Classification_HBI_ViT.ipynb

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\n",
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Meddebma/pyradiomics/blob/master/Classification_HBI_ViT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wiFnM05m6uL2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a99000cb-c63f-4a91-8025-fd9bae5470e1"
      },
      "source": [
        "%pip install \"monai--weekly[nibabel, skimage, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil]\"\n",
        "%pip install itkwidgets\n",
        "%pip install SimpleITK\n",
        "%pip install itk-io\n",
        "%pip install vit-pytorch"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting monai--weekly[gdown,ignite,itk,lmdb,nibabel,pillow,psutil,skimage,tensorboard,torchvision,tqdm]\n",
            "  Downloading monai_weekly-0.9.dev2148-py3-none-any.whl (710 kB)\n",
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            "Successfully installed itk-5.2.1.post1 itk-core-5.2.1.post1 itk-filtering-5.2.1.post1 itk-io-5.2.1.post1 itk-numerics-5.2.1.post1 itk-registration-5.2.1.post1 itk-segmentation-5.2.1.post1 monai-weekly-0.9.dev2148 pytorch-ignite-0.4.6\n",
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            "Installing collected packages: traittypes, zstandard, itk-meshtopolydata, ipympl, ipydatawidgets, itkwidgets\n",
            "Successfully installed ipydatawidgets-4.2.0 ipympl-0.8.2 itk-meshtopolydata-0.7.1 itkwidgets-0.32.1 traittypes-0.2.1 zstandard-0.16.0\n",
            "Collecting SimpleITK\n",
            "  Downloading SimpleITK-2.1.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (48.4 MB)\n",
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            "  Downloading vit_pytorch-0.24.2-py3-none-any.whl (42 kB)\n",
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            "Successfully installed einops-0.3.2 vit-pytorch-0.24.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WHt5hRV4MYoT",
        "outputId": "dc41d033-82eb-48db-c0ad-c818cfcaa984"
      },
      "source": [
        "# Copyright 2020 MONAI Consortium\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "import logging\n",
        "import os\n",
        "import sys\n",
        "import tempfile\n",
        "import shutil\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "import numpy as np\n",
        "import SimpleITK as sitk\n",
        "import monai\n",
        "from monai.networks.nets import DenseNet121, Classifier, EfficientNet, EfficientNetBN, ViT\n",
        "from monai.apps import download_and_extract\n",
        "from monai.config import print_config\n",
        "from monai.utils import first\n",
        "from monai.visualize import GradCAM\n",
        "from monai.metrics import ConfusionMatrixMetric, get_confusion_matrix\n",
        "from monai.data import CacheDataset, DataLoader, ImageDataset, ITKReader, PILReader, Dataset, partition_dataset_classes, partition_dataset\n",
        "from sklearn.metrics import (\n",
        "    classification_report, confusion_matrix,\n",
        "    ConfusionMatrixDisplay, \n",
        ")\n",
        "from monai.visualize import blend_images, matshow3d, plot_2d_or_3d_image\n",
        "from monai.data import decollate_batch\n",
        "from monai.metrics import ROCAUCMetric\n",
        "from torch.utils.data import DataLoader \n",
        "from monai.transforms import Transform\n",
        "from monai.transforms import (\n",
        "    AddChannel,\n",
        "    Compose,\n",
        "    LoadImage,\n",
        "    RandRotate90,\n",
        "    Invertd,\n",
        "    Lambda,\n",
        "    Resize,\n",
        "    Orientation,\n",
        "    Orientationd,\n",
        "    EnsureChannelFirstd,\n",
        "    ScaleIntensity,\n",
        "    AsChannelFirst,\n",
        "    AsChannelLast,\n",
        "    Activations,\n",
        "    AddChannel,\n",
        "    AddChanneld,\n",
        "    ToTensord,\n",
        "    Resized,\n",
        "    AsDiscrete,\n",
        "    Spacingd,\n",
        "    EnsureChannelFirst,\n",
        "    RepeatChannel,\n",
        "    RemoveRepeatedChannel,\n",
        "    ScaleIntensityRanged,\n",
        "    SplitChannel,\n",
        "    SqueezeDim,\n",
        "    ToTensor,\n",
        "    CropForegroundd,\n",
        "    Randomizable,\n",
        "    LoadImaged,\n",
        "    ToTensord,\n",
        "    MaskIntensityd,\n",
        "    Flip,\n",
        "    MaskIntensity,\n",
        "    ScaleIntensityd,\n",
        "    EnsureType,\n",
        "    EnsureTyped,\n",
        ")\n",
        "import numpy as np\n",
        "import statsmodels.formula.api as smf\n",
        "import statsmodels.api as sm\n",
        "import pandas as pd\n",
        "import seaborn as sns \n",
        "import itk\n",
        "import tensorboard\n",
        "from itkwidgets import view\n",
        "\n",
        "from glob import glob\n",
        "import logging\n",
        "from abc import ABC, abstractmethod\n",
        "from typing import Any, Callable, Dict, Generator, Hashable, Iterable, List, Optional, Tuple\n",
        "from monai.utils import first, set_determinism\n",
        "from math import ceil\n",
        "#from downloaddata import fetch_data as fdata\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install -q itkwidgets\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "import numpy as np\n",
