diff --git a/R/datasets.R b/R/datasets.R
index d43dfc394..c1b63f4c2 100644
--- a/R/datasets.R
+++ b/R/datasets.R
@@ -6,8 +6,19 @@
 #' Dataset of 50,000 32x32 color training images, labeled over 10 categories,
 #' and 10,000 test images.
 #'
+#' @param convert When `TRUE` (default) the datasets are returned as R arrays.
+#' If `FALSE`, objects are returned as NumPy arrays.
+#'
 #' @returns Lists of training and test data: `train$x, train$y, test$x, test$y`.
 #'
+#' ```{r cifar10-str-true}
+#' str(dataset_cifar10())
+#' ```
+#'
+#' ```{r cifar10-str-false}
+#' str(dataset_cifar10(convert = FALSE))
+#' ```
+#'
 #' The `x` data is an array of RGB image data with shape (num_samples, 3, 32,
 #' 32).
 #'
@@ -17,8 +28,11 @@
 #' @family datasets
 #'
 #' @export
-dataset_cifar10 <- function() {
-  dataset <- keras$datasets$cifar10$load_data()
+dataset_cifar10 <- function(convert = TRUE) {
+  dataset <- call_dataset_loader(
+    loader = keras$datasets$cifar10$load_data,
+    convert = convert
+  )
   as_dataset_list(dataset)
 }
 
@@ -30,9 +44,18 @@ dataset_cifar10 <- function() {
 #' and 10,000 test images.
 #'
 #' @param label_mode one of "fine", "coarse".
+#' @inheritParams dataset_cifar10
 #'
 #' @returns Lists of training and test data: `train$x, train$y, test$x, test$y`.
 #'
+#' ```{r cifar100-str-true}
+#' str(dataset_cifar100())
+#' ```
+#'
+#' ```{r cifar100-str-false}
+#' str(dataset_cifar100(convert = FALSE))
+#' ```
+#'
 #' The `x` data is an array of RGB image data with shape (num_samples, 3, 32, 32).
 #'
 #' The `y` data is an array of category labels with shape (num_samples).
@@ -40,9 +63,13 @@ dataset_cifar10 <- function() {
 #' @family datasets
 #'
 #' @export
-dataset_cifar100 <- function(label_mode = c("fine", "coarse")) {
-  dataset <- keras$datasets$cifar100$load_data(
-    label_mode = match.arg(label_mode)
+dataset_cifar100 <- function(label_mode = c("fine", "coarse"), convert = TRUE) {
+  dataset <- call_dataset_loader(
+    loader = keras$datasets$cifar100$load_data,
+    convert = convert,
+    args = list(
+      label_mode = match.arg(label_mode)
+    )
   )
   as_dataset_list(dataset)
 }
@@ -75,9 +102,19 @@ dataset_cifar100 <- function(label_mode = c("fine", "coarse")) {
 #' @param oov_char Words that were cut out because of the `num_words` or
 #'   `skip_top` limit will be replaced with this character.
 #' @param index_from Index actual words with this index and higher.
+#' @inheritParams dataset_cifar10
 #'
 #' @returns Lists of training and test data: `train$x, train$y, test$x, test$y`.
 #'
+#' ```
+#' train/
+#' ├─ x
+#' └─ y
+#' test/
+#' ├─ x
+#' └─ y
+#' ```
+#'
 #'   The `x` data includes integer sequences. If the `num_words` argument was
 #'   specific, the maximum possible index value is `num_words-1`. If the
 #'   `maxlen` argument was specified, the largest possible sequence length is
@@ -85,6 +122,14 @@ dataset_cifar100 <- function(label_mode = c("fine", "coarse")) {
 #'
 #'   The `y` data includes a set of integer labels (0 or 1).
 #'
+#' ```{r imdb-str-true}
+#' str(dataset_imdb())
+#' ```
+#'
+#' ```{r imdb-str-false}
+#' str(dataset_imdb(convert = FALSE))
+#' ```
+#'
 #'   The `dataset_imdb_word_index()` function returns a list where the
 #'   names are words and the values are integer.
 #'
@@ -92,19 +137,24 @@ dataset_cifar100 <- function(label_mode = c("fine", "coarse")) {
 #'
 #' @export
 dataset_imdb <- function(path = "imdb.npz", num_words = NULL, skip_top = 0L, maxlen = NULL,
-                         seed = 113L, start_char = 1L, oov_char = 2L, index_from = 3L) {
-  dataset <- keras$datasets$imdb$load_data(
-    path = path,
-    num_words = as_nullable_integer(num_words),
-    skip_top = as.integer(skip_top),
-    maxlen = as_nullable_integer(maxlen),
-    seed = as.integer(seed),
-    start_char = as.integer(start_char),
-    oov_char = as.integer(oov_char),
-    index_from = as.integer(index_from)
+                         seed = 113L, start_char = 1L, oov_char = 2L, index_from = 3L,
+                         convert = TRUE) {
+  dataset <- call_dataset_loader(
+    loader = keras$datasets$imdb$load_data,
+    convert = convert,
+    args = list(
+      path = path,
+      num_words = as_nullable_integer(num_words),
+      skip_top = as.integer(skip_top),
+      maxlen = as_nullable_integer(maxlen),
+      seed = as.integer(seed),
+      start_char = as.integer(start_char),
+      oov_char = as.integer(oov_char),
+      index_from = as.integer(index_from)
+    )
   )
 
