利用情感分析选择年夜饭

时间：2025-07-25 | 作者： | 阅读：0

本文围绕自然语言处理中的情感分析展开，先介绍NLP基本开发流程和原理，说明文本分类通用步骤及词向量表示原因。接着讲解词向量到句子向量的方法、相关神经网络，最后以年夜饭评论为例，用PaddlePaddle和PaddleNLP构建LSTM模型完成情感分析，包括数据处理、模型搭建、训练及预测等过程。

目标任务

本项目将会先分解说明nlp的基本开发流程和原理，然后通过构建LSTM算法来完成情感分析

原理介绍

第一步：输入一个自然语言第二步：切词（或者切字）第三步：转换成ID（根据词语在词汇表的位置也就是id）第四步：生成数组（在id位置是1其他位置是0）

注：按照图中的情况进行假设词汇表的长度是5w，那么3个词生成的数组就是（3，5w）

第五步：上面的数组乘以数组（数组的维度是词汇表长度*5的矩阵）第六步：生成一个新的矩阵（句子长度 * 词向量的长度）

以图为例子： 3个词每一个词用5维的向量表示

第六步：批量处理

如下图：128个数据进行统一的处理就生成了一个3 * 5* 128的三维Tensor，Tensor的大小就是(128, 5, 3)[在里面句子的长度要相等长的要截断，短的要补齐]

第七步：通过黑盒得到一个句子向量（句子的长度这个维度被抹除了）

? ? ? ?

词向量到句子向量

加权平均法：把单个的词向量加起来就是句子向量 ? ? ? ? ? ? ? ?
序列建模法：针对加权法的缺点改进的建模方法
预训练模型法

循环神经网络RNN

RNN的关键点：词向量从左往右逐词处理，不断的挑整网络。每个时刻调用的是同一个网络

? ? ? ?

循环神经网络—长短时记忆网络LSTM

里面也是依次逐词处理的网络利用情感分析选择年夜饭_wishdown.com ? ? ? ?

里面涉及了历史记忆和历史的遗忘值，有就计算，没有就不管利用情感分析选择年夜饭_wishdown.com ? ? ? ?

全连接层、线性分类分类器

全连接层顾名思义：输入层和隐藏层逐个连接利用情感分析选择年夜饭_wishdown.com ? ? ? ?

快来选一顿好吃的年夜饭：看看如何自定义数据集，实现文本分类中的情感分析任务

情感分析是自然语言处理领域一个老生常谈的任务。句子情感分析目的是为了判别说者的情感倾向，比如在某些话题上给出的的态度明确的观点，或者反映的情绪状态等。情感分析有着广泛应用，比如电商评论分析、舆情分析等。

? ? ? ?

环境介绍

PaddlePaddle框架，AI Studio平台已经默认安装最新版2.0。
PaddleNLP，深度兼容框架2.0，是飞桨框架2.0在NLP领域的最佳实践。

这里使用的是beta版本，马上也会发布rc版哦。AI Studio平台后续会默认安装PaddleNLP，在此之前可使用如下命令安装。

In [1]

# 下载paddlenlp!pip install --upgrade paddlenlp==2.0.0b4 -i https://pypi.org/simple登录后复制 ? ?

查看安装的版本

In [2]

import paddleimport paddlenlpprint(paddle.__version__, paddlenlp.__version__)登录后复制 ? ? ? ?

2.0.1 2.0.0b4登录后复制 ? ? ? ?

PaddleNLP和Paddle框架是什么关系？

利用情感分析选择年夜饭_wishdown.com ? ? ? ?

? ? ? ?

Paddle框架是基础底座，提供深度学习任务全流程API。PaddleNLP基于Paddle框架开发，适用于NLP任务。

PaddleNLP中数据处理、数据集、组网单元等API未来会沉淀到框架paddle.text中。

代码中继承?class TSVDataset(paddle.io.Dataset)

使用飞桨完成深度学习任务的通用流程

数据集和数据处理paddle.io.Datasetpaddle.io.DataLoaderpaddlenlp.data
组网和网络配置

paddle.nn.Embeddingpaddlenlp.seq2vec paddle.nn.Linearpaddle.tanh

paddle.nn.CrossEntropyLoss paddle.metric.Accuracy paddle.optimizer

model.prepare

网络训练和评估model.fitmodel.evaluate
预测 model.predict

In [3]

import numpy as npfrom functools import partialimport paddle.nn as nnimport paddle.nn.functional as Fimport paddlenlp as ppnlpfrom paddlenlp.data import Pad, Stack, Tuplefrom paddlenlp.datasets import MapDatasetWrapperfrom utils import load_vocab, convert_example登录后复制 ? ?

