final_project_colab_training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLJNxNFXFbcl"
      },
      "source": [
        "# Histopathologic Cancer Detection (Kaggel Project)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4UIE7Pxc_XZ"
      },
      "source": [
        "## Load data for Google Colab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvPC8W8wJvOo"
      },
      "source": [
        "## Step 1: Brief description of the problem and data\n",
        "\n",
        "### Problem Description\n",
        "The goal of this project is to train a model that is capable of identifying metastatic cancer in small image patches taken from larger digital pathology scans [1].\n",
        "\n",
        "### Data\n",
        "The PatchCamelyon (PCam) benchmark is an image classification dataset containing 327,680 96x96 color images from histopathologic lymph node scans, each labeled with a binary indication of metastatic tissue. It serves as a middle-ground benchmark for machine learning models, larger than CIFAR-10 but smaller than ImageNet, and is trainable on a single GPU. It has been slightly modified for Kaggle. [1], [2]\n",
        "\n",
        "The Kaggel data contains two folders with train and test images:\n",
        "- 219995 images for training\n",
        "- 57428 images for testing.\n",
        "\n",
        "It also contains the labels for the training labels.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39ebk20mJvOq"
      },
      "source": [
        "### Data Handling on Colab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMd7sxpyQrZ3",
        "outputId": "c0d0633d-4b93-4cb1-8b56-556aca40349b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['.config', 'train_labels.csv', 'drive', 'test', 'sample_submission.csv', 'train', 'histopathologic-cancer-detection.zip', 'sample_data']\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "print(os.listdir('/content'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FhjIcAWWFre5",
        "outputId": "3c825e65-6bb2-4f43-ef12-fec5d83381ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAl9igNqF5N3",
        "outputId": "682fdea2-99ee-4289-eaad-22e97da491d9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['sample_submission.csv',\n",
              " 'histopathologic-cancer-detection.zip',\n",
              " 'train_labels.csv',\n",
              " 'histopathologic-cancer-detection',\n",
              " 'submission.csv',\n",
              " 'final-project-colab.ipynb',\n",
              " 'final_project_colab_training.ipynb',\n",
              " 'final_project_colab_training-1.ipynb']"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "path = '/content/drive/MyDrive/DTSA-Project/'\n",
        "drive_contents = os.listdir(path)\n",
        "drive_contents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPWCqELCRcR1",
        "outputId": "dd0d5844-7b81-4487-e7a7-04374a644c0e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "File copied successfully.\n"
          ]
        }
      ],
      "source": [
        "import shutil\n",
        "\n",
        "# Copy the file from Google Drive to Colab\n",
        "source_path = path + 'histopathologic-cancer-detection.zip'\n",
        "destination_path = '/content/histopathologic-cancer-detection.zip'\n",
        "\n",
        "shutil.copy(source_path, destination_path)\n",
        "print(\"File copied successfully.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xCndx2oKRuGE",
        "outputId": "a940f2c6-0e3e-4c9d-8694-478385d1e9e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Zip file extracted successfully.\n"
          ]
        }
      ],
      "source": [
        "import zipfile\n",
        "\n",
        "# Unpack zip file\n",
        "destination_folder = '/content/'\n",
        "with zipfile.ZipFile(destination_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(destination_folder)\n",
        "\n",
        "print(\"Zip file extracted successfully.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E48jjNilFbcq"
      },
      "source": [
        "### Imports and checks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "NlhOIpqgFbcr"
      },
      "outputs": [],
      "source": [
        "# Core Libraries\n",
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Image Handling\n",
        "from PIL import Image\n",
        "\n",
        "# TensorFlow and Keras\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import EfficientNetB0, MobileNetV2\n",
        "from tensorflow.keras.layers import (\n",
        "    Dense, GlobalAveragePooling2D, Dropout, Conv2D,\n",
        "    MaxPooling2D, Flatten, BatchNormalization\n",
        ")\n",
        "from tensorflow.keras.models import Model, Sequential, clone_model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.metrics import Precision\n",
        "\n",
        "# Device Management\n",
        "from tensorflow.python.client import device_lib\n",
        "\n",
        "# Keras\n",
        "import keras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXrXDexfFbcs",
        "outputId": "b7696a78-c4b8-4e8a-e0fa-58bde38b3f2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3.5.0\n",
            "2.17.1\n",
            "[name: \"/device:CPU:0\"\n",
            "device_type: \"CPU\"\n",
            "memory_limit: 268435456\n",
            "locality {\n",
            "}\n",
            "incarnation: 5754853645924336277\n",
            "xla_global_id: -1\n",
            ", name: \"/device:GPU:0\"\n",
            "device_type: \"GPU\"\n",
            "memory_limit: 40427651072\n",
            "locality {\n",
            "  bus_id: 1\n",
            "  links {\n",
            "  }\n",
            "}\n",
            "incarnation: 12206192955971910474\n",
            "physical_device_desc: \"device: 0, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:00:04.0, compute capability: 8.0\"\n",
            "xla_global_id: 416903419\n",
            "]\n",
            "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
          ]
        }
      ],
      "source": [
        "# Check Keras version\n",
        "print(keras.__version__)\n",
        "\n",
        "# Check Tensorflow version\n",
        "print(tf.__version__)\n",
        "\n",
        "# Check GPU\n",
        "print(device_lib.list_local_devices())\n",
        "print(tf.config.list_physical_devices('GPU'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMjlBK0FFbcu"
      },
      "source": [
        "## Step 2: EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oGdBVmIFFbcu"
      },
      "source": [
        "Sample images and class distribution."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gue1na5aFbcu"
      },
      "source": [
        "### Load labels and connect to file names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wz2J9A3bFbcv",
        "outputId": "2990de62-58ce-410e-f8a2-1d1b53cb7519"
      },
      "outputs": [
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              "  </div>\n"
            ],
            "text/plain": [
              "                                         id label  \\\n",
              "0  f38a6374c348f90b587e046aac6079959adf3835     0   \n",
              "1  c18f2d887b7ae4f6742ee445113fa1aef383ed77     1   \n",
              "2  755db6279dae599ebb4d39a9123cce439965282d     0   \n",
              "3  bc3f0c64fb968ff4a8bd33af6971ecae77c75e08     0   \n",
              "4  068aba587a4950175d04c680d38943fd488d6a9d     0   \n",
              "\n",
              "                                       filename  \\\n",
              "0  f38a6374c348f90b587e046aac6079959adf3835.tif   \n",
              "1  c18f2d887b7ae4f6742ee445113fa1aef383ed77.tif   \n",
              "2  755db6279dae599ebb4d39a9123cce439965282d.tif   \n",
              "3  bc3f0c64fb968ff4a8bd33af6971ecae77c75e08.tif   \n",
              "4  068aba587a4950175d04c680d38943fd488d6a9d.tif   \n",
              "\n",
              "                                            filepath  \n",
              "0  /content/train/f38a6374c348f90b587e046aac60799...  \n",
              "1  /content/train/c18f2d887b7ae4f6742ee445113fa1a...  \n",
              "2  /content/train/755db6279dae599ebb4d39a9123cce4...  \n",
              "3  /content/train/bc3f0c64fb968ff4a8bd33af6971eca...  \n",
              "4  /content/train/068aba587a4950175d04c680d38943f...  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "path = '/content/'\n",
        "\n",
        "# Load the CSV\n",
        "labels_df = pd.read_csv(path + 'train_labels.csv')\n",
        "labels_df['label'] = labels_df['label'].astype(str)\n",
        "\n",
        "# Add extension and path\n",
        "labels_df['filename'] = labels_df['id'].apply(lambda x: x + '.tif')\n",
        "image_dir = path + 'train'\n",
        "labels_df['filepath'] = labels_df['filename'].apply(lambda x: os.path.join(image_dir, x))\n",
        "\n",
        "labels_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YI3zaRgzFbcv",
        "outputId": "247cfd28-f88f-40be-ca0d-fcc4465141b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(220025, 4)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labels_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L8TIL2d4Fbcv"
      },
      "source": [
        "The table with the training labels consists of 220,025 rows and 2 columns. After adding the file names and file paths for further processing, the DataFrame has 4 columns. The 'train' folder contains one image per row, while the 'test' folder holds test images for which labels need to be predicted for the competition."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xzRjCcHFbcv"
      },
      "source": [
        "### Sample Images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "Z5GLvB_TFbcw",
        "outputId": "5a710002-0c3b-40f3-8f28-7587502fc747"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 100 validated image filenames belonging to 2 classes.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "labels_df_sample = labels_df.sample(n=100)\n",
        "\n",
        "# Load images and labels\n",
        "datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = datagen.flow_from_dataframe(\n",
        "    dataframe=labels_df_sample,\n",
        "    x_col='filepath',\n",
        "    y_col='label',\n",
        "    target_size=(96, 96),\n",
        "    batch_size=32,\n",
        "    class_mode='binary',\n",
        "    shuffle=True\n",
        ")\n",
        "\n",
        "# Plot sample images\n",
        "images, labels = next(train_generator)\n",
        "\n",
        "plt.figure(figsize=(5, 5))\n",
        "for i in range(9):\n",
        "    plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(images[i])\n",
        "    plt.title(f\"Label: {labels[i]}\")\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEQjk8VLFbcw"
      },
      "source": [
        "### Class Distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "OyS5CfdOFbcw",
        "outputId": "27cb135b-0848-4f89-f534-db9835416186"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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1zoNabgiSnJwsLFmyRBg+fLhgZWUlGBgYCFZWVoKTk5OwY8eOGjeFEQRBuHPnjvDmm28K3bp1E/T19WvMW33Dk/Xr1z92m+q64YkgCEJYWJgwaNAgwdjYWJDL5cL48eOFkydPPnLeX375RXjllVcEc3NzwdDQUOjTp4/w3nvvCeXl5Y+84YkgCMKRI0cEZ2dnwdTUVJDJZDX6f9QNTwRBEEpKSoSNGzcKgwYNEtq1aycYGRkJffv2FdasWSMUFhY2aNvr83okPTJBaOCpjURERPTU8DNqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpIwBjUREZGE8YYnLaiqqgo3b96Eqalpvb4ykIiI/hkEQcC9e/egVCof+wUqDOoWdPPmTVhbW7d0G0RE1EKuX79e4wtg/o5B3YJMTU0B/PU/Si6Xt3A3RET0tGg0GlhbW4s5UBcGdQuqPtwtl8sZ1EREbVB9PvbkyWREREQSxqAmIiKSMAY1tRohISGQyWRaDzs7O3H88uXLePnll9G5c2fI5XK89tpryM/Pb9KcABAYGIgOHTrA2tq6xlcKHj58GC+99FLzbSQR0d/wM2pqVZ577jkcP35cfK6n99ePcElJCdzd3TFw4EAkJCQAAN5++2289NJLOHXqVJ2XPzxqTgD49ttvERUVhWPHjuHixYuYM2cOPDw80KlTJ6jVaqxdu1ZrXSKi5sagplZFT08PVlZWNZafPHkSV65cwU8//SSemLd//36Ym5sjISEBbm5uDZ4TAM6fP48xY8bAwcEBDg4OWLJkCfLy8tCpUyesXLkSCxYsQLdu3Zpn44iIasFD39SqXLx4EUqlEj169MCMGTNw7do1AEBZWRlkMhkMDQ3FWiMjI+jo6ODEiRONmhMABg4ciPT0dNy9excZGRm4f/8+evXqhRMnTuDs2bNYtGjRk9lQIqL/h0FNrYajoyMiIyMRGxuLsLAw5OXlYdSoUbh37x5GjBgBExMTrFq1Cn/++SdKSkqwfPlyVFZW4tatW42aEwA8PDzw+uuvY9iwYZg9ezb2798PExMTLFiwAOHh4QgLC0OfPn3g7OyMnJycp/VWEFEbIhMEQWjpJtoqjUYDhUIBtVrN66gboaioCDY2NtiyZQv8/Pxw7NgxLFiwAHl5edDR0cG0adPwyy+/YPjw4QgLC2vUnLXZsGEDioqK4OvrC3d3d5w7dw4xMTHYuXMnMjIymnMTiegfqiG///kZNbVaZmZmePbZZ3Hp0iUAgLu7Oy5fvow//vgDenp6MDMzg5WVFXr06NHoOf/uwoUL+OKLL/DTTz/hs88+w+jRo9G5c2e89tprmDNnDu7du1evOw0REdUXD31Tq1VcXIzLly+jS5cuWss7deoEMzMzJCQkoKCgAJMmTWrynMBfN9F/8803sWXLFrRv3x6VlZWoqKgAAPG/lZWVTdgiIqKaGNTUaixfvhxJSUm4cuUKUlJS8PLLL0NXVxfTpk0DAOzbtw+nTp3C5cuX8cUXX+DVV1/F0qVL0adPH3EOV1dX7Ny5s95zPuyTTz5B586dxeumnZ2dkZCQgFOnTmHr1q2wt7eHmZnZk30TiKjN4aFvajVu3LiBadOm4c6dO+jcuTNcXFxw6tQpdO7cGQCQm5uL1atXo7CwEN27d8fatWuxdOlSrTmqD43Xd85q+fn5eP/995GSkiIuGz58OJYtWwZPT09YWFhg//79T3Driait4slkLai5TyZLXzS/GboiqpvD9vCWboGo1WvI738e+iYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpIwBjUREZGESSqok5OT8dJLL0GpVEImkyE6Olocq6iowKpVq9C/f3+YmJhAqVRi1qxZuHnzptYchYWFmDFjBuRyOczMzODn54fi4mKtmqysLIwaNQpGRkawtrZGaGhojV4OHz4MOzs7GBkZoX///vjuu++0xgVBQHBwMLp06QJjY2O4ubnh4sWLzfdmEBERQWJBXVJSgoEDB2LXrl01xv7880+cPXsWb7/9Ns6ePYuvv/4aubm5mDRpklbdjBkzkJOTg7i4OMTExCA5ORnz5s0TxzUaDdzd3WFjY4OMjAxs2rQJISEhiIiIEGtSUlIwbdo0+Pn54aeffoKXlxe8vLyQnZ0t1oSGhmL79u0IDw9HWloaTExM4OHhgdLS0ifwzhARUVslEwRBaOkmaiOTyXDkyBF4eXk9subMmTMYPnw4rl69im7duuH8+fOwt7fHmTNn4ODgAACIjY3FhAkTcOPGDSiVSoSFhWHt2rVQqVQwMDAAAAQFBSE6OhoXLlwAAEyZMgUlJSWIiYkRX2vEiBEYNGgQwsPDIQgClEolli1bhuXLlwMA1Go1LC0tERkZialTp9ZrGzUaDRQKBdRqNeRyeWPeJi3pi+Y3eQ6ix3HYHt7SLRC1eg35/S+pPeqGUqvVkMlkMDMzAwCkpqbCzMxMDGkAcHNzg46ODtLS0sSa0aNHiyENAB4eHsjNzcXdu3fFGjc3N63X8vDwQGpqKgAgLy8PKpVKq0ahUMDR0VGsqU1ZWRk0Go3Wg4iIqC6tNqhLS0uxatUqTJs2TfxrRKVSwcLCQqtOT08PHTp0gEqlEmssLS21aqqfP67m4fGH16utpjYbN26EQqEQH9bW1g3aZiIiantaZVBXVFTgtddegyAICAsLa+l26m316tVQq9Xi4/r16y3dEhERSZxeSzfQUNUhffXqVSQkJGgd27eyskJBQYFW/YMHD1BYWAgrKyuxJj8/X6um+vnjah4er17WpUsXrZpBgwY9sndDQ0MYGho2ZHOJiKiNa1V71NUhffHiRRw/fhwdO3bUGndyckJRUREyMjLEZQkJCaiqqoKjo6NYk5ycjIqKCrEmLi4Offr0gbm5uVgTHx+vNXdcXBycnJwAALa2trCystKq0Wg0SEtLE2uIiIiag6SCuri4GJmZmcjMzATw10lbmZmZuHbtGioqKjB58mSkp6fjwIEDqKyshEqlgkqlQnl5OQCgb9++GD9+PObOnYvTp0/j5MmTCAgIwNSpU6FUKgEA06dPh4GBAfz8/JCTk4Mvv/wS27ZtQ2BgoNjH4sWLERsbi82bN+PChQsICQlBeno6AgICAPx1RvqSJUvw3nvv4ZtvvsG5c+cwa9YsKJXKOs9SJyIiaihJXZ6VmJiIsWPH1lju4+ODkJAQ2Nra1rrejz/+iDFjxgD464YnAQEB+Pbbb6GjowNvb29s374d7du3F+uzsrLg7++PM2fOoFOnTli4cCFWrVqlNefhw4exbt06XLlyBb1790ZoaCgmTJggjguCgPXr1yMiIgJFRUVwcXHB7t278eyzz9Z7e3l5FrVGvDyLqOka8vtfUkHd1jCoqTViUBM1XZu5jpqIiOifjkFNREQkYQxqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpIwBjUREZGESSqok5OT8dJLL0GpVEImkyE6OlprXBAEBAcHo0uXLjA2NoabmxsuXryoVVNYWIgZM2ZALpfDzMwMfn5+KC4u1qrJysrCqFGjYGRkBGtra4SGhtbo5fDhw7Czs4ORkRH69++P7777rsG9EBERNZWkgrqkpAQDBw7Erl27ah0PDQ3F9u3bER4ejrS0NJiYmMDDwwOlpaVizYwZM5CTk4O4uDjExMQgOTkZ8+bNE8c1Gg3c3d1hY2ODjIwMbNq0CSEhIYiIiBBrUlJSMG3aNPj5+eGnn36Cl5cXvLy8kJ2d3aBeiIiImkomCILQ0k3URiaT4ciRI/Dy8gLw1x6sUqnEsmXLsHz5cgCAWq2GpaUlIiMjMXXqVJw/fx729vY4c+YMHBwcAACxsbGYMGECbty4AaVSibCwMKxduxYqlQoGBgYAgKCgIERHR+PChQsAgClTpqCkpAQxMTFiPyNGjMCgQYMQHh5er17qQ6PRQKFQQK1WQy6XN/k9S180v8lzED2Ow/bwlm6BqNVryO9/Se1R1yUvLw8qlQpubm7iMoVCAUdHR6SmpgIAUlNTYWZmJoY0ALi5uUFHRwdpaWlizejRo8WQBgAPDw/k5ubi7t27Ys3Dr1NdU/069emFiIioOei1dAP1pVKpAACWlpZayy0tLcUxlUoFCwsLrXE9PT106NBBq8bW1rbGHNVj5ubmUKlUj32dx/VSm7KyMpSVlYnPNRpNHVtMRETUivao/wk2btwIhUIhPqytrVu6JSIikrhWE9RWVlYAgPz8fK3l+fn54piVlRUKCgq0xh88eIDCwkKtmtrmePg1HlXz8PjjeqnN6tWroVarxcf169cfs9VERNTWtZqgtrW1hZWVFeLj48VlGo0GaWlpcHJyAgA4OTmhqKgIGRkZYk1CQgKqqqrg6Ogo1iQnJ6OiokKsiYuLQ58+fWBubi7WPPw61TXVr1OfXmpjaGgIuVyu9SAiIqqLpIK6uLgYmZmZyMzMBPDXSVuZmZm4du0aZDIZlixZgvfeew/ffPMNzp07h1mzZkGpVIpnhvft2xfjx4/H3Llzcfr0aZw8eRIBAQGYOnUqlEolAGD69OkwMDCAn58fcnJy8OWXX2Lbtm0IDAwU+1i8eDFiY2OxefNmXLhwASEhIUhPT0dAQAAA1KsXIiKi5iCpk8nS09MxduxY8Xl1ePr4+CAyMhIrV65ESUkJ5s2bh6KiIri4uCA2NhZGRkbiOgcOHEBAQABcXV2ho6MDb29vbN++XRxXKBQ4duwY/P39MXToUHTq1AnBwcFa11qPHDkSUVFRWLduHdasWYPevXsjOjoa/fr1E2vq0wsREVFTSfY66raA11FTa8TrqIma7h95HTUREVFbxKAmIiKSMAY1ERGRhDGoiYiIJIxBTUREJGEMaiIiIgljUBMREUkYg5qIqBX697//Ld4lsVppaSn8/f3RsWNHtG/fHt7e3jW+k+DvZs+eDZlMpvUYP368OF5WVoaZM2dCLpfj2WefxfHjx7XW37RpExYuXNis20baJHVnMiIierwzZ85gz549GDBggNbypUuX4ujRozh8+DAUCgUCAgLwyiuv4OTJk3XON378eOzbt098bmhoKP47IiICGRkZSE1Nxffff4/p06cjPz8fMpkMeXl52Lt3L9LT05t3A0kL96iJiFqR4uJizJgxA3v37hW/SAgA1Go1Pv30U2zZsgXjxo3D0KFDsW/fPqSkpODUqVN1zmloaAgrKyvx8fC858+fx6RJk/Dcc8/B398ft2/fxh9//AEAWLBgAT788EN+wdATxqAmImpF/P394enpCTc3N63lGRkZqKio0FpuZ2eHbt26ITU1tc45ExMTYWFhgT59+mDBggW4c+eOODZw4ECcOHEC9+/fxw8//IAuXbqgU6dOOHDgAIyMjPDyyy837wZSDTz0TUTUShw8eBBnz57FmTNnaoypVCoYGBjAzMxMa7mlpSVUKtUj5xw/fjxeeeUV2Nra4vLly1izZg1efPFFpKamQldXF3PmzEFWVhbs7e3RqVMnHDp0CHfv3kVwcDASExOxbt06HDx4ED179sRnn32GZ555prk3u81jUBMRtQLXr1/H4sWLERcX16zf0jd16lTx3/3798eAAQPQs2dPJCYmwtXVFfr6+ti1a5fWOr6+vli0aBF++uknREdH4+eff0ZoaCgWLVqEr776qtl6o7/w0DcRUSuQkZGBgoICDBkyBHp6etDT00NSUhK2b98OPT09WFpaory8HEVFRVrr5efnw8rKqt6v06NHD3Tq1AmXLl2qdfzHH39ETk4OAgICkJiYiAkTJsDExASvvfYaEhMTm7CF9CjcoyYiagVcXV1x7tw5rWW+vr6ws7PDqlWrYG1tDX19fcTHx8Pb2xsAkJubi2vXrsHJyaner3Pjxg3cuXMHXbp0qTFWffnXgQMHoKuri8rKSlR/U3JFRQUqKyubsIX0KNyjJiJqBUxNTdGvXz+th4mJCTp27Ih+/fpBoVDAz88PgYGB+PHHH5GRkQFfX184OTlhxIgR4jx2dnY4cuQIgL/OIF+xYgVOnTqFK1euID4+Hv/617/Qq1cveHh41Ojh3XffxYQJEzB48GAAgLOzM77++mtkZWVh586dcHZ2fjpvRhvDPWoion+IrVu3QkdHB97e3igrK4OHhwd2796tVZObmwu1Wg0A0NXVRVZWFvbv34+ioiIolUq4u7vj3Xff1bqWGgCys7Nx6NAhZGZmissmT56MxMREjBo1Cn369EFUVNQT38a2SCZUH7egp06j0UChUECtVjfLdYjpi+Y3Q1dEdXPYHt7SLdRqfgpvukFPXvhIh2aZpyG//3nom4iISMIY1ERERBLGoCYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiIiCWtVQV1ZWYm3334btra2MDY2Rs+ePfHuu+/i4a/UFgQBwcHB6NKlC4yNjeHm5oaLFy9qzVNYWIgZM2ZALpfDzMwMfn5+KC4u1qrJysrCqFGjYGRkBGtra4SGhtbo5/Dhw7Czs4ORkRH69++P77777slsOBERtVmtKqg//PBDhIWFYefOnTh//jw+/PBDhIaGYseOHWJNaGgotm/fjvDwcKSlpcHExAQeHh4oLS0Va2bMmIGcnBzExcUhJiYGycnJmDdvnjiu0Wjg7u4OGxsbZGRkYNOmTQgJCUFERIRYk5KSgmnTpsHPzw8//fQTvLy84OXlhezs7KfzZhARUZsgEx7eHZW4iRMnwtLSEp9++qm4zNvbG8bGxvjiiy8gCAKUSiWWLVuG5cuXAwDUajUsLS0RGRmJqVOn4vz587C3t8eZM2fg4OAAAIiNjcWECRNw48YNKJVKhIWFYe3atVCpVDAwMAAABAUFITo6GhcuXAAATJkyBSUlJYiJiRF7GTFiBAYNGoTw8PB6bY9Go4FCoYBarYZcLm/y+5O+aH6T5yB6HIft9fv5ftrmp6S3dAvUBoSPdGiWeRry+79V7VGPHDkS8fHx+PXXXwEAP//8M06cOIEXX3wRAJCXlweVSgU3NzdxHYVCAUdHR6SmpgIAUlNTYWZmJoY0ALi5uUFHRwdpaWlizejRo8WQBgAPDw/k5ubi7t27Ys3Dr1NdU/06tSkrK4NGo9F6EBER1UWvpRtoiKCgIGg0GtjZ2UFXVxeVlZV4//33MWPGDACASqUCAFhaWmqtZ2lpKY6pVCpYWFhojevp6aFDhw5aNba2tjXmqB4zNzeHSqWq83Vqs3HjRmzYsKGhm01ERG1Yq9qjPnToEA4cOICoqCicPXsW+/fvx0cffYT9+/e3dGv1snr1aqjVavFx/fr1lm6JiIgkrtFBPW7cOMTHxz9y/Mcff8S4ceMaO32tVqxYgaCgIEydOhX9+/fHzJkzsXTpUmzcuBEAYGVlBQDIz8/XWi8/P18cs7KyQkFBgdb4gwcPUFhYqFVT2xwPv8ajaqrHa2NoaAi5XK71ICIiqkujgzoxMbFGUD2soKAASUlJjZ2+Vn/++Sd0dLRb1tXVRVVVFQDA1tYWVlZWWn9AaDQapKWlwcnJCQDg5OSEoqIiZGRkiDUJCQmoqqqCo6OjWJOcnIyKigqxJi4uDn369IG5ublY8/c/VOLi4sTXISIiag5NOvQtk8keOXbp0iWYmpo2ZfoaXnrpJbz//vs4evQorly5giNHjmDLli14+eWXxX6WLFmC9957D9988w3OnTuHWbNmQalUwsvLCwDQt29fjB8/HnPnzsXp06dx8uRJBAQEYOrUqVAqlQCA6dOnw8DAAH5+fsjJycGXX36Jbdu2ITAwUOxl8eLFiI2NxebNm3HhwgWEhIQgPT0dAQEBzbrNRETUtjXoZLL9+/drfR783nvvYe/evTXqioqKkJWVhQkTJjS9w4fs2LEDb7/9Nt566y0UFBRAqVTizTffRHBwsFizcuVKlJSUYN68eSgqKoKLiwtiY2NhZGQk1hw4cAABAQFwdXWFjo4OvL29sX37dnFcoVDg2LFj8Pf3x9ChQ9GpUycEBwdrXWs9cuRIREVFYd26dVizZg169+6N6Oho9OvXr1m3mYiI2rYGXUcdFhaG3bt3AwBycnLQtWtXKBQK7QllMpiYmGDo0KEIDg6ucYY1/X+8jppaI15HTW1ZS1xH3aA96gULFmDBggUA/vo8eNu2bZg0aVLjOyUiIqI6Nfo66ry8vObsg4iIiGrR5Bue3Lt3D1evXsXdu3dR21H00aNHN/UliIiI2qxGB/Uff/yBhQsX4quvvkJlZWWNcUEQIJPJah0jIiKi+ml0UM+bNw/ffvstFi1ahFGjRonXFxMREVHzaXRQHzt2DEuXLq31e5qJiIioeTT6hift2rVD9+7dm7EVIiIi+rtGB/Xrr7+OI0eONGcvRERE9DeNPvQ9efJkJCUlYfz48Zg3bx6sra2hq6tbo27IkCFNapCIiKgta3RQu7i4iP+Oi4urMc6zvomIiJqu0UG9b9++5uyDiIiIatHooPbx8WnOPoiIiKgWTfqaSyIiInqyGr1HPWfOnMfWyGQyfPrpp419CSIiojav0UGdkJAAmUymtayyshK3bt1CZWUlOnfuDBMTkyY3SERE1JY1OqivXLlS6/KKigrs2bMHH3/8ca1ngxMREVH9Nftn1Pr6+ggICIC7uzsCAgKae3oiIqI25YmdTDZw4EAkJyc/qemJiIjahCcW1HFxcWjXrt2Tmp6IiKhNaPRn1O+8806ty4uKipCcnIyzZ88iKCio0Y0RERFRE4I6JCSk1uXm5ubo2bMnwsPDMXfu3MZOT0RERGhCUFdVVTVnH0RERFQL3pmMiIhIwhq9R10tKSkJR48exdWrVwEANjY28PT0xPPPP9/k5oiIiNq6Rgd1eXk5pk2bhujoaAiCADMzMwB/nUy2efNmvPzyy/jf//1f6OvrN1evREREbU6jD31v2LABR44cwbJly3Dr1i0UFhaisLAQKpUKy5cvx9dff/3IM8OJiIiofhod1FFRUfDx8UFoaCgsLS3F5RYWFvjwww8xa9Ys/M///E+zNElERNRWNTqob926BUdHx0eOOzo6QqVSNXZ6IiIiQhOCumvXrkhMTHzkeFJSErp27drY6YmIiAhNCGofHx8cOnQI8+fPR25uLiorK1FVVYXc3FwsWLAAhw8fxuzZs5uxVSIioran0Wd9r1mzBpcvX0ZERAT27t0LHZ2/Mr+qqgqCIMDHxwdr1qxptkaJiIjaokYHta6uLiIjIxEYGIjvvvtO6zrqCRMmYMCAAc3WJBERUVvVoEPfpaWlmD9/Pnbs2CEuGzBgAIKCghAWFoawsDAEBQUhMTERb731FioqKpq94d9//x2vv/46OnbsCGNjY/Tv3x/p6eniuCAICA4ORpcuXWBsbAw3NzdcvHhRa47CwkLMmDEDcrkcZmZm8PPzQ3FxsVZNVlYWRo0aBSMjI1hbWyM0NLRGL4cPH4adnR2MjIzQv39/fPfdd82+vURE1LY1KKgjIiIQGRkJT0/POus8PT3x2Wef4ZNPPmlSc3939+5dODs7Q19fH99//z1++eUXbN68Gebm5mJNaGgotm/fjvDwcKSlpcHExAQeHh4oLS0Va2bMmIGcnBzExcUhJiYGycnJmDdvnjiu0Wjg7u4OGxsbZGRkYNOmTQgJCUFERIRYk5KSgmnTpsHPzw8//fQTvLy84OXlhezs7GbdZiIiattkgiAI9S12cXGBjY0NDhw48NjamTNn4urVq0hOTm5Sgw8LCgrCyZMn8d///rfWcUEQoFQqsWzZMixfvhwAoFarYWlpicjISEydOhXnz5+Hvb09zpw5AwcHBwBAbGwsJkyYgBs3bkCpVCIsLAxr166FSqWCgYGB+NrR0dG4cOECAGDKlCkoKSlBTEyM+PojRozAoEGDEB4eXq/t0Wg0UCgUUKvVkMvljX5fqqUvmt/kOYgex2F7/X6+n7b5KemPLyJqovCRDs0yT0N+/zdoj/rcuXNwcXGpV+3IkSORlZXVkOkf65tvvoGDgwNeffVVWFhYYPDgwdi7d684npeXB5VKBTc3N3GZQqGAo6MjUlNTAQCpqakwMzMTQxoA3NzcoKOjg7S0NLFm9OjRYkgDgIeHB3Jzc3H37l2x5uHXqa6pfp3alJWVQaPRaD2IiIjq0qCgLi8v1wqvuhgYGKCsrKxRTT3Kb7/9hrCwMPTu3Rs//PADFixYgEWLFmH//v0AIN5g5eE7pVU/rx5TqVSwsLDQGtfT00OHDh20amqb4+HXeFRNXTd52bhxIxQKhfiwtrZu0PYTEVHb06CgViqV9f4MNjs7G0qlslFNPUpVVRWGDBmCDz74AIMHD8a8efMwd+7ceh9qbmmrV6+GWq0WH9evX2/ploiISOIaFNRubm74/PPPUVBQUGddQUEBPv/8c7zwwgtNau7vunTpAnt7e61lffv2xbVr1wAAVlZWAID8/Hytmvz8fHHMysqqRv8PHjxAYWGhVk1tczz8Go+qqR6vjaGhIeRyudaDiIioLg0K6lWrVqG0tBTjxo0TP8/9u7S0NLi6uqK0tBQrVqxoliarOTs7Izc3V2vZr7/+ChsbGwCAra0trKysEB8fL45rNBqkpaXByckJAODk5ISioiJkZGSINQkJCaiqqhLvXe7k5ITk5GSty8vi4uLQp08f8QxzJycnrdeprql+HSIioubQoBue9OjRA4cOHcK0adMwcuRI9OjRA/3794epqSnu3buH7OxsXL58Ge3atcPBgwfRs2fPZm126dKlGDlyJD744AO89tprOH36NCIiIsTLpmQyGZYsWYL33nsPvXv3hq2tLd5++20olUp4eXkB+GsPfPz48eIh84qKCgQEBGDq1Kniofrp06djw4YN8PPzw6pVq5CdnY1t27Zh69atYi+LFy/G888/j82bN8PT0xMHDx5Eenq61iVcRERETdXgO5N5enoiKysLH374IWJiYhAdHS2OKZVKzJ07FytXrkSPHj2as08AwLBhw3DkyBGsXr0a77zzDmxtbfHxxx9jxowZYs3KlStRUlKCefPmoaioCC4uLoiNjYWRkZFYc+DAAQQEBMDV1RU6Ojrw9vbG9u3bxXGFQoFjx47B398fQ4cORadOnRAcHKx1rfXIkSMRFRWFdevWYc2aNejduzeio6PRr1+/Zt9uIiJquxp0HXVt7t27B41GA7lcDlNT0+bqq03gddTUGvE6amrLWuI66kbf67uaqakpA5qIiOgJafTXXBIREdGTx6AmIiKSMAY1ERGRhDGoiYiIJIxBTUREJGEMaiIiIgljUBMREUkYg5qIiEjCGNREREQSxqAmIiKSMAY1ERGRhDGoiYiIJIxBTUREJGEMaiIiIgljUBMREUkYg5qIiEjCGNREREQSxqAmIiKSMAY1ERGRhDGoiYiIJIxBTUREJGEMaiIiIgljUBMREUkYg5qIiEjCGNREREQSxqAmIiKSMAY1ERGRhDGoiYiIJIxBTUREJGEMaiIiIgljUBMREUlYqw7qf//735DJZFiyZIm4rLS0FP7+/ujYsSPat28Pb29v5Ofna6137do1eHp6ol27drCwsMCKFSvw4MEDrZrExEQMGTIEhoaG6NWrFyIjI2u8/q5du9C9e3cYGRnB0dERp0+ffhKbSUREbVirDeozZ85gz549GDBggNbypUuX4ttvv8Xhw4eRlJSEmzdv4pVXXhHHKysr4enpifLycqSkpGD//v2IjIxEcHCwWJOXlwdPT0+MHTsWmZmZWLJkCd544w388MMPYs2XX36JwMBArF+/HmfPnsXAgQPh4eGBgoKCJ7/xRETUZrTKoC4uLsaMGTOwd+9emJubi8vVajU+/fRTbNmyBePGjcPQoUOxb98+pKSk4NSpUwCAY8eO4ZdffsEXX3yBQYMG4cUXX8S7776LXbt2oby8HAAQHh4OW1tbbN68GX379kVAQAAmT56MrVu3iq+1ZcsWzJ07F76+vrC3t0d4eDjatWuHzz777Om+GURE9I/WKoPa398fnp6ecHNz01qekZGBiooKreV2dnbo1q0bUlNTAQCpqano378/LC0txRoPDw9oNBrk5OSINX+f28PDQ5yjvLwcGRkZWjU6Ojpwc3MTa4iIiJqDXks30FAHDx7E2bNncebMmRpjKpUKBgYGMDMz01puaWkJlUol1jwc0tXj1WN11Wg0Gty/fx93795FZWVlrTUXLlx4ZO9lZWUoKysTn2s0msdsLRERtXWtao/6+vXrWLx4MQ4cOAAjI6OWbqfBNm7cCIVCIT6sra1buiUiIpK4VhXUGRkZKCgowJAhQ6Cnpwc9PT0kJSVh+/bt0NPTg6WlJcrLy1FUVKS1Xn5+PqysrAAAVlZWNc4Cr37+uBq5XA5jY2N06tQJurq6tdZUz1Gb1atXQ61Wi4/r16836n0gIqK2o1UFtaurK86dO4fMzEzx4eDggBkzZoj/1tfXR3x8vLhObm4url27BicnJwCAk5MTzp07p3V2dlxcHORyOezt7cWah+eorqmew8DAAEOHDtWqqaqqQnx8vFhTG0NDQ8jlcq0HERFRXVrVZ9Smpqbo16+f1jITExN07NhRXO7n54fAwEB06NABcrkcCxcuhJOTE0aMGAEAcHd3h729PWbOnInQ0FCoVCqsW7cO/v7+MDQ0BADMnz8fO3fuxMqVKzFnzhwkJCTg0KFDOHr0qPi6gYGB8PHxgYODA4YPH46PP/4YJSUl8PX1fUrvBhERtQWtKqjrY+vWrdDR0YG3tzfKysrg4eGB3bt3i+O6urqIiYnBggUL4OTkBBMTE/j4+OCdd94Ra2xtbXH06FEsXboU27ZtQ9euXfHJJ5/Aw8NDrJkyZQpu376N4OBgqFQqDBo0CLGxsTVOMCMiImoKmSAIQks30VZpNBooFAqo1epmOQyevmh+M3RFVDeH7eEt3UKt5qekt3QL1AaEj3Rolnka8vu/VX1GTURE1NYwqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYS1qqDeuHEjhg0bBlNTU1hYWMDLywu5ublaNaWlpfD390fHjh3Rvn17eHt7Iz8/X6vm2rVr8PT0RLt27WBhYYEVK1bgwYMHWjWJiYkYMmQIDA0N0atXL0RGRtboZ9euXejevTuMjIzg6OiI06dPN/s2ExFR29aqgjopKQn+/v44deoU4uLiUFFRAXd3d5SUlIg1S5cuxbfffovDhw8jKSkJN2/exCuvvCKOV1ZWwtPTE+Xl5UhJScH+/fsRGRmJ4OBgsSYvLw+enp4YO3YsMjMzsWTJErzxxhv44YcfxJovv/wSgYGBWL9+Pc6ePYuBAwfCw8MDBQUFT+fNICKiNkEmCILQ0k001u3bt2FhYYGkpCSMHj0aarUanTt3RlRUFCZPngwAuHDhAvr27YvU1FSMGDEC33//PSZOnIibN2/C0tISABAeHo5Vq1bh9u3bMDAwwKpVq3D06FFkZ2eLrzV16lQUFRUhNjYWAODo6Ihhw4Zh586dAICqqipYW1tj4cKFCAoKqlf/Go0GCoUCarUacrm8ye9H+qL5TZ6D6HEctoe3dAu1mp+S3tItUBsQPtKhWeZpyO//VrVH/XdqtRoA0KFDBwBARkYGKioq4ObmJtbY2dmhW7duSE1NBQCkpqaif//+YkgDgIeHBzQaDXJycsSah+eorqmeo7y8HBkZGVo1Ojo6cHNzE2tqU1ZWBo1Go/UgIiKqS6sN6qqqKixZsgTOzs7o168fAEClUsHAwABmZmZatZaWllCpVGLNwyFdPV49VleNRqPB/fv38ccff6CysrLWmuo5arNx40YoFArxYW1t3fANJyKiNqXVBrW/vz+ys7Nx8ODBlm6l3lavXg21Wi0+rl+/3tItERGRxOm1dAONERAQgJiYGCQnJ6Nr167icisrK5SXl6OoqEhrrzo/Px9WVlZizd/Pzq4+K/zhmr+fKZ6fnw+5XA5jY2Po6upCV1e31prqOWpjaGgIQ0PDhm8wERG1Wa1qj1oQBAQEBODIkSNISEiAra2t1vjQoUOhr6+P+Ph4cVlubi6uXbsGJycnAICTkxPOnTundXZ2XFwc5HI57O3txZqH56iuqZ7DwMAAQ4cO1aqpqqpCfHy8WENERNQcWtUetb+/P6KiovB//s//gampqfh5sEKhgLGxMRQKBfz8/BAYGIgOHTpALpdj4cKFcHJywogRIwAA7u7usLe3x8yZMxEaGgqVSoV169bB399f3NudP38+du7ciZUrV2LOnDlISEjAoUOHcPToUbGXwMBA+Pj4wMHBAcOHD8fHH3+MkpIS+Pr6Pv03hoiI/rFaVVCHhYUBAMaMGaO1fN++fZg9ezYAYOvWrdDR0YG3tzfKysrg4eGB3bt3i7W6urqIiYnBggUL4OTkBBMTE/j4+OCdd94Ra2xtbXH06FEsXboU27ZtQ9euXfHJJ5/Aw8NDrJkyZQpu376N4OBgqFQqDBo0CLGxsTVOMCMiImqKVn0ddWvH66ipNeJ11NSW8TpqIiIi0sKgJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUTbRr1y50794dRkZGcHR0xOnTp1u6JSIi+gdhUDfBl19+icDAQKxfvx5nz57FwIED4eHhgYKCgpZujYiI/iEY1E2wZcsWzJ07F76+vrC3t0d4eDjatWuHzz77rKVbIyKifwgGdSOVl5cjIyMDbm5u4jIdHR24ubkhNTW1BTsjIqJ/Er2WbqC1+uOPP1BZWQlLS0ut5ZaWlrhw4UKt65SVlaGsrEx8rlarAQAajaZZeiouL2+WeYjq0lw/r82tvKS4pVugNqC5fv6r5xEE4bG1DOqnaOPGjdiwYUON5dbW1i3QDVEj7dnX0h0QtZjm/um/d+8eFApFnTUM6kbq1KkTdHV1kZ+fr7U8Pz8fVlZWta6zevVqBAYGis+rqqpQWFiIjh07QiaTPdF+qSaNRgNra2tcv34dcrm8pdsheqr489+yBEHAvXv3oFQqH1vLoG4kAwMDDB06FPHx8fDy8gLwV/DGx8cjICCg1nUMDQ1haGiotczMzOwJd0qPI5fL+YuK2iz+/Lecx+1JV2NQN0FgYCB8fHzg4OCA4cOH4+OPP0ZJSQl8fX1bujUiIvqHYFA3wZQpU3D79m0EBwdDpVJh0KBBiI2NrXGCGRERUWMxqJsoICDgkYe6SdoMDQ2xfv36Gh9HELUF/PlvPWRCfc4NJyIiohbBG54QERFJGIOaiIhIwhjUREREEsagpjaJX09KbVVycjJeeuklKJVKyGQyREdHt3RL9BgMampz+PWk1JaVlJRg4MCB2LVrV0u3QvXEs76pzXF0dMSwYcOwc+dOAH/dUc7a2hoLFy5EUFBQC3dH9PTIZDIcOXJEvLsiSRP3qKlN4deTElFrw6CmNqWurydVqVQt1BUR0aMxqImIiCSMQU1tSmO+npSIqCUxqKlNefjrSatVfz2pk5NTC3ZGRFQ7fikHtTn8elJqy4qLi3Hp0iXxeV5eHjIzM9GhQwd069atBTujR+HlWdQm7dy5E5s2bRK/nnT79u1wdHRs6baInrjExESMHTu2xnIfHx9ERkY+/YbosRjUREREEsbPqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqInoiejevTtmz57d0m0QtXoMaiJqsMuXL+PNN99Ejx49YGRkBLlcDmdnZ2zbtg33799v6faI/lH4pRxE1CBHjx7Fq6++CkNDQ8yaNQv9+vVDeXk5Tpw4gRUrViAnJwcREREt3SbRPwaDmojqLS8vD1OnToWNjQ0SEhLQpUsXcczf3x+XLl3C0aNHW7BDon8eHvomonoLDQ1FcXExPv30U62QrtarVy8sXry41nULCwuxfPly9O/fH+3bt4dcLseLL76In3/+uUbtjh078Nxzz6Fdu3YwNzeHg4MDoqKixPF79+5hyZIl6N69OwwNDWFhYYEXXngBZ8+ebb6NJZII7lETUb19++236NGjB0aOHNngdX/77TdER0fj1Vdfha2tLfLz87Fnzx48//zz+OWXX6BUKgEAe/fuxaJFizB58mQsXrwYpaWlyMrKQlpaGqZPnw4AmD9/Pv7zn/8gICAA9vb2uHPnDk6cOIHz589jyJAhzbrNRC2NX3NJRPWi0WigUCjwr3/9C9HR0Y+t7969O8aMGSN+x3FZWRn09fWho/P/D+RduXIFdnZ2WLt2Ld5++20AgJeXFy5duoTs7OxHzm1mZobXX38dO3fubNI2EbUGPPRNRPWi0WgAAKampo1a39DQUAzpyspK3LlzB+3bt0efPn20DlmbmZnhxo0bOHPmzCPnMjMzQ1paGm7evNmoXohaEwY1EdWLXC4H8Nfnw41RVVWFrVu3onfv3jA0NESnTp3QuXNnZGVlQa1Wi3WrVq1C+/btMXz4cPTu3Rv+/v44efKk1lyhoaHIzs6GtbU1hg8fjpCQEPz222+N3zgiCWNQE1G9yOVyKJXKOg9J1+WDDz5AYGAgRo8ejS+++AI//PAD4uLi8Nxzz6Gqqkqs69u3L3Jzc3Hw4EG4uLjgq6++gouLC9avXy/WvPbaa/jtt9+wY8cOKJVKbNq0Cc899xy+//77Jm8nkdTwM2oiqrc333wTERERSElJgZOTU521f/+MetCgQejQoQMSEhK06rp27YpevXohMTGx1nnKy8vxyiuvIDY2FsXFxTAyMqpRU1BQgCFDhqB79+44ceJEo7aNSKq4R01E9bZy5UqYmJjgjTfeQH5+fo3xy5cvY9u2bbWuq6uri7/vFxw+fBi///671rI7d+5oPTcwMIC9vT0EQUBFRQUqKyu1DpUDgIWFBZRKJcrKyhqzWUSSxsuziKjeevbsiaioKEyZMgV9+/bVujNZSkoKDh8+/Mj7e0+cOBHvvPMOfH19MXLkSJw7dw4HDhxAjx49tOrc3d1hZWUFZ2dnWFpa4vz589i5cyc8PT1hamqKoqIidO3aFZMnT8bAgQPRvn17HD9+HGfOnMHmzZufwrtA9JQJREQN9Ouvvwpz584VunfvLhgYGAimpqaCs7OzsGPHDqG0tFQQBEGwsbERfHx8xHVKS0uFZcuWCV26dBGMjY0FZ2dnITU1VXj++eeF559/Xqzbs2ePMHr0aKFjx46CoaGh0LNnT2HFihWCWq0WBEEQysrKhBUrVggDBw4UTE1NBRMTE2HgwIHC7t27n+ZbQPTU8DNqIiIiCeNn1ERERBLGoCYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpIwBjUREZGEMaiJiIgkjEFNREQkYf8XnxhidAdWtGsAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Percentages\n",
