ch13/ch13_part3_lightning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72fe9fd2-6094-4d6c-9f8a-a5a806926be5",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Machine Learning with PyTorch and Scikit-Learn  \n",
    "# -- Code Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4297f74f-73dd-4451-85b7-7bceaf6200f8",
   "metadata": {},
   "source": [
    "## Package version checks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a87052e-bf50-41b4-87bf-9dcd4b3240ac",
   "metadata": {},
   "source": [
    "Add folder to path in order to load from the check_packages.py script:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a2013cb-28f8-4ba0-8008-f3953344060d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '..')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "972c1221-f5ba-49ee-9143-f16968cbdbf6",
   "metadata": {},
   "source": [
    "Check recommended package versions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b6b29ad-bf77-42b3-b0ea-a3d14174be02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] torch 1.10.0\n",
      "[OK] torchvision 0.11.2\n",
      "[OK] tensorboard 2.7.0\n",
      "[OK] pytorch_lightning 1.5.8\n",
      "[OK] torchmetrics 0.6.2\n"
     ]
    }
   ],
   "source": [
    "from python_environment_check import check_packages\n",
    "\n",
    "\n",
    "d = {\n",
    "    'torch': '1.8',\n",
    "    'torchvision': '0.9.0',\n",
    "    'tensorboard': '2.7.0',\n",
    "    'pytorch_lightning': '1.5.2',\n",
    "    'torchmetrics': '0.6.2'\n",
    "}\n",
    "check_packages(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c2ba66-8617-409a-b271-99697fc240cc",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Chapter 13: Going Deeper -- the Mechanics of PyTorch (Part 3/3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8e2cbe-0e20-47b1-936c-8657e46890f4",
   "metadata": {},
   "source": [
    "**Outline**\n",
    "\n",
    "- [Higher-level PyTorch APIs: a short introduction to PyTorch Lightning](#Higher-level-PyTorch-APIs-a-short-introduction-to-PyTorch-Lightning)\n",
    "  - [Setting up the PyTorch Lightning model](#Setting-up-the-PyTorch-Lightning-model)\n",
    "  - [Setting up the data loaders for Lightning](#Setting-up-the-data-loaders-for-Lightning)\n",
    "  - [Training the model using the PyTorch Lightning Trainer class](#Training-the-model-using-the-PyTorch-Lightning-Trainer-class)\n",
    "  - [Evaluating the model using TensorBoard](#Evaluating-the-model-using-TensorBoard)\n",
    "- [Summary](#Summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4337ae3e-0e22-456b-944f-84188da4d5f2",
   "metadata": {},
   "source": [
    "## Higher-level PyTorch APIs: a short introduction to PyTorch Lightning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6385b5ca-7919-456e-a4f7-65ab57e904de",
   "metadata": {},
   "source": [
    "### Setting up the PyTorch Lightning model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795b2a55",
   "metadata": {},
   "source": [
    "## Higher-level PyTorch APIs: a short introduction to PyTorch Lightning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaa08b43",
   "metadata": {},
   "source": [
    "### Setting up the PyTorch Lightning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4b5be90",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytorch_lightning as pl\n",
    "import torch \n",
    "import torch.nn as nn \n",
    "\n",
    "from torchmetrics import Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "148a926d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiLayerPerceptron(pl.LightningModule):\n",
    "    def __init__(self, image_shape=(1, 28, 28), hidden_units=(32, 16)):\n",
    "        super().__init__()\n",
    "        \n",
    "        # new PL attributes:\n",
    "        self.train_acc = Accuracy()\n",
    "        self.valid_acc = Accuracy()\n",
    "        self.test_acc = Accuracy()\n",
    "        \n",
    "        # Model similar to previous section:\n",
    "        input_size = image_shape[0] * image_shape[1] * image_shape[2] \n",
    "        all_layers = [nn.Flatten()]\n",
    "        for hidden_unit in hidden_units: \n",
    "            layer = nn.Linear(input_size, hidden_unit) \n",
    "            all_layers.append(layer) \n",
    "            all_layers.append(nn.ReLU()) \n",
    "            input_size = hidden_unit \n",
    " \n",