        "from monai.transforms import Transform, AddChannel, AsChannelFirst\n",
        "\n",
        "\n",
        "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
        "print_config()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MONAI version: 0.9.dev2148\n",
            "Numpy version: 1.19.5\n",
            "Pytorch version: 1.10.0+cu111\n",
            "MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
            "MONAI rev id: 63c18670e4d46414eb3b5917fb0632e09bc3f8ed\n",
            "\n",
            "Optional dependencies:\n",
            "Pytorch Ignite version: 0.4.6\n",
            "Nibabel version: 3.0.2\n",
            "scikit-image version: 0.18.3\n",
            "Pillow version: 7.1.2\n",
            "Tensorboard version: 2.7.0\n",
            "gdown version: 3.6.4\n",
            "TorchVision version: 0.11.1+cu111\n",
            "tqdm version: 4.62.3\n",
            "lmdb version: 0.99\n",
            "psutil version: 5.4.8\n",
            "pandas version: 1.1.5\n",
            "einops version: 0.3.2\n",
            "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "\n",
            "For details about installing the optional dependencies, please visit:\n",
            "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BvUL9l6t_tDM"
      },
      "source": [
        "**Data Direction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHeS8Rt6_zWD"
      },
      "source": [
        "data_dir= \"/content/drive/My Drive/Brain_AI\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1S2v-B-AITM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f03ca0ea-29fd-474a-bbc9-d16eabd94bc5"
      },
      "source": [
        "labels_all = pd.read_excel (r'/content/drive/MyDrive/Brain_AI/data/Projekt1/Brain_AI.xlsx')\n",
        "l=labels_all[\"Label\"].to_numpy()\n",
        "print(l)\n",
        "print(len(l))\n",
        "#print(labels_all[\"label\"])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 1 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 1 0 1\n",
            " 0 1 1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1]\n",
            "58\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyjyR96rS0Bl",
        "outputId": "55830865-d2d9-4978-973a-c70c1eff6f6a"
      },
      "source": [
        "images = sorted(glob(os.path.join(data_dir, \"data/Projekt1/\", \"*.nii.gz\")))\n",
        "#segmentations = sorted(glob(os.path.join(data_dir, \"Projekt2/segmentation/*\", \"*.nii.gz\")))\n",
        "image1 = \"/content/drive/MyDrive/Brain_AI/data/Projekt1/A018_2_FLIRT_CT_toMNI.nii.gz\"\n",
        "#for i in images:\n",
        " # print(os.path.basename(i))\n",
        "\n",
        "labels= l\n",
        "#labels= np.array([0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1], dtype=np.int64)\n",
        "print(len(images))\n",
        "#print(len(segmentations))\n",
        "print(len(labels))\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "58\n",
            "58\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393,
          "referenced_widgets": [
            "42e5bc30ded2449194626c1cb26d26a4",
            "c17a6dc8598a4c10a814a646e665511f",
            "f0a4b91354e04263b42b449d859a44da",
            "a00c27f5d31f476a9816f95750645c11",
            "c472aef8fd7e47918f5b84a1f2c4970d",
            "f4459d788d1a426f90f5d93e0a3530ac",
            "3f02b8380a624e1b969412d01934ca4c"
          ]
        },
        "id": "vBnbVTeKNUc0",
        "outputId": "b9f2802f-ff21-4e6e-c025-1437f0230fc2"
      },
      "source": [
        "#!pip install ipywidgets\n",
        "img = itk.imread(images[3])\n",
        "\n",
        "#view(img)\n",
        "print(img.shape)\n",
        "from ipywidgets import interact, interactive, IntSlider, ToggleButtons\n",
        "#print(f\"The image object has the following dimensions: height: {height}, width:{width}, depth:{depth}\")\n",
        "# Define a function to visualize the data\n",
        "for i in img:\n",
        "  def explore_3dimage(layer):\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    plt.imshow(img[layer, :,  :], cmap='gray');\n",
        "    plt.title('Explore Layers of Brain CT', fontsize=20)\n",
        "    plt.axis('off')\n",
        "    return layer\n",
        "    \n",
        "# Run the ipywidgets interact() function to explore the data\n",
        "interact(explore_3dimage, layer=(1, img.shape[2] - 1));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(181, 217, 181)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42e5bc30ded2449194626c1cb26d26a4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=90, description='layer', max=180, min=1), Output()), _dom_classes=('widg…"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJJwFbO47yGV"
      },
      "source": [
        "**Loading Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scT_G0HLMNjH",
        "outputId": "4f446e29-aef3-4a41-bb32-334a2a58a00c"
      },
      "source": [
        "set_determinism(seed=0)\n",
        "\n",
        "\n",
        "data = [{\"image\": i, \"label\": l} for i, l in zip(images, labels)]\n",
        "\n",
        "transforms = Compose([\n",
        "    LoadImaged(keys=[\"image\"]),\n",
        "    AddChanneld(keys=[\"image\"]),\n",
        "    Orientationd(keys=[\"image\"], axcodes=\"PLI\"),\n",
        "    Resized(keys=[\"image\"], spatial_size=(96, 96, 96)),\n",
        "    ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
