在多次损失 keras 上用单个 output 训练 model

Question

I am building an image segmentation model using keras and I want to train my model on multiple loss functions. I have seen this link but I am looking for a simpler and straight-forward solutions for this situation as my loss functions are quite complex. Can someone tell me how to build a model with single output with multiple losses in keras.

Answer 1

You can use multiple losses with one output using weighted loss, which is a sum of your losses multiplied by weight. Create your custom loss which will return a sum of other losses with coefficients and pass it to model.compile . There is an example here .

Answer 2

This is just an example from here . You could play around with it.

def custom_losses(y_true, y_pred):
    alpha = 0.6
    squared_difference = tf.square(y_true - y_pred)
    Huber = tf.keras.losses.huber(y_true, y_pred)
    return tf.reduce_mean(squared_difference, axis=-1) + (alpha*Huber)

model.compile(optimizer='adam', loss=custom_losses,metrics=['MeanSquaredError'])