Input Function

Estimators can receive data through input functions. Input functions take an arbitrary data source (in-memory data sets, streaming data, custom data format, and so on) and generate Tensors that can be supplied to TensorFlow models. The tfestimators package includes an input_fn() function that can create TensorFlow input functions from common R data sources (e.g. data frames and matrices). It’s also possible to write a fully custom input function.

Here, we define a helper function that will return an input function for a subset of our mtcars data set.

library(tfestimators)

# return an input_fn for a given subset of data
mtcars_input_fn <- function(data) {
  input_fn(data, 
           features = c("disp", "cyl"), 
           response = "mpg")
}

Feature Columns

Next, we define the feature columns for our model. Feature columns are used to specify how Tensors received from the input function should be combined and transformed before entering the model training, evaluation, and prediction steps. A feature column can be a plain mapping to some input column (e.g. column_numeric() for a column of numerical data), or a transformation of other feature columns (e.g. column_crossed() to define a new column as the cross of two other feature columns).

Here, we create a list of feature columns containing two numeric variables - disp and cyl:

cols <- feature_columns(
  column_numeric("disp"),
  column_numeric("cyl")
)

You can also define multiple feature columns at once:

cols <- feature_columns( 
  column_numeric("disp", "cyl")
)

By using the family of feature column functions we can define various transformations on the data before using it for modeling.

Estimator

Next, we create the estimator by calling the linear_regressor() function and passing it a set of feature columns:

model <- linear_regressor(feature_columns = cols)

Training

We’re now ready to train our model, using the train() function. We’ll partition the mtcars data set into separate training and validation data sets, and feed the training data set into train(). We’ll hold 20% of the data aside for validation.

indices <- sample(1:nrow(mtcars), size = 0.80 * nrow(mtcars))
train <- mtcars[indices, ]
test  <- mtcars[-indices, ]

# train the model
model %>% train(mtcars_input_fn(train))

Evaluation

We can evaluate the model’s accuracy using the evaluate() function, using our ‘test’ data set for validation.

model %>% evaluate(mtcars_input_fn(test))

Prediction

After we’ve finished training out model, we can use it to generate predictions from new data.

new_obs <- mtcars[1:3, ]
model %>% predict(mtcars_input_fn(new_obs))