Imagenette_128_5_epoch-bs64.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "conda activate base\n",
    "mamba env remove -n over9000\n",
    "mamba env create\n",
    "conda activate over9000\n",
    "ipython kernel install --user --name=over9000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline: Adam + OneCycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_VISIBLE_DEVICES=2\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_VISIBLE_DEVICES=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To use this log_lamb_rs, please run 'pip install tensorboardx'. Also you must have Tensorboard running to see results\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
     ]
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
     },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\u001b[?25l"
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      "text/plain": [
       "Output()"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.30000114440918, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:02 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">36.49it/s</span>  </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K{'avg_val_loss': 8.30000114440918, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:02 • 0:00:00\u001b[0m \u001b[38;5;249m36.49it/s\u001b[0m  "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.7135426998138428, 'avg_acc': 0.438556045293808}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:18 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.57it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.37 v_num: 36 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:02 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">28.09it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.7135426998138428, 'avg_acc': 0.438556045293808}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:18 • 0:00:00\u001b[0m \u001b[38;5;249m13.57it/s\u001b[0m \u001b[37mloss: 2.37 v_num: 36 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:02 • 0:00:00\u001b[0m \u001b[38;5;249m28.09it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.079819917678833, 'avg_acc': 0.39669737219810486}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.79it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.1 v_num: 36 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.88it/s</span>                     </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.079819917678833, 'avg_acc': 0.39669737219810486}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.79it/s\u001b[0m \u001b[37mloss: 2.1 v_num: 36 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.88it/s\u001b[0m                     "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.0034713745117188, 'avg_acc': 0.6664506196975708}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.62it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.95 v_num: 36 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.84it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.0034713745117188, 'avg_acc': 0.6664506196975708}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.62it/s\u001b[0m \u001b[37mloss: 1.95 v_num: 36 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.84it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7600547075271606, 'avg_acc': 0.7428475618362427}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.21it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.71 v_num: 36 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.08it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7600547075271606, 'avg_acc': 0.7428475618362427}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.21it/s\u001b[0m \u001b[37mloss: 1.71 v_num: 36 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.08it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6324437856674194, 'avg_acc': 0.7957949042320251}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.98it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.64 v_num: 36 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.14it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6324437856674194, 'avg_acc': 0.7957949042320251}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.98it/s\u001b[0m \u001b[37mloss: 1.64 v_num: 36 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.14it/s\u001b[0m                      "
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
     ]
    },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
      ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
     ]
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
     },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
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     "output_type": "display_data"
    },
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a7a868b447b49549ddca394b9361e7c",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.69334602355957, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">30.17it/s</span>  </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K{'avg_val_loss': 8.69334602355957, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m30.17it/s\u001b[0m  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.6893038749694824, 'avg_acc': 0.4753864109516144}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.76it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.42 v_num: 37 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.31it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.6893038749694824, 'avg_acc': 0.4753864109516144}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.76it/s\u001b[0m \u001b[37mloss: 2.42 v_num: 37 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.31it/s\u001b[0m                      "
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.7047853469848633, 'avg_acc': 0.4821188449859619}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.70it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.24 v_num: 37 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.16it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.7047853469848633, 'avg_acc': 0.4821188449859619}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.70it/s\u001b[0m \u001b[37mloss: 2.24 v_num: 37 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.16it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9595744609832764, 'avg_acc': 0.668262779712677}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.77it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.96 v_num: 37 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">32.80it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9595744609832764, 'avg_acc': 0.668262779712677}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.77it/s\u001b[0m \u001b[37mloss: 1.96 v_num: 37 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m32.80it/s\u001b[0m                      "
      ]
     },
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    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7602472305297852, 'avg_acc': 0.7325268983840942}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.97it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.69 v_num: 37 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.46it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7602472305297852, 'avg_acc': 0.7325268983840942}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.97it/s\u001b[0m \u001b[37mloss: 1.69 v_num: 37 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.46it/s\u001b[0m                      "
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6469000577926636, 'avg_acc': 0.7824500799179077}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.82it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.64 v_num: 37 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">36.19it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6469000577926636, 'avg_acc': 0.7824500799179077}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.82it/s\u001b[0m \u001b[37mloss: 1.64 v_num: 37 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m36.19it/s\u001b[0m                      "
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
     ]
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
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    },
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e687620c79b4d0fb79ce9324ae4aefe",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 9.606526374816895, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">42.67it/s</span>  </pre>\n"
      ],
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       "\r",
       "\u001b[2K{'avg_val_loss': 9.606526374816895, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m42.67it/s\u001b[0m  "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.9446494579315186, 'avg_acc': 0.4105822443962097}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.61it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.48 v_num: 38 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.01it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.9446494579315186, 'avg_acc': 0.4105822443962097}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.61it/s\u001b[0m \u001b[37mloss: 2.48 v_num: 38 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.01it/s\u001b[0m                      "
      ]
     },
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.6712536811828613, 'avg_acc': 0.4680299162864685}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.86it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.12 v_num: 38 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.17it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.6712536811828613, 'avg_acc': 0.4680299162864685}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.86it/s\u001b[0m \u001b[37mloss: 2.12 v_num: 38 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.17it/s\u001b[0m                      "
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9976752996444702, 'avg_acc': 0.6550619006156921}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.65it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.9 v_num: 38 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">32.90it/s</span>                     </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9976752996444702, 'avg_acc': 0.6550619006156921}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.65it/s\u001b[0m \u001b[37mloss: 1.9 v_num: 38 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m32.90it/s\u001b[0m                     "
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7097102403640747, 'avg_acc': 0.7600086331367493}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.91it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.78 v_num: 38 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.90it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7097102403640747, 'avg_acc': 0.7600086331367493}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.91it/s\u001b[0m \u001b[37mloss: 1.78 v_num: 38 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.90it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.652773380279541, 'avg_acc': 0.7859662771224976}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.56it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.64 v_num: 38 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.23it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.652773380279541, 'avg_acc': 0.7859662771224976}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.56it/s\u001b[0m \u001b[37mloss: 1.64 v_num: 38 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.23it/s\u001b[0m                      "
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
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       "\u001b[?25l"
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     },
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       "model_id": "be7b5877deb64261a98094b3ae2db686",
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      "text/plain": [
       "Output()"
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.549235343933105, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">39.23it/s</span>  </pre>\n"
      ],
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       "\r",
       "\u001b[2K{'avg_val_loss': 8.549235343933105, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m39.23it/s\u001b[0m  "
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.1947197914123535, 'avg_acc': 0.38890886306762695}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:14 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.18it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.48 v_num: 39 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">41.08it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.1947197914123535, 'avg_acc': 0.38890886306762695}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:14 • 0:00:00\u001b[0m \u001b[38;5;249m15.18it/s\u001b[0m \u001b[37mloss: 2.48 v_num: 39 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m41.08it/s\u001b[0m                      "
