experiment.ipynb

{
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  "metadata": {
    "colab": {
      "name": "GRU_D.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "MuQjsNxfkOzv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Load Data"
      ]
    },
    {
      "metadata": {
        "id": "XmteRNV-qmor",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "cellView": "code",
        "outputId": "fdbe4de7-6403-4e81-acf5-2836314a9253",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525221895187,
          "user_tz": 240,
          "elapsed": 6710,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install pydrive\n",
        "!pip install tqdm"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pydrive\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/e0/0e64788e5dd58ce2d6934549676243dc69d982f198524be9b99e9c2a4fd5/PyDrive-1.3.1.tar.gz (987kB)\n",
            "\u001b[K    100% |████████████████████████████████| 993kB 6.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.6/dist-packages (from pydrive) (1.6.7)\n",
            "Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pydrive) (4.1.2)\n",
            "Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.6/dist-packages (from pydrive) (3.12)\n",
            "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (3.0.0)\n",
            "Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (1.11.0)\n",
            "Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (0.11.3)\n",
            "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive) (0.2.1)\n",
            "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive) (0.4.2)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive) (3.4.2)\n",
            "Building wheels for collected packages: pydrive\n",
            "  Running setup.py bdist_wheel for pydrive ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /content/.cache/pip/wheels/fa/d2/9a/d3b6b506c2da98289e5d417215ce34b696db856643bad779f4\n",
            "Successfully built pydrive\n",
            "Installing collected packages: pydrive\n",
            "Successfully installed pydrive-1.3.1\n",
            "Collecting tqdm\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c2/d4/bf1d99216672eef50392c009a8d0f282c4b643e4f764c962ff7b611ebce4/tqdm-4.23.1-py2.py3-none-any.whl (42kB)\n",
            "\u001b[K    100% |████████████████████████████████| 51kB 2.1MB/s \n",
            "\u001b[?25hInstalling collected packages: tqdm\n",
            "Successfully installed tqdm-4.23.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RwsY2r4pYR0R",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "122a102e-ce8d-49f0-e83e-612710e4a669",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525229232861,
          "user_tz": 240,
          "elapsed": 3340,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "# choose a local (colab) directory to store the data.\n",
        "local_download_path = os.path.expanduser('./')\n",
        "try:\n",
        "  os.makedirs(local_download_path)\n",
        "except: pass\n",
        "\n",
        "# 2. Auto-iterate using the query syntax\n",
        "#    https://developers.google.com/drive/v2/web/search-parameters\n",
        "file_list = drive.ListFile(\n",
        "    {'q': \"'1JAugBFXNsExZgx4bdAJcT6aSvAUmRji4' in parents\"}).GetList()\n",
        "\n",
        "for f in file_list[:-1]:\n",
        "#   3. Create & download by id.\n",
        "  print('title: %s, id: %s' % (f['title'], f['id']))\n",
        "  fname = os.path.join(local_download_path, f['title'])\n",
        "  print('downloading to {}'.format(fname))\n",
        "  f_ = drive.CreateFile({'id': f['id']})\n",
        "  f_.GetContentFile(fname)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "title: dataset.py, id: 1UyGCzyl7YCybvztsc1Nebq02zW-P6PBz\n",
            "downloading to ./dataset.py\n",
            "title: all_data.csv, id: 1Q3LVcYMheO4X0znpLiCxBaURhqzMOMvB\n",
            "downloading to ./all_data.csv\n",
            "title: utils.py, id: 10SrGViJ6RZOThFlTWqMXQQ2RnDhOO9j7\n",
            "downloading to ./utils.py\n",
            "title: Outcomes-a.txt, id: 1APBfJOF66ouhT9jNUI_WYo0somujrAhE\n",
            "downloading to ./Outcomes-a.txt\n",
            "title: model.py, id: 1HK0qg48MwEoh20HPVx6RnypEhCZg9ehb\n",
            "downloading to ./model.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "91ovPt4uGg7N",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# http://pytorch.org/\n",
        "from os import path\n",
        "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
        "platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
        "\n",
        "accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'\n",
        "\n",
        "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.3.0.post4-{platform}-linux_x86_64.whl torchvision\n",
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3dy3II4wSmqv",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "import model\n",
        "import importlib\n",
        "importlib.reload(model)\n",
        "import dataset\n",
        "importlib.reload(dataset)\n",
        "import utils\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.autograd import Variable\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "# import skorch\n",
        "import os\n",
        "from time import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4SWxnpL0IgkJ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# read data\n",
        "outcomes = pd.read_csv('Outcomes-a.txt')\n",
        "df = pd.read_csv('./all_data.csv').set_index('record_id')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GfOA6eSE8dFc",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# stratified k-fold\n",
        "num_fold = 10\n",
        "skf = StratifiedKFold(n_splits=num_fold)\n",
        "\n",
        "train_dfs, test_dfs = [], []\n",
        "\n",
        "for train, test in skf.split(outcomes,outcomes['In-hospital_death']):\n",
        "    train_dfs.append(train)\n",
        "    test_dfs.append(test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yG7nk06uw1oC",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "train_ds = dataset.PhysioNET(df, outcomes)\n",
        "test_ds = dataset.PhysioNET(df, outcomes, means=train_ds.means, stds=train_ds.stds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lO44y4yaS6ro",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "NUM_FEATURES = 33\n",
        "batch_size = 128\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_ds,\n",
        "                                           batch_size=batch_size,\n",
        "                                           collate_fn=dataset.PhysioNET.collate_batch,\n",
        "                                           shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_ds,\n",
        "                                          batch_size=batch_size,\n",
        "                                          collate_fn=dataset.PhysioNET.collate_batch,\n",
        "                                          shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hvs9ir1tl--I",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "033a9c7e-9f75-4afa-df3f-dfbae035d46c",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525096176287,
          "user_tz": 240,
          "elapsed": 332,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "sum(outcomes['In-hospital_death'] == 0), sum(outcomes['In-hospital_death'] == 1)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3446, 554)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "oiHLwuZMXbGs",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "hidden_size = 64\n",
        "gru_dropout = 0.3\n",
        "decoder_dropout = 0.5\n",
        "output_size = 2\n",
        "\n",
        "loss_func = torch.nn.NLLLoss(\n",
        "    weight=Variable(torch.FloatTensor([1/sum(outcomes['In-hospital_death'] == 0), \n",
        "                                       1/sum(outcomes['In-hospital_death'] == 1)\n",
        "                                      ])).cuda())\n",
        "\n",
        "num_epochs = 40\n",
        "log_interval = int((len(train_dfs[0]) / batch_size) / 3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hahjq05Wkd_a",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "class GRU_decoder(nn.Module):\n",
        "  def __init__(self, input_size, hidden_size, output_size,\n",
        "              gru_dropout=0.3, decoder_dropout=0.5, batch_first=True):\n",
        "    super(GRU_decoder, self).__init__()\n",
        "    self.gru = nn.GRU(input_size, hidden_size, \n",
        "                      batch_first=batch_first, dropout=gru_dropout)\n",
        "    self.hidden_size = hidden_size\n",
        "    self.decoder = nn.Sequential(\n",
        "                                nn.BatchNorm1d(hidden_size),\n",
        "#                                 nn.Dropout(decoder_dropout),\n",
        "                                nn.Linear(hidden_size, output_size),\n",
        "                                nn.LogSoftmax()\n",
        "                                )\n",
        "  \n",
        "  def forward(self, x):\n",
        "    batch_size = max(x.batch_sizes)\n",
        "    _, hidden = self.gru(x)\n",
        "    output = self.decoder(hidden.view(batch_size, self.hidden_size))\n",
        "\n",
        "    return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Y_Zhwy9ekUiP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# GRU-mean"
      ]
    },
    {
      "metadata": {
        "id": "xFg_NJS8xvCE",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def test_model_gru_mean(loader, model, concat_input=False):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    model.eval()\n",
        "\n",
        "    predictions = []\n",
        "    truths = []\n",
        "    \n",
        "    total_loss = 0\n",
        "\n",
        "    for data, label in loader:\n",
        "        x, delta, m, x_forward, lengths = data\n",
        "\n",
        "        sorted_lengths, sorted_idx = lengths.sort(descending=True)\n",
        "\n",
        "        x = Variable(x[sorted_idx].float()).cuda()\n",
        "        delta = Variable(delta[sorted_idx].float()).cuda()\n",
        "        m = Variable(m[sorted_idx].float()).cuda()\n",
        "\n",
        "        x[m.data.byte()] = 0.\n",
        "\n",
        "        output = model(pack_padded_sequence(x, sorted_lengths.numpy(), batch_first=True))\n",
        "        \n",
        "        label = label[sorted_idx]\n",
        "        loss = loss_func(output, Variable(label).cuda())\n",
        "            \n",
        "        total_loss += loss.data[0]\n",
        "        predicted = (output.cpu().max(1)[1].data.long()).view(-1)\n",
        "        predictions += list(predicted.numpy())\n",
        "        truths += list(label)\n",
        "        total += label.size(0)\n",
        "        correct += (predicted == label).sum()\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    return 100 * correct / total, roc_auc_score(truths, predictions), total_loss/len(loader)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "N_oFhIUfmWJt",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 27577
        },
        "outputId": "c614244a-00a8-4c41-9ad5-bfe032e91cd8",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525230625097,
          "user_tz": 240,
          "elapsed": 766038,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from torch.nn.utils.rnn import pack_padded_sequence\n",
        "gru_mean_train_loss_histories = []\n",
        "gru_mean_test_auc_histories = []\n",
        "gru_mean_val_loss_histories = []\n",
        "gru_mean_val_acc_histories = []\n",
        "\n",
        "\n",
        "for k in range(num_fold):\n",
        "    train_ds.set_active_indices(train_dfs[k])\n",
        "    test_ds.set_active_indices(test_dfs[k], new_means=train_ds.means, new_stds=train_ds.stds)\n",
        "    train_loader = torch.utils.data.DataLoader(dataset=train_ds,\n",
        "                                           batch_size=batch_size,\n",
        "                                           collate_fn=dataset.PhysioNET.collate_batch,\n",
        "                                           shuffle=True)\n",
        "    test_loader = torch.utils.data.DataLoader(dataset=test_ds,\n",
        "                                          batch_size=batch_size,\n",
        "                                          collate_fn=dataset.PhysioNET.collate_batch,\n",
        "                                          shuffle=True)\n",
        "\n",
        "    gru_mean = GRU_decoder(NUM_FEATURES, hidden_size, output_size, \n",
        "                      gru_dropout, decoder_dropout)\n",
        "    gru_mean.cuda()\n",
        "\n",
        "    optimizer = torch.optim.Adam(gru_mean.parameters(), lr=1e-3)\n",
        "    \n",
        "    gru_mean_train_loss_history = []\n",
        "    gru_mean_test_auc_history = []\n",
        "    gru_mean_val_loss_history = []\n",
        "    gru_mean_val_acc_history = []\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        start = time()\n",
        "        total_loss = 0.\n",
        "\n",
        "        for i, (data, label) in enumerate(train_loader):\n",
        "            x, delta, m, x_forward, lengths = data\n",
        "\n",
        "            sorted_lengths, sorted_idx = lengths.sort(descending=True)\n",
        "            \n",
        "            x = Variable(x[sorted_idx].float()).cuda()\n",
        "            delta = Variable(delta[sorted_idx].float()).cuda()\n",
        "            m = Variable(m[sorted_idx].float()).cuda()\n",
        "\n",
        "            x[m.data.byte()] = 0.\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            output = gru_mean(pack_padded_sequence(x, sorted_lengths.numpy(), batch_first=True))\n",
        "\n",
        "            loss = loss_func(output, Variable(label[sorted_idx].long()).cuda())\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            total_loss += loss.data[0]\n",
        "\n",
        "            # report performance\n",
        "            if (i + 1) % log_interval == 0:\n",
        "                val_acc, test_auc, val_loss = test_model_gru_mean(test_loader, gru_mean)\n",
        "                gru_mean_train_loss_history.append(total_loss/(i+1))\n",
        "                gru_mean_test_auc_history.append(test_auc)\n",
        "                gru_mean_val_acc_history.append(val_acc)\n",
        "                gru_mean_val_loss_history.append(val_loss)\n",
        "                print('Fold:{8}, Epoch: [{0}/{1}], Step: [{2}/{3}], Loss: {4}, Validation Loss:{5}, Validation Acc:{6}, AUC:{7}'.format(\n",
        "                    epoch + 1, num_epochs, i + 1, len(train_loader), total_loss/(i+1), val_loss, val_acc, test_auc, k + 1))\n",
        "        print(\"Epoch %d time: %.4f\" % (epoch, time()-start))\n",
        "        \n",
        "    gru_mean_train_loss_histories.append(gru_mean_train_loss_history)\n",
        "    gru_mean_test_auc_histories.append(gru_mean_test_auc_history)\n",
        "    gru_mean_val_loss_histories.append(gru_mean_val_loss_history)\n",
        "    gru_mean_val_acc_histories.append(gru_mean_val_acc_history)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:1, Epoch: [1/40], Step: [9/29], Loss: 0.6857337355613708, Validation Loss:0.6832905560731888, Validation Acc:86.28428927680798, AUC:0.5313664596273292\n",
            "Fold:1, Epoch: [1/40], Step: [18/29], Loss: 0.6648596458964877, Validation Loss:0.6736467182636261, Validation Acc:77.55610972568579, AUC:0.6003105590062112\n",
            "Fold:1, Epoch: [1/40], Step: [27/29], Loss: 0.6521315972010294, Validation Loss:0.6325764209032059, Validation Acc:72.81795511221945, AUC:0.6625258799171843\n",
            "Epoch 0 time: 1.9274\n",
            "Fold:1, Epoch: [2/40], Step: [9/29], Loss: 0.6126415398385789, Validation Loss:0.6020378172397614, Validation Acc:77.55610972568579, AUC:0.7050207039337474\n",
            "Fold:1, Epoch: [2/40], Step: [18/29], Loss: 0.5883073856433233, Validation Loss:0.5762902200222015, Validation Acc:76.55860349127182, AUC:0.7216614906832297\n",
            "Fold:1, Epoch: [2/40], Step: [27/29], Loss: 0.5780441639599977, Validation Loss:0.5824973881244659, Validation Acc:68.5785536159601, AUC:0.6902432712215321\n",
            "Epoch 1 time: 1.8846\n",
            "Fold:1, Epoch: [3/40], Step: [9/29], Loss: 0.5486837261252933, Validation Loss:0.5396271497011185, Validation Acc:69.57605985037407, AUC:0.7259575569358178\n",
            "Fold:1, Epoch: [3/40], Step: [18/29], Loss: 0.5557175758812163, Validation Loss:0.5414432436227798, Validation Acc:70.07481296758105, AUC:0.7138975155279503\n",
            "Fold:1, Epoch: [3/40], Step: [27/29], Loss: 0.5619027007509161, Validation Loss:0.5231326371431351, Validation Acc:68.3291770573566, AUC:0.7037525879917185\n",
            "Epoch 2 time: 1.9105\n",
            "Fold:1, Epoch: [4/40], Step: [9/29], Loss: 0.5483100877867805, Validation Loss:0.5565923005342484, Validation Acc:72.31920199501246, AUC:0.7269409937888199\n",
            "Fold:1, Epoch: [4/40], Step: [18/29], Loss: 0.5394097483820386, Validation Loss:0.5633815228939056, Validation Acc:66.33416458852868, AUC:0.6846790890269152\n",
            "Fold:1, Epoch: [4/40], Step: [27/29], Loss: 0.5469760398070017, Validation Loss:0.497650571167469, Validation Acc:69.82543640897755, AUC:0.6974896480331263\n",
            "Epoch 3 time: 1.9113\n",
            "Fold:1, Epoch: [5/40], Step: [9/29], Loss: 0.5845965213245816, Validation Loss:0.5504950881004333, Validation Acc:73.56608478802993, AUC:0.7341873706004141\n",
            "Fold:1, Epoch: [5/40], Step: [18/29], Loss: 0.5674876653485827, Validation Loss:0.5092995837330818, Validation Acc:71.82044887780549, AUC:0.7240424430641821\n",
            "Fold:1, Epoch: [5/40], Step: [27/29], Loss: 0.5512973059106756, Validation Loss:0.5350645184516907, Validation Acc:69.82543640897755, AUC:0.6974896480331263\n",
            "Epoch 4 time: 1.8892\n",
            "Fold:1, Epoch: [6/40], Step: [9/29], Loss: 0.506885525253084, Validation Loss:0.5178039968013763, Validation Acc:74.56359102244389, AUC:0.7250258799171843\n",
            "Fold:1, Epoch: [6/40], Step: [18/29], Loss: 0.5451659974124696, Validation Loss:0.5351862534880638, Validation Acc:69.57605985037407, AUC:0.7184782608695652\n",
            "Fold:1, Epoch: [6/40], Step: [27/29], Loss: 0.544279052151574, Validation Loss:0.5380575805902481, Validation Acc:68.82793017456359, AUC:0.7216097308488613\n",
            "Epoch 5 time: 1.9265\n",
            "Fold:1, Epoch: [7/40], Step: [9/29], Loss: 0.5439868900511, Validation Loss:0.5582871586084366, Validation Acc:73.06733167082294, AUC:0.7088509316770186\n",
            "Fold:1, Epoch: [7/40], Step: [18/29], Loss: 0.5412079393863678, Validation Loss:0.4915350005030632, Validation Acc:71.57107231920199, AUC:0.730072463768116\n",
            "Fold:1, Epoch: [7/40], Step: [27/29], Loss: 0.5353249178992378, Validation Loss:0.5227585732936859, Validation Acc:69.07730673316708, AUC:0.7155797101449275\n",
            "Epoch 6 time: 1.8886\n",
            "Fold:1, Epoch: [8/40], Step: [9/29], Loss: 0.5338077710734473, Validation Loss:0.5714374855160713, Validation Acc:70.57356608478803, AUC:0.7317546583850931\n",
            "Fold:1, Epoch: [8/40], Step: [18/29], Loss: 0.520227978626887, Validation Loss:0.5631414577364922, Validation Acc:75.31172069825436, AUC:0.7069358178053831\n",
            "Fold:1, Epoch: [8/40], Step: [27/29], Loss: 0.5216598212718964, Validation Loss:0.5326045751571655, Validation Acc:76.80798004987531, AUC:0.7231107660455486\n",
            "Epoch 7 time: 1.8762\n",
            "Fold:1, Epoch: [9/40], Step: [9/29], Loss: 0.5185669859250387, Validation Loss:0.5847451463341713, Validation Acc:65.08728179551122, AUC:0.7297877846790891\n",
            "Fold:1, Epoch: [9/40], Step: [18/29], Loss: 0.5341857175032297, Validation Loss:0.5095982402563095, Validation Acc:71.3216957605985, AUC:0.7286231884057971\n",
            "Fold:1, Epoch: [9/40], Step: [27/29], Loss: 0.5168827065715084, Validation Loss:0.505587637424469, Validation Acc:73.56608478802993, AUC:0.7192287784679089\n",
            "Epoch 8 time: 1.8986\n",
            "Fold:1, Epoch: [10/40], Step: [9/29], Loss: 0.5136189725663927, Validation Loss:0.4737064316868782, Validation Acc:71.82044887780549, AUC:0.7240424430641821\n",
            "Fold:1, Epoch: [10/40], Step: [18/29], Loss: 0.5110570142666498, Validation Loss:0.566466711461544, Validation Acc:71.3216957605985, AUC:0.7211438923395446\n",
            "Fold:1, Epoch: [10/40], Step: [27/29], Loss: 0.5157554304158246, Validation Loss:0.5305830985307693, Validation Acc:73.31670822942644, AUC:0.71777950310559\n",
            "Epoch 9 time: 1.8683\n",
            "Fold:1, Epoch: [11/40], Step: [9/29], Loss: 0.5220252275466919, Validation Loss:0.5518497750163078, Validation Acc:77.80548628428927, AUC:0.7139492753623189\n",
            "Fold:1, Epoch: [11/40], Step: [18/29], Loss: 0.5013998332950804, Validation Loss:0.5423646420240402, Validation Acc:72.56857855361596, AUC:0.705952380952381\n",
            "Fold:1, Epoch: [11/40], Step: [27/29], Loss: 0.504423666883398, Validation Loss:0.48701105266809464, Validation Acc:71.07231920199501, AUC:0.7196946169772257\n",
            "Epoch 10 time: 1.8734\n",
            "Fold:1, Epoch: [12/40], Step: [9/29], Loss: 0.4923052622212304, Validation Loss:0.5277013853192329, Validation Acc:72.81795511221945, AUC:0.7148809523809524\n"
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            "Fold:1, Epoch: [12/40], Step: [18/29], Loss: 0.48786015974150765, Validation Loss:0.5196767598390579, Validation Acc:67.08229426433915, AUC:0.7339026915113871\n",
            "Fold:1, Epoch: [12/40], Step: [27/29], Loss: 0.4958559407128228, Validation Loss:0.5114088207483292, Validation Acc:73.06733167082294, AUC:0.7462474120082816\n",
            "Epoch 11 time: 1.8893\n",
            "Fold:1, Epoch: [13/40], Step: [9/29], Loss: 0.47738444474008346, Validation Loss:0.45667243376374245, Validation Acc:74.81296758104739, AUC:0.7189958592132505\n",
            "Fold:1, Epoch: [13/40], Step: [18/29], Loss: 0.4804428650273217, Validation Loss:0.4994466006755829, Validation Acc:74.81296758104739, AUC:0.7115165631469978\n",
            "Fold:1, Epoch: [13/40], Step: [27/29], Loss: 0.4847057086450082, Validation Loss:0.5030740275979042, Validation Acc:73.31670822942644, AUC:0.71777950310559\n",
            "Epoch 12 time: 1.8877\n",
            "Fold:1, Epoch: [14/40], Step: [9/29], Loss: 0.47120216819975114, Validation Loss:0.49639520794153214, Validation Acc:66.33416458852868, AUC:0.7295548654244306\n",
            "Fold:1, Epoch: [14/40], Step: [18/29], Loss: 0.49298598534531063, Validation Loss:0.492448627948761, Validation Acc:73.31670822942644, AUC:0.7551759834368529\n",
            "Fold:1, Epoch: [14/40], Step: [27/29], Loss: 0.48061451536637767, Validation Loss:0.5228479281067848, Validation Acc:65.83541147132169, AUC:0.726656314699793\n",
            "Epoch 13 time: 1.8779\n",
            "Fold:1, Epoch: [15/40], Step: [9/29], Loss: 0.5048232409689162, Validation Loss:0.5532922819256783, Validation Acc:78.30423940149626, AUC:0.671972049689441\n",
            "Fold:1, Epoch: [15/40], Step: [18/29], Loss: 0.49089719189537895, Validation Loss:0.553811565041542, Validation Acc:76.05985037406484, AUC:0.6813664596273291\n",
            "Fold:1, Epoch: [15/40], Step: [27/29], Loss: 0.48633920815255904, Validation Loss:0.5596541315317154, Validation Acc:63.591022443890274, AUC:0.7136128364389234\n",
            "Epoch 14 time: 1.9046\n",
            "Fold:1, Epoch: [16/40], Step: [9/29], Loss: 0.4573419756359524, Validation Loss:0.5455495044589043, Validation Acc:72.56857855361596, AUC:0.705952380952381\n",
            "Fold:1, Epoch: [16/40], Step: [18/29], Loss: 0.46398112674554187, Validation Loss:0.4669721908867359, Validation Acc:74.06483790523691, AUC:0.7146480331262939\n",
            "Fold:1, Epoch: [16/40], Step: [27/29], Loss: 0.4710236732606535, Validation Loss:0.6360856741666794, Validation Acc:66.58354114713217, AUC:0.7160455486542442\n",
            "Epoch 15 time: 1.9053\n",
            "Fold:1, Epoch: [17/40], Step: [9/29], Loss: 0.46985822253757054, Validation Loss:0.5180916041135788, Validation Acc:76.55860349127182, AUC:0.7141821946169772\n",
            "Fold:1, Epoch: [17/40], Step: [18/29], Loss: 0.46313101384374833, Validation Loss:0.4991818815469742, Validation Acc:77.0573566084788, AUC:0.7320393374741201\n",
            "Fold:1, Epoch: [17/40], Step: [27/29], Loss: 0.4626623116157673, Validation Loss:0.6321378350257874, Validation Acc:63.84039900249377, AUC:0.7075828157349897\n",
            "Epoch 16 time: 1.9321\n",
            "Fold:1, Epoch: [18/40], Step: [9/29], Loss: 0.46087347798877293, Validation Loss:0.5195269733667374, Validation Acc:71.07231920199501, AUC:0.7196946169772257\n",
            "Fold:1, Epoch: [18/40], Step: [18/29], Loss: 0.4648935728602939, Validation Loss:0.520529218018055, Validation Acc:74.3142144638404, AUC:0.7011387163561077\n",
            "Fold:1, Epoch: [18/40], Step: [27/29], Loss: 0.45450909270180595, Validation Loss:0.5819179192185402, Validation Acc:71.57107231920199, AUC:0.7151138716356108\n",
            "Epoch 17 time: 1.9023\n",
            "Fold:1, Epoch: [19/40], Step: [9/29], Loss: 0.45439686708980137, Validation Loss:0.6193296760320663, Validation Acc:69.07730673316708, AUC:0.7305383022774327\n",
            "Fold:1, Epoch: [19/40], Step: [18/29], Loss: 0.455915951066547, Validation Loss:0.5671717077493668, Validation Acc:76.30922693266832, AUC:0.7276915113871636\n",
            "Fold:1, Epoch: [19/40], Step: [27/29], Loss: 0.4476066529750824, Validation Loss:0.6586527898907661, Validation Acc:75.56109725685785, AUC:0.7383022774327123\n",
            "Epoch 18 time: 1.9037\n",
            "Fold:1, Epoch: [20/40], Step: [9/29], Loss: 0.4683166311846839, Validation Loss:0.5564216375350952, Validation Acc:67.08229426433915, AUC:0.6890269151138717\n",
            "Fold:1, Epoch: [20/40], Step: [18/29], Loss: 0.46324073274930316, Validation Loss:0.5575809255242348, Validation Acc:75.56109725685785, AUC:0.723343685300207\n",
            "Fold:1, Epoch: [20/40], Step: [27/29], Loss: 0.45290367470847237, Validation Loss:0.5452578812837601, Validation Acc:69.07730673316708, AUC:0.7155797101449275\n",
            "Epoch 19 time: 1.9061\n",
            "Fold:1, Epoch: [21/40], Step: [9/29], Loss: 0.413136750459671, Validation Loss:0.5378983020782471, Validation Acc:75.31172069825436, AUC:0.7218944099378882\n",
            "Fold:1, Epoch: [21/40], Step: [18/29], Loss: 0.4209752016597324, Validation Loss:0.5676749125123024, Validation Acc:70.07481296758105, AUC:0.7138975155279503\n",
            "Fold:1, Epoch: [21/40], Step: [27/29], Loss: 0.4320883607422864, Validation Loss:0.5993472710251808, Validation Acc:72.06982543640898, AUC:0.7404503105590061\n",
            "Epoch 20 time: 1.8918\n",
            "Fold:1, Epoch: [22/40], Step: [9/29], Loss: 0.4313865866925981, Validation Loss:0.569813072681427, Validation Acc:72.31920199501246, AUC:0.7045031055900621\n",
            "Fold:1, Epoch: [22/40], Step: [18/29], Loss: 0.43055184185504913, Validation Loss:0.5265364199876785, Validation Acc:75.06234413965088, AUC:0.7428830227743272\n",
            "Fold:1, Epoch: [22/40], Step: [27/29], Loss: 0.43130905981417056, Validation Loss:0.5231088176369667, Validation Acc:71.57107231920199, AUC:0.7525103519668737\n",
            "Epoch 21 time: 1.8978\n",
            "Fold:1, Epoch: [23/40], Step: [9/29], Loss: 0.45131691959169173, Validation Loss:0.5482459738850594, Validation Acc:72.56857855361596, AUC:0.7283902691511387\n",
            "Fold:1, Epoch: [23/40], Step: [18/29], Loss: 0.44325480196211076, Validation Loss:0.5568445399403572, Validation Acc:74.56359102244389, AUC:0.6801501035196688\n"
          ],
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          "text": [
            "Fold:1, Epoch: [23/40], Step: [27/29], Loss: 0.4295798153788955, Validation Loss:0.5758865177631378, Validation Acc:79.80049875311721, AUC:0.7405020703933747\n",
            "Epoch 22 time: 1.8649\n",
            "Fold:1, Epoch: [24/40], Step: [9/29], Loss: 0.4432029161188338, Validation Loss:0.5869854241609573, Validation Acc:71.57107231920199, AUC:0.7076345755693582\n",
            "Fold:1, Epoch: [24/40], Step: [18/29], Loss: 0.43858107758892906, Validation Loss:0.5513206794857979, Validation Acc:74.3142144638404, AUC:0.7160973084886129\n",
            "Fold:1, Epoch: [24/40], Step: [27/29], Loss: 0.42630214933995847, Validation Loss:0.5471227243542671, Validation Acc:76.05985037406484, AUC:0.7262422360248447\n",
            "Epoch 23 time: 1.8852\n",
            "Fold:1, Epoch: [25/40], Step: [9/29], Loss: 0.4165589147143894, Validation Loss:0.5819828882813454, Validation Acc:64.08977556109726, AUC:0.716511387163561\n",
            "Fold:1, Epoch: [25/40], Step: [18/29], Loss: 0.42399610413445366, Validation Loss:0.532167911529541, Validation Acc:78.05486284289277, AUC:0.6854813664596273\n",
            "Fold:1, Epoch: [25/40], Step: [27/29], Loss: 0.419538390857202, Validation Loss:0.6143566071987152, Validation Acc:77.3067331670823, AUC:0.7185300207039338\n",
            "Epoch 24 time: 1.8975\n",
            "Fold:1, Epoch: [26/40], Step: [9/29], Loss: 0.43219072620073956, Validation Loss:0.5600856393575668, Validation Acc:71.3216957605985, AUC:0.713664596273292\n",
            "Fold:1, Epoch: [26/40], Step: [18/29], Loss: 0.40573372609085506, Validation Loss:0.576941080391407, Validation Acc:76.80798004987531, AUC:0.7380693581780539\n",
            "Fold:1, Epoch: [26/40], Step: [27/29], Loss: 0.41385354156847354, Validation Loss:0.5535447522997856, Validation Acc:70.07481296758105, AUC:0.736335403726708\n",
            "Epoch 25 time: 1.8857\n",
            "Fold:1, Epoch: [27/40], Step: [9/29], Loss: 0.37645481692420113, Validation Loss:0.6593589559197426, Validation Acc:80.54862842892769, AUC:0.7373706004140786\n",
            "Fold:1, Epoch: [27/40], Step: [18/29], Loss: 0.3811703787909614, Validation Loss:0.529094398021698, Validation Acc:69.32668329177058, AUC:0.724508281573499\n",
            "Fold:1, Epoch: [27/40], Step: [27/29], Loss: 0.3982678695961281, Validation Loss:0.6151163801550865, Validation Acc:71.3216957605985, AUC:0.7361024844720496\n",
            "Epoch 26 time: 1.8850\n",
            "Fold:1, Epoch: [28/40], Step: [9/29], Loss: 0.4040396743350559, Validation Loss:0.6030571907758713, Validation Acc:79.05236907730674, AUC:0.6912784679089027\n",
            "Fold:1, Epoch: [28/40], Step: [18/29], Loss: 0.4021696249643962, Validation Loss:0.5415274277329445, Validation Acc:72.56857855361596, AUC:0.7134316770186335\n",
            "Fold:1, Epoch: [28/40], Step: [27/29], Loss: 0.4021527215286537, Validation Loss:0.5662393867969513, Validation Acc:79.30174563591022, AUC:0.737603519668737\n",
            "Epoch 27 time: 1.8908\n",
            "Fold:1, Epoch: [29/40], Step: [9/29], Loss: 0.4082510670026143, Validation Loss:0.90740055590868, Validation Acc:69.32668329177058, AUC:0.7394668737060042\n",
            "Fold:1, Epoch: [29/40], Step: [18/29], Loss: 0.40013956526915234, Validation Loss:0.5349043905735016, Validation Acc:73.81546134663341, AUC:0.7206780538302278\n",
            "Fold:1, Epoch: [29/40], Step: [27/29], Loss: 0.39978410910677026, Validation Loss:0.6382235586643219, Validation Acc:75.56109725685785, AUC:0.7083850931677018\n",
            "Epoch 28 time: 1.9022\n",
            "Fold:1, Epoch: [30/40], Step: [9/29], Loss: 0.3848779830667708, Validation Loss:0.5731216222047806, Validation Acc:68.82793017456359, AUC:0.7066511387163561\n",
            "Fold:1, Epoch: [30/40], Step: [18/29], Loss: 0.38427912029955125, Validation Loss:0.6276610195636749, Validation Acc:75.56109725685785, AUC:0.7532608695652174\n",
            "Fold:1, Epoch: [30/40], Step: [27/29], Loss: 0.3982946033830996, Validation Loss:0.6391987800598145, Validation Acc:70.57356608478803, AUC:0.7242753623188406\n",
            "Epoch 29 time: 1.8916\n",
            "Fold:1, Epoch: [31/40], Step: [9/29], Loss: 0.4224785367647807, Validation Loss:0.6122386306524277, Validation Acc:77.55610972568579, AUC:0.7424171842650104\n",
            "Fold:1, Epoch: [31/40], Step: [18/29], Loss: 0.39883938597308266, Validation Loss:0.5505017787218094, Validation Acc:76.30922693266832, AUC:0.720212215320911\n",
            "Fold:1, Epoch: [31/40], Step: [27/29], Loss: 0.39565118926542775, Validation Loss:0.6219552457332611, Validation Acc:72.31920199501246, AUC:0.7344202898550725\n",
            "Epoch 30 time: 1.8787\n",
            "Fold:1, Epoch: [32/40], Step: [9/29], Loss: 0.38172738750775653, Validation Loss:0.5332718566060066, Validation Acc:78.30423940149626, AUC:0.7318064182194617\n",
            "Fold:1, Epoch: [32/40], Step: [18/29], Loss: 0.37699773410956067, Validation Loss:0.637589693069458, Validation Acc:76.05985037406484, AUC:0.7262422360248447\n",
            "Fold:1, Epoch: [32/40], Step: [27/29], Loss: 0.3754284116956923, Validation Loss:0.5433353334665298, Validation Acc:77.3067331670823, AUC:0.7409679089026915\n",
            "Epoch 31 time: 1.8854\n",
            "Fold:1, Epoch: [33/40], Step: [9/29], Loss: 0.3555928170681, Validation Loss:0.561437614262104, Validation Acc:72.06982543640898, AUC:0.7254917184265011\n",
            "Fold:1, Epoch: [33/40], Step: [18/29], Loss: 0.367231880625089, Validation Loss:0.6327799558639526, Validation Acc:75.56109725685785, AUC:0.7158643892339545\n",
            "Fold:1, Epoch: [33/40], Step: [27/29], Loss: 0.3742341189472764, Validation Loss:0.7018252611160278, Validation Acc:78.30423940149626, AUC:0.7318064182194617\n",
            "Epoch 32 time: 1.9231\n",
            "Fold:1, Epoch: [34/40], Step: [9/29], Loss: 0.39884020222557914, Validation Loss:0.6663115918636322, Validation Acc:70.57356608478803, AUC:0.7167960662525881\n",
            "Fold:1, Epoch: [34/40], Step: [18/29], Loss: 0.3726358066002528, Validation Loss:0.6704204678535461, Validation Acc:80.54862842892769, AUC:0.6700569358178055\n",
            "Fold:1, Epoch: [34/40], Step: [27/29], Loss: 0.37650944347734805, Validation Loss:0.6239123046398163, Validation Acc:71.07231920199501, AUC:0.7196946169772257\n",
            "Epoch 33 time: 1.8962\n"
          ],
          "name": "stdout"
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          "text": [
            "Fold:1, Epoch: [35/40], Step: [9/29], Loss: 0.36799559328291154, Validation Loss:0.7089001759886742, Validation Acc:79.05236907730674, AUC:0.7137163561076605\n",
            "Fold:1, Epoch: [35/40], Step: [18/29], Loss: 0.37160670426156783, Validation Loss:0.6342690661549568, Validation Acc:80.54862842892769, AUC:0.7523291925465838\n",
            "Fold:1, Epoch: [35/40], Step: [27/29], Loss: 0.3614325368845904, Validation Loss:0.579759269952774, Validation Acc:69.32668329177058, AUC:0.7469461697722567\n",
            "Epoch 34 time: 1.8769\n",
            "Fold:1, Epoch: [36/40], Step: [9/29], Loss: 0.3372863829135895, Validation Loss:0.9537073224782944, Validation Acc:82.29426433915212, AUC:0.7175983436853002\n",
            "Fold:1, Epoch: [36/40], Step: [18/29], Loss: 0.34788192477491164, Validation Loss:0.5550107285380363, Validation Acc:80.79800498753117, AUC:0.7238612836438924\n",
            "Fold:1, Epoch: [36/40], Step: [27/29], Loss: 0.3506289279019391, Validation Loss:0.5811344236135483, Validation Acc:74.06483790523691, AUC:0.7370859213250518\n",
            "Epoch 35 time: 1.9108\n",
            "Fold:1, Epoch: [37/40], Step: [9/29], Loss: 0.33509203791618347, Validation Loss:0.5971883162856102, Validation Acc:77.3067331670823, AUC:0.7260093167701863\n",
            "Fold:1, Epoch: [37/40], Step: [18/29], Loss: 0.33799804747104645, Validation Loss:0.6422745883464813, Validation Acc:80.29925187032418, AUC:0.7209627329192547\n",
            "Fold:1, Epoch: [37/40], Step: [27/29], Loss: 0.34605825168115123, Validation Loss:0.536741342395544, Validation Acc:74.3142144638404, AUC:0.7385351966873707\n",
            "Epoch 36 time: 1.8689\n",
            "Fold:1, Epoch: [38/40], Step: [9/29], Loss: 0.35585197144084507, Validation Loss:0.601452648639679, Validation Acc:72.06982543640898, AUC:0.7404503105590061\n",
            "Fold:1, Epoch: [38/40], Step: [18/29], Loss: 0.326649730404218, Validation Loss:0.6019320487976074, Validation Acc:82.29426433915212, AUC:0.6876811594202898\n",
            "Fold:1, Epoch: [38/40], Step: [27/29], Loss: 0.3334731172632288, Validation Loss:0.5413375869393349, Validation Acc:72.31920199501246, AUC:0.741899585921325\n",
            "Epoch 37 time: 1.8856\n",
            "Fold:1, Epoch: [39/40], Step: [9/29], Loss: 0.3405085835191939, Validation Loss:0.5803078636527061, Validation Acc:80.29925187032418, AUC:0.7359213250517598\n",
            "Fold:1, Epoch: [39/40], Step: [18/29], Loss: 0.3325598720047209, Validation Loss:0.5547279566526413, Validation Acc:76.80798004987531, AUC:0.7380693581780539\n",
            "Fold:1, Epoch: [39/40], Step: [27/29], Loss: 0.33192409409417045, Validation Loss:0.633213646709919, Validation Acc:75.56109725685785, AUC:0.7083850931677018\n",
            "Epoch 38 time: 1.8860\n",
            "Fold:1, Epoch: [40/40], Step: [9/29], Loss: 0.31353509426116943, Validation Loss:0.7009229958057404, Validation Acc:70.57356608478803, AUC:0.7392339544513457\n",
            "Fold:1, Epoch: [40/40], Step: [18/29], Loss: 0.3239678674274021, Validation Loss:0.630028486251831, Validation Acc:82.29426433915212, AUC:0.6951604554865425\n",
            "Fold:1, Epoch: [40/40], Step: [27/29], Loss: 0.32762382207093416, Validation Loss:0.5487577691674232, Validation Acc:75.06234413965088, AUC:0.727924430641822\n",
            "Epoch 39 time: 1.8761\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:2, Epoch: [1/40], Step: [9/29], Loss: 0.6494616932339139, Validation Loss:0.6739252060651779, Validation Acc:73.81546134663341, AUC:0.6832815734989648\n",
            "Fold:2, Epoch: [1/40], Step: [18/29], Loss: 0.6334423191017575, Validation Loss:0.6613774001598358, Validation Acc:74.3142144638404, AUC:0.6861801242236025\n",
            "Fold:2, Epoch: [1/40], Step: [27/29], Loss: 0.6267094435515227, Validation Loss:0.6422221064567566, Validation Acc:72.31920199501246, AUC:0.7119824016563147\n",
            "Epoch 0 time: 1.9130\n",
            "Fold:2, Epoch: [2/40], Step: [9/29], Loss: 0.5826908349990845, Validation Loss:0.5739967748522758, Validation Acc:69.07730673316708, AUC:0.7454968944099378\n",
            "Fold:2, Epoch: [2/40], Step: [18/29], Loss: 0.5889057252142165, Validation Loss:0.5736114531755447, Validation Acc:68.5785536159601, AUC:0.7425983436853002\n",
            "Fold:2, Epoch: [2/40], Step: [27/29], Loss: 0.5813185495358927, Validation Loss:0.5535089746117592, Validation Acc:70.82294264339153, AUC:0.7257246376811594\n",
            "Epoch 1 time: 1.9275\n",
            "Fold:2, Epoch: [3/40], Step: [9/29], Loss: 0.5666183531284332, Validation Loss:0.5605958551168442, Validation Acc:70.07481296758105, AUC:0.7213768115942029\n",
            "Fold:2, Epoch: [3/40], Step: [18/29], Loss: 0.5526764492193857, Validation Loss:0.5360382944345474, Validation Acc:67.58104738154613, AUC:0.7068840579710145\n",
            "Fold:2, Epoch: [3/40], Step: [27/29], Loss: 0.5496736146785595, Validation Loss:0.6147210597991943, Validation Acc:71.57107231920199, AUC:0.6851966873706004\n",
            "Epoch 2 time: 1.9047\n",
            "Fold:2, Epoch: [4/40], Step: [9/29], Loss: 0.5260079271263547, Validation Loss:0.5781634747982025, Validation Acc:68.07980049875312, AUC:0.7172619047619048\n",
            "Fold:2, Epoch: [4/40], Step: [18/29], Loss: 0.5293185628122754, Validation Loss:0.5365684702992439, Validation Acc:72.56857855361596, AUC:0.705952380952381\n",
            "Fold:2, Epoch: [4/40], Step: [27/29], Loss: 0.544029900321254, Validation Loss:0.621046170592308, Validation Acc:71.57107231920199, AUC:0.6851966873706004\n",
            "Epoch 3 time: 1.8826\n",
            "Fold:2, Epoch: [5/40], Step: [9/29], Loss: 0.543381091621187, Validation Loss:0.5490709841251373, Validation Acc:67.58104738154613, AUC:0.7068840579710145\n",
            "Fold:2, Epoch: [5/40], Step: [18/29], Loss: 0.5352173944314321, Validation Loss:0.565972089767456, Validation Acc:69.82543640897755, AUC:0.7049689440993789\n",
            "Fold:2, Epoch: [5/40], Step: [27/29], Loss: 0.5347444238486113, Validation Loss:0.575643002986908, Validation Acc:69.07730673316708, AUC:0.708100414078675\n",
            "Epoch 4 time: 1.9016\n",
            "Fold:2, Epoch: [6/40], Step: [9/29], Loss: 0.5136828588114845, Validation Loss:0.5898022800683975, Validation Acc:69.57605985037407, AUC:0.6810817805383023\n",
            "Fold:2, Epoch: [6/40], Step: [18/29], Loss: 0.5130998657809364, Validation Loss:0.554902195930481, Validation Acc:74.3142144638404, AUC:0.7086180124223601\n",
            "Fold:2, Epoch: [6/40], Step: [27/29], Loss: 0.52076123158137, Validation Loss:0.5438467413187027, Validation Acc:69.82543640897755, AUC:0.7124482401656315\n",
            "Epoch 5 time: 1.8614\n",
            "Fold:2, Epoch: [7/40], Step: [9/29], Loss: 0.5013423528936174, Validation Loss:0.7217962145805359, Validation Acc:71.57107231920199, AUC:0.6702380952380953\n",
            "Fold:2, Epoch: [7/40], Step: [18/29], Loss: 0.519864386982388, Validation Loss:0.5569547712802887, Validation Acc:72.81795511221945, AUC:0.6999223602484471\n",
            "Fold:2, Epoch: [7/40], Step: [27/29], Loss: 0.518998490439521, Validation Loss:0.5743731260299683, Validation Acc:69.57605985037407, AUC:0.70351966873706\n",
            "Epoch 6 time: 1.9086\n",
            "Fold:2, Epoch: [8/40], Step: [9/29], Loss: 0.5146378378073374, Validation Loss:0.5454634130001068, Validation Acc:65.33665835411472, AUC:0.6863612836438924\n",
            "Fold:2, Epoch: [8/40], Step: [18/29], Loss: 0.5120494266351064, Validation Loss:0.5249068215489388, Validation Acc:76.05985037406484, AUC:0.6963250517598344\n",
            "Fold:2, Epoch: [8/40], Step: [27/29], Loss: 0.5100545430624926, Validation Loss:0.5413721352815628, Validation Acc:69.07730673316708, AUC:0.6856625258799172\n",
            "Epoch 7 time: 1.9023\n",
            "Fold:2, Epoch: [9/40], Step: [9/29], Loss: 0.5041072997781966, Validation Loss:0.5370234325528145, Validation Acc:70.32418952618454, AUC:0.7003881987577638\n",
            "Fold:2, Epoch: [9/40], Step: [18/29], Loss: 0.4933729138639238, Validation Loss:0.47242050990462303, Validation Acc:71.3216957605985, AUC:0.6912267080745342\n",
            "Fold:2, Epoch: [9/40], Step: [27/29], Loss: 0.5113801658153534, Validation Loss:0.5997097045183182, Validation Acc:71.07231920199501, AUC:0.6972567287784678\n",
            "Epoch 8 time: 1.8860\n",
            "Fold:2, Epoch: [10/40], Step: [9/29], Loss: 0.4945512678888109, Validation Loss:0.5926191359758377, Validation Acc:71.82044887780549, AUC:0.6716873706004141\n",
            "Fold:2, Epoch: [10/40], Step: [18/29], Loss: 0.5008093350463443, Validation Loss:0.572508342564106, Validation Acc:72.06982543640898, AUC:0.7105331262939959\n",
            "Fold:2, Epoch: [10/40], Step: [27/29], Loss: 0.5006680775571752, Validation Loss:0.5539213865995407, Validation Acc:75.31172069825436, AUC:0.6994565217391304\n",
            "Epoch 9 time: 1.9050\n",
            "Fold:2, Epoch: [11/40], Step: [9/29], Loss: 0.48094728257921004, Validation Loss:0.5637141764163971, Validation Acc:71.82044887780549, AUC:0.6716873706004141\n",
            "Fold:2, Epoch: [11/40], Step: [18/29], Loss: 0.4899931367900636, Validation Loss:0.5785925015807152, Validation Acc:70.07481296758105, AUC:0.7213768115942029\n",
            "Fold:2, Epoch: [11/40], Step: [27/29], Loss: 0.5060739340605559, Validation Loss:0.485938236117363, Validation Acc:71.57107231920199, AUC:0.7151138716356108\n",
            "Epoch 10 time: 1.8882\n",
            "Fold:2, Epoch: [12/40], Step: [9/29], Loss: 0.505744583076901, Validation Loss:0.5341670736670494, Validation Acc:77.3067331670823, AUC:0.7110507246376812\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "Fold:2, Epoch: [12/40], Step: [18/29], Loss: 0.48531095021300846, Validation Loss:0.5818400681018829, Validation Acc:67.83042394014963, AUC:0.6933747412008282\n",
            "Fold:2, Epoch: [12/40], Step: [27/29], Loss: 0.4916985211548982, Validation Loss:0.6117839962244034, Validation Acc:70.57356608478803, AUC:0.7018374741200828\n",
            "Epoch 11 time: 1.8949\n",
            "Fold:2, Epoch: [13/40], Step: [9/29], Loss: 0.4931459426879883, Validation Loss:0.5305612161755562, Validation Acc:74.3142144638404, AUC:0.6861801242236025\n",
            "Fold:2, Epoch: [13/40], Step: [18/29], Loss: 0.4900320801470015, Validation Loss:0.6595611050724983, Validation Acc:69.57605985037407, AUC:0.70351966873706\n",
            "Fold:2, Epoch: [13/40], Step: [27/29], Loss: 0.4808314354331405, Validation Loss:0.5216756388545036, Validation Acc:69.57605985037407, AUC:0.7259575569358178\n",
            "Epoch 12 time: 1.8935\n",
            "Fold:2, Epoch: [14/40], Step: [9/29], Loss: 0.4828173948658837, Validation Loss:0.552091933786869, Validation Acc:78.30423940149626, AUC:0.7243271221532089\n",
            "Fold:2, Epoch: [14/40], Step: [18/29], Loss: 0.48440306385358173, Validation Loss:0.5303849950432777, Validation Acc:76.80798004987531, AUC:0.6931935817805384\n",
            "Fold:2, Epoch: [14/40], Step: [27/29], Loss: 0.4841171149854307, Validation Loss:0.5297320634126663, Validation Acc:63.591022443890274, AUC:0.7061335403726708\n",
            "Epoch 13 time: 1.8864\n",
            "Fold:2, Epoch: [15/40], Step: [9/29], Loss: 0.4794884655210707, Validation Loss:0.5124445483088493, Validation Acc:73.31670822942644, AUC:0.7103002070393375\n",
            "Fold:2, Epoch: [15/40], Step: [18/29], Loss: 0.47063247197204167, Validation Loss:0.5362822711467743, Validation Acc:72.31920199501246, AUC:0.7344202898550725\n",
            "Fold:2, Epoch: [15/40], Step: [27/29], Loss: 0.47019185070638303, Validation Loss:0.46694116294384, Validation Acc:72.81795511221945, AUC:0.7373188405797102\n",
            "Epoch 14 time: 1.9139\n",
            "Fold:2, Epoch: [16/40], Step: [9/29], Loss: 0.4759531517823537, Validation Loss:0.6349786520004272, Validation Acc:74.3142144638404, AUC:0.6487836438923394\n",
            "Fold:2, Epoch: [16/40], Step: [18/29], Loss: 0.4743351207839118, Validation Loss:0.5426455363631248, Validation Acc:68.5785536159601, AUC:0.7201604554865424\n",
            "Fold:2, Epoch: [16/40], Step: [27/29], Loss: 0.4790820512506697, Validation Loss:0.6501087546348572, Validation Acc:69.82543640897755, AUC:0.7274068322981366\n",
            "Epoch 15 time: 1.8918\n",
            "Fold:2, Epoch: [17/40], Step: [9/29], Loss: 0.4557445678446028, Validation Loss:0.5884446352720261, Validation Acc:78.80299251870325, AUC:0.7047877846790891\n",
            "Fold:2, Epoch: [17/40], Step: [18/29], Loss: 0.4650314235024982, Validation Loss:0.5128118768334389, Validation Acc:68.82793017456359, AUC:0.6991718426501036\n",
            "Fold:2, Epoch: [17/40], Step: [27/29], Loss: 0.4599531579900671, Validation Loss:0.5416397452354431, Validation Acc:67.83042394014963, AUC:0.7158126293995859\n",
            "Epoch 16 time: 1.8925\n",
            "Fold:2, Epoch: [18/40], Step: [9/29], Loss: 0.4499573939376407, Validation Loss:0.5118482857942581, Validation Acc:73.06733167082294, AUC:0.7238095238095239\n",
            "Fold:2, Epoch: [18/40], Step: [18/29], Loss: 0.4523839553197225, Validation Loss:0.5454044193029404, Validation Acc:74.3142144638404, AUC:0.7235766045548654\n",
            "Fold:2, Epoch: [18/40], Step: [27/29], Loss: 0.4569916073922758, Validation Loss:0.4675390273332596, Validation Acc:74.56359102244389, AUC:0.7399844720496895\n",
            "Epoch 17 time: 1.8893\n",
            "Fold:2, Epoch: [19/40], Step: [9/29], Loss: 0.4324873619609409, Validation Loss:0.5737172588706017, Validation Acc:78.80299251870325, AUC:0.7347049689440994\n",
            "Fold:2, Epoch: [19/40], Step: [18/29], Loss: 0.45054736899005043, Validation Loss:0.5920507311820984, Validation Acc:70.32418952618454, AUC:0.7228260869565217\n",
            "Fold:2, Epoch: [19/40], Step: [27/29], Loss: 0.45007756573182567, Validation Loss:0.5830069333314896, Validation Acc:72.06982543640898, AUC:0.7105331262939959\n",
            "Epoch 18 time: 1.8904\n",
            "Fold:2, Epoch: [20/40], Step: [9/29], Loss: 0.4592566986878713, Validation Loss:0.5465731099247932, Validation Acc:72.06982543640898, AUC:0.6880952380952382\n",
            "Fold:2, Epoch: [20/40], Step: [18/29], Loss: 0.4405384875006146, Validation Loss:0.4839561767876148, Validation Acc:78.55361596009975, AUC:0.7182971014492754\n",
            "Fold:2, Epoch: [20/40], Step: [27/29], Loss: 0.4564066418895015, Validation Loss:0.5661137029528618, Validation Acc:68.82793017456359, AUC:0.7216097308488613\n",
            "Epoch 19 time: 1.9587\n",
            "Fold:2, Epoch: [21/40], Step: [9/29], Loss: 0.4425078597333696, Validation Loss:0.5334376320242882, Validation Acc:79.05236907730674, AUC:0.7436335403726708\n",
            "Fold:2, Epoch: [21/40], Step: [18/29], Loss: 0.43797797461350757, Validation Loss:0.5903928577899933, Validation Acc:76.05985037406484, AUC:0.6813664596273291\n",
            "Fold:2, Epoch: [21/40], Step: [27/29], Loss: 0.44224936653066566, Validation Loss:0.5310184881091118, Validation Acc:67.83042394014963, AUC:0.6933747412008282\n",
            "Epoch 20 time: 1.9031\n",
            "Fold:2, Epoch: [22/40], Step: [9/29], Loss: 0.4360552827517192, Validation Loss:0.5697680711746216, Validation Acc:70.57356608478803, AUC:0.7167960662525881\n",
            "Fold:2, Epoch: [22/40], Step: [18/29], Loss: 0.42944062252839404, Validation Loss:0.5302065387368202, Validation Acc:71.82044887780549, AUC:0.7016045548654244\n",
            "Fold:2, Epoch: [22/40], Step: [27/29], Loss: 0.4340214254679503, Validation Loss:0.5171045586466789, Validation Acc:68.82793017456359, AUC:0.7216097308488613\n",
            "Epoch 21 time: 1.8616\n",
            "Fold:2, Epoch: [23/40], Step: [9/29], Loss: 0.4496498472160763, Validation Loss:0.5471590906381607, Validation Acc:75.81047381546135, AUC:0.7098343685300207\n",
            "Fold:2, Epoch: [23/40], Step: [18/29], Loss: 0.4226180513699849, Validation Loss:0.5028579235076904, Validation Acc:76.55860349127182, AUC:0.7291407867494825\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:2, Epoch: [23/40], Step: [27/29], Loss: 0.42733281409298934, Validation Loss:0.5669566318392754, Validation Acc:75.06234413965088, AUC:0.690527950310559\n",
            "Epoch 22 time: 1.9137\n",
            "Fold:2, Epoch: [24/40], Step: [9/29], Loss: 0.407301836543613, Validation Loss:0.5800575017929077, Validation Acc:71.82044887780549, AUC:0.7315217391304347\n",
            "Fold:2, Epoch: [24/40], Step: [18/29], Loss: 0.4318618459833993, Validation Loss:0.48235954344272614, Validation Acc:65.5860349127182, AUC:0.7326863354037266\n",
            "Fold:2, Epoch: [24/40], Step: [27/29], Loss: 0.43284768528408474, Validation Loss:0.5157025083899498, Validation Acc:76.30922693266832, AUC:0.7052536231884058\n",
            "Epoch 23 time: 1.8883\n",
            "Fold:2, Epoch: [25/40], Step: [9/29], Loss: 0.4406922194692824, Validation Loss:0.5298228114843369, Validation Acc:76.05985037406484, AUC:0.7337215320910974\n",
            "Fold:2, Epoch: [25/40], Step: [18/29], Loss: 0.42345068355401355, Validation Loss:0.5950354933738708, Validation Acc:74.56359102244389, AUC:0.6801501035196688\n",
            "Fold:2, Epoch: [25/40], Step: [27/29], Loss: 0.42477015212730124, Validation Loss:0.5484525188803673, Validation Acc:63.591022443890274, AUC:0.7136128364389234\n",
            "Epoch 24 time: 1.9137\n",
            "Fold:2, Epoch: [26/40], Step: [9/29], Loss: 0.3957013024224175, Validation Loss:0.5479566752910614, Validation Acc:79.30174563591022, AUC:0.737603519668737\n",
            "Fold:2, Epoch: [26/40], Step: [18/29], Loss: 0.4120000849167506, Validation Loss:0.6653671264648438, Validation Acc:72.31920199501246, AUC:0.7194616977225674\n",
            "Fold:2, Epoch: [26/40], Step: [27/29], Loss: 0.42127864338733534, Validation Loss:0.6400835439562798, Validation Acc:78.05486284289277, AUC:0.7228778467908903\n",
            "Epoch 25 time: 1.9184\n",
            "Fold:2, Epoch: [27/40], Step: [9/29], Loss: 0.41688641905784607, Validation Loss:0.552156925201416, Validation Acc:77.0573566084788, AUC:0.7170807453416148\n",
            "Fold:2, Epoch: [27/40], Step: [18/29], Loss: 0.4025007983048757, Validation Loss:0.6002517119050026, Validation Acc:71.07231920199501, AUC:0.7196946169772257\n",
            "Fold:2, Epoch: [27/40], Step: [27/29], Loss: 0.40027759031013205, Validation Loss:0.506962426006794, Validation Acc:69.82543640897755, AUC:0.7124482401656315\n",
            "Epoch 26 time: 1.8935\n",
            "Fold:2, Epoch: [28/40], Step: [9/29], Loss: 0.37448469797770184, Validation Loss:0.5431004539132118, Validation Acc:76.55860349127182, AUC:0.7291407867494825\n",
            "Fold:2, Epoch: [28/40], Step: [18/29], Loss: 0.3846152507596546, Validation Loss:0.5661862716078758, Validation Acc:78.05486284289277, AUC:0.7079192546583851\n",
            "Fold:2, Epoch: [28/40], Step: [27/29], Loss: 0.3852103650569916, Validation Loss:0.5461728796362877, Validation Acc:69.82543640897755, AUC:0.7199275362318841\n",
            "Epoch 27 time: 1.8984\n",
            "Fold:2, Epoch: [29/40], Step: [9/29], Loss: 0.44784462120797897, Validation Loss:0.5862748548388481, Validation Acc:66.83291770573567, AUC:0.6950569358178054\n",
            "Fold:2, Epoch: [29/40], Step: [18/29], Loss: 0.4257964574628406, Validation Loss:0.6386833265423775, Validation Acc:78.55361596009975, AUC:0.7482142857142857\n",
            "Fold:2, Epoch: [29/40], Step: [27/29], Loss: 0.41819699494927015, Validation Loss:0.584822341799736, Validation Acc:78.05486284289277, AUC:0.7228778467908903\n",
            "Epoch 28 time: 1.8920\n",
            "Fold:2, Epoch: [30/40], Step: [9/29], Loss: 0.4188571638531155, Validation Loss:0.5537038743495941, Validation Acc:69.32668329177058, AUC:0.7394668737060042\n",
            "Fold:2, Epoch: [30/40], Step: [18/29], Loss: 0.39471587042013806, Validation Loss:0.5486544817686081, Validation Acc:74.56359102244389, AUC:0.7399844720496895\n",
            "Fold:2, Epoch: [30/40], Step: [27/29], Loss: 0.38847616314888, Validation Loss:0.5240837782621384, Validation Acc:78.80299251870325, AUC:0.7272256728778468\n",
            "Epoch 29 time: 1.8911\n",
            "Fold:2, Epoch: [31/40], Step: [9/29], Loss: 0.3462567561202579, Validation Loss:0.7310905158519745, Validation Acc:74.3142144638404, AUC:0.7160973084886129\n",
            "Fold:2, Epoch: [31/40], Step: [18/29], Loss: 0.3752701398399141, Validation Loss:0.5837484672665596, Validation Acc:71.3216957605985, AUC:0.7211438923395446\n",
            "Fold:2, Epoch: [31/40], Step: [27/29], Loss: 0.37662626637352836, Validation Loss:0.6306446641683578, Validation Acc:72.81795511221945, AUC:0.7373188405797102\n",
            "Epoch 30 time: 1.9056\n",
            "Fold:2, Epoch: [32/40], Step: [9/29], Loss: 0.4018772310680813, Validation Loss:0.5340283587574959, Validation Acc:78.30423940149626, AUC:0.7168478260869565\n",
            "Fold:2, Epoch: [32/40], Step: [18/29], Loss: 0.4075796521372265, Validation Loss:0.6090818867087364, Validation Acc:75.81047381546135, AUC:0.7322722567287785\n",
            "Fold:2, Epoch: [32/40], Step: [27/29], Loss: 0.3879564514866582, Validation Loss:0.5986234843730927, Validation Acc:76.55860349127182, AUC:0.7216614906832297\n",
            "Epoch 31 time: 1.9431\n",
            "Fold:2, Epoch: [33/40], Step: [9/29], Loss: 0.3573659459749858, Validation Loss:0.6502106562256813, Validation Acc:71.07231920199501, AUC:0.7196946169772257\n",
            "Fold:2, Epoch: [33/40], Step: [18/29], Loss: 0.37161203887727523, Validation Loss:0.6284044235944748, Validation Acc:78.80299251870325, AUC:0.6973084886128365\n",
            "Fold:2, Epoch: [33/40], Step: [27/29], Loss: 0.3696346669285386, Validation Loss:0.6574080884456635, Validation Acc:77.80548628428927, AUC:0.7438664596273291\n",
            "Epoch 32 time: 1.9214\n",
            "Fold:2, Epoch: [34/40], Step: [9/29], Loss: 0.3878200418419308, Validation Loss:0.5449730381369591, Validation Acc:79.05236907730674, AUC:0.7137163561076605\n",
            "Fold:2, Epoch: [34/40], Step: [18/29], Loss: 0.37009325954649186, Validation Loss:0.637864001095295, Validation Acc:74.81296758104739, AUC:0.6890786749482403\n",
            "Fold:2, Epoch: [34/40], Step: [27/29], Loss: 0.3721732795238495, Validation Loss:0.5749664604663849, Validation Acc:77.0573566084788, AUC:0.7245600414078675\n",
            "Epoch 33 time: 1.9311\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:2, Epoch: [35/40], Step: [9/29], Loss: 0.36523980895678204, Validation Loss:0.5566506162285805, Validation Acc:76.30922693266832, AUC:0.7276915113871636\n",
            "Fold:2, Epoch: [35/40], Step: [18/29], Loss: 0.37206995156076217, Validation Loss:0.6746736913919449, Validation Acc:70.82294264339153, AUC:0.7182453416149068\n",
            "Fold:2, Epoch: [35/40], Step: [27/29], Loss: 0.3656635174044856, Validation Loss:0.6479437127709389, Validation Acc:67.58104738154613, AUC:0.7218426501035197\n",
            "Epoch 34 time: 1.8940\n",
            "Fold:2, Epoch: [36/40], Step: [9/29], Loss: 0.36926212244563633, Validation Loss:0.7895097956061363, Validation Acc:78.05486284289277, AUC:0.7228778467908903\n",
            "Fold:2, Epoch: [36/40], Step: [18/29], Loss: 0.3514266891611947, Validation Loss:0.700888991355896, Validation Acc:78.05486284289277, AUC:0.6854813664596273\n",
            "Fold:2, Epoch: [36/40], Step: [27/29], Loss: 0.3479185325128061, Validation Loss:0.5768466889858246, Validation Acc:75.31172069825436, AUC:0.7069358178053831\n",
            "Epoch 35 time: 1.9121\n",
            "Fold:2, Epoch: [37/40], Step: [9/29], Loss: 0.34089019563462997, Validation Loss:0.6100661754608154, Validation Acc:75.81047381546135, AUC:0.7098343685300207\n",
            "Fold:2, Epoch: [37/40], Step: [18/29], Loss: 0.3407291852765613, Validation Loss:0.6072380617260933, Validation Acc:75.81047381546135, AUC:0.6948757763975155\n",
            "Fold:2, Epoch: [37/40], Step: [27/29], Loss: 0.3455628741670538, Validation Loss:0.5980623811483383, Validation Acc:72.06982543640898, AUC:0.7030538302277433\n",
            "Epoch 36 time: 1.8944\n",
            "Fold:2, Epoch: [38/40], Step: [9/29], Loss: 0.32474591003523934, Validation Loss:0.6733117252588272, Validation Acc:78.05486284289277, AUC:0.7004399585921326\n",
            "Fold:2, Epoch: [38/40], Step: [18/29], Loss: 0.3287377440267139, Validation Loss:0.5998576879501343, Validation Acc:72.56857855361596, AUC:0.6984730848861284\n",
            "Fold:2, Epoch: [38/40], Step: [27/29], Loss: 0.3311896335195612, Validation Loss:0.7628127783536911, Validation Acc:75.31172069825436, AUC:0.7368530020703935\n",
            "Epoch 37 time: 1.8963\n",
            "Fold:2, Epoch: [39/40], Step: [9/29], Loss: 0.3167513459920883, Validation Loss:0.590659536421299, Validation Acc:77.80548628428927, AUC:0.6915113871635611\n",
            "Fold:2, Epoch: [39/40], Step: [18/29], Loss: 0.32461777743366027, Validation Loss:0.9261511862277985, Validation Acc:75.06234413965088, AUC:0.7129658385093168\n",
            "Fold:2, Epoch: [39/40], Step: [27/29], Loss: 0.32531808095949666, Validation Loss:0.7229867354035378, Validation Acc:66.83291770573567, AUC:0.6800983436853001\n",
            "Epoch 38 time: 1.8899\n",
            "Fold:2, Epoch: [40/40], Step: [9/29], Loss: 0.33080962631437516, Validation Loss:0.6479822546243668, Validation Acc:79.55112219451371, AUC:0.6866977225672877\n",
            "Fold:2, Epoch: [40/40], Step: [18/29], Loss: 0.3284863730271657, Validation Loss:0.8945261389017105, Validation Acc:77.0573566084788, AUC:0.679684265010352\n",
            "Fold:2, Epoch: [40/40], Step: [27/29], Loss: 0.33572755809183474, Validation Loss:0.5926767811179161, Validation Acc:72.31920199501246, AUC:0.7269409937888199\n",
            "Epoch 39 time: 1.9095\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:3, Epoch: [1/40], Step: [9/29], Loss: 0.6777824030982124, Validation Loss:0.6786085963249207, Validation Acc:83.79052369077307, AUC:0.5916666666666667\n",
            "Fold:3, Epoch: [1/40], Step: [18/29], Loss: 0.6559954881668091, Validation Loss:0.6669751554727554, Validation Acc:81.79551122194513, AUC:0.6773033126293997\n",
            "Fold:3, Epoch: [1/40], Step: [27/29], Loss: 0.6411566999223497, Validation Loss:0.6432152092456818, Validation Acc:78.80299251870325, AUC:0.6898291925465839\n",
            "Epoch 0 time: 1.9026\n",
            "Fold:3, Epoch: [2/40], Step: [9/29], Loss: 0.5923511981964111, Validation Loss:0.5971574783325195, Validation Acc:73.56608478802993, AUC:0.6967908902691511\n",
            "Fold:3, Epoch: [2/40], Step: [18/29], Loss: 0.5787793265448676, Validation Loss:0.5931979715824127, Validation Acc:69.82543640897755, AUC:0.6974896480331263\n",
            "Fold:3, Epoch: [2/40], Step: [27/29], Loss: 0.5773669613732232, Validation Loss:0.6471059620380402, Validation Acc:72.56857855361596, AUC:0.6909937888198757\n",
            "Epoch 1 time: 1.8813\n",
            "Fold:3, Epoch: [3/40], Step: [9/29], Loss: 0.5929801066716512, Validation Loss:0.599296011030674, Validation Acc:69.07730673316708, AUC:0.7006211180124224\n",
            "Fold:3, Epoch: [3/40], Step: [18/29], Loss: 0.56164640850491, Validation Loss:0.5908998623490334, Validation Acc:69.57605985037407, AUC:0.7184782608695652\n",
            "Fold:3, Epoch: [3/40], Step: [27/29], Loss: 0.5535364978843265, Validation Loss:0.5609138533473015, Validation Acc:71.82044887780549, AUC:0.7165631469979297\n",
            "Epoch 2 time: 1.8874\n",
            "Fold:3, Epoch: [4/40], Step: [9/29], Loss: 0.5512862967120277, Validation Loss:0.5467695817351341, Validation Acc:69.07730673316708, AUC:0.7155797101449275\n",
            "Fold:3, Epoch: [4/40], Step: [18/29], Loss: 0.5401982631948259, Validation Loss:0.545212559401989, Validation Acc:64.58852867830424, AUC:0.7119306418219462\n",
            "Fold:3, Epoch: [4/40], Step: [27/29], Loss: 0.5423916843202379, Validation Loss:0.544343888759613, Validation Acc:63.591022443890274, AUC:0.7136128364389234\n",
            "Epoch 3 time: 1.9216\n",
            "Fold:3, Epoch: [5/40], Step: [9/29], Loss: 0.5309308105044894, Validation Loss:0.5034175664186478, Validation Acc:73.06733167082294, AUC:0.701371635610766\n",
            "Fold:3, Epoch: [5/40], Step: [18/29], Loss: 0.5536488112476137, Validation Loss:0.5638984143733978, Validation Acc:73.31670822942644, AUC:0.7551759834368529\n",
            "Fold:3, Epoch: [5/40], Step: [27/29], Loss: 0.539801643954383, Validation Loss:0.6323938071727753, Validation Acc:68.82793017456359, AUC:0.7290890269151138\n",
            "Epoch 4 time: 1.9020\n",
            "Fold:3, Epoch: [6/40], Step: [9/29], Loss: 0.556581500503752, Validation Loss:0.6251276880502701, Validation Acc:60.598503740648376, AUC:0.7037008281573499\n",
            "Fold:3, Epoch: [6/40], Step: [18/29], Loss: 0.5254064516888725, Validation Loss:0.5795626863837242, Validation Acc:71.82044887780549, AUC:0.7614389233954452\n",
            "Fold:3, Epoch: [6/40], Step: [27/29], Loss: 0.5238092740376791, Validation Loss:0.6255368962883949, Validation Acc:74.56359102244389, AUC:0.7474637681159421\n",
            "Epoch 5 time: 1.9165\n",
            "Fold:3, Epoch: [7/40], Step: [9/29], Loss: 0.48921311232778764, Validation Loss:0.505398653447628, Validation Acc:75.56109725685785, AUC:0.7457815734989649\n",
            "Fold:3, Epoch: [7/40], Step: [18/29], Loss: 0.509335019522243, Validation Loss:0.5549373552203178, Validation Acc:71.57107231920199, AUC:0.7076345755693582\n",
            "Fold:3, Epoch: [7/40], Step: [27/29], Loss: 0.5150758723417918, Validation Loss:0.5247753337025642, Validation Acc:69.57605985037407, AUC:0.7483954451345756\n",
            "Epoch 6 time: 1.9044\n",
            "Fold:3, Epoch: [8/40], Step: [9/29], Loss: 0.5193132758140564, Validation Loss:0.478736013174057, Validation Acc:73.56608478802993, AUC:0.7641045548654245\n",
            "Fold:3, Epoch: [8/40], Step: [18/29], Loss: 0.5238189697265625, Validation Loss:0.5388473570346832, Validation Acc:66.58354114713217, AUC:0.738483436853002\n",
            "Fold:3, Epoch: [8/40], Step: [27/29], Loss: 0.5110410860291233, Validation Loss:0.5130920186638832, Validation Acc:79.30174563591022, AUC:0.737603519668737\n",
            "Epoch 7 time: 1.9118\n",
            "Fold:3, Epoch: [9/40], Step: [9/29], Loss: 0.49734969602690804, Validation Loss:0.5088626742362976, Validation Acc:75.31172069825436, AUC:0.744332298136646\n",
            "Fold:3, Epoch: [9/40], Step: [18/29], Loss: 0.5082067880365584, Validation Loss:0.48620205372571945, Validation Acc:72.06982543640898, AUC:0.7479296066252589\n",
            "Fold:3, Epoch: [9/40], Step: [27/29], Loss: 0.5049192739857568, Validation Loss:0.476703405380249, Validation Acc:76.30922693266832, AUC:0.7725672877846791\n",
            "Epoch 8 time: 1.9345\n",
            "Fold:3, Epoch: [10/40], Step: [9/29], Loss: 0.5232750475406647, Validation Loss:0.5225640088319778, Validation Acc:72.81795511221945, AUC:0.7522774327122153\n",
            "Fold:3, Epoch: [10/40], Step: [18/29], Loss: 0.4988461881875992, Validation Loss:0.5567551478743553, Validation Acc:79.30174563591022, AUC:0.7600414078674949\n",
            "Fold:3, Epoch: [10/40], Step: [27/29], Loss: 0.4906909355410823, Validation Loss:0.5529945716261864, Validation Acc:73.56608478802993, AUC:0.7566252587991718\n",
            "Epoch 9 time: 1.8842\n",
            "Fold:3, Epoch: [11/40], Step: [9/29], Loss: 0.4993441369798448, Validation Loss:0.5098160654306412, Validation Acc:66.33416458852868, AUC:0.7145962732919255\n",
            "Fold:3, Epoch: [11/40], Step: [18/29], Loss: 0.4929889622661803, Validation Loss:0.537607803940773, Validation Acc:67.58104738154613, AUC:0.7368012422360248\n",
            "Fold:3, Epoch: [11/40], Step: [27/29], Loss: 0.4886290618666896, Validation Loss:0.5070252418518066, Validation Acc:69.82543640897755, AUC:0.7274068322981366\n",
            "Epoch 10 time: 1.8873\n",
            "Fold:3, Epoch: [12/40], Step: [9/29], Loss: 0.4682871136400435, Validation Loss:0.49793048948049545, Validation Acc:78.55361596009975, AUC:0.763172877846791\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:3, Epoch: [12/40], Step: [18/29], Loss: 0.48464446266492206, Validation Loss:0.5562574565410614, Validation Acc:75.56109725685785, AUC:0.76074016563147\n",
            "Fold:3, Epoch: [12/40], Step: [27/29], Loss: 0.47852815522087944, Validation Loss:0.5068038180470467, Validation Acc:77.55610972568579, AUC:0.7573757763975155\n",
            "Epoch 11 time: 1.8892\n",
            "Fold:3, Epoch: [13/40], Step: [9/29], Loss: 0.47459150354067486, Validation Loss:0.5118795931339264, Validation Acc:62.34413965087282, AUC:0.7138457556935819\n",
            "Fold:3, Epoch: [13/40], Step: [18/29], Loss: 0.4662146088149812, Validation Loss:0.49788492918014526, Validation Acc:78.55361596009975, AUC:0.7332556935817804\n",
            "Fold:3, Epoch: [13/40], Step: [27/29], Loss: 0.47359557836144056, Validation Loss:0.555800274014473, Validation Acc:78.55361596009975, AUC:0.763172877846791\n",
            "Epoch 12 time: 1.8751\n",
            "Fold:3, Epoch: [14/40], Step: [9/29], Loss: 0.48090306917826336, Validation Loss:0.525348886847496, Validation Acc:79.05236907730674, AUC:0.7735507246376812\n",
            "Fold:3, Epoch: [14/40], Step: [18/29], Loss: 0.47908446027172935, Validation Loss:0.4810660257935524, Validation Acc:77.0573566084788, AUC:0.7843944099378882\n",
