404 lines
10 KiB
Plaintext
404 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f66f7a20",
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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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"# 自定义层\n",
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"\n",
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"深度学习成功背后的一个因素是神经网络的灵活性：\n",
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"我们可以用创造性的方式组合不同的层，从而设计出适用于各种任务的架构。\n",
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"例如，研究人员发明了专门用于处理图像、文本、序列数据和执行动态规划的层。\n",
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"有时我们会遇到或要自己发明一个现在在深度学习框架中还不存在的层。\n",
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"在这些情况下，必须构建自定义层。本节将展示如何构建自定义层。\n",
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"\n",
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"## 不带参数的层\n",
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"\n",
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"首先，我们(**构造一个没有任何参数的自定义层**)。\n",
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"回忆一下在 :numref:`sec_model_construction`对块的介绍，\n",
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"这应该看起来很眼熟。\n",
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"下面的`CenteredLayer`类要从其输入中减去均值。\n",
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"要构建它，我们只需继承基础层类并实现前向传播功能。\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": 1,
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"id": "cc3b353a",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:07:16.604374Z",
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"iopub.status.busy": "2023-08-18T07:07:16.603752Z",
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"iopub.status.idle": "2023-08-18T07:07:17.492480Z",
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"shell.execute_reply": "2023-08-18T07:07:17.491482Z"
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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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"source": [
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"import torch\n",
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"import torch.nn.functional as F\n",
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"from torch import nn\n",
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"\n",
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"\n",
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"class CenteredLayer(nn.Module):\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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"\n",
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" def forward(self, X):\n",
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" return X - X.mean()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a3c321cf",
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"metadata": {
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"origin_pos": 5
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},
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"source": [
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"让我们向该层提供一些数据，验证它是否能按预期工作。\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": "dec68045",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:07:17.497408Z",
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"iopub.status.busy": "2023-08-18T07:07:17.497077Z",
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"iopub.status.idle": "2023-08-18T07:07:17.508357Z",
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"shell.execute_reply": "2023-08-18T07:07:17.507175Z"
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},
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"origin_pos": 7,
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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([-2., -1., 0., 1., 2.])"
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]
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},
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"execution_count": 2,
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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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"layer = CenteredLayer()\n",
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"layer(torch.FloatTensor([1, 2, 3, 4, 5]))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9d38600d",
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"metadata": {
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"origin_pos": 10
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},
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"source": [
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"现在，我们可以[**将层作为组件合并到更复杂的模型中**]。\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": "1b903c3c",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:07:17.513247Z",
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"iopub.status.busy": "2023-08-18T07:07:17.512547Z",
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"iopub.status.idle": "2023-08-18T07:07:17.518968Z",
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"shell.execute_reply": "2023-08-18T07:07:17.517886Z"
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},
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"origin_pos": 12,
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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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"net = nn.Sequential(nn.Linear(8, 128), CenteredLayer())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4c48076d",
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"metadata": {
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"origin_pos": 14
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},
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"source": [
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"作为额外的健全性检查，我们可以在向该网络发送随机数据后，检查均值是否为0。\n",
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"由于我们处理的是浮点数，因为存储精度的原因，我们仍然可能会看到一个非常小的非零数。\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": 4,
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"id": "6ab302a0",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:07:17.523517Z",
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"iopub.status.busy": "2023-08-18T07:07:17.523140Z",
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"iopub.status.idle": "2023-08-18T07:07:17.534718Z",
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"shell.execute_reply": "2023-08-18T07:07:17.533593Z"
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},
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"origin_pos": 16,
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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(7.4506e-09, grad_fn=<MeanBackward0>)"
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]
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},
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"execution_count": 4,
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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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"Y = net(torch.rand(4, 8))\n",
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"Y.mean()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ca107571",
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"metadata": {
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"origin_pos": 19
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},
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"source": [
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"## [**带参数的层**]\n",
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"\n",
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"以上我们知道了如何定义简单的层，下面我们继续定义具有参数的层，\n",
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"这些参数可以通过训练进行调整。\n",
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"我们可以使用内置函数来创建参数，这些函数提供一些基本的管理功能。\n",
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"比如管理访问、初始化、共享、保存和加载模型参数。\n",
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"这样做的好处之一是：我们不需要为每个自定义层编写自定义的序列化程序。\n",
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"\n",
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"现在，让我们实现自定义版本的全连接层。\n",
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"回想一下，该层需要两个参数，一个用于表示权重，另一个用于表示偏置项。\n",
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"在此实现中，我们使用修正线性单元作为激活函数。\n",
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"该层需要输入参数：`in_units`和`units`，分别表示输入数和输出数。\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": 5,
