What is the purpose of weights and biases in tensorflow word2vec example?

Question

I\'m trying to understand how word2vec example works and don\'t really understand what is the purpose of weights and biases passed into nse_loss function. There are two vari

Answer 1

Weights and biases are updated here:

_, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)

The optimizer does the following - computes the gradient and then does the update step.

The similarity is a separate computation is called in a different location and is used to validate the results. Which happens in the following section of code:

if step % 10000 == 0: sim = similarity.eval()

The validation of the embedding relies upon the similarity embedding.