{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1, 2, 3],\n",
      "        [4, 5, 6]])\n",
      "a_max tensor([[3],\n",
      "        [6]])\n",
      "tensor([[-2, -1,  0],\n",
      "        [-2, -1,  0]])\n",
      "tensor([ 6, 15])\n",
      "torch.Size([2, 3])\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "a = torch.tensor([[1, 2, 3], [4, 5, 6]])\n",
    "print(a)\n",
    "a_max = torch.max(a, 1, keepdim=True).values\n",
    "print(\"a_max\", a_max)\n",
    "a_max = torch.reshape(a_max, (2, 1))\n",
    "print(a - a_max)\n",
    "b = torch.sum(a, 1)\n",
    "print(b)\n",
    "print(a.size())\n",
    "print(a.size()[1])\n",
    "print(a.reshape(-1,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0, 1, 0],\n",
      "        [0, 0, 1]])\n",
      "tensor([[0, 2, 0],\n",
      "        [0, 0, 6]])\n"
     ]
    }
   ],
   "source": [
    "label = torch.tensor([1, 2])\n",
    "one_hot = torch.nn.functional.one_hot(label, 3)\n",
    "print(one_hot)\n",
    "print(one_hot * a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.tensor([[1, 2], [3, 4]])\n",
    "b = torch.tensor([5, 6])\n",
    "print(torch.matmul(a, b))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "media_cognition",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}