RQ2_RQ3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f15c6d6c-70b1-4988-aa09-93fa2780cbbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import timm\n",
    "import torch\n",
    "import pandas as pd\n",
    "import plotly.io as pio\n",
    "import plotly.graph_objects as go\n",
    "from fastai.vision.all import *\n",
    "from fastai.callback.tracker import *\n",
    "from sklearn.model_selection import *\n",
    "from plotly.subplots import make_subplots\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "\n",
    "pio.renderers.default = 'iframe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44328ccb-7a54-4545-8d06-d4cab4141aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mean_std(path, batch_size=64):\n",
    "    \"\"\"\n",
    "    Calculate the mean and standard deviation of images in a given path.\n",
    "\n",
    "    Parameters:\n",
    "    - path: Path to the images.\n",
    "    - batch_size: Number of images to load at once.\n",
    "\n",
    "    Returns:\n",
    "    - A tuple (mean, std) of the mean and standard deviation of the images.\n",
    "    \"\"\"\n",
    "    \n",
    "    datablock = DataBlock(\n",
    "        blocks=(ImageBlock, CategoryBlock), \n",
    "        get_items=get_image_files, \n",
    "        get_y=parent_label,\n",
    "        item_tfms=Resize(224)\n",
    "    )\n",
    "\n",
    "    dls = datablock.dataloaders(path, bs=batch_size)\n",
    "\n",
    "    n = 0\n",
    "    sum_ = torch.tensor([0.0, 0.0, 0.0], device='cuda')\n",
    "    sum_sq = torch.tensor([0.0, 0.0, 0.0], device='cuda')\n",
    "    \n",
    "    for x, _ in dls.train:\n",
    "        sum_ += x.sum(dim=[0, 2, 3])\n",
    "        sum_sq += (x**2).sum(dim=[0, 2, 3])\n",
    "        n += x.shape[0] * x.shape[2] * x.shape[3]\n",
    "\n",
    "    mean = sum_ / n\n",
    "    std = (sum_sq / n - mean**2).sqrt()\n",
    "\n",
    "    return mean, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3fd3668d-6c72-42b0-b420-8f1c1d41356c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_validate_model(dataset_name, mean, std, n_folds=3):\n",
    "    \"\"\"\n",
    "    Trains and validates a machine learning model using cross-validation on a malware dataset.\n",
    "\n",
    "    Parameters:\n",
    "    - dataset_name (str): The name of the dataset or the path where the dataset images are stored. \n",
    "    - mean (tuple): A tuple of mean values for each channel of the input images, used for normalization.\n",
    "    - std (tuple): A tuple of standard deviation values for each channel of the input images, used for normalization.\n",
    "    - n_folds (int, optional): The number of folds to use for cross-validation. Defaults to 3.\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    files = get_image_files(dataset_name)\n",
    "    labels = [parent_label(o) for o in files]\n",
    "\n",
    "    kf = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=42)\n",
    "\n",
    "    model_parent_dir = Path('models') / dataset_name\n",
    "    model_parent_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    for fold, (train_idx, valid_idx) in enumerate(kf.split(files, labels)):\n",
    "        print(f\"Starting Training: Dataset - {dataset_name}, Fold - {fold+1}/{n_folds}\")\n",
    "\n",
    "        model_save_path = Path(dataset_name) / f'model_fold_{fold+1}'\n",
    "\n",
    "        # Split the data\n",
    "        train_files = [files[i] for i in train_idx]\n",
    "        valid_files = [files[i] for i in valid_idx]\n",
    "\n",
    "        datablock = DataBlock(\n",
    "            blocks=(ImageBlock, CategoryBlock),\n",
    "            get_items=lambda x: x,\n",
    "            splitter=FuncSplitter(lambda x: x in valid_files),\n",
    "            get_y=parent_label,\n",
    "            item_tfms=Resize(224, method='squish'),\n",
    "            batch_tfms=[Normalize.from_stats(mean, std)]  \n",
    "        )\n",
    "\n",
    "        dls = datablock.dataloaders(train_files + valid_files, bs=16)\n",
    "\n",
    "        learn = vision_learner(dls,\n",
    "                               arch='convnextv2_nano.fcmae_ft_in1k',\n",
    "                               metrics=[accuracy],\n",
    "                               loss_func=nn.CrossEntropyLoss(),\n",
    "                               cbs=EarlyStoppingCallback(patience=6)\n",
    "                              ).to_fp32()\n",
    "\n",
    "        learn.fine_tune(12, 0.01, cbs=SaveModelCallback(fname=model_save_path))\n",
    "        print(f\"Training Completed: Dataset - {dataset_name}, Fold - {fold+1}/{n_folds}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "532c47af-3687-42e1-9219-632554f2d8eb",
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   "source": [
    "def test_model(dataset_name, mean, std, n_folds=3):\n",
    "    \"\"\"\n",
    "    Evaluates a vision model's performance on a test dataset across multiple folds.\n",
    "\n",
    "    Parameters:\n",
    "    - dataset_name (str): The name of the dataset or the path where the dataset images are stored. \n",
    "    - mean (tuple): A tuple of mean values for each channel of the input images, used for normalization.\n",
    "    - std (tuple): A tuple of standard deviation values for each channel of the input images, used for normalization.\n",
    "    - n_folds (int, optional): The number of folds for which the models were trained and will now be evaluated.\n",
    "      Defaults to 3.\n",
    "\n",
    "    Returns:\n",
    "    - A tuple containing the average accuracy and F1 score across all evaluated folds\n",
    "      \n",
    "    \"\"\"\n",
    "\n",
    "    accuracies = []\n",
    "    f1_scores = []\n",
    "\n",
    "    # Path for the dataset\n",
    "    path = Path(dataset_name)\n",
    "    test_path = path/\"test\"\n",
    "\n",
    "    # Prepare the DataBlock for testing\n",
    "    datablock = DataBlock(\n",
    "        blocks=(ImageBlock, CategoryBlock),\n",
    "        get_items=get_image_files,\n",
    "        get_y=parent_label,\n",
    "        item_tfms=Resize(224, method='squish'),\n",
    "        batch_tfms = Normalize.from_stats(mean, std)\n",
    "    )\n",
    "\n",
    "    # Get test items\n",
    "    test_items = get_image_files(test_path)\n",
    "\n",
    "    for fold in range(n_folds):\n",
    "        print(f\"Starting Testing: Dataset - {dataset_name}, Fold - {fold+1}/{n_folds}\")\n",
    "\n",
    "        # Create a test dataloader\n",
    "        test_dls = datablock.dataloaders(test_path, bs=32)\n",
    "        test_dl = test_dls.test_dl(test_items, with_labels=True)\n",
    "\n",
    "        # Load the model\n",
    "        learn = vision_learner(test_dls, arch='convnextv2_nano.fcmae_ft_in1k', metrics=[accuracy])\n",
    "        learn.load(f'{dataset_name}/model_fold_{fold+1}')\n",
    "\n",
    "        # Get predictions\n",
    "        test_preds, test_labels = learn.get_preds(dl=test_dl)\n",
    "\n",
    "        preds_labels = [test_dls.vocab[np.argmax(pred)] for pred in test_preds]\n",
    "        true_labels = [test_dls.vocab[label.item()] for label in test_labels]\n",
    "\n",
    "        accuracy_1 = accuracy_score(true_labels, preds_labels)\n",
    "        f1 = f1_score(true_labels, preds_labels, average='weighted')\n",
    "\n",
    "        accuracies.append(accuracy_1)\n",
    "        f1_scores.append(f1)\n",
    "\n",
    "        print(f\"Testing Completed: Dataset - {dataset_name}, Fold - {fold+1}/{n_folds}\")\n",
    "\n",
    "    return np.mean(accuracies), np.mean(f1_scores)"
   ]
  },
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   "execution_count": 5,
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     "text": [
      "Starting Training: Dataset - Blended, Fold - 1/3\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>0.980543</td>\n",
