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simplify symbol remapping
In Keras 2.13, almost all symbols moved from `keras.*` to `keras.src.*`. The first approach registered aliases for all the necessary S3 methods, and checked for both `keras.src.*` and `keras.src.*` in various parts in the sources. The new, simpler approach involves registereing a class filter with reticualte, so any S3 class name starting with `keras.src.*` is automatically renamed to `keras.*`.
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-71
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‎NAMESPACE

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@@ -3,38 +3,26 @@
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S3method("$",py_R6ClassGenerator)
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S3method(as.data.frame,keras_training_history)
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S3method(compile,keras.engine.training.Model)
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S3method(compile,keras.src.engine.training.Model)
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S3method(evaluate,keras.engine.training.Model)
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S3method(evaluate,keras.src.engine.training.Model)
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S3method(export_savedmodel,keras.engine.training.Model)
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S3method(export_savedmodel,keras.src.engine.training.Model)
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S3method(fit,keras.engine.training.Model)
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S3method(fit,keras.src.engine.training.Model)
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S3method(format,keras.engine.training.Model)
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S3method(format,keras.src.engine.training.Model)
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S3method(pillar::type_sum,py_R6ClassGenerator)
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S3method(plot,keras.engine.training.Model)
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S3method(plot,keras.src.engine.training.Model)
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S3method(plot,keras_training_history)
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S3method(predict,keras.engine.training.Model)
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S3method(predict,keras.src.engine.training.Model)
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S3method(print,keras.engine.training.Model)
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S3method(print,keras.src.engine.training.Model)
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S3method(print,keras_training_history)
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S3method(print,kerastools.model.RModel)
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S3method(print,py_R6ClassGenerator)
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S3method(py_str,keras.engine.training.Model)
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S3method(py_str,keras.src.engine.training.Model)
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S3method(py_to_r,keras.utils.generic_utils.SharedObjectConfig)
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S3method(py_to_r_wrapper,keras.engine.base_layer.Layer)
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S3method(py_to_r_wrapper,keras.engine.training.Model)
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S3method(py_to_r_wrapper,keras.src.engine.base_layer.Layer)
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S3method(py_to_r_wrapper,keras.src.engine.training.Model)
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S3method(py_to_r_wrapper,kerastools.model.RModel)
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S3method(r_to_py,R6ClassGenerator)
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S3method(r_to_py,keras_layer_wrapper)
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S3method(summary,keras.engine.training.Model)
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S3method(summary,keras.src.engine.training.Model)
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S3method(summary,kerastools.model.RModel)
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export("%<-%")
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export("%<-active%")

‎R/layers-core.R

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@@ -562,4 +562,4 @@ compose_layer.keras.models.Sequential <- function(object, layer, ...) {
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}
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compose_layer.keras.engine.sequential.Sequential <- compose_layer.keras.models.Sequential
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compose_layer.keras.src.engine.sequential.Sequential <- compose_layer.keras.models.Sequential
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# compose_layer.keras.src.engine.sequential.Sequential <- compose_layer.keras.models.Sequential

‎R/layers-embedding.R

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@@ -65,7 +65,7 @@ layer_embedding <-
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input_dim = as.integer,
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output_dim = as.integer,
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input_length = as_nullable_integer,
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batch_size = as_nullable_integer,
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batch_size = as_nullable_integer
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), ignore = "object")
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create_layer(keras$layers$Embedding, object, args)
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}

‎R/layers-normalization.R

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#' })
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#' ```
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#'
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#' @param object Layer or model object
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#'
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#' @param axis Integer, the axis that should be normalized (typically the features
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#' axis). For instance, after a `Conv2D` layer with
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#' `data_format="channels_first"`, set `axis=1` in `BatchNormalization`.