            "Fold:3, Epoch: [14/40], Step: [27/29], Loss: 0.4730101040116063, Validation Loss:0.5548749044537544, Validation Acc:71.57107231920199, AUC:0.745031055900621\n",
            "Epoch 13 time: 1.9118\n",
            "Fold:3, Epoch: [15/40], Step: [9/29], Loss: 0.41943560043970746, Validation Loss:0.509942039847374, Validation Acc:82.7930174563591, AUC:0.6980590062111801\n",
            "Fold:3, Epoch: [15/40], Step: [18/29], Loss: 0.4630880173709657, Validation Loss:0.508294440805912, Validation Acc:68.3291770573566, AUC:0.748628364389234\n",
            "Fold:3, Epoch: [15/40], Step: [27/29], Loss: 0.46125231959201674, Validation Loss:0.599929504096508, Validation Acc:75.31172069825436, AUC:0.7592908902691511\n",
            "Epoch 14 time: 1.9063\n",
            "Fold:3, Epoch: [16/40], Step: [9/29], Loss: 0.46722380651368034, Validation Loss:0.5812173783779144, Validation Acc:77.55610972568579, AUC:0.7424171842650104\n",
            "Fold:3, Epoch: [16/40], Step: [18/29], Loss: 0.45694806675116223, Validation Loss:0.4645710811018944, Validation Acc:73.56608478802993, AUC:0.7566252587991718\n",
            "Fold:3, Epoch: [16/40], Step: [27/29], Loss: 0.455612536933687, Validation Loss:0.510262057185173, Validation Acc:71.07231920199501, AUC:0.7421325051759834\n",
            "Epoch 15 time: 1.8956\n",
            "Fold:3, Epoch: [17/40], Step: [9/29], Loss: 0.45532281200091046, Validation Loss:0.511027380824089, Validation Acc:80.0498753117207, AUC:0.7643892339544514\n",
            "Fold:3, Epoch: [17/40], Step: [18/29], Loss: 0.4573131584458881, Validation Loss:0.49552807211875916, Validation Acc:80.54862842892769, AUC:0.7523291925465838\n",
            "Fold:3, Epoch: [17/40], Step: [27/29], Loss: 0.45816173597618387, Validation Loss:0.4962894916534424, Validation Acc:75.31172069825436, AUC:0.7518115942028986\n",
            "Epoch 16 time: 1.9106\n",
            "Fold:3, Epoch: [18/40], Step: [9/29], Loss: 0.43090102738804287, Validation Loss:0.5824076980352402, Validation Acc:79.55112219451371, AUC:0.7689699792960663\n",
            "Fold:3, Epoch: [18/40], Step: [18/29], Loss: 0.42796871562798816, Validation Loss:0.4702518582344055, Validation Acc:71.57107231920199, AUC:0.745031055900621\n",
            "Fold:3, Epoch: [18/40], Step: [27/29], Loss: 0.45190992289119297, Validation Loss:0.5167132839560509, Validation Acc:78.80299251870325, AUC:0.7347049689440994\n",
            "Epoch 17 time: 1.8973\n",
            "Fold:3, Epoch: [19/40], Step: [9/29], Loss: 0.44035953945583767, Validation Loss:0.5564219206571579, Validation Acc:81.54613466334165, AUC:0.743167701863354\n",
            "Fold:3, Epoch: [19/40], Step: [18/29], Loss: 0.43746156990528107, Validation Loss:0.6309929490089417, Validation Acc:74.81296758104739, AUC:0.763871635610766\n",
            "Fold:3, Epoch: [19/40], Step: [27/29], Loss: 0.44186642876377813, Validation Loss:0.5980489626526833, Validation Acc:76.55860349127182, AUC:0.7665372670807454\n",
            "Epoch 18 time: 1.9113\n",
            "Fold:3, Epoch: [20/40], Step: [9/29], Loss: 0.4542255633407169, Validation Loss:0.5789425671100616, Validation Acc:78.05486284289277, AUC:0.7752329192546584\n",
            "Fold:3, Epoch: [20/40], Step: [18/29], Loss: 0.43427664041519165, Validation Loss:0.6150996461510658, Validation Acc:74.06483790523691, AUC:0.7370859213250518\n",
            "Fold:3, Epoch: [20/40], Step: [27/29], Loss: 0.4274094921571237, Validation Loss:0.5099763348698616, Validation Acc:71.57107231920199, AUC:0.7525103519668737\n",
            "Epoch 19 time: 1.8801\n",
            "Fold:3, Epoch: [21/40], Step: [9/29], Loss: 0.4243471688694424, Validation Loss:0.5799112617969513, Validation Acc:78.55361596009975, AUC:0.7108178053830227\n",
            "Fold:3, Epoch: [21/40], Step: [18/29], Loss: 0.42155906723605263, Validation Loss:0.49582235515117645, Validation Acc:75.81047381546135, AUC:0.7472308488612837\n",
            "Fold:3, Epoch: [21/40], Step: [27/29], Loss: 0.4250148722419032, Validation Loss:0.502279169857502, Validation Acc:71.07231920199501, AUC:0.7421325051759834\n",
            "Epoch 20 time: 1.9055\n",
            "Fold:3, Epoch: [22/40], Step: [9/29], Loss: 0.4264361924595303, Validation Loss:0.5370962023735046, Validation Acc:74.06483790523691, AUC:0.752044513457557\n",
            "Fold:3, Epoch: [22/40], Step: [18/29], Loss: 0.4127185510264503, Validation Loss:0.627603143453598, Validation Acc:79.55112219451371, AUC:0.7315734989648033\n",
            "Fold:3, Epoch: [22/40], Step: [27/29], Loss: 0.4196163404870916, Validation Loss:0.47739919275045395, Validation Acc:78.55361596009975, AUC:0.7556935817805384\n",
            "Epoch 21 time: 1.9199\n",
            "Fold:3, Epoch: [23/40], Step: [9/29], Loss: 0.420670833852556, Validation Loss:0.583797350525856, Validation Acc:76.55860349127182, AUC:0.7590579710144927\n",
            "Fold:3, Epoch: [23/40], Step: [18/29], Loss: 0.4053342127137714, Validation Loss:0.525909312069416, Validation Acc:78.55361596009975, AUC:0.7556935817805384\n"
          ],
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          "text": [
            "Fold:3, Epoch: [23/40], Step: [27/29], Loss: 0.41057353218396503, Validation Loss:0.5505351573228836, Validation Acc:68.5785536159601, AUC:0.727639751552795\n",
            "Epoch 22 time: 1.8917\n",
            "Fold:3, Epoch: [24/40], Step: [9/29], Loss: 0.4191837211449941, Validation Loss:0.5366534665226936, Validation Acc:78.30423940149626, AUC:0.7318064182194617\n",
            "Fold:3, Epoch: [24/40], Step: [18/29], Loss: 0.40462035106288063, Validation Loss:0.5318002179265022, Validation Acc:72.31920199501246, AUC:0.7493788819875775\n",
            "Fold:3, Epoch: [24/40], Step: [27/29], Loss: 0.40444237104168645, Validation Loss:0.5123046115040779, Validation Acc:78.05486284289277, AUC:0.7453157349896481\n",
            "Epoch 23 time: 1.8956\n",
            "Fold:3, Epoch: [25/40], Step: [9/29], Loss: 0.41755036181873745, Validation Loss:0.586162343621254, Validation Acc:76.55860349127182, AUC:0.736620082815735\n",
            "Fold:3, Epoch: [25/40], Step: [18/29], Loss: 0.40314407812224495, Validation Loss:0.5406608283519745, Validation Acc:73.06733167082294, AUC:0.7238095238095239\n",
            "Fold:3, Epoch: [25/40], Step: [27/29], Loss: 0.3946432250517386, Validation Loss:0.6685502380132675, Validation Acc:78.55361596009975, AUC:0.7182971014492754\n",
            "Epoch 24 time: 1.9122\n",
            "Fold:3, Epoch: [26/40], Step: [9/29], Loss: 0.392872949441274, Validation Loss:0.47359707951545715, Validation Acc:75.81047381546135, AUC:0.7472308488612837\n",
            "Fold:3, Epoch: [26/40], Step: [18/29], Loss: 0.38690098623434704, Validation Loss:0.5866476371884346, Validation Acc:74.56359102244389, AUC:0.7549430641821946\n",
            "Fold:3, Epoch: [26/40], Step: [27/29], Loss: 0.39106332924630904, Validation Loss:0.5870865732431412, Validation Acc:77.0573566084788, AUC:0.7619565217391303\n",
            "Epoch 25 time: 1.9070\n",
            "Fold:3, Epoch: [27/40], Step: [9/29], Loss: 0.39543866780069137, Validation Loss:0.6199682205915451, Validation Acc:76.55860349127182, AUC:0.6842650103519669\n",
            "Fold:3, Epoch: [27/40], Step: [18/29], Loss: 0.40147604213820565, Validation Loss:0.7740882039070129, Validation Acc:80.0498753117207, AUC:0.6746376811594202\n",
            "Fold:3, Epoch: [27/40], Step: [27/29], Loss: 0.40691153760309573, Validation Loss:0.6582468748092651, Validation Acc:75.31172069825436, AUC:0.7218944099378882\n",
            "Epoch 26 time: 1.9110\n",
            "Fold:3, Epoch: [28/40], Step: [9/29], Loss: 0.3809185259872013, Validation Loss:0.5340871289372444, Validation Acc:75.31172069825436, AUC:0.7218944099378882\n",
            "Fold:3, Epoch: [28/40], Step: [18/29], Loss: 0.37886439594957566, Validation Loss:0.6447752267122269, Validation Acc:75.56109725685785, AUC:0.723343685300207\n",
            "Fold:3, Epoch: [28/40], Step: [27/29], Loss: 0.3862471028610512, Validation Loss:0.5365194901823997, Validation Acc:70.32418952618454, AUC:0.7377846790890269\n",
            "Epoch 27 time: 1.9051\n",
            "Fold:3, Epoch: [29/40], Step: [9/29], Loss: 0.3755401505364312, Validation Loss:0.5802205353975296, Validation Acc:76.80798004987531, AUC:0.7305900621118013\n",
            "Fold:3, Epoch: [29/40], Step: [18/29], Loss: 0.3647718098428514, Validation Loss:0.6557265296578407, Validation Acc:80.54862842892769, AUC:0.6999741200828157\n",
            "Fold:3, Epoch: [29/40], Step: [27/29], Loss: 0.37056513627370197, Validation Loss:0.5885066837072372, Validation Acc:75.56109725685785, AUC:0.7158643892339545\n",
            "Epoch 28 time: 1.9594\n",
            "Fold:3, Epoch: [30/40], Step: [9/29], Loss: 0.3655126392841339, Validation Loss:0.5860063508152962, Validation Acc:76.80798004987531, AUC:0.7006728778467909\n",
            "Fold:3, Epoch: [30/40], Step: [18/29], Loss: 0.36543380386299557, Validation Loss:0.595836266875267, Validation Acc:76.80798004987531, AUC:0.715631469979296\n",
            "Fold:3, Epoch: [30/40], Step: [27/29], Loss: 0.3619822418248212, Validation Loss:0.5607920587062836, Validation Acc:72.31920199501246, AUC:0.7269409937888199\n",
            "Epoch 29 time: 1.9250\n",
            "Fold:3, Epoch: [31/40], Step: [9/29], Loss: 0.3943609827094608, Validation Loss:0.7892446368932724, Validation Acc:78.30423940149626, AUC:0.6944099378881987\n",
            "Fold:3, Epoch: [31/40], Step: [18/29], Loss: 0.3620956407652961, Validation Loss:0.556025430560112, Validation Acc:75.31172069825436, AUC:0.6994565217391304\n",
            "Fold:3, Epoch: [31/40], Step: [27/29], Loss: 0.36264653669463265, Validation Loss:0.7343303263187408, Validation Acc:78.80299251870325, AUC:0.6673913043478261\n",
            "Epoch 30 time: 1.9099\n",
            "Fold:3, Epoch: [32/40], Step: [9/29], Loss: 0.3707161280843947, Validation Loss:0.6270695626735687, Validation Acc:75.81047381546135, AUC:0.7098343685300207\n",
            "Fold:3, Epoch: [32/40], Step: [18/29], Loss: 0.34310677978727555, Validation Loss:0.5974269807338715, Validation Acc:77.0573566084788, AUC:0.6946428571428572\n",
            "Fold:3, Epoch: [32/40], Step: [27/29], Loss: 0.3455483814080556, Validation Loss:0.7266856878995895, Validation Acc:74.81296758104739, AUC:0.6965579710144927\n",
            "Epoch 31 time: 1.8892\n",
            "Fold:3, Epoch: [33/40], Step: [9/29], Loss: 0.3491304549905989, Validation Loss:0.7713045328855515, Validation Acc:78.05486284289277, AUC:0.6780020703933747\n",
            "Fold:3, Epoch: [33/40], Step: [18/29], Loss: 0.3403078251414829, Validation Loss:0.5705514214932919, Validation Acc:73.06733167082294, AUC:0.7163302277432713\n",
            "Fold:3, Epoch: [33/40], Step: [27/29], Loss: 0.3388708940258733, Validation Loss:0.5755786821246147, Validation Acc:76.05985037406484, AUC:0.6963250517598344\n",
            "Epoch 32 time: 1.9213\n",
            "Fold:3, Epoch: [34/40], Step: [9/29], Loss: 0.3238345831632614, Validation Loss:0.6934893876314163, Validation Acc:76.30922693266832, AUC:0.7127329192546584\n",
            "Fold:3, Epoch: [34/40], Step: [18/29], Loss: 0.32442806081639397, Validation Loss:0.7211742699146271, Validation Acc:77.3067331670823, AUC:0.6886128364389233\n",
            "Fold:3, Epoch: [34/40], Step: [27/29], Loss: 0.3280700163708793, Validation Loss:0.6858851611614227, Validation Acc:77.0573566084788, AUC:0.6647256728778468\n",
            "Epoch 33 time: 1.8827\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:3, Epoch: [35/40], Step: [9/29], Loss: 0.3170631527900696, Validation Loss:0.8181767165660858, Validation Acc:77.3067331670823, AUC:0.696092132505176\n",
            "Fold:3, Epoch: [35/40], Step: [18/29], Loss: 0.3167020198371675, Validation Loss:0.6601053327322006, Validation Acc:73.56608478802993, AUC:0.7192287784679089\n",
            "Fold:3, Epoch: [35/40], Step: [27/29], Loss: 0.3179593340114311, Validation Loss:0.7600864768028259, Validation Acc:77.0573566084788, AUC:0.6871635610766046\n",
            "Epoch 34 time: 1.9077\n",
            "Fold:3, Epoch: [36/40], Step: [9/29], Loss: 0.28484700785742867, Validation Loss:0.8661025613546371, Validation Acc:79.80049875311721, AUC:0.6881469979296065\n",
            "Fold:3, Epoch: [36/40], Step: [18/29], Loss: 0.293351670106252, Validation Loss:0.7801835238933563, Validation Acc:76.05985037406484, AUC:0.6738871635610767\n",
            "Fold:3, Epoch: [36/40], Step: [27/29], Loss: 0.3060610835199003, Validation Loss:0.7004043012857437, Validation Acc:74.06483790523691, AUC:0.6772515527950311\n",
            "Epoch 35 time: 1.8953\n",
            "Fold:3, Epoch: [37/40], Step: [9/29], Loss: 0.2860194957918591, Validation Loss:1.1524008810520172, Validation Acc:77.3067331670823, AUC:0.643737060041408\n",
            "Fold:3, Epoch: [37/40], Step: [18/29], Loss: 0.2922979336645868, Validation Loss:0.7711039632558823, Validation Acc:76.05985037406484, AUC:0.703804347826087\n",
            "Fold:3, Epoch: [37/40], Step: [27/29], Loss: 0.2943195352951686, Validation Loss:0.6389411762356758, Validation Acc:76.30922693266832, AUC:0.682815734989648\n",
            "Epoch 36 time: 1.8786\n",
            "Fold:3, Epoch: [38/40], Step: [9/29], Loss: 0.26922723485363853, Validation Loss:0.8295514285564423, Validation Acc:73.81546134663341, AUC:0.6758022774327122\n",
            "Fold:3, Epoch: [38/40], Step: [18/29], Loss: 0.29195038891500896, Validation Loss:0.7908021062612534, Validation Acc:76.30922693266832, AUC:0.6753364389233955\n",
            "Fold:3, Epoch: [38/40], Step: [27/29], Loss: 0.285313770726875, Validation Loss:0.7610851377248764, Validation Acc:73.56608478802993, AUC:0.6818322981366459\n",
            "Epoch 37 time: 1.9017\n",
            "Fold:3, Epoch: [39/40], Step: [9/29], Loss: 0.2954269200563431, Validation Loss:0.8825058490037918, Validation Acc:81.29675810473816, AUC:0.6370082815734989\n",
            "Fold:3, Epoch: [39/40], Step: [18/29], Loss: 0.2946980388628112, Validation Loss:0.883431687951088, Validation Acc:73.06733167082294, AUC:0.6789337474120083\n",
            "Fold:3, Epoch: [39/40], Step: [27/29], Loss: 0.28839730664535806, Validation Loss:0.7440554946660995, Validation Acc:77.0573566084788, AUC:0.6572463768115943\n",
            "Epoch 38 time: 1.8836\n",
            "Fold:3, Epoch: [40/40], Step: [9/29], Loss: 0.26568912631935543, Validation Loss:0.6120027750730515, Validation Acc:71.57107231920199, AUC:0.7076345755693582\n",
            "Fold:3, Epoch: [40/40], Step: [18/29], Loss: 0.2767804182238049, Validation Loss:0.8019969612360001, Validation Acc:76.30922693266832, AUC:0.6528985507246376\n",
            "Fold:3, Epoch: [40/40], Step: [27/29], Loss: 0.2648013096164774, Validation Loss:0.7740015834569931, Validation Acc:77.80548628428927, AUC:0.6915113871635611\n",
            "Epoch 39 time: 1.9256\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:4, Epoch: [1/40], Step: [9/29], Loss: 0.6678158971998427, Validation Loss:0.675664022564888, Validation Acc:74.06483790523691, AUC:0.6697722567287784\n",
            "Fold:4, Epoch: [1/40], Step: [18/29], Loss: 0.6465583013163673, Validation Loss:0.6531295329332352, Validation Acc:73.81546134663341, AUC:0.6982401656314701\n",
            "Fold:4, Epoch: [1/40], Step: [27/29], Loss: 0.6276900128081992, Validation Loss:0.5937930420041084, Validation Acc:73.31670822942644, AUC:0.7028209109730849\n",
            "Epoch 0 time: 1.8892\n",
            "Fold:4, Epoch: [2/40], Step: [9/29], Loss: 0.6014205349816216, Validation Loss:0.5601830333471298, Validation Acc:75.56109725685785, AUC:0.7009057971014493\n",
            "Fold:4, Epoch: [2/40], Step: [18/29], Loss: 0.5840424928400252, Validation Loss:0.5911731719970703, Validation Acc:73.81546134663341, AUC:0.6982401656314701\n",
            "Fold:4, Epoch: [2/40], Step: [27/29], Loss: 0.5799645626986468, Validation Loss:0.5884832590818405, Validation Acc:71.57107231920199, AUC:0.6777173913043478\n",
            "Epoch 1 time: 1.8825\n",
            "Fold:4, Epoch: [3/40], Step: [9/29], Loss: 0.5452769266234504, Validation Loss:0.5704913586378098, Validation Acc:72.81795511221945, AUC:0.6924430641821946\n",
            "Fold:4, Epoch: [3/40], Step: [18/29], Loss: 0.5470400551954905, Validation Loss:0.582799956202507, Validation Acc:69.07730673316708, AUC:0.6931418219461697\n",
            "Fold:4, Epoch: [3/40], Step: [27/29], Loss: 0.554012699259652, Validation Loss:0.5868715941905975, Validation Acc:67.33167082294264, AUC:0.6680383022774327\n",
            "Epoch 2 time: 1.9016\n",
            "Fold:4, Epoch: [4/40], Step: [9/29], Loss: 0.5679300758573744, Validation Loss:0.5602783560752869, Validation Acc:66.33416458852868, AUC:0.6771997929606626\n",
            "Fold:4, Epoch: [4/40], Step: [18/29], Loss: 0.5468444575866064, Validation Loss:0.5421480908989906, Validation Acc:74.56359102244389, AUC:0.7100672877846791\n",
            "Fold:4, Epoch: [4/40], Step: [27/29], Loss: 0.5390548408031464, Validation Loss:0.5675415620207787, Validation Acc:75.06234413965088, AUC:0.6980072463768117\n",
            "Epoch 3 time: 1.8900\n",
            "Fold:4, Epoch: [5/40], Step: [9/29], Loss: 0.513537218173345, Validation Loss:0.5495768561959267, Validation Acc:72.06982543640898, AUC:0.6955745341614906\n",
            "Fold:4, Epoch: [5/40], Step: [18/29], Loss: 0.5252115113867654, Validation Loss:0.5745363980531693, Validation Acc:68.07980049875312, AUC:0.6948240165631471\n",
            "Fold:4, Epoch: [5/40], Step: [27/29], Loss: 0.5332204611213119, Validation Loss:0.6707136631011963, Validation Acc:55.36159600997506, AUC:0.6732660455486543\n",
            "Epoch 4 time: 1.8812\n",
            "Fold:4, Epoch: [6/40], Step: [9/29], Loss: 0.5238772034645081, Validation Loss:0.6632224768400192, Validation Acc:73.06733167082294, AUC:0.7088509316770186\n",
            "Fold:4, Epoch: [6/40], Step: [18/29], Loss: 0.5195992655224271, Validation Loss:0.5758152306079865, Validation Acc:72.81795511221945, AUC:0.6774844720496895\n",
            "Fold:4, Epoch: [6/40], Step: [27/29], Loss: 0.5210097564591302, Validation Loss:0.6035661995410919, Validation Acc:74.56359102244389, AUC:0.672670807453416\n",
            "Epoch 5 time: 1.8685\n",
            "Fold:4, Epoch: [7/40], Step: [9/29], Loss: 0.5281813641389211, Validation Loss:0.5661797076463699, Validation Acc:75.56109725685785, AUC:0.6859472049689441\n",
            "Fold:4, Epoch: [7/40], Step: [18/29], Loss: 0.5189721766445372, Validation Loss:0.6005266308784485, Validation Acc:70.82294264339153, AUC:0.7032867494824018\n",
            "Fold:4, Epoch: [7/40], Step: [27/29], Loss: 0.5131556733890816, Validation Loss:0.6490522846579552, Validation Acc:71.57107231920199, AUC:0.6926759834368529\n",
            "Epoch 6 time: 1.8798\n",
            "Fold:4, Epoch: [8/40], Step: [9/29], Loss: 0.4855212801032596, Validation Loss:0.5753390192985535, Validation Acc:75.31172069825436, AUC:0.6919772256728779\n",
            "Fold:4, Epoch: [8/40], Step: [18/29], Loss: 0.5048921157916387, Validation Loss:0.7186440601944923, Validation Acc:72.56857855361596, AUC:0.6760351966873707\n",
            "Fold:4, Epoch: [8/40], Step: [27/29], Loss: 0.505370893964061, Validation Loss:0.5710409432649612, Validation Acc:73.31670822942644, AUC:0.6953416149068323\n",
            "Epoch 7 time: 1.8897\n",
            "Fold:4, Epoch: [9/40], Step: [9/29], Loss: 0.4873298704624176, Validation Loss:0.6214397102594376, Validation Acc:63.092269326683294, AUC:0.6957556935817806\n",
            "Fold:4, Epoch: [9/40], Step: [18/29], Loss: 0.4981326874759462, Validation Loss:0.5727289542555809, Validation Acc:65.5860349127182, AUC:0.6803312629399587\n",
            "Fold:4, Epoch: [9/40], Step: [27/29], Loss: 0.5050094502943533, Validation Loss:0.578639343380928, Validation Acc:76.05985037406484, AUC:0.6963250517598344\n",
            "Epoch 8 time: 1.8614\n",
            "Fold:4, Epoch: [10/40], Step: [9/29], Loss: 0.49238064885139465, Validation Loss:0.6069103330373764, Validation Acc:76.05985037406484, AUC:0.6888457556935819\n",
            "Fold:4, Epoch: [10/40], Step: [18/29], Loss: 0.4916379716661241, Validation Loss:0.6080399006605148, Validation Acc:74.81296758104739, AUC:0.7189958592132505\n",
            "Fold:4, Epoch: [10/40], Step: [27/29], Loss: 0.4853373434808519, Validation Loss:0.5966364666819572, Validation Acc:82.7930174563591, AUC:0.6905797101449275\n",
            "Epoch 9 time: 1.8821\n",
            "Fold:4, Epoch: [11/40], Step: [9/29], Loss: 0.4883822401364644, Validation Loss:0.6048495471477509, Validation Acc:65.5860349127182, AUC:0.7102484472049688\n",
            "Fold:4, Epoch: [11/40], Step: [18/29], Loss: 0.49424904584884644, Validation Loss:0.562116838991642, Validation Acc:74.06483790523691, AUC:0.6772515527950311\n",
            "Fold:4, Epoch: [11/40], Step: [27/29], Loss: 0.4915515294781438, Validation Loss:0.6225033104419708, Validation Acc:74.06483790523691, AUC:0.6922101449275362\n",
            "Epoch 10 time: 1.8840\n",
            "Fold:4, Epoch: [12/40], Step: [9/29], Loss: 0.48449884520636666, Validation Loss:0.7004274725914001, Validation Acc:70.07481296758105, AUC:0.7138975155279503\n"
          ],
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            "Fold:4, Epoch: [12/40], Step: [18/29], Loss: 0.47469186782836914, Validation Loss:0.5979284048080444, Validation Acc:74.3142144638404, AUC:0.7160973084886129\n",
            "Fold:4, Epoch: [12/40], Step: [27/29], Loss: 0.4809776379002465, Validation Loss:0.6448119431734085, Validation Acc:79.05236907730674, AUC:0.6912784679089027\n",
            "Epoch 11 time: 1.9028\n",
            "Fold:4, Epoch: [13/40], Step: [9/29], Loss: 0.4994972248872121, Validation Loss:0.6582285314798355, Validation Acc:65.08728179551122, AUC:0.6849120082815735\n",
            "Fold:4, Epoch: [13/40], Step: [18/29], Loss: 0.4938460091749827, Validation Loss:0.610889345407486, Validation Acc:70.07481296758105, AUC:0.698938923395445\n",
            "Fold:4, Epoch: [13/40], Step: [27/29], Loss: 0.4924092215520364, Validation Loss:0.6075630486011505, Validation Acc:77.55610972568579, AUC:0.7199792960662525\n",
            "Epoch 12 time: 1.8756\n",
            "Fold:4, Epoch: [14/40], Step: [9/29], Loss: 0.4584730201297336, Validation Loss:0.6178497076034546, Validation Acc:73.06733167082294, AUC:0.7163302277432713\n",
            "Fold:4, Epoch: [14/40], Step: [18/29], Loss: 0.4682159473498662, Validation Loss:0.8435901552438736, Validation Acc:73.56608478802993, AUC:0.7192287784679089\n",
            "Fold:4, Epoch: [14/40], Step: [27/29], Loss: 0.46565294707262955, Validation Loss:0.5808152332901955, Validation Acc:71.3216957605985, AUC:0.713664596273292\n",
            "Epoch 13 time: 1.8836\n",
            "Fold:4, Epoch: [15/40], Step: [9/29], Loss: 0.460574875275294, Validation Loss:0.6060693711042404, Validation Acc:74.56359102244389, AUC:0.7325051759834369\n",
            "Fold:4, Epoch: [15/40], Step: [18/29], Loss: 0.4553821169667774, Validation Loss:0.7153043448925018, Validation Acc:74.81296758104739, AUC:0.7115165631469978\n",
            "Fold:4, Epoch: [15/40], Step: [27/29], Loss: 0.45483103063371444, Validation Loss:0.6108037829399109, Validation Acc:74.3142144638404, AUC:0.7160973084886129\n",
            "Epoch 14 time: 1.8968\n",
            "Fold:4, Epoch: [16/40], Step: [9/29], Loss: 0.4290721383359697, Validation Loss:0.5598127245903015, Validation Acc:73.31670822942644, AUC:0.7327380952380953\n",
            "Fold:4, Epoch: [16/40], Step: [18/29], Loss: 0.44405747950077057, Validation Loss:0.8337797671556473, Validation Acc:76.30922693266832, AUC:0.7276915113871636\n",
            "Fold:4, Epoch: [16/40], Step: [27/29], Loss: 0.4383391660672647, Validation Loss:0.6010860651731491, Validation Acc:72.06982543640898, AUC:0.7479296066252589\n",
            "Epoch 15 time: 1.9469\n",
            "Fold:4, Epoch: [17/40], Step: [9/29], Loss: 0.4488391611311171, Validation Loss:0.6390775591135025, Validation Acc:72.81795511221945, AUC:0.7447981366459627\n",
            "Fold:4, Epoch: [17/40], Step: [18/29], Loss: 0.437879827287462, Validation Loss:0.9132313132286072, Validation Acc:79.30174563591022, AUC:0.7076863354037267\n",
            "Fold:4, Epoch: [17/40], Step: [27/29], Loss: 0.4393900412100333, Validation Loss:0.5833532363176346, Validation Acc:72.06982543640898, AUC:0.7329710144927535\n",
            "Epoch 16 time: 1.8972\n",
            "Fold:4, Epoch: [18/40], Step: [9/29], Loss: 0.4168347650104099, Validation Loss:0.5706644654273987, Validation Acc:74.06483790523691, AUC:0.6922101449275362\n",
            "Fold:4, Epoch: [18/40], Step: [18/29], Loss: 0.41910141209761304, Validation Loss:0.6067615002393723, Validation Acc:68.3291770573566, AUC:0.7261904761904762\n",
            "Fold:4, Epoch: [18/40], Step: [27/29], Loss: 0.43949631849924725, Validation Loss:0.572680875658989, Validation Acc:76.55860349127182, AUC:0.7067028985507247\n",
            "Epoch 17 time: 1.9039\n",
            "Fold:4, Epoch: [19/40], Step: [9/29], Loss: 0.44230948554144967, Validation Loss:0.6600861102342606, Validation Acc:58.35411471321696, AUC:0.6906573498964804\n",
            "Fold:4, Epoch: [19/40], Step: [18/29], Loss: 0.45101840959654915, Validation Loss:0.8214670121669769, Validation Acc:73.31670822942644, AUC:0.71777950310559\n",
            "Fold:4, Epoch: [19/40], Step: [27/29], Loss: 0.4506905266532191, Validation Loss:0.7866982221603394, Validation Acc:78.30423940149626, AUC:0.7168478260869565\n",
            "Epoch 18 time: 1.8910\n",
            "Fold:4, Epoch: [20/40], Step: [9/29], Loss: 0.44766343302196926, Validation Loss:0.6230182722210884, Validation Acc:77.80548628428927, AUC:0.6989906832298137\n",
            "Fold:4, Epoch: [20/40], Step: [18/29], Loss: 0.42844213876459336, Validation Loss:0.6936939656734467, Validation Acc:68.07980049875312, AUC:0.7322204968944099\n",
            "Fold:4, Epoch: [20/40], Step: [27/29], Loss: 0.4247397747304704, Validation Loss:0.7342463731765747, Validation Acc:72.31920199501246, AUC:0.6970238095238096\n",
            "Epoch 19 time: 1.8661\n",
            "Fold:4, Epoch: [21/40], Step: [9/29], Loss: 0.4178576138284471, Validation Loss:0.7344764843583107, Validation Acc:72.56857855361596, AUC:0.7358695652173914\n",
            "Fold:4, Epoch: [21/40], Step: [18/29], Loss: 0.4285975694656372, Validation Loss:0.6134645044803619, Validation Acc:75.81047381546135, AUC:0.7322722567287785\n",
            "Fold:4, Epoch: [21/40], Step: [27/29], Loss: 0.4240622266575142, Validation Loss:0.6050593629479408, Validation Acc:75.56109725685785, AUC:0.7009057971014493\n",
            "Epoch 20 time: 1.8662\n",
            "Fold:4, Epoch: [22/40], Step: [9/29], Loss: 0.40063514974382186, Validation Loss:0.6133003309369087, Validation Acc:71.07231920199501, AUC:0.6972567287784678\n",
            "Fold:4, Epoch: [22/40], Step: [18/29], Loss: 0.39671920239925385, Validation Loss:0.732935942709446, Validation Acc:74.56359102244389, AUC:0.7175465838509316\n",
            "Fold:4, Epoch: [22/40], Step: [27/29], Loss: 0.4118891016200737, Validation Loss:0.8873465806245804, Validation Acc:75.56109725685785, AUC:0.7083850931677018\n",
            "Epoch 21 time: 1.9109\n",
            "Fold:4, Epoch: [23/40], Step: [9/29], Loss: 0.4132164882289039, Validation Loss:0.6406857222318649, Validation Acc:69.07730673316708, AUC:0.7454968944099378\n",
            "Fold:4, Epoch: [23/40], Step: [18/29], Loss: 0.4113437897629208, Validation Loss:0.588878870010376, Validation Acc:77.55610972568579, AUC:0.7199792960662525\n"
          ],
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          "text": [
            "Fold:4, Epoch: [23/40], Step: [27/29], Loss: 0.41383263689500316, Validation Loss:0.8497997671365738, Validation Acc:73.31670822942644, AUC:0.6878623188405797\n",
            "Epoch 22 time: 1.8766\n",
            "Fold:4, Epoch: [24/40], Step: [9/29], Loss: 0.4022791451878018, Validation Loss:0.6646214425563812, Validation Acc:75.81047381546135, AUC:0.7397515527950311\n",
            "Fold:4, Epoch: [24/40], Step: [18/29], Loss: 0.40007009274429745, Validation Loss:0.7794514298439026, Validation Acc:78.80299251870325, AUC:0.7197463768115943\n",
            "Fold:4, Epoch: [24/40], Step: [27/29], Loss: 0.40285193478619613, Validation Loss:0.8627423495054245, Validation Acc:77.3067331670823, AUC:0.7035714285714286\n",
            "Epoch 23 time: 1.8898\n",
            "Fold:4, Epoch: [25/40], Step: [9/29], Loss: 0.39962319533030194, Validation Loss:0.7174234688282013, Validation Acc:69.57605985037407, AUC:0.7334368530020703\n",
            "Fold:4, Epoch: [25/40], Step: [18/29], Loss: 0.385659396648407, Validation Loss:0.7546574026346207, Validation Acc:75.56109725685785, AUC:0.723343685300207\n",
            "Fold:4, Epoch: [25/40], Step: [27/29], Loss: 0.3997157171920494, Validation Loss:0.6197784394025803, Validation Acc:76.05985037406484, AUC:0.7187629399585921\n",
            "Epoch 24 time: 1.8692\n",
            "Fold:4, Epoch: [26/40], Step: [9/29], Loss: 0.37706323133574593, Validation Loss:0.6692259535193443, Validation Acc:70.32418952618454, AUC:0.6704710144927535\n",
            "Fold:4, Epoch: [26/40], Step: [18/29], Loss: 0.40512438780731624, Validation Loss:0.614826463162899, Validation Acc:67.08229426433915, AUC:0.7114648033126295\n",
            "Fold:4, Epoch: [26/40], Step: [27/29], Loss: 0.3957517522352713, Validation Loss:0.9726591557264328, Validation Acc:78.30423940149626, AUC:0.6794513457556935\n",
            "Epoch 25 time: 1.9358\n",
            "Fold:4, Epoch: [27/40], Step: [9/29], Loss: 0.3928874101903703, Validation Loss:0.7186098620295525, Validation Acc:73.56608478802993, AUC:0.7192287784679089\n",
            "Fold:4, Epoch: [27/40], Step: [18/29], Loss: 0.39456309709284043, Validation Loss:0.6044590398669243, Validation Acc:71.57107231920199, AUC:0.7375517598343685\n",
            "Fold:4, Epoch: [27/40], Step: [27/29], Loss: 0.38921453996940897, Validation Loss:0.7148458659648895, Validation Acc:70.07481296758105, AUC:0.7138975155279503\n",
            "Epoch 26 time: 1.9019\n",
            "Fold:4, Epoch: [28/40], Step: [9/29], Loss: 0.4010804262426164, Validation Loss:0.7404021918773651, Validation Acc:77.55610972568579, AUC:0.6900621118012422\n",
            "Fold:4, Epoch: [28/40], Step: [18/29], Loss: 0.3925410840246413, Validation Loss:0.7527316063642502, Validation Acc:75.06234413965088, AUC:0.6830486542443065\n",
            "Fold:4, Epoch: [28/40], Step: [27/29], Loss: 0.37964145563266893, Validation Loss:0.6908975392580032, Validation Acc:74.3142144638404, AUC:0.7086180124223601\n",
            "Epoch 27 time: 1.8670\n",
            "Fold:4, Epoch: [29/40], Step: [9/29], Loss: 0.3803427550527785, Validation Loss:1.0844987630844116, Validation Acc:76.30922693266832, AUC:0.6753364389233955\n",
            "Fold:4, Epoch: [29/40], Step: [18/29], Loss: 0.3641037129693561, Validation Loss:0.7523180246353149, Validation Acc:72.06982543640898, AUC:0.6880952380952382\n",
            "Fold:4, Epoch: [29/40], Step: [27/29], Loss: 0.36716251571973163, Validation Loss:0.7087571993470192, Validation Acc:73.31670822942644, AUC:0.6729037267080746\n",
            "Epoch 28 time: 1.8683\n",
            "Fold:4, Epoch: [30/40], Step: [9/29], Loss: 0.3466947376728058, Validation Loss:0.7056970745325089, Validation Acc:75.81047381546135, AUC:0.6649585921325052\n",
            "Fold:4, Epoch: [30/40], Step: [18/29], Loss: 0.35045768651697373, Validation Loss:0.8037726730108261, Validation Acc:72.56857855361596, AUC:0.705952380952381\n",
            "Fold:4, Epoch: [30/40], Step: [27/29], Loss: 0.3568920173026897, Validation Loss:0.7096939831972122, Validation Acc:74.06483790523691, AUC:0.6697722567287784\n",
            "Epoch 29 time: 1.8914\n",
            "Fold:4, Epoch: [31/40], Step: [9/29], Loss: 0.3706955379909939, Validation Loss:1.0026490539312363, Validation Acc:74.3142144638404, AUC:0.731055900621118\n",
            "Fold:4, Epoch: [31/40], Step: [18/29], Loss: 0.36397144695123035, Validation Loss:0.9479468315839767, Validation Acc:73.31670822942644, AUC:0.6953416149068323\n",
            "Fold:4, Epoch: [31/40], Step: [27/29], Loss: 0.36288497183058, Validation Loss:0.7642006427049637, Validation Acc:75.56109725685785, AUC:0.670988612836439\n",
            "Epoch 30 time: 1.8988\n",
            "Fold:4, Epoch: [32/40], Step: [9/29], Loss: 0.3394463724560208, Validation Loss:0.7574736475944519, Validation Acc:72.81795511221945, AUC:0.6999223602484471\n",
            "Fold:4, Epoch: [32/40], Step: [18/29], Loss: 0.3423385868469874, Validation Loss:0.7305877804756165, Validation Acc:75.06234413965088, AUC:0.6606107660455486\n",
            "Fold:4, Epoch: [32/40], Step: [27/29], Loss: 0.3411867419878642, Validation Loss:0.7257076427340508, Validation Acc:74.81296758104739, AUC:0.6666407867494825\n",
            "Epoch 31 time: 1.9062\n",
            "Fold:4, Epoch: [33/40], Step: [9/29], Loss: 0.3278220875395669, Validation Loss:0.7363632619380951, Validation Acc:77.55610972568579, AUC:0.6526656314699795\n",
            "Fold:4, Epoch: [33/40], Step: [18/29], Loss: 0.31981134828593993, Validation Loss:0.9470154941082001, Validation Acc:72.56857855361596, AUC:0.6909937888198757\n",
            "Fold:4, Epoch: [33/40], Step: [27/29], Loss: 0.33304893253026185, Validation Loss:0.7293363586068153, Validation Acc:77.55610972568579, AUC:0.6825828157349897\n",
            "Epoch 32 time: 1.9143\n",
            "Fold:4, Epoch: [34/40], Step: [9/29], Loss: 0.32437680330541396, Validation Loss:0.7060641758143902, Validation Acc:79.80049875311721, AUC:0.6357919254658385\n",
            "Fold:4, Epoch: [34/40], Step: [18/29], Loss: 0.3298279999030961, Validation Loss:1.1131160408258438, Validation Acc:73.81546134663341, AUC:0.6608436853002071\n",
            "Fold:4, Epoch: [34/40], Step: [27/29], Loss: 0.3361687930645766, Validation Loss:0.7588251829147339, Validation Acc:71.57107231920199, AUC:0.6777173913043478\n",
            "Epoch 33 time: 1.8760\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:4, Epoch: [35/40], Step: [9/29], Loss: 0.3245171689324909, Validation Loss:0.8537300676107407, Validation Acc:81.04738154613466, AUC:0.6280797101449275\n",
            "Fold:4, Epoch: [35/40], Step: [18/29], Loss: 0.32165723625156617, Validation Loss:0.731583796441555, Validation Acc:77.55610972568579, AUC:0.6526656314699795\n",
            "Fold:4, Epoch: [35/40], Step: [27/29], Loss: 0.32418722410996753, Validation Loss:1.0144252479076385, Validation Acc:72.81795511221945, AUC:0.6924430641821946\n",
            "Epoch 34 time: 1.8734\n",
            "Fold:4, Epoch: [36/40], Step: [9/29], Loss: 0.3083607421980964, Validation Loss:0.8249622508883476, Validation Acc:77.0573566084788, AUC:0.6273291925465838\n",
            "Fold:4, Epoch: [36/40], Step: [18/29], Loss: 0.31808203127649093, Validation Loss:0.8723621368408203, Validation Acc:76.80798004987531, AUC:0.6408385093167702\n",
            "Fold:4, Epoch: [36/40], Step: [27/29], Loss: 0.3240098202670062, Validation Loss:0.8225263208150864, Validation Acc:62.8428927680798, AUC:0.6569099378881987\n",
            "Epoch 35 time: 1.8857\n",
            "Fold:4, Epoch: [37/40], Step: [9/29], Loss: 0.3109845949543847, Validation Loss:1.0260172188282013, Validation Acc:80.54862842892769, AUC:0.6251811594202898\n",
            "Fold:4, Epoch: [37/40], Step: [18/29], Loss: 0.3078329728709327, Validation Loss:0.7730347290635109, Validation Acc:75.06234413965088, AUC:0.6456521739130435\n",
            "Fold:4, Epoch: [37/40], Step: [27/29], Loss: 0.32074402217511777, Validation Loss:1.5258840471506119, Validation Acc:73.06733167082294, AUC:0.6415372670807454\n",
            "Epoch 36 time: 1.8868\n",
            "Fold:4, Epoch: [38/40], Step: [9/29], Loss: 0.31004998750156826, Validation Loss:0.904535636305809, Validation Acc:73.81546134663341, AUC:0.6458850931677018\n",
            "Fold:4, Epoch: [38/40], Step: [18/29], Loss: 0.30347564982043373, Validation Loss:0.8487286120653152, Validation Acc:74.06483790523691, AUC:0.6473343685300208\n",
            "Fold:4, Epoch: [38/40], Step: [27/29], Loss: 0.3044406561939805, Validation Loss:0.9274429231882095, Validation Acc:77.3067331670823, AUC:0.6138198757763975\n",
            "Epoch 37 time: 1.8704\n",
            "Fold:4, Epoch: [39/40], Step: [9/29], Loss: 0.2794719354973899, Validation Loss:1.2055498957633972, Validation Acc:74.06483790523691, AUC:0.6548136645962732\n",
            "Fold:4, Epoch: [39/40], Step: [18/29], Loss: 0.29329654905531144, Validation Loss:0.8746878355741501, Validation Acc:70.32418952618454, AUC:0.6779503105590061\n",
            "Fold:4, Epoch: [39/40], Step: [27/29], Loss: 0.2917929909847401, Validation Loss:0.927001491189003, Validation Acc:73.56608478802993, AUC:0.6369565217391304\n",
            "Epoch 38 time: 1.8710\n",
            "Fold:4, Epoch: [40/40], Step: [9/29], Loss: 0.30078621374236214, Validation Loss:0.8834415674209595, Validation Acc:72.31920199501246, AUC:0.6222308488612837\n",
            "Fold:4, Epoch: [40/40], Step: [18/29], Loss: 0.2937273871567514, Validation Loss:0.9229796528816223, Validation Acc:71.82044887780549, AUC:0.6791666666666668\n",
            "Fold:4, Epoch: [40/40], Step: [27/29], Loss: 0.28571546960760047, Validation Loss:0.9836503267288208, Validation Acc:78.30423940149626, AUC:0.634575569358178\n",
            "Epoch 39 time: 1.8557\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:5, Epoch: [1/40], Step: [9/29], Loss: 0.6833906835979886, Validation Loss:0.6862355917692184, Validation Acc:81.5, AUC:0.5488801054018446\n",
            "Fold:5, Epoch: [1/40], Step: [18/29], Loss: 0.6562008162339529, Validation Loss:0.6696883291006088, Validation Acc:72.5, AUC:0.6342555994729907\n",
            "Fold:5, Epoch: [1/40], Step: [27/29], Loss: 0.6435414530612804, Validation Loss:0.6352630853652954, Validation Acc:74.25, AUC:0.6673254281949934\n",
            "Epoch 0 time: 1.8717\n",
            "Fold:5, Epoch: [2/40], Step: [9/29], Loss: 0.5725578665733337, Validation Loss:0.5976213812828064, Validation Acc:71.25, AUC:0.7187088274044796\n",
            "Fold:5, Epoch: [2/40], Step: [18/29], Loss: 0.5912066929870181, Validation Loss:0.5605137199163437, Validation Acc:72.5, AUC:0.7412384716732543\n",
            "Fold:5, Epoch: [2/40], Step: [27/29], Loss: 0.5864742133352492, Validation Loss:0.5738217383623123, Validation Acc:73.0, AUC:0.7059288537549406\n",
            "Epoch 1 time: 1.8917\n",
            "Fold:5, Epoch: [3/40], Step: [9/29], Loss: 0.5421085755030314, Validation Loss:0.5297151729464531, Validation Acc:75.0, AUC:0.7251646903820818\n",
            "Fold:5, Epoch: [3/40], Step: [18/29], Loss: 0.5478830105728574, Validation Loss:0.5040061548352242, Validation Acc:67.75, AUC:0.7289855072463769\n",
            "Fold:5, Epoch: [3/40], Step: [27/29], Loss: 0.5664037024533307, Validation Loss:0.5695562660694122, Validation Acc:70.75, AUC:0.7005270092226615\n",
            "Epoch 2 time: 1.8928\n",
            "Fold:5, Epoch: [4/40], Step: [9/29], Loss: 0.5300096107853783, Validation Loss:0.540027529001236, Validation Acc:78.75, AUC:0.7163372859025033\n",
            "Fold:5, Epoch: [4/40], Step: [18/29], Loss: 0.5571887890497843, Validation Loss:0.545378565788269, Validation Acc:72.5, AUC:0.7565217391304347\n",
            "Fold:5, Epoch: [4/40], Step: [27/29], Loss: 0.5505194178333989, Validation Loss:0.5695867389440536, Validation Acc:68.25, AUC:0.7089591567852438\n",
            "Epoch 3 time: 1.8949\n",
            "Fold:5, Epoch: [5/40], Step: [9/29], Loss: 0.5267920957671272, Validation Loss:0.5144847854971886, Validation Acc:77.75, AUC:0.7181818181818181\n",
            "Fold:5, Epoch: [5/40], Step: [18/29], Loss: 0.538657933473587, Validation Loss:0.531038224697113, Validation Acc:73.75, AUC:0.7484848484848485\n",
            "Fold:5, Epoch: [5/40], Step: [27/29], Loss: 0.5456420249409146, Validation Loss:0.5991626307368279, Validation Acc:76.0, AUC:0.7156785243741766\n",
            "Epoch 4 time: 1.8771\n",
            "Fold:5, Epoch: [6/40], Step: [9/29], Loss: 0.5200916296905942, Validation Loss:0.5578179806470871, Validation Acc:78.0, AUC:0.7119894598155466\n",
            "Fold:5, Epoch: [6/40], Step: [18/29], Loss: 0.5264317293961843, Validation Loss:0.5147126391530037, Validation Acc:73.5, AUC:0.7546772068511198\n",
            "Fold:5, Epoch: [6/40], Step: [27/29], Loss: 0.5442631288811013, Validation Loss:0.4953320771455765, Validation Acc:74.0, AUC:0.7652173913043478\n",
            "Epoch 5 time: 1.8650\n",
            "Fold:5, Epoch: [7/40], Step: [9/29], Loss: 0.5149097045262655, Validation Loss:0.496281236410141, Validation Acc:73.75, AUC:0.7561264822134387\n",
            "Fold:5, Epoch: [7/40], Step: [18/29], Loss: 0.5330403132571114, Validation Loss:0.5151855275034904, Validation Acc:76.25, AUC:0.7400527009222662\n",
            "Fold:5, Epoch: [7/40], Step: [27/29], Loss: 0.529339485698276, Validation Loss:0.5247343182563782, Validation Acc:75.5, AUC:0.7280632411067194\n",
            "Epoch 6 time: 1.9039\n",
            "Fold:5, Epoch: [8/40], Step: [9/29], Loss: 0.5369322763548957, Validation Loss:0.5769109800457954, Validation Acc:77.25, AUC:0.7229249011857708\n",
            "Fold:5, Epoch: [8/40], Step: [18/29], Loss: 0.5121425059106615, Validation Loss:0.5142590925097466, Validation Acc:77.5, AUC:0.7167325428194993\n",
            "Fold:5, Epoch: [8/40], Step: [27/29], Loss: 0.5233878548498507, Validation Loss:0.5528492778539658, Validation Acc:72.25, AUC:0.7321475625823453\n",
            "Epoch 7 time: 1.8814\n",
            "Fold:5, Epoch: [9/40], Step: [9/29], Loss: 0.5442530347241296, Validation Loss:0.5404975861310959, Validation Acc:71.25, AUC:0.7187088274044796\n",
            "Fold:5, Epoch: [9/40], Step: [18/29], Loss: 0.5376424905326631, Validation Loss:0.4831496924161911, Validation Acc:76.75, AUC:0.7353096179183136\n",
            "Fold:5, Epoch: [9/40], Step: [27/29], Loss: 0.5243827678539135, Validation Loss:0.48855168372392654, Validation Acc:77.25, AUC:0.7229249011857708\n",
            "Epoch 8 time: 1.8925\n",
            "Fold:5, Epoch: [10/40], Step: [9/29], Loss: 0.48023374875386554, Validation Loss:0.5427213981747627, Validation Acc:70.75, AUC:0.7387351778656126\n",
            "Fold:5, Epoch: [10/40], Step: [18/29], Loss: 0.5131714956627952, Validation Loss:0.5024933815002441, Validation Acc:75.0, AUC:0.7480895915678525\n",
            "Fold:5, Epoch: [10/40], Step: [27/29], Loss: 0.5083442893293169, Validation Loss:0.5489778518676758, Validation Acc:80.5, AUC:0.7417654808959157\n",
            "Epoch 9 time: 1.8881\n",
            "Fold:5, Epoch: [11/40], Step: [9/29], Loss: 0.47723930411868626, Validation Loss:0.5149385780096054, Validation Acc:75.75, AUC:0.7447957839262187\n",
            "Fold:5, Epoch: [11/40], Step: [18/29], Loss: 0.5061037275526259, Validation Loss:0.4683711379766464, Validation Acc:67.25, AUC:0.7108036890645587\n",
            "Fold:5, Epoch: [11/40], Step: [27/29], Loss: 0.5069534601988616, Validation Loss:0.606453038752079, Validation Acc:80.0, AUC:0.7235836627140975\n",
            "Epoch 10 time: 1.8856\n",
            "Fold:5, Epoch: [12/40], Step: [9/29], Loss: 0.4989633063475291, Validation Loss:0.45838872343301773, Validation Acc:76.25, AUC:0.7476943346508564\n",
            "Fold:5, Epoch: [12/40], Step: [18/29], Loss: 0.4957653416527642, Validation Loss:0.5173409208655357, Validation Acc:75.0, AUC:0.7480895915678525\n"
          ],
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          "text": [
            "Fold:5, Epoch: [12/40], Step: [27/29], Loss: 0.4957544262762423, Validation Loss:0.5286562442779541, Validation Acc:76.75, AUC:0.7429512516469038\n",
            "Epoch 11 time: 1.8672\n",
            "Fold:5, Epoch: [13/40], Step: [9/29], Loss: 0.4636710782845815, Validation Loss:0.4854790270328522, Validation Acc:76.75, AUC:0.7353096179183136\n",
            "Fold:5, Epoch: [13/40], Step: [18/29], Loss: 0.47456783221827614, Validation Loss:0.5115380808711052, Validation Acc:77.75, AUC:0.7334650856389987\n",
            "Fold:5, Epoch: [13/40], Step: [27/29], Loss: 0.4884812026112168, Validation Loss:0.53199652582407, Validation Acc:68.5, AUC:0.7180500658761528\n",
            "Epoch 12 time: 1.8910\n",
            "Fold:5, Epoch: [14/40], Step: [9/29], Loss: 0.5095437069733938, Validation Loss:0.5390520468354225, Validation Acc:74.75, AUC:0.7313570487483532\n",
            "Fold:5, Epoch: [14/40], Step: [18/29], Loss: 0.48539773457580143, Validation Loss:0.48020850121974945, Validation Acc:77.75, AUC:0.7258234519104085\n",
            "Fold:5, Epoch: [14/40], Step: [27/29], Loss: 0.4863837206805194, Validation Loss:0.44045979529619217, Validation Acc:76.75, AUC:0.7276679841897234\n",
            "Epoch 13 time: 1.8836\n",
            "Fold:5, Epoch: [15/40], Step: [9/29], Loss: 0.4713081320126851, Validation Loss:0.4601814076304436, Validation Acc:77.5, AUC:0.7320158102766798\n",
            "Fold:5, Epoch: [15/40], Step: [18/29], Loss: 0.4755946430895064, Validation Loss:0.5209418386220932, Validation Acc:76.75, AUC:0.7047430830039526\n",
            "Fold:5, Epoch: [15/40], Step: [27/29], Loss: 0.48054028219646877, Validation Loss:0.539730928838253, Validation Acc:78.75, AUC:0.7392621870882741\n",
            "Epoch 14 time: 1.9006\n",
            "Fold:5, Epoch: [16/40], Step: [9/29], Loss: 0.4838443994522095, Validation Loss:0.44860105961561203, Validation Acc:75.75, AUC:0.752437417654809\n",
            "Fold:5, Epoch: [16/40], Step: [18/29], Loss: 0.48551366726557416, Validation Loss:0.5594544485211372, Validation Acc:75.75, AUC:0.7295125164690383\n",
            "Fold:5, Epoch: [16/40], Step: [27/29], Loss: 0.48192120039904557, Validation Loss:0.5002576038241386, Validation Acc:72.0, AUC:0.7612648221343873\n",
            "Epoch 15 time: 1.8767\n",
            "Fold:5, Epoch: [17/40], Step: [9/29], Loss: 0.4764951268831889, Validation Loss:0.5100483596324921, Validation Acc:80.75, AUC:0.7202898550724637\n",
            "Fold:5, Epoch: [17/40], Step: [18/29], Loss: 0.4666878663831287, Validation Loss:0.4552506357431412, Validation Acc:75.25, AUC:0.7189723320158103\n",
            "Fold:5, Epoch: [17/40], Step: [27/29], Loss: 0.4624759919113583, Validation Loss:0.5848707035183907, Validation Acc:69.5, AUC:0.7620553359683795\n",
            "Epoch 16 time: 1.8769\n",
            "Fold:5, Epoch: [18/40], Step: [9/29], Loss: 0.45817887783050537, Validation Loss:0.5343113839626312, Validation Acc:81.5, AUC:0.7322793148880105\n",
            "Fold:5, Epoch: [18/40], Step: [18/29], Loss: 0.4587618129120933, Validation Loss:0.4629395678639412, Validation Acc:77.25, AUC:0.7534914361001318\n",
            "Fold:5, Epoch: [18/40], Step: [27/29], Loss: 0.47222321452917876, Validation Loss:0.5098663046956062, Validation Acc:72.75, AUC:0.7579710144927536\n",
            "Epoch 17 time: 1.8741\n",
            "Fold:5, Epoch: [19/40], Step: [9/29], Loss: 0.46494186255666947, Validation Loss:0.5109603479504585, Validation Acc:76.0, AUC:0.7462450592885377\n",
            "Fold:5, Epoch: [19/40], Step: [18/29], Loss: 0.45195258326000637, Validation Loss:0.4848966598510742, Validation Acc:74.25, AUC:0.743741765480896\n",
            "Fold:5, Epoch: [19/40], Step: [27/29], Loss: 0.4543571549433249, Validation Loss:0.4475306272506714, Validation Acc:79.0, AUC:0.7407114624505929\n",
            "Epoch 18 time: 1.8849\n",
            "Fold:5, Epoch: [20/40], Step: [9/29], Loss: 0.43491020798683167, Validation Loss:0.5019880309700966, Validation Acc:74.75, AUC:0.7237154150197629\n",
            "Fold:5, Epoch: [20/40], Step: [18/29], Loss: 0.4529593288898468, Validation Loss:0.48400431871414185, Validation Acc:78.0, AUC:0.7501976284584979\n",
            "Fold:5, Epoch: [20/40], Step: [27/29], Loss: 0.46066198967121263, Validation Loss:0.5256171450018883, Validation Acc:79.0, AUC:0.7254281949934123\n",
            "Epoch 19 time: 1.9005\n",
            "Fold:5, Epoch: [21/40], Step: [9/29], Loss: 0.45589524176385665, Validation Loss:0.4773738235235214, Validation Acc:71.25, AUC:0.7187088274044796\n",
            "Fold:5, Epoch: [21/40], Step: [18/29], Loss: 0.4549194839265611, Validation Loss:0.5026412233710289, Validation Acc:77.5, AUC:0.7625823451910408\n",
            "Fold:5, Epoch: [21/40], Step: [27/29], Loss: 0.46320412887467277, Validation Loss:0.5368165597319603, Validation Acc:72.5, AUC:0.7641633728590251\n",
            "Epoch 20 time: 1.9248\n",
            "Fold:5, Epoch: [22/40], Step: [9/29], Loss: 0.45488356550534564, Validation Loss:0.57717564702034, Validation Acc:75.5, AUC:0.7433465085638999\n",
            "Fold:5, Epoch: [22/40], Step: [18/29], Loss: 0.45412393245432114, Validation Loss:0.4892065227031708, Validation Acc:74.0, AUC:0.7652173913043478\n",
            "Fold:5, Epoch: [22/40], Step: [27/29], Loss: 0.45021873160644815, Validation Loss:0.4922693148255348, Validation Acc:77.0, AUC:0.7444005270092227\n",
            "Epoch 21 time: 1.8827\n",
            "Fold:5, Epoch: [23/40], Step: [9/29], Loss: 0.4368732538488176, Validation Loss:0.49087081104516983, Validation Acc:80.75, AUC:0.7432147562582345\n",
            "Fold:5, Epoch: [23/40], Step: [18/29], Loss: 0.44300763143433464, Validation Loss:0.45566439628601074, Validation Acc:74.0, AUC:0.7499341238471674\n",
            "Fold:5, Epoch: [23/40], Step: [27/29], Loss: 0.44106672428272387, Validation Loss:0.48322541266679764, Validation Acc:77.75, AUC:0.77167325428195\n",
            "Epoch 22 time: 1.9317\n",
            "Fold:5, Epoch: [24/40], Step: [9/29], Loss: 0.4455797142452664, Validation Loss:0.42229006811976433, Validation Acc:76.75, AUC:0.7123847167325428\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:5, Epoch: [24/40], Step: [18/29], Loss: 0.4289906985229916, Validation Loss:0.5570736676454544, Validation Acc:74.0, AUC:0.7728590250329381\n",
            "Fold:5, Epoch: [24/40], Step: [27/29], Loss: 0.43138832185003495, Validation Loss:0.5288577899336815, Validation Acc:77.25, AUC:0.7611330698287221\n",
            "Epoch 23 time: 1.8895\n",
            "Fold:5, Epoch: [25/40], Step: [9/29], Loss: 0.440896964735455, Validation Loss:0.44982901960611343, Validation Acc:79.25, AUC:0.749802371541502\n",
            "Fold:5, Epoch: [25/40], Step: [18/29], Loss: 0.4335771898428599, Validation Loss:0.46109338104724884, Validation Acc:72.75, AUC:0.7579710144927536\n",
            "Fold:5, Epoch: [25/40], Step: [27/29], Loss: 0.428626952347932, Validation Loss:0.48671403527259827, Validation Acc:77.5, AUC:0.7320158102766798\n",
            "Epoch 24 time: 1.8782\n",
            "Fold:5, Epoch: [26/40], Step: [9/29], Loss: 0.4406403965420193, Validation Loss:0.4692898690700531, Validation Acc:75.75, AUC:0.7600790513833993\n",
            "Fold:5, Epoch: [26/40], Step: [18/29], Loss: 0.42322784331109786, Validation Loss:0.5675060749053955, Validation Acc:84.0, AUC:0.7009222661396574\n",
            "Fold:5, Epoch: [26/40], Step: [27/29], Loss: 0.4295760006816299, Validation Loss:0.471446692943573, Validation Acc:77.5, AUC:0.7549407114624506\n",
            "Epoch 25 time: 1.8727\n",
            "Fold:5, Epoch: [27/40], Step: [9/29], Loss: 0.4500608311759101, Validation Loss:0.4632488787174225, Validation Acc:76.75, AUC:0.7353096179183136\n",
            "Fold:5, Epoch: [27/40], Step: [18/29], Loss: 0.42442987693680656, Validation Loss:0.5697703287005424, Validation Acc:73.25, AUC:0.7455862977602108\n",
            "Fold:5, Epoch: [27/40], Step: [27/29], Loss: 0.4296125129417137, Validation Loss:0.47427307069301605, Validation Acc:77.0, AUC:0.752042160737813\n",
            "Epoch 26 time: 1.8889\n",
            "Fold:5, Epoch: [28/40], Step: [9/29], Loss: 0.4263697763284047, Validation Loss:0.5150527209043503, Validation Acc:75.25, AUC:0.7266139657444005\n",
            "Fold:5, Epoch: [28/40], Step: [18/29], Loss: 0.4210648536682129, Validation Loss:0.6280325353145599, Validation Acc:80.0, AUC:0.7541501976284585\n",
            "Fold:5, Epoch: [28/40], Step: [27/29], Loss: 0.4159336608869058, Validation Loss:0.5203403159976006, Validation Acc:78.75, AUC:0.7469038208168642\n",
            "Epoch 27 time: 1.8804\n",
            "Fold:5, Epoch: [29/40], Step: [9/29], Loss: 0.4225936366452111, Validation Loss:0.4563228636980057, Validation Acc:72.5, AUC:0.7488801054018445\n",
            "Fold:5, Epoch: [29/40], Step: [18/29], Loss: 0.4202081908782323, Validation Loss:0.47263652831315994, Validation Acc:76.0, AUC:0.7538866930171277\n",
            "Fold:5, Epoch: [29/40], Step: [27/29], Loss: 0.41110012818265845, Validation Loss:0.45823971927165985, Validation Acc:79.5, AUC:0.789459815546772\n",
            "Epoch 28 time: 1.8605\n",
            "Fold:5, Epoch: [30/40], Step: [9/29], Loss: 0.41997428403960335, Validation Loss:0.5573837459087372, Validation Acc:81.5, AUC:0.7552042160737814\n",
            "Fold:5, Epoch: [30/40], Step: [18/29], Loss: 0.4030536843670739, Validation Loss:0.4352191239595413, Validation Acc:80.75, AUC:0.7737812911725955\n",
            "Fold:5, Epoch: [30/40], Step: [27/29], Loss: 0.4008855908005326, Validation Loss:0.4666823148727417, Validation Acc:78.25, AUC:0.7822134387351778\n",
            "Epoch 29 time: 1.8936\n",
            "Fold:5, Epoch: [31/40], Step: [9/29], Loss: 0.41829124093055725, Validation Loss:0.46735113859176636, Validation Acc:77.5, AUC:0.7625823451910408\n",
            "Fold:5, Epoch: [31/40], Step: [18/29], Loss: 0.4008180499076843, Validation Loss:0.4530911073088646, Validation Acc:74.25, AUC:0.7361001317523057\n",
            "Fold:5, Epoch: [31/40], Step: [27/29], Loss: 0.40782528232645104, Validation Loss:0.46370673179626465, Validation Acc:80.5, AUC:0.7494071146245059\n",
            "Epoch 30 time: 1.8897\n",
            "Fold:5, Epoch: [32/40], Step: [9/29], Loss: 0.39132193062040543, Validation Loss:0.44500670209527016, Validation Acc:78.25, AUC:0.7516469038208169\n",
            "Fold:5, Epoch: [32/40], Step: [18/29], Loss: 0.39355696572197807, Validation Loss:0.5233027935028076, Validation Acc:76.0, AUC:0.7538866930171277\n",
            "Fold:5, Epoch: [32/40], Step: [27/29], Loss: 0.3944881735024629, Validation Loss:0.5029160529375076, Validation Acc:78.25, AUC:0.7287220026350462\n",
            "Epoch 31 time: 1.8737\n",
            "Fold:5, Epoch: [33/40], Step: [9/29], Loss: 0.3985314534770118, Validation Loss:0.5321388617157936, Validation Acc:76.0, AUC:0.7462450592885377\n",
            "Fold:5, Epoch: [33/40], Step: [18/29], Loss: 0.38380296859476304, Validation Loss:0.5206582546234131, Validation Acc:80.75, AUC:0.7661396574440053\n",
            "Fold:5, Epoch: [33/40], Step: [27/29], Loss: 0.38718393555393926, Validation Loss:0.48659319430589676, Validation Acc:78.25, AUC:0.7363636363636364\n",
            "Epoch 32 time: 1.8689\n",
            "Fold:5, Epoch: [34/40], Step: [9/29], Loss: 0.36426177952024674, Validation Loss:0.5033364221453667, Validation Acc:75.5, AUC:0.7586297760210804\n",
            "Fold:5, Epoch: [34/40], Step: [18/29], Loss: 0.370052319433954, Validation Loss:0.5140598118305206, Validation Acc:75.75, AUC:0.7371541501976284\n",
            "Fold:5, Epoch: [34/40], Step: [27/29], Loss: 0.3823129578872963, Validation Loss:0.461929339915514, Validation Acc:79.0, AUC:0.7789196310935442\n",
            "Epoch 33 time: 1.9022\n",
            "Fold:5, Epoch: [35/40], Step: [9/29], Loss: 0.3727293974823422, Validation Loss:0.48433617502450943, Validation Acc:77.25, AUC:0.7611330698287221\n",
            "Fold:5, Epoch: [35/40], Step: [18/29], Loss: 0.36883147888713413, Validation Loss:0.5572770461440086, Validation Acc:81.75, AUC:0.7184453227931489\n",
            "Fold:5, Epoch: [35/40], Step: [27/29], Loss: 0.3732418980863359, Validation Loss:0.47898661345243454, Validation Acc:76.5, AUC:0.7567852437417655\n",
            "Epoch 34 time: 1.8831\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:5, Epoch: [36/40], Step: [9/29], Loss: 0.37091665466626483, Validation Loss:0.5096260458230972, Validation Acc:79.5, AUC:0.7665349143610012\n",
            "Fold:5, Epoch: [36/40], Step: [18/29], Loss: 0.3683970123529434, Validation Loss:0.494676373898983, Validation Acc:83.5, AUC:0.7667984189723321\n",
            "Fold:5, Epoch: [36/40], Step: [27/29], Loss: 0.36544687549273175, Validation Loss:0.49252423271536827, Validation Acc:81.0, AUC:0.7446640316205534\n",
            "Epoch 35 time: 1.8934\n",
            "Fold:5, Epoch: [37/40], Step: [9/29], Loss: 0.36505796843104893, Validation Loss:0.5962687358260155, Validation Acc:72.75, AUC:0.7274044795783926\n",
            "Fold:5, Epoch: [37/40], Step: [18/29], Loss: 0.34653113451268935, Validation Loss:0.5676888674497604, Validation Acc:79.0, AUC:0.7177865612648221\n",
            "Fold:5, Epoch: [37/40], Step: [27/29], Loss: 0.36333241286101164, Validation Loss:0.6631342098116875, Validation Acc:77.0, AUC:0.7596837944664032\n",
            "Epoch 36 time: 1.8907\n",
            "Fold:5, Epoch: [38/40], Step: [9/29], Loss: 0.3777097596062554, Validation Loss:0.5986148193478584, Validation Acc:79.75, AUC:0.7374176548089592\n",
            "Fold:5, Epoch: [38/40], Step: [18/29], Loss: 0.3524517011311319, Validation Loss:0.5006041824817657, Validation Acc:80.5, AUC:0.7111989459815546\n",
            "Fold:5, Epoch: [38/40], Step: [27/29], Loss: 0.34725865335376177, Validation Loss:0.5098735615611076, Validation Acc:79.25, AUC:0.7574440052700921\n",
            "Epoch 37 time: 1.8848\n",
            "Fold:5, Epoch: [39/40], Step: [9/29], Loss: 0.362907071908315, Validation Loss:0.5447517782449722, Validation Acc:73.5, AUC:0.7546772068511198\n",
            "Fold:5, Epoch: [39/40], Step: [18/29], Loss: 0.3660238848792182, Validation Loss:0.5464328229427338, Validation Acc:78.0, AUC:0.7425559947299079\n",
            "Fold:5, Epoch: [39/40], Step: [27/29], Loss: 0.3562487209284747, Validation Loss:0.5797229558229446, Validation Acc:78.5, AUC:0.7454545454545455\n",
            "Epoch 38 time: 1.8984\n",
            "Fold:5, Epoch: [40/40], Step: [9/29], Loss: 0.34485948582490283, Validation Loss:0.5155058354139328, Validation Acc:76.5, AUC:0.7491436100131753\n",
            "Fold:5, Epoch: [40/40], Step: [18/29], Loss: 0.3378918742140134, Validation Loss:0.5691868588328362, Validation Acc:83.25, AUC:0.7194993412384716\n",
            "Fold:5, Epoch: [40/40], Step: [27/29], Loss: 0.3347112155622906, Validation Loss:0.5080836340785027, Validation Acc:79.75, AUC:0.7374176548089592\n",
            "Epoch 39 time: 1.8881\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:6, Epoch: [1/40], Step: [9/29], Loss: 0.7066651384035746, Validation Loss:0.6866744160652161, Validation Acc:44.75, AUC:0.5727272727272728\n",
            "Fold:6, Epoch: [1/40], Step: [18/29], Loss: 0.68229541182518, Validation Loss:0.6718823462724686, Validation Acc:71.0, AUC:0.663768115942029\n",
            "Fold:6, Epoch: [1/40], Step: [27/29], Loss: 0.6565897994571261, Validation Loss:0.6388621777296066, Validation Acc:75.0, AUC:0.6793148880105402\n",
            "Epoch 0 time: 1.8939\n",
            "Fold:6, Epoch: [2/40], Step: [9/29], Loss: 0.5791283580991957, Validation Loss:0.5929653346538544, Validation Acc:74.5, AUC:0.6993412384716732\n",
            "Fold:6, Epoch: [2/40], Step: [18/29], Loss: 0.5941910213894315, Validation Loss:0.5807497203350067, Validation Acc:78.0, AUC:0.719631093544137\n",
            "Fold:6, Epoch: [2/40], Step: [27/29], Loss: 0.5838209247147595, Validation Loss:0.554600179195404, Validation Acc:75.25, AUC:0.71133069828722\n",
            "Epoch 1 time: 1.8670\n",
            "Fold:6, Epoch: [3/40], Step: [9/29], Loss: 0.5625186794333987, Validation Loss:0.5044202655553818, Validation Acc:73.75, AUC:0.7179183135704875\n",
            "Fold:6, Epoch: [3/40], Step: [18/29], Loss: 0.5662386086252, Validation Loss:0.581941083073616, Validation Acc:74.0, AUC:0.7346508563899868\n",
            "Fold:6, Epoch: [3/40], Step: [27/29], Loss: 0.5606136322021484, Validation Loss:0.5328844785690308, Validation Acc:71.0, AUC:0.7096179183135705\n",
            "Epoch 2 time: 1.8747\n",
            "Fold:6, Epoch: [4/40], Step: [9/29], Loss: 0.5463141004244486, Validation Loss:0.5795176476240158, Validation Acc:72.25, AUC:0.693939393939394\n",
            "Fold:6, Epoch: [4/40], Step: [18/29], Loss: 0.5417360183265474, Validation Loss:0.5055086389183998, Validation Acc:70.0, AUC:0.7267457180500658\n",
            "Fold:6, Epoch: [4/40], Step: [27/29], Loss: 0.5469097174980022, Validation Loss:0.5629159063100815, Validation Acc:69.25, AUC:0.7223978919631093\n",
            "Epoch 3 time: 1.8812\n",
            "Fold:6, Epoch: [5/40], Step: [9/29], Loss: 0.5419556829664443, Validation Loss:0.5201388821005821, Validation Acc:68.5, AUC:0.7486166007905138\n",
            "Fold:6, Epoch: [5/40], Step: [18/29], Loss: 0.5442164871427748, Validation Loss:0.5320267304778099, Validation Acc:67.5, AUC:0.696969696969697\n",
            "Fold:6, Epoch: [5/40], Step: [27/29], Loss: 0.5436754922072092, Validation Loss:0.5270285829901695, Validation Acc:74.0, AUC:0.7117259552042161\n",
            "Epoch 4 time: 1.8787\n",
            "Fold:6, Epoch: [6/40], Step: [9/29], Loss: 0.5056945780913035, Validation Loss:0.5201843082904816, Validation Acc:77.5, AUC:0.7472990777338603\n",
            "Fold:6, Epoch: [6/40], Step: [18/29], Loss: 0.5178720239135954, Validation Loss:0.48558859527111053, Validation Acc:71.5, AUC:0.7354413702239789\n",
            "Fold:6, Epoch: [6/40], Step: [27/29], Loss: 0.5293558449656875, Validation Loss:0.5874543488025665, Validation Acc:70.0, AUC:0.6808959156785244\n",
            "Epoch 5 time: 1.8834\n",
            "Fold:6, Epoch: [7/40], Step: [9/29], Loss: 0.5278919968340132, Validation Loss:0.5123745277523994, Validation Acc:69.75, AUC:0.6947299077733862\n",
            "Fold:6, Epoch: [7/40], Step: [18/29], Loss: 0.512938411699401, Validation Loss:0.5261400192975998, Validation Acc:75.0, AUC:0.7251646903820818\n",
            "Fold:6, Epoch: [7/40], Step: [27/29], Loss: 0.5197927863509567, Validation Loss:0.5154828727245331, Validation Acc:75.25, AUC:0.7495388669301714\n",
            "Epoch 6 time: 1.8598\n",
            "Fold:6, Epoch: [8/40], Step: [9/29], Loss: 0.503091788954205, Validation Loss:0.507111944258213, Validation Acc:72.0, AUC:0.7230566534914361\n",
            "Fold:6, Epoch: [8/40], Step: [18/29], Loss: 0.5207589368025461, Validation Loss:0.49285978823900223, Validation Acc:79.25, AUC:0.7345191040843215\n",
            "Fold:6, Epoch: [8/40], Step: [27/29], Loss: 0.5190044542153677, Validation Loss:0.5484205782413483, Validation Acc:74.5, AUC:0.722266139657444\n",
            "Epoch 7 time: 1.8669\n",
            "Fold:6, Epoch: [9/40], Step: [9/29], Loss: 0.50653033124076, Validation Loss:0.5240957364439964, Validation Acc:68.0, AUC:0.7304347826086957\n",
            "Fold:6, Epoch: [9/40], Step: [18/29], Loss: 0.515827500157886, Validation Loss:0.5310495868325233, Validation Acc:69.5, AUC:0.7391304347826089\n",
            "Fold:6, Epoch: [9/40], Step: [27/29], Loss: 0.5124152589727331, Validation Loss:0.6338940337300301, Validation Acc:79.0, AUC:0.7330698287220027\n",
            "Epoch 8 time: 1.8735\n",
            "Fold:6, Epoch: [10/40], Step: [9/29], Loss: 0.5035863154464297, Validation Loss:0.5438604354858398, Validation Acc:70.75, AUC:0.7234519104084323\n",
            "Fold:6, Epoch: [10/40], Step: [18/29], Loss: 0.4943403999010722, Validation Loss:0.46500394493341446, Validation Acc:75.75, AUC:0.7295125164690383\n",
            "Fold:6, Epoch: [10/40], Step: [27/29], Loss: 0.5010876169911137, Validation Loss:0.48195502907037735, Validation Acc:72.0, AUC:0.7383399209486166\n",
            "Epoch 9 time: 1.8756\n",
            "Fold:6, Epoch: [11/40], Step: [9/29], Loss: 0.4686283833450741, Validation Loss:0.46449222788214684, Validation Acc:77.75, AUC:0.7411067193675889\n",
            "Fold:6, Epoch: [11/40], Step: [18/29], Loss: 0.4783179859320323, Validation Loss:0.544971838593483, Validation Acc:75.5, AUC:0.7586297760210804\n",
            "Fold:6, Epoch: [11/40], Step: [27/29], Loss: 0.49434588573597094, Validation Loss:0.52507234364748, Validation Acc:67.5, AUC:0.7122529644268774\n",
            "Epoch 10 time: 1.8736\n",
            "Fold:6, Epoch: [12/40], Step: [9/29], Loss: 0.47158022390471566, Validation Loss:0.558312863111496, Validation Acc:78.0, AUC:0.719631093544137\n",
            "Fold:6, Epoch: [12/40], Step: [18/29], Loss: 0.5001552138063643, Validation Loss:0.5424060076475143, Validation Acc:77.75, AUC:0.6952569169960474\n",
            "Fold:6, Epoch: [12/40], Step: [27/29], Loss: 0.4900214782467595, Validation Loss:0.5107444077730179, Validation Acc:76.5, AUC:0.7415019762845849\n",
            "Epoch 11 time: 1.8697\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:6, Epoch: [13/40], Step: [9/29], Loss: 0.4809192253483666, Validation Loss:0.5533819198608398, Validation Acc:67.75, AUC:0.7213438735177865\n",
            "Fold:6, Epoch: [13/40], Step: [18/29], Loss: 0.4953445858425564, Validation Loss:0.5394679009914398, Validation Acc:76.0, AUC:0.7233201581027668\n",
            "Fold:6, Epoch: [13/40], Step: [27/29], Loss: 0.4884067486833643, Validation Loss:0.5641774758696556, Validation Acc:80.5, AUC:0.7417654808959157\n",
            "Epoch 12 time: 1.8636\n",
            "Fold:6, Epoch: [14/40], Step: [9/29], Loss: 0.4687102933724721, Validation Loss:0.6556596979498863, Validation Acc:76.5, AUC:0.7491436100131753\n",
            "Fold:6, Epoch: [14/40], Step: [18/29], Loss: 0.46733389298121136, Validation Loss:0.5183191075921059, Validation Acc:74.0, AUC:0.7346508563899868\n",
            "Fold:6, Epoch: [14/40], Step: [27/29], Loss: 0.4728092650572459, Validation Loss:0.6123722344636917, Validation Acc:62.25, AUC:0.7200263504611331\n",
            "Epoch 13 time: 1.8887\n",
            "Fold:6, Epoch: [15/40], Step: [9/29], Loss: 0.4776345756318834, Validation Loss:0.5257756114006042, Validation Acc:80.75, AUC:0.7050065876152831\n",
            "Fold:6, Epoch: [15/40], Step: [18/29], Loss: 0.4707774387465583, Validation Loss:0.6151989549398422, Validation Acc:72.25, AUC:0.724505928853755\n",
            "Fold:6, Epoch: [15/40], Step: [27/29], Loss: 0.48161493296976443, Validation Loss:0.5672448873519897, Validation Acc:76.75, AUC:0.7123847167325428\n",
            "Epoch 14 time: 1.9163\n",
            "Fold:6, Epoch: [16/40], Step: [9/29], Loss: 0.4805482195483314, Validation Loss:0.5204988420009613, Validation Acc:73.5, AUC:0.7317523056653492\n",
            "Fold:6, Epoch: [16/40], Step: [18/29], Loss: 0.482652732067638, Validation Loss:0.49468912929296494, Validation Acc:77.5, AUC:0.7472990777338603\n",
            "Fold:6, Epoch: [16/40], Step: [27/29], Loss: 0.48031206484194156, Validation Loss:0.6867358237504959, Validation Acc:62.75, AUC:0.6923583662714097\n",
            "Epoch 15 time: 1.8772\n",
            "Fold:6, Epoch: [17/40], Step: [9/29], Loss: 0.47152288754781085, Validation Loss:0.5293233171105385, Validation Acc:81.5, AUC:0.6940711462450593\n",
            "Fold:6, Epoch: [17/40], Step: [18/29], Loss: 0.4742782215277354, Validation Loss:0.5069081336259842, Validation Acc:72.75, AUC:0.7350461133069829\n",
            "Fold:6, Epoch: [17/40], Step: [27/29], Loss: 0.4740441684369688, Validation Loss:0.49078305810689926, Validation Acc:74.5, AUC:0.7146245059288537\n",
            "Epoch 16 time: 1.8837\n",
            "Fold:6, Epoch: [18/40], Step: [9/29], Loss: 0.4417375392383999, Validation Loss:0.5790683552622795, Validation Acc:74.5, AUC:0.7069828722002636\n",
            "Fold:6, Epoch: [18/40], Step: [18/29], Loss: 0.46166212691201103, Validation Loss:0.5841168388724327, Validation Acc:69.75, AUC:0.7100131752305665\n",
            "Fold:6, Epoch: [18/40], Step: [27/29], Loss: 0.45158916270291366, Validation Loss:0.4783152788877487, Validation Acc:77.5, AUC:0.7320158102766798\n",
