Tag Archives: TensorBoard

Visualizing a MLP Neural Network with TensorBoard

The Multi-Layer Perceptron model is supported in Keras as a form of Sequential model container as MLP in its predefined layer type. For visualization of the training results, TensorBoard is handy with only a few line of code to add to the Python program.

log_dir="logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

Finally add callbacks to the corresponding fitting model command to collect model information.

history = model.fit(X_train, Y_train, validation_split=0.2,
epochs=100, batch_size=10


Once the training is completed, start the TensorBoard and point browser to the designated port number.

Click on the Graph tab to see a detailed visualization of the model.

Click on the Distributions tab to check the layer output.

Click on the Histograms tab for a 3D visualization of the dense layers.



Visualizing TensorFlow with TensorBoard

TensorBoard is a tool for visualizing graphs and various metrics for TensorFlow session runs. With a few line of additional code, TensorBoard gathers and report run statistics in a nice graphical interface.tensorboard1

First of all a few lines are required to TensorFlow session as in below:

writer = tf.summary.FileWriter("output", sess.graph)
print(sess.run(GH, options=options, run_metadata=run_metadata))

Before running the session, start TensorBoard using the following command.

Finally run the TensorFlow session and point the browser to the TensorBoard.