    "        all_layers.append(nn.Linear(hidden_units[-1], 10)) \n",
    "        all_layers.append(nn.Softmax(dim=1)) \n",
    "        self.model = nn.Sequential(*all_layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.model(x)\n",
    "        return x\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        logits = self(x)\n",
    "        loss = nn.functional.cross_entropy(self(x), y)\n",
    "        preds = torch.argmax(logits, dim=1)\n",
    "        self.train_acc.update(preds, y)\n",
    "        self.log(\"train_loss\", loss, prog_bar=True)\n",
    "        return loss\n",
    "\n",
    "    def training_epoch_end(self, outs):\n",
    "        self.log(\"train_acc\", self.train_acc.compute())\n",
    "        self.train_acc.reset()\n",
    "    \n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        logits = self(x)\n",
    "        loss = nn.functional.cross_entropy(self(x), y)\n",
    "        preds = torch.argmax(logits, dim=1)\n",
    "        self.valid_acc.update(preds, y)\n",
    "        self.log(\"valid_loss\", loss, prog_bar=True)\n",
    "        return loss\n",
    "    \n",
    "    def validation_epoch_end(self, outs):\n",
    "        self.log(\"valid_acc\", self.valid_acc.compute(), prog_bar=True)\n",
    "        self.valid_acc.reset()\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        logits = self(x)\n",
    "        loss = nn.functional.cross_entropy(self(x), y)\n",
    "        preds = torch.argmax(logits, dim=1)\n",
    "        self.test_acc.update(preds, y)\n",
    "        self.log(\"test_loss\", loss, prog_bar=True)\n",
    "        self.log(\"test_acc\", self.test_acc.compute(), prog_bar=True)\n",
    "        return loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=0.001)\n",
    "        return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6aa402-3264-4949-b52b-7978e904ce96",
   "metadata": {},
   "source": [
    "### Setting up the data loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0cfbe99f-a47a-4f8f-8090-d1b9c44230ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import random_split\n",
    " \n",
    "from torchvision.datasets import MNIST\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8a89532-7600-4875-86f1-2bc69810597e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MnistDataModule(pl.LightningDataModule):\n",
    "    def __init__(self, data_path='./'):\n",
    "        super().__init__()\n",
    "        self.data_path = data_path\n",
    "        self.transform = transforms.Compose([transforms.ToTensor()])\n",
    "        \n",
    "    def prepare_data(self):\n",
    "        MNIST(root=self.data_path, download=True) \n",
    "\n",
    "    def setup(self, stage=None):\n",
    "        # stage is either 'fit', 'validate', 'test', or 'predict'\n",
    "        # here note relevant\n",
    "        mnist_all = MNIST( \n",
    "            root=self.data_path,\n",
    "            train=True,\n",
    "            transform=self.transform,  \n",
    "            download=False\n",
    "        ) \n",
    "\n",
    "        self.train, self.val = random_split(\n",
    "            mnist_all, [55000, 5000], generator=torch.Generator().manual_seed(1)\n",
    "        )\n",
    "\n",
    "        self.test = MNIST( \n",
    "            root=self.data_path,\n",
    "            train=False,\n",
    "            transform=self.transform,  \n",
    "            download=False\n",
    "        ) \n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return DataLoader(self.train, batch_size=64, num_workers=4)\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return DataLoader(self.val, batch_size=64, num_workers=4)\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        return DataLoader(self.test, batch_size=64, num_workers=4)\n",
    "    \n",
    "    \n",
    "torch.manual_seed(1) \n",
    "mnist_dm = MnistDataModule()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b6f8d80-236a-4b01-9cbe-77eb300a0792",
   "metadata": {},
   "source": [
    "### Training the model using the PyTorch Lightning Trainer class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36f5eeff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "\n",
      "  | Name      | Type       | Params\n",
      "-----------------------------------------\n",
      "0 | train_acc | Accuracy   | 0     \n",
      "1 | valid_acc | Accuracy   | 0     \n",
      "2 | test_acc  | Accuracy   | 0     \n",
