DeepLearning-GPU/PyTorch_Lightning_MNIST.ipynb

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    "# MNIST Pytorch Lightning\n",
    "This is a \"Hello world!\" example with PyTorch Lightning.  \n",
    "It trains a convolutional neural network on the MNIST dataset.  \n",
    "\n",
    "Credits:   \n",
    "MNIST dataset, see http://yann.lecun.com/exdb/mnist/  \n",
    "Code adapted from the documentation of the Lightning-AI and PyTorch projects\n"
   ]
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  {
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   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "from torchvision.datasets import MNIST\n",
    "from torchvision import transforms\n",
    "import pytorch_lightning as pl"
   ]
  },
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    "# Define the model and the training and test steps\n",
    "# The model uses convolutional neural network layers\n",
    "\n",
    "class LitMNIST(pl.LightningModule):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 32, 3, 1)\n",
    "        self.conv2 = nn.Conv2d(32, 64, 3, 1)\n",
    "        self.dropout1 = nn.Dropout(0.25)\n",
    "        self.dropout2 = nn.Dropout(0.5)\n",
    "        self.fc1 = nn.Linear(9216, 128)\n",
    "        self.fc2 = nn.Linear(128, 10)\n",
    "        self.loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "        x = F.max_pool2d(x, 2)\n",
    "        x = self.dropout1(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.dropout2(x)\n",
    "        x = self.fc2(x)\n",
    "        output = F.log_softmax(x, dim=1)\n",
    "        return output\n",
    "    \n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters(), lr=1e-3)\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        y_hat = self(x)\n",
    "        loss = self.loss_fn(y_hat, y)\n",
    "        self.log('train_loss', loss)\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        y_hat = self(x)\n",
    "        loss = self.loss_fn(y_hat, y)\n",
    "        preds = torch.argmax(y_hat, dim=1)\n",
    "        accuracy = (preds == y).float().mean()\n",
    "        self.log('val_loss', loss)\n",
    "        self.log('val_accuracy', accuracy)\n",
    "     "
   ]
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     "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: [0]\n",
      "\n",
      "  | Name     | Type             | Params\n",
      "----------------------------------------------\n",
      "0 | conv1    | Conv2d           | 320   \n",
      "1 | conv2    | Conv2d           | 18.5 K\n",
      "2 | dropout1 | Dropout          | 0     \n",
      "3 | dropout2 | Dropout          | 0     \n",
      "4 | fc1      | Linear           | 1.2 M \n",
      "5 | fc2      | Linear           | 1.3 K \n",
      "6 | loss_fn  | CrossEntropyLoss | 0     \n",
      "----------------------------------------------\n",
      "1.2 M     Trainable params\n",
      "0         Non-trainable params\n",
      "1.2 M     Total params\n",
      "4.800     Total estimated model params size (MB)\n"
     ]
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=10` reached.\n"
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   "source": [
    "# Code for reading the data, training the model\n",
    "\n",
    "num_epochs = 10\n",
    "num_workers = 4\n",
    "\n",
    "torch.manual_seed(1)\n",
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.Normalize((0.1307,), (0.3081,))])\n",
    "dataset = MNIST('../data', train=True, download=True, transform=transform)\n",
    "train_dataset, val_dataset = random_split(dataset, [55000, 5000])\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, num_workers=num_workers)\n",
    "val_loader = DataLoader(val_dataset, batch_size=1000, num_workers=num_workers)\n",
    "    \n",
    "model = LitMNIST()\n",
    "trainer = pl.Trainer(max_epochs=num_epochs)\n",
    "trainer.fit(model, train_loader, val_loader)\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=\"font-weight: bold\">      Validate metric      </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_accuracy        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9901999831199646     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.03850765526294708    </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
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       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m     Validate 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      val_accuracy       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9901999831199646    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.03850765526294708   \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
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    "# Evaluate the trained model on the validation dataset\n",
    "\n",
    "trainer.validate(model, val_loader)"
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