How to do weight imbalanced classes for cross entropy loss in Keras?

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Using Keras for image segmentation on a highly imbalanced dataset, and I want to re-weight the classes proportional to pixels values in each class as described here. If a have binary classes with weights = [0.8, 0.2], how can I modify K.sparse_categorical_crossentropy(y_true, y_pred) to re-weight the loss according to the class which the pixel belongs to?

The input has shape (4, 256, 256, 1) (batch, height, width, channels) and the output is a vector of 0's and 1's (4, 65536, 1) (positive and negative class). The model and data is similar to the one here with the difference being the images are grayscale and the masks are binary (2 classes).

keras
3 Answers

This is the custom loss function I used for my semantic segmentation project. It is modified from the categorical_crossentropy function found in keras/backend/tensorflow_backend.py.

def class_weighted_pixelwise_crossentropy(target, output):
    output = tf.clip_by_value(output, 10e-8, 1.-10e-8)
    weights = [0.8, 0.2]
    return -tf.reduce_sum(target * weights * tf.log(output))

Note that my final version did not use class weighting - I found that it encouraged the model to use the underrepresented classes as filler for patches of the image that it was unsure about instead of making more realistic guesses, and thereby hurt performance.

user_17138500

Jessica's answer is clean and works well. I generally recommend it. But for the sake of variety: I have found that sampling regions of interest that include a better ratio between the classes is an effective way to quickly learn skewed pixelwise classes.

In my case, I had two classes like you which makes things easier. I look for areas in the image that have appearances of the less represented class. I crop around it with some random offset a constantly sized bounding box ( i repeat the process multiple times per image). This yields a large set of small images that have fairly equal ratios of each class. I should probably add here that the network will have to be set to input shape of (None, None, num_chanals) for this to then work on your original images.

Because you skip out on the vast majority of pixels ( that belong to the majority class) the training is very fast but doesn't leverage all of the data for the majority class.

user_46968680

In tensorflow 2.x the model.fit method has a class_weight argument to do this natively, passing a dictionary of weights for each class. Documentation

user_98077800
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