        "class_counts = labels_df['label'].value_counts(normalize=True) * 100\n",
        "\n",
        "# Plot histogram with percentages\n",
        "plt.figure(figsize=(5, 4))\n",
        "sns.countplot(x='label', data=labels_df, palette='hls', hue='label', legend=False)\n",
        "\n",
        "for p in plt.gca().patches:\n",
        "    height = p.get_height()\n",
        "    percentage = (height / len(labels_df)) * 100\n",
        "    plt.text(p.get_x() + p.get_width() / 2., height - 5, f'{percentage:.1f}%',\n",
        "             ha='center', fontsize=10)\n",
        "\n",
        "# Plot\n",
        "plt.title('Class Distribution', fontsize=16)\n",
        "plt.xlabel('Class', fontsize=12)\n",
        "plt.ylabel('Count', fontsize=12)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49E2aw_sZxWa"
      },
      "source": [
        "### Color Distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "82O3Wx1zZxWa",
        "outputId": "51b9a3af-4715-4389-e529-a5734b58225f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sample_size = 200\n",
        "sampled_df = labels_df.sample(n=sample_size, random_state=42)\n",
        "\n",
        "# Initialize RGB lists\n",
        "reds, greens, blues = [], [], []\n",
        "\n",
        "for filepath in sampled_df['filepath']:\n",
        "    try:\n",
        "        img = Image.open(filepath)\n",
        "\n",
        "        img_array = np.array(img)\n",
        "\n",
        "        # Extract RGB channels\n",
        "        reds.append(img_array[:, :, 0].flatten())\n",
        "        greens.append(img_array[:, :, 1].flatten())\n",
        "        blues.append(img_array[:, :, 2].flatten())\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing {filepath}: {e}\")\n",
        "\n",
        "# Flatten and plot\n",
        "reds = np.concatenate(reds)\n",
        "greens = np.concatenate(greens)\n",
        "blues = np.concatenate(blues)\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "plt.hist(reds, bins=256, color='red', alpha=0.6, label='Red Channel')\n",
        "plt.hist(greens, bins=256, color='green', alpha=0.6, label='Green Channel')\n",
        "plt.hist(blues, bins=256, color='blue', alpha=0.6, label='Blue Channel')\n",
        "plt.title('Color Distribution of Sampled Images')\n",
        "plt.xlabel('Pixel Intensity')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Mp93OoKFbcw"
      },
      "source": [
        "### EDA Conclusion\n",
        "- There are two class labels: 0 and 1.\n",
        "- The data is not perfectly balanced but is also not highly skewed; therefore, no action is required.\n",
        "- Paths and filenames have been added for further processing.\n",
        "- The distributions of the RGB channels indicate that the channels contain complementary information."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deQE92klZxWa"
      },
      "source": [
        "### Preprocessing and Augmentation and Normalization\n",
        "\n",
        "The following augemtations will be applied when defining the data generators later:\n",
        "- **Rescaling**: Preprocessing for normalized inputs.\n",
        "- **Geometric Transformations**:\n",
        "  - Rotation (±15°)\n",
        "  - Horizontal and vertical shifts (10% of dimensions)\n",
        "  - Horizontal flipping\n",
        "\n",
        "### Next Steps and Analysis Approach\n",
        "Two model types will be trained using an Nvidia A100 GPU on Google Colab: 1) A small custom CNN, including basic hyperparameter tuning, and 2) transfer learning with MobileNetV2 as the base model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b3nzx34WFbcw"
      },
      "source": [
        "## Step 3: Model Architecture"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmTMIyriFbcw"
      },
      "source": [
        "### (Small) CNN from scratch\n",
        "The convolutional layers (32, 64, 128 filters) progressively extract features from the images, with BatchNormalization and MaxPooling2D helping to stabilize training and reduce size. The fully connected layers, with dropout for regularization, make the final predictions, and the sigmoid output layer provides binary classification results.\n",
        "\n",
        "- Convolutional Layers:\n",
        "  - Conv2D: 32 filters, kernel size (3, 3), ReLU activation, followed by BatchNormalization and MaxPooling2D with pool size (2, 2).\n",
        "  - Conv2D: 64 filters, kernel size (3, 3), ReLU activation, followed by BatchNormalization and MaxPooling2D with pool size (2, 2).\n",
        "  - Conv2D: 128 filters, kernel size (3, 3), ReLU activation, followed by BatchNormalization and MaxPooling2D with pool size (2, 2).\n",
        "\n",
        "The increasing number of filters in convolutional layers allows the model to learn progressively more complex and abstract features from the input data.\n",
        "  \n",
        "- Fully Connected Layers:\n",
        "  - Flatten: Flatten the 3D output of the convolutional layers to 1D.\n",
        "  - Dense: 128 units, ReLU activation.\n",
        "  - Dropout: 50% regularization to prevent overfitting.\n",
        "  - Dense: 1 unit, Sigmoid activation for binary classification output.\n",
        "\n",
        "- Output Layer: A binary classification output (0 or 1).\n",
        "\n",
        "### Transfer learning: MobileNetV2 with custom classification layers\n",
        "Considering the size of the dataset and the limited GPU resources available for this project, transfer learning was chosen as an additional method. MobileNetV2 was chosen as base model due to resource limitations.\n",
        "\n",
        "The following custom layers were added:\n",
        "- GlobalAveragePooling2D: Reduces the dimensionality by averaging the feature map across spatial dimensions (height and width).\n",
        "- Dropout Layer 1: Dropout rate of 20% to prevent overfitting by randomly setting a fraction of input units to zero during training.\n",
        "- Dense Layer with ReLU activation: 128 units with ReLU activation, allowing the model to learn more complex patterns.\n",
        "- Dropout Layer 2: Dropout rate of 20% to further prevent overfitting.\n",
        "- Output Layer: A single unit with sigmoid activation, used for binary classification (outputting a value between 0 and 1)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "szUQaS-LFbcx"
      },
      "source": [
        "## Step 4: Results and Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pa_2cy3jFbcx"
      },
      "source": [
        "### Data generators"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwpPi1lGFbcx",
        "outputId": "f22f8953-529f-447f-92da-cde5b61b7564"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 176020 validated image filenames belonging to 2 classes.\n",
            "Found 44005 validated image filenames belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "# Data Generators\n",
        "datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=15,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    validation_split=0.2\n",
        ")\n",
        "\n",
        "# Training data generator\n",
        "train_generator = datagen.flow_from_dataframe(\n",
        "    dataframe=labels_df,\n",
        "    x_col='filepath',\n",
        "    y_col='label',\n",
        "    target_size=(96, 96),\n",
        "    batch_size=32,\n",
        "    class_mode='binary',\n",
        "    subset='training',\n",
        "    shuffle=True\n",
        ")\n",
        "\n",
        "# Validation data generator\n",
        "val_generator = datagen.flow_from_dataframe(\n",
        "    dataframe=labels_df,\n",
        "    x_col='filepath',\n",
        "    y_col='label',\n",
        "    target_size=(96, 96),\n",
        "    batch_size=32,\n",
        "    class_mode='binary',\n",
        "    subset='validation',\n",
        "    shuffle=False\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dM9HAv1UFbcx"
      },
      "source": [
        "### (Small) CNN from scratch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "id": "cEirw5akFbcx",
        "outputId": "ab70c573-f11c-44aa-90a6-6d1550efa83a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">94</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">94</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">94</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">94</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">45</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">45</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_1                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">45</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">45</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_2                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12800</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,638,528</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ],
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m94\u001b[0m, \u001b[38;5;34m94\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m896\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m94\u001b[0m, \u001b[38;5;34m94\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_1                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_2                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12800\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │       \u001b[38;5;34m1,638,528\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,732,801</span> (6.61 MB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,732,801\u001b[0m (6.61 MB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,732,353</span> (6.61 MB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,732,353\u001b[0m (6.61 MB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> (1.75 KB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m448\u001b[0m (1.75 KB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m633s\u001b[0m 114ms/step - AUC: 0.8617 - accuracy: 0.8015 - loss: 0.5250 - precision: 0.7865 - val_AUC: 0.9239 - val_accuracy: 0.8050 - val_loss: 0.4874 - val_precision: 0.9561\n",
            "Epoch 2/3\n",
            "\u001b[1m   1/5500\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m36s\u001b[0m 7ms/step - AUC: 0.9372 - accuracy: 0.9062 - loss: 0.2830 - precision: 0.9000"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/lib/python3.10/contextlib.py:153: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
            "  self.gen.throw(typ, value, traceback)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 129us/step - AUC: 0.9372 - accuracy: 0.9062 - loss: 0.2830 - precision: 0.9000 - val_AUC: 0.7500 - val_accuracy: 0.4000 - val_loss: 1.0453 - val_precision: 1.0000\n",
            "Epoch 3/3\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m619s\u001b[0m 112ms/step - AUC: 0.9298 - accuracy: 0.8647 - loss: 0.3348 - precision: 0.8703 - val_AUC: 0.8414 - val_accuracy: 0.7505 - val_loss: 0.6670 - val_precision: 0.9615\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x79cb97588940>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_seq = Sequential([\n",
        "    # Convolutional Layer 1\n",
        "    Conv2D(32, (3, 3), activation='relu', input_shape=(96, 96, 3)),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "\n",
        "    # Convolutional Layer 2\n",
        "    Conv2D(64, (3, 3), activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "\n",
        "    # Convolutional Layer 3\n",
        "    Conv2D(128, (3, 3), activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "\n",
        "    # Flatten and Fully Connected Layers\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_seq.compile(\n",
        "    optimizer=Adam(learning_rate=0.001),\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['accuracy', 'AUC', Precision()]\n",
        ")\n",
        "\n",
        "# Summary of the model\n",
        "model_seq.summary()\n",
        "\n",
        "# Train the model\n",
        "steps_per_epoch = train_generator.samples // train_generator.batch_size\n",
        "validation_steps = val_generator.samples // val_generator.batch_size\n",
        "\n",
        "model_seq.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=steps_per_epoch,\n",
        "    epochs=3,\n",
        "    validation_data=val_generator,\n",
        "    validation_steps=validation_steps\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JHjafnVUFbcx"
      },
      "source": [
        "### (Small) CNN from scratch: hyperparameter tuning\n",
        "\n",