            "Epoch 17 time: 1.8799\n",
            "Fold:6, Epoch: [19/40], Step: [9/29], Loss: 0.4680347144603729, Validation Loss:0.5559637546539307, Validation Acc:74.0, AUC:0.7040843214756258\n",
            "Fold:6, Epoch: [19/40], Step: [18/29], Loss: 0.4429616116815143, Validation Loss:0.5734096765518188, Validation Acc:72.5, AUC:0.7106719367588933\n",
            "Fold:6, Epoch: [19/40], Step: [27/29], Loss: 0.4430892986279947, Validation Loss:0.5069668740034103, Validation Acc:78.75, AUC:0.7469038208168642\n",
            "Epoch 18 time: 1.8603\n",
            "Fold:6, Epoch: [20/40], Step: [9/29], Loss: 0.46717964940600926, Validation Loss:0.5436559543013573, Validation Acc:64.75, AUC:0.7268774703557312\n",
            "Fold:6, Epoch: [20/40], Step: [18/29], Loss: 0.4585195779800415, Validation Loss:0.5748531892895699, Validation Acc:80.75, AUC:0.7279314888010541\n",
            "Fold:6, Epoch: [20/40], Step: [27/29], Loss: 0.45082968914950333, Validation Loss:0.6666310057044029, Validation Acc:80.0, AUC:0.7006587615283267\n",
            "Epoch 19 time: 1.9158\n",
            "Fold:6, Epoch: [21/40], Step: [9/29], Loss: 0.4379842182000478, Validation Loss:0.49033303558826447, Validation Acc:77.25, AUC:0.7534914361001318\n",
            "Fold:6, Epoch: [21/40], Step: [18/29], Loss: 0.4287771417034997, Validation Loss:0.522397443652153, Validation Acc:75.5, AUC:0.7280632411067194\n",
            "Fold:6, Epoch: [21/40], Step: [27/29], Loss: 0.43162380545227613, Validation Loss:0.5761982500553131, Validation Acc:70.5, AUC:0.7372859025032937\n",
            "Epoch 20 time: 1.8739\n",
            "Fold:6, Epoch: [22/40], Step: [9/29], Loss: 0.43358679281340706, Validation Loss:0.5078411623835564, Validation Acc:78.75, AUC:0.7163372859025033\n",
            "Fold:6, Epoch: [22/40], Step: [18/29], Loss: 0.4340204762087928, Validation Loss:0.5525164902210236, Validation Acc:74.0, AUC:0.7346508563899868\n",
            "Fold:6, Epoch: [22/40], Step: [27/29], Loss: 0.43561149747283373, Validation Loss:0.5131485499441624, Validation Acc:79.25, AUC:0.719235836627141\n",
            "Epoch 21 time: 1.8869\n",
            "Fold:6, Epoch: [23/40], Step: [9/29], Loss: 0.40372686253653634, Validation Loss:0.5167700350284576, Validation Acc:77.5, AUC:0.7320158102766798\n",
            "Fold:6, Epoch: [23/40], Step: [18/29], Loss: 0.42928196986516315, Validation Loss:0.5545507818460464, Validation Acc:72.0, AUC:0.7383399209486166\n",
            "Fold:6, Epoch: [23/40], Step: [27/29], Loss: 0.4248383453598729, Validation Loss:0.49958646297454834, Validation Acc:67.75, AUC:0.7366271409749672\n",
            "Epoch 22 time: 1.8890\n",
            "Fold:6, Epoch: [24/40], Step: [9/29], Loss: 0.4256327516502804, Validation Loss:0.5395683273673058, Validation Acc:82.0, AUC:0.7351778656126482\n",
            "Fold:6, Epoch: [24/40], Step: [18/29], Loss: 0.4433489011393653, Validation Loss:0.5319747179746628, Validation Acc:75.0, AUC:0.7557312252964428\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:6, Epoch: [24/40], Step: [27/29], Loss: 0.43224682410558063, Validation Loss:0.5559778138995171, Validation Acc:75.75, AUC:0.7371541501976284\n",
            "Epoch 23 time: 1.8989\n",
            "Fold:6, Epoch: [25/40], Step: [9/29], Loss: 0.4000306162569258, Validation Loss:0.5734555572271347, Validation Acc:76.5, AUC:0.7109354413702239\n",
            "Fold:6, Epoch: [25/40], Step: [18/29], Loss: 0.4026414222187466, Validation Loss:0.49989795684814453, Validation Acc:71.0, AUC:0.7249011857707509\n",
            "Fold:6, Epoch: [25/40], Step: [27/29], Loss: 0.4009003219781099, Validation Loss:0.5519475415349007, Validation Acc:74.5, AUC:0.7299077733860342\n",
            "Epoch 24 time: 1.8666\n",
            "Fold:6, Epoch: [26/40], Step: [9/29], Loss: 0.3890789482328627, Validation Loss:0.6605836898088455, Validation Acc:78.0, AUC:0.7119894598155466\n",
            "Fold:6, Epoch: [26/40], Step: [18/29], Loss: 0.3997513833973143, Validation Loss:0.5895655527710915, Validation Acc:77.75, AUC:0.7258234519104085\n",
            "Fold:6, Epoch: [26/40], Step: [27/29], Loss: 0.40198143544020476, Validation Loss:0.568452812731266, Validation Acc:75.75, AUC:0.7371541501976284\n",
            "Epoch 25 time: 1.8650\n",
            "Fold:6, Epoch: [27/40], Step: [9/29], Loss: 0.37678466240564984, Validation Loss:0.572325311601162, Validation Acc:68.5, AUC:0.7027667984189724\n",
            "Fold:6, Epoch: [27/40], Step: [18/29], Loss: 0.39422902630435097, Validation Loss:0.6233571320772171, Validation Acc:73.25, AUC:0.7379446640316206\n",
            "Fold:6, Epoch: [27/40], Step: [27/29], Loss: 0.39449948293191417, Validation Loss:0.579516164958477, Validation Acc:80.75, AUC:0.6897233201581028\n",
            "Epoch 26 time: 1.8546\n",
            "Fold:6, Epoch: [28/40], Step: [9/29], Loss: 0.3716649247540368, Validation Loss:0.6100785806775093, Validation Acc:71.0, AUC:0.7172595520421607\n",
            "Fold:6, Epoch: [28/40], Step: [18/29], Loss: 0.3792712887128194, Validation Loss:0.6115253195166588, Validation Acc:73.25, AUC:0.7455862977602108\n",
            "Fold:6, Epoch: [28/40], Step: [27/29], Loss: 0.39299736199555574, Validation Loss:0.5452204272150993, Validation Acc:77.25, AUC:0.7076416337285902\n",
            "Epoch 27 time: 1.8779\n",
            "Fold:6, Epoch: [29/40], Step: [9/29], Loss: 0.3857700228691101, Validation Loss:0.5405895411968231, Validation Acc:73.25, AUC:0.7226613965744401\n",
            "Fold:6, Epoch: [29/40], Step: [18/29], Loss: 0.3856276786989636, Validation Loss:0.5913956686854362, Validation Acc:77.75, AUC:0.710540184453228\n",
            "Fold:6, Epoch: [29/40], Step: [27/29], Loss: 0.3950237973972603, Validation Loss:0.6211939677596092, Validation Acc:76.75, AUC:0.7429512516469038\n",
            "Epoch 28 time: 1.8882\n",
            "Fold:6, Epoch: [30/40], Step: [9/29], Loss: 0.3757564988401201, Validation Loss:0.7763340696692467, Validation Acc:79.0, AUC:0.7330698287220027\n",
            "Fold:6, Epoch: [30/40], Step: [18/29], Loss: 0.376398251288467, Validation Loss:0.7304899096488953, Validation Acc:77.25, AUC:0.7534914361001318\n",
            "Fold:6, Epoch: [30/40], Step: [27/29], Loss: 0.3768136252959569, Validation Loss:0.7787608951330185, Validation Acc:76.5, AUC:0.7415019762845849\n",
            "Epoch 29 time: 1.8807\n",
            "Fold:6, Epoch: [31/40], Step: [9/29], Loss: 0.3997867869006263, Validation Loss:0.615183487534523, Validation Acc:65.75, AUC:0.7173913043478262\n",
            "Fold:6, Epoch: [31/40], Step: [18/29], Loss: 0.3790218101607429, Validation Loss:0.7765906304121017, Validation Acc:80.5, AUC:0.6959156785243741\n",
            "Fold:6, Epoch: [31/40], Step: [27/29], Loss: 0.37434206627033373, Validation Loss:0.5943881496787071, Validation Acc:78.75, AUC:0.7316205533596838\n",
            "Epoch 30 time: 1.8791\n",
            "Fold:6, Epoch: [32/40], Step: [9/29], Loss: 0.33873164653778076, Validation Loss:0.5986293144524097, Validation Acc:78.5, AUC:0.7072463768115942\n",
            "Fold:6, Epoch: [32/40], Step: [18/29], Loss: 0.35437308582994675, Validation Loss:0.5915908142924309, Validation Acc:70.0, AUC:0.7343873517786561\n",
            "Fold:6, Epoch: [32/40], Step: [27/29], Loss: 0.3645101752546098, Validation Loss:0.7401165887713432, Validation Acc:77.25, AUC:0.7305665349143611\n",
            "Epoch 31 time: 1.8802\n",
            "Fold:6, Epoch: [33/40], Step: [9/29], Loss: 0.35640693704287213, Validation Loss:0.5463018640875816, Validation Acc:77.5, AUC:0.7396574440052701\n",
            "Fold:6, Epoch: [33/40], Step: [18/29], Loss: 0.3594655046860377, Validation Loss:0.586500309407711, Validation Acc:75.25, AUC:0.7342555994729908\n",
            "Fold:6, Epoch: [33/40], Step: [27/29], Loss: 0.3607871510364391, Validation Loss:0.6661372780799866, Validation Acc:73.75, AUC:0.7408432147562584\n",
            "Epoch 32 time: 1.9010\n",
            "Fold:6, Epoch: [34/40], Step: [9/29], Loss: 0.350391858153873, Validation Loss:0.5972395539283752, Validation Acc:76.5, AUC:0.7262187088274045\n",
            "Fold:6, Epoch: [34/40], Step: [18/29], Loss: 0.348410427570343, Validation Loss:0.6072778403759003, Validation Acc:78.25, AUC:0.7210803689064558\n",
            "Fold:6, Epoch: [34/40], Step: [27/29], Loss: 0.3471289012167189, Validation Loss:0.5972395092248917, Validation Acc:76.0, AUC:0.7462450592885377\n",
            "Epoch 33 time: 1.8829\n",
            "Fold:6, Epoch: [35/40], Step: [9/29], Loss: 0.3755633367432488, Validation Loss:0.5664151459932327, Validation Acc:77.5, AUC:0.7167325428194993\n",
            "Fold:6, Epoch: [35/40], Step: [18/29], Loss: 0.3598703948987855, Validation Loss:0.9334276914596558, Validation Acc:76.75, AUC:0.720026350461133\n",
            "Fold:6, Epoch: [35/40], Step: [27/29], Loss: 0.3539781509726136, Validation Loss:0.6003290712833405, Validation Acc:73.25, AUC:0.7303030303030303\n",
            "Epoch 34 time: 1.8703\n",
            "Fold:6, Epoch: [36/40], Step: [9/29], Loss: 0.3577878342734443, Validation Loss:0.621569998562336, Validation Acc:80.75, AUC:0.7050065876152831\n"
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            "Fold:6, Epoch: [36/40], Step: [18/29], Loss: 0.3552825351556142, Validation Loss:0.7717238441109657, Validation Acc:75.5, AUC:0.7280632411067194\n",
            "Fold:6, Epoch: [36/40], Step: [27/29], Loss: 0.3407148707796026, Validation Loss:0.5609191209077835, Validation Acc:74.5, AUC:0.7069828722002636\n",
            "Epoch 35 time: 1.8676\n",
            "Fold:6, Epoch: [37/40], Step: [9/29], Loss: 0.32591988146305084, Validation Loss:0.6443467661738396, Validation Acc:79.5, AUC:0.6824769433465087\n",
            "Fold:6, Epoch: [37/40], Step: [18/29], Loss: 0.33217110981543857, Validation Loss:0.6758763790130615, Validation Acc:79.5, AUC:0.7130434782608697\n",
            "Fold:6, Epoch: [37/40], Step: [27/29], Loss: 0.3297563581554978, Validation Loss:0.5774878412485123, Validation Acc:75.75, AUC:0.752437417654809\n",
            "Epoch 36 time: 1.8903\n",
            "Fold:6, Epoch: [38/40], Step: [9/29], Loss: 0.31412547992335427, Validation Loss:0.699804812669754, Validation Acc:76.75, AUC:0.7047430830039526\n",
            "Fold:6, Epoch: [38/40], Step: [18/29], Loss: 0.3311423617932532, Validation Loss:0.6299967989325523, Validation Acc:76.75, AUC:0.720026350461133\n",
            "Fold:6, Epoch: [38/40], Step: [27/29], Loss: 0.3271393947027348, Validation Loss:0.6751492694020271, Validation Acc:77.75, AUC:0.7181818181818181\n",
            "Epoch 37 time: 1.8743\n",
            "Fold:6, Epoch: [39/40], Step: [9/29], Loss: 0.30392981734540725, Validation Loss:0.827276200056076, Validation Acc:79.5, AUC:0.7206851119894598\n",
            "Fold:6, Epoch: [39/40], Step: [18/29], Loss: 0.3191928325427903, Validation Loss:0.6802887916564941, Validation Acc:75.5, AUC:0.7280632411067194\n",
            "Fold:6, Epoch: [39/40], Step: [27/29], Loss: 0.32043717470433974, Validation Loss:0.742353692650795, Validation Acc:74.75, AUC:0.7389986824769433\n",
            "Epoch 38 time: 1.8653\n",
            "Fold:6, Epoch: [40/40], Step: [9/29], Loss: 0.3193608952893151, Validation Loss:0.5821389853954315, Validation Acc:76.25, AUC:0.7094861660079052\n",
            "Fold:6, Epoch: [40/40], Step: [18/29], Loss: 0.3039827918012937, Validation Loss:0.6638753116130829, Validation Acc:77.0, AUC:0.690909090909091\n",
            "Fold:6, Epoch: [40/40], Step: [27/29], Loss: 0.3000414294225198, Validation Loss:0.6593035757541656, Validation Acc:77.0, AUC:0.6832674571805006\n",
            "Epoch 39 time: 1.8655\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:7, Epoch: [1/40], Step: [9/29], Loss: 0.7163951595624288, Validation Loss:0.6818359941244125, Validation Acc:85.96491228070175, AUC:0.5290961945031712\n",
            "Fold:7, Epoch: [1/40], Step: [18/29], Loss: 0.6761118339167701, Validation Loss:0.6446041464805603, Validation Acc:84.21052631578948, AUC:0.6258456659619449\n",
            "Fold:7, Epoch: [1/40], Step: [27/29], Loss: 0.6550224864924395, Validation Loss:0.6381391733884811, Validation Acc:81.95488721804512, AUC:0.6967758985200846\n",
            "Epoch 0 time: 1.8697\n",
            "Fold:7, Epoch: [2/40], Step: [9/29], Loss: 0.5644295414288839, Validation Loss:0.5875825881958008, Validation Acc:79.9498746867168, AUC:0.7156976744186048\n",
            "Fold:7, Epoch: [2/40], Step: [18/29], Loss: 0.577107889784707, Validation Loss:0.564705565571785, Validation Acc:74.18546365914787, AUC:0.7204545454545455\n",
            "Fold:7, Epoch: [2/40], Step: [27/29], Loss: 0.5780759652455648, Validation Loss:0.604420006275177, Validation Acc:72.68170426065163, AUC:0.7270084566596194\n",
            "Epoch 1 time: 1.8498\n",
            "Fold:7, Epoch: [3/40], Step: [9/29], Loss: 0.5639993482165866, Validation Loss:0.5662857741117477, Validation Acc:74.18546365914787, AUC:0.7280919661733616\n",
            "Fold:7, Epoch: [3/40], Step: [18/29], Loss: 0.5506831357876459, Validation Loss:0.5627315938472748, Validation Acc:69.92481203007519, AUC:0.7415697674418604\n",
            "Fold:7, Epoch: [3/40], Step: [27/29], Loss: 0.5493199814248968, Validation Loss:0.5368706956505775, Validation Acc:73.1829573934837, AUC:0.7528276955602536\n",
            "Epoch 2 time: 1.8979\n",
            "Fold:7, Epoch: [4/40], Step: [9/29], Loss: 0.5024483899275461, Validation Loss:0.5978810340166092, Validation Acc:75.68922305764411, AUC:0.7139006342494715\n",
            "Fold:7, Epoch: [4/40], Step: [18/29], Loss: 0.5239282068279054, Validation Loss:0.535371445119381, Validation Acc:72.43107769423558, AUC:0.7561046511627907\n",
            "Fold:7, Epoch: [4/40], Step: [27/29], Loss: 0.5376992203571178, Validation Loss:0.5442964136600494, Validation Acc:69.92481203007519, AUC:0.7262949260042284\n",
            "Epoch 3 time: 1.9391\n",
            "Fold:7, Epoch: [5/40], Step: [9/29], Loss: 0.519380701912774, Validation Loss:0.5810804218053818, Validation Acc:73.93483709273183, AUC:0.7113636363636364\n",
            "Fold:7, Epoch: [5/40], Step: [18/29], Loss: 0.5275590651565127, Validation Loss:0.5454899147152901, Validation Acc:77.19298245614036, AUC:0.7531712473572939\n",
            "Fold:7, Epoch: [5/40], Step: [27/29], Loss: 0.5333186884721121, Validation Loss:0.4846634864807129, Validation Acc:71.17794486215539, AUC:0.7564746300211418\n",
            "Epoch 4 time: 1.8814\n",
            "Fold:7, Epoch: [6/40], Step: [9/29], Loss: 0.5363161398304833, Validation Loss:0.564799502491951, Validation Acc:66.66666666666667, AUC:0.7303118393234673\n",
            "Fold:7, Epoch: [6/40], Step: [18/29], Loss: 0.5287453300423093, Validation Loss:0.5314618349075317, Validation Acc:74.68671679197995, AUC:0.769186046511628\n",
            "Fold:7, Epoch: [6/40], Step: [27/29], Loss: 0.5366610134089435, Validation Loss:0.5721465945243835, Validation Acc:75.43859649122807, AUC:0.765909090909091\n",
            "Epoch 5 time: 1.8860\n",
            "Fold:7, Epoch: [7/40], Step: [9/29], Loss: 0.5050905181301965, Validation Loss:0.5326090082526207, Validation Acc:74.43609022556392, AUC:0.7371828752642706\n",
            "Fold:7, Epoch: [7/40], Step: [18/29], Loss: 0.5202279206779268, Validation Loss:0.5243513211607933, Validation Acc:81.203007518797, AUC:0.7840644820295983\n",
            "Fold:7, Epoch: [7/40], Step: [27/29], Loss: 0.5197457560786495, Validation Loss:0.5256577581167221, Validation Acc:76.44110275689223, AUC:0.771723044397463\n",
            "Epoch 6 time: 1.8601\n",
            "Fold:7, Epoch: [8/40], Step: [9/29], Loss: 0.5176446669631534, Validation Loss:0.5633621662855148, Validation Acc:69.17293233082707, AUC:0.7295718816067653\n",
            "Fold:7, Epoch: [8/40], Step: [18/29], Loss: 0.5102825462818146, Validation Loss:0.5432351008057594, Validation Acc:80.45112781954887, AUC:0.7720665961945031\n",
            "Fold:7, Epoch: [8/40], Step: [27/29], Loss: 0.50631058105716, Validation Loss:0.527953527867794, Validation Acc:79.19799498746868, AUC:0.7647991543340381\n",
            "Epoch 7 time: 1.8704\n",
            "Fold:7, Epoch: [9/40], Step: [9/29], Loss: 0.5199562311172485, Validation Loss:0.5193308368325233, Validation Acc:77.69423558897243, AUC:0.7484408033826639\n",
            "Fold:7, Epoch: [9/40], Step: [18/29], Loss: 0.5119474596447415, Validation Loss:0.6111942082643509, Validation Acc:72.93233082706767, AUC:0.7437367864693445\n",
            "Fold:7, Epoch: [9/40], Step: [27/29], Loss: 0.5005007871875057, Validation Loss:0.5442241206765175, Validation Acc:77.44360902255639, AUC:0.7393498942917548\n",
            "Epoch 8 time: 1.8612\n",
            "Fold:7, Epoch: [10/40], Step: [9/29], Loss: 0.5230367150571611, Validation Loss:0.5726957768201828, Validation Acc:77.19298245614036, AUC:0.7378964059196618\n",
            "Fold:7, Epoch: [10/40], Step: [18/29], Loss: 0.5091719428698221, Validation Loss:0.5608426332473755, Validation Acc:79.69924812030075, AUC:0.7447938689217759\n",
            "Fold:7, Epoch: [10/40], Step: [27/29], Loss: 0.49785101965621664, Validation Loss:0.5584520846605301, Validation Acc:70.92731829573935, AUC:0.7397463002114165\n",
            "Epoch 9 time: 1.8583\n",
            "Fold:7, Epoch: [11/40], Step: [9/29], Loss: 0.47397348946995205, Validation Loss:0.5352913811802864, Validation Acc:80.45112781954887, AUC:0.7338794926004228\n",
            "Fold:7, Epoch: [11/40], Step: [18/29], Loss: 0.47217949231465656, Validation Loss:0.5981484204530716, Validation Acc:75.18796992481202, AUC:0.7491807610993658\n",
            "Fold:7, Epoch: [11/40], Step: [27/29], Loss: 0.48849256833394367, Validation Loss:0.5219733268022537, Validation Acc:70.92731829573935, AUC:0.7397463002114165\n",
            "Epoch 10 time: 1.8649\n",
            "Fold:7, Epoch: [12/40], Step: [9/29], Loss: 0.5031134188175201, Validation Loss:0.5608285292983055, Validation Acc:83.20802005012531, AUC:0.7269556025369979\n"
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          "text": [
            "Fold:7, Epoch: [12/40], Step: [18/29], Loss: 0.5127386467324363, Validation Loss:0.5556478798389435, Validation Acc:78.44611528822055, AUC:0.7604386892177589\n",
            "Fold:7, Epoch: [12/40], Step: [27/29], Loss: 0.49610942712536565, Validation Loss:0.5697596371173859, Validation Acc:75.43859649122807, AUC:0.7582716701902749\n",
            "Epoch 11 time: 1.8658\n",
            "Fold:7, Epoch: [13/40], Step: [9/29], Loss: 0.4954243004322052, Validation Loss:0.592347040772438, Validation Acc:68.42105263157895, AUC:0.7252114164904863\n",
            "Fold:7, Epoch: [13/40], Step: [18/29], Loss: 0.4814089404212104, Validation Loss:0.520276166498661, Validation Acc:77.19298245614036, AUC:0.76080866807611\n",
            "Fold:7, Epoch: [13/40], Step: [27/29], Loss: 0.4837316318794533, Validation Loss:0.5423475950956345, Validation Acc:76.94235588972431, AUC:0.759355179704017\n",
            "Epoch 12 time: 1.8717\n",
            "Fold:7, Epoch: [14/40], Step: [9/29], Loss: 0.4435231685638428, Validation Loss:0.5368569940328598, Validation Acc:78.94736842105263, AUC:0.7404334038054968\n",
            "Fold:7, Epoch: [14/40], Step: [18/29], Loss: 0.4743058400021659, Validation Loss:0.5186833441257477, Validation Acc:80.45112781954887, AUC:0.7415169133192389\n",
            "Fold:7, Epoch: [14/40], Step: [27/29], Loss: 0.4748729610884631, Validation Loss:0.5481285750865936, Validation Acc:72.43107769423558, AUC:0.7561046511627907\n",
            "Epoch 13 time: 1.8640\n",
            "Fold:7, Epoch: [15/40], Step: [9/29], Loss: 0.4540266858206855, Validation Loss:0.5187315046787262, Validation Acc:77.19298245614036, AUC:0.7302589852008458\n",
            "Fold:7, Epoch: [15/40], Step: [18/29], Loss: 0.4522213687499364, Validation Loss:0.7789311856031418, Validation Acc:78.69674185463658, AUC:0.761892177589852\n",
            "Fold:7, Epoch: [15/40], Step: [27/29], Loss: 0.4631141159269545, Validation Loss:0.5001579448580742, Validation Acc:77.69423558897243, AUC:0.7331659619450318\n",
            "Epoch 14 time: 1.8861\n",
            "Fold:7, Epoch: [16/40], Step: [9/29], Loss: 0.45515868730015224, Validation Loss:0.597358837723732, Validation Acc:74.43609022556392, AUC:0.7448202959830867\n",
            "Fold:7, Epoch: [16/40], Step: [18/29], Loss: 0.45789306031333077, Validation Loss:0.5201187878847122, Validation Acc:77.94486215538848, AUC:0.7498942917547567\n",
            "Fold:7, Epoch: [16/40], Step: [27/29], Loss: 0.4693631386315381, Validation Loss:0.49860208481550217, Validation Acc:77.69423558897243, AUC:0.75607822410148\n",
            "Epoch 15 time: 1.9100\n",
            "Fold:7, Epoch: [17/40], Step: [9/29], Loss: 0.437847375869751, Validation Loss:0.5486074760556221, Validation Acc:77.94486215538848, AUC:0.7498942917547567\n",
            "Fold:7, Epoch: [17/40], Step: [18/29], Loss: 0.45531371898121303, Validation Loss:0.5239498093724251, Validation Acc:71.42857142857143, AUC:0.7426532769556025\n",
            "Fold:7, Epoch: [17/40], Step: [27/29], Loss: 0.45242173141903347, Validation Loss:0.8267021477222443, Validation Acc:74.93734335839599, AUC:0.7477272727272727\n",
            "Epoch 16 time: 1.8610\n",
            "Fold:7, Epoch: [18/40], Step: [9/29], Loss: 0.4407120280795627, Validation Loss:0.5766220390796661, Validation Acc:73.43358395989975, AUC:0.7619186046511628\n",
            "Fold:7, Epoch: [18/40], Step: [18/29], Loss: 0.43917320834265816, Validation Loss:0.5566670149564743, Validation Acc:67.16791979949875, AUC:0.7332188160676534\n",
            "Fold:7, Epoch: [18/40], Step: [27/29], Loss: 0.45971749005494295, Validation Loss:0.6082525998353958, Validation Acc:81.95488721804512, AUC:0.7578752642706131\n",
            "Epoch 17 time: 1.8425\n",
            "Fold:7, Epoch: [19/40], Step: [9/29], Loss: 0.4441285596953498, Validation Loss:0.5143071934580803, Validation Acc:79.19799498746868, AUC:0.7647991543340381\n",
            "Fold:7, Epoch: [19/40], Step: [18/29], Loss: 0.45622558063930935, Validation Loss:0.5562040656805038, Validation Acc:71.17794486215539, AUC:0.7259249471458774\n",
            "Fold:7, Epoch: [19/40], Step: [27/29], Loss: 0.4516386897475631, Validation Loss:0.5635137781500816, Validation Acc:77.94486215538848, AUC:0.7498942917547567\n",
            "Epoch 18 time: 1.8493\n",
            "Fold:7, Epoch: [20/40], Step: [9/29], Loss: 0.45617828104231095, Validation Loss:0.5099421590566635, Validation Acc:77.94486215538848, AUC:0.7804439746300211\n",
            "Fold:7, Epoch: [20/40], Step: [18/29], Loss: 0.4412835439046224, Validation Loss:0.5917033106088638, Validation Acc:69.42355889724311, AUC:0.7386627906976745\n",
            "Fold:7, Epoch: [20/40], Step: [27/29], Loss: 0.4444910530690794, Validation Loss:0.7777743190526962, Validation Acc:76.94235588972431, AUC:0.7822674418604652\n",
            "Epoch 19 time: 1.8733\n",
            "Fold:7, Epoch: [21/40], Step: [9/29], Loss: 0.4288605815834469, Validation Loss:0.5513307675719261, Validation Acc:77.44360902255639, AUC:0.7698995771670192\n",
            "Fold:7, Epoch: [21/40], Step: [18/29], Loss: 0.4280845671892166, Validation Loss:0.5620761662721634, Validation Acc:75.93984962406014, AUC:0.776453488372093\n",
            "Fold:7, Epoch: [21/40], Step: [27/29], Loss: 0.43051595820320976, Validation Loss:0.5168543346226215, Validation Acc:79.9498746867168, AUC:0.7691596194503171\n",
            "Epoch 20 time: 1.8858\n",
            "Fold:7, Epoch: [22/40], Step: [9/29], Loss: 0.4072141680452559, Validation Loss:0.6682775616645813, Validation Acc:79.69924812030075, AUC:0.7829809725158563\n",
            "Fold:7, Epoch: [22/40], Step: [18/29], Loss: 0.4163876689142651, Validation Loss:0.7722784578800201, Validation Acc:73.43358395989975, AUC:0.7619186046511628\n",
            "Fold:7, Epoch: [22/40], Step: [27/29], Loss: 0.4261601478965194, Validation Loss:0.5515946000814438, Validation Acc:76.19047619047619, AUC:0.77026955602537\n",
            "Epoch 21 time: 1.8782\n",
            "Fold:7, Epoch: [23/40], Step: [9/29], Loss: 0.4161423378520542, Validation Loss:0.5363560989499092, Validation Acc:83.20802005012531, AUC:0.7804175475687103\n",
            "Fold:7, Epoch: [23/40], Step: [18/29], Loss: 0.42184687819745803, Validation Loss:0.5532889738678932, Validation Acc:75.68922305764411, AUC:0.7673625792811839\n"
          ],
          "name": "stdout"
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        {
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          "text": [
            "Fold:7, Epoch: [23/40], Step: [27/29], Loss: 0.4256455147707904, Validation Loss:0.6119913011789322, Validation Acc:73.93483709273183, AUC:0.7342758985200846\n",
            "Epoch 22 time: 1.8792\n",
            "Fold:7, Epoch: [24/40], Step: [9/29], Loss: 0.4381938676039378, Validation Loss:0.5487574860453606, Validation Acc:75.93984962406014, AUC:0.7535412262156449\n",
            "Fold:7, Epoch: [24/40], Step: [18/29], Loss: 0.4396553337574005, Validation Loss:0.5559204965829849, Validation Acc:80.95238095238095, AUC:0.7596987315010572\n",
            "Fold:7, Epoch: [24/40], Step: [27/29], Loss: 0.4240752613102948, Validation Loss:0.5805596485733986, Validation Acc:78.19548872180451, AUC:0.7666226215644821\n",
            "Epoch 23 time: 1.8772\n",
            "Fold:7, Epoch: [25/40], Step: [9/29], Loss: 0.4128195312288072, Validation Loss:0.6497839093208313, Validation Acc:76.94235588972431, AUC:0.774630021141649\n",
            "Fold:7, Epoch: [25/40], Step: [18/29], Loss: 0.4190438720915053, Validation Loss:0.6775840818881989, Validation Acc:75.93984962406014, AUC:0.7688160676532769\n",
            "Fold:7, Epoch: [25/40], Step: [27/29], Loss: 0.41706029463697364, Validation Loss:0.6036466956138611, Validation Acc:72.68170426065163, AUC:0.7499207188160676\n",
            "Epoch 24 time: 1.8575\n",
            "Fold:7, Epoch: [26/40], Step: [9/29], Loss: 0.4091980258623759, Validation Loss:0.5879203453660011, Validation Acc:78.69674185463658, AUC:0.7542547568710359\n",
            "Fold:7, Epoch: [26/40], Step: [18/29], Loss: 0.41889843841393787, Validation Loss:0.5770995914936066, Validation Acc:80.70175438596492, AUC:0.7658826638477801\n",
            "Fold:7, Epoch: [26/40], Step: [27/29], Loss: 0.415743801328871, Validation Loss:0.6452959030866623, Validation Acc:69.17293233082707, AUC:0.7372093023255815\n",
            "Epoch 25 time: 1.8502\n",
            "Fold:7, Epoch: [27/40], Step: [9/29], Loss: 0.4306548833847046, Validation Loss:0.5443172380328178, Validation Acc:78.44611528822055, AUC:0.7604386892177589\n",
            "Fold:7, Epoch: [27/40], Step: [18/29], Loss: 0.41677805450227523, Validation Loss:0.5939985737204552, Validation Acc:78.94736842105263, AUC:0.7786205073995771\n",
            "Fold:7, Epoch: [27/40], Step: [27/29], Loss: 0.405677424536811, Validation Loss:0.7571711540222168, Validation Acc:76.69172932330827, AUC:0.76553911205074\n",
            "Epoch 26 time: 1.8654\n",
            "Fold:7, Epoch: [28/40], Step: [9/29], Loss: 0.39454078012042576, Validation Loss:0.6131731569766998, Validation Acc:79.69924812030075, AUC:0.7600687103594079\n",
            "Fold:7, Epoch: [28/40], Step: [18/29], Loss: 0.3961479134029812, Validation Loss:0.5641294121742249, Validation Acc:76.44110275689223, AUC:0.7488107822410148\n",
            "Fold:7, Epoch: [28/40], Step: [27/29], Loss: 0.39484456291905157, Validation Loss:0.8431455045938492, Validation Acc:81.203007518797, AUC:0.7764270613107822\n",
            "Epoch 27 time: 1.8508\n",
            "Fold:7, Epoch: [29/40], Step: [9/29], Loss: 0.37517956230375504, Validation Loss:0.574910968542099, Validation Acc:77.44360902255639, AUC:0.7546247357293869\n",
            "Fold:7, Epoch: [29/40], Step: [18/29], Loss: 0.38976894484625924, Validation Loss:0.5997408851981163, Validation Acc:77.69423558897243, AUC:0.75607822410148\n",
            "Fold:7, Epoch: [29/40], Step: [27/29], Loss: 0.3924436723744428, Validation Loss:0.7502192482352257, Validation Acc:77.94486215538848, AUC:0.7651691331923891\n",
            "Epoch 28 time: 1.8448\n",
            "Fold:7, Epoch: [30/40], Step: [9/29], Loss: 0.3655955427222782, Validation Loss:0.6099479347467422, Validation Acc:79.4486215538847, AUC:0.7662526427061311\n",
            "Fold:7, Epoch: [30/40], Step: [18/29], Loss: 0.3757343375020557, Validation Loss:0.5639806464314461, Validation Acc:76.19047619047619, AUC:0.77026955602537\n",
            "Fold:7, Epoch: [30/40], Step: [27/29], Loss: 0.38399055710545293, Validation Loss:0.6375562697649002, Validation Acc:75.93984962406014, AUC:0.7459038054968288\n",
            "Epoch 29 time: 1.8503\n",
            "Fold:7, Epoch: [31/40], Step: [9/29], Loss: 0.3853446940581004, Validation Loss:0.6075921431183815, Validation Acc:75.43859649122807, AUC:0.7429968287526427\n",
            "Fold:7, Epoch: [31/40], Step: [18/29], Loss: 0.3829652551147673, Validation Loss:0.5534075126051903, Validation Acc:73.43358395989975, AUC:0.7466437632135307\n",
            "Fold:7, Epoch: [31/40], Step: [27/29], Loss: 0.3817074155365979, Validation Loss:0.7483239620923996, Validation Acc:73.93483709273183, AUC:0.7571881606765327\n",
            "Epoch 30 time: 1.8805\n",
            "Fold:7, Epoch: [32/40], Step: [9/29], Loss: 0.3610862096150716, Validation Loss:0.5705672651529312, Validation Acc:81.45363408521304, AUC:0.747330866807611\n",
            "Fold:7, Epoch: [32/40], Step: [18/29], Loss: 0.36262010865741306, Validation Loss:1.0160829275846481, Validation Acc:79.9498746867168, AUC:0.7844344608879492\n",
            "Fold:7, Epoch: [32/40], Step: [27/29], Loss: 0.37356185140433135, Validation Loss:0.6407013684511185, Validation Acc:75.18796992481202, AUC:0.7568181818181818\n",
            "Epoch 31 time: 1.8681\n",
            "Fold:7, Epoch: [33/40], Step: [9/29], Loss: 0.4006481303109063, Validation Loss:0.5949747636914253, Validation Acc:77.94486215538848, AUC:0.7651691331923891\n",
            "Fold:7, Epoch: [33/40], Step: [18/29], Loss: 0.37742553899685544, Validation Loss:0.5773829333484173, Validation Acc:77.69423558897243, AUC:0.7331659619450318\n",
            "Fold:7, Epoch: [33/40], Step: [27/29], Loss: 0.366564882574258, Validation Loss:0.8203391581773758, Validation Acc:77.94486215538848, AUC:0.757531712473573\n",
            "Epoch 32 time: 1.8623\n",
            "Fold:7, Epoch: [34/40], Step: [9/29], Loss: 0.35315700040923226, Validation Loss:0.6114953234791756, Validation Acc:76.19047619047619, AUC:0.77026955602537\n",
            "Fold:7, Epoch: [34/40], Step: [18/29], Loss: 0.3563668645090527, Validation Loss:0.6273022815585136, Validation Acc:79.69924812030075, AUC:0.752431289640592\n",
            "Fold:7, Epoch: [34/40], Step: [27/29], Loss: 0.35976938958521243, Validation Loss:0.7552975118160248, Validation Acc:77.44360902255639, AUC:0.7698995771670192\n",
            "Epoch 33 time: 1.8623\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:7, Epoch: [35/40], Step: [9/29], Loss: 0.34726988275845844, Validation Loss:0.8784399405121803, Validation Acc:76.94235588972431, AUC:0.7517177589852009\n",
            "Fold:7, Epoch: [35/40], Step: [18/29], Loss: 0.34489089416133034, Validation Loss:0.5993998721241951, Validation Acc:77.69423558897243, AUC:0.7637156448202961\n",
            "Fold:7, Epoch: [35/40], Step: [27/29], Loss: 0.34622946271189936, Validation Loss:0.6447983309626579, Validation Acc:80.45112781954887, AUC:0.7415169133192389\n",
            "Epoch 34 time: 1.8625\n",
            "Fold:7, Epoch: [36/40], Step: [9/29], Loss: 0.33910291062461007, Validation Loss:0.6062608398497105, Validation Acc:74.18546365914787, AUC:0.7433668076109936\n",
            "Fold:7, Epoch: [36/40], Step: [18/29], Loss: 0.34997208250893486, Validation Loss:0.652812547981739, Validation Acc:78.94736842105263, AUC:0.7175211416490487\n",
            "Fold:7, Epoch: [36/40], Step: [27/29], Loss: 0.3510148315517991, Validation Loss:0.7744725942611694, Validation Acc:79.69924812030075, AUC:0.7218816067653278\n",
            "Epoch 35 time: 1.8821\n",
            "Fold:7, Epoch: [37/40], Step: [9/29], Loss: 0.31093704369333053, Validation Loss:0.6321687996387482, Validation Acc:82.45614035087719, AUC:0.7684196617336152\n",
            "Fold:7, Epoch: [37/40], Step: [18/29], Loss: 0.3211645781993866, Validation Loss:0.8362722098827362, Validation Acc:74.68671679197995, AUC:0.7386363636363636\n",
            "Fold:7, Epoch: [37/40], Step: [27/29], Loss: 0.3328217665354411, Validation Loss:0.7914032265543938, Validation Acc:73.6842105263158, AUC:0.7251849894291754\n",
            "Epoch 36 time: 1.8646\n",
            "Fold:7, Epoch: [38/40], Step: [9/29], Loss: 0.3560244639714559, Validation Loss:0.6493339985609055, Validation Acc:77.44360902255639, AUC:0.7317124735729387\n",
            "Fold:7, Epoch: [38/40], Step: [18/29], Loss: 0.34872040980392033, Validation Loss:0.6471481174230576, Validation Acc:83.95989974937343, AUC:0.6931289640591967\n",
            "Fold:7, Epoch: [38/40], Step: [27/29], Loss: 0.3489981989065806, Validation Loss:0.8615835160017014, Validation Acc:75.93984962406014, AUC:0.7688160676532769\n",
            "Epoch 37 time: 1.8762\n",
            "Fold:7, Epoch: [39/40], Step: [9/29], Loss: 0.2979181723462211, Validation Loss:0.6156233996152878, Validation Acc:77.44360902255639, AUC:0.7393498942917548\n",
            "Fold:7, Epoch: [39/40], Step: [18/29], Loss: 0.31572943677504856, Validation Loss:0.8621591702103615, Validation Acc:75.18796992481202, AUC:0.7568181818181818\n",
            "Fold:7, Epoch: [39/40], Step: [27/29], Loss: 0.3158342159456677, Validation Loss:0.8333821594715118, Validation Acc:78.19548872180451, AUC:0.7437103594080338\n",
            "Epoch 38 time: 1.9551\n",
            "Fold:7, Epoch: [40/40], Step: [9/29], Loss: 0.3185207396745682, Validation Loss:0.6505337655544281, Validation Acc:77.94486215538848, AUC:0.7193446088794925\n",
            "Fold:7, Epoch: [40/40], Step: [18/29], Loss: 0.30592115720113117, Validation Loss:1.0442041531205177, Validation Acc:79.69924812030075, AUC:0.7447938689217759\n",
            "Fold:7, Epoch: [40/40], Step: [27/29], Loss: 0.323989177191699, Validation Loss:0.6304542422294617, Validation Acc:75.43859649122807, AUC:0.7353594080338266\n",
            "Epoch 39 time: 1.8680\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:8, Epoch: [1/40], Step: [9/29], Loss: 0.674059530099233, Validation Loss:0.68528813123703, Validation Acc:61.65413533834587, AUC:0.61723044397463\n",
            "Fold:8, Epoch: [1/40], Step: [18/29], Loss: 0.6431714627477858, Validation Loss:0.6759166717529297, Validation Acc:70.17543859649123, AUC:0.6208245243128963\n",
            "Fold:8, Epoch: [1/40], Step: [27/29], Loss: 0.6355500397858797, Validation Loss:0.6354543417692184, Validation Acc:70.17543859649123, AUC:0.5902748414376322\n",
            "Epoch 0 time: 1.8925\n",
            "Fold:8, Epoch: [2/40], Step: [9/29], Loss: 0.5971736311912537, Validation Loss:0.592745840549469, Validation Acc:71.9298245614035, AUC:0.6691860465116279\n",
            "Fold:8, Epoch: [2/40], Step: [18/29], Loss: 0.5710976653628879, Validation Loss:0.5946035385131836, Validation Acc:72.18045112781955, AUC:0.670639534883721\n",
            "Fold:8, Epoch: [2/40], Step: [27/29], Loss: 0.5656859985104313, Validation Loss:0.6717381477355957, Validation Acc:68.92230576441102, AUC:0.6670190274841438\n",
            "Epoch 1 time: 1.8523\n",
            "Fold:8, Epoch: [3/40], Step: [9/29], Loss: 0.534257560968399, Validation Loss:0.664700984954834, Validation Acc:66.41604010025063, AUC:0.6372093023255815\n",
            "Fold:8, Epoch: [3/40], Step: [18/29], Loss: 0.5481165597836176, Validation Loss:0.6263889968395233, Validation Acc:66.41604010025063, AUC:0.6601215644820296\n",
            "Fold:8, Epoch: [3/40], Step: [27/29], Loss: 0.5406589497018743, Validation Loss:0.6737162917852402, Validation Acc:67.66917293233082, AUC:0.6597515856236786\n",
            "Epoch 2 time: 1.8359\n",
            "Fold:8, Epoch: [4/40], Step: [9/29], Loss: 0.5433201028241051, Validation Loss:0.7318587899208069, Validation Acc:67.41854636591479, AUC:0.6277484143763213\n",
            "Fold:8, Epoch: [4/40], Step: [18/29], Loss: 0.5453618533081479, Validation Loss:0.6196727603673935, Validation Acc:69.92481203007519, AUC:0.6346458773784355\n",
            "Fold:8, Epoch: [4/40], Step: [27/29], Loss: 0.5389865107006497, Validation Loss:0.62160674482584, Validation Acc:71.9298245614035, AUC:0.6462737843551797\n",
            "Epoch 3 time: 1.8528\n",
            "Fold:8, Epoch: [5/40], Step: [9/29], Loss: 0.5442385574181875, Validation Loss:0.6356153637170792, Validation Acc:66.16541353383458, AUC:0.6815803382663848\n",
            "Fold:8, Epoch: [5/40], Step: [18/29], Loss: 0.5425171454747518, Validation Loss:0.6718353182077408, Validation Acc:67.16791979949875, AUC:0.6568446088794926\n",
            "Fold:8, Epoch: [5/40], Step: [27/29], Loss: 0.5327242690104025, Validation Loss:0.7725500911474228, Validation Acc:70.17543859649123, AUC:0.6590116279069768\n",
            "Epoch 4 time: 1.8770\n",
            "Fold:8, Epoch: [6/40], Step: [9/29], Loss: 0.5475705034203, Validation Loss:0.7317693680524826, Validation Acc:62.40601503759399, AUC:0.6597780126849894\n",
            "Fold:8, Epoch: [6/40], Step: [18/29], Loss: 0.540639297829734, Validation Loss:0.6573166400194168, Validation Acc:74.93734335839599, AUC:0.6484408033826639\n",
            "Fold:8, Epoch: [6/40], Step: [27/29], Loss: 0.5287378993299272, Validation Loss:0.6338199749588966, Validation Acc:70.67669172932331, AUC:0.6466437632135306\n",
            "Epoch 5 time: 1.8469\n",
            "Fold:8, Epoch: [7/40], Step: [9/29], Loss: 0.506163027551439, Validation Loss:0.5917737185955048, Validation Acc:66.9172932330827, AUC:0.6477536997885835\n",
            "Fold:8, Epoch: [7/40], Step: [18/29], Loss: 0.5027806311845779, Validation Loss:0.5583045184612274, Validation Acc:71.67919799498746, AUC:0.6753699788583509\n",
            "Fold:8, Epoch: [7/40], Step: [27/29], Loss: 0.5168641971217262, Validation Loss:0.6335462182760239, Validation Acc:74.68671679197995, AUC:0.662262156448203\n",
            "Epoch 6 time: 1.8741\n",
            "Fold:8, Epoch: [8/40], Step: [9/29], Loss: 0.5118890371587541, Validation Loss:0.7048432976007462, Validation Acc:70.67669172932331, AUC:0.6695560253699788\n",
            "Fold:8, Epoch: [8/40], Step: [18/29], Loss: 0.49372371865643394, Validation Loss:0.6183215081691742, Validation Acc:70.92731829573935, AUC:0.6786469344608879\n",
            "Fold:8, Epoch: [8/40], Step: [27/29], Loss: 0.505577681241212, Validation Loss:0.5855843052268028, Validation Acc:72.93233082706767, AUC:0.6520877378435518\n",
            "Epoch 7 time: 1.8780\n",
            "Fold:8, Epoch: [9/40], Step: [9/29], Loss: 0.5043929119904836, Validation Loss:0.6383297443389893, Validation Acc:69.67418546365914, AUC:0.6408298097251585\n",
            "Fold:8, Epoch: [9/40], Step: [18/29], Loss: 0.5131880856222577, Validation Loss:0.5893297642469406, Validation Acc:72.43107769423558, AUC:0.6491807610993657\n",
            "Fold:8, Epoch: [9/40], Step: [27/29], Loss: 0.51025508068226, Validation Loss:0.6333231031894684, Validation Acc:69.67418546365914, AUC:0.6713794926004227\n",
            "Epoch 8 time: 1.8924\n",
            "Fold:8, Epoch: [10/40], Step: [9/29], Loss: 0.49644612934854293, Validation Loss:0.6130741089582443, Validation Acc:69.92481203007519, AUC:0.6651955602536997\n",
            "Fold:8, Epoch: [10/40], Step: [18/29], Loss: 0.4933049927155177, Validation Loss:0.5764323174953461, Validation Acc:70.92731829573935, AUC:0.6633720930232558\n",
            "Fold:8, Epoch: [10/40], Step: [27/29], Loss: 0.48870018234959356, Validation Loss:0.5782180428504944, Validation Acc:71.9298245614035, AUC:0.6691860465116279\n",
            "Epoch 9 time: 1.8866\n",
            "Fold:8, Epoch: [11/40], Step: [9/29], Loss: 0.4741232924991184, Validation Loss:0.6050134599208832, Validation Acc:64.16040100250626, AUC:0.6928646934460888\n",
            "Fold:8, Epoch: [11/40], Step: [18/29], Loss: 0.48021209902233547, Validation Loss:0.6367906183004379, Validation Acc:70.67669172932331, AUC:0.6695560253699788\n",
            "Fold:8, Epoch: [11/40], Step: [27/29], Loss: 0.4863614958745462, Validation Loss:0.5261947438120842, Validation Acc:76.44110275689223, AUC:0.6418868921775898\n",
            "Epoch 10 time: 1.8815\n",
            "Fold:8, Epoch: [12/40], Step: [9/29], Loss: 0.4866009056568146, Validation Loss:0.5610595420002937, Validation Acc:75.93984962406014, AUC:0.6924418604651164\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:8, Epoch: [12/40], Step: [18/29], Loss: 0.4836163818836212, Validation Loss:0.7204980105161667, Validation Acc:75.68922305764411, AUC:0.6909883720930233\n",
            "Fold:8, Epoch: [12/40], Step: [27/29], Loss: 0.48347579770618015, Validation Loss:0.6189289689064026, Validation Acc:63.659147869674186, AUC:0.6823202959830867\n",
            "Epoch 11 time: 1.9107\n",
            "Fold:8, Epoch: [13/40], Step: [9/29], Loss: 0.49080180790689254, Validation Loss:0.6993798166513443, Validation Acc:73.93483709273183, AUC:0.6655391120507399\n",
            "Fold:8, Epoch: [13/40], Step: [18/29], Loss: 0.4811932262447145, Validation Loss:0.6223887726664543, Validation Acc:71.17794486215539, AUC:0.6648255813953488\n",
            "Fold:8, Epoch: [13/40], Step: [27/29], Loss: 0.47606600213933875, Validation Loss:0.5587956011295319, Validation Acc:74.43609022556392, AUC:0.6913583509513742\n",
            "Epoch 12 time: 1.9292\n",
            "Fold:8, Epoch: [14/40], Step: [9/29], Loss: 0.48080049289597404, Validation Loss:0.6034059524536133, Validation Acc:65.16290726817043, AUC:0.6834038054968288\n",
            "Fold:8, Epoch: [14/40], Step: [18/29], Loss: 0.4599965959787369, Validation Loss:0.6654966622591019, Validation Acc:75.93984962406014, AUC:0.669529598308668\n",
            "Fold:8, Epoch: [14/40], Step: [27/29], Loss: 0.46453462817050795, Validation Loss:0.5614883154630661, Validation Acc:67.91979949874687, AUC:0.6764799154334038\n",
            "Epoch 13 time: 1.9159\n",
            "Fold:8, Epoch: [15/40], Step: [9/29], Loss: 0.48827074964841205, Validation Loss:0.6422193497419357, Validation Acc:77.19298245614036, AUC:0.6691596194503171\n",
            "Fold:8, Epoch: [15/40], Step: [18/29], Loss: 0.47043049666616654, Validation Loss:0.6485078781843185, Validation Acc:78.44611528822055, AUC:0.6687896405919661\n",
            "Fold:8, Epoch: [15/40], Step: [27/29], Loss: 0.47208270872080765, Validation Loss:0.7865987494587898, Validation Acc:75.93984962406014, AUC:0.6924418604651164\n",
            "Epoch 14 time: 1.9134\n",
            "Fold:8, Epoch: [16/40], Step: [9/29], Loss: 0.4612805445988973, Validation Loss:0.6464457362890244, Validation Acc:78.19548872180451, AUC:0.6826109936575052\n",
            "Fold:8, Epoch: [16/40], Step: [18/29], Loss: 0.45688558783796096, Validation Loss:0.5404557585716248, Validation Acc:65.41353383458646, AUC:0.6848572938689218\n",
            "Fold:8, Epoch: [16/40], Step: [27/29], Loss: 0.45450949006610447, Validation Loss:0.5561138540506363, Validation Acc:74.18546365914787, AUC:0.674630021141649\n",
            "Epoch 15 time: 1.9006\n",
            "Fold:8, Epoch: [17/40], Step: [9/29], Loss: 0.4324991703033447, Validation Loss:0.6851745247840881, Validation Acc:69.67418546365914, AUC:0.686654334038055\n",
            "Fold:8, Epoch: [17/40], Step: [18/29], Loss: 0.44544360869460636, Validation Loss:0.5665764063596725, Validation Acc:67.66917293233082, AUC:0.6826638477801268\n",
            "Fold:8, Epoch: [17/40], Step: [27/29], Loss: 0.45487159490585327, Validation Loss:0.6969418972730637, Validation Acc:77.44360902255639, AUC:0.6553382663847781\n",
            "Epoch 16 time: 1.8982\n",
            "Fold:8, Epoch: [18/40], Step: [9/29], Loss: 0.4204030434290568, Validation Loss:0.6133928820490837, Validation Acc:70.17543859649123, AUC:0.6742864693446087\n",
            "Fold:8, Epoch: [18/40], Step: [18/29], Loss: 0.4386297927962409, Validation Loss:0.5922509580850601, Validation Acc:70.92731829573935, AUC:0.6939217758985201\n",
            "Fold:8, Epoch: [18/40], Step: [27/29], Loss: 0.4515139306033099, Validation Loss:0.6749601513147354, Validation Acc:73.43358395989975, AUC:0.7160940803382664\n",
            "Epoch 17 time: 1.8737\n",
            "Fold:8, Epoch: [19/40], Step: [9/29], Loss: 0.44628018471929765, Validation Loss:0.6172883212566376, Validation Acc:73.93483709273183, AUC:0.6960887949260042\n",
            "Fold:8, Epoch: [19/40], Step: [18/29], Loss: 0.44117972917026943, Validation Loss:0.5387041941285133, Validation Acc:70.67669172932331, AUC:0.684830866807611\n",
            "Fold:8, Epoch: [19/40], Step: [27/29], Loss: 0.4393768884517528, Validation Loss:0.6870662197470665, Validation Acc:77.19298245614036, AUC:0.6844344608879492\n",
            "Epoch 18 time: 1.8897\n",
            "Fold:8, Epoch: [20/40], Step: [9/29], Loss: 0.4248766865995195, Validation Loss:0.616033285856247, Validation Acc:68.17042606516291, AUC:0.693208245243129\n",
            "Fold:8, Epoch: [20/40], Step: [18/29], Loss: 0.4221595509184731, Validation Loss:0.610572025179863, Validation Acc:77.44360902255639, AUC:0.7011627906976745\n",
            "Fold:8, Epoch: [20/40], Step: [27/29], Loss: 0.43335674206415814, Validation Loss:0.5614958256483078, Validation Acc:73.1829573934837, AUC:0.7070031712473573\n",
            "Epoch 19 time: 1.8854\n",
            "Fold:8, Epoch: [21/40], Step: [9/29], Loss: 0.4097602400514815, Validation Loss:0.7250133827328682, Validation Acc:77.44360902255639, AUC:0.6858879492600422\n",
            "Fold:8, Epoch: [21/40], Step: [18/29], Loss: 0.4306752665175332, Validation Loss:0.6020025163888931, Validation Acc:72.68170426065163, AUC:0.681183932346723\n",
            "Fold:8, Epoch: [21/40], Step: [27/29], Loss: 0.4246125751071506, Validation Loss:0.6680746078491211, Validation Acc:67.91979949874687, AUC:0.6764799154334038\n",
            "Epoch 20 time: 1.8807\n",
            "Fold:8, Epoch: [22/40], Step: [9/29], Loss: 0.40292765696843463, Validation Loss:0.6019328236579895, Validation Acc:75.43859649122807, AUC:0.6971723044397464\n",
            "Fold:8, Epoch: [22/40], Step: [18/29], Loss: 0.4150107039345635, Validation Loss:0.7321991920471191, Validation Acc:73.43358395989975, AUC:0.7313689217758985\n",
            "Fold:8, Epoch: [22/40], Step: [27/29], Loss: 0.4166421989599864, Validation Loss:0.9648332670331001, Validation Acc:69.42355889724311, AUC:0.700475687103594\n",
            "Epoch 21 time: 1.9051\n",
            "Fold:8, Epoch: [23/40], Step: [9/29], Loss: 0.39706098039944965, Validation Loss:0.7154089063405991, Validation Acc:79.4486215538847, AUC:0.6746035940803382\n",
            "Fold:8, Epoch: [23/40], Step: [18/29], Loss: 0.4040204882621765, Validation Loss:0.6871470957994461, Validation Acc:74.68671679197995, AUC:0.6851744186046512\n"
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            "Fold:8, Epoch: [23/40], Step: [27/29], Loss: 0.41531793607605827, Validation Loss:0.608450673520565, Validation Acc:68.67167919799499, AUC:0.6655655391120507\n",
            "Epoch 22 time: 1.8755\n",
            "Fold:8, Epoch: [24/40], Step: [9/29], Loss: 0.39304305447472465, Validation Loss:0.5618125461041927, Validation Acc:75.43859649122807, AUC:0.6971723044397464\n",
            "Fold:8, Epoch: [24/40], Step: [18/29], Loss: 0.4014337841007445, Validation Loss:0.6626675352454185, Validation Acc:78.94736842105263, AUC:0.7022463002114165\n",
            "Fold:8, Epoch: [24/40], Step: [27/29], Loss: 0.4034816832454116, Validation Loss:0.6802528649568558, Validation Acc:74.18546365914787, AUC:0.6899048625792812\n",
            "Epoch 23 time: 1.8836\n",
            "Fold:8, Epoch: [25/40], Step: [9/29], Loss: 0.36033012138472664, Validation Loss:0.5866700261831284, Validation Acc:75.18796992481202, AUC:0.7033562367864693\n",
            "Fold:8, Epoch: [25/40], Step: [18/29], Loss: 0.400129250354237, Validation Loss:0.6937701553106308, Validation Acc:69.92481203007519, AUC:0.6728329809725158\n",
            "Fold:8, Epoch: [25/40], Step: [27/29], Loss: 0.4011810300526796, Validation Loss:0.6183374673128128, Validation Acc:68.42105263157895, AUC:0.6641120507399576\n",
            "Epoch 24 time: 1.8918\n",
            "Fold:8, Epoch: [26/40], Step: [9/29], Loss: 0.42034833629926044, Validation Loss:0.7337624579668045, Validation Acc:75.93984962406014, AUC:0.677167019027484\n",
            "Fold:8, Epoch: [26/40], Step: [18/29], Loss: 0.407165053817961, Validation Loss:0.755973681807518, Validation Acc:78.19548872180451, AUC:0.6826109936575052\n",
            "Fold:8, Epoch: [26/40], Step: [27/29], Loss: 0.4015354492046215, Validation Loss:0.5985947884619236, Validation Acc:76.69172932330827, AUC:0.6815274841437631\n",
            "Epoch 25 time: 1.8837\n",
            "Fold:8, Epoch: [27/40], Step: [9/29], Loss: 0.40552934673097396, Validation Loss:0.6476509645581245, Validation Acc:71.42857142857143, AUC:0.7121035940803382\n",
            "Fold:8, Epoch: [27/40], Step: [18/29], Loss: 0.3958507279555003, Validation Loss:0.7004427015781403, Validation Acc:75.18796992481202, AUC:0.6957188160676533\n",
            "Fold:8, Epoch: [27/40], Step: [27/29], Loss: 0.3939757733433335, Validation Loss:0.6067718863487244, Validation Acc:72.18045112781955, AUC:0.678276955602537\n",
            "Epoch 26 time: 1.8793\n",
            "Fold:8, Epoch: [28/40], Step: [9/29], Loss: 0.36382055282592773, Validation Loss:0.6437013819813728, Validation Acc:76.69172932330827, AUC:0.6968023255813954\n",
            "Fold:8, Epoch: [28/40], Step: [18/29], Loss: 0.360777058535152, Validation Loss:0.7166514471173286, Validation Acc:70.67669172932331, AUC:0.677193446088795\n",
            "Fold:8, Epoch: [28/40], Step: [27/29], Loss: 0.3772582886395631, Validation Loss:0.9438538551330566, Validation Acc:76.19047619047619, AUC:0.6862579281183931\n",
            "Epoch 27 time: 1.8671\n",
            "Fold:8, Epoch: [29/40], Step: [9/29], Loss: 0.4131380584504869, Validation Loss:0.6191144734621048, Validation Acc:75.43859649122807, AUC:0.6742600422832981\n",
            "Fold:8, Epoch: [29/40], Step: [18/29], Loss: 0.3797074837817086, Validation Loss:0.735682412981987, Validation Acc:72.68170426065163, AUC:0.688821353065539\n",
            "Fold:8, Epoch: [29/40], Step: [27/29], Loss: 0.3816867967446645, Validation Loss:0.6658730059862137, Validation Acc:75.68922305764411, AUC:0.6833509513742072\n",
            "Epoch 28 time: 1.8963\n",
            "Fold:8, Epoch: [30/40], Step: [9/29], Loss: 0.3397274812062581, Validation Loss:0.9291080236434937, Validation Acc:75.43859649122807, AUC:0.6895348837209303\n",
            "Fold:8, Epoch: [30/40], Step: [18/29], Loss: 0.36829401221540237, Validation Loss:0.6184373088181019, Validation Acc:76.44110275689223, AUC:0.6953488372093023\n",
            "Fold:8, Epoch: [30/40], Step: [27/29], Loss: 0.372398489051395, Validation Loss:0.6168784350156784, Validation Acc:65.6641604010025, AUC:0.6710359408033827\n",
            "Epoch 29 time: 1.8809\n",
            "Fold:8, Epoch: [31/40], Step: [9/29], Loss: 0.36811837553977966, Validation Loss:0.8374313712120056, Validation Acc:79.19799498746868, AUC:0.6731501057082452\n",
            "Fold:8, Epoch: [31/40], Step: [18/29], Loss: 0.36773130628797746, Validation Loss:0.6925912648439407, Validation Acc:76.94235588972431, AUC:0.6829809725158562\n",
            "Fold:8, Epoch: [31/40], Step: [27/29], Loss: 0.3683599829673767, Validation Loss:0.6092008091509342, Validation Acc:69.67418546365914, AUC:0.686654334038055\n",
            "Epoch 30 time: 1.8981\n",
            "Fold:8, Epoch: [32/40], Step: [9/29], Loss: 0.3755802247259352, Validation Loss:0.806360125541687, Validation Acc:74.18546365914787, AUC:0.6899048625792812\n",
            "Fold:8, Epoch: [32/40], Step: [18/29], Loss: 0.3606724871529473, Validation Loss:0.7038987278938293, Validation Acc:74.93734335839599, AUC:0.65607822410148\n",
            "Fold:8, Epoch: [32/40], Step: [27/29], Loss: 0.358460846874449, Validation Loss:0.9736640751361847, Validation Acc:77.19298245614036, AUC:0.6920718816067654\n",
            "Epoch 31 time: 1.8868\n",
            "Fold:8, Epoch: [33/40], Step: [9/29], Loss: 0.33572712540626526, Validation Loss:0.7433560565114021, Validation Acc:72.18045112781955, AUC:0.678276955602537\n",
            "Fold:8, Epoch: [33/40], Step: [18/29], Loss: 0.33737049831284416, Validation Loss:0.7606444358825684, Validation Acc:74.43609022556392, AUC:0.6913583509513742\n",
            "Fold:8, Epoch: [33/40], Step: [27/29], Loss: 0.3398071693049537, Validation Loss:0.6317372471094131, Validation Acc:75.68922305764411, AUC:0.6757135306553911\n",
            "Epoch 32 time: 1.8795\n",
            "Fold:8, Epoch: [34/40], Step: [9/29], Loss: 0.3245989812744988, Validation Loss:0.7727922648191452, Validation Acc:68.17042606516291, AUC:0.6550211416490486\n",
            "Fold:8, Epoch: [34/40], Step: [18/29], Loss: 0.348458190759023, Validation Loss:0.7542289271950722, Validation Acc:76.94235588972431, AUC:0.6524312896405919\n",
            "Fold:8, Epoch: [34/40], Step: [27/29], Loss: 0.34858555043185196, Validation Loss:0.8459053039550781, Validation Acc:77.44360902255639, AUC:0.6782505285412262\n",
            "Epoch 33 time: 1.8851\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:8, Epoch: [35/40], Step: [9/29], Loss: 0.31511886086728835, Validation Loss:0.7054620534181595, Validation Acc:71.67919799498746, AUC:0.6524577167019027\n",
            "Fold:8, Epoch: [35/40], Step: [18/29], Loss: 0.32372262494431603, Validation Loss:0.7373776212334633, Validation Acc:76.19047619047619, AUC:0.678620507399577\n",
            "Fold:8, Epoch: [35/40], Step: [27/29], Loss: 0.3314128126259203, Validation Loss:0.7671568095684052, Validation Acc:74.18546365914787, AUC:0.674630021141649\n",
            "Epoch 34 time: 1.8778\n",
            "Fold:8, Epoch: [36/40], Step: [9/29], Loss: 0.30617910623550415, Validation Loss:0.6987444125115871, Validation Acc:78.94736842105263, AUC:0.6793340380549683\n",
            "Fold:8, Epoch: [36/40], Step: [18/29], Loss: 0.3240031765566932, Validation Loss:0.8403069972991943, Validation Acc:74.68671679197995, AUC:0.669899577167019\n",
            "Fold:8, Epoch: [36/40], Step: [27/29], Loss: 0.3293812495690805, Validation Loss:1.1524648517370224, Validation Acc:59.64912280701754, AUC:0.6514270613107823\n",
            "Epoch 35 time: 1.8781\n",
            "Fold:8, Epoch: [37/40], Step: [9/29], Loss: 0.33374718493885464, Validation Loss:1.0254336148500443, Validation Acc:80.20050125313283, AUC:0.6713266384778013\n",
            "Fold:8, Epoch: [37/40], Step: [18/29], Loss: 0.33798398822546005, Validation Loss:1.4452592730522156, Validation Acc:79.4486215538847, AUC:0.6516913319238901\n",
            "Fold:8, Epoch: [37/40], Step: [27/29], Loss: 0.3285298816583775, Validation Loss:0.8837418854236603, Validation Acc:75.43859649122807, AUC:0.6742600422832981\n",
            "Epoch 36 time: 1.8633\n",
            "Fold:8, Epoch: [38/40], Step: [9/29], Loss: 0.28818882836235893, Validation Loss:0.8651988357305527, Validation Acc:76.19047619047619, AUC:0.678620507399577\n",
            "Fold:8, Epoch: [38/40], Step: [18/29], Loss: 0.2977516104777654, Validation Loss:0.748773530125618, Validation Acc:72.93233082706767, AUC:0.6673625792811839\n",
            "Fold:8, Epoch: [38/40], Step: [27/29], Loss: 0.302115167732592, Validation Loss:0.8371493518352509, Validation Acc:73.6842105263158, AUC:0.6488107822410148\n",
            "Epoch 37 time: 1.8827\n",
            "Fold:8, Epoch: [39/40], Step: [9/29], Loss: 0.2972870071729024, Validation Loss:0.900411069393158, Validation Acc:77.19298245614036, AUC:0.6691596194503171\n",
            "Fold:8, Epoch: [39/40], Step: [18/29], Loss: 0.3043969107998742, Validation Loss:0.7663369178771973, Validation Acc:70.67669172932331, AUC:0.6466437632135306\n",
            "Fold:8, Epoch: [39/40], Step: [27/29], Loss: 0.3052338107868477, Validation Loss:0.8516291379928589, Validation Acc:75.18796992481202, AUC:0.657531712473573\n",
            "Epoch 38 time: 1.8723\n",
            "Fold:8, Epoch: [40/40], Step: [9/29], Loss: 0.29797670245170593, Validation Loss:1.0570604652166367, Validation Acc:76.94235588972431, AUC:0.6906183932346723\n",
            "Fold:8, Epoch: [40/40], Step: [18/29], Loss: 0.29770463208357495, Validation Loss:0.780294731259346, Validation Acc:67.66917293233082, AUC:0.6521141649048625\n",
            "Fold:8, Epoch: [40/40], Step: [27/29], Loss: 0.3072708166307873, Validation Loss:0.9599102735519409, Validation Acc:72.93233082706767, AUC:0.6597251585623678\n",
            "Epoch 39 time: 1.8768\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Fold:9, Epoch: [1/40], Step: [9/29], Loss: 0.692443675465054, Validation Loss:0.6875843107700348, Validation Acc:84.21052631578948, AUC:0.5494714587737843\n",
            "Fold:9, Epoch: [1/40], Step: [18/29], Loss: 0.6759706636269888, Validation Loss:0.6698276698589325, Validation Acc:76.69172932330827, AUC:0.6433403805496829\n",
            "Fold:9, Epoch: [1/40], Step: [27/29], Loss: 0.6514210347776059, Validation Loss:0.6326776593923569, Validation Acc:72.43107769423558, AUC:0.6491807610993657\n",
            "Epoch 0 time: 1.8877\n",
            "Fold:9, Epoch: [2/40], Step: [9/29], Loss: 0.6091724634170532, Validation Loss:0.5910980999469757, Validation Acc:72.18045112781955, AUC:0.7317389006342494\n",
            "Fold:9, Epoch: [2/40], Step: [18/29], Loss: 0.5917843911382887, Validation Loss:0.564123809337616, Validation Acc:76.94235588972431, AUC:0.7288054968287527\n",
            "Fold:9, Epoch: [2/40], Step: [27/29], Loss: 0.5890464672335872, Validation Loss:0.5349076166749, Validation Acc:72.93233082706767, AUC:0.6826374207188161\n",
            "Epoch 1 time: 1.8862\n",
            "Fold:9, Epoch: [3/40], Step: [9/29], Loss: 0.5568508572048612, Validation Loss:0.5371472537517548, Validation Acc:75.68922305764411, AUC:0.7368128964059196\n",
            "Fold:9, Epoch: [3/40], Step: [18/29], Loss: 0.5643525636858411, Validation Loss:0.5044014230370522, Validation Acc:75.18796992481202, AUC:0.7109936575052854\n",
            "Fold:9, Epoch: [3/40], Step: [27/29], Loss: 0.56526701869788, Validation Loss:0.5339393317699432, Validation Acc:73.6842105263158, AUC:0.7404598308668077\n",
            "Epoch 2 time: 1.8979\n",
            "Fold:9, Epoch: [4/40], Step: [9/29], Loss: 0.5669452216890123, Validation Loss:0.555568128824234, Validation Acc:70.17543859649123, AUC:0.697198731501057\n",
            "Fold:9, Epoch: [4/40], Step: [18/29], Loss: 0.5585044920444489, Validation Loss:0.5486333668231964, Validation Acc:73.1829573934837, AUC:0.7222780126849894\n",
            "Fold:9, Epoch: [4/40], Step: [27/29], Loss: 0.5587160454856025, Validation Loss:0.5397941768169403, Validation Acc:74.18546365914787, AUC:0.7433668076109936\n",
            "Epoch 3 time: 1.8798\n",
            "Fold:9, Epoch: [5/40], Step: [9/29], Loss: 0.5463803774780698, Validation Loss:0.6664948463439941, Validation Acc:78.69674185463658, AUC:0.7542547568710359\n",
            "Fold:9, Epoch: [5/40], Step: [18/29], Loss: 0.5430154916312959, Validation Loss:0.5417430847883224, Validation Acc:74.18546365914787, AUC:0.7357293868921776\n",
            "Fold:9, Epoch: [5/40], Step: [27/29], Loss: 0.5444426106082069, Validation Loss:0.5900070667266846, Validation Acc:69.67418546365914, AUC:0.7401162790697674\n",
            "Epoch 4 time: 1.8992\n",
            "Fold:9, Epoch: [6/40], Step: [9/29], Loss: 0.5374703539742364, Validation Loss:0.5025148317217827, Validation Acc:74.18546365914787, AUC:0.7433668076109936\n",
            "Fold:9, Epoch: [6/40], Step: [18/29], Loss: 0.5375699516799715, Validation Loss:0.48197969794273376, Validation Acc:77.69423558897243, AUC:0.7408033826638477\n",
            "Fold:9, Epoch: [6/40], Step: [27/29], Loss: 0.5290934068185312, Validation Loss:0.5364324599504471, Validation Acc:75.43859649122807, AUC:0.765909090909091\n",
            "Epoch 5 time: 1.8939\n",
            "Fold:9, Epoch: [7/40], Step: [9/29], Loss: 0.5116978287696838, Validation Loss:0.5223246812820435, Validation Acc:78.44611528822055, AUC:0.7451638477801268\n",
            "Fold:9, Epoch: [7/40], Step: [18/29], Loss: 0.5190015186866125, Validation Loss:0.5868391245603561, Validation Acc:76.19047619047619, AUC:0.7397198731501057\n",
            "Fold:9, Epoch: [7/40], Step: [27/29], Loss: 0.5263268539199123, Validation Loss:0.5339701250195503, Validation Acc:71.67919799498746, AUC:0.7441067653276955\n",
            "Epoch 6 time: 1.9097\n",
            "Fold:9, Epoch: [8/40], Step: [9/29], Loss: 0.5371453927622901, Validation Loss:0.5172269940376282, Validation Acc:74.43609022556392, AUC:0.7448202959830867\n",
            "Fold:9, Epoch: [8/40], Step: [18/29], Loss: 0.5329649150371552, Validation Loss:0.512708954513073, Validation Acc:77.94486215538848, AUC:0.7346194503171247\n",
            "Fold:9, Epoch: [8/40], Step: [27/29], Loss: 0.5171480002226653, Validation Loss:0.5708279758691788, Validation Acc:75.43859649122807, AUC:0.765909090909091\n",
            "Epoch 7 time: 1.8865\n",
            "Fold:9, Epoch: [9/40], Step: [9/29], Loss: 0.5208489067024655, Validation Loss:0.5425641909241676, Validation Acc:81.45363408521304, AUC:0.7244186046511627\n",
            "Fold:9, Epoch: [9/40], Step: [18/29], Loss: 0.5185064226388931, Validation Loss:0.6056405901908875, Validation Acc:74.93734335839599, AUC:0.7400898520084567\n",
            "Fold:9, Epoch: [9/40], Step: [27/29], Loss: 0.5099860606370149, Validation Loss:0.4995878264307976, Validation Acc:78.69674185463658, AUC:0.7542547568710359\n",
            "Epoch 8 time: 1.9199\n",
            "Fold:9, Epoch: [10/40], Step: [9/29], Loss: 0.48204277290238273, Validation Loss:0.492828331887722, Validation Acc:77.94486215538848, AUC:0.7193446088794925\n",
            "Fold:9, Epoch: [10/40], Step: [18/29], Loss: 0.5009938991732068, Validation Loss:0.5628971233963966, Validation Acc:80.45112781954887, AUC:0.7262420718816067\n",
            "Fold:9, Epoch: [10/40], Step: [27/29], Loss: 0.5101474600809591, Validation Loss:0.5660714209079742, Validation Acc:72.18045112781955, AUC:0.7317389006342494\n",
            "Epoch 9 time: 1.9006\n",
            "Fold:9, Epoch: [11/40], Step: [9/29], Loss: 0.5071014198991988, Validation Loss:0.5623266845941544, Validation Acc:70.92731829573935, AUC:0.7321088794926004\n",
            "Fold:9, Epoch: [11/40], Step: [18/29], Loss: 0.49234239591492546, Validation Loss:0.46819986030459404, Validation Acc:79.19799498746868, AUC:0.749524312896406\n",
            "Fold:9, Epoch: [11/40], Step: [27/29], Loss: 0.4920293456978268, Validation Loss:0.541031152009964, Validation Acc:81.203007518797, AUC:0.7535147991543341\n",
            "Epoch 10 time: 1.8920\n",
            "Fold:9, Epoch: [12/40], Step: [9/29], Loss: 0.5236982074048784, Validation Loss:0.6737397387623787, Validation Acc:71.42857142857143, AUC:0.7502906976744186\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "Fold:9, Epoch: [12/40], Step: [18/29], Loss: 0.5090713169839647, Validation Loss:0.5665415972471237, Validation Acc:68.92230576441102, AUC:0.7357558139534884\n",
            "Fold:9, Epoch: [12/40], Step: [27/29], Loss: 0.4970442928649761, Validation Loss:0.6163113713264465, Validation Acc:82.95739348370927, AUC:0.7178646934460887\n",
            "Epoch 11 time: 1.8996\n",
            "Fold:9, Epoch: [13/40], Step: [9/29], Loss: 0.4816208846039242, Validation Loss:0.6139069944620132, Validation Acc:82.95739348370927, AUC:0.7255021141649048\n",
            "Fold:9, Epoch: [13/40], Step: [18/29], Loss: 0.4817419797182083, Validation Loss:0.5300796404480934, Validation Acc:74.93734335839599, AUC:0.7553646934460888\n",
            "Fold:9, Epoch: [13/40], Step: [27/29], Loss: 0.48454504432501616, Validation Loss:0.49498268216848373, Validation Acc:78.69674185463658, AUC:0.7466173361522198\n",
            "Epoch 12 time: 1.9176\n",
            "Fold:9, Epoch: [14/40], Step: [9/29], Loss: 0.4674660000536177, Validation Loss:0.5970498621463776, Validation Acc:56.8922305764411, AUC:0.6889006342494715\n",
            "Fold:9, Epoch: [14/40], Step: [18/29], Loss: 0.4947926633887821, Validation Loss:0.7207148373126984, Validation Acc:78.69674185463658, AUC:0.761892177589852\n",
            "Fold:9, Epoch: [14/40], Step: [27/29], Loss: 0.48185431294971043, Validation Loss:0.5859971269965172, Validation Acc:79.4486215538847, AUC:0.7357029598308668\n",
            "Epoch 13 time: 1.8682\n",
            "Fold:9, Epoch: [15/40], Step: [9/29], Loss: 0.46785398655467564, Validation Loss:0.6179542690515518, Validation Acc:82.95739348370927, AUC:0.7025898520084567\n",
            "Fold:9, Epoch: [15/40], Step: [18/29], Loss: 0.46792269580894047, Validation Loss:0.5409439578652382, Validation Acc:79.69924812030075, AUC:0.752431289640592\n",
            "Fold:9, Epoch: [15/40], Step: [27/29], Loss: 0.46739415106949983, Validation Loss:0.7419186756014824, Validation Acc:78.69674185463658, AUC:0.7542547568710359\n",
            "Epoch 14 time: 1.8858\n",
            "Fold:9, Epoch: [16/40], Step: [9/29], Loss: 0.4652942054801517, Validation Loss:0.6773892045021057, Validation Acc:78.19548872180451, AUC:0.7360729386892179\n",
            "Fold:9, Epoch: [16/40], Step: [18/29], Loss: 0.4604234794775645, Validation Loss:0.5413954183459282, Validation Acc:79.19799498746868, AUC:0.7189746300211417\n",
            "Fold:9, Epoch: [16/40], Step: [27/29], Loss: 0.4664058596999557, Validation Loss:0.5549575611948967, Validation Acc:74.68671679197995, AUC:0.7309989429175475\n",
            "Epoch 15 time: 1.8797\n",
            "Fold:9, Epoch: [17/40], Step: [9/29], Loss: 0.45433133178287083, Validation Loss:0.5215670093894005, Validation Acc:80.20050125313283, AUC:0.7324260042283297\n",
            "Fold:9, Epoch: [17/40], Step: [18/29], Loss: 0.46580087807443404, Validation Loss:0.5005552470684052, Validation Acc:76.69172932330827, AUC:0.7349894291754757\n",
            "Fold:9, Epoch: [17/40], Step: [27/29], Loss: 0.46562338096124156, Validation Loss:0.5623198002576828, Validation Acc:79.4486215538847, AUC:0.7662526427061311\n",
            "Epoch 16 time: 1.8783\n",
            "Fold:9, Epoch: [18/40], Step: [9/29], Loss: 0.45302414894104004, Validation Loss:0.6188833341002464, Validation Acc:79.69924812030075, AUC:0.7371564482029598\n",
            "Fold:9, Epoch: [18/40], Step: [18/29], Loss: 0.44690893093744916, Validation Loss:0.5569692850112915, Validation Acc:78.44611528822055, AUC:0.7222515856236786\n",
            "Fold:9, Epoch: [18/40], Step: [27/29], Loss: 0.4473130206267039, Validation Loss:0.5495446175336838, Validation Acc:79.69924812030075, AUC:0.7371564482029598\n",
            "Epoch 17 time: 1.8824\n",
            "Fold:9, Epoch: [19/40], Step: [9/29], Loss: 0.423405475086636, Validation Loss:0.5136740952730179, Validation Acc:77.94486215538848, AUC:0.7498942917547567\n",
            "Fold:9, Epoch: [19/40], Step: [18/29], Loss: 0.4373834778865178, Validation Loss:0.5152292847633362, Validation Acc:77.94486215538848, AUC:0.757531712473573\n",
            "Fold:9, Epoch: [19/40], Step: [27/29], Loss: 0.4460815025700463, Validation Loss:0.6052259430289268, Validation Acc:81.45363408521304, AUC:0.747330866807611\n",
            "Epoch 18 time: 1.8784\n",
            "Fold:9, Epoch: [20/40], Step: [9/29], Loss: 0.4274660580688053, Validation Loss:0.5978631526231766, Validation Acc:70.42606516290726, AUC:0.6910147991543341\n",
            "Fold:9, Epoch: [20/40], Step: [18/29], Loss: 0.4342007901933458, Validation Loss:0.6305025368928909, Validation Acc:79.4486215538847, AUC:0.7127906976744186\n",
            "Fold:9, Epoch: [20/40], Step: [27/29], Loss: 0.43650879241802076, Validation Loss:0.6021325066685677, Validation Acc:81.70426065162907, AUC:0.7029598308668076\n",