       "      <td>0.787374</td>\n",
       "      <td>0.833515</td>\n",
       "      <td>00:48</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 0.7873742580413818.\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.461738</td>\n",
       "      <td>0.407398</td>\n",
       "      <td>0.899847</td>\n",
       "      <td>01:52</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.402120</td>\n",
       "      <td>0.349791</td>\n",
       "      <td>0.911194</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.344568</td>\n",
       "      <td>0.398893</td>\n",
       "      <td>0.916649</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.259740</td>\n",
       "      <td>0.207099</td>\n",
       "      <td>0.948505</td>\n",
       "      <td>01:54</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.172339</td>\n",
       "      <td>0.169942</td>\n",
       "      <td>0.954397</td>\n",
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       "      <td>5</td>\n",
       "      <td>0.121986</td>\n",
       "      <td>0.134625</td>\n",
       "      <td>0.969016</td>\n",
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       "      <td>6</td>\n",
       "      <td>0.095586</td>\n",
       "      <td>0.142557</td>\n",
       "      <td>0.971198</td>\n",
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       "      <td>7</td>\n",
       "      <td>0.035542</td>\n",
       "      <td>0.133480</td>\n",
       "      <td>0.975562</td>\n",
       "      <td>01:54</td>\n",
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       "      <td>8</td>\n",
       "      <td>0.032184</td>\n",
       "      <td>0.130149</td>\n",
       "      <td>0.974907</td>\n",
       "      <td>01:54</td>\n",
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       "      <td>9</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>0.120576</td>\n",
       "      <td>0.979489</td>\n",
       "      <td>01:54</td>\n",
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       "      <td>10</td>\n",
       "      <td>0.002451</td>\n",
       "      <td>0.119046</td>\n",
       "      <td>0.979926</td>\n",
       "      <td>01:53</td>\n",
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       "      <td>11</td>\n",
       "      <td>0.001656</td>\n",
       "      <td>0.118325</td>\n",
       "      <td>0.980580</td>\n",
       "      <td>01:53</td>\n",
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.4073978662490845.\n",
      "Better model found at epoch 1 with valid_loss value: 0.34979110956192017.\n",
      "Better model found at epoch 3 with valid_loss value: 0.20709887146949768.\n",
      "Better model found at epoch 4 with valid_loss value: 0.16994217038154602.\n",
      "Better model found at epoch 5 with valid_loss value: 0.13462522625923157.\n",
      "Better model found at epoch 7 with valid_loss value: 0.13348010182380676.\n",
      "Better model found at epoch 8 with valid_loss value: 0.13014911115169525.\n",
      "Better model found at epoch 9 with valid_loss value: 0.12057558447122574.\n",
      "Better model found at epoch 10 with valid_loss value: 0.11904581636190414.\n",
      "Better model found at epoch 11 with valid_loss value: 0.11832474172115326.\n",
      "Training Completed: Dataset - Blended, Fold - 1/3\n",
      "Starting Training: Dataset - Blended, Fold - 2/3\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
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       "      <td>0</td>\n",
       "      <td>1.011682</td>\n",
       "      <td>0.644392</td>\n",
       "      <td>0.853339</td>\n",
       "      <td>00:48</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 0.6443918347358704.\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.512106</td>\n",
       "      <td>0.269403</td>\n",
       "      <td>0.923396</td>\n",
       "      <td>01:55</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.393758</td>\n",
       "      <td>0.339228</td>\n",
       "      <td>0.913575</td>\n",
       "      <td>01:53</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.356380</td>\n",
       "      <td>0.342587</td>\n",
       "      <td>0.918376</td>\n",
       "      <td>01:53</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.281498</td>\n",
       "      <td>0.235930</td>\n",
       "      <td>0.935836</td>\n",
       "      <td>01:54</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.161152</td>\n",
       "      <td>0.169991</td>\n",
       "      <td>0.957442</td>\n",
       "      <td>01:52</td>\n",
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       "      <td>5</td>\n",
       "      <td>0.115504</td>\n",
       "      <td>0.123165</td>\n",
       "      <td>0.970319</td>\n",
       "      <td>01:52</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.103411</td>\n",
       "      <td>0.113416</td>\n",
       "      <td>0.975775</td>\n",
       "      <td>01:52</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.060467</td>\n",
       "      <td>0.127578</td>\n",
       "      <td>0.970973</td>\n",
       "      <td>01:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.054130</td>\n",
       "      <td>0.117573</td>\n",
       "      <td>0.978175</td>\n",
       "      <td>01:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.009626</td>\n",
       "      <td>0.110910</td>\n",
       "      <td>0.981886</td>\n",
       "      <td>01:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.007976</td>\n",
       "      <td>0.105105</td>\n",
       "      <td>0.983850</td>\n",
       "      <td>01:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.106179</td>\n",
       "      <td>0.983195</td>\n",
       "      <td>01:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.26940256357192993.\n",
      "Better model found at epoch 3 with valid_loss value: 0.2359301596879959.\n",
      "Better model found at epoch 4 with valid_loss value: 0.16999128460884094.\n",
      "Better model found at epoch 5 with valid_loss value: 0.12316501885652542.\n",
      "Better model found at epoch 6 with valid_loss value: 0.11341635882854462.\n",
      "Better model found at epoch 9 with valid_loss value: 0.11091001331806183.\n",
      "Better model found at epoch 10 with valid_loss value: 0.10510455816984177.\n",
      "Training Completed: Dataset - Blended, Fold - 2/3\n",
      "Starting Training: Dataset - Blended, Fold - 3/3\n"
     ]
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       "      <td>0</td>\n",
       "      <td>1.210390</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 0.589425802230835.\n"
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       "      <th>epoch</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.385502</td>\n",
       "      <td>0.322843</td>\n",
       "      <td>0.928416</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.436118</td>\n",
       "      <td>0.311389</td>\n",
       "      <td>0.919904</td>\n",
       "      <td>01:56</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.414839</td>\n",
       "      <td>0.329042</td>\n",
       "      <td>0.916849</td>\n",
       "      <td>01:54</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.303222</td>\n",
       "      <td>0.210622</td>\n",
       "      <td>0.945002</td>\n",
       "      <td>01:54</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.150043</td>\n",
       "      <td>0.162202</td>\n",
       "      <td>0.966827</td>\n",
       "      <td>01:55</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.177689</td>\n",
       "      <td>0.135765</td>\n",
       "      <td>0.968791</td>\n",
       "      <td>01:53</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.147935</td>\n",
       "      <td>0.154374</td>\n",
       "      <td>0.963335</td>\n",
       "      <td>01:54</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.084131</td>\n",
