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])
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.