unit06-dl-tips/6.2-learning-rates/6.2-part5-0-no-scheduler.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e462c50-9f5d-4cc0-beb8-2f8918a6b1e2",
   "metadata": {},
   "source": [
    "# Unit 6. Essential Deep Learning Tips & Tricks\n",
    "\n",
    "# 6.2 Learning Rates and Learning Rate Schedulers\n",
    "\n",
    "## Part 3. Using an Automatic Learning Rate Finder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eec68953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch    : 1.13.1\n",
      "lightning: 2.0.0rc1\n",
      "\n",
      "conda environment: dl-fundamentals\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning --conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e80fef60",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightning as L\n",
    "import torch\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "\n",
    "from shared_utilities import CustomDataModule, LightningModel, PyTorchMLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa7bcf14-8046-4213-accf-544b67e74883",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(123)\n",
    "dm = CustomDataModule()\n",
    "\n",
    "pytorch_model = PyTorchMLP(num_features=100, num_classes=2)\n",
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "17e18376-9168-439c-95bc-cfa94cdf9f8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/setup.py:201: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
      "  rank_zero_warn(\n"
     ]
    }
   ],
   "source": [
    "trainer = L.Trainer(\n",
    "    max_epochs=100,\n",
    "    accelerator=\"cpu\",\n",
    "    devices=\"auto\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"),    \n",
    "    deterministic=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "14d23b49-4c47-4bb1-bf6c-b87aabf0b22d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/callbacks/model_checkpoint.py:612: UserWarning: Checkpoint directory logs/my-model/version_0/checkpoints exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "\n",
      "  | Name      | Type               | Params\n",
      "-------------------------------------------------\n",
      "0 | model     | PyTorchMLP         | 15.3 K\n",
      "1 | train_acc | MulticlassAccuracy | 0     \n",
      "2 | val_acc   | MulticlassAccuracy | 0     \n",
      "3 | test_acc  | MulticlassAccuracy | 0     \n",
      "-------------------------------------------------\n",
      "15.3 K    Trainable params\n",
      "0         Non-trainable params\n",
      "15.3 K    Total params\n",
      "0.061     Total estimated model params size (MB)\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/fabric/loggers/csv_logs.py:188: UserWarning: Experiment logs directory logs/my-model/version_0 exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "712f0e315a444109b768f124d5eb1fd6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "404292d41b2440139dcc87eed0c547f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8052f366a27e49c99a2614ae669168d3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "512b423b739b4e7f8f7a2d4da8123c53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "461c7014ed1146f5b6252ef2713e718a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e32e279b6da546c6bd2ee5e763f1a7e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "117e7440f2e04b5180b9b8a80b4b0d2b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28655fc98bcc400ea7442242568f4ac8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37ae56a631f9444896935ef0d2a2473d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f53a86a4ea2c4484be00683a85a783eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3bb16c4d1fd47c8b769ebaf3e6c8845",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef452d030d844bd59e52cc48684c502a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c1ebe3141994acf80a74f0bb67401d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "219ceddc912b4ff58665b0e14927fd5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cab962e7c7a84233b3f08a9b260a560f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "62700b71040c4e03b39d1c092f412383",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fae3f9499920472b9273743c8508972f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff533774e7fa492c83a849d4f906832f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "626fc25e9cec429bbda5a624dc22142e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "679b6598b3924d10a91b96605860aa1b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15d8c5549dd548a8b19d448f7c47712a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bdcb89a59f634dd9bab8dc167ef93d1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4e2211ffac24b67bd25e7304b7f4272",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aae5e900df2e46c99451561011a0a607",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb91d3c4dafa43a7825f77f11112659a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "353624f211ae4ecc946b5b8724f3eea9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d90f75db83904d46a3fc6638784c2c22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "825446506df6447dad02e46aaeb143c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b4b6a0571c5475eb931abb1db9df56e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76b2f2cb04054997a0a602bcaa2b7935",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb8b359865e14a07a5f9e4b9cd1e0bcc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c72548bc9658450c9be16f112890aaf3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e84a5881b7554c00a6bf7a40f32ff7fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fad4098c296548f18a44ba5784fa9fe4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ccd49da0a0d406dad31cb2c13646641",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e52d0a24c39843f28da3d728ce31ba1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dcc4db8200d44112ad2fd1fd0ba75ab2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2010ecb380024051aae4cf06a6ab26e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a06ab9b72d04452a3f9c843bfcbca20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be62b2a9ffda44e782f4c5415999eeb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71de0795ecdb4919ad8579d2bfb78cb8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54771c23f9b14825adb9ad079bca91bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "797ac657bf0b4f57a5db1356888e122f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2eaed6c082f24aca9cd358563a7f06d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb5a66fd1bae40e787704774a79979ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fdd85678e0194f63b16da9c92a9cb3da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c17c3003903b443181220ad06932cbca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "328ef6983f6546209c857377fa524ed4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dff7d9810fc54584934b0251affa57dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0adeb63cbfa9498e8c79a4af59e9efbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8cf62fbfac714caa9fecec843928e73a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8123fe3690c940ceb96014519dffaeff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5a4d921195a4093a48ae0f34d70f783",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2575a9d7104948938da7457cc562c69f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2456bdf2a5a84637ad4972713c5378cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=100` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer.fit(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d4b7eb3-9f3c-4095-ad85-b37acf4557dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "df_metrics[[\"train_loss\", \"val_loss\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n",
    ")\n",
    "\n",
    "plt.savefig(\"suggest_loss.pdf\")\n",
    "\n",
    "df_metrics[[\"train_acc\", \"val_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.savefig(\"suggest_acc.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed97ce3f-c177-45f7-ac9c-5a90b0f765f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2213cc992c79453ba193122aa5f93e66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "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",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.862500011920929     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.862500011920929    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.862500011920929}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, dataloaders=dm.val_dataloader())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c218eba8-60c6-4a5b-92d1-ede4ba089ce8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee693cf1-f29b-45b7-8107-86830abaf96d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}