        "The hyperparameter tuning approach involves different combinations of learning rates, dropout rates, and batch sizes to identify the optimal set for the model. Specifically, the learning rates tested are 0.01, and 0.001; dropout rates of 0.3, and 0.5; and batch sizes of 32, and 128."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q772WNvpFbcy",
        "outputId": "f4e259a9-00b0-454f-a7e4-fc302a8aeb36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training with learning_rate=0.01, dropout=0.3, batch_size=32...\n",
            "Epoch 1/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m627s\u001b[0m 113ms/step - AUC: 0.8146 - accuracy: 0.7667 - loss: 1.1728 - precision_1: 0.7899 - val_AUC: 0.8448 - val_accuracy: 0.7530 - val_loss: 0.5222 - val_precision_1: 0.9593\n",
            "Epoch 2/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70us/step - AUC: 0.8730 - accuracy: 0.7188 - loss: 0.5929 - precision_1: 0.7778 - val_AUC: 0.8750 - val_accuracy: 0.4000 - val_loss: 0.8571 - val_precision_1: 1.0000\n",
            "Training with learning_rate=0.01, dropout=0.3, batch_size=128...\n",
            "Epoch 1/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m624s\u001b[0m 112ms/step - AUC: 0.8351 - accuracy: 0.7745 - loss: 1.3852 - precision_2: 0.7877 - val_AUC: 0.9046 - val_accuracy: 0.8276 - val_loss: 0.4129 - val_precision_2: 0.7955\n",
            "Epoch 2/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75us/step - AUC: 0.8983 - accuracy: 0.8438 - loss: 0.4054 - precision_2: 0.7500 - val_AUC: 0.6250 - val_accuracy: 0.6000 - val_loss: 0.5262 - val_precision_2: 0.7500\n",
            "Training with learning_rate=0.01, dropout=0.5, batch_size=32...\n",
            "Epoch 1/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m625s\u001b[0m 113ms/step - AUC: 0.7115 - accuracy: 0.6899 - loss: 1.2651 - precision_3: 0.7569 - val_AUC: 0.6397 - val_accuracy: 0.6644 - val_loss: 0.6254 - val_precision_3: 0.9664\n",
            "Epoch 2/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71us/step - AUC: 0.5583 - accuracy: 0.6250 - loss: 0.6449 - precision_3: 0.5000 - val_AUC: 0.6250 - val_accuracy: 0.4000 - val_loss: 0.7831 - val_precision_3: 1.0000\n",
            "Training with learning_rate=0.01, dropout=0.5, batch_size=128...\n",
            "Epoch 1/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m623s\u001b[0m 112ms/step - AUC: 0.7703 - accuracy: 0.7359 - loss: 1.4639 - precision_4: 0.7862 - val_AUC: 0.8306 - val_accuracy: 0.7901 - val_loss: 0.5231 - val_precision_4: 0.7907\n",
            "Epoch 2/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72us/step - AUC: 0.6667 - accuracy: 0.6875 - loss: 0.6949 - precision_4: 0.2222 - val_AUC: 0.5000 - val_accuracy: 0.6000 - val_loss: 0.6875 - val_precision_4: 1.0000\n",
            "Training with learning_rate=0.001, dropout=0.3, batch_size=32...\n",
            "Epoch 1/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m627s\u001b[0m 113ms/step - AUC: 0.8764 - accuracy: 0.8147 - loss: 0.4868 - precision_5: 0.7944 - val_AUC: 0.8376 - val_accuracy: 0.7628 - val_loss: 0.6136 - val_precision_5: 0.9192\n",
            "Epoch 2/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73us/step - AUC: 0.9271 - accuracy: 0.8750 - loss: 0.3174 - precision_5: 0.8462 - val_AUC: 0.7500 - val_accuracy: 0.8000 - val_loss: 1.1287 - val_precision_5: 1.0000\n",
            "Training with learning_rate=0.001, dropout=0.3, batch_size=128...\n",
            "Epoch 1/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m625s\u001b[0m 113ms/step - AUC: 0.8780 - accuracy: 0.8130 - loss: 0.4930 - precision_6: 0.7874 - val_AUC: 0.8015 - val_accuracy: 0.7626 - val_loss: 0.9103 - val_precision_6: 0.8341\n",
            "Epoch 2/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71us/step - AUC: 0.9455 - accuracy: 0.8750 - loss: 0.3606 - precision_6: 0.8000 - val_AUC: 0.5000 - val_accuracy: 0.6000 - val_loss: 1.8102 - val_precision_6: 1.0000\n",
            "Training with learning_rate=0.001, dropout=0.5, batch_size=32...\n",
            "Epoch 1/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m626s\u001b[0m 113ms/step - AUC: 0.8726 - accuracy: 0.8094 - loss: 0.5007 - precision_7: 0.7916 - val_AUC: 0.9024 - val_accuracy: 0.8273 - val_loss: 0.4694 - val_precision_7: 0.8769\n",
            "Epoch 2/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72us/step - AUC: 0.9004 - accuracy: 0.9062 - loss: 0.3775 - precision_7: 1.0000 - val_AUC: 0.2500 - val_accuracy: 0.4000 - val_loss: 0.8288 - val_precision_7: 0.6667\n",
            "Training with learning_rate=0.001, dropout=0.5, batch_size=128...\n",
            "Epoch 1/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m624s\u001b[0m 112ms/step - AUC: 0.8650 - accuracy: 0.8043 - loss: 0.5430 - precision_8: 0.7916 - val_AUC: 0.8474 - val_accuracy: 0.7673 - val_loss: 0.6380 - val_precision_8: 0.8975\n",
            "Epoch 2/2\n",
            "\u001b[1m5500/5500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73us/step - AUC: 0.8651 - accuracy: 0.8125 - loss: 0.4435 - precision_8: 0.7857 - val_AUC: 0.7500 - val_accuracy: 0.6000 - val_loss: 1.4568 - val_precision_8: 1.0000\n",
            "Best model validation accuracy: 0.8275681734085083\n"
          ]
        }
      ],
      "source": [
        "# Parameter ranges\n",
        "learning_rates = [0.01, 0.001]\n",
        "dropout_rates = [0.3, 0.5]\n",
        "batch_sizes = [32, 128]\n",
        "\n",
        "# Model Tracking\n",
        "best_val_acc = 0\n",
        "best_model = None\n",
        "best_history = None\n",
        "\n",
        "# Collect history\n",
        "histories = []\n",
        "\n",
        "for lr in learning_rates:\n",
        "    for dropout in dropout_rates:\n",
        "        for batch_size in batch_sizes:\n",
        "\n",
        "            model_seq_hyper = Sequential([\n",
        "                # Convolutional Layer 1\n",
        "                Conv2D(32, (3, 3), activation='relu', input_shape=(96, 96, 3)),\n",
        "                BatchNormalization(),\n",
        "                MaxPooling2D(pool_size=(2, 2)),\n",
        "\n",
        "                # Convolutional Layer 2\n",
        "                Conv2D(64, (3, 3), activation='relu'),\n",
        "                BatchNormalization(),\n",
        "                MaxPooling2D(pool_size=(2, 2)),\n",
        "\n",
        "                # Convolutional Layer 3\n",
        "                Conv2D(128, (3, 3), activation='relu'),\n",
        "                BatchNormalization(),\n",
        "                MaxPooling2D(pool_size=(2, 2)),\n",
        "\n",
        "                # Flatten and Fully Connected Layers\n",
        "                Flatten(),\n",
        "                Dense(128, activation='relu'),\n",
        "                Dropout(dropout),\n",
        "                Dense(1, activation='sigmoid')\n",
        "            ])\n",
        "\n",
        "            # Recompile\n",
        "            model_seq_hyper.compile(\n",
        "                optimizer=Adam(learning_rate=lr),\n",
        "                loss='binary_crossentropy',\n",
        "                metrics=['accuracy', 'AUC', Precision()]\n",
        "            )\n",
        "\n",
        "            # Train model\n",
        "            print(f\"Training with learning_rate={lr}, dropout={dropout}, batch_size={batch_size}...\")\n",
        "            history = model_seq_hyper.fit(\n",
        "                train_generator,\n",
        "                validation_data=val_generator,\n",
        "                epochs=2,\n",
        "                steps_per_epoch=steps_per_epoch,\n",
        "                validation_steps=validation_steps,\n",
        "                batch_size=batch_size\n",
        "            )\n",
        "\n",
        "            histories.append(history)\n",
        "\n",
        "            val_acc = max(history.history['val_accuracy'])\n",
        "            if val_acc > best_val_acc:\n",
        "                best_val_acc = val_acc\n",
        "                best_model = model_seq_hyper\n",
        "                best_history = history\n",
        "\n",
        "print(f\"Best model validation accuracy: {best_val_acc}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojh_KyOkFbcy"
      },
      "source": [
        "### MobileNetV2 with custom classification layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gBpIaa7WFbcy",
        "outputId": "3b588585-39eb-4e3b-f011-77f4715a3ddb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_96_no_top.h5\n",
            "\u001b[1m9406464/9406464\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_9\"</span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1mModel: \"functional_9\"\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)              </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">        Param # </span>┃<span style=\"font-weight: bold\"> Connected to           </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_9             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ Conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">864</span> │ input_layer_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ bn_Conv1                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ Conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ Conv1_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ bn_Conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ expanded_conv_depthwise   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">288</span> │ Conv1_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ expanded_conv_depthwise_… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ expanded_conv_depthwi… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ expanded_conv_depthwise_… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ expanded_conv_depthwi… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ expanded_conv_project     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ expanded_conv_depthwi… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ expanded_conv_project_BN  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ expanded_conv_project… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ expanded_conv_project… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_expand_BN         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block_1_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_expand_relu       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_1_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_pad               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_1_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)           │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_depthwise         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">864</span> │ block_1_pad[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_depthwise_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block_1_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_depthwise_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_1_depthwise_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_1_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_project_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span> │ block_1_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_1_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_expand_BN         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block_2_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_expand_relu       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_2_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_depthwise         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,296</span> │ block_2_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_depthwise_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block_2_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_depthwise_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_2_depthwise_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_2_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_project_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span> │ block_2_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_1_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│                           │                        │                │ block_2_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_2_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_expand_BN         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block_3_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_expand_relu       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_3_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_pad               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_3_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)           │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_depthwise         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,296</span> │ block_3_pad[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_depthwise_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block_3_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_depthwise_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_3_depthwise_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │ block_3_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_project_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ block_3_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block_3_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_expand_BN         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_4_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_expand_relu       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_4_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_depthwise         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,728</span> │ block_4_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_depthwise_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_4_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_depthwise_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_4_depthwise_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block_4_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_project_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ block_4_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_3_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│                           │                        │                │ block_4_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block_4_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_expand_BN         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_5_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_expand_relu       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_5_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_depthwise         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,728</span> │ block_5_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_depthwise_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_5_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_depthwise_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_5_depthwise_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block_5_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_project_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ block_5_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_4_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
