add cyfi445 labs

CYFI445/projects/CYFI445_p3.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "10ee0fc5",
+   "metadata": {},
+   "source": [
+    "# Mini Project: Autograd with PyTorch\n",
+    "Fill in the code cells below to complete the project.\n",
+    "\n",
+    "Use comments (`# ...`) to record your observations and reflection as requested."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "276c8f5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# --- Imports ---\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "torch.manual_seed(0)  # for reproducibility"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66360d15",
+   "metadata": {},
+   "source": [
+    "## Step 1: Define Data and Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd3741d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the dataset\n",
+    "X = torch.tensor([[1., 2.], [2., 3.], [3., 4.], [4., 5.]])\n",
+    "y = torch.tensor([5., 7., 9., 11.])\n",
+    "\n",
+    "# Initialize parameters with requires_grad=True\n",
+    "w = torch.randn(2, requires_grad=True)\n",
+    "print('Initial weights:', w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6af9174b",
+   "metadata": {},
+   "source": [
+    "## Step 2: Training Loop with Manual Updates\n",
+    "Implement MSE loss, call `loss.backward()`, and manually update weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3804230e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hyperparameters\n",
+    "lr = 0.01\n",
+    "epochs = 50\n",
+    "losses = []\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    # Forward pass: prediction\n",
+    "    y_pred = X @ w  # linear model (no bias)\n",
+    "\n",
+    "    # Compute MSE loss\n",
+    "    loss = ((y_pred - y)**2).mean()\n",
+    "    losses.append(loss.item())\n",
+    "\n",
+    "    # Zero gradients from previous step\n",
+    "    if w.grad is not None:\n",
+    "        w.grad.zero_()\n",
+    "\n",
+    "    # Backward pass\n",
+    "    loss.backward()\n",
+    "\n",
+    "    # Manual gradient descent update\n",
+    "    with torch.no_grad():\n",
+    "        w -= lr * w.grad\n",
+    "\n",
+    "    if (epoch+1) % 10 == 0:\n",
+    "        print(f\"Epoch {epoch+1}, Loss: {loss.item():.6f}\")\n",
+    "\n",
+    "# Comment here: Does the loss decrease as expected?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c1922d7",
+   "metadata": {},
+   "source": [
+    "## Step 3: Plot Loss Curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "522965a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(losses, '-o')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('MSE Loss')\n",
+    "plt.title('Loss vs. Epoch')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# Comment: Briefly describe the shape of the curve (e.g., decreasing smoothly)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75a2aa1a",
+   "metadata": {},
+   "source": [
+    "## Reflection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0051b8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write 3-4 lines of reflection as comments:\n",
+    "# - What did you learn about autograd?\n",
+    "# - What step was confusing or new?\n",
+    "# - What would you try next (optimizer, dataset, etc.)?"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}