Classification_HBI.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Classification_HBI.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "mount_file_id": "1vfp59FiJvZP7P5mTyFDoPrrAIWzhViA6",
      "authorship_tag": "ABX9TyP5nqU3iY7VveH3Jbf+rZG3",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "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.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"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WHt5hRV4MYoT",
        "outputId": "b3583a90-f3f6-4784-f869-febc97d7651c"
      },
      "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\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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1S2v-B-AITM",
        "outputId": "d734e62d-90b5-46e2-d20d-fdbec4d9a25a"
      },
      "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": {
        "id": "HZKrBoKcGBu1"
      },
      "source": [
        "x=[]\n",
        "for i in images:\n",
        "  x.append(os.path.basename(i))\n",
        "\n",
        "\n",
        "df = pd.DataFrame(x) \n",
        "df[\"labels\"]= l\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyjyR96rS0Bl",
        "outputId": "d7e112cd-7b76-40cc-83f5-dc96d16096db"
      },
      "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": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "58\n",
            "58\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBnbVTeKNUc0"
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JgOMkqS2-i4q"
      },
      "source": [
        "**Check transforms**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-trGi0jHGQg",
        "outputId": "04ef6b31-cd5e-42fe-88f5-990f2a5c8896"
      },
      "source": [
        "data = [{\"image\": i, \"label\": l} for i, l in zip(images, labels)]\n",
        "transforms = Compose([\n",
        "    LoadImaged(keys=[\"image\"]),\n",
        "    AddChanneld(keys=[\"image\"]),\n",
        "    #CropForegroundd(keys=[\"image\"], source_key=[\"image\"], margin=-1),\n",
        "    #Spacingd(keys=[\"image\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
        "    Orientationd(keys=[\"image\"], axcodes=\"PLI\"),\n",
        "    Resized(keys=[\"image\"], spatial_size=(96, 96, 96)),\n",
        "    ScaleIntensityRanged(keys=['image'], a_min=-50, a_max=300, b_min=0.0, b_max=1.0, clip=True),\n",
        "    #MaskIntensityd(keys=\"image\", mask_key=\"seg\"),\n",
        "    EnsureTyped(keys=[\"image\"]),\n",
        "])\n",
        "data_partition= partition_dataset_classes(data, labels.tolist(), shuffle=True, ratios=[0.8, 0.2])\n",
        "train_ds = CacheDataset(data=data_partition[1], transform=transforms)\n",
        "train_loader = DataLoader(train_ds, batch_size=2, shuffle=False, num_workers=4, pin_memory=torch.cuda.is_available())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Loading dataset: 100%|██████████| 12/12 [00:01<00:00,  6.53it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ceFSaonDHXsp"
      },
      "source": [
        "device = torch.device(\"cuda\")\n",
        "for data in train_loader:\n",
        "  image = data[\"image\"].to(device).cpu() \n",
        " # print(os.path.basename(image))\n",
        "  #img = itk.imread(image)\n",
        "\n",
        "  def explore_3dimage(layer):\n",
        "    plt.figure(figsize=(8, 4))\n",
        "    channel = 3\n",
        "    #plt.subplot(1, 4, range(data) + 1)\n",
        "    plt.imshow(data[\"image\"][0, 0, :, :,layer], cmap='gray');\n",
        "    plt.title('Explore Layers of Brain CT', fontsize=20)\n",
        "    plt.axis('off')\n",
        "    \n",
        "    return layer\n",
        "  plt.show()    \n",
        "# Run the ipywidgets interact() function to explore the data\n",
        "  interact(explore_3dimage, layer=(0, data[\"image\"].shape[2] - 1));\n",
        "  #plt.figure(\"check\", (18, 6))\n",
        "  #plt.subplot(1, 2, 1)\n",
        "  #plt.imshow(data[\"image\"][0, 0, :, :, 55], cmap=\"gray\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJJwFbO47yGV"
      },
      "source": [
        "**Loading Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scT_G0HLMNjH",
        "outputId": "cd8bff8b-6acc-47b2-b263-62ba6a6ce43a"
      },
      "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": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading dataset: 100%|██████████| 46/46 [00:17<00:00,  2.64it/s]\n",
            "Loading dataset: 100%|██████████| 12/12 [00:04<00:00,  2.75it/s]\n",
            "Loading dataset: 100%|██████████| 12/12 [00:01<00:00,  9.00it/s]\n",
            "`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "xxkzWn0Yco_B"
      },
      "source": [
        "device = torch.device(\"cuda\")\n",
        "for data in train_loader:\n",
        "  image = data[\"image\"].to(device) \n",
        "  #plt.figure(\"check\", (18, 6))\n",
        "  #plt.subplot(1, 2, 1)\n",
        "  #plt.imshow(data[\"image\"][0, 0, :, :, 55], cmap=\"gray\")\n",
        " \n",
        "  matshow3d(\n",
        "    volume=data['image'],\n",
        "    fig=None, title=\"input image\",\n",
        "    figsize=(10, 10),\n",
        "    every_n=10,\n",
        "    frame_dim=-1,\n",
        "    show=True,\n",
        "    cmap=\"gray\",\n",
        ")\n",
        "\n",
        "  #print(inputs.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtjR-k758HU7"
      },
      "source": [
        "**Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGT1akrQ-MKe"
      },
      "source": [
        "# Create DenseNet121, CrossEntropyLoss and Adam optimizer\n",
        "device = torch.device(\"cuda\")\n",
        "model = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)\n",
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), 1e-5)\n",
        "auc_metric = ROCAUCMetric()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LSEwWGo2GMR",
        "outputId": "740d6910-289a-44ed-b8c5-18c2b6a7c7df"
      },
      "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, 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)], 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_dense1.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: 0.6802\n",
            "2/23, train_loss: 0.6869\n",
            "3/23, train_loss: 0.7479\n",
            "4/23, train_loss: 0.6863\n",
            "5/23, train_loss: 0.6194\n",
            "6/23, train_loss: 0.8637\n",
            "7/23, train_loss: 0.6367\n",
            "8/23, train_loss: 0.7701\n",
            "9/23, train_loss: 0.6245\n",
            "10/23, train_loss: 0.7296\n",
            "11/23, train_loss: 0.5438\n",
            "12/23, train_loss: 0.5673\n",
            "13/23, train_loss: 0.7141\n",
            "14/23, train_loss: 0.5697\n",
            "15/23, train_loss: 0.5302\n",
            "16/23, train_loss: 0.7110\n",
            "17/23, train_loss: 0.4897\n",
            "18/23, train_loss: 0.5131\n",
            "19/23, train_loss: 0.8267\n",
            "20/23, train_loss: 0.5792\n",
            "21/23, train_loss: 0.5488\n",
            "22/23, train_loss: 0.9352\n",
            "23/23, train_loss: 0.6676\n",
            "epoch 1 average loss: 0.6627\n",
            "----------\n",
            "epoch 2/100\n",
            "1/23, train_loss: 0.3994\n",
            "2/23, train_loss: 0.4001\n",
            "3/23, train_loss: 0.4980\n",
            "4/23, train_loss: 0.4031\n",
            "5/23, train_loss: 0.3597\n",
            "6/23, train_loss: 0.7986\n",
            "7/23, train_loss: 0.4093\n",
            "8/23, train_loss: 0.6522\n",
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            "10/23, train_loss: 0.5974\n",
            "11/23, train_loss: 0.3553\n",
            "12/23, train_loss: 0.5784\n",
            "13/23, train_loss: 0.5529\n",
            "14/23, train_loss: 0.3663\n",
            "15/23, train_loss: 0.3347\n",
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            "20/23, train_loss: 0.5869\n",
            "21/23, train_loss: 0.3592\n",
            "22/23, train_loss: 0.8348\n",
            "23/23, train_loss: 0.4984\n",
            "epoch 2 average loss: 0.4928\n",
            "saved new best metric model\n",
            "current epoch: 2 current accuracy: 0.5833 current AUC: 0.8611 best accuracy: 0.5833 at epoch 2\n",
            "----------\n",
            "epoch 3/100\n",
            "1/23, train_loss: 0.2968\n",
            "2/23, train_loss: 0.3050\n",
            "3/23, train_loss: 0.3704\n",
            "4/23, train_loss: 0.2826\n",
            "5/23, train_loss: 0.2567\n",
            "6/23, train_loss: 0.7512\n",
            "7/23, train_loss: 0.2975\n",
            "8/23, train_loss: 0.5323\n",
            "9/23, train_loss: 0.2883\n",
            "10/23, train_loss: 0.4755\n",
            "11/23, train_loss: 0.2618\n",
            "12/23, train_loss: 0.5800\n",
            "13/23, train_loss: 0.4237\n",
            "14/23, train_loss: 0.2701\n",
            "15/23, train_loss: 0.2376\n",
            "16/23, train_loss: 0.3633\n",
            "17/23, train_loss: 0.2375\n",
            "18/23, train_loss: 0.2659\n",
            "19/23, train_loss: 0.7185\n",
            "20/23, train_loss: 0.5905\n",
            "21/23, train_loss: 0.2558\n",
            "22/23, train_loss: 0.7006\n",
            "23/23, train_loss: 0.3737\n",
            "epoch 3 average loss: 0.3972\n",
            "----------\n",
            "epoch 4/100\n",
            "1/23, train_loss: 0.2341\n",
            "2/23, train_loss: 0.2488\n",
            "3/23, train_loss: 0.2822\n",
            "4/23, train_loss: 0.2117\n",
            "5/23, train_loss: 0.1968\n",
            "6/23, train_loss: 0.7077\n",
            "7/23, train_loss: 0.2262\n",
            "8/23, train_loss: 0.4292\n",
            "9/23, train_loss: 0.2117\n",
            "10/23, train_loss: 0.3767\n",
            "11/23, train_loss: 0.2034\n",
            "12/23, train_loss: 0.5722\n",
            "13/23, train_loss: 0.3325\n",
            "14/23, train_loss: 0.2064\n",
            "15/23, train_loss: 0.1790\n",
            "16/23, train_loss: 0.2699\n",
            "17/23, train_loss: 0.1853\n",
            "18/23, train_loss: 0.2061\n",
            "19/23, train_loss: 0.6811\n",
            "20/23, train_loss: 0.5817\n",
            "21/23, train_loss: 0.1903\n",
            "22/23, train_loss: 0.5789\n",
            "23/23, train_loss: 0.2879\n",
            "epoch 4 average loss: 0.3304\n",
            "saved new best metric model\n",