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.3004088401794434, 'avg_acc': 0.545158863067627}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.60it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.17 v_num: 39 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">36.96it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.3004088401794434, 'avg_acc': 0.545158863067627}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.60it/s\u001b[0m \u001b[37mloss: 2.17 v_num: 39 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m36.96it/s\u001b[0m                      "
      ]
     },
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     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.4212584495544434, 'avg_acc': 0.5214093327522278}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.25it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.96 v_num: 39 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.61it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.4212584495544434, 'avg_acc': 0.5214093327522278}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.25it/s\u001b[0m \u001b[37mloss: 1.96 v_num: 39 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.61it/s\u001b[0m                      "
      ]
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7367922067642212, 'avg_acc': 0.7542362809181213}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.68it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.74 v_num: 39 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">36.29it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7367922067642212, 'avg_acc': 0.7542362809181213}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.68it/s\u001b[0m \u001b[37mloss: 1.74 v_num: 39 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m36.29it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6492931842803955, 'avg_acc': 0.7832180857658386}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.19it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.67 v_num: 39 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.75it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6492931842803955, 'avg_acc': 0.7832180857658386}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.19it/s\u001b[0m \u001b[37mloss: 1.67 v_num: 39 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.75it/s\u001b[0m                      "
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    {
     "name": "stderr",
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\u001b[?25l"
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     },
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    {
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      "text/plain": [
       "Output()"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.089838027954102, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.48it/s</span>  </pre>\n"
      ],
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       "\r",
       "\u001b[2K{'avg_val_loss': 8.089838027954102, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m33.48it/s\u001b[0m  "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.7152392864227295, 'avg_acc': 0.34172186255455017}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.65it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.43 v_num: 40 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.30it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.7152392864227295, 'avg_acc': 0.34172186255455017}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.65it/s\u001b[0m \u001b[37mloss: 2.43 v_num: 40 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.30it/s\u001b[0m                      "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.001593828201294, 'avg_acc': 0.46092548966407776}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.91it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.14 v_num: 40 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.68it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.001593828201294, 'avg_acc': 0.46092548966407776}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.91it/s\u001b[0m \u001b[37mloss: 2.14 v_num: 40 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.68it/s\u001b[0m                      "
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.8688554763793945, 'avg_acc': 0.6932003498077393}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.31it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.93 v_num: 40 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.85it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.8688554763793945, 'avg_acc': 0.6932003498077393}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.31it/s\u001b[0m \u001b[37mloss: 1.93 v_num: 40 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.85it/s\u001b[0m                      "
      ]
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     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7114951610565186, 'avg_acc': 0.7672930955886841}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:17 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.49it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.72 v_num: 40 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.96it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7114951610565186, 'avg_acc': 0.7672930955886841}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:17 • 0:00:00\u001b[0m \u001b[38;5;249m13.49it/s\u001b[0m \u001b[37mloss: 1.72 v_num: 40 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.96it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6404743194580078, 'avg_acc': 0.7915226221084595}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.83it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.61 v_num: 40 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">32.12it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6404743194580078, 'avg_acc': 0.7915226221084595}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.83it/s\u001b[0m \u001b[37mloss: 1.61 v_num: 40 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m32.12it/s\u001b[0m                      "
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "\n",
       "\u001b[?25h"
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     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
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       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\u001b[?25l"
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       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 7.089400291442871, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.22it/s</span>  </pre>\n"
      ],
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       "\r",
       "\u001b[2K{'avg_val_loss': 7.089400291442871, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m35.22it/s\u001b[0m  "
      ]
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.1711907386779785, 'avg_acc': 0.38558465242385864}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.75it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.44 v_num: 41 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.76it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.1711907386779785, 'avg_acc': 0.38558465242385864}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.75it/s\u001b[0m \u001b[37mloss: 2.44 v_num: 41 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.76it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.317399501800537, 'avg_acc': 0.5577356815338135}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.00it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.15 v_num: 41 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">39.52it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.317399501800537, 'avg_acc': 0.5577356815338135}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m15.00it/s\u001b[0m \u001b[37mloss: 2.15 v_num: 41 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m39.52it/s\u001b[0m                      "
      ]
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     "output_type": "display_data"
    },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.2562482357025146, 'avg_acc': 0.5661122798919678}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.12it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.01 v_num: 41 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.92it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.2562482357025146, 'avg_acc': 0.5661122798919678}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m15.12it/s\u001b[0m \u001b[37mloss: 2.01 v_num: 41 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.92it/s\u001b[0m                      "
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7401235103607178, 'avg_acc': 0.7519680857658386}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.05it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.72 v_num: 41 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">32.34it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7401235103607178, 'avg_acc': 0.7519680857658386}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m15.05it/s\u001b[0m \u001b[37mloss: 1.72 v_num: 41 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m32.34it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6611411571502686, 'avg_acc': 0.777637779712677}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.18it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.63 v_num: 41 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.87it/s</span>                      </pre>\n"
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6611411571502686, 'avg_acc': 0.777637779712677}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.18it/s\u001b[0m \u001b[37mloss: 1.63 v_num: 41 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.87it/s\u001b[0m                      "
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
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       "\n",
       "\u001b[?25h"
      ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
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       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22872a19df294ad8a2e66faa728c602a",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.753585815429688, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">42.27it/s</span>  </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K{'avg_val_loss': 8.753585815429688, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m42.27it/s\u001b[0m  "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.1002652645111084, 'avg_acc': 0.39672136306762695}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.24it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.4 v_num: 42 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.96it/s</span>                     </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.1002652645111084, 'avg_acc': 0.39672136306762695}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.24it/s\u001b[0m \u001b[37mloss: 2.4 v_num: 42 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.96it/s\u001b[0m                     "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.855919122695923, 'avg_acc': 0.4794306755065918}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.79it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.11 v_num: 42 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.82it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.855919122695923, 'avg_acc': 0.4794306755065918}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.79it/s\u001b[0m \u001b[37mloss: 2.11 v_num: 42 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.82it/s\u001b[0m                      "
      ]
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.076504945755005, 'avg_acc': 0.6170794367790222}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:14 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.78it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.89 v_num: 42 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.39it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.076504945755005, 'avg_acc': 0.6170794367790222}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:14 • 0:00:00\u001b[0m \u001b[38;5;249m15.78it/s\u001b[0m \u001b[37mloss: 1.89 v_num: 42 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.39it/s\u001b[0m                      "
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7750765085220337, 'avg_acc': 0.7418754696846008}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.78it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.7 v_num: 42 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.38it/s</span>                     </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7750765085220337, 'avg_acc': 0.7418754696846008}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.78it/s\u001b[0m \u001b[37mloss: 1.7 v_num: 42 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.38it/s\u001b[0m                     "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6312804222106934, 'avg_acc': 0.7980870604515076}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.72it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.6 v_num: 42 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.33it/s</span>                     </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6312804222106934, 'avg_acc': 0.7980870604515076}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.72it/s\u001b[0m \u001b[37mloss: 1.6 v_num: 42 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.33it/s\u001b[0m                     "