        "    #MaskIntensityd(keys=\"image\", mask_key=\"seg\"),\n",
        "    EnsureTyped(keys=[\"image\"]),\n",
        "])\n",
        "\n",
        "\n",
        "data_partition= partition_dataset_classes(data, labels.tolist(), shuffle=True, ratios=[0.8, 0.2])\n",
        "\n",
        "# create a training data loader\n",
        "train_ds = CacheDataset(data=data_partition[0], transform=transforms)\n",
        "train_loader = DataLoader(train_ds, batch_size=2, shuffle=False, num_workers=4, pin_memory=torch.cuda.is_available())\n",
        "\n",
        "# create a validation data loader\n",
        "val_ds = CacheDataset(data=data_partition[1], transform=transforms)\n",
        "val_loader = DataLoader(val_ds, batch_size=2, shuffle=False, num_workers=4, pin_memory=torch.cuda.is_available())\n",
        "\n",
        "# create a test data loader\n",
        "test_ds = CacheDataset(data=data_partition[1], transform=transforms)\n",
        "test_loader = DataLoader(test_ds, batch_size=2, shuffle=False, num_workers=4, pin_memory=torch.cuda.is_available())\n",
        "\n",
        "post_pred = Compose([EnsureType(), Activations(softmax=True)])\n",
        "post_label = Compose([EnsureType(), AsDiscrete(to_onehot=True, n_classes=2)])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading dataset: 100%|██████████| 46/46 [00:12<00:00,  3.58it/s]\n",
            "Loading dataset: 100%|██████████| 12/12 [00:03<00:00,  3.46it/s]\n",
            "Loading dataset: 100%|██████████| 12/12 [00:01<00:00,  6.45it/s]\n",
            "`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtjR-k758HU7"
      },
      "source": [
        "**Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WdSLt-xvpUQ"
      },
      "source": [
        "device = torch.device(\"cuda\")\n",
        "model = monai.networks.nets.ViT(in_channels=1, img_size=(96,96,96), patch_size=(16,16,16), pos_embed='conv', classification=True).to(device)\n",
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), 1e-5)\n",
        "auc_metric = ROCAUCMetric()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LSEwWGo2GMR",
        "outputId": "d2383b64-f4de-4c84-c725-275a184f4c61"
      },
      "source": [
        "# start a typical PyTorch training\n",
        "val_interval = 2\n",
        "best_metric = -1\n",
        "best_metric_epoch = -1\n",
        "epoch_loss_values = []\n",
        "metric_values = []\n",
        "acc_values = []\n",
        "writer = SummaryWriter()\n",
        "max_epochs = 100\n",
        "for epoch in range(max_epochs):\n",
        "        print(\"-\" * 10)\n",
        "        print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
        "        model.train()\n",
        "        epoch_loss = 0\n",
        "        step = 0\n",
        "        for batch_data in train_loader:\n",
        "            step += 1\n",
        "            inputs, labels = batch_data[\"image\"].to(device), batch_data[\"label\"].to(device)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs)\n",
        "            loss = loss_function(outputs[0], labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            epoch_loss += loss.item()\n",
        "            epoch_len = len(train_ds) // train_loader.batch_size\n",
        "            print(f\"{step}/{epoch_len}, train_loss: {loss.item():.4f}\")\n",
        "            writer.add_scalar(\"train_loss\", loss.item(), epoch_len * epoch + step)\n",
        "        epoch_loss /= step\n",
        "        epoch_loss_values.append(epoch_loss)\n",
        "        print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
        "\n",
        "        if (epoch + 1) % val_interval == 0:\n",
        "            model.eval()\n",
        "            with torch.no_grad():\n",
        "                y_pred = torch.tensor([], dtype=torch.float32, device=device)\n",
        "                y = torch.tensor([], dtype=torch.long, device=device)\n",
        "                for val_data in val_loader:\n",
        "                    val_images, val_labels = val_data[\"image\"].to(device), val_data[\"label\"].to(device)\n",
        "                    y_pred = torch.cat([y_pred, model(val_images)[0]], dim=0)\n",
        "                    y = torch.cat([y, val_labels], dim=0)\n",
        "\n",
        "                acc_value = torch.eq(y_pred.argmax(dim=1), y)\n",
        "                acc_metric = acc_value.sum().item() / len(acc_value)\n",
        "                y_onehot = [post_label(i) for i in decollate_batch(y)]\n",
        "                y_pred_act = [post_pred(i) for i in decollate_batch(y_pred)]\n",
        "                auc_metric(y_pred_act, y_onehot)\n",
        "                auc_result = auc_metric.aggregate()\n",
        "                auc_metric.reset()\n",
        "                del y_pred_act, y_onehot\n",
        "                metric_values.append(auc_result)\n",
        "                \n",
        "\n",
        "                if acc_metric > best_metric:\n",
        "                    best_metric = acc_metric\n",
        "                    best_metric_epoch = epoch + 1\n",
        "                    \n",
        "                    torch.save(model.state_dict(), \"/content/drive/My Drive/Brain_AI/best_metric_model_classification_ViT100.pth\")\n",
        "                    print(\"saved new best metric model\")\n",