              "│                           │                        │                │ block_5_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block_5_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_expand_BN         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_6_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_expand_relu       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_6_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_pad               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_6_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)           │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_depthwise         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,728</span> │ block_6_pad[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_depthwise_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)      │            <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_6_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_depthwise_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_6_depthwise_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">12,288</span> │ block_6_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_project_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ block_6_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_6_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_expand_BN         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_7_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_expand_relu       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_7_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_depthwise         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_7_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_depthwise_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_7_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_depthwise_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_7_depthwise_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_7_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_project_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ block_7_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_6_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│                           │                        │                │ block_7_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_7_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_expand_BN         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_8_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_expand_relu       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_8_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_depthwise         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_8_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_depthwise_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_8_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_depthwise_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_8_depthwise_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_8_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_project_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ block_8_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_7_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
              "│                           │                        │                │ block_8_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_8_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_expand_BN         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_9_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_expand_relu       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_9_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_depthwise         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_9_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_depthwise_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_9_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_depthwise_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_9_depthwise_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_9_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_project_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ block_9_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_8_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
              "│                           │                        │                │ block_9_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_9_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_expand_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_10_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_expand_relu      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_10_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_depthwise        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_10_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_depthwise_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_10_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_depthwise_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_10_depthwise_BN… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">36,864</span> │ block_10_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_project_BN       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block_10_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">55,296</span> │ block_10_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_expand_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_11_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_expand_relu      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_11_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_depthwise        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">5,184</span> │ block_11_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_depthwise_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_11_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_depthwise_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_11_depthwise_BN… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">55,296</span> │ block_11_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_project_BN       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block_11_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_10_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│                           │                        │                │ block_11_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">55,296</span> │ block_11_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_expand_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_12_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_expand_relu      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_12_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_depthwise        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">5,184</span> │ block_12_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_depthwise_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_12_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_depthwise_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_12_depthwise_BN… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">55,296</span> │ block_12_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_project_BN       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block_12_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_11_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],    │\n",
              "│                           │                        │                │ block_12_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">55,296</span> │ block_12_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_expand_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_13_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_expand_relu      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_13_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_pad              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_13_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)           │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_depthwise        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">5,184</span> │ block_13_pad[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_depthwise_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_13_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_depthwise_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_13_depthwise_BN… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">92,160</span> │ block_13_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_project_BN       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)      │            <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ block_13_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">153,600</span> │ block_13_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_expand_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_14_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_expand_relu      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_14_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_depthwise        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">8,640</span> │ block_14_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_depthwise_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_14_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_depthwise_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_14_depthwise_BN… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">153,600</span> │ block_14_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_project_BN       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)      │            <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ block_14_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_13_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│                           │                        │                │ block_14_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">153,600</span> │ block_14_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_expand_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_15_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_expand_relu      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_15_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_depthwise        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">8,640</span> │ block_15_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_depthwise_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_15_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_depthwise_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_15_depthwise_BN… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">153,600</span> │ block_15_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_project_BN       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)      │            <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ block_15_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_14_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],    │\n",