数据集和数据处理

自定义数据集

映射式(map-style)数据集需要继承paddle.io.Dataset

__getitem__: 根据给定索引获取数据集中指定样本，在 paddle.io.DataLoader 中需要使用此函数通过下标获取样本。
__len__: 返回数据集样本个数， paddle.io.BatchSampler 中需要样本个数生成下标序列。

验证集：验证模型在训练过程中的表现，通过负反馈调整模型测试集：看模型最后的表现。个人理解：训练集：上课；验证集：周考，月考；测试集：期末考通过SelfDefinedDataset.get_datasets对数据集进行处理得到paddle.io.Dataset类型的结果

In [4]

class SelfDefinedDataset(paddle.io.Dataset): # 继承paddle.io.Dataset生成数据集 def __init__(self, data): super(SelfDefinedDataset, self).__init__() self.data = data def __getitem__(self, idx): return self.data[idx] def __len__(self): return len(self.data) def get_labels(self): return [”0“, ”1“]def txt_to_list(file_name): res_list = [] for line in open(file_name): res_list.append(line.strip().split('t')) return res_listtrainlst = txt_to_list('train.txt')devlst = txt_to_list('dev.txt')testlst = txt_to_list('test.txt')train_ds, dev_ds, test_ds = SelfDefinedDataset.get_datasets([trainlst, devlst, testlst])登录后复制 ? ?In [5]

# 查看数据长什么样label_list = train_ds.get_labels()print(label_list)for i in range(10): print (train_ds[i])登录后复制 ? ? ? ?

['0', '1']['赢在心理，输在出品！杨枝太酸，三文鱼熟了，酥皮焗杏汁杂果可以换个名（九唔搭八）', '0']['服务一般，客人多，服务员少，但食品很不错', '1']['東坡肉竟然有好多毛，問佢地點解，佢地仲話係咁架ue107ue107ue107ue107ue107ue107ue107冇天理，第一次食東坡肉有毛，波羅包就幾好食', '0']['父亲节去的，人很多，口味还可以上菜快！但是结账的时候，算错了没有打折，我也忘记拿清单了。说好打8折的，收银员没有打，人太多一时自己也没有想起。不知道收银员忘记，还是故意那钱露入自己qian包。。', '0']['吃野味，吃个新鲜，你当然一定要来广州吃鹿肉啦*价格便宜，量好足，', '1']['味道几好服务都五错推荐鹅肝乳鸽飞鱼', '1']['作为老字号，水准保持算是不错，龟岗分店可能是位置问题，人不算多，基本不用等位，自从抢了券，去过好几次了，每次都可以打85以上的评分，算是可以了～粉丝煲每次必点，哈哈，鱼也不错，还会来帮衬的，楼下还可以免费停车！', '1']['边到正宗啊？味味都咸死人啦，粤菜讲求鲜甜，五知点解感多人话好吃。', '0']['环境卫生差，出品垃圾，冇下次，不知所为', '0']['和苑真是精致粤菜第一家，服务菜品都一流', '1']登录后复制 ? ? ? ?

数据处理

为了将原始数据处理成模型可以读入的格式，本项目将对数据作以下处理：

首先使用jieba切词，之后将jieba切完后的单词映射词表中单词id。

? ? ? ?

使用paddle.io.DataLoader接口多线程异步加载数据。

其中用到了PaddleNLP中关于数据处理的API。PaddleNLP提供了许多关于NLP任务中构建有效的数据pipeline的常用API

API简介paddlenlp.data.Stack堆叠N个具有相同shape的输入数据来构建一个batch，它的输入必须具有相同的shape，输出便是这些输入的堆叠组成的batch数据。paddlenlp.data.Pad堆叠N个输入数据来构建一个batch，每个输入数据将会被padding到N个输入数据中最大的长度paddlenlp.data.Tuple将多个组batch的函数包装在一起

更多数据处理操作详见：?https://github.com/PaddlePaddle/PaddleNLP/blob/develop/docs/data.md

In [6]

# 下载词汇表文件word_dict.txt，用于构造词-id映射关系。# !wget https://paddlenlp.bj.bcebos.com/data/senta_word_dict.txt# 加载词表vocab = load_vocab('./senta_word_dict.txt')for k, v in vocab.items(): print(k, v) break登录后复制 ? ? ? ?

[PAD] 0登录后复制 ? ? ? ?

构造dataloder

下面的create_data_loader函数用于创建运行和预测时所需要的DataLoader对象。

paddle.io.DataLoader返回一个迭代器，该迭代器根据batch_sampler指定的顺序迭代返回dataset数据。异步加载数据。
batch_sampler：DataLoader通过 batch_sampler 产生的mini-batch索引列表来 dataset 中索引样本并组成mini-batch
collate_fn：指定如何将样本列表组合为mini-batch数据。传给它参数需要是一个callable对象，需要实现对组建的batch的处理逻辑，并返回每个batch的数据。在这里传入的是prepare_input函数，对产生的数据进行pad操作，并返回实际长度等。

In [7]

