1482 lines
54 KiB
Plaintext
1482 lines
54 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "76c80cf7",
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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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"# 情感分析：使用卷积神经网络\n",
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":label:`sec_sentiment_cnn`\n",
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"\n",
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"在 :numref:`chap_cnn`中，我们探讨了使用二维卷积神经网络处理二维图像数据的机制，并将其应用于局部特征，如相邻像素。虽然卷积神经网络最初是为计算机视觉设计的，但它也被广泛用于自然语言处理。简单地说，只要将任何文本序列想象成一维图像即可。通过这种方式，一维卷积神经网络可以处理文本中的局部特征，例如$n$元语法。\n",
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"\n",
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"本节将使用*textCNN*模型来演示如何设计一个表示单个文本 :cite:`Kim.2014`的卷积神经网络架构。与 :numref:`fig_nlp-map-sa-rnn`中使用带有GloVe预训练的循环神经网络架构进行情感分析相比， :numref:`fig_nlp-map-sa-cnn`中唯一的区别在于架构的选择。\n",
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"\n",
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"\n",
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":label:`fig_nlp-map-sa-cnn`\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": "a5b05735",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:56:29.533507Z",
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"iopub.status.busy": "2023-08-18T06:56:29.532815Z",
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"iopub.status.idle": "2023-08-18T06:57:08.360556Z",
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"shell.execute_reply": "2023-08-18T06:57:08.359662Z"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading ../data/aclImdb_v1.tar.gz from http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz...\n"
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]
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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 d2l import torch as d2l\n",
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"\n",
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"batch_size = 64\n",
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"train_iter, test_iter, vocab = d2l.load_data_imdb(batch_size)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ddfb806b",
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"metadata": {
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"origin_pos": 4
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},
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"source": [
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"## 一维卷积\n",
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"\n",
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"在介绍该模型之前，让我们先看看一维卷积是如何工作的。请记住，这只是基于互相关运算的二维卷积的特例。\n",
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"\n",
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"\n",
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":label:`fig_conv1d`\n",
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"\n",
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"如 :numref:`fig_conv1d`中所示，在一维情况下，卷积窗口在输入张量上从左向右滑动。在滑动期间，卷积窗口中某个位置包含的输入子张量（例如， :numref:`fig_conv1d`中的$0$和$1$）和核张量（例如， :numref:`fig_conv1d`中的$1$和$2$）按元素相乘。这些乘法的总和在输出张量的相应位置给出单个标量值（例如， :numref:`fig_conv1d`中的$0\\times1+1\\times2=2$）。\n",
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"\n",
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"我们在下面的`corr1d`函数中实现了一维互相关。给定输入张量`X`和核张量`K`，它返回输出张量`Y`。\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": "06263d9b",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:57:08.364760Z",
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"iopub.status.busy": "2023-08-18T06:57:08.364177Z",
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"iopub.status.idle": "2023-08-18T06:57:08.369258Z",
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"shell.execute_reply": "2023-08-18T06:57:08.368473Z"
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},
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"origin_pos": 5,
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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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"def corr1d(X, K):\n",
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" w = K.shape[0]\n",
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" Y = torch.zeros((X.shape[0] - w + 1))\n",
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" for i in range(Y.shape[0]):\n",
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" Y[i] = (X[i: i + w] * K).sum()\n",
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" return Y"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d0c34067",
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"metadata": {
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"origin_pos": 7
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},
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"source": [
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"我们可以从 :numref:`fig_conv1d`构造输入张量`X`和核张量`K`来验证上述一维互相关实现的输出。\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": "1f6357d7",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:57:08.372600Z",
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"iopub.status.busy": "2023-08-18T06:57:08.372159Z",
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"iopub.status.idle": "2023-08-18T06:57:08.399427Z",
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"shell.execute_reply": "2023-08-18T06:57:08.398684Z"
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},
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"origin_pos": 8,
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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., 5., 8., 11., 14., 17.])"
