Is there a deep learning method for 3D labels? 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?
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=&quot;softmax&quot;))
model.compile( optimizer='adam, loss=&quot;categorical_crossentropy&quot;, 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