            "Epoch 19 time: 1.8705\n",
            "Fold:9, Epoch: [21/40], Step: [9/29], Loss: 0.4616698722044627, Validation Loss:0.5182576552033424, Validation Acc:73.93483709273183, AUC:0.7342758985200846\n",
            "Fold:9, Epoch: [21/40], Step: [18/29], Loss: 0.4440552559163835, Validation Loss:0.5990949422121048, Validation Acc:77.69423558897243, AUC:0.7255285412262156\n",
            "Fold:9, Epoch: [21/40], Step: [27/29], Loss: 0.43794855033909835, Validation Loss:0.5584668442606926, Validation Acc:79.4486215538847, AUC:0.7280655391120507\n",
            "Epoch 20 time: 1.8908\n",
            "Fold:9, Epoch: [22/40], Step: [9/29], Loss: 0.4010421137015025, Validation Loss:0.4996220991015434, Validation Acc:76.94235588972431, AUC:0.7135306553911204\n",
            "Fold:9, Epoch: [22/40], Step: [18/29], Loss: 0.4071974986129337, Validation Loss:0.6550222337245941, Validation Acc:80.20050125313283, AUC:0.7018763213530654\n",
            "Fold:9, Epoch: [22/40], Step: [27/29], Loss: 0.4138706436863652, Validation Loss:0.48450931161642075, Validation Acc:74.18546365914787, AUC:0.7204545454545455\n",
            "Epoch 21 time: 1.9392\n",
            "Fold:9, Epoch: [23/40], Step: [9/29], Loss: 0.39770398868454826, Validation Loss:0.5548864156007767, Validation Acc:79.4486215538847, AUC:0.7051532769556026\n",
            "Fold:9, Epoch: [23/40], Step: [18/29], Loss: 0.40852847695350647, Validation Loss:0.753600537776947, Validation Acc:81.95488721804512, AUC:0.7196881606765327\n"
          ],
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          "text": [
            "Fold:9, Epoch: [23/40], Step: [27/29], Loss: 0.4125802483823564, Validation Loss:0.5981579571962357, Validation Acc:74.68671679197995, AUC:0.7309989429175475\n",
            "Epoch 22 time: 1.8901\n",
            "Fold:9, Epoch: [24/40], Step: [9/29], Loss: 0.43926192323366803, Validation Loss:0.5288942828774452, Validation Acc:74.93734335839599, AUC:0.7324524312896405\n",
            "Fold:9, Epoch: [24/40], Step: [18/29], Loss: 0.4220111883348889, Validation Loss:0.7764449045062065, Validation Acc:74.18546365914787, AUC:0.7128171247357293\n",
            "Fold:9, Epoch: [24/40], Step: [27/29], Loss: 0.4230925882304156, Validation Loss:0.5608448460698128, Validation Acc:74.68671679197995, AUC:0.7386363636363636\n",
            "Epoch 23 time: 1.8944\n",
            "Fold:9, Epoch: [25/40], Step: [9/29], Loss: 0.3680863512886895, Validation Loss:0.6341888457536697, Validation Acc:80.20050125313283, AUC:0.6789640591966173\n",
            "Fold:9, Epoch: [25/40], Step: [18/29], Loss: 0.3916504681110382, Validation Loss:0.5629321187734604, Validation Acc:80.95238095238095, AUC:0.7291490486257928\n",
            "Fold:9, Epoch: [25/40], Step: [27/29], Loss: 0.3942438518559491, Validation Loss:0.6322880238294601, Validation Acc:73.6842105263158, AUC:0.7175475687103594\n",
            "Epoch 24 time: 1.8939\n",
            "Fold:9, Epoch: [26/40], Step: [9/29], Loss: 0.3952970637215508, Validation Loss:0.8252207785844803, Validation Acc:81.45363408521304, AUC:0.7015063424947146\n",
            "Fold:9, Epoch: [26/40], Step: [18/29], Loss: 0.3900211917029487, Validation Loss:0.5854173302650452, Validation Acc:75.43859649122807, AUC:0.6971723044397464\n",
            "Fold:9, Epoch: [26/40], Step: [27/29], Loss: 0.40224458111657035, Validation Loss:0.5746868252754211, Validation Acc:78.94736842105263, AUC:0.6869714587737844\n",
            "Epoch 25 time: 1.9299\n",
            "Fold:9, Epoch: [27/40], Step: [9/29], Loss: 0.40124012033144635, Validation Loss:0.5987931340932846, Validation Acc:67.16791979949875, AUC:0.7103065539112051\n",
            "Fold:9, Epoch: [27/40], Step: [18/29], Loss: 0.39341234001848435, Validation Loss:0.6711259186267853, Validation Acc:78.69674185463658, AUC:0.7237050739957716\n",
            "Fold:9, Epoch: [27/40], Step: [27/29], Loss: 0.38470084137386745, Validation Loss:0.6178280711174011, Validation Acc:81.70426065162907, AUC:0.7258720930232558\n",
            "Epoch 26 time: 1.8755\n",
            "Fold:9, Epoch: [28/40], Step: [9/29], Loss: 0.36987928880585563, Validation Loss:0.5564663037657738, Validation Acc:75.43859649122807, AUC:0.7048097251585623\n",
            "Fold:9, Epoch: [28/40], Step: [18/29], Loss: 0.35734057757589555, Validation Loss:0.762904018163681, Validation Acc:81.203007518797, AUC:0.6618657505285412\n",
            "Fold:9, Epoch: [28/40], Step: [27/29], Loss: 0.37030985951423645, Validation Loss:0.6300151720643044, Validation Acc:76.19047619047619, AUC:0.7015327695560254\n",
            "Epoch 27 time: 1.9006\n",
            "Fold:9, Epoch: [29/40], Step: [9/29], Loss: 0.39060037003623116, Validation Loss:0.6521705538034439, Validation Acc:74.93734335839599, AUC:0.6942653276955604\n",
            "Fold:9, Epoch: [29/40], Step: [18/29], Loss: 0.3622271517912547, Validation Loss:0.7312998622655869, Validation Acc:81.95488721804512, AUC:0.7120507399577166\n",
            "Fold:9, Epoch: [29/40], Step: [27/29], Loss: 0.36794001084786876, Validation Loss:0.6642206162214279, Validation Acc:75.68922305764411, AUC:0.6833509513742072\n",
            "Epoch 28 time: 1.8742\n",
            "Fold:9, Epoch: [30/40], Step: [9/29], Loss: 0.40889538990126717, Validation Loss:0.7579823285341263, Validation Acc:70.92731829573935, AUC:0.686284355179704\n",
            "Fold:9, Epoch: [30/40], Step: [18/29], Loss: 0.36145666738351184, Validation Loss:0.7465226501226425, Validation Acc:77.94486215538848, AUC:0.7040697674418606\n",
            "Fold:9, Epoch: [30/40], Step: [27/29], Loss: 0.3568600548638238, Validation Loss:0.7325151935219765, Validation Acc:81.70426065162907, AUC:0.6876849894291754\n",
            "Epoch 29 time: 1.8856\n",
            "Fold:9, Epoch: [31/40], Step: [9/29], Loss: 0.3631880713833703, Validation Loss:0.6076090112328529, Validation Acc:74.68671679197995, AUC:0.7004492600422833\n",
            "Fold:9, Epoch: [31/40], Step: [18/29], Loss: 0.34141259226534104, Validation Loss:0.6926446706056595, Validation Acc:78.44611528822055, AUC:0.7069767441860466\n",
            "Fold:9, Epoch: [31/40], Step: [27/29], Loss: 0.3449410045588458, Validation Loss:0.7529793530702591, Validation Acc:73.6842105263158, AUC:0.6869978858350951\n",
            "Epoch 30 time: 1.8847\n",
            "Fold:9, Epoch: [32/40], Step: [9/29], Loss: 0.34156331419944763, Validation Loss:0.6967499405145645, Validation Acc:79.19799498746868, AUC:0.6960623678646934\n",
            "Fold:9, Epoch: [32/40], Step: [18/29], Loss: 0.32738881640964085, Validation Loss:0.8087886720895767, Validation Acc:79.4486215538847, AUC:0.6898784355179705\n",
            "Fold:9, Epoch: [32/40], Step: [27/29], Loss: 0.33881064587169224, Validation Loss:0.7442691326141357, Validation Acc:75.68922305764411, AUC:0.6757135306553911\n",
            "Epoch 31 time: 1.8884\n",
            "Fold:9, Epoch: [33/40], Step: [9/29], Loss: 0.39106722672780353, Validation Loss:0.6691946387290955, Validation Acc:71.9298245614035, AUC:0.6920983086680761\n",
            "Fold:9, Epoch: [33/40], Step: [18/29], Loss: 0.3813845117886861, Validation Loss:0.5930946096777916, Validation Acc:76.69172932330827, AUC:0.7044397463002113\n",
            "Fold:9, Epoch: [33/40], Step: [27/29], Loss: 0.3652448742477982, Validation Loss:0.6095103099942207, Validation Acc:74.93734335839599, AUC:0.7095401691331924\n",
            "Epoch 32 time: 1.8873\n",
            "Fold:9, Epoch: [34/40], Step: [9/29], Loss: 0.35741860999001396, Validation Loss:0.7974004223942757, Validation Acc:75.93984962406014, AUC:0.6618921775898521\n",
            "Fold:9, Epoch: [34/40], Step: [18/29], Loss: 0.3423213205403752, Validation Loss:0.6764176338911057, Validation Acc:79.4486215538847, AUC:0.6746035940803382\n",
            "Fold:9, Epoch: [34/40], Step: [27/29], Loss: 0.3266868127716912, Validation Loss:0.8887266218662262, Validation Acc:81.45363408521304, AUC:0.6785940803382664\n",
            "Epoch 33 time: 1.8718\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:9, Epoch: [35/40], Step: [9/29], Loss: 0.30658628874354893, Validation Loss:0.6926331967115402, Validation Acc:68.67167919799499, AUC:0.6732029598308668\n",
            "Fold:9, Epoch: [35/40], Step: [18/29], Loss: 0.3085177366932233, Validation Loss:0.7933869361877441, Validation Acc:80.70175438596492, AUC:0.643683932346723\n",
            "Fold:9, Epoch: [35/40], Step: [27/29], Loss: 0.31898470719655353, Validation Loss:1.0279908180236816, Validation Acc:80.45112781954887, AUC:0.6727801268498943\n",
            "Epoch 34 time: 1.8741\n",
            "Fold:9, Epoch: [36/40], Step: [9/29], Loss: 0.32527710994084674, Validation Loss:0.7183835506439209, Validation Acc:78.19548872180451, AUC:0.6826109936575052\n",
            "Fold:9, Epoch: [36/40], Step: [18/29], Loss: 0.32854366219706005, Validation Loss:0.6842740327119827, Validation Acc:73.93483709273183, AUC:0.673176532769556\n",
            "Fold:9, Epoch: [36/40], Step: [27/29], Loss: 0.33036573783115103, Validation Loss:0.6489253044128418, Validation Acc:77.44360902255639, AUC:0.6706131078224102\n",
            "Epoch 35 time: 1.8793\n",
            "Fold:9, Epoch: [37/40], Step: [9/29], Loss: 0.28588759568002486, Validation Loss:0.7295708805322647, Validation Acc:78.69674185463658, AUC:0.7084302325581396\n",
            "Fold:9, Epoch: [37/40], Step: [18/29], Loss: 0.2984996578759617, Validation Loss:0.6807059049606323, Validation Acc:74.68671679197995, AUC:0.6928118393234672\n",
            "Fold:9, Epoch: [37/40], Step: [27/29], Loss: 0.30741644291966047, Validation Loss:0.9760918766260147, Validation Acc:76.94235588972431, AUC:0.6371564482029598\n",
            "Epoch 36 time: 1.8827\n",
            "Fold:9, Epoch: [38/40], Step: [9/29], Loss: 0.30659838020801544, Validation Loss:0.7496266961097717, Validation Acc:73.1829573934837, AUC:0.6764534883720931\n",
            "Fold:9, Epoch: [38/40], Step: [18/29], Loss: 0.3045526337292459, Validation Loss:0.9458173364400864, Validation Acc:77.69423558897243, AUC:0.6949788583509513\n",
            "Fold:9, Epoch: [38/40], Step: [27/29], Loss: 0.3079316903043676, Validation Loss:0.8792468011379242, Validation Acc:80.20050125313283, AUC:0.6789640591966173\n",
            "Epoch 37 time: 1.8863\n",
            "Fold:9, Epoch: [39/40], Step: [9/29], Loss: 0.31262989342212677, Validation Loss:0.9946570843458176, Validation Acc:75.68922305764411, AUC:0.6757135306553911\n",
            "Fold:9, Epoch: [39/40], Step: [18/29], Loss: 0.3097923621535301, Validation Loss:0.6852084025740623, Validation Acc:74.18546365914787, AUC:0.674630021141649\n",
            "Fold:9, Epoch: [39/40], Step: [27/29], Loss: 0.29563453959094155, Validation Loss:0.7089201435446739, Validation Acc:78.44611528822055, AUC:0.6764270613107823\n",
            "Epoch 38 time: 1.8969\n",
            "Fold:9, Epoch: [40/40], Step: [9/29], Loss: 0.2813734942012363, Validation Loss:0.7841940969228745, Validation Acc:78.69674185463658, AUC:0.6396934460887949\n",
            "Fold:9, Epoch: [40/40], Step: [18/29], Loss: 0.28264082968235016, Validation Loss:0.7106583938002586, Validation Acc:77.69423558897243, AUC:0.6873414376321353\n",
            "Fold:9, Epoch: [40/40], Step: [27/29], Loss: 0.27502572370900047, Validation Loss:0.7724400013685226, Validation Acc:78.44611528822055, AUC:0.6840644820295982\n",
            "Epoch 39 time: 1.8636\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:10, Epoch: [1/40], Step: [9/29], Loss: 0.6978638105922275, Validation Loss:0.6849768310785294, Validation Acc:61.40350877192982, AUC:0.5775898520084567\n",
            "Fold:10, Epoch: [1/40], Step: [18/29], Loss: 0.671059764093823, Validation Loss:0.6658293604850769, Validation Acc:69.67418546365914, AUC:0.6331923890063424\n",
            "Fold:10, Epoch: [1/40], Step: [27/29], Loss: 0.6576247060740436, Validation Loss:0.6603478342294693, Validation Acc:74.18546365914787, AUC:0.7280919661733616\n",
            "Epoch 0 time: 1.8669\n",
            "Fold:10, Epoch: [2/40], Step: [9/29], Loss: 0.6000466015603807, Validation Loss:0.6036451458930969, Validation Acc:75.18796992481202, AUC:0.7339059196617336\n",
            "Fold:10, Epoch: [2/40], Step: [18/29], Loss: 0.5790581686629189, Validation Loss:0.5429927706718445, Validation Acc:74.43609022556392, AUC:0.7371828752642706\n",
            "Fold:10, Epoch: [2/40], Step: [27/29], Loss: 0.5756818552811941, Validation Loss:0.6464346051216125, Validation Acc:74.93734335839599, AUC:0.7400898520084567\n",
            "Epoch 1 time: 1.8772\n",
            "Fold:10, Epoch: [3/40], Step: [9/29], Loss: 0.5471802022722032, Validation Loss:0.6115803420543671, Validation Acc:76.44110275689223, AUC:0.7258985200845667\n",
            "Fold:10, Epoch: [3/40], Step: [18/29], Loss: 0.5460334320863088, Validation Loss:0.5603720098733902, Validation Acc:72.18045112781955, AUC:0.7622885835095138\n",
            "Fold:10, Epoch: [3/40], Step: [27/29], Loss: 0.5486928975140607, Validation Loss:0.649041086435318, Validation Acc:68.92230576441102, AUC:0.7433932346723046\n",
            "Epoch 2 time: 1.8832\n",
            "Fold:10, Epoch: [4/40], Step: [9/29], Loss: 0.5107866757445865, Validation Loss:0.5659767836332321, Validation Acc:71.42857142857143, AUC:0.7197410147991542\n",
            "Fold:10, Epoch: [4/40], Step: [18/29], Loss: 0.5378577328390546, Validation Loss:0.5808132290840149, Validation Acc:67.16791979949875, AUC:0.7255813953488373\n",
            "Fold:10, Epoch: [4/40], Step: [27/29], Loss: 0.5390033710885931, Validation Loss:0.5651803761720657, Validation Acc:77.19298245614036, AUC:0.7226215644820295\n",
            "Epoch 3 time: 1.8539\n",
            "Fold:10, Epoch: [5/40], Step: [9/29], Loss: 0.5369272298283048, Validation Loss:0.5341767594218254, Validation Acc:70.92731829573935, AUC:0.7473837209302325\n",
            "Fold:10, Epoch: [5/40], Step: [18/29], Loss: 0.5322633352544572, Validation Loss:0.5560109168291092, Validation Acc:64.91228070175438, AUC:0.7430496828752642\n",
            "Fold:10, Epoch: [5/40], Step: [27/29], Loss: 0.5292142982836123, Validation Loss:0.648735985159874, Validation Acc:72.18045112781955, AUC:0.7470137420718815\n",
            "Epoch 4 time: 1.9256\n",
            "Fold:10, Epoch: [6/40], Step: [9/29], Loss: 0.533622165520986, Validation Loss:0.5122545957565308, Validation Acc:74.18546365914787, AUC:0.7357293868921776\n",
            "Fold:10, Epoch: [6/40], Step: [18/29], Loss: 0.5316483643319871, Validation Loss:0.5466495305299759, Validation Acc:78.69674185463658, AUC:0.7466173361522198\n",
            "Fold:10, Epoch: [6/40], Step: [27/29], Loss: 0.5194686463585606, Validation Loss:0.5349932685494423, Validation Acc:74.68671679197995, AUC:0.7309989429175475\n",
            "Epoch 5 time: 1.8725\n",
            "Fold:10, Epoch: [7/40], Step: [9/29], Loss: 0.5324670705530379, Validation Loss:0.5368894562125206, Validation Acc:62.65664160401003, AUC:0.6765063424947145\n",
            "Fold:10, Epoch: [7/40], Step: [18/29], Loss: 0.5317900528510412, Validation Loss:0.5282308980822563, Validation Acc:74.68671679197995, AUC:0.7386363636363636\n",
            "Fold:10, Epoch: [7/40], Step: [27/29], Loss: 0.5256304774019454, Validation Loss:0.5646110624074936, Validation Acc:72.68170426065163, AUC:0.7270084566596194\n",
            "Epoch 6 time: 1.8608\n",
            "Fold:10, Epoch: [8/40], Step: [9/29], Loss: 0.49458302723036873, Validation Loss:0.5073832273483276, Validation Acc:75.68922305764411, AUC:0.7444503171247357\n",
            "Fold:10, Epoch: [8/40], Step: [18/29], Loss: 0.4996013310220506, Validation Loss:0.680345706641674, Validation Acc:75.93984962406014, AUC:0.7153541226215645\n",
            "Fold:10, Epoch: [8/40], Step: [27/29], Loss: 0.5063478350639343, Validation Loss:0.5269593968987465, Validation Acc:75.68922305764411, AUC:0.6909883720930233\n",
            "Epoch 7 time: 1.8999\n",
            "Fold:10, Epoch: [9/40], Step: [9/29], Loss: 0.5231279101636674, Validation Loss:0.5359096527099609, Validation Acc:67.66917293233082, AUC:0.7437632135306554\n",
            "Fold:10, Epoch: [9/40], Step: [18/29], Loss: 0.512641837199529, Validation Loss:0.5187686905264854, Validation Acc:76.19047619047619, AUC:0.7091701902748414\n",
            "Fold:10, Epoch: [9/40], Step: [27/29], Loss: 0.512726425020783, Validation Loss:0.589260995388031, Validation Acc:76.44110275689223, AUC:0.6953488372093023\n",
            "Epoch 8 time: 1.8854\n",
            "Fold:10, Epoch: [10/40], Step: [9/29], Loss: 0.5201537244849734, Validation Loss:0.5763778388500214, Validation Acc:76.94235588972431, AUC:0.6753435517970401\n",
            "Fold:10, Epoch: [10/40], Step: [18/29], Loss: 0.5203985604974959, Validation Loss:0.5582138076424599, Validation Acc:72.93233082706767, AUC:0.7284619450317125\n",
            "Fold:10, Epoch: [10/40], Step: [27/29], Loss: 0.5083512939788677, Validation Loss:0.5397903472185135, Validation Acc:76.69172932330827, AUC:0.7349894291754757\n",
            "Epoch 9 time: 1.8846\n",
            "Fold:10, Epoch: [11/40], Step: [9/29], Loss: 0.4936768776840634, Validation Loss:0.5137652233242989, Validation Acc:72.43107769423558, AUC:0.7561046511627907\n",
            "Fold:10, Epoch: [11/40], Step: [18/29], Loss: 0.5004283984502157, Validation Loss:0.5857440605759621, Validation Acc:74.18546365914787, AUC:0.7128171247357293\n",
            "Fold:10, Epoch: [11/40], Step: [27/29], Loss: 0.4957832936887388, Validation Loss:0.5370329841971397, Validation Acc:73.6842105263158, AUC:0.7251849894291754\n",
            "Epoch 10 time: 1.9066\n",
            "Fold:10, Epoch: [12/40], Step: [9/29], Loss: 0.47737138470013935, Validation Loss:0.5400487706065178, Validation Acc:73.1829573934837, AUC:0.7146405919661734\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:10, Epoch: [12/40], Step: [18/29], Loss: 0.48843764927652145, Validation Loss:0.6271394044160843, Validation Acc:76.44110275689223, AUC:0.7106236786469344\n",
            "Fold:10, Epoch: [12/40], Step: [27/29], Loss: 0.49156515796979267, Validation Loss:0.518113262951374, Validation Acc:73.6842105263158, AUC:0.7328224101479915\n",
            "Epoch 11 time: 1.8969\n",
            "Fold:10, Epoch: [13/40], Step: [9/29], Loss: 0.5014203389485677, Validation Loss:0.5337724834680557, Validation Acc:74.68671679197995, AUC:0.7233615221987315\n",
            "Fold:10, Epoch: [13/40], Step: [18/29], Loss: 0.4935745762454139, Validation Loss:0.4779911898076534, Validation Acc:77.19298245614036, AUC:0.7302589852008458\n",
            "Fold:10, Epoch: [13/40], Step: [27/29], Loss: 0.47619536739808543, Validation Loss:0.767862930893898, Validation Acc:74.18546365914787, AUC:0.7204545454545455\n",
            "Epoch 12 time: 1.8841\n",
            "Fold:10, Epoch: [14/40], Step: [9/29], Loss: 0.48193742831548053, Validation Loss:0.5313535556197166, Validation Acc:72.93233082706767, AUC:0.7208245243128963\n",
            "Fold:10, Epoch: [14/40], Step: [18/29], Loss: 0.46963783933056724, Validation Loss:0.5350189059972763, Validation Acc:72.68170426065163, AUC:0.7117336152219873\n",
            "Fold:10, Epoch: [14/40], Step: [27/29], Loss: 0.46731529302067226, Validation Loss:0.5305147171020508, Validation Acc:74.43609022556392, AUC:0.7142706131078225\n",
            "Epoch 13 time: 1.8672\n",
            "Fold:10, Epoch: [15/40], Step: [9/29], Loss: 0.4792832268608941, Validation Loss:0.7108968645334244, Validation Acc:73.93483709273183, AUC:0.7190010570824525\n",
            "Fold:10, Epoch: [15/40], Step: [18/29], Loss: 0.47315631144576603, Validation Loss:0.5316378027200699, Validation Acc:77.44360902255639, AUC:0.6858879492600422\n",
            "Fold:10, Epoch: [15/40], Step: [27/29], Loss: 0.4653159026746397, Validation Loss:0.550609402358532, Validation Acc:72.43107769423558, AUC:0.7255549682875264\n",
            "Epoch 14 time: 1.8691\n",
            "Fold:10, Epoch: [16/40], Step: [9/29], Loss: 0.4654476311471727, Validation Loss:0.5649889186024666, Validation Acc:78.69674185463658, AUC:0.6778805496828753\n",
            "Fold:10, Epoch: [16/40], Step: [18/29], Loss: 0.46124901870886487, Validation Loss:0.5791591703891754, Validation Acc:77.44360902255639, AUC:0.7088002114164904\n",
            "Fold:10, Epoch: [16/40], Step: [27/29], Loss: 0.46195110899430736, Validation Loss:0.4896387495100498, Validation Acc:70.92731829573935, AUC:0.7321088794926004\n",
            "Epoch 15 time: 1.8888\n",
            "Fold:10, Epoch: [17/40], Step: [9/29], Loss: 0.49033227894041276, Validation Loss:0.5031249225139618, Validation Acc:63.40852130325815, AUC:0.7190539112050739\n",
            "Fold:10, Epoch: [17/40], Step: [18/29], Loss: 0.474270717965232, Validation Loss:0.5305957272648811, Validation Acc:79.9498746867168, AUC:0.6851479915433404\n",
            "Fold:10, Epoch: [17/40], Step: [27/29], Loss: 0.46836409635014004, Validation Loss:0.6176342740654945, Validation Acc:80.95238095238095, AUC:0.6833245243128964\n",
            "Epoch 16 time: 1.9152\n",
            "Fold:10, Epoch: [18/40], Step: [9/29], Loss: 0.4614311456680298, Validation Loss:0.5983870327472687, Validation Acc:74.68671679197995, AUC:0.7309989429175475\n",
            "Fold:10, Epoch: [18/40], Step: [18/29], Loss: 0.447524971432156, Validation Loss:0.5359122157096863, Validation Acc:76.69172932330827, AUC:0.757901691331924\n",
            "Fold:10, Epoch: [18/40], Step: [27/29], Loss: 0.4514060737910094, Validation Loss:0.5593578889966011, Validation Acc:80.20050125313283, AUC:0.7247885835095137\n",
            "Epoch 17 time: 1.8754\n",
            "Fold:10, Epoch: [19/40], Step: [9/29], Loss: 0.41998379760318333, Validation Loss:0.5934957712888718, Validation Acc:82.20551378446115, AUC:0.6982293868921776\n",
            "Fold:10, Epoch: [19/40], Step: [18/29], Loss: 0.43124332361751133, Validation Loss:0.5483426749706268, Validation Acc:75.18796992481202, AUC:0.7186310782241014\n",
            "Fold:10, Epoch: [19/40], Step: [27/29], Loss: 0.44039003054300946, Validation Loss:0.5072756484150887, Validation Acc:68.67167919799499, AUC:0.7495771670190275\n",
            "Epoch 18 time: 1.8633\n",
            "Fold:10, Epoch: [20/40], Step: [9/29], Loss: 0.45242323809199864, Validation Loss:0.49048954248428345, Validation Acc:77.94486215538848, AUC:0.7498942917547567\n",
            "Fold:10, Epoch: [20/40], Step: [18/29], Loss: 0.44169017010264927, Validation Loss:0.5467491149902344, Validation Acc:73.93483709273183, AUC:0.7495507399577167\n",
            "Fold:10, Epoch: [20/40], Step: [27/29], Loss: 0.44656217429373, Validation Loss:0.5896730348467827, Validation Acc:74.68671679197995, AUC:0.7309989429175475\n",
            "Epoch 19 time: 1.8567\n",
            "Fold:10, Epoch: [21/40], Step: [9/29], Loss: 0.42537814378738403, Validation Loss:0.5734453648328781, Validation Acc:77.44360902255639, AUC:0.6858879492600422\n",
            "Fold:10, Epoch: [21/40], Step: [18/29], Loss: 0.42305703461170197, Validation Loss:0.5348327308893204, Validation Acc:74.43609022556392, AUC:0.7295454545454545\n",
            "Fold:10, Epoch: [21/40], Step: [27/29], Loss: 0.4376941168749774, Validation Loss:0.5663715824484825, Validation Acc:66.16541353383458, AUC:0.712130021141649\n",
            "Epoch 20 time: 1.8777\n",
            "Fold:10, Epoch: [22/40], Step: [9/29], Loss: 0.43709402283032733, Validation Loss:0.6334286257624626, Validation Acc:77.94486215538848, AUC:0.7269820295983086\n",
            "Fold:10, Epoch: [22/40], Step: [18/29], Loss: 0.4414818336566289, Validation Loss:0.5085379555821419, Validation Acc:78.19548872180451, AUC:0.7055232558139535\n",
            "Fold:10, Epoch: [22/40], Step: [27/29], Loss: 0.4309050617394624, Validation Loss:0.5037086755037308, Validation Acc:78.44611528822055, AUC:0.7222515856236786\n",
            "Epoch 21 time: 1.8590\n",
            "Fold:10, Epoch: [23/40], Step: [9/29], Loss: 0.41326531436708236, Validation Loss:0.5493191629648209, Validation Acc:71.67919799498746, AUC:0.7211945031712472\n",
            "Fold:10, Epoch: [23/40], Step: [18/29], Loss: 0.40497424370712704, Validation Loss:0.6353059858083725, Validation Acc:80.20050125313283, AUC:0.6713266384778013\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold:10, Epoch: [23/40], Step: [27/29], Loss: 0.41889586823957936, Validation Loss:0.5878079012036324, Validation Acc:72.43107769423558, AUC:0.7408298097251586\n",
            "Epoch 22 time: 1.8710\n",
            "Fold:10, Epoch: [24/40], Step: [9/29], Loss: 0.41358620259496903, Validation Loss:0.5621659308671951, Validation Acc:80.95238095238095, AUC:0.7520613107822411\n",
            "Fold:10, Epoch: [24/40], Step: [18/29], Loss: 0.4357303712103102, Validation Loss:0.6350180879235268, Validation Acc:78.69674185463658, AUC:0.7007928118393235\n",
            "Fold:10, Epoch: [24/40], Step: [27/29], Loss: 0.4257527490456899, Validation Loss:0.5569722801446915, Validation Acc:78.19548872180451, AUC:0.7360729386892179\n",
            "Epoch 23 time: 1.8737\n",
            "Fold:10, Epoch: [25/40], Step: [9/29], Loss: 0.41400280594825745, Validation Loss:0.5585311651229858, Validation Acc:63.659147869674186, AUC:0.7205073995771669\n",
            "Fold:10, Epoch: [25/40], Step: [18/29], Loss: 0.4134732054339515, Validation Loss:0.5389377102255821, Validation Acc:76.94235588972431, AUC:0.7058932346723044\n",
            "Fold:10, Epoch: [25/40], Step: [27/29], Loss: 0.42317744427257115, Validation Loss:0.5996268913149834, Validation Acc:80.20050125313283, AUC:0.6789640591966173\n",
            "Epoch 24 time: 1.8703\n",
            "Fold:10, Epoch: [26/40], Step: [9/29], Loss: 0.41218960616323685, Validation Loss:0.6271051093935966, Validation Acc:73.43358395989975, AUC:0.7390063424947146\n",
            "Fold:10, Epoch: [26/40], Step: [18/29], Loss: 0.4111577802234226, Validation Loss:0.5345700085163116, Validation Acc:76.44110275689223, AUC:0.7029862579281184\n",
            "Fold:10, Epoch: [26/40], Step: [27/29], Loss: 0.40674550444991503, Validation Loss:0.5810250341892242, Validation Acc:79.4486215538847, AUC:0.6822410147991543\n",
            "Epoch 25 time: 1.8893\n",
            "Fold:10, Epoch: [27/40], Step: [9/29], Loss: 0.43650462561183506, Validation Loss:0.5222566798329353, Validation Acc:75.43859649122807, AUC:0.7353594080338266\n",
            "Fold:10, Epoch: [27/40], Step: [18/29], Loss: 0.41048544810877907, Validation Loss:0.5404223799705505, Validation Acc:73.1829573934837, AUC:0.7222780126849894\n",
            "Fold:10, Epoch: [27/40], Step: [27/29], Loss: 0.40032659305466545, Validation Loss:0.5287477746605873, Validation Acc:78.94736842105263, AUC:0.6946088794926004\n",
            "Epoch 26 time: 1.8793\n",
            "Fold:10, Epoch: [28/40], Step: [9/29], Loss: 0.412345031897227, Validation Loss:0.5260010957717896, Validation Acc:76.94235588972431, AUC:0.7364429175475687\n",
            "Fold:10, Epoch: [28/40], Step: [18/29], Loss: 0.41311966876188916, Validation Loss:0.5588103234767914, Validation Acc:77.94486215538848, AUC:0.7040697674418606\n",
            "Fold:10, Epoch: [28/40], Step: [27/29], Loss: 0.41121441236248724, Validation Loss:0.49206629022955894, Validation Acc:79.9498746867168, AUC:0.7004228329809724\n",
            "Epoch 27 time: 1.9023\n",
            "Fold:10, Epoch: [29/40], Step: [9/29], Loss: 0.4070950680308872, Validation Loss:0.6376253664493561, Validation Acc:74.18546365914787, AUC:0.7280919661733616\n",
            "Fold:10, Epoch: [29/40], Step: [18/29], Loss: 0.3972597122192383, Validation Loss:0.5540750026702881, Validation Acc:79.69924812030075, AUC:0.7218816067653278\n",
            "Fold:10, Epoch: [29/40], Step: [27/29], Loss: 0.4021036492453681, Validation Loss:0.4846845716238022, Validation Acc:74.43609022556392, AUC:0.7524577167019028\n",
            "Epoch 28 time: 1.8877\n",
            "Fold:10, Epoch: [30/40], Step: [9/29], Loss: 0.38321781489584184, Validation Loss:0.6165926828980446, Validation Acc:77.94486215538848, AUC:0.7040697674418606\n",
            "Fold:10, Epoch: [30/40], Step: [18/29], Loss: 0.3927149689859814, Validation Loss:0.6666123196482658, Validation Acc:79.19799498746868, AUC:0.6960623678646934\n",
            "Fold:10, Epoch: [30/40], Step: [27/29], Loss: 0.3874157965183258, Validation Loss:0.5306858196854591, Validation Acc:76.69172932330827, AUC:0.7197145877378436\n",
            "Epoch 29 time: 1.8788\n",
            "Fold:10, Epoch: [31/40], Step: [9/29], Loss: 0.3707112967967987, Validation Loss:0.5559502094984055, Validation Acc:77.44360902255639, AUC:0.7317124735729387\n",
            "Fold:10, Epoch: [31/40], Step: [18/29], Loss: 0.3724176072412067, Validation Loss:0.5887301862239838, Validation Acc:78.44611528822055, AUC:0.7222515856236786\n",
            "Fold:10, Epoch: [31/40], Step: [27/29], Loss: 0.3677224572058077, Validation Loss:0.8381595760583878, Validation Acc:78.19548872180451, AUC:0.7284355179704016\n",
            "Epoch 30 time: 1.8851\n",
            "Fold:10, Epoch: [32/40], Step: [9/29], Loss: 0.36694074008199906, Validation Loss:0.6003645732998848, Validation Acc:77.69423558897243, AUC:0.7102536997885834\n",
            "Fold:10, Epoch: [32/40], Step: [18/29], Loss: 0.3761836820178562, Validation Loss:0.6480295807123184, Validation Acc:79.9498746867168, AUC:0.6851479915433404\n",
            "Fold:10, Epoch: [32/40], Step: [27/29], Loss: 0.3675776046735269, Validation Loss:0.5803249552845955, Validation Acc:79.69924812030075, AUC:0.6913319238900633\n",
            "Epoch 31 time: 1.8792\n",
            "Fold:10, Epoch: [33/40], Step: [9/29], Loss: 0.3541982803079817, Validation Loss:0.5999442487955093, Validation Acc:75.68922305764411, AUC:0.7139006342494715\n",
            "Fold:10, Epoch: [33/40], Step: [18/29], Loss: 0.3480043063561122, Validation Loss:0.5957974456250668, Validation Acc:78.69674185463658, AUC:0.6855179704016913\n",
            "Fold:10, Epoch: [33/40], Step: [27/29], Loss: 0.35516112490936563, Validation Loss:0.6366761028766632, Validation Acc:76.94235588972431, AUC:0.7440803382663848\n",
            "Epoch 32 time: 1.8688\n",
            "Fold:10, Epoch: [34/40], Step: [9/29], Loss: 0.329345530933804, Validation Loss:0.5825619995594025, Validation Acc:79.69924812030075, AUC:0.7066067653276956\n",
            "Fold:10, Epoch: [34/40], Step: [18/29], Loss: 0.35820479525460136, Validation Loss:0.6345197558403015, Validation Acc:78.44611528822055, AUC:0.6917019027484144\n",
            "Fold:10, Epoch: [34/40], Step: [27/29], Loss: 0.3619547155168321, Validation Loss:0.6668953895568848, Validation Acc:74.68671679197995, AUC:0.7004492600422833\n",
            "Epoch 33 time: 1.8772\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold:10, Epoch: [35/40], Step: [9/29], Loss: 0.346364912059572, Validation Loss:0.5184372998774052, Validation Acc:77.69423558897243, AUC:0.7026162790697674\n",
            "Fold:10, Epoch: [35/40], Step: [18/29], Loss: 0.35855408675140804, Validation Loss:0.5992095172405243, Validation Acc:83.70927318295739, AUC:0.6840380549682875\n",
            "Fold:10, Epoch: [35/40], Step: [27/29], Loss: 0.36300258835156757, Validation Loss:0.567741185426712, Validation Acc:81.203007518797, AUC:0.6924154334038055\n",
            "Epoch 34 time: 1.8807\n",
            "Fold:10, Epoch: [36/40], Step: [9/29], Loss: 0.3761604030927022, Validation Loss:0.6202263236045837, Validation Acc:70.92731829573935, AUC:0.7168340380549683\n",
            "Fold:10, Epoch: [36/40], Step: [18/29], Loss: 0.3582106547223197, Validation Loss:0.6136047393083572, Validation Acc:77.69423558897243, AUC:0.6949788583509513\n",
            "Fold:10, Epoch: [36/40], Step: [27/29], Loss: 0.3505465631131773, Validation Loss:0.5780900791287422, Validation Acc:80.20050125313283, AUC:0.7553382663847781\n",
            "Epoch 35 time: 1.8832\n",
            "Fold:10, Epoch: [37/40], Step: [9/29], Loss: 0.34774156742625767, Validation Loss:0.5607838183641434, Validation Acc:78.44611528822055, AUC:0.6993393234672304\n",
            "Fold:10, Epoch: [37/40], Step: [18/29], Loss: 0.3471587515539593, Validation Loss:0.578995831310749, Validation Acc:79.9498746867168, AUC:0.7156976744186048\n",
            "Fold:10, Epoch: [37/40], Step: [27/29], Loss: 0.33329123976054015, Validation Loss:0.6473346054553986, Validation Acc:73.6842105263158, AUC:0.7251849894291754\n",
            "Epoch 36 time: 1.8805\n",
            "Fold:10, Epoch: [38/40], Step: [9/29], Loss: 0.33203278647528756, Validation Loss:0.5836844332516193, Validation Acc:79.4486215538847, AUC:0.7280655391120507\n",
            "Fold:10, Epoch: [38/40], Step: [18/29], Loss: 0.3226838351951705, Validation Loss:0.6233754083514214, Validation Acc:82.45614035087719, AUC:0.6996828752642706\n",
            "Fold:10, Epoch: [38/40], Step: [27/29], Loss: 0.321060163003427, Validation Loss:0.6537457555532455, Validation Acc:79.4486215538847, AUC:0.6975158562367864\n",
            "Epoch 37 time: 1.8591\n",
            "Fold:10, Epoch: [39/40], Step: [9/29], Loss: 0.317414124806722, Validation Loss:0.6833896934986115, Validation Acc:78.69674185463658, AUC:0.6778805496828753\n",
            "Fold:10, Epoch: [39/40], Step: [18/29], Loss: 0.31710703008704716, Validation Loss:0.7253277599811554, Validation Acc:80.20050125313283, AUC:0.7247885835095137\n",
            "Fold:10, Epoch: [39/40], Step: [27/29], Loss: 0.31345702211062115, Validation Loss:0.6435994356870651, Validation Acc:76.94235588972431, AUC:0.6906183932346723\n",
            "Epoch 38 time: 1.8911\n",
            "Fold:10, Epoch: [40/40], Step: [9/29], Loss: 0.3370460205607944, Validation Loss:0.6557703465223312, Validation Acc:80.20050125313283, AUC:0.7095137420718814\n",
            "Fold:10, Epoch: [40/40], Step: [18/29], Loss: 0.31116489817698795, Validation Loss:0.620567798614502, Validation Acc:80.70175438596492, AUC:0.7124207188160676\n",
            "Fold:10, Epoch: [40/40], Step: [27/29], Loss: 0.3109458800819185, Validation Loss:0.6628529280424118, Validation Acc:72.93233082706767, AUC:0.6902748414376321\n",
            "Epoch 39 time: 1.9040\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6U-o5SYexfKt",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "e55946be-9492-4506-9148-d30cf8a1f9c9",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525230667799,
          "user_tz": 240,
          "elapsed": 526,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(gru_mean_train_loss_history)\n",
        "plt.plot(gru_mean_val_loss_history)"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7e40477e80>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EsvYDO58ZDewDER4O6xfUUrF5UahIuqstIoQxHZsEA6ZptBVgCSYR4XZJJ35w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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7e686cdba8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "L8fkj_NGlaHo",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "bccf3de0-8527-4a27-b1ff-0a2722adf6c8",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525230671609,
          "user_tz": 240,
          "elapsed": 891,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(gru_mean_test_auc_history)"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7e403ddcc0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lKqFLrE6ZYi5X7xrz/AwJAfcagiMGkYTdj5rehyipeOn1BcTNcT12bu1IaqzR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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7e2aa99f60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rqGYs1QCdMJk",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b6d6872c-e69a-418b-9fbc-3a3c260a5b5b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525230659807,
          "user_tz": 240,
          "elapsed": 282,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Best AUC: %.4f\" % np.max([max(i) for i in gru_mean_test_auc_histories[:10]]))\n",
        "print(\"Mean AUC: %.4f, Std: %.4f\" % \n",
        "      (np.mean([max(i) for i in gru_mean_test_auc_histories]), \n",
        "       np.std([max(i) for i in gru_mean_test_auc_histories]))\n",
        "     )"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best AUC: 0.7895\n",
            "Mean AUC: 0.7628, Std: 0.0178\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "veaQRTwTrM5P",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# GRU-Forward"
      ]
    },
    {
      "metadata": {
        "id": "Kxe156AaybHa",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def test_model_gru_forward(loader, model, concat_input=False):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    model.eval()\n",
        "\n",
        "    predictions = []\n",
        "    truths = []\n",
        "    \n",
        "    total_loss = 0\n",
        "\n",
        "    for data, label in loader:\n",
        "        x, delta, m, x_forward, lengths = data\n",
        "\n",
        "        sorted_lengths, sorted_idx = lengths.sort(descending=True)\n",
        "\n",
        "        x = Variable(x[sorted_idx].float()).cuda()\n",
        "        delta = Variable(delta[sorted_idx].float()).cuda()\n",
        "        m = Variable(m[sorted_idx].float()).cuda()\n",
        "        x_forward = Variable(x_forward[sorted_idx].float()).cuda()\n",
        "\n",
        "        x[m.data.byte()] = x_forward[m.data.byte()]\n",
        "\n",
        "        output = model(pack_padded_sequence(x, sorted_lengths.numpy(), batch_first=True))\n",
        "        \n",
        "        label = label[sorted_idx]\n",
        "        loss = loss_func(output, Variable(label).cuda())\n",
        "            \n",
        "        total_loss += loss.data[0]\n",
        "        predicted = (output.cpu().max(1)[1].data.long()).view(-1)\n",
        "        predictions += list(predicted.numpy())\n",
        "        truths += list(label)\n",
        "        total += label.size(0)\n",
        "        correct += (predicted == label).sum()\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    return 100 * correct / total, roc_auc_score(truths, predictions), total_loss/len(loader)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "t-tRl1HZuEO8",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 27577
        },
        "outputId": "dab34c3a-be98-4574-a113-fbbd2df5b6c8",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525231482315,
          "user_tz": 240,
          "elapsed": 802812,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "gru_forward_train_loss_histories = []\n",
        "gru_forward_test_auc_histories = []\n",
        "gru_forward_val_loss_histories = []\n",
        "gru_forward_val_acc_histories = []\n",
        "\n",
        "for k in range(num_fold):\n",
        "    train_ds.set_active_indices(train_dfs[k])\n",
        "    test_ds.set_active_indices(test_dfs[k], new_means=train_ds.means, new_stds=train_ds.stds)\n",
        "    train_loader = torch.utils.data.DataLoader(dataset=train_ds,\n",
        "                                           batch_size=batch_size,\n",
        "                                           collate_fn=dataset.PhysioNET.collate_batch,\n",
        "                                           shuffle=True)\n",
        "    test_loader = torch.utils.data.DataLoader(dataset=test_ds,\n",
        "                                          batch_size=batch_size,\n",
        "                                          collate_fn=dataset.PhysioNET.collate_batch,\n",
        "                                          shuffle=True)\n",
        "\n",
        "    gru_forward = GRU_decoder(NUM_FEATURES, hidden_size, output_size, \n",
        "                      gru_dropout, decoder_dropout)\n",
        "    gru_forward.cuda()\n",
        "\n",
        "    optimizer = torch.optim.Adam(gru_forward.parameters(), lr=1e-3)\n",
        "    \n",
        "    gru_forward_train_loss_history = []\n",
        "    gru_forward_test_auc_history = []\n",
        "    gru_forward_val_loss_history = []\n",
        "    gru_forward_val_acc_history = []\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        start = time()\n",
        "        total_loss = 0.\n",
        "\n",
        "        for i, (data, label) in enumerate(train_loader):\n",
        "            x, delta, m, x_forward, lengths = data\n",
        "\n",
        "            sorted_lengths, sorted_idx = lengths.sort(descending=True)\n",
        "            \n",
        "            x = Variable(x[sorted_idx].float()).cuda()\n",
        "            delta = Variable(delta[sorted_idx].float()).cuda()\n",
        "            m = Variable(m[sorted_idx].float()).cuda()\n",
        "            x_forward = Variable(x_forward[sorted_idx].float()).cuda()\n",
        "\n",
        "            x[m.data.byte()] = x_forward[m.data.byte()]\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            output = gru_forward(pack_padded_sequence(x, sorted_lengths.numpy(), batch_first=True))\n",
        "\n",
        "            loss = loss_func(output, Variable(label[sorted_idx].long()).cuda())\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            total_loss += loss.data[0]\n",
        "\n",
        "            # report performance\n",
        "            if (i + 1) % log_interval == 0:\n",
        "                val_acc, test_auc, val_loss = test_model_gru_forward(test_loader, gru_forward)\n",
        "                gru_forward_train_loss_history.append(total_loss/(i+1))\n",
        "                gru_forward_test_auc_history.append(test_auc)\n",
        "                gru_forward_val_acc_history.append(val_acc)\n",
        "                gru_forward_val_loss_history.append(val_loss)\n",
        "                print('Fold: {8}, Epoch: [{0}/{1}], Step: [{2}/{3}], Loss: {4}, Validation Loss:{5}, Validation Acc:{6}, AUC:{7}'.format(\n",
        "                    epoch + 1, num_epochs, i + 1, len(train_loader), total_loss/(i+1), val_loss, val_acc, test_auc, k+1))\n",
        "        print(\"Epoch %d time: %.4f\" % (epoch, time()-start))\n",
        "\n",
        "    gru_forward_train_loss_histories.append(gru_forward_train_loss_history)\n",
        "    gru_forward_test_auc_histories.append(gru_forward_test_auc_history)\n",
        "    gru_forward_val_loss_histories.append(gru_forward_val_loss_history)\n",
        "    gru_forward_val_acc_histories.append(gru_forward_val_acc_history)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 1, Epoch: [1/40], Step: [9/29], Loss: 0.6912558608584933, Validation Loss:0.6569111794233322, Validation Acc:69.32668329177058, AUC:0.6497153209109732\n",
            "Fold: 1, Epoch: [1/40], Step: [18/29], Loss: 0.6490186386638217, Validation Loss:0.6138132959604263, Validation Acc:72.06982543640898, AUC:0.7180124223602485\n",
            "Fold: 1, Epoch: [1/40], Step: [27/29], Loss: 0.617095360049495, Validation Loss:0.5512276068329811, Validation Acc:70.57356608478803, AUC:0.7242753623188406\n",
            "Epoch 0 time: 2.0742\n",
            "Fold: 1, Epoch: [2/40], Step: [9/29], Loss: 0.5314640071656969, Validation Loss:0.5452506467700005, Validation Acc:67.83042394014963, AUC:0.7307712215320912\n",
            "Fold: 1, Epoch: [2/40], Step: [18/29], Loss: 0.5397722605201933, Validation Loss:0.616474412381649, Validation Acc:67.08229426433915, AUC:0.718944099378882\n",
            "Fold: 1, Epoch: [2/40], Step: [27/29], Loss: 0.5306930354347935, Validation Loss:0.5028433203697205, Validation Acc:69.07730673316708, AUC:0.7454968944099378\n",
            "Epoch 1 time: 1.9449\n",
            "Fold: 1, Epoch: [3/40], Step: [9/29], Loss: 0.5258682800663842, Validation Loss:0.5581059977412224, Validation Acc:69.57605985037407, AUC:0.7109989648033126\n",
            "Fold: 1, Epoch: [3/40], Step: [18/29], Loss: 0.5166437791453468, Validation Loss:0.5324107259511948, Validation Acc:71.3216957605985, AUC:0.713664596273292\n",
            "Fold: 1, Epoch: [3/40], Step: [27/29], Loss: 0.5219460195965238, Validation Loss:0.5487448498606682, Validation Acc:70.82294264339153, AUC:0.7332039337474121\n",
            "Epoch 2 time: 1.9928\n",
            "Fold: 1, Epoch: [4/40], Step: [9/29], Loss: 0.4844238559405009, Validation Loss:0.5195452570915222, Validation Acc:72.31920199501246, AUC:0.7344202898550725\n",
            "Fold: 1, Epoch: [4/40], Step: [18/29], Loss: 0.4996457000573476, Validation Loss:0.5330818891525269, Validation Acc:71.07231920199501, AUC:0.7196946169772257\n",
            "Fold: 1, Epoch: [4/40], Step: [27/29], Loss: 0.4994942954293004, Validation Loss:0.47308898717164993, Validation Acc:72.06982543640898, AUC:0.7404503105590061\n",
            "Epoch 3 time: 1.9691\n",
            "Fold: 1, Epoch: [5/40], Step: [9/29], Loss: 0.4930568171872033, Validation Loss:0.5124521479010582, Validation Acc:70.82294264339153, AUC:0.7332039337474121\n",
            "Fold: 1, Epoch: [5/40], Step: [18/29], Loss: 0.5041157222456403, Validation Loss:0.4803076609969139, Validation Acc:70.57356608478803, AUC:0.7317546583850931\n",
            "Fold: 1, Epoch: [5/40], Step: [27/29], Loss: 0.4958298890678971, Validation Loss:0.48354322463274, Validation Acc:69.32668329177058, AUC:0.7170289855072464\n",
            "Epoch 4 time: 2.0058\n",
            "Fold: 1, Epoch: [6/40], Step: [9/29], Loss: 0.49764299723837113, Validation Loss:0.47284799814224243, Validation Acc:69.07730673316708, AUC:0.7155797101449275\n",
            "Fold: 1, Epoch: [6/40], Step: [18/29], Loss: 0.46318595276938546, Validation Loss:0.4854902997612953, Validation Acc:70.82294264339153, AUC:0.7182453416149068\n",
            "Fold: 1, Epoch: [6/40], Step: [27/29], Loss: 0.4780631098482344, Validation Loss:0.4716149717569351, Validation Acc:69.82543640897755, AUC:0.7274068322981366\n",
            "Epoch 5 time: 1.9963\n",
            "Fold: 1, Epoch: [7/40], Step: [9/29], Loss: 0.49614665574497646, Validation Loss:0.47769877314567566, Validation Acc:68.82793017456359, AUC:0.7066511387163561\n",
            "Fold: 1, Epoch: [7/40], Step: [18/29], Loss: 0.48836616675059, Validation Loss:0.459122933447361, Validation Acc:72.81795511221945, AUC:0.7148809523809524\n",
            "Fold: 1, Epoch: [7/40], Step: [27/29], Loss: 0.4720769590801663, Validation Loss:0.6046371832489967, Validation Acc:71.3216957605985, AUC:0.7286231884057971\n",
            "Epoch 6 time: 1.9604\n",
            "Fold: 1, Epoch: [8/40], Step: [9/29], Loss: 0.45179882314470077, Validation Loss:0.4810495898127556, Validation Acc:71.07231920199501, AUC:0.7271739130434782\n",
            "Fold: 1, Epoch: [8/40], Step: [18/29], Loss: 0.46095649898052216, Validation Loss:0.48700182884931564, Validation Acc:71.57107231920199, AUC:0.730072463768116\n",
            "Fold: 1, Epoch: [8/40], Step: [27/29], Loss: 0.4566590454843309, Validation Loss:0.5309788286685944, Validation Acc:71.57107231920199, AUC:0.7225931677018633\n",
            "Epoch 7 time: 1.9792\n",
            "Fold: 1, Epoch: [9/40], Step: [9/29], Loss: 0.45851726664437187, Validation Loss:0.5748565346002579, Validation Acc:68.82793017456359, AUC:0.7141304347826087\n",
            "Fold: 1, Epoch: [9/40], Step: [18/29], Loss: 0.465348223845164, Validation Loss:0.5086183771491051, Validation Acc:70.07481296758105, AUC:0.7213768115942029\n",
            "Fold: 1, Epoch: [9/40], Step: [27/29], Loss: 0.450093537569046, Validation Loss:0.5072486251592636, Validation Acc:72.06982543640898, AUC:0.7180124223602485\n",
            "Epoch 8 time: 1.9636\n",
            "Fold: 1, Epoch: [10/40], Step: [9/29], Loss: 0.4266505671872033, Validation Loss:0.4743576720356941, Validation Acc:72.81795511221945, AUC:0.7373188405797102\n",
            "Fold: 1, Epoch: [10/40], Step: [18/29], Loss: 0.4325869099961387, Validation Loss:0.5015221014618874, Validation Acc:72.56857855361596, AUC:0.7283902691511387\n",
            "Fold: 1, Epoch: [10/40], Step: [27/29], Loss: 0.43912613171118275, Validation Loss:0.5414802953600883, Validation Acc:71.82044887780549, AUC:0.7240424430641821\n",
            "Epoch 9 time: 1.9702\n",
            "Fold: 1, Epoch: [11/40], Step: [9/29], Loss: 0.4395639995733897, Validation Loss:0.4838442727923393, Validation Acc:73.31670822942644, AUC:0.7252587991718427\n",
            "Fold: 1, Epoch: [11/40], Step: [18/29], Loss: 0.4296194612979889, Validation Loss:0.519169844686985, Validation Acc:67.58104738154613, AUC:0.714363354037267\n",
            "Fold: 1, Epoch: [11/40], Step: [27/29], Loss: 0.4374129264443009, Validation Loss:0.5294065922498703, Validation Acc:70.57356608478803, AUC:0.7167960662525881\n",
            "Epoch 10 time: 1.9726\n",
            "Fold: 1, Epoch: [12/40], Step: [9/29], Loss: 0.3949102858702342, Validation Loss:0.5350043252110481, Validation Acc:74.3142144638404, AUC:0.731055900621118\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 1, Epoch: [12/40], Step: [18/29], Loss: 0.40921059416400063, Validation Loss:0.5291879177093506, Validation Acc:70.82294264339153, AUC:0.7182453416149068\n",
            "Fold: 1, Epoch: [12/40], Step: [27/29], Loss: 0.42032298666459544, Validation Loss:0.5844492763280869, Validation Acc:71.07231920199501, AUC:0.7196946169772257\n",
            "Epoch 11 time: 1.9635\n",
            "Fold: 1, Epoch: [13/40], Step: [9/29], Loss: 0.3824976748890347, Validation Loss:0.6015201359987259, Validation Acc:75.56109725685785, AUC:0.7383022774327123\n",
            "Fold: 1, Epoch: [13/40], Step: [18/29], Loss: 0.4011077798075146, Validation Loss:0.5757630169391632, Validation Acc:74.3142144638404, AUC:0.731055900621118\n",
            "Fold: 1, Epoch: [13/40], Step: [27/29], Loss: 0.41140612297587925, Validation Loss:0.5651413723826408, Validation Acc:72.56857855361596, AUC:0.7209109730848862\n",
            "Epoch 12 time: 1.9566\n",
            "Fold: 1, Epoch: [14/40], Step: [9/29], Loss: 0.3988104462623596, Validation Loss:0.5191020667552948, Validation Acc:72.06982543640898, AUC:0.7105331262939959\n",
            "Fold: 1, Epoch: [14/40], Step: [18/29], Loss: 0.3928534405099021, Validation Loss:0.5051001608371735, Validation Acc:71.82044887780549, AUC:0.7165631469979297\n",
            "Fold: 1, Epoch: [14/40], Step: [27/29], Loss: 0.40220890001014425, Validation Loss:0.5150952935218811, Validation Acc:74.3142144638404, AUC:0.7460144927536232\n",
            "Epoch 13 time: 1.9628\n",
            "Fold: 1, Epoch: [15/40], Step: [9/29], Loss: 0.42179247074657017, Validation Loss:0.713999405503273, Validation Acc:68.82793017456359, AUC:0.6916925465838509\n",
            "Fold: 1, Epoch: [15/40], Step: [18/29], Loss: 0.44076906972461277, Validation Loss:0.5801555961370468, Validation Acc:71.07231920199501, AUC:0.7047360248447205\n",
            "Fold: 1, Epoch: [15/40], Step: [27/29], Loss: 0.4486746203016352, Validation Loss:0.5090976282954216, Validation Acc:74.3142144638404, AUC:0.7385351966873707\n",
            "Epoch 14 time: 1.9623\n",
            "Fold: 1, Epoch: [16/40], Step: [9/29], Loss: 0.4296911226378547, Validation Loss:0.46040961146354675, Validation Acc:73.81546134663341, AUC:0.7431159420289856\n",
            "Fold: 1, Epoch: [16/40], Step: [18/29], Loss: 0.4125562525457806, Validation Loss:0.5182385221123695, Validation Acc:74.56359102244389, AUC:0.7399844720496895\n",
            "Fold: 1, Epoch: [16/40], Step: [27/29], Loss: 0.4111601699281622, Validation Loss:0.5623138770461082, Validation Acc:74.3142144638404, AUC:0.7609730848861284\n",
            "Epoch 15 time: 1.9629\n",
            "Fold: 1, Epoch: [17/40], Step: [9/29], Loss: 0.40197303891181946, Validation Loss:0.49753452092409134, Validation Acc:74.3142144638404, AUC:0.7534937888198756\n",
            "Fold: 1, Epoch: [17/40], Step: [18/29], Loss: 0.4019838869571686, Validation Loss:0.5722744092345238, Validation Acc:74.56359102244389, AUC:0.7325051759834369\n",
            "Fold: 1, Epoch: [17/40], Step: [27/29], Loss: 0.39231319780702945, Validation Loss:0.6089648902416229, Validation Acc:73.81546134663341, AUC:0.7356366459627329\n",
            "Epoch 16 time: 1.9533\n",
            "Fold: 1, Epoch: [18/40], Step: [9/29], Loss: 0.37606311506695217, Validation Loss:0.5736447423696518, Validation Acc:73.56608478802993, AUC:0.7192287784679089\n",
            "Fold: 1, Epoch: [18/40], Step: [18/29], Loss: 0.37316955791579354, Validation Loss:0.5309539586305618, Validation Acc:74.3142144638404, AUC:0.731055900621118\n",
            "Fold: 1, Epoch: [18/40], Step: [27/29], Loss: 0.3764507638083564, Validation Loss:0.5131116174161434, Validation Acc:73.56608478802993, AUC:0.6967908902691511\n",
            "Epoch 17 time: 1.9605\n",
            "Fold: 1, Epoch: [19/40], Step: [9/29], Loss: 0.34070057339138454, Validation Loss:0.5703809261322021, Validation Acc:76.05985037406484, AUC:0.703804347826087\n",
            "Fold: 1, Epoch: [19/40], Step: [18/29], Loss: 0.3559606373310089, Validation Loss:0.5198497697710991, Validation Acc:75.81047381546135, AUC:0.7098343685300207\n",
            "Fold: 1, Epoch: [19/40], Step: [27/29], Loss: 0.3676590190993415, Validation Loss:0.577552005648613, Validation Acc:74.56359102244389, AUC:0.7250258799171843\n",
            "Epoch 18 time: 1.9722\n",
            "Fold: 1, Epoch: [20/40], Step: [9/29], Loss: 0.36010898484124076, Validation Loss:0.5519327148795128, Validation Acc:71.3216957605985, AUC:0.6837474120082816\n",
            "Fold: 1, Epoch: [20/40], Step: [18/29], Loss: 0.36324053671624923, Validation Loss:0.5803442373871803, Validation Acc:72.31920199501246, AUC:0.6596273291925465\n",
            "Fold: 1, Epoch: [20/40], Step: [27/29], Loss: 0.35435821336728557, Validation Loss:0.5985453054308891, Validation Acc:75.31172069825436, AUC:0.6844979296066253\n",
            "Epoch 19 time: 2.0267\n",
            "Fold: 1, Epoch: [21/40], Step: [9/29], Loss: 0.34763996137513054, Validation Loss:0.5727805346250534, Validation Acc:72.56857855361596, AUC:0.6984730848861284\n",
            "Fold: 1, Epoch: [21/40], Step: [18/29], Loss: 0.3513026121589873, Validation Loss:0.6275942176580429, Validation Acc:72.06982543640898, AUC:0.6880952380952382\n",
            "Fold: 1, Epoch: [21/40], Step: [27/29], Loss: 0.3487741903022484, Validation Loss:0.6055784225463867, Validation Acc:70.57356608478803, AUC:0.6644409937888199\n",
            "Epoch 20 time: 1.9774\n",
            "Fold: 1, Epoch: [22/40], Step: [9/29], Loss: 0.3350723816288842, Validation Loss:0.6125518828630447, Validation Acc:74.06483790523691, AUC:0.6996894409937887\n",
            "Fold: 1, Epoch: [22/40], Step: [18/29], Loss: 0.3417360136906306, Validation Loss:0.7664099484682083, Validation Acc:75.06234413965088, AUC:0.6980072463768117\n",
            "Fold: 1, Epoch: [22/40], Step: [27/29], Loss: 0.33952814561349376, Validation Loss:0.6572637557983398, Validation Acc:71.07231920199501, AUC:0.6748188405797101\n",
            "Epoch 21 time: 1.9942\n",
            "Fold: 1, Epoch: [23/40], Step: [9/29], Loss: 0.3151018139388826, Validation Loss:0.7049155384302139, Validation Acc:72.06982543640898, AUC:0.6955745341614906\n",
            "Fold: 1, Epoch: [23/40], Step: [18/29], Loss: 0.31315075440539253, Validation Loss:0.5912091061472893, Validation Acc:74.06483790523691, AUC:0.6772515527950311\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 1, Epoch: [23/40], Step: [27/29], Loss: 0.32042871746752, Validation Loss:0.7946431040763855, Validation Acc:75.31172069825436, AUC:0.6844979296066253\n",
            "Epoch 22 time: 1.9927\n",
            "Fold: 1, Epoch: [24/40], Step: [9/29], Loss: 0.29665739668740165, Validation Loss:0.8208629935979843, Validation Acc:75.31172069825436, AUC:0.6919772256728779\n",
            "Fold: 1, Epoch: [24/40], Step: [18/29], Loss: 0.3027973514464166, Validation Loss:0.7591411471366882, Validation Acc:71.57107231920199, AUC:0.6627587991718428\n",
            "Fold: 1, Epoch: [24/40], Step: [27/29], Loss: 0.3077485373726598, Validation Loss:0.6533182486891747, Validation Acc:72.56857855361596, AUC:0.6760351966873707\n",
            "Epoch 23 time: 1.9913\n",
            "Fold: 1, Epoch: [25/40], Step: [9/29], Loss: 0.2885007990731133, Validation Loss:0.7737295925617218, Validation Acc:74.56359102244389, AUC:0.6651915113871636\n",
            "Fold: 1, Epoch: [25/40], Step: [18/29], Loss: 0.29580464048518074, Validation Loss:0.6738001704216003, Validation Acc:77.0573566084788, AUC:0.679684265010352\n",
            "Fold: 1, Epoch: [25/40], Step: [27/29], Loss: 0.2929206402213485, Validation Loss:0.8494019284844398, Validation Acc:71.57107231920199, AUC:0.6702380952380953\n",
            "Epoch 24 time: 1.9695\n",
            "Fold: 1, Epoch: [26/40], Step: [9/29], Loss: 0.2761162552568648, Validation Loss:0.865661010146141, Validation Acc:72.56857855361596, AUC:0.668555900621118\n",
            "Fold: 1, Epoch: [26/40], Step: [18/29], Loss: 0.2953380023439725, Validation Loss:0.8017760813236237, Validation Acc:75.81047381546135, AUC:0.6724378881987578\n",
            "Fold: 1, Epoch: [26/40], Step: [27/29], Loss: 0.29064343262601783, Validation Loss:0.7787744104862213, Validation Acc:76.80798004987531, AUC:0.7081521739130434\n",
            "Epoch 25 time: 1.9567\n",
            "Fold: 1, Epoch: [27/40], Step: [9/29], Loss: 0.26943139731884, Validation Loss:0.6684171706438065, Validation Acc:72.31920199501246, AUC:0.6596273291925465\n",
            "Fold: 1, Epoch: [27/40], Step: [18/29], Loss: 0.2839438882138994, Validation Loss:0.6953970715403557, Validation Acc:75.31172069825436, AUC:0.6321428571428571\n",
            "Fold: 1, Epoch: [27/40], Step: [27/29], Loss: 0.28295810410269984, Validation Loss:0.8413802087306976, Validation Acc:74.06483790523691, AUC:0.662292960662526\n",
            "Epoch 26 time: 1.9768\n",
            "Fold: 1, Epoch: [28/40], Step: [9/29], Loss: 0.2530204670296775, Validation Loss:0.6682635694742203, Validation Acc:71.57107231920199, AUC:0.6702380952380953\n",
            "Fold: 1, Epoch: [28/40], Step: [18/29], Loss: 0.25327423297696644, Validation Loss:0.7471092492341995, Validation Acc:74.06483790523691, AUC:0.6548136645962732\n",
            "Fold: 1, Epoch: [28/40], Step: [27/29], Loss: 0.25881247719128925, Validation Loss:0.6937639713287354, Validation Acc:74.56359102244389, AUC:0.672670807453416\n",
            "Epoch 27 time: 1.9576\n",
            "Fold: 1, Epoch: [29/40], Step: [9/29], Loss: 0.24057111309634316, Validation Loss:0.6832248121500015, Validation Acc:74.3142144638404, AUC:0.6787008281573499\n",
            "Fold: 1, Epoch: [29/40], Step: [18/29], Loss: 0.23973243021302754, Validation Loss:0.8996796905994415, Validation Acc:76.05985037406484, AUC:0.666407867494824\n",
            "Fold: 1, Epoch: [29/40], Step: [27/29], Loss: 0.24505092369185555, Validation Loss:0.7429491281509399, Validation Acc:73.81546134663341, AUC:0.6608436853002071\n",
            "Epoch 28 time: 1.9917\n",
            "Fold: 1, Epoch: [30/40], Step: [9/29], Loss: 0.24550335937076145, Validation Loss:0.723443828523159, Validation Acc:74.3142144638404, AUC:0.6637422360248447\n",
            "Fold: 1, Epoch: [30/40], Step: [18/29], Loss: 0.2356021064851019, Validation Loss:0.8067147433757782, Validation Acc:74.81296758104739, AUC:0.674120082815735\n",
            "Fold: 1, Epoch: [30/40], Step: [27/29], Loss: 0.24220064116848838, Validation Loss:0.8472062647342682, Validation Acc:75.31172069825436, AUC:0.6919772256728779\n",
            "Epoch 29 time: 1.9524\n",
            "Fold: 1, Epoch: [31/40], Step: [9/29], Loss: 0.19441397156980303, Validation Loss:0.7481147199869156, Validation Acc:77.0573566084788, AUC:0.6572463768115943\n",
            "Fold: 1, Epoch: [31/40], Step: [18/29], Loss: 0.20897394004795286, Validation Loss:0.738485224545002, Validation Acc:78.30423940149626, AUC:0.6794513457556935\n",
            "Fold: 1, Epoch: [31/40], Step: [27/29], Loss: 0.2159050057331721, Validation Loss:0.8264494836330414, Validation Acc:75.81047381546135, AUC:0.6649585921325052\n",
            "Epoch 30 time: 1.9499\n",
            "Fold: 1, Epoch: [32/40], Step: [9/29], Loss: 0.2256661272711224, Validation Loss:0.8158238753676414, Validation Acc:72.81795511221945, AUC:0.6176501035196689\n",
            "Fold: 1, Epoch: [32/40], Step: [18/29], Loss: 0.22058772212929195, Validation Loss:1.1265314668416977, Validation Acc:73.81546134663341, AUC:0.6608436853002071\n",
            "Fold: 1, Epoch: [32/40], Step: [27/29], Loss: 0.21728207722858148, Validation Loss:0.8747325539588928, Validation Acc:76.80798004987531, AUC:0.6707556935817804\n",
            "Epoch 31 time: 1.9670\n",
            "Fold: 1, Epoch: [33/40], Step: [9/29], Loss: 0.19797675477133858, Validation Loss:0.9956357926130295, Validation Acc:77.80548628428927, AUC:0.6615942028985508\n",
            "Fold: 1, Epoch: [33/40], Step: [18/29], Loss: 0.1922860989967982, Validation Loss:0.9670402109622955, Validation Acc:78.05486284289277, AUC:0.6630434782608695\n",
            "Fold: 1, Epoch: [33/40], Step: [27/29], Loss: 0.19187991624629055, Validation Loss:1.0243688076734543, Validation Acc:75.56109725685785, AUC:0.6635093167701863\n",
            "Epoch 32 time: 1.9619\n",
            "Fold: 1, Epoch: [34/40], Step: [9/29], Loss: 0.17329088846842447, Validation Loss:1.0445850789546967, Validation Acc:75.81047381546135, AUC:0.6350414078674949\n",
            "Fold: 1, Epoch: [34/40], Step: [18/29], Loss: 0.1724744252860546, Validation Loss:1.0255167037248611, Validation Acc:76.80798004987531, AUC:0.6632763975155279\n",
            "Fold: 1, Epoch: [34/40], Step: [27/29], Loss: 0.17391920614021797, Validation Loss:0.8402011394500732, Validation Acc:75.81047381546135, AUC:0.6500000000000001\n",
            "Epoch 33 time: 1.9806\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 1, Epoch: [35/40], Step: [9/29], Loss: 0.1717032425933414, Validation Loss:1.0590910613536835, Validation Acc:77.80548628428927, AUC:0.6541149068322981\n",
            "Fold: 1, Epoch: [35/40], Step: [18/29], Loss: 0.17331144048107994, Validation Loss:0.9238729029893875, Validation Acc:75.56109725685785, AUC:0.6410714285714286\n",
            "Fold: 1, Epoch: [35/40], Step: [27/29], Loss: 0.17222058441903856, Validation Loss:1.0409976840019226, Validation Acc:75.56109725685785, AUC:0.6111542443064182\n",
            "Epoch 34 time: 1.9943\n",
            "Fold: 1, Epoch: [36/40], Step: [9/29], Loss: 0.16357229153315225, Validation Loss:0.8504685834050179, Validation Acc:77.80548628428927, AUC:0.6765527950310558\n",
            "Fold: 1, Epoch: [36/40], Step: [18/29], Loss: 0.17159437470965916, Validation Loss:0.9394032061100006, Validation Acc:76.05985037406484, AUC:0.6514492753623188\n",
            "Fold: 1, Epoch: [36/40], Step: [27/29], Loss: 0.17225836079429696, Validation Loss:1.1130431294441223, Validation Acc:73.81546134663341, AUC:0.6384057971014493\n",
            "Epoch 35 time: 1.9694\n",
            "Fold: 1, Epoch: [37/40], Step: [9/29], Loss: 0.15473830865489113, Validation Loss:0.8744859136641026, Validation Acc:79.30174563591022, AUC:0.6553312629399586\n",
            "Fold: 1, Epoch: [37/40], Step: [18/29], Loss: 0.16086661029193136, Validation Loss:1.2236846685409546, Validation Acc:74.56359102244389, AUC:0.6352743271221533\n",
            "Fold: 1, Epoch: [37/40], Step: [27/29], Loss: 0.15836712745604692, Validation Loss:1.0189067125320435, Validation Acc:78.55361596009975, AUC:0.6509834368530021\n",
            "Epoch 36 time: 1.9546\n",
            "Fold: 1, Epoch: [38/40], Step: [9/29], Loss: 0.13590626584159005, Validation Loss:1.1288760304450989, Validation Acc:78.80299251870325, AUC:0.6599120082815735\n",
            "Fold: 1, Epoch: [38/40], Step: [18/29], Loss: 0.13276850680510202, Validation Loss:1.048268973827362, Validation Acc:76.55860349127182, AUC:0.6543478260869564\n",
            "Fold: 1, Epoch: [38/40], Step: [27/29], Loss: 0.13502544937310396, Validation Loss:1.263181447982788, Validation Acc:78.80299251870325, AUC:0.6374741200828157\n",
            "Epoch 37 time: 1.9965\n",
            "Fold: 1, Epoch: [39/40], Step: [9/29], Loss: 0.12506108482678732, Validation Loss:1.2583511471748352, Validation Acc:79.05236907730674, AUC:0.6464026915113871\n",
            "Fold: 1, Epoch: [39/40], Step: [18/29], Loss: 0.11913915930522813, Validation Loss:1.1224945187568665, Validation Acc:77.3067331670823, AUC:0.6362577639751553\n",
            "Fold: 1, Epoch: [39/40], Step: [27/29], Loss: 0.11929271949662103, Validation Loss:1.318805530667305, Validation Acc:75.81047381546135, AUC:0.6574792960662525\n",
            "Epoch 38 time: 1.9755\n",
            "Fold: 1, Epoch: [40/40], Step: [9/29], Loss: 0.13856104306048816, Validation Loss:1.090870276093483, Validation Acc:79.80049875311721, AUC:0.6731884057971015\n",
            "Fold: 1, Epoch: [40/40], Step: [18/29], Loss: 0.1293389161841737, Validation Loss:1.4087757617235184, Validation Acc:80.0498753117207, AUC:0.6372412008281573\n",
            "Fold: 1, Epoch: [40/40], Step: [27/29], Loss: 0.13623864010528283, Validation Loss:1.171902447938919, Validation Acc:75.81047381546135, AUC:0.6500000000000001\n",
            "Epoch 39 time: 1.9581\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 2, Epoch: [1/40], Step: [9/29], Loss: 0.6883715391159058, Validation Loss:0.655120849609375, Validation Acc:79.30174563591022, AUC:0.6927277432712216\n",
            "Fold: 2, Epoch: [1/40], Step: [18/29], Loss: 0.6502097414599525, Validation Loss:0.5746204555034637, Validation Acc:77.55610972568579, AUC:0.7274585921325052\n",
            "Fold: 2, Epoch: [1/40], Step: [27/29], Loss: 0.6219631135463715, Validation Loss:0.5593947768211365, Validation Acc:75.31172069825436, AUC:0.7368530020703935\n",
            "Epoch 0 time: 1.9824\n",
            "Fold: 2, Epoch: [2/40], Step: [9/29], Loss: 0.5296115246084001, Validation Loss:0.5264822840690613, Validation Acc:72.81795511221945, AUC:0.7298395445134574\n",
            "Fold: 2, Epoch: [2/40], Step: [18/29], Loss: 0.5295815236038632, Validation Loss:0.527998298406601, Validation Acc:71.57107231920199, AUC:0.745031055900621\n",
            "Fold: 2, Epoch: [2/40], Step: [27/29], Loss: 0.5376450529804936, Validation Loss:0.5783777311444283, Validation Acc:70.82294264339153, AUC:0.7556418219461697\n",
            "Epoch 1 time: 1.9824\n",
            "Fold: 2, Epoch: [3/40], Step: [9/29], Loss: 0.5131091508600447, Validation Loss:0.5016415789723396, Validation Acc:72.31920199501246, AUC:0.741899585921325\n",
            "Fold: 2, Epoch: [3/40], Step: [18/29], Loss: 0.508501367436515, Validation Loss:0.5055018663406372, Validation Acc:71.82044887780549, AUC:0.7390010351966875\n",
            "Fold: 2, Epoch: [3/40], Step: [27/29], Loss: 0.5152769419882033, Validation Loss:0.5430944934487343, Validation Acc:72.81795511221945, AUC:0.7522774327122153\n",
            "Epoch 2 time: 1.9762\n",
            "Fold: 2, Epoch: [4/40], Step: [9/29], Loss: 0.4903332359260983, Validation Loss:0.523696132004261, Validation Acc:74.56359102244389, AUC:0.7699016563146998\n",
            "Fold: 2, Epoch: [4/40], Step: [18/29], Loss: 0.4983322471380234, Validation Loss:0.5391364321112633, Validation Acc:75.56109725685785, AUC:0.7682194616977225\n",
            "Fold: 2, Epoch: [4/40], Step: [27/29], Loss: 0.49953181324181734, Validation Loss:0.5051329135894775, Validation Acc:75.56109725685785, AUC:0.76074016563147\n",
            "Epoch 3 time: 1.9803\n",
            "Fold: 2, Epoch: [5/40], Step: [9/29], Loss: 0.48731940653589034, Validation Loss:0.5600889623165131, Validation Acc:73.81546134663341, AUC:0.7431159420289856\n",
            "Fold: 2, Epoch: [5/40], Step: [18/29], Loss: 0.4951968524191115, Validation Loss:0.5102224424481392, Validation Acc:72.56857855361596, AUC:0.7508281573498964\n",
            "Fold: 2, Epoch: [5/40], Step: [27/29], Loss: 0.4894140097830031, Validation Loss:0.5818871259689331, Validation Acc:72.81795511221945, AUC:0.7447981366459627\n",
            "Epoch 4 time: 1.9738\n",
            "Fold: 2, Epoch: [6/40], Step: [9/29], Loss: 0.4504624472724067, Validation Loss:0.4918965920805931, Validation Acc:71.57107231920199, AUC:0.7599896480331263\n",
            "Fold: 2, Epoch: [6/40], Step: [18/29], Loss: 0.4794321407874425, Validation Loss:0.4767180532217026, Validation Acc:71.57107231920199, AUC:0.7525103519668737\n",