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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    {
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     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
     },
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     "output_type": "display_data"
    },
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee9101090a554755ac518fcbcbaf1d7b",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 7.334644794464111, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">32.42it/s</span>  </pre>\n"
      ],
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       "\r",
       "\u001b[2K{'avg_val_loss': 7.334644794464111, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m32.42it/s\u001b[0m  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.677807569503784, 'avg_acc': 0.454397052526474}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.52it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.42 v_num: 43 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.05it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.677807569503784, 'avg_acc': 0.454397052526474}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.52it/s\u001b[0m \u001b[37mloss: 2.42 v_num: 43 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.05it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.853297233581543, 'avg_acc': 0.48071473836898804}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.02it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.12 v_num: 43 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.19it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.853297233581543, 'avg_acc': 0.48071473836898804}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.02it/s\u001b[0m \u001b[37mloss: 2.12 v_num: 43 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.19it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.020885705947876, 'avg_acc': 0.6677467226982117}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.86it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.88 v_num: 43 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.89it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.020885705947876, 'avg_acc': 0.6677467226982117}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.86it/s\u001b[0m \u001b[37mloss: 1.88 v_num: 43 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.89it/s\u001b[0m                      "
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7651634216308594, 'avg_acc': 0.7474198341369629}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.75it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.74 v_num: 43 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.64it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7651634216308594, 'avg_acc': 0.7474198341369629}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.75it/s\u001b[0m \u001b[37mloss: 1.74 v_num: 43 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.64it/s\u001b[0m                      "
      ]
     },
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    },
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6597237586975098, 'avg_acc': 0.7854622602462769}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:17 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.46it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.66 v_num: 43 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.27it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6597237586975098, 'avg_acc': 0.7854622602462769}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:17 • 0:00:00\u001b[0m \u001b[38;5;249m13.46it/s\u001b[0m \u001b[37mloss: 1.66 v_num: 43 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.27it/s\u001b[0m                      "
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "\n",
       "\u001b[?25h"
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     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
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     },
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    {
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       "model_id": "d69456d2320b40c6a75533b478fdb13c",
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      "text/plain": [
       "Output()"
      ]
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.096821784973145, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.19it/s</span>  </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K{'avg_val_loss': 8.096821784973145, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m35.19it/s\u001b[0m  "
      ]
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.739636182785034, 'avg_acc': 0.43173959851264954}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.69it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.39 v_num: 44 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.86it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.739636182785034, 'avg_acc': 0.43173959851264954}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.69it/s\u001b[0m \u001b[37mloss: 2.39 v_num: 44 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.86it/s\u001b[0m                      "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.5840487480163574, 'avg_acc': 0.5203173160552979}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.64it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.12 v_num: 44 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.80it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.5840487480163574, 'avg_acc': 0.5203173160552979}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.64it/s\u001b[0m \u001b[37mloss: 2.12 v_num: 44 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.80it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.4315226078033447, 'avg_acc': 0.5403585433959961}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.17it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.94 v_num: 44 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.94it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.4315226078033447, 'avg_acc': 0.5403585433959961}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.17it/s\u001b[0m \u001b[37mloss: 1.94 v_num: 44 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.94it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.8105859756469727, 'avg_acc': 0.7292506694793701}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.92it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.71 v_num: 44 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.36it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.8105859756469727, 'avg_acc': 0.7292506694793701}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.92it/s\u001b[0m \u001b[37mloss: 1.71 v_num: 44 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.36it/s\u001b[0m                      "
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6323498487472534, 'avg_acc': 0.7930227518081665}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.75it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.61 v_num: 44 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.86it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6323498487472534, 'avg_acc': 0.7930227518081665}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.75it/s\u001b[0m \u001b[37mloss: 1.61 v_num: 44 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.86it/s\u001b[0m                      "
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
     ]
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
     },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
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    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53953448fb5e48ee81092a4f59fa3859",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.343656539916992, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">24.49it/s</span>  </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K{'avg_val_loss': 8.343656539916992, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m24.49it/s\u001b[0m  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.713958501815796, 'avg_acc': 0.465137779712677}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.69it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.42 v_num: 45 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.34it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.713958501815796, 'avg_acc': 0.465137779712677}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.69it/s\u001b[0m \u001b[37mloss: 2.42 v_num: 45 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.34it/s\u001b[0m                      "
      ]
     },
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    },
    {
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.478332757949829, 'avg_acc': 0.4939996004104614}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.81it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.15 v_num: 45 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.42it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.478332757949829, 'avg_acc': 0.4939996004104614}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.81it/s\u001b[0m \u001b[37mloss: 2.15 v_num: 45 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.42it/s\u001b[0m                      "
      ]
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     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.1758975982666016, 'avg_acc': 0.6213997602462769}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.09it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.91 v_num: 45 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.08it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.1758975982666016, 'avg_acc': 0.6213997602462769}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.09it/s\u001b[0m \u001b[37mloss: 1.91 v_num: 45 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.08it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7351093292236328, 'avg_acc': 0.7551963329315186}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.71it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.67 v_num: 45 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.04it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7351093292236328, 'avg_acc': 0.7551963329315186}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.71it/s\u001b[0m \u001b[37mloss: 1.67 v_num: 45 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.04it/s\u001b[0m                      "
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6461964845657349, 'avg_acc': 0.7839621901512146}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:17 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.06it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.61 v_num: 45 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">32.22it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6461964845657349, 'avg_acc': 0.7839621901512146}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:17 • 0:00:00\u001b[0m \u001b[38;5;249m13.06it/s\u001b[0m \u001b[37mloss: 1.61 v_num: 45 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m32.22it/s\u001b[0m                      "
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
     ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
     ]
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27ddf77691d046b691cd1956a4423047",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.923526763916016, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">46.40it/s</span>  </pre>\n"
      ],
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       "\r",
       "\u001b[2K{'avg_val_loss': 8.923526763916016, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m46.40it/s\u001b[0m  "
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.4478373527526855, 'avg_acc': 0.36262720823287964}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.52it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.42 v_num: 46 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.16it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.4478373527526855, 'avg_acc': 0.36262720823287964}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.52it/s\u001b[0m \u001b[37mloss: 2.42 v_num: 46 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.16it/s\u001b[0m                      "
      ]
