Advanced Machine Learning with R

Model training

Training the model is quite interesting, I believe. We will pass the model to the fit() function, having specified the features, response, number of epochs, and validation percentage at each epoch. Here, I have gone with 100 epochs, and 20% of the training data for validation:

> epochs <- 100> # Fit the model and store training stats> history <- model %>% fit(    trainT,    train_logy,    epochs = epochs,    validation_split = 0.2,    verbose = 0)

You can examine the history object on your own, but what is very powerful is to plot the training and validation error for each epoch:

> plot(history, metrics = "mean_absolute_error", smooth = FALSE) 

The output of the preceding code is as follows:

Notice how much the error differs between training ...