From 74b1296d23a31583286894853a8529bf1c860e37 Mon Sep 17 00:00:00 2001
From: davidrsch <daviddrsch@gmail.com>
Date: Thu, 4 Sep 2025 11:58:19 +0200
Subject: [PATCH 1/4] Updating docs to fix issue with checks

---
 NAMESPACE                           |   1 +
 R/compile_keras_grid.R              | 150 ++++++++++++++++++++++++----
 R/create_keras_functional_spec.R    |   1 +
 R/create_keras_sequential_spec.R    |   5 +-
 R/generic_functional_fit.R          |   9 +-
 R/generic_sequential_fit.R          |   9 +-
 R/keras_tools.R                     |  32 ++++--
 R/remove_keras_spec.R               |   2 +-
 R/utils.R                           |  27 +++++
 man/compile_keras_grid.Rd           |  35 +++++--
 man/create_keras_functional_spec.Rd |   1 +
 man/create_keras_sequential_spec.Rd |   5 +-
 man/extract_valid_grid.Rd           |  58 +++++++++--
 man/generic_functional_fit.Rd       |   9 +-
 man/generic_sequential_fit.Rd       |   9 +-
 man/inform_errors.Rd                |  57 ++++++++++-
 man/keras_evaluate.Rd               |  32 ++++--
 man/model_exists.Rd                 |  33 ++++++
 man/remove_keras_spec.Rd            |   2 +-
 19 files changed, 401 insertions(+), 76 deletions(-)
 create mode 100644 man/model_exists.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 385f59f..46d821b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -19,6 +19,7 @@ export(keras_losses)
 export(keras_metrics)
 export(keras_optimizers)
 export(loss_function_keras)
+export(model_exists)
 export(optimizer_function)
 export(process_x_functional)
 export(process_x_sequential)
diff --git a/R/compile_keras_grid.R b/R/compile_keras_grid.R
index 259db24..a1d7457 100644
--- a/R/compile_keras_grid.R
+++ b/R/compile_keras_grid.R
@@ -42,35 +42,49 @@
 #' @examples
 #' \donttest{
 #' if (requireNamespace("keras3", quietly = TRUE)) {
+#' library(keras3)
+#' library(parsnip)
+#' library(dials)
 #'
-#' # 1. Define a kerasnip model specification
+#' # 1. Define layer blocks
+#' input_block <- function(model, input_shape) {
+#'   keras_model_sequential(input_shape = input_shape)
+#' }
+#' hidden_block <- function(model, units = 32) {
+#'   model |> layer_dense(units = units, activation = "relu")
+#' }
+#' output_block <- function(model, num_classes) {
+#'   model |> layer_dense(units = num_classes, activation = "softmax")
+#' }
+#'
+#' # 2. Define a kerasnip model specification
 #' create_keras_sequential_spec(
-#'   model_name = "my_mlp",
+#'   model_name = "my_mlp_grid",
 #'   layer_blocks = list(
-#'     input_block,
-#'     hidden_block,
-#'     output_block
+#'     input = input_block,
+#'     hidden = hidden_block,
+#'     output = output_block
 #'   ),
 #'   mode = "classification"
 #' )
 #'
-#' mlp_spec <- my_mlp(
+#' mlp_spec <- my_mlp_grid(
 #'   hidden_units = tune(),
 #'   compile_loss = "categorical_crossentropy",
 #'   compile_optimizer = "adam"
 #' )
 #'
-#' # 2. Create a hyperparameter grid
+#' # 3. Create a hyperparameter grid
 #' # Include an invalid value (-10) to demonstrate error handling
 #' param_grid <- tibble::tibble(
 #'   hidden_units = c(32, 64, -10)
 #' )
 #'
-#' # 3. Prepare dummy data
+#' # 4. Prepare dummy data
 #' x_train <- matrix(rnorm(100 * 10), ncol = 10)
 #' y_train <- factor(sample(0:1, 100, replace = TRUE))
 #'
-#' # 4. Compile models over the grid
+#' # 5. Compile models over the grid
 #' compiled_grid <- compile_keras_grid(
 #'   spec = mlp_spec,
 #'   grid = param_grid,
@@ -79,8 +93,9 @@
 #' )
 #'
 #' print(compiled_grid)
+#' remove_keras_spec("my_mlp_grid")
 #'
-#' # 5. Inspect the results
+#' # 6. Inspect the results
 #' # The row with `hidden_units = -10` will show an error.
