{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting a straight line using Feed Forward Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.tensorboard import SummaryWriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Linear Regression Model\n",
    "class LinearRegressionModel(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(LinearRegressionModel, self).__init__()\n",
    "        self.linear = nn.Linear(input_dim, output_dim)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.linear(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate dummy data\n",
    "torch.manual_seed(42)\n",
    "X = torch.randn(100, 1)\n",
    "y = 3 * X + 2 + 0.1 * torch.randn(100, 1)  # y = 3x + 2 with noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize model, loss function, and optimizer\n",
    "model = LinearRegressionModel(input_dim=1, output_dim=1)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training loop\n",
    "epochs = 1000\n",
    "for epoch in range(epochs):\n",
    "    optimizer.zero_grad()\n",
    "    outputs = model(X)\n",
    "    loss = criterion(outputs, y)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    if (epoch + 1) % 100 == 0:\n",
    "        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print final parameters\n",
    "for name, param in model.named_parameters():\n",
    "    print(f'{name}: {param.data}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot data, predictions, and learned parameters\n",
    "with torch.no_grad():\n",
    "    predicted = model(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(X.numpy(), y.numpy(), label='Original Data')\n",
    "plt.plot(X.numpy(), predicted.numpy(), color='red', label='Fitted Line')\n",
    "plt.legend()\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('y')\n",
    "plt.title('Linear Regression Fit')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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