1417 lines
50 KiB
Plaintext
1417 lines
50 KiB
Plaintext
{
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"cells": [
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"metadata": {
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"source": [
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"# 情感分析：使用循环神经网络\n",
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":label:`sec_sentiment_rnn`\n",
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"\n",
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"与词相似度和类比任务一样，我们也可以将预先训练的词向量应用于情感分析。由于 :numref:`sec_sentiment`中的IMDb评论数据集不是很大，使用在大规模语料库上预训练的文本表示可以减少模型的过拟合。作为 :numref:`fig_nlp-map-sa-rnn`中所示的具体示例，我们将使用预训练的GloVe模型来表示每个词元，并将这些词元表示送入多层双向循环神经网络以获得文本序列表示，该文本序列表示将被转换为情感分析输出 :cite:`Maas.Daly.Pham.ea.2011`。对于相同的下游应用，我们稍后将考虑不同的架构选择。\n",
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"\n",
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"\n",
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":label:`fig_nlp-map-sa-rnn`\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": "39fc24d2",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:05:05.652082Z",
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"iopub.status.busy": "2023-08-18T07:05:05.651505Z",
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"shell.execute_reply": "2023-08-18T07:05:40.291561Z"
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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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"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": "c3e77029",
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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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"## 使用循环神经网络表示单个文本\n",
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"\n",
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"在文本分类任务（如情感分析）中，可变长度的文本序列将被转换为固定长度的类别。在下面的`BiRNN`类中，虽然文本序列的每个词元经由嵌入层（`self.embedding`）获得其单独的预训练GloVe表示，但是整个序列由双向循环神经网络（`self.encoder`）编码。更具体地说，双向长短期记忆网络在初始和最终时间步的隐状态（在最后一层）被连结起来作为文本序列的表示。然后，通过一个具有两个输出（“积极”和“消极”）的全连接层（`self.decoder`），将此单一文本表示转换为输出类别。\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": "744f39e5",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:05:40.299184Z",
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"iopub.status.busy": "2023-08-18T07:05:40.298245Z",
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"shell.execute_reply": "2023-08-18T07:05:40.307994Z"
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},
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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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"outputs": [],
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"source": [
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"class BiRNN(nn.Module):\n",
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" def __init__(self, vocab_size, embed_size, num_hiddens,\n",
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" num_layers, **kwargs):\n",
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" super(BiRNN, self).__init__(**kwargs)\n",
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" self.embedding = nn.Embedding(vocab_size, embed_size)\n",
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" # 将bidirectional设置为True以获取双向循环神经网络\n",
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" self.encoder = nn.LSTM(embed_size, num_hiddens, num_layers=num_layers,\n",
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" bidirectional=True)\n",
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" self.decoder = nn.Linear(4 * num_hiddens, 2)\n",
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"\n",
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" def forward(self, inputs):\n",
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" # inputs的形状是（批量大小，时间步数）\n",
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" # 因为长短期记忆网络要求其输入的第一个维度是时间维，\n",
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" # 所以在获得词元表示之前，输入会被转置。\n",
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" # 输出形状为（时间步数，批量大小，词向量维度）\n",
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" embeddings = self.embedding(inputs.T)\n",
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" self.encoder.flatten_parameters()\n",
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" # 返回上一个隐藏层在不同时间步的隐状态，\n",
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" # outputs的形状是（时间步数，批量大小，2*隐藏单元数）\n",
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" outputs, _ = self.encoder(embeddings)\n",
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" # 连结初始和最终时间步的隐状态，作为全连接层的输入，\n",
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" # 其形状为（批量大小，4*隐藏单元数）\n",
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||
" encoding = torch.cat((outputs[0], outputs[-1]), dim=1)\n",
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" outs = self.decoder(encoding)\n",
|
||
" return outs"
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||
]
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},
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{
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"cell_type": "markdown",
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"id": "98329015",
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"metadata": {
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"origin_pos": 8
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},
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"source": [
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||
"让我们构造一个具有两个隐藏层的双向循环神经网络来表示单个文本以进行情感分析。\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_count": 3,
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"id": "d8b2c0f6",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:05:40.314032Z",
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"iopub.status.busy": "2023-08-18T07:05:40.313553Z",
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"iopub.status.idle": "2023-08-18T07:05:40.403532Z",
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"shell.execute_reply": "2023-08-18T07:05:40.402355Z"
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},
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"origin_pos": 9,
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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, num_hiddens, num_layers = 100, 100, 2\n",
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||
"devices = d2l.try_all_gpus()\n",
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||
"net = BiRNN(len(vocab), embed_size, num_hiddens, num_layers)"
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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": "8215de71",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:05:40.408642Z",
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"iopub.status.busy": "2023-08-18T07:05:40.407984Z",
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"iopub.status.idle": "2023-08-18T07:05:40.420400Z",
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"shell.execute_reply": "2023-08-18T07:05:40.419330Z"
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},
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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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"outputs": [],
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"source": [
|
||
"def init_weights(m):\n",
|
||
" if type(m) == nn.Linear:\n",
|
||
" nn.init.xavier_uniform_(m.weight)\n",
|
||
" if type(m) == nn.LSTM:\n",
|
||
" for param in m._flat_weights_names:\n",
|
||
" if \"weight\" in param:\n",
|
||
" nn.init.xavier_uniform_(m._parameters[param])\n",
|
||
"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": "3b604a0d",
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"metadata": {
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"origin_pos": 13
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},
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"source": [
|
||