        "                print(\n",
        "                    \"current epoch: {} current accuracy: {:.4f} current AUC: {:.4f} best accuracy: {:.4f} at epoch {}\".format(\n",
        "                        epoch + 1, acc_metric, auc_result, best_metric, best_metric_epoch\n",
        "                    )\n",
        "                )\n",
        "                writer.add_scalar(\"val_accuracy\", acc_metric, epoch + 1)\n",
        "print(f\"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}\")\n",
        "writer.close()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------\n",
            "epoch 1/100\n",
            "1/23, train_loss: 0.6922\n",
            "2/23, train_loss: 1.1192\n",
            "3/23, train_loss: 1.1202\n",
            "4/23, train_loss: 1.0869\n",
            "5/23, train_loss: 0.7726\n",
            "6/23, train_loss: 0.7102\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6932\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6931\n",
            "20/23, train_loss: 0.6932\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 1 average loss: 0.7515\n",
            "----------\n",
            "epoch 2/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6930\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6933\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6930\n",
            "20/23, train_loss: 0.6933\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 2 average loss: 0.6931\n",
            "saved new best metric model\n",
            "current epoch: 2 current accuracy: 0.5000 current AUC: 0.7778 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 3/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6930\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6933\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6930\n",
            "20/23, train_loss: 0.6933\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 3 average loss: 0.6931\n",
            "----------\n",
            "epoch 4/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6930\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6933\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6930\n",
            "20/23, train_loss: 0.6933\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 4 average loss: 0.6931\n",
            "current epoch: 4 current accuracy: 0.5000 current AUC: 0.7778 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 5/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6930\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6933\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6930\n",
            "20/23, train_loss: 0.6933\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 5 average loss: 0.6931\n",
            "----------\n",
            "epoch 6/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6930\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6933\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6930\n",
            "20/23, train_loss: 0.6933\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 6 average loss: 0.6931\n",
            "current epoch: 6 current accuracy: 0.5000 current AUC: 0.7778 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 7/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6930\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6933\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6930\n",
            "20/23, train_loss: 0.6933\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 7 average loss: 0.6931\n",
            "----------\n",
            "epoch 8/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6930\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6933\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6930\n",
            "20/23, train_loss: 0.6933\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 8 average loss: 0.6931\n",
            "current epoch: 8 current accuracy: 0.5000 current AUC: 0.7778 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 9/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6930\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
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            "epoch 9 average loss: 0.6931\n",
            "----------\n",
            "epoch 10/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 10 average loss: 0.6931\n",
            "current epoch: 10 current accuracy: 0.5000 current AUC: 0.7778 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 11/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 11 average loss: 0.6931\n",
            "----------\n",
            "epoch 12/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 12 average loss: 0.6931\n",
            "current epoch: 12 current accuracy: 0.5000 current AUC: 0.7847 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 13/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
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            "epoch 13 average loss: 0.6931\n",
            "----------\n",
            "epoch 14/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 14 average loss: 0.6931\n",
            "current epoch: 14 current accuracy: 0.5000 current AUC: 0.7778 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 15/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
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            "epoch 15 average loss: 0.6931\n",
            "----------\n",
