R/learners.R
In joeddav/CNTK-R: R Interface to the Microsoft Cognitive Toolkit (CNTK)
#' Learner
#'
#' Abstraction for learning a subset of parameters of a learnable function
#' using first order gradient values. For example momentum, AdaGrad, RMSProp,
#' etc. are different types of learners with their own algorithms for learning
#' parameter values using first order gradients. To instantiate a concrete
#' learner, use the factory methods in this module.
#'
#' @param parameters – list of network parameters list of parameter associated
#' with this learner
#' @param learning_rate_schedule get_learning_rate(learner)
#' @example \dontrun{
#' reset_learning_rate(learner, learning_rate)
#' update_learner(learner, gradient_values, training_sample_count)
#' }
#'
#' @export
Learner <- function(parameters, learning_rate_schedule) {
cntk$learners$Learner(
parameters,
learningRateSchedule
)
}
#' Get Learner Learning Rate
#'
#' @param learner
#'
#' @export
get_learning_rate <- function(learner) {
learner$get_learning_rate()
}
#' Reset Learner Learning Rate
#'
#' @param learner
#' @param learning_rate
#'
#' @export
reset_learning_rate <- function(learner, learning_rate) {
learner$reset_learning_rate(learning_rate)
}
#' @param learner
#'
#' @param gradient_values
#' @param training_sample_count
#'
#' @export
update_learner <- function(learner, gradient_values, training_sample_count) {
learner$update_learner(gradient_values, training_sample_count)
}
#' @param value
#'
#' @export
UnitType <- function(value) {
reticulate::py_get_attr(cntk$learners$UnitType, value)
}
#' UserLearner
#'
#' Base class of all user-defined learners. To implement your own learning
#' algorithm, derive from this class and override the \code{update()}.
#'
#' Certain optimizers (such as AdaGrad) require additional storage. This can be
#' allocated and initialized during construction.
#'
#' @param parameters – list of network parameters
#' @param lr (output of learning_rate_schedule()) – learning rate schedule_schedule
#' @param as_matrix
#'
#' @seealso \code{\link{update_user_learner}}
#'
#' @export
UserLearner <- function(parameters, lr_schedule, as_matrix = TRUE) {
cntk$learners$UserLearner(
parameters,
lr_schedule,
as_numpy = as_matrix
)
}
#' @param learner
#'
#' @param gradient_values
#' @param training_sample_count
#' @param sweep_end
#'
#' @export
update_user_learner <- function(learner, gradient_values, training_sample_count,
sweep_end) {
learner$update_user_learner(
gradient_values,
training_sample_count,
sweep_end
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param rho (float) – exponential smooth factor for each minibatch.
#' @param epsilon (float, default 0.00001) - added to avoid division by 0
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#' @param use_mean_gradient (bool, default False) – use averaged gradient as input to learner. Defaults to the value returned by default_use_mean_gradient_value().
