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Quantize the weights of a model.

Source: R/layer-methods.R
quantize_weights.Rd

Note that the model must be built first before calling this method. quantize_weights() will recursively call layer$quantize(...) in all layers and will be skipped if the layer doesn't implement the function.

Pass a mode string to use the default configuration for that mode. Advanced users can pass an upstream Keras quantization configuration object via config.

Usage

quantize_weights(object, mode = NULL, config = NULL, filters = NULL, ...)

Arguments

object

A Keras Model or Layer.

mode

Quantization mode supported by the installed Keras version. Optional when config is supplied.

config

An optional upstream Keras quantization configuration object.

filters

Optional filters controlling which layers are quantized. May be a regular expression string, a list of regular expression strings, or a callable. Only layers matching the filter conditions are quantized.

...

Passed on to the object quantization method.

Value

model, invisibly. Note this is just a convenience for usage with |>, the model is modified in-place.

Examples

Quantize a model to int8 with the default configuration:

model <- keras_model_sequential(input_shape = 10) |>
  layer_dense(10)
model |> quantize_weights("int8")

See also

Other layer methods:
count_params()
get_config()
get_weights()
reset_state()