 #' }
 #' }
@@ -194,15 +209,61 @@ compile_keras_grid <- function(spec, grid, x, y) {
 #'
 #' @examples
 #' \donttest{
-#' # Continuing the example from `compile_keras_grid`:
+#' if (requireNamespace("keras3", quietly = TRUE)) {
+#'   library(keras3)
+#'   library(parsnip)
+#'   library(dials)
+#'
+#'   # 1. Define layer blocks
+#'   input_block <- function(model, input_shape) {
+#'     keras_model_sequential(input_shape = input_shape)
+#'   }
+#'   hidden_block <- function(model, units = 32) {
+#'     model |> layer_dense(units = units, activation = "relu")
+#'   }
+#'   output_block <- function(model, num_classes) {
+#'     model |> layer_dense(units = num_classes, activation = "softmax")
+#'   }
+#'
+#'   # 2. Define a kerasnip model specification
+#'   create_keras_sequential_spec(
+#'     model_name = "my_mlp_grid_2",
+#'     layer_blocks = list(
+#'       input = input_block,
+#'       hidden = hidden_block,
+#'       output = output_block
+#'     ),
+#'     mode = "classification"
+#'   )
+#'
+#'   mlp_spec <- my_mlp_grid_2(
+#'     hidden_units = tune(),
+#'     compile_loss = "categorical_crossentropy",
+#'     compile_optimizer = "adam"
+#'   )
+#'
+#'   # 3. Create a hyperparameter grid
+#'   param_grid <- tibble::tibble(
+#'     hidden_units = c(32, 64, -10)
+#'   )
 #'
-#' # `compiled_grid` contains one row with an error.
-#' valid_grid <- extract_valid_grid(compiled_grid)
+#'   # 4. Prepare dummy data
+#'   x_train <- matrix(rnorm(100 * 10), ncol = 10)
+#'   y_train <- factor(sample(0:1, 100, replace = TRUE))
 #'
-#' # `valid_grid` now only contains the rows that compiled successfully.
-#' print(valid_grid)
+#'   # 5. Compile models over the grid
+#'   compiled_grid <- compile_keras_grid(
+#'     spec = mlp_spec,
+#'     grid = param_grid,
+#'     x = x_train,
+#'     y = y_train
+#'   )
 #'
-#' # This clean grid can now be passed to tune::tune_grid().
+#'   # 6. Extract the valid grid
+#'   valid_grid <- extract_valid_grid(compiled_grid)
+#'   print(valid_grid)
+#'   remove_keras_spec("my_mlp_grid_2")
+#' }
 #' }
 #' @export
 extract_valid_grid <- function(compiled_grid) {
@@ -242,11 +303,60 @@ extract_valid_grid <- function(compiled_grid) {
 #'
 #' @examples
 #' \donttest{
-#' # Continuing the example from `compile_keras_grid`:
+#' if (requireNamespace("keras3", quietly = TRUE)) {
+#'   library(keras3)
+#'   library(parsnip)
+#'   library(dials)
+#'
+#'   # 1. Define layer blocks
+#'   input_block <- function(model, input_shape) {
+#'     keras_model_sequential(input_shape = input_shape)
+#'   }
+#'   hidden_block <- function(model, units = 32) {
+#'     model |> layer_dense(units = units, activation = "relu")
+#'   }
+#'   output_block <- function(model, num_classes) {
+#'     model |> layer_dense(units = num_classes, activation = "softmax")
+#'   }
 #'
-#' # `compiled_grid` contains one row with an error.
-#' # This will print a formatted summary of that error.
-#' inform_errors(compiled_grid)
+#'   # 2. Define a kerasnip model specification
+#'   create_keras_sequential_spec(
+#'     model_name = "my_mlp_grid_3",
+#'     layer_blocks = list(
+#'       input = input_block,
+#'       hidden = hidden_block,
+#'       output = output_block
+#'     ),
+#'     mode = "classification"
+#'   )
+#'
+#'   mlp_spec <- my_mlp_grid_3(
+#'     hidden_units = tune(),
+#'     compile_loss = "categorical_crossentropy",
+#'     compile_optimizer = "adam"
+#'   )
+#'
+#'   # 3. Create a hyperparameter grid
+#'   param_grid <- tibble::tibble(
+#'     hidden_units = c(32, 64, -10)
+#'   )
+#'
+#'   # 4. Prepare dummy data
+#'   x_train <- matrix(rnorm(100 * 10), ncol = 10)
