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))
