利用CNN进行中文文本分类（数据集是复旦中文语料）
利用TfidfVectorizer进行中文文本分类（数据集是复旦中文语料）
上一节我们利用了RNN（GRU）对中文文本进行了分类，本节我们将继续使用CNN对中文文本进行分类。
数据处理还是没有变，只是换了个模型，代码如下：
# coding: utf-8 from __future__ import print_function import os
import sys
import time
from datetime import timedelta
import keras import numpy as np
import tensorflow as tf
from sklearn import metrics
#将词汇表中的单词映射成id
def word2id():
vocabulary_path = '/content/drive/My Drive/NLP/dataset/Fudan/vocabulary.txt'
fp1 = open(vocabulary_path,'r',encoding='utf-8')
word2id_dict = {}
for i,line in enumerate(fp1.readlines()):
word2id_dict[line.strip()] = i
print(len(word2id_dict))
fp1.close()
return word2id_dict #得到文本内容及对应的标签
def get_content_label(path):
#data = '/content/drive/My Drive/NLP/dataset/Fudan/data/train_clean_jieba.txt'
fp = open(path,'r',encoding='utf-8')
content_list = []
label_list = []
for line in fp.readlines():
line = line.strip().split('\t')
if len(line) == 2:
content_list.append(line[0])
label_list.append(line[1])
print(content_list[:5])
print(label_list[:5])
fp.close()
return content_list,label_list
#得到标签对应的id
def get_label_id():
label = '/content/drive/My Drive/NLP/dataset/Fudan/label.txt'
label2id_dict = {}
fp = open(label,'r',encoding='utf-8')
for line in fp.readlines():
line = line.strip().split('\t')
label2id_dict[line[0]] = line[1]
#print(label2id_dict)
return label2id_dict
#将文本内容中的词替换成词对应的id，并设定文本的最大长度
#对标签进行one-hot编码
def process(path,max_length):
contents,labels = get_content_label(path)
word_to_id = word2id()
cat_to_id = get_label_id()
data_id = []
label_id = []
for i in range(len(contents)):
data_id.append([word_to_id[x] for x in contents[i] if x in word_to_id])
label_id.append(cat_to_id[labels[i]]) # 使用keras提供的pad_sequences来将文本pad为固定长度
x_pad = keras.preprocessing.sequence.pad_sequences(data_id, max_length)
y_pad = keras.utils.to_categorical(label_id, num_classes=len(cat_to_id)) # 将标签转换为one-hot表示
return x_pad,y_pad def batch_iter(x, y, batch_size=64):
"""生成批次数据"""
data_len = len(x)
num_batch = int((data_len - 1) / batch_size) + 1 indices = np.random.permutation(np.arange(data_len))
x_shuffle = x[indices]
y_shuffle = y[indices] for i in range(num_batch):
start_id = i * batch_size
end_id = min((i + 1) * batch_size, data_len)
yield x_shuffle[start_id:end_id], y_shuffle[start_id:end_id] def evaluate(sess, x_, y_):
"""评估在某一数据上的准确率和损失"""
data_len = len(x_)
batch_eval = batch_iter(x_, y_, 128)
total_loss = 0.0
total_acc = 0.0
for x_batch, y_batch in batch_eval:
batch_len = len(x_batch)
feed_dict = feed_data(x_batch, y_batch, 1.0)
loss, acc = sess.run([model.loss, model.acc], feed_dict=feed_dict)
total_loss += loss * batch_len
total_acc += acc * batch_len return total_loss / data_len, total_acc / data_len def get_time_dif(start_time):
"""获取已使用时间"""
end_time = time.time()
time_dif = end_time - start_time
return timedelta(seconds=int(round(time_dif))) def feed_data(x_batch, y_batch, keep_prob):
feed_dict = {
model.input_x: x_batch,
model.input_y: y_batch,
model.keep_prob: keep_prob
}
return feed_dict def get_training_word2vec_vectors(filename):
with np.load(filename) as data:
return data["embeddings"] class TCNNConfig(object):
"""CNN配置参数""" embedding_dim = 100 # 词向量维度
seq_length = 600 # 序列长度
num_classes = 20 # 类别数