      "3 | model     | Sequential | 25.8 K\n",
      "-----------------------------------------\n",
      "25.8 K    Trainable params\n",
      "0         Non-trainable params\n",
      "25.8 K    Total params\n",
      "0.103     Total estimated model params size (MB)\n"
     ]
    },
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       "Validation sanity check: 0it [00:00, ?it/s]"
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   "source": [
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "\n",
    "\n",
    "mnistclassifier = MultiLayerPerceptron()\n",
    "\n",
    "callbacks = [ModelCheckpoint(save_top_k=1, mode='max', monitor=\"valid_acc\")] # save top 1 model\n",
    "\n",
    "if torch.cuda.is_available(): # if you have GPUs\n",
    "    trainer = pl.Trainer(max_epochs=10, callbacks=callbacks, gpus=1)\n",
    "else:\n",
    "    trainer = pl.Trainer(max_epochs=10, callbacks=callbacks)\n",
    "\n",
    "trainer.fit(model=mnistclassifier, datamodule=mnist_dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a15f2cf-bb14-4ab0-b2bb-9fdeaff6430b",
   "metadata": {},
   "source": [
    "### Evaluating the model using TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "596ae7f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at /Users/sebastian/Desktop/lightning_logs/version_0/checkpoints/epoch=9-step=8599.ckpt\n",
      "Loaded model weights from checkpoint at /Users/sebastian/Desktop/lightning_logs/version_0/checkpoints/epoch=9-step=8599.ckpt\n"
     ]
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_acc': 0.938988208770752, 'test_loss': 1.5173449516296387}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 1.5173449516296387, 'test_acc': 0.938988208770752}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=mnistclassifier, datamodule=mnist_dm, ckpt_path='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2f5ccfc6-27ce-4110-bc3e-b055078afc01",
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    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='figures/01.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3fc54776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 54170), started 7:45:19 ago. (Use '!kill 54170' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-1f4782a460395538\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-1f4782a460395538\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Start tensorboard\n",
    "%load_ext tensorboard\n",
    "%tensorboard --logdir lightning_logs/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb5ba946-fd6e-4eb3-aba3-597ee3ee546d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastian/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py:45: LightningDeprecationWarning: Setting `Trainer(resume_from_checkpoint=)` is deprecated in v1.5 and will be removed in v1.7. Please pass `Trainer.fit(ckpt_path=)` directly instead.\n",
      "  rank_zero_deprecation(\n",
      "Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.model_summary.ModelSummary'>]. Skipping setting a default `ModelSummary` callback.\n",
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "/Users/sebastian/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1906: LightningDeprecationWarning: `trainer.resume_from_checkpoint` is deprecated in v1.5 and will be removed in v1.7. Specify the fit checkpoint path with `trainer.fit(ckpt_path=)` instead.\n",
      "  rank_zero_deprecation(\n",
      "/Users/sebastian/miniforge3/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py:469: LightningDeprecationWarning: DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup.\n",
      "  rank_zero_deprecation(\n",
      "Restoring states from the checkpoint path at lightning_logs/version_0/checkpoints/epoch=9-step=8599.ckpt\n",
      "/Users/sebastian/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py:247: UserWarning: You're resuming from a checkpoint that ended mid-epoch. Training will start from the beginning of the next epoch. This can cause unreliable results if further training is done, consider using an end of epoch checkpoint.\n",
      "  rank_zero_warn(\n",
      "Restored all states from the checkpoint file at lightning_logs/version_0/checkpoints/epoch=9-step=8599.ckpt\n",
      "\n",
      "  | Name      | Type       | Params\n",
      "-----------------------------------------\n",
      "0 | train_acc | Accuracy   | 0     \n",
