learn_pytorch_05_pytorch中张量的属性.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 常用的张量属性\n",
    "1 维度  \n",
    "2 尺寸  \n",
    "3 数据类型  \n",
    "4 创建张量的方法  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.randn(3,4,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.6298,  0.5022,  0.8071,  0.2965,  0.0228],\n",
       "         [-0.2108,  0.0867, -1.3653, -2.0107, -0.6433],\n",
       "         [ 1.7354, -1.0490,  0.4973,  1.2050,  1.4922],\n",
       "         [-0.5689,  0.5220,  0.9226,  1.3008, -1.4385]],\n",
       "\n",
       "        [[-0.0988,  0.3371, -1.2369, -0.1856,  0.4241],\n",
       "         [-0.6580,  0.2351, -0.6472, -0.9870,  0.6752],\n",
       "         [ 1.7218,  0.9167, -0.1385,  0.1248, -0.0132],\n",
       "         [-0.0258,  2.1326,  1.7767,  0.0961, -2.0621]],\n",
       "\n",
       "        [[-0.0133, -1.4248, -0.8008, -1.3679, -1.9875],\n",
       "         [-0.5374,  0.7848,  1.0515, -0.4273, -0.9096],\n",
       "         [-0.2987, -0.8561,  1.4489, -0.1706, -1.1991],\n",
       "         [-0.0398,  0.1823,  1.5778, -0.2630,  0.0550]]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'torch.FloatTensor'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 4, 5])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = a.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 4, 5])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.rand((2,2),requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.2088, 0.9028],\n",
       "        [0.1812, 0.1154]], requires_grad=True)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=torch.tensor([1.0,2],requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "z= x+y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<AddBackward0 object at 0x0000019E42FB59E8>\n"
     ]
    }
   ],
   "source": [
    "print(z.grad_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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