#'
#' @export
learner_adadelta <- function(parameters, lr, rho, epsilon,
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = 0,
gradient_clipping_with_truncation = TRUE,
use_mean_gradient = FALSE) {
cntk$learners$adadelta(
parameters,
lr,
rho,
epsilon,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation,
use_mean_gradient = use_mean_gradient
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param need_ave_multiplier
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#'
#' @export
learner_adagrad <- function(parameters, lr, need_ave_multiplier = TRUE,
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE) {
cntk$learners$adagrad(
parameters,
lr,
need_ave_multiplier = need_ave_multiplier,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param momentum
#' @param unit_gain
#' @param variance_momentum
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#' @param epsilon (float, default 0.00001) - added to avoid division by 0
#' @param adamax
#'
#' @export
learner_adam <- function(parameters, lr, momentum,
unit_gain = cntk$default_unit_gain_value(),
variance_momentum = cntk$ops$momentum_as_time_constant_schedule(720000),
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE,
epsilon = 1e-8, adamax = FALSE) {
cntk$learners$adam(
parameters,
lr,
momentum,
unit_gain = unit_gain,
variance_momentum = variance_momentum,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation,
epsilon = epsilon,
adamax = adamax
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param momentum
#' @param unit_gain
#' @param variance_momentum
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#'
#' @export
learner_fsadagrad <- function(parameters, lr, momentum,
unit_gain = cntk$default_unit_gain_value(),
variance_momentum = cntk$ops$momentum_as_time_constant_schedule(720000),
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE) {
cntk$learners$fsadagrad(
parameters,
lr,
momentum,
unit_gain = unit_gain,
variance_momentum = variance_momentum,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation
)
}
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#'
#' @param unit
#' @param epoch_size
#'
#' @export
learning_rate_schedule <- function(lr, unit, epoch_size = NULL) {
cntk$learners$learning_rate_schedule(
lr,
unit,
epoch_size = to_int(epoch_size)
)
}
#' @param momentum
#'
#' @param epoch_size
#'
#' @export
momentum_as_time_constant_schedule <- function(momentum, epoch_size = NULL) {
cntk$learners$momentum_as_time_constant_schedule(
momentum,
epoch_size = to_int(epoch_size)
)
}
#' @param momentum
#'
#' @param epoch_size
#'
#' @export
momentum_schedule <- function(momentum, epoch_size = NULL) {
cntk$learners$momentum_schedule(
momentum,
epoch_size = to_int(epoch_size)
)
}
#' Creates a Momentum SGD learner instance to learn the parameters.
#'
#' @param parameters – list of network parameters list of network parameters to tune.
#' @param lr (output of learning_rate_schedule()) – learning rate schedule output of \code{learning_rate_schedule}
#' @param momentum output of \code{momentum_schdule} or \code{momentum_as_time_constant_schedule}
#' @param unit_gain logical whether to interpret momentum as a unit-gain filter
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional) double of l1 regularization
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0 double of l2 regularization
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0 double of noise injection
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity. double of gradient clipping threshold per sample
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation logical for gradient clipping with truncation
#' @param use_mean_gradient (bool, default False) – use averaged gradient as input to learner. Defaults to the value returned by default_use_mean_gradient_value(). logical use averaged gradient as input to learner.
#'
#' @references \url{https://www.cntk.ai/pythondocs/cntk.learners.html#cntk.learners.momentum_sgd}
#' @export
learner_momentum_sgd <- function(parameters, lr, momentum,
unit_gain = cntk$default_unit_gain_value(),
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE,
use_mean_gradient = FALSE)
{
cntk$learners$momentum_sgd(
parameters,
lr,
momentum,
unit_gain = unit_gain,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation,
use_mean_gradient = use_mean_gradient
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param momentum
#' @param unit_gain
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#'
#' @export
learner_nesterov <- function(parameters, lr, momentum,
unit_gain = cntk$default_unit_gain_value(),
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE) {
cntk$learners$nesterov(
parameters,
lr,
momentum,
unit_gain = unit_gain,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param gamma
#' @param inc
#' @param dec
#' @param max
#' @param min
#' @param need_ave_multiplier
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#'
#' @export
learner_rmsprop <- function(parameters, lr, gamma, inc, dec, max, min,
need_ave_multiplier = TRUE,
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE) {
cntk$learners$rmsprop(
parameters,
lr,
gamma,
inc,
dec,
max,
min,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#'
#' @export
learner_sgd <- function(parameters, lr,
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE) {
cntk$learners$sgd(
parameters,
lr,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation
)
}
#' @param schedule
#'
#' @param unit
#' @param epoch_size
#'
#' @export
training_parameter_schedule <- function(schedule, unit, epoch_size = NULL) {
cntk$learners$training_parameter_schedule(
schedule,
unit,
epoch_size = to_int(epoch_size)
)
}
#' @param update_func
#'
#' @param parameters – list of network parameters
#'
#' @export
universal_learner <- function(update_func, parameters) {
cntk$learners$universal(update_func, parameters)
}
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#' Learner
#'
#' Abstraction for learning a subset of parameters of a learnable function
#' using first order gradient values. For example momentum, AdaGrad, RMSProp,
#' etc. are different types of learners with their own algorithms for learning
#' parameter values using first order gradients. To instantiate a concrete
#' learner, use the factory methods in this module.