            "current epoch: 4 current accuracy: 0.7500 current AUC: 0.9167 best accuracy: 0.7500 at epoch 4\n",
            "----------\n",
            "epoch 5/100\n",
            "1/23, train_loss: 0.1836\n",
            "2/23, train_loss: 0.2026\n",
            "3/23, train_loss: 0.2174\n",
            "4/23, train_loss: 0.1646\n",
            "5/23, train_loss: 0.1563\n",
            "6/23, train_loss: 0.6694\n",
            "7/23, train_loss: 0.1802\n",
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            "10/23, train_loss: 0.3025\n",
            "11/23, train_loss: 0.1601\n",
            "12/23, train_loss: 0.5622\n",
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            "14/23, train_loss: 0.1616\n",
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            "16/23, train_loss: 0.2053\n",
            "17/23, train_loss: 0.1480\n",
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            "21/23, train_loss: 0.1481\n",
            "22/23, train_loss: 0.4770\n",
            "23/23, train_loss: 0.2281\n",
            "epoch 5 average loss: 0.2812\n",
            "----------\n",
            "epoch 6/100\n",
            "1/23, train_loss: 0.1495\n",
            "2/23, train_loss: 0.1684\n",
            "3/23, train_loss: 0.1710\n",
            "4/23, train_loss: 0.1313\n",
            "5/23, train_loss: 0.1256\n",
            "6/23, train_loss: 0.6380\n",
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            "10/23, train_loss: 0.2450\n",
            "11/23, train_loss: 0.1310\n",
            "12/23, train_loss: 0.5545\n",
            "13/23, train_loss: 0.2150\n",
            "14/23, train_loss: 0.1307\n",
            "15/23, train_loss: 0.1076\n",
            "16/23, train_loss: 0.1623\n",
            "17/23, train_loss: 0.1187\n",
            "18/23, train_loss: 0.1380\n",
            "19/23, train_loss: 0.6251\n",
            "20/23, train_loss: 0.5586\n",
            "21/23, train_loss: 0.1154\n",
            "22/23, train_loss: 0.3898\n",
            "23/23, train_loss: 0.1813\n",
            "epoch 6 average loss: 0.2440\n",
            "current epoch: 6 current accuracy: 0.7500 current AUC: 0.9167 best accuracy: 0.7500 at epoch 4\n",
            "----------\n",
            "epoch 7/100\n",
            "1/23, train_loss: 0.1210\n",
            "2/23, train_loss: 0.1403\n",
            "3/23, train_loss: 0.1375\n",
            "4/23, train_loss: 0.1069\n",
            "5/23, train_loss: 0.1036\n",
            "6/23, train_loss: 0.6148\n",
            "7/23, train_loss: 0.1191\n",
            "8/23, train_loss: 0.2348\n",
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            "10/23, train_loss: 0.1987\n",
            "11/23, train_loss: 0.1068\n",
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            "14/23, train_loss: 0.1052\n",
            "15/23, train_loss: 0.0878\n",
            "16/23, train_loss: 0.1295\n",
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            "21/23, train_loss: 0.0913\n",
            "22/23, train_loss: 0.3189\n",
            "23/23, train_loss: 0.1495\n",
            "epoch 7 average loss: 0.2151\n",
            "----------\n",
            "epoch 8/100\n",
            "1/23, train_loss: 0.0990\n",
            "2/23, train_loss: 0.1183\n",
            "3/23, train_loss: 0.1094\n",
            "4/23, train_loss: 0.0859\n",
            "5/23, train_loss: 0.0845\n",
            "6/23, train_loss: 0.5981\n",
            "7/23, train_loss: 0.0956\n",
            "8/23, train_loss: 0.1938\n",
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            "10/23, train_loss: 0.1639\n",
            "11/23, train_loss: 0.0884\n",
            "12/23, train_loss: 0.5318\n",
            "13/23, train_loss: 0.1418\n",
            "14/23, train_loss: 0.0865\n",
            "15/23, train_loss: 0.0712\n",
            "16/23, train_loss: 0.1027\n",
            "17/23, train_loss: 0.0809\n",
            "18/23, train_loss: 0.0955\n",
            "19/23, train_loss: 0.5815\n",
            "20/23, train_loss: 0.5353\n",
            "21/23, train_loss: 0.0731\n",
            "22/23, train_loss: 0.2594\n",
            "23/23, train_loss: 0.1202\n",
            "epoch 8 average loss: 0.1912\n",
            "current epoch: 8 current accuracy: 0.7500 current AUC: 0.9167 best accuracy: 0.7500 at epoch 4\n",
            "----------\n",
            "epoch 9/100\n",
            "1/23, train_loss: 0.0815\n",
            "2/23, train_loss: 0.0984\n",
            "3/23, train_loss: 0.0890\n",
            "4/23, train_loss: 0.0681\n",
            "5/23, train_loss: 0.0697\n",
            "6/23, train_loss: 0.5727\n",
            "7/23, train_loss: 0.0792\n",
            "8/23, train_loss: 0.1586\n",
            "9/23, train_loss: 0.0666\n",
            "10/23, train_loss: 0.1361\n",
            "11/23, train_loss: 0.0738\n",
            "12/23, train_loss: 0.5203\n",
            "13/23, train_loss: 0.1179\n",
            "14/23, train_loss: 0.0715\n",
            "15/23, train_loss: 0.0574\n",
            "16/23, train_loss: 0.0827\n",
            "17/23, train_loss: 0.0659\n",
            "18/23, train_loss: 0.0785\n",
            "19/23, train_loss: 0.5580\n",
            "20/23, train_loss: 0.5160\n",
            "21/23, train_loss: 0.0588\n",
            "22/23, train_loss: 0.2055\n",
            "23/23, train_loss: 0.0962\n",
            "epoch 9 average loss: 0.1705\n",
            "----------\n",
            "epoch 10/100\n",
            "1/23, train_loss: 0.0670\n",
            "2/23, train_loss: 0.0845\n",
            "3/23, train_loss: 0.0733\n",
            "4/23, train_loss: 0.0568\n",
            "5/23, train_loss: 0.0598\n",
            "6/23, train_loss: 0.5530\n",
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            "10/23, train_loss: 0.1098\n",
            "11/23, train_loss: 0.0609\n",
            "12/23, train_loss: 0.5031\n",
            "13/23, train_loss: 0.0946\n",
            "14/23, train_loss: 0.0590\n",
            "15/23, train_loss: 0.0477\n",
            "16/23, train_loss: 0.0690\n",
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            "20/23, train_loss: 0.5039\n",
            "21/23, train_loss: 0.0493\n",
            "22/23, train_loss: 0.1684\n",
            "23/23, train_loss: 0.0774\n",
            "epoch 10 average loss: 0.1541\n",
            "current epoch: 10 current accuracy: 0.6667 current AUC: 0.9167 best accuracy: 0.7500 at epoch 4\n",
            "----------\n",
            "epoch 11/100\n",
            "1/23, train_loss: 0.0552\n",
            "2/23, train_loss: 0.0702\n",
            "3/23, train_loss: 0.0601\n",
            "4/23, train_loss: 0.0472\n",
            "5/23, train_loss: 0.0500\n",
            "6/23, train_loss: 0.5288\n",
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            "11/23, train_loss: 0.0517\n",
            "12/23, train_loss: 0.4907\n",
            "13/23, train_loss: 0.0790\n",
            "14/23, train_loss: 0.0483\n",
            "15/23, train_loss: 0.0398\n",
            "16/23, train_loss: 0.0574\n",
            "17/23, train_loss: 0.0462\n",
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            "21/23, train_loss: 0.0417\n",
            "22/23, train_loss: 0.1433\n",
            "23/23, train_loss: 0.0672\n",
            "epoch 11 average loss: 0.1404\n",
            "----------\n",
            "epoch 12/100\n",
            "1/23, train_loss: 0.0475\n",
            "2/23, train_loss: 0.0592\n",
            "3/23, train_loss: 0.0506\n",
            "4/23, train_loss: 0.0399\n",
            "5/23, train_loss: 0.0413\n",
            "6/23, train_loss: 0.5076\n",
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            "8/23, train_loss: 0.0894\n",
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            "10/23, train_loss: 0.0784\n",
            "11/23, train_loss: 0.0446\n",
            "12/23, train_loss: 0.4747\n",
            "13/23, train_loss: 0.0678\n",
            "14/23, train_loss: 0.0424\n",
            "15/23, train_loss: 0.0345\n",
            "16/23, train_loss: 0.0490\n",
            "17/23, train_loss: 0.0400\n",
            "18/23, train_loss: 0.0475\n",
            "19/23, train_loss: 0.4940\n",
            "20/23, train_loss: 0.4693\n",
            "21/23, train_loss: 0.0355\n",
            "22/23, train_loss: 0.1194\n",
            "23/23, train_loss: 0.0570\n",
            "epoch 12 average loss: 0.1293\n",
            "current epoch: 12 current accuracy: 0.7500 current AUC: 0.9444 best accuracy: 0.7500 at epoch 4\n",
            "----------\n",
            "epoch 13/100\n",
            "1/23, train_loss: 0.0415\n",
            "2/23, train_loss: 0.0518\n",
            "3/23, train_loss: 0.0445\n",
            "4/23, train_loss: 0.0351\n",
            "5/23, train_loss: 0.0363\n",
            "6/23, train_loss: 0.4896\n",
            "7/23, train_loss: 0.0413\n",
            "8/23, train_loss: 0.0792\n",
            "9/23, train_loss: 0.0330\n",
            "10/23, train_loss: 0.0675\n",
            "11/23, train_loss: 0.0385\n",
            "12/23, train_loss: 0.4606\n",
            "13/23, train_loss: 0.0586\n",
            "14/23, train_loss: 0.0370\n",
            "15/23, train_loss: 0.0299\n",
            "16/23, train_loss: 0.0428\n",
            "17/23, train_loss: 0.0351\n",
            "18/23, train_loss: 0.0420\n",
            "19/23, train_loss: 0.4815\n",
            "20/23, train_loss: 0.4588\n",
            "21/23, train_loss: 0.0312\n",
            "22/23, train_loss: 0.1032\n",
            "23/23, train_loss: 0.0495\n",
            "epoch 13 average loss: 0.1212\n",
            "----------\n",
            "epoch 14/100\n",
            "1/23, train_loss: 0.0362\n",
            "2/23, train_loss: 0.0458\n",
            "3/23, train_loss: 0.0387\n",
            "4/23, train_loss: 0.0304\n",
            "5/23, train_loss: 0.0325\n",
            "6/23, train_loss: 0.4761\n",
            "7/23, train_loss: 0.0364\n",
            "8/23, train_loss: 0.0712\n",
            "9/23, train_loss: 0.0291\n",
            "10/23, train_loss: 0.0598\n",
            "11/23, train_loss: 0.0344\n",
            "12/23, train_loss: 0.4501\n",
            "13/23, train_loss: 0.0526\n",
            "14/23, train_loss: 0.0331\n",
            "15/23, train_loss: 0.0264\n",
            "16/23, train_loss: 0.0373\n",
            "17/23, train_loss: 0.0307\n",
            "18/23, train_loss: 0.0369\n",