      "1 | valid_acc | Accuracy   | 0     \n",
      "2 | test_acc  | Accuracy   | 0     \n",
      "3 | model     | Sequential | 25.8 K\n",
      "-----------------------------------------\n",
      "25.8 K    Trainable params\n",
      "0         Non-trainable params\n",
      "25.8 K    Total params\n",
      "0.103     Total estimated model params size (MB)\n",
      "/Users/sebastian/miniforge3/lib/python3.9/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:617: UserWarning: Checkpoint directory /Users/sebastian/Desktop/lightning_logs/version_0/checkpoints exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n"
     ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastian/miniforge3/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py:469: LightningDeprecationWarning: DataModule.teardown has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.teardown.\n",
      "  rank_zero_deprecation(\n"
     ]
    }
   ],
   "source": [
    "path = 'lightning_logs/version_0/checkpoints/epoch=9-step=8599.ckpt'\n",
    "\n",
    "if torch.cuda.is_available(): # if you have GPUs\n",
    "    trainer = pl.Trainer(\n",
    "        max_epochs=30, callbacks=callbacks, resume_from_checkpoint=path, gpus=1\n",
    "    )\n",
    "else:\n",
    "    trainer = pl.Trainer(\n",
    "        max_epochs=30, callbacks=callbacks, resume_from_checkpoint=path\n",
    "    )\n",
    "\n",
    "trainer.fit(model=mnistclassifier, datamodule=mnist_dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b376098f-48a2-4c3f-83e8-25f31ae0ba70",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='figures/02.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ed175e10-2d44-4b9c-977c-72c3953efa97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 54170), started 7:52:54 ago. (Use '!kill 54170' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-b797f5dcdc7c4773\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-b797f5dcdc7c4773\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir lightning_logs/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e7a3282-8c7c-4808-b20c-7080ee06681a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f398ea1b12fe4c0d9754c478d6b5e5f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_acc': 0.9474525451660156, 'test_loss': 1.504822850227356}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 1.504822850227356, 'test_acc': 0.9474525451660156}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=mnistclassifier, datamodule=mnist_dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32bb7db7-623f-4bae-9b87-fb1b6327997c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at /Users/sebastian/Desktop/lightning_logs/version_0/checkpoints/epoch=23-step=20639.ckpt\n",
      "Loaded model weights from checkpoint at /Users/sebastian/Desktop/lightning_logs/version_0/checkpoints/epoch=23-step=20639.ckpt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53d1542f40294567b8a7eb0b1b44fda4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_acc': 0.9509269595146179, 'test_loss': 1.5060889720916748}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 1.5060889720916748, 'test_acc': 0.9509269595146179}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=mnistclassifier, datamodule=mnist_dm, ckpt_path='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4b4ff35e-0fca-4d16-8f6e-49ecdb25cc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"lightning_logs/version_0/checkpoints/epoch=23-step=20639.ckpt\"\n",
    "model = MultiLayerPerceptron.load_from_checkpoint(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54205ec0-acd9-479c-bbe2-d290407f4c22",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e4056c-de62-4cb9-b594-2b833b4050c2",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Readers may ignore the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68f01866-03bc-4a46-bf54-04e449158e3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] WARNING | Config option `kernel_spec_manager_class` not recognized by `NbConvertApp`.\n",
      "[NbConvertApp] Converting notebook ch13_part3_lightning.ipynb to script\n",
      "[NbConvertApp] Writing 7309 bytes to ch13_part3_lightning.py\n"
     ]
    }
   ],
   "source": [
    "! python ../.convert_notebook_to_script.py --input ch13_part3_lightning.ipynb --output ch13_part3_lightning.py"
   ]
  }
 ],
 "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}