TensorBoard Tutorial: TensorFlow Graph Visualization [Example]

What is TensorBoard?

TensorBoard is the interface used to visualize the graph and other tools to understand, debug, and optimize the model. It is a tool that provides measurements and visualizations for machine learning workflow. It helps to track metrics like loss and accuracy, model graph visualization, project embedding at lower-dimensional spaces, etc.

TensorFlow Graph Visualization using Tensorboard Example

The image below comes from the TensorBoard graph you will generate in this TensorBoard Tutorial. It is the main panel:

TensorFlow Graph Visualization
TensorFlow Graph Visualization

From the picture below, you can see the panel of TensorBoard graph visualization. The panel contains different tabs, which are linked to the level of information you add when you run the model.

TensorBoard Graphs

TensorBoard Graphs
  • Scalars: Show different useful information during the model training
  • Graphs: Show the model
  • Histogram: Display weights with a histogram
  • Distribution: Display the distribution of the weight
  • Projector: Show Principal component analysis and T-SNE algorithm. The technique used for dimensionality reduction

During this TensorBoard Tutorial, you will train a simple deep learning model. You will learn how it works in a future tutorial.

If you look at the graph, you can understand how the model work.

  1. Enqueue the data to the model: Push an amount of data equal to the batch size to the model, i.e., Number of data feed after each iteration
  2. Feed the data to the Tensors
  3. Train the model
  4. Display the number of batches during the training. Save the model on the disk.

TensorFlow Graph Visualization using Tensorboard Example

The basic idea behind tensorboard is that neural network can be something known as a black box and we need a tool to inspect what’s inside this box. You can imagine tensorboard as a flashlight to start dive into the neural network.

It helps to understand the dependencies between operations, how the weights are computed, displays the loss function and much other useful information. When you bring all these pieces of information together, you have a great tool to debug and find how to improve the model.

To give you an idea of how useful the TensorBoard graph can be, look at the picture below:

TensorBoard Graphs
TensorBoard graph

A neural network decides how to connect the different “neurons” and how many layers before the model can predict an outcome. Once you have defined the architecture, you not only need to train the model but also a metrics to compute the accuracy of the prediction. This metric is referred to as a loss function. The objective is to minimize the loss function. In different words, it means the model is making fewer errors. All machine learning algorithms will repeat many times the computations until the loss reaches a flatter line. To minimize this loss function, you need to define a learning rate. It is the speed you want the model to learn. If you set a learning rate too high, the model does not have time to learn anything. This is the case in the left picture. The line is moving up and down, meaning the model predicts with pure guess the outcome. The picture on the right shows that the loss is decreasing over iteration until the curve got flatten, meaning the model found a solution.

TensorBoard is a great tool to visualize such metrics and highlight potential issues. The neural network can take hours to weeks before they find a solution. TensorBoard updates the metrics very often. In this case, you don’t need to wait until the end to see if the model trains correctly. You can open TensorBoard to check how the training is going and make the appropriate change if necessary.

How to use TensorBoard?

In this tutorial, you will learn how to open TensorBoard from the terminal for MacOS and the Command line TensorBoard for Windows.

The code will be explained in a future tutorial, the focus here is on TensorBoard.

First, you need to import the libraries you will use during the training

## Import the library
import tensorflow as tf
import numpy as np

You create the data. It is an array of 10000 rows and 5 columns

X_train = (np.random.sample((10000,5)))
y_train =  (np.random.sample((10000,1)))


(10000, 5)

The codes below transform the data and create the model.

Note that the learning rate is equal to 0.1. If you change this rate to a higher value, the model will not find a solution. This is what happened on the left side of the above picture.

During most of the TensorFlow tutorials, you will use TensorFlow estimator. This is TensorFlow API that contains all the mathematical computations.

To create the log files, you need to specify the path. This is done with the argument model_dir.

In the TensorBoard example below, you store the model inside the working directory, i.e., where you store the notebook or python file. Inside this path, TensorFlow will create a folder called train with a child folder name linreg.

feature_columns = [
      tf.feature_column.numeric_column('x', shape=X_train.shape[1:])]
DNN_reg = tf.estimator.DNNRegressor(feature_columns=feature_columns,
# Indicate where to store the log file    
     hidden_units=[500, 300],    


INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': 'train/linreg', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1818e63828>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}

The last step of this TensorFlow visualize graph example consists to train the model. During the training, TensorFlow writes information in the model directory.

# Train the estimator
train_input = tf.estimator.inputs.numpy_input_fn(    
     x={"x": X_train},    
     y=y_train, shuffle=False,num_epochs=None)


INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 1 into train/linreg/model.ckpt.
INFO:tensorflow:loss = 40.060104, step = 1
INFO:tensorflow:global_step/sec: 197.061
INFO:tensorflow:loss = 10.62989, step = 101 (0.508 sec)
INFO:tensorflow:global_step/sec: 172.487
INFO:tensorflow:loss = 11.255318, step = 201 (0.584 sec)
INFO:tensorflow:global_step/sec: 193.295
INFO:tensorflow:loss = 10.604872, step = 301 (0.513 sec)
INFO:tensorflow:global_step/sec: 175.378
INFO:tensorflow:loss = 10.090343, step = 401 (0.572 sec)
INFO:tensorflow:global_step/sec: 209.737
INFO:tensorflow:loss = 10.057928, step = 501 (0.476 sec)
INFO:tensorflow:global_step/sec: 171.646
INFO:tensorflow:loss = 10.460144, step = 601 (0.583 sec)
INFO:tensorflow:global_step/sec: 192.269
INFO:tensorflow:loss = 10.529617, step = 701 (0.519 sec)
INFO:tensorflow:global_step/sec: 198.264
INFO:tensorflow:loss = 9.100082, step = 801 (0.504 sec)
INFO:tensorflow:global_step/sec: 226.842
INFO:tensorflow:loss = 10.485607, step = 901 (0.441 sec)
INFO:tensorflow:global_step/sec: 152.929
INFO:tensorflow:loss = 10.052481, step = 1001 (0.655 sec)
INFO:tensorflow:global_step/sec: 166.745
INFO:tensorflow:loss = 11.320213, step = 1101 (0.600 sec)
INFO:tensorflow:global_step/sec: 161.854
INFO:tensorflow:loss = 9.603306, step = 1201 (0.619 sec)
INFO:tensorflow:global_step/sec: 179.074
INFO:tensorflow:loss = 11.110269, step = 1301 (0.556 sec)
INFO:tensorflow:global_step/sec: 202.776
INFO:tensorflow:loss = 11.929443, step = 1401 (0.494 sec)
INFO:tensorflow:global_step/sec: 144.161
INFO:tensorflow:loss = 11.951693, step = 1501 (0.694 sec)
INFO:tensorflow:global_step/sec: 154.144
INFO:tensorflow:loss = 8.620987, step = 1601 (0.649 sec)
INFO:tensorflow:global_step/sec: 151.094
INFO:tensorflow:loss = 10.666125, step = 1701 (0.663 sec)
INFO:tensorflow:global_step/sec: 193.644
INFO:tensorflow:loss = 11.0349865, step = 1801 (0.516 sec)
INFO:tensorflow:global_step/sec: 189.707
INFO:tensorflow:loss = 9.860596, step = 1901 (0.526 sec)
INFO:tensorflow:global_step/sec: 176.423
INFO:tensorflow:loss = 10.695, step = 2001 (0.567 sec)
INFO:tensorflow:global_step/sec: 213.066
INFO:tensorflow:loss = 10.426752, step = 2101 (0.471 sec)
INFO:tensorflow:global_step/sec: 220.975
INFO:tensorflow:loss = 10.594796, step = 2201 (0.452 sec)
INFO:tensorflow:global_step/sec: 219.289
INFO:tensorflow:loss = 10.4212265, step = 2301 (0.456 sec)
INFO:tensorflow:global_step/sec: 215.123
INFO:tensorflow:loss = 9.668612, step = 2401 (0.465 sec)
INFO:tensorflow:global_step/sec: 175.65
INFO:tensorflow:loss = 10.009649, step = 2501 (0.569 sec)
INFO:tensorflow:global_step/sec: 206.962
INFO:tensorflow:loss = 10.477722, step = 2601 (0.483 sec)
INFO:tensorflow:global_step/sec: 229.627
INFO:tensorflow:loss = 9.877638, step = 2701 (0.435 sec)
INFO:tensorflow:global_step/sec: 195.792
INFO:tensorflow:loss = 10.274586, step = 2801 (0.512 sec)
INFO:tensorflow:global_step/sec: 176.803
INFO:tensorflow:loss = 10.061047, step = 2901 (0.566 sec)
INFO:tensorflow:Saving checkpoints for 3000 into train/linreg/model.ckpt.
INFO:tensorflow:Loss for final step: 10.73032.

<tensorflow.python.estimator.canned.dnn.DNNRegressor at 0x1818e63630>

For MacOS user

TensorBoard Tutorial For MacOS User

For Windows user

TensorBoard Tutorial For Windows User

You can see this information in thePyTorch TensorBoard.

Now that you have the log events written, you can open Tensorboard. Tensorboard Keras runs on port 6006 (Jupyter runs on port 8888). You can use the Terminal for MacOs users or Anaconda prompt for Windows user.

For MacOS user

# Different for you
cd /Users/Guru99/tuto_TF
source activate hello-tf!

The notebook is stored in the path /Users/Guru99/tuto_TF

For Windows users

cd C:\Users\Admin\Anaconda3
activate hello-tf

The notebook is stored in the path C:\Users\Admin\Anaconda3

To launch Tensorboard, you can use this code

For MacOS user

tensorboard --logdir=./train/linreg

For Windows users

tensorboard --logdir=.\train\linreg

Tensorboard is located in this URL: http://localhost:6006

It could also be located at the following location.

Use TensorBoard

Copy and paste the URL into your favorite browser. You should see this:

Note that, we will learn how to read the graph in the tutorial dedicated to the deep learning.

Use TensorBoard

If you see something like this:

Use TensorBoard

It means Tensorboard cannot find the log file. Make sure you point the cd to the right path or double check if the log event has been creating. If not, re-run the code.

If you want to close TensorBoard Press CTRL+C

Hat Tip: Check your anaconda prompt for the current working directory,

Use TensorBoard

The log file should be created at C:\Users\Admin


TensorBoard is a great tool to visualize your model. Besides, many metrics are displayed during the training, such as the loss, accuracy or weights.

To activate Tensorboard, you need to set the path of your file:

cd /Users/Guru99/tuto_TF

Activate Tensorflow’s environment

activate hello-tf

Launch Tensorboard

tensorboard --logdir=.+ PATH