       "      <td>0.112913</td>\n",
       "      <td>0.977739</td>\n",
       "      <td>01:54</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.027429</td>\n",
       "      <td>0.122825</td>\n",
       "      <td>0.981667</td>\n",
       "      <td>01:55</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.014929</td>\n",
       "      <td>0.102596</td>\n",
       "      <td>0.982322</td>\n",
       "      <td>01:55</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.010521</td>\n",
       "      <td>0.113456</td>\n",
       "      <td>0.983195</td>\n",
       "      <td>01:53</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>0.111008</td>\n",
       "      <td>0.983413</td>\n",
       "      <td>01:55</td>\n",
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.32284313440322876.\n",
      "Better model found at epoch 1 with valid_loss value: 0.31138917803764343.\n",
      "Better model found at epoch 3 with valid_loss value: 0.21062220633029938.\n",
      "Better model found at epoch 4 with valid_loss value: 0.16220200061798096.\n",
      "Better model found at epoch 5 with valid_loss value: 0.13576488196849823.\n",
      "Better model found at epoch 7 with valid_loss value: 0.11291314661502838.\n",
      "Better model found at epoch 9 with valid_loss value: 0.10259585827589035.\n",
      "Training Completed: Dataset - Blended, Fold - 3/3\n",
      "Starting Training: Dataset - Dumpware, Fold - 1/3\n"
     ]
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       "      <td>0</td>\n",
       "      <td>1.231591</td>\n",
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       "      <td>00:16</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 0.8591794371604919.\n"
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       "  <thead>\n",
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       "      <td>0</td>\n",
       "      <td>0.577124</td>\n",
       "      <td>0.441660</td>\n",
       "      <td>0.888967</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.391640</td>\n",
       "      <td>0.596349</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.523149</td>\n",
       "      <td>1.676084</td>\n",
       "      <td>0.706704</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.451702</td>\n",
       "      <td>0.703525</td>\n",
       "      <td>0.881983</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.237272</td>\n",
       "      <td>0.991189</td>\n",
       "      <td>0.744413</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.225836</td>\n",
       "      <td>2.275283</td>\n",
       "      <td>0.775838</td>\n",
       "      <td>00:37</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.119524</td>\n",
       "      <td>0.437526</td>\n",
       "      <td>0.891061</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.109274</td>\n",
       "      <td>0.222606</td>\n",
       "      <td>0.951117</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.033541</td>\n",
       "      <td>0.092809</td>\n",
       "      <td>0.975559</td>\n",
       "      <td>00:37</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.023308</td>\n",
       "      <td>0.086303</td>\n",
       "      <td>0.976257</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.007869</td>\n",
       "      <td>0.080458</td>\n",
       "      <td>0.978352</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.002909</td>\n",
       "      <td>0.078228</td>\n",
       "      <td>0.979749</td>\n",
       "      <td>00:37</td>\n",
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.4416596293449402.\n",
      "Better model found at epoch 6 with valid_loss value: 0.437526136636734.\n",
      "Better model found at epoch 7 with valid_loss value: 0.2226061224937439.\n",
      "Better model found at epoch 8 with valid_loss value: 0.09280931204557419.\n",
      "Better model found at epoch 9 with valid_loss value: 0.08630312979221344.\n",
      "Better model found at epoch 10 with valid_loss value: 0.08045753836631775.\n",
      "Better model found at epoch 11 with valid_loss value: 0.07822753489017487.\n",
      "Training Completed: Dataset - Dumpware, Fold - 1/3\n",
      "Starting Training: Dataset - Dumpware, Fold - 2/3\n"
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       "      <td>0</td>\n",
       "      <td>1.274598</td>\n",
       "      <td>1.261833</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 1.2618331909179688.\n"
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       "      <td>0</td>\n",
       "      <td>0.554567</td>\n",
       "      <td>0.630593</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.565394</td>\n",
       "      <td>0.456137</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.474206</td>\n",
       "      <td>3.101507</td>\n",
       "      <td>0.638015</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.408671</td>\n",
       "      <td>0.625596</td>\n",
       "      <td>0.862334</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.289046</td>\n",
       "      <td>0.765981</td>\n",
       "      <td>0.781971</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.205436</td>\n",
       "      <td>0.651237</td>\n",
       "      <td>0.812718</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.187593</td>\n",
       "      <td>0.326304</td>\n",
       "      <td>0.927324</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.065642</td>\n",
       "      <td>0.373255</td>\n",
       "      <td>0.918938</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.061211</td>\n",
       "      <td>0.112034</td>\n",
       "      <td>0.967855</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.014257</td>\n",
       "      <td>0.088766</td>\n",
       "      <td>0.979036</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.005398</td>\n",
       "      <td>0.093440</td>\n",
       "      <td>0.976939</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.004077</td>\n",
       "      <td>0.089866</td>\n",
       "      <td>0.980433</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.6305925846099854.\n",
      "Better model found at epoch 1 with valid_loss value: 0.45613667368888855.\n",
      "Better model found at epoch 6 with valid_loss value: 0.3263035714626312.\n",
      "Better model found at epoch 8 with valid_loss value: 0.11203388124704361.\n",
      "Better model found at epoch 9 with valid_loss value: 0.0887664183974266.\n",
      "Training Completed: Dataset - Dumpware, Fold - 2/3\n",
      "Starting Training: Dataset - Dumpware, Fold - 3/3\n"
     ]
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       "      <td>0</td>\n",
       "      <td>1.205854</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 1.0947954654693604.\n"
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       "  <thead>\n",
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       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.569340</td>\n",
       "      <td>0.818567</td>\n",
       "      <td>0.801537</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.567645</td>\n",
       "      <td>3.049089</td>\n",
       "      <td>0.468204</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.427206</td>\n",
       "      <td>1.811540</td>\n",
       "      <td>0.588400</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.371669</td>\n",
       "      <td>0.550314</td>\n",
       "      <td>0.839972</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.265511</td>\n",
       "      <td>0.420999</td>\n",
       "      <td>0.904263</td>\n",
       "      <td>00:38</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.186515</td>\n",
       "      <td>0.403854</td>\n",
       "      <td>0.916841</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.132108</td>\n",
       "      <td>0.253317</td>\n",
