learner_momentum_sgd: Creates a Momentum SGD learner instance to learn the...
In joeddav/CNTK-R: R Interface to the Microsoft Cognitive Toolkit (CNTK)

Creates a Momentum SGD learner instance to learn the parameters.

learner_momentum_sgd(parameters, lr, momentum,
  unit_gain = cntk$default_unit_gain_value(), l1_regularization_weight = 0,
  l2_regularization_weight = 0, gaussian_noise_injection_std_dev = 0,
  gradient_clipping_threshold_per_sample = np$inf,
  gradient_clipping_with_truncation = TRUE, use_mean_gradient = FALSE)

`parameters`	– list of network parameters list of network parameters to tune.
`lr`	(output of learning_rate_schedule()) – learning rate schedule output of `learning_rate_schedule`
`momentum`	output of `momentum_schdule` or `momentum_as_time_constant_schedule`
`unit_gain`	logical whether to interpret momentum as a unit-gain filter
`l1_regularization_weight`	(float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional) double of l1 regularization
`l2_regularization_weight`	(float, optional) – the L2 regularization weight per sample, defaults to 0.0 double of l2 regularization
`gaussian_noise_injection_std_dev`	(float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0 double of noise injection
`gradient_clipping_threshold_per_sample`	(float, optional) – clipping threshold per sample, defaults to infinity. double of gradient clipping threshold per sample
`gradient_clipping_with_truncation`	(bool, default True) – use gradient clipping with truncation logical for gradient clipping with truncation
`use_mean_gradient`	(bool, default False) – use averaged gradient as input to learner. Defaults to the value returned by default_use_mean_gradient_value(). logical use averaged gradient as input to learner.