRUTpw4wdGjR2nSJHvy5AoVKnD16lXzN/Pjjz+ayUTvu+8+Nm7cCBhJRlNSUqhUyVhkf+TIEQIDA3O1UZML9o6WuNWteoVy8kmPJrJi4BSZ++RGSUvNGeViT6ZOnSqenp4ya9asQougsifFLWpORKRHjx7y+eefi4jI3r17pU2bNtKgQQOpW7euTJ482YzwEhH5/vvvJSwsTHx9fcXPz0/Gjx+fo/74+HgZPHiwBAQESHBwsHzzzTciIvL1119LnTp1pGnTpvLMM8+Y0VNDhgyRr7/+OlsdUVFRAsjChQvN92JjY6V///4SFBQkfn5+8uSTT+a4dnJysgwdOlQCAwMlNDRUNm3aJCIiJ06ckICAgFzb49y5cxIRESGBgYESEhIiv/zyS7Z2io2NlWbNmklgYKAMHTpUfH195cSJE/LDDz9IUFCQhISESHh4uERFRclff/0ljRs3lqCgIAkMDMxmfybTp08Xf39/CQkJkWbNmsnPP/+cq13Dhg2TH3/8MV8bcvtcs2fPlsDAQAkMDJRmzZrJsWPHJCUlRbp06SK+vr7Ss2dPadOmjURGRuZ63Vth2rRpUqdOHWnQoIGsWbPGfL9r164SExMjIiLffvutBAYGSnBwsLRp00b+97//iYgRKdeiRQsJDg6WkJAQM1pTROSZZ56RVatW3bZ9fxdHi5orNBmIwqKGd3l5s3F9nKsPJ+GeQIa9eb+9TeLnn38mNTWVBx54gNTUVGJjY6le/eZis46IloHQFJSdO3cya9YsvvjiC3ubUqSkpqbSpk0btm7darckxY4mA+GQQ3MeKUKaawW7BypcvHiRYcOG0bp1a6ZMmQKAm5tbiXVCGs2tEBYWRrt27Yr1gtbC4PTp00yfPl1nyr8FHNMRpUKak6fdHJGI8Omnn+Lr68sXX3zBhAkT8pQu1mjuZoYNG2ZGE94t1K9fn7Zt29rbDIfCIV12KQukWN0pa6c1RGvWrGHYsGG0bNmSDz74QE9KajQazW3gkD2iDBcPRFSRhm4nJSWxbds2ALp168bKlSvZsmWLdkIajUZzmzimI3I1Fvd5VSwaR7R27VoCAwPp2rUrV65cQSlFRESETlCq0Wg0dwCHvJOmlbI5okIemouJieGhhx6iW7duuLm58f3332dbLa7RaDSa28chHVG6q+EMynoXXo/owoUL+Pv7s3r1aqZNm8aePXto06ZNoV1PU3C0DIR9ZSAyiYqKwsXFheXLl+d6vKTIQGzatImwsDACAwMZMmQIGRkZgLHQuXfv3gQHB9OkSRP2798PGHn2WrdubZbTFAB7L2S61a16hXLyVc8XZcH4Lfku6Pq7nDlzxnz9zjvvyLFjxwrlOo5KcVvQqmUg8qawZCBEjLZr166ddO3aNcdi3kxKggyExWKR6tWryx9//CEiIi+//LJ8/PHHIiLy/PPPy+TJk0VE5NChQ9K+fXvzvMmTJ8uXX35ZRJ8kJ462oNUho+bS3Crc8dQ+V69eZdKkSXz44Yf89ttvhIWFMXr06Dt6jZJG5ML5XDh1/I7WWblmHdoNLXh6fy0DUfQyEADvvvsuffv2JSoqKs/vpiTIQMTFxeHq6kqDBkaC344dO/L6668zfPhwDh48aKYO8vX15eTJk5w/f54qVarQq1cvXnzxRR555JHbsvFuwSEdUYqbN/feIUckInz99df885//5Ny5c4waNYq6devekbo1hYuWgTAoahmImJgYVqxYQWRkZJ6OqKTIQFSsWJGMjAyio6MJDw9n+fLlZqLUkJAQvv32W1q1asX27ds5deoUZ86coUqVKgQGBubrpDXZcUhHlO5W/o6EbosIffr04bvvviMsLIxVq1YRHl4sM2AUS26l53In0TIQ2SlqGYh//vOfvPHGG/lGjZYUGQilFEuWLOG5554jNTWVTp06md/zxIkTGTNmDKGhoQQFBdGwYUPzmLOzM66ursTHx1O2bNk7alNJxCEdEarUbQ3NpaenU6pUKZRS3H///bRv356RI0fedSvAHZVMGYikpCQ6d+7MvHnzGD16NP7+/mzZsiVb2dxkIEJCQv7Wdf+uDMSkSZOA6zIQ7u5FmxEkqwRDqVKlqFWrVjYZiDVr1jBp0iQ6dOjAK6+8wvbt29m4cSPLly9n7ty5bNq0KVt90dHRDBw4EDAczpo1a3BxcaFXr15mmfxkILLaALnLQDz55JPZrplVBsLDw4O2bdvmKQNR0B5RQWQgwPg+f/75Z8BwlJmZuL28vPj0009Nu2vXrk2dOnXM81JTU4v8u3ZY7D1Jdatb9Qrl5N0Rq+X0obi8Z+ryITIyUnx9feW77777