646 lines
22 KiB
Plaintext
646 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1dca9252",
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"metadata": {
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"origin_pos": 0
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},
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"source": [
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"# 层和块\n",
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":label:`sec_model_construction`\n",
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"\n",
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"之前首次介绍神经网络时，我们关注的是具有单一输出的线性模型。\n",
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"在这里，整个模型只有一个输出。\n",
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"注意，单个神经网络\n",
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"（1）接受一些输入；\n",
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"（2）生成相应的标量输出；\n",
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"（3）具有一组相关 *参数*（parameters），更新这些参数可以优化某目标函数。\n",
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"\n",
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"然后，当考虑具有多个输出的网络时，\n",
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"我们利用矢量化算法来描述整层神经元。\n",
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"像单个神经元一样，层（1）接受一组输入，\n",
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"（2）生成相应的输出，\n",
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"（3）由一组可调整参数描述。\n",
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"当我们使用softmax回归时，一个单层本身就是模型。\n",
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"然而，即使我们随后引入了多层感知机，我们仍然可以认为该模型保留了上面所说的基本架构。\n",
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"\n",
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"对于多层感知机而言，整个模型及其组成层都是这种架构。\n",
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"整个模型接受原始输入（特征），生成输出（预测），\n",
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"并包含一些参数（所有组成层的参数集合）。\n",
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"同样，每个单独的层接收输入（由前一层提供），\n",
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"生成输出（到下一层的输入），并且具有一组可调参数，\n",
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"这些参数根据从下一层反向传播的信号进行更新。\n",
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"\n",
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"事实证明，研究讨论“比单个层大”但“比整个模型小”的组件更有价值。\n",
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"例如，在计算机视觉中广泛流行的ResNet-152架构就有数百层，\n",
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"这些层是由*层组*（groups of layers）的重复模式组成。\n",
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"这个ResNet架构赢得了2015年ImageNet和COCO计算机视觉比赛\n",
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"的识别和检测任务 :cite:`He.Zhang.Ren.ea.2016`。\n",
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"目前ResNet架构仍然是许多视觉任务的首选架构。\n",
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"在其他的领域，如自然语言处理和语音，\n",
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"层组以各种重复模式排列的类似架构现在也是普遍存在。\n",
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"\n",
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"为了实现这些复杂的网络，我们引入了神经网络*块*的概念。\n",
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"*块*（block）可以描述单个层、由多个层组成的组件或整个模型本身。\n",
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"使用块进行抽象的一个好处是可以将一些块组合成更大的组件，\n",
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"这一过程通常是递归的，如 :numref:`fig_blocks`所示。\n",
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"通过定义代码来按需生成任意复杂度的块，\n",
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"我们可以通过简洁的代码实现复杂的神经网络。\n",
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"\n",
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"\n",
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":label:`fig_blocks`\n",
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"\n",
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"从编程的角度来看，块由*类*（class）表示。\n",
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"它的任何子类都必须定义一个将其输入转换为输出的前向传播函数，\n",
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"并且必须存储任何必需的参数。\n",
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"注意，有些块不需要任何参数。\n",
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"最后，为了计算梯度，块必须具有反向传播函数。\n",
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"在定义我们自己的块时，由于自动微分（在 :numref:`sec_autograd` 中引入）\n",
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"提供了一些后端实现，我们只需要考虑前向传播函数和必需的参数。\n",
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"\n",
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"在构造自定义块之前，(**我们先回顾一下多层感知机**)\n",
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"（ :numref:`sec_mlp_concise` ）的代码。\n",
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"下面的代码生成一个网络，其中包含一个具有256个单元和ReLU激活函数的全连接隐藏层，\n",
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"然后是一个具有10个隐藏单元且不带激活函数的全连接输出层。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "9895e279",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:57:00.244437Z",
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"iopub.status.busy": "2023-08-18T06:57:00.243813Z",
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"iopub.status.idle": "2023-08-18T06:57:01.320999Z",
