evaluate.ggml_sequential_model

evaluate.ggml_functional_model

Computes loss and metrics on test data. This is the keras-compatible
interface; it delegates to <code>ggml_evaluate</code>.

Provides 'R' bindings to the 'GGML' tensor library for machine
learning, optimized for 'Vulkan' GPU acceleration with a transparent CPU
fallback. The package features a 'Keras'-like sequential API and a
'PyTorch'-style 'autograd' engine for building, training, and deploying
neural networks. Key capabilities include high-performance 5D tensor
operations, 'f16' precision, and efficient quantization. It supports
native 'ONNX' model import (50+ operators) and 'GGUF' weight loading
from the 'llama.cpp' and 'Hugging Face' ecosystems. Designed for
zero-overhead inference via dedicated weight buffering, it integrates
seamlessly as a 'parsnip' engine for 'tidymodels' and provides
first-class learners for the 'mlr3' framework.
See <https://github.com/ggml-org/ggml> for more information about the
underlying library.

Yuri Baramykov

ggmlR

'GGML' Tensor Operations for Machine Learning

evaluate.ggml_sequential_model function

<dl><dt>x</dt>
<dd>A trained model object.</dd>
<dt>test_x</dt>
<dd>Test data.</dd>
<dt>test_y</dt>
<dd>Test labels.</dd>
<dt>batch_size</dt>
<dd>Batch size (default 32).</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code>ggml_evaluate</code>.</dd></dl>

Arguments

Evaluate a Model — evaluate.ggml_sequential_model

<dl>

<dt>x</dt>
<dd>A trained model object.</dd>


<dt>test_x</dt>
<dd>Test data.</dd>


<dt>test_y</dt>
<dd>Test labels.</dd>


<dt>batch_size</dt>
<dd>Batch size (default 32).</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code>ggml_evaluate</code>.</dd>

</dl>

evaluate.ggml_sequential_model: Evaluate a Model

Description

Usage

Value

Arguments