       "      <td>0.945493</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.054928</td>\n",
       "      <td>0.159793</td>\n",
       "      <td>0.976939</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.034952</td>\n",
       "      <td>0.131372</td>\n",
       "      <td>0.981132</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.015023</td>\n",
       "      <td>0.134238</td>\n",
       "      <td>0.979036</td>\n",
       "      <td>00:36</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.119845</td>\n",
       "      <td>0.979036</td>\n",
       "      <td>00:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.123404</td>\n",
       "      <td>0.979036</td>\n",
       "      <td>00:36</td>\n",
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.8185669779777527.\n",
      "Better model found at epoch 3 with valid_loss value: 0.5503140091896057.\n",
      "Better model found at epoch 4 with valid_loss value: 0.4209985136985779.\n",
      "Better model found at epoch 5 with valid_loss value: 0.4038538932800293.\n",
      "Better model found at epoch 6 with valid_loss value: 0.253317266702652.\n",
      "Better model found at epoch 7 with valid_loss value: 0.15979252755641937.\n",
      "Better model found at epoch 8 with valid_loss value: 0.131372332572937.\n",
      "Better model found at epoch 10 with valid_loss value: 0.11984460055828094.\n",
      "Training Completed: Dataset - Dumpware, Fold - 3/3\n",
      "Starting Training: Dataset - Malevis_224, Fold - 1/3\n"
     ]
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       "      <td>0</td>\n",
       "      <td>1.329620</td>\n",
       "      <td>0.775540</td>\n",
       "      <td>0.808941</td>\n",
       "      <td>00:48</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 0.7755398750305176.\n"
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       "  <thead>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.488377</td>\n",
       "      <td>0.375847</td>\n",
       "      <td>0.894348</td>\n",
       "      <td>02:01</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.474267</td>\n",
       "      <td>0.411678</td>\n",
       "      <td>0.885913</td>\n",
       "      <td>01:59</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.475127</td>\n",
       "      <td>0.358146</td>\n",
       "      <td>0.905947</td>\n",
       "      <td>01:58</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.359428</td>\n",
       "      <td>0.337197</td>\n",
       "      <td>0.916913</td>\n",
       "      <td>01:57</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.231076</td>\n",
       "      <td>0.223951</td>\n",
       "      <td>0.945803</td>\n",
       "      <td>01:57</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.176855</td>\n",
       "      <td>0.219636</td>\n",
       "      <td>0.941797</td>\n",
       "      <td>01:59</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.149663</td>\n",
       "      <td>0.203639</td>\n",
       "      <td>0.954239</td>\n",
       "      <td>01:58</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.068553</td>\n",
       "      <td>0.217523</td>\n",
       "      <td>0.959722</td>\n",
       "      <td>01:57</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.036920</td>\n",
       "      <td>0.164119</td>\n",
       "      <td>0.970055</td>\n",
       "      <td>01:59</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.019448</td>\n",
       "      <td>0.161701</td>\n",
       "      <td>0.972374</td>\n",
       "      <td>02:00</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.011738</td>\n",
       "      <td>0.153333</td>\n",
       "      <td>0.974483</td>\n",
       "      <td>01:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.003673</td>\n",
       "      <td>0.150670</td>\n",
       "      <td>0.974694</td>\n",
       "      <td>01:57</td>\n",
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       "</table>"
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.37584739923477173.\n",
      "Better model found at epoch 2 with valid_loss value: 0.3581458330154419.\n",
      "Better model found at epoch 3 with valid_loss value: 0.33719733357429504.\n",
      "Better model found at epoch 4 with valid_loss value: 0.22395065426826477.\n",
      "Better model found at epoch 5 with valid_loss value: 0.2196355164051056.\n",
      "Better model found at epoch 6 with valid_loss value: 0.2036389261484146.\n",
      "Better model found at epoch 8 with valid_loss value: 0.16411876678466797.\n",
      "Better model found at epoch 9 with valid_loss value: 0.1617012321949005.\n",
      "Better model found at epoch 10 with valid_loss value: 0.15333318710327148.\n",
      "Better model found at epoch 11 with valid_loss value: 0.15066978335380554.\n",
      "Training Completed: Dataset - Malevis_224, Fold - 1/3\n",
      "Starting Training: Dataset - Malevis_224, Fold - 2/3\n"
     ]
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       "      <td>0</td>\n",
       "      <td>1.292367</td>\n",
       "      <td>0.912404</td>\n",
       "      <td>0.779629</td>\n",
       "      <td>00:50</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 0.912404477596283.\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.518873</td>\n",
       "      <td>0.398159</td>\n",
       "      <td>0.897933</td>\n",
       "      <td>02:00</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.476512</td>\n",
       "      <td>0.418960</td>\n",
       "      <td>0.879376</td>\n",
       "      <td>01:58</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.422583</td>\n",
       "      <td>0.433055</td>\n",
       "      <td>0.885491</td>\n",
       "      <td>01:57</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.373924</td>\n",
       "      <td>0.262054</td>\n",
       "      <td>0.932518</td>\n",
       "      <td>01:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.241891</td>\n",
       "      <td>0.231562</td>\n",
       "      <td>0.945171</td>\n",
       "      <td>01:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.143794</td>\n",
       "      <td>0.192672</td>\n",
       "      <td>0.954660</td>\n",
       "      <td>01:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.106514</td>\n",
       "      <td>0.173365</td>\n",
       "      <td>0.957824</td>\n",
       "      <td>01:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.060241</td>\n",
       "      <td>0.207528</td>\n",
       "      <td>0.956769</td>\n",
       "      <td>01:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.028631</td>\n",
       "      <td>0.181887</td>\n",
       "      <td>0.967946</td>\n",
       "      <td>01:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.013827</td>\n",
       "      <td>0.173374</td>\n",
       "      <td>0.970477</td>\n",
       "      <td>01:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.003429</td>\n",
       "      <td>0.183042</td>\n",
       "      <td>0.971109</td>\n",
       "      <td>01:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.002078</td>\n",
       "      <td>0.178542</td>\n",
       "      <td>0.971742</td>\n",
       "      <td>01:58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.3981594741344452.\n",
      "Better model found at epoch 3 with valid_loss value: 0.2620542049407959.\n",
      "Better model found at epoch 4 with valid_loss value: 0.2315620332956314.\n",
      "Better model found at epoch 5 with valid_loss value: 0.19267182052135468.\n",
      "Better model found at epoch 6 with valid_loss value: 0.17336511611938477.\n",
      "Training Completed: Dataset - Malevis_224, Fold - 2/3\n",
      "Starting Training: Dataset - Malevis_224, Fold - 3/3\n"
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       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
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       "      <th>time</th>\n",