+#'   y_train <- factor(sample(0:1, 100, replace = TRUE))
+#'
+#'   # 5. Compile models over the grid
+#'   compiled_grid <- compile_keras_grid(
+#'     spec = mlp_spec,
+#'     grid = param_grid,
+#'     x = x_train,
+#'     y = y_train
+#'   )
+#'
+#'   # 6. Inform about errors
+#'   inform_errors(compiled_grid)
+#'   remove_keras_spec("my_mlp_grid_3")
+#' }
 #' }
 #' @export
 inform_errors <- function(compiled_grid, n = 10) {
diff --git a/R/create_keras_functional_spec.R b/R/create_keras_functional_spec.R
index cbf476c..5103f70 100644
--- a/R/create_keras_functional_spec.R
+++ b/R/create_keras_functional_spec.R
@@ -119,6 +119,7 @@
 #'   # model_spec <- my_resnet_spec(num_dense_path = 2, dense_path_units = 32)
 #'
 #'   print(model_spec)
+#'   remove_keras_spec("my_resnet_spec")
 #'   # tune::tunable(model_spec)
 #' }
 #' }
diff --git a/R/create_keras_sequential_spec.R b/R/create_keras_sequential_spec.R
index de1bbce..0369a7d 100644
--- a/R/create_keras_sequential_spec.R
+++ b/R/create_keras_sequential_spec.R
@@ -84,7 +84,7 @@
 #'
 #' # 2. Create the spec, providing blocks in the correct order.
 #' create_keras_sequential_spec(
-#' model_name = "my_mlp",
+#' model_name = "my_mlp_seq_spec",
 #'   layer_blocks = list(
 #'     input = input_block,
 #'     hidden = hidden_block,
@@ -95,7 +95,7 @@
 #'
 #' # 3. Use the newly created specification function!
 #' # Note the new arguments `num_hidden` and `hidden_units`.
-#' model_spec <- my_mlp(
+#' model_spec <- my_mlp_seq_spec(
 #'   num_hidden = 2,
 #'   hidden_units = 64,
 #'   epochs = 10,
@@ -103,6 +103,7 @@
 #' )
 #'
 #' print(model_spec)
+#' remove_keras_spec("my_mlp_seq_spec")
 #' }
 #' }
 create_keras_sequential_spec <- function(
diff --git a/R/generic_functional_fit.R b/R/generic_functional_fit.R
index 35864d9..7befb9f 100644
--- a/R/generic_functional_fit.R
+++ b/R/generic_functional_fit.R
@@ -57,13 +57,14 @@
 #' # It is called internally by `parsnip::fit()`.
 #' # For example:
 #' \donttest{
+#' library(parsnip)
 #' # create_keras_functional_spec(...) defines my_functional_model
 #'
-#' spec <- my_functional_model(hidden_units = 128, fit_epochs = 10) |>
-#'   set_engine("keras")
+#' # spec <- my_functional_model(hidden_units = 128, fit_epochs = 10) |>
+#' #   set_engine("keras")
 #'
-#' # This call to fit() would invoke generic_functional_fit() internally
-#' fitted_model <- fit(spec, y ~ x, data = training_data)
+#' # # This call to fit() would invoke generic_functional_fit() internally
+#' # fitted_model <- fit(spec, y ~ x, data = training_data)
 #' }
 #' @keywords internal
 #' @export
diff --git a/R/generic_sequential_fit.R b/R/generic_sequential_fit.R
index be5da61..3f49ef7 100644
--- a/R/generic_sequential_fit.R
+++ b/R/generic_sequential_fit.R
@@ -57,13 +57,14 @@
 #' # It is called internally by `parsnip::fit()`.
 #' # For example:
 #' \donttest{
+#' library(parsnip)
 #' # create_keras_sequential_spec(...) defines my_sequential_model
 #'
-#' spec <- my_sequential_model(hidden_1_units = 128, fit_epochs = 10) |>
-#'   set_engine("keras")
+#' # spec <- my_sequential_model(hidden_1_units = 128, fit_epochs = 10) |>
+#' #   set_engine("keras")
 #'
-#' # This call to fit() would invoke generic_sequential_fit() internally
-#' fitted_model <- fit(spec, y ~ x, data = training_data)
+#' # # This call to fit() would invoke generic_sequential_fit() internally
+#' # fitted_model <- fit(spec, y ~ x, data = training_data)
 #' }
 #' @keywords internal
 #' @export
diff --git a/R/keras_tools.R b/R/keras_tools.R
index f5cd5f0..2313ee8 100644
--- a/R/keras_tools.R
+++ b/R/keras_tools.R
@@ -20,15 +20,32 @@
 #' @examples
