Python机器学习NLP自然语言处理基本操作电影影评分析

目录

• 概述
• RNN
• 权重共享
• 计算过程
• LSTM
• 阶段
• 代码
• 预处理
• 主函数

概述

从今天开始我们将开启一段自然语言处理 (NLP) 的旅程. 自然语言处理可以让来处理, 理解, 以及运用人类的语言, 实现机器语言和人类语言之间的沟通桥梁.

RNN

RNN (Recurrent Neural Network), 即循环神经网络. RNN 相较于 CNN, 可以帮助我们更好的处理序列信息, 挖掘前后信息之间的联系. 对于 NLP 这类的任务, 语料的前后概率有极大的联系. 比如: “明天天气真好” 的概率 > “明天天气篮球”.

权重共享

传统神经网络:

RNN:

RNN 的权重共享和 CNN 的权重共享类似, 不同时刻共享一个权重, 大大减少了参数数量.

计算过程

计算状态 (State)

计算输出:

LSTM

LSTM (Long Short Term Memory), 即长短期记忆模型. LSTM 是一种特殊的 RNN 模型, 解决了长序列训练过程中的梯度消失和梯度爆炸的问题. 相较于普通 RNN, LSTM 能够在更长的序列中有更好的表现. 相比 RNN 只有一个传递状态 ht, LSTM 有两个传递状态： ct (cell state) 和 ht (hidden state).

阶段

LSTM 通过门来控制传输状态。

LSTM 总共分为三个阶段:

• 忘记阶段: 对上一个节点传进来的输入进行选择性忘记
• 选择记忆阶段: 将这个阶段的记忆有选择性的进行记忆. 哪些重要则着重记录下来, 哪些不重要, 则少记录一些
• 输出阶段: 决定哪些将会被当成当前状态的输出

代码

预处理

import pandas as pd
import re
from bs4 import BeautifulSoup
from sklearn.model_selection import train_test_split
import tensorflow as tf
# 停用词
stop_words = pd.read_csv("data/stopwords.txt", index_col=False, quoting=3, sep="\n", names=["stop_words"])
stop_words = [word.strip() for word in stop_words["stop_words"].values]
# 用pandas读取训练数据
def load_data():
    # 语料
    data = pd.read_csv("data/labeledTrainData.tsv", sep="\t", escapechar="\\")
    print(data[:5])
    print("评论数量:", len(data))
    return data
def pre_process(text):
    # 去除网页链接
    text = BeautifulSoup(text, "html.parser").get_text()
    # 去除标点
    text = re.sub("[^a-zA-Z]", " ", text)
    # 分词
    words = text.lower().split()
    # 去除停用词
    words = [w for w in words if w not in stop_words]
    return " ".join(words)
def split_data():
    # 读取文件
    data = pd.read_csv("data/train.csv")
    print(data.head())
    # 实例化
    tokenizer = tf.keras.preprocessing.text.Tokenizer()
    # 拟合
    tokenizer.fit_on_texts(data["review"])
    # 词袋
    word_index = tokenizer.word_index
    print(word_index)
    print(len(word_index))
    # 转换成数组
    sequence = tokenizer.texts_to_sequences(data["review"])
    # 填充
    character = tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=200)
    # 标签转换
    labels = tf.keras.utils.to_categorical(data["sentiment"])
    # 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(character, labels, test_size=0.2,
                                                        random_state=0)
    return X_train, X_test, y_train, y_test
if __name__ == '__main__':
    # #
    # data = load_data()
    # data["review"] = data["review"].apply(pre_process)
    # print(data.head())
    #
    # # 保存
    # data.to_csv("data.csv")
    split_data()

