figure tensorboardact ivat ion histograms the embeddings tab gives you way to inspect the embedding locations and spatial relationships of the , words in the input vocabularyas learned by the initial embedding layer because the embedding space is -dimensionaltensorboard automatically reduces it to or using dimensionality-reduction algorithm of your choiceeither principal component analysis (pcaor -distributed stochastic neighbor embedding ( -snein figure in the point cloudyou can clearly see two clusterswords with positive connotation and words with negative connotation the visualization makes it immediately obvious that embeddings trained jointly with specific objective result in models that are completely specific to the underlying task--that' the reason using pretrained generic word embeddings is rarely good idea licensed to