# Reads data and generates mini-batches.def create_dataloader(dataset, trans_function=None, mode='train', batch_size=1, pad_token_id=0, batchify_fn=None): if trans_function: dataset = dataset.apply(trans_function, lazy=True) # return_list 数据是否以list形式返回 # collate_fn 指定如何将样本列表组合为mini-batch数据。传给它参数需要是一个callable对象，需要实现对组建的batch的处理逻辑，并返回每个batch的数据。在这里传入的是`prepare_input`函数，对产生的数据进行pad操作，并返回实际长度等。 dataloader = paddle.io.DataLoader( dataset, return_list=True, batch_size=batch_size, collate_fn=batchify_fn) return dataloader# python中的偏函数partial，把一个函数的某些参数固定住（也就是设置默认值），返回一个新的函数，调用这个新函数会更简单。trans_function = partial( convert_example, vocab=vocab, unk_token_id=vocab.get('[UNK]', 1), is_test=False)# 将读入的数据batch化处理，便于模型batch化运算。# batch中的每个句子将会padding到这个batch中的文本最大长度batch_max_seq_len。# 当文本长度大于batch_max_seq时，将会截断到batch_max_seq_len；当文本长度小于batch_max_seq时，将会padding补齐到batch_max_seq_len.batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=vocab['[PAD]']), # input_ids Stack(dtype=”int64“), # seq len Stack(dtype=”int64“) # label): [data for data in fn(samples)]train_loader = create_dataloader( train_ds, trans_function=trans_function, batch_size=128, mode='train', batchify_fn=batchify_fn)dev_loader = create_dataloader( dev_ds, trans_function=trans_function, batch_size=128, mode='validation', batchify_fn=batchify_fn)test_loader = create_dataloader( test_ds, trans_function=trans_function, batch_size=128, mode='test', batchify_fn=batchify_fn)登录后复制 ? ?

模型搭建

使用LSTMencoder搭建一个BiLSTM模型用于进行句子建模，得到句子的向量表示。

然后接一个线性变换层，完成二分类任务。

paddle.nn.Embedding组建word-embedding层
ppnlp.seq2vec.LSTMEncoder组建句子建模层
paddle.nn.Linear构造二分类器

利用情感分析选择年夜饭_wishdown.com ? ? ? ?

? ? ? ?图1：seq2vec示意图 ? ? ? ?

除LSTM外，seq2vec还提供了许多语义表征方法，详细可参考：seq2vec介绍

In [8]

class LSTMModel(nn.Layer): def __init__(self, vocab_size, num_classes, emb_dim=128, padding_idx=0, lstm_hidden_size=198, direction='forward', lstm_layers=1, dropout_rate=0, pooling_type=None, fc_hidden_size=96): super().__init__() # 首先将输入word id 查表后映射成 word embedding self.embedder = nn.Embedding( num_embeddings=vocab_size, embedding_dim=emb_dim, padding_idx=padding_idx) # 将word embedding经过LSTMEncoder变换到文本语义表征空间中 self.lstm_encoder = ppnlp.seq2vec.RNNEncoder( emb_dim, lstm_hidden_size, num_layers=lstm_layers, direction=direction, dropout=dropout_rate, pooling_type=pooling_type) # LSTMEncoder.get_output_dim()方法可以获取经过encoder之后的文本表示hidden_size self.fc = nn.Linear(self.lstm_encoder.get_output_dim(), fc_hidden_size) # 最后的分类器 self.output_layer = nn.Linear(fc_hidden_size, num_classes) def forward(self, text, seq_len): # text shape: (batch_size, num_tokens) # print('input :', text.shape) # Shape: (batch_size, num_tokens, embedding_dim) embedded_text = self.embedder(text) # print('after word-embeding:', embedded_text.shape) # Shape: (batch_size, num_tokens, num_directions*lstm_hidden_size) # num_directions = 2 if direction is 'bidirectional' else 1 text_repr = self.lstm_encoder(embedded_text, sequence_length=seq_len) # print('after lstm:', text_repr.shape) # Shape: (batch_size, fc_hidden_size) fc_out = paddle.tanh(self.fc(text_repr)) # print('after Linear classifier:', fc_out.shape) # Shape: (batch_size, num_classes) logits = self.output_layer(fc_out) # print('output:', logits.shape) # probs 分类概率值 probs = F.softmax(logits, axis=-1) # print('output probability:', probs.shape) return probsmodel= LSTMModel( len(vocab), len(label_list), direction='bidirectional', padding_idx=vocab['[PAD]'])model = paddle.Model(model)登录后复制 ? ?

模型配置和训练

模型配置

In [9]

optimizer = paddle.optimizer.Adam( parameters=model.parameters(), learning_rate=5e-5)loss = paddle.nn.CrossEntropyLoss()metric = paddle.metric.Accuracy()model.prepare(optimizer, loss, metric)登录后复制 ? ?In [10]

# 设置visualdl路径log_dir = './visualdl'callback = paddle.callbacks.VisualDL(log_dir=log_dir)登录后复制 ? ?

模型训练

训练过程中会输出loss、acc等信息。这里设置了10个epoch，在训练集上准确率约97%。

? ? ? ?

In [11]

model.fit(train_loader, dev_loader, epochs=10, save_dir='./checkpoints', save_freq=5, callbacks=callback)登录后复制 ? ? ? ?

The loss value printed in the log is the current step, and the metric is the average value of previous step.Epoch 1/10登录后复制 ? ? ? ?

Building prefix dict from the default dictionary ...登录后复制 ? ? ? ?