              "│                           │                        │                │ block_15_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_expand (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">153,600</span> │ block_15_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_expand_BN        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_16_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_expand_relu      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_16_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_depthwise        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">8,640</span> │ block_16_expand_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_depthwise_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_16_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_depthwise_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_16_depthwise_BN… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_project (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">307,200</span> │ block_16_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_project_BN       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │ block_16_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ Conv_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">409,600</span> │ block_16_project_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ Conv_1_bn                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">5,120</span> │ Conv_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ out_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ Conv_1_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ global_average_pooling2d  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)           │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ out_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)           │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ global_average_poolin… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">163,968</span> │ dropout_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │ dropout_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
              "</pre>\n"
            ],
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to          \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_9             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m96\u001b[0m, \u001b[38;5;34m96\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
              "│ (\u001b[38;5;33mInputLayer\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ Conv1 (\u001b[38;5;33mConv2D\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │            \u001b[38;5;34m864\u001b[0m │ input_layer_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ bn_Conv1                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │            \u001b[38;5;34m128\u001b[0m │ Conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ Conv1_relu (\u001b[38;5;33mReLU\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ bn_Conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ expanded_conv_depthwise   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │            \u001b[38;5;34m288\u001b[0m │ Conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ expanded_conv_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │            \u001b[38;5;34m128\u001b[0m │ expanded_conv_depthwi… │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ expanded_conv_depthwise_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ expanded_conv_depthwi… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ expanded_conv_project     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │            \u001b[38;5;34m512\u001b[0m │ expanded_conv_depthwi… │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ expanded_conv_project_BN  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m64\u001b[0m │ expanded_conv_project… │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_expand (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │          \u001b[38;5;34m1,536\u001b[0m │ expanded_conv_project… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_expand_BN         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │            \u001b[38;5;34m384\u001b[0m │ block_1_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_expand_relu       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ block_1_expand_BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_pad               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ block_1_expand_relu[\u001b[38;5;34m0\u001b[0m… │\n",
              "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)           │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_depthwise         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │            \u001b[38;5;34m864\u001b[0m │ block_1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_depthwise_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │            \u001b[38;5;34m384\u001b[0m │ block_1_depthwise[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_depthwise_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ block_1_depthwise_BN[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_project (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m)     │          \u001b[38;5;34m2,304\u001b[0m │ block_1_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_1_project_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m)     │             \u001b[38;5;34m96\u001b[0m │ block_1_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_expand (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │          \u001b[38;5;34m3,456\u001b[0m │ block_1_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_expand_BN         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │            \u001b[38;5;34m576\u001b[0m │ block_2_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_expand_relu       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_2_expand_BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_depthwise         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │          \u001b[38;5;34m1,296\u001b[0m │ block_2_expand_relu[\u001b[38;5;34m0\u001b[0m… │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_depthwise_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │            \u001b[38;5;34m576\u001b[0m │ block_2_depthwise[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_depthwise_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_2_depthwise_BN[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_project (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m)     │          \u001b[38;5;34m3,456\u001b[0m │ block_2_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_project_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m)     │             \u001b[38;5;34m96\u001b[0m │ block_2_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_2_add (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ block_1_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│                           │                        │                │ block_2_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_expand (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │          \u001b[38;5;34m3,456\u001b[0m │ block_2_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_expand_BN         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │            \u001b[38;5;34m576\u001b[0m │ block_3_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_expand_relu       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_3_expand_BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_pad               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_3_expand_relu[\u001b[38;5;34m0\u001b[0m… │\n",
              "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)           │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_depthwise         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │          \u001b[38;5;34m1,296\u001b[0m │ block_3_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_depthwise_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │            \u001b[38;5;34m576\u001b[0m │ block_3_depthwise[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_depthwise_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m144\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_3_depthwise_BN[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_project (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │          \u001b[38;5;34m4,608\u001b[0m │ block_3_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_3_project_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │            \u001b[38;5;34m128\u001b[0m │ block_3_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_expand (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │          \u001b[38;5;34m6,144\u001b[0m │ block_3_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_expand_BN         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │            \u001b[38;5;34m768\u001b[0m │ block_4_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_expand_relu       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_4_expand_BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_depthwise         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │          \u001b[38;5;34m1,728\u001b[0m │ block_4_expand_relu[\u001b[38;5;34m0\u001b[0m… │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_depthwise_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │            \u001b[38;5;34m768\u001b[0m │ block_4_depthwise[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_depthwise_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_4_depthwise_BN[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_project (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │          \u001b[38;5;34m6,144\u001b[0m │ block_4_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_project_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │            \u001b[38;5;34m128\u001b[0m │ block_4_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_4_add (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ block_3_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│                           │                        │                │ block_4_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_expand (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │          \u001b[38;5;34m6,144\u001b[0m │ block_4_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_expand_BN         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │            \u001b[38;5;34m768\u001b[0m │ block_5_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_expand_relu       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_5_expand_BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_depthwise         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │          \u001b[38;5;34m1,728\u001b[0m │ block_5_expand_relu[\u001b[38;5;34m0\u001b[0m… │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_depthwise_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │            \u001b[38;5;34m768\u001b[0m │ block_5_depthwise[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_depthwise_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_5_depthwise_BN[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_project (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │          \u001b[38;5;34m6,144\u001b[0m │ block_5_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_project_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │            \u001b[38;5;34m128\u001b[0m │ block_5_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_5_add (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ block_4_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
              "│                           │                        │                │ block_5_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_expand (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │          \u001b[38;5;34m6,144\u001b[0m │ block_5_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_expand_BN         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │            \u001b[38;5;34m768\u001b[0m │ block_6_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_expand_relu       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_6_expand_BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_pad               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block_6_expand_relu[\u001b[38;5;34m0\u001b[0m… │\n",