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"shell.execute_reply": "2023-08-18T06:57:01.320186Z"
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},
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"origin_pos": 2,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[ 0.0343, 0.0264, 0.2505, -0.0243, 0.0945, 0.0012, -0.0141, 0.0666,\n",
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" -0.0547, -0.0667],\n",
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" [ 0.0772, -0.0274, 0.2638, -0.0191, 0.0394, -0.0324, 0.0102, 0.0707,\n",
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" -0.1481, -0.1031]], grad_fn=<AddmmBackward0>)"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"from torch.nn import functional as F\n",
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"\n",
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"net = nn.Sequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))\n",
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"\n",
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"X = torch.rand(2, 20)\n",
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"net(X)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "be949c0e",
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"metadata": {
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"origin_pos": 6,
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"tab": [
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"pytorch"
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]
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},
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"source": [
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"在这个例子中，我们通过实例化`nn.Sequential`来构建我们的模型，\n",
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"层的执行顺序是作为参数传递的。\n",
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"简而言之，(**`nn.Sequential`定义了一种特殊的`Module`**)，\n",
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"即在PyTorch中表示一个块的类，\n",
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"它维护了一个由`Module`组成的有序列表。\n",
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"注意，两个全连接层都是`Linear`类的实例，\n",
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"`Linear`类本身就是`Module`的子类。\n",
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"另外，到目前为止，我们一直在通过`net(X)`调用我们的模型来获得模型的输出。\n",
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"这实际上是`net.__call__(X)`的简写。\n",
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"这个前向传播函数非常简单：\n",
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"它将列表中的每个块连接在一起，将每个块的输出作为下一个块的输入。\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a3ce5ce8",
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"metadata": {
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"origin_pos": 9
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},
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"source": [
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"## [**自定义块**]\n",
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"\n",
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"要想直观地了解块是如何工作的，最简单的方法就是自己实现一个。\n",
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"在实现我们自定义块之前，我们简要总结一下每个块必须提供的基本功能。\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "24ea84f7",
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"metadata": {
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"origin_pos": 11,
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"tab": [
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"pytorch"
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]
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},
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"source": [
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"1. 将输入数据作为其前向传播函数的参数。\n",
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"1. 通过前向传播函数来生成输出。请注意，输出的形状可能与输入的形状不同。例如，我们上面模型中的第一个全连接的层接收一个20维的输入，但是返回一个维度为256的输出。\n",
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"1. 计算其输出关于输入的梯度，可通过其反向传播函数进行访问。通常这是自动发生的。\n",
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"1. 存储和访问前向传播计算所需的参数。\n",
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"1. 根据需要初始化模型参数。\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "572894df",
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"metadata": {
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"origin_pos": 12
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},
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"source": [
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"在下面的代码片段中，我们从零开始编写一个块。\n",
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"它包含一个多层感知机，其具有256个隐藏单元的隐藏层和一个10维输出层。\n",
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"注意，下面的`MLP`类继承了表示块的类。\n",
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"我们的实现只需要提供我们自己的构造函数（Python中的`__init__`函数）和前向传播函数。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "876df867",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:57:01.325541Z",
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"iopub.status.busy": "2023-08-18T06:57:01.324828Z",
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"iopub.status.idle": "2023-08-18T06:57:01.330411Z",
