R Deep Learning Cookbook

How to do it...

The data is imported using a standard function from R, as shown in the following code.

1. The data is imported using the read.csv file and transformed into the matrix format followed by selecting the features used to model as defined in xFeatures and yFeatures. The next step in TensorFlow is to set up a graph to run optimization:

# Loading input and test dataxFeatures = c("Temperature", "Humidity", "Light", "CO2", "HumidityRatio")yFeatures = "Occupancy"occupancy_train <-as.matrix(read.csv("datatraining.txt",stringsAsFactors = T))occupancy_test <- as.matrix(read.csv("datatest.txt",stringsAsFactors = T))# subset features for modeling and transform to numeric valuesoccupancy_train<-apply(occupancy_train[, c(xFeatures, yFeatures)], ...