2021-03-21 13:02:03,274 - DEBUG - Building prefix dict from the default dictionary ...Dumping model to file cache /tmp/jieba.cache2021-03-21 13:02:04,016 - DEBUG - Dumping model to file cache /tmp/jieba.cacheLoading model cost 0.798 seconds.2021-03-21 13:02:04,073 - DEBUG - Loading model cost 0.798 seconds.Prefix dict has been built successfully.2021-03-21 13:02:04,075 - DEBUG - Prefix dict has been built successfully./opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working return (isinstance(seq, collections.Sequence) and登录后复制 ? ? ? ?

step 10/125 - loss: 0.7010 - acc: 0.4813 - 216ms/stepstep 20/125 - loss: 0.6931 - acc: 0.5043 - 151ms/stepstep 30/125 - loss: 0.6910 - acc: 0.5154 - 129ms/stepstep 40/125 - loss: 0.6890 - acc: 0.5174 - 117ms/stepstep 50/125 - loss: 0.6860 - acc: 0.5197 - 110ms/stepstep 60/125 - loss: 0.6942 - acc: 0.5180 - 105ms/stepstep 70/125 - loss: 0.6905 - acc: 0.5180 - 102ms/stepstep 80/125 - loss: 0.6869 - acc: 0.5222 - 100ms/stepstep 90/125 - loss: 0.6870 - acc: 0.5398 - 98ms/stepstep 100/125 - loss: 0.6823 - acc: 0.5445 - 97ms/stepstep 110/125 - loss: 0.6776 - acc: 0.5452 - 96ms/stepstep 120/125 - loss: 0.6747 - acc: 0.5577 - 95ms/stepstep 125/125 - loss: 0.6774 - acc: 0.5620 - 93ms/stepsave checkpoint at /home/aistudio/checkpoints/0Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.6720 - acc: 0.6695 - 84ms/stepstep 20/84 - loss: 0.6739 - acc: 0.6648 - 69ms/stepstep 30/84 - loss: 0.6749 - acc: 0.6620 - 65ms/stepstep 40/84 - loss: 0.6735 - acc: 0.6637 - 62ms/stepstep 50/84 - loss: 0.6778 - acc: 0.6620 - 61ms/stepstep 60/84 - loss: 0.6721 - acc: 0.6638 - 61ms/stepstep 70/84 - loss: 0.6746 - acc: 0.6664 - 60ms/stepstep 80/84 - loss: 0.6749 - acc: 0.6652 - 59ms/stepstep 84/84 - loss: 0.6649 - acc: 0.6647 - 57ms/stepEval samples: 10646Epoch 2/10step 10/125 - loss: 0.6739 - acc: 0.6898 - 113ms/stepstep 20/125 - loss: 0.6524 - acc: 0.7191 - 100ms/stepstep 30/125 - loss: 0.6025 - acc: 0.7500 - 95ms/stepstep 40/125 - loss: 0.5736 - acc: 0.7623 - 92ms/stepstep 50/125 - loss: 0.4809 - acc: 0.7683 - 91ms/stepstep 60/125 - loss: 0.4591 - acc: 0.7763 - 90ms/stepstep 70/125 - loss: 0.4734 - acc: 0.7831 - 91ms/stepstep 80/125 - loss: 0.4487 - acc: 0.7861 - 92ms/stepstep 90/125 - loss: 0.5213 - acc: 0.7900 - 94ms/stepstep 100/125 - loss: 0.5303 - acc: 0.7891 - 96ms/stepstep 110/125 - loss: 0.4789 - acc: 0.7930 - 99ms/stepstep 120/125 - loss: 0.4611 - acc: 0.7969 - 101ms/stepstep 125/125 - loss: 0.4887 - acc: 0.7984 - 99ms/stepEval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.5155 - acc: 0.8523 - 96ms/stepstep 20/84 - loss: 0.4754 - acc: 0.8484 - 80ms/stepstep 30/84 - loss: 0.5009 - acc: 0.8469 - 76ms/stepstep 40/84 - loss: 0.4709 - acc: 0.8500 - 73ms/stepstep 50/84 - loss: 0.4760 - acc: 0.8497 - 71ms/stepstep 60/84 - loss: 0.4576 - acc: 0.8479 - 70ms/stepstep 70/84 - loss: 0.4642 - acc: 0.8493 - 69ms/stepstep 80/84 - loss: 0.4890 - acc: 0.8485 - 68ms/stepstep 84/84 - loss: 0.4549 - acc: 0.8494 - 66ms/stepEval samples: 10646Epoch 3/10step 10/125 - loss: 0.5171 - acc: 0.8313 - 123ms/stepstep 20/125 - loss: 0.4559 - acc: 0.8297 - 112ms/stepstep 30/125 - loss: 0.4608 - acc: 0.8344 - 108ms/stepstep 40/125 - loss: 0.4628 - acc: 0.8424 - 105ms/stepstep 50/125 - loss: 0.4640 - acc: 0.8470 - 105ms/stepstep 60/125 - loss: 0.3650 - acc: 0.8522 - 103ms/stepstep 70/125 - loss: 0.4364 - acc: 0.8560 - 103ms/stepstep 80/125 - loss: 0.4144 - acc: 0.8560 - 103ms/stepstep 90/125 - loss: 0.4244 - acc: 0.8583 - 103ms/stepstep 100/125 - loss: 0.4586 - acc: 0.8584 - 103ms/stepstep 110/125 - loss: 0.4421 - acc: 0.8598 - 104ms/stepstep 120/125 - loss: 0.4119 - acc: 0.8621 - 104ms/stepstep 125/125 - loss: 0.3894 - acc: 0.8623 - 102ms/stepEval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.4168 - acc: 0.8977 - 96ms/stepstep 20/84 - loss: 0.4086 - acc: 0.9012 - 78ms/stepstep 30/84 - loss: 0.4200 - acc: 0.9000 - 72ms/stepstep 40/84 - loss: 0.3959 - acc: 0.9014 - 70ms/stepstep 50/84 - loss: 0.4019 - acc: 0.9022 - 69ms/stepstep 60/84 - loss: 0.4229 - acc: 0.9014 - 68ms/stepstep 70/84 - loss: 0.4447 - acc: 0.9001 - 67ms/stepstep 80/84 - loss: 0.4186 - acc: 0.9011 - 66ms/stepstep 84/84 - loss: 0.4398 - acc: 0.9015 - 64ms/stepEval samples: 10646Epoch 4/10step 10/125 - loss: 0.4333 - acc: 0.8930 - 