## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)

## -----------------------------------------------------------------------------
# input <- input_fn(mtcars,
#                   features = c("drat", "mpg", "am"),
#                   response = "vs",
#                   batch_size = 128,
#                   epochs = 3)

## -----------------------------------------------------------------------------
# input <- input_fn(vs ~ drat + mpg + am, data = mtcars,
#                   batch_size = 128,
#                   epochs = 3)

## -----------------------------------------------------------------------------
# cols <- feature_columns(
#   column_numeric("drat"),
#   column_indicator("am")
# )

## -----------------------------------------------------------------------------
# # construct feature columns
# linear_feature_columns <- feature_columns(column_numeric("mpg"))
# dnn_feature_columns <- feature_columns(column_numeric("drat"))
# 
# # generate classifier
# classifier <-
# 	dnn_linear_combined_classifier(
# 	  linear_feature_columns = linear_feature_columns,
# 	  dnn_feature_columns = dnn_feature_columns,
# 	  dnn_hidden_units = c(3, 3),
# 	  dnn_optimizer = "Adagrad"
# 	)

## -----------------------------------------------------------------------------
# classifier %>%
#   train(input_fn = input, steps = 2)

## -----------------------------------------------------------------------------
# predictions <- predict(classifier, input_fn = input)

## -----------------------------------------------------------------------------
# predictions <- predict(
#   classifier,
#   input_fn = input,
#   predict_keys = "probabilities")

## -----------------------------------------------------------------------------
# predictions <- predict(
#   classifier,
#   input_fn = input,
#   predict_keys = "logistic")

## -----------------------------------------------------------------------------
# saved_model_dir <- model_dir(classifier)

## -----------------------------------------------------------------------------
# library(tfestimators)
# linear_feature_columns <- feature_columns(column_numeric("mpg"))
# dnn_feature_columns <- feature_columns(column_numeric("drat"))
# 
# loaded_model <-
# 	dnn_linear_combined_classifier(
# 	  linear_feature_columns = linear_feature_columns,
# 	  dnn_feature_columns = dnn_feature_columns,
# 	  dnn_hidden_units = c(3, 3),
# 	  dnn_optimizer = "Adagrad",
# 	  model_dir = saved_model_dir
# 	)
# loaded_model