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## ----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)
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