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.4203691482543945, 'avg_acc': 0.5498272180557251}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:17 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.25it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.16 v_num: 46 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.44it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.4203691482543945, 'avg_acc': 0.5498272180557251}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:17 • 0:00:00\u001b[0m \u001b[38;5;249m13.25it/s\u001b[0m \u001b[37mloss: 2.16 v_num: 46 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.44it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9327261447906494, 'avg_acc': 0.6818956136703491}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.59it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.97 v_num: 46 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.46it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9327261447906494, 'avg_acc': 0.6818956136703491}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.59it/s\u001b[0m \u001b[37mloss: 1.97 v_num: 46 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.46it/s\u001b[0m                      "
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9108930826187134, 'avg_acc': 0.6841397881507874}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.40it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.77 v_num: 46 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.12it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9108930826187134, 'avg_acc': 0.6841397881507874}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.40it/s\u001b[0m \u001b[37mloss: 1.77 v_num: 46 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.12it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.677293300628662, 'avg_acc': 0.7776497602462769}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.20it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.67 v_num: 46 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.32it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.677293300628662, 'avg_acc': 0.7776497602462769}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.20it/s\u001b[0m \u001b[37mloss: 1.67 v_num: 46 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.32it/s\u001b[0m                      "
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
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       "\u001b[?25l"
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     },
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    {
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      "text/plain": [
       "Output()"
      ]
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 7.098374366760254, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">41.86it/s</span>  </pre>\n"
      ],
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       "\r",
       "\u001b[2K{'avg_val_loss': 7.098374366760254, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m41.86it/s\u001b[0m  "
      ]
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    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.5468015670776367, 'avg_acc': 0.300163209438324}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:17 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.68it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.34 v_num: 47 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.93it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.5468015670776367, 'avg_acc': 0.300163209438324}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:17 • 0:00:00\u001b[0m \u001b[38;5;249m13.68it/s\u001b[0m \u001b[37mloss: 2.34 v_num: 47 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.93it/s\u001b[0m                      "
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.8298025131225586, 'avg_acc': 0.41205835342407227}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.61it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.12 v_num: 47 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">36.88it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.8298025131225586, 'avg_acc': 0.41205835342407227}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.61it/s\u001b[0m \u001b[37mloss: 2.12 v_num: 47 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m36.88it/s\u001b[0m                      "
      ]
     },
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     "output_type": "display_data"
    },
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.0297675132751465, 'avg_acc': 0.6331244707107544}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.70it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.9 v_num: 47 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.80it/s</span>                     </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.0297675132751465, 'avg_acc': 0.6331244707107544}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.70it/s\u001b[0m \u001b[37mloss: 1.9 v_num: 47 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.80it/s\u001b[0m                     "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7405568361282349, 'avg_acc': 0.7580165266990662}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.61it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.75 v_num: 47 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.52it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7405568361282349, 'avg_acc': 0.7580165266990662}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.61it/s\u001b[0m \u001b[37mloss: 1.75 v_num: 47 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.52it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6475733518600464, 'avg_acc': 0.7892544865608215}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.66it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.63 v_num: 47 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.08it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6475733518600464, 'avg_acc': 0.7892544865608215}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.66it/s\u001b[0m \u001b[37mloss: 1.63 v_num: 47 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.08it/s\u001b[0m                      "
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\u001b[?25l"
      ]
     },
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    {
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       "model_id": "1bdf227689cb498a8b6ac7025cb84fbf",
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      "text/plain": [
       "Output()"
      ]
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     "metadata": {},
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 9.213560104370117, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">46.21it/s</span>  </pre>\n"
      ],
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       "\r",
       "\u001b[2K{'avg_val_loss': 9.213560104370117, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m46.21it/s\u001b[0m  "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.630695343017578, 'avg_acc': 0.47817060351371765}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.75it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.36 v_num: 48 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.57it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.630695343017578, 'avg_acc': 0.47817060351371765}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.75it/s\u001b[0m \u001b[37mloss: 2.36 v_num: 48 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.57it/s\u001b[0m                      "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.3757455348968506, 'avg_acc': 0.5315020084381104}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.86it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.19 v_num: 48 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.90it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.3757455348968506, 'avg_acc': 0.5315020084381104}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.86it/s\u001b[0m \u001b[37mloss: 2.19 v_num: 48 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.90it/s\u001b[0m                      "
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.5464389324188232, 'avg_acc': 0.5372864007949829}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.42it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.87 v_num: 48 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.97it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.5464389324188232, 'avg_acc': 0.5372864007949829}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.42it/s\u001b[0m \u001b[37mloss: 1.87 v_num: 48 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.97it/s\u001b[0m                      "
      ]
     },
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    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7441966533660889, 'avg_acc': 0.750912070274353}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.60it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.73 v_num: 48 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.44it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7441966533660889, 'avg_acc': 0.750912070274353}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.60it/s\u001b[0m \u001b[37mloss: 1.73 v_num: 48 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.44it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6416096687316895, 'avg_acc': 0.7892544865608215}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.17it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.6 v_num: 48 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.77it/s</span>                     </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6416096687316895, 'avg_acc': 0.7892544865608215}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.17it/s\u001b[0m \u001b[37mloss: 1.6 v_num: 48 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.77it/s\u001b[0m                     "
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "\n",
       "\u001b[?25h"
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     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
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       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\u001b[?25l"
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       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.525567054748535, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.51it/s</span>  </pre>\n"
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       "\r",
       "\u001b[2K{'avg_val_loss': 8.525567054748535, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m33.51it/s\u001b[0m  "
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.1483616828918457, 'avg_acc': 0.39969757199287415}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.17it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.43 v_num: 49 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.39it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.1483616828918457, 'avg_acc': 0.39969757199287415}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.17it/s\u001b[0m \u001b[37mloss: 2.43 v_num: 49 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.39it/s\u001b[0m                      "
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.2803831100463867, 'avg_acc': 0.5688603520393372}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.15it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.12 v_num: 49 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.73it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.2803831100463867, 'avg_acc': 0.5688603520393372}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.15it/s\u001b[0m \u001b[37mloss: 2.12 v_num: 49 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.73it/s\u001b[0m                      "
      ]
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    },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9890161752700806, 'avg_acc': 0.6690068244934082}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.94it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.97 v_num: 49 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.43it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9890161752700806, 'avg_acc': 0.6690068244934082}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.94it/s\u001b[0m \u001b[37mloss: 1.97 v_num: 49 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.43it/s\u001b[0m                      "
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    },
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7716517448425293, 'avg_acc': 0.745367705821991}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.08it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.75 v_num: 49 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">32.92it/s</span>                      </pre>\n"
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7716517448425293, 'avg_acc': 0.745367705821991}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m15.08it/s\u001b[0m \u001b[37mloss: 1.75 v_num: 49 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m32.92it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6532070636749268, 'avg_acc': 0.7834461331367493}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.16it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.66 v_num: 49 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">36.07it/s</span>                      </pre>\n"