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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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"X, K = torch.tensor([0, 1, 2, 3, 4, 5, 6]), torch.tensor([1, 2])\n",
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"corr1d(X, K)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7bd7511d",
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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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"对于任何具有多个通道的一维输入，卷积核需要具有相同数量的输入通道。然后，对于每个通道，对输入的一维张量和卷积核的一维张量执行互相关运算，将所有通道上的结果相加以产生一维输出张量。 :numref:`fig_conv1d_channel`演示了具有3个输入通道的一维互相关操作。\n",
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"\n",
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"\n",
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":label:`fig_conv1d_channel`\n",
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"\n",
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"我们可以实现多个输入通道的一维互相关运算，并在 :numref:`fig_conv1d_channel`中验证结果。\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": "ab10c163",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:57:08.446606Z",
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"iopub.status.busy": "2023-08-18T06:57:08.446039Z",
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"iopub.status.idle": "2023-08-18T06:57:08.454551Z",
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"shell.execute_reply": "2023-08-18T06:57:08.453800Z"
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},
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"origin_pos": 10,
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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., 8., 14., 20., 26., 32.])"
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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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"def corr1d_multi_in(X, K):\n",
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" # 首先，遍历'X'和'K'的第0维（通道维）。然后，把它们加在一起\n",
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" return sum(corr1d(x, k) for x, k in zip(X, K))\n",
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"\n",
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"X = torch.tensor([[0, 1, 2, 3, 4, 5, 6],\n",
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" [1, 2, 3, 4, 5, 6, 7],\n",
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" [2, 3, 4, 5, 6, 7, 8]])\n",
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"K = torch.tensor([[1, 2], [3, 4], [-1, -3]])\n",
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"corr1d_multi_in(X, K)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0ae59cc7",
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"metadata": {
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"origin_pos": 11
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},
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"source": [
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"注意，多输入通道的一维互相关等同于单输入通道的二维互相关。举例说明， :numref:`fig_conv1d_channel`中的多输入通道一维互相关的等价形式是 :numref:`fig_conv1d_2d`中的单输入通道二维互相关，其中卷积核的高度必须与输入张量的高度相同。\n",
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"\n",
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"\n",
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":label:`fig_conv1d_2d`\n",
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"\n",
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" :numref:`fig_conv1d`和 :numref:`fig_conv1d_channel`中的输出都只有一个通道。与 :numref:`subsec_multi-output-channels`中描述的具有多个输出通道的二维卷积相同，我们也可以为一维卷积指定多个输出通道。\n",
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"\n",
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"## 最大时间汇聚层\n",
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"\n",
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"类似地，我们可以使用汇聚层从序列表示中提取最大值，作为跨时间步的最重要特征。textCNN中使用的*最大时间汇聚层*的工作原理类似于一维全局汇聚 :cite:`Collobert.Weston.Bottou.ea.2011`。对于每个通道在不同时间步存储值的多通道输入，每个通道的输出是该通道的最大值。请注意，最大时间汇聚允许在不同通道上使用不同数量的时间步。\n",
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"\n",
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"## textCNN模型\n",
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"\n",
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"使用一维卷积和最大时间汇聚，textCNN模型将单个预训练的词元表示作为输入，然后获得并转换用于下游应用的序列表示。\n",
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"\n",
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"对于具有由$d$维向量表示的$n$个词元的单个文本序列，输入张量的宽度、高度和通道数分别为$n$、$1$和$d$。textCNN模型将输入转换为输出，如下所示：\n",
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"\n",
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"1. 定义多个一维卷积核，并分别对输入执行卷积运算。具有不同宽度的卷积核可以捕获不同数目的相邻词元之间的局部特征。\n",
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"1. 在所有输出通道上执行最大时间汇聚层，然后将所有标量汇聚输出连结为向量。\n",
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"1. 使用全连接层将连结后的向量转换为输出类别。Dropout可以用来减少过拟合。\n",
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"\n",
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"\n",
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":label:`fig_conv1d_textcnn`\n",
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"\n",
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" :numref:`fig_conv1d_textcnn`通过一个具体的例子说明了textCNN的模型架构。输入是具有11个词元的句子，其中每个词元由6维向量表示。因此，我们有一个宽度为11的6通道输入。定义两个宽度为2和4的一维卷积核，分别具有4个和5个输出通道。它们产生4个宽度为$11-2+1=10$的输出通道和5个宽度为$11-4+1=8$的输出通道。尽管这9个通道的宽度不同，但最大时间汇聚层给出了一个连结的9维向量，该向量最终被转换为用于二元情感预测的2维输出向量。\n",
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"\n",
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"### 定义模型\n",
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"\n",
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"我们在下面的类中实现textCNN模型。与 :numref:`sec_sentiment_rnn`的双向循环神经网络模型相比，除了用卷积层代替循环神经网络层外，我们还使用了两个嵌入层：一个是可训练权重，另一个是固定权重。\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": "b2e051a1",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:57:08.458036Z",
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"iopub.status.busy": "2023-08-18T06:57:08.457474Z",
