pytorch_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "n_epochs = 3\n",
    "batch_size_train = 64\n",
    "batch_size_test = 1000\n",
    "learning_rate = 0.01\n",
    "momentum = 0.5\n",
    "log_interval = 10\n",
    "\n",
    "random_seed = 1\n",
    "\n",
    "torch.backends.cudnn.enabled = False\n",
    "torch.manual_seed(random_seed)\n",
    "\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor()])\n",
    "\n",
    "# Load the MNIST dataset\n",
    "train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
    "test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(dataset=test_dataset, batch_size=1000, shuffle=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000\n",
      "10000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(len(train_dataset))\n",
    "print(len(test_dataset))\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "for i in range(5):\n",
    "    image, label = train_dataset[52]\n",
    "    plt.imshow(image.squeeze())\n",
    "    plt.title(f'THIS IS {label}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleNN, self).__init__()\n",
    "        self.fc1 = nn.Linear(28*28, 128) #28x28 to 128 (pixels to 128 features)\n",
    "        self.fc2 = nn.Linear(128, 10) # 128 features to 10 (0 - 9)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 28*28) #flatten\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "    \n",
    "model = SimpleNN() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5, Loss: 0.3588151124589987\n",
      "Epoch 2/5, Loss: 0.1686050516328832\n",
      "Epoch 3/5, Loss: 0.11658498387374698\n",
      "Epoch 4/5, Loss: 0.08745877094902416\n",
      "Epoch 5/5, Loss: 0.06909481643327772\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Define loss function and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# Training the model\n",
    "def train_model(num_epochs=5):\n",
    "    for epoch in range(num_epochs):\n",
    "        running_loss = 0.0\n",
    "        for images, labels in train_loader:\n",
    "            # find maxima/minima\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss.item()\n",
    "        \n",
    "        print(f'Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(train_loader)}')\n",
    "\n",
    "train_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.21%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Testing the model\n",
    "def test_model():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for images, labels in test_loader:\n",
    "            outputs = model(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "    print(f'Accuracy: {100 * correct / total}%')\n",
    "\n",
    "test_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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