Lab 32- Pytorch - Tensors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b09afa39",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "312ed4bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.tensor([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34da0518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "106ec0e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "34fabcfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fee7d00e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.tensor([1,2,3], device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7aafa310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc7eeb48",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.tensor([4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2978c383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee20be86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37059cfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f7b3508",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = nn.Parameter(torch.tensor(1.7), requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0fa96676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor(1.7000, requires_grad=True)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7810f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = x.detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "952a04fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.7000)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "228f5df2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}