{
 "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/02_softmax.ipynb)\n",
    "\n",
    "# 🟢 Easy: Implement Softmax\n",
    "\n",
    "Implement the **Softmax** function from scratch.\n",
    "\n",
    "$$\\text{softmax}(x_i) = \\frac{e^{x_i}}{\\sum_j e^{x_j}}$$\n",
    "\n",
    "### Signature\n",
    "```python\n",
    "def my_softmax(x: torch.Tensor, dim: int = -1) -> torch.Tensor:\n",
    "    ...\n",
    "```\n",
    "\n",
    "### Rules\n",
    "- Do **NOT** use `torch.softmax`, `F.softmax`, or `torch.nn.Softmax`\n",
    "- Must be **numerically stable** (hint: subtract `max` before `exp`)\n",
    "\n",
    "### Example\n",
    "```\n",
    "Input:  tensor([1., 2., 3.])\n",
    "Output: tensor([0.0900, 0.2447, 0.6652])  # sums to 1.0\n",
    "```"
   ],
   "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"
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ✏️ YOUR IMPLEMENTATION HERE\n",
    "\n",
    "def my_softmax(x: torch.Tensor, dim: int = -1) -> torch.Tensor:\n",
    "    pass  # Replace this"
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 🧪 Debug\n",
    "x = torch.tensor([1.0, 2.0, 3.0])\n",
    "print(\"Output:\", my_softmax(x, dim=-1))\n",
    "print(\"Sum:   \", my_softmax(x, dim=-1).sum())  # should be ~1.0\n",
    "print(\"Ref:   \", torch.softmax(x, dim=-1))"
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ✅ SUBMIT\n",
    "from torch_judge import check\n",
    "check(\"softmax\")"
   ],
   "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
}