from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
import numpy as np

frames=[singleteamone_tweets,singleteamtwo_tweets,twoteam_tweets]
All_data = pd.concat(frames)

#creating a bag of words model
from sklearn.feature_extraction.text import CountVectorizer
cv= CountVectorizer()
X=cv.fit_transform(All_data.iloc[:,0]).toarray()  #.iloc[:,0]
Y=All_data.iloc[:,1].values


#training and test dataset

from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.25,random_state=0)

#Reshape 
#X_train = np.random.rand(3,4)
X_train = np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))





classifier=Sequential()
#LSTM layers and Dropout regularization
classifier.add(LSTM(units=30,return_sequences=True,input_shape=(X_train.shape[1],1)))
classifier.add(Dropout(0.2))

classifier.add(LSTM(units=30,return_sequences=True))
classifier.add(Dropout(0.2))

classifier.add(LSTM(units=30,return_sequences=True))
classifier.add(Dropout(0.2))

classifier.add(LSTM(units=30))
classifier.add(Dropout(0.2))

#output layer
classifier.add(Dense(units=1,init='uniform',activation='sigmoid'))

我在检查其显示属性错误的准确性时遇到错误：-历史记录对象没有属性“评估”。我试图修复它无法正常工作。AttributeError：“历史记录”对象没有属性“评估”

＃编译RNN classifier.compile（optimizer ='adam'，loss ='binary_crossentropy'，metrics = ['accuracy']）

model=classifier.fit(X_train,epochs=30,batch_size=15)



# Final evaluation of the model

test_loss,test_acc = model.evaluate(X_test)