DucknetBrainSegmentation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "1wKzqw1elHipOAmplhmKy4xu2ZsIeZAPp",
      "authorship_tag": "ABX9TyO8RvbuKRrD5uvx+u6c0kMo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shrprabh/ArticulateAI/blob/main/DucknetBrainSegmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#  Enable GPU in Google Colab"
      ],
      "metadata": {
        "id": "GBai_oDclMIM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confirming if GPU is enabled"
      ],
      "metadata": {
        "id": "oEBUSZQDlao1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n"
      ],
      "metadata": {
        "id": "qz9G9eRMlYDa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68207113-acf2-45e3-a53d-9b9133b7d463"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num GPUs Available:  1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking GPU details and current utilization"
      ],
      "metadata": {
        "id": "uksIsqhalkJI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "DFJ8oX2bLT_p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ce5d986-3587-4a60-f0fc-aebb58a5ebed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Oct 16 02:21:28 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   59C    P8              10W /  70W |      3MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0APDb8N1LWSI",
        "outputId": "8580fdce-ef8b-420a-c700-c39ed01bc9ad"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls '/content/drive/MyDrive/Colab Notebooks/DUCK-Net-Colab/'\n",
        "\n",
        "%pip install tensorflow\n",
        "%pip install albumentations\n",
        "%pip install matplotlib\n",
        "%pip install scikit-learn\n",
        "%pip install opencv-python\n",
        "%pip install opencv-python-headless\n",
        "%pip install opencv-contrib-python\n",
        "%pip install tqdm\n",
        "import sys\n",
        "sys.path.append('/content/drive/MyDrive/Colab Notebooks/DUCK-Net-Colab/')  # Replace with the actual path to your module\n",
        "# Importing the necessary libraries\n",
        "import tensorflow as tf\n",
        "import albumentations as albu\n",
        "import numpy as np\n",
        "import gc\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.callbacks import CSVLogger  # Import Keras from TensorFlow\n",
        "from datetime import datetime\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import jaccard_score, precision_score, recall_score, accuracy_score, f1_score\n",
        "\n",
        "from ModelArchitecture.DiceLoss import dice_metric_loss\n",
        "from ModelArchitecture import DUCK_Net\n",
        "from ImageLoader import ImageLoader2D\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v3Wk-tYKMEnk",
        "outputId": "8dbe0248-ff33-4c80-c9cb-90817de5cc96"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CustomLayers\tImageLoader\t     ModelSaveTensorFLow     requirements.txt\n",
            "DataSet\t\tLICENSE\t\t     ProgressFull\t     results_DuckNet_17_kvasir.txt\n",
            "DUCK-Block.png\tModelArchitecture    QualitativeResults.png  Table1.png\n",
            "DUCK-Net.png\tModelNotebook.ipynb  README.md\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.0)\n",
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            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/albumentations/__init__.py:13: UserWarning: A new version of Albumentations is available: 1.4.18 (you have 1.4.15). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.\n",
            "  check_for_updates()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Checking the number of GPUs available\n",
        "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n",
        "\n",
        "# Checking the TensorFlow version\n",
        "print(\"TensorFlow version:\", tf.__version__)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GOSFIS3ORErl",
        "outputId": "d0fe4e77-0cf9-4330-a49f-70937cd22e9b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num GPUs Available:  1\n",
            "TensorFlow version: 2.17.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_size = 352\n",
        "dataset_type = 'cvc-clinicdb' # Options: kvasir/cvc-clinicdb/cvc-colondb/etis-laribpolypdb\n",
        "learning_rate = 1e-4\n",
        "seed_value = 58800\n",
        "filters = 17 # Number of filters, the paper presents the results with 17 and 34\n",
        "optimizer = tf.keras.optimizers.RMSprop(learning_rate=learning_rate)\n",
        "\n",
        "ct = datetime.now()\n",
        "\n",
        "model_type = \"DuckNet\"\n",
        "\n",
        "progress_path = '/content/drive/MyDrive/Colab Notebooks/DUCK-Net-Colab/ProgressFull/' + dataset_type + '_progress_csv_' + model_type + '_filters_' + str(filters) +  '_' + str(ct) + '.csv'\n",
        "progressfull_path = '/content/drive/MyDrive/Colab Notebooks/DUCK-Net-Colab/ProgressFull/' + dataset_type + '_progress_' + model_type + '_filters_' + str(filters) + '_' + str(ct) + '.txt'\n",
        "plot_path = '/content/drive/MyDrive/Colab Notebooks/DUCK-Net-Colab/ProgressFull/' + dataset_type + '_progress_plot_' + model_type + '_filters_' + str(filters) + '_' + str(ct) + '.png'\n",