            "19/23, train_loss: 0.4672\n",
            "20/23, train_loss: 0.4476\n",
            "21/23, train_loss: 0.0277\n",
            "22/23, train_loss: 0.0902\n",
            "23/23, train_loss: 0.0431\n",
            "epoch 14 average loss: 0.1145\n",
            "saved new best metric model\n",
            "current epoch: 14 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 15/100\n",
            "1/23, train_loss: 0.0316\n",
            "2/23, train_loss: 0.0413\n",
            "3/23, train_loss: 0.0345\n",
            "4/23, train_loss: 0.0270\n",
            "5/23, train_loss: 0.0290\n",
            "6/23, train_loss: 0.4633\n",
            "7/23, train_loss: 0.0318\n",
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            "10/23, train_loss: 0.0524\n",
            "11/23, train_loss: 0.0304\n",
            "12/23, train_loss: 0.4419\n",
            "13/23, train_loss: 0.0467\n",
            "14/23, train_loss: 0.0295\n",
            "15/23, train_loss: 0.0237\n",
            "16/23, train_loss: 0.0333\n",
            "17/23, train_loss: 0.0274\n",
            "18/23, train_loss: 0.0328\n",
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            "20/23, train_loss: 0.4347\n",
            "21/23, train_loss: 0.0250\n",
            "22/23, train_loss: 0.0795\n",
            "23/23, train_loss: 0.0387\n",
            "epoch 15 average loss: 0.1085\n",
            "----------\n",
            "epoch 16/100\n",
            "1/23, train_loss: 0.0289\n",
            "2/23, train_loss: 0.0370\n",
            "3/23, train_loss: 0.0307\n",
            "4/23, train_loss: 0.0242\n",
            "5/23, train_loss: 0.0259\n",
            "6/23, train_loss: 0.4502\n",
            "7/23, train_loss: 0.0285\n",
            "8/23, train_loss: 0.0544\n",
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            "10/23, train_loss: 0.0466\n",
            "11/23, train_loss: 0.0276\n",
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            "16/23, train_loss: 0.0295\n",
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            "21/23, train_loss: 0.0222\n",
            "22/23, train_loss: 0.0710\n",
            "23/23, train_loss: 0.0343\n",
            "epoch 16 average loss: 0.1038\n",
            "current epoch: 16 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 17/100\n",
            "1/23, train_loss: 0.0259\n",
            "2/23, train_loss: 0.0335\n",
            "3/23, train_loss: 0.0281\n",
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            "21/23, train_loss: 0.0205\n",
            "22/23, train_loss: 0.0660\n",
            "23/23, train_loss: 0.0325\n",
            "epoch 17 average loss: 0.1012\n",
            "----------\n",
            "epoch 18/100\n",
            "1/23, train_loss: 0.0239\n",
            "2/23, train_loss: 0.0296\n",
            "3/23, train_loss: 0.0256\n",
            "4/23, train_loss: 0.0198\n",
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            "21/23, train_loss: 0.0186\n",
            "22/23, train_loss: 0.0594\n",
            "23/23, train_loss: 0.0296\n",
            "epoch 18 average loss: 0.1007\n",
            "current epoch: 18 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 19/100\n",
            "1/23, train_loss: 0.0224\n",
            "2/23, train_loss: 0.0313\n",
            "3/23, train_loss: 0.0244\n",
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            "21/23, train_loss: 0.0184\n",
            "22/23, train_loss: 0.0618\n",
            "23/23, train_loss: 0.0309\n",
            "epoch 19 average loss: 0.1083\n",
            "----------\n",
            "epoch 20/100\n",
            "1/23, train_loss: 0.0235\n",
            "2/23, train_loss: 0.0287\n",
            "3/23, train_loss: 0.0252\n",
            "4/23, train_loss: 0.0187\n",
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            "21/23, train_loss: 0.0170\n",
            "22/23, train_loss: 0.0535\n",
            "23/23, train_loss: 0.0288\n",
            "epoch 20 average loss: 0.1048\n",
            "current epoch: 20 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 21/100\n",
            "1/23, train_loss: 0.0214\n",
            "2/23, train_loss: 0.0307\n",
            "3/23, train_loss: 0.0220\n",
            "4/23, train_loss: 0.0182\n",
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            "21/23, train_loss: 0.0161\n",
            "22/23, train_loss: 0.0538\n",
            "23/23, train_loss: 0.0256\n",
            "epoch 21 average loss: 0.1011\n",
            "----------\n",
            "epoch 22/100\n",
            "1/23, train_loss: 0.0190\n",
            "2/23, train_loss: 0.0248\n",
            "3/23, train_loss: 0.0199\n",
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            "14/23, train_loss: 0.0186\n",
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            "21/23, train_loss: 0.0152\n",
            "22/23, train_loss: 0.0450\n",
            "23/23, train_loss: 0.0240\n",
            "epoch 22 average loss: 0.0936\n",
            "current epoch: 22 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 23/100\n",
            "1/23, train_loss: 0.0179\n",
            "2/23, train_loss: 0.0235\n",
            "3/23, train_loss: 0.0182\n",
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            "14/23, train_loss: 0.0171\n",
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            "21/23, train_loss: 0.0138\n",
            "22/23, train_loss: 0.0436\n",
            "23/23, train_loss: 0.0217\n",
            "epoch 23 average loss: 0.0938\n",
            "----------\n",
            "epoch 24/100\n",
            "1/23, train_loss: 0.0163\n",
            "2/23, train_loss: 0.0220\n",
            "3/23, train_loss: 0.0168\n",
            "4/23, train_loss: 0.0136\n",
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            "14/23, train_loss: 0.0169\n",
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            "21/23, train_loss: 0.0129\n",
            "22/23, train_loss: 0.0386\n",
            "23/23, train_loss: 0.0205\n",
            "epoch 24 average loss: 0.0891\n",
            "current epoch: 24 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 25/100\n",
            "1/23, train_loss: 0.0157\n",
            "2/23, train_loss: 0.0196\n",
            "3/23, train_loss: 0.0162\n",
            "4/23, train_loss: 0.0128\n",
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            "21/23, train_loss: 0.0122\n",
            "22/23, train_loss: 0.0359\n",
            "23/23, train_loss: 0.0191\n",
            "epoch 25 average loss: 0.0843\n",
            "----------\n",
            "epoch 26/100\n",
            "1/23, train_loss: 0.0145\n",
            "2/23, train_loss: 0.0195\n",
            "3/23, train_loss: 0.0147\n",
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            "21/23, train_loss: 0.0114\n",
            "22/23, train_loss: 0.0358\n",
            "23/23, train_loss: 0.0183\n",
            "epoch 26 average loss: 0.0842\n",
            "current epoch: 26 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 27/100\n",
            "1/23, train_loss: 0.0139\n",
            "2/23, train_loss: 0.0174\n",
            "3/23, train_loss: 0.0143\n",
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            "21/23, train_loss: 0.0111\n",
            "22/23, train_loss: 0.0326\n",
            "23/23, train_loss: 0.0172\n",
            "epoch 27 average loss: 0.0799\n",
            "----------\n",
            "epoch 28/100\n",
            "1/23, train_loss: 0.0133\n",
            "2/23, train_loss: 0.0169\n",
            "3/23, train_loss: 0.0134\n",
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            "21/23, train_loss: 0.0101\n",
            "22/23, train_loss: 0.0302\n",
            "23/23, train_loss: 0.0159\n",
            "epoch 28 average loss: 0.0758\n",
            "current epoch: 28 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 29/100\n",
            "1/23, train_loss: 0.0122\n",
            "2/23, train_loss: 0.0154\n",
            "3/23, train_loss: 0.0127\n",
            "4/23, train_loss: 0.0101\n",
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            "21/23, train_loss: 0.0095\n",
            "22/23, train_loss: 0.0276\n",
            "23/23, train_loss: 0.0148\n",
            "epoch 29 average loss: 0.0713\n",
            "----------\n",
            "epoch 30/100\n",
            "1/23, train_loss: 0.0114\n",
            "2/23, train_loss: 0.0147\n",
            "3/23, train_loss: 0.0119\n",
            "4/23, train_loss: 0.0096\n",
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            "21/23, train_loss: 0.0091\n",
            "22/23, train_loss: 0.0260\n",
            "23/23, train_loss: 0.0142\n",
            "epoch 30 average loss: 0.0697\n",
            "current epoch: 30 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 31/100\n",
            "1/23, train_loss: 0.0112\n",
            "2/23, train_loss: 0.0137\n",
            "3/23, train_loss: 0.0115\n",
            "4/23, train_loss: 0.0093\n",
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            "21/23, train_loss: 0.0087\n",
            "22/23, train_loss: 0.0254\n",
            "23/23, train_loss: 0.0136\n",
            "epoch 31 average loss: 0.0714\n",
            "----------\n",
            "epoch 32/100\n",
            "1/23, train_loss: 0.0106\n",
            "2/23, train_loss: 0.0147\n",
            "3/23, train_loss: 0.0108\n",
            "4/23, train_loss: 0.0089\n",
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            "21/23, train_loss: 0.0085\n",
            "22/23, train_loss: 0.0251\n",
            "23/23, train_loss: 0.0135\n",
            "epoch 32 average loss: 0.0735\n",
            "current epoch: 32 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 33/100\n",
            "1/23, train_loss: 0.0105\n",
            "2/23, train_loss: 0.0128\n",
            "3/23, train_loss: 0.0111\n",
            "4/23, train_loss: 0.0088\n",
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            "22/23, train_loss: 0.0238\n",
            "23/23, train_loss: 0.0128\n",
            "epoch 33 average loss: 0.0717\n",
            "----------\n",
            "epoch 34/100\n",
            "1/23, train_loss: 0.0100\n",
            "2/23, train_loss: 0.0136\n",
            "3/23, train_loss: 0.0102\n",
            "4/23, train_loss: 0.0084\n",