            "Fold: 2, Epoch: [6/40], Step: [27/29], Loss: 0.4776640092885053, Validation Loss:0.5241067111492157, Validation Acc:73.06733167082294, AUC:0.7612060041407868\n",
            "Epoch 5 time: 1.9609\n",
            "Fold: 2, Epoch: [7/40], Step: [9/29], Loss: 0.4557155668735504, Validation Loss:0.49744780361652374, Validation Acc:72.31920199501246, AUC:0.7493788819875775\n",
            "Fold: 2, Epoch: [7/40], Step: [18/29], Loss: 0.4565569625960456, Validation Loss:0.4840734899044037, Validation Acc:72.31920199501246, AUC:0.7568581780538303\n",
            "Fold: 2, Epoch: [7/40], Step: [27/29], Loss: 0.46377050324722574, Validation Loss:0.4792279526591301, Validation Acc:74.3142144638404, AUC:0.7684523809523811\n",
            "Epoch 6 time: 1.9813\n",
            "Fold: 2, Epoch: [8/40], Step: [9/29], Loss: 0.4405315121014913, Validation Loss:0.4922224432229996, Validation Acc:74.81296758104739, AUC:0.7713509316770186\n",
            "Fold: 2, Epoch: [8/40], Step: [18/29], Loss: 0.4472015168931749, Validation Loss:0.5251510962843895, Validation Acc:72.81795511221945, AUC:0.7597567287784679\n",
            "Fold: 2, Epoch: [8/40], Step: [27/29], Loss: 0.4572384401603981, Validation Loss:0.5402322560548782, Validation Acc:72.81795511221945, AUC:0.7672360248447205\n",
            "Epoch 7 time: 1.9712\n",
            "Fold: 2, Epoch: [9/40], Step: [9/29], Loss: 0.45480648014280534, Validation Loss:0.5129292160272598, Validation Acc:71.57107231920199, AUC:0.7525103519668737\n",
            "Fold: 2, Epoch: [9/40], Step: [18/29], Loss: 0.44612206849786973, Validation Loss:0.5047062486410141, Validation Acc:72.81795511221945, AUC:0.7597567287784679\n",
            "Fold: 2, Epoch: [9/40], Step: [27/29], Loss: 0.4504295973866074, Validation Loss:0.5079568848013878, Validation Acc:71.57107231920199, AUC:0.7375517598343685\n",
            "Epoch 8 time: 1.9798\n",
            "Fold: 2, Epoch: [10/40], Step: [9/29], Loss: 0.4373876419332292, Validation Loss:0.502717949450016, Validation Acc:73.81546134663341, AUC:0.7730331262939959\n",
            "Fold: 2, Epoch: [10/40], Step: [18/29], Loss: 0.4506256795591778, Validation Loss:0.5425883159041405, Validation Acc:74.81296758104739, AUC:0.7713509316770186\n",
            "Fold: 2, Epoch: [10/40], Step: [27/29], Loss: 0.44347231807532134, Validation Loss:0.7045791447162628, Validation Acc:74.06483790523691, AUC:0.7670031055900621\n",
            "Epoch 9 time: 2.0008\n",
            "Fold: 2, Epoch: [11/40], Step: [9/29], Loss: 0.4313420554002126, Validation Loss:0.5286295115947723, Validation Acc:75.81047381546135, AUC:0.7771480331262939\n",
            "Fold: 2, Epoch: [11/40], Step: [18/29], Loss: 0.4291762693060769, Validation Loss:0.654221348464489, Validation Acc:75.56109725685785, AUC:0.76074016563147\n",
            "Fold: 2, Epoch: [11/40], Step: [27/29], Loss: 0.43564268615510726, Validation Loss:0.5042877495288849, Validation Acc:75.31172069825436, AUC:0.7592908902691511\n",
            "Epoch 10 time: 1.9931\n",
            "Fold: 2, Epoch: [12/40], Step: [9/29], Loss: 0.44139065013991463, Validation Loss:0.48142074048519135, Validation Acc:74.3142144638404, AUC:0.7759316770186335\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 2, Epoch: [12/40], Step: [18/29], Loss: 0.436930634909206, Validation Loss:0.488810732960701, Validation Acc:74.3142144638404, AUC:0.7759316770186335\n",
            "Fold: 2, Epoch: [12/40], Step: [27/29], Loss: 0.43625472872345533, Validation Loss:0.5216220691800117, Validation Acc:75.81047381546135, AUC:0.7846273291925465\n",
            "Epoch 11 time: 2.0315\n",
            "Fold: 2, Epoch: [13/40], Step: [9/29], Loss: 0.4373357660240597, Validation Loss:0.5746172219514847, Validation Acc:77.0573566084788, AUC:0.7843944099378882\n",
            "Fold: 2, Epoch: [13/40], Step: [18/29], Loss: 0.42380958961115944, Validation Loss:0.5873330384492874, Validation Acc:75.56109725685785, AUC:0.7682194616977225\n",
            "Fold: 2, Epoch: [13/40], Step: [27/29], Loss: 0.41966285970475936, Validation Loss:0.5987511053681374, Validation Acc:75.56109725685785, AUC:0.76074016563147\n",
            "Epoch 12 time: 2.0113\n",
            "Fold: 2, Epoch: [14/40], Step: [9/29], Loss: 0.38754041658507454, Validation Loss:0.5365887433290482, Validation Acc:77.0573566084788, AUC:0.7619565217391303\n",
            "Fold: 2, Epoch: [14/40], Step: [18/29], Loss: 0.4094017727507485, Validation Loss:0.5765773057937622, Validation Acc:77.55610972568579, AUC:0.764855072463768\n",
            "Fold: 2, Epoch: [14/40], Step: [27/29], Loss: 0.4057927871191943, Validation Loss:0.5862003266811371, Validation Acc:75.81047381546135, AUC:0.7621894409937888\n",
            "Epoch 13 time: 1.9951\n",
            "Fold: 2, Epoch: [15/40], Step: [9/29], Loss: 0.37694552540779114, Validation Loss:0.7996925190091133, Validation Acc:75.06234413965088, AUC:0.7578416149068322\n",
            "Fold: 2, Epoch: [15/40], Step: [18/29], Loss: 0.3733836743566725, Validation Loss:0.5794131979346275, Validation Acc:75.06234413965088, AUC:0.7503623188405797\n",
            "Fold: 2, Epoch: [15/40], Step: [27/29], Loss: 0.39513411897200124, Validation Loss:0.5434692725539207, Validation Acc:75.31172069825436, AUC:0.7518115942028986\n",
            "Epoch 14 time: 1.9767\n",
            "Fold: 2, Epoch: [16/40], Step: [9/29], Loss: 0.3846363690164354, Validation Loss:0.561134085059166, Validation Acc:77.0573566084788, AUC:0.7469979296066253\n",
            "Fold: 2, Epoch: [16/40], Step: [18/29], Loss: 0.39228033191627926, Validation Loss:0.5349339172244072, Validation Acc:78.30423940149626, AUC:0.7692028985507248\n",
            "Fold: 2, Epoch: [16/40], Step: [27/29], Loss: 0.39272185056297865, Validation Loss:0.5213516354560852, Validation Acc:76.80798004987531, AUC:0.7679865424430641\n",
            "Epoch 15 time: 1.9823\n",
            "Fold: 2, Epoch: [17/40], Step: [9/29], Loss: 0.38682523038652206, Validation Loss:0.5793462991714478, Validation Acc:71.57107231920199, AUC:0.730072463768116\n",
            "Fold: 2, Epoch: [17/40], Step: [18/29], Loss: 0.38865649534596336, Validation Loss:0.563894048333168, Validation Acc:75.06234413965088, AUC:0.7354037267080746\n",
            "Fold: 2, Epoch: [17/40], Step: [27/29], Loss: 0.3893937821741457, Validation Loss:0.5693677589297295, Validation Acc:76.05985037406484, AUC:0.7486801242236025\n",
            "Epoch 16 time: 2.0529\n",
            "Fold: 2, Epoch: [18/40], Step: [9/29], Loss: 0.37633029619852704, Validation Loss:0.588149294257164, Validation Acc:76.30922693266832, AUC:0.7426501035196689\n",
            "Fold: 2, Epoch: [18/40], Step: [18/29], Loss: 0.3799324217769835, Validation Loss:0.5611813217401505, Validation Acc:79.30174563591022, AUC:0.7525621118012422\n",
            "Fold: 2, Epoch: [18/40], Step: [27/29], Loss: 0.3747788502110375, Validation Loss:0.8463643342256546, Validation Acc:78.05486284289277, AUC:0.7453157349896481\n",
            "Epoch 17 time: 1.9969\n",
            "Fold: 2, Epoch: [19/40], Step: [9/29], Loss: 0.37819158368640476, Validation Loss:0.6288869455456734, Validation Acc:75.31172069825436, AUC:0.7218944099378882\n",
            "Fold: 2, Epoch: [19/40], Step: [18/29], Loss: 0.3624771386384964, Validation Loss:0.5886786878108978, Validation Acc:74.56359102244389, AUC:0.7025879917184266\n",
            "Fold: 2, Epoch: [19/40], Step: [27/29], Loss: 0.37158074975013733, Validation Loss:0.6614025086164474, Validation Acc:77.0573566084788, AUC:0.7395186335403727\n",
            "Epoch 18 time: 1.9963\n",
            "Fold: 2, Epoch: [20/40], Step: [9/29], Loss: 0.362580355670717, Validation Loss:0.6772302091121674, Validation Acc:79.30174563591022, AUC:0.7525621118012422\n",
            "Fold: 2, Epoch: [20/40], Step: [18/29], Loss: 0.3591921677192052, Validation Loss:0.7385242879390717, Validation Acc:76.30922693266832, AUC:0.7351708074534161\n",
            "Fold: 2, Epoch: [20/40], Step: [27/29], Loss: 0.3599956289485649, Validation Loss:0.5587596744298935, Validation Acc:74.3142144638404, AUC:0.7160973084886129\n",
            "Epoch 19 time: 1.9970\n",
            "Fold: 2, Epoch: [21/40], Step: [9/29], Loss: 0.4280731976032257, Validation Loss:0.65237857401371, Validation Acc:78.55361596009975, AUC:0.7257763975155279\n",
            "Fold: 2, Epoch: [21/40], Step: [18/29], Loss: 0.41330309874481624, Validation Loss:0.5107257962226868, Validation Acc:78.05486284289277, AUC:0.7378364389233955\n",
            "Fold: 2, Epoch: [21/40], Step: [27/29], Loss: 0.40808869622371813, Validation Loss:0.5523105710744858, Validation Acc:76.05985037406484, AUC:0.7337215320910974\n",
            "Epoch 20 time: 1.9893\n",
            "Fold: 2, Epoch: [22/40], Step: [9/29], Loss: 0.34235551291041905, Validation Loss:0.6538831293582916, Validation Acc:74.81296758104739, AUC:0.7115165631469978\n",
            "Fold: 2, Epoch: [22/40], Step: [18/29], Loss: 0.3575241350465351, Validation Loss:0.5634701028466225, Validation Acc:76.80798004987531, AUC:0.7231107660455486\n",
            "Fold: 2, Epoch: [22/40], Step: [27/29], Loss: 0.3785780535803901, Validation Loss:0.5201565772294998, Validation Acc:76.30922693266832, AUC:0.7351708074534161\n",
            "Epoch 21 time: 1.9820\n",
            "Fold: 2, Epoch: [23/40], Step: [9/29], Loss: 0.3425975806183285, Validation Loss:0.5689615532755852, Validation Acc:75.81047381546135, AUC:0.7098343685300207\n",
            "Fold: 2, Epoch: [23/40], Step: [18/29], Loss: 0.3355571726957957, Validation Loss:0.8191972002387047, Validation Acc:76.80798004987531, AUC:0.7231107660455486\n"
          ],
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          "text": [
            "Fold: 2, Epoch: [23/40], Step: [27/29], Loss: 0.3470921814441681, Validation Loss:0.5903094559907913, Validation Acc:76.80798004987531, AUC:0.7231107660455486\n",
            "Epoch 22 time: 1.9793\n",
            "Fold: 2, Epoch: [24/40], Step: [9/29], Loss: 0.3110935654905107, Validation Loss:0.6779312714934349, Validation Acc:78.55361596009975, AUC:0.7257763975155279\n",
            "Fold: 2, Epoch: [24/40], Step: [18/29], Loss: 0.32247630258401233, Validation Loss:0.5967532098293304, Validation Acc:77.0573566084788, AUC:0.7245600414078675\n",
            "Fold: 2, Epoch: [24/40], Step: [27/29], Loss: 0.3326618759720414, Validation Loss:0.5700784102082253, Validation Acc:78.05486284289277, AUC:0.7228778467908903\n",
            "Epoch 23 time: 2.0038\n",
            "Fold: 2, Epoch: [25/40], Step: [9/29], Loss: 0.3000051958693398, Validation Loss:0.5225240960717201, Validation Acc:79.05236907730674, AUC:0.7137163561076605\n",
            "Fold: 2, Epoch: [25/40], Step: [18/29], Loss: 0.3179246162374814, Validation Loss:0.5921424627304077, Validation Acc:79.55112219451371, AUC:0.7315734989648033\n",
            "Fold: 2, Epoch: [25/40], Step: [27/29], Loss: 0.319710624438745, Validation Loss:0.51047876290977, Validation Acc:78.55361596009975, AUC:0.7332556935817804\n",
            "Epoch 24 time: 1.9989\n",
            "Fold: 2, Epoch: [26/40], Step: [9/29], Loss: 0.31584618820084465, Validation Loss:0.6186680048704147, Validation Acc:77.80548628428927, AUC:0.7064699792960663\n",
            "Fold: 2, Epoch: [26/40], Step: [18/29], Loss: 0.2947813380095694, Validation Loss:0.73951156437397, Validation Acc:78.05486284289277, AUC:0.7004399585921326\n",
            "Fold: 2, Epoch: [26/40], Step: [27/29], Loss: 0.3118535462352965, Validation Loss:0.6242806389927864, Validation Acc:76.80798004987531, AUC:0.7081521739130434\n",
            "Epoch 25 time: 2.0321\n",
            "Fold: 2, Epoch: [27/40], Step: [9/29], Loss: 0.30986572636498344, Validation Loss:0.6734265387058258, Validation Acc:77.3067331670823, AUC:0.7260093167701863\n",
            "Fold: 2, Epoch: [27/40], Step: [18/29], Loss: 0.3028150010440085, Validation Loss:0.5994030609726906, Validation Acc:79.80049875311721, AUC:0.7105848861283645\n",
            "Fold: 2, Epoch: [27/40], Step: [27/29], Loss: 0.3054552955759896, Validation Loss:0.835004523396492, Validation Acc:80.29925187032418, AUC:0.7209627329192547\n",
            "Epoch 26 time: 1.9822\n",
            "Fold: 2, Epoch: [28/40], Step: [9/29], Loss: 0.2520672198798921, Validation Loss:0.6384374275803566, Validation Acc:79.05236907730674, AUC:0.6987577639751552\n",
            "Fold: 2, Epoch: [28/40], Step: [18/29], Loss: 0.26997588409317863, Validation Loss:0.6299926713109016, Validation Acc:80.29925187032418, AUC:0.7134834368530021\n",
            "Fold: 2, Epoch: [28/40], Step: [27/29], Loss: 0.28255730977764837, Validation Loss:0.7042485773563385, Validation Acc:79.55112219451371, AUC:0.7091356107660456\n",
            "Epoch 27 time: 1.9854\n",
            "Fold: 2, Epoch: [29/40], Step: [9/29], Loss: 0.26852646635638344, Validation Loss:0.6322904601693153, Validation Acc:77.3067331670823, AUC:0.7110507246376812\n",
            "Fold: 2, Epoch: [29/40], Step: [18/29], Loss: 0.2725927440656556, Validation Loss:0.6255303993821144, Validation Acc:78.30423940149626, AUC:0.7093685300207041\n",
            "Fold: 2, Epoch: [29/40], Step: [27/29], Loss: 0.2783097503361879, Validation Loss:0.6282324120402336, Validation Acc:78.80299251870325, AUC:0.7047877846790891\n",
            "Epoch 28 time: 1.9961\n",
            "Fold: 2, Epoch: [30/40], Step: [9/29], Loss: 0.2730155603753196, Validation Loss:0.6920610517263412, Validation Acc:80.54862842892769, AUC:0.6999741200828157\n",
            "Fold: 2, Epoch: [30/40], Step: [18/29], Loss: 0.27299434194962185, Validation Loss:0.8328271806240082, Validation Acc:78.55361596009975, AUC:0.7182971014492754\n",
            "Fold: 2, Epoch: [30/40], Step: [27/29], Loss: 0.26918760935465497, Validation Loss:1.0062963962554932, Validation Acc:78.80299251870325, AUC:0.7047877846790891\n",
            "Epoch 29 time: 1.9773\n",
            "Fold: 2, Epoch: [31/40], Step: [9/29], Loss: 0.22242041594452328, Validation Loss:0.6290345881134272, Validation Acc:81.04738154613466, AUC:0.7103519668737059\n",
            "Fold: 2, Epoch: [31/40], Step: [18/29], Loss: 0.2352238984571563, Validation Loss:0.9376611039042473, Validation Acc:80.0498753117207, AUC:0.7120341614906833\n",
            "Fold: 2, Epoch: [31/40], Step: [27/29], Loss: 0.24860850859571387, Validation Loss:0.7029553055763245, Validation Acc:80.0498753117207, AUC:0.697075569358178\n",
            "Epoch 30 time: 1.9817\n",
            "Fold: 2, Epoch: [32/40], Step: [9/29], Loss: 0.22835717267460293, Validation Loss:0.7031048163771629, Validation Acc:80.79800498753117, AUC:0.7014233954451345\n",
            "Fold: 2, Epoch: [32/40], Step: [18/29], Loss: 0.23481007665395737, Validation Loss:0.6644452884793282, Validation Acc:80.29925187032418, AUC:0.7134834368530021\n",
            "Fold: 2, Epoch: [32/40], Step: [27/29], Loss: 0.23561788046801532, Validation Loss:0.86510269343853, Validation Acc:80.29925187032418, AUC:0.7134834368530021\n",
            "Epoch 31 time: 1.9889\n",
            "Fold: 2, Epoch: [33/40], Step: [9/29], Loss: 0.22022764715883467, Validation Loss:0.8375427424907684, Validation Acc:80.29925187032418, AUC:0.7134834368530021\n",
            "Fold: 2, Epoch: [33/40], Step: [18/29], Loss: 0.22683703237109715, Validation Loss:0.6938315816223621, Validation Acc:81.04738154613466, AUC:0.7103519668737059\n",
            "Fold: 2, Epoch: [33/40], Step: [27/29], Loss: 0.2254954626162847, Validation Loss:0.8308022320270538, Validation Acc:80.54862842892769, AUC:0.7298913043478261\n",
            "Epoch 32 time: 1.9956\n",
            "Fold: 2, Epoch: [34/40], Step: [9/29], Loss: 0.21306092374854618, Validation Loss:0.8271273151040077, Validation Acc:79.55112219451371, AUC:0.6717391304347826\n",
            "Fold: 2, Epoch: [34/40], Step: [18/29], Loss: 0.2202398313416375, Validation Loss:0.8924580812454224, Validation Acc:83.79052369077307, AUC:0.7337732919254659\n",
            "Fold: 2, Epoch: [34/40], Step: [27/29], Loss: 0.22662079444638006, Validation Loss:0.8598903566598892, Validation Acc:81.29675810473816, AUC:0.7192805383022776\n",
            "Epoch 33 time: 1.9759\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 2, Epoch: [35/40], Step: [9/29], Loss: 0.2141808056169086, Validation Loss:0.755571261048317, Validation Acc:79.05236907730674, AUC:0.6912784679089027\n",
            "Fold: 2, Epoch: [35/40], Step: [18/29], Loss: 0.20447476870483822, Validation Loss:0.6938890814781189, Validation Acc:80.0498753117207, AUC:0.7120341614906833\n",
            "Fold: 2, Epoch: [35/40], Step: [27/29], Loss: 0.20954406592581007, Validation Loss:0.8171291351318359, Validation Acc:80.29925187032418, AUC:0.6985248447204969\n",
            "Epoch 34 time: 1.9682\n",
            "Fold: 2, Epoch: [36/40], Step: [9/29], Loss: 0.1894908050696055, Validation Loss:1.256307765841484, Validation Acc:81.29675810473816, AUC:0.6893633540372671\n",
            "Fold: 2, Epoch: [36/40], Step: [18/29], Loss: 0.18618671347697577, Validation Loss:0.7912481650710106, Validation Acc:82.54364089775561, AUC:0.719047619047619\n",
            "Fold: 2, Epoch: [36/40], Step: [27/29], Loss: 0.18713014600453554, Validation Loss:0.7789588123559952, Validation Acc:82.29426433915212, AUC:0.7175983436853002\n",
            "Epoch 35 time: 1.9877\n",
            "Fold: 2, Epoch: [37/40], Step: [9/29], Loss: 0.15981889516115189, Validation Loss:1.0520568042993546, Validation Acc:82.54364089775561, AUC:0.7115683229813664\n",
            "Fold: 2, Epoch: [37/40], Step: [18/29], Loss: 0.16394211848576865, Validation Loss:1.0041799396276474, Validation Acc:82.7930174563591, AUC:0.7204968944099378\n",
            "Fold: 2, Epoch: [37/40], Step: [27/29], Loss: 0.16744042270713383, Validation Loss:0.8615443371236324, Validation Acc:83.29177057356608, AUC:0.6934782608695651\n",
            "Epoch 36 time: 1.9665\n",
            "Fold: 2, Epoch: [38/40], Step: [9/29], Loss: 0.14965030633740956, Validation Loss:1.3252663612365723, Validation Acc:80.79800498753117, AUC:0.6864648033126294\n",
            "Fold: 2, Epoch: [38/40], Step: [18/29], Loss: 0.14983793016937044, Validation Loss:1.13200244307518, Validation Acc:79.05236907730674, AUC:0.6987577639751552\n",
            "Fold: 2, Epoch: [38/40], Step: [27/29], Loss: 0.15304268372279625, Validation Loss:0.915407033637166, Validation Acc:83.0423940149626, AUC:0.699508281573499\n",
            "Epoch 37 time: 2.0129\n",
            "Fold: 2, Epoch: [39/40], Step: [9/29], Loss: 0.16598456270164913, Validation Loss:0.9411545172333717, Validation Acc:79.05236907730674, AUC:0.68379917184265\n",
            "Fold: 2, Epoch: [39/40], Step: [18/29], Loss: 0.17534039831823772, Validation Loss:1.0344948321580887, Validation Acc:80.29925187032418, AUC:0.6835662525879918\n",
            "Fold: 2, Epoch: [39/40], Step: [27/29], Loss: 0.17480182840868277, Validation Loss:1.181482270359993, Validation Acc:79.80049875311721, AUC:0.6582298136645963\n",
            "Epoch 38 time: 1.9859\n",
            "Fold: 2, Epoch: [40/40], Step: [9/29], Loss: 0.13728622595469156, Validation Loss:1.0091938972473145, Validation Acc:81.29675810473816, AUC:0.674404761904762\n",
            "Fold: 2, Epoch: [40/40], Step: [18/29], Loss: 0.14055146856440437, Validation Loss:1.0287808775901794, Validation Acc:80.79800498753117, AUC:0.6789855072463767\n",
            "Fold: 2, Epoch: [40/40], Step: [27/29], Loss: 0.13877339109226508, Validation Loss:1.4919031262397766, Validation Acc:83.54114713216957, AUC:0.6874482401656316\n",
            "Epoch 39 time: 1.9875\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 3, Epoch: [1/40], Step: [9/29], Loss: 0.701544808016883, Validation Loss:0.6366740614175797, Validation Acc:63.34164588528678, AUC:0.6897256728778467\n",
            "Fold: 3, Epoch: [1/40], Step: [18/29], Loss: 0.6508993208408356, Validation Loss:0.5958560407161713, Validation Acc:70.57356608478803, AUC:0.7167960662525881\n",
            "Fold: 3, Epoch: [1/40], Step: [27/29], Loss: 0.6281415036431065, Validation Loss:0.6048111319541931, Validation Acc:68.3291770573566, AUC:0.711231884057971\n",
            "Epoch 0 time: 1.9863\n",
            "Fold: 3, Epoch: [2/40], Step: [9/29], Loss: 0.5422672695583768, Validation Loss:0.549284391105175, Validation Acc:67.83042394014963, AUC:0.7158126293995859\n",
            "Fold: 3, Epoch: [2/40], Step: [18/29], Loss: 0.5303132997618781, Validation Loss:0.5362729653716087, Validation Acc:69.07730673316708, AUC:0.7155797101449275\n",
            "Fold: 3, Epoch: [2/40], Step: [27/29], Loss: 0.536182928968359, Validation Loss:0.5290656238794327, Validation Acc:70.07481296758105, AUC:0.7138975155279503\n",
            "Epoch 1 time: 1.9769\n",
            "Fold: 3, Epoch: [3/40], Step: [9/29], Loss: 0.5153066780832078, Validation Loss:0.46467143297195435, Validation Acc:71.57107231920199, AUC:0.745031055900621\n",
            "Fold: 3, Epoch: [3/40], Step: [18/29], Loss: 0.5191067821449704, Validation Loss:0.4949224218726158, Validation Acc:72.56857855361596, AUC:0.7433488612836439\n",
            "Fold: 3, Epoch: [3/40], Step: [27/29], Loss: 0.5176483359601762, Validation Loss:0.46721509099006653, Validation Acc:73.56608478802993, AUC:0.7416666666666667\n",
            "Epoch 2 time: 1.9945\n",
            "Fold: 3, Epoch: [4/40], Step: [9/29], Loss: 0.5065899756219652, Validation Loss:0.479978010058403, Validation Acc:73.06733167082294, AUC:0.738768115942029\n",
            "Fold: 3, Epoch: [4/40], Step: [18/29], Loss: 0.5019820183515549, Validation Loss:0.4952216073870659, Validation Acc:72.81795511221945, AUC:0.7373188405797102\n",
            "Fold: 3, Epoch: [4/40], Step: [27/29], Loss: 0.5059628486633301, Validation Loss:0.5061108022928238, Validation Acc:73.56608478802993, AUC:0.7491459627329192\n",
            "Epoch 3 time: 1.9945\n",
            "Fold: 3, Epoch: [5/40], Step: [9/29], Loss: 0.5141661531395383, Validation Loss:0.5470788851380348, Validation Acc:73.06733167082294, AUC:0.7163302277432713\n",
            "Fold: 3, Epoch: [5/40], Step: [18/29], Loss: 0.48969460858239067, Validation Loss:0.5206186175346375, Validation Acc:73.06733167082294, AUC:0.7238095238095239\n",
            "Fold: 3, Epoch: [5/40], Step: [27/29], Loss: 0.48927412430445355, Validation Loss:0.4947902336716652, Validation Acc:73.31670822942644, AUC:0.7402173913043478\n",
            "Epoch 4 time: 2.0319\n",
            "Fold: 3, Epoch: [6/40], Step: [9/29], Loss: 0.4729754428068797, Validation Loss:0.4978719502687454, Validation Acc:75.81047381546135, AUC:0.7397515527950311\n",
            "Fold: 3, Epoch: [6/40], Step: [18/29], Loss: 0.4775824480586582, Validation Loss:0.5540479347109795, Validation Acc:76.55860349127182, AUC:0.7665372670807454\n",
            "Fold: 3, Epoch: [6/40], Step: [27/29], Loss: 0.4807726939519246, Validation Loss:0.5710661634802818, Validation Acc:76.80798004987531, AUC:0.7605072463768117\n",
            "Epoch 5 time: 2.0059\n",
            "Fold: 3, Epoch: [7/40], Step: [9/29], Loss: 0.4732121096716987, Validation Loss:0.5512630939483643, Validation Acc:75.31172069825436, AUC:0.7518115942028986\n",
            "Fold: 3, Epoch: [7/40], Step: [18/29], Loss: 0.46715451611412895, Validation Loss:0.5128062814474106, Validation Acc:74.56359102244389, AUC:0.7474637681159421\n",
            "Fold: 3, Epoch: [7/40], Step: [27/29], Loss: 0.4699908947503125, Validation Loss:0.6090023592114449, Validation Acc:77.0573566084788, AUC:0.7769151138716356\n",
            "Epoch 6 time: 2.0205\n",
            "Fold: 3, Epoch: [8/40], Step: [9/29], Loss: 0.44373830490642124, Validation Loss:0.44126854836940765, Validation Acc:77.0573566084788, AUC:0.7843944099378882\n",
            "Fold: 3, Epoch: [8/40], Step: [18/29], Loss: 0.46001722580856746, Validation Loss:0.4771587625145912, Validation Acc:72.56857855361596, AUC:0.7583074534161491\n",
            "Fold: 3, Epoch: [8/40], Step: [27/29], Loss: 0.4669103545171243, Validation Loss:0.48856325447559357, Validation Acc:72.56857855361596, AUC:0.7657867494824017\n",
            "Epoch 7 time: 1.9638\n",
            "Fold: 3, Epoch: [9/40], Step: [9/29], Loss: 0.4576173689630296, Validation Loss:0.5046799406409264, Validation Acc:76.80798004987531, AUC:0.753027950310559\n",
            "Fold: 3, Epoch: [9/40], Step: [18/29], Loss: 0.45886653496159446, Validation Loss:0.4830399677157402, Validation Acc:76.80798004987531, AUC:0.7605072463768117\n",
            "Fold: 3, Epoch: [9/40], Step: [27/29], Loss: 0.45509083403481376, Validation Loss:0.538735643029213, Validation Acc:77.0573566084788, AUC:0.7619565217391303\n",
            "Epoch 8 time: 1.9869\n",
            "Fold: 3, Epoch: [10/40], Step: [9/29], Loss: 0.4516023529900445, Validation Loss:0.5489078536629677, Validation Acc:76.30922693266832, AUC:0.7501293995859213\n",
            "Fold: 3, Epoch: [10/40], Step: [18/29], Loss: 0.4491860651307636, Validation Loss:0.435746930539608, Validation Acc:76.55860349127182, AUC:0.7740165631469978\n",
            "Fold: 3, Epoch: [10/40], Step: [27/29], Loss: 0.44144550186616405, Validation Loss:0.4803525283932686, Validation Acc:77.0573566084788, AUC:0.7694358178053831\n",
            "Epoch 9 time: 1.9983\n",
            "Fold: 3, Epoch: [11/40], Step: [9/29], Loss: 0.40849463807211983, Validation Loss:0.4487174153327942, Validation Acc:76.55860349127182, AUC:0.7515786749482402\n",
            "Fold: 3, Epoch: [11/40], Step: [18/29], Loss: 0.4235741181506051, Validation Loss:0.4932542070746422, Validation Acc:75.56109725685785, AUC:0.7756987577639752\n",
            "Fold: 3, Epoch: [11/40], Step: [27/29], Loss: 0.42495099261954977, Validation Loss:0.4834509715437889, Validation Acc:78.55361596009975, AUC:0.7706521739130434\n",
            "Epoch 10 time: 1.9743\n",
            "Fold: 3, Epoch: [12/40], Step: [9/29], Loss: 0.4033932089805603, Validation Loss:0.6274589970707893, Validation Acc:74.56359102244389, AUC:0.7624223602484471\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 3, Epoch: [12/40], Step: [18/29], Loss: 0.4070543481243981, Validation Loss:0.47343859821558, Validation Acc:76.80798004987531, AUC:0.7829451345755694\n",
            "Fold: 3, Epoch: [12/40], Step: [27/29], Loss: 0.4239314772464611, Validation Loss:0.4984486550092697, Validation Acc:77.55610972568579, AUC:0.7573757763975155\n",
            "Epoch 11 time: 1.9754\n",
            "Fold: 3, Epoch: [13/40], Step: [9/29], Loss: 0.4071637623839908, Validation Loss:0.5191812738776207, Validation Acc:75.31172069825436, AUC:0.7518115942028986\n",
            "Fold: 3, Epoch: [13/40], Step: [18/29], Loss: 0.40889571772681343, Validation Loss:0.5017911046743393, Validation Acc:76.80798004987531, AUC:0.7605072463768117\n",
            "Fold: 3, Epoch: [13/40], Step: [27/29], Loss: 0.407443478151604, Validation Loss:0.4724747762084007, Validation Acc:77.3067331670823, AUC:0.7634057971014493\n",
            "Epoch 12 time: 1.9832\n",
            "Fold: 3, Epoch: [14/40], Step: [9/29], Loss: 0.4063895410961575, Validation Loss:0.4516165480017662, Validation Acc:77.0573566084788, AUC:0.7619565217391303\n",
            "Fold: 3, Epoch: [14/40], Step: [18/29], Loss: 0.3899625970257653, Validation Loss:0.4915992543101311, Validation Acc:77.3067331670823, AUC:0.7634057971014493\n",
            "Fold: 3, Epoch: [14/40], Step: [27/29], Loss: 0.3976572608506238, Validation Loss:0.45451976358890533, Validation Acc:77.3067331670823, AUC:0.7634057971014493\n",
            "Epoch 13 time: 1.9847\n",
            "Fold: 3, Epoch: [15/40], Step: [9/29], Loss: 0.39456761876742047, Validation Loss:0.47398296743631363, Validation Acc:76.55860349127182, AUC:0.7590579710144927\n",
            "Fold: 3, Epoch: [15/40], Step: [18/29], Loss: 0.3885144342978795, Validation Loss:0.4459066092967987, Validation Acc:75.06234413965088, AUC:0.7578416149068322\n",
            "Fold: 3, Epoch: [15/40], Step: [27/29], Loss: 0.38620897023766126, Validation Loss:0.5355663150548935, Validation Acc:75.56109725685785, AUC:0.7457815734989649\n",
            "Epoch 14 time: 1.9811\n",
            "Fold: 3, Epoch: [16/40], Step: [9/29], Loss: 0.3755075865321689, Validation Loss:0.5759268403053284, Validation Acc:75.56109725685785, AUC:0.7308229813664597\n",
            "Fold: 3, Epoch: [16/40], Step: [18/29], Loss: 0.3817580160167482, Validation Loss:0.5147121176123619, Validation Acc:78.05486284289277, AUC:0.7752329192546584\n",
            "Fold: 3, Epoch: [16/40], Step: [27/29], Loss: 0.38675256901317173, Validation Loss:0.5034849569201469, Validation Acc:76.80798004987531, AUC:0.753027950310559\n",
            "Epoch 15 time: 1.9908\n",
            "Fold: 3, Epoch: [17/40], Step: [9/29], Loss: 0.35562224520577324, Validation Loss:0.44859258085489273, Validation Acc:76.30922693266832, AUC:0.7576086956521739\n",
            "Fold: 3, Epoch: [17/40], Step: [18/29], Loss: 0.3687877323892381, Validation Loss:0.5521207675337791, Validation Acc:75.06234413965088, AUC:0.7428830227743272\n",
            "Fold: 3, Epoch: [17/40], Step: [27/29], Loss: 0.3691254026359982, Validation Loss:0.5939958393573761, Validation Acc:76.80798004987531, AUC:0.753027950310559\n",
            "Epoch 16 time: 2.0075\n",
            "Fold: 3, Epoch: [18/40], Step: [9/29], Loss: 0.3539324667718675, Validation Loss:0.4655301198363304, Validation Acc:76.55860349127182, AUC:0.7665372670807454\n",
            "Fold: 3, Epoch: [18/40], Step: [18/29], Loss: 0.34259523782465195, Validation Loss:0.5057771056890488, Validation Acc:76.30922693266832, AUC:0.7650879917184265\n",
            "Fold: 3, Epoch: [18/40], Step: [27/29], Loss: 0.35228747350198253, Validation Loss:0.48587020486593246, Validation Acc:73.81546134663341, AUC:0.7431159420289856\n",
            "Epoch 17 time: 1.9948\n",
            "Fold: 3, Epoch: [19/40], Step: [9/29], Loss: 0.34737655851576066, Validation Loss:0.5917695015668869, Validation Acc:80.54862842892769, AUC:0.7598084886128366\n",
            "Fold: 3, Epoch: [19/40], Step: [18/29], Loss: 0.3543354603979323, Validation Loss:0.5604158639907837, Validation Acc:77.80548628428927, AUC:0.7588250517598344\n",
            "Fold: 3, Epoch: [19/40], Step: [27/29], Loss: 0.34765741670573197, Validation Loss:0.5154167264699936, Validation Acc:76.55860349127182, AUC:0.7590579710144927\n",
            "Epoch 18 time: 2.0209\n",
            "Fold: 3, Epoch: [20/40], Step: [9/29], Loss: 0.3099873595767551, Validation Loss:0.5163827016949654, Validation Acc:78.05486284289277, AUC:0.7602743271221533\n",
            "Fold: 3, Epoch: [20/40], Step: [18/29], Loss: 0.3210634109046724, Validation Loss:0.45707354322075844, Validation Acc:77.55610972568579, AUC:0.764855072463768\n",
            "Fold: 3, Epoch: [20/40], Step: [27/29], Loss: 0.33232397613701997, Validation Loss:0.5509957894682884, Validation Acc:77.3067331670823, AUC:0.7559265010351967\n",
            "Epoch 19 time: 1.9824\n",
            "Fold: 3, Epoch: [21/40], Step: [9/29], Loss: 0.31658921970261467, Validation Loss:0.4943352937698364, Validation Acc:77.3067331670823, AUC:0.7634057971014493\n",
            "Fold: 3, Epoch: [21/40], Step: [18/29], Loss: 0.31457412160105175, Validation Loss:0.512942261993885, Validation Acc:77.80548628428927, AUC:0.766304347826087\n",
            "Fold: 3, Epoch: [21/40], Step: [27/29], Loss: 0.3224462828150502, Validation Loss:0.5359949171543121, Validation Acc:74.56359102244389, AUC:0.7549430641821946\n",
            "Epoch 20 time: 1.9738\n",
            "Fold: 3, Epoch: [22/40], Step: [9/29], Loss: 0.28935688071780735, Validation Loss:0.5668570324778557, Validation Acc:79.55112219451371, AUC:0.7390527950310558\n",
            "Fold: 3, Epoch: [22/40], Step: [18/29], Loss: 0.3083221647474501, Validation Loss:0.6226222515106201, Validation Acc:78.55361596009975, AUC:0.7706521739130434\n",
            "Fold: 3, Epoch: [22/40], Step: [27/29], Loss: 0.3082559500579481, Validation Loss:0.7057681605219841, Validation Acc:77.0573566084788, AUC:0.7544772256728779\n",
            "Epoch 21 time: 1.9697\n",
            "Fold: 3, Epoch: [23/40], Step: [9/29], Loss: 0.29577631254990894, Validation Loss:0.5667699575424194, Validation Acc:78.30423940149626, AUC:0.7692028985507248\n",
            "Fold: 3, Epoch: [23/40], Step: [18/29], Loss: 0.29517438179916805, Validation Loss:0.5411444380879402, Validation Acc:79.05236907730674, AUC:0.7436335403726708\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "Fold: 3, Epoch: [23/40], Step: [27/29], Loss: 0.295907492990847, Validation Loss:0.645946130156517, Validation Acc:77.55610972568579, AUC:0.7349378881987577\n",
            "Epoch 22 time: 1.9995\n",
            "Fold: 3, Epoch: [24/40], Step: [9/29], Loss: 0.2629606028397878, Validation Loss:0.5619351789355278, Validation Acc:75.81047381546135, AUC:0.7547101449275362\n",
            "Fold: 3, Epoch: [24/40], Step: [18/29], Loss: 0.28623606264591217, Validation Loss:0.659294456243515, Validation Acc:74.56359102244389, AUC:0.7549430641821946\n",
            "Fold: 3, Epoch: [24/40], Step: [27/29], Loss: 0.281036306310583, Validation Loss:0.5778498314321041, Validation Acc:77.55610972568579, AUC:0.7424171842650104\n",
            "Epoch 23 time: 1.9863\n",
            "Fold: 3, Epoch: [25/40], Step: [9/29], Loss: 0.26136780778567, Validation Loss:0.5409585386514664, Validation Acc:79.55112219451371, AUC:0.7540113871635611\n",
            "Fold: 3, Epoch: [25/40], Step: [18/29], Loss: 0.26596685002247494, Validation Loss:0.8234086036682129, Validation Acc:79.80049875311721, AUC:0.7405020703933747\n",
            "Fold: 3, Epoch: [25/40], Step: [27/29], Loss: 0.26432749408262746, Validation Loss:0.6190650388598442, Validation Acc:77.55610972568579, AUC:0.7125\n",
            "Epoch 24 time: 1.9811\n",
            "Fold: 3, Epoch: [26/40], Step: [9/29], Loss: 0.26831717789173126, Validation Loss:0.6210550367832184, Validation Acc:75.81047381546135, AUC:0.7621894409937888\n",
            "Fold: 3, Epoch: [26/40], Step: [18/29], Loss: 0.2658652381764518, Validation Loss:0.574018806219101, Validation Acc:80.29925187032418, AUC:0.7658385093167701\n",
            "Fold: 3, Epoch: [26/40], Step: [27/29], Loss: 0.26256724529796177, Validation Loss:0.5796225443482399, Validation Acc:79.30174563591022, AUC:0.737603519668737\n",
            "Epoch 25 time: 1.9844\n",
            "Fold: 3, Epoch: [27/40], Step: [9/29], Loss: 0.23779814607567257, Validation Loss:0.5678229629993439, Validation Acc:78.55361596009975, AUC:0.7556935817805384\n",
            "Fold: 3, Epoch: [27/40], Step: [18/29], Loss: 0.24782232112354702, Validation Loss:0.6916613802313805, Validation Acc:75.81047381546135, AUC:0.7098343685300207\n",
            "Fold: 3, Epoch: [27/40], Step: [27/29], Loss: 0.24690251935411384, Validation Loss:0.5725658126175404, Validation Acc:76.80798004987531, AUC:0.715631469979296\n",
            "Epoch 26 time: 1.9802\n",
            "Fold: 3, Epoch: [28/40], Step: [9/29], Loss: 0.21873375442292955, Validation Loss:1.1164253801107407, Validation Acc:79.80049875311721, AUC:0.75546066252588\n",
            "Fold: 3, Epoch: [28/40], Step: [18/29], Loss: 0.22164885865317452, Validation Loss:0.8894639909267426, Validation Acc:78.80299251870325, AUC:0.7347049689440994\n",
            "Fold: 3, Epoch: [28/40], Step: [27/29], Loss: 0.22196464461308937, Validation Loss:0.71240234375, Validation Acc:79.30174563591022, AUC:0.7450828157349897\n",
            "Epoch 27 time: 2.0000\n",
            "Fold: 3, Epoch: [29/40], Step: [9/29], Loss: 0.22543329828315312, Validation Loss:0.7829795107245445, Validation Acc:79.55112219451371, AUC:0.7465320910973086\n",
            "Fold: 3, Epoch: [29/40], Step: [18/29], Loss: 0.2162609121037854, Validation Loss:0.6655258312821388, Validation Acc:78.55361596009975, AUC:0.7182971014492754\n",
            "Fold: 3, Epoch: [29/40], Step: [27/29], Loss: 0.21492149460094948, Validation Loss:0.7078235819935799, Validation Acc:79.80049875311721, AUC:0.7405020703933747\n",
            "Epoch 28 time: 1.9851\n",
            "Fold: 3, Epoch: [30/40], Step: [9/29], Loss: 0.18153423070907593, Validation Loss:0.7547957301139832, Validation Acc:77.80548628428927, AUC:0.7139492753623189\n",
            "Fold: 3, Epoch: [30/40], Step: [18/29], Loss: 0.191677981780635, Validation Loss:0.6874265745282173, Validation Acc:79.80049875311721, AUC:0.7255434782608696\n",
            "Fold: 3, Epoch: [30/40], Step: [27/29], Loss: 0.18619154255699227, Validation Loss:0.7503817528486252, Validation Acc:80.0498753117207, AUC:0.7494306418219462\n",
            "Epoch 29 time: 2.0012\n",
            "Fold: 3, Epoch: [31/40], Step: [9/29], Loss: 0.18676080223586825, Validation Loss:0.8964232355356216, Validation Acc:78.05486284289277, AUC:0.7303571428571428\n",
            "Fold: 3, Epoch: [31/40], Step: [18/29], Loss: 0.18397310086422497, Validation Loss:0.67688212916255, Validation Acc:81.04738154613466, AUC:0.7028726708074534\n",
            "Fold: 3, Epoch: [31/40], Step: [27/29], Loss: 0.18878300957105779, Validation Loss:0.7679730951786041, Validation Acc:81.04738154613466, AUC:0.7178312629399586\n",
            "Epoch 30 time: 1.9959\n",
            "Fold: 3, Epoch: [32/40], Step: [9/29], Loss: 0.17297828528616163, Validation Loss:0.747773602604866, Validation Acc:81.29675810473816, AUC:0.72675983436853\n",
            "Fold: 3, Epoch: [32/40], Step: [18/29], Loss: 0.18522864166233274, Validation Loss:0.6774311307817698, Validation Acc:79.80049875311721, AUC:0.718064182194617\n",
            "Fold: 3, Epoch: [32/40], Step: [27/29], Loss: 0.18263244601311507, Validation Loss:0.8862267285585403, Validation Acc:80.0498753117207, AUC:0.734472049689441\n",
            "Epoch 31 time: 2.0099\n",
            "Fold: 3, Epoch: [33/40], Step: [9/29], Loss: 0.15654276559750238, Validation Loss:1.0056387335062027, Validation Acc:79.05236907730674, AUC:0.721195652173913\n",
            "Fold: 3, Epoch: [33/40], Step: [18/29], Loss: 0.14816872734162542, Validation Loss:0.7397947162389755, Validation Acc:81.29675810473816, AUC:0.7342391304347826\n",
            "Fold: 3, Epoch: [33/40], Step: [27/29], Loss: 0.14707462517199693, Validation Loss:0.9101306051015854, Validation Acc:84.78802992518703, AUC:0.7694875776397516\n",
            "Epoch 32 time: 1.9897\n",
            "Fold: 3, Epoch: [34/40], Step: [9/29], Loss: 0.11902106139394972, Validation Loss:0.7707178592681885, Validation Acc:81.29675810473816, AUC:0.7491977225672877\n",
            "Fold: 3, Epoch: [34/40], Step: [18/29], Loss: 0.12303454014990065, Validation Loss:0.9719088599085808, Validation Acc:80.0498753117207, AUC:0.7195134575569359\n",
            "Fold: 3, Epoch: [34/40], Step: [27/29], Loss: 0.13100296065763192, Validation Loss:0.9016156494617462, Validation Acc:79.55112219451371, AUC:0.7166149068322982\n",
            "Epoch 33 time: 1.9909\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 3, Epoch: [35/40], Step: [9/29], Loss: 0.1448469642135832, Validation Loss:1.0521447658538818, Validation Acc:81.29675810473816, AUC:0.72675983436853\n",
            "Fold: 3, Epoch: [35/40], Step: [18/29], Loss: 0.13431414465109506, Validation Loss:1.0207863450050354, Validation Acc:80.54862842892769, AUC:0.7373706004140786\n",
            "Fold: 3, Epoch: [35/40], Step: [27/29], Loss: 0.13165741800158112, Validation Loss:1.289250686764717, Validation Acc:82.29426433915212, AUC:0.7250776397515529\n",
            "Epoch 34 time: 2.0216\n",
            "Fold: 3, Epoch: [36/40], Step: [9/29], Loss: 0.12029702299171025, Validation Loss:0.8416251465678215, Validation Acc:84.03990024937656, AUC:0.7352225672877848\n",
            "Fold: 3, Epoch: [36/40], Step: [18/29], Loss: 0.13162759608692592, Validation Loss:0.7921601682901382, Validation Acc:80.0498753117207, AUC:0.7269927536231883\n",
            "Fold: 3, Epoch: [36/40], Step: [27/29], Loss: 0.12431759470038944, Validation Loss:0.8733320981264114, Validation Acc:79.05236907730674, AUC:0.721195652173913\n",
            "Epoch 35 time: 1.9830\n",
            "Fold: 3, Epoch: [37/40], Step: [9/29], Loss: 0.12322766913308038, Validation Loss:0.8868081420660019, Validation Acc:82.29426433915212, AUC:0.7250776397515529\n",
            "Fold: 3, Epoch: [37/40], Step: [18/29], Loss: 0.11111656452218692, Validation Loss:0.8453634232282639, Validation Acc:83.0423940149626, AUC:0.7219461697722567\n",
            "Fold: 3, Epoch: [37/40], Step: [27/29], Loss: 0.1130797369060693, Validation Loss:1.0210384726524353, Validation Acc:82.04488778054863, AUC:0.7460662525879916\n",
            "Epoch 36 time: 1.9949\n",
            "Fold: 3, Epoch: [38/40], Step: [9/29], Loss: 0.08765890325109164, Validation Loss:1.0764958560466766, Validation Acc:81.79551122194513, AUC:0.7146997929606625\n",
            "Fold: 3, Epoch: [38/40], Step: [18/29], Loss: 0.08970655562976997, Validation Loss:1.1442518532276154, Validation Acc:82.7930174563591, AUC:0.7504140786749481\n",
            "Fold: 3, Epoch: [38/40], Step: [27/29], Loss: 0.0997191474393562, Validation Loss:0.7448669523000717, Validation Acc:83.54114713216957, AUC:0.7398033126293996\n",
            "Epoch 37 time: 1.9885\n",
            "Fold: 3, Epoch: [39/40], Step: [9/29], Loss: 0.08352522179484367, Validation Loss:0.8433824181556702, Validation Acc:82.54364089775561, AUC:0.7489648033126294\n",
            "Fold: 3, Epoch: [39/40], Step: [18/29], Loss: 0.08177179180913502, Validation Loss:0.9252227693796158, Validation Acc:84.03990024937656, AUC:0.7651397515527949\n",
            "Fold: 3, Epoch: [39/40], Step: [27/29], Loss: 0.08536739923335888, Validation Loss:1.2146200090646744, Validation Acc:84.03990024937656, AUC:0.7277432712215322\n",
            "Epoch 38 time: 1.9763\n",
            "Fold: 3, Epoch: [40/40], Step: [9/29], Loss: 0.06463123071524832, Validation Loss:1.1113134026527405, Validation Acc:81.79551122194513, AUC:0.7146997929606625\n",
            "Fold: 3, Epoch: [40/40], Step: [18/29], Loss: 0.07182458715720309, Validation Loss:1.1249559223651886, Validation Acc:82.04488778054863, AUC:0.723628364389234\n",
            "Fold: 3, Epoch: [40/40], Step: [27/29], Loss: 0.07208154095267807, Validation Loss:1.3385148793458939, Validation Acc:82.7930174563591, AUC:0.7204968944099378\n",
            "Epoch 39 time: 1.9903\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 4, Epoch: [1/40], Step: [9/29], Loss: 0.6353322201304965, Validation Loss:0.646671935915947, Validation Acc:67.33167082294264, AUC:0.6755175983436852\n",
            "Fold: 4, Epoch: [1/40], Step: [18/29], Loss: 0.6138668855031332, Validation Loss:0.5913992375135422, Validation Acc:70.57356608478803, AUC:0.7093167701863353\n",
            "Fold: 4, Epoch: [1/40], Step: [27/29], Loss: 0.5858673883808984, Validation Loss:0.567462146282196, Validation Acc:70.82294264339153, AUC:0.7257246376811594\n",
            "Epoch 0 time: 1.9674\n",
            "Fold: 4, Epoch: [2/40], Step: [9/29], Loss: 0.5427635080284543, Validation Loss:0.5698417127132416, Validation Acc:71.07231920199501, AUC:0.7196946169772257\n",
            "Fold: 4, Epoch: [2/40], Step: [18/29], Loss: 0.5227116197347641, Validation Loss:0.5643634647130966, Validation Acc:71.07231920199501, AUC:0.7122153209109732\n",
            "Fold: 4, Epoch: [2/40], Step: [27/29], Loss: 0.5205555701697314, Validation Loss:0.5631855204701424, Validation Acc:72.06982543640898, AUC:0.7254917184265011\n",
            "Epoch 1 time: 1.9695\n",
            "Fold: 4, Epoch: [3/40], Step: [9/29], Loss: 0.5224828322728475, Validation Loss:0.5097014978528023, Validation Acc:71.82044887780549, AUC:0.7240424430641821\n",
            "Fold: 4, Epoch: [3/40], Step: [18/29], Loss: 0.5068222963147693, Validation Loss:0.5494688227772713, Validation Acc:71.82044887780549, AUC:0.7165631469979297\n",
            "Fold: 4, Epoch: [3/40], Step: [27/29], Loss: 0.5076662224751932, Validation Loss:0.5047792717814445, Validation Acc:72.56857855361596, AUC:0.7283902691511387\n",
            "Epoch 2 time: 1.9651\n",
            "Fold: 4, Epoch: [4/40], Step: [9/29], Loss: 0.5046918590863546, Validation Loss:0.6447112411260605, Validation Acc:73.31670822942644, AUC:0.7402173913043478\n",
            "Fold: 4, Epoch: [4/40], Step: [18/29], Loss: 0.4993705103794734, Validation Loss:0.5288068726658821, Validation Acc:73.31670822942644, AUC:0.7327380952380953\n",
            "Fold: 4, Epoch: [4/40], Step: [27/29], Loss: 0.4917208066693059, Validation Loss:0.5035807266831398, Validation Acc:73.56608478802993, AUC:0.7267080745341614\n",
            "Epoch 3 time: 1.9670\n",
            "Fold: 4, Epoch: [5/40], Step: [9/29], Loss: 0.46031679378615487, Validation Loss:0.6225274205207825, Validation Acc:73.56608478802993, AUC:0.7192287784679089\n",
            "Fold: 4, Epoch: [5/40], Step: [18/29], Loss: 0.48767225444316864, Validation Loss:0.6612690016627312, Validation Acc:72.56857855361596, AUC:0.7209109730848862\n",
            "Fold: 4, Epoch: [5/40], Step: [27/29], Loss: 0.48372324528517546, Validation Loss:0.5193527042865753, Validation Acc:72.81795511221945, AUC:0.7298395445134574\n",
            "Epoch 4 time: 1.9988\n",
            "Fold: 4, Epoch: [6/40], Step: [9/29], Loss: 0.48676902055740356, Validation Loss:0.5470594018697739, Validation Acc:76.30922693266832, AUC:0.7351708074534161\n",
            "Fold: 4, Epoch: [6/40], Step: [18/29], Loss: 0.4746297597885132, Validation Loss:0.5307199060916901, Validation Acc:73.81546134663341, AUC:0.7281573498964804\n",
            "Fold: 4, Epoch: [6/40], Step: [27/29], Loss: 0.4764279237499944, Validation Loss:0.531037911772728, Validation Acc:74.56359102244389, AUC:0.7325051759834369\n",
            "Epoch 5 time: 1.9806\n",
            "Fold: 4, Epoch: [7/40], Step: [9/29], Loss: 0.46215028564135235, Validation Loss:0.5427528694272041, Validation Acc:73.81546134663341, AUC:0.7206780538302278\n",
            "Fold: 4, Epoch: [7/40], Step: [18/29], Loss: 0.4710678706566493, Validation Loss:0.477509468793869, Validation Acc:72.06982543640898, AUC:0.7329710144927535\n",
            "Fold: 4, Epoch: [7/40], Step: [27/29], Loss: 0.4649824853296633, Validation Loss:0.5207867696881294, Validation Acc:73.06733167082294, AUC:0.738768115942029\n",
            "Epoch 6 time: 1.9677\n",
            "Fold: 4, Epoch: [8/40], Step: [9/29], Loss: 0.44996461934513515, Validation Loss:0.5175761729478836, Validation Acc:74.81296758104739, AUC:0.7563923395445136\n",
            "Fold: 4, Epoch: [8/40], Step: [18/29], Loss: 0.4593020694123374, Validation Loss:0.5223400965332985, Validation Acc:75.31172069825436, AUC:0.7518115942028986\n",
            "Fold: 4, Epoch: [8/40], Step: [27/29], Loss: 0.45639658636516994, Validation Loss:0.5309433490037918, Validation Acc:74.81296758104739, AUC:0.7339544513457558\n",
            "Epoch 7 time: 1.9854\n",
            "Fold: 4, Epoch: [9/40], Step: [9/29], Loss: 0.42656030919816756, Validation Loss:0.5569845363497734, Validation Acc:73.31670822942644, AUC:0.7402173913043478\n",
            "Fold: 4, Epoch: [9/40], Step: [18/29], Loss: 0.45015044179227615, Validation Loss:0.518604651093483, Validation Acc:72.31920199501246, AUC:0.7194616977225674\n",
            "Fold: 4, Epoch: [9/40], Step: [27/29], Loss: 0.44767850306298995, Validation Loss:0.5145512595772743, Validation Acc:72.31920199501246, AUC:0.7119824016563147\n",
            "Epoch 8 time: 1.9686\n",
            "Fold: 4, Epoch: [10/40], Step: [9/29], Loss: 0.4268696771727668, Validation Loss:0.6880343109369278, Validation Acc:75.81047381546135, AUC:0.7472308488612837\n",
            "Fold: 4, Epoch: [10/40], Step: [18/29], Loss: 0.44139817357063293, Validation Loss:0.5086842179298401, Validation Acc:75.56109725685785, AUC:0.76074016563147\n",
            "Fold: 4, Epoch: [10/40], Step: [27/29], Loss: 0.4478689365916782, Validation Loss:0.5564122200012207, Validation Acc:74.81296758104739, AUC:0.7414337474120083\n",
            "Epoch 9 time: 1.9581\n",
            "Fold: 4, Epoch: [11/40], Step: [9/29], Loss: 0.43651389082272846, Validation Loss:0.5640285760164261, Validation Acc:75.31172069825436, AUC:0.7218944099378882\n",
            "Fold: 4, Epoch: [11/40], Step: [18/29], Loss: 0.42950258486800724, Validation Loss:0.5481202453374863, Validation Acc:75.31172069825436, AUC:0.7218944099378882\n",
            "Fold: 4, Epoch: [11/40], Step: [27/29], Loss: 0.43526351231115834, Validation Loss:0.5225235223770142, Validation Acc:75.81047381546135, AUC:0.7397515527950311\n",
            "Epoch 10 time: 1.9545\n",
            "Fold: 4, Epoch: [12/40], Step: [9/29], Loss: 0.4234086043304867, Validation Loss:0.5208600461483002, Validation Acc:75.56109725685785, AUC:0.7308229813664597\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 4, Epoch: [12/40], Step: [18/29], Loss: 0.42536669307284886, Validation Loss:0.5390901267528534, Validation Acc:75.31172069825436, AUC:0.7518115942028986\n",
            "Fold: 4, Epoch: [12/40], Step: [27/29], Loss: 0.4129730893505944, Validation Loss:0.6730941757559776, Validation Acc:74.56359102244389, AUC:0.7325051759834369\n",
            "Epoch 11 time: 1.9752\n",
            "Fold: 4, Epoch: [13/40], Step: [9/29], Loss: 0.4182065659099155, Validation Loss:0.5537313222885132, Validation Acc:77.55610972568579, AUC:0.7424171842650104\n",
            "Fold: 4, Epoch: [13/40], Step: [18/29], Loss: 0.42282159129778546, Validation Loss:0.5269138664007187, Validation Acc:76.80798004987531, AUC:0.7305900621118013\n",
            "Fold: 4, Epoch: [13/40], Step: [27/29], Loss: 0.4096852022188681, Validation Loss:0.5275447443127632, Validation Acc:76.30922693266832, AUC:0.7276915113871636\n",
            "Epoch 12 time: 1.9402\n",
            "Fold: 4, Epoch: [14/40], Step: [9/29], Loss: 0.4049089286062453, Validation Loss:0.6260179132223129, Validation Acc:75.06234413965088, AUC:0.7428830227743272\n",
            "Fold: 4, Epoch: [14/40], Step: [18/29], Loss: 0.3950960917605294, Validation Loss:0.4994613006711006, Validation Acc:75.81047381546135, AUC:0.7322722567287785\n",
            "Fold: 4, Epoch: [14/40], Step: [27/29], Loss: 0.3982294742707853, Validation Loss:0.6138829663395882, Validation Acc:75.56109725685785, AUC:0.7158643892339545\n",
            "Epoch 13 time: 1.9428\n",
            "Fold: 4, Epoch: [15/40], Step: [9/29], Loss: 0.38069968422253925, Validation Loss:0.7841204404830933, Validation Acc:76.80798004987531, AUC:0.7006728778467909\n",
            "Fold: 4, Epoch: [15/40], Step: [18/29], Loss: 0.3877221710152096, Validation Loss:0.5571547374129295, Validation Acc:75.56109725685785, AUC:0.7158643892339545\n",
            "Fold: 4, Epoch: [15/40], Step: [27/29], Loss: 0.39351872271961635, Validation Loss:0.555031418800354, Validation Acc:71.82044887780549, AUC:0.7315217391304347\n",
            "Epoch 14 time: 1.9673\n",
            "Fold: 4, Epoch: [16/40], Step: [9/29], Loss: 0.40224732293023, Validation Loss:0.9120578989386559, Validation Acc:75.31172069825436, AUC:0.7069358178053831\n",
            "Fold: 4, Epoch: [16/40], Step: [18/29], Loss: 0.404682735602061, Validation Loss:0.6594108641147614, Validation Acc:74.56359102244389, AUC:0.6876293995859213\n",
            "Fold: 4, Epoch: [16/40], Step: [27/29], Loss: 0.39526098525082626, Validation Loss:0.6488159447908401, Validation Acc:77.55610972568579, AUC:0.6975414078674949\n",
            "Epoch 15 time: 1.9583\n",
            "Fold: 4, Epoch: [17/40], Step: [9/29], Loss: 0.3709296186765035, Validation Loss:0.5922728106379509, Validation Acc:76.55860349127182, AUC:0.7141821946169772\n",
            "Fold: 4, Epoch: [17/40], Step: [18/29], Loss: 0.3732518073585298, Validation Loss:0.5289187580347061, Validation Acc:74.56359102244389, AUC:0.7250258799171843\n",
            "Fold: 4, Epoch: [17/40], Step: [27/29], Loss: 0.37614451183213127, Validation Loss:0.5232831388711929, Validation Acc:75.31172069825436, AUC:0.7218944099378882\n",
            "Epoch 16 time: 1.9536\n",
            "Fold: 4, Epoch: [18/40], Step: [9/29], Loss: 0.36174538400438094, Validation Loss:0.6598581150174141, Validation Acc:77.80548628428927, AUC:0.7214285714285714\n",
            "Fold: 4, Epoch: [18/40], Step: [18/29], Loss: 0.3500058700640996, Validation Loss:0.6977111399173737, Validation Acc:76.80798004987531, AUC:0.715631469979296\n",
            "Fold: 4, Epoch: [18/40], Step: [27/29], Loss: 0.36280262139108443, Validation Loss:0.56605114787817, Validation Acc:76.80798004987531, AUC:0.7006728778467909\n",
            "Epoch 17 time: 1.9893\n",
            "Fold: 4, Epoch: [19/40], Step: [9/29], Loss: 0.3607485459910499, Validation Loss:0.5907551720738411, Validation Acc:79.80049875311721, AUC:0.7405020703933747\n",
            "Fold: 4, Epoch: [19/40], Step: [18/29], Loss: 0.3435322460201051, Validation Loss:0.8148281872272491, Validation Acc:79.30174563591022, AUC:0.7301242236024844\n",
            "Fold: 4, Epoch: [19/40], Step: [27/29], Loss: 0.34663963980144924, Validation Loss:0.7592049613595009, Validation Acc:76.05985037406484, AUC:0.7262422360248447\n",
            "Epoch 18 time: 1.9806\n",
            "Fold: 4, Epoch: [20/40], Step: [9/29], Loss: 0.3837710420290629, Validation Loss:0.5697645917534828, Validation Acc:75.81047381546135, AUC:0.7098343685300207\n",
            "Fold: 4, Epoch: [20/40], Step: [18/29], Loss: 0.38058659268750084, Validation Loss:0.5588936656713486, Validation Acc:76.05985037406484, AUC:0.7262422360248447\n",
            "Fold: 4, Epoch: [20/40], Step: [27/29], Loss: 0.3681464438085203, Validation Loss:0.5759402364492416, Validation Acc:76.55860349127182, AUC:0.7216614906832297\n",
            "Epoch 19 time: 1.9769\n",
            "Fold: 4, Epoch: [21/40], Step: [9/29], Loss: 0.3424726658397251, Validation Loss:0.5539955124258995, Validation Acc:79.55112219451371, AUC:0.7465320910973086\n",
            "Fold: 4, Epoch: [21/40], Step: [18/29], Loss: 0.34296274847454494, Validation Loss:0.6392699331045151, Validation Acc:80.29925187032418, AUC:0.7134834368530021\n",
            "Fold: 4, Epoch: [21/40], Step: [27/29], Loss: 0.3406315865340056, Validation Loss:0.59625693410635, Validation Acc:77.55610972568579, AUC:0.7199792960662525\n",
            "Epoch 20 time: 1.9881\n",
            "Fold: 4, Epoch: [22/40], Step: [9/29], Loss: 0.308614045381546, Validation Loss:0.5646981298923492, Validation Acc:77.3067331670823, AUC:0.7334886128364391\n",
            "Fold: 4, Epoch: [22/40], Step: [18/29], Loss: 0.313742582168844, Validation Loss:0.5203993022441864, Validation Acc:79.30174563591022, AUC:0.7301242236024844\n",
            "Fold: 4, Epoch: [22/40], Step: [27/29], Loss: 0.309921407589206, Validation Loss:0.6575985103845596, Validation Acc:78.55361596009975, AUC:0.7257763975155279\n",
            "Epoch 21 time: 1.9777\n",
            "Fold: 4, Epoch: [23/40], Step: [9/29], Loss: 0.2843506766690148, Validation Loss:0.6212886273860931, Validation Acc:78.05486284289277, AUC:0.6780020703933747\n",
            "Fold: 4, Epoch: [23/40], Step: [18/29], Loss: 0.2851528252164523, Validation Loss:0.6016656644642353, Validation Acc:77.55610972568579, AUC:0.6900621118012422\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 4, Epoch: [23/40], Step: [27/29], Loss: 0.3020706171238864, Validation Loss:0.5663463994860649, Validation Acc:78.30423940149626, AUC:0.7318064182194617\n",
            "Epoch 22 time: 2.0201\n",
            "Fold: 4, Epoch: [24/40], Step: [9/29], Loss: 0.2936828268898858, Validation Loss:0.635952815413475, Validation Acc:77.80548628428927, AUC:0.7139492753623189\n",
            "Fold: 4, Epoch: [24/40], Step: [18/29], Loss: 0.2982679042551253, Validation Loss:0.6316420286893845, Validation Acc:77.3067331670823, AUC:0.7035714285714286\n",
            "Fold: 4, Epoch: [24/40], Step: [27/29], Loss: 0.300178787222615, Validation Loss:0.6834622174501419, Validation Acc:78.05486284289277, AUC:0.7153985507246376\n",
            "Epoch 23 time: 2.0186\n",
            "Fold: 4, Epoch: [25/40], Step: [9/29], Loss: 0.2830113073190053, Validation Loss:0.9017034620046616, Validation Acc:79.80049875311721, AUC:0.7105848861283645\n",
            "Fold: 4, Epoch: [25/40], Step: [18/29], Loss: 0.27301784522003597, Validation Loss:0.803853452205658, Validation Acc:80.0498753117207, AUC:0.7269927536231883\n",
            "Fold: 4, Epoch: [25/40], Step: [27/29], Loss: 0.26823519611800156, Validation Loss:0.8347279280424118, Validation Acc:78.80299251870325, AUC:0.7197463768115943\n",
            "Epoch 24 time: 2.0121\n",
            "Fold: 4, Epoch: [26/40], Step: [9/29], Loss: 0.2566096418433719, Validation Loss:0.6049402728676796, Validation Acc:77.80548628428927, AUC:0.7513457556935819\n",
            "Fold: 4, Epoch: [26/40], Step: [18/29], Loss: 0.2725352893273036, Validation Loss:0.8049831539392471, Validation Acc:77.0573566084788, AUC:0.6946428571428572\n",
            "Fold: 4, Epoch: [26/40], Step: [27/29], Loss: 0.28311894447715197, Validation Loss:0.7002146244049072, Validation Acc:79.05236907730674, AUC:0.6912784679089027\n",
            "Epoch 25 time: 2.0285\n",
            "Fold: 4, Epoch: [27/40], Step: [9/29], Loss: 0.25466974907451206, Validation Loss:0.6958603411912918, Validation Acc:80.79800498753117, AUC:0.7014233954451345\n",
            "Fold: 4, Epoch: [27/40], Step: [18/29], Loss: 0.25609911067618263, Validation Loss:0.7659403532743454, Validation Acc:80.0498753117207, AUC:0.7045548654244307\n",
            "Fold: 4, Epoch: [27/40], Step: [27/29], Loss: 0.24639923704995048, Validation Loss:0.6561385616660118, Validation Acc:78.80299251870325, AUC:0.7122670807453415\n",
            "Epoch 26 time: 1.9545\n",
            "Fold: 4, Epoch: [28/40], Step: [9/29], Loss: 0.21333658198515573, Validation Loss:0.8888835459947586, Validation Acc:81.54613466334165, AUC:0.705771221532091\n",
            "Fold: 4, Epoch: [28/40], Step: [18/29], Loss: 0.22239495979415047, Validation Loss:0.9289143681526184, Validation Acc:80.29925187032418, AUC:0.6910455486542443\n",
            "Fold: 4, Epoch: [28/40], Step: [27/29], Loss: 0.2284323865616763, Validation Loss:0.6777231208980083, Validation Acc:80.29925187032418, AUC:0.7284420289855073\n",
            "Epoch 27 time: 1.9638\n",
            "Fold: 4, Epoch: [29/40], Step: [9/29], Loss: 0.19448256658183205, Validation Loss:0.7700245156884193, Validation Acc:80.54862842892769, AUC:0.6924948240165631\n",
            "Fold: 4, Epoch: [29/40], Step: [18/29], Loss: 0.21071968393193352, Validation Loss:1.0408496111631393, Validation Acc:77.80548628428927, AUC:0.6615942028985508\n",
            "Fold: 4, Epoch: [29/40], Step: [27/29], Loss: 0.21847977572017246, Validation Loss:0.8509110808372498, Validation Acc:78.80299251870325, AUC:0.6973084886128365\n",
            "Epoch 28 time: 1.9984\n",
            "Fold: 4, Epoch: [30/40], Step: [9/29], Loss: 0.18568775223361123, Validation Loss:0.8104378804564476, Validation Acc:82.04488778054863, AUC:0.7086697722567288\n",
            "Fold: 4, Epoch: [30/40], Step: [18/29], Loss: 0.18157067977719837, Validation Loss:0.82304497808218, Validation Acc:82.29426433915212, AUC:0.7325569358178053\n",
            "Fold: 4, Epoch: [30/40], Step: [27/29], Loss: 0.19577156172858345, Validation Loss:0.8469835817813873, Validation Acc:79.30174563591022, AUC:0.7151656314699794\n",
            "Epoch 29 time: 1.9712\n",
            "Fold: 4, Epoch: [31/40], Step: [9/29], Loss: 0.1858741400970353, Validation Loss:0.9816868752241135, Validation Acc:80.54862842892769, AUC:0.7224120082815735\n",
            "Fold: 4, Epoch: [31/40], Step: [18/29], Loss: 0.19272731865445772, Validation Loss:0.7378420233726501, Validation Acc:82.04488778054863, AUC:0.671273291925466\n",
            "Fold: 4, Epoch: [31/40], Step: [27/29], Loss: 0.2003736283492159, Validation Loss:1.14003124833107, Validation Acc:80.29925187032418, AUC:0.7134834368530021\n",
            "Epoch 30 time: 1.9658\n",
            "Fold: 4, Epoch: [32/40], Step: [9/29], Loss: 0.18782231377230751, Validation Loss:0.7947258502244949, Validation Acc:80.0498753117207, AUC:0.6895962732919254\n",
            "Fold: 4, Epoch: [32/40], Step: [18/29], Loss: 0.1814141281776958, Validation Loss:0.8160795047879219, Validation Acc:79.80049875311721, AUC:0.6881469979296065\n",
            "Fold: 4, Epoch: [32/40], Step: [27/29], Loss: 0.17873884351165206, Validation Loss:1.0287707149982452, Validation Acc:80.54862842892769, AUC:0.6999741200828157\n",
            "Epoch 31 time: 1.9777\n",
            "Fold: 4, Epoch: [33/40], Step: [9/29], Loss: 0.149590236445268, Validation Loss:0.877247542142868, Validation Acc:81.04738154613466, AUC:0.7028726708074534\n",
            "Fold: 4, Epoch: [33/40], Step: [18/29], Loss: 0.14848357521825367, Validation Loss:1.0374521017074585, Validation Acc:82.04488778054863, AUC:0.686231884057971\n",
            "Fold: 4, Epoch: [33/40], Step: [27/29], Loss: 0.15339946884799888, Validation Loss:0.8565112724900246, Validation Acc:80.54862842892769, AUC:0.7298913043478261\n",
            "Epoch 32 time: 2.0392\n",
            "Fold: 4, Epoch: [34/40], Step: [9/29], Loss: 0.12764113810327318, Validation Loss:0.9366975575685501, Validation Acc:81.29675810473816, AUC:0.7043219461697723\n",
            "Fold: 4, Epoch: [34/40], Step: [18/29], Loss: 0.1446895268228319, Validation Loss:0.8308292776346207, Validation Acc:80.54862842892769, AUC:0.6999741200828157\n",
            "Fold: 4, Epoch: [34/40], Step: [27/29], Loss: 0.14421327108586277, Validation Loss:1.1300039291381836, Validation Acc:80.54862842892769, AUC:0.7074534161490683\n",
            "Epoch 33 time: 1.9785\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 4, Epoch: [35/40], Step: [9/29], Loss: 0.11953088061677085, Validation Loss:0.8403369933366776, Validation Acc:81.29675810473816, AUC:0.7192805383022776\n",
            "Fold: 4, Epoch: [35/40], Step: [18/29], Loss: 0.12489807688527638, Validation Loss:1.2748801410198212, Validation Acc:82.04488778054863, AUC:0.6787525879917184\n",
            "Fold: 4, Epoch: [35/40], Step: [27/29], Loss: 0.12520216239823234, Validation Loss:0.984127089381218, Validation Acc:80.29925187032418, AUC:0.6686076604554866\n",
            "Epoch 34 time: 1.9784\n",
            "Fold: 4, Epoch: [36/40], Step: [9/29], Loss: 0.11030816576547092, Validation Loss:0.961438000202179, Validation Acc:79.05236907730674, AUC:0.6912784679089027\n",
            "Fold: 4, Epoch: [36/40], Step: [18/29], Loss: 0.13410030657218563, Validation Loss:1.337429478764534, Validation Acc:81.54613466334165, AUC:0.6982919254658384\n",
            "Fold: 4, Epoch: [36/40], Step: [27/29], Loss: 0.14029821436162349, Validation Loss:1.2280049175024033, Validation Acc:82.7930174563591, AUC:0.7130175983436853\n",
            "Epoch 35 time: 1.9977\n",
            "Fold: 4, Epoch: [37/40], Step: [9/29], Loss: 0.11534941693147023, Validation Loss:1.2494795471429825, Validation Acc:81.29675810473816, AUC:0.7118012422360247\n",
            "Fold: 4, Epoch: [37/40], Step: [18/29], Loss: 0.12584593850705358, Validation Loss:1.4093283265829086, Validation Acc:81.54613466334165, AUC:0.6833333333333333\n",
            "Fold: 4, Epoch: [37/40], Step: [27/29], Loss: 0.12534885080876174, Validation Loss:1.1972430646419525, Validation Acc:80.79800498753117, AUC:0.7014233954451345\n",
            "Epoch 36 time: 1.9940\n",
            "Fold: 4, Epoch: [38/40], Step: [9/29], Loss: 0.10537144458956188, Validation Loss:1.3359429091215134, Validation Acc:82.04488778054863, AUC:0.6937111801242236\n",
            "Fold: 4, Epoch: [38/40], Step: [18/29], Loss: 0.10382046260767513, Validation Loss:1.433007687330246, Validation Acc:82.54364089775561, AUC:0.6966097308488612\n",
            "Fold: 4, Epoch: [38/40], Step: [27/29], Loss: 0.10184302360371307, Validation Loss:1.105221375823021, Validation Acc:83.0423940149626, AUC:0.6920289855072465\n",
            "Epoch 37 time: 1.9772\n",
            "Fold: 4, Epoch: [39/40], Step: [9/29], Loss: 0.09718721898065673, Validation Loss:1.027633547782898, Validation Acc:81.79551122194513, AUC:0.6997412008281573\n",
            "Fold: 4, Epoch: [39/40], Step: [18/29], Loss: 0.09741588930288951, Validation Loss:1.0500361323356628, Validation Acc:81.29675810473816, AUC:0.6893633540372671\n",
            "Fold: 4, Epoch: [39/40], Step: [27/29], Loss: 0.10209898457483009, Validation Loss:1.2338121086359024, Validation Acc:80.29925187032418, AUC:0.6910455486542443\n",
            "Epoch 38 time: 1.9826\n",
            "Fold: 4, Epoch: [40/40], Step: [9/29], Loss: 0.09636750030848715, Validation Loss:1.0081117674708366, Validation Acc:81.79551122194513, AUC:0.6997412008281573\n",
            "Fold: 4, Epoch: [40/40], Step: [18/29], Loss: 0.0886702032552825, Validation Loss:0.9839475378394127, Validation Acc:82.7930174563591, AUC:0.645703933747412\n",
            "Fold: 4, Epoch: [40/40], Step: [27/29], Loss: 0.09286100031049163, Validation Loss:0.9519522413611412, Validation Acc:80.54862842892769, AUC:0.6700569358178055\n",
            "Epoch 39 time: 1.9730\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 5, Epoch: [1/40], Step: [9/29], Loss: 0.6796833674112955, Validation Loss:0.6437118500471115, Validation Acc:67.0, AUC:0.7246376811594202\n",
            "Fold: 5, Epoch: [1/40], Step: [18/29], Loss: 0.639765116903517, Validation Loss:0.5629177317023277, Validation Acc:74.0, AUC:0.7575757575757576\n",
            "Fold: 5, Epoch: [1/40], Step: [27/29], Loss: 0.6152426158940351, Validation Loss:0.5513497963547707, Validation Acc:75.25, AUC:0.7648221343873517\n",
            "Epoch 0 time: 1.9759\n",
            "Fold: 5, Epoch: [2/40], Step: [9/29], Loss: 0.5172710915406545, Validation Loss:0.49256809055805206, Validation Acc:74.0, AUC:0.7499341238471674\n",
            "Fold: 5, Epoch: [2/40], Step: [18/29], Loss: 0.5445963856246736, Validation Loss:0.5093178674578667, Validation Acc:74.0, AUC:0.7499341238471674\n",
            "Fold: 5, Epoch: [2/40], Step: [27/29], Loss: 0.5444248528392227, Validation Loss:0.5221119895577431, Validation Acc:74.75, AUC:0.7466403162055336\n",
            "Epoch 1 time: 1.9589\n",
            "Fold: 5, Epoch: [3/40], Step: [9/29], Loss: 0.5059322946601443, Validation Loss:0.48542700707912445, Validation Acc:75.75, AUC:0.7447957839262187\n",
            "Fold: 5, Epoch: [3/40], Step: [18/29], Loss: 0.5161469827095667, Validation Loss:0.5091811865568161, Validation Acc:75.0, AUC:0.7404479578392622\n",
            "Fold: 5, Epoch: [3/40], Step: [27/29], Loss: 0.519326948457294, Validation Loss:0.484568789601326, Validation Acc:75.25, AUC:0.7342555994729908\n",
            "Epoch 2 time: 1.9821\n",
            "Fold: 5, Epoch: [4/40], Step: [9/29], Loss: 0.4795777632130517, Validation Loss:0.5754912197589874, Validation Acc:77.25, AUC:0.7534914361001318\n",
            "Fold: 5, Epoch: [4/40], Step: [18/29], Loss: 0.4909043245845371, Validation Loss:0.45202942192554474, Validation Acc:77.5, AUC:0.7549407114624506\n",
            "Fold: 5, Epoch: [4/40], Step: [27/29], Loss: 0.5035469399558173, Validation Loss:0.4808117747306824, Validation Acc:77.5, AUC:0.7472990777338603\n",
            "Epoch 3 time: 1.9813\n",
            "Fold: 5, Epoch: [5/40], Step: [9/29], Loss: 0.5015262961387634, Validation Loss:0.4914463832974434, Validation Acc:76.0, AUC:0.7309617918313571\n",
            "Fold: 5, Epoch: [5/40], Step: [18/29], Loss: 0.4971574942270915, Validation Loss:0.5161752626299858, Validation Acc:77.0, AUC:0.7444005270092227\n",
            "Fold: 5, Epoch: [5/40], Step: [27/29], Loss: 0.49785780906677246, Validation Loss:0.4451978877186775, Validation Acc:76.0, AUC:0.7538866930171277\n",
            "Epoch 4 time: 1.9568\n",
            "Fold: 5, Epoch: [6/40], Step: [9/29], Loss: 0.4965401821666294, Validation Loss:0.5391766354441643, Validation Acc:76.5, AUC:0.7338603425559947\n",
            "Fold: 5, Epoch: [6/40], Step: [18/29], Loss: 0.48617806865109336, Validation Loss:0.50300732254982, Validation Acc:77.0, AUC:0.7444005270092227\n",
            "Fold: 5, Epoch: [6/40], Step: [27/29], Loss: 0.4854427796823007, Validation Loss:0.5245055332779884, Validation Acc:77.25, AUC:0.7611330698287221\n",
            "Epoch 5 time: 1.9715\n",
