Question :
Hi I have been trying to make a custom loss function in keras for dice_error_coefficient. It has its implementations in tensorboard and I tried using the same function in keras with tensorflow but it keeps returning a NoneType when I used model.train_on_batch or model.fit where as it gives proper values when used in metrics in the model. Can please someone help me out with what should i do? I have tried following libraries like KerasFCN by ahundt where he has used custom loss functions but none of it seems to work. The target and output in the code are y_true and y_pred respectively as used in the losses.py file in keras.
def dice_hard_coe(target, output, threshold=0.5, axis=[1,2], smooth=1e5):
"""References

 `WikiDice <https://en.wikipedia.org/wiki/SĂ¸rensenâ€“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)
hard_dice = tf.reduce_mean(hard_dice)
return hard_dice
Answer #1:
There are two steps in implementing a parameterized custom loss function in Keras. First, writing a method for the coefficient/metric. Second, writing a wrapper function to format things the way Keras needs them to be.

It’s actually quite a bit cleaner to use the Keras backend instead of tensorflow directly for simple custom loss functions like DICE. Here’s an example of the coefficient implemented that way:
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)

Now for the tricky part. Keras loss functions must only take (y_true, y_pred) as parameters. So we need a separate function that returns another function.
def dice_loss(smooth, thresh): def dice(y_true, y_pred) return dice_coef(y_true, y_pred, smooth, thresh) return dice
Finally, you can use it as follows in Keras compile.
# build model
model = my_model()
# get the loss function
model_dice = dice_loss(smooth=1e5, thresh=0.5)
# compile model
model.compile(loss=model_dice)
Answer #2:
According to the documentation, you can use a custom loss function like this:
Any callable with the signature
loss_fn(y_true, y_pred)
that returns an array of losses (one of sample in the input batch) can be passed to compile() as a loss. Note that sample weighting is automatically supported for any such loss.
As a simple example:
def my_loss_fn(y_true, y_pred):
squared_difference = tf.square(y_true  y_pred)
return tf.reduce_mean(squared_difference, axis=1) # Note the `axis=1`
model.compile(optimizer='adam', loss=my_loss_fn)
Complete example:
import tensorflow as tf
import numpy as np
def my_loss_fn(y_true, y_pred):
squared_difference = tf.square(y_true  y_pred)
return tf.reduce_mean(squared_difference, axis=1) # Note the `axis=1`
model = tf.keras.Sequential([
tf.keras.layers.Dense(8, activation='relu'),
tf.keras.layers.Dense(16, activation='relu'),
tf.keras.layers.Dense(1)])
model.compile(optimizer='adam', loss=my_loss_fn)
x = np.random.rand(1000)
y = x**2
history = model.fit(x, y, epochs=10)