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            "22/23, train_loss: 0.0235\n",
            "23/23, train_loss: 0.0122\n",
            "epoch 34 average loss: 0.0699\n",
            "current epoch: 34 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 35/100\n",
            "1/23, train_loss: 0.0095\n",
            "2/23, train_loss: 0.0118\n",
            "3/23, train_loss: 0.0098\n",
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            "21/23, train_loss: 0.0076\n",
            "22/23, train_loss: 0.0211\n",
            "23/23, train_loss: 0.0119\n",
            "epoch 35 average loss: 0.0684\n",
            "----------\n",
            "epoch 36/100\n",
            "1/23, train_loss: 0.0095\n",
            "2/23, train_loss: 0.0127\n",
            "3/23, train_loss: 0.0098\n",
            "4/23, train_loss: 0.0081\n",
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            "21/23, train_loss: 0.0075\n",
            "22/23, train_loss: 0.0231\n",
            "23/23, train_loss: 0.0122\n",
            "epoch 36 average loss: 0.0744\n",
            "current epoch: 36 current accuracy: 0.7500 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 37/100\n",
            "1/23, train_loss: 0.0092\n",
            "2/23, train_loss: 0.0124\n",
            "3/23, train_loss: 0.0097\n",
            "4/23, train_loss: 0.0075\n",
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            "14/23, train_loss: 0.0091\n",
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            "21/23, train_loss: 0.0073\n",
            "22/23, train_loss: 0.0230\n",
            "23/23, train_loss: 0.0128\n",
            "epoch 37 average loss: 0.0784\n",
            "----------\n",
            "epoch 38/100\n",
            "1/23, train_loss: 0.0101\n",
            "2/23, train_loss: 0.0148\n",
            "3/23, train_loss: 0.0106\n",
            "4/23, train_loss: 0.0085\n",
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            "21/23, train_loss: 0.0080\n",
            "22/23, train_loss: 0.0248\n",
            "23/23, train_loss: 0.0136\n",
            "epoch 38 average loss: 0.0850\n",
            "current epoch: 38 current accuracy: 0.7500 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 39/100\n",
            "1/23, train_loss: 0.0107\n",
            "2/23, train_loss: 0.0130\n",
            "3/23, train_loss: 0.0107\n",
            "4/23, train_loss: 0.0082\n",
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            "11/23, train_loss: 0.0098\n",
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            "14/23, train_loss: 0.0098\n",
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            "21/23, train_loss: 0.0073\n",
            "22/23, train_loss: 0.0220\n",
            "23/23, train_loss: 0.0124\n",
            "epoch 39 average loss: 0.0760\n",
            "----------\n",
            "epoch 40/100\n",
            "1/23, train_loss: 0.0096\n",
            "2/23, train_loss: 0.0132\n",
            "3/23, train_loss: 0.0096\n",
            "4/23, train_loss: 0.0078\n",
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            "21/23, train_loss: 0.0071\n",
            "22/23, train_loss: 0.0227\n",
            "23/23, train_loss: 0.0118\n",
            "epoch 40 average loss: 0.0745\n",
            "current epoch: 40 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 41/100\n",
            "1/23, train_loss: 0.0090\n",
            "2/23, train_loss: 0.0113\n",
            "3/23, train_loss: 0.0090\n",
            "4/23, train_loss: 0.0070\n",
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            "11/23, train_loss: 0.0085\n",
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            "14/23, train_loss: 0.0094\n",
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            "16/23, train_loss: 0.0089\n",
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            "21/23, train_loss: 0.0068\n",
            "22/23, train_loss: 0.0198\n",
            "23/23, train_loss: 0.0109\n",
            "epoch 41 average loss: 0.0681\n",
            "----------\n",
            "epoch 42/100\n",
            "1/23, train_loss: 0.0085\n",
            "2/23, train_loss: 0.0106\n",
            "3/23, train_loss: 0.0082\n",
            "4/23, train_loss: 0.0067\n",
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            "11/23, train_loss: 0.0089\n",
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            "16/23, train_loss: 0.0089\n",
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            "21/23, train_loss: 0.0063\n",
            "22/23, train_loss: 0.0196\n",
            "23/23, train_loss: 0.0101\n",
            "epoch 42 average loss: 0.0676\n",
            "current epoch: 42 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 43/100\n",
            "1/23, train_loss: 0.0078\n",
            "2/23, train_loss: 0.0102\n",
            "3/23, train_loss: 0.0081\n",
            "4/23, train_loss: 0.0065\n",
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            "21/23, train_loss: 0.0062\n",
            "22/23, train_loss: 0.0169\n",
            "23/23, train_loss: 0.0097\n",
            "epoch 43 average loss: 0.0636\n",
            "----------\n",
            "epoch 44/100\n",
            "1/23, train_loss: 0.0079\n",
            "2/23, train_loss: 0.0098\n",
            "3/23, train_loss: 0.0077\n",
            "4/23, train_loss: 0.0062\n",
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            "21/23, train_loss: 0.0056\n",
            "22/23, train_loss: 0.0161\n",
            "23/23, train_loss: 0.0092\n",
            "epoch 44 average loss: 0.0588\n",
            "current epoch: 44 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 45/100\n",
            "1/23, train_loss: 0.0072\n",
            "2/23, train_loss: 0.0093\n",
            "3/23, train_loss: 0.0072\n",
            "4/23, train_loss: 0.0059\n",
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            "21/23, train_loss: 0.0055\n",
            "22/23, train_loss: 0.0149\n",
            "23/23, train_loss: 0.0086\n",
            "epoch 45 average loss: 0.0567\n",
            "----------\n",
            "epoch 46/100\n",
            "1/23, train_loss: 0.0069\n",
            "2/23, train_loss: 0.0085\n",
            "3/23, train_loss: 0.0069\n",
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            "21/23, train_loss: 0.0051\n",
            "22/23, train_loss: 0.0147\n",
            "23/23, train_loss: 0.0083\n",
            "epoch 46 average loss: 0.0555\n",
            "current epoch: 46 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 47/100\n",
            "1/23, train_loss: 0.0066\n",
            "2/23, train_loss: 0.0086\n",
            "3/23, train_loss: 0.0067\n",
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            "21/23, train_loss: 0.0052\n",
            "22/23, train_loss: 0.0141\n",
            "23/23, train_loss: 0.0084\n",
            "epoch 47 average loss: 0.0571\n",
            "----------\n",
            "epoch 48/100\n",
            "1/23, train_loss: 0.0068\n",
            "2/23, train_loss: 0.0082\n",
            "3/23, train_loss: 0.0066\n",
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            "21/23, train_loss: 0.0049\n",
            "22/23, train_loss: 0.0146\n",
            "23/23, train_loss: 0.0081\n",
            "epoch 48 average loss: 0.0600\n",
            "current epoch: 48 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 49/100\n",
            "1/23, train_loss: 0.0066\n",
            "2/23, train_loss: 0.0091\n",
            "3/23, train_loss: 0.0068\n",
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            "21/23, train_loss: 0.0051\n",
            "22/23, train_loss: 0.0141\n",
            "23/23, train_loss: 0.0083\n",
            "epoch 49 average loss: 0.0613\n",
            "----------\n",
            "epoch 50/100\n",
            "1/23, train_loss: 0.0068\n",
            "2/23, train_loss: 0.0085\n",
            "3/23, train_loss: 0.0065\n",
            "4/23, train_loss: 0.0052\n",
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            "11/23, train_loss: 0.0062\n",
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            "21/23, train_loss: 0.0048\n",
            "22/23, train_loss: 0.0139\n",
            "23/23, train_loss: 0.0081\n",
            "epoch 50 average loss: 0.0571\n",
            "current epoch: 50 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 51/100\n",
            "1/23, train_loss: 0.0065\n",
            "2/23, train_loss: 0.0085\n",
            "3/23, train_loss: 0.0064\n",
            "4/23, train_loss: 0.0053\n",
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            "21/23, train_loss: 0.0047\n",
            "22/23, train_loss: 0.0130\n",
            "23/23, train_loss: 0.0077\n",
            "epoch 51 average loss: 0.0565\n",
            "----------\n",
            "epoch 52/100\n",
            "1/23, train_loss: 0.0062\n",
            "2/23, train_loss: 0.0078\n",
            "3/23, train_loss: 0.0061\n",
            "4/23, train_loss: 0.0050\n",
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            "14/23, train_loss: 0.0058\n",
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            "21/23, train_loss: 0.0045\n",
            "22/23, train_loss: 0.0131\n",
            "23/23, train_loss: 0.0073\n",
            "epoch 52 average loss: 0.0539\n",
            "current epoch: 52 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 53/100\n",
            "1/23, train_loss: 0.0058\n",
            "2/23, train_loss: 0.0075\n",
            "3/23, train_loss: 0.0058\n",
            "4/23, train_loss: 0.0048\n",
            "5/23, train_loss: 0.0054\n",
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            "13/23, train_loss: 0.0087\n",
            "14/23, train_loss: 0.0062\n",
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            "16/23, train_loss: 0.0056\n",
            "17/23, train_loss: 0.0053\n",
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            "21/23, train_loss: 0.0044\n",
            "22/23, train_loss: 0.0119\n",
            "23/23, train_loss: 0.0072\n",
            "epoch 53 average loss: 0.0520\n",
            "----------\n",
            "epoch 54/100\n",
            "1/23, train_loss: 0.0058\n",
            "2/23, train_loss: 0.0071\n",
            "3/23, train_loss: 0.0056\n",