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6532070636749268, 'avg_acc': 0.7834461331367493}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m15.16it/s\u001b[0m \u001b[37mloss: 1.66 v_num: 49 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m36.07it/s\u001b[0m                      "
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
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       "\n",
       "\u001b[?25h"
      ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
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       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d50c0d21b2e842adbb25ed3ae9828717",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 7.782278060913086, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">39.46it/s</span>  </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K{'avg_val_loss': 7.782278060913086, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m39.46it/s\u001b[0m  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.7394957542419434, 'avg_acc': 0.4810507893562317}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.49it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.43 v_num: 50 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.81it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.7394957542419434, 'avg_acc': 0.4810507893562317}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.49it/s\u001b[0m \u001b[37mloss: 2.43 v_num: 50 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.81it/s\u001b[0m                      "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.2893807888031006, 'avg_acc': 0.5782209634780884}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.96it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.13 v_num: 50 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">41.04it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.2893807888031006, 'avg_acc': 0.5782209634780884}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.96it/s\u001b[0m \u001b[37mloss: 2.13 v_num: 50 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m41.04it/s\u001b[0m                      "
      ]
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9805846214294434, 'avg_acc': 0.6601262092590332}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.39it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.97 v_num: 50 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">36.21it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9805846214294434, 'avg_acc': 0.6601262092590332}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.39it/s\u001b[0m \u001b[37mloss: 1.97 v_num: 50 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m36.21it/s\u001b[0m                      "
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7665431499481201, 'avg_acc': 0.7438316345214844}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.90it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.79 v_num: 50 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.69it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7665431499481201, 'avg_acc': 0.7438316345214844}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.90it/s\u001b[0m \u001b[37mloss: 1.79 v_num: 50 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.69it/s\u001b[0m                      "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6419981718063354, 'avg_acc': 0.7904905676841736}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.58it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.66 v_num: 50 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.07it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6419981718063354, 'avg_acc': 0.7904905676841736}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.58it/s\u001b[0m \u001b[37mloss: 1.66 v_num: 50 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.07it/s\u001b[0m                      "
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
     },
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     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
     },
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    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8fccf75f1df4ab1903d0fed84072ce7",
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.14699649810791, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">39.92it/s</span>  </pre>\n"
      ],
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       "\r",
       "\u001b[2K{'avg_val_loss': 8.14699649810791, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m39.92it/s\u001b[0m  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.2332088947296143, 'avg_acc': 0.3750479817390442}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.93it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.35 v_num: 51 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.75it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.2332088947296143, 'avg_acc': 0.3750479817390442}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.93it/s\u001b[0m \u001b[37mloss: 2.35 v_num: 51 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.75it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 4.381516933441162, 'avg_acc': 0.33563748002052307}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.31it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.18 v_num: 51 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.09it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 4.381516933441162, 'avg_acc': 0.33563748002052307}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.31it/s\u001b[0m \u001b[37mloss: 2.18 v_num: 51 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.09it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.1520907878875732, 'avg_acc': 0.6087509989738464}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.10it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.92 v_num: 51 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">41.41it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.1520907878875732, 'avg_acc': 0.6087509989738464}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.10it/s\u001b[0m \u001b[37mloss: 1.92 v_num: 51 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m41.41it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7896231412887573, 'avg_acc': 0.7294906973838806}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.52it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.72 v_num: 51 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.20it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7896231412887573, 'avg_acc': 0.7294906973838806}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.52it/s\u001b[0m \u001b[37mloss: 1.72 v_num: 51 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.20it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6383230686187744, 'avg_acc': 0.7912826538085938}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.42it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.67 v_num: 51 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.57it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6383230686187744, 'avg_acc': 0.7912826538085938}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.42it/s\u001b[0m \u001b[37mloss: 1.67 v_num: 51 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.57it/s\u001b[0m                      "
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "\n",
       "\u001b[?25h"
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     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
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     },
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    {
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       "model_id": "f696de23cd774659be3d182e677b91c1",
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      "text/plain": [
       "Output()"
      ]
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 8.352052688598633, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.22it/s</span>  </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K{'avg_val_loss': 8.352052688598633, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m35.22it/s\u001b[0m  "
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.9486446380615234, 'avg_acc': 0.43293970823287964}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:14 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.77it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.44 v_num: 52 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.89it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.9486446380615234, 'avg_acc': 0.43293970823287964}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:14 • 0:00:00\u001b[0m \u001b[38;5;249m15.77it/s\u001b[0m \u001b[37mloss: 2.44 v_num: 52 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.89it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.420336961746216, 'avg_acc': 0.5122407674789429}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.12it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.19 v_num: 52 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.30it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.420336961746216, 'avg_acc': 0.5122407674789429}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m15.12it/s\u001b[0m \u001b[37mloss: 2.19 v_num: 52 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.30it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9599254131317139, 'avg_acc': 0.6710829138755798}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.36it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.89 v_num: 52 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.08it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9599254131317139, 'avg_acc': 0.6710829138755798}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.36it/s\u001b[0m \u001b[37mloss: 1.89 v_num: 52 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.08it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.751685380935669, 'avg_acc': 0.7499039769172668}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.26it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.74 v_num: 52 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.32it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.751685380935669, 'avg_acc': 0.7499039769172668}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m15.26it/s\u001b[0m \u001b[37mloss: 1.74 v_num: 52 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.32it/s\u001b[0m                      "
      ]
     },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6521391868591309, 'avg_acc': 0.7867343425750732}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.08it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.63 v_num: 52 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.97it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6521391868591309, 'avg_acc': 0.7867343425750732}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m15.08it/s\u001b[0m \u001b[37mloss: 1.63 v_num: 52 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.97it/s\u001b[0m                      "
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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     "name": "stderr",
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     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
     ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
     },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
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    {
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       "model_id": "9abadc8ba5f2474ca4d159de712b97dd",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 7.7617340087890625, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">56.47it/s</span>  </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K{'avg_val_loss': 7.7617340087890625, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m56.47it/s\u001b[0m  "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.8493235111236572, 'avg_acc': 0.3241167366504669}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.66it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.4 v_num: 53 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">36.37it/s</span>                     </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.8493235111236572, 'avg_acc': 0.3241167366504669}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.66it/s\u001b[0m \u001b[37mloss: 2.4 v_num: 53 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m36.37it/s\u001b[0m                     "
      ]