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"shell.execute_reply": "2023-08-18T06:57:01.329591Z"
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},
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"origin_pos": 14,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"class MLP(nn.Module):\n",
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" # 用模型参数声明层。这里，我们声明两个全连接的层\n",
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" def __init__(self):\n",
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" # 调用MLP的父类Module的构造函数来执行必要的初始化。\n",
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" # 这样，在类实例化时也可以指定其他函数参数，例如模型参数params（稍后将介绍）\n",
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" super().__init__()\n",
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" self.hidden = nn.Linear(20, 256) # 隐藏层\n",
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" self.out = nn.Linear(256, 10) # 输出层\n",
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"\n",
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" # 定义模型的前向传播，即如何根据输入X返回所需的模型输出\n",
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" def forward(self, X):\n",
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" # 注意，这里我们使用ReLU的函数版本，其在nn.functional模块中定义。\n",
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" return self.out(F.relu(self.hidden(X)))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8327a09c",
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"metadata": {
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"origin_pos": 17
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},
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"source": [
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"我们首先看一下前向传播函数，它以`X`作为输入，\n",
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"计算带有激活函数的隐藏表示，并输出其未规范化的输出值。\n",
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"在这个`MLP`实现中，两个层都是实例变量。\n",
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"要了解这为什么是合理的，可以想象实例化两个多层感知机（`net1`和`net2`），\n",
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"并根据不同的数据对它们进行训练。\n",
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"当然，我们希望它们学到两种不同的模型。\n",
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"\n",
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"接着我们[**实例化多层感知机的层，然后在每次调用前向传播函数时调用这些层**]。\n",
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"注意一些关键细节：\n",
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"首先，我们定制的`__init__`函数通过`super().__init__()`\n",
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"调用父类的`__init__`函数，\n",
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"省去了重复编写模版代码的痛苦。\n",
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"然后，我们实例化两个全连接层，\n",
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"分别为`self.hidden`和`self.out`。\n",
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"注意，除非我们实现一个新的运算符，\n",
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"否则我们不必担心反向传播函数或参数初始化，\n",
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"系统将自动生成这些。\n",
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"\n",
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"我们来试一下这个函数：\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "f7a34ec3",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:57:01.334346Z",
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"iopub.status.busy": "2023-08-18T06:57:01.333603Z",
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||
"iopub.status.idle": "2023-08-18T06:57:01.340473Z",
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"shell.execute_reply": "2023-08-18T06:57:01.339676Z"
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},
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"origin_pos": 19,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[ 0.0669, 0.2202, -0.0912, -0.0064, 0.1474, -0.0577, -0.3006, 0.1256,\n",
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" -0.0280, 0.4040],\n",
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" [ 0.0545, 0.2591, -0.0297, 0.1141, 0.1887, 0.0094, -0.2686, 0.0732,\n",
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" -0.0135, 0.3865]], grad_fn=<AddmmBackward0>)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"net = MLP()\n",
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"net(X)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "37aaa7fc",
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"metadata": {
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"origin_pos": 21
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},
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"source": [
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"块的一个主要优点是它的多功能性。\n",
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"我们可以子类化块以创建层（如全连接层的类）、\n",
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"整个模型（如上面的`MLP`类）或具有中等复杂度的各种组件。\n",
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"我们在接下来的章节中充分利用了这种多功能性，\n",
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"比如在处理卷积神经网络时。\n",
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"\n",
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"## [**顺序块**]\n",
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"\n",