-  as_sequences_dataset_list(dataset)
+  as_sequences_dataset_list(dataset, convert = convert)
 
 }
 
@@ -136,30 +186,52 @@ dataset_imdb_word_index <- function(path = "imdb_word_index.json") {
 #' @param oov_char words that were cut out because of the `num_words` or
 #'   `skip_top` limit will be replaced with this character.
 #' @param index_from index actual words with this index and higher.
+#' @inheritParams dataset_cifar10
 #'
 #' @returns Lists of training and test data: `train$x, train$y, test$x, test$y`
 #'   with same format as [dataset_imdb()]. The `dataset_reuters_word_index()`
 #'   function returns a list where the names are words and the values are
 #'   integer. e.g. `word_index[["giraffe"]]` might return `1234`.
 #'
+#' ```
+#' train/
+#' ├─ x
+#' └─ y
+#' test/
+#' ├─ x
+#' └─ y
+#' ```
+#'
+#' ```{r reuters-str-true}
+#' str(dataset_reuters())
+#' ```
+#'
+#' ```{r reuters-str-false}
+#' str(dataset_reuters(convert = FALSE))
+#' ```
+#'
 #' @family datasets
 #'
 #' @export
 dataset_reuters <- function(path = "reuters.npz", num_words = NULL, skip_top = 0L, maxlen = NULL,
                             test_split = 0.2, seed = 113L, start_char = 1L, oov_char = 2L,
-                            index_from = 3L) {
-  dataset <- keras$datasets$reuters$load_data(
-    path = path,
-    num_words = as_nullable_integer(num_words),
-    skip_top = as.integer(skip_top),
-    maxlen = as_nullable_integer(maxlen),
-    test_split = test_split,
-    seed = as.integer(seed),
-    start_char = as.integer(start_char),
-    oov_char = as.integer(oov_char),
-    index_from = as.integer(index_from)
+                            index_from = 3L, convert = TRUE) {
+  dataset <- call_dataset_loader(
+    loader = keras$datasets$reuters$load_data,
+    convert = convert,
+    args = list(
+      path = path,
+      num_words = as_nullable_integer(num_words),
+      skip_top = as.integer(skip_top),
+      maxlen = as_nullable_integer(maxlen),
+      test_split = test_split,
+      seed = as.integer(seed),
+      start_char = as.integer(start_char),
+      oov_char = as.integer(oov_char),
+      index_from = as.integer(index_from)
+    )
   )
-  as_sequences_dataset_list(dataset)
+  as_sequences_dataset_list(dataset, convert = convert)
 }
 
 
@@ -176,16 +248,29 @@ dataset_reuters_word_index <- function(path = "reuters_word_index.pkl") {
 #' Dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images.
 #'
 #' @param path Path where to cache the dataset locally (relative to ~/.keras/datasets).
+#' @inheritParams dataset_cifar10
 #'
 #' @returns Lists of training and test data: `train$x, train$y, test$x, test$y`, where
 #'   `x` is an array of grayscale image data with shape (num_samples, 28, 28) and `y`
 #'   is an array of digit labels (integers in range 0-9) with shape (num_samples).
 #'
+#' ```{r mnist-str-true}
+#' str(dataset_mnist())
+#' ```
+#'
+#' ```{r mnist-str-false}
+#' str(dataset_mnist(convert = FALSE))
+#' ```
+#'
 #' @family datasets
 #'
 #' @export
-dataset_mnist <- function(path = "mnist.npz") {
-  dataset <- keras$datasets$mnist$load_data(path)
+dataset_mnist <- function(path = "mnist.npz", convert = TRUE) {
+  dataset <- call_dataset_loader(
+    loader = keras$datasets$mnist$load_data,
+    convert = convert,
+    args = list(path = path)
+  )
   as_dataset_list(dataset)
 }
 
