nn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyper-parameters\n",
    "input_size = 1\n",
    "output_size = 1\n",
    "num_epochs = 60\n",
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Toy dataset\n",
    "x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], \n",
    "                    [9.779], [6.182], [7.59], [2.167], [7.042], \n",
    "                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)\n",
    "\n",
    "y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], \n",
    "                    [3.366], [2.596], [2.53], [1.221], [2.827], \n",
    "                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear regression model\n",
    "model = nn.Linear(input_size, output_size)\n",
    "\n",
    "# Loss and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [5/60], Loss: 41.2294\n",
      "Epoch [10/60], Loss: 16.9871\n",
      "Epoch [15/60], Loss: 7.1656\n",
      "Epoch [20/60], Loss: 3.1863\n",
      "Epoch [25/60], Loss: 1.5738\n",
      "Epoch [30/60], Loss: 0.9201\n",
      "Epoch [35/60], Loss: 0.6547\n",
      "Epoch [40/60], Loss: 0.5468\n",
      "Epoch [45/60], Loss: 0.5026\n",
      "Epoch [50/60], Loss: 0.4842\n",
      "Epoch [55/60], Loss: 0.4763\n",
      "Epoch [60/60], Loss: 0.4727\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "for epoch in range(num_epochs):\n",
    "    # Convert numpy arrays to torch tensors\n",
    "    inputs = torch.from_numpy(x_train)\n",
    "    targets = torch.from_numpy(y_train)\n",
    "\n",
    "    # Forward pass\n",
    "    outputs = model(inputs)\n",
    "    loss = criterion(outputs, targets)\n",
    "    \n",
    "    # Backward and optimize\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    if (epoch+1) % 5 == 0:\n",
    "        print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the graph\n",
    "predicted = model(torch.from_numpy(x_train)).detach().numpy()\n",
    "plt.plot(x_train, y_train, 'ro', label='Original data')\n",
    "plt.plot(x_train, predicted, label='Fitted line')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Save the model checkpoint\n",
    "torch.save(model.state_dict(), 'model.ckpt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyper-parameters \n",
    "input_size = 28 * 28    # 784\n",
    "num_classes = 10\n",
    "num_epochs = 5\n",
    "batch_size = 100\n",
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST dataset (images and labels)\n",
    "train_dataset = torchvision.datasets.MNIST(root='./data', \n",
    "                                           train=True, \n",
    "                                           transform=transforms.ToTensor(),\n",
    "                                           download=True)\n",
    "\n",
    "test_dataset = torchvision.datasets.MNIST(root='./data', \n",
    "                                          train=False, \n",
    "                                          transform=transforms.ToTensor())\n",
    "\n",
    "# Data loader (input pipeline)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "                                          batch_size=batch_size, \n",
    "                                          shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic regression model\n",
    "model = nn.Linear(input_size, num_classes)\n",
    "\n",
    "# Loss and optimizer\n",
    "# nn.CrossEntropyLoss() computes softmax internally\n",
    "criterion = nn.CrossEntropyLoss()  \n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/5], Step [100/600], Loss: 2.2256\n",
      "Epoch [1/5], Step [200/600], Loss: 2.1370\n",
      "Epoch [1/5], Step [300/600], Loss: 2.0095\n",
      "Epoch [1/5], Step [400/600], Loss: 1.9410\n",
      "Epoch [1/5], Step [500/600], Loss: 1.8547\n",
      "Epoch [1/5], Step [600/600], Loss: 1.8080\n",
      "Epoch [2/5], Step [100/600], Loss: 1.7334\n",
      "Epoch [2/5], Step [200/600], Loss: 1.6616\n",
      "Epoch [2/5], Step [300/600], Loss: 1.6308\n",
      "Epoch [2/5], Step [400/600], Loss: 1.5190\n",
      "Epoch [2/5], Step [500/600], Loss: 1.5292\n",
      "Epoch [2/5], Step [600/600], Loss: 1.5024\n",
      "Epoch [3/5], Step [100/600], Loss: 1.3856\n",
      "Epoch [3/5], Step [200/600], Loss: 1.2991\n",
      "Epoch [3/5], Step [300/600], Loss: 1.3667\n",
      "Epoch [3/5], Step [400/600], Loss: 1.3003\n",
      "Epoch [3/5], Step [500/600], Loss: 1.3011\n",
      "Epoch [3/5], Step [600/600], Loss: 1.3345\n",
      "Epoch [4/5], Step [100/600], Loss: 1.2729\n",
      "Epoch [4/5], Step [200/600], Loss: 1.2387\n",
      "Epoch [4/5], Step [300/600], Loss: 1.2031\n",
      "Epoch [4/5], Step [400/600], Loss: 1.1419\n",
      "Epoch [4/5], Step [500/600], Loss: 1.0957\n",
      "Epoch [4/5], Step [600/600], Loss: 1.0451\n",
      "Epoch [5/5], Step [100/600], Loss: 1.0997\n",
      "Epoch [5/5], Step [200/600], Loss: 1.0798\n",
      "Epoch [5/5], Step [300/600], Loss: 1.1927\n",
      "Epoch [5/5], Step [400/600], Loss: 1.0408\n",
      "Epoch [5/5], Step [500/600], Loss: 1.0493\n",
      "Epoch [5/5], Step [600/600], Loss: 0.9638\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "total_step = len(train_loader)\n",
    "for epoch in range(num_epochs):\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "        # Reshape images to (batch_size, input_size)\n",