     },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.1577911376953125, 'avg_acc': 0.610071063041687}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.70it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.19 v_num: 53 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">33.00it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.1577911376953125, 'avg_acc': 0.610071063041687}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.70it/s\u001b[0m \u001b[37mloss: 2.19 v_num: 53 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m33.00it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.958350658416748, 'avg_acc': 0.6778513789176941}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.93it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.96 v_num: 53 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">36.35it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.958350658416748, 'avg_acc': 0.6778513789176941}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.93it/s\u001b[0m \u001b[37mloss: 1.96 v_num: 53 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m36.35it/s\u001b[0m                      "
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     },
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7929359674453735, 'avg_acc': 0.731302797794342}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.74it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.79 v_num: 53 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.01it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7929359674453735, 'avg_acc': 0.731302797794342}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.74it/s\u001b[0m \u001b[37mloss: 1.79 v_num: 53 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.01it/s\u001b[0m                      "
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6730154752731323, 'avg_acc': 0.7753816246986389}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.62it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.66 v_num: 53 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.04it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6730154752731323, 'avg_acc': 0.7753816246986389}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.62it/s\u001b[0m \u001b[37mloss: 1.66 v_num: 53 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.04it/s\u001b[0m                      "
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     "name": "stderr",
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
     ]
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
      ]
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf7164d25acd443da94e20de74e14eee",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 6.79327392578125, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">46.94it/s</span>  </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K{'avg_val_loss': 6.79327392578125, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m46.94it/s\u001b[0m  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.8566105365753174, 'avg_acc': 0.44086021184921265}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:14 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">16.10it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.43 v_num: 54 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.03it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.8566105365753174, 'avg_acc': 0.44086021184921265}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:14 • 0:00:00\u001b[0m \u001b[38;5;249m16.10it/s\u001b[0m \u001b[37mloss: 2.43 v_num: 54 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.03it/s\u001b[0m                      "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.383004665374756, 'avg_acc': 0.5200892686843872}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.45it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.16 v_num: 54 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.90it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.383004665374756, 'avg_acc': 0.5200892686843872}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.45it/s\u001b[0m \u001b[37mloss: 2.16 v_num: 54 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.90it/s\u001b[0m                      "
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.2575104236602783, 'avg_acc': 0.5876775979995728}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.85it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.87 v_num: 54 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">38.03it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.2575104236602783, 'avg_acc': 0.5876775979995728}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.85it/s\u001b[0m \u001b[37mloss: 1.87 v_num: 54 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m38.03it/s\u001b[0m                      "
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7578407526016235, 'avg_acc': 0.7565163969993591}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.09it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.73 v_num: 54 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">39.70it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7578407526016235, 'avg_acc': 0.7565163969993591}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m14.09it/s\u001b[0m \u001b[37mloss: 1.73 v_num: 54 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m39.70it/s\u001b[0m                      "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6442296504974365, 'avg_acc': 0.7869983315467834}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.13it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.6 v_num: 54 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">35.00it/s</span>                     </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.6442296504974365, 'avg_acc': 0.7869983315467834}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m15.13it/s\u001b[0m \u001b[37mloss: 1.6 v_num: 54 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m35.00it/s\u001b[0m                     "
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
       "\u001b[?25h"
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     "output_type": "stream",
     "text": [
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 51                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 51                                                                         \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
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    {
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       "model_id": "51fd5dc0610242d78f8483c56122767a",
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      "text/plain": [
       "Output()"
      ]
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 6.810842990875244, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:00 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">25.51it/s</span>  </pre>\n"
      ],
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       "\r",
       "\u001b[2K{'avg_val_loss': 6.810842990875244, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:00 • 0:00:00\u001b[0m \u001b[38;5;249m25.51it/s\u001b[0m  "
      ]
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     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.426271915435791, 'avg_acc': 0.37166377902030945}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Epoch 0/4 </span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.72it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.43 v_num: 55 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.02it/s</span>                      </pre>\n"
      ],
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       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 3.426271915435791, 'avg_acc': 0.37166377902030945}\n",
       "\u001b[37mEpoch 0/4 \u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.72it/s\u001b[0m \u001b[37mloss: 2.43 v_num: 55 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.02it/s\u001b[0m                      "
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.441312551498413, 'avg_acc': 0.5278778076171875}\n",
       "Epoch 1/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:16 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">13.90it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 2.22 v_num: 55 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">37.49it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 2.441312551498413, 'avg_acc': 0.5278778076171875}\n",
       "Epoch 1/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:16 • 0:00:00\u001b[0m \u001b[38;5;249m13.90it/s\u001b[0m \u001b[37mloss: 2.22 v_num: 55 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m37.49it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9706518650054932, 'avg_acc': 0.6614943146705627}\n",
       "Epoch 2/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:14 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">15.47it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.94 v_num: 55 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">41.01it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.9706518650054932, 'avg_acc': 0.6614943146705627}\n",
       "Epoch 2/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:14 • 0:00:00\u001b[0m \u001b[38;5;249m15.47it/s\u001b[0m \u001b[37mloss: 1.94 v_num: 55 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m41.01it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7318105697631836, 'avg_acc': 0.7602606415748596}\n",
       "Epoch 3/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.87it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.75 v_num: 55 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">34.69it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.7318105697631836, 'avg_acc': 0.7602606415748596}\n",
       "Epoch 3/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.87it/s\u001b[0m \u001b[37mloss: 1.75 v_num: 55 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m34.69it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.662052035331726, 'avg_acc': 0.7816700339317322}\n",
       "Epoch 4/4  <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">209/209</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:15 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">14.72it/s</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">loss: 1.68 v_num: 55 </span>\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Validation</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">62/62  </span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">39.45it/s</span>                      </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K\u001b[1A\u001b[2K{'avg_val_loss': 1.662052035331726, 'avg_acc': 0.7816700339317322}\n",
       "Epoch 4/4  \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m209/209\u001b[0m \u001b[38;5;245m0:00:15 • 0:00:00\u001b[0m \u001b[38;5;249m14.72it/s\u001b[0m \u001b[37mloss: 1.68 v_num: 55 \u001b[0m\n",
       "\u001b[37mValidation\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m62/62  \u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m39.45it/s\u001b[0m                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7957949  0.78245008 0.78596628 0.78321809 0.79152262 0.77763778\n",
      " 0.79808706 0.78546226 0.79302275 0.78396219 0.77764976 0.78925449\n",
      " 0.78925449 0.78344613 0.79049057 0.79128265 0.78673434 0.77538162\n",
      " 0.78699833 0.78167003]\n",
      "mean = 0.7864643216133118, std = 0.005877042298987194, ci-95 = (0.7836423266416727, 0.789286316584951)\n"
     ]
    }
   ],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-3 --gpu 1 \\\n",
    "                --opt adam "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RangerLars (RAdam + LARS + Lookahead) + flat_and_anneal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only 4 epoch\n",
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 4 --lr 1e-2 --gpu 2,3 \\\n",
    "                --opt rangerlars --sched_type flat_and_anneal --ann_start 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all 5 epoch\n",
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 1e-2 --gpu 2,3 \\\n",
    "                --opt rangerlars --sched_type flat_and_anneal --ann_start 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ralamb (RAdam + LARS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-3 --gpu 2,3 \\\n",
    "                --opt ralamb --sched_type flat_and_anneal --ann_start 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ranger (RAdam + Lookahead)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 1e-2 --gpu 2,3 \\\n",
    "                --opt ranger --sched_type flat_and_anneal --ann_start 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Novograd + bigger lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-2 --gpu 2,3 \\\n",
    "                --opt novograd --sched_type flat_and_anneal --ann_start 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Radam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 1e-2 --gpu 2,3 \\\n",
    "                --opt radam --sched_type flat_and_anneal --ann_start 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lookahead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 1e-2 --gpu 2,3 \\\n",
    "                --opt lookahead "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lamb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 1e-2 --gpu 2,3 \\\n",