@@ -224,18 +309,6 @@ dataset_mnist <- function(path = "mnist.npz") {
 #' values for block groups with few households and many empty houses,
 #' such as vacation resorts.
 #'
-#' @returns
-#' Nested list of arrays: `(x_train, y_train), (x_test, y_test)`.
-#'
-#' **`x_train`, `x_test`**: arrays with shape `(num_samples, 8)`
-#'   containing either the training samples (for `x_train`),
-#'   or test samples (for `y_train`).
-#'
-#' **`y_train`, `y_test`**: arrays of shape `(num_samples)`
-#'     containing the target scalars. The targets are float scalars
-#'     typically between 25,000 and 500,000 that represent
-#'     the home prices in dollars.
-#'
 #' @param version
 #' `"small"` or `"large"`. The small version
 #' contains 600 samples, the large version contains
@@ -254,15 +327,32 @@ dataset_mnist <- function(path = "mnist.npz") {
 #' Random seed for shuffling the data
 #' before computing the test split.
 #'
+#' @inheritParams dataset_cifar10
+#'
+#' @returns
+#' Nested list of arrays: `(x_train, y_train), (x_test, y_test)`.
+#'
+#' ```{r california-housing-str-true}
+#' str(dataset_california_housing())
+#' ```
+#'
+#' ```{r california-housing-str-false}
+#' str(dataset_california_housing(convert = FALSE))
+#' ```
+#'
 #' @export
 #' @family datasets
 #' @tether keras.datasets.california_housing.load_data
 dataset_california_housing <-
 function (version = "large", path = "california_housing.npz",
-    test_split = 0.2, seed = 113L)
+    test_split = 0.2, seed = 113L, convert = TRUE)
 {
-    args <- capture_args(list(seed = as_integer))
-    dataset <- do.call(keras$datasets$california_housing$load_data, args)
+    args <- capture_args(list(seed = as_integer), ignore = "convert")
+    dataset <- call_dataset_loader(
+      loader = keras$datasets$california_housing$load_data,
+      convert = convert,
+      args = args
+    )
     as_dataset_list(dataset)
 }
 
@@ -280,6 +370,14 @@ function (version = "large", path = "california_housing.npz",
 #'   `x` is an array of grayscale image data with shape (num_samples, 28, 28) and `y`
 #'   is an array of article labels (integers in range 0-9) with shape (num_samples).
 #'
+#' ```{r fashion-mnist-str-true}
+#' str(dataset_fashion_mnist())
+#' ```
+#'
+#' ```{r fashion-mnist-str-false}
+#' str(dataset_fashion_mnist(convert = FALSE))
+#' ```
+#'
 #' @details Dataset of 60,000 28x28 grayscale images of 10 fashion categories,
 #' along with a test set of 10,000 images. This dataset can be used as a drop-in
 #' replacement for MNIST. The class labels are:
@@ -294,12 +392,16 @@ function (version = "large", path = "california_housing.npz",
 #' * 7 - Sneaker
 #' * 8 - Bag
 #' * 9 - Ankle boot
+#' @inheritParams dataset_cifar10
 #'
 #' @family datasets
 #'
 #' @export
-dataset_fashion_mnist <- function() {
-  dataset <- keras$datasets$fashion_mnist$load_data()
+dataset_fashion_mnist <- function(convert = TRUE) {
+  dataset <- call_dataset_loader(
+    loader = keras$datasets$fashion_mnist$load_data,
+    convert = convert
+  )
   as_dataset_list(dataset)
 }
 
@@ -314,6 +416,7 @@ dataset_fashion_mnist <- function() {
 #' @param test_split fraction of the data to reserve as test set.
 #' @param seed Random seed for shuffling the data before computing the test
 #'   split.
+#' @inheritParams dataset_cifar10
 #'
 #' @returns Lists of training and test data: `train$x, train$y, test$x, test$y`.
 #'
@@ -321,14 +424,27 @@ dataset_fashion_mnist <- function() {
 #' the Boston suburbs in the late 1970s. Targets are the median values of the
 #' houses at a location (in k$).
 #'
+#' ```{r boston-housing-str-true}
+#' str(dataset_boston_housing())
+#' ```
+#'
+#' ```{r boston-housing-str-false}
+#' str(dataset_boston_housing(convert = FALSE))
+#' ```
+#'
 #' @family datasets
 #'
 #' @export
-dataset_boston_housing <- function(path = "boston_housing.npz", test_split = 0.2, seed = 113L) {
-  dataset <- keras$datasets$boston_housing$load_data(
-    path = path,
-    seed = as.integer(seed),
-    test_split = test_split
+dataset_boston_housing <- function(path = "boston_housing.npz", test_split = 0.2, seed = 113L,
+                                   convert = TRUE) {
+  dataset <- call_dataset_loader(
+    loader = keras$datasets$boston_housing$load_data,
+    convert = convert,
+    args = list(
+      path = path,
+      seed = as.integer(seed),
+      test_split = test_split
+    )
   )
   as_dataset_list(dataset)
 }
@@ -336,6 +452,16 @@ dataset_boston_housing <- function(path = "boston_housing.npz", test_split = 0.2
 
 
 