              "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)           │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_depthwise         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m192\u001b[0m)      │          \u001b[38;5;34m1,728\u001b[0m │ block_6_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_depthwise_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m192\u001b[0m)      │            \u001b[38;5;34m768\u001b[0m │ block_6_depthwise[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_depthwise_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m192\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_6_depthwise_BN[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_project (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │         \u001b[38;5;34m12,288\u001b[0m │ block_6_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_6_project_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │            \u001b[38;5;34m256\u001b[0m │ block_6_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_expand (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │         \u001b[38;5;34m24,576\u001b[0m │ block_6_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_expand_BN         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m1,536\u001b[0m │ block_7_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_expand_relu       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_7_expand_BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_depthwise         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m3,456\u001b[0m │ block_7_expand_relu[\u001b[38;5;34m0\u001b[0m… │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_depthwise_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m1,536\u001b[0m │ block_7_depthwise[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_depthwise_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_7_depthwise_BN[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_project (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │         \u001b[38;5;34m24,576\u001b[0m │ block_7_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_project_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │            \u001b[38;5;34m256\u001b[0m │ block_7_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_7_add (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ block_6_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│                           │                        │                │ block_7_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_expand (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │         \u001b[38;5;34m24,576\u001b[0m │ block_7_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_expand_BN         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m1,536\u001b[0m │ block_8_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_expand_relu       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_8_expand_BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_depthwise         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m3,456\u001b[0m │ block_8_expand_relu[\u001b[38;5;34m0\u001b[0m… │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_depthwise_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m1,536\u001b[0m │ block_8_depthwise[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_depthwise_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_8_depthwise_BN[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_project (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │         \u001b[38;5;34m24,576\u001b[0m │ block_8_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_project_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │            \u001b[38;5;34m256\u001b[0m │ block_8_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_8_add (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ block_7_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
              "│                           │                        │                │ block_8_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_expand (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │         \u001b[38;5;34m24,576\u001b[0m │ block_8_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_expand_BN         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m1,536\u001b[0m │ block_9_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_expand_relu       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_9_expand_BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_depthwise         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m3,456\u001b[0m │ block_9_expand_relu[\u001b[38;5;34m0\u001b[0m… │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_depthwise_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m1,536\u001b[0m │ block_9_depthwise[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_depthwise_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_9_depthwise_BN[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_project (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │         \u001b[38;5;34m24,576\u001b[0m │ block_9_depthwise_rel… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_project_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │            \u001b[38;5;34m256\u001b[0m │ block_9_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_9_add (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ block_8_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
              "│                           │                        │                │ block_9_project_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_expand (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │         \u001b[38;5;34m24,576\u001b[0m │ block_9_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_expand_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m1,536\u001b[0m │ block_10_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_expand_relu      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_10_expand_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_depthwise        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m3,456\u001b[0m │ block_10_expand_relu[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_depthwise_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │          \u001b[38;5;34m1,536\u001b[0m │ block_10_depthwise[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_depthwise_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_10_depthwise_BN… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_project (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)       │         \u001b[38;5;34m36,864\u001b[0m │ block_10_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_10_project_BN       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)       │            \u001b[38;5;34m384\u001b[0m │ block_10_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_expand (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │         \u001b[38;5;34m55,296\u001b[0m │ block_10_project_BN[\u001b[38;5;34m0\u001b[0m… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_expand_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │          \u001b[38;5;34m2,304\u001b[0m │ block_11_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_expand_relu      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_11_expand_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_depthwise        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │          \u001b[38;5;34m5,184\u001b[0m │ block_11_expand_relu[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_depthwise_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │          \u001b[38;5;34m2,304\u001b[0m │ block_11_depthwise[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_depthwise_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_11_depthwise_BN… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_project (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)       │         \u001b[38;5;34m55,296\u001b[0m │ block_11_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_project_BN       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)       │            \u001b[38;5;34m384\u001b[0m │ block_11_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_11_add (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ block_10_project_BN[\u001b[38;5;34m0\u001b[0m… │\n",
              "│                           │                        │                │ block_11_project_BN[\u001b[38;5;34m0\u001b[0m… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_expand (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │         \u001b[38;5;34m55,296\u001b[0m │ block_11_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_expand_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │          \u001b[38;5;34m2,304\u001b[0m │ block_12_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_expand_relu      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_12_expand_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_depthwise        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │          \u001b[38;5;34m5,184\u001b[0m │ block_12_expand_relu[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_depthwise_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │          \u001b[38;5;34m2,304\u001b[0m │ block_12_depthwise[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_depthwise_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_12_depthwise_BN… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_project (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)       │         \u001b[38;5;34m55,296\u001b[0m │ block_12_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_project_BN       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)       │            \u001b[38;5;34m384\u001b[0m │ block_12_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_12_add (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ block_11_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],    │\n",
              "│                           │                        │                │ block_12_project_BN[\u001b[38;5;34m0\u001b[0m… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_expand (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │         \u001b[38;5;34m55,296\u001b[0m │ block_12_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_expand_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │          \u001b[38;5;34m2,304\u001b[0m │ block_13_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_expand_relu      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_13_expand_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_pad              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_13_expand_relu[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)           │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_depthwise        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │          \u001b[38;5;34m5,184\u001b[0m │ block_13_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_depthwise_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │          \u001b[38;5;34m2,304\u001b[0m │ block_13_depthwise[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_depthwise_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m576\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_13_depthwise_BN… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_project (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)      │         \u001b[38;5;34m92,160\u001b[0m │ block_13_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_13_project_BN       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)      │            \u001b[38;5;34m640\u001b[0m │ block_13_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_expand (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │        \u001b[38;5;34m153,600\u001b[0m │ block_13_project_BN[\u001b[38;5;34m0\u001b[0m… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_expand_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │          \u001b[38;5;34m3,840\u001b[0m │ block_14_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_expand_relu      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_14_expand_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_depthwise        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │          \u001b[38;5;34m8,640\u001b[0m │ block_14_expand_relu[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_depthwise_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │          \u001b[38;5;34m3,840\u001b[0m │ block_14_depthwise[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_depthwise_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_14_depthwise_BN… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_project (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)      │        \u001b[38;5;34m153,600\u001b[0m │ block_14_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_project_BN       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)      │            \u001b[38;5;34m640\u001b[0m │ block_14_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_14_add (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_13_project_BN[\u001b[38;5;34m0\u001b[0m… │\n",
              "│                           │                        │                │ block_14_project_BN[\u001b[38;5;34m0\u001b[0m… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_expand (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │        \u001b[38;5;34m153,600\u001b[0m │ block_14_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_expand_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │          \u001b[38;5;34m3,840\u001b[0m │ block_15_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_expand_relu      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_15_expand_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_depthwise        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │          \u001b[38;5;34m8,640\u001b[0m │ block_15_expand_relu[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_depthwise_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │          \u001b[38;5;34m3,840\u001b[0m │ block_15_depthwise[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_depthwise_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_15_depthwise_BN… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_project (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)      │        \u001b[38;5;34m153,600\u001b[0m │ block_15_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_project_BN       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)      │            \u001b[38;5;34m640\u001b[0m │ block_15_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_15_add (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_14_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],    │\n",
