原理讲解
TextRNN相关论文：Recurrent Neural Network for Text Classification with Multi-Task Learning
核心点
textRNN原理.png
RNN的几个策略：
1.直接使用RNN的最后一个单元输出向量作为文本特征
2.使用双向RNN的两个方向输出向量的连接(concatenate)或均值作为文本特征
3.将所有RNN单元的输出向量的均值pooling或者max-pooling作为文本特征
本文实现
Text-RNN 的网络结构：
text-RNN实现.png
定义网络结构：
from tensorflow.keras import Input,Model
from tensorflow.kears.layers import Embedding , Dense,Dropout,LSTM
class Text-RNN(object):
def __init__(self , maxlen , max_features ,embedding_dims,class_num=5,last_activation = 'softmax'):
self.maxlen = maxlen
self.max_features = max_features
self.embedding_dims = embedding_dims
self.class_num = class_num
self.last_activation = last_activation
def get_model(self):
input = Input((self.maxlen,))
embedding = Embedding(self.max_features, self.embedding_dims,input_length = self.maxlen)(input)
x = LSTM(128)(embedding)
output = Dense(self.class_num , activation= self.last_activation)(x)
model = Model(inputs = input , outputs = output)
return model
from tensorflow.proprecessing import sequence
import random
from sklearn.model_selection import train_test_split
from tensorflow.keras.callback import EarlyStopping
from tensoflow.keras.utils import to_categorical
from utils import *
#路径配置
data_dir = './processed_data'
vocab_file = './vocab/vocab.txt'
vocab_size = 40000
#神经网络配置
max_features = 40001
maxlen = 400
batch_size = 64
embedding_dims = 50
epochs = 10
print('数据预处理与加载数据')
#如果不存在词汇表，重建
if not os.path.exists(vocab_file):
build_vocab(data_dir , vocab_file , vocab_size)
#获得 词汇/类别 与id的映射
categories , cat_to_id = read_category()
words , word_to_id = read_vocab(vocab_file)
#全部数据
x , y =read_files(data_dir)
data = list(zip(x,y))
del x, y
#打乱数据即
random.shuffle(data)
#切分训练集与测试机
train_data, test_data = train_test_split(data)
#对文本的词id和类别id进行编码
x_train = encode_sentences([content[0] for content in train_data] , word_to_id)
y_train = to_categorical(encode_cate([content[1] for content in train_data),cat_to_id)
x_test = encode_sentences([content[0] for content in test_data], word_to_id)
y_test = to_categorical(encode_cate([content[1] for content in test_data], cat_to_id))
print('对序列做padding，保证是 samples*timestep 的维度')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
print('构建模型...')
model = TextRNN(maxlen, max_features, embedding_dims).get_model()
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])
print('Train...')
early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, mode='max')
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=[early_stopping],
validation_data=(x_test, y_test))
print('Test...')
result = model.predict(x_test)
import matplotlib.pyplot as plt
plt.switch_backend('agg')
%matplotlib inline
fig1 = plt.figure()
plt.plot(history.history['loss'],'r',linewidth=3.0)
plt.plot(history.history['val_loss'],'b',linewidth=3.0)
plt.legend(['Training loss', 'Validation Loss'],fontsize=18)
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Loss',fontsize=16)
plt.title('Loss Curves :CNN',fontsize=16)
fig1.savefig('loss_cnn.png')
plt.show()
fig2=plt.figure()
plt.plot(history.history['accuracy'],'r',linewidth=3.0)
plt.plot(history.history['val_accuracy'],'b',linewidth=3.0)
plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Accuracy',fontsize=16)
plt.title('Accuracy Curves : CNN',fontsize=16)
fig2.savefig('accuracy_cnn.png')
plt.show()
from tensorflow.keras.utils import plot_model
# model.summary()
plot_model(model, show_shapes=True, show_layer_names=True)
