{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93841636",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "369f04fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1., 1., 1., 1., 1.])\n"
     ]
    }
   ],
   "source": [
    "a = torch.ones(5)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b534974c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5 0.4 2. ]\n"
     ]
    }
   ],
   "source": [
    "a_np = np.array([0.5,0.4,2])\n",
    "print(a_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "616503d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.5000, 0.4000, 2.0000], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "b_tensor = torch.tensor(a_np)\n",
    "print(b_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "93bd75d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "b = a.numpy()\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4871cf5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'torch.Tensor'>\n"
     ]
    }
   ],
   "source": [
    "print(type(b))\n",
    "print(type(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eea8a7bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1., 1., 1., 1., 1.], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "    x =  torch.ones(5, device= device)\n",
    "    print(x)\n",
    "    #z = x.numpy()\n",
    "    #print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d38da1d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1., 1., 1., 1., 1.], device='cuda:0')\n",
      "tensor([1., 1., 1., 1., 1.])\n",
      "[1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "    x =  torch.ones(5, device= device)\n",
    "    print(x)\n",
    "    x = x.to(\"cpu\")\n",
    "    print(x)\n",
    "    z = x.numpy()\n",
    "    print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf220e0f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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