131ms/stepstep 20/125 - loss: 0.4103 - acc: 0.8926 - 113ms/stepstep 30/125 - loss: 0.3948 - acc: 0.9000 - 109ms/stepstep 40/125 - loss: 0.4312 - acc: 0.9045 - 107ms/stepstep 50/125 - loss: 0.4069 - acc: 0.9020 - 106ms/stepstep 60/125 - loss: 0.4027 - acc: 0.9049 - 104ms/stepstep 70/125 - loss: 0.4955 - acc: 0.9011 - 104ms/stepstep 80/125 - loss: 0.3805 - acc: 0.8979 - 103ms/stepstep 90/125 - loss: 0.3931 - acc: 0.8979 - 104ms/stepstep 100/125 - loss: 0.3674 - acc: 0.8988 - 104ms/stepstep 110/125 - loss: 0.3908 - acc: 0.8998 - 104ms/stepstep 120/125 - loss: 0.3746 - acc: 0.9027 - 104ms/stepstep 125/125 - loss: 0.3734 - acc: 0.9037 - 102ms/stepEval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.3905 - acc: 0.9266 - 97ms/stepstep 20/84 - loss: 0.3848 - acc: 0.9320 - 82ms/stepstep 30/84 - loss: 0.3714 - acc: 0.9336 - 76ms/stepstep 40/84 - loss: 0.3695 - acc: 0.9361 - 77ms/stepstep 50/84 - loss: 0.3676 - acc: 0.9372 - 75ms/stepstep 60/84 - loss: 0.3807 - acc: 0.9380 - 74ms/stepstep 70/84 - loss: 0.3835 - acc: 0.9377 - 73ms/stepstep 80/84 - loss: 0.3630 - acc: 0.9379 - 73ms/stepstep 84/84 - loss: 0.4244 - acc: 0.9383 - 70ms/stepEval samples: 10646Epoch 5/10step 10/125 - loss: 0.4770 - acc: 0.9094 - 124ms/stepstep 20/125 - loss: 0.3861 - acc: 0.9227 - 112ms/stepstep 30/125 - loss: 0.3744 - acc: 0.9318 - 106ms/stepstep 40/125 - loss: 0.3799 - acc: 0.9361 - 104ms/stepstep 50/125 - loss: 0.3660 - acc: 0.9391 - 103ms/stepstep 60/125 - loss: 0.3525 - acc: 0.9428 - 101ms/stepstep 70/125 - loss: 0.3703 - acc: 0.9446 - 100ms/stepstep 80/125 - loss: 0.3534 - acc: 0.9438 - 100ms/stepstep 90/125 - loss: 0.3415 - acc: 0.9451 - 100ms/stepstep 100/125 - loss: 0.3525 - acc: 0.9451 - 100ms/stepstep 110/125 - loss: 0.3530 - acc: 0.9462 - 100ms/stepstep 120/125 - loss: 0.3838 - acc: 0.9477 - 99ms/stepstep 125/125 - loss: 0.3552 - acc: 0.9478 - 97ms/stepEval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.3792 - acc: 0.9492 - 99ms/stepstep 20/84 - loss: 0.3733 - acc: 0.9488 - 80ms/stepstep 30/84 - loss: 0.3702 - acc: 0.9500 - 74ms/stepstep 40/84 - loss: 0.3499 - acc: 0.9525 - 71ms/stepstep 50/84 - loss: 0.3756 - acc: 0.9519 - 70ms/stepstep 60/84 - loss: 0.3550 - acc: 0.9522 - 69ms/stepstep 70/84 - loss: 0.3693 - acc: 0.9521 - 67ms/stepstep 80/84 - loss: 0.3517 - acc: 0.9520 - 66ms/stepstep 84/84 - loss: 0.4341 - acc: 0.9524 - 63ms/stepEval samples: 10646Epoch 6/10step 10/125 - loss: 0.3712 - acc: 0.9469 - 128ms/stepstep 20/125 - loss: 0.3570 - acc: 0.9543 - 115ms/stepstep 30/125 - loss: 0.3519 - acc: 0.9576 - 108ms/stepstep 40/125 - loss: 0.3670 - acc: 0.9576 - 104ms/stepstep 50/125 - loss: 0.3500 - acc: 0.9587 - 103ms/stepstep 60/125 - loss: 0.3303 - acc: 0.9605 - 103ms/stepstep 70/125 - loss: 0.3565 - acc: 0.9610 - 102ms/stepstep 80/125 - loss: 0.3389 - acc: 0.9604 - 102ms/stepstep 90/125 - loss: 0.3361 - acc: 0.9602 - 102ms/stepstep 100/125 - loss: 0.3479 - acc: 0.9597 - 101ms/stepstep 110/125 - loss: 0.3415 - acc: 0.9599 - 101ms/stepstep 120/125 - loss: 0.3643 - acc: 0.9613 - 101ms/stepstep 125/125 - loss: 0.3519 - acc: 0.9610 - 99ms/stepsave checkpoint at /home/aistudio/checkpoints/5Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.3761 - acc: 0.9484 - 101ms/stepstep 20/84 - loss: 0.3602 - acc: 0.9520 - 83ms/stepstep 30/84 - loss: 0.3653 - acc: 0.9526 - 78ms/stepstep 40/84 - loss: 0.3450 - acc: 0.9549 - 75ms/stepstep 50/84 - loss: 0.3758 - acc: 0.9553 - 75ms/stepstep 60/84 - loss: 0.3358 - acc: 0.9564 - 74ms/stepstep 70/84 - loss: 0.3652 - acc: 0.9557 - 72ms/stepstep 80/84 - loss: 0.3458 - acc: 0.9563 - 70ms/stepstep 84/84 - loss: 0.3526 - acc: 0.9570 - 67ms/stepEval samples: 10646Epoch 7/10step 10/125 - loss: 0.3576 - acc: 0.9531 - 129ms/stepstep 20/125 - loss: 0.3430 - acc: 0.9641 - 116ms/stepstep 30/125 - loss: 0.3442 - acc: 0.9661 - 110ms/stepstep 40/125 - loss: 0.3624 - acc: 0.9648 - 106ms/stepstep 50/125 - loss: 0.3434 - acc: 0.9659 - 105ms/stepstep 60/125 - loss: 0.3276 - acc: 0.9684 - 103ms/stepstep 70/125 - loss: 0.3427 - acc: 0.9692 - 102ms/stepstep 80/125 - loss: 0.3296 - acc: 0.9683 - 101ms/stepstep 90/125 - loss: 0.3288 - acc: 0.9681 - 101ms/stepstep 100/125 - loss: 0.3370 - acc: 0.9675 - 101ms/stepstep 110/125 - loss: 0.3326 - acc: 0.9679 - 101ms/stepstep 120/125 - loss: 0.3567 - acc: 0.9689 - 