## ----setup, include = FALSE---------------------------------------------------
library(keras)
knitr::opts_chunk$set(comment = NA, eval = FALSE)

## -----------------------------------------------------------------------------
# library(keras)
# 
# keras_model_simple_mlp <- function(num_classes,
#                                    use_bn = FALSE, use_dp = FALSE,
#                                    name = NULL) {
# 
#   # define and return a custom model
#   keras_model_custom(name = name, function(self) {
# 
#     # create layers we'll need for the call (this code executes once)
#     self$dense1 <- layer_dense(units = 32, activation = "relu")
#     self$dense2 <- layer_dense(units = num_classes, activation = "softmax")
#     if (use_dp)
#       self$dp <- layer_dropout(rate = 0.5)
#     if (use_bn)
#       self$bn <- layer_batch_normalization(axis = -1)
# 
#     # implement call (this code executes during training & inference)
#     function(inputs, mask = NULL, training = FALSE) {
#       x <- self$dense1(inputs)
#       if (use_dp)
#         x <- self$dp(x)
#       if (use_bn)
#         x <- self$bn(x)
#       self$dense2(x)
#     }
#   })
# }

## -----------------------------------------------------------------------------
# library(keras)
# 
# # create the model
# model <- keras_model_simple_mlp(num_classes = 10, use_dp = TRUE)
# 
# # compile graph
# model %>% compile(
#   loss = 'categorical_crossentropy',
#   optimizer = optimizer_rmsprop(),
#   metrics = c('accuracy')
# )
# 
# # Generate dummy data
# data <- matrix(runif(1000*100), nrow = 1000, ncol = 100)
# labels <- matrix(round(runif(1000, min = 0, max = 9)), nrow = 1000, ncol = 1)
# 
# # Convert labels to categorical one-hot encoding
# one_hot_labels <- to_categorical(labels, num_classes = 10)
# 
# # Train the model
# model %>% fit(data, one_hot_labels, epochs=10, batch_size=32)