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Last Updated: Mar 27, 2024
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TensorBoard

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Prerita Agarwal
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23 Jul, 2024 @ 01:30 PM

Introduction

We'll go over the TensorBoard lesson in this article, a visualization tool that helps you comprehend various metrics of your neural network model and the training process. First, we'll go through TensorBoard and how to set it up.

TensorBoard is a web interface that allows you to visualize numerous parameters of a neural network model and its training metrics. Such visualizations are beneficial when you're working with neural network models and want to watch the metrics. It belongs to the Tensorflow group and is open-source.

Deep learning algorithms outperform traditional methods as data volume grows. However, it has the disadvantage of increasing algorithm complexity, which reduces the algorithm's interpretability. The intricacy also makes hyperparameter tuning difficult. tensorboard is the answer. The TensorFlow team designed a visualization addon to make neural networks easier to understand. It can be used to create a variety of graphs. Accuracy, Error, Weight distribution, and so on are a few of them.

Applications

TensorBoard can be used for a variety of tasks, including:

  • Metrics like accuracy and loss can be visualized.
  • Visualize the graph of the model.
  • Understand how weights and biases vary throughout training by looking at histograms.
  • Visualize text, image, and audio data.
  • Embeddings can be seen in a lower-dimensional space.
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Components Of TensorBoard

It is made up of five parts, which are as follows:

  1. Scalars:  Given the enormous number of epochs, it's tough to look at the accuracy and inaccuracy for each one, and there's a danger of becoming caught in local minima rather than global ones. This section can be used to tackle these two difficulties. It shows the accuracy and error graphs concerning the epoch.

    Img_src
  2. Graphs: The "model.summary()" findings are visualised in this section. In other words, it improves the aesthetics of neural network architecture. This makes the process of comprehending architecture much easier.

    Img_src
  3. Distributions: Neural networks have a lot of layers, and each layer is made up of a lot of biases and weights. The distribution of these hyperparameters is depicted in this section.

    Img_src
  4. Histograms: Consists of the histogram of these hyperparameters.

    Img_src
  5. Time-Series: Consists of the values for the same over time. These sections are important for analyzing the hyperparameters' trends and controlling them.

Installing TensorBoard

We can download the tensorboard using the pip command given below:

pip install tensorboard

Starting TensorBoard

To begin, we must first launch the TensorBoard service. To do so, type the commands below into the command prompt. The –logdir argument specifies where TensorBoard data will be saved for visualization. The directory name is 'logs' in this case.

tensorboard --logdir logs

As illustrated below, this will start the TensorBoard service on the default port 6066. http://localhost:6006/ is the URL for the TesnorBoard dashboard.

In the Jupyter notebook, you can issue the following command in the cell.

%tensorboard --logdir logs

Implementation

Let us take that we are working on a dataset and have already compiled our model. Now, how do we visualize the training process using tensorBoard? For that, we call the callback function.

A callback function records the model and its results (error, accuracy, bias, weights, and so on) regularly (epochs). This is necessary because the graphs are based on these values at each epoch.

Please note that the parent path for log_dir below should be the same as the logdir value we gave while starting the TensorBoard service in the second step.

tf_callbacks = tf.keras.callbacks.TensorBoard(log_dir = "logs/fit" , histogram_freq = 1)

Finally, we use the fit() function to train the model. We train it for five epochs, and you'll see that we also passed the callback object we built earlier.

model.fit(x_train, y_train, epochs=200, callbacks = tf_callbacks)

Comparing Different Models Using TensorBoard

Creating a decent Neural Network is a difficult task requiring numerous runs to test various parameters. TensorBoard allows you to compare and visualize the performance of all model runs in the dashboard.

We'll construct training logs in several subfolders within the main folder. The sample below will clarify things for you.

We build the TensorBoard Keras callback object in the first run, and the logs are saved in the 'run1' folder within the main logs folder.

tb_callback = tf.keras.callbacks.
TensorBoard(log_dir="logs/run1", histogram_freq=1)
model.fit(X_train, y_train, epochs=5, callbacks=[tb_callback])

The log path for the second run is run2, as seen below.

tb_callback = tf.keras.callbacks.
TensorBoard(log_dir="logs/run2", histogram_freq=1)
model.fit(X_train, y_train, epochs=5, callbacks=[tb_callback])

When we look at the TensorBoard dashboard now, the accuracy and loss graph will provide information for both runs in orange and blue lines.

Img_src

FAQs

1. What am I able to accomplish with TensorBoard?
TensorBoard is a platform that provides the measurements and visualizations required for machine learning. It allows you to keep track of experimental parameters such as loss and accuracy, visualize the model graph, project embeddings to a lower-dimensional space, etc.

2. What is a callback in TensorBoard?
TensorFlow comes with a visualization tool called TensorBoard. This callback logs TensorBoard events, such as Summary charts of metrics, Visualization of a training graph Histograms of activation.

3. Is TensorBoard a real-time platform?
Without Tensorflow installed, Tensorboard does not show real-time updates when training.

4. Is TensorFlow required for TensorBoard?
TensorBoard is a visualization tool included with every TensorFlow installation. We'll use TensorFlow for many things (like training a huge deep neural network), and they can be complex and confusing.

Key Takeaways

Let us brief out the article.

Firstly, we saw the meaning of tensorboard, its various applications, and its different components. Later we saw how to install tensorboard with its starting process. Lastly, we saw the implementation of tensorboard and how it can be compared to different models using tensorboard. That's all from the article. 


Check out this problem - Largest Rectangle in Histogram

I hope you all like this article. Want to learn more about Data Analysis? Here is an excellent course that can guide you in learning. Can also refer to our Machine Learning course.

Happy Learning, Ninjas!

Topics covered
1.
Introduction
2.
Applications
3.
Components Of TensorBoard
4.
Installing TensorBoard
5.
Starting TensorBoard
6.
Implementation
7.
Comparing Different Models Using TensorBoard
8.
FAQs
9.
Key Takeaways