W+ff7RS3YIWdO3fKfffdJ+np6ZKUlCS1a9c2Mz4nJSVJ9+7dZc6cOSIismfPHqlbt6458WyxWOT999/PUf+ECRNkzJgx5v6lS5dERKRu3bpy8OBBsVgs0qdPn3yzbz///PPy6KOPSteuXc33Bg0aJG+++aa5v2vXrhzXfuutt2TYsGEiIvLHH3/IfffdJykpKflm3x4wYIDMmjVLRIwggitXrmRrp9mzZ8uoUaNERGTTpk0CyIkTJyQmJkaSk5NFxMhK3rNnT4mPj5fz58+LiMiVK1fE29s712tmkttnz6R69epm/XnZcOPnWrdunTRp0kTi4+NFxAgeOn/+vHz33XfSo0cPETECA9zc3G47+/b+/fuzBSvUrl07R7CCiJjtkZKSIu3bt5eNGzeKiMjly5clNTVVRETmz58vjz32mHnOxYsXxcfH57bsux0cLVjBIcO3Uc63vIYoNjaWIUOG0K5dO1JTU3V3uYTQsGFDgoODWbx4MaVLl2blypVMmzYNHx8fgoKCaNy4MaNGjQIgODiY2bNnM2jQIPz8/AgMDDQn+LMyadIkLl++TGBgICEhIeYT9vTp0+nRowctWrSgatWq+do1YMAAvvzyS3NYDmDOnDlER0cTHByMv79/rlpII0eOxGq1EhQUxIABA1i4cCFubm75Xuudd94hMjKSoKAgGjVqlG34D+CRRx4hOjqaoKAgPv/8czNYY9++fTRp0oTQ0FBee+01Jk2aRHx8PD169CA4OJj777+ft99+O99r50enTp3YunVrvjbkds7DDz9M8+bNCQoKol+/fsTHx9OlSxcyMjLw8/Nj4sSJ2eZ2/i4BAQH0798ff39/unTpwrx588xRkW7dupkKqzNmzMDPz4/g4GAefPBB2rdvDxgZrgMDA/Hx8WHt2rW88847Zt2RkZF07979tm28a7C3J7zVrXqFcvLuE2slI6PgYZ5fffWVVKhQQUqVKiUvvfSSJCYmFvhcTXaKQ49I4xjs2LFDHn30UXubYRd69+5t9rztgaP1iBxyjsjZKQ1n54J35jIyMggMDOSDDz7A39+/EC3TaDSZZJWBuJvmX9PS0ujVq5cZ8q25OQ45NOfqbs33eGJiIhMnTuS9994D4NFHH+Wnn37STkijKWLuRhkIV1dXBg8ebG8zHAqHdETunirPY6tXryYgIIA33njDjG5RSmWLeNLcHkYvX6PRFEcc8f/TAR2RwjOXHHNnzpyhT58+PPjgg5QpU4YtW7Ywe/ZsO9hXsnF3dycuLs4hf+waTUlHRIiLi3O4sHEHnCNS3FO1XI53jx8/zrp163j99dcZO3Ysrq6udrCt5FO9enXOnDlDbGysvU3RaDS54O7uTvXq1e1txi3hgI4IqtauAsD27dv59ddfGTNmDK1bt+b06dM5Vlpr7iylSpWidu3a9jZDo9GUIAp1aE4p1UUp9YdS6phSamIux92UUkttx39XStUqQK2U8nZl5MiRNGvWjLfffpvExEQA7YQ0Go3GASk0R6SUcgbmAV0Bf2CQUurGsLXhwGURqQfMAt64Wb1Jaak0adWIDz/8kNGjR7Nv375sKUI0Go1G41gU5tBcE+CYiBwHUEotAXoCWZd99wQm214vB+YqpZTkMxN+KTGRcD8/1qxZQ1hYWOFYrtFoNJoiozAdUTXgzyz7Z4CmeZURkQyl1FXgHuBi1kJKqSeAzFTPqdHR0ftvTPd/l1KRG9rqLka3xXV0W1xHt8V1fOxtQF44RLCCiMwH5gMopaJFRGs1oNsiK7otrqPb4jq6La6jlIq2tw15UZjBCjFAjSz71W3v5VpGKeUClAPiCtEmjUaj0RQzCtMRRQH1lVK1lVKuwEBg1Q1lVgFDbK/7AZvymx/SaDQaTcmj0IbmbHM+o4B1gDOwQEQOKKWmYGSBXQV8AnyhlDoGXMJwVjdjfmHZ7IDotriObovr6La4jm6L6xTbtlC6A6LRaDQae+KAueY0Go1GU5LQjkij0Wg0dqXYOqLCSQ/kmBSgLcYqpQ4qpfYqpTYqpWraw86i4GZtkaVcX6WUKKVKbOhuQdpCKdXf9ts4oJT6qqhtLCoK8D9yn1IqUim1y/Z/0s0edhY2SqkFSqkLSqn9eRxXSqk5tnbaq5QqHlkB7C0Rm9uGEdzwP6AO4ArsAfxvKDMS+MD2eiCw1N5227Et2gEettdP381tYStXFtgC/AaE29tuO/4u6gO7gAq2/cr2ttuObTEfeNr22h84aW+7C6ktWgNhwP48jncD1gIKaAb8bm+bRaTY9ojM9EAikgZkpgfKSk/gM9vr5UAHVTLV727aFiISKSJJtt