The performance of the model can be evaluated using AUC:

# Performance on Train
library(pROC) 
ypred <- sess$run(tf$nn$sigmoid(multilayer_perceptron(x, weights, biases)))
roc_obj <- roc(occupancy_train[, yFeatures], as.numeric(ypred))

# Performance on Test
nRowt<-nrow(occupancy_test)
xt <- tf$constant(unlist(occupancy_test[, xFeatures]), shape=c(nRowt, nFeatures), dtype=np$float32) #
ypredt <- sess$run(tf$nn$sigmoid(multilayer_perceptron(xt, weights, biases)))
roc_objt <- roc(occupancy_test[, yFeatures], as.numeric(ypredt))

AUC can be visualized using the plot.auc function from the pROC package, as shown in the image following the next command. The performance of train and test (hold out) is very similar.

plot.roc(roc_obj, col = "green", lty=2, lwd=2)
plot.roc(roc_objt, add=T, col="red", lty=4, lwd=2)