+call_dataset_loader <- function(loader, convert, args = list()) {
+  if (convert) {
+    return(do.call(loader, args))
+  }
+
+  dataset <- do.call(r_to_py(loader), args)
+  iterate(dataset, iterate, simplify = FALSE)
+}
+
+
 as_dataset_list <- function(dataset) {
   list(
     train = list(
@@ -349,15 +475,28 @@ as_dataset_list <- function(dataset) {
   )
 }
 
-as_sequences_dataset_list <- function(dataset) {
-  list(
-    train = list(
-      x = lapply(dataset[[1]][[1]], identity),
-      y = as.integer(dataset[[1]][[2]])
-    ),
-    test = list(
-      x = lapply(dataset[[2]][[1]], identity),
-      y = as.integer(dataset[[2]][[2]])
+as_sequences_dataset_list <- function(dataset, convert) {
+  if (convert) {
+    list(
+      train = list(
+        x = lapply(dataset[[1]][[1]], identity),
+        y = as.integer(dataset[[1]][[2]])
+      ),
+      test = list(
+        x = lapply(dataset[[2]][[1]], identity),
+        y = as.integer(dataset[[2]][[2]])
+      )
     )
-  )
+  } else {
+    list(
+      train = list(
+        x = dataset[[1]][[1]],
+        y = dataset[[1]][[2]]
+      ),
+      test = list(
+        x = dataset[[2]][[1]],
+        y = dataset[[2]][[2]]
+      )
+    )
+  }
 }
diff --git a/man/callback_reduce_lr_on_plateau.Rd b/man/callback_reduce_lr_on_plateau.Rd
index dc0459cdb..cefbd6318 100644
--- a/man/callback_reduce_lr_on_plateau.Rd
+++ b/man/callback_reduce_lr_on_plateau.Rd
@@ -10,7 +10,7 @@ callback_reduce_lr_on_plateau(
   patience = 10L,
   verbose = 0L,
   mode = "auto",
-  min_delta = 1e-04,
+  min_delta = 0.0001,
   cooldown = 0L,
   min_lr = 0,
   ...
diff --git a/man/dataset_boston_housing.Rd b/man/dataset_boston_housing.Rd
index 05df5a5e5..38b151320 100644
--- a/man/dataset_boston_housing.Rd
+++ b/man/dataset_boston_housing.Rd
@@ -7,7 +7,8 @@
 dataset_boston_housing(
   path = "boston_housing.npz",
   test_split = 0.2,
-  seed = 113L
+  seed = 113L,
+  convert = TRUE
 )
 }
 \arguments{
@@ -18,6 +19,9 @@ dataset_boston_housing(
 
 \item{seed}{Random seed for shuffling the data before computing the test
 split.}
+
+\item{convert}{When \code{TRUE} (default) the datasets are returned as R arrays.
+If \code{FALSE}, objects are returned as NumPy arrays.}
 }
 \value{
 Lists of training and test data: \verb{train$x, train$y, test$x, test$y}.
@@ -25,6 +29,32 @@ Lists of training and test data: \verb{train$x, train$y, test$x, test$y}.
 Samples contain 13 attributes of houses at different locations around
 the Boston suburbs in the late 1970s. Targets are the median values of the
 houses at a location (in k$).
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_boston_housing())
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: num [1:404, 1:13] 1.2325 0.0218 4.8982 0.0396 3.6931 ...
+##   ..$ y: num [1:404(1d)] 15.2 42.3 50 21.1 17.7 18.5 11.3 15.6 15.6 14.4 ...
+##  $ test :List of 2
+##   ..$ x: num [1:102, 1:13] 18.0846 0.1233 0.055 1.2735 0.0715 ...
+##   ..$ y: num [1:102(1d)] 7.2 18.8 19 27 22.2 24.5 31.2 22.9 20.5 23.2 ...
+
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_boston_housing(convert = FALSE))
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: <numpy.ndarray shape(404,13), dtype=float64>
+##   ..$ y: <numpy.ndarray shape(404), dtype=float64>
+##  $ test :List of 2
+##   ..$ x: <numpy.ndarray shape(102,13), dtype=float64>
+##   ..$ y: <numpy.ndarray shape(102), dtype=float64>
+
+}\if{html}{\out{</div>}}
 }
 \description{
 Dataset taken from the StatLib library which is maintained at Carnegie Mellon
diff --git a/man/dataset_california_housing.Rd b/man/dataset_california_housing.Rd
index d77b77369..3ac4968ac 100644
--- a/man/dataset_california_housing.Rd
+++ b/man/dataset_california_housing.Rd
@@ -8,7 +8,8 @@ dataset_california_housing(
   version = "large",
   path = "california_housing.npz",
   test_split = 0.2,
-  seed = 113L
+  seed = 113L,
+  convert = TRUE
 )
 }
 \arguments{
@@ -25,18 +26,38 @@ deprecated \code{boston_housing} dataset.}
 
 \item{seed}{Random seed for shuffling the data
 before computing the test split.}
+
+\item{convert}{When \code{TRUE} (default) the datasets are returned as R arrays.
+If \code{FALSE}, objects are returned as NumPy arrays.}
 }
 \value{
 Nested list of arrays: \verb{(x_train, y_train), (x_test, y_test)}.
 