            "Fold: 5, Epoch: [7/40], Step: [9/29], Loss: 0.5069896313879225, Validation Loss:0.4551592916250229, Validation Acc:73.5, AUC:0.7317523056653492\n",
            "Fold: 5, Epoch: [7/40], Step: [18/29], Loss: 0.48590224319034153, Validation Loss:0.44323717057704926, Validation Acc:78.25, AUC:0.7669301712779975\n",
            "Fold: 5, Epoch: [7/40], Step: [27/29], Loss: 0.48133970741872434, Validation Loss:0.4560765400528908, Validation Acc:76.25, AUC:0.7553359683794467\n",
            "Epoch 6 time: 1.9906\n",
            "Fold: 5, Epoch: [8/40], Step: [9/29], Loss: 0.459774418009652, Validation Loss:0.5059444978833199, Validation Acc:75.75, AUC:0.7677206851119894\n",
            "Fold: 5, Epoch: [8/40], Step: [18/29], Loss: 0.46574177510208553, Validation Loss:0.5639589801430702, Validation Acc:75.5, AUC:0.7433465085638999\n",
            "Fold: 5, Epoch: [8/40], Step: [27/29], Loss: 0.4735728877562064, Validation Loss:0.43071820586919785, Validation Acc:75.75, AUC:0.752437417654809\n",
            "Epoch 7 time: 1.9907\n",
            "Fold: 5, Epoch: [9/40], Step: [9/29], Loss: 0.43787383039792377, Validation Loss:0.5254601985216141, Validation Acc:73.25, AUC:0.7455862977602108\n",
            "Fold: 5, Epoch: [9/40], Step: [18/29], Loss: 0.45347688098748523, Validation Loss:0.4262266829609871, Validation Acc:74.0, AUC:0.7499341238471674\n",
            "Fold: 5, Epoch: [9/40], Step: [27/29], Loss: 0.4585082641354314, Validation Loss:0.4564487710595131, Validation Acc:75.75, AUC:0.7677206851119894\n",
            "Epoch 8 time: 2.0050\n",
            "Fold: 5, Epoch: [10/40], Step: [9/29], Loss: 0.4535801675584581, Validation Loss:0.4228294938802719, Validation Acc:76.5, AUC:0.7797101449275362\n",
            "Fold: 5, Epoch: [10/40], Step: [18/29], Loss: 0.4540146523051792, Validation Loss:0.5052637830376625, Validation Acc:76.25, AUC:0.7400527009222662\n",
            "Fold: 5, Epoch: [10/40], Step: [27/29], Loss: 0.4510806743745451, Validation Loss:0.4546562284231186, Validation Acc:77.0, AUC:0.7444005270092227\n",
            "Epoch 9 time: 1.9956\n",
            "Fold: 5, Epoch: [11/40], Step: [9/29], Loss: 0.43613513973024154, Validation Loss:0.44559622555971146, Validation Acc:76.5, AUC:0.7491436100131753\n",
            "Fold: 5, Epoch: [11/40], Step: [18/29], Loss: 0.4503490659925673, Validation Loss:0.43927377462387085, Validation Acc:75.0, AUC:0.7786561264822135\n",
            "Fold: 5, Epoch: [11/40], Step: [27/29], Loss: 0.44697427970391734, Validation Loss:0.4432148188352585, Validation Acc:75.75, AUC:0.78300395256917\n",
            "Epoch 10 time: 1.9915\n",
            "Fold: 5, Epoch: [12/40], Step: [9/29], Loss: 0.41912628875838387, Validation Loss:0.4515400156378746, Validation Acc:78.0, AUC:0.7578392621870883\n",
            "Fold: 5, Epoch: [12/40], Step: [18/29], Loss: 0.42458319332864547, Validation Loss:0.5404449477791786, Validation Acc:78.5, AUC:0.7607378129117259\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 5, Epoch: [12/40], Step: [27/29], Loss: 0.43633654713630676, Validation Loss:0.5954433977603912, Validation Acc:76.0, AUC:0.7462450592885377\n",
            "Epoch 11 time: 1.9804\n",
            "Fold: 5, Epoch: [13/40], Step: [9/29], Loss: 0.44480568170547485, Validation Loss:0.4415273740887642, Validation Acc:75.75, AUC:0.7447957839262187\n",
            "Fold: 5, Epoch: [13/40], Step: [18/29], Loss: 0.43822933236757916, Validation Loss:0.48587897419929504, Validation Acc:76.75, AUC:0.7658761528326746\n",
            "Fold: 5, Epoch: [13/40], Step: [27/29], Loss: 0.4219100166250158, Validation Loss:0.4923290014266968, Validation Acc:76.0, AUC:0.7691699604743082\n",
            "Epoch 12 time: 1.9849\n",
            "Fold: 5, Epoch: [14/40], Step: [9/29], Loss: 0.4067094094223446, Validation Loss:0.4674277976155281, Validation Acc:79.25, AUC:0.7650856389986824\n",
            "Fold: 5, Epoch: [14/40], Step: [18/29], Loss: 0.4113010780678855, Validation Loss:0.4657614380121231, Validation Acc:81.25, AUC:0.7613965744400527\n",
            "Fold: 5, Epoch: [14/40], Step: [27/29], Loss: 0.4185751235043561, Validation Loss:0.4601432979106903, Validation Acc:79.0, AUC:0.7483530961791831\n",
            "Epoch 13 time: 1.9841\n",
            "Fold: 5, Epoch: [15/40], Step: [9/29], Loss: 0.3934314548969269, Validation Loss:0.45713581144809723, Validation Acc:74.5, AUC:0.7833992094861659\n",
            "Fold: 5, Epoch: [15/40], Step: [18/29], Loss: 0.3979968875646591, Validation Loss:0.4610138610005379, Validation Acc:75.25, AUC:0.7724637681159421\n",
            "Fold: 5, Epoch: [15/40], Step: [27/29], Loss: 0.4108213495325159, Validation Loss:0.4861922115087509, Validation Acc:75.5, AUC:0.7433465085638999\n",
            "Epoch 14 time: 1.9835\n",
            "Fold: 5, Epoch: [16/40], Step: [9/29], Loss: 0.4221957226594289, Validation Loss:0.5185543447732925, Validation Acc:77.25, AUC:0.7534914361001318\n",
            "Fold: 5, Epoch: [16/40], Step: [18/29], Loss: 0.4016644325521257, Validation Loss:0.4607420712709427, Validation Acc:77.25, AUC:0.7611330698287221\n",
            "Fold: 5, Epoch: [16/40], Step: [27/29], Loss: 0.39281455675760907, Validation Loss:0.49523136764764786, Validation Acc:78.5, AUC:0.7607378129117259\n",
            "Epoch 15 time: 1.9477\n",
            "Fold: 5, Epoch: [17/40], Step: [9/29], Loss: 0.3596123854319255, Validation Loss:0.482706181704998, Validation Acc:78.75, AUC:0.7698287220026352\n",
            "Fold: 5, Epoch: [17/40], Step: [18/29], Loss: 0.38521554403834873, Validation Loss:0.46798183023929596, Validation Acc:74.0, AUC:0.7499341238471674\n",
            "Fold: 5, Epoch: [17/40], Step: [27/29], Loss: 0.38814346547479983, Validation Loss:0.5111544132232666, Validation Acc:74.5, AUC:0.7299077733860342\n",
            "Epoch 16 time: 1.9922\n",
            "Fold: 5, Epoch: [18/40], Step: [9/29], Loss: 0.37699121567938065, Validation Loss:0.4891633987426758, Validation Acc:77.0, AUC:0.7596837944664032\n",
            "Fold: 5, Epoch: [18/40], Step: [18/29], Loss: 0.38640350931220585, Validation Loss:0.5012095645070076, Validation Acc:76.0, AUC:0.7538866930171277\n",
            "Fold: 5, Epoch: [18/40], Step: [27/29], Loss: 0.37875819868511623, Validation Loss:0.5072345584630966, Validation Acc:74.5, AUC:0.7375494071146246\n",
            "Epoch 17 time: 1.9792\n",
            "Fold: 5, Epoch: [19/40], Step: [9/29], Loss: 0.353125403324763, Validation Loss:0.5116973146796227, Validation Acc:78.5, AUC:0.7454545454545455\n",
            "Fold: 5, Epoch: [19/40], Step: [18/29], Loss: 0.34999804033173454, Validation Loss:0.4998365342617035, Validation Acc:79.75, AUC:0.7679841897233202\n",
            "Fold: 5, Epoch: [19/40], Step: [27/29], Loss: 0.3698339782379292, Validation Loss:0.4521397650241852, Validation Acc:76.5, AUC:0.7262187088274045\n",
            "Epoch 18 time: 1.9939\n",
            "Fold: 5, Epoch: [20/40], Step: [9/29], Loss: 0.37100578678978813, Validation Loss:0.5000597909092903, Validation Acc:75.0, AUC:0.7480895915678525\n",
            "Fold: 5, Epoch: [20/40], Step: [18/29], Loss: 0.36285434166590375, Validation Loss:0.4841061607003212, Validation Acc:77.0, AUC:0.7749670619235836\n",
            "Fold: 5, Epoch: [20/40], Step: [27/29], Loss: 0.3655938506126404, Validation Loss:0.6481526643037796, Validation Acc:78.0, AUC:0.7578392621870883\n",
            "Epoch 19 time: 1.9634\n",
            "Fold: 5, Epoch: [21/40], Step: [9/29], Loss: 0.3154181059863832, Validation Loss:0.5199063867330551, Validation Acc:75.75, AUC:0.7371541501976284\n",
            "Fold: 5, Epoch: [21/40], Step: [18/29], Loss: 0.32381713804271484, Validation Loss:0.6896313577890396, Validation Acc:77.75, AUC:0.7640316205533597\n",
            "Fold: 5, Epoch: [21/40], Step: [27/29], Loss: 0.34933540832113336, Validation Loss:0.4714500308036804, Validation Acc:73.0, AUC:0.744137022397892\n",
            "Epoch 20 time: 1.9749\n",
            "Fold: 5, Epoch: [22/40], Step: [9/29], Loss: 0.3225754631890191, Validation Loss:0.517421156167984, Validation Acc:75.0, AUC:0.7251646903820818\n",
            "Fold: 5, Epoch: [22/40], Step: [18/29], Loss: 0.3386782606442769, Validation Loss:0.625087320804596, Validation Acc:79.75, AUC:0.7374176548089592\n",
            "Fold: 5, Epoch: [22/40], Step: [27/29], Loss: 0.3406174690635116, Validation Loss:0.5598572716116905, Validation Acc:79.25, AUC:0.7268774703557311\n",
            "Epoch 21 time: 1.9716\n",
            "Fold: 5, Epoch: [23/40], Step: [9/29], Loss: 0.32411569688055253, Validation Loss:0.6955917179584503, Validation Acc:75.75, AUC:0.721870882740448\n",
            "Fold: 5, Epoch: [23/40], Step: [18/29], Loss: 0.32317743450403214, Validation Loss:0.5654206424951553, Validation Acc:77.25, AUC:0.7382081686429512\n",
            "Fold: 5, Epoch: [23/40], Step: [27/29], Loss: 0.33601893374213465, Validation Loss:0.5980904921889305, Validation Acc:78.75, AUC:0.7621870882740448\n",
            "Epoch 22 time: 1.9662\n",
            "Fold: 5, Epoch: [24/40], Step: [9/29], Loss: 0.3193235529793633, Validation Loss:0.5706832781434059, Validation Acc:73.25, AUC:0.7379446640316206\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 5, Epoch: [24/40], Step: [18/29], Loss: 0.3317714316977395, Validation Loss:0.5775044858455658, Validation Acc:72.75, AUC:0.7121212121212123\n",
            "Fold: 5, Epoch: [24/40], Step: [27/29], Loss: 0.33933236532741123, Validation Loss:0.5504928156733513, Validation Acc:77.0, AUC:0.7596837944664032\n",
            "Epoch 23 time: 1.9851\n",
            "Fold: 5, Epoch: [25/40], Step: [9/29], Loss: 0.32980528639422524, Validation Loss:0.5243633463978767, Validation Acc:79.0, AUC:0.7407114624505929\n",
            "Fold: 5, Epoch: [25/40], Step: [18/29], Loss: 0.3250005402498775, Validation Loss:0.6123516485095024, Validation Acc:77.75, AUC:0.7411067193675889\n",
            "Fold: 5, Epoch: [25/40], Step: [27/29], Loss: 0.32254862509391924, Validation Loss:0.5000912360846996, Validation Acc:78.5, AUC:0.791304347826087\n",
            "Epoch 24 time: 1.9984\n",
            "Fold: 5, Epoch: [26/40], Step: [9/29], Loss: 0.317599344584677, Validation Loss:0.553213931620121, Validation Acc:77.25, AUC:0.7611330698287221\n",
            "Fold: 5, Epoch: [26/40], Step: [18/29], Loss: 0.31281917211082244, Validation Loss:0.5165949650108814, Validation Acc:78.25, AUC:0.7363636363636364\n",
            "Fold: 5, Epoch: [26/40], Step: [27/29], Loss: 0.3016424101811868, Validation Loss:0.6857149079442024, Validation Acc:78.5, AUC:0.7225296442687746\n",
            "Epoch 25 time: 2.0667\n",
            "Fold: 5, Epoch: [27/40], Step: [9/29], Loss: 0.2837136338154475, Validation Loss:0.5506014972925186, Validation Acc:79.75, AUC:0.7527009222661396\n",
            "Fold: 5, Epoch: [27/40], Step: [18/29], Loss: 0.2862819160024325, Validation Loss:0.5980849713087082, Validation Acc:77.25, AUC:0.7305665349143611\n",
            "Fold: 5, Epoch: [27/40], Step: [27/29], Loss: 0.3019152841082326, Validation Loss:0.6769856885075569, Validation Acc:77.25, AUC:0.7458498023715415\n",
            "Epoch 26 time: 1.9765\n",
            "Fold: 5, Epoch: [28/40], Step: [9/29], Loss: 0.29137663543224335, Validation Loss:0.4711443595588207, Validation Acc:80.25, AUC:0.7555994729907773\n",
            "Fold: 5, Epoch: [28/40], Step: [18/29], Loss: 0.2903803206152386, Validation Loss:0.53184524923563, Validation Acc:79.0, AUC:0.7177865612648221\n",
            "Fold: 5, Epoch: [28/40], Step: [27/29], Loss: 0.2910325902479666, Validation Loss:0.5077618062496185, Validation Acc:76.5, AUC:0.7262187088274045\n",
            "Epoch 27 time: 1.9806\n",
            "Fold: 5, Epoch: [29/40], Step: [9/29], Loss: 0.29549594554636216, Validation Loss:0.537297748029232, Validation Acc:80.0, AUC:0.738866930171278\n",
            "Fold: 5, Epoch: [29/40], Step: [18/29], Loss: 0.3019203154577149, Validation Loss:0.650932252407074, Validation Acc:79.25, AUC:0.7345191040843215\n",
            "Fold: 5, Epoch: [29/40], Step: [27/29], Loss: 0.29667365661373846, Validation Loss:0.5110236182808876, Validation Acc:78.0, AUC:0.719631093544137\n",
            "Epoch 28 time: 1.9898\n",
            "Fold: 5, Epoch: [30/40], Step: [9/29], Loss: 0.27328409254550934, Validation Loss:0.7175379991531372, Validation Acc:77.75, AUC:0.7334650856389987\n",
            "Fold: 5, Epoch: [30/40], Step: [18/29], Loss: 0.26379874762561584, Validation Loss:0.5934511795639992, Validation Acc:78.75, AUC:0.7163372859025033\n",
            "Fold: 5, Epoch: [30/40], Step: [27/29], Loss: 0.2612957728129846, Validation Loss:0.91164780408144, Validation Acc:80.0, AUC:0.708300395256917\n",
            "Epoch 29 time: 2.0133\n",
            "Fold: 5, Epoch: [31/40], Step: [9/29], Loss: 0.24894296129544577, Validation Loss:0.84743632376194, Validation Acc:79.5, AUC:0.7206851119894598\n",
            "Fold: 5, Epoch: [31/40], Step: [18/29], Loss: 0.2516348593764835, Validation Loss:0.6749975830316544, Validation Acc:77.5, AUC:0.7014492753623189\n",
            "Fold: 5, Epoch: [31/40], Step: [27/29], Loss: 0.248921365097717, Validation Loss:0.6423596441745758, Validation Acc:78.25, AUC:0.7134387351778656\n",
            "Epoch 30 time: 1.9711\n",
            "Fold: 5, Epoch: [32/40], Step: [9/29], Loss: 0.25678053829405045, Validation Loss:0.9549610763788223, Validation Acc:78.0, AUC:0.7425559947299079\n",
            "Fold: 5, Epoch: [32/40], Step: [18/29], Loss: 0.24483907388316262, Validation Loss:0.7963948845863342, Validation Acc:77.75, AUC:0.7181818181818181\n",
            "Fold: 5, Epoch: [32/40], Step: [27/29], Loss: 0.24883895615736643, Validation Loss:0.6700655370950699, Validation Acc:78.75, AUC:0.7086956521739131\n",
            "Epoch 31 time: 1.9782\n",
            "Fold: 5, Epoch: [33/40], Step: [9/29], Loss: 0.24109233915805817, Validation Loss:0.7780252546072006, Validation Acc:79.75, AUC:0.6915678524374176\n",
            "Fold: 5, Epoch: [33/40], Step: [18/29], Loss: 0.22763058377636802, Validation Loss:0.6893212683498859, Validation Acc:80.0, AUC:0.7159420289855073\n",
            "Fold: 5, Epoch: [33/40], Step: [27/29], Loss: 0.22312099072668287, Validation Loss:0.9413532316684723, Validation Acc:79.0, AUC:0.687220026350461\n",
            "Epoch 32 time: 1.9561\n",
            "Fold: 5, Epoch: [34/40], Step: [9/29], Loss: 0.21517072121302286, Validation Loss:0.7031876891851425, Validation Acc:81.0, AUC:0.7140974967061923\n",
            "Fold: 5, Epoch: [34/40], Step: [18/29], Loss: 0.20522938585943645, Validation Loss:0.7066091746091843, Validation Acc:78.25, AUC:0.7134387351778656\n",
            "Fold: 5, Epoch: [34/40], Step: [27/29], Loss: 0.21551242525930758, Validation Loss:0.8451844751834869, Validation Acc:76.0, AUC:0.6774703557312252\n",
            "Epoch 33 time: 1.9790\n",
            "Fold: 5, Epoch: [35/40], Step: [9/29], Loss: 0.18419847223493788, Validation Loss:0.9600892066955566, Validation Acc:78.75, AUC:0.6934123847167325\n",
            "Fold: 5, Epoch: [35/40], Step: [18/29], Loss: 0.21283993704451454, Validation Loss:0.8155089765787125, Validation Acc:79.75, AUC:0.7068511198945981\n",
            "Fold: 5, Epoch: [35/40], Step: [27/29], Loss: 0.20929551400520183, Validation Loss:1.0928982943296432, Validation Acc:76.5, AUC:0.7032938076416337\n",
            "Epoch 34 time: 1.9808\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "Fold: 5, Epoch: [36/40], Step: [9/29], Loss: 0.18909665279918247, Validation Loss:0.906574085354805, Validation Acc:81.25, AUC:0.7231884057971014\n",
            "Fold: 5, Epoch: [36/40], Step: [18/29], Loss: 0.1963732060458925, Validation Loss:0.7936539724469185, Validation Acc:79.5, AUC:0.697760210803689\n",
            "Fold: 5, Epoch: [36/40], Step: [27/29], Loss: 0.19264167105710064, Validation Loss:1.049040898680687, Validation Acc:79.5, AUC:0.7130434782608697\n",
            "Epoch 35 time: 1.9711\n",
            "Fold: 5, Epoch: [37/40], Step: [9/29], Loss: 0.1759079173207283, Validation Loss:1.1996884644031525, Validation Acc:80.25, AUC:0.7097496706192359\n",
            "Fold: 5, Epoch: [37/40], Step: [18/29], Loss: 0.17111668694350454, Validation Loss:1.0595375895500183, Validation Acc:78.25, AUC:0.6981554677206852\n",
            "Fold: 5, Epoch: [37/40], Step: [27/29], Loss: 0.17178112792747993, Validation Loss:0.8347408175468445, Validation Acc:79.25, AUC:0.68866930171278\n",
            "Epoch 36 time: 1.9816\n",
            "Fold: 5, Epoch: [38/40], Step: [9/29], Loss: 0.1585250207119518, Validation Loss:0.7598904613405466, Validation Acc:79.5, AUC:0.697760210803689\n",
            "Fold: 5, Epoch: [38/40], Step: [18/29], Loss: 0.16698145576649243, Validation Loss:1.1595892906188965, Validation Acc:80.75, AUC:0.6820816864295125\n",
            "Fold: 5, Epoch: [38/40], Step: [27/29], Loss: 0.16957328275397973, Validation Loss:1.0843543112277985, Validation Acc:77.75, AUC:0.6876152832674571\n",
            "Epoch 37 time: 1.9793\n",
            "Fold: 5, Epoch: [39/40], Step: [9/29], Loss: 0.16275609036286673, Validation Loss:0.960443764925003, Validation Acc:79.5, AUC:0.697760210803689\n",
            "Fold: 5, Epoch: [39/40], Step: [18/29], Loss: 0.14622356328699324, Validation Loss:1.3844456523656845, Validation Acc:80.5, AUC:0.6806324110671936\n",
            "Fold: 5, Epoch: [39/40], Step: [27/29], Loss: 0.15006868761998635, Validation Loss:0.9043010920286179, Validation Acc:77.5, AUC:0.6861660079051383\n",
            "Epoch 38 time: 1.9776\n",
            "Fold: 5, Epoch: [40/40], Step: [9/29], Loss: 0.14197937730285856, Validation Loss:1.1425447762012482, Validation Acc:80.75, AUC:0.7050065876152831\n",
            "Fold: 5, Epoch: [40/40], Step: [18/29], Loss: 0.14101327665978008, Validation Loss:0.96355901658535, Validation Acc:82.0, AUC:0.696969696969697\n",
            "Fold: 5, Epoch: [40/40], Step: [27/29], Loss: 0.1393870896211377, Validation Loss:1.3079652190208435, Validation Acc:80.0, AUC:0.6930171277997365\n",
            "Epoch 39 time: 1.9614\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 6, Epoch: [1/40], Step: [9/29], Loss: 0.6716341508759393, Validation Loss:0.6593283116817474, Validation Acc:72.5, AUC:0.6342555994729907\n",
            "Fold: 6, Epoch: [1/40], Step: [18/29], Loss: 0.6392578250832028, Validation Loss:0.5907562226057053, Validation Acc:72.75, AUC:0.7121212121212123\n",
            "Fold: 6, Epoch: [1/40], Step: [27/29], Loss: 0.6197854368774979, Validation Loss:0.556463211774826, Validation Acc:72.0, AUC:0.7230566534914361\n",
            "Epoch 0 time: 1.9884\n",
            "Fold: 6, Epoch: [2/40], Step: [9/29], Loss: 0.5483770569165548, Validation Loss:0.5652511566877365, Validation Acc:67.5, AUC:0.7198945981554676\n",
            "Fold: 6, Epoch: [2/40], Step: [18/29], Loss: 0.5435418122344546, Validation Loss:0.5387541279196739, Validation Acc:67.75, AUC:0.7213438735177865\n",
            "Fold: 6, Epoch: [2/40], Step: [27/29], Loss: 0.5403035967438309, Validation Loss:0.5507407709956169, Validation Acc:70.0, AUC:0.7114624505928854\n",
            "Epoch 1 time: 1.9849\n",
            "Fold: 6, Epoch: [3/40], Step: [9/29], Loss: 0.5477733247809939, Validation Loss:0.489076629281044, Validation Acc:71.75, AUC:0.7368906455862978\n",
            "Fold: 6, Epoch: [3/40], Step: [18/29], Loss: 0.5167716079288058, Validation Loss:0.5043142586946487, Validation Acc:70.75, AUC:0.7310935441370225\n",
            "Fold: 6, Epoch: [3/40], Step: [27/29], Loss: 0.5132678780290816, Validation Loss:0.5653410032391548, Validation Acc:71.25, AUC:0.7339920948616601\n",
            "Epoch 2 time: 1.9588\n",
            "Fold: 6, Epoch: [4/40], Step: [9/29], Loss: 0.4606310162279341, Validation Loss:0.6128631234169006, Validation Acc:70.0, AUC:0.7343873517786561\n",
            "Fold: 6, Epoch: [4/40], Step: [18/29], Loss: 0.48477374182807076, Validation Loss:0.5155122727155685, Validation Acc:70.0, AUC:0.7420289855072464\n",
            "Fold: 6, Epoch: [4/40], Step: [27/29], Loss: 0.4989673914732756, Validation Loss:0.5145723149180412, Validation Acc:73.0, AUC:0.7364953886693018\n",
            "Epoch 3 time: 1.9722\n",
            "Fold: 6, Epoch: [5/40], Step: [9/29], Loss: 0.45814598600069684, Validation Loss:0.5022729188203812, Validation Acc:76.75, AUC:0.7353096179183136\n",
            "Fold: 6, Epoch: [5/40], Step: [18/29], Loss: 0.47787212166521287, Validation Loss:0.5788730978965759, Validation Acc:72.0, AUC:0.745981554677207\n",
            "Fold: 6, Epoch: [5/40], Step: [27/29], Loss: 0.4883226520485348, Validation Loss:0.5011786222457886, Validation Acc:72.0, AUC:0.7306982872200264\n",
            "Epoch 4 time: 1.9883\n",
            "Fold: 6, Epoch: [6/40], Step: [9/29], Loss: 0.48015190495385063, Validation Loss:0.5178806036710739, Validation Acc:76.5, AUC:0.7109354413702239\n",
            "Fold: 6, Epoch: [6/40], Step: [18/29], Loss: 0.47163890467749703, Validation Loss:0.4775880500674248, Validation Acc:75.5, AUC:0.7433465085638999\n",
            "Fold: 6, Epoch: [6/40], Step: [27/29], Loss: 0.47463213514398644, Validation Loss:0.5187627449631691, Validation Acc:74.25, AUC:0.7513833992094862\n",
            "Epoch 5 time: 2.0060\n",
            "Fold: 6, Epoch: [7/40], Step: [9/29], Loss: 0.46148522694905597, Validation Loss:0.4758142903447151, Validation Acc:74.75, AUC:0.7389986824769433\n",
            "Fold: 6, Epoch: [7/40], Step: [18/29], Loss: 0.4669502377510071, Validation Loss:0.4602819234132767, Validation Acc:72.5, AUC:0.7335968379446641\n",
            "Fold: 6, Epoch: [7/40], Step: [27/29], Loss: 0.4669725309919428, Validation Loss:0.5439092516899109, Validation Acc:73.75, AUC:0.7408432147562584\n",
            "Epoch 6 time: 1.9659\n",
            "Fold: 6, Epoch: [8/40], Step: [9/29], Loss: 0.4269729753335317, Validation Loss:0.5708978995680809, Validation Acc:71.25, AUC:0.6957839262187088\n",
            "Fold: 6, Epoch: [8/40], Step: [18/29], Loss: 0.44913682010438705, Validation Loss:0.5558847337961197, Validation Acc:73.5, AUC:0.7241106719367588\n",
            "Fold: 6, Epoch: [8/40], Step: [27/29], Loss: 0.4554550559432418, Validation Loss:0.4918123111128807, Validation Acc:75.5, AUC:0.7433465085638999\n",
            "Epoch 7 time: 1.9799\n",
            "Fold: 6, Epoch: [9/40], Step: [9/29], Loss: 0.42807894945144653, Validation Loss:0.5007690787315369, Validation Acc:70.75, AUC:0.7310935441370225\n",
            "Fold: 6, Epoch: [9/40], Step: [18/29], Loss: 0.42429647346337634, Validation Loss:0.5692836493253708, Validation Acc:74.5, AUC:0.7451910408432149\n",
            "Fold: 6, Epoch: [9/40], Step: [27/29], Loss: 0.4364993781955154, Validation Loss:0.5476847440004349, Validation Acc:75.25, AUC:0.7189723320158103\n",
            "Epoch 8 time: 1.9747\n",
            "Fold: 6, Epoch: [10/40], Step: [9/29], Loss: 0.4444603655073378, Validation Loss:0.5736936777830124, Validation Acc:70.75, AUC:0.7005270092226615\n",
            "Fold: 6, Epoch: [10/40], Step: [18/29], Loss: 0.4211997472577625, Validation Loss:0.525276891887188, Validation Acc:73.5, AUC:0.7241106719367588\n",
            "Fold: 6, Epoch: [10/40], Step: [27/29], Loss: 0.43222070512948213, Validation Loss:0.45426497980952263, Validation Acc:76.0, AUC:0.7309617918313571\n",
            "Epoch 9 time: 1.9983\n",
            "Fold: 6, Epoch: [11/40], Step: [9/29], Loss: 0.39208248919910854, Validation Loss:0.5110700130462646, Validation Acc:73.25, AUC:0.7226613965744401\n",
            "Fold: 6, Epoch: [11/40], Step: [18/29], Loss: 0.40404553214708966, Validation Loss:0.6155871525406837, Validation Acc:75.25, AUC:0.7036890645586298\n",
            "Fold: 6, Epoch: [11/40], Step: [27/29], Loss: 0.42115664702874644, Validation Loss:0.5275087803602219, Validation Acc:75.5, AUC:0.7204216073781291\n",
            "Epoch 10 time: 1.9815\n",
            "Fold: 6, Epoch: [12/40], Step: [9/29], Loss: 0.3950787021054162, Validation Loss:0.5818321406841278, Validation Acc:72.75, AUC:0.704479578392622\n",
            "Fold: 6, Epoch: [12/40], Step: [18/29], Loss: 0.3941703803009457, Validation Loss:0.5524091497063637, Validation Acc:73.75, AUC:0.7026350461133068\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "Fold: 6, Epoch: [12/40], Step: [27/29], Loss: 0.40552902111300715, Validation Loss:0.6164166927337646, Validation Acc:75.75, AUC:0.7600790513833993\n",
            "Epoch 11 time: 1.9767\n",
            "Fold: 6, Epoch: [13/40], Step: [9/29], Loss: 0.3916052149401771, Validation Loss:0.5762092396616936, Validation Acc:75.75, AUC:0.7065876152832674\n",
            "Fold: 6, Epoch: [13/40], Step: [18/29], Loss: 0.398213740852144, Validation Loss:0.5433705225586891, Validation Acc:74.25, AUC:0.713175230566535\n",
            "Fold: 6, Epoch: [13/40], Step: [27/29], Loss: 0.39864015579223633, Validation Loss:0.47637294605374336, Validation Acc:73.0, AUC:0.7212121212121212\n",
            "Epoch 12 time: 1.9683\n",
            "Fold: 6, Epoch: [14/40], Step: [9/29], Loss: 0.4001469512780507, Validation Loss:0.6246930733323097, Validation Acc:72.75, AUC:0.7121212121212123\n",
            "Fold: 6, Epoch: [14/40], Step: [18/29], Loss: 0.3969770189788606, Validation Loss:0.47816693037748337, Validation Acc:72.0, AUC:0.7306982872200264\n",
            "Fold: 6, Epoch: [14/40], Step: [27/29], Loss: 0.3913831412792206, Validation Loss:0.6311633735895157, Validation Acc:75.75, AUC:0.7371541501976284\n",
            "Epoch 13 time: 1.9550\n",
            "Fold: 6, Epoch: [15/40], Step: [9/29], Loss: 0.36002830333179897, Validation Loss:0.543589286506176, Validation Acc:76.5, AUC:0.7109354413702239\n",
            "Fold: 6, Epoch: [15/40], Step: [18/29], Loss: 0.3867095940642887, Validation Loss:0.5876342281699181, Validation Acc:74.5, AUC:0.7451910408432149\n",
            "Fold: 6, Epoch: [15/40], Step: [27/29], Loss: 0.37110975936607077, Validation Loss:0.5935946255922318, Validation Acc:74.75, AUC:0.7466403162055336\n",
            "Epoch 14 time: 2.0007\n",
            "Fold: 6, Epoch: [16/40], Step: [9/29], Loss: 0.366699477036794, Validation Loss:0.5781900510191917, Validation Acc:77.75, AUC:0.7258234519104085\n",
            "Fold: 6, Epoch: [16/40], Step: [18/29], Loss: 0.3580260111225976, Validation Loss:0.5642427504062653, Validation Acc:78.5, AUC:0.7454545454545455\n",
            "Fold: 6, Epoch: [16/40], Step: [27/29], Loss: 0.3704118706561901, Validation Loss:0.6836091876029968, Validation Acc:76.75, AUC:0.7429512516469038\n",
            "Epoch 15 time: 1.9896\n",
            "Fold: 6, Epoch: [17/40], Step: [9/29], Loss: 0.36361701289812726, Validation Loss:0.633999839425087, Validation Acc:73.0, AUC:0.68300395256917\n",
            "Fold: 6, Epoch: [17/40], Step: [18/29], Loss: 0.36316247946686214, Validation Loss:0.5777561292052269, Validation Acc:74.0, AUC:0.7117259552042161\n",
            "Fold: 6, Epoch: [17/40], Step: [27/29], Loss: 0.35254283525325636, Validation Loss:0.525578610599041, Validation Acc:78.0, AUC:0.7501976284584979\n",
            "Epoch 16 time: 1.9614\n",
            "Fold: 6, Epoch: [18/40], Step: [9/29], Loss: 0.33570514619350433, Validation Loss:0.7107154279947281, Validation Acc:77.75, AUC:0.7258234519104085\n",
            "Fold: 6, Epoch: [18/40], Step: [18/29], Loss: 0.341498963534832, Validation Loss:0.5890603438019753, Validation Acc:76.0, AUC:0.7080368906455863\n",
            "Fold: 6, Epoch: [18/40], Step: [27/29], Loss: 0.34363322732625184, Validation Loss:0.6776546388864517, Validation Acc:74.75, AUC:0.700790513833992\n",
            "Epoch 17 time: 1.9791\n",
            "Fold: 6, Epoch: [19/40], Step: [9/29], Loss: 0.3263999604516559, Validation Loss:0.5547318756580353, Validation Acc:80.0, AUC:0.7312252964426877\n",
            "Fold: 6, Epoch: [19/40], Step: [18/29], Loss: 0.33617084804508424, Validation Loss:0.6146823540329933, Validation Acc:79.5, AUC:0.7359683794466403\n",
            "Fold: 6, Epoch: [19/40], Step: [27/29], Loss: 0.33589942863693945, Validation Loss:0.7101282626390457, Validation Acc:72.25, AUC:0.724505928853755\n",
            "Epoch 18 time: 2.0157\n",
            "Fold: 6, Epoch: [20/40], Step: [9/29], Loss: 0.3519384496741825, Validation Loss:0.5984525457024574, Validation Acc:78.5, AUC:0.7225296442687746\n",
            "Fold: 6, Epoch: [20/40], Step: [18/29], Loss: 0.3356862962245941, Validation Loss:0.6272823959589005, Validation Acc:80.75, AUC:0.7508563899868248\n",
            "Fold: 6, Epoch: [20/40], Step: [27/29], Loss: 0.3366875284247928, Validation Loss:0.5404122229665518, Validation Acc:77.75, AUC:0.7258234519104085\n",
            "Epoch 19 time: 2.0116\n",
            "Fold: 6, Epoch: [21/40], Step: [9/29], Loss: 0.3077600697676341, Validation Loss:0.7683669626712799, Validation Acc:77.75, AUC:0.7028985507246377\n",
            "Fold: 6, Epoch: [21/40], Step: [18/29], Loss: 0.2963286547197236, Validation Loss:0.7654910385608673, Validation Acc:80.0, AUC:0.7159420289855073\n",
            "Fold: 6, Epoch: [21/40], Step: [27/29], Loss: 0.3065989354142436, Validation Loss:0.6178018376231194, Validation Acc:78.5, AUC:0.7225296442687746\n",
            "Epoch 20 time: 1.9779\n",
            "Fold: 6, Epoch: [22/40], Step: [9/29], Loss: 0.3000747991932763, Validation Loss:0.6167401671409607, Validation Acc:77.5, AUC:0.7243741765480897\n",
            "Fold: 6, Epoch: [22/40], Step: [18/29], Loss: 0.2926393821835518, Validation Loss:0.5964018851518631, Validation Acc:79.25, AUC:0.7115942028985508\n",
            "Fold: 6, Epoch: [22/40], Step: [27/29], Loss: 0.29413323545897446, Validation Loss:0.6514765173196793, Validation Acc:81.0, AUC:0.737022397891963\n",
            "Epoch 21 time: 1.9780\n",
            "Fold: 6, Epoch: [23/40], Step: [9/29], Loss: 0.2815646645095613, Validation Loss:0.729324571788311, Validation Acc:80.25, AUC:0.686824769433465\n",
            "Fold: 6, Epoch: [23/40], Step: [18/29], Loss: 0.28353700869613224, Validation Loss:0.7259418964385986, Validation Acc:80.5, AUC:0.7417654808959157\n",
            "Fold: 6, Epoch: [23/40], Step: [27/29], Loss: 0.2830348108653669, Validation Loss:0.7949724644422531, Validation Acc:76.5, AUC:0.6803689064558629\n",
            "Epoch 22 time: 1.9683\n",
            "Fold: 6, Epoch: [24/40], Step: [9/29], Loss: 0.26725074814425576, Validation Loss:0.9851424247026443, Validation Acc:78.25, AUC:0.6905138339920948\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 6, Epoch: [24/40], Step: [18/29], Loss: 0.2644685498542256, Validation Loss:0.7586672455072403, Validation Acc:79.5, AUC:0.6901185770750988\n",
            "Fold: 6, Epoch: [24/40], Step: [27/29], Loss: 0.2721180612290347, Validation Loss:1.0003237128257751, Validation Acc:78.0, AUC:0.6814229249011857\n",
            "Epoch 23 time: 1.9775\n",
            "Fold: 6, Epoch: [25/40], Step: [9/29], Loss: 0.2622603293922212, Validation Loss:0.7384486421942711, Validation Acc:79.0, AUC:0.7101449275362319\n",
            "Fold: 6, Epoch: [25/40], Step: [18/29], Loss: 0.2543814107775688, Validation Loss:0.7964230626821518, Validation Acc:80.5, AUC:0.6959156785243741\n",
            "Fold: 6, Epoch: [25/40], Step: [27/29], Loss: 0.25464116588786795, Validation Loss:0.9080393463373184, Validation Acc:80.75, AUC:0.697364953886693\n",
            "Epoch 24 time: 1.9787\n",
            "Fold: 6, Epoch: [26/40], Step: [9/29], Loss: 0.22561556928687626, Validation Loss:1.0206004679203033, Validation Acc:75.0, AUC:0.6563899868247695\n",
            "Fold: 6, Epoch: [26/40], Step: [18/29], Loss: 0.2536109470658832, Validation Loss:0.7978759557008743, Validation Acc:79.0, AUC:0.687220026350461\n",
            "Fold: 6, Epoch: [26/40], Step: [27/29], Loss: 0.24713784511442538, Validation Loss:0.7693253867328167, Validation Acc:80.0, AUC:0.7006587615283267\n",
            "Epoch 25 time: 1.9629\n",
            "Fold: 6, Epoch: [27/40], Step: [9/29], Loss: 0.23444457186592949, Validation Loss:0.9525510519742966, Validation Acc:79.0, AUC:0.6948616600790514\n",
            "Fold: 6, Epoch: [27/40], Step: [18/29], Loss: 0.22570429742336273, Validation Loss:0.7661507949233055, Validation Acc:82.0, AUC:0.7122529644268775\n",
            "Fold: 6, Epoch: [27/40], Step: [27/29], Loss: 0.22659468209301983, Validation Loss:0.8519974797964096, Validation Acc:78.0, AUC:0.6814229249011857\n",
            "Epoch 26 time: 1.9857\n",
            "Fold: 6, Epoch: [28/40], Step: [9/29], Loss: 0.2015029788017273, Validation Loss:0.9523310661315918, Validation Acc:78.5, AUC:0.6843214756258235\n",
            "Fold: 6, Epoch: [28/40], Step: [18/29], Loss: 0.19636836813555825, Validation Loss:1.0171220153570175, Validation Acc:80.5, AUC:0.6806324110671936\n",
            "Fold: 6, Epoch: [28/40], Step: [27/29], Loss: 0.20024527305806125, Validation Loss:1.064589649438858, Validation Acc:81.0, AUC:0.6835309617918314\n",
            "Epoch 27 time: 1.9702\n",
            "Fold: 6, Epoch: [29/40], Step: [9/29], Loss: 0.1979510419898563, Validation Loss:0.8602529242634773, Validation Acc:81.0, AUC:0.6835309617918314\n",
            "Fold: 6, Epoch: [29/40], Step: [18/29], Loss: 0.20797189076741537, Validation Loss:0.8357902094721794, Validation Acc:77.5, AUC:0.7014492753623189\n",
            "Fold: 6, Epoch: [29/40], Step: [27/29], Loss: 0.2019559200163241, Validation Loss:0.875464204698801, Validation Acc:80.5, AUC:0.6959156785243741\n",
            "Epoch 28 time: 1.9829\n",
            "Fold: 6, Epoch: [30/40], Step: [9/29], Loss: 0.1792468883925014, Validation Loss:0.85132797062397, Validation Acc:80.75, AUC:0.6897233201581028\n",
            "Fold: 6, Epoch: [30/40], Step: [18/29], Loss: 0.18607408760322464, Validation Loss:1.0723457336425781, Validation Acc:79.25, AUC:0.6657444005270092\n",
            "Fold: 6, Epoch: [30/40], Step: [27/29], Loss: 0.1840714975087731, Validation Loss:1.0342788100242615, Validation Acc:80.0, AUC:0.6853754940711463\n",
            "Epoch 29 time: 1.9713\n",
            "Fold: 6, Epoch: [31/40], Step: [9/29], Loss: 0.20394240816434225, Validation Loss:0.8313679024577141, Validation Acc:77.5, AUC:0.6861660079051383\n",
            "Fold: 6, Epoch: [31/40], Step: [18/29], Loss: 0.18147474113437864, Validation Loss:0.9985868185758591, Validation Acc:82.25, AUC:0.6831357048748353\n",
            "Fold: 6, Epoch: [31/40], Step: [27/29], Loss: 0.18065135070571192, Validation Loss:0.8609727658331394, Validation Acc:80.5, AUC:0.6959156785243741\n",
            "Epoch 30 time: 1.9785\n",
            "Fold: 6, Epoch: [32/40], Step: [9/29], Loss: 0.1477013271715906, Validation Loss:1.0514793694019318, Validation Acc:81.25, AUC:0.67733860342556\n",
            "Fold: 6, Epoch: [32/40], Step: [18/29], Loss: 0.1414126261240906, Validation Loss:1.1102699041366577, Validation Acc:81.5, AUC:0.686429512516469\n",
            "Fold: 6, Epoch: [32/40], Step: [27/29], Loss: 0.1491272289443899, Validation Loss:1.3238971084356308, Validation Acc:80.0, AUC:0.6853754940711463\n",
            "Epoch 31 time: 2.0074\n",
            "Fold: 6, Epoch: [33/40], Step: [9/29], Loss: 0.14078963630729252, Validation Loss:1.0121757090091705, Validation Acc:81.0, AUC:0.6911725955204217\n",
            "Fold: 6, Epoch: [33/40], Step: [18/29], Loss: 0.14284422745307288, Validation Loss:1.2374280989170074, Validation Acc:81.5, AUC:0.686429512516469\n",
            "Fold: 6, Epoch: [33/40], Step: [27/29], Loss: 0.146322267750899, Validation Loss:1.592192381620407, Validation Acc:82.25, AUC:0.6831357048748353\n",
            "Epoch 32 time: 1.9903\n",
            "Fold: 6, Epoch: [34/40], Step: [9/29], Loss: 0.15416917949914932, Validation Loss:1.3389296382665634, Validation Acc:81.75, AUC:0.7184453227931489\n",
            "Fold: 6, Epoch: [34/40], Step: [18/29], Loss: 0.16585084464814928, Validation Loss:1.2467506229877472, Validation Acc:81.75, AUC:0.6878787878787879\n",
            "Fold: 6, Epoch: [34/40], Step: [27/29], Loss: 0.17317722158299553, Validation Loss:0.9409340471029282, Validation Acc:77.75, AUC:0.6952569169960474\n",
            "Epoch 33 time: 1.9652\n",
            "Fold: 6, Epoch: [35/40], Step: [9/29], Loss: 0.14055490659342873, Validation Loss:1.5266581028699875, Validation Acc:79.25, AUC:0.6733860342555994\n",
            "Fold: 6, Epoch: [35/40], Step: [18/29], Loss: 0.14987380430102348, Validation Loss:1.4030819237232208, Validation Acc:82.0, AUC:0.6511198945981556\n",
            "Fold: 6, Epoch: [35/40], Step: [27/29], Loss: 0.14846681693085917, Validation Loss:1.0657193213701248, Validation Acc:82.25, AUC:0.675494071146245\n",
            "Epoch 34 time: 1.9795\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 6, Epoch: [36/40], Step: [9/29], Loss: 0.12642885165082085, Validation Loss:1.5938231348991394, Validation Acc:80.75, AUC:0.6820816864295125\n",
            "Fold: 6, Epoch: [36/40], Step: [18/29], Loss: 0.12393365303675334, Validation Loss:1.129671186208725, Validation Acc:82.25, AUC:0.6678524374176549\n",
            "Fold: 6, Epoch: [36/40], Step: [27/29], Loss: 0.12725243220726648, Validation Loss:1.2132637798786163, Validation Acc:83.25, AUC:0.6736495388669302\n",
            "Epoch 35 time: 1.9911\n",
            "Fold: 6, Epoch: [37/40], Step: [9/29], Loss: 0.09181725854674976, Validation Loss:1.1627579554915428, Validation Acc:80.25, AUC:0.656258234519104\n",
            "Fold: 6, Epoch: [37/40], Step: [18/29], Loss: 0.09851176974674065, Validation Loss:1.5442547798156738, Validation Acc:82.25, AUC:0.6525691699604743\n",
            "Fold: 6, Epoch: [37/40], Step: [27/29], Loss: 0.09467919805535564, Validation Loss:1.4371928870677948, Validation Acc:82.75, AUC:0.655467720685112\n",
            "Epoch 36 time: 1.9812\n",
            "Fold: 6, Epoch: [38/40], Step: [9/29], Loss: 0.09948690897888607, Validation Loss:1.2548648416996002, Validation Acc:82.0, AUC:0.666403162055336\n",
            "Fold: 6, Epoch: [38/40], Step: [18/29], Loss: 0.11320333472556537, Validation Loss:1.5679846405982971, Validation Acc:78.0, AUC:0.6661396574440054\n",
            "Fold: 6, Epoch: [38/40], Step: [27/29], Loss: 0.11320982487113387, Validation Loss:1.2558786571025848, Validation Acc:80.25, AUC:0.6791831357048749\n",
            "Epoch 37 time: 1.9624\n",
            "Fold: 6, Epoch: [39/40], Step: [9/29], Loss: 0.13065078854560852, Validation Loss:1.2450503557920456, Validation Acc:81.75, AUC:0.6725955204216074\n",
            "Fold: 6, Epoch: [39/40], Step: [18/29], Loss: 0.12078837719228533, Validation Loss:1.4655247032642365, Validation Acc:79.5, AUC:0.6519104084321475\n",
            "Fold: 6, Epoch: [39/40], Step: [27/29], Loss: 0.11766558388868968, Validation Loss:1.287019520998001, Validation Acc:82.25, AUC:0.675494071146245\n",
            "Epoch 38 time: 1.9539\n",
            "Fold: 6, Epoch: [40/40], Step: [9/29], Loss: 0.10035022720694542, Validation Loss:1.5966330170631409, Validation Acc:81.0, AUC:0.6606060606060606\n",
            "Fold: 6, Epoch: [40/40], Step: [18/29], Loss: 0.10137334031363328, Validation Loss:1.2526365667581558, Validation Acc:81.5, AUC:0.686429512516469\n",
            "Fold: 6, Epoch: [40/40], Step: [27/29], Loss: 0.09838552759201438, Validation Loss:1.548413097858429, Validation Acc:80.75, AUC:0.6591567852437418\n",
            "Epoch 39 time: 1.9829\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 7, Epoch: [1/40], Step: [9/29], Loss: 0.650065905518002, Validation Loss:0.6512775272130966, Validation Acc:72.18045112781955, AUC:0.6477272727272727\n",
            "Fold: 7, Epoch: [1/40], Step: [18/29], Loss: 0.6187446580992805, Validation Loss:0.5673056468367577, Validation Acc:72.93233082706767, AUC:0.7208245243128963\n",
            "Fold: 7, Epoch: [1/40], Step: [27/29], Loss: 0.6075213297649666, Validation Loss:0.5367090478539467, Validation Acc:72.93233082706767, AUC:0.7284619450317125\n",
            "Epoch 0 time: 1.9877\n",
            "Fold: 7, Epoch: [2/40], Step: [9/29], Loss: 0.5103562341796027, Validation Loss:0.55821792781353, Validation Acc:71.67919799498746, AUC:0.7364693446088795\n",
            "Fold: 7, Epoch: [2/40], Step: [18/29], Loss: 0.5271779944499334, Validation Loss:0.48875217139720917, Validation Acc:72.93233082706767, AUC:0.7437367864693445\n",
            "Fold: 7, Epoch: [2/40], Step: [27/29], Loss: 0.5350604962419581, Validation Loss:0.5411014780402184, Validation Acc:70.67669172932331, AUC:0.7306553911205074\n",
            "Epoch 1 time: 1.9930\n",
            "Fold: 7, Epoch: [3/40], Step: [9/29], Loss: 0.5248215231630537, Validation Loss:0.514172799885273, Validation Acc:74.18546365914787, AUC:0.7510042283298097\n",
            "Fold: 7, Epoch: [3/40], Step: [18/29], Loss: 0.5244068387481902, Validation Loss:0.48836714029312134, Validation Acc:74.68671679197995, AUC:0.7615486257928119\n",
            "Fold: 7, Epoch: [3/40], Step: [27/29], Loss: 0.5173735795197664, Validation Loss:0.5528100728988647, Validation Acc:75.18796992481202, AUC:0.7568181818181818\n",
            "Epoch 2 time: 1.9722\n",
            "Fold: 7, Epoch: [4/40], Step: [9/29], Loss: 0.48730888962745667, Validation Loss:0.48677246272563934, Validation Acc:75.43859649122807, AUC:0.7506342494714588\n",
            "Fold: 7, Epoch: [4/40], Step: [18/29], Loss: 0.4933250430557463, Validation Loss:0.5477399006485939, Validation Acc:75.43859649122807, AUC:0.765909090909091\n",
            "Fold: 7, Epoch: [4/40], Step: [27/29], Loss: 0.5023681466226224, Validation Loss:0.5291969925165176, Validation Acc:74.68671679197995, AUC:0.7615486257928119\n",
            "Epoch 3 time: 2.0045\n",
            "Fold: 7, Epoch: [5/40], Step: [9/29], Loss: 0.509300000137753, Validation Loss:0.45988089591264725, Validation Acc:75.68922305764411, AUC:0.7597251585623679\n",
            "Fold: 7, Epoch: [5/40], Step: [18/29], Loss: 0.4978655096557405, Validation Loss:0.45064105093479156, Validation Acc:76.19047619047619, AUC:0.77026955602537\n",
            "Fold: 7, Epoch: [5/40], Step: [27/29], Loss: 0.4962559629369665, Validation Loss:0.4698624312877655, Validation Acc:74.18546365914787, AUC:0.773916490486258\n",
            "Epoch 4 time: 1.9790\n",
            "Fold: 7, Epoch: [6/40], Step: [9/29], Loss: 0.49995255801412797, Validation Loss:0.4748629033565521, Validation Acc:76.69172932330827, AUC:0.773176532769556\n",
            "Fold: 7, Epoch: [6/40], Step: [18/29], Loss: 0.47778522140449947, Validation Loss:0.4704020544886589, Validation Acc:76.19047619047619, AUC:0.77026955602537\n",
            "Fold: 7, Epoch: [6/40], Step: [27/29], Loss: 0.48942126609660963, Validation Loss:0.4936549812555313, Validation Acc:75.93984962406014, AUC:0.7840909090909092\n",
            "Epoch 5 time: 2.0235\n",
            "Fold: 7, Epoch: [7/40], Step: [9/29], Loss: 0.4665168159537845, Validation Loss:0.4616432189941406, Validation Acc:77.94486215538848, AUC:0.7804439746300211\n",
            "Fold: 7, Epoch: [7/40], Step: [18/29], Loss: 0.46872557202974957, Validation Loss:0.4286298155784607, Validation Acc:76.94235588972431, AUC:0.774630021141649\n",
            "Fold: 7, Epoch: [7/40], Step: [27/29], Loss: 0.4725756479634179, Validation Loss:0.5018600299954414, Validation Acc:75.93984962406014, AUC:0.7688160676532769\n",
            "Epoch 6 time: 1.9973\n",
            "Fold: 7, Epoch: [8/40], Step: [9/29], Loss: 0.4523693091339535, Validation Loss:0.4783118814229965, Validation Acc:72.93233082706767, AUC:0.7666490486257929\n",
            "Fold: 7, Epoch: [8/40], Step: [18/29], Loss: 0.4547654456562466, Validation Loss:0.47538796812295914, Validation Acc:74.93734335839599, AUC:0.7859143763213531\n",
            "Fold: 7, Epoch: [8/40], Step: [27/29], Loss: 0.471989330318239, Validation Loss:0.4868575483560562, Validation Acc:75.18796992481202, AUC:0.7797304439746301\n",
            "Epoch 7 time: 1.9774\n",
            "Fold: 7, Epoch: [9/40], Step: [9/29], Loss: 0.4418206943405999, Validation Loss:0.44671814143657684, Validation Acc:77.94486215538848, AUC:0.7804439746300211\n",
            "Fold: 7, Epoch: [9/40], Step: [18/29], Loss: 0.4447441084517373, Validation Loss:0.4577217847108841, Validation Acc:74.43609022556392, AUC:0.7677325581395349\n",
            "Fold: 7, Epoch: [9/40], Step: [27/29], Loss: 0.4550597744959372, Validation Loss:0.4580771178007126, Validation Acc:74.18546365914787, AUC:0.7662790697674419\n",
            "Epoch 8 time: 1.9541\n",
            "Fold: 7, Epoch: [10/40], Step: [9/29], Loss: 0.440882596704695, Validation Loss:0.44356314092874527, Validation Acc:77.19298245614036, AUC:0.7837209302325583\n",
            "Fold: 7, Epoch: [10/40], Step: [18/29], Loss: 0.44195552004708183, Validation Loss:0.4586706906557083, Validation Acc:78.69674185463658, AUC:0.7924418604651163\n",
            "Fold: 7, Epoch: [10/40], Step: [27/29], Loss: 0.4573923824010072, Validation Loss:0.4270823150873184, Validation Acc:74.68671679197995, AUC:0.776823467230444\n",
            "Epoch 9 time: 1.9623\n",
            "Fold: 7, Epoch: [11/40], Step: [9/29], Loss: 0.4563690192169613, Validation Loss:0.5308396071195602, Validation Acc:75.43859649122807, AUC:0.765909090909091\n",
            "Fold: 7, Epoch: [11/40], Step: [18/29], Loss: 0.44511095848348403, Validation Loss:0.43595439940690994, Validation Acc:76.69172932330827, AUC:0.773176532769556\n",
            "Fold: 7, Epoch: [11/40], Step: [27/29], Loss: 0.44571876415499934, Validation Loss:0.43382345139980316, Validation Acc:77.69423558897243, AUC:0.7789904862579281\n",
            "Epoch 10 time: 1.9803\n",
            "Fold: 7, Epoch: [12/40], Step: [9/29], Loss: 0.4399993320306142, Validation Loss:0.4754948616027832, Validation Acc:79.69924812030075, AUC:0.7753435517970402\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 7, Epoch: [12/40], Step: [18/29], Loss: 0.4333707226647271, Validation Loss:0.459086112678051, Validation Acc:77.19298245614036, AUC:0.76080866807611\n",
            "Fold: 7, Epoch: [12/40], Step: [27/29], Loss: 0.42585459461918584, Validation Loss:0.4862659424543381, Validation Acc:74.93734335839599, AUC:0.7859143763213531\n",
            "Epoch 11 time: 1.9832\n",
            "Fold: 7, Epoch: [13/40], Step: [9/29], Loss: 0.4076017869843377, Validation Loss:0.4521505907177925, Validation Acc:79.69924812030075, AUC:0.7677061310782242\n",
            "Fold: 7, Epoch: [13/40], Step: [18/29], Loss: 0.41064438554975724, Validation Loss:0.48899806290864944, Validation Acc:79.19799498746868, AUC:0.7724365750528541\n",
            "Fold: 7, Epoch: [13/40], Step: [27/29], Loss: 0.41841334214916936, Validation Loss:0.41811956092715263, Validation Acc:76.69172932330827, AUC:0.7808139534883721\n",
            "Epoch 12 time: 2.0221\n",
            "Fold: 7, Epoch: [14/40], Step: [9/29], Loss: 0.415748483604855, Validation Loss:0.4991399794816971, Validation Acc:76.94235588972431, AUC:0.766992600422833\n",
            "Fold: 7, Epoch: [14/40], Step: [18/29], Loss: 0.40866265694300336, Validation Loss:0.4501902312040329, Validation Acc:76.69172932330827, AUC:0.757901691331924\n",
            "Fold: 7, Epoch: [14/40], Step: [27/29], Loss: 0.4149070371080328, Validation Loss:0.42205873876810074, Validation Acc:78.69674185463658, AUC:0.7695295983086681\n",
            "Epoch 13 time: 1.9615\n",
            "Fold: 7, Epoch: [15/40], Step: [9/29], Loss: 0.3907867835627662, Validation Loss:0.4528375267982483, Validation Acc:74.68671679197995, AUC:0.769186046511628\n",
            "Fold: 7, Epoch: [15/40], Step: [18/29], Loss: 0.40475982924302417, Validation Loss:0.4901660084724426, Validation Acc:76.94235588972431, AUC:0.7822674418604652\n",
            "Fold: 7, Epoch: [15/40], Step: [27/29], Loss: 0.4053011017817038, Validation Loss:0.44417963176965714, Validation Acc:79.19799498746868, AUC:0.7724365750528541\n",
            "Epoch 14 time: 1.9928\n",
            "Fold: 7, Epoch: [16/40], Step: [9/29], Loss: 0.37747460934850907, Validation Loss:0.4883616864681244, Validation Acc:79.4486215538847, AUC:0.7662526427061311\n",
            "Fold: 7, Epoch: [16/40], Step: [18/29], Loss: 0.39402781923611957, Validation Loss:0.4861944317817688, Validation Acc:79.69924812030075, AUC:0.7677061310782242\n",
            "Fold: 7, Epoch: [16/40], Step: [27/29], Loss: 0.3962888607272395, Validation Loss:0.5446608513593674, Validation Acc:76.44110275689223, AUC:0.756448202959831\n",
            "Epoch 15 time: 1.9656\n",
            "Fold: 7, Epoch: [17/40], Step: [9/29], Loss: 0.36420966850386727, Validation Loss:0.47064004093408585, Validation Acc:78.94736842105263, AUC:0.7633456659619451\n",
            "Fold: 7, Epoch: [17/40], Step: [18/29], Loss: 0.38101884722709656, Validation Loss:0.5003251656889915, Validation Acc:78.94736842105263, AUC:0.755708245243129\n",
            "Fold: 7, Epoch: [17/40], Step: [27/29], Loss: 0.38585150572988725, Validation Loss:0.4632490202784538, Validation Acc:77.44360902255639, AUC:0.762262156448203\n",
            "Epoch 16 time: 1.9620\n",
            "Fold: 7, Epoch: [18/40], Step: [9/29], Loss: 0.3427235616577996, Validation Loss:0.5438965782523155, Validation Acc:78.44611528822055, AUC:0.768076109936575\n",
            "Fold: 7, Epoch: [18/40], Step: [18/29], Loss: 0.35228902929359013, Validation Loss:0.6813963130116463, Validation Acc:78.44611528822055, AUC:0.7833509513742072\n",
            "Fold: 7, Epoch: [18/40], Step: [27/29], Loss: 0.3705586879341691, Validation Loss:0.47317932546138763, Validation Acc:78.19548872180451, AUC:0.7589852008456659\n",
            "Epoch 17 time: 1.9734\n",
            "Fold: 7, Epoch: [19/40], Step: [9/29], Loss: 0.3517694373925527, Validation Loss:0.6808437928557396, Validation Acc:79.19799498746868, AUC:0.7724365750528541\n",
            "Fold: 7, Epoch: [19/40], Step: [18/29], Loss: 0.3620093762874603, Validation Loss:0.5588674545288086, Validation Acc:78.94736842105263, AUC:0.755708245243129\n",
            "Fold: 7, Epoch: [19/40], Step: [27/29], Loss: 0.3605290827927766, Validation Loss:0.5373740792274475, Validation Acc:78.94736842105263, AUC:0.755708245243129\n",
            "Epoch 18 time: 1.9638\n",
            "Fold: 7, Epoch: [20/40], Step: [9/29], Loss: 0.3434010197718938, Validation Loss:0.7328895255923271, Validation Acc:78.44611528822055, AUC:0.7604386892177589\n",
            "Fold: 7, Epoch: [20/40], Step: [18/29], Loss: 0.3479590076539252, Validation Loss:0.5148884132504463, Validation Acc:78.44611528822055, AUC:0.768076109936575\n",
            "Fold: 7, Epoch: [20/40], Step: [27/29], Loss: 0.34747620589203304, Validation Loss:0.5849953293800354, Validation Acc:78.69674185463658, AUC:0.7771670190274841\n",
            "Epoch 19 time: 1.9790\n",
            "Fold: 7, Epoch: [21/40], Step: [9/29], Loss: 0.3426524235142602, Validation Loss:0.6297214776277542, Validation Acc:78.19548872180451, AUC:0.7818974630021142\n",
            "Fold: 7, Epoch: [21/40], Step: [18/29], Loss: 0.34104697902997333, Validation Loss:0.5054569654166698, Validation Acc:80.20050125313283, AUC:0.7400634249471458\n",
            "Fold: 7, Epoch: [21/40], Step: [27/29], Loss: 0.3413493611194469, Validation Loss:0.5520514473319054, Validation Acc:80.95238095238095, AUC:0.7367864693446088\n",
            "Epoch 20 time: 2.0046\n",
            "Fold: 7, Epoch: [22/40], Step: [9/29], Loss: 0.3323327932092879, Validation Loss:0.5754580721259117, Validation Acc:77.69423558897243, AUC:0.7637156448202961\n",
            "Fold: 7, Epoch: [22/40], Step: [18/29], Loss: 0.32960044344266254, Validation Loss:0.6344563364982605, Validation Acc:78.19548872180451, AUC:0.7589852008456659\n",
            "Fold: 7, Epoch: [22/40], Step: [27/29], Loss: 0.32460443178812665, Validation Loss:0.5264752246439457, Validation Acc:78.94736842105263, AUC:0.7480708245243128\n",
            "Epoch 21 time: 1.9708\n",
            "Fold: 7, Epoch: [23/40], Step: [9/29], Loss: 0.30038467711872524, Validation Loss:0.5446920096874237, Validation Acc:79.69924812030075, AUC:0.7600687103594079\n",
            "Fold: 7, Epoch: [23/40], Step: [18/29], Loss: 0.3142499609125985, Validation Loss:0.6195804327726364, Validation Acc:79.4486215538847, AUC:0.758615221987315\n"
          ],
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            "Fold: 7, Epoch: [23/40], Step: [27/29], Loss: 0.31501265256493177, Validation Loss:0.7035250663757324, Validation Acc:79.19799498746868, AUC:0.7571617336152219\n",
            "Epoch 22 time: 1.9692\n",
            "Fold: 7, Epoch: [24/40], Step: [9/29], Loss: 0.31774792406294083, Validation Loss:0.7723073661327362, Validation Acc:79.19799498746868, AUC:0.74188689217759\n",
            "Fold: 7, Epoch: [24/40], Step: [18/29], Loss: 0.34276078475846183, Validation Loss:0.655981570482254, Validation Acc:78.69674185463658, AUC:0.7695295983086681\n",
            "Fold: 7, Epoch: [24/40], Step: [27/29], Loss: 0.33102617219642355, Validation Loss:0.6217847615480423, Validation Acc:77.19298245614036, AUC:0.7378964059196618\n",
            "Epoch 23 time: 1.9793\n",
            "Fold: 7, Epoch: [25/40], Step: [9/29], Loss: 0.3000805659426583, Validation Loss:0.8477342426776886, Validation Acc:79.19799498746868, AUC:0.6884249471458774\n",
            "Fold: 7, Epoch: [25/40], Step: [18/29], Loss: 0.3015717342495918, Validation Loss:0.7060779705643654, Validation Acc:80.70175438596492, AUC:0.7429704016913319\n",
            "Fold: 7, Epoch: [25/40], Step: [27/29], Loss: 0.30677761247864477, Validation Loss:0.6440936326980591, Validation Acc:78.44611528822055, AUC:0.7604386892177589\n",
            "Epoch 24 time: 1.9784\n",
            "Fold: 7, Epoch: [26/40], Step: [9/29], Loss: 0.2823085967037413, Validation Loss:0.860702857375145, Validation Acc:79.19799498746868, AUC:0.7342494714587737\n",
            "Fold: 7, Epoch: [26/40], Step: [18/29], Loss: 0.2900528791877959, Validation Loss:0.9089610055088997, Validation Acc:78.44611528822055, AUC:0.7604386892177589\n",
            "Fold: 7, Epoch: [26/40], Step: [27/29], Loss: 0.28113641451906274, Validation Loss:0.9228916242718697, Validation Acc:79.69924812030075, AUC:0.7218816067653278\n",
            "Epoch 25 time: 1.9755\n",
            "Fold: 7, Epoch: [27/40], Step: [9/29], Loss: 0.25723181002669865, Validation Loss:0.5645548291504383, Validation Acc:80.70175438596492, AUC:0.7124207188160676\n",
            "Fold: 7, Epoch: [27/40], Step: [18/29], Loss: 0.2689789566728804, Validation Loss:0.5821547955274582, Validation Acc:79.4486215538847, AUC:0.7433403805496829\n",
            "Fold: 7, Epoch: [27/40], Step: [27/29], Loss: 0.2656650063064363, Validation Loss:0.6979692876338959, Validation Acc:77.44360902255639, AUC:0.7469873150105708\n",
            "Epoch 26 time: 1.9660\n",
            "Fold: 7, Epoch: [28/40], Step: [9/29], Loss: 0.22822039326032004, Validation Loss:0.7075200825929642, Validation Acc:78.69674185463658, AUC:0.7160676532769557\n",
            "Fold: 7, Epoch: [28/40], Step: [18/29], Loss: 0.23231828543874952, Validation Loss:0.6808924973011017, Validation Acc:79.4486215538847, AUC:0.7280655391120507\n",
            "Fold: 7, Epoch: [28/40], Step: [27/29], Loss: 0.24361134403281742, Validation Loss:0.8560441434383392, Validation Acc:80.45112781954887, AUC:0.7186046511627907\n",
            "Epoch 27 time: 1.9668\n",
            "Fold: 7, Epoch: [29/40], Step: [9/29], Loss: 0.24224991765287188, Validation Loss:0.9899582415819168, Validation Acc:80.20050125313283, AUC:0.7095137420718814\n",
            "Fold: 7, Epoch: [29/40], Step: [18/29], Loss: 0.23541818724738228, Validation Loss:0.7628072947263718, Validation Acc:79.69924812030075, AUC:0.7142441860465117\n",
            "Fold: 7, Epoch: [29/40], Step: [27/29], Loss: 0.2346266375647651, Validation Loss:0.6981510296463966, Validation Acc:77.19298245614036, AUC:0.6844344608879492\n",
            "Epoch 28 time: 1.9736\n",
            "Fold: 7, Epoch: [30/40], Step: [9/29], Loss: 0.2167923864391115, Validation Loss:0.9971552342176437, Validation Acc:81.203007518797, AUC:0.7000528541226214\n",
            "Fold: 7, Epoch: [30/40], Step: [18/29], Loss: 0.2234634268614981, Validation Loss:0.8696215003728867, Validation Acc:80.20050125313283, AUC:0.6789640591966173\n",
            "Fold: 7, Epoch: [30/40], Step: [27/29], Loss: 0.21968654460377163, Validation Loss:0.752182126045227, Validation Acc:80.45112781954887, AUC:0.7186046511627907\n",
            "Epoch 29 time: 1.9724\n",
            "Fold: 7, Epoch: [31/40], Step: [9/29], Loss: 0.18468241227997673, Validation Loss:0.8483436703681946, Validation Acc:78.44611528822055, AUC:0.7069767441860466\n",
            "Fold: 7, Epoch: [31/40], Step: [18/29], Loss: 0.18100348114967346, Validation Loss:0.7503907531499863, Validation Acc:81.203007518797, AUC:0.6771405919661734\n",
            "Fold: 7, Epoch: [31/40], Step: [27/29], Loss: 0.19582712098404212, Validation Loss:1.029063656926155, Validation Acc:78.69674185463658, AUC:0.6702431289640592\n",
            "Epoch 30 time: 1.9746\n",
            "Fold: 7, Epoch: [32/40], Step: [9/29], Loss: 0.19972961644331613, Validation Loss:1.536413624882698, Validation Acc:80.20050125313283, AUC:0.6636892177589852\n",
            "Fold: 7, Epoch: [32/40], Step: [18/29], Loss: 0.18797181215551165, Validation Loss:0.9942706525325775, Validation Acc:79.9498746867168, AUC:0.6698731501057082\n",
            "Fold: 7, Epoch: [32/40], Step: [27/29], Loss: 0.18779034636638783, Validation Loss:1.0928996205329895, Validation Acc:78.19548872180451, AUC:0.6978858350951374\n",
            "Epoch 31 time: 1.9679\n",
            "Fold: 7, Epoch: [33/40], Step: [9/29], Loss: 0.1707277869184812, Validation Loss:0.9521024972200394, Validation Acc:81.203007518797, AUC:0.6771405919661734\n",
            "Fold: 7, Epoch: [33/40], Step: [18/29], Loss: 0.1689022539390458, Validation Loss:0.9818425178527832, Validation Acc:82.45614035087719, AUC:0.6767706131078224\n",
            "Fold: 7, Epoch: [33/40], Step: [27/29], Loss: 0.17280450739242412, Validation Loss:0.8653955906629562, Validation Acc:79.69924812030075, AUC:0.6989693446088794\n",
            "Epoch 32 time: 1.9740\n",
            "Fold: 7, Epoch: [34/40], Step: [9/29], Loss: 0.16693230718374252, Validation Loss:0.9752698838710785, Validation Acc:80.20050125313283, AUC:0.6789640591966173\n",
            "Fold: 7, Epoch: [34/40], Step: [18/29], Loss: 0.16693341152535546, Validation Loss:1.0710453242063522, Validation Acc:79.19799498746868, AUC:0.6731501057082452\n",
            "Fold: 7, Epoch: [34/40], Step: [27/29], Loss: 0.1705308280609272, Validation Loss:1.1043784469366074, Validation Acc:80.45112781954887, AUC:0.6727801268498943\n",
            "Epoch 33 time: 1.9619\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 7, Epoch: [35/40], Step: [9/29], Loss: 0.1581978334320916, Validation Loss:0.9682429879903793, Validation Acc:80.20050125313283, AUC:0.6560517970401691\n",
            "Fold: 7, Epoch: [35/40], Step: [18/29], Loss: 0.1631162220405208, Validation Loss:0.9021141976118088, Validation Acc:80.95238095238095, AUC:0.6527748414376321\n",
            "Fold: 7, Epoch: [35/40], Step: [27/29], Loss: 0.1566838064679393, Validation Loss:1.0881555080413818, Validation Acc:81.70426065162907, AUC:0.6724101479915433\n",
            "Epoch 34 time: 1.9803\n",
            "Fold: 7, Epoch: [36/40], Step: [9/29], Loss: 0.13361738787757027, Validation Loss:0.9649168252944946, Validation Acc:80.95238095238095, AUC:0.6680496828752642\n",
            "Fold: 7, Epoch: [36/40], Step: [18/29], Loss: 0.13123443060451084, Validation Loss:1.0224681496620178, Validation Acc:81.70426065162907, AUC:0.6647727272727273\n",
            "Fold: 7, Epoch: [36/40], Step: [27/29], Loss: 0.13338772649014438, Validation Loss:1.177668035030365, Validation Acc:78.94736842105263, AUC:0.65642177589852\n",
            "Epoch 35 time: 1.9768\n",
            "Fold: 7, Epoch: [37/40], Step: [9/29], Loss: 0.1253861760099729, Validation Loss:1.0015606060624123, Validation Acc:79.4486215538847, AUC:0.6593287526427061\n",
            "Fold: 7, Epoch: [37/40], Step: [18/29], Loss: 0.12237402341432041, Validation Loss:0.9610425382852554, Validation Acc:81.95488721804512, AUC:0.6662262156448203\n",
            "Fold: 7, Epoch: [37/40], Step: [27/29], Loss: 0.12240527680626621, Validation Loss:1.3844982087612152, Validation Acc:81.70426065162907, AUC:0.641860465116279\n",
            "Epoch 36 time: 1.9803\n",
            "Fold: 7, Epoch: [38/40], Step: [9/29], Loss: 0.10454728288782968, Validation Loss:1.2025322318077087, Validation Acc:81.45363408521304, AUC:0.640406976744186\n",
            "Fold: 7, Epoch: [38/40], Step: [18/29], Loss: 0.0984166951643096, Validation Loss:1.2179954648017883, Validation Acc:81.70426065162907, AUC:0.634223044397463\n",
            "Fold: 7, Epoch: [38/40], Step: [27/29], Loss: 0.10022252108211871, Validation Loss:1.7846203744411469, Validation Acc:82.45614035087719, AUC:0.6614957716701902\n",
            "Epoch 37 time: 1.9982\n",
            "Fold: 7, Epoch: [39/40], Step: [9/29], Loss: 0.07167598812116517, Validation Loss:1.2245992869138718, Validation Acc:81.70426065162907, AUC:0.6571353065539112\n",
            "Fold: 7, Epoch: [39/40], Step: [18/29], Loss: 0.08036026731133461, Validation Loss:1.6317558884620667, Validation Acc:80.95238095238095, AUC:0.6375\n",
            "Fold: 7, Epoch: [39/40], Step: [27/29], Loss: 0.08356959386556237, Validation Loss:1.0960650444030762, Validation Acc:81.45363408521304, AUC:0.6556818181818181\n",
            "Epoch 38 time: 1.9759\n",
            "Fold: 7, Epoch: [40/40], Step: [9/29], Loss: 0.0835274439305067, Validation Loss:1.2310198992490768, Validation Acc:81.45363408521304, AUC:0.625132135306554\n",
            "Fold: 7, Epoch: [40/40], Step: [18/29], Loss: 0.08407374937087297, Validation Loss:1.7941390573978424, Validation Acc:80.70175438596492, AUC:0.6513213530655391\n",
            "Fold: 7, Epoch: [40/40], Step: [27/29], Loss: 0.08792087139078865, Validation Loss:1.6412096619606018, Validation Acc:80.45112781954887, AUC:0.649867864693446\n",
            "Epoch 39 time: 1.9811\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 8, Epoch: [1/40], Step: [9/29], Loss: 0.6696984900368584, Validation Loss:0.6519713997840881, Validation Acc:64.91228070175438, AUC:0.6590380549682876\n",
            "Fold: 8, Epoch: [1/40], Step: [18/29], Loss: 0.6442987322807312, Validation Loss:0.6178998351097107, Validation Acc:69.67418546365914, AUC:0.686654334038055\n",
            "Fold: 8, Epoch: [1/40], Step: [27/29], Loss: 0.6232574162659822, Validation Loss:0.5342147722840309, Validation Acc:67.91979949874687, AUC:0.7070295983086683\n",
            "Epoch 0 time: 1.9665\n",
            "Fold: 8, Epoch: [2/40], Step: [9/29], Loss: 0.5340674850675795, Validation Loss:0.6081254482269287, Validation Acc:67.16791979949875, AUC:0.7179439746300212\n",
            "Fold: 8, Epoch: [2/40], Step: [18/29], Loss: 0.537108204431004, Validation Loss:0.5357119292020798, Validation Acc:67.91979949874687, AUC:0.7223044397463002\n",
            "Fold: 8, Epoch: [2/40], Step: [27/29], Loss: 0.5342066177615413, Validation Loss:0.5565975457429886, Validation Acc:68.42105263157895, AUC:0.7175739957716702\n",
            "Epoch 1 time: 1.9883\n",
            "Fold: 8, Epoch: [3/40], Step: [9/29], Loss: 0.5045284297731187, Validation Loss:0.5107134208083153, Validation Acc:71.42857142857143, AUC:0.7579281183932347\n",
            "Fold: 8, Epoch: [3/40], Step: [18/29], Loss: 0.4993995726108551, Validation Loss:0.5253032669425011, Validation Acc:70.92731829573935, AUC:0.7397463002114165\n",
            "Fold: 8, Epoch: [3/40], Step: [27/29], Loss: 0.502121052256337, Validation Loss:0.4962615594267845, Validation Acc:69.92481203007519, AUC:0.7186575052854124\n",
            "Epoch 2 time: 2.0169\n",
            "Fold: 8, Epoch: [4/40], Step: [9/29], Loss: 0.5101151433255937, Validation Loss:0.5015429258346558, Validation Acc:69.92481203007519, AUC:0.7186575052854124\n",
            "Fold: 8, Epoch: [4/40], Step: [18/29], Loss: 0.49849238163895077, Validation Loss:0.5988207533955574, Validation Acc:69.42355889724311, AUC:0.7081131078224101\n",
            "Fold: 8, Epoch: [4/40], Step: [27/29], Loss: 0.5099604173942849, Validation Loss:0.5707774460315704, Validation Acc:70.17543859649123, AUC:0.7277484143763214\n",
            "Epoch 3 time: 1.9609\n",
            "Fold: 8, Epoch: [5/40], Step: [9/29], Loss: 0.47116777963108486, Validation Loss:0.6225066930055618, Validation Acc:70.92731829573935, AUC:0.7321088794926004\n",
            "Fold: 8, Epoch: [5/40], Step: [18/29], Loss: 0.4828370345963372, Validation Loss:0.526709571480751, Validation Acc:71.9298245614035, AUC:0.7455602536997885\n",
            "Fold: 8, Epoch: [5/40], Step: [27/29], Loss: 0.48671740183123835, Validation Loss:0.49467042833566666, Validation Acc:71.9298245614035, AUC:0.7226479915433404\n",
            "Epoch 4 time: 1.9879\n",
            "Fold: 8, Epoch: [6/40], Step: [9/29], Loss: 0.4980473087893592, Validation Loss:0.5324804186820984, Validation Acc:72.18045112781955, AUC:0.7241014799154334\n",
            "Fold: 8, Epoch: [6/40], Step: [18/29], Loss: 0.49282170832157135, Validation Loss:0.49014023691415787, Validation Acc:71.9298245614035, AUC:0.7379228329809725\n",
            "Fold: 8, Epoch: [6/40], Step: [27/29], Loss: 0.4764323698149787, Validation Loss:0.544949471950531, Validation Acc:70.92731829573935, AUC:0.7168340380549683\n",
            "Epoch 5 time: 1.9870\n",
            "Fold: 8, Epoch: [7/40], Step: [9/29], Loss: 0.48880815837118363, Validation Loss:0.48644836992025375, Validation Acc:71.67919799498746, AUC:0.7059196617336152\n",
            "Fold: 8, Epoch: [7/40], Step: [18/29], Loss: 0.46800804138183594, Validation Loss:0.4569985121488571, Validation Acc:72.93233082706767, AUC:0.7208245243128963\n",
            "Fold: 8, Epoch: [7/40], Step: [27/29], Loss: 0.4675461440174668, Validation Loss:0.5211130529642105, Validation Acc:72.43107769423558, AUC:0.7255549682875264\n",
            "Epoch 6 time: 1.9539\n",
            "Fold: 8, Epoch: [8/40], Step: [9/29], Loss: 0.4597222540113661, Validation Loss:0.4548190087080002, Validation Acc:72.68170426065163, AUC:0.7346458773784356\n",
            "Fold: 8, Epoch: [8/40], Step: [18/29], Loss: 0.4519030253092448, Validation Loss:0.45578134804964066, Validation Acc:72.43107769423558, AUC:0.7484672304439746\n",
            "Fold: 8, Epoch: [8/40], Step: [27/29], Loss: 0.4613512335000215, Validation Loss:0.4834827184677124, Validation Acc:68.92230576441102, AUC:0.7433932346723046\n",
            "Epoch 7 time: 1.9789\n",
            "Fold: 8, Epoch: [9/40], Step: [9/29], Loss: 0.45084882775942486, Validation Loss:0.6060153618454933, Validation Acc:73.1829573934837, AUC:0.7222780126849894\n",
            "Fold: 8, Epoch: [9/40], Step: [18/29], Loss: 0.4665074563688702, Validation Loss:0.5191551148891449, Validation Acc:75.68922305764411, AUC:0.7215380549682875\n",
            "Fold: 8, Epoch: [9/40], Step: [27/29], Loss: 0.4560076892375946, Validation Loss:0.6819830909371376, Validation Acc:75.18796992481202, AUC:0.7415433403805497\n",
            "Epoch 8 time: 2.0122\n",
            "Fold: 8, Epoch: [10/40], Step: [9/29], Loss: 0.48666849732398987, Validation Loss:0.486285075545311, Validation Acc:69.92481203007519, AUC:0.7492071881606766\n",
            "Fold: 8, Epoch: [10/40], Step: [18/29], Loss: 0.4533701472812229, Validation Loss:0.515590213239193, Validation Acc:72.18045112781955, AUC:0.7393763213530655\n",
            "Fold: 8, Epoch: [10/40], Step: [27/29], Loss: 0.4442841046386295, Validation Loss:0.5384566336870193, Validation Acc:73.1829573934837, AUC:0.7375528541226216\n",
            "Epoch 9 time: 1.9958\n",
            "Fold: 8, Epoch: [11/40], Step: [9/29], Loss: 0.42661604285240173, Validation Loss:0.5567537322640419, Validation Acc:74.43609022556392, AUC:0.7295454545454545\n",
            "Fold: 8, Epoch: [11/40], Step: [18/29], Loss: 0.41814269456598496, Validation Loss:0.48907483369112015, Validation Acc:74.68671679197995, AUC:0.7462737843551798\n",
            "Fold: 8, Epoch: [11/40], Step: [27/29], Loss: 0.42906083442546705, Validation Loss:0.5289112403988838, Validation Acc:73.6842105263158, AUC:0.7480972515856237\n",
            "Epoch 10 time: 2.0019\n",