            "4/23, train_loss: 0.0047\n",
            "5/23, train_loss: 0.0053\n",
            "6/23, train_loss: 0.2492\n",
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            "11/23, train_loss: 0.0055\n",
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            "14/23, train_loss: 0.0054\n",
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            "21/23, train_loss: 0.0042\n",
            "22/23, train_loss: 0.0118\n",
            "23/23, train_loss: 0.0068\n",
            "epoch 54 average loss: 0.0480\n",
            "current epoch: 54 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 55/100\n",
            "1/23, train_loss: 0.0053\n",
            "2/23, train_loss: 0.0065\n",
            "3/23, train_loss: 0.0053\n",
            "4/23, train_loss: 0.0044\n",
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            "6/23, train_loss: 0.2331\n",
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            "11/23, train_loss: 0.0052\n",
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            "14/23, train_loss: 0.0052\n",
            "15/23, train_loss: 0.0039\n",
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            "21/23, train_loss: 0.0040\n",
            "22/23, train_loss: 0.0110\n",
            "23/23, train_loss: 0.0064\n",
            "epoch 55 average loss: 0.0452\n",
            "----------\n",
            "epoch 56/100\n",
            "1/23, train_loss: 0.0051\n",
            "2/23, train_loss: 0.0063\n",
            "3/23, train_loss: 0.0050\n",
            "4/23, train_loss: 0.0042\n",
            "5/23, train_loss: 0.0046\n",
            "6/23, train_loss: 0.2296\n",
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            "11/23, train_loss: 0.0051\n",
            "12/23, train_loss: 0.2342\n",
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            "14/23, train_loss: 0.0052\n",
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            "16/23, train_loss: 0.0049\n",
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            "21/23, train_loss: 0.0039\n",
            "22/23, train_loss: 0.0105\n",
            "23/23, train_loss: 0.0063\n",
            "epoch 56 average loss: 0.0441\n",
            "current epoch: 56 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 57/100\n",
            "1/23, train_loss: 0.0050\n",
            "2/23, train_loss: 0.0062\n",
            "3/23, train_loss: 0.0049\n",
            "4/23, train_loss: 0.0040\n",
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            "11/23, train_loss: 0.0049\n",
            "12/23, train_loss: 0.2387\n",
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            "14/23, train_loss: 0.0047\n",
            "15/23, train_loss: 0.0037\n",
            "16/23, train_loss: 0.0050\n",
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            "21/23, train_loss: 0.0038\n",
            "22/23, train_loss: 0.0105\n",
            "23/23, train_loss: 0.0060\n",
            "epoch 57 average loss: 0.0443\n",
            "----------\n",
            "epoch 58/100\n",
            "1/23, train_loss: 0.0047\n",
            "2/23, train_loss: 0.0059\n",
            "3/23, train_loss: 0.0048\n",
            "4/23, train_loss: 0.0039\n",
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            "11/23, train_loss: 0.0048\n",
            "12/23, train_loss: 0.2376\n",
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            "14/23, train_loss: 0.0048\n",
            "15/23, train_loss: 0.0035\n",
            "16/23, train_loss: 0.0046\n",
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            "21/23, train_loss: 0.0037\n",
            "22/23, train_loss: 0.0099\n",
            "23/23, train_loss: 0.0058\n",
            "epoch 58 average loss: 0.0436\n",
            "current epoch: 58 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 59/100\n",
            "1/23, train_loss: 0.0047\n",
            "2/23, train_loss: 0.0058\n",
            "3/23, train_loss: 0.0046\n",
            "4/23, train_loss: 0.0038\n",
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            "11/23, train_loss: 0.0046\n",
            "12/23, train_loss: 0.2498\n",
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            "14/23, train_loss: 0.0047\n",
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            "16/23, train_loss: 0.0045\n",
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            "21/23, train_loss: 0.0036\n",
            "22/23, train_loss: 0.0098\n",
            "23/23, train_loss: 0.0058\n",
            "epoch 59 average loss: 0.0443\n",
            "----------\n",
            "epoch 60/100\n",
            "1/23, train_loss: 0.0046\n",
            "2/23, train_loss: 0.0056\n",
            "3/23, train_loss: 0.0045\n",
            "4/23, train_loss: 0.0037\n",
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            "11/23, train_loss: 0.0044\n",
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            "13/23, train_loss: 0.0070\n",
            "14/23, train_loss: 0.0044\n",
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            "16/23, train_loss: 0.0046\n",
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            "21/23, train_loss: 0.0036\n",
            "22/23, train_loss: 0.0094\n",
            "23/23, train_loss: 0.0056\n",
            "epoch 60 average loss: 0.0453\n",
            "current epoch: 60 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 61/100\n",
            "1/23, train_loss: 0.0044\n",
            "2/23, train_loss: 0.0058\n",
            "3/23, train_loss: 0.0044\n",
            "4/23, train_loss: 0.0036\n",
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            "11/23, train_loss: 0.0046\n",
            "12/23, train_loss: 0.2549\n",
            "13/23, train_loss: 0.0066\n",
            "14/23, train_loss: 0.0045\n",
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            "16/23, train_loss: 0.0043\n",
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            "21/23, train_loss: 0.0034\n",
            "22/23, train_loss: 0.0095\n",
            "23/23, train_loss: 0.0055\n",
            "epoch 61 average loss: 0.0445\n",
            "----------\n",
            "epoch 62/100\n",
            "1/23, train_loss: 0.0043\n",
            "2/23, train_loss: 0.0054\n",
            "3/23, train_loss: 0.0043\n",
            "4/23, train_loss: 0.0035\n",
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            "11/23, train_loss: 0.0045\n",
            "12/23, train_loss: 0.2310\n",
            "13/23, train_loss: 0.0064\n",
            "14/23, train_loss: 0.0047\n",
            "15/23, train_loss: 0.0033\n",
            "16/23, train_loss: 0.0042\n",
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            "21/23, train_loss: 0.0035\n",
            "22/23, train_loss: 0.0089\n",
            "23/23, train_loss: 0.0055\n",
            "epoch 62 average loss: 0.0448\n",
            "current epoch: 62 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 63/100\n",
            "1/23, train_loss: 0.0045\n",
            "2/23, train_loss: 0.0054\n",
            "3/23, train_loss: 0.0042\n",
            "4/23, train_loss: 0.0036\n",
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            "14/23, train_loss: 0.0041\n",
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            "21/23, train_loss: 0.0033\n",
            "22/23, train_loss: 0.0098\n",
            "23/23, train_loss: 0.0053\n",
            "epoch 63 average loss: 0.0491\n",
            "----------\n",
            "epoch 64/100\n",
            "1/23, train_loss: 0.0042\n",
            "2/23, train_loss: 0.0057\n",
            "3/23, train_loss: 0.0043\n",
            "4/23, train_loss: 0.0035\n",
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            "11/23, train_loss: 0.0050\n",
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            "14/23, train_loss: 0.0050\n",
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            "21/23, train_loss: 0.0034\n",
            "22/23, train_loss: 0.0087\n",
            "23/23, train_loss: 0.0054\n",
            "epoch 64 average loss: 0.0483\n",
            "current epoch: 64 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 65/100\n",
            "1/23, train_loss: 0.0045\n",
            "2/23, train_loss: 0.0054\n",
            "3/23, train_loss: 0.0043\n",
            "4/23, train_loss: 0.0035\n",
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            "11/23, train_loss: 0.0043\n",
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            "16/23, train_loss: 0.0043\n",
            "17/23, train_loss: 0.0036\n",
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            "21/23, train_loss: 0.0032\n",
            "22/23, train_loss: 0.0089\n",
            "23/23, train_loss: 0.0053\n",
            "epoch 65 average loss: 0.0442\n",
            "----------\n",
            "epoch 66/100\n",
            "1/23, train_loss: 0.0042\n",
            "2/23, train_loss: 0.0053\n",
            "3/23, train_loss: 0.0041\n",
            "4/23, train_loss: 0.0034\n",
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            "21/23, train_loss: 0.0031\n",
            "22/23, train_loss: 0.0085\n",
            "23/23, train_loss: 0.0050\n",
            "epoch 66 average loss: 0.0425\n",
            "current epoch: 66 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 67/100\n",
            "1/23, train_loss: 0.0041\n",
            "2/23, train_loss: 0.0050\n",
            "3/23, train_loss: 0.0040\n",
            "4/23, train_loss: 0.0033\n",
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            "14/23, train_loss: 0.0039\n",
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            "21/23, train_loss: 0.0030\n",
            "22/23, train_loss: 0.0083\n",
            "23/23, train_loss: 0.0049\n",
            "epoch 67 average loss: 0.0411\n",
            "----------\n",
            "epoch 68/100\n",
            "1/23, train_loss: 0.0040\n",
            "2/23, train_loss: 0.0050\n",
            "3/23, train_loss: 0.0039\n",
            "4/23, train_loss: 0.0032\n",
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            "14/23, train_loss: 0.0043\n",
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            "16/23, train_loss: 0.0037\n",
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            "21/23, train_loss: 0.0031\n",