3fMNZslUQK8rsAmIqRtzClKI0rYgrSFiOAeSJyGUBELhSxjUVFQdpCAC/b63LAX0VoX5EhIlswIpDzoifwuRj8BpRXSlUtGuvyprg6otzSA1XLq4yIZACZ6YFKGgVpi6wMx3jiKYnctOd61lAAAAXsSURBVC1sQw01ROS/RWmYHSjI76IB0EAptU0p9ZtSqkuRWVe0FKQtJgOPKqXOAGuAZ4vGtGLHrd5PigSHSPGjKRhKqUeBcKCNvW2xB0opJ+BtYKidTSkuuGAMz7XF6CVvUUoFicgVu1plHwYBC0XkLaVUc4z1i4EiYrW3YZri2yPS6YGuU5C2QCn1APAvIEJEUovItqLmZm1RFggENiulTmKMga8qoQELBfldnAFWiUi6iJwAjmA4ppJGQdpiOLAMQER+BdwxEqLebRToflLUFFdHpNMDXeembaGUagh8iOGESuo8ANykLUTkqohUFJFaIlILY74sQkSKbbLH26Ag/yPfYfSGUEpVxBiqO16URhYRBWmL00AHAKWUH4Yjuhv17lcBg23Rc82AqyJy1t5GFcuhOSm89EAORwHbYgbgCXxti9c4LSIRdjO6kChgW9wVFLAt1gGdlFIHAQswXkRK3KhBAdtiHPCRUuo5jMCFoSXxwVUptRjj4aOibT7sVaAUgIh8gDE/1g04BiQBj9vH0uzoFD8ajUajsSvFdWhOo9FoNHcJ2hFpNBqNxq5oR6TRaDQau6IdkUaj0WjsinZEGo1Go7Er2hFpiiVKKYtSaneWrVY+ZRPuwPUWKqVO2K6107b6/lbr+Fgp5W97/dINx365XRtt9WS2y36l1PdKqfI3KR9aUjNNa0oOOnxbUyxRSiWIiOedLptPHQuB1SKyXCnVCZgpIsG3Ud9t23SzepVSnwFHROTf+ZQfipGBfNSdtkWjuVPoHpHGIVBKedq0lnYqpfYppXJk3VZKVVVKbcnSY2hle7+TUupX27lfK6Vu5iC2APVs54611bVfKfVP23tllFL/VUrtsb0/wPb+ZqVUuFJqOlDaZsci27EE298lSqnuWWxeqJTqp5RyVkrNUEpF2XRinixAs/yKLWGlUqqJ7TPuUkr9opTysWUZmAIMsNkywGb7AqXUdlvZ3LKXazRFi711KPSmt9w2jEwAu23bCowsIF62YxUxVoZn9ugTbH/HAf+yvXbGyD1XEcOxlLG9PwF4JZfrLQT62V4/BPwONAL2AWUwMlccABoCfYGPspxbzvZ3Mzb9o0ybspTJtLE38JnttStGJuTSwBPAJNv7bkA0UDsXOxOyfL6vgS62fS/Axfb6AeAb2+uhwNws5/8HeNT2ujxG/rky9v6+9XZ3b8UyxY9GAySLSGjmjlKqFPAfpVRrwIrRE6gCnMtyThSwwFb2OxHZrZRqgyGEts2W/sgVoyeRGzOUUpMwcpANx8hNtkJEEm02fAu0An4A3lJKvYExnPfzLXyutcA7Sik3oAuwRUSSbcOBwUqpfrZy5TASlJ644fzSSqndts9/CPgxS/nPlFL1MVLYlMrj+p2ACKXU87Z9d+A+W10ajV3QjkjjKDwCVAIaiUi6MrJru2ctICJbbI6qO7BQKfU2cBn4UUQGFeAa40VkeeaOUqpDboVE5IgydI+6AdOUUhtFZEpBPoSIpCilNgOdgQEYIm5gKGY+KyLrblJFsoiEKqU8MHKrPQPMwRADjBSR3rbAjs15nK+AviLyR0Hs1WiKAj1HpHEUygEXbE6oHVDzxgJKqZrAeRH5CPgYQzL5N6ClUipzzqeMUqpBAa/5M9BLKeWhlCqDMaz2s1LqXiBJRL7ESDgblsu56baeWW4sxUg2mdm7AsOpPJ15jlKqge2auSKGIu9oYJy6LoOSmc5/aJai8RhDlJmsA55Vtu6hMjK3azR2RTsijaOwCAhXSu0DBgOHcynTFtijlNqF0dt4R0RiMW7Mi5VSezGG5XwLckER2Ykxd7QdY87oYxHZBQQB221DZK8C03I5fT6wNzNY4QbWY4gXbhBD2hoMx3kQ2KmU2o8h65HviIXNlr0Yom9vAq/bPnvW8yIB/8xgBYyeUymbbQds+xqNXdHh2xqNRqOxK7pHpNFoNBq7oh2RRqPRaOyKdkQajUajsSvaEWk0Go3GrmhHpNFoNBq7oh2RRqPRaOyKdkQajUajsSv/D8iVdaOw8BjrAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **13 - CONCLUSION**"
      ],
      "metadata": {
