Is there a deep learning method for 3D labels?

Question

As the question says, I want to feed labels into a neural net that are three dimensional. Let's say that I have 3 possible labels and each one of my data points corresponds to a percentage of those labels. e.g, my first datapoint contains 20% of label A, 30% of label B, and 50% of label C.
Is there any architecture able to deal with this shape of label data?

10xAI · Accepted Answer

Since the probability are summing up to zero, so you can simply treat it as Multi-class problem and use a network with Softmax at the end.
Last layer and compile -

model.add(keras.layers.Dense( 3, activation="softmax"))
model.compile( optimizer='adam, loss="categorical_crossentropy", metrics='accuracy')

Metrics - Accuracy is not appropriate. Define a custom metrics based on the interpretation of 3 probabilities
The labels will be as per the probability-
e.g. This is for MNIST 10 digits -

Digit 1 - [0.05, 0.55, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]

Prediction - [0.064, 0.356, 0.059, 0.069, 0.068, 0.050, 0.044, 0.122, 0.064, 0.101]

Code for MNIST - Colab link

Is there a deep learning method for 3D labels?

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