update URLs

Author: t-kalinowski

DESCRIPTION

@@ -23,7 +23,7 @@ Description: Interface to 'Keras' <https://keras.io>, a high-level neural
   combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
 Encoding: UTF-8
 License: MIT + file LICENSE
-URL: https://keras.rstudio.com
+URL: https://tensorflow.rstudio.com/
 BugReports: https://github.com/rstudio/keras/issues
 Depends:
     R (>= 3.4)

R/metrics.R

@@ -1307,7 +1307,7 @@ metric_sparse_top_k_categorical_accuracy <- py_metric_wrapper(
 #' metric by name just like you do with built in keras metrics.
 #'
 #' Documentation on the available backend tensor functions can be
-#' found at <https://keras.rstudio.com/articles/backend.html#backend-functions>.
+#' found at <https://tensorflow.rstudio.com/reference/keras/#backend>.
 #'
 #' Alternative ways of supplying custom metrics:
 #'  +  `custom_metric():` Arbitrary R function.

R/model-custom.R

@@ -2,16 +2,14 @@
 #' (Deprecated) Create a Keras custom model
 #'
 #' `keras_model_custom()` is soft-deprecated. Please define custom models by
-#' subclassing `keras$Model` directly using [`%py_class%`] or [`R6::R6Class()`].
+#' subclassing `keras$Model` directly using [`%py_class%`] or [`R6::R6Class()`],
+#' or by calling `new_model_class()`.
 #'
 #' @param model_fn Function that returns an R custom model
 #' @param name Optional name for model
 #'
 #' @return A Keras model
 #'
-#' @details For documentation on using custom models, see
-#'   <https://keras.rstudio.com/articles/custom_models.html>.
-#'
 #' @keywords internal
 #' @export
 keras_model_custom <- function(model_fn, name = NULL) {

R/optimizers.R

@@ -72,7 +72,7 @@
 #' @return Optimizer for use with \code{\link{compile.keras.engine.training.Model}}.
 #'
 #' @seealso
-#'   +  <https://www.tensorflow.org/api_docs/python/keras/optimizers/optimizer_experimental/adadelta/Adadelta>
+#'   +  <https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/Adadelta>
 #' @export
 optimizer_adadelta <-
 function(learning_rate = 0.001, rho = 0.95, epsilon = 1e-07,
@@ -150,7 +150,7 @@ do.call(keras$optimizers$Adadelta, args)
 #' @return Optimizer for use with \code{\link{compile.keras.engine.training.Model}}.
 #'
 #' @seealso
-#'   +  <https://www.tensorflow.org/api_docs/python/keras/optimizers/optimizer_experimental/adagrad/Adagrad>
+#'   +  <https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/Adagrad>
 #' @export
 optimizer_adagrad <-
 function(learning_rate = 0.001, initial_accumulator_value = 0.1,
@@ -170,7 +170,7 @@ do.call(keras$optimizers$Adagrad, args)
 #' adaptive estimation of first-order and second-order moments.
 #'
 #' According to
-#' [Kingma et al., 2014](http://arxiv.org/abs/1412.6980),
+#' [Kingma et al., 2014](https://arxiv.org/abs/1412.6980),
 #' the method is "*computationally
 #' efficient, has little memory requirement, invariant to diagonal rescaling of
 #' gradients, and is well suited for problems that are large in terms of
@@ -241,7 +241,7 @@ do.call(keras$optimizers$Adagrad, args)
 #' @return Optimizer for use with \code{\link{compile.keras.engine.training.Model}}.
 #'
 #' @seealso
-#'   +  <https://www.tensorflow.org/api_docs/python/keras/optimizers/optimizer_experimental/adam/Adam>
+#'   +  <https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam>
 #' @export
 optimizer_adam <-
 function(learning_rate = 0.001, beta_1 = 0.9, beta_2 = 0.999,
@@ -340,7 +340,7 @@ do.call(keras$optimizers$Adam, args)
 #' @return Optimizer for use with \code{\link{compile.keras.engine.training.Model}}.
 #'
 #' @seealso
-#'   +  <https://www.tensorflow.org/api_docs/python/keras/optimizers/optimizer_experimental/adamax/Adamax>
+#'   +  <https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/Adamax>
 #' @export
 optimizer_adamax <-
 function(learning_rate = 0.001, beta_1 = 0.9, beta_2 = 0.999,
@@ -469,7 +469,7 @@ do.call(keras$optimizers$Adamax, args)
 #' @return Optimizer for use with \code{\link{compile.keras.engine.training.Model}}.
 #'
 #' @seealso
-#'   +  <https://www.tensorflow.org/api_docs/python/keras/optimizers/optimizer_experimental/ftrl/Ftrl>
+#'   +  <https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/Ftrl>
 #' @export
 optimizer_ftrl <-
 function(learning_rate = 0.001, learning_rate_power = -0.5,
@@ -552,7 +552,7 @@ do.call(keras$optimizers$Ftrl, args)
 #' @return Optimizer for use with \code{\link{compile.keras.engine.training.Model}}.
 #'
 #' @seealso
-#'   +  <https://www.tensorflow.org/api_docs/python/keras/optimizers/optimizer_experimental/nadam/Nadam>
+#'   +  <https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/Nadam>
 #' @export
 optimizer_nadam <-
 function(learning_rate = 0.001, beta_1 = 0.9, beta_2 = 0.999,
@@ -642,7 +642,7 @@ do.call(keras$optimizers$Nadam, args)
 #' @return Optimizer for use with \code{\link{compile.keras.engine.training.Model}}.
 #'
 #' @seealso
-#'   +  <https://www.tensorflow.org/api_docs/python/keras/optimizers/optimizer_experimental/rmsprop/RMSprop>
+#'   +  <https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/RMSprop>
 #' @export
 optimizer_rmsprop <-
 function(learning_rate = 0.001, rho = 0.9, momentum = 0, epsilon = 1e-07,
@@ -736,7 +736,7 @@ do.call(keras$optimizers$RMSprop, args)
 #' @return Optimizer for use with \code{\link{compile.keras.engine.training.Model}}.
 #'
 #' @seealso
-#'   +  <https://www.tensorflow.org/api_docs/python/keras/optimizers/optimizer_experimental/sgd/SGD>
+#'   +  <https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/SGD>
 #' @export
 optimizer_sgd <-
 function(learning_rate = 0.01, momentum = 0, nesterov = FALSE,

