The complete source code for the previous example is as follows:

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
from keras.layers import Conv1D, GlobalMaxPooling1D
 
# fix random seed for reproducibility 
numpy.random.seed(7) 

# load the dataset but only keep the top n words, zero the rest 
top_words = 5000 
(X_train, y_train), (X_test, y_test) =              
                             imdb.load_data(num_words=top_words) 

# 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_vector_length = 32 
model = Sequential() 
model.add(Embedding(top_words, embedding_vector_length, 
                       input_length=max_review_length))
model.add(Conv1D (padding="same", activation="relu", kernel_size=3, num_filter=32))
model.add(GlobalMaxPooling1D ())
 
model.add(LSTM(100)) 
model.add(Dense(1, activation='sigmoid')) 
model.compile(loss='binary_crossentropy', 
                 optimizer='adam', 
                   metrics=['accuracy']) 
print(model.summary()) 

model.fit(X_train, y_train, 
     validation_data=(X_test, y_test), 
           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))