Why do my target labels need to begin at 0 for sparse categorical cross entropy to work? I'm following a guide here to implement image segmentation in Keras.
One thing I'm confused about are these lines:
# Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:
y[j] -= 1

The ground truth targets are .png files with either 1,2 or 3 in a particular pixel position to indicate the following:
Pixel Annotations: 1: Foreground 2:Background 3: Not classified

When I remove this -1, my sparse_categorical_crossentropy values come out as nan during training.
Epoch 1/15
 25/196 [==&gt;...........................] - ETA: 27s - loss: nan - accuracy: 0.0348 - sparse_categorical_crossentropy: nan  

Why is this the case? If the possible integer values are 1, 2, 3, why would I need to alter them to begin at 0 to be correctly used?
If I include the -1, the training looks correct:
Epoch 1/15
196/196 [==============================] - 331s 2s/step - loss: 2.0959 - accuracy: 0.6280 - sparse_categorical_crossentropy: 2.0959 - val_loss: 1.9682 - val_accuracy: 0.5749 - val_sparse_categorical_crossentropy: 1.9682
Have a look at this stackoverflow answer, it seems to be caused by that the fact that your labels need to be zero indexed as the argmax function also return the index based on a zero indexed array.