keras - Neural Network

library('vivid')
library("keras")

This guide is designed as a quick-stop reference of how to use some of the more popular machine learning R packages with vivid. In the following example, we use the air quality data for regression.

keras - Neural Network

The keras package in R is an interface to the original keras Python library. It allows R users to build and train neural network models using the high-level Keras API. To use keras the data must first be normalised. Due to the complexity custom model fits in keras, producing a custom predict function to use with vivid may require some work. In the example below, we show how to use vivid when building a keras model composed of a linear stack of layers (i.e., using keras_model_sequential). Additionally, currently the PDP plots in vivid only work for the development version on GitHub.

# load data
aq <- na.omit(airquality)
# Set up data
train_data <- aq %>%
  as.matrix()
train_targets <- aq$Ozone

# Normalize the data
mean <- apply(train_data, 2, mean)                                  
std <- apply(train_data, 2, sd)
aqTrain <- scale(train_data, center = mean, scale = std)  

# Define model architecture
build_model <- function() {                                
  model <- keras_model_sequential() %>%
    layer_dense(units = 64, activation = "relu",
                input_shape = dim(aqTrain)[[2]]) %>%
    layer_dense(units = 64, activation = "relu") %>%
    layer_dense(units = 1)
  model %>% compile(
    optimizer = "rmsprop",
    loss = "mse",
    metrics = c("mae")
  )
}

# Train the final model
model <- build_model()
model %>% fit(train_data, train_targets, epochs = 80, 
              batch_size = 16, verbose = 0)

# Create usable data frame from scaled data
aqFinal <- as.data.frame(aqTrain)

# predict function for keras
vi_fun <- function(fit, data,...) {
  predict(fit, x = as.matrix(data))
}

# vivid
vi <- vivi(data = aqFinal, 
           fit = model,
           response = 'Ozone',
           predictFun = vi_fun)

Heatmap

viviHeatmap(mat = vi)

Figure 1: Heatmap of a keras neural network regression fit displaying 2-way interaction strength on the off diagonal and individual variable importance on the diagonal.

PDP

pdpPairs(data = aqFinal, 
         fit =  model, 
         response = "Ozone", 
         nmax = 500, 
         gridSize = 10,         
         nIce = 100,
         predictFun = vi_fun)

Figure 2: Generalized pairs partial dependence plot for a keras neural network regression fit.