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"现在我们可以更仔细地看看`Sequential`类是如何工作的，\n",
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"回想一下`Sequential`的设计是为了把其他模块串起来。\n",
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"为了构建我们自己的简化的`MySequential`，\n",
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"我们只需要定义两个关键函数：\n",
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"\n",
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"1. 一种将块逐个追加到列表中的函数；\n",
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"1. 一种前向传播函数，用于将输入按追加块的顺序传递给块组成的“链条”。\n",
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"\n",
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"下面的`MySequential`类提供了与默认`Sequential`类相同的功能。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "dd09709c",
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"metadata": {
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||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T06:57:01.344392Z",
|
||
"iopub.status.busy": "2023-08-18T06:57:01.343695Z",
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||
"iopub.status.idle": "2023-08-18T06:57:01.349458Z",
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||
"shell.execute_reply": "2023-08-18T06:57:01.348481Z"
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},
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||
"origin_pos": 23,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
|
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"class MySequential(nn.Module):\n",
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" def __init__(self, *args):\n",
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" super().__init__()\n",
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" for idx, module in enumerate(args):\n",
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" # 这里，module是Module子类的一个实例。我们把它保存在'Module'类的成员\n",
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" # 变量_modules中。_module的类型是OrderedDict\n",
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" self._modules[str(idx)] = module\n",
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"\n",
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" def forward(self, X):\n",
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" # OrderedDict保证了按照成员添加的顺序遍历它们\n",
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" for block in self._modules.values():\n",
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" X = block(X)\n",
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" return X"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a44d091",
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"metadata": {
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"origin_pos": 27,
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"tab": [
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"pytorch"
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]
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},
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"source": [
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"`__init__`函数将每个模块逐个添加到有序字典`_modules`中。\n",
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"读者可能会好奇为什么每个`Module`都有一个`_modules`属性？\n",
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"以及为什么我们使用它而不是自己定义一个Python列表？\n",
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"简而言之，`_modules`的主要优点是：\n",
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"在模块的参数初始化过程中，\n",
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"系统知道在`_modules`字典中查找需要初始化参数的子块。\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0272bce5",
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"metadata": {
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"origin_pos": 29
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},
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"source": [
|
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"当`MySequential`的前向传播函数被调用时，\n",
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"每个添加的块都按照它们被添加的顺序执行。\n",
|
||
"现在可以使用我们的`MySequential`类重新实现多层感知机。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "9672de9a",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T06:57:01.353302Z",
|
||
"iopub.status.busy": "2023-08-18T06:57:01.352727Z",
|
||
"iopub.status.idle": "2023-08-18T06:57:01.360268Z",
|
||
"shell.execute_reply": "2023-08-18T06:57:01.359462Z"
|
||
},
|
||
"origin_pos": 31,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"tensor([[ 2.2759e-01, -4.7003e-02, 4.2846e-01, -1.2546e-01, 1.5296e-01,\n",
|
||
" 1.8972e-01, 9.7048e-02, 4.5479e-04, -3.7986e-02, 6.4842e-02],\n",
|
||
" [ 2.7825e-01, -9.7517e-02, 4.8541e-01, -2.4519e-01, -8.4580e-02,\n",
|
||
" 2.8538e-01, 3.6861e-02, 2.9411e-02, -1.0612e-01, 1.2620e-01]],\n",
|
||
" grad_fn=<AddmmBackward0>)"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"net = MySequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))\n",
|
||
"net(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "189aa472",
|
||
"metadata": {
|
||
"origin_pos": 33
|
||
},
|
||
"source": [
|
||
"请注意，`MySequential`的用法与之前为`Sequential`类编写的代码相同\n",
|
||
"（如 :numref:`sec_mlp_concise` 中所述）。\n",
|
||
"\n",
|
||
"## [**在前向传播函数中执行代码**]\n",
|
||
"\n",
|
||
