Can we use multiple loss functions in same layer?

Question

Can we use mulitple loss function in this architecture: I have two different type of loss functions and want to use it on last layer [Output] loss functions :

• b

Answer 1

You can calculate two different losses. Then get weighted mean and return as the final value of the loss. Technically, it can be implemented like this (that is an example, I didn't run it):

def joint_loss(y_true, y_pred):
    part_binary_crossentropy = 0.4
    part_custom = 0.6

    # binary_crossentropy
    loss_binary_crossentropy = tf.keras.losses.binary_crossentropy(y_true, y_pred)

    # custom_loss
    loss_custom = some_custom_loss(y_true, y_pred))

    return part_binary_crossentropy * loss_binary_crossentropy + part_custom * loss_custom


model.compile(loss=joint_loss, optimizer='Adam')