kerasnip (version 0.1.0)

create_keras_sequential_spec: Create a Custom Keras Sequential Model Specification for Tidymodels

Description

This function acts as a factory to generate a new parsnip model specification based on user-defined blocks of Keras layers using the Sequential API. This is the ideal choice for creating models that are a simple, linear stack of layers. For models with complex, non-linear topologies, see create_keras_functional_spec().

Usage

create_keras_sequential_spec(
  model_name,
  layer_blocks,
  mode = c("regression", "classification"),
  ...,
  env = parent.frame()
)

Value

Invisibly returns NULL. Its primary side effect is to create a new model specification function (e.g., my_mlp()) in the specified environment and register the model with parsnip so it can be used within the tidymodels framework.

Arguments

model_name

A character string for the name of the new model specification function (e.g., "custom_cnn"). This should be a valid R function name.

layer_blocks

A named, ordered list of functions. Each function defines a "block" of Keras layers. The function must take a Keras model object as its first argument and return the modified model. Other arguments to the function will become tunable parameters in the final model specification.

mode

A character string, either "regression" or "classification".

...

Reserved for future use. Currently not used.

env

The environment in which to create the new model specification function and its associated update() method. Defaults to the calling environment (parent.frame()).

Model Architecture (Sequential API)

kerasnip builds the model by applying the functions in layer_blocks in the order they are provided. Each function receives the Keras model built by the previous function and returns a modified version.

1. The first block must initialize the model (e.g., with keras_model_sequential()). It can accept an input_shape argument, which kerasnip will provide automatically during fitting.

2. Subsequent blocks add layers to the model.

3. The final block should add the output layer. For classification, it can accept a num_classes argument, which is provided automatically.

A key feature of this function is the automatic creation of num_{block_name} arguments (e.g., num_hidden). This allows you to control how many times each block is repeated, making it easy to tune the depth of your network.

Details

This function generates all the boilerplate needed to create a custom, tunable parsnip model specification that uses the Keras Sequential API.

The function inspects the arguments of your layer_blocks functions (ignoring special arguments like input_shape and num_classes) and makes them available as arguments in the generated model specification, prefixed with the block's name (e.g., dense_units).

The new model specification function and its update() method are created in the environment specified by the env argument.

See Also

remove_keras_spec(), parsnip::new_model_spec(), create_keras_functional_spec()

Examples

# \donttest{
if (requireNamespace("keras3", quietly = TRUE)) {
library(keras3)
library(parsnip)
library(dials)

# 1. Define layer blocks for a complete model.
# The first block must initialize the model. `input_shape` is passed automatically.
input_block <- function(model, input_shape) {
  keras_model_sequential(input_shape = input_shape)
}
# A block for hidden layers. `units` will become a tunable parameter.
hidden_block <- function(model, units = 32) {
  model |> layer_dense(units = units, activation = "relu")
}

# The output block. `num_classes` is passed automatically for classification.
output_block <- function(model, num_classes) {
  model |> layer_dense(units = num_classes, activation = "softmax")
}

# 2. Create the spec, providing blocks in the correct order.
create_keras_sequential_spec(
model_name = "my_mlp_seq_spec",
  layer_blocks = list(
    input = input_block,
    hidden = hidden_block,
    output = output_block
  ),
  mode = "classification"
)

# 3. Use the newly created specification function!
# Note the new arguments `num_hidden` and `hidden_units`.
model_spec <- my_mlp_seq_spec(
  num_hidden = 2,
  hidden_units = 64,
  epochs = 10,
  learn_rate = 0.01
)

print(model_spec)
remove_keras_spec("my_mlp_seq_spec")
}
# }