-\strong{\code{x_train}, \code{x_test}}: arrays with shape \verb{(num_samples, 8)}
-containing either the training samples (for \code{x_train}),
-or test samples (for \code{y_train}).
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_california_housing())
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: num [1:16512, 1:8] -118 -118 -122 -118 -123 ...
+##   ..$ y: num [1:16512(1d)] 252300 146900 290900 141300 500001 ...
+##  $ test :List of 2
+##   ..$ x: num [1:4128, 1:8] -118 -120 -121 -122 -117 ...
+##   ..$ y: num [1:4128(1d)] 397900 227900 172100 186500 148900 ...
+
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_california_housing(convert = FALSE))
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: <numpy.ndarray shape(16512,8), dtype=float32>
+##   ..$ y: <numpy.ndarray shape(16512), dtype=float32>
+##  $ test :List of 2
+##   ..$ x: <numpy.ndarray shape(4128,8), dtype=float32>
+##   ..$ y: <numpy.ndarray shape(4128), dtype=float32>
 
-\strong{\code{y_train}, \code{y_test}}: arrays of shape \code{(num_samples)}
-containing the target scalars. The targets are float scalars
-typically between 25,000 and 500,000 that represent
-the home prices in dollars.
+}\if{html}{\out{</div>}}
 }
 \description{
 This dataset was obtained from the \href{https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html}{StatLib repository}.
diff --git a/man/dataset_cifar10.Rd b/man/dataset_cifar10.Rd
index 1d0c29857..dd8a77ea1 100644
--- a/man/dataset_cifar10.Rd
+++ b/man/dataset_cifar10.Rd
@@ -4,11 +4,41 @@
 \alias{dataset_cifar10}
 \title{CIFAR10 small image classification}
 \usage{
-dataset_cifar10()
+dataset_cifar10(convert = TRUE)
+}
+\arguments{
+\item{convert}{When \code{TRUE} (default) the datasets are returned as R arrays.
+If \code{FALSE}, objects are returned as NumPy arrays.}
 }
 \value{
 Lists of training and test data: \verb{train$x, train$y, test$x, test$y}.
 
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_cifar10())
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: int [1:50000, 1:32, 1:32, 1:3] 59 154 255 28 170 159 164 28 134 125 ...
+##   ..$ y: int [1:50000, 1] 6 9 9 4 1 1 2 7 8 3 ...
+##  $ test :List of 2
+##   ..$ x: int [1:10000, 1:32, 1:32, 1:3] 158 235 158 155 65 179 160 83 23 217 ...
+##   ..$ y: int [1:10000, 1] 3 8 8 0 6 6 1 6 3 1 ...
+
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_cifar10(convert = FALSE))
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: <numpy.ndarray shape(50000,32,32,3), dtype=uint8>
+##   ..$ y: <numpy.ndarray shape(50000,1), dtype=uint8>
+##  $ test :List of 2
+##   ..$ x: <numpy.ndarray shape(10000,32,32,3), dtype=uint8>
+##   ..$ y: <numpy.ndarray shape(10000,1), dtype=uint8>
+
+}\if{html}{\out{</div>}}
+
 The \code{x} data is an array of RGB image data with shape (num_samples, 3, 32,
 32).
 
diff --git a/man/dataset_cifar100.Rd b/man/dataset_cifar100.Rd
index c7b429b9b..278ace00d 100644
--- a/man/dataset_cifar100.Rd
+++ b/man/dataset_cifar100.Rd
@@ -4,14 +4,43 @@
 \alias{dataset_cifar100}
 \title{CIFAR100 small image classification}
 \usage{
-dataset_cifar100(label_mode = c("fine", "coarse"))
+dataset_cifar100(label_mode = c("fine", "coarse"), convert = TRUE)
 }
 \arguments{
 \item{label_mode}{one of "fine", "coarse".}
+
+\item{convert}{When \code{TRUE} (default) the datasets are returned as R arrays.
+If \code{FALSE}, objects are returned as NumPy arrays.}
 }
 \value{
 Lists of training and test data: \verb{train$x, train$y, test$x, test$y}.
 
