Custom Loss Function in Keras: Implementing the Dice Error Coefficient

In this article, we'll explore how to create a custom loss function in Keras, focusing on the Dice error coefficient. We'll learn to implement a parameterized coefficient and wrap it for compatibility with Keras' requirements.

Implementing the Coefficient

Our custom loss function will require both a coefficient and a wrapper function. The coefficient measures the Dice error, which compares the target and predicted values. We can use the Python expression below:

def dice_hard_coe(y_true, y_pred, threshold=0.5, axis=[1,2], smooth=1e-5):
    # Calculate intersection, labels, and compute hard dice coefficient
    output = tf.cast(output > threshold, dtype=tf.float32)
    target = tf.cast(target > threshold, dtype=tf.float32)
    inse = tf.reduce_sum(tf.multiply(output, target), axis=axis)
    l = tf.reduce_sum(output, axis=axis)
    r = tf.reduce_sum(target, axis=axis)
    hard_dice = (2. * inse   smooth) / (l   r   smooth)
    # Return the mean hard dice coefficient
    return hard_dice

Creating the Wrapper Function

Keras requires loss functions to only take (y_true, y_pred) as parameters. Therefore, we need a wrapper function that returns another function that conforms to this requirement. Our wrapper function will be:

def dice_loss(smooth, thresh):
    def dice(y_true, y_pred):
        # Calculate the dice coefficient using the coefficient function
        return -dice_coef(y_true, y_pred, smooth, thresh)
    # Return the dice loss function
    return dice

Using the Custom Loss Function

Now, we can use our custom Dice loss function in Keras by compiling the model with it:

# Build the model
model = my_model()
# Get the Dice loss function
model_dice = dice_loss(smooth=1e-5, thresh=0.5)
# Compile the model
model.compile(loss=model_dice)

By implementing the custom Dice error coefficient in this way, we can effectively evaluate model performance for image segmentation and other tasks where Dice error is a relevant metric.