 #' \donttest{
 #' if (requireNamespace("keras3", quietly = TRUE)) {
+#' library(keras3)
+#' library(parsnip)
 #'
-#' # 1. Define and fit a model ----
+#' # 1. Define layer blocks
+#' input_block <- function(model, input_shape) {
+#'   keras_model_sequential(input_shape = input_shape)
+#' }
+#' hidden_block <- function(model, units = 32) {
+#'   model |> layer_dense(units = units, activation = "relu")
+#' }
+#' output_block <- function(model, num_classes) {
+#'   model |> layer_dense(units = num_classes, activation = "softmax")
+#' }
+#'
+#' # 2. Define and fit a model ----
 #' create_keras_sequential_spec(
-#'   model_name = "my_mlp",
-#'   layer_blocks = list(input_block, hidden_block, output_block),
+#'   model_name = "my_mlp_tools",
+#'   layer_blocks = list(
+#'     input = input_block,
+#'     hidden = hidden_block,
+#'     output = output_block
+#'   ),
 #'   mode = "classification"
 #' )
 #'
-#' mlp_spec <- my_mlp(
+#' mlp_spec <- my_mlp_tools(
 #'   hidden_units = 32,
 #'   compile_loss = "categorical_crossentropy",
 #'   compile_optimizer = "adam",
@@ -42,20 +59,21 @@
 #'
 #' fitted_mlp <- fit(mlp_spec, y ~ x, data = train_df)
 #'
-#' # 2. Evaluate the model on new data ----
+#' # 3. Evaluate the model on new data ----
 #' x_test <- matrix(rnorm(50 * 10), ncol = 10)
 #' y_test <- factor(sample(0:1, 50, replace = TRUE))
 #'
 #' eval_metrics <- keras_evaluate(fitted_mlp, x_test, y_test)
 #' print(eval_metrics)
 #'
-#' # 3. Extract the Keras model object ----
+#' # 4. Extract the Keras model object ----
 #' keras_model <- extract_keras_model(fitted_mlp)
 #' summary(keras_model)
 #'
-#' # 4. Extract the training history ----
+#' # 5. Extract the training history ----
 #' history <- extract_keras_history(fitted_mlp)
 #' plot(history)
+#' remove_keras_spec("my_mlp_tools")
 #' }
 #' }
 #' @export
diff --git a/R/remove_keras_spec.R b/R/remove_keras_spec.R
index 54518c1..f0091bf 100644
--- a/R/remove_keras_spec.R
+++ b/R/remove_keras_spec.R
@@ -55,7 +55,7 @@
 #'
 #'   # Check it's gone
 #'   !exists("my_temp_model")
-#'   !"my_temp_model" %in% parsnip::show_engines(NULL)$model
+#'   !model_exists("my_temp_model")
 #' }
 #' }
 remove_keras_spec <- function(model_name, env = parent.frame()) {
diff --git a/R/utils.R b/R/utils.R
index c3cd94d..c814a19 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -416,3 +416,30 @@ get_model_env <- function() {
   current <- utils::getFromNamespace("parsnip", ns = "parsnip")
   current
 }
+
+#' Check if a Kerasnip Model Specification Exists
+#'
+#' @description
+#' This is an internal helper function to check if a model specification has been
+#' registered in the `parsnip` model environment.
+#'
+#' @param model_name A character string giving the name of the model
+#'   specification function to check (e.g., "my_mlp").
+#' @return A logical value, `TRUE` if the model exists, `FALSE` otherwise.
+#' @examples
+#' \donttest{
+#' if (requireNamespace("parsnip", quietly = TRUE)) {
+#'   library(parsnip)
+#'
+#'   # Check for a model that exists in parsnip
+#'   model_exists("mlp")
+#'
+#'   # Check for a model that does not exist
+#'   model_exists("non_existent_model")
+#' }
+#' }
+#' @keywords internal
+#' @export
+model_exists <- function(model_name) {
+  model_name %in% ls(get_model_env())
+}
diff --git a/man/compile_keras_grid.Rd b/man/compile_keras_grid.Rd
index 04f649c..57115ba 100644
--- a/man/compile_keras_grid.Rd
+++ b/man/compile_keras_grid.Rd
@@ -54,35 +54,49 @@ inspect which architectures are valid.
 \examples{
 \donttest{
 if (requireNamespace("keras3", quietly = TRUE)) {
+library(keras3)
+library(parsnip)
+library(dials)
 
