6_2DUnet_evaluation.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oO6FC3e-QlqG",
        "outputId": "f595f108-4e4f-4172-a921-fcb882e51b7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: namex, optree, ml-dtypes, h5py, tensorboard, keras, tensorflow\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "Collecting patchify\n",
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          ]
        }
      ],
      "source": [
        "!pip install tensorflow==2.16.0rc0\n",
        "!PYTHONHASHSEED=0\n",
        "!pip install patchify"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import other modules\n",
        "from matplotlib import pyplot as plt\n",
        "import zipfile\n",
        "from shutil import copyfile\n",
        "from time import time\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random as python_random\n",
        "import os\n",
        "import shutil\n",
        "import glob\n",
        "from patchify import patchify, unpatchify\n",
        "import random\n",
        "\n",
        "# Import TensorFlow/Keras\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Conv3D, Conv3DTranspose, MaxPooling3D, concatenate, Dropout, Activation, BatchNormalization, GroupNormalization\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "import tensorflow.keras.backend as K\n",
        "\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W17oitOgQuUe",
        "outputId": "92aebe60-c92b-4a90-809d-13189db887fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Percorso del file ZIP su Google Drive\n",
        "drive_zip_path = '/content/drive/MyDrive/DL_PROJECT/Data/Test_128.zip'\n",
        "local_extract_path = '/content/Test_Data'\n",
        "os.makedirs(local_extract_path, exist_ok=True)\n",
        "\n",
        "# Decomprimere il file ZIP nella directory locale\n",
        "with zipfile.ZipFile(drive_zip_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(local_extract_path)\n",
        "\n",
        "print(\"Decompressione completata.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i3AUf6vFRBc6",
        "outputId": "86bc13d4-06e0-4f25-cf8e-0b33f4b2fc39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decompressione completata.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Import the numpy library for array manipulation\n",
        "\n",
        "# Function to load images from a given directory and list of image file names\n",
        "def load_img(img_dir, img_list):\n",
        "    images = []  # Initialize an empty list to store the images\n",
        "    for image_name in img_list:  # Loop through each image name in the list\n",
        "        if image_name.split('.')[-1] == 'npy':  # Check if the file is a .npy file\n",
        "            volume = np.load(img_dir + image_name)  # Load the .npy file as a numpy array\n",
        "            # Extract 128 images along the third axis (z-axis) from each volume\n",
        "            for j in range(volume.shape[2]):\n",
        "                images.append(volume[:, :, j, :])  # Append each slice to the images list\n",
        "    images = np.array(images)  # Convert the list of images to a numpy array\n",
        "    return images  # Return the numpy array of images\n",
        "\n",
        "# Generator function to load and yield image and mask batches\n",
        "def image_loader(img_dir, img_list, mask_dir, mask_list, batch_size=256):\n",
        "    assert batch_size == 256, \"Batch size must be 256 to match the number of slices per two volumes.\"  # Ensure batch size is 256\n",
        "\n",
        "    L = len(img_list)  # Get the length of the image list\n",
        "\n",
        "    while True:  # Infinite loop to keep yielding batches\n",
        "        for i in range(0, L, 2):  # Iterate through the image list in steps of 2\n",
        "            if i + 1 < L:  # Check if there are at least two more volumes to load\n",
        "                img = load_img(img_dir, [img_list[i], img_list[i+1]])  # Load images from two volumes\n",
        "                mask = load_img(mask_dir, [mask_list[i], mask_list[i+1]])  # Load corresponding masks\n",
        "\n",
        "                # Combine images and masks into a list of tuples and shuffle them\n",
        "                combined = list(zip(img, mask))\n",
        "                np.random.shuffle(combined)\n",
        "                img[:], mask[:] = zip(*combined)  # Unzip the shuffled list back into images and masks\n",
        "\n",
        "                yield (img, mask)  # Yield the shuffled images and masks as a batch\n",
        "            else:\n",
        "                # Handle the case where there is an odd number of volumes\n",
        "                img = load_img(img_dir, [img_list[i]])  # Load images from the last volume\n",
        "                mask = load_img(mask_dir, [mask_list[i]])  # Load corresponding masks\n",
        "\n",
        "                # Combine images and masks into a list of tuples and shuffle them\n",
        "                combined = list(zip(img, mask))\n",
        "                np.random.shuffle(combined)\n",
        "                img[:], mask[:] = zip(*combined)  # Unzip the shuffled list back into images and masks\n",
        "\n",
        "                yield (img, mask)  # Yield the shuffled images and masks as a batch"
      ],
      "metadata": {
        "id": "1FlyiUxCRI9Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow.keras.backend as K\n",
        "\n",
        "def DiceCoefficient(y_true, y_pred, smooth = 1e-6):\n",
        "\n",
        "    y_pred = tf.keras.activations.softmax(y_pred, axis = -1)\n",
        "\n",
        "    # Cast to float32 datatype\n",
        "    y_true = K.cast(y_true, 'float32')\n",
        "    y_pred = K.cast(y_pred, 'float32')\n",
        "\n",
        "    # Flatten label and prediction tensors\n",
        "    inputs = K.flatten(y_pred)\n",
        "    targets = K.flatten(y_true)\n",
        "\n",
        "    intersection = K.sum(inputs * targets)\n",
        "    dice = (2 * intersection + smooth) / (K.sum(targets) + K.sum(inputs) + smooth)\n",
        "    return dice"
      ],
      "metadata": {
        "id": "5UqRt79JQvLK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.saving.load_model('/content/drive/MyDrive/DL_PROJECT/2D_FINAL/64_Base.keras', compile = True , custom_objects={'DiceCoefficient' : DiceCoefficient})"
      ],
      "metadata": {
        "id": "GFfxTdUeQzWx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "03NqmcaLVoPY",
        "outputId": "0573850e-f9db-4897-f9c3-8124e4aab105"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"2D_U-Net\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"2D_U-Net\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
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          "output_type": "display_data",
          "data": {
