Hands-On Natural Language Processing with Python

Visualization of word embeddings

We will embed the trained word vectors into TensorBoard and visualize the learned vectors by projecting them down to two dimensions. For such a projection, we can use methods such as t-SNE or PCA, which are available in TensorBoard. The following screenshot shows how a projection using PCA appears on TensorBoard:

The preceding visualization illustrates how TensorBoard shows embeddings on its projector. However, this visualization does not look useful, and it makes no sense when it's viewed, as it uses PCA dimensionality reduction. Hence, we will switch the visualization mode to using t-SNE, which is another ...