num_filters = 256 # 卷积核数目
kernel_size = 5 # 卷积核尺寸
vocab_size = 183664 # 词汇表达小 hidden_dim = 128 # 全连接层神经元 dropout_keep_prob = 0.5 # dropout保留比例
learning_rate = 1e-3 # 学习率 batch_size = 64 # 每批训练大小
num_epochs = 10 # 总迭代轮次 print_per_batch = 20 # 每多少轮输出一次结果
save_per_batch = 10 # 每多少轮存入tensorboard
pre_trianing = None
vector_word_npz = '/content/drive/My Drive/NLP/dataset/Fudan/vector_word.npz' class TextCNN(object):
"""文本分类，CNN模型""" def __init__(self, config):
self.config = config # 三个待输入的数据
self.input_x = tf.placeholder(tf.int32, [None, self.config.seq_length], name='input_x')
self.input_y = tf.placeholder(tf.float32, [None, self.config.num_classes], name='input_y')
self.keep_prob = tf.placeholder(tf.float32, name='keep_prob') self.cnn() def cnn(self):
"""CNN模型"""
# 词向量映射
with tf.device('/cpu:0'):
#embedding = tf.get_variable('embedding', [self.config.vocab_size, self.config.embedding_dim])
embedding = tf.get_variable("embeddings", shape=[self.config.vocab_size, self.config.embedding_dim],
initializer=tf.constant_initializer(self.config.pre_trianing))
embedding_inputs = tf.nn.embedding_lookup(embedding, self.input_x) with tf.name_scope("cnn"):
# CNN layer
conv = tf.layers.conv1d(embedding_inputs, self.config.num_filters, self.config.kernel_size, name='conv')
# global max pooling layer
gmp = tf.reduce_max(conv, reduction_indices=[1], name='gmp') with tf.name_scope("score"):
# 全连接层，后面接dropout以及relu激活
fc = tf.layers.dense(gmp, self.config.hidden_dim, name='fc1')
fc = tf.contrib.layers.dropout(fc, self.keep_prob)
fc = tf.nn.relu(fc) # 分类器
self.logits = tf.layers.dense(fc, self.config.num_classes, name='fc2')
self.y_pred_cls = tf.argmax(tf.nn.softmax(self.logits), 1) # 预测类别 with tf.name_scope("optimize"):
# 损失函数，交叉熵
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.input_y)
self.loss = tf.reduce_mean(cross_entropy)
# 优化器
self.optim = tf.train.AdamOptimizer(learning_rate=self.config.learning_rate).minimize(self.loss) with tf.name_scope("accuracy"):
# 准确率
correct_pred = tf.equal(tf.argmax(self.input_y, 1), self.y_pred_cls)
self.acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) def train():
print("Configuring TensorBoard and Saver...")
# 配置 Tensorboard，重新训练时，请将tensorboard文件夹删除，不然图会覆盖
tensorboard_dir = 'tensorboard/textcnn'
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir) tf.summary.scalar("loss", model.loss)
tf.summary.scalar("accuracy", model.acc)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(tensorboard_dir)
save_dir = 'checkpoint/textcnn/'
save_path = os.path.join(save_dir, 'best_validation') # 最佳验证结果保存路径
# 配置 Saver
saver = tf.train.Saver()
if not os.path.exists(save_dir):
os.makedirs(save_dir) print("Loading training and validation data...")
# 载入训练集与验证集
start_time = time.time()
train_dir = '/content/drive/My Drive/NLP/dataset/Fudan/data/train_clean_jieba.txt'
val_dir = '/content/drive/My Drive/NLP/dataset/Fudan/data/test_clean_jieba.txt'
x_train, y_train = process(train_dir, config.seq_length)
x_val, y_val = process(val_dir, config.seq_length)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif) # 创建session
session = tf.Session()
session.run(tf.global_variables_initializer())
writer.add_graph(session.graph) print('Training and evaluating...')
