learn_pytorch_06_pytorch中张量的操作之基本数学运算.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 四则运算：加减乘除\n",
    "torch.add(input, alpha=1, other, out=None)  \n",
    "torch.addcdiv(tensor, value=1, tensor1, tensor2, out=None)  \n",
    "torch.addcmul(tensor, value=1, tensor1, tensor2, out=None)  \n",
    "torch.sub(input, other, out=None)  \n",
    "torch.mul(input, other, out=None)  \n",
    "torch.div(input, other, out=None)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.6585, 0.5872, 0.1523, 0.6315],\n",
      "        [0.8839, 0.2742, 0.5890, 0.5073],\n",
      "        [0.9062, 0.5147, 0.4520, 0.7539]])\n",
      "tensor([[0.1982, 0.7813, 0.2600, 0.0372],\n",
      "        [0.0693, 0.2151, 0.2451, 0.2351],\n",
      "        [0.0554, 0.3463, 0.8022, 0.6366]])\n"
     ]
    }
   ],
   "source": [
    "a = torch.rand(3,4)\n",
    "b = torch.rand(3,4)\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.8567, 1.3685, 0.4122, 0.6687],\n",
       "        [0.9532, 0.4894, 0.8341, 0.7424],\n",
       "        [0.9616, 0.8609, 1.2543, 1.3906]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.add(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 3.3524,  1.7254,  1.1657, 17.5210],\n",
       "        [13.7119,  1.5031,  2.6063,  2.8464],\n",
       "        [16.5303,  2.1327,  1.4472,  1.2102]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.addcdiv(torch.rand(3,4),a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.4602, -0.1941, -0.1077,  0.5944],\n",
       "        [ 0.8146,  0.0591,  0.3439,  0.2722],\n",
       "        [ 0.8508,  0.1684, -0.3502,  0.1173]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.sub(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.1305, 0.4588, 0.0396, 0.0235],\n",
       "        [0.0612, 0.0590, 0.1444, 0.1193],\n",
       "        [0.0502, 0.1782, 0.3626, 0.4800]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.mul(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.3170, 1.1744, 0.3045, 1.2631],\n",
       "        [1.7678, 0.5485, 1.1780, 1.0146],\n",
       "        [1.8125, 1.0293, 0.9040, 1.5079]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.mul(a,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.6585, 0.5872, 0.1523, 0.6315],\n",
       "        [0.8839, 0.2742, 0.5890, 0.5073],\n",
       "        [0.9062, 0.5147, 0.4520, 0.7539]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.div(a,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 对数，指数，幂函数运算\n",
    "torch.log()  \n",
    "torch.log10()  \n",
    "torch.log2()  \n",
    "torch.exp()  \n",
    "torch.pow()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.tensor([3.0,4.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3., 4.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0986, 1.3863])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.log(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.4771, 0.6021])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.log10(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.5850, 2.0000])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.log2(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([20.0855, 54.5981])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.exp(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 9., 16.])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.pow(x,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 三角函数计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0000])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.sin(torch.tensor([3.1515926/2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cos(torch.tensor([0.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tan(torch.tensor([0.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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