How to Implement Your Own Loss Function in Keras?

Custom Loss Function Implementation in Keras

In Keras, custom loss functions can be implemented to address specific training requirements. One such function is the dice error coefficient, which measures the overlap between ground truth and predicted labels.

To create a custom loss function in Keras, follow these steps:

1. Implement the Coefficient Function

The dice error coefficient can be written as:

dice coefficient = (2 * intersection) / (sum(ground_truth) + sum(predictions))

Using Keras backend functions, you can implement the coefficient function:

<code class="python">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)</code>

2. Wrap the Function as a Loss Function

Keras loss functions accept only (y_true, y_pred) as input. Therefore, wrap the coefficient function in a function that returns the loss:

<code class="python">def dice_loss(smooth, thresh):
  def dice(y_true, y_pred):
    return -dice_coef(y_true, y_pred, smooth, thresh)
  return dice</code>

3. Compile the Model

Finally, compile the model using the custom loss function:

<code class="python"># 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)</code>

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