        "model_path = '/content/drive/MyDrive/Colab Notebooks/DUCK-Net-Colab/ModelSaveTensorFLow/' + dataset_type + '/' + model_type + '_filters_' + str(filters) + '_' + str(ct)\n",
        "\n",
        "EPOCHS = 1\n",
        "min_loss_for_saving = 1\n"
      ],
      "metadata": {
        "id": "poRYiN4uRTS1"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls \"/content/drive/MyDrive/Colab Notebooks/DUCK-Net-Colab/DataSet/Kvasir-SEG/train/images/\"\n",
        "!ls \"/content/drive/MyDrive/Colab Notebooks/DUCK-Net-Colab/DataSet/Kvasir-SEG/train/masks/\"\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "An2lOV25cck5",
        "outputId": "88894f1e-4d25-49bb-9e49-fef98a4f2a03"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            " 19.jpg        '303 (1).jpg'   406.jpg\t      508.jpg\t     612.jpg   715.jpg\t      9.jpg\n",
            " 1.jpg\t        303.jpg        407.jpg\t      509.jpg\t     613.jpg   716.jpg\n",
            " 200.jpg        304.jpg        408.jpg\t      50.jpg\t     614.jpg   717.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, Y = ImageLoader2D.load_data(img_size, img_size, -1, 'kvasir')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3OgUrwrnRZAO",
        "outputId": "bcea592f-a6a8-4a26-c900-237d02fe1967"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks/DUCK-Net-Colab/DataSet/Kvasir-SEG/train/\n",
            "ETst\n",
            "/content/drive/MyDrive/Colab Notebooks/DUCK-Net-Colab/DataSet/Kvasir-SEG/train/images/\n",
            "Number of images found: 800\n",
            "Resizing training images and masks: 800\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "800it [03:36,  3.70it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Splitting the data, seed for reproducibility"
      ],
      "metadata": {
        "id": "wp-Hm-0seGCT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if X.size > 0 and Y.size > 0:\n",
        "    print(f\"Shape of X: {X.shape}\")\n",
        "    print(f\"Shape of Y: {Y.shape}\")\n",
        "    x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.1, shuffle=True, random_state=seed_value)\n",
        "    x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.111, shuffle=True, random_state=seed_value)\n",
        "else:\n",
        "    print(\"Dataset is empty. Cannot perform train-test split.\")\n"
      ],
      "metadata": {
        "id": "Ae0-5SDXeLbF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2a40102-4ee5-4233-e409-9de32fe67d99"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X: (800, 352, 352, 3)\n",
            "Shape of Y: (800, 352, 352, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining the augmentations"
      ],
      "metadata": {
        "id": "GhTHdTrlfXI5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "aug_train = albu.Compose([\n",
        "    albu.HorizontalFlip(),\n",
        "    albu.VerticalFlip(),\n",
        "    albu.ColorJitter(brightness=(0.6,1.6), contrast=0.2, saturation=0.1, hue=0.01, always_apply=True),\n",
        "    albu.Affine(scale=(0.5,1.5), translate_percent=(-0.125,0.125), rotate=(-180,180), shear=(-22.5,22), always_apply=True),\n",
        "])\n",
        "\n",
        "\n",
        "def augment_images():\n",
        "    x_train_out = []\n",
        "    y_train_out = []\n",
        "\n",
        "    for i in range (len(x_train)):\n",
        "        ug = aug_train(image=x_train[i], mask=y_train[i])\n",
        "        x_train_out.append(ug['image'])\n",
        "        y_train_out.append(ug['mask'])\n",
        "\n",
        "    return np.array(x_train_out), np.array(y_train_out)\n"
      ],
      "metadata": {
        "id": "VkGYnJgGfTa3"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating the model"
      ],
      "metadata": {
        "id": "OkRXJtiNfyia"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = DUCK_Net.create_model(img_height=img_size, img_width=img_size, input_chanels=3, out_classes=1, starting_filters=filters)\n"
      ],
      "metadata": {
        "id": "uK_TyK_3f0fs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef26212b-21c5-4ca6-cdc4-dcfbdb709c4a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting DUCK-Net\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Compiling the model"
      ],
      "metadata": {
        "id": "oSo1mIqrf8qO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=optimizer, loss=dice_metric_loss)\n"
      ],
      "metadata": {
        "id": "otgDDAG9f4Rt"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the model"
      ],
      "metadata": {
        "id": "3LSqXzvWgGmP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "step = 0\n",
        "\n",
        "for epoch in range(0, EPOCHS):\n",
        "\n",
        "    print(f'Training, epoch {epoch}')\n",
        "    print('Learning Rate: ' + str(learning_rate))\n",
        "\n",
        "    step += 1\n",
        "\n",
        "    image_augmented, mask_augmented = augment_images()\n",
        "\n",
        "    csv_logger = CSVLogger(progress_path, append=True, separator=';')\n",
        "\n",
        "    model.fit(x=image_augmented, y=mask_augmented, epochs=1, batch_size=4, validation_data=(x_valid, y_valid), verbose=1, callbacks=[csv_logger])\n",
        "\n",