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_cifar100())
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: int [1:50000, 1:32, 1:32, 1:3] 255 255 250 124 43 190 50 178 122 255 ...
+##   ..$ y: num [1:50000, 1] 19 29 0 11 1 86 90 28 23 31 ...
+##  $ test :List of 2
+##   ..$ x: int [1:10000, 1:32, 1:32, 1:3] 199 113 61 93 80 168 37 175 233 182 ...
+##   ..$ y: num [1:10000, 1] 49 33 72 51 71 92 15 14 23 0 ...
+
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_cifar100(convert = FALSE))
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: <numpy.ndarray shape(50000,32,32,3), dtype=uint8>
+##   ..$ y: <numpy.ndarray shape(50000,1), dtype=int64>
+##  $ test :List of 2
+##   ..$ x: <numpy.ndarray shape(10000,32,32,3), dtype=uint8>
+##   ..$ y: <numpy.ndarray shape(10000,1), dtype=int64>
+
+}\if{html}{\out{</div>}}
+
 The \code{x} data is an array of RGB image data with shape (num_samples, 3, 32, 32).
 
 The \code{y} data is an array of category labels with shape (num_samples).
diff --git a/man/dataset_fashion_mnist.Rd b/man/dataset_fashion_mnist.Rd
index ade51bb97..c0d06a4fe 100644
--- a/man/dataset_fashion_mnist.Rd
+++ b/man/dataset_fashion_mnist.Rd
@@ -4,12 +4,42 @@
 \alias{dataset_fashion_mnist}
 \title{Fashion-MNIST database of fashion articles}
 \usage{
-dataset_fashion_mnist()
+dataset_fashion_mnist(convert = TRUE)
+}
+\arguments{
+\item{convert}{When \code{TRUE} (default) the datasets are returned as R arrays.
+If \code{FALSE}, objects are returned as NumPy arrays.}
 }
 \value{
 Lists of training and test data: \verb{train$x, train$y, test$x, test$y}, where
 \code{x} is an array of grayscale image data with shape (num_samples, 28, 28) and \code{y}
 is an array of article labels (integers in range 0-9) with shape (num_samples).
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_fashion_mnist())
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: int [1:60000, 1:28, 1:28] 0 0 0 0 0 0 0 0 0 0 ...
+##   ..$ y: int [1:60000(1d)] 9 0 0 3 0 2 7 2 5 5 ...
+##  $ test :List of 2
+##   ..$ x: int [1:10000, 1:28, 1:28] 0 0 0 0 0 0 0 0 0 0 ...
+##   ..$ y: int [1:10000(1d)] 9 2 1 1 6 1 4 6 5 7 ...
+
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_fashion_mnist(convert = FALSE))
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: <numpy.ndarray shape(60000,28,28), dtype=uint8>
+##   ..$ y: <numpy.ndarray shape(60000), dtype=uint8>
+##  $ test :List of 2
+##   ..$ x: <numpy.ndarray shape(10000,28,28), dtype=uint8>
+##   ..$ y: <numpy.ndarray shape(10000), dtype=uint8>
+
+}\if{html}{\out{</div>}}
 }
 \description{
 Dataset of 60,000 28x28 grayscale images of the 10 fashion article classes,
diff --git a/man/dataset_imdb.Rd b/man/dataset_imdb.Rd
index ea71f2190..fc8b5d299 100644
--- a/man/dataset_imdb.Rd
+++ b/man/dataset_imdb.Rd
@@ -13,7 +13,8 @@ dataset_imdb(
   seed = 113L,
   start_char = 1L,
   oov_char = 2L,
-  index_from = 3L
+  index_from = 3L,
+  convert = TRUE
 )
 
 dataset_imdb_word_index(path = "imdb_word_index.json")
@@ -39,10 +40,21 @@ Set to 1 because 0 is usually the padding character.}
 \code{skip_top} limit will be replaced with this character.}
 
 \item{index_from}{Index actual words with this index and higher.}
+
+\item{convert}{When \code{TRUE} (default) the datasets are returned as R arrays.
+If \code{FALSE}, objects are returned as NumPy arrays.}
 }
 \value{
 Lists of training and test data: \verb{train$x, train$y, test$x, test$y}.
 
+\if{html}{\out{<div class="sourceCode">}}\preformatted{train/
+├─ x
+└─ y
+test/
+├─ x
+└─ y
+}\if{html}{\out{</div>}}
+
 The \code{x} data includes integer sequences. If the \code{num_words} argument was
 specific, the maximum possible index value is \code{num_words-1}. If the
 \code{maxlen} argument was specified, the largest possible sequence length is
@@ -50,6 +62,46 @@ specific, the maximum possible index value is \code{num_words-1}. If the
 
 The \code{y} data includes a set of integer labels (0 or 1).
 