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"iopub.status.idle": "2023-08-18T06:57:08.466575Z",
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"shell.execute_reply": "2023-08-18T06:57:08.465450Z"
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},
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"origin_pos": 13,
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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 TextCNN(nn.Module):\n",
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" def __init__(self, vocab_size, embed_size, kernel_sizes, num_channels,\n",
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" **kwargs):\n",
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" super(TextCNN, self).__init__(**kwargs)\n",
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" self.embedding = nn.Embedding(vocab_size, embed_size)\n",
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" # 这个嵌入层不需要训练\n",
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" self.constant_embedding = nn.Embedding(vocab_size, embed_size)\n",
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" self.dropout = nn.Dropout(0.5)\n",
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" self.decoder = nn.Linear(sum(num_channels), 2)\n",
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" # 最大时间汇聚层没有参数，因此可以共享此实例\n",
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" self.pool = nn.AdaptiveAvgPool1d(1)\n",
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" self.relu = nn.ReLU()\n",
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" # 创建多个一维卷积层\n",
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" self.convs = nn.ModuleList()\n",
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" for c, k in zip(num_channels, kernel_sizes):\n",
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" self.convs.append(nn.Conv1d(2 * embed_size, c, k))\n",
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"\n",
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" def forward(self, inputs):\n",
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" # 沿着向量维度将两个嵌入层连结起来，\n",
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" # 每个嵌入层的输出形状都是（批量大小，词元数量，词元向量维度）连结起来\n",
|
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" embeddings = torch.cat((\n",
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" self.embedding(inputs), self.constant_embedding(inputs)), dim=2)\n",
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" # 根据一维卷积层的输入格式，重新排列张量，以便通道作为第2维\n",
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" embeddings = embeddings.permute(0, 2, 1)\n",
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" # 每个一维卷积层在最大时间汇聚层合并后，获得的张量形状是（批量大小，通道数，1）\n",
|
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" # 删除最后一个维度并沿通道维度连结\n",
|
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" encoding = torch.cat([\n",
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" torch.squeeze(self.relu(self.pool(conv(embeddings))), dim=-1)\n",
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" for conv in self.convs], dim=1)\n",
|
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" outputs = self.decoder(self.dropout(encoding))\n",
|
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" return outputs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ffdedc58",
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"metadata": {
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"origin_pos": 15
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},
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"source": [
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"让我们创建一个textCNN实例。它有3个卷积层，卷积核宽度分别为3、4和5，均有100个输出通道。\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": "b263a464",
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"metadata": {
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"execution": {
|
|
"iopub.execute_input": "2023-08-18T06:57:08.470313Z",
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"iopub.status.busy": "2023-08-18T06:57:08.469519Z",
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"iopub.status.idle": "2023-08-18T06:57:08.572337Z",
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"shell.execute_reply": "2023-08-18T06:57:08.571481Z"
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},
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"origin_pos": 17,
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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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"embed_size, kernel_sizes, nums_channels = 100, [3, 4, 5], [100, 100, 100]\n",
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"devices = d2l.try_all_gpus()\n",
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"net = TextCNN(len(vocab), embed_size, kernel_sizes, nums_channels)\n",
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"\n",
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"def init_weights(m):\n",
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" if type(m) in (nn.Linear, nn.Conv1d):\n",
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" nn.init.xavier_uniform_(m.weight)\n",
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"\n",
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"net.apply(init_weights);"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6a8fe95d",
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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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"### 加载预训练词向量\n",
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"\n",
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"与 :numref:`sec_sentiment_rnn`相同，我们加载预训练的100维GloVe嵌入作为初始化的词元表示。这些词元表示（嵌入权重）在`embedding`中将被训练，在`constant_embedding`中将被固定。\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": "3926166b",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:57:08.576318Z",
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"iopub.status.busy": "2023-08-18T06:57:08.575732Z",
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"iopub.status.idle": "2023-08-18T06:57:35.120291Z",