            "epoch 16/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 16 average loss: 0.6931\n",
            "current epoch: 16 current accuracy: 0.5000 current AUC: 0.7847 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 17/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
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            "epoch 17 average loss: 0.6931\n",
            "----------\n",
            "epoch 18/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 18 average loss: 0.6931\n",
            "current epoch: 18 current accuracy: 0.5000 current AUC: 0.7847 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 19/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 19 average loss: 0.6931\n",
            "----------\n",
            "epoch 20/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 20 average loss: 0.6931\n",
            "current epoch: 20 current accuracy: 0.5000 current AUC: 0.7917 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 21/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 21 average loss: 0.6931\n",
            "----------\n",
            "epoch 22/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 22 average loss: 0.6931\n",
            "current epoch: 22 current accuracy: 0.5000 current AUC: 0.8056 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 23/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 23 average loss: 0.6931\n",
            "----------\n",
            "epoch 24/100\n",
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            "epoch 24 average loss: 0.6931\n",
            "current epoch: 24 current accuracy: 0.5000 current AUC: 0.8056 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 25/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 25 average loss: 0.6931\n",
            "----------\n",
            "epoch 26/100\n",
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            "epoch 26 average loss: 0.6931\n",
            "current epoch: 26 current accuracy: 0.5000 current AUC: 0.8125 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 27/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 27 average loss: 0.6931\n",
            "----------\n",
            "epoch 28/100\n",
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            "epoch 28 average loss: 0.6931\n",
            "current epoch: 28 current accuracy: 0.5000 current AUC: 0.7986 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 29/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 29 average loss: 0.6931\n",
            "----------\n",
            "epoch 30/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 30 average loss: 0.6931\n",
            "current epoch: 30 current accuracy: 0.5000 current AUC: 0.8056 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 31/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 31 average loss: 0.6931\n",
            "----------\n",
            "epoch 32/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 32 average loss: 0.6931\n",
            "current epoch: 32 current accuracy: 0.5000 current AUC: 0.8125 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 33/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 33 average loss: 0.6931\n",
            "----------\n",
            "epoch 34/100\n",
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            "epoch 34 average loss: 0.6931\n",
            "current epoch: 34 current accuracy: 0.5000 current AUC: 0.8125 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 35/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 35 average loss: 0.6931\n",
            "----------\n",
            "epoch 36/100\n",
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            "epoch 36 average loss: 0.6931\n",
            "current epoch: 36 current accuracy: 0.5000 current AUC: 0.8194 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 37/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 37 average loss: 0.6931\n",
            "----------\n",
            "epoch 38/100\n",
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            "epoch 38 average loss: 0.6931\n",
            "current epoch: 38 current accuracy: 0.5000 current AUC: 0.8194 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 39/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 39 average loss: 0.6931\n",
            "----------\n",
            "epoch 40/100\n",
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            "epoch 40 average loss: 0.6931\n",
            "current epoch: 40 current accuracy: 0.5000 current AUC: 0.8125 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 41/100\n",
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            "epoch 41 average loss: 0.6931\n",
            "----------\n",
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            "epoch 42 average loss: 0.6931\n",
            "current epoch: 42 current accuracy: 0.5000 current AUC: 0.8264 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 43/100\n",
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            "epoch 43 average loss: 0.6931\n",
            "----------\n",
            "epoch 44/100\n",
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            "epoch 44 average loss: 0.6931\n",
            "current epoch: 44 current accuracy: 0.5000 current AUC: 0.8194 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 45/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 45 average loss: 0.6931\n",