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_imdb())
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x:List of 25000
+##   .. ..$ : int [1:218] 1 14 22 16 43 530 973 1622 1385 65 ...
+##   .. ..$ : int [1:189] 1 194 1153 194 8255 78 228 5 6 1463 ...
+##   .. ..$ : int [1:141] 1 14 47 8 30 31 7 4 249 108 ...
+##   .. ..$ : int [1:550] 1 4 18609 16085 33 2804 4 2040 432 111 ...
+##   .. ..$ : int [1:147] 1 249 1323 7 61 113 10 10 13 1637 ...
+##   .. ..$ : int [1:43] 1 778 128 74 12 630 163 15 4 1766 ...
+##   .. .. [list output truncated]
+##   ..$ y: int [1:25000] 1 0 0 1 0 0 1 0 1 0 ...
+##  $ test :List of 2
+##   ..$ x:List of 25000
+##   .. ..$ : int [1:68] 1 591 202 14 31 6 717 10 10 18142 ...
+##   .. ..$ : int [1:260] 1 14 22 3443 6 176 7 5063 88 12 ...
+##   .. ..$ : int [1:603] 1 111 748 4368 1133 33782 24563 4 87 1551 ...
+##   .. ..$ : int [1:181] 1 13 1228 119 14 552 7 20 190 14 ...
+##   .. ..$ : int [1:108] 1 40 49 85 84 1040 146 6 783 254 ...
+##   .. ..$ : int [1:132] 1 146 427 5718 14 20 218 112 2962 32 ...
+##   .. .. [list output truncated]
+##   ..$ y: int [1:25000] 0 1 1 0 1 1 1 0 0 1 ...
+
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_imdb(convert = FALSE))
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: <numpy.ndarray shape(25000), dtype=object>
+##   ..$ y: <numpy.ndarray shape(25000), dtype=int64>
+##  $ test :List of 2
+##   ..$ x: <numpy.ndarray shape(25000), dtype=object>
+##   ..$ y: <numpy.ndarray shape(25000), dtype=int64>
+
+}\if{html}{\out{</div>}}
+
 The \code{dataset_imdb_word_index()} function returns a list where the
 names are words and the values are integer.
 }
diff --git a/man/dataset_mnist.Rd b/man/dataset_mnist.Rd
index 50b56f4d1..5c3815579 100644
--- a/man/dataset_mnist.Rd
+++ b/man/dataset_mnist.Rd
@@ -4,15 +4,44 @@
 \alias{dataset_mnist}
 \title{MNIST database of handwritten digits}
 \usage{
-dataset_mnist(path = "mnist.npz")
+dataset_mnist(path = "mnist.npz", convert = TRUE)
 }
 \arguments{
 \item{path}{Path where to cache the dataset locally (relative to ~/.keras/datasets).}
+
+\item{convert}{When \code{TRUE} (default) the datasets are returned as R arrays.
+If \code{FALSE}, objects are returned as NumPy arrays.}
 }
 \value{
 Lists of training and test data: \verb{train$x, train$y, test$x, test$y}, where
 \code{x} is an array of grayscale image data with shape (num_samples, 28, 28) and \code{y}
 is an array of digit labels (integers in range 0-9) with shape (num_samples).
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_mnist())
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: int [1:60000, 1:28, 1:28] 0 0 0 0 0 0 0 0 0 0 ...
+##   ..$ y: int [1:60000(1d)] 5 0 4 1 9 2 1 3 1 4 ...
+##  $ test :List of 2
+##   ..$ x: int [1:10000, 1:28, 1:28] 0 0 0 0 0 0 0 0 0 0 ...
+##   ..$ y: int [1:10000(1d)] 7 2 1 0 4 1 4 9 5 9 ...
+
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_mnist(convert = FALSE))
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: <numpy.ndarray shape(60000,28,28), dtype=uint8>
+##   ..$ y: <numpy.ndarray shape(60000), dtype=uint8>
+##  $ test :List of 2
+##   ..$ x: <numpy.ndarray shape(10000,28,28), dtype=uint8>
+##   ..$ y: <numpy.ndarray shape(10000), dtype=uint8>
+
+}\if{html}{\out{</div>}}
 }
 \description{
 Dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images.
diff --git a/man/dataset_reuters.Rd b/man/dataset_reuters.Rd
index 7a79bc0ca..e07172675 100644
--- a/man/dataset_reuters.Rd
+++ b/man/dataset_reuters.Rd
@@ -14,7 +14,8 @@ dataset_reuters(
   seed = 113L,
   start_char = 1L,
   oov_char = 2L,
-  index_from = 3L
+  index_from = 3L,
+  convert = TRUE
 )
 
 dataset_reuters_word_index(path = "reuters_word_index.pkl")
@@ -42,12 +43,63 @@ Set to 1 because 0 is usually the padding character.}
 \code{skip_top} limit will be replaced with this character.}
 