主函数

import tensorflow as tf
from lstm_pre_processing import split_data
def main():
    # 读取数据
    X_train, X_test, y_train, y_test = split_data()
    print(X_train[:5])
    print(y_train[:5])
    # 超参数
    EMBEDDING_DIM = 200  # embedding 维度
    optimizer = tf.keras.optimizers.RMSprop()  # 优化器
    loss = tf.losses.CategoricalCrossentropy(from_logits=True)  # 损失
    # 模型
    model = tf.keras.Sequential([
        tf.keras.layers.Embedding(73424, EMBEDDING_DIM),
        tf.keras.layers.LSTM(200, dropout=0.2, recurrent_dropout=0.2),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(64, activation="relu"),
        tf.keras.layers.Dense(2, activation="softmax")
    ])
    model.build(input_shape=[None, 20])
    print(model.summary())
    # 组合
    model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
    # 训练
    model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=32)
    # 保存模型
    model.save("movie_model.h5")
if __name__ == '__main__':
    # 主函数
    main()

输出结果:

2021-09-14 22:20:56.974310: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
   Unnamed: 0      id  sentiment                                             review
0           0  5814_8          1  stuff moment mj ve started listening music wat...
1           1  2381_9          1  classic war worlds timothy hines entertaining ...
2           2  7759_3          0  film starts manager nicholas bell investors ro...
3           3  3630_4          0  assumed praised film filmed opera didn read do...
4           4  9495_8          1  superbly trashy wondrously unpretentious explo...
73423
[[15958   623 12368  4459   622   835    30   152  2097  2408 35364 57143
    892  2997   766 42223   967   266 25276   157   108   696  1631   198
   2576  9850  3745    27    52  3789  9503   696   526    52   354   862
    474    38     2   101 11027   696  6456 22390   969  5873  5376  4044
    623  1401  2069   718   618    92    96   138  1345   714    96    18
    123  1770   518  3314   354   983  1888   520    83    73   983     2
     28 28635  1044  2054   401  1071    85  8565  8957  7226   804    46
    224   447  2113  2691  5742    10     5  3217   943  5045   980   373
     28   873   438   389    41    23    19    56   122     9   253 27176
   2149    19    90 57144    53  4874   696  6558   136  2067 10682    48
    518  1482     9  3668  1587  3786     2   110    10   506 25150 20744
    340    33   316    17  4824  3892   978    14 10150  2596   766 42223
   5082  4784   700   198  6276  5254   700   198  2334   696 20879     5
     86    30     2   583  2872 30601    30    86    28    83    73    32
     96    18     2   224   708    30   167     7  3791   216    45   513
      2  2310   513  1860  4536  1925   414  1321   578  7434   851   696
    997  5354 57145   162    30     2    91  1839]
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   9287  1609  7100  1065    75  9800  3344    76  5021    47   380  3015
  14366  6523  1396   851 22330  3465 20861  7106  6374   340    60 19035
   3089  5081     3     7  1695 10735  3582    92  6374   176  8348    60
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2021-09-14 22:21:02.212681: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-09-14 22:21:02.213245: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: /usr/lib/x86_64-linux-gnu/libcuda.so.1: file too short; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64/:/usr/lib/x86_64-linux-gnu
2021-09-14 22:21:02.213268: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)
2021-09-14 22:21:02.213305: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (5aa046a4f47b): /proc/driver/nvidia/version does not exist
2021-09-14 22:21:02.213624: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX512F
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-09-14 22:21:02.216309: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding (Embedding)        (None, None, 200)         14684800  
_________________________________________________________________
lstm (LSTM)                  (None, 200)               320800    
_________________________________________________________________
dropout (Dropout)            (None, 200)               0         
_________________________________________________________________
dense (Dense)                (None, 64)                12864     
_________________________________________________________________
dense_1 (Dense)              (None, 2)                 130       
=================================================================
Total params: 15,018,594
Trainable params: 15,018,594
Non-trainable params: 0
_________________________________________________________________
None
2021-09-14 22:21:02.515404: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-09-14 22:21:02.547745: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2300000000 Hz
Epoch 1/2
313/313 [==============================] - 97s 302ms/step - loss: 0.5112 - accuracy: 0.7510 - val_loss: 0.3607 - val_accuracy: 0.8628
Epoch 2/2
313/313 [==============================] - 94s 300ms/step - loss: 0.2090 - accuracy: 0.9236 - val_loss: 0.3078 - val_accuracy: 0.8790

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