TOP_WORDS = 5000 MAX_WORDS = 500 #load dataset (X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=TOP_WORDS) X_train = sequence.pad_sequences(X_train, maxlen=MAX_WORDS) X_test = sequence.pad_sequences(X_test, maxlen=MAX_WORDS) # create model model = Sequential() model.add(Embedding(TOP_WORDS, 32, input_length=MAX_WORDS)) mode.add(Flatten()) model.add(Dense(250, activation="relu")) model.add(Dense(1, activation="sigmoid")) model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"]) model.Summary() # Fit model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch=128, verbose=2) # eval scores = model.evaluate(X_test, y_test, verbose=0) print("accuracy: %.2f%%" % (scores[1] * 100))