 \item{index_from}{index actual words with this index and higher.}
+
+\item{convert}{When \code{TRUE} (default) the datasets are returned as R arrays.
+If \code{FALSE}, objects are returned as NumPy arrays.}
 }
 \value{
 Lists of training and test data: \verb{train$x, train$y, test$x, test$y}
 with same format as \code{\link[=dataset_imdb]{dataset_imdb()}}. The \code{dataset_reuters_word_index()}
 function returns a list where the names are words and the values are
 integer. e.g. \code{word_index[["giraffe"]]} might return \code{1234}.
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{train/
+├─ x
+└─ y
+test/
+├─ x
+└─ y
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_reuters())
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x:List of 8982
+##   .. ..$ : int [1:87] 1 27595 28842 8 43 10 447 5 25 207 ...
+##   .. ..$ : int [1:56] 1 3267 699 3434 2295 56 16784 7511 9 56 ...
+##   .. ..$ : int [1:139] 1 53 12 284 15 14 272 26 53 959 ...
+##   .. ..$ : int [1:224] 1 4 686 867 558 4 37 38 309 2276 ...
+##   .. ..$ : int [1:101] 1 8295 111 8 25 166 40 638 10 436 ...
+##   .. ..$ : int [1:116] 1 4 37 38 309 213 349 1632 48 193 ...
+##   .. .. [list output truncated]
+##   ..$ y: int [1:8982] 3 4 3 4 4 4 4 3 3 16 ...
+##  $ test :List of 2
+##   ..$ x:List of 2246
+##   .. ..$ : int [1:145] 1 4 1378 2025 9 697 4622 111 8 25 ...
+##   .. ..$ : int [1:745] 1 2768 283 122 7 4 89 544 463 29 ...
+##   .. ..$ : int [1:228] 1 4 309 2276 4759 5 2015 403 1920 33 ...
+##   .. ..$ : int [1:172] 1 11786 13716 65 9 249 1096 8 16 515 ...
+##   .. ..$ : int [1:187] 1 470 354 18270 4231 62 2373 509 1687 5138 ...
+##   .. ..$ : int [1:80] 1 53 134 26 14 102 26 39 5150 18 ...
+##   .. .. [list output truncated]
+##   ..$ y: int [1:2246] 3 10 1 4 4 3 3 3 3 3 ...
+
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_reuters(convert = FALSE))
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
+##  $ train:List of 2
+##   ..$ x: <numpy.ndarray shape(8982), dtype=object>
+##   ..$ y: <numpy.ndarray shape(8982), dtype=int64>
+##  $ test :List of 2
+##   ..$ x: <numpy.ndarray shape(2246), dtype=object>
+##   ..$ y: <numpy.ndarray shape(2246), dtype=int64>
+
+}\if{html}{\out{</div>}}
 }
 \description{
 Dataset of 11,228 newswires from Reuters, labeled over 46 topics. As with
diff --git a/man/layer_tfsm.Rd b/man/layer_tfsm.Rd
index ba6b79213..ef9337506 100644
--- a/man/layer_tfsm.Rd
+++ b/man/layer_tfsm.Rd
@@ -59,8 +59,8 @@ model |> export_savedmodel("path/to/artifact")
 ## Output Type:
 ##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None)
 ## Captures:
-##   127701998122768: TensorSpec(shape=(), dtype=tf.resource, name=None)
-##   127701998119504: TensorSpec(shape=(), dtype=tf.resource, name=None)
+##   131333390867856: TensorSpec(shape=(), dtype=tf.resource, name=None)
+##   131333390868240: TensorSpec(shape=(), dtype=tf.resource, name=None)
 
 }\if{html}{\out{</div>}}
 
diff --git a/man/learning_rate_schedule_polynomial_decay.Rd b/man/learning_rate_schedule_polynomial_decay.Rd
index 327b60e5c..cccccea16 100644
--- a/man/learning_rate_schedule_polynomial_decay.Rd
+++ b/man/learning_rate_schedule_polynomial_decay.Rd
@@ -7,7 +7,7 @@
 learning_rate_schedule_polynomial_decay(
   initial_learning_rate,
   decay_steps,
-  end_learning_rate = 1e-04,
+  end_learning_rate = 0.0001,
   power = 1,
   cycle = FALSE,
   name = "PolynomialDecay"
diff --git a/man/roxygen/meta.R b/man/roxygen/meta.R
index 9b0fb06f7..afb267bb7 100644
--- a/man/roxygen/meta.R
+++ b/man/roxygen/meta.R
@@ -13,6 +13,15 @@ local({
   }
   if (isNamespaceLoaded('roxygen2')) register_tether_tag_parser()
   else setHook(packageEvent("roxygen2", "onLoad"), register_tether_tag_parser)
+
+  options(
+    paged.print = FALSE,
+    str = utils::strOptions(list.len = 6),
+    width = 76,
+    scipen = 1, # default ==0, positive valus bias fixed, negative bias scientific
+    keras.plot.history.theme_bw = TRUE,
+    pillar.print_min = 5
+  )
 })
 
 local({