Keras documentation: Optimizers Abstract optimizer base class. If you intend to create your own optimization algorithm, please inherit from this class and override the following methods: build: …

Keras documentation: Optimizers Apply gradients to variables. Arguments grads_and_vars: List of (gradient, variable) pairs. name: string, defaults to None. The name of the namescope to use when …

Keras documentation: Adam Optimizer that implements the Adam algorithm. Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second …

Arguments optimizer: String (name of optimizer) or optimizer instance. See keras.optimizers. loss: Loss function. May be a string (name of loss function), or a keras.losses.Loss instance. See keras.losses. …

Keras documentation: Adagrad Optimizer that implements the Adagrad algorithm. Adagrad is an optimizer with parameter-specific learning rates, which are adapted relative to how frequently a …

Keras documentation: AdamW Optimizer that implements the AdamW algorithm. AdamW optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second …

Keras documentation: SGD Gradient descent (with momentum) optimizer. Update rule for parameter w with gradient g when momentum is 0:

Keras documentation: Lion Optimizer that implements the Lion algorithm. The Lion optimizer is a stochastic-gradient-descent method that uses the sign operator to control the magnitude of the …

Keras documentation: Muon Optimizer that implements the Muon algorithm. Note that this optimizer should not be used in the following layers: Embedding layer Final output fully connected layer Any …

Keras documentation: Adadelta Optimizer that implements the Adadelta algorithm. Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to …