#'
#' @param parameters – list of network parameters list of parameter associated
#' with this learner
#' @param learning_rate_schedule get_learning_rate(learner)
#' @example \dontrun{
#' reset_learning_rate(learner, learning_rate)
#' update_learner(learner, gradient_values, training_sample_count)
#' }
#'
#' @export
Learner <- function(parameters, learning_rate_schedule) {
cntk$learners$Learner(
parameters,
learningRateSchedule
)
}
#' Get Learner Learning Rate
#'
#' @param learner
#'
#' @export
get_learning_rate <- function(learner) {
learner$get_learning_rate()
}
#' Reset Learner Learning Rate
#'
#' @param learner
#' @param learning_rate
#'
#' @export
reset_learning_rate <- function(learner, learning_rate) {
learner$reset_learning_rate(learning_rate)
}
#' @param learner
#'
#' @param gradient_values
#' @param training_sample_count
#'
#' @export
update_learner <- function(learner, gradient_values, training_sample_count) {
learner$update_learner(gradient_values, training_sample_count)
}
#' @param value
#'
#' @export
UnitType <- function(value) {
reticulate::py_get_attr(cntk$learners$UnitType, value)
}
#' UserLearner
#'
#' Base class of all user-defined learners. To implement your own learning
#' algorithm, derive from this class and override the \code{update()}.
#'
#' Certain optimizers (such as AdaGrad) require additional storage. This can be
#' allocated and initialized during construction.
#'
#' @param parameters – list of network parameters
#' @param lr (output of learning_rate_schedule()) – learning rate schedule_schedule
#' @param as_matrix
#'
#' @seealso \code{\link{update_user_learner}}
#'
#' @export
UserLearner <- function(parameters, lr_schedule, as_matrix = TRUE) {
cntk$learners$UserLearner(
parameters,
lr_schedule,
as_numpy = as_matrix
)
}
#' @param learner
#'
#' @param gradient_values
#' @param training_sample_count
#' @param sweep_end
#'
#' @export
update_user_learner <- function(learner, gradient_values, training_sample_count,
sweep_end) {
learner$update_user_learner(
gradient_values,
training_sample_count,
sweep_end
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param rho (float) – exponential smooth factor for each minibatch.
#' @param epsilon (float, default 0.00001) - added to avoid division by 0
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#' @param use_mean_gradient (bool, default False) – use averaged gradient as input to learner. Defaults to the value returned by default_use_mean_gradient_value().
#'
#' @export
learner_adadelta <- function(parameters, lr, rho, epsilon,
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = 0,
gradient_clipping_with_truncation = TRUE,
use_mean_gradient = FALSE) {
cntk$learners$adadelta(
parameters,
lr,
rho,
epsilon,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation,
use_mean_gradient = use_mean_gradient
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param need_ave_multiplier
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#'
#' @export
learner_adagrad <- function(parameters, lr, need_ave_multiplier = TRUE,
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE) {
cntk$learners$adagrad(
parameters,
lr,
need_ave_multiplier = need_ave_multiplier,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param momentum
#' @param unit_gain
#' @param variance_momentum
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#' @param epsilon (float, default 0.00001) - added to avoid division by 0
#' @param adamax
#'
#' @export
learner_adam <- function(parameters, lr, momentum,
unit_gain = cntk$default_unit_gain_value(),
variance_momentum = cntk$ops$momentum_as_time_constant_schedule(720000),
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE,
epsilon = 1e-8, adamax = FALSE) {
cntk$learners$adam(
parameters,
lr,
momentum,
unit_gain = unit_gain,
variance_momentum = variance_momentum,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation,
epsilon = epsilon,
adamax = adamax
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param momentum
#' @param unit_gain
#' @param variance_momentum
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#'
#' @export
learner_fsadagrad <- function(parameters, lr, momentum,
unit_gain = cntk$default_unit_gain_value(),
variance_momentum = cntk$ops$momentum_as_time_constant_schedule(720000),
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE) {
cntk$learners$fsadagrad(
parameters,
lr,
momentum,
unit_gain = unit_gain,
variance_momentum = variance_momentum,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation
)
}
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#'
#' @param unit
#' @param epoch_size
#'
#' @export
learning_rate_schedule <- function(lr, unit, epoch_size = NULL) {
cntk$learners$learning_rate_schedule(
lr,
unit,
epoch_size = to_int(epoch_size)
)
}
#' @param momentum
#'
#' @param epoch_size
#'
#' @export
momentum_as_time_constant_schedule <- function(momentum, epoch_size = NULL) {
cntk$learners$momentum_as_time_constant_schedule(
momentum,
epoch_size = to_int(epoch_size)
)
}
#' @param momentum
#'
#' @param epoch_size
#'
#' @export
momentum_schedule <- function(momentum, epoch_size = NULL) {
cntk$learners$momentum_schedule(
momentum,
epoch_size = to_int(epoch_size)
)
}
#' Creates a Momentum SGD learner instance to learn the parameters.
