comments

Author: t-kalinowski

R/LearningRateSchedule.R

@@ -1,7 +1,7 @@
 #' Define a custom `LearningRateSchedule` class
 #'
 #' @description
-#' Subclass the keras learning rate schedule base class.
+#' Subclass the Keras `LearningRateSchedule` base class.
 #'
 #' You can use a learning rate schedule to modulate how the learning rate
 #' of your optimizer changes over time.
@@ -35,19 +35,17 @@
 #' ```{r}
 #' my_custom_learning_rate_schedule <- LearningRateSchedule(
 #'   classname = "MyLRSchedule",
-#'   initialize = function( initial_learning_rate) {
 #'
+#'   initialize = function(initial_learning_rate) {
 #'     self$initial_learning_rate <- initial_learning_rate
 #'   },
 #'
-#'
 #'   call = function(step) {
 #'     # note that `step` is a tensor
 #'     # and call() will be traced via tf_function() or similar.
 #'
 #'     str(step) # <KerasVariable shape=(), dtype=int64, path=SGD/iteration>
 #'
-#'
 #'     # print 'step' every 1000 steps
 #'     op_cond((step %% 1000) == 0,
 #'             \() {tensorflow::tf$print(step); NULL},
@@ -116,5 +114,3 @@ LearningRateSchedule <- function(classname,
   )
 
 }
-
-# TODO: should all optimizer accept a plain R function to `learning_rate`?

R/applications.R

@@ -3961,8 +3961,7 @@ set_preprocessing_attributes <- function(object, module) {
         x = function(x) {
           if (!is_py_object(x))
             x <- np_array(x)
-          if (inherits(x, "numpy.ndarray") &&
-              !py_bool(x$flags$writeable))
+          if (inherits(x, "numpy.ndarray") && !py_bool(x$flags$writeable))
             x <- x$copy()
           x
         }

R/layer-r-helpers.R

@@ -70,7 +70,7 @@ compose_layer <- function(object, layer, ...) {
 
 # TODO: use formals(x) in py_to_r_wrapper.Layer() to construct a better wrapper fn
 # (( though, all layer.__call__ signatures are generally (...), unless user
-#     implemented __call__() directly insteald of call() ))
+#     implemented __call__() directly instead of call() ))
 
 # This is used for:
 # - ALL layer instances (custom and builtin) and