            "----------\n",
            "epoch 46/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 46 average loss: 0.6931\n",
            "current epoch: 46 current accuracy: 0.5000 current AUC: 0.8125 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 47/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 47 average loss: 0.6931\n",
            "----------\n",
            "epoch 48/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 48 average loss: 0.6931\n",
            "current epoch: 48 current accuracy: 0.5000 current AUC: 0.8194 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 49/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 49 average loss: 0.6931\n",
            "----------\n",
            "epoch 50/100\n",
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            "epoch 50 average loss: 0.6931\n",
            "current epoch: 50 current accuracy: 0.5000 current AUC: 0.8125 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 51/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 51 average loss: 0.6931\n",
            "----------\n",
            "epoch 52/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 52 average loss: 0.6931\n",
            "current epoch: 52 current accuracy: 0.5000 current AUC: 0.8264 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 53/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 53 average loss: 0.6931\n",
            "----------\n",
            "epoch 54/100\n",
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            "epoch 54 average loss: 0.6931\n",
            "current epoch: 54 current accuracy: 0.5000 current AUC: 0.8194 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 55/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 55 average loss: 0.6931\n",
            "----------\n",
            "epoch 56/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 56 average loss: 0.6931\n",
            "current epoch: 56 current accuracy: 0.5000 current AUC: 0.8194 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 57/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 57 average loss: 0.6931\n",
            "----------\n",
            "epoch 58/100\n",
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            "epoch 58 average loss: 0.6931\n",
            "current epoch: 58 current accuracy: 0.5000 current AUC: 0.8125 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 59/100\n",
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            "epoch 59 average loss: 0.6931\n",
            "----------\n",
            "epoch 60/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 60 average loss: 0.6931\n",
            "current epoch: 60 current accuracy: 0.5000 current AUC: 0.8264 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 61/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 61 average loss: 0.6931\n",
            "----------\n",
            "epoch 62/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 62 average loss: 0.6931\n",
            "current epoch: 62 current accuracy: 0.5000 current AUC: 0.8403 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 63/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 63 average loss: 0.6931\n",
            "----------\n",
            "epoch 64/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 64 average loss: 0.6931\n",
            "current epoch: 64 current accuracy: 0.5000 current AUC: 0.8472 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 65/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 65 average loss: 0.6931\n",
            "----------\n",
            "epoch 66/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 66 average loss: 0.6931\n",
            "current epoch: 66 current accuracy: 0.5000 current AUC: 0.8264 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 67/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 67 average loss: 0.6931\n",
            "----------\n",
            "epoch 68/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 68 average loss: 0.6931\n",
            "current epoch: 68 current accuracy: 0.5000 current AUC: 0.8333 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 69/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 69 average loss: 0.6931\n",
            "----------\n",
            "epoch 70/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 70 average loss: 0.6931\n",
            "current epoch: 70 current accuracy: 0.5000 current AUC: 0.8333 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 71/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 71 average loss: 0.6931\n",
            "----------\n",
            "epoch 72/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 72 average loss: 0.6931\n",
            "current epoch: 72 current accuracy: 0.5000 current AUC: 0.8333 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 73/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 73 average loss: 0.6931\n",
            "----------\n",
            "epoch 74/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 74 average loss: 0.6931\n",
            "current epoch: 74 current accuracy: 0.5000 current AUC: 0.8403 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 75/100\n",
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            "epoch 75 average loss: 0.6931\n",
            "----------\n",