        "id": "72nq3KEOYUVN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **In Model Training From Scratch Phase:**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "*   Initial Learning rate was ***0.001***\n",
        "*   The Include Top was set to ***True***\n",
        "*   Our Model shape is ***(224, 224, 3)***\n",
        "*   Weights are set to ***NONE***\n",
        "*   We used the Optimizer ***Adam***\n",
        "*   Our Loss Function was ***Sparse Categorical Crossentropy***\n",
        "*   Batch Size was ***32***\n",
        "*   Epoch Size was ***40***\n",
        "\n",
        "**Results:**\n",
        "\n",
        "\n",
        "> *Train Accuracy:* **83%**\n",
        "\n",
        "> *Validation Accuracy:* **76%**\n",
        "\n",
        "> *Test Accuracy:* **78%**\n",
        "\n",
        "#### **In Transfer Learning Phase:**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "* Our Model shape is ***(224, 224, 3)***\n",
        "* The Include Top was set to ***False***\n",
        "* Weights is set to ***ImageNet***\n",
        "* We used the Optimizer ***Adam***\n",
        "* Learning Rate was updated to ***0.0001***\n",
        "* We used ***Average Pooling***\n",
        "* Our Loss Function was ***Sparse Categorical Crossentropy***\n",
        "* We Used Regularization ***Dropout of 20%***\n",
        "* Batch Size was ***32***\n",
        "* Epoch Size was ***25***\n",
        "\n",
        "**Results:**\n",
        "\n",
        "\n",
        "> *Train Accuracy:* **76%**\n",
        "\n",
        "> *Validation Accuracy:* **71%**\n",
        "\n",
        "> *Test Accuracy:* **77%**\n",
        "\n",
        "#### **In Unfreezing Model Phase:**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "* We used ***Batch Normalization***\n",
        "* We used the Optimizer ***Adam***\n",
        "* Our Loss Function was ***Sparse Categorical Crossentropy***\n",
        "* Learning Rate was set to ***0.0001***\n",
        "* Batch Size was ***32***\n",
        "* Epoch Size was ***15***\n",
        "\n",
        "**Results:**\n",
        "\n",
        "\n",
        "> *Train Accuracy:* **90%**\n",
        "\n",
        "> *Validation Accuracy:* **81%**\n",
        "\n",
        "> *Test Accuracy:* **87%**"
      ],
      "metadata": {
        "id": "eEZBZkNyYbiD"
      }
    }
  ]
}