#'
#' @param parameters – list of network parameters list of network parameters to tune.
#' @param lr (output of learning_rate_schedule()) – learning rate schedule output of \code{learning_rate_schedule}
#' @param momentum output of \code{momentum_schdule} or \code{momentum_as_time_constant_schedule}
#' @param unit_gain logical whether to interpret momentum as a unit-gain filter
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional) double of l1 regularization
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0 double of l2 regularization
#' @param 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
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity. double of gradient clipping threshold per sample
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation logical for gradient clipping with truncation
#' @param 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.
#'
#' @references \url{https://www.cntk.ai/pythondocs/cntk.learners.html#cntk.learners.momentum_sgd}
#' @export
learner_momentum_sgd <- function(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)
{
cntk$learners$momentum_sgd(
parameters,
lr,
momentum,
unit_gain = unit_gain,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation,
use_mean_gradient = use_mean_gradient
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param momentum
#' @param unit_gain
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#'
#' @export
learner_nesterov <- function(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) {
cntk$learners$nesterov(
parameters,
lr,
momentum,
unit_gain = unit_gain,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param gamma
#' @param inc
#' @param dec
#' @param max
#' @param min
#' @param need_ave_multiplier
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#'
#' @export
learner_rmsprop <- function(parameters, lr, gamma, inc, dec, max, min,
need_ave_multiplier = TRUE,
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE) {
cntk$learners$rmsprop(
parameters,
lr,
gamma,
inc,
dec,
max,
min,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation
)
}
#' @param parameters – list of network parameters
#'
#' @param lr (output of learning_rate_schedule()) – learning rate schedule
#' @param l1_regularization_weight (float, optional) – the L1 regularization weight per sample, defaults to 0.0(float, optional)
#' @param l2_regularization_weight (float, optional) – the L2 regularization weight per sample, defaults to 0.0
#' @param gaussian_noise_injection_std_dev (float, optional) – the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0
#' @param gradient_clipping_threshold_per_sample (float, optional) – clipping threshold per sample, defaults to infinity.
#' @param gradient_clipping_with_truncation (bool, default True) – use gradient clipping with truncation
#'
#' @export
learner_sgd <- function(parameters, lr,
l1_regularization_weight = 0,
l2_regularization_weight = 0,
gaussian_noise_injection_std_dev = 0,
gradient_clipping_threshold_per_sample = np$inf,
gradient_clipping_with_truncation = TRUE) {
cntk$learners$sgd(
parameters,
lr,
l1_regularization_weight = l1_regularization_weight,
l2_regularization_weight = l2_regularization_weight,
gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev,
gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample,
gradient_clipping_with_truncation = gradient_clipping_with_truncation
)
}
#' @param schedule
#'
#' @param unit
#' @param epoch_size
#'
#' @export
training_parameter_schedule <- function(schedule, unit, epoch_size = NULL) {
cntk$learners$training_parameter_schedule(
schedule,
unit,
epoch_size = to_int(epoch_size)
)
}
#' @param update_func
#'
#' @param parameters – list of network parameters
#'
#' @export
universal_learner <- function(update_func, parameters) {
cntk$learners$universal(update_func, parameters)
}
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