torch_basic_functions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "14d535f1",
   "metadata": {},
   "source": [
    "# torch basic functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8b9c334",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9aad8d30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[ 0.,  1.,  2.],\n",
      "         [ 3.,  4.,  5.]],\n",
      "\n",
      "        [[ 6.,  7.,  8.],\n",
      "         [ 9., 10., 11.]]])\n",
      "torch.Size([2, 2, 3])\n"
     ]
    }
   ],
   "source": [
    "t = np.array([[[0, 1, 2],\n",
    "                [3, 4, 5]],\n",
    "                [[6, 7, 8],\n",
    "                [9, 10, 11]]])\n",
    "ft = torch.FloatTensor(t)\n",
    "print(ft)\n",
    "print(ft.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "052e1737",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.,  1.,  2.],\n",
      "        [ 3.,  4.,  5.],\n",
      "        [ 6.,  7.,  8.],\n",
      "        [ 9., 10., 11.]])\n",
      "torch.Size([4, 3])\n"
     ]
    }
   ],
   "source": [
    "print(ft.view([-1, 3]))\n",
    "print(ft.view([-1, 3]).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82a3d2dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[ 0.,  1.,  2.]],\n",
      "\n",
      "        [[ 3.,  4.,  5.]],\n",
      "\n",
      "        [[ 6.,  7.,  8.]],\n",
      "\n",
      "        [[ 9., 10., 11.]]])\n",
      "torch.Size([4, 1, 3])\n"
     ]
    }
   ],
   "source": [
    "print(ft.view([-1, 1, 3]))\n",
    "print(ft.view([-1, 1, 3]).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39b1fc09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.],\n",
      "        [1.],\n",
      "        [2.]])\n",
      "torch.Size([3, 1])\n"
     ]
    }
   ],
   "source": [
    "ft = torch.FloatTensor([[0], [1], [2]])\n",
    "print(ft)\n",
    "print(ft.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa3af1d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 1., 2.])\n",
      "torch.Size([3])\n"
     ]
    }
   ],
   "source": [
    "print(ft.squeeze())\n",
    "print(ft.squeeze().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2794bc96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3])\n"
     ]
    }
   ],
   "source": [
    "ft = torch.Tensor([0, 1, 2])\n",
    "print(ft.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5f896ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 1., 2.]])\n",
      "torch.Size([1, 3])\n"
     ]
    }
   ],
   "source": [
    "print(ft.unsqueeze(0))\n",
    "print(ft.unsqueeze(0).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd0f1c63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.],\n",
      "        [1.],\n",
      "        [2.]])\n",
      "torch.Size([3, 1])\n"
     ]
    }
   ],
   "source": [
    "print(ft.unsqueeze(1))\n",
    "print(ft.unsqueeze(1).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ba0721f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 2.],\n",
      "        [3., 4.]])\n",
      "tensor([[5., 6.],\n",
      "        [7., 8.]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.FloatTensor([[1, 2], [3, 4]])\n",
    "y = torch.FloatTensor([[5, 6], [7, 8]])\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e5b055f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 2.],\n",
      "        [3., 4.],\n",
      "        [5., 6.],\n",
      "        [7., 8.]])\n"
     ]
    }
   ],
   "source": [
    "print(torch.cat([x, y], dim = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "624475bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 2., 5., 6.],\n",
      "        [3., 4., 7., 8.]])\n"
     ]
    }
   ],
   "source": [
    "print(torch.cat([x, y], dim = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1862215d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.FloatTensor([1, 4])\n",
    "y = torch.FloatTensor([2, 5])\n",
    "z = torch.FloatTensor([3, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa338ea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 4.],\n",
      "        [2., 5.],\n",
      "        [3., 6.]])\n"
     ]
    }
   ],
   "source": [
    "print(torch.stack([x, y, z]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c7c6c94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 2., 3.],\n",
      "        [4., 5., 6.]])\n"
     ]
    }
   ],
   "source": [
    "print(torch.stack([x, y, z], dim=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1b23373d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 1., 2.],\n",
      "        [2., 1., 0.]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.FloatTensor([[0, 1, 2], [2, 1, 0]])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fed3385e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 1., 1.],\n",
      "        [1., 1., 1.]])\n",
      "tensor([[0., 0., 0.],\n",
      "        [0., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "print(torch.ones_like(x))\n",
    "print(torch.zeros_like(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3bbc07ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 2., 4.],\n",
      "        [4., 2., 0.]])\n",
      "tensor([[0., 1., 2.],\n",
      "        [2., 1., 0.]])\n"
     ]
    }
   ],
   "source": [
    "print(x.mul(2.))\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4700b53d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 2., 4.],\n",
      "        [4., 2., 0.]])\n",
      "tensor([[0., 2., 4.],\n",
      "        [4., 2., 0.]])\n"
     ]
    }
   ],
   "source": [
    "print(x.mul_(2.))\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bfcfe94",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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