start_time = time.time()
total_batch = 0 # 总批次
best_acc_val = 0.0 # 最佳验证集准确率
last_improved = 0 # 记录上一次提升批次
require_improvement = 1000 # 如果超过1000轮未提升，提前结束训练 flag = False
for epoch in range(config.num_epochs):
print('Epoch:', epoch + 1)
batch_train = batch_iter(x_train, y_train, config.batch_size)
for x_batch, y_batch in batch_train:
feed_dict = feed_data(x_batch, y_batch, config.dropout_keep_prob) if total_batch % config.save_per_batch == 0:
# 每多少轮次将训练结果写入tensorboard scalar
s = session.run(merged_summary, feed_dict=feed_dict)
writer.add_summary(s, total_batch) if total_batch % config.print_per_batch == 0:
# 每多少轮次输出在训练集和验证集上的性能
feed_dict[model.keep_prob] = 1.0
loss_train, acc_train = session.run([model.loss, model.acc], feed_dict=feed_dict)
loss_val, acc_val = evaluate(session, x_val, y_val) # todo if acc_val > best_acc_val:
# 保存最好结果
best_acc_val = acc_val
last_improved = total_batch
saver.save(sess=session, save_path=save_path)
improved_str = '*'
else:
improved_str = '' time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>6.2}, Train Acc: {2:>7.2%},' \
+ ' Val Loss: {3:>6.2}, Val Acc: {4:>7.2%}, Time: {5} {6}'
print(msg.format(total_batch, loss_train, acc_train, loss_val, acc_val, time_dif, improved_str)) feed_dict[model.keep_prob] = config.dropout_keep_prob
session.run(model.optim, feed_dict=feed_dict) # 运行优化
total_batch += 1 if total_batch - last_improved > require_improvement:
# 验证集正确率长期不提升，提前结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break # 跳出循环
if flag: # 同上
break def test():
print("Loading test data...")
start_time = time.time()
test_dir = '/content/drive/My Drive/NLP/dataset/Fudan/data/test_clean_jieba.txt'
x_test, y_test = process(test_dir, config.seq_length)
save_path = 'checkpoint/textcnn/best_validation' session = tf.Session()
session.run(tf.global_variables_initializer()) saver = tf.train.Saver()
saver.restore(sess=session, save_path=save_path) # 读取保存的模型 print('Testing...')
loss_test, acc_test = evaluate(session, x_test, y_test)
msg = 'Test Loss: {0:>6.2}, Test Acc: {1:>7.2%}'
print(msg.format(loss_test, acc_test)) batch_size = 128
data_len = len(x_test)
num_batch = int((data_len - 1) / batch_size) + 1 y_test_cls = np.argmax(y_test, 1)
y_pred_cls = np.zeros(shape=len(x_test), dtype=np.int32) # 保存预测结果
for i in range(num_batch): # 逐批次处理
start_id = i * batch_size
end_id = min((i + 1) * batch_size, data_len)
feed_dict = {
model.input_x: x_test[start_id:end_id],
model.keep_prob: 1.0
}
y_pred_cls[start_id:end_id] = session.run(model.y_pred_cls, feed_dict=feed_dict)
categories = get_label_id().values()
# 评估
print("Precision, Recall and F1-Score...")
print(metrics.classification_report(y_test_cls, y_pred_cls, target_names=categories)) # 混淆矩阵
print("Confusion Matrix...")
cm = metrics.confusion_matrix(y_test_cls, y_pred_cls)
print(cm) time_dif = get_time_dif(start_time)
print("Time usage:", time_dif) if __name__ == '__main__':
print('Configuring CNN model...')
