pytorch_ipynb/lightning/lightning-mlp-best-model.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "07c2ba66-8617-409a-b271-99697fc240cc",
   "metadata": {},
   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5e8d7ae-5738-4e17-a2db-3e9cc48febe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Sebastian Raschka\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.9.6\n",
      "IPython version      : 7.29.0\n",
      "\n",
      "torch            : 1.8.0\n",
      "pytorch_lightning: 1.5.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p torch,pytorch_lightning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795b2a55",
   "metadata": {},
   "source": [
    "## Higher-level PyTorch APIs: a short introduction to PyTorch Lightning (checkpointing the best model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a23ff4-53e8-4d24-925c-48e5cd934dd6",
   "metadata": {},
   "source": [
    "### Showing how to checkpoint the best model"
   ]
  },
  {
   "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": "82acead3-d939-400b-a8b6-f64e337c010e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "\n",
    "callbacks = [ModelCheckpoint(save_top_k=1, mode='max', monitor=\"valid_acc\")] # save top 1 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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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   "source": [
    "mnistclassifier = MultiLayerPerceptron()\n",
    "\n",
    "if torch.cuda.is_available(): # if you have GPUs\n",
    "    trainer = pl.Trainer(max_epochs=300, callbacks=callbacks, gpu=1)\n",
    "else:\n",
    "    trainer = pl.Trainer(max_epochs=300, 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": "markdown",
   "id": "1e4d76e6-c9e9-49f2-ac98-3ac7348aa74d",
   "metadata": {},
   "source": [
    "- By default, the last model is usually evaluated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "596ae7f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bf97b2b834b4367b0b05c3d0feb83b6",
       "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': 0.9600539803504944, 'test_loss': 1.498080849647522}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 1.498080849647522, 'test_acc': 0.9600539803504944}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=mnistclassifier, datamodule=mnist_dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2f5ccfc6-27ce-4110-bc3e-b055078afc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='lightning-mlp-best-model_images/1.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fc54776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 54170), started 6:25:38 ago. (Use '!kill 54170' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-a420c872f57ec160\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-a420c872f57ec160\");\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": "markdown",
   "id": "e8cd9566-d46c-4d96-9c33-20669af59d26",
   "metadata": {},
   "source": [
    "Based on the TensorBoard output above, we can see that the best model is not the model after the final training step. We can load the best for evaluation as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cf0d4ae7-9529-4aa8-88cc-5e0eed7a56ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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 /Users/sebastian/Desktop/lightning_logs/version_0/checkpoints/epoch=273-step=235639.ckpt\n",
      "Loaded model weights from checkpoint at /Users/sebastian/Desktop/lightning_logs/version_0/checkpoints/epoch=273-step=235639.ckpt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "120e3cbef6e644be8b0c4c3986d31935",
       "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': 0.9628065228462219, 'test_loss': 1.4963043928146362}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 1.4963043928146362, 'test_acc': 0.9628065228462219}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=mnistclassifier, datamodule=mnist_dm, ckpt_path='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deabe84d-4f8e-4518-9679-efc573fc3329",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}