{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensor\n",
    "\n",
    "[![](https://gitee.com/mindspore/docs/raw/r1.2/docs/programming_guide/source_zh_cn/_static/logo_source.png)](https://gitee.com/mindspore/docs/blob/r1.2/docs/programming_guide/source_zh_cn/tensor.ipynb) [![](https://gitee.com/mindspore/docs/raw/r1.2/docs/programming_guide/source_zh_cn/_static/logo_notebook.png)](https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/r1.2/programming_guide/mindspore_tensor.ipynb) [![](https://gitee.com/mindspore/docs/raw/r1.2/docs/programming_guide/source_zh_cn/_static/logo_modelarts.png)](https://console.huaweicloud.com/modelarts/?region=cn-north-4#/notebook/loading?share-url-b64=aHR0cHM6Ly9vYnMuZHVhbHN0YWNrLmNuLW5vcnRoLTQubXlodWF3ZWljbG91ZC5jb20vbWluZHNwb3JlLXdlYnNpdGUvbm90ZWJvb2svbW9kZWxhcnRzL3Byb2dyYW1taW5nX2d1aWRlL21pbmRzcG9yZV90ZW5zb3IuaXB5bmI=&image_id=65f636a0-56cf-49df-b941-7d2a07ba8c8c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 概述\n",
    "\n",
    "张量（Tensor）是MindSpore网络运算中的基本数据结构。张量中的数据类型可参考[dtype](https://www.mindspore.cn/doc/programming_guide/zh-CN/r1.2/dtype.html)。\n",
    "\n",
    "不同维度的张量分别表示不同的数据，0维张量表示标量，1维张量表示向量，2维张量表示矩阵，3维张量可以表示彩色图像的RGB三通道等等。\n",
    "\n",
    "> 本文中的所有示例，支持在PyNative模式下运行。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 张量构造\n",
    "\n",
    "构造张量时，支持传入`Tensor`、`float`、`int`、`bool`、`tuple`、`list`和`NumPy.array`类型，其中`tuple`和`list`里只能存放`float`、`int`、`bool`类型数据。\n",
    "\n",
    "`Tensor`初始化时，可指定dtype。如果没有指定dtype，初始值`int`、`float`、`bool`分别生成数据类型为`mindspore.int32`、`mindspore.float32`、`mindspore.bool_`的0维Tensor，\n",
    "初始值`tuple`和`list`生成的1维`Tensor`数据类型与`tuple`和`list`里存放的数据类型相对应，如果包含多种不同类型的数据，则按照优先级：`bool` < `int` < `float`，选择相对优先级最高类型所对应的mindspore数据类型。\n",
    "如果初始值是`Tensor`，则生成的`Tensor`数据类型与其一致；如果初始值是`NumPy.array`，则生成的`Tensor`数据类型与之对应。\n",
    "\n",
    "代码样例如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-03T02:59:50.340750Z",
     "start_time": "2021-02-03T02:59:49.571048Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]] \n",
      "\n",
      " 1 \n",
      "\n",
      " 2 \n",
      "\n",
      " True \n",
      "\n",
      " [1 2 3] \n",
      "\n",
      " [4. 5. 6.] \n",
      "\n",
      " [4. 5. 6.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from mindspore import Tensor\n",
    "from mindspore import dtype as mstype\n",
    "\n",
    "x = Tensor(np.array([[1, 2], [3, 4]]), mstype.int32)\n",
    "y = Tensor(1.0, mstype.int32)\n",
    "z = Tensor(2, mstype.int32)\n",
    "m = Tensor(True, mstype.bool_)\n",
    "n = Tensor((1, 2, 3), mstype.int16)\n",
    "p = Tensor([4.0, 5.0, 6.0], mstype.float64)\n",
    "q = Tensor(p, mstype.float64)\n",
    "\n",
    "print(x, \"\\n\\n\", y, \"\\n\\n\", z, \"\\n\\n\", m, \"\\n\\n\", n, \"\\n\\n\", p, \"\\n\\n\", q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 张量的属性和方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 属性\n",
    "\n",
    "张量的属性包括形状（shape）和数据类型（dtype）。\n",
    "\n",
    "   * 形状：`Tensor`的shape，是一个tuple。\n",
    "\n",
    "   * 数据类型：`Tensor`的dtype，是MindSpore的一个数据类型。\n",
    "\n",
    "代码样例如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-03T02:59:50.347520Z",
     "start_time": "2021-02-03T02:59:50.342826Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 2) Int32\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from mindspore import Tensor\n",
    "from mindspore import dtype as mstype\n",
    "\n",
    "x = Tensor(np.array([[1, 2], [3, 4]]), mstype.int32)\n",
    "x_shape = x.shape\n",
    "x_dtype = x.dtype\n",
    "\n",
    "print(x_shape, x_dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方法\n",
    "\n",
    "张量的方法包括`all`、`any`和`asnumpy`，`all`和`any`方法目前只支持Ascend，并且要求`Tensor`的数据类型是`mindspore.bool_`。\n",
    "\n",
    "- `all(axis, keep_dims)`：在指定维度上通过`and`操作进行归约，`axis`代表归约维度，`keep_dims`表示是否保留归约后的维度。\n",
    "\n",
    "- `any(axis, keep_dims)`：在指定维度上通过`or`操作进行归约，参数含义同`all`。\n",
    "\n",
    "- `asnumpy()`：将`Tensor`转换为`NumPy`的`array`。\n",
    "\n",
    "代码样例如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-03T02:59:50.374128Z",
     "start_time": "2021-02-03T02:59:50.349665Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False \n",
      "\n",
      " True \n",
      "\n",
      " [[ True  True]\n",
      " [False False]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from mindspore import Tensor\n",
    "from mindspore import dtype as mstype\n",
    "\n",
    "x = Tensor(np.array([[True, True], [False, False]]), mstype.bool_)\n",
    "x_all = x.all()\n",
    "x_any = x.any()\n",
    "x_array = x.asnumpy()\n",
    "\n",
    "print(x_all, \"\\n\\n\", x_any, \"\\n\\n\", x_array)"
   ]
  }
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