"## 加载预训练的词向量\n",
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||
"\n",
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||
"下面，我们为词表中的单词加载预训练的100维（需要与`embed_size`一致）的GloVe嵌入。\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": "50827d9a",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:05:40.424891Z",
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"iopub.status.busy": "2023-08-18T07:05:40.424223Z",
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"iopub.status.idle": "2023-08-18T07:06:05.190359Z",
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"shell.execute_reply": "2023-08-18T07:06:05.188903Z"
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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": [
|
||
"glove_embedding = d2l.TokenEmbedding('glove.6b.100d')"
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||
]
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},
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{
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"cell_type": "markdown",
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"id": "a1f48ad7",
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"metadata": {
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"origin_pos": 15
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},
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"source": [
|
||
"打印词表中所有词元向量的形状。\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": "2ca5c11f",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:06:05.196382Z",
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"iopub.status.busy": "2023-08-18T07:06:05.195537Z",
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"iopub.status.idle": "2023-08-18T07:06:05.260164Z",
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"shell.execute_reply": "2023-08-18T07:06:05.258848Z"
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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": [
|
||
"torch.Size([49346, 100])"
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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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],
|
||
"source": [
|
||
"embeds = glove_embedding[vocab.idx_to_token]\n",
|
||
"embeds.shape"
|
||
]
|
||
},
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{
|
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"cell_type": "markdown",
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"id": "c8621b1b",
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"metadata": {
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"origin_pos": 17
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},
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"source": [
|
||
"我们使用这些预训练的词向量来表示评论中的词元，并且在训练期间不要更新这些向量。\n"
|
||
]
|
||
},
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{
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"cell_type": "code",
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||
"execution_count": 7,
|
||
"id": "1f86f2b0",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:06:05.267876Z",
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"iopub.status.busy": "2023-08-18T07:06:05.266887Z",
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"iopub.status.idle": "2023-08-18T07:06:05.276563Z",
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"shell.execute_reply": "2023-08-18T07:06:05.275272Z"
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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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"source": [
|
||
"net.embedding.weight.data.copy_(embeds)\n",
|
||
"net.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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"id": "fa9d2712",
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"metadata": {
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"origin_pos": 21
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},
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"source": [
|
||
"## 训练和评估模型\n",
|
||
"\n",
|
||
"现在我们可以训练双向循环神经网络进行情感分析。\n"
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]
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},
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{
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"cell_type": "code",
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"pytorch"
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{
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"name": "stdout",
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"text": [
|
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"loss 0.262, train acc 0.893, test acc 0.864\n",
|
||
"2902.4 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]\n"
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|
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|
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"source": [
|
||
"我们定义以下函数来使用训练好的模型`net`预测文本序列的情感。\n"
|
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]
|
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},
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{
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},
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"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"#@save\n",
|
||
"def predict_sentiment(net, vocab, sequence):\n",
|
||
" \"\"\"预测文本序列的情感\"\"\"\n",
|
||
" sequence = torch.tensor(vocab[sequence.split()], device=d2l.try_gpu())\n",
|
||
" label = torch.argmax(net(sequence.reshape(1, -1)), dim=1)\n",
|
||
" return 'positive' if label == 1 else 'negative'"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "532d7858",
|
||
"metadata": {
|
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"origin_pos": 29
|
||
},
|
||
"source": [
|
||
"最后，让我们使用训练好的模型对两个简单的句子进行情感预测。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "41cb5e96",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T07:07:16.160003Z",
|
||
"iopub.status.busy": "2023-08-18T07:07:16.159129Z",
|
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|
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},
|
||
"origin_pos": 30,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'positive'"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predict_sentiment(net, vocab, 'this movie is so great')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "84350bf3",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T07:07:16.171422Z",
|
||
"iopub.status.busy": "2023-08-18T07:07:16.170827Z",
|
||
"iopub.status.idle": "2023-08-18T07:07:16.177666Z",
|
||
"shell.execute_reply": "2023-08-18T07:07:16.176801Z"
|
||
},
|
||
"origin_pos": 31,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'negative'"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predict_sentiment(net, vocab, 'this movie is so bad')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f13f4f0d",
|
||
"metadata": {
|
||
"origin_pos": 32
|
||
},
|
||
"source": [
|
||
"## 小结\n",
|
||
"\n",
|
||
"* 预训练的词向量可以表示文本序列中的各个词元。\n",
|
||
"* 双向循环神经网络可以表示文本序列。例如通过连结初始和最终时间步的隐状态，可以使用全连接的层将该单个文本表示转换为类别。\n",
|
||
"\n",
|
||
"## 练习\n",
|
||
"\n",
|
||
"1. 增加迭代轮数可以提高训练和测试的准确性吗？调优其他超参数怎么样？\n",
|
||
"1. 使用较大的预训练词向量，例如300维的GloVe嵌入。它是否提高了分类精度？\n",
|
||
"1. 是否可以通过spaCy词元化来提高分类精度？需要安装Spacy（`pip install spacy`）和英语语言包（`python -m spacy download en`）。在代码中，首先导入Spacy（`import spacy`）。然后，加载Spacy英语软件包（`spacy_en = spacy.load('en')`）。最后，定义函数`def tokenizer(text): return [tok.text for tok in spacy_en.tokenizer(text)]`并替换原来的`tokenizer`函数。请注意GloVe和spaCy中短语标记的不同形式。例如，短语标记“new york”在GloVe中的形式是“new-york”，而在spaCy词元化之后的形式是“new york”。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d148e971",
|
||
"metadata": {
|
||
"origin_pos": 34,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"source": [
|
||
"[Discussions](https://discuss.d2l.ai/t/5724)\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"language_info": {
|
||
"name": "python"
|
||
},
|
||
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|
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|
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