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我已經使用keras對ANN分類器進行了編碼，現在我正在學習自己編寫用於文本和時間序列預測的keras中的RNN。在網上搜索了一段時間後，我發現了Jason Brownlee的tutorial，這對於RNN的初學者來說是一個很好的選擇。原文將IMDb數據集用於LSTM文本分類，但由於其數據集大小較大，我將其更改爲小型sms垃圾郵件檢測數據集。如何在數據集中使用keras RNN進行文本分類？
# LSTM with dropout for sequence classification in the IMDB dataset
import numpy
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
import pandaas as pd
from sklearn.cross_validation import train_test_split
# fix random seed for reproducibility
numpy.random.seed(7)
url = 'https://raw.githubusercontent.com/justmarkham/pydata-dc-2016-tutorial/master/sms.tsv'
sms = pd.read_table(url, header=None, names=['label', 'message'])
# convert label to a numerical variable
sms['label_num'] = sms.label.map({'ham':0, 'spam':1})
X = sms.message
y = sms.label_num
print(X.shape)
print(y.shape)
# load the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
top_words = 5000
# truncate and pad input sequences
max_review_length = 500
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)
# create the model
embedding_vecor_length = 32
model = Sequential()
model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length, dropout=0.2))
model.add(LSTM(100, dropout_W=0.2, dropout_U=0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(X_train, y_train, nb_epoch=3, batch_size=64)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
我已經成功地將數據集處理成了訓練和測試集，但現在應如何爲此數據集建立我的RNN模型？