Tensorflow Embedding Flask Projector | by Jesko Rehberg

TensorBoard embedding projector offers a powerful toolset for visualizing and exploring high-dimensional data. It aids in understanding complex relationships, identifying clusters, and gaining insights from data.

This article explains how to embed the TensorBoard projector into a website using Flask. You can use this knowledge to create customized data visualization experiences.

First you need to import the necessary libraries:

• TensorFlow: for creating and managing the embeddings

• TensorBoard Projector: for configuring the embeddings visualization

• NumPy: for numerical operations

• Flask: for creating the web application

• subprocess: for launching TensorBoard

import tensorflow as tf
from tensorboard.plugins import projector
import numpy as np
from flask import Flask, send_from_directory
import subprocessp

Create a Flask application instance and define the home route (‘/’) which renders the ‘index.html’ template. We will come back to this later.

app = Flask(__name__)
@app.route('/')
def home():
return send_from_directory('.', 'index.html')

Define the static files route to serve files from the current directory.

@app.route('/<path:path>')
def static_files(path):
return send_from_directory('.', path)

Define a function, launch_tensorboard(), to launch TensorBoard using the subprocess module.

def launch_tensorboard():
subprocess.Popen(["tensorboard", "--logdir=."])

Load the embedding vectors from the ‘similarity_matrixE.tsv’ file into a NumPy array.

if __name__ == '__main__':
vectors_path = 'similarity_matrixE.tsv'
vectors = []
with open(vectors_path, 'r') as f:
for line in f:
vector = line.strip().split('\t')
vector = [float(value) for value in vector]
vectors.append(vector)
vectors = np.array(vectors)