        "    prediction_valid = model.predict(x_valid, verbose=0)\n",
        "    loss_valid = dice_metric_loss(y_valid, prediction_valid)\n",
        "\n",
        "    loss_valid = loss_valid.numpy()\n",
        "    print(\"Loss Validation: \" + str(loss_valid))\n",
        "\n",
        "    prediction_test = model.predict(x_test, verbose=0)\n",
        "    loss_test = dice_metric_loss(y_test, prediction_test)\n",
        "    loss_test = loss_test.numpy()\n",
        "    print(\"Loss Test: \" + str(loss_test))\n",
        "\n",
        "    with open(progressfull_path, 'a') as f:\n",
        "        f.write('epoch: ' + str(epoch) + '\\nval_loss: ' + str(loss_valid) + '\\ntest_loss: ' + str(loss_test) + '\\n\\n\\n')\n",
        "\n",
        "    if min_loss_for_saving > loss_valid:\n",
        "        min_loss_for_saving = loss_valid\n",
        "        print(\"Saved model with val_loss: \", loss_valid)\n",
        "        model.save(model_path + '.h5')\n",
        "\n",
        "    del image_augmented\n",
        "    del mask_augmented\n",
        "\n",
        "    gc.collect()"
      ],
      "metadata": {
        "id": "28PS-4JLgNn2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bb92427-6e65-41cf-dc43-0157a76c8f9b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training, epoch 0\n",
            "Learning Rate: 0.0001\n",
            "\u001b[1m160/160\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m468s\u001b[0m 1s/step - loss: 0.6787 - val_loss: 0.6164\n",
            "Loss Validation: 0.60657513\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss Test: 0.64096653\n",
            "Saved model with val_loss:  0.60657513\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assuming you have a trained model named 'model'\n",
        "\n",
        "# Generate predictions for training, testing, and validation datasets"
      ],
      "metadata": {
        "id": "6ZVKi9_HgYAs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_train = model.predict(x_train)\n",
        "prediction_test = model.predict(x_test)\n",
        "prediction_valid = model.predict(x_valid)"
      ],
      "metadata": {
        "id": "XGIc4dhNgex1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35f280f7-dc7d-414e-8952-e22039581487"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 1s/step\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1s/step\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Calculate recall scores"
      ],
      "metadata": {
        "id": "izc-7sGygoyO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "recall_test = recall_score(np.ndarray.flatten(np.array(y_test, dtype=bool)),\n",
        "                           np.ndarray.flatten(prediction_test > 0.5))\n",
        "recall_valid = recall_score(np.ndarray.flatten(np.array(y_valid, dtype=bool)),\n",
        "                            np.ndarray.flatten(prediction_valid > 0.5))\n",
        "\n",
        "print(\"Recall finished\")\n"
      ],
      "metadata": {
        "id": "17riA1Fegnfn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebdf217c-e77f-4984-c27d-180fccb6c5fa"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Calculate Accuracy Scores"
      ],
      "metadata": {
        "id": "7IX9-dBpgxMp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_train = accuracy_score(np.ndarray.flatten(np.array(y_train, dtype=bool)),\n",
        "                                np.ndarray.flatten(prediction_train > 0.5))\n",
        "accuracy_test = accuracy_score(np.ndarray.flatten(np.array(y_test, dtype=bool)),\n",
        "                               np.ndarray.flatten(prediction_test > 0.5))\n",
        "accuracy_valid = accuracy_score(np.ndarray.flatten(np.array(y_valid, dtype=bool)),\n",
        "                                np.ndarray.flatten(prediction_valid > 0.5))\n",
        "\n",
        "print(\"Accuracy finished\")"
      ],
      "metadata": {
        "id": "TffP6k92g3Xl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf4220e5-6599-4cf7-bfe8-4f7c38997530"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Construct the final file name"
      ],
      "metadata": {
        "id": "XWMLL67Qg5kY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_file = 'results_' + model_type + '_' + str(filters) + '_' + dataset_type + '.txt'\n",
        "print(final_file)\n"
      ],
      "metadata": {
        "id": "px05LBOihBoi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41cac9d9-989f-4204-f3e7-9fd16b6fdf4c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results_DuckNet_17_cvc-clinicdb.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Write results to final file name"
      ],
      "metadata": {
        "id": "Z4oTTCAXgmaT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(final_file, 'a') as f:\n",
        "    f.write(dataset_type + '\\n\\n')\n",
        "    f.write('recall_test: ' + str(recall_test) + ' recall_valid: ' + str(recall_valid) + '\\n\\n')\n",
        "    f.write('accuracy_train: ' + str(accuracy_train) + ' accuracy_test: ' + str(accuracy_test) + ' accuracy_valid: ' + str(accuracy_valid) + '\\n\\n')\n"
      ],
      "metadata": {
        "id": "YHF2QBKchgi3"
      },
      "execution_count": 18,
      "outputs": []
    }
  ]
}