    "                --opt lamb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DiffGrad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 1e-2 --gpu 2,3 \\\n",
    "                --opt diffgrad "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaMod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 1e-2 --gpu 2,3 \\\n",
    "                --opt adamod "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Madam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 1e-2 --gpu 2,3 \\\n",
    "                --opt madam --sched_type flat_and_anneal --ann_start 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apollo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-3 --gpu 2,3 \\\n",
    "                --opt apollo "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-3 --gpu 2,3 \\\n",
    "                --opt adam ## AdaBelief\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-3 --gpu 2,3 \\\n",
    "                --opt adabelief --eps 1e-16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-3 --gpu 2,3 \\\n",
    "                --opt adam %run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-3 --gpu 2,3 \\\n",
    "                --opt adam ## MADGRAD\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-3 --gpu 2,3 \\\n",
    "                --opt madgrad "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-3 --gpu 1 \\\n",
    "                --opt adan --eps 1e-16 --mom 0.98 --alpha 0.92 --sched_type flat_and_anneal --ann_start 0.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TGAdamW\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To use this log_lamb_rs, please run 'pip install tensorboardx'. Also you must have Tensorboard running to see results\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [2]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name      </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ model     │ XResNet                    │ 25.6 M │\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName     \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model     │ XResNet                    │ 25.6 M │\n",
       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss_func │ LabelSmoothingCrossEntropy │      0 │\n",
       "└───┴───────────┴────────────────────────────┴────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 25.6 M                                                                                           \n",
       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
       "<span style=\"font-weight: bold\">Total params</span>: 25.6 M                                                                                               \n",
       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 102                                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mTrainable params\u001b[0m: 25.6 M                                                                                           \n",
       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
       "\u001b[1mTotal params\u001b[0m: 25.6 M                                                                                               \n",
       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 102                                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6fa4820a2a62438c92ee90d6f82a3df0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\r",
       "\u001b[2K{'avg_val_loss': 9.26771354675293, 'avg_acc': 0.0}\n",
       "<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">Sanity Checking</span> <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">2/2</span> <span style=\"color: #8a8a8a; text-decoration-color: #8a8a8a\">0:00:01 • 0:00:00</span> <span style=\"color: #b2b2b2; text-decoration-color: #b2b2b2\">44.52it/s</span>  </pre>\n"
      ],
      "text/plain": [
       "\r",
       "\u001b[2K{'avg_val_loss': 9.26771354675293, 'avg_acc': 0.0}\n",
       "\u001b[37mSanity Checking\u001b[0m \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[37m2/2\u001b[0m \u001b[38;5;245m0:00:01 • 0:00:00\u001b[0m \u001b[38;5;249m44.52it/s\u001b[0m  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "MisconfigurationException",
     "evalue": "The closure hasn't been executed. HINT: did you call `optimizer_closure()` in your `optimizer_step` hook? It could also happen because the `optimizer.step(optimizer_closure)` call did not execute it internally.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMisconfigurationException\u001b[0m                 Traceback (most recent call last)",
      "File \u001b[0;32m~/over9000/train.py:169\u001b[0m\n\u001b[1;32m    165\u001b[0m     trainer\u001b[38;5;241m.\u001b[39mfit(lmodel, dls[\u001b[38;5;241m0\u001b[39m], dls[\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m    166\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lmodel\u001b[38;5;241m.\u001b[39mresult[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mavg_acc\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m    168\u001b[0m \u001b[38;5;129;43m@call_parse\u001b[39;49m\n\u001b[0;32m--> 169\u001b[0m \u001b[38;5;28;43;01mdef\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;21;43mmain\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    170\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mNumber of run\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgpu\u001b[49m\u001b[43m:\u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGPU to run on\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[43m        \u001b[49m\u001b[43mwoof\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mUse imagewoof (otherwise imagenette)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    173\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mLearning rate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-3\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    174\u001b[0m \u001b[43m        \u001b[49m\u001b[43msize\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSize (px: 128,192,224)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m128\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    175\u001b[0m \u001b[43m        \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAlpha\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.99\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmom\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMomentum\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.9\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    177\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepsilon\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-6\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[43m        \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mNumber of epochs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    179\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbs\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mBatch size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m256\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[43m        \u001b[49m\u001b[43mopt\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mOptimizer (adam,rms,sgd)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43madam\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    181\u001b[0m \u001b[43m        \u001b[49m\u001b[43march\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mArchitecture (xresnet34, xresnet50)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mxresnet50\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    182\u001b[0m \u001b[43m        \u001b[49m\u001b[43msched_type\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mLR schedule type\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mone_cycle\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[43m        \u001b[49m\u001b[43mann_start\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mParam\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMixup\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    184\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    186\u001b[0m \u001b[43m    \u001b[49m\u001b[43macc\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    187\u001b[0m \u001b[43m        \u001b[49m\u001b[43m[\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgpu\u001b[49m\u001b[43m,\u001b[49m\u001b[43mwoof\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmom\u001b[49m\u001b[43m,\u001b[49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43march\u001b[49m\u001b[43m,\u001b[49m\u001b[43msched_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43mann_start\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    188\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    190\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43macc\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/fastcore/script.py:125\u001b[0m, in \u001b[0;36mcall_parse\u001b[0;34m(func, nested)\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[38;5;28msetattr\u001b[39m(mod, func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, _f)\n\u001b[1;32m    124\u001b[0m     SCRIPT_INFO\u001b[38;5;241m.\u001b[39mfunc \u001b[38;5;241m=\u001b[39m func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[0;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_f\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    126\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;28;01mreturn\u001b[39;00m _f\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/fastcore/script.py:119\u001b[0m, in \u001b[0;36mcall_parse.<locals>._f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m xtra \u001b[38;5;241m=\u001b[39m otherwise(args\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxtra\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m), eq(\u001b[38;5;241m1\u001b[39m), p\u001b[38;5;241m.\u001b[39mprog)\n\u001b[1;32m    118\u001b[0m tfunc \u001b[38;5;241m=\u001b[39m trace(func) \u001b[38;5;28;01mif\u001b[39;00m args\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpdb\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m) \u001b[38;5;28;01melse\u001b[39;00m func\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmerge\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs_from_prog\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxtra\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/over9000/train.py:187\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(run, gpu, woof, lr, size, alpha, mom, eps, epochs, bs, opt, arch, sched_type, ann_start)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;129m@call_parse\u001b[39m\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmain\u001b[39m(\n\u001b[1;32m    170\u001b[0m         run: Param(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of run\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mint\u001b[39m)\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    183\u001b[0m         ann_start: Param(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMixup\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mfloat\u001b[39m)\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1.0\u001b[39m,\n\u001b[1;32m    184\u001b[0m         ):\n\u001b[1;32m    186\u001b[0m     acc \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\n\u001b[0;32m--> 187\u001b[0m         [train(gpu,woof,lr,size,alpha,mom,eps,epochs,bs,opt,arch,sched_type,ann_start)\n\u001b[1;32m    188\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(run)])\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;28mprint\u001b[39m(acc)\n\u001b[1;32m    191\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mmean(acc)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, std = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mstd(acc)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, ci-95 = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msms\u001b[38;5;241m.\u001b[39mDescrStatsW(acc)\u001b[38;5;241m.\u001b[39mtconfint_mean()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/over9000/train.py:187\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;129m@call_parse\u001b[39m\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmain\u001b[39m(\n\u001b[1;32m    170\u001b[0m         run: Param(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of run\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mint\u001b[39m)\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    183\u001b[0m         ann_start: Param(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMixup\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mfloat\u001b[39m)\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1.0\u001b[39m,\n\u001b[1;32m    184\u001b[0m         ):\n\u001b[1;32m    186\u001b[0m     acc \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\n\u001b[0;32m--> 187\u001b[0m         [\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgpu\u001b[49m\u001b[43m,\u001b[49m\u001b[43mwoof\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmom\u001b[49m\u001b[43m,\u001b[49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43march\u001b[49m\u001b[43m,\u001b[49m\u001b[43msched_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43mann_start\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    188\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(run)])\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;28mprint\u001b[39m(acc)\n\u001b[1;32m    191\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mmean(acc)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, std = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mstd(acc)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, ci-95 = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msms\u001b[38;5;241m.