How to Implement Parameterized Custom Loss Functions in Keras?

Custom Loss Functions in Keras: A Detailed Guide

Custom loss functions allow you to tailor your model's training process to a specific problem or metric. In Keras, implementing parameterized custom loss functions requires following a specific procedure.

Creating the Coefficient/Metric Method

First, define a method for calculating the coefficient or metric you want to use as the loss function. For example, for the Dice coefficient, you can write the following code:

import keras.backend as K
def dice_coef(y_true, y_pred, smooth, thresh):
    y_pred = y_pred > thresh
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)

    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

Wrapper Function for Keras

Keras loss functions only accept (y_true, y_pred) as parameters. To fit into this format, create a wrapper function that returns the loss function:

def dice_loss(smooth, thresh):
  def dice(y_true, y_pred)
    return -dice_coef(y_true, y_pred, smooth, thresh)
  return dice

Using the Custom Loss Function

Now you can use your custom loss function in Keras by compiling it with the loss argument:

# build model 
model = my_model()
# get the loss function
model_dice = dice_loss(smooth=1e-5, thresh=0.5)
# compile model
model.compile(loss=model_dice)

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