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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,
    adam_weight_decay=0.004,
    epsilon=1e-07,
    weight_decay=0.004,
    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=1,
    momentum=0.95,
    ns_steps=5,
    nesterov=True,
    rms_rate=0.2,
    **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, 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, or a 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.
  • adam_weight_decay: Float. If set, weight decay is applied when using the Adam optimizer.
  • 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 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}}
  • rms_rate: Float. A parameter from https://arxiv.org/abs/2502.16982 that can enhance the stability of Muon, allowing it to use the same learning rate and weight decay as Adam. Defaults to 0.2. Set to None to disable this feature.
Muon
Muon class