{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/03_linear.ipynb)\n",
    "\n",
    "# 🟡 Medium: Simple Linear Layer\n",
    "\n",
    "Implement a fully-connected linear layer: **y = xW^T + b**\n",
    "\n",
    "### Signature\n",
    "```python\n",
    "class SimpleLinear:\n",
    "    def __init__(self, in_features: int, out_features: int): ...\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor: ...\n",
    "```\n",
    "\n",
    "### Requirements\n",
    "- `self.weight`: shape `(out_features, in_features)`, init with `randn * (1/√in_features)`\n",
    "- `self.bias`: shape `(out_features,)`, init as zeros\n",
    "- Both must have `requires_grad=True`\n",
    "- `forward(x)` computes `x @ W^T + b`\n",
    "- Do **NOT** use `torch.nn.Linear`"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Install torch-judge in Colab (no-op in JupyterLab/Docker)\n",
    "try:\n",
    "    import google.colab\n",
    "    get_ipython().run_line_magic('pip', 'install -q torch-judge')\n",
    "except ImportError:\n",
    "    pass\n"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import math"
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ✏️ YOUR IMPLEMENTATION HERE\n",
    "\n",
    "class SimpleLinear:\n",
    "    def __init__(self, in_features: int, out_features: int):\n",
    "        pass  # Initialize weight and bias\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        pass  # Compute y = x @ W^T + b"
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 🧪 Debug\n",
    "layer = SimpleLinear(8, 4)\n",
    "print(\"W shape:\", layer.weight.shape)  # should be (4, 8)\n",
    "print(\"b shape:\", layer.bias.shape)    # should be (4,)\n",
    "\n",
    "x = torch.randn(2, 8)\n",
    "y = layer.forward(x)\n",
    "print(\"Output shape:\", y.shape)        # should be (2, 4)"
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ✅ SUBMIT\n",
    "from torch_judge import check\n",
    "check(\"linear\")"
   ],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}