-# 1. Define a kerasnip model specification
+# 1. Define layer blocks
+input_block <- function(model, input_shape) {
+  keras_model_sequential(input_shape = input_shape)
+}
+hidden_block <- function(model, units = 32) {
+  model |> layer_dense(units = units, activation = "relu")
+}
+output_block <- function(model, num_classes) {
+  model |> layer_dense(units = num_classes, activation = "softmax")
+}
+
+# 2. Define a kerasnip model specification
 create_keras_sequential_spec(
-  model_name = "my_mlp",
+  model_name = "my_mlp_grid",
   layer_blocks = list(
-    input_block,
-    hidden_block,
-    output_block
+    input = input_block,
+    hidden = hidden_block,
+    output = output_block
   ),
   mode = "classification"
 )
 
-mlp_spec <- my_mlp(
+mlp_spec <- my_mlp_grid(
   hidden_units = tune(),
   compile_loss = "categorical_crossentropy",
   compile_optimizer = "adam"
 )
 
-# 2. Create a hyperparameter grid
+# 3. Create a hyperparameter grid
 # Include an invalid value (-10) to demonstrate error handling
 param_grid <- tibble::tibble(
   hidden_units = c(32, 64, -10)
 )
 
-# 3. Prepare dummy data
+# 4. Prepare dummy data
 x_train <- matrix(rnorm(100 * 10), ncol = 10)
 y_train <- factor(sample(0:1, 100, replace = TRUE))
 
-# 4. Compile models over the grid
+# 5. Compile models over the grid
 compiled_grid <- compile_keras_grid(
   spec = mlp_spec,
   grid = param_grid,
@@ -91,8 +105,9 @@ compiled_grid <- compile_keras_grid(
 )
 
 print(compiled_grid)
+remove_keras_spec("my_mlp_grid")
 
-# 5. Inspect the results
+# 6. Inspect the results
 # The row with `hidden_units = -10` will show an error.
 }
 }
diff --git a/man/create_keras_functional_spec.Rd b/man/create_keras_functional_spec.Rd
index 5a1c629..ba41f23 100644
--- a/man/create_keras_functional_spec.Rd
+++ b/man/create_keras_functional_spec.Rd
@@ -136,6 +136,7 @@ if (requireNamespace("keras3", quietly = TRUE)) {
   # model_spec <- my_resnet_spec(num_dense_path = 2, dense_path_units = 32)
 
   print(model_spec)
+  remove_keras_spec("my_resnet_spec")
   # tune::tunable(model_spec)
 }
 }
diff --git a/man/create_keras_sequential_spec.Rd b/man/create_keras_sequential_spec.Rd
index b7c061c..54e1228 100644
--- a/man/create_keras_sequential_spec.Rd
+++ b/man/create_keras_sequential_spec.Rd
@@ -98,7 +98,7 @@ output_block <- function(model, num_classes) {
 
 # 2. Create the spec, providing blocks in the correct order.
 create_keras_sequential_spec(
-model_name = "my_mlp",
+model_name = "my_mlp_seq_spec",
   layer_blocks = list(
     input = input_block,
     hidden = hidden_block,
@@ -109,7 +109,7 @@ model_name = "my_mlp",
 
 # 3. Use the newly created specification function!
 # Note the new arguments `num_hidden` and `hidden_units`.
-model_spec <- my_mlp(
+model_spec <- my_mlp_seq_spec(
   num_hidden = 2,
   hidden_units = 64,
   epochs = 10,
@@ -117,6 +117,7 @@ model_spec <- my_mlp(
 )
 
 print(model_spec)
+remove_keras_spec("my_mlp_seq_spec")
 }
 }
 }
diff --git a/man/extract_valid_grid.Rd b/man/extract_valid_grid.Rd
index ff12a80..1befbec 100644
--- a/man/extract_valid_grid.Rd
+++ b/man/extract_valid_grid.Rd
@@ -28,14 +28,60 @@ problematic hyperparameter combinations before proceeding to the full
 }
 \examples{
 \donttest{
-# Continuing the example from `compile_keras_grid`:
+if (requireNamespace("keras3", quietly = TRUE)) {
+  library(keras3)
+  library(parsnip)
+  library(dials)
 