    "        images = images.reshape(-1, input_size)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if (i+1) % 100 == 0:\n",
    "            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' \n",
    "                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model on the 10000 test images: 82 %\n"
     ]
    }
   ],
   "source": [
    "# Test the model\n",
    "# In test phase, we don't need to compute gradients (for memory efficiency)\n",
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for images, labels in test_loader:\n",
    "        images = images.reshape(-1, input_size)\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum()\n",
    "\n",
    "    print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))\n",
    "\n",
    "# Save the model checkpoint\n",
    "torch.save(model.state_dict(), 'model.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feed Forward NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Device configuration\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Hyper-parameters \n",
    "input_size = 784\n",
    "hidden_size = 500\n",
    "num_classes = 10\n",
    "num_epochs = 5\n",
    "batch_size = 100\n",
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST dataset \n",
    "train_dataset = torchvision.datasets.MNIST(root='./data', \n",
    "                                           train=True, \n",
    "                                           transform=transforms.ToTensor(),  \n",
    "                                           download=True)\n",
    "\n",
    "test_dataset = torchvision.datasets.MNIST(root='./data', \n",
    "                                          train=False, \n",
    "                                          transform=transforms.ToTensor())\n",
    "\n",
    "# Data loader\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "                                          batch_size=batch_size, \n",
    "                                          shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fully connected neural network with one hidden layer\n",
    "class NeuralNet(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_classes):\n",
    "        super(NeuralNet, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, hidden_size) \n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(hidden_size, num_classes)  \n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = self.fc1(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NeuralNet(input_size, hidden_size, num_classes).to(device)\n",
    "\n",
    "# Loss and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/5], Step [100/600], Loss: 0.4591\n",
      "Epoch [1/5], Step [200/600], Loss: 0.2855\n",
      "Epoch [1/5], Step [300/600], Loss: 0.1880\n",
      "Epoch [1/5], Step [400/600], Loss: 0.1510\n",
      "Epoch [1/5], Step [500/600], Loss: 0.2956\n",
      "Epoch [1/5], Step [600/600], Loss: 0.0951\n",
      "Epoch [2/5], Step [100/600], Loss: 0.2085\n",
      "Epoch [2/5], Step [200/600], Loss: 0.0763\n",
      "Epoch [2/5], Step [300/600], Loss: 0.1956\n",
      "Epoch [2/5], Step [400/600], Loss: 0.1513\n",
      "Epoch [2/5], Step [500/600], Loss: 0.0885\n",
      "Epoch [2/5], Step [600/600], Loss: 0.0549\n",
      "Epoch [3/5], Step [100/600], Loss: 0.0816\n",
      "Epoch [3/5], Step [200/600], Loss: 0.0989\n",
      "Epoch [3/5], Step [300/600], Loss: 0.0779\n",
      "Epoch [3/5], Step [400/600], Loss: 0.0317\n",
      "Epoch [3/5], Step [500/600], Loss: 0.1032\n",
      "Epoch [3/5], Step [600/600], Loss: 0.0227\n",
      "Epoch [4/5], Step [100/600], Loss: 0.1377\n",
      "Epoch [4/5], Step [200/600], Loss: 0.0546\n",
      "Epoch [4/5], Step [300/600], Loss: 0.0252\n",
      "Epoch [4/5], Step [400/600], Loss: 0.0134\n",
      "Epoch [4/5], Step [500/600], Loss: 0.0703\n",
      "Epoch [4/5], Step [600/600], Loss: 0.0636\n",
      "Epoch [5/5], Step [100/600], Loss: 0.0366\n",
      "Epoch [5/5], Step [200/600], Loss: 0.0494\n",
      "Epoch [5/5], Step [300/600], Loss: 0.0302\n",
      "Epoch [5/5], Step [400/600], Loss: 0.0198\n",
      "Epoch [5/5], Step [500/600], Loss: 0.0407\n",
      "Epoch [5/5], Step [600/600], Loss: 0.0123\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "total_step = len(train_loader)\n",
    "for epoch in range(num_epochs):\n",
    "    for i, (images, labels) in enumerate(train_loader):  \n",
    "        # Move tensors to the configured device\n",
    "        images = images.reshape(-1, 28*28).to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if (i+1) % 100 == 0:\n",
    "            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' \n",
    "                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 97.95 %\n"
     ]
    }
   ],
   "source": [
    "# Test the model\n",
    "# In test phase, we don't need to compute gradients (for memory efficiency)\n",
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for images, labels in test_loader:\n",
    "        images = images.reshape(-1, 28*28).to(device)\n",
    "        labels = labels.to(device)\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('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))\n",
    "\n",
    "# Save the model checkpoint\n",
    "torch.save(model.state_dict(), 'model.ckpt')"
   ]
  }
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