{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b05be39e",
   "metadata": {
    "origin_pos": 0
   },
   "source": [
    "# 参数管理\n",
    "\n",
    "在选择了架构并设置了超参数后，我们就进入了训练阶段。\n",
    "此时，我们的目标是找到使损失函数最小化的模型参数值。\n",
    "经过训练后，我们将需要使用这些参数来做出未来的预测。\n",
    "此外，有时我们希望提取参数，以便在其他环境中复用它们，\n",
    "将模型保存下来，以便它可以在其他软件中执行，\n",
    "或者为了获得科学的理解而进行检查。\n",
    "\n",
    "之前的介绍中，我们只依靠深度学习框架来完成训练的工作，\n",
    "而忽略了操作参数的具体细节。\n",
    "本节，我们将介绍以下内容：\n",
    "\n",
    "* 访问参数，用于调试、诊断和可视化；\n",
    "* 参数初始化；\n",
    "* 在不同模型组件间共享参数。\n",
    "\n",
    "(**我们首先看一下具有单隐藏层的多层感知机。**)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab7ef7a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:09.649068Z",
     "iopub.status.busy": "2023-08-18T07:01:09.648305Z",
     "iopub.status.idle": "2023-08-18T07:01:10.928992Z",
     "shell.execute_reply": "2023-08-18T07:01:10.927959Z"
    },
    "origin_pos": 2,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.0970],\n",
       "        [-0.0827]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "net = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 1))\n",
    "X = torch.rand(size=(2, 4))\n",
    "net(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa004a12",
   "metadata": {
    "origin_pos": 5
   },
   "source": [
    "## [**参数访问**]\n",
    "\n",
    "我们从已有模型中访问参数。\n",
    "当通过`Sequential`类定义模型时，\n",
    "我们可以通过索引来访问模型的任意层。\n",
    "这就像模型是一个列表一样，每层的参数都在其属性中。\n",
    "如下所示，我们可以检查第二个全连接层的参数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e2fff9a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:10.933865Z",
     "iopub.status.busy": "2023-08-18T07:01:10.933267Z",
     "iopub.status.idle": "2023-08-18T07:01:10.939922Z",
     "shell.execute_reply": "2023-08-18T07:01:10.938931Z"
    },
    "origin_pos": 7,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('weight', tensor([[-0.0427, -0.2939, -0.1894,  0.0220, -0.1709, -0.1522, -0.0334, -0.2263]])), ('bias', tensor([0.0887]))])\n"
     ]
    }
   ],
   "source": [
    "print(net[2].state_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b77c779c",
   "metadata": {
    "origin_pos": 9
   },
   "source": [
    "输出的结果告诉我们一些重要的事情：\n",
    "首先，这个全连接层包含两个参数，分别是该层的权重和偏置。\n",
    "两者都存储为单精度浮点数（float32）。\n",
    "注意，参数名称允许唯一标识每个参数，即使在包含数百个层的网络中也是如此。\n",
    "\n",
    "### [**目标参数**]\n",
    "\n",
    "注意，每个参数都表示为参数类的一个实例。\n",
    "要对参数执行任何操作，首先我们需要访问底层的数值。\n",
    "有几种方法可以做到这一点。有些比较简单，而另一些则比较通用。\n",
    "下面的代码从第二个全连接层（即第三个神经网络层）提取偏置，\n",
    "提取后返回的是一个参数类实例，并进一步访问该参数的值。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0682fff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:10.945104Z",
     "iopub.status.busy": "2023-08-18T07:01:10.944250Z",
     "iopub.status.idle": "2023-08-18T07:01:10.951764Z",
     "shell.execute_reply": "2023-08-18T07:01:10.950790Z"
    },
    "origin_pos": 11,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'torch.nn.parameter.Parameter'>\n",
      "Parameter containing:\n",
      "tensor([0.0887], requires_grad=True)\n",
      "tensor([0.0887])\n"
     ]
    }
   ],
   "source": [
    "print(type(net[2].bias))\n",
    "print(net[2].bias)\n",
    "print(net[2].bias.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90565b1",
   "metadata": {
    "origin_pos": 14,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "参数是复合的对象，包含值、梯度和额外信息。\n",
    "这就是我们需要显式参数值的原因。\n",
    "除了值之外，我们还可以访问每个参数的梯度。\n",
    "在上面这个网络中，由于我们还没有调用反向传播，所以参数的梯度处于初始状态。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3cf4d55b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:10.956378Z",
     "iopub.status.busy": "2023-08-18T07:01:10.955542Z",
     "iopub.status.idle": "2023-08-18T07:01:10.961810Z",
     "shell.execute_reply": "2023-08-18T07:01:10.960767Z"
    },
    "origin_pos": 16,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net[2].weight.grad == None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e647c1",
   "metadata": {
    "origin_pos": 17
   },
   "source": [
    "### [**一次性访问所有参数**]\n",
    "\n",
    "当我们需要对所有参数执行操作时，逐个访问它们可能会很麻烦。\n",
    "当我们处理更复杂的块（例如，嵌套块）时，情况可能会变得特别复杂，\n",
    "因为我们需要递归整个树来提取每个子块的参数。\n",
    "下面，我们将通过演示来比较访问第一个全连接层的参数和访问所有层。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "916939ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:10.966725Z",
     "iopub.status.busy": "2023-08-18T07:01:10.965969Z",
