My model is trying to predict scores for 163 items using variety of inputs. It uses keras on tensorflow backend. Following the approach in Keras - Save image embedding of the mnist data set to capture layer weights, I am capturing embedding data for final layer which is Dense(163). Since final dense layer is getting 128 inputs, weights matrix is 128x163. In Tensorboard Projector, I can see it visualizes 128 points very well. However when I try to map it to my real world items using meta data, I have 163 items names but Tensorboard Projecter is visualizing 128x163 weight matrix by dimension 0 i.e. 128 points. Is there any way to make it visualize points by dimension 1 (163 points) in Tensorboard Projector?
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