kerasnip (version 0.1.0)

generic_sequential_fit: Internal Fitting Engine for Sequential API Models

Description

This function serves as the internal engine for fitting kerasnip models that are based on the Keras sequential API. It is not intended to be called directly by the user. The function is invoked by parsnip::fit() when a kerasnip sequential model specification is used.

Usage

generic_sequential_fit(formula, data, layer_blocks, ...)

Value

A list containing the fitted model and other metadata. This list is stored in the fit slot of the parsnip model fit object. The list contains the following elements:

• fit: The raw, fitted Keras model object.

• history: The Keras training history object.

• lvl: A character vector of the outcome factor levels (for classification) or NULL (for regression).

Arguments

formula

A formula specifying the predictor and outcome variables, passed down from the parsnip::fit() call.

data

A data frame containing the training data, passed down from the parsnip::fit() call.

layer_blocks

A named list of layer block functions. This is passed internally from the parsnip model specification.

...

Additional arguments passed down from the model specification. These can include:

• Layer Parameters: Arguments for the layer blocks, prefixed with the block name (e.g., dense_units = 64).

• Architecture Parameters: Arguments to control the number of times a block is repeated, in the format num_{block_name} (e.g., num_dense = 2).

• Compile Parameters: Arguments to customize model compilation, prefixed with compile_ (e.g., compile_loss = "mae", compile_optimizer = "sgd").

• Fit Parameters: Arguments to customize model fitting, prefixed with fit_ (e.g., fit_callbacks = list(...), fit_class_weight = list(...)).

Details

Generic Fitting Function for Sequential Keras Models

The function orchestrates the three main steps of the model fitting process:

1. Build and Compile: It calls build_and_compile_sequential_model() to construct the Keras model architecture based on the provided layer_blocks and hyperparameters.

2. Process Data: It preprocesses the input (x) and output (y) data into the format expected by Keras.

3. Fit Model: It calls keras3::fit() with the compiled model and processed data, passing along any fitting-specific arguments (e.g., epochs, batch_size, callbacks).

Examples

# This function is not called directly by users.
# 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")

# # This call to fit() would invoke generic_sequential_fit() internally
# fitted_model <- fit(spec, y ~ x, data = training_data)
# }