How to gather the logits according to a 2D index?

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How to gather the logits according to a 2D index?



Assume the sequence [[2, 1, 4], [3, 4, 2]] is generated by a pre-trained LSTM. It's dimension is (2*3) meaning batch-size = 2 and 3 time steps in each sample.


[[2, 1, 4], [3, 4, 2]]


(2*3)


batch-size = 2


3 time steps



Then for example, there are 5 features in total so the logits may be:


5 features


[[[0.2, 0.2, 0.1, 0.1, 0.2],
[0.3, 0.2, 0.1, 0.1, 0.1],
[0.1, 0.2, 0.1, 0.1, 0.3]],
[[0.2, 0.2, 0.1, 0.1, 0.2],
[0.2, 0.2, 0.1, 0.1, 0.2],
[0.2, 0.2, 0.1, 0.1, 0.2]]]



I want to use the sequence as the index to get the corresponding probabilities from the logits for each sample and each time step. Regarding the example above, the final result I want to get is


[[0.1, 0.2, 0.3],[0.1, 0.2, 0.1]]



I knew that I probably need tf.stack() but I'm confused about how to handle the dimension. Appreciate for any help!




1 Answer
1



I think I found a way.


tf.losses.sparse_softmax_cross_entropy(labels = None, logits = None)



labels is the index and logits is the output of a model.


labels


logits






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