在 Keras TF 中处理和组合两个损失 function

Question

Is there a way to have two loss functions in Keras in which the second loss function takes the output from the first loss function?

I am working on a Neural Network with Keras and I want to add another custom function to the Loss term inside the model.compile() to regularize and somehow penalize it, which is the form:

model.compile(loss_1='mean_squared_error', optimizer=Adam(lr=learning_rate), metrics=['mae'])

I would like to add another loss function as a sum of the predicted values from the Loss_1 outputs so that I can tell the Neural Network to minimize the sum of the predicted values from the Loss_1 model. How can I do that (loss_2)?

Something like:

model.compile(loss_1='mean_squared_error', loss_2= np.sum(****PREDICTED_OUTPUT_FROM_LOSS_FUNCTION_1****), optimizer=Adam(lr=learning_rate), metrics=['mae'])

how can this be implemented?

Answer 1

You should define a custom loss function

def custom_loss_function(y_true, y_pred):
   squared_difference = tf.square(y_true - y_pred)
   absolute_difference = tf.abs(y_true - y_pred)
   
   loss = tf.reduce_mean(squared_difference, axis=-1) + tf.reduce_mean(absolute_difference, axis=-1)

   return loss

model.compile(optimizer='adam', loss=custom_loss_function)

I believe that would solve your problem