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       "      <td>0</td>\n",
       "      <td>1.304601</td>\n",
       "      <td>0.779206</td>\n",
       "      <td>0.823914</td>\n",
       "      <td>00:49</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 0.7792062759399414.\n"
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       "  <thead>\n",
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       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>0.334520</td>\n",
       "      <td>0.909743</td>\n",
       "      <td>01:57</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.485898</td>\n",
       "      <td>0.327902</td>\n",
       "      <td>0.915647</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.493211</td>\n",
       "      <td>0.380227</td>\n",
       "      <td>0.901729</td>\n",
       "      <td>01:58</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.369989</td>\n",
       "      <td>0.317868</td>\n",
       "      <td>0.909110</td>\n",
       "      <td>01:57</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.204645</td>\n",
       "      <td>0.293574</td>\n",
       "      <td>0.933361</td>\n",
       "      <td>02:00</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.214055</td>\n",
       "      <td>0.193163</td>\n",
       "      <td>0.951286</td>\n",
       "      <td>01:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.074717</td>\n",
       "      <td>0.147838</td>\n",
       "      <td>0.966048</td>\n",
       "      <td>02:00</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.058658</td>\n",
       "      <td>0.169969</td>\n",
       "      <td>0.965415</td>\n",
       "      <td>01:57</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.018255</td>\n",
       "      <td>0.195807</td>\n",
       "      <td>0.966048</td>\n",
       "      <td>01:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.005336</td>\n",
       "      <td>0.169629</td>\n",
       "      <td>0.971953</td>\n",
       "      <td>01:58</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.002287</td>\n",
       "      <td>0.174087</td>\n",
       "      <td>0.971742</td>\n",
       "      <td>01:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.002781</td>\n",
       "      <td>0.178768</td>\n",
       "      <td>0.972164</td>\n",
       "      <td>01:58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.334520161151886.\n",
      "Better model found at epoch 1 with valid_loss value: 0.3279019296169281.\n",
      "Better model found at epoch 3 with valid_loss value: 0.31786805391311646.\n",
      "Better model found at epoch 4 with valid_loss value: 0.2935737371444702.\n",
      "Better model found at epoch 5 with valid_loss value: 0.1931631863117218.\n",
      "Better model found at epoch 6 with valid_loss value: 0.14783796668052673.\n",
      "Training Completed: Dataset - Malevis_224, Fold - 3/3\n",
      "Starting Training: Dataset - Malimg, Fold - 1/3\n"
     ]
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       "  <thead>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.597869</td>\n",
       "      <td>0.422423</td>\n",
       "      <td>0.935432</td>\n",
       "      <td>00:34</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 0.42242348194122314.\n"
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       "  <thead>\n",
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       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.203977</td>\n",
       "      <td>0.112924</td>\n",
       "      <td>0.965628</td>\n",
       "      <td>01:20</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.258238</td>\n",
       "      <td>0.103241</td>\n",
       "      <td>0.976550</td>\n",
       "      <td>01:18</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.250782</td>\n",
       "      <td>0.270217</td>\n",
       "      <td>0.949566</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.177347</td>\n",
       "      <td>0.113045</td>\n",
       "      <td>0.980084</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.141379</td>\n",
       "      <td>0.178945</td>\n",
       "      <td>0.965307</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.071609</td>\n",
       "      <td>0.050716</td>\n",
       "      <td>0.986829</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.119141</td>\n",
       "      <td>0.061877</td>\n",
       "      <td>0.992612</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.031158</td>\n",
       "      <td>0.077355</td>\n",
       "      <td>0.987151</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.009551</td>\n",
       "      <td>0.054219</td>\n",
       "      <td>0.991969</td>\n",
       "      <td>01:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.005801</td>\n",
       "      <td>0.053487</td>\n",
       "      <td>0.993575</td>\n",
       "      <td>01:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.005183</td>\n",
       "      <td>0.048835</td>\n",
       "      <td>0.993254</td>\n",
       "      <td>01:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.001486</td>\n",
       "      <td>0.047925</td>\n",
       "      <td>0.993254</td>\n",
       "      <td>01:18</td>\n",
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       "</table>"
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.11292378604412079.\n",
      "Better model found at epoch 1 with valid_loss value: 0.10324142873287201.\n",
      "Better model found at epoch 5 with valid_loss value: 0.05071607232093811.\n",
      "Better model found at epoch 10 with valid_loss value: 0.048834968358278275.\n",
      "Better model found at epoch 11 with valid_loss value: 0.047925010323524475.\n",
      "Training Completed: Dataset - Malimg, Fold - 1/3\n",
      "Starting Training: Dataset - Malimg, Fold - 2/3\n"
     ]
    },
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       "  <thead>\n",
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       "      <td>0</td>\n",
       "      <td>0.480334</td>\n",
       "      <td>0.368101</td>\n",
       "      <td>0.926759</td>\n",
       "      <td>00:34</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 0.3681011199951172.\n"
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       "      <td>0</td>\n",
       "      <td>0.152684</td>\n",
       "      <td>1.583811</td>\n",
       "      <td>0.726309</td>\n",
       "      <td>01:18</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.221334</td>\n",
       "      <td>0.114233</td>\n",
       "      <td>0.971089</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.320356</td>\n",
       "      <td>0.374149</td>\n",
       "      <td>0.943784</td>\n",
       "      <td>01:20</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.214702</td>\n",
       "      <td>0.130198</td>\n",
       "      <td>0.975908</td>\n",
       "      <td>01:21</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.199592</td>\n",
       "      <td>0.226206</td>\n",
       "      <td>0.964664</td>\n",
       "      <td>01:20</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.093521</td>\n",
       "      <td>0.075572</td>\n",
       "      <td>0.982975</td>\n",
       "      <td>01:19</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.091826</td>\n",
       "      <td>0.058836</td>\n",
       "      <td>0.984260</td>\n",
       "      <td>01:20</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.035625</td>\n",
       "      <td>0.040340</td>\n",
       "      <td>0.989078</td>\n",
       "      <td>01:21</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.032619</td>\n",
       "      <td>0.015988</td>\n",
       "      <td>0.992933</td>\n",
       "      <td>01:19</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.017410</td>\n",
       "      <td>0.027789</td>\n",