config = TCNNConfig()
config.pre_trianing = get_training_word2vec_vectors(config.vector_word_npz)
model = TextCNN(config)
test()
结果如下：
Epoch: 8
Iter: 1080, Train Loss: 0.13, Train Acc: 95.31%, Val Loss: 0.44, Val Acc: 87.19%, Time: 0:04:33
Iter: 1100, Train Loss: 0.24, Train Acc: 95.31%, Val Loss: 0.44, Val Acc: 87.03%, Time: 0:04:38
Iter: 1120, Train Loss: 0.19, Train Acc: 93.75%, Val Loss: 0.43, Val Acc: 87.38%, Time: 0:04:42
Iter: 1140, Train Loss: 0.17, Train Acc: 92.19%, Val Loss: 0.42, Val Acc: 87.80%, Time: 0:04:47 *
Iter: 1160, Train Loss: 0.21, Train Acc: 90.62%, Val Loss: 0.41, Val Acc: 87.89%, Time: 0:04:53 *
Iter: 1180, Train Loss: 0.34, Train Acc: 89.06%, Val Loss: 0.43, Val Acc: 87.57%, Time: 0:04:57
Iter: 1200, Train Loss: 0.22, Train Acc: 92.19%, Val Loss: 0.41, Val Acc: 87.62%, Time: 0:05:01
Iter: 1220, Train Loss: 0.24, Train Acc: 90.62%, Val Loss: 0.41, Val Acc: 87.87%, Time: 0:05:06
Epoch: 9
Iter: 1240, Train Loss: 0.096, Train Acc: 95.31%, Val Loss: 0.4, Val Acc: 88.34%, Time: 0:05:11 *
Iter: 1260, Train Loss: 0.21, Train Acc: 92.19%, Val Loss: 0.41, Val Acc: 87.98%, Time: 0:05:16
Iter: 1280, Train Loss: 0.13, Train Acc: 95.31%, Val Loss: 0.42, Val Acc: 88.14%, Time: 0:05:20
Iter: 1300, Train Loss: 0.1, Train Acc: 98.44%, Val Loss: 0.43, Val Acc: 87.76%, Time: 0:05:25
Iter: 1320, Train Loss: 0.27, Train Acc: 92.19%, Val Loss: 0.39, Val Acc: 87.93%, Time: 0:05:29
Iter: 1340, Train Loss: 0.19, Train Acc: 92.19%, Val Loss: 0.45, Val Acc: 87.67%, Time: 0:05:33
Iter: 1360, Train Loss: 0.27, Train Acc: 92.19%, Val Loss: 0.42, Val Acc: 87.57%, Time: 0:05:38
Iter: 1380, Train Loss: 0.17, Train Acc: 92.19%, Val Loss: 0.41, Val Acc: 88.07%, Time: 0:05:42
Epoch: 10
Iter: 1400, Train Loss: 0.1, Train Acc: 98.44%, Val Loss: 0.39, Val Acc: 88.64%, Time: 0:05:47 *
Iter: 1420, Train Loss: 0.069, Train Acc: 96.88%, Val Loss: 0.4, Val Acc: 88.46%, Time: 0:05:51
Iter: 1440, Train Loss: 0.15, Train Acc: 98.44%, Val Loss: 0.41, Val Acc: 88.16%, Time: 0:05:56
Iter: 1460, Train Loss: 0.073, Train Acc: 98.44%, Val Loss: 0.4, Val Acc: 88.38%, Time: 0:06:00
Iter: 1480, Train Loss: 0.16, Train Acc: 95.31%, Val Loss: 0.42, Val Acc: 88.12%, Time: 0:06:05
Iter: 1500, Train Loss: 0.21, Train Acc: 92.19%, Val Loss: 0.41, Val Acc: 87.79%, Time: 0:06:09
Iter: 1520, Train Loss: 0.16, Train Acc: 93.75%, Val Loss: 0.41, Val Acc: 88.03%, Time: 0:06:13
进行测试，测试结果如下：
Testing...
2020-10-19 12:51:46.979827: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-10-19 12:51:47.221023: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
Test Loss: 0.39, Test Acc: 88.64%
Precision, Recall and F1-Score...
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
precision recall f1-score support 0 0.33 0.05 0.09 61
1 0.89 0.96 0.93 1022
2 0.39 0.15 0.22 59
3 0.89 0.95 0.92 1254
4 0.33 0.08 0.12 52
5 0.83 0.90 0.86 1026
6 0.95 0.98 0.96 1358
7 0.67 0.04 0.08 45
8 0.39 0.28 0.32 76
9 0.85 0.94 0.89 742
10 0.00 0.00 0.00 34
11 0.00 0.00 0.00 28
12 0.96 0.96 0.96 1218
13 0.87 0.92 0.89 642
14 0.50 0.15 0.23 33
15 0.67 0.07 0.13 27
16 0.91 0.91 0.91 1601
17 0.86 0.11 0.20 53
18 0.00 0.00 0.00 34
19 0.74 0.69 0.72 468 accuracy 0.89 9833
macro avg 0.60 0.46 0.47 9833
weighted avg 0.87 0.89 0.87 9833 Confusion Matrix...