"`Sequential`类使模型构造变得简单，\n",
|
||
"允许我们组合新的架构，而不必定义自己的类。\n",
|
||
"然而，并不是所有的架构都是简单的顺序架构。\n",
|
||
"当需要更强的灵活性时，我们需要定义自己的块。\n",
|
||
"例如，我们可能希望在前向传播函数中执行Python的控制流。\n",
|
||
"此外，我们可能希望执行任意的数学运算，\n",
|
||
"而不是简单地依赖预定义的神经网络层。\n",
|
||
"\n",
|
||
"到目前为止，\n",
|
||
"我们网络中的所有操作都对网络的激活值及网络的参数起作用。\n",
|
||
"然而，有时我们可能希望合并既不是上一层的结果也不是可更新参数的项，\n",
|
||
"我们称之为*常数参数*（constant parameter）。\n",
|
||
"例如，我们需要一个计算函数\n",
|
||
"$f(\\mathbf{x},\\mathbf{w}) = c \\cdot \\mathbf{w}^\\top \\mathbf{x}$的层，\n",
|
||
"其中$\\mathbf{x}$是输入，\n",
|
||
"$\\mathbf{w}$是参数，\n",
|
||
"$c$是某个在优化过程中没有更新的指定常量。\n",
|
||
"因此我们实现了一个`FixedHiddenMLP`类，如下所示：\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "9ad09596",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T06:57:01.364000Z",
|
||
"iopub.status.busy": "2023-08-18T06:57:01.363468Z",
|
||
"iopub.status.idle": "2023-08-18T06:57:01.369665Z",
|
||
"shell.execute_reply": "2023-08-18T06:57:01.368755Z"
|
||
},
|
||
"origin_pos": 35,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"class FixedHiddenMLP(nn.Module):\n",
|
||
" def __init__(self):\n",
|
||
" super().__init__()\n",
|
||
" # 不计算梯度的随机权重参数。因此其在训练期间保持不变\n",
|
||
" self.rand_weight = torch.rand((20, 20), requires_grad=False)\n",
|
||
" self.linear = nn.Linear(20, 20)\n",
|
||
"\n",
|
||
" def forward(self, X):\n",
|
||
" X = self.linear(X)\n",
|
||
" # 使用创建的常量参数以及relu和mm函数\n",
|
||
" X = F.relu(torch.mm(X, self.rand_weight) + 1)\n",
|
||
" # 复用全连接层。这相当于两个全连接层共享参数\n",
|
||
" X = self.linear(X)\n",
|
||
" # 控制流\n",
|
||
" while X.abs().sum() > 1:\n",
|
||
" X /= 2\n",
|
||
" return X.sum()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "06017344",
|
||
"metadata": {
|
||
"origin_pos": 38
|
||
},
|
||
"source": [
|
||
"在这个`FixedHiddenMLP`模型中，我们实现了一个隐藏层，\n",
|
||
"其权重（`self.rand_weight`）在实例化时被随机初始化，之后为常量。\n",
|
||
"这个权重不是一个模型参数，因此它永远不会被反向传播更新。\n",
|
||
"然后，神经网络将这个固定层的输出通过一个全连接层。\n",
|
||
"\n",
|
||
"注意，在返回输出之前，模型做了一些不寻常的事情：\n",
|
||
"它运行了一个while循环，在$L_1$范数大于$1$的条件下，\n",
|
||
"将输出向量除以$2$，直到它满足条件为止。\n",
|
||
"最后，模型返回了`X`中所有项的和。\n",
|
||
"注意，此操作可能不会常用于在任何实际任务中，\n",
|
||
"我们只展示如何将任意代码集成到神经网络计算的流程中。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "00ebc567",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T06:57:01.373508Z",
|
||
"iopub.status.busy": "2023-08-18T06:57:01.372789Z",
|
||
"iopub.status.idle": "2023-08-18T06:57:01.380049Z",
|
||
"shell.execute_reply": "2023-08-18T06:57:01.379025Z"
|
||
},
|
||
"origin_pos": 40,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"tensor(0.1862, grad_fn=<SumBackward0>)"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"net = FixedHiddenMLP()\n",
|
||
"net(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "80b18eb2",
|
||
"metadata": {
|
||
"origin_pos": 41
|
||
},
|
||
"source": [
|
||
"我们可以[**混合搭配各种组合块的方法**]。\n",
|
||
"在下面的例子中，我们以一些想到的方法嵌套块。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "6ca3b399",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T06:57:01.384091Z",
|
||
"iopub.status.busy": "2023-08-18T06:57:01.383236Z",
|
||
"iopub.status.idle": "2023-08-18T06:57:01.394649Z",
|
||
"shell.execute_reply": "2023-08-18T06:57:01.393535Z"
|
||
},
|
||
"origin_pos": 43,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"tensor(0.2183, grad_fn=<SumBackward0>)"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"class NestMLP(nn.Module):\n",
|
||
" def __init__(self):\n",
|
||
" super().__init__()\n",
|
||
" self.net = nn.Sequential(nn.Linear(20, 64), nn.ReLU(),\n",
|
||
" nn.Linear(64, 32), nn.ReLU())\n",
|
||
" self.linear = nn.Linear(32, 16)\n",
|
||
"\n",
|
||
" def forward(self, X):\n",
|
||
" return self.linear(self.net(X))\n",
|
||
"\n",
|
||
"chimera = nn.Sequential(NestMLP(), nn.Linear(16, 20), FixedHiddenMLP())\n",
|
||
"chimera(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3b12e280",
|
||
"metadata": {
|
||
"origin_pos": 46
|
||
},
|
||
"source": [
|
||
"## 效率\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e26229d3",
|
||
"metadata": {
|
||
"origin_pos": 48,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"source": [
|
||
"读者可能会开始担心操作效率的问题。\n",
|
||
"毕竟，我们在一个高性能的深度学习库中进行了大量的字典查找、\n",
|
||
"代码执行和许多其他的Python代码。\n",
|
||
"Python的问题[全局解释器锁](https://wiki.python.org/moin/GlobalInterpreterLock)\n",
|
||
"是众所周知的。\n",
|
||
"在深度学习环境中，我们担心速度极快的GPU可能要等到CPU运行Python代码后才能运行另一个作业。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4fa617e6",
|
||
"metadata": {
|
||
"origin_pos": 51
|
||
},
|
||
"source": [
|
||
"## 小结\n",
|
||
"\n",
|
||
"* 一个块可以由许多层组成；一个块可以由许多块组成。\n",
|
||
"* 块可以包含代码。\n",
|
||
"* 块负责大量的内部处理，包括参数初始化和反向传播。\n",
|
||
"* 层和块的顺序连接由`Sequential`块处理。\n",
|
||
"\n",
|
||
"## 练习\n",
|
||
"\n",
|
||
"1. 如果将`MySequential`中存储块的方式更改为Python列表，会出现什么样的问题？\n",
|
||
"1. 实现一个块，它以两个块为参数，例如`net1`和`net2`，并返回前向传播中两个网络的串联输出。这也被称为平行块。\n",
|
||
"1. 假设我们想要连接同一网络的多个实例。实现一个函数，该函数生成同一个块的多个实例，并在此基础上构建更大的网络。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c29846c8",
|
||
"metadata": {
|
||
"origin_pos": 53,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"source": [
|
||
"[Discussions](https://discuss.d2l.ai/t/1827)\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"language_info": {
|
||
"name": "python"
|
||
},
|
||
"required_libs": []
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
} |