     "iopub.status.idle": "2023-08-18T07:01:10.972600Z",
     "shell.execute_reply": "2023-08-18T07:01:10.971655Z"
    },
    "origin_pos": 19,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('weight', torch.Size([8, 4])) ('bias', torch.Size([8]))\n",
      "('0.weight', torch.Size([8, 4])) ('0.bias', torch.Size([8])) ('2.weight', torch.Size([1, 8])) ('2.bias', torch.Size([1]))\n"
     ]
    }
   ],
   "source": [
    "print(*[(name, param.shape) for name, param in net[0].named_parameters()])\n",
    "print(*[(name, param.shape) for name, param in net.named_parameters()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9cc1e2f",
   "metadata": {
    "origin_pos": 21
   },
   "source": [
    "这为我们提供了另一种访问网络参数的方式，如下所示。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "116207ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:10.977269Z",
     "iopub.status.busy": "2023-08-18T07:01:10.976623Z",
     "iopub.status.idle": "2023-08-18T07:01:10.983222Z",
     "shell.execute_reply": "2023-08-18T07:01:10.982309Z"
    },
    "origin_pos": 23,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0887])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.state_dict()['2.bias'].data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ae2721",
   "metadata": {
    "origin_pos": 26
   },
   "source": [
    "### [**从嵌套块收集参数**]\n",
    "\n",
    "让我们看看，如果我们将多个块相互嵌套，参数命名约定是如何工作的。\n",
    "我们首先定义一个生成块的函数（可以说是“块工厂”），然后将这些块组合到更大的块中。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "712e31fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:10.988088Z",
     "iopub.status.busy": "2023-08-18T07:01:10.987352Z",
     "iopub.status.idle": "2023-08-18T07:01:10.998245Z",
     "shell.execute_reply": "2023-08-18T07:01:10.997197Z"
    },
    "origin_pos": 28,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.2596],\n",
       "        [0.2596]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def block1():\n",
    "    return nn.Sequential(nn.Linear(4, 8), nn.ReLU(),\n",
    "                         nn.Linear(8, 4), nn.ReLU())\n",
    "\n",
    "def block2():\n",
    "    net = nn.Sequential()\n",
    "    for i in range(4):\n",
    "        # 在这里嵌套\n",
    "        net.add_module(f'block {i}', block1())\n",
    "    return net\n",
    "\n",
    "rgnet = nn.Sequential(block2(), nn.Linear(4, 1))\n",
    "rgnet(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9958fb",
   "metadata": {
    "origin_pos": 31
   },
   "source": [
    "[**设计了网络后，我们看看它是如何工作的。**]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7d7717d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:11.002889Z",
     "iopub.status.busy": "2023-08-18T07:01:11.002264Z",
     "iopub.status.idle": "2023-08-18T07:01:11.007643Z",
     "shell.execute_reply": "2023-08-18T07:01:11.006464Z"
    },
    "origin_pos": 33,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Sequential(\n",
      "    (block 0): Sequential(\n",
      "      (0): Linear(in_features=4, out_features=8, bias=True)\n",
      "      (1): ReLU()\n",
      "      (2): Linear(in_features=8, out_features=4, bias=True)\n",
      "      (3): ReLU()\n",
      "    )\n",
      "    (block 1): Sequential(\n",
      "      (0): Linear(in_features=4, out_features=8, bias=True)\n",
      "      (1): ReLU()\n",
      "      (2): Linear(in_features=8, out_features=4, bias=True)\n",
      "      (3): ReLU()\n",
      "    )\n",
      "    (block 2): Sequential(\n",
      "      (0): Linear(in_features=4, out_features=8, bias=True)\n",
      "      (1): ReLU()\n",
      "      (2): Linear(in_features=8, out_features=4, bias=True)\n",
      "      (3): ReLU()\n",
      "    )\n",
      "    (block 3): Sequential(\n",
      "      (0): Linear(in_features=4, out_features=8, bias=True)\n",
      "      (1): ReLU()\n",
      "      (2): Linear(in_features=8, out_features=4, bias=True)\n",
      "      (3): ReLU()\n",
      "    )\n",
      "  )\n",
      "  (1): Linear(in_features=4, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(rgnet)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c49f699",
   "metadata": {
    "origin_pos": 35
   },
   "source": [
    "因为层是分层嵌套的，所以我们也可以像通过嵌套列表索引一样访问它们。\n",
    "下面，我们访问第一个主要的块中、第二个子块的第一层的偏置项。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "939ba4d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:11.012522Z",
