kerasnip (version 0.1.0)

inp_spec: Remap Layer Block Arguments for Model Specification

Description

Creates a wrapper function around a Keras layer block to rename its arguments. This is a powerful helper for defining the layer_blocks in create_keras_functional_spec() and create_keras_sequential_spec(), allowing you to connect reusable blocks into a model graph without writing verbose anonymous functions.

Usage

inp_spec(block, input_map)

Value

A new function (a closure) that wraps the block function with renamed arguments, ready to be used in a layer_blocks list.

Arguments

block

A function that defines a Keras layer or a set of layers. The first arguments should be the input tensor(s).

input_map

A single character string or a named character vector that specifies how to rename/remap the arguments of block.

Details

inp_spec() makes your model definitions cleaner and more readable. It handles the metaprogramming required to create a new function with the correct argument names, while preserving the original block's hyperparameters and their default values.

The function supports two modes of operation based on input_map:

1. Single Input Renaming: If input_map is a single character string, the wrapper function renames the first argument of the block function to the provided string. This is the common case for blocks that take a single tensor input.

2. Multiple Input Mapping: If input_map is a named character vector, the names must match the argument names of block and each value must be the name of an upstream layer block whose output should be fed into that argument. This orientation matches the syntax (e.g., c(numeric = "processed_numerical")). This is used for blocks with multiple inputs, like a concatenation layer.

Note: Prior releases accepted the opposite orientation (c(processed_numerical = "numeric")). Existing code written in that style must flip the names/values when upgrading to this version.

Examples

# \donttest{
# --- Example Blocks ---
# A standard dense block with one input tensor and one hyperparameter.
dense_block <- function(tensor, units = 16) {
  tensor |> keras3::layer_dense(units = units, activation = "relu")
}

# A block that takes two tensors as input.
concat_block <- function(input_a, input_b) {
  keras3::layer_concatenate(list(input_a, input_b))
}

# An output block with one input.
output_block <- function(tensor) {
  tensor |> keras3::layer_dense(units = 1)
}

# --- Usage ---
layer_blocks <- list(
  main_input = keras3::layer_input,
  path_a = inp_spec(dense_block, "main_input"),
  path_b = inp_spec(dense_block, "main_input"),
  concatenated = inp_spec(
    concat_block,
    c(input_a = "path_a", input_b = "path_b")
  ),
  output = inp_spec(output_block, "concatenated")
)
# }