# The sum of multi-class prediction is not 1 using tensorflow and keras?

I am studying how to do text classification with multiple labels using tensorflow. Let's say my model is like:

model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, 50, weights=[embedding_matrix], trainable=False),
tf.keras.layers.LSTM(128),
tf.keras.layers.Dense(4, activation='sigmoid')])


I have 4 classes, the prediction function has the following results:

pred=model.predict(X_test)
pred
array([[0.915674  , 0.4272042 , 0.69613266, 0.3520468 ],
[0.915674  , 0.42720422, 0.69613266, 0.35204676],
[0.915674  , 0.4272042 , 0.69613266, 0.3520468 ],
[0.9156739 , 0.42720422, 0.69613266, 0.3520468 ],
......


You can see that every data has 4 prediction values. But the sum of them is not 1, which I do not understand. Did I do something wrong, or how to interpret this?