101ms/stepstep 125/125 - loss: 0.3450 - acc: 0.9682 - 99ms/stepEval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.3743 - acc: 0.9547 - 101ms/stepstep 20/84 - loss: 0.3683 - acc: 0.9547 - 83ms/stepstep 30/84 - loss: 0.3621 - acc: 0.9552 - 77ms/stepstep 40/84 - loss: 0.3402 - acc: 0.9568 - 73ms/stepstep 50/84 - loss: 0.3642 - acc: 0.9572 - 71ms/stepstep 60/84 - loss: 0.3561 - acc: 0.9576 - 70ms/stepstep 70/84 - loss: 0.3590 - acc: 0.9569 - 68ms/stepstep 80/84 - loss: 0.3467 - acc: 0.9563 - 67ms/stepstep 84/84 - loss: 0.4090 - acc: 0.9570 - 64ms/stepEval samples: 10646Epoch 8/10step 10/125 - loss: 0.3474 - acc: 0.9578 - 118ms/stepstep 20/125 - loss: 0.3465 - acc: 0.9641 - 104ms/stepstep 30/125 - loss: 0.3451 - acc: 0.9667 - 102ms/stepstep 40/125 - loss: 0.3570 - acc: 0.9658 - 100ms/stepstep 50/125 - loss: 0.3404 - acc: 0.9680 - 100ms/stepstep 60/125 - loss: 0.3243 - acc: 0.9698 - 99ms/stepstep 70/125 - loss: 0.3353 - acc: 0.9709 - 98ms/stepstep 80/125 - loss: 0.3346 - acc: 0.9704 - 98ms/stepstep 90/125 - loss: 0.3228 - acc: 0.9703 - 98ms/stepstep 100/125 - loss: 0.3342 - acc: 0.9701 - 98ms/stepstep 110/125 - loss: 0.3223 - acc: 0.9710 - 98ms/stepstep 120/125 - loss: 0.3479 - acc: 0.9721 - 98ms/stepstep 125/125 - loss: 0.3624 - acc: 0.9718 - 97ms/stepEval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.3666 - acc: 0.9523 - 104ms/stepstep 20/84 - loss: 0.3573 - acc: 0.9563 - 87ms/stepstep 30/84 - loss: 0.3554 - acc: 0.9570 - 81ms/stepstep 40/84 - loss: 0.3370 - acc: 0.9588 - 77ms/stepstep 50/84 - loss: 0.3662 - acc: 0.9592 - 74ms/stepstep 60/84 - loss: 0.3248 - acc: 0.9612 - 72ms/stepstep 70/84 - loss: 0.3667 - acc: 0.9603 - 71ms/stepstep 80/84 - loss: 0.3448 - acc: 0.9604 - 69ms/stepstep 84/84 - loss: 0.3349 - acc: 0.9613 - 66ms/stepEval samples: 10646Epoch 9/10step 10/125 - loss: 0.3650 - acc: 0.9594 - 121ms/stepstep 20/125 - loss: 0.3495 - acc: 0.9637 - 114ms/stepstep 30/125 - loss: 0.3436 - acc: 0.9669 - 109ms/stepstep 40/125 - loss: 0.3573 - acc: 0.9674 - 106ms/stepstep 50/125 - loss: 0.3390 - acc: 0.9694 - 104ms/stepstep 60/125 - loss: 0.3239 - acc: 0.9714 - 103ms/stepstep 70/125 - loss: 0.3281 - acc: 0.9729 - 102ms/stepstep 80/125 - loss: 0.3261 - acc: 0.9729 - 101ms/stepstep 90/125 - loss: 0.3198 - acc: 0.9734 - 100ms/stepstep 100/125 - loss: 0.3306 - acc: 0.9729 - 100ms/stepstep 110/125 - loss: 0.3193 - acc: 0.9737 - 101ms/stepstep 120/125 - loss: 0.3468 - acc: 0.9745 - 100ms/stepstep 125/125 - loss: 0.3413 - acc: 0.9743 - 99ms/stepEval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.3647 - acc: 0.9539 - 99ms/stepstep 20/84 - loss: 0.3593 - acc: 0.9578 - 79ms/stepstep 30/84 - loss: 0.3548 - acc: 0.9589 - 74ms/stepstep 40/84 - loss: 0.3333 - acc: 0.9598 - 73ms/stepstep 50/84 - loss: 0.3658 - acc: 0.9605 - 71ms/stepstep 60/84 - loss: 0.3247 - acc: 0.9617 - 70ms/stepstep 70/84 - loss: 0.3626 - acc: 0.9610 - 69ms/stepstep 80/84 - loss: 0.3414 - acc: 0.9614 - 67ms/stepstep 84/84 - loss: 0.3232 - acc: 0.9621 - 65ms/stepEval samples: 10646Epoch 10/10step 10/125 - loss: 0.3456 - acc: 0.9641 - 122ms/stepstep 20/125 - loss: 0.3336 - acc: 0.9711 - 111ms/stepstep 30/125 - loss: 0.3376 - acc: 0.9737 - 108ms/stepstep 40/125 - loss: 0.3581 - acc: 0.9732 - 104ms/stepstep 50/125 - loss: 0.3378 - acc: 0.9742 - 102ms/stepstep 60/125 - loss: 0.3228 - acc: 0.9757 - 101ms/stepstep 70/125 - loss: 0.3313 - acc: 0.9767 - 99ms/stepstep 80/125 - loss: 0.3334 - acc: 0.9762 - 99ms/stepstep 90/125 - loss: 0.3175 - acc: 0.9764 - 99ms/stepstep 100/125 - loss: 0.3304 - acc: 0.9762 - 99ms/stepstep 110/125 - loss: 0.3193 - acc: 0.9767 - 99ms/stepstep 120/125 - loss: 0.3469 - acc: 0.9773 - 99ms/stepstep 125/125 - loss: 0.3359 - acc: 0.9770 - 97ms/stepEval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.3624 - acc: 0.9578 - 95ms/stepstep 20/84 - loss: 0.3622 - acc: 0.9609 - 78ms/stepstep 30/84 - loss: 0.3528 - acc: 0.9620 - 74ms/stepstep 40/84 - loss: 0.3319 - acc: 0.9633 - 73ms/stepstep 50/84 - loss: 0.3561 - acc: 0.9639 - 71ms/stepstep 60/84 - loss: 0.3247 - acc: 0.9654 - 70ms/stepstep 70/84 - loss: 0.3520 - acc: 0.9647 - 69ms/stepstep 80/84 - loss: 0.3471 - acc: 0.9647 - 67ms/stepstep 84/84 - loss: 0.3202 - acc: 0.9653 - 64ms/stepEval samples: 10646save checkpoint at /home/aistudio/checkpoints/final登录后复制 ? ? ? ?

