{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "170e84e3",
   "metadata": {},
   "source": [
    "# 自定义Cell的反向\n",
    "\n",
    "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r1.8/resource/_static/logo_notebook.png)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r1.8/tutorials/experts/zh_cn/network/mindspore_custom_cell_reverse.ipynb) [![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r1.8/resource/_static/logo_download_code.png)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r1.8/tutorials/experts/zh_cn/network/mindspore_custom_cell_reverse.py) [![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r1.8/resource/_static/logo_source.png)](https://gitee.com/mindspore/docs/blob/r1.8/tutorials/experts/source_zh_cn/network/custom_cell_reverse.ipynb)\n",
    "\n",
    "用户可以自定义nn.Cell对象的反向传播（计算）函数，从而控制nn.Cell对象梯度计算的过程，定位梯度问题。\n",
    "\n",
    "自定义bprop函数的使用方法是：在定义的nn.Cell对象里面增加一个用户自定义的bprop函数。训练的过程中会使用用户自定义的bprop函数来生成反向图。\n",
    "\n",
    "示例代码:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "05282818",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-02T09:14:55.896896Z",
     "start_time": "2022-03-02T09:14:55.881233Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Tensor(shape=[], dtype=Float32, value= 3), Tensor(shape=[], dtype=Float32, value= 2))\n"
     ]
    }
   ],
   "source": [
    "ms.set_context(mode=ms.PYNATIVE_MODE)\n",
    "\n",
    "class Net(nn.Cell):\n",
    "    def construct(self, x, y):\n",
    "        z = x * y\n",
    "        z = z * y\n",
    "        return z\n",
    "\n",
    "    def bprop(self, x, y, out, dout):\n",
    "        x_dout = x + y\n",
    "        y_dout = x * y\n",
    "        return x_dout, y_dout\n",
    "\n",
    "grad_all = ops.GradOperation(get_all=True)\n",
    "output = grad_all(Net())(ms.Tensor(1, ms.float32), ms.Tensor(2, ms.float32))\n",
    "print(output)"
   ]
  }
 ],
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