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- # LSTM 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
- # 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_vecor_length = 32
- model = Sequential()
- model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
- 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, epochs=3, batch_size=64)
- # Final evaluation of the model
- scores = model.evaluate(X_test, y_test, verbose=0)
- print("Accuracy: %.2f%%" % (scores[1]*100))
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