-# `compiled_grid` contains one row with an error.
-valid_grid <- extract_valid_grid(compiled_grid)
+  # 1. Define layer blocks
+  input_block <- function(model, input_shape) {
+    keras_model_sequential(input_shape = input_shape)
+  }
+  hidden_block <- function(model, units = 32) {
+    model |> layer_dense(units = units, activation = "relu")
+  }
+  output_block <- function(model, num_classes) {
+    model |> layer_dense(units = num_classes, activation = "softmax")
+  }
 
-# `valid_grid` now only contains the rows that compiled successfully.
-print(valid_grid)
+  # 2. Define a kerasnip model specification
+  create_keras_sequential_spec(
+    model_name = "my_mlp_grid_2",
+    layer_blocks = list(
+      input = input_block,
+      hidden = hidden_block,
+      output = output_block
+    ),
+    mode = "classification"
+  )
 
-# This clean grid can now be passed to tune::tune_grid().
+  mlp_spec <- my_mlp_grid_2(
+    hidden_units = tune(),
+    compile_loss = "categorical_crossentropy",
+    compile_optimizer = "adam"
+  )
+
+  # 3. Create a hyperparameter grid
+  param_grid <- tibble::tibble(
+    hidden_units = c(32, 64, -10)
+  )
+
+  # 4. Prepare dummy data
+  x_train <- matrix(rnorm(100 * 10), ncol = 10)
+  y_train <- factor(sample(0:1, 100, replace = TRUE))
+
+  # 5. Compile models over the grid
+  compiled_grid <- compile_keras_grid(
+    spec = mlp_spec,
+    grid = param_grid,
+    x = x_train,
+    y = y_train
+  )
+
+  # 6. Extract the valid grid
+  valid_grid <- extract_valid_grid(compiled_grid)
+  print(valid_grid)
+  remove_keras_spec("my_mlp_grid_2")
+}
 }
 }
diff --git a/man/generic_functional_fit.Rd b/man/generic_functional_fit.Rd
index 333d558..2bb9388 100644
--- a/man/generic_functional_fit.Rd
+++ b/man/generic_functional_fit.Rd
@@ -69,13 +69,14 @@ and processed data, passing along any fitting-specific arguments (e.g.,
 # It is called internally by `parsnip::fit()`.
 # For example:
 \donttest{
+library(parsnip)
 # create_keras_functional_spec(...) defines my_functional_model
 
-spec <- my_functional_model(hidden_units = 128, fit_epochs = 10) |>
-  set_engine("keras")
+# spec <- my_functional_model(hidden_units = 128, fit_epochs = 10) |>
+#   set_engine("keras")
 
-# This call to fit() would invoke generic_functional_fit() internally
-fitted_model <- fit(spec, y ~ x, data = training_data)
+# # This call to fit() would invoke generic_functional_fit() internally
+# fitted_model <- fit(spec, y ~ x, data = training_data)
 }
 }
 \keyword{internal}
diff --git a/man/generic_sequential_fit.Rd b/man/generic_sequential_fit.Rd
index d35d6f9..523a533 100644
--- a/man/generic_sequential_fit.Rd
+++ b/man/generic_sequential_fit.Rd
@@ -69,13 +69,14 @@ and processed data, passing along any fitting-specific arguments (e.g.,
 # It is called internally by `parsnip::fit()`.
 # For example:
 \donttest{
+library(parsnip)
 # create_keras_sequential_spec(...) defines my_sequential_model
 
-spec <- my_sequential_model(hidden_1_units = 128, fit_epochs = 10) |>
-  set_engine("keras")
+# spec <- my_sequential_model(hidden_1_units = 128, fit_epochs = 10) |>
+#   set_engine("keras")
 
-# This call to fit() would invoke generic_sequential_fit() internally
-fitted_model <- fit(spec, y ~ x, data = training_data)
+# # This call to fit() would invoke generic_sequential_fit() internally
+# fitted_model <- fit(spec, y ~ x, data = training_data)
 }
 }
 \keyword{internal}
diff --git a/man/inform_errors.Rd b/man/inform_errors.Rd
index c892bbd..cd23035 100644
--- a/man/inform_errors.Rd
+++ b/man/inform_errors.Rd
@@ -29,10 +29,59 @@ some hyperparameter combinations may lead to invalid Keras models.
 }
 \examples{
 \donttest{
-# Continuing the example from `compile_keras_grid`:
+if (requireNamespace("keras3", quietly = TRUE)) {
+  library(keras3)
+  library(parsnip)
+  library(dials)
 
