Tidy

Author: t-kalinowski

R/utils.R

@@ -530,12 +530,6 @@ keras_array <- function(x, dtype = NULL) {
   if (is.null(x))
     return(x)
 
-  # reflect HDF5
-  # TODO: is this still relevent? Is it still the correct S3 class?
-  # keras 3 (and tf.keras 2) seems to not export a HDF5 wrapper ...
-  if (inherits(x, "keras.utils.io_utils.HDF5Matrix"))
-    return(x)
-
   # reflect tensors
   if (keras$ops$is_tensor(x))
     return(x)

tests/testthat/test-Layer.R

@@ -263,7 +263,6 @@ test_succeeds("custom layers can accept standard layer args like input_shape", {
 
 })
 
-# TODO: document change: inputs to the keras api being tensors/arrays now strictly enforced
 
 test_that("calling Layer() doesn't initialize Python", {
   expect_no_error(callr::r(function() {

tests/testthat/test-generators.R

@@ -144,9 +144,6 @@ test_succeeds("R function can be used as custom generator with multiple inputs",
     list(tuple(keras_array(1), keras_array(2)),
           keras_array(3))
   }
-  # TODO: this might need a PR up stream, or some chages to as_generator.function()
-  # right now it's very picky about tuple vs lists in generator yielded values.
-  # and tree.map_structure() / tf.next.flatten() are picky too.
 
   model %>% compile(loss = "mse", optimizer = "sgd")
   model %>% fit(generator, steps_per_epoch = 10,

tests/testthat/test-layers.R

@@ -3,7 +3,6 @@ context("layers")
 test_call_succeeds("layer_input", {
   layer_input(shape = c(32))
   input <- layer_input(shape = c(32), sparse = TRUE)
-  # TODO: arg rename: ragged -> sparse
 })
 
 test_call_succeeds("layer_dense", {