ggml_compile: Compile a Sequential Model

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Compile a Sequential Model

Description

Configures the model for training: infers shapes, creates backend. Weight tensors are created at training time when batch_size is known.

Usage

## S3 method for class 'ggml_functional_model'
ggml_compile(
  model,
  optimizer = "adam",
  loss = "categorical_crossentropy",
  metrics = c("accuracy"),
  backend = "auto"
)

ggml_compile(
  model,
  optimizer = "adam",
  loss = "categorical_crossentropy",
  metrics = c("accuracy"),
  backend = "auto"
)

## S3 method for class 'ggml_sequential_model'
ggml_compile(
  model,
  optimizer = "adam",
  loss = "categorical_crossentropy",
  metrics = c("accuracy"),
  backend = "auto"
)

Arguments

model	A ggml_sequential_model object
optimizer	Optimizer name: "adam" or "sgd"
loss	Loss function name: "categorical_crossentropy" or "mse"
metrics	Character vector of metrics (currently "accuracy")
backend	Backend to use: "auto" (GPU if available, else CPU), "cpu", or "vulkan"

Value

The compiled model (invisibly).

Examples

model <- ggml_model_sequential() |>
  ggml_layer_conv_2d(32, c(3,3), activation = "relu",
                     input_shape = c(28, 28, 1)) |>
  ggml_layer_max_pooling_2d(c(2, 2)) |>
  ggml_layer_flatten() |>
  ggml_layer_dense(10, activation = "softmax")
model <- ggml_compile(model, optimizer = "adam",
                      loss = "categorical_crossentropy")

ggmlR documentation built on July 14, 2026, 1:08 a.m.