-# `compiled_grid` contains one row with an error.
-# This will print a formatted summary of that error.
-inform_errors(compiled_grid)
+  # 1. Define layer blocks
+  input_block <- function(model, input_shape) {
+    keras_model_sequential(input_shape = input_shape)
+  }
+  hidden_block <- function(model, units = 32) {
+    model |> layer_dense(units = units, activation = "relu")
+  }
+  output_block <- function(model, num_classes) {
+    model |> layer_dense(units = num_classes, activation = "softmax")
+  }
+
+  # 2. Define a kerasnip model specification
+  create_keras_sequential_spec(
+    model_name = "my_mlp_grid_3",
+    layer_blocks = list(
+      input = input_block,
+      hidden = hidden_block,
+      output = output_block
+    ),
+    mode = "classification"
+  )
+
+  mlp_spec <- my_mlp_grid_3(
+    hidden_units = tune(),
+    compile_loss = "categorical_crossentropy",
+    compile_optimizer = "adam"
+  )
+
+  # 3. Create a hyperparameter grid
+  param_grid <- tibble::tibble(
+    hidden_units = c(32, 64, -10)
+  )
+
+  # 4. Prepare dummy data
+  x_train <- matrix(rnorm(100 * 10), ncol = 10)
+  y_train <- factor(sample(0:1, 100, replace = TRUE))
+
+  # 5. Compile models over the grid
+  compiled_grid <- compile_keras_grid(
+    spec = mlp_spec,
+    grid = param_grid,
+    x = x_train,
+    y = y_train
+  )
+
+  # 6. Inform about errors
+  inform_errors(compiled_grid)
+  remove_keras_spec("my_mlp_grid_3")
+}
 }
 }
diff --git a/man/keras_evaluate.Rd b/man/keras_evaluate.Rd
index 1970e43..9df1aea 100644
--- a/man/keras_evaluate.Rd
+++ b/man/keras_evaluate.Rd
@@ -33,15 +33,32 @@ Evaluate a Fitted Kerasnip Model on New Data
 \examples{
 \donttest{
 if (requireNamespace("keras3", quietly = TRUE)) {
+library(keras3)
+library(parsnip)
 
-# 1. Define and fit a model ----
+# 1. Define layer blocks
+input_block <- function(model, input_shape) {
+  keras_model_sequential(input_shape = input_shape)
+}
+hidden_block <- function(model, units = 32) {
+  model |> layer_dense(units = units, activation = "relu")
+}
+output_block <- function(model, num_classes) {
+  model |> layer_dense(units = num_classes, activation = "softmax")
+}
+
+# 2. Define and fit a model ----
 create_keras_sequential_spec(
-  model_name = "my_mlp",
-  layer_blocks = list(input_block, hidden_block, output_block),
+  model_name = "my_mlp_tools",
+  layer_blocks = list(
+    input = input_block,
+    hidden = hidden_block,
+    output = output_block
+  ),
   mode = "classification"
 )
 
-mlp_spec <- my_mlp(
+mlp_spec <- my_mlp_tools(
   hidden_units = 32,
   compile_loss = "categorical_crossentropy",
   compile_optimizer = "adam",
@@ -55,20 +72,21 @@ train_df <- data.frame(x = I(x_train), y = y_train)
 
 fitted_mlp <- fit(mlp_spec, y ~ x, data = train_df)
 
-# 2. Evaluate the model on new data ----
+# 3. Evaluate the model on new data ----
 x_test <- matrix(rnorm(50 * 10), ncol = 10)
 y_test <- factor(sample(0:1, 50, replace = TRUE))
 
 eval_metrics <- keras_evaluate(fitted_mlp, x_test, y_test)
 print(eval_metrics)
 
-# 3. Extract the Keras model object ----
+# 4. Extract the Keras model object ----
 keras_model <- extract_keras_model(fitted_mlp)
 summary(keras_model)
 