‎R/model-persistence.R

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@@ -342,8 +342,7 @@ model_from_yaml <- function(yaml, custom_objects = NULL) {
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#' @export
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serialize_model <- function(model, include_optimizer = TRUE) {
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if (!inherits(model, c("keras.engine.training.Model",
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"keras.src.engine.training.Model")))
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if (!inherits(model, "keras.engine.training.Model"))
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stop("You must pass a Keras model object to serialize_model")
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# write hdf5 file to temp file

‎R/model.R

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@@ -555,8 +555,7 @@ resolve_input_data <- function(x, y = NULL) {
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args$x <- as_generator(x)
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} else if (inherits(x, "python.builtin.iterator")) {
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args$x <- x
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} else if (inherits(x, c("keras.src.utils.data_utils.Sequence",
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"keras.utils.data_utils.Sequence"))) {
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} else if (inherits(x, "keras.utils.data_utils.Sequence")) {
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args$x <- x
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} else {
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if (!is.null(x))
@@ -577,8 +576,7 @@ resolve_validation_data <- function(validation_data) {
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args$validation_data <- as_generator(validation_data)
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else if (inherits(validation_data, "python.builtin.iterator"))
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args$validation_data <- validation_data
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else if (inherits(validation_data, c("keras.src.utils.data_utils.Sequence",
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"keras.utils.data_utils.Sequence")))
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else if (inherits(validation_data, "keras.utils.data_utils.Sequence"))
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args$validation_data <- validation_data
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else {
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args$validation_data <- keras_array(validation_data)
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FALSE
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}
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is_main_thread_generator.keras.src.preprocessing.sequence.TimeseriesGenerator <-
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is_main_thread_generator.keras_preprocessing.sequence.TimeseriesGenerator
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is_tensorflow_dataset <- function(x) {
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inherits(x, "tensorflow.python.data.ops.dataset_ops.DatasetV2") ||

‎R/package.R

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@@ -107,7 +107,7 @@ keras <- NULL
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# delay load keras
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keras <<- import(implementation_module, delay_load = list(
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priority = 10,
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priority = 10, # tensorflow priority == 5
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environment = "r-tensorflow",
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if (identical(module, "tensorflow.keras"))
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module <- "tensorflow.python.keras"
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# replace "tensorflow.python.keras.*" with "keras.*"
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classes <- sub(paste0("^", module), "keras", classes)
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# All python symbols moved in v2.13 under .src
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classes <- sub("^keras\\.src\\.", "keras.", classes)
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# let KerasTensor inherit all the S3 methods of tf.Tensor, but
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# KerasTensor methods take precedence.
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if(any(c("keras.src.engine.keras_tensor.KerasTensor",
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"keras.engine.keras_tensor.KerasTensor") %in% classes))
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if(any("keras.engine.keras_tensor.KerasTensor" %in% classes))
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classes <- unique(c("keras.engine.keras_tensor.KerasTensor",
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"tensorflow.tensor",
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classes))

‎R/zzz.R

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## keras internal symbols moved under `src` in TF 2.12
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#' @export
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compile.keras.src.engine.training.Model <- compile.keras.engine.training.Model
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#' @export
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evaluate.keras.src.engine.training.Model <- evaluate.keras.engine.training.Model
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#' @export
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export_savedmodel.keras.src.engine.training.Model <- export_savedmodel.keras.engine.training.Model
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#' @export
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fit.keras.src.engine.training.Model <- fit.keras.engine.training.Model
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#' @export
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format.keras.src.engine.training.Model <- format.keras.engine.training.Model
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#' @export
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plot.keras.src.engine.training.Model <- plot.keras.engine.training.Model
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#' @export
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predict.keras.src.engine.training.Model <- predict.keras.engine.training.Model
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#' @export
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print.keras.src.engine.training.Model <- print.keras.engine.training.Model
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#' @export
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py_str.keras.src.engine.training.Model <- py_str.keras.engine.training.Model
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#' @export
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py_to_r_wrapper.keras.src.engine.base_layer.Layer <- py_to_r_wrapper.keras.engine.base_layer.Layer
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#' @export
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py_to_r_wrapper.keras.src.engine.training.Model <- py_to_r_wrapper.keras.engine.training.Model
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#' @export
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summary.keras.src.engine.training.Model <- summary.keras.engine.training.Model
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as_generator.keras.src.utils.data_utils.Sequence <- as_generator.keras_preprocessing.sequence.TimeseriesGenerator

‎man/layer_batch_normalization.Rd

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‎tests/testthat/test-metrics.R

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@@ -143,8 +143,7 @@ test_metric <- function(metric, ...) {
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m <- metric(...)
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expect_s3_class(m, c("keras.metrics.Metric",
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'keras.metrics.base_metric.Metric',
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'keras.src.metrics.base_metric.Metric'))
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'keras.metrics.base_metric.Metric'))
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define_model() %>%
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compile(loss = loss,

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