tidy

Author: t-kalinowski

R/layers-preprocessing.R

@@ -2360,15 +2360,6 @@ set_vocabulary <- function(object, vocabulary, idf_weights=NULL, ...) {
 }
 
 
-## TODO: TextVectorization has a compile() method. investigate if this is
-## actually useful to export
-#compile.keras.engine.base_preprocessing_layer.PreprocessingLayer <-
-function(object, run_eagerly = NULL, steps_per_execution = NULL, ...) {
-  args <- capture_args(ignore="object")
-  do.call(object$compile, args)
-}
-
-
 #' A preprocessing layer to convert raw audio signals to Mel spectrograms.
 #'
 #' @description
@@ -2585,11 +2576,12 @@ function (object, fft_length = 2048L, sequence_stride = 512L,
 adapt <- function(object, data, ..., batch_size=NULL, steps=NULL) {
   if (!is_py_object(data))
     data <- keras_array(data)
-  # TODO: use as_tensor() here
 
-  args <- capture_args(list(batch_size = as_nullable_integer,
-                             step = as_nullable_integer),
-                        ignore = c("object", "data"))
+  args <- capture_args(
+    list(batch_size = as_nullable_integer,
+         step = as_nullable_integer),
+    ignore = c("object", "data")
+  )
   # `data` named to `dataset` in keras3 keras.utils.FeatureSpace
   # pass it as a positional arg
   args <- c(list(data), args)

R/metrics.R

@@ -3673,11 +3673,3 @@ py_to_r_wrapper.keras.src.metrics.metric.Metric <- py_to_r_wrapper.keras.src.los
 custom_metric <- function(name, metric_fn) {
   py_func2(metric_fn, convert = TRUE, name = name)
 }
-
-# TODO: export ??
-# can be used w/ activations, regularizers, metrics, loss, anything else
-# where it helps to have a name
-custom_fn <- function(name, fn) {
-  py_func2(fn, TRUE, name)
-}
-

R/model-training.R

@@ -913,7 +913,6 @@ function (object, x, y = NULL, sample_weight = NULL, class_weight = NULL)
 #' @export
 summary.keras.src.models.model.Model <- function(object, ...) {
     writeLines(f <- format.keras.src.models.model.Model(object, ...))
-  # TODO: knit_print...?
     invisible(f)
 }
 
@@ -927,6 +926,7 @@ function(x,
          expand_nested = FALSE,
          show_trainable = NA,
          ...,
+         # TODO: add force_ascii arg
          # force_ascii ... (impl in man/roxygen/meta.R)
          # width = getOption("width"),
          # rich = TRUE, ??
@@ -945,9 +945,6 @@ function(x,
       args$show_trainable <- built && as.logical(length(x$non_trainable_weights))
     }
 
-    # args$print_fn <- function(x, ...) {browser(); x}
-
-    # Do we need to check for model$built before calling summary?
     with_rich_config(
       out <- trimws(py_capture_output(do.call(x$summary, args)))
     )

R/package.R

@@ -51,7 +51,6 @@ keras <- NULL
 
 .onLoad <- function(libname, pkgname) {
 
-  # TODO: remove this requireNamespace()
   # tensorflow:::.onLoad() registers some reticulate class filter hooks
   # we need to identify tensors reliably.
   requireNamespace("tensorflow", quietly = TRUE)

R/py-classes.R

@@ -458,6 +458,14 @@ def wrap_fn(r_fn):
 }
 
 
+# Export this if the API calls for a bare named function somewhere
+custom_fn <- function(name, fn) {
+  py_func2(fn, TRUE, name)
+}
+
+
+
+
 # TODO: (maybe?) factor out a py_class() function,
 # funnel r_to_py.R6ClassGenerator() and %py_class%() to go through py_class()
 # export py_class()

R/r-utils.R

@@ -464,10 +464,6 @@ as_py_function <- function(fn, default_name = "r_func") {
     attr(fn, "name", TRUE) %||%
     default_name
 
-  # TODO: try to generate a pretty name using deparse(substitute(x)) would need
-  # to update capture_args() to construct calls to transformers so that
-  # substitute will work here.
-  # if(is.null(name)) { name <- as_py_name(deparse1(substitute(x)))}
   py_func2(fn, convert = TRUE, name = name)
 }