            "epoch 76/100\n",
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            "epoch 76 average loss: 0.6931\n",
            "current epoch: 76 current accuracy: 0.5000 current AUC: 0.8542 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 77/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 77 average loss: 0.6931\n",
            "----------\n",
            "epoch 78/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 78 average loss: 0.6931\n",
            "current epoch: 78 current accuracy: 0.5000 current AUC: 0.8472 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 79/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 79 average loss: 0.6931\n",
            "----------\n",
            "epoch 80/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 80 average loss: 0.6931\n",
            "current epoch: 80 current accuracy: 0.5000 current AUC: 0.8333 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 81/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 81 average loss: 0.6931\n",
            "----------\n",
            "epoch 82/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 82 average loss: 0.6931\n",
            "current epoch: 82 current accuracy: 0.5000 current AUC: 0.8403 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 83/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 83 average loss: 0.6931\n",
            "----------\n",
            "epoch 84/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 84 average loss: 0.6931\n",
            "current epoch: 84 current accuracy: 0.5000 current AUC: 0.8542 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 85/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 85 average loss: 0.6931\n",
            "----------\n",
            "epoch 86/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 86 average loss: 0.6931\n",
            "current epoch: 86 current accuracy: 0.5000 current AUC: 0.8472 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 87/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 87 average loss: 0.6931\n",
            "----------\n",
            "epoch 88/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 88 average loss: 0.6931\n",
            "current epoch: 88 current accuracy: 0.5000 current AUC: 0.8403 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 89/100\n",
            "1/23, train_loss: 0.6931\n",
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            "epoch 89 average loss: 0.6931\n",
            "----------\n",
            "epoch 90/100\n",
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            "epoch 90 average loss: 0.6931\n",
            "current epoch: 90 current accuracy: 0.5000 current AUC: 0.8472 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 91/100\n",
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            "epoch 91 average loss: 0.6931\n",
            "----------\n",
            "epoch 92/100\n",
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            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6931\n",
            "20/23, train_loss: 0.6932\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 92 average loss: 0.6931\n",
            "current epoch: 92 current accuracy: 0.5000 current AUC: 0.8333 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 93/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6931\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6932\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6931\n",
            "20/23, train_loss: 0.6932\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 93 average loss: 0.6931\n",
            "----------\n",
            "epoch 94/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6931\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6932\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6931\n",
            "20/23, train_loss: 0.6932\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 94 average loss: 0.6931\n",
            "current epoch: 94 current accuracy: 0.5000 current AUC: 0.8611 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 95/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6931\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6932\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6931\n",
            "20/23, train_loss: 0.6932\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 95 average loss: 0.6931\n",
            "----------\n",
            "epoch 96/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6931\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6932\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6931\n",
            "20/23, train_loss: 0.6932\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 96 average loss: 0.6931\n",
            "current epoch: 96 current accuracy: 0.5000 current AUC: 0.8472 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 97/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6931\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6932\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6931\n",
            "20/23, train_loss: 0.6932\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 97 average loss: 0.6931\n",
            "----------\n",
            "epoch 98/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6931\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6932\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6931\n",