       "      <td>0.992290</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.007340</td>\n",
       "      <td>0.020116</td>\n",
       "      <td>0.992933</td>\n",
       "      <td>01:19</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.006783</td>\n",
       "      <td>0.020855</td>\n",
       "      <td>0.992612</td>\n",
       "      <td>01:19</td>\n",
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 1.5838109254837036.\n",
      "Better model found at epoch 1 with valid_loss value: 0.11423292011022568.\n",
      "Better model found at epoch 5 with valid_loss value: 0.07557228207588196.\n",
      "Better model found at epoch 6 with valid_loss value: 0.05883572995662689.\n",
      "Better model found at epoch 7 with valid_loss value: 0.040339596569538116.\n",
      "Better model found at epoch 8 with valid_loss value: 0.01598820462822914.\n",
      "Training Completed: Dataset - Malimg, Fold - 2/3\n",
      "Starting Training: Dataset - Malimg, Fold - 3/3\n"
     ]
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      "Better model found at epoch 0 with valid_loss value: 0.5372514724731445.\n"
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       "      <td>0</td>\n",
       "      <td>0.165011</td>\n",
       "      <td>0.099091</td>\n",
       "      <td>0.972053</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.245205</td>\n",
       "      <td>0.234028</td>\n",
       "      <td>0.945712</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.327772</td>\n",
       "      <td>3.749593</td>\n",
       "      <td>0.778028</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.198021</td>\n",
       "      <td>0.104536</td>\n",
       "      <td>0.979120</td>\n",
       "      <td>01:17</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.163752</td>\n",
       "      <td>0.085859</td>\n",
       "      <td>0.983617</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.112377</td>\n",
       "      <td>0.081543</td>\n",
       "      <td>0.980084</td>\n",
       "      <td>01:17</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.056967</td>\n",
       "      <td>0.136531</td>\n",
       "      <td>0.973659</td>\n",
       "      <td>01:18</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.054565</td>\n",
       "      <td>0.042061</td>\n",
       "      <td>0.989399</td>\n",
       "      <td>01:18</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.030700</td>\n",
       "      <td>0.041637</td>\n",
       "      <td>0.991005</td>\n",
       "      <td>01:18</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.013123</td>\n",
       "      <td>0.032702</td>\n",
       "      <td>0.991005</td>\n",
       "      <td>01:18</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.011410</td>\n",
       "      <td>0.026527</td>\n",
       "      <td>0.993254</td>\n",
       "      <td>01:18</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.005791</td>\n",
       "      <td>0.024883</td>\n",
       "      <td>0.992612</td>\n",
       "      <td>01:18</td>\n",
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.09909100830554962.\n",
      "Better model found at epoch 4 with valid_loss value: 0.08585895597934723.\n",
      "Better model found at epoch 5 with valid_loss value: 0.08154323697090149.\n",
      "Better model found at epoch 7 with valid_loss value: 0.04206132888793945.\n",
      "Better model found at epoch 8 with valid_loss value: 0.041636500507593155.\n",
      "Better model found at epoch 9 with valid_loss value: 0.0327015295624733.\n",
      "Better model found at epoch 10 with valid_loss value: 0.026526765897870064.\n",
      "Better model found at epoch 11 with valid_loss value: 0.024882981553673744.\n",
      "Training Completed: Dataset - Malimg, Fold - 3/3\n",
      "Starting Training: Dataset - Microsoft_2015, Fold - 1/3\n"
     ]
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       "      <td>0</td>\n",
       "      <td>0.763949</td>\n",
       "      <td>0.578326</td>\n",
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      "Better model found at epoch 0 with valid_loss value: 0.5783255100250244.\n"
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       "  <thead>\n",
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       "      <td>0</td>\n",
       "      <td>0.356115</td>\n",
       "      <td>0.286065</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.282110</td>\n",
       "      <td>0.472178</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.322356</td>\n",
       "      <td>0.245295</td>\n",
       "      <td>0.932873</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.242699</td>\n",
       "      <td>0.151615</td>\n",
       "      <td>0.964641</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.201084</td>\n",
       "      <td>0.111747</td>\n",
       "      <td>0.969061</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.127529</td>\n",
       "      <td>0.074461</td>\n",
       "      <td>0.983149</td>\n",
       "      <td>01:34</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.129559</td>\n",
       "      <td>0.105029</td>\n",
       "      <td>0.973757</td>\n",
       "      <td>01:34</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.063430</td>\n",
       "      <td>0.098571</td>\n",
       "      <td>0.977072</td>\n",
       "      <td>01:34</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.021936</td>\n",
       "      <td>0.096720</td>\n",
       "      <td>0.981768</td>\n",
       "      <td>01:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.014311</td>\n",
       "      <td>0.070968</td>\n",
       "      <td>0.986188</td>\n",
       "      <td>01:33</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.004242</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>0.986740</td>\n",
       "      <td>01:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.002133</td>\n",
       "      <td>0.068198</td>\n",
       "      <td>0.986740</td>\n",
       "      <td>01:44</td>\n",
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.28606536984443665.\n",
      "Better model found at epoch 2 with valid_loss value: 0.24529460072517395.\n",
      "Better model found at epoch 3 with valid_loss value: 0.1516152173280716.\n",
      "Better model found at epoch 4 with valid_loss value: 0.11174660921096802.\n",
      "Better model found at epoch 5 with valid_loss value: 0.07446132600307465.\n",
      "Better model found at epoch 9 with valid_loss value: 0.07096826285123825.\n",
      "Better model found at epoch 11 with valid_loss value: 0.06819812208414078.\n",
      "Training Completed: Dataset - Microsoft_2015, Fold - 1/3\n",
      "Starting Training: Dataset - Microsoft_2015, Fold - 2/3\n"
     ]
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      "Better model found at epoch 0 with valid_loss value: 0.49119603633880615.\n"
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       "      <td>0.181630</td>\n",
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       "      <td>0.193186</td>\n",
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       "      <td>0.239480</td>\n",
       "      <td>0.132780</td>\n",
       "      <td>0.962431</td>\n",
       "      <td>01:34</td>\n",
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       "      <td>0.228783</td>\n",
       "      <td>0.096133</td>\n",
       "      <td>0.970166</td>\n",
       "      <td>01:33</td>\n",
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       "      <td>0.159564</td>\n",
       "      <td>0.095665</td>\n",
       "      <td>0.974586</td>\n",
       "      <td>01:37</td>\n",
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       "      <td>0.077341</td>\n",
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       "      <td>0.066049</td>\n",
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       "      <td>0.007419</td>\n",