     "iopub.status.busy": "2023-08-18T07:01:11.011839Z",
     "iopub.status.idle": "2023-08-18T07:01:11.018508Z",
     "shell.execute_reply": "2023-08-18T07:01:11.017590Z"
    },
    "origin_pos": 37,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.1999, -0.4073, -0.1200, -0.2033, -0.1573,  0.3546, -0.2141, -0.2483])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgnet[0][1][0].bias.data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0383b6a9",
   "metadata": {
    "origin_pos": 40
   },
   "source": [
    "## 参数初始化\n",
    "\n",
    "知道了如何访问参数后，现在我们看看如何正确地初始化参数。\n",
    "我们在 :numref:`sec_numerical_stability`中讨论了良好初始化的必要性。\n",
    "深度学习框架提供默认随机初始化，\n",
    "也允许我们创建自定义初始化方法，\n",
    "满足我们通过其他规则实现初始化权重。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0418f044",
   "metadata": {
    "origin_pos": 42,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "默认情况下，PyTorch会根据一个范围均匀地初始化权重和偏置矩阵，\n",
    "这个范围是根据输入和输出维度计算出的。\n",
    "PyTorch的`nn.init`模块提供了多种预置初始化方法。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b0b932a",
   "metadata": {
    "origin_pos": 45
   },
   "source": [
    "### [**内置初始化**]\n",
    "\n",
    "让我们首先调用内置的初始化器。\n",
    "下面的代码将所有权重参数初始化为标准差为0.01的高斯随机变量，\n",
    "且将偏置参数设置为0。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f00d5e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:11.023955Z",
     "iopub.status.busy": "2023-08-18T07:01:11.023046Z",
     "iopub.status.idle": "2023-08-18T07:01:11.033287Z",
     "shell.execute_reply": "2023-08-18T07:01:11.032096Z"
    },
    "origin_pos": 47,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.0214, -0.0015, -0.0100, -0.0058]), tensor(0.))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def init_normal(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.normal_(m.weight, mean=0, std=0.01)\n",
    "        nn.init.zeros_(m.bias)\n",
    "net.apply(init_normal)\n",
    "net[0].weight.data[0], net[0].bias.data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753e540b",
   "metadata": {
    "origin_pos": 50
   },
   "source": [
    "我们还可以将所有参数初始化为给定的常数，比如初始化为1。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "49ee306c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:11.038321Z",
     "iopub.status.busy": "2023-08-18T07:01:11.037607Z",
     "iopub.status.idle": "2023-08-18T07:01:11.049009Z",
     "shell.execute_reply": "2023-08-18T07:01:11.047793Z"
    },
    "origin_pos": 52,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1., 1., 1., 1.]), tensor(0.))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def init_constant(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.constant_(m.weight, 1)\n",
    "        nn.init.zeros_(m.bias)\n",
    "net.apply(init_constant)\n",
    "net[0].weight.data[0], net[0].bias.data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e086279d",
   "metadata": {
    "origin_pos": 55
   },
   "source": [
    "我们还可以[**对某些块应用不同的初始化方法**]。\n",
    "例如，下面我们使用Xavier初始化方法初始化第一个神经网络层，\n",
    "然后将第三个神经网络层初始化为常量值42。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a90ffaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:11.054335Z",
     "iopub.status.busy": "2023-08-18T07:01:11.053550Z",
     "iopub.status.idle": "2023-08-18T07:01:11.063215Z",
     "shell.execute_reply": "2023-08-18T07:01:11.062244Z"
    },
    "origin_pos": 57,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 0.5236,  0.0516, -0.3236,  0.3794])\n",
      "tensor([[42., 42., 42., 42., 42., 42., 42., 42.]])\n"
     ]
    }
   ],
   "source": [
    "def init_xavier(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.xavier_uniform_(m.weight)\n",
    "def init_42(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.constant_(m.weight, 42)\n",
    "\n",
    "net[0].apply(init_xavier)\n",
    "net[2].apply(init_42)\n",
    "print(net[0].weight.data[0])\n",
    "print(net[2].weight.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "581dcade",
   "metadata": {
    "origin_pos": 60
   },
   "source": [
    "### [**自定义初始化**]\n",
    "\n",
    "有时，深度学习框架没有提供我们需要的初始化方法。\n",
    "在下面的例子中，我们使用以下的分布为任意权重参数$w$定义初始化方法：\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    w \\sim \\begin{cases}\n",
    "        U(5, 10) & \\text{ 可能性 } \\frac{1}{4} \\\\\n",
    "            0    & \\text{ 可能性 } \\frac{1}{2} \\\\\n",
