Apply different loss function to part of tensor in keras

Question

I’m trying to build a custom loss function where it will apply different function to different part of tensor based on groundtruth.

Say for example the groundtruth is:

[0 1 1 0]

I want to apply log(n) to index 1, 2 (which is those whose value is 1 in the ground truth) of the output tensor, and apply log(n-1) to the rest.

How will I be able to achieve it?

Asked By: smallha61109

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Source

Answer 1

You can create two masks.

The first one masks out zeros, so you can apply it to your first loss function in which you only apply log(n) to those values of 1’s.
The second mask masks out ones, so you can apply it to your second loss function in which you apply log(n-1) to those values of 0’s.

Something like:

input = tf.constant([0, 1, 1, 0], tf.float32)
mask1 = tf.cast(tf.equal(input, 1.0), tf.float32)
loss1 = tf.log(input) * mask1

mask2 = tf.cast(tf.equal(input, 0.0), tf.float32)
loss2 = tf.log(input - 1) * mask2

overall_loss = tf.add(loss1, loss2)

Answered By: greeness

Answer 2

@greeness answer can be further simplified by directly using the input for masking without converting to bool and back to float:

ground_truth = tf.constant([0, 1, 1, 0], tf.float32)
your_tensor = tf.constant([1, 0, 1, 0], tf.float32)

loss = tf.log(ground_truth * your_tensor + (1 - ground_truth ) * (your_tensor - 1))
# loss = tf.log([0, 0, 1, -1])

Answered By: Miron Foerster

Apply different loss function to part of tensor in keras

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