       "      <td>0.051461</td>\n",
       "      <td>0.988674</td>\n",
       "      <td>01:38</td>\n",
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       "      <td>0.056523</td>\n",
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       "      <td>01:37</td>\n",
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       "      <td>0.001340</td>\n",
       "      <td>0.059264</td>\n",
       "      <td>0.987293</td>\n",
       "      <td>01:36</td>\n",
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     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.18162959814071655.\n",
      "Better model found at epoch 3 with valid_loss value: 0.13277983665466309.\n",
      "Better model found at epoch 4 with valid_loss value: 0.09613289684057236.\n",
      "Better model found at epoch 5 with valid_loss value: 0.09566512703895569.\n",
      "Better model found at epoch 6 with valid_loss value: 0.06813988834619522.\n",
      "Better model found at epoch 8 with valid_loss value: 0.06604884564876556.\n",
      "Better model found at epoch 9 with valid_loss value: 0.051461413502693176.\n",
      "Training Completed: Dataset - Microsoft_2015, Fold - 2/3\n",
      "Starting Training: Dataset - Microsoft_2015, Fold - 3/3\n"
     ]
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      "Better model found at epoch 0 with valid_loss value: 0.5172196626663208.\n"
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       "      <td>0.315101</td>\n",
       "      <td>0.302010</td>\n",
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       "      <td>0.404239</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0.166409</td>\n",
       "      <td>0.959392</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.179810</td>\n",
       "      <td>0.127847</td>\n",
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       "      <td>01:36</td>\n",
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       "      <td>0.144400</td>\n",
       "      <td>0.133795</td>\n",
       "      <td>0.963536</td>\n",
       "      <td>01:36</td>\n",
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       "      <td>6</td>\n",
       "      <td>0.083098</td>\n",
       "      <td>0.128961</td>\n",
       "      <td>0.965746</td>\n",
       "      <td>01:38</td>\n",
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       "      <td>7</td>\n",
       "      <td>0.052923</td>\n",
       "      <td>0.083602</td>\n",
       "      <td>0.978453</td>\n",
       "      <td>01:37</td>\n",
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       "      <td>8</td>\n",
       "      <td>0.019956</td>\n",
       "      <td>0.064867</td>\n",
       "      <td>0.984530</td>\n",
       "      <td>01:36</td>\n",
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       "      <td>9</td>\n",
       "      <td>0.009530</td>\n",
       "      <td>0.077086</td>\n",
       "      <td>0.983978</td>\n",
       "      <td>01:38</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.008261</td>\n",
       "      <td>0.070683</td>\n",
       "      <td>0.986464</td>\n",
       "      <td>01:37</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.004966</td>\n",
       "      <td>0.072764</td>\n",
       "      <td>0.987017</td>\n",
       "      <td>01:36</td>\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.3020101487636566.\n",
      "Better model found at epoch 1 with valid_loss value: 0.2120930552482605.\n",
      "Better model found at epoch 3 with valid_loss value: 0.16640914976596832.\n",
      "Better model found at epoch 4 with valid_loss value: 0.12784743309020996.\n",
      "Better model found at epoch 7 with valid_loss value: 0.08360247313976288.\n",
      "Better model found at epoch 8 with valid_loss value: 0.0648670569062233.\n",
      "Training Completed: Dataset - Microsoft_2015, Fold - 3/3\n"
     ]
    }
   ],
   "source": [
    "datasets = ['Virus-MNIST','Dumpware','Malevis_224', 'Malimg','Microsoft_2015', 'Blended']\n",
    "\n",
    "for dataset in datasets:\n",
    "    training_path = Path(dataset)/\"train\"\n",
    "    mean, std = get_mean_std(training_path)\n",
    "    train_validate_model(dataset,mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "398d3266-3dc6-4737-bbdf-800a2b28e500",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Testing: Dataset - Virus-MNIST, Fold - 1/3\n"
     ]
    },
    {
     "data": {
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     "text": [
      "Testing Completed: Dataset - Virus-MNIST, Fold - 1/3\n",
      "Starting Testing: Dataset - Virus-MNIST, Fold - 2/3\n"
     ]
    },
    {
     "data": {
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     "text": [
      "Testing Completed: Dataset - Virus-MNIST, Fold - 2/3\n",
      "Starting Testing: Dataset - Virus-MNIST, Fold - 3/3\n"
     ]
    },
    {
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     "text": [
      "Testing Completed: Dataset - Virus-MNIST, Fold - 3/3\n",
      "Starting Testing: Dataset - Dumpware, Fold - 1/3\n"
     ]
    },
    {
     "data": {
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     "text": [
      "Testing Completed: Dataset - Dumpware, Fold - 1/3\n",
      "Starting Testing: Dataset - Dumpware, Fold - 2/3\n"
     ]
    },
    {
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     "text": [
      "Testing Completed: Dataset - Dumpware, Fold - 2/3\n",
      "Starting Testing: Dataset - Dumpware, Fold - 3/3\n"
     ]
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      "Testing Completed: Dataset - Dumpware, Fold - 3/3\n",
      "Starting Testing: Dataset - Malevis_224, Fold - 1/3\n"
     ]
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      "Testing Completed: Dataset - Malevis_224, Fold - 1/3\n",
      "Starting Testing: Dataset - Malevis_224, Fold - 2/3\n"
     ]
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      "Testing Completed: Dataset - Malevis_224, Fold - 2/3\n",
      "Starting Testing: Dataset - Malevis_224, Fold - 3/3\n"
     ]
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      "Testing Completed: Dataset - Malevis_224, Fold - 3/3\n",
      "Starting Testing: Dataset - Malimg, Fold - 1/3\n"
     ]
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      "Testing Completed: Dataset - Malimg, Fold - 1/3\n",
      "Starting Testing: Dataset - Malimg, Fold - 2/3\n"
     ]
    },
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      "Testing Completed: Dataset - Malimg, Fold - 2/3\n",
      "Starting Testing: Dataset - Malimg, Fold - 3/3\n"
     ]
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     "text": [
      "Testing Completed: Dataset - Malimg, Fold - 3/3\n",
      "Starting Testing: Dataset - Microsoft_2015, Fold - 1/3\n"
     ]
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      "Testing Completed: Dataset - Microsoft_2015, Fold - 1/3\n",
      "Starting Testing: Dataset - Microsoft_2015, Fold - 2/3\n"
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      "Testing Completed: Dataset - Microsoft_2015, Fold - 2/3\n",
      "Starting Testing: Dataset - Microsoft_2015, Fold - 3/3\n"
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      "Testing Completed: Dataset - Microsoft_2015, Fold - 3/3\n",
      "Starting Testing: Dataset - Blended, Fold - 1/3\n"
     ]
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      "Testing Completed: Dataset - Blended, Fold - 1/3\n",
      "Starting Testing: Dataset - Blended, Fold - 2/3\n"
     ]
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     "text": [
      "Testing Completed: Dataset - Blended, Fold - 2/3\n",
      "Starting Testing: Dataset - Blended, Fold - 3/3\n"
     ]
    },
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     "text": [