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"shell.execute_reply": "2023-08-18T06:57:35.119200Z"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading ../data/glove.6B.100d.zip from http://d2l-data.s3-accelerate.amazonaws.com/glove.6B.100d.zip...\n"
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]
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}
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],
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"source": [
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"glove_embedding = d2l.TokenEmbedding('glove.6b.100d')\n",
|
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"embeds = glove_embedding[vocab.idx_to_token]\n",
|
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"net.embedding.weight.data.copy_(embeds)\n",
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"net.constant_embedding.weight.data.copy_(embeds)\n",
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"net.constant_embedding.weight.requires_grad = False"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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},
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"source": [
|
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"### 训练和评估模型\n",
|
|
"\n",
|
|
"现在我们可以训练textCNN模型进行情感分析。\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": {
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},
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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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"outputs": [
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{
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"name": "stdout",
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"text": [
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"loss 0.066, train acc 0.978, test acc 0.861\n",
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"4609.1 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]\n"
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]
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"text/plain": [
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]
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},
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"metadata": {
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}
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"source": [
|
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"lr, num_epochs = 0.001, 5\n",
|
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"trainer = torch.optim.Adam(net.parameters(), lr=lr)\n",
|
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"loss = nn.CrossEntropyLoss(reduction=\"none\")\n",
|
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"d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"origin_pos": 27
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},
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"source": [
|
|
"下面，我们使用训练好的模型来预测两个简单句子的情感。\n"
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]
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},
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{
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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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"'positive'"
|
|
]
|
|
},
|
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"execution_count": 9,
|
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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": [
|
|
"d2l.predict_sentiment(net, vocab, 'this movie is so great')"
|
|
]
|
|
},
|
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{
|
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"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "802c833b",
|
|
"metadata": {
|
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"execution": {
|
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|
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|
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|
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},
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"origin_pos": 29,
|
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"tab": [
|
|
"pytorch"
|
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]
|
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},
|
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"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'negative'"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
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],
|
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"source": [
|
|
"d2l.predict_sentiment(net, vocab, 'this movie is so bad')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
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"id": "2fbca6d3",
|
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"metadata": {
|
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"origin_pos": 30
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},
|
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"source": [
|
|
"## 小结\n",
|
|
"\n",
|
|
"* 一维卷积神经网络可以处理文本中的局部特征，例如$n$元语法。\n",
|
|
"* 多输入通道的一维互相关等价于单输入通道的二维互相关。\n",
|
|
"* 最大时间汇聚层允许在不同通道上使用不同数量的时间步长。\n",
|
|
"* textCNN模型使用一维卷积层和最大时间汇聚层将单个词元表示转换为下游应用输出。\n",
|
|
"\n",
|
|
"## 练习\n",
|
|
"\n",
|
|
"1. 调整超参数，并比较 :numref:`sec_sentiment_rnn`中用于情感分析的架构和本节中用于情感分析的架构，例如在分类精度和计算效率方面。\n",
|
|
"1. 请试着用 :numref:`sec_sentiment_rnn`练习中介绍的方法进一步提高模型的分类精度。\n",
|
|
"1. 在输入表示中添加位置编码。它是否提高了分类的精度？\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1c40650d",
|
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"metadata": {
|
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"origin_pos": 32,
|
|
"tab": [
|
|
"pytorch"
|
|
]
|
|
},
|
|
"source": [
|
|
"[Discussions](https://discuss.d2l.ai/t/5720)\n"
|
|
]
|
|
}
|
|
],
|
|
"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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"nbformat_minor": 5
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