Visualizing word embeddings in TensorBoard

In the previous section, we learned how to build word2vec model for generating word embeddings using gensim. Now, we will see how to visualize those embeddings using TensorBoard. Visualizing word embeddings help us to understand the projection space and also helps us to easily validate the embeddings. TensorBoard provides us a built-in visualizer called the embedding projector for interactively visualizing and analyzing the high-dimensional data like our word embeddings. We will learn how can we use the TensorBoard's projector for visualizing the word embeddings step by step.

Import the required libraries:

import warnings
warnings.filterwarnings(action='ignore')

import tensorflow as tf 
from tensorflow.contrib.tensorboard.plugins import projector tf.logging.set_verbosity(tf.logging.ERROR)

import numpy as np
import gensim 
import...