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              "┃\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 (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m4\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │          \u001b[38;5;34m2,368\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation (\u001b[38;5;33mActivation\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\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;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ activation[\u001b[38;5;34m0\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;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m36,928\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling2d             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │         \u001b[38;5;34m73,856\u001b[0m │ max_pooling2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ activation_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m147,584\u001b[0m │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_3 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling2d_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m295,168\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_4 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ activation_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m590,080\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_5 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling2d_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │      \u001b[38;5;34m1,180,160\u001b[0m │ max_pooling2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_6 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ activation_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │      \u001b[38;5;34m2,359,808\u001b[0m │ dropout_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_7 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling2d_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │      \u001b[38;5;34m4,719,616\u001b[0m │ max_pooling2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_8 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ conv2d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ activation_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │      \u001b[38;5;34m9,438,208\u001b[0m │ dropout_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_9 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ conv2d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_transpose          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │      \u001b[38;5;34m2,097,664\u001b[0m │ activation_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m1024\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ conv2d_transpose[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
              "│                           │                        │                │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │      \u001b[38;5;34m4,719,104\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_10             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ activation_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │      \u001b[38;5;34m2,359,808\u001b[0m │ dropout_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_11             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_transpose_1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m524,544\u001b[0m │ activation_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_1             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_1[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │                        │                │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │      \u001b[38;5;34m1,179,904\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_12             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ activation_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m590,080\u001b[0m │ dropout_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_13             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_transpose_2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m131,200\u001b[0m │ activation_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_2             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_2[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │                        │                │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m295,040\u001b[0m │ concatenate_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_14             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ activation_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m147,584\u001b[0m │ dropout_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_15             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_transpose_3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m32,832\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_3             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │              \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_3[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │                        │                │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m73,792\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_16             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ conv2d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m36,928\u001b[0m │ dropout_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_17             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ conv2d_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m4\u001b[0m)    │            \u001b[38;5;34m260\u001b[0m │ activation_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)              </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">        Param # </span>┃<span style=\"font-weight: bold\"> Connected to           </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,368</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ dropout[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling2d             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_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\">MaxPooling2D</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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │ max_pooling2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ dropout_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling2d_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_3[<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\">MaxPooling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │ max_pooling2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling2d_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_5[<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\">MaxPooling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │ max_pooling2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ dropout_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling2d_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_7[<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\">MaxPooling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,719,616</span> │ max_pooling2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,438,208</span> │ dropout_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_transpose          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,097,664</span> │ activation_9[<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\">Conv2DTranspose</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│                           │                        │                │ activation_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,719,104</span> │ concatenate[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_10             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_10[<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\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ dropout_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_11             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_11[<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\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_transpose_1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">524,544</span> │ activation_11[<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\">Conv2DTranspose</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_1             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │                        │                │ activation_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,179,904</span> │ concatenate_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_12             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_12[<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\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_6 (<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\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_13 (<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\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ dropout_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_13             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_13[<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\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_transpose_2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">131,200</span> │ activation_13[<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\">Conv2DTranspose</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_2             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │                        │                │ activation_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_14 (<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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">295,040</span> │ concatenate_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_14             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_14[<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\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_15 (<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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ dropout_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_15             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_15[<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\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_transpose_3        │ (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">32,832</span> │ activation_15[<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\">Conv2DTranspose</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_3             │ (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │                        │                │ activation_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_16 (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">73,792</span> │ concatenate_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_16             │ (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_16[<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\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_8 (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_17 (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ dropout_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_17             │ (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_17[<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\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_18 (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span> │ activation_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m93,097,549\u001b[0m (355.14 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">93,097,549</span> (355.14 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m31,032,516\u001b[0m (118.38 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">31,032,516</span> (118.38 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m62,065,033\u001b[0m (236.76 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">62,065,033</span> (236.76 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_img_dir = '/content/Test_Data/X_test/'\n",
        "test_mask_dir = '/content/Test_Data/Y_test/'\n",
        "\n",
        "test_img_list = sorted(os.listdir(test_img_dir))\n",
        "test_mask_list = sorted(os.listdir(test_mask_dir))"
      ],
      "metadata": {
        "id": "SDOfub_fTnGu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 256\n",
        "\n",
        "test_img_datagen = image_loader(test_img_dir, test_img_list, test_mask_dir, test_mask_list, batch_size)\n",
        "\n",
        "steps_per_epoch = len(test_img_list)//2\n",
        "print(steps_per_epoch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NcUXNFGPTm3n",
        "outputId": "e4b4a810-ca45-4e63-86e7-df534993a4a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "36\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluation = model.evaluate(\n",
        "    test_img_datagen,\n",
        "    verbose = 1,\n",
        "    steps = steps_per_epoch,\n",
        "    callbacks = None,\n",
        "    return_dict = True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53eCrNERTRrK",
        "outputId": "92e7b3a5-b1ae-492f-de22-6b732de07f6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m149s\u001b[0m 2s/step - accuracy: 0.9158 - loss: 0.2101 - one_hot_io_u: 0.7709 - one_hot_io_u_1: 0.2691 - one_hot_io_u_2: 0.3808 - one_hot_io_u_3: 0.5521 - one_hot_mean_io_u: 0.4932\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQ5wA6bbwuG0",
        "outputId": "33317133-7bae-4126-dea0-2629cd36e3a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.9409926533699036,\n",
              " 'loss': 0.2141338437795639,\n",
              " 'one_hot_io_u': 0.680842399597168,\n",
              " 'one_hot_io_u_1': 0.3214501440525055,\n",
              " 'one_hot_io_u_2': 0.36830729246139526,\n",
              " 'one_hot_io_u_3': 0.5814285278320312,\n",
              " 'one_hot_mean_io_u': 0.488007128238678}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}