            "Fold: 8, Epoch: [12/40], Step: [9/29], Loss: 0.3994024462170071, Validation Loss:0.46549487113952637, Validation Acc:72.68170426065163, AUC:0.7422832980972515\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 8, Epoch: [12/40], Step: [18/29], Loss: 0.41430558760960895, Validation Loss:0.49318827688694, Validation Acc:72.93233082706767, AUC:0.7360993657505285\n",
            "Fold: 8, Epoch: [12/40], Step: [27/29], Loss: 0.41949216304001985, Validation Loss:0.5270381346344948, Validation Acc:73.6842105263158, AUC:0.7557346723044397\n",
            "Epoch 11 time: 2.0000\n",
            "Fold: 8, Epoch: [13/40], Step: [9/29], Loss: 0.3930242525206672, Validation Loss:0.53511643409729, Validation Acc:74.43609022556392, AUC:0.7295454545454545\n",
            "Fold: 8, Epoch: [13/40], Step: [18/29], Loss: 0.4155591130256653, Validation Loss:0.4816332310438156, Validation Acc:75.18796992481202, AUC:0.7491807610993658\n",
            "Fold: 8, Epoch: [13/40], Step: [27/29], Loss: 0.4122665723164876, Validation Loss:0.4361652322113514, Validation Acc:73.43358395989975, AUC:0.7237315010570824\n",
            "Epoch 12 time: 1.9591\n",
            "Fold: 8, Epoch: [14/40], Step: [9/29], Loss: 0.4045919742849138, Validation Loss:0.43818484246730804, Validation Acc:74.68671679197995, AUC:0.7386363636363636\n",
            "Fold: 8, Epoch: [14/40], Step: [18/29], Loss: 0.40837763581011033, Validation Loss:0.4418030045926571, Validation Acc:73.6842105263158, AUC:0.7251849894291754\n",
            "Fold: 8, Epoch: [14/40], Step: [27/29], Loss: 0.4067329896820916, Validation Loss:0.5142546892166138, Validation Acc:74.43609022556392, AUC:0.7371828752642706\n",
            "Epoch 13 time: 2.0085\n",
            "Fold: 8, Epoch: [15/40], Step: [9/29], Loss: 0.37698937124676174, Validation Loss:0.4854852259159088, Validation Acc:75.18796992481202, AUC:0.7339059196617336\n",
            "Fold: 8, Epoch: [15/40], Step: [18/29], Loss: 0.3805981046623654, Validation Loss:0.5445843636989594, Validation Acc:74.43609022556392, AUC:0.7295454545454545\n",
            "Fold: 8, Epoch: [15/40], Step: [27/29], Loss: 0.3947368871282648, Validation Loss:0.5428681522607803, Validation Acc:74.18546365914787, AUC:0.7357293868921776\n",
            "Epoch 14 time: 1.9680\n",
            "Fold: 8, Epoch: [16/40], Step: [9/29], Loss: 0.4024701284037696, Validation Loss:0.5588507726788521, Validation Acc:73.93483709273183, AUC:0.7419133192389007\n",
            "Fold: 8, Epoch: [16/40], Step: [18/29], Loss: 0.3860697133673562, Validation Loss:0.5564597845077515, Validation Acc:72.93233082706767, AUC:0.7437367864693445\n",
            "Fold: 8, Epoch: [16/40], Step: [27/29], Loss: 0.3818310918631377, Validation Loss:0.6155526265501976, Validation Acc:73.43358395989975, AUC:0.6931818181818181\n",
            "Epoch 15 time: 1.9679\n",
            "Fold: 8, Epoch: [17/40], Step: [9/29], Loss: 0.3604801495869954, Validation Loss:0.5542614832520485, Validation Acc:76.69172932330827, AUC:0.7349894291754757\n",
            "Fold: 8, Epoch: [17/40], Step: [18/29], Loss: 0.37230849431620705, Validation Loss:0.5232344269752502, Validation Acc:75.18796992481202, AUC:0.7339059196617336\n",
            "Fold: 8, Epoch: [17/40], Step: [27/29], Loss: 0.3685483789002454, Validation Loss:0.5060968175530434, Validation Acc:74.93734335839599, AUC:0.7248150105708245\n",
            "Epoch 16 time: 1.9751\n",
            "Fold: 8, Epoch: [18/40], Step: [9/29], Loss: 0.36852473351690507, Validation Loss:0.5790082365274429, Validation Acc:75.43859649122807, AUC:0.7124471458773785\n",
            "Fold: 8, Epoch: [18/40], Step: [18/29], Loss: 0.36214642888969845, Validation Loss:0.48858625814318657, Validation Acc:74.68671679197995, AUC:0.7004492600422833\n",
            "Fold: 8, Epoch: [18/40], Step: [27/29], Loss: 0.35963939958148533, Validation Loss:0.49831216782331467, Validation Acc:76.44110275689223, AUC:0.7182610993657506\n",
            "Epoch 17 time: 1.9853\n",
            "Fold: 8, Epoch: [19/40], Step: [9/29], Loss: 0.3493406805727217, Validation Loss:0.6106525957584381, Validation Acc:74.68671679197995, AUC:0.7233615221987315\n",
            "Fold: 8, Epoch: [19/40], Step: [18/29], Loss: 0.36088333361678654, Validation Loss:0.6191599294543266, Validation Acc:74.68671679197995, AUC:0.7004492600422833\n",
            "Fold: 8, Epoch: [19/40], Step: [27/29], Loss: 0.3520018557707469, Validation Loss:0.7180518731474876, Validation Acc:74.93734335839599, AUC:0.7171775898520085\n",
            "Epoch 18 time: 1.9793\n",
            "Fold: 8, Epoch: [20/40], Step: [9/29], Loss: 0.35328197479248047, Validation Loss:0.59508116543293, Validation Acc:73.1829573934837, AUC:0.7146405919661734\n",
            "Fold: 8, Epoch: [20/40], Step: [18/29], Loss: 0.3419207524922159, Validation Loss:0.6569547802209854, Validation Acc:73.6842105263158, AUC:0.7022727272727273\n",
            "Fold: 8, Epoch: [20/40], Step: [27/29], Loss: 0.33707599452248327, Validation Loss:0.5735393166542053, Validation Acc:72.93233082706767, AUC:0.6902748414376321\n",
            "Epoch 19 time: 2.0074\n",
            "Fold: 8, Epoch: [21/40], Step: [9/29], Loss: 0.3324628207418654, Validation Loss:0.9351375848054886, Validation Acc:74.18546365914787, AUC:0.6822674418604653\n",
            "Fold: 8, Epoch: [21/40], Step: [18/29], Loss: 0.3311006956630283, Validation Loss:0.5390943698585033, Validation Acc:76.94235588972431, AUC:0.6906183932346723\n",
            "Fold: 8, Epoch: [21/40], Step: [27/29], Loss: 0.33774193348707976, Validation Loss:0.6352064609527588, Validation Acc:75.43859649122807, AUC:0.7429968287526427\n",
            "Epoch 20 time: 1.9645\n",
            "Fold: 8, Epoch: [22/40], Step: [9/29], Loss: 0.29751166701316833, Validation Loss:0.5585826560854912, Validation Acc:78.94736842105263, AUC:0.7251585623678647\n",
            "Fold: 8, Epoch: [22/40], Step: [18/29], Loss: 0.3193586683935589, Validation Loss:0.5530154369771481, Validation Acc:77.94486215538848, AUC:0.7193446088794925\n",
            "Fold: 8, Epoch: [22/40], Step: [27/29], Loss: 0.323818055567918, Validation Loss:0.8303784653544426, Validation Acc:74.18546365914787, AUC:0.7128171247357293\n",
            "Epoch 21 time: 1.9543\n",
            "Fold: 8, Epoch: [23/40], Step: [9/29], Loss: 0.31207139127784306, Validation Loss:0.8869650363922119, Validation Acc:75.93984962406014, AUC:0.6924418604651164\n",
            "Fold: 8, Epoch: [23/40], Step: [18/29], Loss: 0.2956782289677196, Validation Loss:0.6213183254003525, Validation Acc:79.4486215538847, AUC:0.7204281183932347\n"
          ],
          "name": "stdout"
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          "text": [
            "Fold: 8, Epoch: [23/40], Step: [27/29], Loss: 0.299701107872857, Validation Loss:0.5289529673755169, Validation Acc:75.18796992481202, AUC:0.6880813953488373\n",
            "Epoch 22 time: 1.9769\n",
            "Fold: 8, Epoch: [24/40], Step: [9/29], Loss: 0.2723090731435352, Validation Loss:0.5930287912487984, Validation Acc:79.19799498746868, AUC:0.6960623678646934\n",
            "Fold: 8, Epoch: [24/40], Step: [18/29], Loss: 0.27299730148580337, Validation Loss:0.5969082750380039, Validation Acc:76.69172932330827, AUC:0.6738900634249471\n",
            "Fold: 8, Epoch: [24/40], Step: [27/29], Loss: 0.2854371849033568, Validation Loss:0.5645793676376343, Validation Acc:75.18796992481202, AUC:0.6957188160676533\n",
            "Epoch 23 time: 1.9715\n",
            "Fold: 8, Epoch: [25/40], Step: [9/29], Loss: 0.2835395799742805, Validation Loss:0.5513888746500015, Validation Acc:76.44110275689223, AUC:0.7335359408033827\n",
            "Fold: 8, Epoch: [25/40], Step: [18/29], Loss: 0.29644012120034957, Validation Loss:0.5166234541684389, Validation Acc:73.93483709273183, AUC:0.7037262156448203\n",
            "Fold: 8, Epoch: [25/40], Step: [27/29], Loss: 0.29556369229599283, Validation Loss:0.7572790533304214, Validation Acc:80.45112781954887, AUC:0.7262420718816067\n",
            "Epoch 24 time: 1.9859\n",
            "Fold: 8, Epoch: [26/40], Step: [9/29], Loss: 0.26707372897201115, Validation Loss:0.7597992420196533, Validation Acc:79.4486215538847, AUC:0.7357029598308668\n",
            "Fold: 8, Epoch: [26/40], Step: [18/29], Loss: 0.2579934406611655, Validation Loss:0.6255882903933525, Validation Acc:79.4486215538847, AUC:0.7051532769556026\n",
            "Fold: 8, Epoch: [26/40], Step: [27/29], Loss: 0.25905809744640634, Validation Loss:0.6163092479109764, Validation Acc:79.9498746867168, AUC:0.7004228329809724\n",
            "Epoch 25 time: 1.9606\n",
            "Fold: 8, Epoch: [27/40], Step: [9/29], Loss: 0.2316394630405638, Validation Loss:0.7489399611949921, Validation Acc:79.69924812030075, AUC:0.660782241014799\n",
            "Fold: 8, Epoch: [27/40], Step: [18/29], Loss: 0.23243260466390187, Validation Loss:0.5878235101699829, Validation Acc:80.95238095238095, AUC:0.7367864693446088\n",
            "Fold: 8, Epoch: [27/40], Step: [27/29], Loss: 0.23880466432483108, Validation Loss:0.6103861480951309, Validation Acc:76.69172932330827, AUC:0.7120771670190276\n",
            "Epoch 26 time: 2.0055\n",
            "Fold: 8, Epoch: [28/40], Step: [9/29], Loss: 0.22254560556676653, Validation Loss:0.786747619509697, Validation Acc:79.9498746867168, AUC:0.6851479915433404\n",
            "Fold: 8, Epoch: [28/40], Step: [18/29], Loss: 0.23263633085621727, Validation Loss:0.8449853509664536, Validation Acc:79.19799498746868, AUC:0.6807875264270614\n",
            "Fold: 8, Epoch: [28/40], Step: [27/29], Loss: 0.24804512438950715, Validation Loss:0.6190442889928818, Validation Acc:74.68671679197995, AUC:0.669899577167019\n",
            "Epoch 27 time: 1.9603\n",
            "Fold: 8, Epoch: [29/40], Step: [9/29], Loss: 0.21685146457619137, Validation Loss:0.7093802317976952, Validation Acc:78.94736842105263, AUC:0.7022463002114165\n",
            "Fold: 8, Epoch: [29/40], Step: [18/29], Loss: 0.21311863511800766, Validation Loss:0.7051739171147346, Validation Acc:81.70426065162907, AUC:0.6876849894291754\n",
            "Fold: 8, Epoch: [29/40], Step: [27/29], Loss: 0.22780535839222096, Validation Loss:0.7452639192342758, Validation Acc:82.20551378446115, AUC:0.6829545454545455\n",
            "Epoch 28 time: 1.9774\n",
            "Fold: 8, Epoch: [30/40], Step: [9/29], Loss: 0.23129470646381378, Validation Loss:0.670566700398922, Validation Acc:81.95488721804512, AUC:0.7120507399577166\n",
            "Fold: 8, Epoch: [30/40], Step: [18/29], Loss: 0.2076198168926769, Validation Loss:0.8290160447359085, Validation Acc:81.70426065162907, AUC:0.6953224101479916\n",
            "Fold: 8, Epoch: [30/40], Step: [27/29], Loss: 0.2021612897515297, Validation Loss:0.7954516112804413, Validation Acc:83.70927318295739, AUC:0.7222251585623678\n",
            "Epoch 29 time: 2.0012\n",
            "Fold: 8, Epoch: [31/40], Step: [9/29], Loss: 0.20549924837218392, Validation Loss:0.8492707312107086, Validation Acc:79.9498746867168, AUC:0.6927854122621564\n",
            "Fold: 8, Epoch: [31/40], Step: [18/29], Loss: 0.20830466681056553, Validation Loss:0.890972763299942, Validation Acc:80.45112781954887, AUC:0.7262420718816067\n",
            "Fold: 8, Epoch: [31/40], Step: [27/29], Loss: 0.19772556810467332, Validation Loss:0.6736362911760807, Validation Acc:81.203007518797, AUC:0.6847780126849894\n",
            "Epoch 30 time: 1.9660\n",
            "Fold: 8, Epoch: [32/40], Step: [9/29], Loss: 0.1797104212972853, Validation Loss:1.1966370791196823, Validation Acc:81.45363408521304, AUC:0.6709566596194503\n",
            "Fold: 8, Epoch: [32/40], Step: [18/29], Loss: 0.17533839659558403, Validation Loss:0.9318923503160477, Validation Acc:81.203007518797, AUC:0.6847780126849894\n",
            "Fold: 8, Epoch: [32/40], Step: [27/29], Loss: 0.18452165165433176, Validation Loss:0.9830345436930656, Validation Acc:84.46115288220551, AUC:0.7189482029598309\n",
            "Epoch 31 time: 1.9601\n",
            "Fold: 8, Epoch: [33/40], Step: [9/29], Loss: 0.18149768395556343, Validation Loss:0.776052676141262, Validation Acc:79.69924812030075, AUC:0.6684196617336152\n",
            "Fold: 8, Epoch: [33/40], Step: [18/29], Loss: 0.1823611727191342, Validation Loss:0.7937415242195129, Validation Acc:80.20050125313283, AUC:0.7171511627906977\n",
            "Fold: 8, Epoch: [33/40], Step: [27/29], Loss: 0.1754993368630056, Validation Loss:0.78726876527071, Validation Acc:82.70676691729324, AUC:0.7011363636363637\n",
            "Epoch 32 time: 1.9492\n",
            "Fold: 8, Epoch: [34/40], Step: [9/29], Loss: 0.15615539915031856, Validation Loss:1.0068793296813965, Validation Acc:82.20551378446115, AUC:0.6753171247357294\n",
            "Fold: 8, Epoch: [34/40], Step: [18/29], Loss: 0.1521410486764378, Validation Loss:1.230664312839508, Validation Acc:83.20802005012531, AUC:0.7269556025369979\n",
            "Fold: 8, Epoch: [34/40], Step: [27/29], Loss: 0.15264848316157306, Validation Loss:1.0921742171049118, Validation Acc:81.95488721804512, AUC:0.6738636363636363\n",
            "Epoch 33 time: 1.9612\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 8, Epoch: [35/40], Step: [9/29], Loss: 0.1421410996052954, Validation Loss:1.0935319364070892, Validation Acc:83.95989974937343, AUC:0.6778541226215645\n",
            "Fold: 8, Epoch: [35/40], Step: [18/29], Loss: 0.1354619926876492, Validation Loss:0.8211340717971325, Validation Acc:81.70426065162907, AUC:0.6571353065539112\n",
            "Fold: 8, Epoch: [35/40], Step: [27/29], Loss: 0.1418214961886406, Validation Loss:0.8970738053321838, Validation Acc:80.95238095238095, AUC:0.6680496828752642\n",
            "Epoch 34 time: 1.9591\n",
            "Fold: 8, Epoch: [36/40], Step: [9/29], Loss: 0.1319979793495602, Validation Loss:0.8686988428235054, Validation Acc:83.70927318295739, AUC:0.6840380549682875\n",
            "Fold: 8, Epoch: [36/40], Step: [18/29], Loss: 0.13731163491805395, Validation Loss:1.002640277147293, Validation Acc:82.95739348370927, AUC:0.6491279069767442\n",
            "Fold: 8, Epoch: [36/40], Step: [27/29], Loss: 0.13545650547301327, Validation Loss:1.0428577214479446, Validation Acc:82.70676691729324, AUC:0.6705866807610994\n",
            "Epoch 35 time: 1.9558\n",
            "Fold: 8, Epoch: [37/40], Step: [9/29], Loss: 0.14562343806028366, Validation Loss:0.9937428385019302, Validation Acc:81.203007518797, AUC:0.7076902748414375\n",
            "Fold: 8, Epoch: [37/40], Step: [18/29], Loss: 0.14520758555995095, Validation Loss:0.893566370010376, Validation Acc:82.70676691729324, AUC:0.6934989429175475\n",
            "Fold: 8, Epoch: [37/40], Step: [27/29], Loss: 0.14299665032713502, Validation Loss:0.8548740223050117, Validation Acc:83.20802005012531, AUC:0.6887684989429176\n",
            "Epoch 36 time: 1.9676\n",
            "Fold: 8, Epoch: [38/40], Step: [9/29], Loss: 0.12391387505663766, Validation Loss:0.8922739289700985, Validation Acc:83.20802005012531, AUC:0.6505813953488372\n",
            "Fold: 8, Epoch: [38/40], Step: [18/29], Loss: 0.12092581080893676, Validation Loss:0.8602252900600433, Validation Acc:82.20551378446115, AUC:0.6753171247357294\n",
            "Fold: 8, Epoch: [38/40], Step: [27/29], Loss: 0.1258079573788025, Validation Loss:0.9680828154087067, Validation Acc:80.95238095238095, AUC:0.6527748414376321\n",
            "Epoch 37 time: 1.9812\n",
            "Fold: 8, Epoch: [39/40], Step: [9/29], Loss: 0.10929548657602733, Validation Loss:0.9398152157664299, Validation Acc:81.70426065162907, AUC:0.6800475687103594\n",
            "Fold: 8, Epoch: [39/40], Step: [18/29], Loss: 0.1092700097295973, Validation Loss:1.2591873854398727, Validation Acc:82.70676691729324, AUC:0.6705866807610994\n",
            "Fold: 8, Epoch: [39/40], Step: [27/29], Loss: 0.10654348548915651, Validation Loss:1.5284655541181564, Validation Acc:82.95739348370927, AUC:0.6644027484143763\n",
            "Epoch 38 time: 1.9704\n",
            "Fold: 8, Epoch: [40/40], Step: [9/29], Loss: 0.07795736938714981, Validation Loss:1.1059777736663818, Validation Acc:81.95488721804512, AUC:0.6815010570824525\n",
            "Fold: 8, Epoch: [40/40], Step: [18/29], Loss: 0.09642402248250113, Validation Loss:1.227755069732666, Validation Acc:82.45614035087719, AUC:0.6538583509513742\n",
            "Fold: 8, Epoch: [40/40], Step: [27/29], Loss: 0.09336894971353037, Validation Loss:1.381969392299652, Validation Acc:83.95989974937343, AUC:0.6854915433403805\n",
            "Epoch 39 time: 2.0750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 9, Epoch: [1/40], Step: [9/29], Loss: 0.6736121641265022, Validation Loss:0.6569283902645111, Validation Acc:78.19548872180451, AUC:0.6367864693446088\n",
            "Fold: 9, Epoch: [1/40], Step: [18/29], Loss: 0.6321336229642233, Validation Loss:0.6269540935754776, Validation Acc:74.68671679197995, AUC:0.7004492600422833\n",
            "Fold: 9, Epoch: [1/40], Step: [27/29], Loss: 0.6103350155883365, Validation Loss:0.5629214942455292, Validation Acc:72.43107769423558, AUC:0.6950052854122621\n",
            "Epoch 0 time: 2.0167\n",
            "Fold: 9, Epoch: [2/40], Step: [9/29], Loss: 0.5465873115592532, Validation Loss:0.5276937857270241, Validation Acc:70.42606516290726, AUC:0.7215644820295983\n",
            "Fold: 9, Epoch: [2/40], Step: [18/29], Loss: 0.5263380308945974, Validation Loss:0.5260047987103462, Validation Acc:73.1829573934837, AUC:0.7451902748414376\n",
            "Fold: 9, Epoch: [2/40], Step: [27/29], Loss: 0.5309814733487589, Validation Loss:0.5859098508954048, Validation Acc:72.43107769423558, AUC:0.7331923890063425\n",
            "Epoch 1 time: 1.9890\n",
            "Fold: 9, Epoch: [3/40], Step: [9/29], Loss: 0.5017934110429552, Validation Loss:0.5157409161329269, Validation Acc:72.43107769423558, AUC:0.7255549682875264\n",
            "Fold: 9, Epoch: [3/40], Step: [18/29], Loss: 0.5007068978415595, Validation Loss:0.5566806793212891, Validation Acc:71.42857142857143, AUC:0.7197410147991542\n",
            "Fold: 9, Epoch: [3/40], Step: [27/29], Loss: 0.5068838110676518, Validation Loss:0.4929013028740883, Validation Acc:73.43358395989975, AUC:0.7466437632135307\n",
            "Epoch 2 time: 1.9884\n",
            "Fold: 9, Epoch: [4/40], Step: [9/29], Loss: 0.5065445800622305, Validation Loss:0.593235932290554, Validation Acc:71.17794486215539, AUC:0.7411997885835095\n",
            "Fold: 9, Epoch: [4/40], Step: [18/29], Loss: 0.5039914548397064, Validation Loss:0.5542928129434586, Validation Acc:69.92481203007519, AUC:0.7339323467230443\n",
            "Fold: 9, Epoch: [4/40], Step: [27/29], Loss: 0.49112273697499875, Validation Loss:0.4912573993206024, Validation Acc:72.18045112781955, AUC:0.7241014799154334\n",
            "Epoch 3 time: 1.9707\n",
            "Fold: 9, Epoch: [5/40], Step: [9/29], Loss: 0.47641173005104065, Validation Loss:0.537439689040184, Validation Acc:73.6842105263158, AUC:0.7251849894291754\n",
            "Fold: 9, Epoch: [5/40], Step: [18/29], Loss: 0.4800795581605699, Validation Loss:0.5557282716035843, Validation Acc:73.6842105263158, AUC:0.7404598308668077\n",
            "Fold: 9, Epoch: [5/40], Step: [27/29], Loss: 0.49028019772635567, Validation Loss:0.48957981169223785, Validation Acc:71.9298245614035, AUC:0.7684725158562369\n",
            "Epoch 4 time: 1.9793\n",
            "Fold: 9, Epoch: [6/40], Step: [9/29], Loss: 0.4378821783595615, Validation Loss:0.5148186832666397, Validation Acc:72.43107769423558, AUC:0.7331923890063425\n",
            "Fold: 9, Epoch: [6/40], Step: [18/29], Loss: 0.45801888902982074, Validation Loss:0.570366807281971, Validation Acc:72.68170426065163, AUC:0.7346458773784356\n",
            "Fold: 9, Epoch: [6/40], Step: [27/29], Loss: 0.4739351769288381, Validation Loss:0.6094311699271202, Validation Acc:73.6842105263158, AUC:0.7404598308668077\n",
            "Epoch 5 time: 1.9832\n",
            "Fold: 9, Epoch: [7/40], Step: [9/29], Loss: 0.47039401200082565, Validation Loss:0.5082847699522972, Validation Acc:74.43609022556392, AUC:0.7448202959830867\n",
            "Fold: 9, Epoch: [7/40], Step: [18/29], Loss: 0.46316468550099266, Validation Loss:0.4858861267566681, Validation Acc:76.69172932330827, AUC:0.76553911205074\n",
            "Fold: 9, Epoch: [7/40], Step: [27/29], Loss: 0.4625863167974684, Validation Loss:0.5030373260378838, Validation Acc:75.43859649122807, AUC:0.7506342494714588\n",
            "Epoch 6 time: 1.9706\n",
            "Fold: 9, Epoch: [8/40], Step: [9/29], Loss: 0.4615219500329759, Validation Loss:0.4891284964978695, Validation Acc:72.68170426065163, AUC:0.7422832980972515\n",
            "Fold: 9, Epoch: [8/40], Step: [18/29], Loss: 0.45673410097757977, Validation Loss:0.49998947978019714, Validation Acc:72.18045112781955, AUC:0.7317389006342494\n",
            "Fold: 9, Epoch: [8/40], Step: [27/29], Loss: 0.45460570962340746, Validation Loss:0.5093066543340683, Validation Acc:73.6842105263158, AUC:0.7404598308668077\n",
            "Epoch 7 time: 1.9669\n",
            "Fold: 9, Epoch: [9/40], Step: [9/29], Loss: 0.4261273509926266, Validation Loss:0.5512363836169243, Validation Acc:72.93233082706767, AUC:0.7437367864693445\n",
            "Fold: 9, Epoch: [9/40], Step: [18/29], Loss: 0.4319560064209832, Validation Loss:0.5537406578660011, Validation Acc:74.93734335839599, AUC:0.7477272727272727\n",
            "Fold: 9, Epoch: [9/40], Step: [27/29], Loss: 0.44094331396950615, Validation Loss:0.5432896837592125, Validation Acc:73.43358395989975, AUC:0.7313689217758985\n",
            "Epoch 8 time: 1.9495\n",
            "Fold: 9, Epoch: [10/40], Step: [9/29], Loss: 0.44174155592918396, Validation Loss:0.4820440411567688, Validation Acc:76.44110275689223, AUC:0.771723044397463\n",
            "Fold: 9, Epoch: [10/40], Step: [18/29], Loss: 0.43957003785504234, Validation Loss:0.4652307406067848, Validation Acc:75.18796992481202, AUC:0.7644556025369978\n",
            "Fold: 9, Epoch: [10/40], Step: [27/29], Loss: 0.4469803074995677, Validation Loss:0.5640686675906181, Validation Acc:76.44110275689223, AUC:0.771723044397463\n",
            "Epoch 9 time: 1.9676\n",
            "Fold: 9, Epoch: [11/40], Step: [9/29], Loss: 0.42035626702838474, Validation Loss:0.5295433476567268, Validation Acc:74.93734335839599, AUC:0.770639534883721\n",
            "Fold: 9, Epoch: [11/40], Step: [18/29], Loss: 0.42787875566217637, Validation Loss:0.58134526014328, Validation Acc:74.43609022556392, AUC:0.7524577167019028\n",
            "Fold: 9, Epoch: [11/40], Step: [27/29], Loss: 0.4346607945583485, Validation Loss:0.48766619712114334, Validation Acc:76.19047619047619, AUC:0.7626321353065539\n",
            "Epoch 10 time: 1.9831\n",
            "Fold: 9, Epoch: [12/40], Step: [9/29], Loss: 0.4147283103730943, Validation Loss:0.5808981582522392, Validation Acc:76.44110275689223, AUC:0.756448202959831\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 9, Epoch: [12/40], Step: [18/29], Loss: 0.4180173807673984, Validation Loss:0.4598902240395546, Validation Acc:75.43859649122807, AUC:0.765909090909091\n",
            "Fold: 9, Epoch: [12/40], Step: [27/29], Loss: 0.4284043003011633, Validation Loss:0.553376168012619, Validation Acc:76.94235588972431, AUC:0.766992600422833\n",
            "Epoch 11 time: 1.9603\n",
            "Fold: 9, Epoch: [13/40], Step: [9/29], Loss: 0.4203322794702318, Validation Loss:0.6201809197664261, Validation Acc:79.9498746867168, AUC:0.7691596194503171\n",
            "Fold: 9, Epoch: [13/40], Step: [18/29], Loss: 0.4150671280092663, Validation Loss:0.4978117123246193, Validation Acc:78.94736842105263, AUC:0.7709830866807612\n",
            "Fold: 9, Epoch: [13/40], Step: [27/29], Loss: 0.406768097921654, Validation Loss:0.5682880133390427, Validation Acc:77.69423558897243, AUC:0.75607822410148\n",
            "Epoch 12 time: 1.9649\n",
            "Fold: 9, Epoch: [14/40], Step: [9/29], Loss: 0.3781947195529938, Validation Loss:0.4677891656756401, Validation Acc:78.44611528822055, AUC:0.7298890063424947\n",
            "Fold: 9, Epoch: [14/40], Step: [18/29], Loss: 0.3750530845589108, Validation Loss:0.6305915787816048, Validation Acc:79.19799498746868, AUC:0.7571617336152219\n",
            "Fold: 9, Epoch: [14/40], Step: [27/29], Loss: 0.3955146548924623, Validation Loss:0.5045051649212837, Validation Acc:75.68922305764411, AUC:0.7597251585623679\n",
            "Epoch 13 time: 2.0023\n",
            "Fold: 9, Epoch: [15/40], Step: [9/29], Loss: 0.3928883605533176, Validation Loss:0.4748111814260483, Validation Acc:77.94486215538848, AUC:0.7651691331923891\n",
            "Fold: 9, Epoch: [15/40], Step: [18/29], Loss: 0.3843869086768892, Validation Loss:0.5793057754635811, Validation Acc:77.44360902255639, AUC:0.7698995771670192\n",
            "Fold: 9, Epoch: [15/40], Step: [27/29], Loss: 0.3964722178600453, Validation Loss:0.44103559851646423, Validation Acc:77.94486215538848, AUC:0.7728065539112051\n",
            "Epoch 14 time: 1.9821\n",
            "Fold: 9, Epoch: [16/40], Step: [9/29], Loss: 0.34241215056843227, Validation Loss:0.611652284860611, Validation Acc:80.70175438596492, AUC:0.750607822410148\n",
            "Fold: 9, Epoch: [16/40], Step: [18/29], Loss: 0.3796060085296631, Validation Loss:0.5793218016624451, Validation Acc:75.68922305764411, AUC:0.7444503171247357\n",
            "Fold: 9, Epoch: [16/40], Step: [27/29], Loss: 0.37783012235606156, Validation Loss:0.49460770934820175, Validation Acc:75.68922305764411, AUC:0.7520877378435519\n",
            "Epoch 15 time: 1.9754\n",
            "Fold: 9, Epoch: [17/40], Step: [9/29], Loss: 0.34337854716512894, Validation Loss:0.5412885919213295, Validation Acc:79.4486215538847, AUC:0.758615221987315\n",
            "Fold: 9, Epoch: [17/40], Step: [18/29], Loss: 0.3543928994072808, Validation Loss:0.4515438675880432, Validation Acc:79.4486215538847, AUC:0.7662526427061311\n",
            "Fold: 9, Epoch: [17/40], Step: [27/29], Loss: 0.3657463546152468, Validation Loss:0.518165610730648, Validation Acc:77.69423558897243, AUC:0.75607822410148\n",
            "Epoch 16 time: 1.9689\n",
            "Fold: 9, Epoch: [18/40], Step: [9/29], Loss: 0.33896080984009636, Validation Loss:0.5402482450008392, Validation Acc:78.19548872180451, AUC:0.7513477801268499\n",
            "Fold: 9, Epoch: [18/40], Step: [18/29], Loss: 0.36259294549624127, Validation Loss:0.5399873107671738, Validation Acc:78.19548872180451, AUC:0.7742600422832981\n",
            "Fold: 9, Epoch: [18/40], Step: [27/29], Loss: 0.3626771127736127, Validation Loss:0.6275842860341072, Validation Acc:80.45112781954887, AUC:0.7720665961945031\n",
            "Epoch 17 time: 1.9814\n",
            "Fold: 9, Epoch: [19/40], Step: [9/29], Loss: 0.3469252652592129, Validation Loss:0.5612286552786827, Validation Acc:78.69674185463658, AUC:0.7313424947145877\n",
            "Fold: 9, Epoch: [19/40], Step: [18/29], Loss: 0.3438532178600629, Validation Loss:0.5634360313415527, Validation Acc:80.45112781954887, AUC:0.7415169133192389\n",
            "Fold: 9, Epoch: [19/40], Step: [27/29], Loss: 0.34517932103739846, Validation Loss:0.6479912474751472, Validation Acc:79.19799498746868, AUC:0.749524312896406\n",
            "Epoch 18 time: 1.9850\n",
            "Fold: 9, Epoch: [20/40], Step: [9/29], Loss: 0.3296607765886519, Validation Loss:0.675086572766304, Validation Acc:80.45112781954887, AUC:0.7720665961945031\n",
            "Fold: 9, Epoch: [20/40], Step: [18/29], Loss: 0.32670681178569794, Validation Loss:0.5872954502701759, Validation Acc:79.69924812030075, AUC:0.7600687103594079\n",
            "Fold: 9, Epoch: [20/40], Step: [27/29], Loss: 0.323857503356757, Validation Loss:0.5227077975869179, Validation Acc:80.95238095238095, AUC:0.7520613107822411\n",
            "Epoch 19 time: 1.9771\n",
            "Fold: 9, Epoch: [21/40], Step: [9/29], Loss: 0.30373063021235996, Validation Loss:0.5269459672272205, Validation Acc:77.69423558897243, AUC:0.7255285412262156\n",
            "Fold: 9, Epoch: [21/40], Step: [18/29], Loss: 0.3199043456051085, Validation Loss:0.5796100422739983, Validation Acc:77.69423558897243, AUC:0.7331659619450318\n",
            "Fold: 9, Epoch: [21/40], Step: [27/29], Loss: 0.32494164672162795, Validation Loss:0.6163780391216278, Validation Acc:79.69924812030075, AUC:0.7371564482029598\n",
            "Epoch 20 time: 1.9768\n",
            "Fold: 9, Epoch: [22/40], Step: [9/29], Loss: 0.3131379849380917, Validation Loss:0.5237539485096931, Validation Acc:77.94486215538848, AUC:0.757531712473573\n",
            "Fold: 9, Epoch: [22/40], Step: [18/29], Loss: 0.30523748447497684, Validation Loss:0.5983330011367798, Validation Acc:78.19548872180451, AUC:0.7284355179704016\n",
            "Fold: 9, Epoch: [22/40], Step: [27/29], Loss: 0.3177384364384192, Validation Loss:0.5115359053015709, Validation Acc:78.94736842105263, AUC:0.7480708245243128\n",
            "Epoch 21 time: 1.9865\n",
            "Fold: 9, Epoch: [23/40], Step: [9/29], Loss: 0.2668464067909453, Validation Loss:0.5276591405272484, Validation Acc:78.44611528822055, AUC:0.6993393234672304\n",
            "Fold: 9, Epoch: [23/40], Step: [18/29], Loss: 0.283703217903773, Validation Loss:0.4842519797384739, Validation Acc:81.203007518797, AUC:0.7382399577167018\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 9, Epoch: [23/40], Step: [27/29], Loss: 0.29170051879352993, Validation Loss:0.6334417834877968, Validation Acc:80.20050125313283, AUC:0.7400634249471458\n",
            "Epoch 22 time: 1.9845\n",
            "Fold: 9, Epoch: [24/40], Step: [9/29], Loss: 0.2885088523228963, Validation Loss:0.6248004734516144, Validation Acc:77.69423558897243, AUC:0.7331659619450318\n",
            "Fold: 9, Epoch: [24/40], Step: [18/29], Loss: 0.2963971305224631, Validation Loss:0.7133083045482635, Validation Acc:78.69674185463658, AUC:0.7389799154334039\n",
            "Fold: 9, Epoch: [24/40], Step: [27/29], Loss: 0.29072883283650436, Validation Loss:0.5689352676272392, Validation Acc:81.45363408521304, AUC:0.7396934460887948\n",
            "Epoch 23 time: 1.9841\n",
            "Fold: 9, Epoch: [25/40], Step: [9/29], Loss: 0.27042947047286564, Validation Loss:0.78773233294487, Validation Acc:80.20050125313283, AUC:0.7324260042283297\n",
            "Fold: 9, Epoch: [25/40], Step: [18/29], Loss: 0.27275944501161575, Validation Loss:0.5494852177798748, Validation Acc:81.45363408521304, AUC:0.747330866807611\n",
            "Fold: 9, Epoch: [25/40], Step: [27/29], Loss: 0.2642327221455397, Validation Loss:0.623219795525074, Validation Acc:81.70426065162907, AUC:0.7258720930232558\n",
            "Epoch 24 time: 1.9581\n",
            "Fold: 9, Epoch: [26/40], Step: [9/29], Loss: 0.25486692951785195, Validation Loss:0.7834440022706985, Validation Acc:79.9498746867168, AUC:0.7156976744186048\n",
            "Fold: 9, Epoch: [26/40], Step: [18/29], Loss: 0.2655134192771382, Validation Loss:0.6841309070587158, Validation Acc:79.19799498746868, AUC:0.7036997885835096\n",
            "Fold: 9, Epoch: [26/40], Step: [27/29], Loss: 0.26403883320313914, Validation Loss:0.7153454720973969, Validation Acc:80.95238095238095, AUC:0.7215116279069768\n",
            "Epoch 25 time: 1.9842\n",
            "Fold: 9, Epoch: [27/40], Step: [9/29], Loss: 0.223754925860299, Validation Loss:0.8036994785070419, Validation Acc:80.20050125313283, AUC:0.7095137420718814\n",
            "Fold: 9, Epoch: [27/40], Step: [18/29], Loss: 0.22829486678044, Validation Loss:0.9015510827302933, Validation Acc:81.203007518797, AUC:0.7229651162790698\n",
            "Fold: 9, Epoch: [27/40], Step: [27/29], Loss: 0.23605248387213107, Validation Loss:0.6108915843069553, Validation Acc:81.70426065162907, AUC:0.7182346723044397\n",
            "Epoch 26 time: 1.9805\n",
            "Fold: 9, Epoch: [28/40], Step: [9/29], Loss: 0.20675266120168898, Validation Loss:0.7273223847150803, Validation Acc:80.45112781954887, AUC:0.7033298097251585\n",
            "Fold: 9, Epoch: [28/40], Step: [18/29], Loss: 0.21279238992267185, Validation Loss:0.7909642904996872, Validation Acc:82.70676691729324, AUC:0.7164112050739957\n",
            "Fold: 9, Epoch: [28/40], Step: [27/29], Loss: 0.22088150956012584, Validation Loss:0.7366476356983185, Validation Acc:81.203007518797, AUC:0.7382399577167018\n",
            "Epoch 27 time: 1.9746\n",
            "Fold: 9, Epoch: [29/40], Step: [9/29], Loss: 0.23098219765557182, Validation Loss:0.630725309252739, Validation Acc:77.19298245614036, AUC:0.7378964059196618\n",
            "Fold: 9, Epoch: [29/40], Step: [18/29], Loss: 0.2386445990867085, Validation Loss:0.6271159127354622, Validation Acc:78.44611528822055, AUC:0.7146141649048626\n",
            "Fold: 9, Epoch: [29/40], Step: [27/29], Loss: 0.2438918743972425, Validation Loss:0.7316429018974304, Validation Acc:79.9498746867168, AUC:0.7004228329809724\n",
            "Epoch 28 time: 1.9850\n",
            "Fold: 9, Epoch: [30/40], Step: [9/29], Loss: 0.2081301791800393, Validation Loss:0.6673616021871567, Validation Acc:82.45614035087719, AUC:0.6920454545454545\n",
            "Fold: 9, Epoch: [30/40], Step: [18/29], Loss: 0.21319889194435543, Validation Loss:0.6582612916827202, Validation Acc:80.95238095238095, AUC:0.6909619450317125\n",
            "Fold: 9, Epoch: [30/40], Step: [27/29], Loss: 0.21041051712301043, Validation Loss:0.8807391673326492, Validation Acc:80.70175438596492, AUC:0.6818710359408033\n",
            "Epoch 29 time: 1.9628\n",
            "Fold: 9, Epoch: [31/40], Step: [9/29], Loss: 0.19168498118718466, Validation Loss:0.7121777422726154, Validation Acc:81.45363408521304, AUC:0.6862315010570823\n",
            "Fold: 9, Epoch: [31/40], Step: [18/29], Loss: 0.1803451023168034, Validation Loss:0.7755013480782509, Validation Acc:81.45363408521304, AUC:0.6709566596194503\n",
            "Fold: 9, Epoch: [31/40], Step: [27/29], Loss: 0.18544253596553095, Validation Loss:0.8393030166625977, Validation Acc:82.20551378446115, AUC:0.7058668076109936\n",
            "Epoch 30 time: 1.9928\n",
            "Fold: 9, Epoch: [32/40], Step: [9/29], Loss: 0.2168357620636622, Validation Loss:0.9803841859102249, Validation Acc:79.19799498746868, AUC:0.7036997885835096\n",
            "Fold: 9, Epoch: [32/40], Step: [18/29], Loss: 0.20083220799763998, Validation Loss:0.8126770704984665, Validation Acc:80.70175438596492, AUC:0.6895084566596195\n",
            "Fold: 9, Epoch: [32/40], Step: [27/29], Loss: 0.19644762299678944, Validation Loss:0.8829513788223267, Validation Acc:81.45363408521304, AUC:0.6938689217758984\n",
            "Epoch 31 time: 2.0515\n",
            "Fold: 9, Epoch: [33/40], Step: [9/29], Loss: 0.17364857925309074, Validation Loss:0.7811683267354965, Validation Acc:80.95238095238095, AUC:0.6909619450317125\n",
            "Fold: 9, Epoch: [33/40], Step: [18/29], Loss: 0.17344357901149327, Validation Loss:0.9607407599687576, Validation Acc:79.9498746867168, AUC:0.6775105708245243\n",
            "Fold: 9, Epoch: [33/40], Step: [27/29], Loss: 0.16781249200856244, Validation Loss:1.0115164369344711, Validation Acc:81.203007518797, AUC:0.7000528541226214\n",
            "Epoch 32 time: 1.9845\n",
            "Fold: 9, Epoch: [34/40], Step: [9/29], Loss: 0.16872339695692062, Validation Loss:0.8113364353775978, Validation Acc:81.45363408521304, AUC:0.6862315010570823\n",
            "Fold: 9, Epoch: [34/40], Step: [18/29], Loss: 0.16370560311608845, Validation Loss:0.9196426868438721, Validation Acc:81.95488721804512, AUC:0.6967758985200846\n",
            "Fold: 9, Epoch: [34/40], Step: [27/29], Loss: 0.1650378808931068, Validation Loss:0.7323283217847347, Validation Acc:79.9498746867168, AUC:0.6927854122621564\n",
            "Epoch 33 time: 1.9726\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 9, Epoch: [35/40], Step: [9/29], Loss: 0.14841111914979088, Validation Loss:0.8970371931791306, Validation Acc:81.95488721804512, AUC:0.6891384778012685\n",
            "Fold: 9, Epoch: [35/40], Step: [18/29], Loss: 0.14761063994632828, Validation Loss:1.17062246799469, Validation Acc:82.20551378446115, AUC:0.6600422832980972\n",
            "Fold: 9, Epoch: [35/40], Step: [27/29], Loss: 0.13922120299604204, Validation Loss:0.8658620193600655, Validation Acc:83.45864661654136, AUC:0.6902219873150106\n",
            "Epoch 34 time: 1.9754\n",
            "Fold: 9, Epoch: [36/40], Step: [9/29], Loss: 0.12962564991580117, Validation Loss:0.9476313441991806, Validation Acc:80.20050125313283, AUC:0.6713266384778013\n",
            "Fold: 9, Epoch: [36/40], Step: [18/29], Loss: 0.1364925280213356, Validation Loss:0.7966097984462976, Validation Acc:81.95488721804512, AUC:0.6891384778012685\n",
            "Fold: 9, Epoch: [36/40], Step: [27/29], Loss: 0.13523627514088596, Validation Loss:0.8953332826495171, Validation Acc:82.95739348370927, AUC:0.6720401691331924\n",
            "Epoch 35 time: 1.9781\n",
            "Fold: 9, Epoch: [37/40], Step: [9/29], Loss: 0.11956661939620972, Validation Loss:0.9940301477909088, Validation Acc:83.20802005012531, AUC:0.6734936575052854\n",
            "Fold: 9, Epoch: [37/40], Step: [18/29], Loss: 0.12568181918727028, Validation Loss:0.9885510802268982, Validation Acc:81.45363408521304, AUC:0.6785940803382664\n",
            "Fold: 9, Epoch: [37/40], Step: [27/29], Loss: 0.13198016473540553, Validation Loss:1.1408518254756927, Validation Acc:81.95488721804512, AUC:0.6891384778012685\n",
            "Epoch 36 time: 1.9937\n",
            "Fold: 9, Epoch: [38/40], Step: [9/29], Loss: 0.12019570834106869, Validation Loss:1.1428709030151367, Validation Acc:83.20802005012531, AUC:0.6887684989429176\n",
            "Fold: 9, Epoch: [38/40], Step: [18/29], Loss: 0.12189334630966187, Validation Loss:1.0470100045204163, Validation Acc:82.20551378446115, AUC:0.6600422832980972\n",
            "Fold: 9, Epoch: [38/40], Step: [27/29], Loss: 0.12339537094036739, Validation Loss:0.969111293554306, Validation Acc:81.45363408521304, AUC:0.6709566596194503\n",
            "Epoch 37 time: 1.9766\n",
            "Fold: 9, Epoch: [39/40], Step: [9/29], Loss: 0.10988234769966868, Validation Loss:0.9434657245874405, Validation Acc:81.95488721804512, AUC:0.6891384778012685\n",
            "Fold: 9, Epoch: [39/40], Step: [18/29], Loss: 0.10895131838818391, Validation Loss:0.9548719450831413, Validation Acc:81.95488721804512, AUC:0.6662262156448203\n",
            "Fold: 9, Epoch: [39/40], Step: [27/29], Loss: 0.11429452965105022, Validation Loss:1.1718412935733795, Validation Acc:81.95488721804512, AUC:0.6585887949260042\n",
            "Epoch 38 time: 1.9682\n",
            "Fold: 9, Epoch: [40/40], Step: [9/29], Loss: 0.1726065849264463, Validation Loss:0.804714024066925, Validation Acc:82.45614035087719, AUC:0.7302325581395349\n",
            "Fold: 9, Epoch: [40/40], Step: [18/29], Loss: 0.16497755754325125, Validation Loss:1.0010529905557632, Validation Acc:79.9498746867168, AUC:0.6545983086680761\n",
            "Fold: 9, Epoch: [40/40], Step: [27/29], Loss: 0.163189849919743, Validation Loss:1.2023646831512451, Validation Acc:82.70676691729324, AUC:0.6629492600422833\n",
            "Epoch 39 time: 1.9946\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 10, Epoch: [1/40], Step: [9/29], Loss: 0.6225320961740282, Validation Loss:0.6420807391405106, Validation Acc:76.94235588972431, AUC:0.7288054968287527\n",
            "Fold: 10, Epoch: [1/40], Step: [18/29], Loss: 0.6160288585556878, Validation Loss:0.5512501373887062, Validation Acc:75.43859649122807, AUC:0.7277219873150106\n",
            "Fold: 10, Epoch: [1/40], Step: [27/29], Loss: 0.5882063364541089, Validation Loss:0.5059917941689491, Validation Acc:76.44110275689223, AUC:0.7488107822410148\n",
            "Epoch 0 time: 1.9532\n",
            "Fold: 10, Epoch: [2/40], Step: [9/29], Loss: 0.5152785016430749, Validation Loss:0.5538319945335388, Validation Acc:75.43859649122807, AUC:0.765909090909091\n",
            "Fold: 10, Epoch: [2/40], Step: [18/29], Loss: 0.5181636297040515, Validation Loss:0.4732418581843376, Validation Acc:75.18796992481202, AUC:0.7415433403805497\n",
            "Fold: 10, Epoch: [2/40], Step: [27/29], Loss: 0.5250867185769258, Validation Loss:0.5387229695916176, Validation Acc:74.43609022556392, AUC:0.7295454545454545\n",
            "Epoch 1 time: 1.9794\n",
            "Fold: 10, Epoch: [3/40], Step: [9/29], Loss: 0.48850178056293064, Validation Loss:0.4971250966191292, Validation Acc:74.43609022556392, AUC:0.7371828752642706\n",
            "Fold: 10, Epoch: [3/40], Step: [18/29], Loss: 0.5000946008496814, Validation Loss:0.505871132016182, Validation Acc:75.43859649122807, AUC:0.7353594080338266\n",
            "Fold: 10, Epoch: [3/40], Step: [27/29], Loss: 0.5123929248915778, Validation Loss:0.541288286447525, Validation Acc:74.93734335839599, AUC:0.7324524312896405\n",
            "Epoch 2 time: 1.9850\n",
            "Fold: 10, Epoch: [4/40], Step: [9/29], Loss: 0.5374838279353248, Validation Loss:0.5036647319793701, Validation Acc:73.93483709273183, AUC:0.7037262156448203\n",
            "Fold: 10, Epoch: [4/40], Step: [18/29], Loss: 0.5052429338296255, Validation Loss:0.5190344229340553, Validation Acc:75.43859649122807, AUC:0.7124471458773785\n",
            "Fold: 10, Epoch: [4/40], Step: [27/29], Loss: 0.5033628112739987, Validation Loss:0.5096434950828552, Validation Acc:76.44110275689223, AUC:0.7335359408033827\n",
            "Epoch 3 time: 1.9784\n",
            "Fold: 10, Epoch: [5/40], Step: [9/29], Loss: 0.5009553167555068, Validation Loss:0.5050601810216904, Validation Acc:75.43859649122807, AUC:0.7277219873150106\n",
            "Fold: 10, Epoch: [5/40], Step: [18/29], Loss: 0.5008514871199926, Validation Loss:0.5123550668358803, Validation Acc:74.43609022556392, AUC:0.7295454545454545\n",
            "Fold: 10, Epoch: [5/40], Step: [27/29], Loss: 0.49311484893163043, Validation Loss:0.5696592330932617, Validation Acc:76.19047619047619, AUC:0.7244450317124737\n",
            "Epoch 4 time: 1.9729\n",
            "Fold: 10, Epoch: [6/40], Step: [9/29], Loss: 0.4574444029066298, Validation Loss:0.49320050328969955, Validation Acc:76.19047619047619, AUC:0.7244450317124737\n",
            "Fold: 10, Epoch: [6/40], Step: [18/29], Loss: 0.4778045995367898, Validation Loss:0.4740113168954849, Validation Acc:74.68671679197995, AUC:0.7309989429175475\n",
            "Fold: 10, Epoch: [6/40], Step: [27/29], Loss: 0.483244933463909, Validation Loss:0.5167740359902382, Validation Acc:75.18796992481202, AUC:0.7339059196617336\n",
            "Epoch 5 time: 1.9774\n",
            "Fold: 10, Epoch: [7/40], Step: [9/29], Loss: 0.452354308631685, Validation Loss:0.5875451788306236, Validation Acc:76.94235588972431, AUC:0.7440803382663848\n",
            "Fold: 10, Epoch: [7/40], Step: [18/29], Loss: 0.4608182824320263, Validation Loss:0.47474344819784164, Validation Acc:75.68922305764411, AUC:0.7291754756871036\n",
            "Fold: 10, Epoch: [7/40], Step: [27/29], Loss: 0.46452164539584406, Validation Loss:0.4547997787594795, Validation Acc:73.93483709273183, AUC:0.7419133192389007\n",
            "Epoch 6 time: 1.9680\n",
            "Fold: 10, Epoch: [8/40], Step: [9/29], Loss: 0.4471394287215339, Validation Loss:0.46168725937604904, Validation Acc:75.93984962406014, AUC:0.7382663847780128\n",
            "Fold: 10, Epoch: [8/40], Step: [18/29], Loss: 0.4531567477517658, Validation Loss:0.4543846994638443, Validation Acc:77.44360902255639, AUC:0.7317124735729387\n",
            "Fold: 10, Epoch: [8/40], Step: [27/29], Loss: 0.45789483851856655, Validation Loss:0.5270223021507263, Validation Acc:75.43859649122807, AUC:0.7506342494714588\n",
            "Epoch 7 time: 1.9837\n",
            "Fold: 10, Epoch: [9/40], Step: [9/29], Loss: 0.40922777851422626, Validation Loss:0.521482564508915, Validation Acc:75.43859649122807, AUC:0.7353594080338266\n",
            "Fold: 10, Epoch: [9/40], Step: [18/29], Loss: 0.43600746658113265, Validation Loss:0.4954524263739586, Validation Acc:72.68170426065163, AUC:0.7346458773784356\n",
            "Fold: 10, Epoch: [9/40], Step: [27/29], Loss: 0.4430721943025236, Validation Loss:0.451384536921978, Validation Acc:74.93734335839599, AUC:0.7477272727272727\n",
            "Epoch 8 time: 2.0156\n",
            "Fold: 10, Epoch: [10/40], Step: [9/29], Loss: 0.4454728927877214, Validation Loss:0.45437146723270416, Validation Acc:78.19548872180451, AUC:0.7437103594080338\n",
            "Fold: 10, Epoch: [10/40], Step: [18/29], Loss: 0.4224009546968672, Validation Loss:0.5727189406752586, Validation Acc:78.44611528822055, AUC:0.7604386892177589\n",
            "Fold: 10, Epoch: [10/40], Step: [27/29], Loss: 0.4343448943561978, Validation Loss:0.49796488136053085, Validation Acc:76.44110275689223, AUC:0.7411733615221987\n",
            "Epoch 9 time: 1.9546\n",
            "Fold: 10, Epoch: [11/40], Step: [9/29], Loss: 0.4651191367043389, Validation Loss:0.5235219970345497, Validation Acc:77.19298245614036, AUC:0.7455338266384779\n",
            "Fold: 10, Epoch: [11/40], Step: [18/29], Loss: 0.44236041605472565, Validation Loss:0.526085838675499, Validation Acc:76.69172932330827, AUC:0.750264270613108\n",
            "Fold: 10, Epoch: [11/40], Step: [27/29], Loss: 0.4338650968339708, Validation Loss:0.5373491197824478, Validation Acc:74.43609022556392, AUC:0.7295454545454545\n",
            "Epoch 10 time: 1.9735\n",
            "Fold: 10, Epoch: [12/40], Step: [9/29], Loss: 0.42839675810601974, Validation Loss:0.49018900841474533, Validation Acc:78.69674185463658, AUC:0.7389799154334039\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 10, Epoch: [12/40], Step: [18/29], Loss: 0.4248755027850469, Validation Loss:0.5260873213410378, Validation Acc:80.20050125313283, AUC:0.7553382663847781\n",
            "Fold: 10, Epoch: [12/40], Step: [27/29], Loss: 0.4149392467957956, Validation Loss:0.4697164446115494, Validation Acc:76.44110275689223, AUC:0.7488107822410148\n",
            "Epoch 11 time: 1.9957\n",
            "Fold: 10, Epoch: [13/40], Step: [9/29], Loss: 0.42053870691193473, Validation Loss:0.4332958832383156, Validation Acc:76.44110275689223, AUC:0.756448202959831\n",
            "Fold: 10, Epoch: [13/40], Step: [18/29], Loss: 0.41595039930608535, Validation Loss:0.6428600922226906, Validation Acc:76.44110275689223, AUC:0.7488107822410148\n",
            "Fold: 10, Epoch: [13/40], Step: [27/29], Loss: 0.41239175973115144, Validation Loss:0.5158121213316917, Validation Acc:78.44611528822055, AUC:0.7451638477801268\n",
            "Epoch 12 time: 1.9819\n",
            "Fold: 10, Epoch: [14/40], Step: [9/29], Loss: 0.3864024049705929, Validation Loss:0.5471866801381111, Validation Acc:77.44360902255639, AUC:0.762262156448203\n",
            "Fold: 10, Epoch: [14/40], Step: [18/29], Loss: 0.39028560949696434, Validation Loss:0.4900956302881241, Validation Acc:76.94235588972431, AUC:0.7440803382663848\n",
            "Fold: 10, Epoch: [14/40], Step: [27/29], Loss: 0.39763287151301346, Validation Loss:0.49003521353006363, Validation Acc:78.94736842105263, AUC:0.7480708245243128\n",
            "Epoch 13 time: 2.0073\n",
            "Fold: 10, Epoch: [15/40], Step: [9/29], Loss: 0.4003533025582631, Validation Loss:0.470050610601902, Validation Acc:79.69924812030075, AUC:0.7447938689217759\n",
            "Fold: 10, Epoch: [15/40], Step: [18/29], Loss: 0.40024362835619187, Validation Loss:0.49578728526830673, Validation Acc:78.69674185463658, AUC:0.7313424947145877\n",
            "Fold: 10, Epoch: [15/40], Step: [27/29], Loss: 0.39039517000869467, Validation Loss:0.4486764930188656, Validation Acc:78.19548872180451, AUC:0.7437103594080338\n",
            "Epoch 14 time: 1.9633\n",
            "Fold: 10, Epoch: [16/40], Step: [9/29], Loss: 0.36081378327475655, Validation Loss:0.49408385902643204, Validation Acc:78.44611528822055, AUC:0.7451638477801268\n",
            "Fold: 10, Epoch: [16/40], Step: [18/29], Loss: 0.3690965572992961, Validation Loss:0.661466047167778, Validation Acc:79.4486215538847, AUC:0.7357029598308668\n",
            "Fold: 10, Epoch: [16/40], Step: [27/29], Loss: 0.3766753044393327, Validation Loss:0.4921477548778057, Validation Acc:78.44611528822055, AUC:0.7298890063424947\n",
            "Epoch 15 time: 1.9920\n",
            "Fold: 10, Epoch: [17/40], Step: [9/29], Loss: 0.3467959231800503, Validation Loss:0.5077631399035454, Validation Acc:79.69924812030075, AUC:0.752431289640592\n",
            "Fold: 10, Epoch: [17/40], Step: [18/29], Loss: 0.3681266854206721, Validation Loss:0.6759523823857307, Validation Acc:79.69924812030075, AUC:0.7371564482029598\n",
            "Fold: 10, Epoch: [17/40], Step: [27/29], Loss: 0.36592320049250565, Validation Loss:0.5900155007839203, Validation Acc:78.69674185463658, AUC:0.7237050739957716\n",
            "Epoch 16 time: 1.9950\n",
            "Fold: 10, Epoch: [18/40], Step: [9/29], Loss: 0.33440251813994515, Validation Loss:0.7249450832605362, Validation Acc:77.44360902255639, AUC:0.7088002114164904\n",
            "Fold: 10, Epoch: [18/40], Step: [18/29], Loss: 0.3441485779152976, Validation Loss:0.5281241983175278, Validation Acc:80.45112781954887, AUC:0.7338794926004228\n",
            "Fold: 10, Epoch: [18/40], Step: [27/29], Loss: 0.3480151450192487, Validation Loss:0.5487166941165924, Validation Acc:77.19298245614036, AUC:0.7226215644820295\n",
            "Epoch 17 time: 1.9619\n",
            "Fold: 10, Epoch: [19/40], Step: [9/29], Loss: 0.3432396617200639, Validation Loss:0.5961163491010666, Validation Acc:78.19548872180451, AUC:0.7055232558139535\n",
            "Fold: 10, Epoch: [19/40], Step: [18/29], Loss: 0.33973875972959733, Validation Loss:0.5526123344898224, Validation Acc:73.1829573934837, AUC:0.6993657505285412\n",
            "Fold: 10, Epoch: [19/40], Step: [27/29], Loss: 0.3426696326997545, Validation Loss:0.7166433110833168, Validation Acc:75.68922305764411, AUC:0.7215380549682875\n",
            "Epoch 18 time: 1.9797\n",
            "Fold: 10, Epoch: [20/40], Step: [9/29], Loss: 0.3347015016608768, Validation Loss:0.6146877333521843, Validation Acc:79.9498746867168, AUC:0.7004228329809724\n",
            "Fold: 10, Epoch: [20/40], Step: [18/29], Loss: 0.3334652963611815, Validation Loss:0.5303006656467915, Validation Acc:81.45363408521304, AUC:0.7244186046511627\n",
            "Fold: 10, Epoch: [20/40], Step: [27/29], Loss: 0.330456734807403, Validation Loss:0.5219188705086708, Validation Acc:78.44611528822055, AUC:0.7528012684989428\n",
            "Epoch 19 time: 1.9587\n",
            "Fold: 10, Epoch: [21/40], Step: [9/29], Loss: 0.31239862905608284, Validation Loss:0.620087668299675, Validation Acc:77.19298245614036, AUC:0.7226215644820295\n",
            "Fold: 10, Epoch: [21/40], Step: [18/29], Loss: 0.3323888298537996, Validation Loss:0.5394320376217365, Validation Acc:78.19548872180451, AUC:0.7131606765327696\n",
            "Fold: 10, Epoch: [21/40], Step: [27/29], Loss: 0.3289673626422882, Validation Loss:0.7658159360289574, Validation Acc:78.69674185463658, AUC:0.7313424947145877\n",
            "Epoch 20 time: 1.9679\n",
            "Fold: 10, Epoch: [22/40], Step: [9/29], Loss: 0.28409327732192147, Validation Loss:0.6427267491817474, Validation Acc:79.4486215538847, AUC:0.7204281183932347\n",
            "Fold: 10, Epoch: [22/40], Step: [18/29], Loss: 0.3007913910680347, Validation Loss:0.6341535449028015, Validation Acc:77.94486215538848, AUC:0.7117071881606766\n",
            "Fold: 10, Epoch: [22/40], Step: [27/29], Loss: 0.3083368319052237, Validation Loss:0.5876320898532867, Validation Acc:77.69423558897243, AUC:0.7331659619450318\n",
            "Epoch 21 time: 1.9841\n",
            "Fold: 10, Epoch: [23/40], Step: [9/29], Loss: 0.2894253051943249, Validation Loss:0.8019053786993027, Validation Acc:78.44611528822055, AUC:0.7222515856236786\n",
            "Fold: 10, Epoch: [23/40], Step: [18/29], Loss: 0.2968263137671683, Validation Loss:0.6437210291624069, Validation Acc:77.69423558897243, AUC:0.7255285412262156\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Fold: 10, Epoch: [23/40], Step: [27/29], Loss: 0.29352679407155075, Validation Loss:0.6756426841020584, Validation Acc:77.94486215538848, AUC:0.6964323467230443\n",
            "Epoch 22 time: 1.9826\n",
            "Fold: 10, Epoch: [24/40], Step: [9/29], Loss: 0.27513547903961605, Validation Loss:0.6250706613063812, Validation Acc:78.19548872180451, AUC:0.7055232558139535\n",
            "Fold: 10, Epoch: [24/40], Step: [18/29], Loss: 0.2747253618306584, Validation Loss:0.5700031537562609, Validation Acc:79.4486215538847, AUC:0.7127906976744186\n",
            "Fold: 10, Epoch: [24/40], Step: [27/29], Loss: 0.2824798872073491, Validation Loss:0.6417055577039719, Validation Acc:79.4486215538847, AUC:0.7433403805496829\n",
            "Epoch 23 time: 1.9789\n",
            "Fold: 10, Epoch: [25/40], Step: [9/29], Loss: 0.28998840351899463, Validation Loss:0.6524326391518116, Validation Acc:77.94486215538848, AUC:0.6964323467230443\n",
            "Fold: 10, Epoch: [25/40], Step: [18/29], Loss: 0.27753301627106136, Validation Loss:0.6968980319797993, Validation Acc:79.19799498746868, AUC:0.6960623678646934\n",
            "Fold: 10, Epoch: [25/40], Step: [27/29], Loss: 0.2776614835968724, Validation Loss:0.7864367961883545, Validation Acc:80.20050125313283, AUC:0.7400634249471458\n",
            "Epoch 24 time: 2.0039\n",
            "Fold: 10, Epoch: [26/40], Step: [9/29], Loss: 0.2667659918467204, Validation Loss:0.8492130041122437, Validation Acc:80.20050125313283, AUC:0.7171511627906977\n",
            "Fold: 10, Epoch: [26/40], Step: [18/29], Loss: 0.25777827699979144, Validation Loss:0.7910740226507187, Validation Acc:80.20050125313283, AUC:0.7018763213530654\n",
            "Fold: 10, Epoch: [26/40], Step: [27/29], Loss: 0.2580994776001683, Validation Loss:0.7637552917003632, Validation Acc:79.9498746867168, AUC:0.7080602536997885\n",
            "Epoch 25 time: 1.9585\n",
            "Fold: 10, Epoch: [27/40], Step: [9/29], Loss: 0.22932656606038412, Validation Loss:0.6899735331535339, Validation Acc:78.19548872180451, AUC:0.6902484143763212\n",
            "Fold: 10, Epoch: [27/40], Step: [18/29], Loss: 0.2415740026368035, Validation Loss:1.0459492728114128, Validation Acc:79.4486215538847, AUC:0.6975158562367864\n",
            "Fold: 10, Epoch: [27/40], Step: [27/29], Loss: 0.24118010258233105, Validation Loss:0.7800547629594803, Validation Acc:80.45112781954887, AUC:0.6880549682875264\n",
            "Epoch 26 time: 1.9835\n",
            "Fold: 10, Epoch: [28/40], Step: [9/29], Loss: 0.22060507535934448, Validation Loss:0.7825525999069214, Validation Acc:78.69674185463658, AUC:0.6931553911205074\n",
            "Fold: 10, Epoch: [28/40], Step: [18/29], Loss: 0.23200666738881004, Validation Loss:0.7280495390295982, Validation Acc:80.95238095238095, AUC:0.6833245243128964\n",
            "Fold: 10, Epoch: [28/40], Step: [27/29], Loss: 0.23082915665926756, Validation Loss:0.7394515573978424, Validation Acc:78.94736842105263, AUC:0.6946088794926004\n",
            "Epoch 27 time: 1.9835\n",
            "Fold: 10, Epoch: [29/40], Step: [9/29], Loss: 0.19805853565533957, Validation Loss:0.8022654429078102, Validation Acc:79.9498746867168, AUC:0.7233350951374207\n",
            "Fold: 10, Epoch: [29/40], Step: [18/29], Loss: 0.2161257283555137, Validation Loss:1.2319616377353668, Validation Acc:80.95238095238095, AUC:0.6985993657505285\n",
            "Fold: 10, Epoch: [29/40], Step: [27/29], Loss: 0.2161327655668612, Validation Loss:0.7194790467619896, Validation Acc:79.69924812030075, AUC:0.6760570824524312\n",
            "Epoch 28 time: 1.9520\n",
            "Fold: 10, Epoch: [30/40], Step: [9/29], Loss: 0.18696225517325932, Validation Loss:0.7862226814031601, Validation Acc:81.45363408521304, AUC:0.7091437632135306\n",
            "Fold: 10, Epoch: [30/40], Step: [18/29], Loss: 0.19836995667881435, Validation Loss:0.8576964884996414, Validation Acc:80.45112781954887, AUC:0.6880549682875264\n",
            "Fold: 10, Epoch: [30/40], Step: [27/29], Loss: 0.19198368765689708, Validation Loss:0.7669892273843288, Validation Acc:79.69924812030075, AUC:0.6836945031712472\n",
            "Epoch 29 time: 1.9675\n",
            "Fold: 10, Epoch: [31/40], Step: [9/29], Loss: 0.1762708524862925, Validation Loss:0.8548701107501984, Validation Acc:79.4486215538847, AUC:0.6669661733615222\n",
            "Fold: 10, Epoch: [31/40], Step: [18/29], Loss: 0.17828274187114504, Validation Loss:0.8289271593093872, Validation Acc:81.95488721804512, AUC:0.6891384778012685\n",
            "Fold: 10, Epoch: [31/40], Step: [27/29], Loss: 0.17732559310065377, Validation Loss:0.9696351140737534, Validation Acc:81.95488721804512, AUC:0.7120507399577166\n",
            "Epoch 30 time: 1.9828\n",
            "Fold: 10, Epoch: [32/40], Step: [9/29], Loss: 0.15091552916500303, Validation Loss:1.0527233332395554, Validation Acc:79.69924812030075, AUC:0.660782241014799\n",
            "Fold: 10, Epoch: [32/40], Step: [18/29], Loss: 0.158040352165699, Validation Loss:1.056452438235283, Validation Acc:81.45363408521304, AUC:0.6633192389006343\n",
            "Fold: 10, Epoch: [32/40], Step: [27/29], Loss: 0.16432092393989917, Validation Loss:0.8857927098870277, Validation Acc:80.95238095238095, AUC:0.6756871035940804\n",
            "Epoch 31 time: 1.9583\n",
            "Fold: 10, Epoch: [33/40], Step: [9/29], Loss: 0.16026559472084045, Validation Loss:0.9218013361096382, Validation Acc:81.203007518797, AUC:0.6695031712473573\n",
            "Fold: 10, Epoch: [33/40], Step: [18/29], Loss: 0.15178286449776757, Validation Loss:0.9536996409296989, Validation Acc:81.70426065162907, AUC:0.6876849894291754\n",
            "Fold: 10, Epoch: [33/40], Step: [27/29], Loss: 0.15307706539277677, Validation Loss:1.2676019370555878, Validation Acc:81.203007518797, AUC:0.6847780126849894\n",
            "Epoch 32 time: 1.9853\n",
            "Fold: 10, Epoch: [34/40], Step: [9/29], Loss: 0.1285330355167389, Validation Loss:1.1318368166685104, Validation Acc:80.20050125313283, AUC:0.6789640591966173\n",
            "Fold: 10, Epoch: [34/40], Step: [18/29], Loss: 0.14025574425856271, Validation Loss:1.317197024822235, Validation Acc:81.95488721804512, AUC:0.6738636363636363\n",
            "Fold: 10, Epoch: [34/40], Step: [27/29], Loss: 0.13603028434294243, Validation Loss:1.4574550986289978, Validation Acc:80.70175438596492, AUC:0.6818710359408033\n",
            "Epoch 33 time: 1.9818\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 10, Epoch: [35/40], Step: [9/29], Loss: 0.12332456641727024, Validation Loss:1.180028259754181, Validation Acc:80.95238095238095, AUC:0.6833245243128964\n",
            "Fold: 10, Epoch: [35/40], Step: [18/29], Loss: 0.11789957371850808, Validation Loss:0.9233919084072113, Validation Acc:81.45363408521304, AUC:0.6785940803382664\n",
            "Fold: 10, Epoch: [35/40], Step: [27/29], Loss: 0.1236340415974458, Validation Loss:1.302696704864502, Validation Acc:81.70426065162907, AUC:0.6571353065539112\n",
            "Epoch 34 time: 2.0158\n",
            "Fold: 10, Epoch: [36/40], Step: [9/29], Loss: 0.10042609439955817, Validation Loss:1.6966489106416702, Validation Acc:82.70676691729324, AUC:0.6629492600422833\n",
            "Fold: 10, Epoch: [36/40], Step: [18/29], Loss: 0.10992798995640543, Validation Loss:1.1320454478263855, Validation Acc:80.45112781954887, AUC:0.6651427061310782\n",
            "Fold: 10, Epoch: [36/40], Step: [27/29], Loss: 0.11367860519223744, Validation Loss:0.9900888442061841, Validation Acc:80.95238095238095, AUC:0.6527748414376321\n",
            "Epoch 35 time: 1.9860\n",
            "Fold: 10, Epoch: [37/40], Step: [9/29], Loss: 0.12963839371999106, Validation Loss:1.2483932077884674, Validation Acc:82.95739348370927, AUC:0.6644027484143763\n",
            "Fold: 10, Epoch: [37/40], Step: [18/29], Loss: 0.12815550590554872, Validation Loss:1.2245025336742401, Validation Acc:80.95238095238095, AUC:0.6756871035940804\n",
            "Fold: 10, Epoch: [37/40], Step: [27/29], Loss: 0.1429801599846946, Validation Loss:0.9743543714284897, Validation Acc:78.69674185463658, AUC:0.6778805496828753\n",
            "Epoch 36 time: 1.9711\n",
            "Fold: 10, Epoch: [38/40], Step: [9/29], Loss: 0.10905037737554973, Validation Loss:1.0848298966884613, Validation Acc:81.95488721804512, AUC:0.6662262156448203\n",
            "Fold: 10, Epoch: [38/40], Step: [18/29], Loss: 0.10675889915890163, Validation Loss:1.1165456473827362, Validation Acc:83.20802005012531, AUC:0.6811310782241015\n",
            "Fold: 10, Epoch: [38/40], Step: [27/29], Loss: 0.11317002773284912, Validation Loss:1.0014144331216812, Validation Acc:80.45112781954887, AUC:0.6651427061310782\n",
            "Epoch 37 time: 1.9741\n",
            "Fold: 10, Epoch: [39/40], Step: [9/29], Loss: 0.08958668468727006, Validation Loss:1.4782723635435104, Validation Acc:82.45614035087719, AUC:0.6309460887949261\n",
            "Fold: 10, Epoch: [39/40], Step: [18/29], Loss: 0.09272249498301083, Validation Loss:1.7004221826791763, Validation Acc:82.45614035087719, AUC:0.6538583509513742\n",
            "Fold: 10, Epoch: [39/40], Step: [27/29], Loss: 0.11405317347358775, Validation Loss:1.205251783132553, Validation Acc:78.44611528822055, AUC:0.645877378435518\n",
            "Epoch 38 time: 1.9974\n",
            "Fold: 10, Epoch: [40/40], Step: [9/29], Loss: 0.1360189426276419, Validation Loss:1.0267730206251144, Validation Acc:81.45363408521304, AUC:0.6633192389006343\n",
            "Fold: 10, Epoch: [40/40], Step: [18/29], Loss: 0.14607652835547924, Validation Loss:1.5102931559085846, Validation Acc:81.45363408521304, AUC:0.63276955602537\n",
            "Fold: 10, Epoch: [40/40], Step: [27/29], Loss: 0.1324091530232518, Validation Loss:1.4954914152622223, Validation Acc:81.45363408521304, AUC:0.6633192389006343\n",
            "Epoch 39 time: 1.9573\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hURIthaO16Zq",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "73d5405a-e9a5-49ca-e279-90788a94bf80",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525231487125,
          "user_tz": 240,
          "elapsed": 435,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(gru_forward_train_loss_history)\n",
        "plt.plot(gru_forward_val_loss_history)"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7e2a8c5278>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XslokxcYNcCglppkSpTwRFikxiyAIglgDSJIEXyIAu8FWcM6sNVWsE85u1KGg1TTGHS1K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\nIcRlwtuoY9bYs9/vjbL68vNbxvDH9w4QE+rHd2YkYA1wr8Z157wkfvX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uu+/y9ttvc+ON\nN/LAAw8M6XhMnTqV7du343Q6qa+vp7W1dUjHIyYmhpycHABKS0vx8fEhISGB7OxsYOjF40y9XRej\nR4/mwIEDNDU10dLSwp49exg3bpyHW3pprF27Fr1ez0MPPeR6biDiMah3UVq+fDnZ2dkoisKyZctI\nSkrydJMuuVWrVvHSSy8RFxfneu6ZZ57h8ccfp6Ojg/DwcH7961+j1+s92ErPeOmll4iIiGDq1Kk8\n8sgjQzYeK1euZPXq1QD88Ic/JC0tbcjGo6WlhaVLl1JbW4vdbufHP/4xFouFX/ziFzidTkaPHs1j\njz3m6WYOuIMHD/Lss89SWlqKTqfDZrOxfPlyHn300R7XxUcffcQrr7yCoigsWbKExYsXe7r5/a63\neNTW1mI0Gl2du4SEBJ544ol+j8egTsJCCCHEYDZoh6OFEEKIwU6SsBBCCOEhkoSFEEIID5EkLIQQ\nQniIJGEhhBDCQyQJCyGEEB4iSVgIIYTwEEnCQgghhIf8f1wTJBafVgrcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7e403c8390>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "irsoxNz12BpI",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "d0848cfd-5fbe-4448-d2fb-89c1465b93b1",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525231489364,
          "user_tz": 240,
          "elapsed": 441,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(gru_forward_test_auc_history)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7e40486080>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FqeUZmVwRk5EUtg/68dDBXfjwvkGMzSTwFz84u+6D4CdVIRs2FeesaT9oQPlB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\nRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7e2ab09ac8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2Xx2b8OSf4pc",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c1e2e545-487a-4c49-9da8-b8a84c5e6393",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525231494194,
          "user_tz": 240,
          "elapsed": 1897,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Best AUC: %.4f\" % np.max([max(i) for i in gru_forward_test_auc_histories]))\n",
        "print(\"Mean AUC: %.4f, Std: %.4f\" % \n",
        "      (np.mean([max(i) for i in gru_forward_test_auc_histories]), \n",
        "       np.std([max(i) for i in gru_forward_test_auc_histories]))\n",
        "     )"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best AUC: 0.7924\n",
            "Mean AUC: 0.7733, Std: 0.0131\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4Bk-uWhFdrKG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# GRU-Simple"
      ]
    },
    {
      "metadata": {
        "id": "Rz73cjZC4ken",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def test_model_gru_simple(loader, model, concat_input=False):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    model.eval()\n",
        "\n",
        "    predictions = []\n",
        "    truths = []\n",
        "    \n",
        "    total_loss = 0\n",
        "\n",
        "    for data, label in loader:\n",
        "        x, delta, m, x_forward, lengths = data\n",
        "\n",
        "        sorted_lengths, sorted_idx = lengths.sort(descending=True)\n",
        "\n",
        "        x = Variable(x[sorted_idx].float()).cuda()\n",
        "        delta = Variable(delta[sorted_idx].float()).cuda()\n",
        "        m = Variable(m[sorted_idx].float()).cuda()\n",
        "        x_forward = Variable(x_forward[sorted_idx].float()).cuda()\n",
        "\n",
        "        x[m.data.byte()] = 0.\n",
        "        \n",
        "        simple_input = torch.cat([x, m, delta], dim=-1)\n",
        "\n",
        "        output = model(pack_padded_sequence(simple_input, sorted_lengths.numpy(), batch_first=True))\n",
        "        \n",
        "        label = label[sorted_idx]\n",
        "        loss = loss_func(output, Variable(label).cuda())\n",
        "            \n",
        "        total_loss += loss.data[0]\n",
        "        predicted = (output.cpu().max(1)[1].data.long()).view(-1)\n",
        "        predictions += list(predicted.numpy())\n",
        "        truths += list(label)\n",
        "        total += label.size(0)\n",
        "        correct += (predicted == label).sum()\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    return 100 * correct / total, roc_auc_score(truths, predictions), total_loss/len(loader)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "b8ZxzKDMdudQ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "outputId": "afd8ab26-35d2-4bb5-d8bd-fb592084e920"
      },
      "cell_type": "code",
      "source": [
        "gru_simple_train_loss_histories = []\n",
        "gru_simple_test_auc_histories = []\n",
        "gru_simple_val_loss_histories = []\n",
        "gru_simple_val_acc_histories = []\n",
        "\n",
        "for k in range(num_fold):\n",
        "    train_ds.set_active_indices(train_dfs[k])\n",
        "    test_ds.set_active_indices(test_dfs[k], new_means=train_ds.means, new_stds=train_ds.stds)\n",
        "    train_loader = torch.utils.data.DataLoader(dataset=train_ds,\n",
        "                                           batch_size=batch_size,\n",
        "                                           collate_fn=dataset.PhysioNET.collate_batch,\n",
        "                                           shuffle=True)\n",
        "    test_loader = torch.utils.data.DataLoader(dataset=test_ds,\n",
        "                                          batch_size=batch_size,\n",
        "                                          collate_fn=dataset.PhysioNET.collate_batch,\n",
        "                                          shuffle=True)\n",
        "\n",
        "    gru_simple = GRU_decoder(3*NUM_FEATURES, 43, output_size, \n",
        "                      gru_dropout, decoder_dropout)\n",
        "    gru_simple.cuda()\n",
        "\n",