    "        U(-10, -5) & \\text{ 可能性 } \\frac{1}{4}\n",
    "    \\end{cases}\n",
    "\\end{aligned}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12502b7c",
   "metadata": {
    "origin_pos": 62,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "同样，我们实现了一个`my_init`函数来应用到`net`。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9166f6e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:11.068164Z",
     "iopub.status.busy": "2023-08-18T07:01:11.067460Z",
     "iopub.status.idle": "2023-08-18T07:01:11.079228Z",
     "shell.execute_reply": "2023-08-18T07:01:11.078069Z"
    },
    "origin_pos": 66,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init weight torch.Size([8, 4])\n",
      "Init weight torch.Size([1, 8])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[5.4079, 9.3334, 5.0616, 8.3095],\n",
       "        [0.0000, 7.2788, -0.0000, -0.0000]], grad_fn=<SliceBackward0>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_init(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        print(\"Init\", *[(name, param.shape)\n",
    "                        for name, param in m.named_parameters()][0])\n",
    "        nn.init.uniform_(m.weight, -10, 10)\n",
    "        m.weight.data *= m.weight.data.abs() >= 5\n",
    "\n",
    "net.apply(my_init)\n",
    "net[0].weight[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030a52c5",
   "metadata": {
    "origin_pos": 69
   },
   "source": [
    "注意，我们始终可以直接设置参数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5b9af1f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:11.084158Z",
     "iopub.status.busy": "2023-08-18T07:01:11.083416Z",
     "iopub.status.idle": "2023-08-18T07:01:11.092672Z",
     "shell.execute_reply": "2023-08-18T07:01:11.091537Z"
    },
    "origin_pos": 71,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([42.0000, 10.3334,  6.0616,  9.3095])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net[0].weight.data[:] += 1\n",
    "net[0].weight.data[0, 0] = 42\n",
    "net[0].weight.data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4144ff7",
   "metadata": {
    "origin_pos": 75
   },
   "source": [
    "## [**参数绑定**]\n",
    "\n",
    "有时我们希望在多个层间共享参数：\n",
    "我们可以定义一个稠密层，然后使用它的参数来设置另一个层的参数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69660fa7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:11.097767Z",
     "iopub.status.busy": "2023-08-18T07:01:11.096948Z",
     "iopub.status.idle": "2023-08-18T07:01:11.108904Z",
     "shell.execute_reply": "2023-08-18T07:01:11.107763Z"
    },
    "origin_pos": 77,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([True, True, True, True, True, True, True, True])\n",
      "tensor([True, True, True, True, True, True, True, True])\n"
     ]
    }
   ],
   "source": [
    "# 我们需要给共享层一个名称，以便可以引用它的参数\n",
    "shared = nn.Linear(8, 8)\n",
    "net = nn.Sequential(nn.Linear(4, 8), nn.ReLU(),\n",
    "                    shared, nn.ReLU(),\n",
    "                    shared, nn.ReLU(),\n",
    "                    nn.Linear(8, 1))\n",
    "net(X)\n",
    "# 检查参数是否相同\n",
    "print(net[2].weight.data[0] == net[4].weight.data[0])\n",
    "net[2].weight.data[0, 0] = 100\n",
    "# 确保它们实际上是同一个对象，而不只是有相同的值\n",
    "print(net[2].weight.data[0] == net[4].weight.data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81dc2c3c",
   "metadata": {
    "origin_pos": 81,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "这个例子表明第三个和第五个神经网络层的参数是绑定的。\n",
    "它们不仅值相等，而且由相同的张量表示。\n",
    "因此，如果我们改变其中一个参数，另一个参数也会改变。\n",
    "这里有一个问题：当参数绑定时，梯度会发生什么情况？\n",
    "答案是由于模型参数包含梯度，因此在反向传播期间第二个隐藏层\n",
    "（即第三个神经网络层）和第三个隐藏层（即第五个神经网络层）的梯度会加在一起。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8e6259",
   "metadata": {
    "origin_pos": 82
   },
   "source": [
    "## 小结\n",
    "\n",
    "* 我们有几种方法可以访问、初始化和绑定模型参数。\n",
    "* 我们可以使用自定义初始化方法。\n",
    "\n",
    "## 练习\n",
    "\n",
    "1. 使用 :numref:`sec_model_construction` 中定义的`FancyMLP`模型，访问各个层的参数。\n",
    "1. 查看初始化模块文档以了解不同的初始化方法。\n",
    "1. 构建包含共享参数层的多层感知机并对其进行训练。在训练过程中，观察模型各层的参数和梯度。\n",
    "1. 为什么共享参数是个好主意？\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ead65cf9",
   "metadata": {
    "origin_pos": 84,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "[Discussions](https://discuss.d2l.ai/t/1829)\n"
   ]
  }
 ],
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