Tag Archives: Multi Layer Perceptron

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
,callbacks=[tensorboard_callback])

tfb1

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.
tfb2

Click on the Distributions tab to check the layer output.
tfb3

Click on the Histograms tab for a 3D visualization of the dense layers.
tfb4

 

 

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