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"id": "8c4a7999",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:07:17.539101Z",
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"iopub.status.busy": "2023-08-18T07:07:17.538729Z",
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"iopub.status.idle": "2023-08-18T07:07:17.546162Z",
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"shell.execute_reply": "2023-08-18T07:07:17.545105Z"
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},
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"origin_pos": 21,
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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 MyLinear(nn.Module):\n",
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" def __init__(self, in_units, units):\n",
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" super().__init__()\n",
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" self.weight = nn.Parameter(torch.randn(in_units, units))\n",
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" self.bias = nn.Parameter(torch.randn(units,))\n",
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" def forward(self, X):\n",
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" linear = torch.matmul(X, self.weight.data) + self.bias.data\n",
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" return F.relu(linear)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "442183c6",
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"metadata": {
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"origin_pos": 25,
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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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"接下来，我们实例化`MyLinear`类并访问其模型参数。\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": 6,
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"id": "4490005a",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:07:17.550522Z",
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"iopub.status.busy": "2023-08-18T07:07:17.550152Z",
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"iopub.status.idle": "2023-08-18T07:07:17.558364Z",
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"shell.execute_reply": "2023-08-18T07:07:17.557338Z"
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},
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"origin_pos": 28,
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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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"Parameter containing:\n",
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"tensor([[ 0.1775, -1.4539, 0.3972],\n",
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" [-0.1339, 0.5273, 1.3041],\n",
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" [-0.3327, -0.2337, -0.6334],\n",
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" [ 1.2076, -0.3937, 0.6851],\n",
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" [-0.4716, 0.0894, -0.9195]], requires_grad=True)"
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]
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},
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"execution_count": 6,
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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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"linear = MyLinear(5, 3)\n",
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"linear.weight"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7dcc8fd9",
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"metadata": {
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"origin_pos": 30
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},
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"source": [
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"我们可以[**使用自定义层直接执行前向传播计算**]。\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": 7,
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"id": "25f2aabf",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:07:17.562706Z",
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"iopub.status.busy": "2023-08-18T07:07:17.562337Z",
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"iopub.status.idle": "2023-08-18T07:07:17.570015Z",
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"shell.execute_reply": "2023-08-18T07:07:17.568916Z"
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},
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"origin_pos": 32,
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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., 0., 0.],\n",
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" [0., 0., 0.]])"
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]
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},
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"execution_count": 7,
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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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"linear(torch.rand(2, 5))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c92ac1e0",
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"metadata": {
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"origin_pos": 35
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},
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"source": [
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"我们还可以(**使用自定义层构建模型**)，就像使用内置的全连接层一样使用自定义层。\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": 8,
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"id": "fb2953e8",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:07:17.574378Z",
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"iopub.status.busy": "2023-08-18T07:07:17.574000Z",
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"iopub.status.idle": "2023-08-18T07:07:17.582792Z",
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"shell.execute_reply": "2023-08-18T07:07:17.581735Z"
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},
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"origin_pos": 37,
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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.],\n",
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" [0.]])"
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]
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},
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"execution_count": 8,
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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 = nn.Sequential(MyLinear(64, 8), MyLinear(8, 1))\n",
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"net(torch.rand(2, 64))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5a23d1ab",
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"metadata": {
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"origin_pos": 40
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},
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"source": [
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"## 小结\n",
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"\n",
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"* 我们可以通过基本层类设计自定义层。这允许我们定义灵活的新层，其行为与深度学习框架中的任何现有层不同。\n",
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"* 在自定义层定义完成后，我们就可以在任意环境和网络架构中调用该自定义层。\n",
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"* 层可以有局部参数，这些参数可以通过内置函数创建。\n",
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"\n",
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"## 练习\n",
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"\n",
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"1. 设计一个接受输入并计算张量降维的层，它返回$y_k = \\sum_{i, j} W_{ijk} x_i x_j$。\n",
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"1. 设计一个返回输入数据的傅立叶系数前半部分的层。\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": "2d5d22c2",
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"metadata": {
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"origin_pos": 42,
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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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"[Discussions](https://discuss.d2l.ai/t/1835)\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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},
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"required_libs": []
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},
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"nbformat": 4,
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"nbformat_minor": 5
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} |