- You can create custom loss functions to suit specific tasks.pythonCopy code
import tensorflow.keras.backend as K def custom_loss(y_true, y_pred): return K.mean(K.square(y_pred - y_true) * K.exp(-K.abs(y_true))) model.compile(optimizer='adam', loss=custom_loss) - Custom loss functions are useful when default loss functions don’t meet the exact requirements of the task.
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