{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 自定义模型\n",
    "\n",
    "`CPU` `GPU` `Linux` `入门`\n",
    "\n",
    "\n",
    "luojianet_ms.nn提供了神经网络模型构建的基础模块，如nn.Conv2D等基础操作算子。\n",
    "\n",
    "自定义模型需要继承自基类nn.Module\n",
    "\n",
    "首先导入本文档需要的模块和接口，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import luojianet_ms\n",
    "import luojianet_ms.nn as nn\n",
    "from luojianet_ms import Tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型类\n",
    "\n",
    "LuoJiaNET采用nn.Module作为基类，所有网络结构中使用的算子都需要继承自该类，并重写`__init__`方法和`call`方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeNet5(nn.Module):\n",
    "    \"\"\"\n",
    "    Lenet网络结构\n",
    "    \"\"\"\n",
    "    def __init__(self, num_class=10, num_channel=1):\n",
    "        super(LeNet5, self).__init__()\n",
    "        # 定义所需要的运算\n",
    "        self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')\n",
    "        self.fc1 = nn.Dense(16 * 5 * 5, 120)\n",
    "        self.fc2 = nn.Dense(120, 84)\n",
    "        self.fc3 = nn.Dense(84, num_class)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.flatten = nn.Flatten()\n",
    "\n",
    "    def call(self, x):\n",
    "        # 使用定义好的运算构建前向网络\n",
    "        x = self.conv1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.max_pool2d(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.max_pool2d(x)\n",
    "        x = self.flatten(x)\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型参数\n",
    "\n",
    "通过调用nn.Conv，nn.ReLU，nn.MaxPool2d，nn.Flatten,nn.Dense等操作搭建网络结构后，使用的权重和偏置参数会在之后训练中进行优化。`nn.Module`中使用`parameters_and_names()`方法访问所有参数。\n",
    "\n",
    "下面的示例中，调用parameters_and_names遍历每个参数，并打印网络各层名字和属性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('conv1.weight', Parameter (name=conv1.weight, shape=(6, 1, 5, 5), dtype=Float32, requires_grad=True))\n",
      "('conv2.weight', Parameter (name=conv2.weight, shape=(16, 6, 5, 5), dtype=Float32, requires_grad=True))\n",
      "('fc1.weight', Parameter (name=fc1.weight, shape=(120, 400), dtype=Float32, requires_grad=True))\n",
      "('fc1.bias', Parameter (name=fc1.bias, shape=(120,), dtype=Float32, requires_grad=True))\n",
      "('fc2.weight', Parameter (name=fc2.weight, shape=(84, 120), dtype=Float32, requires_grad=True))\n",
      "('fc2.bias', Parameter (name=fc2.bias, shape=(84,), dtype=Float32, requires_grad=True))\n",
      "('fc3.weight', Parameter (name=fc3.weight, shape=(10, 84), dtype=Float32, requires_grad=True))\n",
      "('fc3.bias', Parameter (name=fc3.bias, shape=(10,), dtype=Float32, requires_grad=True))\n"
     ]
    }
   ],
   "source": [
    "model = LeNet5()\n",
    "for m in model.parameters_and_names():\n",
    "    print(m)"
   ]
  }
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