启动VisualDL查看训练过程可视化结果

启动步骤：

1、切换到本界面左侧「可视化」
2、日志文件路径选择 'visualdl'
3、点击「启动VisualDL」后点击「打开VisualDL」，即可查看可视化结果： Accuracy和Loss的实时变化趋势如下：
? ? ? ? ? ?

In [12]

results = model.evaluate(dev_loader)print(”Finally test acc: %.5f“ % results['acc'])登录后复制 ? ? ? ?

Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 10/84 - loss: 0.3624 - acc: 0.9578 - 95ms/stepstep 20/84 - loss: 0.3622 - acc: 0.9609 - 79ms/stepstep 30/84 - loss: 0.3528 - acc: 0.9620 - 74ms/stepstep 40/84 - loss: 0.3319 - acc: 0.9633 - 71ms/stepstep 50/84 - loss: 0.3561 - acc: 0.9639 - 69ms/stepstep 60/84 - loss: 0.3247 - acc: 0.9654 - 67ms/stepstep 70/84 - loss: 0.3520 - acc: 0.9647 - 66ms/stepstep 80/84 - loss: 0.3471 - acc: 0.9647 - 65ms/stepstep 84/84 - loss: 0.3202 - acc: 0.9653 - 63ms/stepEval samples: 10646Finally test acc: 0.96534登录后复制 ? ? ? ?