-# 4. Extract the training history ----
+# 5. Extract the training history ----
 history <- extract_keras_history(fitted_mlp)
 plot(history)
+remove_keras_spec("my_mlp_tools")
 }
 }
 }
diff --git a/man/model_exists.Rd b/man/model_exists.Rd
new file mode 100644
index 0000000..17bfc93
--- /dev/null
+++ b/man/model_exists.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.R
+\name{model_exists}
+\alias{model_exists}
+\title{Check if a Kerasnip Model Specification Exists}
+\usage{
+model_exists(model_name)
+}
+\arguments{
+\item{model_name}{A character string giving the name of the model
+specification function to check (e.g., "my_mlp").}
+}
+\value{
+A logical value, \code{TRUE} if the model exists, \code{FALSE} otherwise.
+}
+\description{
+This is an internal helper function to check if a model specification has been
+registered in the \code{parsnip} model environment.
+}
+\examples{
+\donttest{
+if (requireNamespace("parsnip", quietly = TRUE)) {
+  library(parsnip)
+
+  # Check for a model that exists in parsnip
+  model_exists("mlp")
+
+  # Check for a model that does not exist
+  model_exists("non_existent_model")
+}
+}
+}
+\keyword{internal}
diff --git a/man/remove_keras_spec.Rd b/man/remove_keras_spec.Rd
index 6e5fa36..1062faf 100644
--- a/man/remove_keras_spec.Rd
+++ b/man/remove_keras_spec.Rd
@@ -64,7 +64,7 @@ if (requireNamespace("keras3", quietly = TRUE)) {
 
   # Check it's gone
   !exists("my_temp_model")
-  !"my_temp_model" \%in\% parsnip::show_engines(NULL)$model
+  !model_exists("my_temp_model")
 }
 }
 }

From eff9d5bc996d2d3305160628d3a96a2f1be81095 Mon Sep 17 00:00:00 2001
From: davidrsch <daviddrsch@gmail.com>
Date: Thu, 4 Sep 2025 15:45:32 +0200
Subject: [PATCH 2/4] Fix for macos check error?

---
 .github/workflows/R-CMD-check.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index a46eb86..6a023ca 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -41,6 +41,7 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       R_KEEP_PKG_SOURCE: yes
+      OMP_NUM_THREADS: 1
 
     steps:
       - uses: actions/checkout@v4

From b7ac2de5d53f459dba50362960a5e5a2cdbfbf7d Mon Sep 17 00:00:00 2001
From: davidrsch <daviddrsch@gmail.com>
Date: Thu, 4 Sep 2025 16:01:09 +0200
Subject: [PATCH 3/4] Adding more Badges to readme

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README.md b/README.md
index 33b7036..8226fc7 100644
--- a/README.md
+++ b/README.md
@@ -4,6 +4,10 @@
 <!-- badges: start -->
 [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
 [![R-CMD-check](https://github.com/davidrsch/kerasnip/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/davidrsch/kerasnip/actions/workflows/R-CMD-check.yaml)
+[![Codecov test
+coverage](https://codecov.io/gh/davidrsch/kerasnip/graph/badge.svg)](https://app.codecov.io/gh/davidrsch/kerasnip)
+[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/kerasnip)](https://cran.r-project.org/package=kerasnip)
+[![Downloads](https://cranlogs.r-pkg.org/badges/kerasnip)](https://cran.r-project.org/package=kerasnip)
 <!-- badges: end -->
 
 The goal of `kerasnip` is to provide a seamless bridge between the `keras` and `tidymodels` frameworks. It allows for the dynamic creation of `parsnip` model specifications for Keras models, making them fully compatible with `tidymodels` workflows.

From a0314fd06e2e1f42092674a4bf2935014bbd1c1a Mon Sep 17 00:00:00 2001
From: davidrsch <daviddrsch@gmail.com>
Date: Thu, 4 Sep 2025 16:01:27 +0200
Subject: [PATCH 4/4] Trying to fix macOS checks

---
 .github/workflows/R-CMD-check.yaml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 6a023ca..e0b85a8 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -41,7 +41,6 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       R_KEEP_PKG_SOURCE: yes
-      OMP_NUM_THREADS: 1
 
     steps:
       - uses: actions/checkout@v4
@@ -81,7 +80,7 @@ jobs:
       - name: Install Keras backend
         # The keras3 package is installed by the previous step (as a dependency)
         # This step installs the required Python libraries (e.g., tensorflow).
-        run: keras3::install_keras()
+        run: keras3::install_keras(envname = "r-reticulate")
         shell: Rscript {0}
 
       - uses: r-lib/actions/check-r-package@v2