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► Keras 3 API documentation / Optimizers / Muon

Muon

[source]

Muon class

keras.optimizers.Muon(
    learning_rate=0.001,
    adam_beta_1=0.9,
    adam_beta_2=0.999,
    epsilon=1e-07,
    weight_decay=0.1,
    clipnorm=None,
    clipvalue=None,
    global_clipnorm=None,
    use_ema=False,
    ema_momentum=0.99,
    ema_overwrite_frequency=None,
    loss_scale_factor=None,
    gradient_accumulation_steps=None,
    name="muon",
    exclude_layers=None,
    exclude_embeddings=True,
    muon_a=3.4445,
    muon_b=-4.775,
    muon_c=2.0315,
    adam_lr_ratio=0.1,
    momentum=0.95,
    ns_steps=6,
    nesterov=True,
    **kwargs
)

Optimizer that implements the Muon algorithm.

Note that this optimizer should not be used in the following layers:

  1. Embedding layer
  2. Final output fully connected layer
  3. Any {0,1}-D variables

These should all be optimized using AdamW.

The Muon optimizer can use both the Muon update step or the AdamW update step based on the following:

  • For any variable that isn't 2D, 3D or 4D, the AdamW step will be used. This is not configurable.
  • If the argument exclude_embeddings (defaults to True) is set to True, the AdamW step will be used.
  • For any variablewith a name that matches an expression listed in the argument exclude_layers (a list), the AdamW step will be used.
  • Any other variable uses the Muon step.

Typically, you only need to pass the name of your densely-connected output layer to exclude_layers, e.g. exclude_layers=["output_dense"].

References

Arguments

  • learning_rate: A float, keras.optimizers.schedules.LearningRateSchedule instance, or a callable that takes no arguments and returns the actual value to use. The learning rate. Defaults to 0.001.
  • adam_beta_1: A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 1st moment estimates. Defaults to 0.9.
  • adam_beta_2: A float value or a constant float tensor, ora callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 2nd moment estimates. Defaults to 0.999.
  • epsilon: A small constant for numerical stability. This is "epsilon hat" in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper. It be used at Adamw.Defaults to 1e-7.
  • exclude_layers: List of strings, keywords of layer names to exclude. All layers with keywords in their path will use adamw.
  • exclude_embeddings: Boolean value If True, embedding layers will use adamw.
  • muon_a: Float, parameter a of the muon algorithm. It is recommended to use the default value
  • muon_b: Float, parameter b of the muon algorithm. It is recommended to use the default value
  • muon_c: Float, parameter c of the muon algorithm. It is recommended to use the default value
  • adam_lr_ratio: Float, the ratio of the learning rate when using Adam to the main learning rate. it is recommended to set it to 0.1
  • momentum: Float, momentum used by internal SGD.
  • ns_steps: Integer, number of Newton-Schulz iterations to run.
  • nesterov: Boolean, whether to use Nesterov-style momentum {{base_optimizer_keyword_args}}
Muon
Muon class