TensorBoard for training performance visualization

For visualizing deep network training performance, TensorBoard is a useful tool that is available as part of the TensorFlow package. We will rerun the deep network model that we used in Chapter 2, Deep Neural Networks for Multi-Class Classification, where we used CTG data to develop a multi-class classification model for patients. For the code related to data processing, the model architecture, and compiling the model, you can refer to Chapter 2, Deep Neural Networks for Multi-Class Classification.

The following is the code for model_one from Chapter 2, Deep Neural Networks for Multi-Class Classification:

# Fitting model and TensorBoard
setwd("~/Desktop/")
model_one <- model %>% fit(training, 
                         trainLabels, 
                         epochs = 200,  
                         batch_size = 32,
  ...