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   "source": [
    "# 模型层\n",
    "\n",
    "[![](https://gitee.com/mindspore/docs/raw/r1.3/resource/_static/logo_source.png)](https://gitee.com/mindspore/docs/blob/r1.3/docs/mindspore/programming_guide/source_zh_cn/layer.ipynb) [![](https://gitee.com/mindspore/docs/raw/r1.3/resource/_static/logo_notebook.png)](https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/r1.3/programming_guide/zh_cn/mindspore_layer.ipynb) [![](https://gitee.com/mindspore/docs/raw/r1.3/resource/_static/logo_modelarts.png)](https://authoring-modelarts-cnnorth4.huaweicloud.com/console/lab?share-url-b64=aHR0cHM6Ly9vYnMuZHVhbHN0YWNrLmNuLW5vcnRoLTQubXlodWF3ZWljbG91ZC5jb20vbWluZHNwb3JlLXdlYnNpdGUvbm90ZWJvb2svbW9kZWxhcnRzL3Byb2dyYW1taW5nX2d1aWRlL21pbmRzcG9yZV9sYXllci5pcHluYg==&imageid=65f636a0-56cf-49df-b941-7d2a07ba8c8c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 概述\n",
    "\n",
    "在讲述了`Cell`的使用方法后可知，MindSpore能够以`Cell`为基类构造网络结构。\n",
    "\n",
    "为了方便用户的使用，MindSpore框架内置了大量的模型层，用户可以通过接口直接调用。\n",
    "\n",
    "同样，用户也可以自定义模型，此内容在“构建自定义网络”中介绍。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 内置模型层\n",
    "\n",
    "MindSpore框架在`mindspore.nn`的layer层内置了丰富的接口，主要内容如下：\n",
    "\n",
    "- 激活层\n",
    "\n",
    "    激活层内置了大量的激活函数，在定义网络结构中经常使用。激活函数为网络加入了非线性运算，使得网络能够拟合效果更好。\n",
    "\n",
    "    主要接口有`Softmax`、`Relu`、`Elu`、`Tanh`、`Sigmoid`等。\n",
    "    \n",
    "\n",
    "- 基础层\n",
    "\n",
    "    基础层实现了网络中一些常用的基础结构，例如全连接层、Onehot编码、Dropout、平铺层等都在此部分实现。\n",
    "\n",
    "    主要接口有`Dense`、`Flatten`、`Dropout`、`Norm`、`OneHot`等。\n",
    "    \n",
    "\n",
    "- 容器层\n",
    "\n",
    "    容器层主要功能是实现一些存储多个Cell的数据结构。\n",
    "\n",
    "    主要接口有`SequentialCell`、`CellList`等。\n",
    "    \n",
    "\n",
    "- 卷积层\n",
    "\n",
    "    卷积层提供了一些卷积计算的功能，如普通卷积、深度卷积和卷积转置等。\n",
    "\n",
    "    主要接口有`Conv2d`、`Conv1d`、`Conv2dTranspose`、`Conv1dTranspose`等。\n",
    "    \n",
    "\n",
    "- 池化层\n",
    "\n",
    "    池化层提供了平均池化和最大池化等计算的功能。\n",
    "\n",
    "    主要接口有`AvgPool2d`、`MaxPool2d`和`AvgPool1d`。\n",
    "    \n",
    "\n",
    "- 嵌入层\n",
    "\n",
    "    嵌入层提供word embedding的计算功能，将输入的单词映射为稠密向量。\n",
    "\n",
    "    主要接口有`Embedding`、`EmbeddingLookup`、`EmbeddingLookUpSplitMode`等。\n",
    "    \n",
    "\n",
    "- 长短记忆循环层\n",
    "\n",
    "    长短记忆循环层提供LSTM计算功能。其中`LSTM`内部会调用`LSTMCell`接口，`LSTMCell`是一个LSTM单元，对一个LSTM层做运算，当涉及多LSTM网络层运算时，使用`LSTM`接口。\n",
    "\n",
    "    主要接口有`LSTM`和`LSTMCell`。\n",
    "    \n",
    "\n",
    "- 标准化层\n",
    "\n",
    "    标准化层提供了一些标准化的方法，即通过线性变换等方式将数据转换成均值和标准差。\n",
    "\n",
    "    主要接口有`BatchNorm1d`、`BatchNorm2d`、`LayerNorm`、`GroupNorm`、`GlobalBatchNorm`等。\n",
    "    \n",
    "\n",
    "- 数学计算层\n",
    "\n",
    "    数学计算层提供一些算子拼接而成的计算功能，例如数据生成和一些数学计算等。\n",
    "\n",
    "    主要接口有`ReduceLogSumExp`、`Range`、`LinSpace`、`LGamma`等。\n",
    "    \n",
    "\n",
    "- 图片层\n",
    "\n",
    "    图片计算层提供了一些矩阵计算相关的功能，将图片数据进行一些变换与计算。\n",
    "\n",
    "    主要接口有`ImageGradients`、`SSIM`、`MSSSIM`、`PSNR`、`CentralCrop`等。\n",
    "    \n",
    "\n",
    "- 量化层\n",
    "\n",
    "    量化是指将数据从float的形式转换成一段数据范围的int类型，所以量化层提供了一些数据量化的方法和模型层结构封装。\n",
    "\n",
    "    主要接口有`Conv2dBnAct`、`DenseBnAct`、`Conv2dBnFoldQuant`、`LeakyReLUQuant`等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 应用实例\n",
    "\n",
    "MindSpore的模型层在`mindspore.nn`下，使用方法如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-08T01:01:31.944015Z",
     "start_time": "2021-02-08T01:01:31.917571Z"
    }
   },
   "outputs": [],
   "source": [
    "import mindspore.nn as nn\n",
    "\n",
    "class Net(nn.Cell):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal')\n",
    "        self.bn = nn.BatchNorm2d(64)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.fc = nn.Dense(64 * 222 * 222, 3)\n",
    "\n",
    "    def construct(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.bn(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.flatten(x)\n",
    "        out = self.fc(x)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "依然是上述网络构造的用例，从这个用例中可以看出，程序调用了`Conv2d`、`BatchNorm2d`、`ReLU`、`Flatten`和`Dense`模型层的接口。\n",
    "\n",
    "在`Net`初始化方法里被定义，然后在`construct`方法里真正运行，这些模型层接口有序的连接，形成一个可执行的网络。"
   ]
  }
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