      "Testing Completed: Dataset - Blended, Fold - 3/3\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "datasets = ['Virus-MNIST','Dumpware','Malevis_224', 'Malimg','Microsoft_2015', 'Blended']\n",
    "\n",
    "for dataset in datasets:\n",
    "    training_path = Path(dataset)/\"train\"\n",
    "    mean, std = get_mean_std(training_path)\n",
    "    avg_accuracy, avg_f1 = test_model(dataset,mean, std)\n",
    "    results.append({'Dataset': dataset, 'Average Accuracy': avg_accuracy, 'Average F1 Score': avg_f1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e65213b3-8d74-475a-a82a-0c2258006d1a",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Average Accuracy</th>\n",
       "      <th>Average F1 Score</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.943223</td>\n",
       "      <td>0.940035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dumpware</td>\n",
       "      <td>0.994580</td>\n",
       "      <td>0.994552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Malevis_224</td>\n",
       "      <td>0.989075</td>\n",
       "      <td>0.989093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Malimg</td>\n",
       "      <td>0.995009</td>\n",
       "      <td>0.995005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Microsoft_2015</td>\n",
       "      <td>0.994485</td>\n",
       "      <td>0.994473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Blended</td>\n",
       "      <td>0.992524</td>\n",
       "      <td>0.992526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Dataset  Average Accuracy  Average F1 Score\n",
       "0     Virus-MNIST          0.943223          0.940035\n",
       "1        Dumpware          0.994580          0.994552\n",
       "2     Malevis_224          0.989075          0.989093\n",
       "3          Malimg          0.995009          0.995005\n",
       "4  Microsoft_2015          0.994485          0.994473\n",
       "5         Blended          0.992524          0.992526"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame(results)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f47cbe8e-af82-4b55-9072-2f9620b56819",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [\"Virus-MNIST\",\"Dumpware\", \"MaleVis\", \"Malimg\", \"Microsoft 2015\",\"Blended\"]\n",
    "our_approach_f1 = [94.0, 99.5, 98.9, 99.5, 99.5, 99.3] \n",
    "approach_2_f1 = [86.1, 95.8, 95.4, 97.7, 96.1, 95.0]\n",
    "approach_3_f1 = [88.1, 96.2, 98.7, 97.0, 96.0, None]  # None for missing data\n",
    "\n",
    "colors = [\"#4682B4\", \"#967969\", \"#008080\"] \n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    \n",
    "    go.Bar(name='ConvNeXt V2',\n",
    "           x=datasets, \n",
    "           y=our_approach_f1, \n",
    "           text=our_approach_f1,\n",
    "           marker_color=colors[0],\n",
    "           textposition='inside'),\n",
    "    \n",
    "    go.Bar(name='Approach 2',\n",
    "           x=datasets,\n",
    "           y=approach_2_f1, \n",
    "           text=approach_2_f1,\n",
    "           marker_color=colors[1], \n",
    "           textposition='inside'),\n",
    "    \n",
    "    go.Bar(name='Approach 3',\n",
    "           x=datasets, \n",
    "           y=approach_3_f1,\n",
    "           text=approach_3_f1,\n",
    "           marker_color=colors[2], \n",
    "           textposition='inside')\n",
    "])\n",
    "\n",
    "\n",
    "fig.update_layout(barmode='group', \n",
    "                  height = 400, \n",
    "                  plot_bgcolor='rgb(233,233,233)',\n",
    "                     legend=dict(\n",
    "                        orientation=\"h\",\n",
    "                        yanchor=\"top\",\n",
    "                        y=1.2,\n",
    "                        xanchor=\"left\",\n",
    "                        x=0.25,\n",
    "                        font_size=15,\n",
    "                        font_color=\"black\",\n",
    "                    )\n",
    "                 )\n",
    "\n",
    "fig.update_xaxes(showline=True,\n",
    "                 linewidth=1.5, \n",
    "                 linecolor='black',\n",
    "                 mirror=False,\n",
    "                 color=\"black\",\n",
    "                 tickfont_size=16\n",
    "                )\n",
    "\n",
    "\n",
    "fig.update_yaxes(showline=True, \n",
    "                 linewidth=1.5, \n",
    "                 linecolor='black', \n",
    "                 mirror=False, \n",
    "                 color=\"black\",\n",
    "                 title_text=r'$\\text{F1 score } (\\%)$',\n",
    "                 title_font_color=\"black\",\n",
    "                 title_font_size=15,\n",
    "                 tickfont_size=16)\n",
    "\n",
    "\n",
    "fig.update_traces(marker_line_color='black', \n",
    "                  marker_line_width=0.8, \n",
    "                  textangle=0)\n",
    "fig.show()\n",
    "\n",
    "pio.write_image(fig, \n",
    "                \"Datasets_Evaluation.png\", \n",
    "                height = 400, \n",
    "                width= 800,\n",
    "                scale=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c40c73a-b8aa-40be-a6dd-edd08e134ac2",
   "metadata": {},
   "source": [
    "| Dataset         | Our Approach F1 | Approach 2                      | F1 score      | Approach 3                      | F1 score      |\n",
    "|-----------------|-----------------|---------------------------------|---------------|---------------------------------|---------------|\n",
    "| Virus-MNIST     | 0.940035        | ECOC-SVM                        | 86.05         | FACILE                          | 88.08         |\n",
    "| Dumpware        | 0.994552        | DenseNet 121 + CycleGAN+CNN     | 95.82         | Kernel Subspace                 | 96.20         |\n",
    "| Malevis_224     | 0.989093        | ShuffleNet + DenseNet-201       | 95.37         | MobileNetV2                     | 97.76         |\n",
    "| Malimg          | 0.994661        | EfficientNetB4                  | 97.66         | Kernel Subspace                 | 97.00         |\n",
    "| Microsoft_2015  | 0.994627        | ResNet50                        | 96.08         | CycleGAN+CNN                    | 96.00         |\n",
    "| Blended         | 0.992526        | ResNet50                        | 95.00         |           -                     |    -          | "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "61d93035-c445-41a1-a0d3-73fd737dfb8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of parameters in convnext_nano.in12k_ft_in1k: 15.59\n",
      "Number of parameters in densenet121.tv_in1k: 7.98\n",
      "Number of parameters in densenet201.tv_in1k: 20.01\n",
      "Number of parameters in resnet50.tv2_in1k: 25.56\n",
      "Number of parameters in efficientnet_b4.ra2_in1k: 19.34\n",
      "Number of parameters in densenet161.tv_in1k: 28.68\n",
      "Number of parameters in mobilenetv2_100: 3.5\n",
      "Number of parameters in resnet18: 11.69\n"
     ]
    }
   ],
   "source": [
    "model_names = ['convnext_nano.in12k_ft_in1k',\n",
    "               'densenet121.tv_in1k',\n",
    "               'densenet201.tv_in1k',\n",
    "               'resnet50.tv2_in1k',\n",
    "               'efficientnet_b4.ra2_in1k',\n",
    "               'densenet161.tv_in1k',\n",
    "               'mobilenetv2_100',\n",
    "               'resnet18'\n",
    "              ]\n",
    "\n",
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "for model_name in model_names:\n",
    "    model = timm.create_model(model_name, pretrained=True)\n",
    "    num_parameters = count_parameters(model)\n",
    "    print(f\"Number of parameters in {model_name}: {round(num_parameters/1000000,2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1ea1c79-8128-4242-90af-7ccccfd84757",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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