Pytorch_torch_tensors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1., -1.],\n",
       "        [ 1., -1.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor([[1., -1.], \n",
    "              [1., -1.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 2, 3],\n",
       "        [4, 5, 6]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor(np.array([[1, 2, 3], \n",
    "                       [4, 5, 6]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(6)\n",
      "tensor([[1, 8, 3],\n",
      "        [4, 5, 6]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor([[1, 2, 3], \n",
    "                  [4, 5, 6]])\n",
    "print(x[1][2])\n",
    "x[0][1] = 8\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.tensor([[1]])\n",
    "print(x)\n",
    "x.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.5000])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.tensor([2.5])\n",
    "print(x)\n",
    "x.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 2, 3, 1, 2, 3],\n",
       "        [1, 2, 3, 1, 2, 3],\n",
       "        [1, 2, 3, 1, 2, 3],\n",
       "        [1, 2, 3, 1, 2, 3],\n",
       "        [1, 2, 3, 1, 2, 3]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.tensor([1, 2, 3])\n",
    "x.repeat(5, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 4])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.randn(4, 4)\n",
    "x.size()\n",
    "#torch.Size([4, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x.view(16)\n",
    "y.size()\n",
    "#torch.Size([16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 8])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = x.view(-1, 8)  # the size -1 is inferred from other dimensions\n",
    "z.size()\n",
    "#torch.Size([2, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 2])\n",
      "tensor([[ 1.1631, -0.6798],\n",
      "        [-0.7029,  0.0496],\n",
      "        [-0.2426,  0.1410],\n",
      "        [ 0.4116,  0.1582],\n",
      "        [-0.2458, -1.1343],\n",
      "        [ 0.8123,  1.3286],\n",
      "        [-1.4117,  0.1714],\n",
      "        [ 1.5756,  0.0974]])\n"
     ]
    }
   ],
   "source": [
    "p = y.view(-1, 2)\n",
    "print(p.size())\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
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   "cell_type": "code",
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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