Optimizer factory class.

This class builds learning rate and optimizer based on an optimization config. To use this class, you need to do the following: (1) Define optimization config, this includes optimizer, and learning rate schedule. (2) Initialize the class using the optimization config. (3) Build learning rate. (4) Build optimizer.

This is a typical example for using this class:

params = {
      'optimizer': {
          'type': 'sgd',
          'sgd': {'momentum': 0.9}
      'learning_rate': {
          'type': 'stepwise',
          'stepwise': {'boundaries': [10000, 20000],
                       'values': [0.1, 0.01, 0.001]}
      'warmup': {
          'type': 'linear',
          'linear': {'warmup_steps': 500, 'warmup_learning_rate': 0.01}
opt_config = OptimizationConfig(params)
opt_factory = OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
optimizer = opt_factory.build_optimizer(lr)

config OptimizationConfig instance contain optimization config.



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Build learning rate.

Builds learning rate from config. Learning rate schedule is built according to the learning rate config. If learning rate type is consant, lr_config.learning_rate is returned.

tf.keras.optimizers.schedules.LearningRateSchedule instance. If learning rate type is consant, lr_config.learning_rate is returned.


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Build optimizer.

Builds optimizer from config. It takes learning rate as input, and builds the optimizer according to the optimizer config. Typically, the learning rate built using self.build_lr() is passed as an argument to this method.

lr A floating point value, or a tf.keras.optimizers.schedules.LearningRateSchedule instance.
gradient_aggregator Optional function to overwrite gradient aggregation.
gradient_transformers Optional list of functions to use to transform gradients before applying updates to Variables. The functions are applied after gradient_aggregator. The functions should accept and return a list of (gradient, variable) tuples. clipvalue, clipnorm, global_clipnorm should not be set when gradient_transformers is passed.
postprocessor An optional function for postprocessing the optimizer. It takes an optimizer and returns an optimizer.
use_legacy_optimizer A boolean that indicates if using legacy optimizers.

tf.keras.optimizers.legacy.Optimizer or tf.keras.optimizers.experimental.Optimizer instance.