Setting up a neural network using TensorFlow

In this section, we will cover an application of TensorFlow in setting up a two-layer neural network model.

Getting ready

To start modeling, load the tensorflow package in the environment. R loads the default tf environment variable and also the NumPy library from Python in the np variable:

library("tensorflow") # Load Tensorflow 
np <- import("numpy") # Load numpy library

How to do it...

1. The data is imported using the standard function from R, as shown in the following code. The data is imported using the read.csv file and transformed into the matrix format followed by selecting the features used for the modeling as defined in xFeatures and yFeatures:

# Loading input and test data
xFeatures = 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...