              "│                           │                        │                │ block_15_project_BN[\u001b[38;5;34m0\u001b[0m… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_expand (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │        \u001b[38;5;34m153,600\u001b[0m │ block_15_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_expand_BN        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │          \u001b[38;5;34m3,840\u001b[0m │ block_16_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_expand_relu      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_16_expand_BN[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_depthwise        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │          \u001b[38;5;34m8,640\u001b[0m │ block_16_expand_relu[\u001b[38;5;34m…\u001b[0m │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_depthwise_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │          \u001b[38;5;34m3,840\u001b[0m │ block_16_depthwise[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_depthwise_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block_16_depthwise_BN… │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                    │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_project (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m320\u001b[0m)      │        \u001b[38;5;34m307,200\u001b[0m │ block_16_depthwise_re… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block_16_project_BN       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m320\u001b[0m)      │          \u001b[38;5;34m1,280\u001b[0m │ block_16_project[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ Conv_1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m1280\u001b[0m)     │        \u001b[38;5;34m409,600\u001b[0m │ block_16_project_BN[\u001b[38;5;34m0\u001b[0m… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ Conv_1_bn                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m1280\u001b[0m)     │          \u001b[38;5;34m5,120\u001b[0m │ Conv_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ out_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m1280\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ Conv_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ global_average_pooling2d  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1280\u001b[0m)           │              \u001b[38;5;34m0\u001b[0m │ out_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1280\u001b[0m)           │              \u001b[38;5;34m0\u001b[0m │ global_average_poolin… │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_18 (\u001b[38;5;33mDense\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m163,968\u001b[0m │ dropout_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │              \u001b[38;5;34m0\u001b[0m │ dense_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_19 (\u001b[38;5;33mDense\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m129\u001b[0m │ dropout_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,422,081</span> (9.24 MB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,422,081\u001b[0m (9.24 MB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">164,097</span> (641.00 KB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m164,097\u001b[0m (641.00 KB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,257,984</span> (8.61 MB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,257,984\u001b[0m (8.61 MB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "\u001b[1m5501/5501\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m646s\u001b[0m 116ms/step - AUC: 0.8941 - accuracy: 0.8212 - loss: 0.3985 - precision_9: 0.8034 - val_AUC: 0.9254 - val_accuracy: 0.8522 - val_loss: 0.3389 - val_precision_9: 0.8558\n",
            "Epoch 2/3\n",
            "\u001b[1m5501/5501\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10us/step - AUC: 0.0000e+00 - accuracy: 0.0000e+00 - loss: 0.0000e+00 - precision_9: 0.0000e+00\n",
            "Epoch 3/3\n",
            "\u001b[1m5501/5501\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m625s\u001b[0m 114ms/step - AUC: 0.9170 - accuracy: 0.8447 - loss: 0.3546 - precision_9: 0.8356 - val_AUC: 0.9310 - val_accuracy: 0.8594 - val_loss: 0.3272 - val_precision_9: 0.8372\n"
          ]
        }
      ],
      "source": [
        "input_shape = (96, 96, 3)\n",
        "\n",
        "# Load pre-trained MobileNetV2 model\n",
        "base_model = MobileNetV2(include_top=False, weights='imagenet',\n",
        "                         input_shape=input_shape)\n",
        "\n",
        "# Freeze base model (no fine tuning)\n",
        "base_model.trainable = False\n",
        "\n",
        "# Custom classification layers\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "output = Dense(1, activation='sigmoid')(x)\n",
        "\n",
        "# Defien and compile the model\n",
        "model = Model(inputs=base_model.input, outputs=output)\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy',\n",
        "              metrics=['accuracy', 'AUC', Precision()])\n",
        "\n",
        "model.summary()\n",
        "\n",
        "# Train\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    validation_data=val_generator,\n",
        "    epochs=3,\n",
        "    steps_per_epoch=len(train_generator),\n",
        "    validation_steps=len(val_generator)\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJFiE8mLZxWc"
      },
      "source": [
        "### Summary of Model Performance and discussion\n",
        "\n",
        "| Model   | Learning Rate | Dropout | Batch Size | Epoch | AUC   | Accuracy | Loss   | Precision | Val AUC | Val Accuracy | Val Loss | Val Precision |\n",
        "|---------|---------------|---------|------------|-------|-------|----------|--------|-----------|---------|--------------|---------|---------------|\n",
        "| Model 1 | 0.01          | 0.3     | 32         | 1     | 0.8146 | 0.7667   | 1.1728 | 0.7899    | 0.8448  | 0.7530       | 0.5222  | 0.9593        |\n",
        "|         |               |         |            | 2     | 0.8730 | 0.7188   | 0.5929 | 0.7778    | 0.8750  | 0.4000       | 0.8571  | 1.0000        |\n",
        "|         | 0.01          | 0.3     | 128        | 1     | 0.8351 | 0.7745   | 1.3852 | 0.7877    | 0.9046  | 0.8276       | 0.4129  | 0.7955        |\n",
        "|         |               |         |            | 2     | 0.8983 | 0.8438   | 0.4054 | 0.7500    | 0.6250  | 0.6000       | 0.5262  | 0.7500        |\n",
        "|         | 0.01          | 0.5     | 32         | 1     | 0.7115 | 0.6899   | 1.2651 | 0.7569    | 0.6397  | 0.6644       | 0.6254  | 0.9664        |\n",
        "|         |               |         |            | 2     | 0.5583 | 0.6250   | 0.6449 | 0.5000    | 0.6250  | 0.4000       | 0.7831  | 1.0000        |\n",
        "|         | 0.01          | 0.5     | 128        | 1     | 0.7703 | 0.7359   | 1.4639 | 0.7862    | 0.8306  | 0.7901       | 0.5231  | 0.7907        |\n",
        "|         |               |         |            | 2     | 0.6667 | 0.6875   | 0.6949 | 0.2222    | 0.5000  | 0.6000       | 0.6875  | 1.0000        |\n",
        "|         | 0.001         | 0.3     | 32         | 1     | 0.8764 | 0.8147   | 0.4868 | 0.7944    | 0.8376  | 0.7628       | 0.6136  | 0.9192        |\n",
        "|         |               |         |            | 2     | 0.9271 | 0.8750   | 0.3174 | 0.8462    | 0.7500  | 0.8000       | 1.1287  | 1.0000        |\n",
        "|         | 0.001         | 0.3     | 128        | 1     | 0.8780 | 0.8130   | 0.4930 | 0.7874    | 0.8015  | 0.7626       | 0.9103  | 0.8341        |\n",
        "|         |               |         |            | 2     | 0.9455 | 0.8750   | 0.3606 | 0.8000    | 0.5000  | 0.6000       | 1.8102  | 1.0000        |\n",
        "|         | 0.001         | 0.5     | 32         | 1     | 0.8726 | 0.8094   | 0.5007 | 0.7916    | 0.9024  | 0.8273       | 0.4694  | 0.8769        |\n",
        "|         |               |         |            | 2     | 0.9004 | 0.9062   | 0.3775 | 1.0000    | 0.2500  | 0.4000       | 0.8288  | 0.6667        |\n",
        "|         | 0.001         | 0.5     | 128        | 1     | 0.8650 | 0.8043   | 0.5430 | 0.7916    | 0.8474  | 0.7673       | 0.6380  | 0.8975        |\n",
        "|         |               |         |            | 2     | 0.8651 | 0.8125   | 0.4435 | 0.7857    | 0.7500  | 0.6000       | 1.4568  | 1.0000        |\n",
        "| Model 2 | -             | -       | -          | 1     | 0.8941 | 0.8212   | 0.3985 | 0.8034    | 0.9254  | 0.8522       | 0.3389  | 0.8558        |\n",
        "|         |               |         |            | 2     | 0.0000 | 0.0000   | 0.0000 | 0.0000    | -       | -            | -       | -             |\n",
        "|         |               |         |            | 3     | 0.9170 | 0.8447   | 0.3546 | 0.8356    | 0.9310  | 0.8594       | 0.3272  | 0.8372        |\n",
        "\n",
        "\n",
        "\n",
        "Model 2 is better than Model 1, with a higher validation accuracy of 85.94% compared to 82.73%. It also has a higher validation AUC (0.9310 vs. 0.9046) and lower validation loss (0.3272 vs. 0.4129). This means Model 2 performs better overall and is the best choice.\n",
        "\n",
        "In Model 1, hyperparameter tuning was done by adjusting learning rate, dropout, and batch size. The best validation accuracy of 82.73% was achieved with a learning rate of 0.01, dropout of 0.3, and a batch size of 128. However, other combinations resulted in lower accuracy, suggesting that tuning had limited success overall. While some improvement was seen, Model 1's performance still fell short of Model 2, indicating that further optimization or a different model architecture might be needed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5T4m4IrFbcz"
      },
      "source": [
        "### Prediction using the small custom CNN for submission"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKduZUu3Fbcz",
        "outputId": "2e92f8d4-1a2f-4cd3-9339-7ed5eaed7185"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 57458 validated image filenames.\n"
          ]
        }
      ],
      "source": [
        "# DataFrame for the test images\n",
        "test_folder = path + 'test'\n",
        "test_files = os.listdir(test_folder)\n",
        "test_df = pd.DataFrame({\n",
        "    'filepath': [os.path.join(test_folder, file) for file in test_files],\n",
        "    'id': [file.split('.')[0] for file in test_files]\n",
        "})\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "test_generator = test_datagen.flow_from_dataframe(\n",
        "    dataframe=test_df,\n",
        "    x_col='filepath',\n",
        "    y_col=None,\n",
        "    target_size=(96, 96),\n",
        "    batch_size=32,\n",
        "    class_mode=None,\n",
        "    shuffle=False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQ8j-kcbFbc0",
        "outputId": "9dd2f253-de57-4669-868b-499a2fe6a440"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m1796/1796\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 23ms/step\n"
          ]
        }
      ],
      "source": [
        "# Predict classes for the test set\n",
        "predictions = best_model.predict(test_generator, verbose=1)\n",
        "labels = (predictions > 0.5).astype(int).flatten()\n",
        "\n",
        "# Save to CSV\n",
        "test_df['label'] = labels\n",
        "submission = test_df[['id', 'label']]\n",
        "submission.to_csv(\"submission.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5F0WuSz5VoUV",
        "outputId": "76e53be9-54c7-4add-9117-96731e98292a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "File copied successfully.\n"
          ]
        }
      ],
      "source": [
        "shutil.copy('submission.csv', 'drive/MyDrive/DTSA-Project/submission.csv')\n",
        "print(\"File copied successfully.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jR9TuPNvFbc0"
      },
      "source": [
        "## Step 5: Conclusion\n",
        "\n",
        "In this project, two modeling approaches were used to identify metastatic cancer in small image patches: 1) a small custom CNN and 2) transfer learning (with fixed weights) combined with custom layers. Both models performed reasonably well. Model 2 performed slightly better than model 1, achieving a validation accuracy of 0.85. To further improve the performance of the custom CNN, hyperparameter tuning was applied. However, this did not lead to substantial improvements.\n",
        "\n",
        "Hyperparameter tuning for Model 1 had limited success overall. In the future, more parameters could be considered for tuning. However, a more promising approach might be to further optimize the model architecture itself. Initially, transfer learning with larger models (e.g., ResNet) was attempted, but the approach was discontinued due to hardware limitations, leading me to pivot to the smaller MobileNetV2 model.\n",
        "\n",
        "To further enhance the model, one potential approach is to use a larger base model, as demonstrated by [3], where ResNet was used alongside a single neuron layer with a sigmoid activation. This was not attempted in this project due to hardware limitations. EDA could also be improved by analyzing RGB channel intensities, as there appears to be a correlation between the green channel's intensity and the occurrence of cancer (as demonstrated by [4])."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KLXQx9nBFbc1"
      },
      "source": [
        "## References\n",
        "[1] Veeling, B. (n.d.). *PatchCamelyon (PCam) benchmark dataset*. GitHub. Retrieved January 5, 2025, from [https://github.com/basveeling/pcam](https://github.com/basveeling/pcam)\n",
        "\n",
        "[2] Kaggle. (n.d.). *Histopathologic cancer detection*. Retrieved January 5, 2025, from [https://www.kaggle.com/c/histopathologic-cancer-detection/overview](https://www.kaggle.com/c/histopathologic-cancer-detection/overview)\n",
        "\n",
        "[3] Azkalot1. (n.d.). *Histopathologic Cancer Detection*. GitHub. Retrieved January 5, 2025, from https://github.com/azkalot1/Histopathologic-Cancer-Detection/tree/master\n",
        "\n",
        "[4] Pietka, T. (n.d.). *BS-SM-TP-HCD Visualizations*. Kaggle. Retrieved January 5, 2025, from https://www.kaggle.com/code/terripietka/bs-sm-tp-hcd-visualizations"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
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