What is TensorBoard

Understanding TensorBoard: The Perfect Tool for Visualizing Machine Learning datasets
In the world of deep learning, image and voice recognition systems, machine learning is based on massive quantities of data. One crucial task in machine learning projects is visualizing such datasets to understand their structure and track the progress of sophisticated algorithms. TensorBoard, an inbuilt TensorFlow application, is designed to address this problem. In this article, we will explore TensorBoard - an intuitive, well-designed, and easy-to-use platform that allows one to visualize different kinds of data.
  • What is TensorBoard?
  • The Architecture of TensorBoard
  • Key Features of TensorBoard
  • TensorBoard Visualization Examples
  • How to use TensorBoard effectively.
What is TensorBoard?
TensorBoard is a tool designed by TensorFlow, a prominent deep learning library. TensorBoard provides powerful ways to visualize machine learning models, computational graphs, and other information relevant to deep learning. TensorBoard aims to provide a straightforward interface to visualize all the changes that happen during the training of the model intuitively. TensorBoard helps data scientists develop machine learning algorithms correctly and keeps track of how well these algorithms perform over time. TensorBoard allows a user to ascertain whether the model is overfitting or underfitting, find out if the gradients are vanishing or exploding and discover if the error is backpropagating correctly. TensorBoard provides graphs, images, and other tools to allow a machine learning model developer to investigate various aspects of model performance and correctness.
The Architecture of TensorBoard
TensorBoard is a client-server application. The server runs in the background and logs events and data to a directory on the host machine. The client (browser) can read and visualize the logs from this directory. In short, TensorBoard reads data written to a log directory and displays that data in a series of web-pages.
Key Features of TensorBoard
The key features of TensorBoard are:
  • Dashboard or Homepage
  • Scalars
  • Images
  • Graphs
  • Histograms
  • Distribution Plots
  • Projector
  • Text Embeddings
  • Audio
TensorBoard Visualization Examples
Here are some examples of data visualization techniques you can perform with TensorBoard.
Scalars Visualization
Scalars are one-dimensional data (e.g., accuracy, loss) that change over time throughout the model training. The most common scalar visualization is the scalar chart. To create one, activate your TensorBoard server and write the following code snippet: ```python import tensorflow as tf # Make a tensor named 'three' with value 3. three = tf.constant([3]) # Make a summary operation. summary_three = tf.summary.scalar('three', three) with tf.Session() as sess: # Create a writer object. writer = tf.summary.FileWriter('/logs', sess.graph) for i in range(1, 100): # Run the summary operation. summary_value = sess.run(summary_three) # Log the summary to disk. writer.add_summary(summary_value, i) ```  Scalars Visualization allows users to track model performance over time and helps the data scientist to analyze model training trends.
Images Visualization
Images Visualization is another powerful feature of TensorBoard. It allows one to visualize images being processed by a convolutional neural network. To create a session with image data, you can use the following code snippet: ```python import tensorflow as tf # Make a summary operation. image = tf.placeholder(tf.float32, [None, 56, 56, 3]) summary_image = tf.summary.image("Test Image", image) with tf.Session() as sess: # Create a writer object. writer = tf.summary.FileWriter('/logs', sess.graph) # Define the image. img_data = (np.random.rand(1, 56, 56, 3) * 255).astype(np.uint8) for i in range(3): # Run the summary operation. summary_image_value = sess.run(summary_image, feed_dict={image: img_data}) # Log the summary to disk. writer.add_summary(summary_image_value, i) ``` Images Visualization allows one to understand the convolutional neural network’s decisions and see how the model processes image data over time.
Projector Visualization
Projector visualization is a powerful tool for visualizing high-dimensional data such as text embeddings. Projector Visualization is ideal for analyzing such data, as it allows one to plot data in 3D space and better understand how different data points relate to one another. ```python from tensorflow.contrib.tensorboard.plugins import projector # TensorBoard requires that you give it a list of metadata for each of your embedding points. metadata = './metadata.tsv' metadata_file = open(metadata, 'w') metadata_file.write('Name\tLabel\n') for i in range(0, 100): metadata_file.write('Point %d\t%d\n' % (i, i % 3)) metadata_file.close() # Point the two files to directory containing the tensor of embeddings. embedding = tf.Variable(np.zeros((100, 100)), name="test_embedding") writer = tf.summary.FileWriter('/logs/embedding-example', sess.graph) embedding_config = projector.ProjectorConfig() embedding = embedding_config.embeddings.add() embedding.tensor_name = embedding.name = "test_embedding" embedding.metadata_path = metadata # Save a checkpoint for the embedding, adding images if desired saver = tf.train.Saver() saver.save(sess, '/logs/embedding-example/test_embedding.ckpt', global_step=100) projector.visualize_embeddings(writer, embedding_config) ```  Projector Visualization allows one to easily analyze and explore high-dimensional data. The user can zoom and drag points around to better understand the nature of the dataset and how its different components relate to each other.
How to use TensorBoard effectively
To make the most out of TensorBoard, the first step is to configure the log directory. By default, TensorFlow writes events data to the “./events/” folder. However, it is a good practice to create separate folders for training, validation, and test datasets and write them explicitly by giving an appropriate name. The second best-practice tip is to write summaries periodically instead of writing them all together. TensorBoard has an option to refresh the graph and update scalars, histograms, and other visualizations automatically. Writing too many summaries at once can overwhelm TensorBoard and cause the browser to lag. Lastly, TensorBoard supports many types of visualizations. It is important to determine which visualization is most relevant to the data one working with, and use them to analyze the data effectively.
TensorBoard is a powerful tool for generating visualizations of machine learning models. It is not only useful for visualizing data but also helps in monitoring the model performance so that the model can be optimized. By using TensorBoard, the data scientist can gain insights into:
  • The behavior of the model
  • The performance of the model
  • Identify possible flaws and areas of improvement.
Using TensorBoard for deep learning projects can streamline the workflow, providing a better understanding of the dataset, and improving the model's performance.