[[ 3 1 0 42 0 5 0 0 4 3 0 0 0 2
0 0 1 0 0 0]
[ 0 983 0 5 0 1 0 0 0 0 0 0 8 3
0 0 14 1 0 7]
[ 1 2 9 3 0 4 2 0 3 1 0 0 2 15
3 0 13 0 0 1]
[ 0 3 0 1195 0 12 2 0 0 16 0 0 3 2
0 0 8 0 0 13]
[ 0 6 1 1 4 14 5 0 5 0 0 0 1 1
0 0 14 0 0 0]
[ 0 7 0 16 0 924 1 0 3 5 0 0 1 0
0 0 39 0 0 30]
[ 0 1 0 3 0 0 1328 1 1 0 0 0 1 17
0 0 5 0 0 1]
[ 0 0 0 13 0 12 0 2 0 8 0 0 1 2
0 0 0 0 0 7]
[ 2 1 1 7 0 39 0 0 21 0 0 0 0 4
0 0 0 0 0 1]
[ 0 1 0 10 0 10 1 0 1 696 0 0 0 0
0 0 3 0 0 20]
[ 0 0 0 4 0 0 0 0 0 15 0 0 0 1
0 0 1 0 0 13]
[ 0 0 0 2 1 0 5 0 2 0 0 0 0 10
1 0 7 0 0 0]
[ 0 11 0 1 1 1 8 0 3 0 0 0 1175 6
0 0 7 0 0 5]
[ 0 0 0 6 0 0 31 0 0 1 0 0 12 589
0 0 3 0 0 0]
[ 0 2 4 1 1 1 0 0 1 0 0 0 4 6
5 1 7 0 0 0]
[ 0 0 2 1 0 1 6 0 0 0 0 0 0 11
0 2 4 0 0 0]
[ 0 70 2 10 2 39 5 0 2 2 0 0 7 0
0 0 1451 0 0 11]
[ 3 4 0 10 3 12 0 0 6 3 0 0 0 0
0 0 5 6 0 1]
[ 0 7 4 0 0 1 0 0 1 1 0 0 6 5
1 0 7 0 0 1]
[ 0 4 0 7 0 43 5 0 1 72 0 0 1 1
0 0 11 0 0 323]]
Time usage: 0:00:13
至此使用传统的TF-IDF+朴素贝叶斯、RNN（LSTM、GRU）和CNN从数据的处理到模型的训练和测试就全部完成了，接下来准备弄弄Transformer和Bert了，欢迎关注。
参考：
https://github.com/gaussic/text-classification-cnn-rnn
利用CNN进行中文文本分类（数据集是复旦中文语料）的更多相关文章
- 基于tensorflow的文本分类总结（数据集是复旦中文语料）
代码已上传到github:https://github.com/taishan1994/tensorflow-text-classification 往期精彩: 利用TfidfVectorizer进行 ...
- 利用RNN进行中文文本分类（数据集是复旦中文语料）
利用TfidfVectorizer进行中文文本分类(数据集是复旦中文语料) 1.训练词向量 数据预处理参考利用TfidfVectorizer进行中文文本分类(数据集是复旦中文语料) ,现在我们有了分词 ...
- 基于Text-CNN模型的中文文本分类实战 流川枫 发表于AI星球订阅
Text-CNN 1.文本分类 转眼学生生涯就结束了,在家待就业期间正好有一段空闲期,可以对曾经感兴趣的一些知识点进行总结. 本文介绍NLP中文本分类任务中核心流程进行了系统的介绍,文末给出一个基于T ...
- 基于Text-CNN模型的中文文本分类实战
Text-CNN 1.文本分类 转眼学生生涯就结束了,在家待就业期间正好有一段空闲期,可以对曾经感兴趣的一些知识点进行总结. 本文介绍NLP中文本分类任务中核心流程进行了系统的介绍,文末给出一个基于T ...
