Visualizing word embeddings with TensorBoard

When we wanted to visualize word embeddings in Chapter 3, Word2vec – Learning Word Embeddings, we manually implemented the visualization with the t-SNE algorithm. However, you also could use TensorBoard to visualize word embeddings. TensorBoard is a visualization tool provided with TensorFlow. You can use TensorBoard to visualize the TensorFlow variables in your program. This allows you to see how different variables behave over time (for example, model loss/accuracy), so you can identify potential issues in your model.

TensorBoard enables you to visualize scalar values (e.g. loss values over training iterations) and vectors as histograms (e.g. model’s layer node activations). Apart from this, TensorBoard also allows you to visualize word embeddings. Therefore, it takes all the required code implementation away from you, if you need to analyze what the embeddings look like. Next, we will see how we can use TensorBoard...