title: How PCA in TensorBoard projector is connected to the trained model?
body: <p>I am trying to implement a multi-label classifier on structured data in TensorFlow. I am using a NN with two fully connected layers, but I have also integrated embeddings as described in this <a href="https://www.tensorflow.org/programmers_guide/embedding" rel="nofollow noreferrer">example</a>, so I can see the PCA graph in TensorBoard projector. I have noticed that the PCA graph remains the same when I change my model. So how is are TensorBoard projections connected to the model if at all? And how can I use the results from this PCA to make predictions and e.g. find the nearest neighbors to the new sample? </p>