- 万字总结Keras深度学习中文文本分类
摘要:文章将详细讲解Keras实现经典的深度学习文本分类算法,包括LSTM.BiLSTM.BiLSTM+Attention和CNN.TextCNN. 本文分享自华为云社区<Keras深度学习中文 ...
- Chinese-Text-Classification，用卷积神经网络基于 Tensorflow 实现的中文文本分类。
用卷积神经网络基于 Tensorflow 实现的中文文本分类 项目地址: https://github.com/fendouai/Chinese-Text-Classification 欢迎提问:ht ...
- 中文文本分类之CharCNN
文本分类是自然语言处理中一个非常经典的任务,可用的模型非常多,相关的开源代码也非常多了.这篇博客用一个CNN模型,对新闻文本进行分类. 全部代码有4个模块:1.数据处理模块(命名为:cnews_loa ...
- 中文文本分类之TextRNN
RNN模型由于具有短期记忆功能,因此天然就比较适合处理自然语言等序列问题,尤其是引入门控机制后,能够解决长期依赖问题,捕获输入样本之间的长距离联系.本文的模型是堆叠两层的LSTM和GRU模型,模型的结 ...
- 利用TfidfVectorizer进行中文文本分类（数据集是复旦中文语料）
1.对语料进行分析 基本目录如下: 其中train存放的是训练集,answer存放的是测试集,具体看下train中的文件: 下面有20个文件夹,对应着20个类,我们继续看下其中的文件,以C3-Art为 ...
随机推荐
- nginx 1.12 HTTPS双向认证配置
使用openssl生成相关证书: #生成CA私钥,私钥会被加密,需要设置密码 openssl genrsa -aes256 -out ca.key 2048 #生成CA证书签名请求,需要输入CA私钥密 ...
- Python-in is == 区别
in 判断单个元素是否在序列中, 对字典来说只能判断key,在不在关系 print("ab" in "abcdefg") print("abc&quo ...
- Centos-分割文件-split
split 分割文件,将一个文件分割为多个 相关选项 -b 指定文件大小,可以在size后面添加单位后缀,b表示512字节,k表示1KB,m表示MB -n 指定分割文件的长度,默认为1000行 -d ...
- Java知识系统回顾整理01基础05控制流程01if
一.if if(表达式1){ 表达式2: } 如果表达式1的值是true, 就执行表达式2 public class HelloWorld { public static void main(Stri ...
- Splay浅谈
Splay是众多平衡树之一,它的功能十分强大,但常数极大.在LCT和许多数据结构中都能用到. Splay的核心操作,就是rotate.为了使树不是一条链,而是平衡的,我们需要旋转来维护形态.理论很简单 ...
- Keil ARm新建项目
一.新建一个工程 选好芯片后确认,完成创建 二.新建一个文件 保存为后缀名为*.c的文件 三.把文件添加进项目里面 四.测试 发现有警告 五.给项目添加特定的文件,去除警告或错误 现在保存项目的文件夹 ...
- 使用css实现轮播图
使用css3实现图片轮播 前言:实现图片轮播的方式有很多种 ,例如js ,css 等等. 本文主要讲述使用纯css3实现轮播图 工具介绍: 使用的编辑器: Hbuilder 进入正题 html代码: ...
- background-size 详解
backgroun-size:cover; .是按照等比缩放铺满整个区域.主要用于图片比div小的时候,将图片按照某一边的比例扩大以填充整个div背景. .优点:图片不会被拉升,且实用于div长度和宽 ...
- S3C6410 LCD驱动分析（转）
一. 理论分析1. 几个概念:FIMC : Fully Interactive Mobile Camera (完全交互式移动摄像机)FIMD: Fully Interactive Mob ...
- day32 Pyhton hashlib模块 总结异常处理
一.当用明文密码进行信息存储的时候,会导致密码的泄露,如何解决问题 通过导入hashlib模块,利用里面存在的算法对字符串进行加密计算得到一串密文的结果 1.这个过程不可逆 2.对于同一个字符串,同一 ...