Input Function

Estimators can accept data from arbitrary data sources through an ‘input function’. The input function selects feature and response columns from the input source as well as defines how data will be drawn (e.g. batch size, epochs, etc.). The tfestimators package provides the input_fn() helper function for generating input functions from common R data structures, e.g. R matrices and data frames.

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, num_epochs = 1) {
  input_fn(data, 
           features = c("disp", "cyl"), 
           response = "mpg",
           batch_size = 32,
           num_epochs = num_epochs)
}

Feature Columns

Next, we define the feature columns for our model. Feature columns are mappings of raw input data to the data that we’ll actually feed into our training, evaluation, and prediction steps. Here, we create a list of feature columns containing the disp and cyl variables:

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, num_epochs = 10))

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 our model, we can use it to generate predictions from new data.

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

Model Persistence

Models created via tfestimators are persisted on disk. To obtain the location of where the model artifacts are stored, we can call model_dir():

saved_model_dir <- model_dir(model)

And subsequently load the saved model (in a new session) by passing the directory to the model_dir argument of the model constructor and use it for prediction or continue training:

library(tfestimators)
cols <- feature_columns( 
  column_numeric("disp", "cyl")
)
loaded_model <- linear_regressor(feature_columns = cols,
                                 model_dir = saved_model_dir)
loaded_model