            "22/23, train_loss: 0.0078\n",
            "23/23, train_loss: 0.0049\n",
            "epoch 68 average loss: 0.0422\n",
            "current epoch: 68 current accuracy: 0.8333 current AUC: 0.9722 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 69/100\n",
            "1/23, train_loss: 0.0040\n",
            "2/23, train_loss: 0.0049\n",
            "3/23, train_loss: 0.0038\n",
            "4/23, train_loss: 0.0032\n",
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            "11/23, train_loss: 0.0039\n",
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            "14/23, train_loss: 0.0036\n",
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            "21/23, train_loss: 0.0030\n",
            "22/23, train_loss: 0.0090\n",
            "23/23, train_loss: 0.0049\n",
            "epoch 69 average loss: 0.0468\n",
            "----------\n",
            "epoch 70/100\n",
            "1/23, train_loss: 0.0038\n",
            "2/23, train_loss: 0.0050\n",
            "3/23, train_loss: 0.0039\n",
            "4/23, train_loss: 0.0032\n",
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            "14/23, train_loss: 0.0046\n",
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            "16/23, train_loss: 0.0037\n",
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            "21/23, train_loss: 0.0030\n",
            "22/23, train_loss: 0.0076\n",
            "23/23, train_loss: 0.0048\n",
            "epoch 70 average loss: 0.0437\n",
            "current epoch: 70 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 71/100\n",
            "1/23, train_loss: 0.0039\n",
            "2/23, train_loss: 0.0048\n",
            "3/23, train_loss: 0.0037\n",
            "4/23, train_loss: 0.0030\n",
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            "11/23, train_loss: 0.0039\n",
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            "13/23, train_loss: 0.0054\n",
            "14/23, train_loss: 0.0040\n",
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            "16/23, train_loss: 0.0036\n",
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            "21/23, train_loss: 0.0029\n",
            "22/23, train_loss: 0.0076\n",
            "23/23, train_loss: 0.0047\n",
            "epoch 71 average loss: 0.0414\n",
            "----------\n",
            "epoch 72/100\n",
            "1/23, train_loss: 0.0038\n",
            "2/23, train_loss: 0.0047\n",
            "3/23, train_loss: 0.0037\n",
            "4/23, train_loss: 0.0031\n",
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            "11/23, train_loss: 0.0039\n",
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            "14/23, train_loss: 0.0037\n",
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            "16/23, train_loss: 0.0039\n",
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            "21/23, train_loss: 0.0029\n",
            "22/23, train_loss: 0.0081\n",
            "23/23, train_loss: 0.0047\n",
            "epoch 72 average loss: 0.0427\n",
            "current epoch: 72 current accuracy: 0.8333 current AUC: 0.9722 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 73/100\n",
            "1/23, train_loss: 0.0036\n",
            "2/23, train_loss: 0.0046\n",
            "3/23, train_loss: 0.0036\n",
            "4/23, train_loss: 0.0029\n",
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            "11/23, train_loss: 0.0036\n",
            "12/23, train_loss: 0.2118\n",
            "13/23, train_loss: 0.0054\n",
            "14/23, train_loss: 0.0037\n",
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            "16/23, train_loss: 0.0034\n",
            "17/23, train_loss: 0.0031\n",
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            "21/23, train_loss: 0.0027\n",
            "22/23, train_loss: 0.0074\n",
            "23/23, train_loss: 0.0046\n",
            "epoch 73 average loss: 0.0416\n",
            "----------\n",
            "epoch 74/100\n",
            "1/23, train_loss: 0.0037\n",
            "2/23, train_loss: 0.0049\n",
            "3/23, train_loss: 0.0036\n",
            "4/23, train_loss: 0.0030\n",
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            "11/23, train_loss: 0.0041\n",
            "12/23, train_loss: 0.2154\n",
            "13/23, train_loss: 0.0055\n",
            "14/23, train_loss: 0.0039\n",
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            "16/23, train_loss: 0.0036\n",
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            "21/23, train_loss: 0.0028\n",
            "22/23, train_loss: 0.0075\n",
            "23/23, train_loss: 0.0047\n",
            "epoch 74 average loss: 0.0434\n",
            "current epoch: 74 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 75/100\n",
            "1/23, train_loss: 0.0038\n",
            "2/23, train_loss: 0.0046\n",
            "3/23, train_loss: 0.0036\n",
            "4/23, train_loss: 0.0029\n",
            "5/23, train_loss: 0.0034\n",
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            "11/23, train_loss: 0.0036\n",
            "12/23, train_loss: 0.2179\n",
            "13/23, train_loss: 0.0056\n",
            "14/23, train_loss: 0.0035\n",
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            "16/23, train_loss: 0.0035\n",
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            "21/23, train_loss: 0.0027\n",
            "22/23, train_loss: 0.0074\n",
            "23/23, train_loss: 0.0045\n",
            "epoch 75 average loss: 0.0426\n",
            "----------\n",
            "epoch 76/100\n",
            "1/23, train_loss: 0.0037\n",
            "2/23, train_loss: 0.0050\n",
            "3/23, train_loss: 0.0036\n",
            "4/23, train_loss: 0.0030\n",
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            "6/23, train_loss: 0.2256\n",
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            "11/23, train_loss: 0.0041\n",
            "12/23, train_loss: 0.2253\n",
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            "14/23, train_loss: 0.0037\n",
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            "16/23, train_loss: 0.0036\n",
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            "21/23, train_loss: 0.0027\n",
            "22/23, train_loss: 0.0076\n",
            "23/23, train_loss: 0.0044\n",
            "epoch 76 average loss: 0.0427\n",
            "current epoch: 76 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 77/100\n",
            "1/23, train_loss: 0.0035\n",
            "2/23, train_loss: 0.0044\n",
            "3/23, train_loss: 0.0035\n",
            "4/23, train_loss: 0.0028\n",
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            "11/23, train_loss: 0.0036\n",
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            "14/23, train_loss: 0.0038\n",
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            "16/23, train_loss: 0.0034\n",
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            "21/23, train_loss: 0.0027\n",
            "22/23, train_loss: 0.0071\n",
            "23/23, train_loss: 0.0044\n",
            "epoch 77 average loss: 0.0405\n",
            "----------\n",
            "epoch 78/100\n",
            "1/23, train_loss: 0.0035\n",
            "2/23, train_loss: 0.0046\n",
            "3/23, train_loss: 0.0033\n",
            "4/23, train_loss: 0.0028\n",
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            "11/23, train_loss: 0.0037\n",
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            "13/23, train_loss: 0.0056\n",
            "14/23, train_loss: 0.0034\n",
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            "16/23, train_loss: 0.0034\n",
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            "21/23, train_loss: 0.0026\n",
            "22/23, train_loss: 0.0071\n",
            "23/23, train_loss: 0.0042\n",
            "epoch 78 average loss: 0.0379\n",
            "current epoch: 78 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 79/100\n",
            "1/23, train_loss: 0.0033\n",
            "2/23, train_loss: 0.0040\n",
            "3/23, train_loss: 0.0033\n",
            "4/23, train_loss: 0.0026\n",
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            "14/23, train_loss: 0.0033\n",
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            "21/23, train_loss: 0.0025\n",
            "22/23, train_loss: 0.0066\n",
            "23/23, train_loss: 0.0040\n",
            "epoch 79 average loss: 0.0354\n",
            "----------\n",
            "epoch 80/100\n",
            "1/23, train_loss: 0.0032\n",
            "2/23, train_loss: 0.0041\n",
            "3/23, train_loss: 0.0031\n",
            "4/23, train_loss: 0.0026\n",
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            "11/23, train_loss: 0.0034\n",
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            "13/23, train_loss: 0.0047\n",
            "14/23, train_loss: 0.0035\n",
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            "16/23, train_loss: 0.0031\n",
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            "21/23, train_loss: 0.0025\n",
            "22/23, train_loss: 0.0066\n",
            "23/23, train_loss: 0.0041\n",
            "epoch 80 average loss: 0.0373\n",
            "current epoch: 80 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 81/100\n",
            "1/23, train_loss: 0.0033\n",
            "2/23, train_loss: 0.0040\n",
            "3/23, train_loss: 0.0031\n",
            "4/23, train_loss: 0.0025\n",
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            "14/23, train_loss: 0.0031\n",
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            "16/23, train_loss: 0.0033\n",
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            "21/23, train_loss: 0.0024\n",
            "22/23, train_loss: 0.0067\n",
            "23/23, train_loss: 0.0039\n",
            "epoch 81 average loss: 0.0375\n",
            "----------\n",
            "epoch 82/100\n",
            "1/23, train_loss: 0.0031\n",
            "2/23, train_loss: 0.0040\n",
            "3/23, train_loss: 0.0030\n",
            "4/23, train_loss: 0.0025\n",
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            "14/23, train_loss: 0.0033\n",
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            "21/23, train_loss: 0.0024\n",