        "    optimizer = torch.optim.Adam(gru_simple.parameters(), lr=1e-3)\n",
        "    \n",
        "    gru_simple_train_loss_history = []\n",
        "    gru_simple_test_auc_history = []\n",
        "    gru_simple_val_loss_history = []\n",
        "    gru_simple_val_acc_history = []\n",
        "\n",
        "    for epoch in range(60):\n",
        "        start = time()\n",
        "        total_loss = 0.\n",
        "\n",
        "        for i, (data, label) in enumerate(train_loader):\n",
        "            x, delta, m, x_forward, lengths = data\n",
        "\n",
        "            sorted_lengths, sorted_idx = lengths.sort(descending=True)\n",
        "\n",
        "            x = Variable(x[sorted_idx].float()).cuda()\n",
        "            delta = Variable(delta[sorted_idx].float()).cuda()\n",
        "            m = Variable(m[sorted_idx].float()).cuda()\n",
        "            x_forward = Variable(x_forward[sorted_idx].float()).cuda()\n",
        "\n",
        "            x[m.data.byte()] = 0.\n",
        "\n",
        "            simple_input = torch.cat([x, m, delta], dim=-1)\n",
        "            optimizer.zero_grad()\n",
        "            output = gru_simple(pack_padded_sequence(simple_input, sorted_lengths.numpy(), batch_first=True))\n",
        "\n",
        "            label = label[sorted_idx]\n",
        "            loss = loss_func(output, Variable(label).cuda())\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            total_loss += loss.data[0]\n",
        "\n",
        "            # report performance\n",
        "            if (i + 1) % log_interval == 0:\n",
        "                val_acc, test_auc, val_loss = test_model_gru_simple(test_loader, gru_simple, concat_input=True)\n",
        "                gru_simple_train_loss_history.append(total_loss/(i+1))\n",
        "                gru_simple_test_auc_history.append(test_auc)\n",
        "                gru_simple_val_acc_history.append(val_acc)\n",
        "                gru_simple_val_loss_history.append(val_loss)\n",
        "                print('Fold: {8}, Epoch: [{0}/{1}], Step: [{2}/{3}], Loss: {4}, Validation Loss:{5}, Validation Acc:{6}, AUC:{7}'.format(\n",
        "                    epoch + 1, num_epochs, i + 1, len(train_loader), total_loss/(i+1), val_loss, val_acc, test_auc, k+1))\n",
        "        print(\"Epoch %d time: %.4f\" % (epoch, time()-start))\n",
        "\n",
        "    gru_simple_train_loss_histories.append(gru_simple_train_loss_history)\n",
        "    gru_simple_test_auc_histories.append(gru_simple_test_auc_history)\n",
        "    gru_simple_val_loss_histories.append(gru_simple_val_loss_history)\n",
        "    gru_simple_val_acc_histories.append(gru_simple_val_acc_history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 1, Epoch: [1/40], Step: [9/29], Loss: 0.7008877860175239, Validation Loss:0.7109838724136353, Validation Acc:26.433915211970074, AUC:0.5051501035196688\n",
            "Fold: 1, Epoch: [1/40], Step: [18/29], Loss: 0.7031718293825785, Validation Loss:0.6868892014026642, Validation Acc:44.13965087281795, AUC:0.5182971014492754\n",
            "Fold: 1, Epoch: [1/40], Step: [27/29], Loss: 0.7012105915281508, Validation Loss:0.689066544175148, Validation Acc:45.13715710723192, AUC:0.5315734989648033\n",
            "Epoch 0 time: 1.9704\n",
            "Fold: 1, Epoch: [2/40], Step: [9/29], Loss: 0.6777586539586385, Validation Loss:0.6685870140790939, Validation Acc:50.37406483790524, AUC:0.5694875776397516\n",
            "Fold: 1, Epoch: [2/40], Step: [18/29], Loss: 0.6744779480828179, Validation Loss:0.6709707230329514, Validation Acc:52.61845386533666, AUC:0.5675724637681159\n",
            "Fold: 1, Epoch: [2/40], Step: [27/29], Loss: 0.669917243498343, Validation Loss:0.6614110618829727, Validation Acc:55.61097256857855, AUC:0.5924430641821947\n",
            "Epoch 1 time: 1.8639\n",
            "Fold: 1, Epoch: [3/40], Step: [9/29], Loss: 0.6566013097763062, Validation Loss:0.6565311402082443, Validation Acc:55.61097256857855, AUC:0.5774844720496894\n",
            "Fold: 1, Epoch: [3/40], Step: [18/29], Loss: 0.6508063640859392, Validation Loss:0.7150153815746307, Validation Acc:54.114713216957604, AUC:0.5687888198757765\n",
            "Fold: 1, Epoch: [3/40], Step: [27/29], Loss: 0.6552236654140331, Validation Loss:0.6777697652578354, Validation Acc:56.35910224438903, AUC:0.5967908902691512\n",
            "Epoch 2 time: 1.8772\n",
            "Fold: 1, Epoch: [4/40], Step: [9/29], Loss: 0.627114733060201, Validation Loss:0.6402128636837006, Validation Acc:61.34663341645885, AUC:0.6482142857142856\n",
            "Fold: 1, Epoch: [4/40], Step: [18/29], Loss: 0.6396996312671237, Validation Loss:0.6705391705036163, Validation Acc:59.10224438902743, AUC:0.6127329192546583\n",
            "Fold: 1, Epoch: [4/40], Step: [27/29], Loss: 0.64512539351428, Validation Loss:0.6890942454338074, Validation Acc:60.099750623441395, AUC:0.6110507246376812\n",
            "Epoch 3 time: 1.8726\n",
            "Fold: 1, Epoch: [5/40], Step: [9/29], Loss: 0.6411842372682359, Validation Loss:0.6450607627630234, Validation Acc:61.34663341645885, AUC:0.6108178053830228\n",
            "Fold: 1, Epoch: [5/40], Step: [18/29], Loss: 0.6410239934921265, Validation Loss:0.6827835887670517, Validation Acc:63.591022443890274, AUC:0.6014233954451346\n",
            "Fold: 1, Epoch: [5/40], Step: [27/29], Loss: 0.6385186469113385, Validation Loss:0.6776557415723801, Validation Acc:59.600997506234414, AUC:0.5931935817805384\n",
            "Epoch 4 time: 1.8743\n",
            "Fold: 1, Epoch: [6/40], Step: [9/29], Loss: 0.6348866290516324, Validation Loss:0.6477510631084442, Validation Acc:57.605985037406484, AUC:0.6040372670807453\n",
            "Fold: 1, Epoch: [6/40], Step: [18/29], Loss: 0.6383944584263695, Validation Loss:0.7139740735292435, Validation Acc:62.34413965087282, AUC:0.6614906832298137\n",
            "Fold: 1, Epoch: [6/40], Step: [27/29], Loss: 0.634760097221092, Validation Loss:0.6506262272596359, Validation Acc:59.10224438902743, AUC:0.6351708074534161\n",
            "Epoch 5 time: 1.8919\n",
            "Fold: 1, Epoch: [7/40], Step: [9/29], Loss: 0.6318816939989725, Validation Loss:0.6760134994983673, Validation Acc:68.3291770573566, AUC:0.6439182194616978\n",
            "Fold: 1, Epoch: [7/40], Step: [18/29], Loss: 0.6266265710194906, Validation Loss:0.6531603634357452, Validation Acc:69.07730673316708, AUC:0.6632246376811595\n",
            "Fold: 1, Epoch: [7/40], Step: [27/29], Loss: 0.6299094999278033, Validation Loss:0.6612764745950699, Validation Acc:66.83291770573567, AUC:0.6800983436853001\n",
            "Epoch 6 time: 1.8593\n",
            "Fold: 1, Epoch: [8/40], Step: [9/29], Loss: 0.6216763787799411, Validation Loss:0.6379446983337402, Validation Acc:65.5860349127182, AUC:0.6653726708074534\n",
            "Fold: 1, Epoch: [8/40], Step: [18/29], Loss: 0.6380375226338705, Validation Loss:0.6346546858549118, Validation Acc:70.07481296758105, AUC:0.6690217391304348\n",
            "Fold: 1, Epoch: [8/40], Step: [27/29], Loss: 0.627911110719045, Validation Loss:0.6226905882358551, Validation Acc:70.32418952618454, AUC:0.6181159420289856\n",
            "Epoch 7 time: 1.8762\n",
            "Fold: 1, Epoch: [9/40], Step: [9/29], Loss: 0.6249291035864089, Validation Loss:0.6386987715959549, Validation Acc:65.5860349127182, AUC:0.6204968944099378\n",
            "Fold: 1, Epoch: [9/40], Step: [18/29], Loss: 0.6158783965640597, Validation Loss:0.7040962278842926, Validation Acc:69.57605985037407, AUC:0.6287267080745342\n",
            "Fold: 1, Epoch: [9/40], Step: [27/29], Loss: 0.6171200562406469, Validation Loss:0.68385149538517, Validation Acc:67.83042394014963, AUC:0.6260610766045549\n",
            "Epoch 8 time: 1.8721\n",
            "Fold: 1, Epoch: [10/40], Step: [9/29], Loss: 0.6113518012894524, Validation Loss:0.712510272860527, Validation Acc:69.57605985037407, AUC:0.6586438923395446\n",
            "Fold: 1, Epoch: [10/40], Step: [18/29], Loss: 0.6005476282702552, Validation Loss:0.6555027067661285, Validation Acc:67.08229426433915, AUC:0.6665890269151138\n",
            "Fold: 1, Epoch: [10/40], Step: [27/29], Loss: 0.6064988352634288, Validation Loss:0.6337877959012985, Validation Acc:70.07481296758105, AUC:0.6241459627329193\n",
            "Epoch 9 time: 1.8683\n",
            "Fold: 1, Epoch: [11/40], Step: [9/29], Loss: 0.5886108213000827, Validation Loss:0.6201498135924339, Validation Acc:67.33167082294264, AUC:0.6381211180124224\n",
            "Fold: 1, Epoch: [11/40], Step: [18/29], Loss: 0.6038116680251228, Validation Loss:0.6180707812309265, Validation Acc:64.33915211970074, AUC:0.6656055900621117\n",
            "Fold: 1, Epoch: [11/40], Step: [27/29], Loss: 0.6007913064073633, Validation Loss:0.6976301819086075, Validation Acc:69.07730673316708, AUC:0.6482660455486543\n",
            "Epoch 10 time: 1.8855\n",
            "Fold: 1, Epoch: [12/40], Step: [9/29], Loss: 0.5906606382793851, Validation Loss:0.6317171901464462, Validation Acc:68.5785536159601, AUC:0.6378881987577639\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Fold: 1, Epoch: [12/40], Step: [18/29], Loss: 0.5880346894264221, Validation Loss:0.6345724612474442, Validation Acc:67.08229426433915, AUC:0.6591097308488613\n",
            "Fold: 1, Epoch: [12/40], Step: [27/29], Loss: 0.5791966517766317, Validation Loss:0.6046360209584236, Validation Acc:63.84039900249377, AUC:0.6327898550724638\n",
            "Epoch 11 time: 1.8776\n",
            "Fold: 1, Epoch: [13/40], Step: [9/29], Loss: 0.5749481916427612, Validation Loss:0.6393613368272781, Validation Acc:66.83291770573567, AUC:0.6352225672877847\n",
            "Fold: 1, Epoch: [13/40], Step: [18/29], Loss: 0.5706495940685272, Validation Loss:0.6284272521734238, Validation Acc:69.32668329177058, AUC:0.6347567287784679\n",
            "Fold: 1, Epoch: [13/40], Step: [27/29], Loss: 0.5762844681739807, Validation Loss:0.6077932715415955, Validation Acc:74.06483790523691, AUC:0.6548136645962732\n",
            "Epoch 12 time: 1.8521\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VRgHsSUiduKF",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "266d152a-6fa1-4aea-a353-fb003741db92",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525235087782,
          "user_tz": 240,
          "elapsed": 1900,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(gru_simple_train_loss_history)\n",
        "plt.plot(gru_simple_val_loss_history)"
      ],
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7e198c7550>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 200
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QD/XzRQ0sx1wNC0cv5zZAEmGCINYthkUi3C62beN4apwLUFbLue1JaFqYlSk7k6XOue0/\nzAkDwER2sul7MhGOyM64yOpe4eqFC6F2nLBZRtDdEzwcGeKvlUQJGyJDvuNVQ+FogiCIDmL6WpRI\nhJtxLHUSf/3K3+HJE0+jbGrQLB0xpVqE/U7Vtm1kNFeEc+cBADPFBEQ3rNuuCPcFewA42458j/Nw\ndGViFtDYCeuWAcMyEJLqLx3a3bsTITnI1yJWQ+FogiCIDmKSE26bVDkNAHh99kTVoA5n1Z8AoU64\nuMyF8lx+Gnm9gLxewM7ubRAFsbUIu6/tCzqLf7JVFdKVcHS1E64vwkycWe64mndufyu+8I5P8eNU\nI63AxCwSYYIg1i0G5YTbhrnS8eREjQg7eWEVWlU4mrlgADiXP89D0SOxDRgKx3E2d44XbDV7TybC\n1b3CelWfMBPPRjuF2fcbiawsyogGGi+goIlZBEEQHcTyOuFlDDGuRZggnstOY644B6AiwoCTR612\nwhmPc00U5nAu74SkB0NxbIqNoGxqSLjHqv+ezAk74ejqqVnV1dEsHN1oEEjJZE440PA9myFSTpgg\nCKJz+MZWtjFo4mKGDeKwYeNI8jgA8Jww4OSFa0XYccKSIMGGjf2JQwCAwfAANsVGADTPC2tWcydc\nPbYyIKkQIDR0wiwc3Sgn3AqWE17OGzYSYYIg1i2+nDD1CTdF84yQPDT3OgBnZCUjIAUaivD27i0A\ngCPJYwBcEY62IcJV4ejqqVlaVThaFEQE5WBDJ1x050Y3ygm3otKiRCJMEASxZPx9whSOboa3Zzet\nZQA4IysZTji67BuokXXbk/b0OQMxDMuAIsroCXRjNLYRAHCmqQhXFWbVVEf7+4QBJ9/b0gkvVoQF\nKswiCILoGL6xlbRFqSn1BnHEVH842obtK3BjTvgSdyoVAMRDAxAFESE5iJ5AN88v16NsaZBFGWEl\nBFEQa/qEq8dWAs1FuGg2r45uBbUoEQRBdBDTE4JeiBM2LROJQmPxWK3MFBKLzn2zUPNAuI9/L6pU\nKokrSxwqYs1EeDgyiJ5ANwAgHh7gj0eUMPJNli3opo6AqEIURETkcM1O4erCLMAR4bJZrlt1veSc\nsEjV0QRBEB3DG1ZcSIvST8/+Ah9//j5MFxIX4rQuCBPZc/jE85/Fv5x5alGvZ6HfPQM7ADiVyLI7\n5hGoPzUro2WhiAqCUoBPoxoMeURYDqNklhqKmmZqUNx8b1SN1A7rMOuFo0OwYfv2BjNKPCe8uOpo\nCkcTBEF0kMWOrZwrzgMAkqVUx8/pQvHC1MuwYSNZXtw5s9DvnvhOAEBM9ffT1pualdFy6HLnS2+M\nOluKBj1OOKw44ygbjZksWxovuooqERSMok+wNVODAMF3M9CsV5gVbC0+J0zhaIIgiI6x2AUOTDTq\n5UlXI5Zt4ZWZ/QD86wEXgmZqkAQJu/q3AfC3JwG14WjLtpDVcnwE5A1D12BXz3bs7d/DXxNR2Ezo\n2jWFQCUcDVRC396VhpqlQ5UUCILAv9esV7hkLrE6egWGdcitn0IQBLE28X6Y6gtoUWK5RW2NFHOd\nTJ/mYycXW4BWNjWokopNXRuwKTbCK54ZzLGycHTBKMK0TS7Co7GN+ONrPux7TTMRtm0bZVOD4jps\n1g6V1/PoDjjH1Eyd9wgzmjthtzBLWmQ4Wlz+YR0kwgRBrFsM24AoiLBsa4FO2BXhNeKEX57ex79e\nrAhrpgZVVCBLMv7L9f+x5vHqdYZse1IsUH8ZAlBZPVgwakXYsE3YsHmYO+oKds4zNUsztZq1gxUR\nruOEjRIEVFYvLhRRECFAoJwwQRBEJ7Asi38gL0SEmROut8R+tWFaJl6Z2c/DuYutjtYsnQtiPapz\nwqwyutFGIgCIuOdUzwlXF11F3fC3PxxdccqMVk44KAd84euFIokS5YQJgiA6gWEbPDS5XnPCR1Mn\nkNPzuGbwCkiCtDQn3FSEWU7YCUe3J8KOE24qwjwc7eaENa8TruSMGZWccK0Il8wygotsT2JIgkgT\nswiCIDqBaZkIyAsX4eIaEuHTmbMAgMv6L4EiKovKY7P8bDMRZo9pC3DCYdmtjq4nwlW7gpmTZ1Oz\nLNuC7hZmeWnlhBdbGc2QBInC0QRBEEvFtm2YtuVxwu25G9Myeci1bK1+EWb51qgShSLJi3LChmXA\nhu3rx61mceFotzCrTv62Eo72V0ezqVnlKqfMCDeojrZtGyWjtOjKaIYjwuSECYIgloRlW46wuB/i\neptOuOQZRrHYdp/lpOhOpAorIaiiAt1c+KIK5kqb54T9LUps53BbIlw1hAOoHUlZCUfn3eO7hV+q\nv1Uq2MAJl00NNuxFD+pgSKJEE7MIgiCWCnMzsiBBEeW2w9HeD/fVGI4+PHcUk7nz/O8sfx2WQ244\neuHnXJ2frUeNEy4zJxxt+Bo+rKPO6MrqDUmRqnB02j1+t9rlP2YDJ8x2CS92ZCVDEkQKRxMEsf4Y\nT5/Bo8e+V3fm74WAibAkSpBFue1Vht4P99UmwpZt4UsHvoF/PPoY/x4TuLAcgiIpi6qObhT69VI9\ntjKjZRGSg3zsZD0UUYYqqcjXaVHiG5Lc4yqijKAU5K45wzY5VbVANXLCrKKdwtEEQRB1+MW55/Hj\niaeWbR4zywFLggRZWIgTrojwamtRKpsadMvgLhRwnHBAUiGJEhRRgW4ZvnWD7VDtSutRHY7OaNmm\noWhGRA43qI52w9GePHRUCfPq6LRW3wkrogxFlGtEmO0SXmphlkgtSgRBrEfK7odkqcEauk7Dw9Gu\nE243J+wLR6+ywiz2s/OKWtEo8ipkVVScdYMLFBGeExbbq47WTR15vdCWCIeVUP3q6DruO6pGkdML\nsG2b32h01xkGEpSDKJpV4Wg+LasD4WiLwtEEQawzWMFTve03FwLT44QXkhMueER4tTlhdoNQMIo8\nrF8witz9sdDwQkPS7YSjFVGGAAFls4z5UhI2bPSH+ho+nxFRIiiZ5Zpip3prCqNKGKZtomSWkGbh\n6ConDDih96Je5YRZTpjC0QRBELWwD3r2YdkJ5kvJhuLK3KAkuDnhdqujV3FhFruRsWGjoDtCXDRK\nCLtDMVhod6FtSu0UZgmCMw6ybGqYLSUBAAPBNkTYLaSqzguXqyZmAZWpWVktX3HCddy244Qb5YSX\nWB1NIkwQxHqk3GEnPFucx8eeuw8/O/tM3ceZ83LC0dICnLC3MGt1tSh5Q+V5Pc//zsLRypJFuHFO\nGAACkoKyWearHttxwpUKab8I63XD0ZVe4bSWQcgtNqs5phyCYRk+x89EeMlOWHRmjS80r75YSIQJ\nglgWeE64Q054ujADy7a4IFTjr45WFpATdkRYgNB2TriZI+8kXpeeNwq+ymigEo5e6M0Du85mOWHA\nKc7STA1zJednPtBWOJr1CvtzuGWrVvgr6wwdJ1zPBQOeqVmef0vFjuWEnU1Ky1XF35YIHz16FHfc\ncQcefPDBmseeffZZvPe978Xdd9+NL3zhCx0/QYIg1gcs/NipwqyMW0XbSHB81dGiDBt2W0MY2Id5\nVI20lROeLc7hY8/dh5+cebrdU180PhHWC/yGgYWjFdFZjLdQJ9xOThhw2pTKplZxwu2EoxsM7GC/\nN6/wMxFOldPIGwV0BWrzwYBHhD3CzkL1ncgJA8u3zrClCBcKBXzyk5/EjTfeWPfxT33qU/j85z+P\nhx9+GM888wyOHz/e8ZMkCGLt0+nCLDZRqZHgeId1yO6e2HaqhpkI96hdba1APJs7D8u2MFWYafvc\nF4vX+eX0gm9QB1DJry7YCbcpwqorwrOleSii3F51tFx/dGX1xCygEo4+l5sGUD8fDHiWONRzwkue\nmOXI4qoRYVVVcf/992NwcLDmsYmJCXR3d2PDhg0QRRG33nornnvuuQtyogRBrF3YMH6gc+FoNjax\nUciYV0eLEhTBcYjthIzZhzlzYa2Ks5grzNUZzdhpvDcweT3PRTjEw9FsROdCRbhWEOsRkAKwYWM6\nP4O+YF9bKwMjDXLC1cM6gIoTZhPBqgd1MCpOuPJvqZITDrU8p2ZwJ7xMbUpyyyfIMmS5/tMSiQT6\n+irhiL6+PkxMTDQ9Xm9vGLIsLfA0mxOPt74bW+vQNa4P1vs1Nro+79hCWzI78nPQjrsfwJLNj/ed\nI0/ip+PP4q9+7f9D1HA+3LuiYURM54O5qzeAvlDz99ZRRkAOoCcSBeaAaI+K/nDlNdXnXjjj3AyU\n7dIF//0KExV3ZisGpKAjFEN9vYjHY+idd6qLQ1F5QecinXH+HB7oAdD499gVjgDzTnvRxu54W+8x\ngn5+vr39YTw/8Qp+ZdM1gOSc+8ahPp4XNkNDAIDzhSnntX313yOecs5TiVTONWtmIYkSNg/HIUvN\npa3ZeYeDjpPu6Quht8W/lU7QUoQ7TTJZ27S9FOLxGBKJbOsnrmHoGtcH6/0am11fqpzmX6cL+Y78\nHGayTptMvlTkx3tt8ggmM1M4ee485nOOOJaKBkzNqXSdTqRhhpqbgGwxj5AUhK07gcLziSSssPNR\nWe8azyYdwUgVsxf89zufqxw/kU7C1tzQaVFAIpGFVnREei6ZQSLQ/rmk8s7PKp82gB40vg6jEjyN\niV1tXS9bD5zIpPDovh/hW8e+g+n5eeSKBQgQkJorQhCcGyrNcH5P7KZN0tS672GWHAc+PZdEIpiF\naZk4nTqLjeEhJOdr51R7afX/UHf/rczMpmEEO1e73Ej4l/QOg4ODmJ2d5X+fnp6uG7YmCOLipuwJ\no3aqMIvnhD35TxY6LpvlmsIsoM1wtOmswwtU7c9tBOuZrbcpqNNUF2YV9OrCLDcnvMBK7YXkhBnt\ntCcBlcUMBb2A1xJjAIDD88egubuCvSHtoBTk4WCgjXC0m9qYLiSgWwY2xUbaOqdm8JzwMoWjlyTC\no6OjyOVyOHv2LAzDwE9/+lO86U1v6tS5EQSxTvCuB/R+vRQqOWGvCDtfl00NpitEsih5qoabi5Nt\n287wCznIBadZhbRt25h3c8JFo3TBV+D5c8Ke6mjZL8KL7RMOtOwTrohwO4M6gMoNwkxhFsdT4wCA\nY6kTKBklvkuYIQgCzwsD9adlAZXccbKUAgBMZCcBoDMivMzV0S3D0WNjY7jvvvswOTkJWZbxxBNP\n4Pbbb8fo6CjuvPNOfPzjH8ef/MmfAADuuusubNu27YKfNEEQawuvkHXCCZuWyecneyuBWbFP2dT4\nOjpJkNt2wpqlw7ItBD0i3MwJ5/S87yYgb7Sep5wozMGChaFwvOnz6lE0SlBEGbKoIKfnETPcHDCb\nHS0tVoTbL8xitOuE2Sals7lzznuICopGCUWjVLfFKapG+MjKenOjAWA0NgJFVPB60unG6aQIs0r6\n5eoTbinCl19+OR544IGGj19//fV45JFHOnpSBEGsL8peJ9yBFqWcnocNJ3enW7XhaM3U+OpCSRCb\nirBt2/jnEz/Ejp6t/EM8LIfaCkfPVg0KyWn5liL8pQPfgGVb+Itf+bOmz6tHySwhKAURkAP+cDSb\nHc1blBY2brNsaZBFGaLQPDjqdcrt9AgzInKYn9OdW96C748/CaD+hC7mchVRaTh4QxFl7OrZjkPz\nryNVTuNMdhICBIxEN7R9To24buhqlIwyBsMDSz5WO9DELIIgLjjenHDRLC15JCAb1AE0CkeXeU5P\nFp1VhgDq7hTO6Xk8eeZn+OH4j7moBeUg77ltFo5mk6NYOJjlhX828Qz+8sXP14i+YRmYys/wMOpC\nKRklhOQgokrEEWGj6Kz2c8WsEo5eeE641bQsoOKEQ3KIh5nbgbUpDYXjePNIZeZEPefNRLhbjTVt\ngdrTtwsAcHjuKM7mJjEcGWzp5Nthe/cWfOCyu/mN24WGRJggiAuOV8icnuGljXhkRVmAU5jFRJ0J\nctnUPWMrZShSYyfMXPrZ3DnkdEfcw3KoEo5uMrqS9Qhvjo0CqKwY3D97EKezE0iXM/7nu9uHNEtf\nVP64aJQQlAOIuNuGkuUUvwEAPOHoBQ/r0NsSMBYdGAj2Luj4bH70FQN7EVOj3LF6lzcw2MCORtOy\nGEyEn558HmVT60goeiUgESYI4oLDhE52i16WOrAj63HC3v25/upoTzhaaFyYxcLjpm3iWPIkACAk\neXPCjQWNOeHNXY4Is4EdrCWr2kUnCpVukuql9K0wLROapSMoBbmzzGo5hNyvAW91dIMpYpaJr409\nhFdnDvi+r5lay+UNQMW5tpsSNkfNAAAgAElEQVQPZsRcd3tF/DIAwCW9O33H8+J1ws3YGBlGlxrD\n6awzm4JEmCAIogGsIrrbdTdLLc7KuE6Y5TB1U4Npmdz9ap7CLFmQK2Mr64mwJ199JHkMABBS2mtR\nmis67UlbYkyEnYX0LNxcrqoETxTn+NcLFWHvbOSIR3i9Tri6Olq3DN8Ny3QhgZdnXsM/Hfuur/Co\nbGltOWHWGjQQ6l/Qud+55Tb81q53YlvXFgDtiXArJywIAnfDALApSiJMEARRF+YIKyK8tOIsJsJ9\nAWdykmbpvrCxt0VJEkUuTkadELBXKE9lHFcV8oSjm+WEZ0vziKlR9Aad82CjJCth8Son7BPh5kMl\nqqnsyw0iIlfaeJqFo7974nF8/Lm/5LnupOvQk+UUDs4dAeAUpummXtMuVI8dPdvwzu1vxVtGF9aK\nuim2EbdvejPP8e7q3YHt3VtxWd/umueywraeFiIMAJd6Xj8a27igc1otkAgTBLFoCnqxLUHlTtjt\n+1xqrzBzdywsqpm6v1XJ1HiIWvI54dowrff8mTsMeYd1NMgJW7aFZCmF/mAfH0iR1wu+oqvq60wU\nFx+OLnpEOKpWnHDI54T9W5TO56dRMkv8fVPlyrk9Nfmc+1wDNmxfD3AjREHE27b+Kr/pWCwBScWf\nXPv/4KaNN9Q8tnfgUty19Q7cuOH6lse5pNdxwvFQ/5K3J60Uyz62kiCI9cPnXvkiBoK9+L+v/DdN\nn1c2qp1wZ3LCrE1Gt3QIqFTSls0yRHfykSw2n5hVHTIGXCcsNnfCqXIapm2iP9iLqBsezul534jO\nctUNymzB44QXmBfn4WgpwEUfgK9KuXpiVt5wCsXmSyls6dqEVMk5N1VScXjuKGaL8wi6Fc/t5ISX\nA0WU8evbf62t53YHYnjvrnehb4GFYqsJcsIEQSwKy7YwnZ/B+XzrFX5adU54iU44o2URlIJcgDSz\nXjjaccKib2xlbTianctQuDJyNyQHuSg1ygnznbqhPgSkACRBcpywx216Bdy0TMyWKn3FRX1h4WgW\nvnbC0fVzwpIoQRRE6O77sveYd0drshuE2ze9GTZs/MKtLAZaD+pYrdy26WZcGd+70qexaEiECYJY\nFGVTgw27rdxmTTi6gRMemz2Me1/4m5q1d9VktRy61KivEMkrlmVPONo/trI2HM3cKisWApgIN6+O\nZjOjB9yVflEljJyeR7LkccKem41kOQXLtnjYd8FO2D3PYHVhVlW/rioqPCfNnDALkSc9IhyRw3hh\n6uW6KwWJ5YNEmCCIRcHEt2iUWo74K5saZEHiPaCN8sgH5g5jIncOk7mphseybMsZ2ahGfWMavWJZ\nNsuw6i1wqDMPmN0g7OnzinDriVkstMvyoxElgryer3LCletMuKFo1kqz8Opoti+3cXU0ACiSAt0d\nv1mocsLJchphOYSIEsYlfTuR1rI45+7ubWdYB9F5SIQJglgUTERs2E0riAFHjAJSgI8hbOQCc5rT\nZ9tsQAYbWRlTY54xjf5wtGbqnsIsz8SsJi1Kg+E4egLdEAURqqhAdMddlhucS95wzpW11ESVCIpG\niYepneuuvJYVR7HBHgutjuaFWdU5YbnWCeumgZJR5qM9590bg1QpjZ5ANwBge/dWAMCReacta7Xk\nhC82qDCLIIhF4XVyrQSlZJQRkAMIygH+93qwgqtmos6e01XlhC2Py3XGVlbC0U0Ls1iYVwrgN3b+\nOjLlDG+lCYhqQyfMpmMxV8r+9Lp4nxN225MqIuy/EbFtG98f/xG2dW/F3v5Lat7PG45WJYWHnUPV\nTthd7lDw/E7mS0kUjRJKZgk9QSbCTs/uYS7C5IRXAhJhgiAWhVd4W4VWNVNDVyDGnXCjiVls4lQz\nEWY9wjFPTlgzNd8eWmeLEhtbWRHhuhOzXKEMSAFcN3SV7zFVaizC7FyZK42wULtZQrcaQ1rLNnDC\nTji6Oi+e1jL44akfY1fP9roizH7GrBUnokSglVM1OWFFUqCVdV9ePa8XMFNIAAB6XSc8Gt0IRVR4\n+JxEeGWgcDRBEIvCK7yFFpW+JbOMoBRAqIUTZrOb67UNMSpOOMaFQ7cMX9hYMzVeCS0JlcKsZk64\nXp+sKikNbwjyegGyIPHXeffgDkWGAPivM1GYQ0gO8WlThSoRZnlb9icAHJw7gntf+BtktCy/cWE3\nMsx51+SERQW6qXOnzjiZPg0APBwtiRK2dm3ij1NOeGUgESYIYlG0G442LAOmbSIgBfgWnnrV0f4d\nwc3WBzphXX9OWPMtLShbFScsC83D0SWzDEVUIIlSzWOqpDacw5zXC4goYR669hZLDbu7gtnNhGVb\nmC3OIR7qhyRKUEUFpaqf2XyxUjzFQun7Zg5gIncOh+Zer3HC3YEuiILoe1/AyQnbsHnEgE2gGuci\nXBm0sc0NSTvXSjnhlYDC0QRBLAq/CDcORzMnGZDUigDVcbqsncb7mmpMy8Rz51+CKirY2bMN59z8\nq3dYhyiIMCyDV0tLogTZbrzKsOy69Hqoosq3NFWv1cvrBR7aBfxOuC/YC0WsuOh0OQPDNhF3XXBI\nDtY6YTcsbNkW0loGfcFenkc+lZnwjK10zvU3d74Dc6VkTRiZbYxiG5xGYxtxaO51nEifAgD0Bivn\nvN0nwuSEVwJywgRBLAqv+y00ccIsJBtwxSMgB+rmhL2LBho54ddmD2K+lMQbN1yHiBL2DNTQeTia\nOcOiUYQAgVc5A4Bu1t+ixM6tmoCkwoZd019sWiaKRtHnQr0Vy72BbgQklYtwznX4UTUKwGmBqo4G\nzHvGXbKvZ91K61OZMyiZZQQklS+tGI4M1s0ds+hASnNaqEajzkxlNqjDe+PAFiqwayWWHxJhgiAW\nRbvh6LKn8Alw1gTWywmz9iTnNfVF+CdnngYA3OYuEPAO62Dh6JjiCF3BKPKZ0WyF4oKdcIMlDuym\nwyu8UY8g9wR7EJAC/NpZkVTEzd+G5CCKRonvQQb8ueD5UhK6ZXDhnMydR6ac5fngZrBxmynmhKP+\nxQY9XveuRjDkhs7JCa8MJMIEQSyKdqujmYAxoQvKgbo54aye87ymVqTH02cwnjmNy/v3YCjijJj0\nOmEWfo65brOgF3jFtCAIkEW5ZmylbTs9zoEGItxoYEeeV0Y3csI9CMoeEXZ/Vmy5fVAOwrRNn8P2\nivBcMYn54jzv82Uh6naWFCjuz4SFo0eiwzxUH5SCCFYdY1fPdgBA1L15IZYXygkTBLEofNXRbTlh\nR9CCUhCapcO0TF8xlNcJ1xsV+S9nfg4AuH3TLfx7isiqo3VYrmAxEdYs3SeSsiDXFGax0ZvBBuFo\nJmjVTjhX1SMMVJywAAE9gS4ejrZtu+KEqyqai0YJqqTCtm3Ml5K893e+lOT54OHIEKby087Prh0R\ndkPvzEV3qTF0B7qQKqd5j7CXd+94O64evAID7kYqYnkhJ0wQRFtMFxL4h0OP8FByqe3CrCoRdoWk\n2u02c8Lj6TPYlziALV2bsLt3B/++6hYhaZbGlxbEPI5O9vQOy6JU0yfM3qdROJq17VRP8GJO2FuM\nxZY4dKlRSKKEgBSAZVswLIMXnTHxZT8D9nMrGkWUTY1XK8+XkjwffP3Q1fw92nHCqhuiz2hZCBAQ\nlIPoc0drevPBjLASxp6+XS2PS1wYSIQJgmiL58+/hF9OvYwj80cBOH2ubFpTs41A1YVZzHUWq/LC\nuQaFWbZt49vHvw8A+I0dv+6rUuY5YbNSDc3mUwPOBiWGE472i3CpKl9dTaMlDtXTsgAn5H3N4BW4\nZuhK3zFLZpn3UYdrnLDz/Tm3EGsoPIiYEnVEuOQ44T19O7nYN7pZ8MLcu2VbCMlBiIKIXrctqaeO\nCBMrC4WjCYJoC7aJJ+32n5aMIqJKGJZttijMYi1Krgg3mJqVdd2lAMEX/j0wewgn0uN4w8Bl2NW7\n3fcaSXBW92mWBtO2IIuyr3hJ9oS7w3IIs6V5WLbFK4z5yMom1dFAvZxwrQgDwO/t/Z2a15ZNDQXD\nX5hV7YRZPrgv2IO+YC8mc+f4woeBUD+2dG3CwbkjbYajK/2+TPTZvl0S4dUHOWGCINqCtc1k3IIf\nxwkHEZJDNT2vXqpDvo3mR+e0HAQI6FJjXIRt28Z3Tj4OAQLes+PtNccWBAGKKEM3nepoVVR8rTbe\nUZabu0ahmRrOu/lVoH0nXJ0TrohwpOY1DHbMsllGvsoJh7gI+7cc9QV70RfsgWGbGM+cdjYmyWE+\n2aqtwiyPCLO9w/1uvpeJMbF6IBEmiDVCQS/gsy99AQfnXl+R92czhtNaFoZlQLd0BOUQwnKozRYl\nR9BCDZxwTs8jqkR8VcW6ZeB8fhq7endg2B0FWY0qOlOtNFODKqm+Vhtv4dfWrs0AnJ7b6nNr5IRV\njxN+ZWY/xpMT/FwBf1tSNT4nrPtzwqGGTrgXfSFHKPN6AQOhfgiCgJ1uBXN3oKvh+zEUyeuEnfe7\nYfgavGfHXbjODZUTqwcSYYJYAXRTb7n+r5qT6dMYz5zG2Ozhps977vxLfLRjp7Bsi1fbprUMF4+w\nHOQ9r412CleHowMNnHBWyyGqRhDwLE1g4h5r4jgVSeHDOlTJ74TZCkMA2MZEOD3Bv8fz1S1alL43\n/iN8dexBfOXlhwG054SZsJeNMvJGAUEpwG8KqkWYhfodJ1xxq2zO9O7eHfgPV/07vHnkxobvx1C9\n4WhX9AOSiju3vIV6gVchJMIEsQJ89eCD+NzLX1jQa+Zct1QwCg2fM1NI4MHD/4gvvPbVpvOXF0q6\nnOEimylnK7tt3XC0DbvhUgbuhFlhVp350aZlomAUEVOifF6zZVtchJuFYVVR4cM6VFH1h6PFykfc\nhsgQVFHxOeFSi+poJmjsBuRs+jxs20Zez0OA0PS8vOHogl7koWjneiotSoAT6pdFGTE1gn6PCLMx\nlwBwSd/OtqZaeWdAV+esidUHiTBBrABns+dxLjfV0D3WY67ktKxUb8fxwsRipjCL75x8fGkn6SHp\nHhdwnHCJO+EQF5SCVj8kXaraUsSql70tSTy8q0a4eGmmzvuPq3fmenGcsAbN0qFKqs/VenPCkihh\nc9cozuen+fm3CkcPRwYRkoO4ZeRGXDGwF0WjhFQ5jbxeQFgJ8QKvelQXZkU811AvHN0b6IYoiH4n\nHFx47269wixi9UIiTCyZrJbjH6JEe+SNgusem+/h9TLnbtlptjYw42nz+enEL3AseaLpMX828Qwe\nPf69lu+d9Exzyml5fiMQlIMIK46gNLo5qJ6YxSp02UQnoCLCMTVaycNamqe1p7EIswEXlm1BFRVf\nyJXNjGZs7doMGzbOZM8CqLjxRuHogVA//vLNH8fdl/wGRqMbAABT+Rmev24GE+G8UUDZ1KqccEWE\nNVNHVs9x8a0Xjl4I/sKsxj83YnVAIkwsmb955X/hS/u/sdKnsWbQLYOHinNNXG0188wJNwlHsyUI\nd2y+FQIE/NOx7zY95s/PPoMfn3nKNzIRcKqSP/vS/8Sjx77nvreTs2QLDaaLzoJ4rxPON3DCzG0y\ncWTFRSmPCLPzjiqRioM0NM/6viZO2CM6jhP2Vkf7P+Kq88KtwtEAfAsTAOB8YRqFquUN9WDCzvK9\n4bpOuMhvcJj4huQgf+6iRFiq3HiQE179kAgTSyKr5TBVmMF0IbHSp7JmKHiEt1l+t5qKE278GrZD\n9g0Dl2FzbBTnCzNNQ96sN3ds9oj/HI0ixjNn8ML0K7Btm1dGb46NAgCm887vO+gWZjU7r7JZhioq\nXMwichiyKPPQOVAZ1BFTo5XeXEurzFxulhP2iG51YZZU7YS7HREed/PC5apBIs1g1dmn0mdg2dbC\nRdjz/IAUgAABRaPEc/1sqhUADIbjUCXVt3awXeoVZhGrFxJhYkmczZ0D4IRIF5LfvJjxhm2b5Xe9\nFI0Sd8DNKpGZo+xSo+gN9sCwjIapAraODwAOzB2qOk6WHy9VTnMnzNp8pgozABxxDLdwwiXTvypQ\nEAR0q12+cHSWt/xEfQVNxXZywh6hVUV/i5J3bCXghMJ7At04lTkD27bbcsKMwXAcgiDgeGocABCR\nW4SjZec82J5gr2iLgugssjBLGJtzboDYDQ4AvH/Pe/EHV/5+05xzIygnvLYgESaWxNmsI8ILzW9e\nzOQ9otiuCHvDxTbshgsTmBOOqTHurJKePbVevOJ8NHnC1zLlzS2PZ84gWUpBlVRscN3gtDvwgg3r\naHQt0/kZzBRmMRwe9H2/J9CFjJaF6W418jphtorPKWhqIydc5YRlUeYFWWKVCAPA1q5NyGhZJMup\nlsM6vCiijKHIANKac/MQURcfjgacG4uclsfL0/sQVSK4tG83f2xjdBg7e7a1PKe650nV0WuKtkT4\nM5/5DO6++27cc8892L9/v++xhx56CHfffTd+53d+B5/+9KcvyEkSqxfmhIGF5TcvZhbjhOfcYf6M\nRqHfrJaFIsoISgGeY5yryvcycp4xkYZl4Mj8Mc9xKiJ8OjOBZCmFvkAPz+ey0ZX+cHTtjcHPzj4D\nALhl9Cbf93sC3bBh8wrprGchQsCzuaioLzwnDFSKorxjKxnMcU5kz6FslCFAaHuh/WjXBv51tIUT\nZu6a3RhV30iE5CDSWgY5PY9rh67yDRZZCv5wNInwaqelCL/wwgs4ffo0HnnkEXz605/2CW0ul8NX\nv/pVPPTQQ3j44Ydx4sQJ7Nu374KeMLG6OJs7z79uV1AudryFVc3yu16YkLIe0nyDCumMlkNMjUEQ\nBPS2cMJMaJkD8w4B8Yrw0eRx5I0CeoM96FJjvmOE3YlZQG04uqAX8Pz5l9Ab6MGVA3t9j1WKs5y8\nMHPC/hYlb064mRP2iLDIRNg5RnVhFgCMxpwl92ezk06oXFJ9SyGaMdI1zL9unRP2C3ukShC9M65v\nGL4ancIfjqac8GqnpQg/99xzuOOOOwAAO3bsQDqdRi7n/IdRFAWKoqBQKMAwDBSLRXR304DwiwXN\n1DCdn+F/z1ObUlv4nHCbhVnMCW9yXVy9gi7btpHTcnyfbl+gvXD03oE9iCoRjM0d5rlmlhMWIOBM\ndtI5XrCnZmxisEk4+plzL0CzdLxl05tqXB531G5eOKvnIAoiwnLIM6+53NawDr8TVtw/nWNUF2YB\nwGh0BIBzA1k2ym2FovlrPU64lQhXT6eqzs+y1q7B8AC2xDa1fQ6tkERnqYUqKr58ObE6aSnCs7Oz\n6O2t9K319fUhkXAqIwOBAP7gD/4Ad9xxB2677TZceeWV2LZtcXkMYu1xLj8FG3alH5KccFssKhzt\nOuHNsZGGrysaRRi2iS4mwq5rrm4/YlSKuGLY278HGS3LlxuwMDErxAKA3kAvwnLI13sbkoIIsT5h\nrXJOpmXi52efhSoquGnD9TXvzXqFWZvSTGEWvYEeiILoG3JRNIpQRaWm39eL2iwcXScn3B2IIaZG\nMeE64UaDOurhd8LNw9FMCCvPr3bC7lznoWvbduLtoooKFWWtERZ8m2TbNv86l8vhS1/6Eh5//HFE\no1F88IMfxJEjR7Bnz56Gr+/tDUOWO5P7YMTjsdZPWuOsxmt8LeO4s8sGd+PV82MQg9aSznM1XmOn\nicdjsMYru2l1QWvrutN6CgE5gJ3Dm4CTgBio/VlPZhxnG+/qQzwew4AdhSopyJiZuu9hTTnnMRqP\nQ5eL+OXUyygrecTjMZSPOLnYG7dejfH9pwEAW+LDGBzsQm+oG4n8HAJyAMNDPby4qqAX+ftMpM8h\nWU7hli1vxJaNtYsXttjDwEFAk4pQYjZyeh57BnciHo9h0HIcvBwAylYZkUC46c+oNxnlXw/0dCEe\njyEaDAFZIBYN1X3tjr7N2Dd1CIIgYCg60Pa/vaheEdVNQ3HEu5u/LqSGoJXcn/PQAAbCledfumE7\nDs0fxl17b8FApLP/9ke7NyAWiC76/9R6/7+4mq6vpQgPDg5idnaW/31mZgbxeBwAcOLECWzatAl9\nfc5oteuuuw5jY2NNRTiZ7KxbisdjSCSyHT3mamO1XuPh806rxrbIVryKMUwn5xd9nqv1GjsJu8a5\nbKU/NpXPtrxu27YxnZtDf7AXZtFxTNOpZM3rTienAACqGeCP9QZ6kMjV/73MpJybKCMvQDUc13Rq\n+hy2B3ZiLpeCKIjYHNjCny/rQSQSWUSlKBKYQ0gK8uMGJBV5rcD/Pj7v1ArExK7611d0xOx8chb7\nTzsFYXFlAIlEFsWsI+rJbA65cgGxQPN/G1qh0q5VyptIJLIQLedGv1w06752MDAE4BBs24Zky23/\n24vHY+gN9CBZTqGcBRJa89cpQkW0SxkbiXzl+W/suwHXvela2AUJiUJn/+3/0RX/DqIgLur/1Hr/\nv7hS19dI+FuGo9/0pjfhiSeeAAAcPHgQg4ODiEadO8+RkRGcOHECpZJz1zw2NoatW7d26JSJ1c7Z\n7DmIgshbKVZbOHoiO4n/+sxncDoz0frJy0hOL0CAgJgabSsnXDCKKJkl9AV7ebVrvZ91hrf5VP6z\n9wV7kdPzdZc5eEdFsnYm1tOa0XKIKVFsjAzznCt7TnfAOb53wXxIDvmKxViut9HqPW9OeNIt7tvo\njoX0z1wuthw44W3JYeFotUl1NAA+ghJob1CHl+3dWxBVIk3XGPJje8Li3tA0o1MV0dXIoryoHmNi\n+WnphK+55hrs3bsX99xzDwRBwMc+9jE8+uijiMViuPPOO/H7v//7+MAHPgBJknD11VfjuuuuW47z\nJlYYy7YwmT+P4fAg/0BdbSL83PmXkCyncCI1ji1dnSt8WSp5o4CwHEJMifoWIzSCLW7oD/XxvGK9\nqmqe4w1URLjXLc6aL6X42MXK851NQN5cJRvKkdWyGArHIYkStnVvwanMGXS7edwu1fl9e6dYheUQ\n0ppnyQMTYbW+CKuSgogcRqqcxrmc4+BHIk6+lQlXRsvCht10WhZQlRN2v65URzcQYTe3DrQ3qMPL\nv9rzWyibelsCys4jrIQ7nvcl1gdt5YT/9E//1Pd3b7j5nnvuwT333NPZsyJWPbPFeWimhpHoRt56\nsdpE+PDc6wBWX/9ywd3AE1HCOJd3Nik1cy1sXGV/sJcX2zAH/cPxH+OlmX34z9f9h8qgDqWSI/UO\n7NBMDV/c/zV86PIPYEfPVuT0PCJKGKIgIqpEoIgK5ktJlN2NRMxRf+DS9yGn53mlbX0nHMT5/DS/\nFjbQggl3PboDXZgvpaDkzkMRZcTDAwAqLjZVSrvHbuWE/cM6vMdoJJTxUL+zMtHUFuyEg3LQd+1N\nn+sRYYKoB8UriEXB+jsHQr1QJAWqqKyqFqVEYQ4zRaeWYTWdl7OLtoCIEuEfzM22IgFAwr2O/lCf\nUyksSPw1B2YPYSo/jVOZM7ytiFVHA+C9wvPlJH5x7pfIajkccsck5vQc3wQkCAL6gj2YLyX5cVir\nU2+wB5s8zpE54ZDkD0d7dwozJ9zTIBwNOCJcMkuYzE9hQ2SI34gwJ5xyQ+OtRFj1VE4HqqqjGzlh\nURB5SHqhTnghsPOgGc5EI0iEiUWRcT9k2fCGiBJBvsEoxZXg0Pzr/OvV5ITLZhmmbSKihCsRhBZ5\n4YNzRyBAwLauzRAEAWEljIJegG3bmHG3GZ1InWqYEwYcN70/cRAAMFVIwLItFPQi3+3LnpvXC5h1\ne5JjHjH3wpxwyDMIgn2P3ZylyxmIgti0l5a1KVm2xfPBgNP3K0Dg19Nq4IS3H1cRW7coMUajztCO\nCyvCzrEjNDSDaACJMLEoMtx1MREOryrHeWiuIsKdOK+54nxHFlSw4qWwHOYC1SyMn9GyOJE6hW3d\nW3juPaKEkTcKyOo5vurvRHocWS0HWZB8gy1YOPrl6X2893emkEBed/YZR+uErs9knF27jUR4JLoB\nqqTynmWgsnJvtjgHwBlr2a12NQ2ze4u2RjwiLAgCVEmBDacdstmgDsA/rKPihN2ccJO8LZuc1cpp\nLwW2xIHGRxKNoHEqxKLI8CKgijCczWkwLKPpYIXlQLcMHE0ex1B4EFktu+Rc9XQhgU88/1e4Kn45\nfv/yf72kqtO8wWYkh5sWWTH2Jw7Cho2r45fz74XlMKbyM75pZePp0wjKQT6yktET6IYAAbNucZcs\nSJgpzvKbqGonDACns041uTe37KUn0I3P3fIJCKi8j1eEbdtGupzhItcIb6h6JLLB91hACvCFEq1C\nub6xle7Xcfd82DXV4/qhq5EspXHd0FVNj78UKk6YRJioDzlhYlGwwhvmhMNtuLrl4kRqHJql47L+\n3Ygo4Yar/NqFid2+xBgeO/GDJR2L/XwiSpiHWZv9zPYlxgAAV3pEOKKEYcPG6azjWAOSipJZRqqc\nrnGvsijz31FIDuGK+F4YloHTzO16pj6xSupTbktX9ZxoL6Ig+sR+IOTMCkgU55HXCzBtEz0NKqMZ\nPZ6irY3RYd9j3hBzyxYljxNmN4CXD1yKv3zzx7G7d0fD16mSinds/zXfjUin4dXRlBMmGkAifJGQ\n0/I46AnRLpVMuTYcDSy/CJeMMv76lb/Dy9Ov8e+xfPBlfZcgqkSc0Ktn0hvgFEjV652tB2v9EQUR\nPz7zFB55/TG8OPVqw5nMzWA/n7AS5mMPG+WEC3oBryePY3NsBP2uyDmvdT7Qx9POJKtrBytOrqtO\nCJmFma8YuAwbXcd5Iu0MWvGHox3XyPK6jcLR9WDOc7Y056mMbi7C7PEuNVbzXt7lBy0Ls1hFtKj4\nohSrwX3ywqxVcC7E6mTNi3CrytKLgZnCLF6aerXpc3505qf44mtf5bOBl0pGyyIkB/kHYHSF2pRO\nZyZwPDWOZ879kn/vROoUHyISUSIwbZPvjWX8eOIp/L9P/zeew2wGC92+b/e7EVOieGryWXzj0MP4\nxC8/y4uY2qXgccKRJnt4AWD/7CFYtoWr4m/wfZ8VdDERvmljZTZzrI57ZRXSV8Uvx1DEnXbnLqav\nF46uHKt9EQ7JIUTVCMQKl6UAACAASURBVGaLc3wedCsRZs6bFUh58Ymw0l5OuHphwmpgo9v77M15\nE4SXNS3CR5PH8XuP/iffHtSLke+c+CG+fujhmp2zXphra0d02iGjZXmrCuBxwm1uBeoUUwUnVDye\nOQPTMqGZOiayk9gUG4EqqQ0d+kvT+6BbOl6dOdDyPVj+e3v3Vnzsxj/DH131Ifzq5lugmRq+d/JH\nCzpfbziaOeHqnPDhuaP42thD+Nax7wIArhr0izBzVWkti5AcwtauzTwiUS+EfMvITbhl5CZc1n8J\nhsKOCCfcfwdRTzi6J1AppBIg+B5rh6HoAOaLSd5a1GhQByOmRvFv9r4fv7nrHTWPeTcbtQrlSoIE\nAYIvLL1auKRvJz57y3/jU+UIopo1LcKM15PHV/oUVpTJvDP2r9HydsAJRwNAstR6QlMrDMtATs/7\nQp+VnPDyVkhPuflazdRwLj+FM9mzMG0T27udmcdMSLznldVymHDX843NHUYrMp6+2ZAcwp6+XXjP\njrswGt2Il6ZfxdnsubbPt1VOOFVO44v7v4aXZ15DQFLxjm1v5cLJ8La7DIXjEAQBO7q38nOsZlfv\ndtx9yXsgizLioQFfQZX3+ZIoceGMKOEFj1QcisZh2CbPN7dywgBw7dCV2BCpXfCwkJwwq6ZejU4Y\nuLDV18TaZ02L8Igbxjqba/9DcL2hmzoSBcfVNMtRsuKkVBtjElvBcqTeD9mVygkzJwwAJ9KneIh2\nuytK7Ly8xVneyMnJ9OmW55zVsjXOUBREvGvH22HDxj+f/GHb58siBRE5XHfS2L6ZMVi2hffsuAuf\nuunP8fZtv1pzDG+7y6A7ZWpP3y4AldxsI1RJ8YWdo1UV0Cx/HF1AKJoxFHHO5UT6FID2RLgR3nB0\nO9Opbh19E24eeeOi348gVoo1LcIRJYyBcB8mF+BE1hvThQTvp2w2h7iTIlzdIwxUdqsutwhP56d5\nGPJk6hROuiK8zd2DG61zXofnjwIAro6/AZZt8fGWjchoWUTVSE1r0mV9u7GrZzsOzb2O8fSZts7X\n64T5pDFPCP+VmdcgQMANw9c0nDXsLTgaDDku+aaNN+CPr/4w9vY33mDG8Drr6iUETKC7GrQnNT1u\n1DnulFt3sBQRZq42KAXbagl794634/ZNb170+xHESrGmRRgAtvaMIq1luTBcbHgLrRoJrG3bXITb\nWRjQivoi3LrntdMU9CLSWhY7e7YhqkRwMn0aJ9On0Bvo4cVI1U7Ytm0cnj+KmBLFW7c6LvNAi5B0\nVsvVzbUKgoBbRm8CABxPnWz4etMycTw1DtMykdcLkATJ0z8a4T+zVDmNE+lT2NmzramAeUWYFVqJ\ngohdvdvbWhLAXhOWQzUhZybCCynK4seNDvCvZUHiTn8xBNzJV60GdRDEWmfti3DvKABgMnt+hc9k\nZfCKcKNwdNEo8WlPrGhmKaSrRlYCKxOOZqHoDZEhbOvegmQ5hZye5/lg57z8TvhcfgoZLYs9fbsx\nGt2A3kAPDs69zhfTV6OZGkpmuWHPLKvsZev4AOBo8gQPedu2jW++/m389St/h6+98gjyeh5hJcTF\nMqyE+LmxIrFrBq9oet2+cHRooMkz68OccL3+WBaOXqoIdwW6lrQ1qNLaQ/lUYn2z9kW4x1lRt1rz\nwufz0/jh+I87MvKw0fEBxwklGwhszh1XCDibaap7ZhcKd8KelXlhOQQBQttzmjVTx3dPPM4FfTGw\noqzh8CAvTAIq+WCgIjTMCbNxlpf174YgCLh84FIUjSIPY1fDJ4M1EOGBUB9USeUibNkWvjL2AD6/\n735858Tj+PHEU3j2/AsAgCdPPI3Z4jy/MQCcm4SSWYZpmXhlZj8ECLiyqiWpGm9hFts8tBCGws5K\nw+p8MFAZmrGY4/aFevis5laV0a1QafEBcZGwDkTYccKrVYQfPf49fG/8CV4x2mmm8tOIyGHEQ/18\n9Vs1XmHULB1FowjLtvDl/X+Pn078YsHvyYTJ+0ErCiJCcrDtFqWXpl/F46d/gp+dfWbB78+YKjg3\nIEORQWzzuF+/E/Y7dOZQL+3bDQB4w8ClAIBXZvbXfY9M1UahakRBxEhkGFOFGeiWwecyA8ATp3+C\nbx//PrrVGP7wyn8LxZ2H7A3Tsl7hZ869gJPpU9jVs50vQ2hEQApAFET0Bnp8BUztwjYWMdfrZXv3\nVvzptX+AN21ceJGTKIh8qMhS8sFApUWJKouJ9c6aFuHzc3l8+v4DECwZ+86exPefO4WSZizb+x+Y\nPYSHDn+rYSizoBf4h/5MIdHx99dNHYniHIYjQ+gN9CBvFOpOgcq5oslaU1LlDBLFObw2exBPTT67\n4Pet3qDEWMgSh2NuDvW0OyJxMbBxksORQWyJjUISJKii4huMwCuQtTxs28bp7ASGwnEuqnt6d6FL\njeGl6Vehm3rNe2Tr5L+r2RjdAMu2MJ2f4Y76ndvfhsv6LkFQCuLfX/F7uLR/N373yt8E4Bd0dpPw\nyNFvAwDPMTdDEATcsflW/OrmW1o+tx4xNYo/uupDePeOt9d9fFv3Fr47eKGwGdJLF2FywsTFwZpe\n4GBZNoolE1ahC1ZkHv/09DE88cIE3nfbTuzdHcKx1ElcP3T1knJTzfjB+JM4k53EjRuv97kvxmuJ\ngzwMzXbbeimbzsKDxY7Xm3IrozdEh2BYzs1Hspyu6Stlodh4uB8zhVkky2mU3O07M4VZ5PT8ggYz\nZLQsJEGqyddFlAjmSynYtt3yZ34syUT47KJD9VP5GcSUKD/3d25/KyRB9BUbSaKzVSin55Esp1A0\nStwFs8ffOHwtnjzzM7w2e7BmmH8rJwyA76WdzJ3nIvyGgUvxtq23+xZavHXnrSjmdZ9r39O3G4fn\nj+Lygcvw5pFfqdszW49GAtouzWYqLwUmwq3mRreCiXCraVkEsdZZ0054JB7Flz96B27bcykEAXjL\nr8RgWha+8cMj+PsD38LfH/pmw1zfUnEGPjghcDYCsJpXEpUQZ6JQK8JfP/gQPv3Lz3EHZts29iXG\n+Ho6wCm2Ojpbv/L2fH4KgBNeZCMA6xVnMRFmRUSpcspXSLRQN5rWsoip0ZrWkbASgmmbmG8yNARw\n1gKy/HXJLGGmzs+mFZqpY66UxHBkkH/vzi1vwe113GHEnR/Nrrl6hOCNG64DADx37sWa11bPyK7H\nxioRDkoBLqbejVKCIODmkV/xvf/Vg2/AJ276KN63+91tC/BqhhWK9QS7WzyzOSwMvdCpXQSx1ljT\nIsxg4rJ9B/Dv37UXllTCsYwTBj40d6Qj7zFXnMdjx3/AP8iPzB/j/blsOIGXgl7A6/PHMRLdAEWU\na8LRlm3hWPIk0loWB91z3JcYw/0H/gHfOvodAI4of2n/N/Cxn3yubgsWK8raGBlCr/uhV68FiU3L\n2hR19r+mSmlfDr3dHld2Thl3V2w1ve5WnL947l7c++L/4D8r27bxjYPfxJf3/z1s2+ahaPaBvZiQ\n9IwbBRjyiHAjWJj8bLa+CA9FnMKu15PHMVf030Bk9OaFWc7xnGKmo6kTmC7MYGvX5iWtO1zL3Ljx\nerx317twdYsK71bs7NmG9+1+z6Jy0wSxllgXnxRsb+mx1ElcsWMAo5ekAcERSLZRRzd1/PeX/w4P\nHf4WdKv9vLFhGfjuySfwiV9+Fk+e+RkePvIogMrAB0WUcTJ9qiak+trsIZi2iWsHr0Q8NICZ4qyv\nKnmumORLBV6cdpYvPD35HP97spTC4fmjmMidg2lbODx3tObcmAhviAxzJ5xq4oRHYswJpzGZO8/d\nxqlM+yJcNIowLANdgdrw7Lu2vx3v2XEX9vTuwkR2El858ABKRgk/n3wWL06/gtdmD2L/7CEuwsy1\nsv217WLbNr9xGQ63FuGoEoFhmxjPOFGRjZHaYfo3brgeNmz8y5mf+XL82TaccEgOoT/Yy0dh1ktN\nXCwEJBW3bbp50TllhiRKuHX0pkW1ShHEWmJdiPBIdAM2Robx0vQ+HEuehNg/AdsSIJa6cSY7iayW\nwysz+3EiPY5nz7+AL772NV/Itxk/mXgaj5/6MSJyGKPRjRjPnMaJ1Ck+8OGq+BuQ1wuYrnK6L0/v\nAwBcPXgFBsMDKJsaryoGgIncJP96bO4IzmTO4vXkcQQkFaZt4qdnf4EnT/+MP4fdTDAMy8BEdhJR\nJYKYGuXDKeq1KTER3hSrjPlMldPY0b0Fg6EBnMqcaTsvW29QByOqRnDnlrfgj67+EO7YfCtmirP4\nytiDeOz493kL0w/Gn8Sx5EmE5BDeOHwNJEHi+2vbIafn8dWxB/Gdk49DFRVc1n9Jy9ewkObx1EmE\n5GDdquCrB69ATI3iqcnn8MlffhYHZg+515uDKIgt+1U3ety1t0WKIAiiGetChEVBxL/a81sQIOCr\nYw8iUUpgUNyK8oyTY3v+zBh+Mfk8BAi4pHcXjiaP4/Ov3t+wqtnLi1OvQhYk/PkbP4L37noXAODh\n1/+JD3xg21G8eeGjyeM4PH8U27u3YjA8gLgbdvWGpNmozd09O2BYBr469iAA4Ld3vwfdahd+fvZZ\nHE2dwJ7eXegNdePw/FGfUD55+udIldN8ww4LBTcKR8uijJgSRVgO4Yzr2EaiG7G1ezOKRqnt6u00\nd4bNC2/etf1t2Na1GYfnj0K3DPzrS38b1w5dibO5c5grzWNnz1aokoqR6DAms+egmzoeOvx/fGHs\nevzj64/h1cQBbO/eio/e8JGaIrR6sMK3sqlhY2RD3aKxoBzAf7n+P+LNIzdivpTClw/8A7JaDhkt\ni5hSm/+uhhVnCRCwtXtTy3MiCIIA1okIA05bxS2jNyHr5vB+64q34MYtjkB9+8iPcTJzGkaqH/aJ\n63Ht4JU4nZ3AU274t2SU8b2TP8ILU6/4xi6ey03hXH4Ke/v3IKpEsLNnG7bENvEw8GX9u7GDibCb\nFzYtE4+8/hgECPhtV7TZkP2Ep0J6ws3JvmfnXQCA2dI8IkoY1w1dhds23cyrne/c8hZcNbwXeb3A\nw51T+Wk8fupf0K3G8O7tTpVsUA4iJAd5r7BhGTz8zaqfBUFAT6BSMDMa24it7ozl8TbdaDMn7EUS\nJfxfe9+PgVA/fnXzLbgyfjnevvUO3ia1s2c7AGBL12YYtomvv/p/8Oz5FzGRncRfvfR5PDP5S/4z\nYNi2jaPJE+gJdOMj13yY/1xb4R2O0Wyva0+gG/dc8ht45/a3wrIt7E8cRFbL+rZFNYI54Q2RIept\nJQiibdaNCAPAu7a/Ff3BXvQHe3FZ32783ltuQFAIQwg7whTN78Rrx+cxqr8RITmI748/iXQ5g6+M\nPYAfnvoX/P2hb+I//+ITeOz4DwD8/+3deUCVVf748ffdL3DZuYCAIDsIKOC+jKJpi1NWZmplTb/2\nGtumxhpnJp3vfFtnamb6zlrT1FTWWI6ZrZapueGCIgKKCAiy7/vO5fn9ceUqgYKKIvh5/VP3uds5\n95H7uec853w+1mT6YC23BtbVrVf5n0oSH+kWhpe9GQetPdk1uQBsKdhBSVMZ03wn4e9kTSTieXK0\ndvoq4IL6IlwMzgQ4jbRdQ5zsPR6dWst030mYdA4EOvkT7hpC7IjRAByuzMTSaWF1xlo6FAuLwxd0\nmyZ1MThT3VpDZXMVK3b+L5/lbASsGbO6pmRPD8K+phG2Qge5/VxF3jXd3Z99oO52rqyavJwFIdZ6\nsd4OnkzwjrN9dgABTtZR46bs7dhp7bg9/Ba0ah0fHP0vP9++ir+lvG370VPZUk19ewOBzgHntPDp\n9C1gXYuozqZrUdHukiTaOttx7CN5BlgLRmhVGiLdw/p8rBBCdBnS+4R/yKg1snzCYyiKYtsrGus1\nmt0lSbgYnHli/jWs+td+PtlSyLzrE/i64Gte3PtH6tsbGO0WTrBLILuK9vLtia2MdPRlf2kKerWO\naI/RtveINcfg4+CNo95kWzQS5BJAasUR3j28hv1lKTjo7JkfdK3tObbp6JMj4fq2Bmrb6oh2t2Zr\nmuM/k/XZXzLzZKIGO60dv5z0M3RqHSqVihivCFSoSK08TEFDETm1ecR7jmGsOapb/10NLhQ3lvL+\nkY9pbG8irfII1426ilZLW48grFfrMNu5oygKOrWW7NpcLJ2WPmvIdl377s80MNBj6ve28FuY5Tfd\nNiINcPSz3Xd7xC3Ee44h3C2UzfnbyajKJK3yCHZaO+6OWmIrU9j1w6G/TP0cCXfxsHPD39HPtr3N\nSdd3EHY1urByynIc+5ghEEKI0w2rkTBgW6jUJcZsDaA/8p2C2dmB2+eE0tpmYe92OxzVbtS3N+CI\nmYqU0Xi1xfDTsfegV+t478hHlDVXEOMxultqQI1aw/IJj/HTsffajgU7W6ek95Tsx1Fn4q7Ixd1G\nX056EwaN3rZXuKsIfNdCqbHmaFZOXm5L+Wd9jqOtgoxJ70Cgsz95dfkcLE8l1CWIOyJu7dH3rm1K\nmTXZgHU6verkyLUrh3LX/k1f0wjUJxNbhLoEU9xYystJr3OsOvusn29pYxlqlbrPurVnotfobDME\nYB0dh7kEc0P4HFvhAg87NxaF3civJz2Ns96Jw1UZdCqdHD+5ijvwHFcfn34uRjj0PRIG6/7dLk79\nGAmDtQLRha4KFkJcWYZdEP6hsR5RPBn/MFcHJAAwNdqbKVHeFJY3U54SQUepP2UHoskrbuYfn6XT\n2mDHwtD5tHdaE2j46kIpLG/o9po6tbbbiHGaz0Tm+ifwyNh7+J+pzxJ9Mh9xF5VKhae9mfLmCjqV\nTtse3a79zf3RVSd2tFs4j4y9F6PW0OMxXduUNCoN0e4RKCi2rVRdo8GuBVynjwjvGr2YySPGU9hQ\nzB+T/0FaRe+l/RRFoaSpDLOdR58j5v5Sq9Q8Hv8gd8be0uM+a4GFCBrbm8itO0Fu7Qk0Kg0jz+Fz\ng1N997Bz7/Vz603saUUUZJuMEOJiGfZBWKVSEeISaLuGqFKpuP+G0bzy0BSW/mg8M81X89iNE3jo\nxija2jv587pU6vK9Udf40dliz5r1tfz6rb18vefMe2ntdfbcFDKPKPeIM16r9LTzoL2zg5rWWtsC\nq679zf2R4Dede6Lu4IExP0Gv0fX6mK7EFXP9ZzLBOx7Atp+2KxAFOQegU+uIOW2K3VFv4s7IRTwZ\n/zAalYYPj66juaO5x+vXtTXQ3NHSLUvVxdY1ZZ9clkp+QyF+jj7oztD/M3E2OKFRaRjl1P9Vy572\nHrYfSX0tQhNCiPN1xc6debjYMSver9uxoopGNuzM5eOt2Rh0YxgXbsZpgoHEwyV8vDWLkV4mgkY4\nsS2lCEunwuTRXrg59S+3bVdpuGPVOZyoL8CoMeJudOvjWacYtQbbArEzGesRxeNxDxLiEmjL/NQ1\nvdw1He3t4MUfE57v9fkhLoFcO2o2Xxz/lk+yvuT2iO6j09KTVYv6kyBjoIS7haJVa9lRuJtOpZMg\np3NPhOGgs+fJ+IfxsOv/5w0wecR4/nvsM3z6OYUthBDn6ooNwr2ZPz2QTkVBrVJx1Tg/HO2t14Lj\nw828vPoAf1+fhlqtor7JOlX93++zGRPkzsKEYHzNZ5+y7ErR+O6RNQCEugQNeGEJjVpjS8zvYeeG\ng9beVlrQoZ85eK8OmEVyWSo7i/Zg0OgJdw0hzDUYvUZvq9/b30VZA8Gg0RPmEmxLVjLK+dwWZXUJ\nPI/nJfhNI84zptuKciGEGEgShE+jVqlYMKNndZkQX2fuuDqMd78+ikGv4cbpgTib9Ow4VExKdiWp\nOVXMivclJsgNL1d7PF3tegTYMebRTKudhKJ0YtQamXhyuvhiUalU+Dv52a4JO/YzCGvVWu6MXMSf\nkt9gc/52NudvJ8I1lEfj7qfk5MroSzkdDRDtEWkLwue6MvpC/HBftRBCDDQJwv2UEOuLn4cJTzc7\nnE6OkBNifTmYVcF/Nh3ju/0FfLe/AIDIAFcevDHK9jiwbjv64fTuxTbKaeSphVnnsLjI38mP56f9\nkuN1eaw79jlHq7Ooba231e/1uoTT0QDR7hF8hPXarJvR9ZK+txBCXEwShM9BiF/PUVFsiAdRo9xI\nyaqguKqJjLxqjuRV89t39rFswRgCvAdvUU/AaQuRzrUknFFrINItjKk+E1l7bAMp5WmUNJXhYnDu\n9wrjgeJu58Zc/wTcjK4XrTa0EEIMhmG/OvpS0GnVjI/w5Iapo3hqSSw3/SiQqrpWXvkwmbLqpr5f\n4CLxd7QGYRUq7M8zlWKsORqwZo+qaa29pIuyTndTyDxm+E0ZlPcWQoiLpV9B+IUXXmDx4sUsWbKE\nQ4cOdbuvuLiY2267jYULF/Lcc89dlEYOJWqVivnTAvnJdRE0t3bw10/SaGs/VSiiw9LJztRiTpSe\nqg+cllPJq2sOsikpn4bm9gFri7PBEXejG05603nv63U1uhDgNNJW8/dSXw8WQojhrM/p6L1795KX\nl8eaNWvIzs5mxYoVrFmzxnb/Sy+9xD333MPcuXP5zW9+Q1FRET4+55ZMYTiaMdaHnKI6tqUU8fZX\nGcwc60NLm4W132dTVNGIQa/h6cWxaDVq/vJJGq3tFtKPV/HRliyWLRjDmODzy0j1Q/fFLO1RCOFc\nxZljbEH4Ul8PFkKI4azPIJyYmMicOXMACA4Opra2loaGBkwmE52dnezfv5/XXnsNgJUrV17c1g4x\nd8wNJa+knj2HS9lz2LrHVgXEh5k5eKyC1z5KQa9T09Zu4Z55kdQ3t/Hxlmy+2XdiwIKwv6Nf3w/q\nQ6w5hvXZ1qIWMhIWQoiB02cQrqioICrqVKEANzc3ysvLMZlMVFVV4eDgwIsvvkh6ejrjx4/nqaee\nOuvrubrao9UOTMrDLmbz5ZvR6KVl09l2sJCquhZaWi0kxPsRMtKFrQcKeO2D/TS3wj03RHFzQggA\nB7MqyThRg9HBYNunDIPbRzOOjMrwI7emgCj/IFyMF6ctl/N5HCjDvY/DvX8gfRwOLqf+nfPq6K4a\ntV3/X1payl133YWvry8PPPAAW7duJSEh4YzPrx7ghUpmsyPl5fV9P3AQTQjtXve2vLyeqJHOPL5w\nDFX1rUwb7Wnrw5ggN47l1/Dd7lymxVjzO5vNjhQU1ZBf2kCwr9OgrBBeHLqAksYy2utVlNcP/Oc9\nFM7jhRrufRzu/QPp43AwWP07U+Dvc2GWp6cnFRWn6uCWlZVhNlszJrm6uuLj44O/vz8ajYYpU6Zw\n7NixAWry8Dcm2IOEWN9uQXVcuHW6d//Rctux1OwKVv5rLy+8v9+2F/lS83f0u+gJRoQQ4krTZxCe\nNm0aGzdai8Onp6fj6emJyWRN/KDVahk5ciS5ubm2+wMDAy9ea68A3m72+JodSDteRXNrBx9tyWLF\nX3dSXtOMXqvmk+3HqW1sG+xmCiGEGAB9TkfHx8cTFRXFkiVLUKlUrFy5knXr1uHo6MjcuXNZsWIF\nzz77LIqiEBYWxuzZsy9Fu4e1cWFmNuzM5fn39lNU0Yiv2YG7r4sgt7ie1d9msnZrFvf+2FoFqaml\nnf9sziK/tIEnF43FyUHfx6sLIYS4XPTrmvDTTz/d7XZERITt/wMCAvjwww8HtlVXuPiTQbioopGw\nkS785oEpNDe2MsrbkW0pRexMLcHFZMDBqGPT/nyq6loBeP/bTB65KZoOSyff7S/A3clIXJgHGrXk\nZBFCiMuRpK28DI30NDElyguDXsttV4VistfT3NiKRq1m6dVhvPJBMl8k5gGgUau4aXogablVJGWU\nsSutmL1HyjiUXQmAh7OR+DAzRr0GF0cDU6O80es0KIpCdlEdZmcjzqZLm4ZSCCGElUo5fbnzJTDQ\nq9KG+0o+6NnHippmSqqbaG614Gd2YIS7A8WVjax6ex/tHZ0ARAW6YXY2sjOtxHYMrEF53pQAdqeX\nkplfQ9QoV55aEnfJ+/RDV+J5HG6Ge/9A+jgcXG6ro2UkPAR5uNjh4dI9F/QIdwcWzAhizeYsJkd5\ncc+8SLQaNQtmBlNa1URbRyep2ZV8m5TPu19bywLqtWoyTtTQ1NKBvVH+KQghxKUm37zDyDUT/YkP\nM+PhbLRtezLZ6TD5Wqs/RQa4Mn3MCLYmFxIfZuZofg2f7jjO4dwqxkdIJiwhhLjUZMXOMGN2sTtr\nMg8fDwdunxtGRICrLTVmSnbFGR8vhBDi4pEgfAUL8HbEyUFPak4VnSeXBrR3dPLV7jye/utOdqeX\n9Pu1FEUhNaeS7w8WcomXGQghxJAl09FXMLVKRUyQGztTS8grqcdiUXjz83TKa1oA+GhLFuPCzei0\nGhqa2yksb8DV0YCbkxGt5tTvt6Mnqvl4azY5RXUAeLrYETnKbVD6JIQQQ4kE4SvcmGAPdqaW8Mn2\nHDJP1NBhUZg7fiQdlk62JBeyLaWYqdHevLz6AIUVjQDodWru/fFoJkR4cqyghlfXHKTDohDh70LG\niRq2HiySICyEEP0gQfgKFzXKDY1aRVpOFTqtmkdviWFsiAd1TW3sTCvmi8RcjuRVU1jRyJhgd0x2\nOg5klvOPT9OpqW/ls125dHbCk4vGEh3oxnNv7eVAZjl1jW3dsncVV1oD+Ah3h0HqqRBCXH7kmvAV\nzt6oJSrQDTuDlqcWxzI2xFrxyclez1XxftQ0tHEgs5ywkS4sWxDDfdeP5meLYtHp1Hz43TEamtu5\n4+owYoLcUalUzIj1wdKpsDO1GIDm1g4+2JTJr/65h//5dxLV9a2D2V0hhLisyEhY8NObo2nv6MTe\nqOt2/JpJ/mw9WIhRr+Xhm6Jt14FD/Jx58tax/HV9GtNivJkV52t7ztRob9Zuzeb7g0WoVCo27j1B\nbWMbDkYtjS0drN2azf03jKbD0knS0TIamzvo7FT40biRGOUnoRDiCiNBWKDTatBpNT2OO9nr+e29\nkzDqNT0CdNhIF/6wbFqP7VAORh0TIzzZmVbCR1uyMOg13Dg9kGsn+fPS+wdITC9hQqQn3+w9QcaJ\nGtvzthws5Df/rGBQPwAAIABJREFUbyI6rURiIcSVQ4KwOCs3J+MZ7zvTfuR5UwIoq2kmJsidWfG+\nOJwM4LfPDeXF9w/w+tpDAMSFejAlypsDx8rZnV7Kd/sLuHaSP/VNbaTnVuHnYcLX7HDWfc9CCDGU\nSRAWA26EuwO/WDqux/FQPxemRXuzM62EeZMDWDAzCLVKRUSAK2k5VXy2K5dQP2f+sSGdilrrNiln\nBz2xoR6MD/ck3N+l29ao/lAUhZKqJnKL6xk1wlEWhgkhLitSwGEIGE59tHR2UlHTgpebfbfju46U\n8c9P02y3E2J9aGm3kH68ivqmdgBcTHpmxfkyM9a3z7rJXVusvtqdR01DG2CtODVvcgDXTx01KNPe\nw+k89ma49w+kj8OBFHAQVzSNWt0jAAPMmxrIZ9uyKatpZunV4bbFXp2dCscKath7pIzE9BI+2X6c\nz3blMiHCi1lxvphd7bA3aLsF1ezCWt7+KoOiikbsDBomRnoy0tPEluRCPttl3XL17NJ41DLNLYQY\nZBKExWVBp1Xz7B3xNDS342s22Y6r1SrC/V0J93dlYUIwu9JK+G5/AYnpJSSellYzLtSDxVeFklVQ\nwztfZWCxKMyM9eHmGUE42VtHzbPj/fjbp2mk5VSRfryKmCD3S95PIYQ4nQRhcdlwNhlwNhnOeL+d\nQctV4/yYFe/L4dwqDhwtp6G5nbKaZpKPVZCaU0mHRcHeoOXhW6OJ+kHWLjuDlgUzgkjLqeLbffkS\nhIUQg06CsBhy1CoV0YHuRAdag6iiKOw9Usaazccw6rU8ekvMGRdgjfJ2IszPmbTjVRRWNOLr4WB7\njS3Jhfh6OBDu73rJ+iKEuLJJEBZDnkqlYtJoLyZEeKKgoFGffdHV3An+ZBaksikpn59cGwHAjtRi\n3v8mE4New2/vnYiHs92laLoQ4gonmRHEsKFWq/oMwGC9fuzhbGRXWgmZ+TVU1DTz4aZjaNQqWtss\nvPNVhq0cY0NzO8eL60jJqqC1zXKxuyCEuMLISFhccdRqFTfPCOKfnx3m5dUHcHUy0NJm4Z55kSQd\nLeNQdiVvf5VBYXkjx4vrbM+bHe/L0qvDB7HlQojhRkbC4oo0JcqbX9w5Di83e6rqWokL9WBajDc/\nuTYCO4OWHYeKyS2pIzLAlTnj/HA26dmZVkJza0e318kqqGXV23s5UTrw+w4VRaGsuonkY+XsSiuW\nkbgQw5CMhMUVK8TXmVX/bwKHsiuJDnJDpVLh6mjgiVvHkFdSz7hwT1wdrau1TfY61m8/zu70EmbF\n+wHWIPnRlixOlDbw9lcZ/Pqu8ajVA7P3WFEU/vZpOkkZZbZjnzjlsOSqMOLDPCSVpxDDhIyExRVN\nr9MwPsITo/7U79FQPxfmjB9pC8AAM8b6oFGr2JJcaLtefPREDVmFtWjUKvJK6tmSXAhASVUT+zLK\n+P5gIfsyyuhvUrq6pjbbY3ellZCUUYa/p4lbZgZx7UR/ahra+MsnqWxLKRqo7gshBpmMhIXoBxeT\ngbgwM0kZZWQV1hLq58LnibkAPHJzNP/8/AjrtmVzJK+aA5nl3Z778E3RTIjwPONrK4rCZztzWb/j\nOFGBbtwyM4j/fHcMg17DsltibCu1p0R7s/Jfe0k+VsHMWN8zvp4QYuiQICxEP82O8yUpo4z3NmYS\nG+rB4dxqIgNciQs1s3BmK+99k8mBzHICRzgxebQXOp2aD749xgebMnskDqltbKO4ohE7g5atBwv5\n/mAReq2a9OPWbF4Ad8wN67ZVaqSnCXcnI7nFdSiKIlPSQgwDEoSF6KdwfxfiQj1IPlZBQXkDANdP\nHQXAzFhfFMDLzZ7RAa62AFnX2Mb67cf5ZFsOT9wxDkVR2JVWwupvM2k5baGVv6eJJxaN5eCxCv6z\n+RjBPs7Miu852g30cSIpo4zKuhbZyyzEMCBBWIh+UqlUPHrLGCpqm0nLsY5WI/xdAOu2p9knF2yd\n7rpJAexOL2XzgQLKaltobmknq7AWO4OGayf5Y7EoGPQarpvkj51BS0KcL5OjvNBq1L0WmAgc4UhS\nRhnHi+slCAsxDEgQFuIceTjbkRDXv2uyOq2ae34cyV8/SSU1uwKAUD9n7rt+NGaX3oPo6YvEfiho\nhBMAx4vqznqdWQgxNEgQFuIiC/F15rVl03Fzc6CgyDoKPt/ruQHejqhUkHMyiUhlbQvfpxRRWtVE\nU2sH/++6CNycjAPZfCHERSRBWIhLRKNRY2+8sD85o16Lj7sDeSX1dFg6+cPHKRRVNNru3324lHmT\nAy60qUKIS0SCsBBDTOAIJworGvloSxZFFY1MjvLi6gkj+Z93kjiWXwMXEISLKxt5/b+poCiY7HUk\nxPoyLWbEALZeCHG6fiXreOGFF1i8eDFLlizh0KFDvT7m1Vdf5c477xzQxgkhegr0sV4X3pRUgF6r\n5taEEEZ5O2F2MZJVWEvnD5KDKIpCaXUTSRllbEkupLGl3XZfVV0LxZWNtse9+/VRSquaaG7tIKeo\njg82HaOlrXuqTiHEwOlzJLx3717y8vJYs2YN2dnZrFixgjVr1nR7TFZWFvv27UOn0120hgohrLoW\nZwHMnXAqs1eYnws700ooKm/Ez9MEWAPrO19lsP1Qse053ycX8vRtcZRUNvGHj1Noa7fw0I3RtLVb\nOJpfQ2yIB48tHMOnO47z6Y7jJKZZU3V2dioUlDfgZzYNWHpOIa50fY6EExMTmTNnDgDBwcHU1tbS\n0NDQ7TEvvfQSTz755MVpoRCiG1+zA3qdGpOdjusmnZp6Dh1p3S6VWVBjO/ZFYh7bDxXjZzaxaFYI\n06K9OVHWwEurD/D7Ncm0tlnQatT8bX0aq7/NRK9Vc/ucUAAS4nzRqFVs2l9Ap6Lw1hdHWPX2Pn7+\nt118si1HCkoIMQD6HAlXVFQQFRVlu+3m5kZ5eTkmk/WX9rp165g4cSK+vpJGT4hLQatR8+StYzHq\ntd0WeoX6OQNwrKCW2fF+JGWUsW5bDm5OBp5aPBZnk4FORUGjUbEtpRitRs2yBTE4Ouj4w5oUmlo7\nWDAjCI+TW6ecHfRMjPQiMb2Ev36SxoHMcjycjTQ0t/PZrlw6LJ3cOivE9v4NTW18vCWLg1kVPDg/\nCn8vR9t9ls5ONiUVUFDWwJ3XhKPXaS7RpyXE5e2cF2adnoy+pqaGdevW8fbbb1NaWtqv57u62qPV\nDuwfoNns2PeDhjjp4/AwUH3s7XU8PEy4mAxkF9bSjop/fXkEo17DqvunEOjjbHvcU0snEBWSS7Cv\nM+EB1nSav/d2JvloGddNDUSnPTVBduvcMBLTSziQWY6bk5HfPz4Do17L0pVfkVVUZ2vHvsMlvPqn\n7TQ2W683bzpQyDN3TQAgt7iOP/0nmayCWgAmRI/gqgn+A/I5DAb5dzr0XU796zMIe3p6UlFRYbtd\nVlaG2WwGYPfu3VRVVXHHHXfQ1tbGiRMneOGFF1ixYsUZX6+6umkAmn2K2exIefnA13K9nEgfh4dL\n0cdgHyf2Z5bz3BuJtLRZeOCG0Zh06h7vOyHUA8B23KiGKZGe1FQ3dnuci1FL2EgXjhfX8dObo+ls\n66CprQN/L0dyCmspKKrBoNPw7y8O09LawaJZIexKK2HXoWKOZpej12lY8cZuGprbGR9uJuloOV/u\nyGHMKNeL+jlcLPLvdOgbrP6dKfD3eU142rRpbNy4EYD09HQ8PT1tU9HXXnstX375JR999BF//vOf\niYqKOmsAFkJcXF1T0qVVTUyN9mZylPcFv+Zjt8Tw/P2TCDxtQViIrzOWToXc4joamts5UVJPZKAb\n107yZ854PzoVha0HC1m7NZuG5nYWJgTzyM0xRAa4kllQS0mV9cf4D1dyC3Gl6XMkHB8fT1RUFEuW\nLEGlUrFy5UrWrVuHo6Mjc+fOvRRtFEL0U7i/dYTp6WrHHXPDBuQ17Y067I3ddz6E+Drzzb58sgpr\nqW1sQwFiw6wzZJNGe/Hxliw2JRXQ0mbB1+zA1RNGAta6zEfyqtmeUsT4CE9eX3sID2cjt84KIezk\nwrIfyi2pw2SnO6dc2c2tHWTkVTMmxB2NWsqmi8tXv64JP/30091uR0RE9HiMn58f77333sC0Sghx\nXgK8Hbn3x5GEjnTBznDxcvEE+1pH3NmFdZTXNAMQF2bNZW3QafjRWB++3nMCgDuvDkersQbC+DAP\nHIxatqUUsTm5kLY2C7WNbby0+gAJsT7cdW3375bC8gaef3c/fp4mVt49oV9tq21s448fpZBXWs/C\nhGDJICYua/ITUYhhZlrMCDzPUBxioLg6GnB3MpBVWEv68WocjFqC/U6NZGfH+2LQa0iI9ek2wtVp\nNUyJ8qaxpQOLpZOHb4pmxZ3jGOFuz9aDRRSWn9r+qCgKq7/NxNKpkFdS3+2+MymvaebF9/eTV1qP\nCvh2Xz7tHZ0D2nchBpIEYSHEeQn2daahuZ3KuhYiA1zRnJbAw8PZjtd+Oo2l14T3eN61k/yJDzPz\nxK1jGR/hSYivMzf/KAiAHamnkorsyygj40QNziY9AInpPXdgFJY30Npu3a9c29DK7z5Mpqy6meun\nBnDNRH9qG9tITC8Z0H6fi4qaZr7anUdzq2QdE72TICyEOC8hvqe2PY0OdOtxv51B22tNZDcnI8sW\nxDB61KnnjA2xTlMnppXQYemkubWDNZuz0GrUPL04FjuDht2HS7ot5Pr+YCG/fmsvv3pzD/uPlvPH\njw9RUdvC/GmjWDAjmLkTRqJRq/h6z4kzLgBTFIXE9BKW/20Xf/80zZais7C8gW/25XdL8dkf6blV\n7DlcSkNzO4eyK/nNO/v4eGu2bWr+XLW2W0jKKGPP4VKSj5XbfnCI4UMKOAghzkuI36kgHDWqZxA+\nFzqtmslR3ny3v4CUrEp2pRVTXd/K/Gmj8DWbGBfuyY5DxWSeqCEiwJX03Cre/yYTO4OG6vpW/vJJ\nKgAzxo7gxumBgHXKfEqUNztSi3n36wxcTAaaWy1UN7TS3NKOXqehpqGV48XW7SoVtS0UVzYRNcqN\nb5PysXQqfL4rl4UJwYwPN/dYnPZDu9KKeevzIyiAClCwJlbR69TsSC1m/vRR57RIrMPSyZ8+TiHj\nxKkMaH5mE4/eEnPGWtRi6JEgLIQ4L35mE3YGDU4OhgEJCtNjRvDd/gLe/vIITa0dRAa4cv3UUQDW\nYHqomG+T8skva2D9juOoVPD4wrEY9Ro+3HQMNycDd14T3q1W87WT/ElML2FbSvEZ3hXiQj1YNCuE\njXtPsPVgEfllDbg7GZkQ4cmW5ELe+SqDd77KwMGoZUa8HzdNDUCn1VDf1EZaThWODjqq61p55+sM\n7Axarhrnx9ET1Vg6Fe64OoztKcVsSS4kNaeK2BCPM7ajtd3CKx8k42LSc9ucUL7Zl0/GiRqig9yI\nC/Egp7iOnakl/PbfSSxbEHPG1eRiaJEgLIQ4L1qNmqeXxA1YCsoAb0dGeprIL2vA7GLk4Zuibauq\nw/1dcHU0kHysguRjFahUcN/1o22B6Jk74nt9TR8PB3573yRqG1oB0Os0uDoacDBqaevopLNTwdHe\nes35zmvCCfJxprq+hasn+GPQa5gz3o9NSQUUVTZSUN7AV7tyyTheyaTR3ny28ziNLaeu9Rr1Gn62\nOJYgH6dubVChYktyIdsOFtmCcENzO2nHK2ls7mBWnC9qtYodh4o5XlwHQNrxKto7OvHxcODhG6Ox\nM2iZhfU6/OpvMvnr+jRefGByjxXwlbUtuDoZer0MIC5PEoSFEOft9AQeA+H6qaPYsOM4D90Yhcnu\n1PSvWqXiJ9eGcyi7ksARToT7u/R737C3mz3ebvY9jut+kD5XpVIxfUz32sluTkYWzbbmx27vsLBm\naw6bk/I5XlyPUa/hxumBqIDapjamx4zo9fMI8HYkwNuRQ9mVJKaXsDO1mCN51XRdpu7sVJg9zpeN\ne0+g06pZNCuET3ccR69V8+iCmG6BNiHWl/rGNj7ZfpzPd+V2y92990gpf/80nRljfbj7up7bSMXl\nSYKwEOKyMSHCkwkRnr3eNybYgzHBZ57Ovdh0Wg1PLInDz8OewrJGfjw1ABeToV/PnRnrw7tfH+XN\nzw4DEOzrxJggd75NKuC/27Jp67BQUdtCQpwvV43zY1qMNx0WpdsPkS7XTPRnW0ox3+zLZ8ZYH7zc\n7Cksb+DtLzMA2J5SxJxxfrZyluLyJkFYCCH6SaVSkRB77hXjJkV6sSe9FGeTnusmBRDgbc0j7OFi\nx5ufHea/3+egAq6ZaM0sZtSf+atZr9OweHYIf12fxj8/P0x8uJltB4tobbcwK96XLQcKWft9Nk/c\nOva8+iguLQnCQghxkdkZtL1et5482os9h0s5lF3JuHAzXq49p817My7cTHSQG2k5VWQXWa8jXzfJ\nn4UJwRRXNHIou5JD2ZUE+TjZKt9p1KqzrvDu7FSobWzDw0NG0JeSBGEhhBgkKpWKu6+LYN22HH48\npf/pNVUqFY8vHENucT31ze1oNSpGj3JDpVJx66wQfvvvJP74cUqP57k6Ghjl7Yi9QUtHp0KHpROL\nRaG+uY38sgba2ju5/6ZoppzhkoAYeBKEhRBiELmYDNwzL/Kcn6dRq205vE8XOMKJxbNDbPWbAVBB\nW3snJ0rrST5W0eM5apUKHw97Kutaef+rI0T6Off7ere4MBKEhRBimLlmoj/XTOx5XFGsU84dHZ1o\nNGq0GhVajRqdVo1Wo2ZrciHvbjzKR1uyeOCGqEvf8D7UNbWx70gZoX7OjPQ0ddsTPlRJEBZCiCuE\nSqU66wh3xlgfdqWXsDu9lMmjvQZ1NXpvVn+Tyb6MMgDcnQzMHufHVfF+A7ZXfTBI7mghhBAAqNUq\nHlowBoA/fnyIX7+155wLYNQ1tfG/7yYNeOGM4spGkjLKGOFuz+TRXjS1dvDxlmx+8cZu9hzuWdxj\nqJAgLIQQwiY8wI2fLR5LXKgHpVVN/PPzw9Q1tvV4nKIotpXXp9u49wQ5RXWs2ZzVo+BEXWMb21KK\nzquq1JeJeSjAghlBPDA/ilcensq8yQE0Nrfzjw3pvPt1Bu0dQ6/AhQRhIYQQ3UQHuvPoLWO4ZWYw\nigIHs7ov5qqsbeGXb+7hX18c6Xa8obmdzQcKAWvA3XLy/zsVha3Jhax4YzfvfJXBGxvSz1jZqjfl\nNc0kppfi4+FAXJgZAAejjoUJwfzm3omM9DSx9WARr3yQPOTqR0sQFkII0av4kwFv/9Fy27GG5nZe\n++ggJVVN7Ewrobiy0Xbfd/sLaG2zcP3UAOwMGr7ak0dpdRO//zCZdzceRUFhpKeJlOxKPtuZy/Hi\nOl5afYD/fTeJnJP7nXvz+a5cOhWF66cE9MiL7eVqzy/vHMf4cDPZRXVs2p8/wJ/CxSVBWAghRK/M\nLnaM9DRxJK+K5tYO2tot/GltCsWVTbZ60t/tLwCgubWDTUn5mOx0zJscwJxxI6lvaudXb+4h40QN\ncaEePH//ZH5+WxzuTkY+3XGc//13Epn5NeQU1fH8u0ms/jYTS2f3keyh7Eq2HyrGx8OBCZG971/W\n6zT85LoITHY6PtuZayvYMRRIEBZCCHFG48LMdFgUDmVXsmZLFtmFdUwe7cXy2+NwdTSwM7WEppZ2\nPtx0jMaWDuaO98Oo13L1xJHYG7SoVHD7nFCWLYjBxWTAZKdj2YIYjHoNPmYHfn5bHM/cHoe3uz3f\n7S9ga3KR7b1rG9v41xeH0WpUPHDD6LPWY3Yw6rh5RhAtbRbWfp9Nh6WTppb2Xq9bX05ki5IQQogz\nig8zs37HcT7ZnkNZdTO+Zgfuvi4CrUbN7Hhf/vt9Di++f4DCikYCvB2ZO8Ga/9rBqOPXd49HBXj+\nIB1ngLcjf1g2HZ1ObZteXn57PCveSGT99hwmRnpiZ9Dy1heHqWtqZ8nsEPy9HPts68yxPmxNLmRn\nagk7U62rs2+YOoqbZwQN7IcygGQkLIQQ4ox8zQ54utpRVt2MTqvmoflRtn25M2N90WvVFFY04uPh\nwM8Wje1WfMLL1b5HAO5i0Gu6Xd91dtBz47RAGls6+M93x3htzUHScqqIDnJjzsnA3he12poGNMTP\nmcgAV0x2OjbuPUFNP6anNyXls/qbTOqaeq4Ev5gkCAshhDgjlUplKy+55KpQfM2nCjyY7HTMnx5I\n2EgXnl4Si6O9/oLea/Y4P0a425OYXkrGiRriw8z89OaYHouxziZwhBMrlo7j57fFsWBmEG0dnXyR\nmHfW5zQ0t/PRliy+O1DAr97cw+4B3uN8NjIdLYQQ4qxumDqK2FAPgkY49bhv3uQA5k3uf/GJs9Fq\n1Cy9Opw/rzvEzLG+LEwIRq0+/9SU02NG8NXuPL4/WMjYEHe+2ZdPbnE9Ib7OjA1xZ/qYEWjUavYd\nKaXDojB6lCvZhXW88dlhIke54exwYT8q+kOCsBBCiLPS6zQE+/QsFnExRAa48n+Pz7ig4NtFq1Ez\nf1ogb31xhNfWWKtKuToaOJhVwcGsCsprWliYEMzOtBJUKrjv+tFYLAr55Q042Z+57ONAkiAshBDi\nsjIQAbjL5CgvNh8opLG5ncWzQ5g7NZDDWeX87oNkNu49QeAIR3KK6ogOcrPl1XZ3Ng7Y+/dFgrAQ\nQohhS6NW88u7xtmuK6tUKjxd7Lhjbhiv//cQf/80HYBp0SMGpX2yMEsIIcSw1tvCrthQD+JCPbB0\nKtgZtMSFDk7FKAnCQgghrki3zQnFZKdjZqzPoJVDlOloIYQQVyQPZzv+8Oi0c9oCNdAkCAshhLhi\nnS0V5qUg09FCCCHEIJEgLIQQQgwSCcJCCCHEIOnXNeEXXniBlJQUVCoVK1asYMyYMbb7du/ezWuv\nvYZarSYwMJDnn38e9SDPsQshhBBDQZ/Rcu/eveTl5bFmzRqef/55nn/++W73P/fcc7z++uv85z//\nobGxke3bt1+0xgohhBDDSZ9BODExkTlz5gAQHBxMbW0tDQ0NtvvXrVuHt7c3AG5ublRXV1+kpgoh\nhBDDS5/T0RUVFURFRdluu7m5UV5ejslkLWfV9d+ysjJ27tzJ448/ftbXc3W1R6sd2E3RZnPfxZ6H\nOunj8DDc+zjc+wfSx+HgcurfOe8TVhSlx7HKykoeeughVq5ciaur61mfX13ddK5veVZmsyPl5fUD\n+pqXG+nj8DDc+zjc+wfSx+FgsPp3psDf53S0p6cnFRUVtttlZWWYzWbb7YaGBu6//36eeOIJpk+f\nPgBNFUIIIa4MfQbhadOmsXHjRgDS09Px9PS0TUEDvPTSS/zkJz9hxowZF6+VQgghxDDU53R0fHw8\nUVFRLFmyBJVKxcqVK1m3bh2Ojo5Mnz6d9evXk5eXx9q1awG4/vrrWbx48UVvuBBCCDHUqZTeLvIK\nIYQQ4qKTrBpCCCHEIJEgLIQQQgwSCcJCCCHEIJEgLIQQQgwSCcJCCCHEIJEgLIQQQgySc05beTk5\nW4nFoeyVV15h//79dHR08OCDD7J582bS09NxcXEB4N577yUhIWFwG3kB9uzZw+OPP05oaCgAYWFh\n3HfffSxfvhyLxYLZbOZ3v/sder1+kFt6fj7++GM2bNhgu52WlkZ0dDRNTU3Y29sD8MwzzxAdHT1Y\nTbwgmZmZPPLII9x9990sXbqU4uLiXs/dhg0b+Pe//41arWbRokXceuutg930fumtf7/4xS/o6OhA\nq9Xyu9/9DrPZTFRUFPHx8bbnvfPOO2g0A5sX/2L5YR+fffbZXr9jhuo5hJ59fOyxx2wFhmpqaoiN\njeXBBx/khhtusP0turq68vrrr1/ahipD1J49e5QHHnhAURRFycrKUhYtWjTILRoYiYmJyn333aco\niqJUVVUpM2fOVJ555hll8+bNg9yygbN7927l0Ucf7Xbs2WefVb788ktFURTl1VdfVVavXj0YTRtw\ne/bsUVatWqUsXbpUOXr06GA354I1NjYqS5cuVX71q18p7733nqIovZ+7xsZG5eqrr1bq6uqU5uZm\n5cc//rFSXV09mE3vl976t3z5cuWLL75QFEVR3n//feXll19WFEVRJk6cOGjtvBC99bG375iheg4V\npfc+nu7ZZ59VUlJSlPz8fOXmm28ehBaeMmSno/sqsThUTZgwgT/96U8AODk50dzcjMViGeRWXXx7\n9uzhqquuAmDWrFkkJiYOcosGxl/+8hceeeSRwW7GgNHr9bz55pt4enrajvV27lJSUoiJicHR0RGj\n0Uh8fDwHDhwYrGb3W2/9W7lyJddccw1gHSnV1NQMVvMGRG997M1QPYdw9j7m5ORQX19/2cycDtkg\nXFFR0a1iU1eJxaFOo9HYpizXrl3LjBkz0Gg0vP/++9x11108+eSTVFVVDXIrL1xWVhYPPfQQt912\nGzt37qS5udk2/ezu7j4szuWhQ4cYMWKEreDJ66+/zh133MFzzz1HS0vLILfu/Gi1WoxGY7djvZ27\niooK3NzcbI8ZKn+fvfXP3t4ejUaDxWLhgw8+4IYbbgCgra2Np556iiVLlvD2228PRnPPS299BHp8\nxwzVcwhn7iPAu+++y9KlS223KyoqeOyxx1iyZEm3y0iXypC+Jnw6ZZhl39y0aRNr167lX//6F2lp\nabi4uBAZGckbb7zBn//8Z5577rnBbuJ5GzVqFMuWLeO6664jPz+fu+66q9tof7icy7Vr13LzzTcD\ncNdddxEeHo6/vz8rV65k9erV3HvvvYPcwoF3pnM31M+pxWJh+fLlTJ48mSlTpgCwfPly5s+fj0ql\nYunSpYwfP56YmJhBbun5ufHGG3t8x8TFxXV7zFA/h2D94bR//35WrVoFgIuLC48//jjz58+nvr6e\nW2+9lcmTJ/c5SzCQhuxIuK8Si0PZ9u3b+fvf/86bb76Jo6MjU6ZMITIyEoDZs2eTmZk5yC2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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7dd49e7400>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "OtlC5UgygAru",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "ddb98431-6ec1-4431-c54d-46d65b08a94b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525235095926,
          "user_tz": 240,
          "elapsed": 1861,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(gru_simple_test_auc_history)"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7e6846c358>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WuDnnoOmPhxLVvpOUs/czNUPOudVDch405UzP91ru1zh889kLOHtlpefvK7Cx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\ngiCIGoOEM0EQBEHUGCScCYIgCKLG+P/F+w4Ay7LLdQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7e403db390>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rEC5eLIFgFjs",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "70d5f1df-d4ac-47a8-c315-84a621852a48",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525235097758,
          "user_tz": 240,
          "elapsed": 894,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Best AUC: %.4f\" % np.max([max(i) for i in gru_simple_test_auc_histories]))\n",
        "print(\"Mean AUC: %.4f, Std: %.4f\" % \n",
        "      (np.mean([max(i) for i in gru_simple_test_auc_histories]), \n",
        "       np.std([max(i) for i in gru_simple_test_auc_histories]))\n",
        "     )"
      ],
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best AUC: 0.7235\n",
            "Mean AUC: 0.6755, Std: 0.0264\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6KE4c-oXzcvx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# GRU-D"
      ]
    },
    {
      "metadata": {
        "id": "HtcMC-Qpe0jF",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def test_model_gru_d(loader, model):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    model.eval()\n",
        "\n",
        "    predictions = []\n",
        "    truths = []\n",
        "    \n",
        "    total_loss = 0\n",
        "\n",
        "    for data, label in loader:\n",
        "        x, delta, m, x_forward, lengths = data\n",
        "\n",
        "        sorted_lengths, sorted_idx = lengths.sort(descending=True)\n",
        "\n",
        "        x = Variable(x[sorted_idx].float()).cuda()\n",
        "        delta = Variable(delta[sorted_idx].float()).cuda()\n",
        "        m = Variable(m[sorted_idx].float()).cuda()\n",
        "        x_forward = Variable(x_forward[sorted_idx].float()).cuda()\n",
        "\n",
        "        dummy = pack_padded_sequence(x, sorted_lengths.numpy(), batch_first=True)\n",
        "\n",
        "        output, hidden = gru_d(x, delta, m, x_forward, dummy.batch_sizes)\n",
        "\n",
        "        label = label[sorted_idx]\n",
        "        loss = loss_func(output, Variable(label).cuda())\n",
        "\n",
        "        total_loss += loss.data[0]\n",
        "        predicted = (output.cpu().max(1)[1].data.long()).view(-1)\n",
        "#         predicted = ((output.cpu().data > 0.5).long()).view(-1)\n",
        "        predictions += list(predicted.numpy())\n",
        "        truths += list(label.numpy())\n",
        "        total += label.size(0)\n",
        "        correct += (predicted == label).sum()\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    return 100 * correct / total, roc_auc_score(truths, predictions), total_loss/len(loader)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2ZlxoWGwzeJa",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "class GRUD(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, output_size,\n",
        "                 gru_dropout=0.3, decoder_dropout=0.5, batch_first=True):\n",
        "        super(GRUD, self).__init__()\n",
        "        self.input_size = input_size\n",
        "        self.hidden_size = hidden_size\n",
        "        self.output_size = output_size\n",
        "        self.batch_first = batch_first\n",
        "#         self.feature_means = Variable(torch.FloatTensor(feature_means), requires_grad=False)\n",
        "        # initialize weights and biases\n",
        "        self.W_r = nn.Parameter(torch.FloatTensor(input_size, hidden_size).normal_(0, 0.02))\n",
        "        self.U_r = nn.Parameter(torch.FloatTensor(hidden_size, hidden_size).normal_(0, 0.02))\n",
        "        self.V_r = nn.Parameter(torch.FloatTensor(input_size, hidden_size).normal_(0, 0.02))\n",
        "        self.b_r = nn.Parameter(torch.FloatTensor(hidden_size).zero_())\n",
        "\n",
        "        self.W_z = nn.Parameter(torch.FloatTensor(input_size, hidden_size).normal_(0, 0.02))\n",
        "        self.U_z = nn.Parameter(torch.FloatTensor(hidden_size, hidden_size).normal_(0, 0.02))\n",
        "        self.V_z = nn.Parameter(torch.FloatTensor(input_size, hidden_size).normal_(0, 0.02))\n",
        "        self.b_z = nn.Parameter(torch.FloatTensor(hidden_size).zero_())\n",
        "\n",
        "        self.W = nn.Parameter(torch.FloatTensor(input_size, hidden_size).normal_(0, 0.02))\n",
        "        self.U = nn.Parameter(torch.FloatTensor(hidden_size, hidden_size).normal_(0, 0.02))\n",
        "        self.V = nn.Parameter(torch.FloatTensor(input_size, hidden_size).normal_(0, 0.02))\n",
        "        self.b = nn.Parameter(torch.FloatTensor(hidden_size).zero_())\n",
        "\n",
        "        # since W_gamma_x is diagonal, just initialize 1-d\n",
        "        self.W_gamma_x = nn.Parameter(torch.FloatTensor(input_size).normal_(0, 0.02))\n",
        "        self.b_gamma_x = nn.Parameter(torch.FloatTensor(input_size).zero_())\n",
        "\n",
        "        self.W_gamma_h = nn.Parameter(torch.FloatTensor(input_size, hidden_size).normal_(0, 0.02))\n",
        "        self.b_gamma_h = nn.Parameter(torch.FloatTensor(hidden_size).zero_())\n",
        "        self.gru_dropout = gru_dropout\n",
        "        \n",
        "        self.decoder = nn.Linear(hidden_size, output_size)\n",
        "        self.bn = nn.BatchNorm1d(hidden_size)\n",
        "        self.decoder_dropout = nn.Dropout(p=decoder_dropout)\n",
        "        \n",
        "    def forward(self, x, delta, m, x_forward, batch_sizes, h_t=None):\n",
        "        \"\"\"\n",
        "        \n",
        "        :param x: features input [batch_size, seq_len, num_features]\n",
        "        :param delta: time interval of feature observation [batch_size, seq_len, num_features]\n",
        "        :param m: masking vector {0, 1} of [batch_size, seq_len, num_features]\n",
        "        :param x_forward: place to replace missing values with [b, seqlen, numf]\n",
        "        :param h_t: initial hidden state [batch_size, seq_len, hidden_size]\n",
        "        :return: output [batch_size, output_size], hidden_state [batch_size, hidden_size]\n",
        "        \"\"\"\n",
        "        batch_size, seq_len, input_size = x.size()\n",
        "        if h_t is None:\n",
        "            # initialize to zero\n",
        "            h_t = Variable(torch.FloatTensor(batch_size, self.hidden_size).zero_())\n",
        "            if x.is_cuda:\n",
        "                h_t = h_t.cuda()\n",
        "          \n",
        "        # compute decays\n",
        "        decay_x = delta*self.W_gamma_x + self.b_gamma_x\n",
        "        zeroes = Variable(torch.zeros(decay_x.size()))\n",
        "        if decay_x.is_cuda:\n",
        "            zeroes = zeroes.cuda()\n",
        "        gamma_x_t = torch.exp(-torch.max(zeroes, decay_x))\n",
        "            \n",
        "        decay_h = torch.matmul(m, self.W_gamma_h) + self.b_gamma_h\n",
        "        zeroes = Variable(torch.zeros(decay_h.size()))\n",
        "        if decay_h.is_cuda:\n",
        "            zeroes = zeroes.cuda()\n",
        "        gamma_x_h = torch.exp(-torch.max(zeroes, decay_h))\n",
        "            \n",
        "        # replace missing values\n",
        "        x_replace = decay_x * x_forward + (1-decay_x) * 0.001\n",
        "        x[m.byte()] = x_replace[m.byte()]\n",
        "                  \n",
        "        # dropout masks, one for each batch\n",
        "        dropout_rate = self.gru_dropout if self.training else 0.\n",
        "        \n",
        "        W_dropout = Variable((torch.FloatTensor(self.W.size()).uniform_() > dropout_rate).float())\n",
        "        U_dropout = Variable((torch.FloatTensor(self.U.size()).uniform_() > dropout_rate).float())\n",
        "        V_dropout = Variable((torch.FloatTensor(self.V.size()).uniform_() > dropout_rate).float())\n",
        "        \n",
        "        if decay_h.is_cuda:\n",
        "            W_dropout = W_dropout.cuda()\n",
        "            U_dropout = U_dropout.cuda()\n",
        "            V_dropout = V_dropout.cuda()\n",
        "        \n",
        "        for t in range(seq_len):\n",
        "            # decay h\n",
        "            update_range = Variable(torch.LongTensor(list(range(batch_sizes[t]))))\n",
        "            if decay_h.is_cuda:\n",
        "                update_range = update_range.cuda()\n",
        "            h_t = h_t.clone().index_copy_(0, update_range, gamma_x_h[:batch_sizes[t],t,:] * h_t[:batch_sizes[t]])\n",
        "\n",
        "            z_t = F.sigmoid(torch.matmul(x[:batch_sizes[t], t, :], self.W_z) + torch.matmul(h_t[:batch_sizes[t]], self.U_z) + torch.matmul(1-m[:batch_sizes[t], t, :], self.V_z) + self.b_z)\n",
        "            r_t = F.sigmoid(torch.matmul(x[:batch_sizes[t], t, :], self.W_r) + torch.matmul(h_t[:batch_sizes[t]], self.U_r) + torch.matmul(1-m[:batch_sizes[t], t, :], self.V_r) + self.b_r)\n",
        "            h_tilde_t = F.tanh(torch.matmul(x[:batch_sizes[t], t, :], self.W*W_dropout) + torch.matmul(h_t[:batch_sizes[t]]*r_t, self.U*U_dropout) + torch.matmul(1-m[:batch_sizes[t], t, :], self.V*V_dropout) + self.b)\n",
        "            h_t = h_t.clone()\n",
        "            h_t = h_t.clone().index_copy_(0, update_range, (1 - z_t) * h_t[:batch_sizes[t]] + z_t * h_tilde_t)\n",
        "            \n",
        "        if batch_size > 1:\n",
        "            h_t = self.bn(h_t)\n",
        "\n",
        "        output = F.log_softmax(self.decoder(self.decoder_dropout(h_t)), dim=-1)\n",
        "        \n",
        "        return output, h_t\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "t5o1LIqAzene",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "gru_d = GRUD(NUM_FEATURES, 49, output_size, \n",
        "                      gru_dropout, decoder_dropout)\n",
        "gru_d.cuda()\n",
        "\n",
        "optimizer = torch.optim.Adam(gru_d.parameters(), lr=1e-3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hiol7HzvdpLD",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "ac08e59e-5307-4ba3-eb98-a310e1435949"
      },
      "cell_type": "code",
      "source": [
        "gru_d_train_loss_histories = []\n",
        "gru_d_test_auc_histories = []\n",
        "gru_d_val_loss_histories = []\n",
        "gru_d_val_acc_histories = []\n",
        "\n",
        "for k in range(num_fold):\n",
        "    train_ds.set_active_indices(train_dfs[k])\n",
        "    test_ds.set_active_indices(test_dfs[k], new_means=train_ds.means, new_stds=train_ds.stds)\n",
        "    train_loader = torch.utils.data.DataLoader(dataset=train_ds,\n",
        "                                           batch_size=batch_size,\n",
        "                                           collate_fn=dataset.PhysioNET.collate_batch,\n",
        "                                           shuffle=True)\n",
        "    test_loader = torch.utils.data.DataLoader(dataset=test_ds,\n",
        "                                          batch_size=batch_size,\n",
        "                                          collate_fn=dataset.PhysioNET.collate_batch,\n",
        "                                          shuffle=True)\n",
        "\n",
        "    gru_d = GRUD(NUM_FEATURES, 49, output_size, \n",
        "                      0.0, decoder_dropout)\n",
        "    gru_d.cuda()\n",
        "\n",
        "    optimizer = torch.optim.Adam(gru_d.parameters(), lr=1e-3)\n",
        "\n",
        "    gru_d_train_loss_history = []\n",
        "    gru_d_test_auc_history = []\n",
        "    gru_d_val_loss_history = []\n",
        "    gru_d_val_acc_history = []\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        start = time()\n",
        "        total_loss = 0.\n",
        "\n",
        "        for i, (data, label) in enumerate(train_loader):\n",
        "            x, delta, m, x_forward, lengths = data\n",
        "\n",
        "            sorted_lengths, sorted_idx = lengths.sort(descending=True)\n",
        "\n",
        "            x = Variable(x[sorted_idx].float()).cuda()\n",
        "            delta = Variable(delta[sorted_idx].float()).cuda()\n",
        "            m = Variable(m[sorted_idx].float()).cuda()\n",
        "            x_forward = Variable(x_forward[sorted_idx].float()).cuda()\n",
        "            \n",
        "            dummy = pack_padded_sequence(x, sorted_lengths.numpy(), batch_first=True)\n",
        "            optimizer.zero_grad()\n",
        "            \n",
        "            output, hidden = gru_d(x, delta, m, x_forward, torch.LongTensor(dummy.batch_sizes).cuda())\n",
        "\n",
        "            label = label[sorted_idx]\n",
        "            loss = loss_func(output, Variable(label).cuda())\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            total_loss += loss.data[0]\n",
        "\n",
        "            # report performance\n",
        "            if (i + 1) % log_interval == 0:\n",
        "                val_acc, test_auc, val_loss = test_model_gru_d(test_loader, gru_d)\n",
        "                gru_d_train_loss_history.append(total_loss/(i+1))\n",
        "                gru_d_test_auc_history.append(test_auc)\n",
        "                gru_d_val_acc_history.append(val_acc)\n",
        "                gru_d_val_loss_history.append(val_loss)\n",
        "                print('Fold: {8}, Epoch: [{0}/{1}], Step: [{2}/{3}], Loss: {4}, Validation Loss:{5}, Validation Acc:{6}, AUC:{7}'.format(\n",
        "                    epoch + 1, num_epochs, i + 1, len(train_loader), total_loss/(i+1), val_loss, val_acc, test_auc, k+1))\n",
        "#         print(\"Epoch %d time: %.4f\" % (epoch, time()-start))\n",
        "    gru_d_train_loss_histories.append(gru_d_train_loss_history)\n",
        "    gru_d_test_auc_histories.append(gru_d_test_auc_history)\n",
        "    gru_d_val_loss_histories.append(gru_d_val_loss_history)\n",
        "    gru_d_val_acc_histories.append(gru_d_val_acc_history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fold: 1, Epoch: [1/40], Step: [9/29], Loss: 0.6582479808065627, Validation Loss:0.6737809181213379, Validation Acc:72.31920199501246, AUC:0.6970238095238096\n",
            "Fold: 1, Epoch: [1/40], Step: [18/29], Loss: 0.6587927374574873, Validation Loss:0.6195915043354034, Validation Acc:61.596009975062344, AUC:0.6870600414078675\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "g4Jeocx3d3Vv",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "9cec170b-f6ee-4116-9c58-fcf166d7236c",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525238006602,
          "user_tz": 240,
          "elapsed": 2178,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(gru_d_train_loss_history)\n",
        "plt.plot(gru_d_val_loss_history)"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7e68ee2978>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SZu/lyUQeS7UVVPUq1htFyIKEjJLu2CfM2pMmeU44OJcbdY0ho9dw9CBOuNly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\ngg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7e68ee2be0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "d3HuNuekilhm",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "163bd95c-6eb9-466a-9ee4-23d585dd0329",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525124363595,
          "user_tz": 240,
          "elapsed": 2306,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(gru_d_test_auc_history)"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6f205e5240>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HaojEklm7Kc0vDhqZCuLXJ8fQ3mzGB67vyHl+g0ELnVaT08giEI7jwtAcutvt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\nSUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f6ef12cb198>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "TnlSHZCAYLxq",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "bec6de66-c67b-4735-b62c-3c2fd345e616",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1525237987978,
          "user_tz": 240,
          "elapsed": 1428,
          "user": {
            "displayName": "Tim Hsu",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "117310307035761107620"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Best AUC: %.4f\" % np.max([max(i) for i in gru_d_test_auc_histories]))\n",
        "print(\"Mean AUC: %.4f, Std: %.4f\" % \n",
        "      (np.mean([max(i) for i in gru_d_test_auc_histories]), \n",
        "       np.std([max(i) for i in gru_d_test_auc_histories]))\n",
        "     )"
      ],
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best AUC: 0.7892\n",
            "Mean AUC: 0.7892, Std: 0.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DHFkWMrlQ1hb",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}