预测

In [13]

label_map = {0: 'negative', 1: 'positive'}results = model.predict(test_loader, batch_size=128)[0]predictions = []for batch_probs in results: # 映射分类label idx = np.argmax(batch_probs, axis=-1) idx = idx.tolist() labels = [label_map[i] for i in idx] predictions.extend(labels)# 看看预测数据前5个样例分类结果for idx, data in enumerate(test_ds.data[:10]): print('Data: {} t Label: {}'.format(data[0], predictions[idx]))登录后复制 ? ? ? ?

Predict begin...step 42/42 [==============================] - ETA: 4s - 106ms/st - ETA: 3s - 103ms/st - ETA: 3s - 102ms/st - ETA: 3s - 104ms/st - ETA: 3s - 100ms/st - ETA: 2s - 97ms/step - ETA: 2s - 93ms/ste - ETA: 2s - 90ms/ste - ETA: 2s - 87ms/ste - ETA: 1s - 85ms/ste - ETA: 1s - 83ms/ste - ETA: 1s - 81ms/ste - ETA: 1s - 79ms/ste - ETA: 1s - 78ms/ste - ETA: 0s - 77ms/ste - ETA: 0s - 77ms/ste - ETA: 0s - 76ms/ste - ETA: 0s - 75ms/ste - ETA: 0s - 73ms/ste - ETA: 0s - 71ms/ste - 68ms/step Predict samples: 5353Data: 楼面经理服务态度极差，等位和埋单都差，楼面小妹还挺好 Label: negativeData: 欺负北方人没吃过鲍鱼是怎么着？简直敷衍到可笑的程度，团购连青菜都是两人份？！难吃到死，菜色还特别可笑，什么时候粤菜的小菜改成拍黄瓜了？！把团购客人当sha子，可这满大厅的sha子谁还会再来？！ Label: negativeData: 如果大家有时间而且不怕麻烦的话可以去这里试试，点一个饭等左2个钟，没错！是两个钟！期间催了n遍都说马上到，结果？呵呵。乳鸽的味道，太咸，可能不新鲜吧……要用重口味盖住异味。上菜超级慢！中途还搞什么表演，麻烦有人手的话就上菜啊，表什么演？！？！要大家饿着看表演吗？最后结账还算错单，我真心服了……有一种店叫不会有下次，大概就是指它吧 Label: negativeData: 偌大的一个大厅就一个人点菜，点菜速度超级慢，菜牌上多个菜停售，连续点了两个没标停售的菜也告知没有，粥上来是凉的，榴莲酥火大了，格格肉超级油腻而且咸?????? Label: negativeData: 泥撕雞超級好吃！！！吃了一個再叫一個還想打包的節奏！ Label: positiveData: 作为地道的广州人，从小就跟着家人在西关品尝各式美食，今日带着家中长辈来这个老字号泮溪酒家真实失望透顶，出品差、服务差、洗手间邋遢弥漫着浓郁尿骚味、丢广州人的脸、丢广州老字号的脸。 Label: negativeData: 辣味道很赞哦！猪肚鸡一直是我们的最爱，每次来都必点，服务很给力，环境很好，值得分享哦！西洋菜 Label: positiveData: 第一次吃到這麼脏的火鍋：吃着吃著吃出一條尾指粗的黑毛毛蟲——惡心！脏！！！第一次吃到這麼無招诺幕疱伔?眨何覀兒魡救藛T時，某女部長立即使服務員迅速取走蟲所在的碗，任我們多次叫「放下」論理，她們也置若罔聞轉身將蟲毁屍滅跡，還嘻皮笑臉辯稱只是把碗換走,態度行為惡劣——jian詐！毫無招牛。??。。‘斎贿?袆傋?聲r的情形：第一次吃到這樣的火鍋：所有肉食熟食都上桌了，鍋底遲遲沒上，足足等了半小時才姍姍來遲；---差！！第一次吃到這樣的火鍋：1元雞鍋、1碟6塊小牛肉、1碟小腐皮、1碟5塊裝的普通肥牛、1碟數片的細碎牛肚結帳便2百多元；---不值！！以下省略千字差評......白云路的稻香是最差、最失禮的稻香，天河城、華廈的都比它好上過萬倍！！白云路的稻香是史上最差的餐廳！！！ Label: negativeData: 文昌鸡份量很少且很咸，其他菜味道很一般！服务态度差差差！还要10%的服务费、 Label: negativeData: 这个网站的评价真是越来越不可信了，搞不懂为什么这么多好评。真的是很一般，不要迷信什么哪里回来的大厨吧。环境和出品若是当作普通茶餐厅来看待就还说得过去，但是价格又不是茶餐厅的价格，这就很尴尬了。。服务也是有待提高。 Label: negative登录后复制 ? ? ? ?

这里只采用了一个基础的模型，就得到了较高的的准确率。

可以试试预训练模型，能得到更好的效果！参考如何通过预训练模型Fine-tune下游任务

首先就是先处理数据，把文本转换成词向量，接着构造神经网络，进行训练，调整网络模型，最后获得一个比较好的结果