fit_k_ontram_augmented_data: Function for estimating a 'k_ontram' or 'k_ontram_ci' model...

In LucasKookUZH/ontram-pkg: Ordinal transformation model neural networks

Function for estimating a k_ontram or k_ontram_ci model with augmented images as input

Description

Function for estimating a k_ontram or k_ontram_ci model with augmented images as input

Usage

fit_k_ontram_augmented_data(
  m,
  im_train,
  im_val = NULL,
  x_train = NULL,
  x_val = NULL,
  y_train,
  y_val = NULL,
  generator,
  epochs = 10,
  mim_as_mbl = FALSE,
  bs = 32,
  history = TRUE,
  save_best_only = TRUE,
  patience = 1,
  filepath = NULL
)

Examples

mnist <- dataset_mnist()
c(c(x_train, y_train), c(x_val, y_val)) %<-% mnist
y_train <- to_categorical(y_train)
y_val <- to_categorical(y_val)
x_train <- array_reshape(x_train, c(60000, 28, 28, 1))
x_val <- array_reshape(x_val, c(10000, 28, 28, 1))
x_train <- x_train / 255
x_val <- x_val / 255
nim_train <- 100
nim_val <- 50
x_train <- x_train[1:nim_train, , , , drop = FALSE]
y_train <- y_train[1:nim_train, , drop = FALSE]
x_val <- x_val[1:nim_val, , , , drop = FALSE]
y_val <- y_val[1:nim_val, , drop = FALSE]

mbl <- k_mod_baseline(ncol(y_train))
mim <-  keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu",
                input_shape = c(28, 28, 1)) %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_flatten() %>%
  layer_dense(units = 32, activation = "relu") %>%
  layer_dense(units = 1L)
m <- k_ontram(mbl, mim)
compile(m, optimizer = optimizer_adam(learning_rate = 10^-4), loss = k_ontram_loss(ncol(y_train)))

datagen <- image_data_generator(
  rotation_range = 20,
  width_shift_range = 0.2,
  height_shift_range = 0.2,
  shear_range = 0.15,
  zoom_range = 0.15,
  fill_mode = "nearest"
)

f <- fit_k_ontram_augmented_data(m = m, im_train = x_train, im_val = x_val, y_train = y_train, y_val = y_val,
                                    generator = datagen, epochs = 10, mim_as_mbl = FALSE, bs = 32,
                                    history = TRUE, save_best_only = TRUE)
f$hist
f$best_epoch

LucasKookUZH/ontram-pkg documentation built on March 27, 2023, 6:05 p.m.