            "22/23, train_loss: 0.0061\n",
            "23/23, train_loss: 0.0037\n",
            "epoch 82 average loss: 0.0346\n",
            "current epoch: 82 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 83/100\n",
            "1/23, train_loss: 0.0030\n",
            "2/23, train_loss: 0.0037\n",
            "3/23, train_loss: 0.0029\n",
            "4/23, train_loss: 0.0024\n",
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            "21/23, train_loss: 0.0023\n",
            "22/23, train_loss: 0.0061\n",
            "23/23, train_loss: 0.0038\n",
            "epoch 83 average loss: 0.0341\n",
            "----------\n",
            "epoch 84/100\n",
            "1/23, train_loss: 0.0030\n",
            "2/23, train_loss: 0.0038\n",
            "3/23, train_loss: 0.0029\n",
            "4/23, train_loss: 0.0024\n",
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            "21/23, train_loss: 0.0023\n",
            "22/23, train_loss: 0.0061\n",
            "23/23, train_loss: 0.0037\n",
            "epoch 84 average loss: 0.0339\n",
            "current epoch: 84 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 85/100\n",
            "1/23, train_loss: 0.0029\n",
            "2/23, train_loss: 0.0036\n",
            "3/23, train_loss: 0.0028\n",
            "4/23, train_loss: 0.0023\n",
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            "21/23, train_loss: 0.0022\n",
            "22/23, train_loss: 0.0056\n",
            "23/23, train_loss: 0.0035\n",
            "epoch 85 average loss: 0.0329\n",
            "----------\n",
            "epoch 86/100\n",
            "1/23, train_loss: 0.0028\n",
            "2/23, train_loss: 0.0035\n",
            "3/23, train_loss: 0.0027\n",
            "4/23, train_loss: 0.0023\n",
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            "14/23, train_loss: 0.0030\n",
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            "21/23, train_loss: 0.0022\n",
            "22/23, train_loss: 0.0058\n",
            "23/23, train_loss: 0.0036\n",
            "epoch 86 average loss: 0.0330\n",
            "current epoch: 86 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 87/100\n",
            "1/23, train_loss: 0.0028\n",
            "2/23, train_loss: 0.0034\n",
            "3/23, train_loss: 0.0027\n",
            "4/23, train_loss: 0.0022\n",
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            "13/23, train_loss: 0.0043\n",
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            "21/23, train_loss: 0.0021\n",
            "22/23, train_loss: 0.0056\n",
            "23/23, train_loss: 0.0034\n",
            "epoch 87 average loss: 0.0322\n",
            "----------\n",
            "epoch 88/100\n",
            "1/23, train_loss: 0.0027\n",
            "2/23, train_loss: 0.0033\n",
            "3/23, train_loss: 0.0026\n",
            "4/23, train_loss: 0.0022\n",
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            "12/23, train_loss: 0.1740\n",
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            "14/23, train_loss: 0.0029\n",
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            "21/23, train_loss: 0.0020\n",
            "22/23, train_loss: 0.0052\n",
            "23/23, train_loss: 0.0033\n",
            "epoch 88 average loss: 0.0309\n",
            "current epoch: 88 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 89/100\n",
            "1/23, train_loss: 0.0026\n",
            "2/23, train_loss: 0.0033\n",
            "3/23, train_loss: 0.0025\n",
            "4/23, train_loss: 0.0021\n",
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            "12/23, train_loss: 0.1710\n",
            "13/23, train_loss: 0.0037\n",
            "14/23, train_loss: 0.0026\n",
            "15/23, train_loss: 0.0019\n",
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            "21/23, train_loss: 0.0020\n",
            "22/23, train_loss: 0.0053\n",
            "23/23, train_loss: 0.0032\n",
            "epoch 89 average loss: 0.0305\n",
            "----------\n",
            "epoch 90/100\n",
            "1/23, train_loss: 0.0025\n",
            "2/23, train_loss: 0.0033\n",
            "3/23, train_loss: 0.0025\n",
            "4/23, train_loss: 0.0021\n",
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            "12/23, train_loss: 0.1705\n",
            "13/23, train_loss: 0.0038\n",
            "14/23, train_loss: 0.0025\n",
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            "21/23, train_loss: 0.0020\n",
            "22/23, train_loss: 0.0053\n",
            "23/23, train_loss: 0.0032\n",
            "epoch 90 average loss: 0.0309\n",
            "current epoch: 90 current accuracy: 0.8333 current AUC: 0.9444 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 91/100\n",
            "1/23, train_loss: 0.0025\n",
            "2/23, train_loss: 0.0031\n",
            "3/23, train_loss: 0.0024\n",
            "4/23, train_loss: 0.0020\n",
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            "6/23, train_loss: 0.1597\n",
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            "11/23, train_loss: 0.0025\n",
            "12/23, train_loss: 0.1681\n",
            "13/23, train_loss: 0.0037\n",
            "14/23, train_loss: 0.0026\n",
            "15/23, train_loss: 0.0018\n",
            "16/23, train_loss: 0.0024\n",
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            "21/23, train_loss: 0.0019\n",
            "22/23, train_loss: 0.0049\n",
            "23/23, train_loss: 0.0031\n",
            "epoch 91 average loss: 0.0301\n",
            "----------\n",
            "epoch 92/100\n",
            "1/23, train_loss: 0.0025\n",
            "2/23, train_loss: 0.0031\n",
            "3/23, train_loss: 0.0024\n",
            "4/23, train_loss: 0.0020\n",
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            "10/23, train_loss: 0.0034\n",
            "11/23, train_loss: 0.0025\n",
            "12/23, train_loss: 0.1640\n",
            "13/23, train_loss: 0.0036\n",
            "14/23, train_loss: 0.0024\n",
            "15/23, train_loss: 0.0019\n",
            "16/23, train_loss: 0.0024\n",
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            "21/23, train_loss: 0.0019\n",
            "22/23, train_loss: 0.0049\n",
            "23/23, train_loss: 0.0030\n",
            "epoch 92 average loss: 0.0293\n",
            "current epoch: 92 current accuracy: 0.8333 current AUC: 0.9722 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 93/100\n",
            "1/23, train_loss: 0.0024\n",
            "2/23, train_loss: 0.0029\n",
            "3/23, train_loss: 0.0023\n",
            "4/23, train_loss: 0.0019\n",
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            "6/23, train_loss: 0.1478\n",
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            "11/23, train_loss: 0.0024\n",
            "12/23, train_loss: 0.1678\n",
            "13/23, train_loss: 0.0035\n",
            "14/23, train_loss: 0.0024\n",
            "15/23, train_loss: 0.0018\n",
            "16/23, train_loss: 0.0023\n",
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            "21/23, train_loss: 0.0018\n",
            "22/23, train_loss: 0.0048\n",
            "23/23, train_loss: 0.0029\n",
            "epoch 93 average loss: 0.0296\n",
            "----------\n",
            "epoch 94/100\n",
            "1/23, train_loss: 0.0024\n",
            "2/23, train_loss: 0.0030\n",
            "3/23, train_loss: 0.0023\n",
            "4/23, train_loss: 0.0019\n",
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            "11/23, train_loss: 0.0025\n",
            "12/23, train_loss: 0.1802\n",
            "13/23, train_loss: 0.0034\n",
            "14/23, train_loss: 0.0024\n",
            "15/23, train_loss: 0.0018\n",
            "16/23, train_loss: 0.0023\n",
            "17/23, train_loss: 0.0021\n",
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            "21/23, train_loss: 0.0018\n",
            "22/23, train_loss: 0.0048\n",
            "23/23, train_loss: 0.0030\n",
            "epoch 94 average loss: 0.0305\n",
            "current epoch: 94 current accuracy: 0.8333 current AUC: 0.9722 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 95/100\n",
            "1/23, train_loss: 0.0023\n",
            "2/23, train_loss: 0.0029\n",
            "3/23, train_loss: 0.0023\n",
            "4/23, train_loss: 0.0018\n",
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            "11/23, train_loss: 0.0023\n",
            "12/23, train_loss: 0.1743\n",
            "13/23, train_loss: 0.0034\n",
            "14/23, train_loss: 0.0022\n",
            "15/23, train_loss: 0.0017\n",
            "16/23, train_loss: 0.0023\n",
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            "21/23, train_loss: 0.0018\n",
            "22/23, train_loss: 0.0047\n",
            "23/23, train_loss: 0.0029\n",
            "epoch 95 average loss: 0.0299\n",
            "----------\n",
            "epoch 96/100\n",
            "1/23, train_loss: 0.0023\n",
            "2/23, train_loss: 0.0029\n",
            "3/23, train_loss: 0.0022\n",
            "4/23, train_loss: 0.0018\n",
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            "6/23, train_loss: 0.1500\n",
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            "11/23, train_loss: 0.0025\n",
            "12/23, train_loss: 0.1629\n",
            "13/23, train_loss: 0.0033\n",
            "14/23, train_loss: 0.0024\n",
            "15/23, train_loss: 0.0017\n",
            "16/23, train_loss: 0.0022\n",
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            "21/23, train_loss: 0.0017\n",
            "22/23, train_loss: 0.0045\n",
            "23/23, train_loss: 0.0028\n",
            "epoch 96 average loss: 0.0289\n",
            "current epoch: 96 current accuracy: 0.8333 current AUC: 0.9722 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 97/100\n",
            "1/23, train_loss: 0.0022\n",
            "2/23, train_loss: 0.0027\n",
            "3/23, train_loss: 0.0022\n",
            "4/23, train_loss: 0.0018\n",
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            "11/23, train_loss: 0.0022\n",
            "12/23, train_loss: 0.1537\n",
            "13/23, train_loss: 0.0032\n",
            "14/23, train_loss: 0.0022\n",
