【学习记录】深度学习中的双向RNN实现

最新推荐文章于 2024-08-30 13:03:12 发布

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#深度学习 #rnn #学习

本文通过Keras实现了一个情感分析模型，使用IMDb数据集对电影评论进行正面与负面情感的分类。从数据预处理到构建双向LSTM+Dropout的模型，展示了文本情感分析的基本步骤和实践技巧。

代码展示：

import numpy as np
import pandas as pd

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense,Dropout,Activation,LSTM
from keras.layers import Embedding,GRU,Bidirectional
from keras.callbacks import EarlyStopping
from keras.datasets import  imdb

n_words = 1000
(X_train,y_train),(X_test,y_test) = imdb.load_data(num_words=n_words)
print("train seq:{}".format(len(X_train)))
print("test seq:{}".format(len(X_test)))
print("train example:{}".format(X_train[0]))
print("test example:{}".format(X_test[0]))

max_len = 200
X_train = sequence.pad_sequences(X_train,maxlen=max_len)
X_test = sequence.pad_sequences(X_test,maxlen=max_len)


#model
model = Sequential()
model.add(Embedding(n_words ,50,input_length=max_len))
model.add(Dropout(0.2))
model.add(Bidirectional(LSTM(100,dropout = 0.2,recurrent_dropout= 0.2)))
model.add(Dense(250,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['acc'])
model.summary()


callbacks = [EarlyStopping(monitor='val_acc',patience=3)]
batch_size = 1024
n_epochs  = 10
model.fit(X_train,y_train,batch_size=batch_size,epochs=n_epochs,validation_split=0.2,callbacks=callbacks)
print('acc on test set :{}'.format(model.evaluate(X_test,y_test,batch_size=batch_size)[1]))

实现截图：