added support for tb projector word embeddings #2853
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I've added feat['vec'].squeeze() to support unflattend layers for image models (e.g. pooling layers). Changed docs and tests to show the different options how to pass the callback (fit_one_cycle(5, cbs=cbs), get_preds(dl=dl, cbs=cbs)). |
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Added support for exporting word embeddings for Tensorboard projector (function tensorboard_word_embeddings) and updated tests and documentation.
tensorboard_word_embeddings will automatically get the correct layer and vocab for a fast.ai model - for transformers or other models the embedding layer and vocab can be passed as parameters.