            "20/23, train_loss: 0.6932\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 98 average loss: 0.6931\n",
            "current epoch: 98 current accuracy: 0.5000 current AUC: 0.8542 best accuracy: 0.5000 at epoch 2\n",
            "----------\n",
            "epoch 99/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6931\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6932\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6931\n",
            "20/23, train_loss: 0.6932\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 99 average loss: 0.6931\n",
            "----------\n",
            "epoch 100/100\n",
            "1/23, train_loss: 0.6931\n",
            "2/23, train_loss: 0.6931\n",
            "3/23, train_loss: 0.6931\n",
            "4/23, train_loss: 0.6931\n",
            "5/23, train_loss: 0.6931\n",
            "6/23, train_loss: 0.6931\n",
            "7/23, train_loss: 0.6931\n",
            "8/23, train_loss: 0.6931\n",
            "9/23, train_loss: 0.6931\n",
            "10/23, train_loss: 0.6931\n",
            "11/23, train_loss: 0.6931\n",
            "12/23, train_loss: 0.6932\n",
            "13/23, train_loss: 0.6931\n",
            "14/23, train_loss: 0.6931\n",
            "15/23, train_loss: 0.6931\n",
            "16/23, train_loss: 0.6931\n",
            "17/23, train_loss: 0.6931\n",
            "18/23, train_loss: 0.6931\n",
            "19/23, train_loss: 0.6931\n",
            "20/23, train_loss: 0.6932\n",
            "21/23, train_loss: 0.6931\n",
            "22/23, train_loss: 0.6931\n",
            "23/23, train_loss: 0.6931\n",
            "epoch 100 average loss: 0.6931\n",
            "current epoch: 100 current accuracy: 0.5000 current AUC: 0.8403 best accuracy: 0.5000 at epoch 2\n",
            "train completed, best_metric: 0.5000 at epoch: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "MLNwxbkS2Nbl",
        "outputId": "092e2f00-abf4-4471-c693-2665c06bfd67"
      },
      "source": [
        "plt.figure(\"train\", (12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Epoch Average Loss\")\n",
        "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
        "y = epoch_loss_values\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.plot(x, y)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Val AUC\")\n",
        "x = [val_interval * (i + 1) for i in range(len(metric_values))]\n",
        "y = metric_values\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.plot(x, y)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIsk3v3gKTrB"
      },
      "source": [
        "**Evaluation Metrics**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-LyP68KcHELg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "54e3fc66-7633-46da-d40f-fd39312eef2d"
      },
      "source": [
        "from enum import Enum\n",
        "from sklearn.metrics import (\n",
        "    classification_report, confusion_matrix,\n",
        "    ConfusionMatrixDisplay\n",
        ")\n",
        "class Diagnosis(Enum):\n",
        "    Normal_Befund = 0\n",
        "    Hypoxischer_Hirnschaden = 1\n",
        "    \n",
        "\n",
        "%matplotlib inline\n",
        "# Load best model\n",
        "model.load_state_dict(torch.load(\"/content/drive/My Drive/Brain_AI/best_metric_model_classification_ViT100.pth\"))\n",
        "model.to(device)\n",
        "model.eval()\n",
        "\n",
        "with torch.no_grad():\n",
        "    y_pred = torch.tensor([], dtype=torch.float32, device=device)\n",
        "    y = torch.tensor([], dtype=torch.long, device=device)\n",
        "\n",
        "    for val_data in val_loader:\n",
        "        val_images, val_labels = val_data[\"image\"].to(\n",
        "            device), val_data[\"label\"].to(device),\n",
        "\n",
        "        outputs = model(val_images)\n",
        "        y_pred = torch.cat([y_pred, outputs.argmax(dim=1)], dim=0)\n",
        "        y = torch.cat([y, val_labels], dim=0)\n",
        "\n",
        "print(classification_report(\n",
        "    y.cpu().numpy(),\n",
        "    y_pred.cpu().numpy(),\n",
        "    target_names=[d.name for d in Diagnosis]))\n",
        "\n",
        "cm = confusion_matrix (\n",
        "    y.cpu().numpy(),\n",
        "    y_pred.cpu().numpy(),\n",
        "    normalize='true',\n",
        ")\n",
        "cmm = ConfusionMatrixMetric (include_background=True, metric_name=[\"sensitivity\",\"specificity\",\"negative predictive value\"])\n",
        "\n",
        "disp = ConfusionMatrixDisplay(\n",
        "    confusion_matrix=cm,\n",
        "    display_labels=[d.name for d in Diagnosis],\n",
        ")\n",
        "disp.plot(ax=plt.subplots(1, 1, facecolor='white')[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                         precision    recall  f1-score   support\n",
            "\n",
            "          Normal_Befund       0.50      1.00      0.67         6\n",
            "Hypoxischer_Hirnschaden       0.00      0.00      0.00         6\n",
            "\n",
            "               accuracy                           0.50        12\n",
            "              macro avg       0.25      0.50      0.33        12\n",
            "           weighted avg       0.25      0.50      0.33        12\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7eff471d1750>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}