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

# load the dataset 
top_words = 5000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)

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)

# embeddings
embedding_vector_length = 32
model = Sequential()
model.add(Embedding(top_words, embedding_vector_length, input_length=max_review_length))

# lstm
model.add(LSTM(100))

# actual output
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),epochs=3,batch_size=64)

# evaluation
scores = model.evaluate(X_test,y_test,verbose=0)
print("Accuracy: %.2f%%"% (scores[1]*100))