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            "16/23, train_loss: 0.0021\n",
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            "21/23, train_loss: 0.0017\n",
            "22/23, train_loss: 0.0044\n",
            "23/23, train_loss: 0.0027\n",
            "epoch 97 average loss: 0.0277\n",
            "----------\n",
            "epoch 98/100\n",
            "1/23, train_loss: 0.0022\n",
            "2/23, train_loss: 0.0028\n",
            "3/23, train_loss: 0.0021\n",
            "4/23, train_loss: 0.0018\n",
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            "21/23, train_loss: 0.0017\n",
            "22/23, train_loss: 0.0042\n",
            "23/23, train_loss: 0.0027\n",
            "epoch 98 average loss: 0.0276\n",
            "current epoch: 98 current accuracy: 0.8333 current AUC: 0.9722 best accuracy: 0.8333 at epoch 14\n",
            "----------\n",
            "epoch 99/100\n",
            "1/23, train_loss: 0.0022\n",
            "2/23, train_loss: 0.0026\n",
            "3/23, train_loss: 0.0021\n",
            "4/23, train_loss: 0.0017\n",
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            "21/23, train_loss: 0.0016\n",
            "22/23, train_loss: 0.0042\n",
            "23/23, train_loss: 0.0026\n",
            "epoch 99 average loss: 0.0274\n",
            "----------\n",
            "epoch 100/100\n",
            "1/23, train_loss: 0.0021\n",
            "2/23, train_loss: 0.0027\n",
            "3/23, train_loss: 0.0020\n",
            "4/23, train_loss: 0.0017\n",
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            "21/23, train_loss: 0.0016\n",
            "22/23, train_loss: 0.0042\n",
            "23/23, train_loss: 0.0026\n",
            "epoch 100 average loss: 0.0276\n",
            "current epoch: 100 current accuracy: 0.8333 current AUC: 0.9722 best accuracy: 0.8333 at epoch 14\n",
            "train completed, best_metric: 0.8333 at epoch: 14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "MLNwxbkS2Nbl",
        "outputId": "f0bdee3b-f532-4733-914e-00dffea036d1"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "-LyP68KcHELg",
        "outputId": "5264376c-c702-49dc-8ebc-9c2c42fea4bb"
      },
      "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_dense1.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": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                         precision    recall  f1-score   support\n",
            "\n",
            "          Normal_Befund       0.75      1.00      0.86         6\n",
            "Hypoxischer_Hirnschaden       1.00      0.67      0.80         6\n",
            "\n",
            "               accuracy                           0.83        12\n",
            "              macro avg       0.88      0.83      0.83        12\n",
            "           weighted avg       0.88      0.83      0.83        12\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fe73ecd7610>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nm0LXsvlgp_N"
      },
      "source": [
        "#files = [{\"img\": img} for img in images]\n",
        "#val_files  = [{\"image\": i, \"seg\": s, \"label\": l} for i, s, l in zip(images[120:], segmentations[120:], labels[120:])]\n",
        "\n",
        "post_transforms = Compose([\n",
        "        #Activationsd(keys=\"pred\", sigmoid=True),\n",
        "        #AsDiscreted(keys=\"pred\", threshold_values=True),\n",
        "        Invertd(\n",
        "            keys=\"y_pred\",  # invert the `pred` data field, also support multiple fields\n",
        "            transform=transforms,\n",
        "            #loader=val_loader,\n",
        "            orig_keys=\"files\",  # get the previously applied pre_transforms information on the `img` data field,\n",
        "                              # then invert `pred` based on this information. we can use same info\n",
        "                              # for multiple fields, also support different orig_keys for different fields\n",
        "            #meta_keys=\"pred_meta_dict\",  # key field to save inverted meta data, every item maps to `keys`\n",
        "            #orig_meta_keys=\"img_meta_dict\",  # get the meta data from `img_meta_dict` field when inverting,\n",
        "                                             # for example, may need the `affine` to invert `Spacingd` transform,\n",
        "                                             # multiple fields can use the same meta data to invert\n",
        "            #meta_key_postfix=\"meta_dict\",  # if `meta_keys=None`, use \"{keys}_{meta_key_postfix}\" as the meta key,\n",
        "                                           # if `orig_meta_keys=None`, use \"{orig_keys}_{meta_key_postfix}\",\n",
        "                                           # otherwise, no need this arg during inverting\n",
        "            nearest_interp=True,  # change to use \"nearest\" mode in interpolation when inverting\n",
        "            to_tensor=True,  # convert to PyTorch Tensor after inverting\n",
        "        ),\n",
        "        #SaveImaged(keys=\"pred\", meta_keys=\"pred_meta_dict\", output_dir=\"./out\", output_postfix=\"seg\", resample=False),\n",
        "    ])\n",
        "\n",
        "#test_ds = CacheDataset(data=data_partition[1], transform=transforms)\n",
        "\n",
        "test_loader = DataLoader(val_ds, batch_size=1, num_workers=4)\n",
        "itera = iter(test_loader)\n",
        "\n",
        "\n",
        "def get_next_im():\n",
        "    test_data = next(itera)\n",
        "    return test_data[\"image\"].to(device), test_data[\"label\"].unsqueeze(0).to(device)\n",
        "\n",
        "\n",
        "def plot_occlusion_heatmap(im, heatmap):\n",
        "    plt.subplots(1, 2)\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.imshow(np.squeeze(im.cpu()))\n",
        "    plt.colorbar()\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.imshow(heatmap)\n",
        "    plt.colorbar()\n",
        "    plt.show()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "vfFYizJOg1TG",
        "outputId": "ae9fd564-b840-442f-b2ff-263b49c4d2a9"
      },
      "source": [
        "# Get a random image and its corresponding label\n",
        "img, label = get_next_im()\n",
        "\n",
        "# Get the occlusion sensitivity map\n",
        "occ_sens = monai.visualize.OcclusionSensitivity(\n",
        "    nn_module=model, mask_size=12, n_batch=0, stride=12)\n",
        "# Get the CAM\n",
        "cam = monai.visualize.GradCAM(\n",
        "    nn_module=model, target_layers=\"class_layers.relu\"\n",
        ")\n",
        "# Only get a single slice to save time.\n",
        "# For the other dimensions (channel, width, height), use\n",
        "# -1 to use 0 and img.shape[x]-1 for min and max, respectively\n",
        "depth_slice = img.shape[2] // 2\n",
        "\n",
        "occ_sens_b_box = [-1, -1, depth_slice, depth_slice, -1, -1, -1,-1]\n",
        "\n",
        "occ_result, _ = occ_sens(x=img, b_box=occ_sens_b_box)\n",
        "occ_result = occ_result[..., label.item()]\n",
        "#occ_result = occ_result[None]\n",
        "cam_result = cam(x=img, class_idx=None)\n",
        "#cam_result = cam_result[..., depth_slice]\n",
        "#cam_result = cam_result[:, :, :, :, depth_slice]\n",
        "\n",
        "fig, axes = plt.subplots(1, 3, figsize=(45, 15), facecolor='white')\n",
        "\n",
        "for i, im in enumerate([img[:, :, :, :, depth_slice], occ_result, cam_result[:, :, :, :, depth_slice]]):\n",
        "    cmap = 'gray' if i == 0 else 'jet'\n",
        "    ax = axes[i]\n",
        "    if isinstance(im, torch.Tensor):\n",
        "        im = im.cpu().detach()    \n",
        "    #im_show = ax.imshow(im[0][0], cmap=cmap)\n",
        "    im_show = ax.imshow(np.squeeze(im[0][0]), cmap=cmap)\n",
        "    ax.axis('off')\n",
        "    fig.colorbar(im_show, ax=ax)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Computing occlusion sensitivity: 100%|██████████| 64/64 [00:00<00:00, 1742.04it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 3240x1080 with 6 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZ7pxMVV_SWt",
        "outputId": "51e090cc-802a-4226-a1ed-88ab6175ff68"
      },
      "source": [
        "print(img.shape)\n",
        "print(occ_result.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([1, 1, 96, 96, 96])\n",
            "torch.Size([1, 1, 1, 96, 96])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhK5F99UDgAz",
        "outputId": "7e209b4c-401d-46fe-d43e-780443929497"
      },
      "source": [
        "ret = blend_images(image=img, label=cam_result, alpha=0.5, cmap=\"hsv\", rescale_arrays=False)\n",
        "print(ret.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([3, 1, 96, 96, 96])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vnIXoWUcEhWq",
        "outputId": "245d6212-a746-4ac9-d8b4-1eb23f7aa7f0"
      },
      "source": [
        "for i in range(5, 10):\n",
        "    # plot the slice 50 - 100 of image, label and blend result\n",
        "    slice_index = 10 * i\n",
        "    plt.figure(\"blend image and label\", (12, 4))\n",
        "    plt.subplot(1, 3, 1)\n",
        "    plt.title(f\"image slice {slice_index}\")\n",
        "    plt.imshow(img[0, 0, :, :, slice_index].cpu().detach(), cmap=\"gray\")\n",
        "    plt.subplot(1, 3, 2)\n",
        "    plt.title(f\"label slice {slice_index}\")\n",
        "    plt.imshow(cam_result[0, 0, :, :, slice_index].cpu().detach())\n",
        "    plt.subplot(1, 3, 3)\n",
        "    plt.title(f\"blend slice {slice_index}\")\n",
        "    # switch the channel dim to the last dim\n",
        "    plt.imshow(torch.moveaxis(ret[0, 0, :, :, slice_index].cpu().detach(),1,1))\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXwf045wOV2N"
      },
      "source": [
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from monai.visualize import plot_2d_or_3d_image\n",
        "with SummaryWriter(log_dir=\"logs\") as writer:\n",
        "    plot_2d_or_3d_image(img, step=0, writer=writer, tag=\"Input\")\n",
        "    plot_2d_or_3d_image(cam_result, step=0, writer=writer, tag=\"CAM\")\n",
        "    plot_2d_or_3d_image(occ_result, step=0, writer=writer, tag=\"OccSens\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nv3hzdFVO3Ce"
      },
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir logs\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}