\u001b[39mDescrStatsW(acc)\u001b[38;5;241m.\u001b[39mtconfint_mean()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/over9000/train.py:165\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(gpu, woof, lr, size, alpha, mom, eps, epochs, bs, opt, arch, sched_type, ann_start)\u001b[0m\n\u001b[1;32m    162\u001b[0m lmodel \u001b[38;5;241m=\u001b[39m LModel(m, opt_func, lr, sched_type, total_steps, ann_start)\n\u001b[1;32m    164\u001b[0m trainer \u001b[38;5;241m=\u001b[39m pl\u001b[38;5;241m.\u001b[39mTrainer(accelerator\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgpu\u001b[39m\u001b[38;5;124m'\u001b[39m, devices\u001b[38;5;241m=\u001b[39mgpu, max_epochs\u001b[38;5;241m=\u001b[39mepochs, callbacks\u001b[38;5;241m=\u001b[39m[lr_monitor, RichProgressBar()])\u001b[38;5;66;03m#, precision=16) \u001b[39;00m\n\u001b[0;32m--> 165\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdls\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdls\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    166\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lmodel\u001b[38;5;241m.\u001b[39mresult[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mavg_acc\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:696\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m    677\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    678\u001b[0m \u001b[38;5;124;03mRuns the full optimization routine.\u001b[39;00m\n\u001b[1;32m    679\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[38;5;124;03m    datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.\u001b[39;00m\n\u001b[1;32m    694\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    695\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m model\n\u001b[0;32m--> 696\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    697\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m    698\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:650\u001b[0m, in \u001b[0;36mTrainer._call_and_handle_interrupt\u001b[0;34m(self, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    648\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    649\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 650\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    651\u001b[0m \u001b[38;5;66;03m# TODO(awaelchli): Unify both exceptions below, where `KeyboardError` doesn't re-raise\u001b[39;00m\n\u001b[1;32m    652\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exception:\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:735\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m    731\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m ckpt_path \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresume_from_checkpoint\n\u001b[1;32m    732\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__set_ckpt_path(\n\u001b[1;32m    733\u001b[0m     ckpt_path, model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    734\u001b[0m )\n\u001b[0;32m--> 735\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    737\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m    738\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1166\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m   1162\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39mrestore_training_state()\n\u001b[1;32m   1164\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39mresume_end()\n\u001b[0;32m-> 1166\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1168\u001b[0m log\u001b[38;5;241m.\u001b[39mdetail(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1169\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_teardown()\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1252\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1250\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredicting:\n\u001b[1;32m   1251\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_predict()\n\u001b[0;32m-> 1252\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1283\u001b[0m, in \u001b[0;36mTrainer._run_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1280\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_loop\u001b[38;5;241m.\u001b[39mtrainer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m   1282\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1283\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py:200\u001b[0m, in \u001b[0;36mLoop.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    199\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m    202\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py:271\u001b[0m, in \u001b[0;36mFitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    267\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher\u001b[38;5;241m.\u001b[39msetup(\n\u001b[1;32m    268\u001b[0m     dataloader, batch_to_device\u001b[38;5;241m=\u001b[39mpartial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39m_call_strategy_hook, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch_to_device\u001b[39m\u001b[38;5;124m\"\u001b[39m, dataloader_idx\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    269\u001b[0m )\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_epoch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 271\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py:200\u001b[0m, in \u001b[0;36mLoop.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    199\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m    202\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py:203\u001b[0m, in \u001b[0;36mTrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_progress\u001b[38;5;241m.\u001b[39mincrement_started()\n\u001b[1;32m    202\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_batch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 203\u001b[0m         batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_progress\u001b[38;5;241m.\u001b[39mincrement_processed()\n\u001b[1;32m    207\u001b[0m \u001b[38;5;66;03m# update non-plateau LR schedulers\u001b[39;00m\n\u001b[1;32m    208\u001b[0m \u001b[38;5;66;03m# update epoch-interval ones only when we are at the end of training epoch\u001b[39;00m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py:200\u001b[0m, in \u001b[0;36mLoop.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    199\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m    202\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py:87\u001b[0m, in \u001b[0;36mTrainingBatchLoop.advance\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39mautomatic_optimization:\n\u001b[1;32m     84\u001b[0m     optimizers \u001b[38;5;241m=\u001b[39m _get_active_optimizers(\n\u001b[1;32m     85\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39moptimizers, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39moptimizer_frequencies, kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch_idx\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     86\u001b[0m     )\n\u001b[0;32m---> 87\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     89\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_loop\u001b[38;5;241m.\u001b[39mrun(kwargs)\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py:200\u001b[0m, in \u001b[0;36mLoop.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    199\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m    202\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:201\u001b[0m, in \u001b[0;36mOptimizerLoop.advance\u001b[0;34m(self, optimizers, kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madvance\u001b[39m(\u001b[38;5;28mself\u001b[39m, optimizers: List[Tuple[\u001b[38;5;28mint\u001b[39m, Optimizer]], kwargs: OrderedDict) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:  \u001b[38;5;66;03m# type: ignore[override]\u001b[39;00m\n\u001b[1;32m    199\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_kwargs(kwargs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimizer_idx, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_hiddens)\n\u001b[0;32m--> 201\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_optimization\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptim_progress\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_position\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    202\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    203\u001b[0m         \u001b[38;5;66;03m# automatic optimization assumes a loss needs to be returned for extras to be considered as the batch\u001b[39;00m\n\u001b[1;32m    204\u001b[0m         \u001b[38;5;66;03m# would be skipped otherwise\u001b[39;00m\n\u001b[1;32m    205\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_outputs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimizer_idx] \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39masdict()\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:250\u001b[0m, in \u001b[0;36mOptimizerLoop._run_optimization\u001b[0;34m(self, kwargs, optimizer)\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m    243\u001b[0m \u001b[38;5;66;03m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m    244\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m    245\u001b[0m \u001b[38;5;66;03m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m    246\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    247\u001b[0m     \u001b[38;5;66;03m# the `batch_idx` is optional with inter-batch parallelism\u001b[39;00m\n\u001b[1;32m    248\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step(optimizer, opt_idx, kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch_idx\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m0\u001b[39m), closure)\n\u001b[0;32m--> 250\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconsume_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    253\u001b[0m     \u001b[38;5;66;03m# if no result, user decided to skip optimization\u001b[39;00m\n\u001b[1;32m    254\u001b[0m     \u001b[38;5;66;03m# otherwise update running loss + reset accumulated loss\u001b[39;00m\n\u001b[1;32m    255\u001b[0m     \u001b[38;5;66;03m# TODO: find proper way to handle updating running loss\u001b[39;00m\n\u001b[1;32m    256\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mfit_loop\u001b[38;5;241m.\u001b[39mepoch_loop\u001b[38;5;241m.\u001b[39mbatch_loop\u001b[38;5;241m.\u001b[39m_update_running_loss(result\u001b[38;5;241m.\u001b[39mloss)\n",
      "File \u001b[0;32m~/.anaconda/envs/over9000/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/closure.py:51\u001b[0m, in \u001b[0;36mAbstractClosure.consume_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;124;03m\"\"\"The cached result from the last time the closure was called.\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \n\u001b[1;32m     47\u001b[0m \u001b[38;5;124;03mOnce accessed, the internal reference gets reset and the consumer will have to hold on to the reference as long\u001b[39;00m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;124;03mas necessary.\u001b[39;00m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MisconfigurationException(\n\u001b[1;32m     52\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe closure hasn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt been executed.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     53\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m HINT: did you call `optimizer_closure()` in your `optimizer_step` hook? It could also happen because\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     54\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m the `optimizer.step(optimizer_closure)` call did not execute it internally.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     55\u001b[0m     )\n\u001b[1;32m     56\u001b[0m result, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result, \u001b[38;5;28;01mNone\u001b[39;00m  \u001b[38;5;66;03m# free memory\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "\u001b[0;31mMisconfigurationException\u001b[0m: The closure hasn't been executed. HINT: did you call `optimizer_closure()` in your `optimizer_step` hook? It could also happen because the `optimizer.step(optimizer_closure)` call did not execute it internally."
     ]
    }
   ],
   "source": [
    "%run train.py --run 20 --woof 0 --size 128 --bs 64 --epoch 5 --lr 3e-3 --gpu 1 \\\n",
    "                --opt tgadamw "
   ]
  }
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