How to use sample weights with tensorflow datasets?

Question

I have been training a unet model for multiclass semantic segmentation in python using Tensorflow and Tensorflow Datasets.

I've noticed that one of my classes seems to be underrepresented in training. After doing some research, I found out about sample weights and thought it might be a good solution to my problem, but I've been having trouble deciphering the documentation on how to use it or find examples of it being used.

Could someone help explain how sample weights come into play with datasets for training or point me to an example where it is being implemented? Or even what type of input the model.fit function is expecting would be helpful.

Answer 1

From the documentation of tf.keras model.fit():

sample_weight

[...] This argument is not supported when x is a dataset, generator, or keras.utils.Sequence instance, instead provide the sample_weights as the third element of x.

What is meant by that? This is demonstrated for the Dataset case in one of the official documentation turorials:

sample_weight = np.ones(shape=(len(y_train),))
sample_weight[y_train == 5] = 2.0

# Create a Dataset that includes sample weights
# (3rd element in the return tuple).
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train, sample_weight))

# Shuffle and slice the dataset.
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)

model = get_compiled_model()
model.fit(train_dataset, epochs=1)

See the link for a full-fledged example.