R/package.R

@@ -16,7 +16,7 @@
 #'   [CNTK](https://github.com/Microsoft/cntk),
 #'   or [Theano](https://github.com/Theano/Theano).
 #'
-#' See the package website at <https://keras.rstudio.com> for complete documentation.
+#' See the package website at <https://tensorflow.rstudio.com> for complete documentation.
 #'
 #' @import methods
 #' @import R6

R/py-classes.R

@@ -317,6 +317,8 @@ r_formals_to_py__signature__ <- function(fn) {
 # *) method calls respect user-supplied `convert` values for all args
 #
 
+# @seealso <https://tensorflow.rstudio.com/articles/new-guides/python_subclasses.html>
+
 
 #' Make a python class constructor
 #'
@@ -329,7 +331,6 @@ r_formals_to_py__signature__ <- function(fn) {
 #' @export
 #' @aliases py_class
 #'
-#' @seealso <https://keras.rstudio.com/articles/new-guides/python_subclasses.html>
 #'
 #' @examples
 #' \dontrun{

index.Rmd

@@ -112,7 +112,7 @@ y_test <- to_categorical(y_test, 10)
 
 ### Defining the Model
 
-The core data structure of Keras is a model, a way to organize layers. The simplest type of model is the [Sequential model](https://keras.rstudio.com/articles/sequential_model.html), a linear stack of layers.
+The core data structure of Keras is a model, a way to organize layers. The simplest type of model is the [Sequential model](https://tensorflow.rstudio.com/articles/sequential_model.html), a linear stack of layers.
 
 We begin by creating a sequential model and then adding layers using the pipe (`%>%`) operator:
 

man-roxygen/roxlate-keras-backend.R

@@ -1,11 +1,11 @@
 #' @md
-#' 
+#'
 #' @section Keras Backend:
-#' 
+#'
 #' This function is part of a set of Keras backend functions that enable
-#' lower level access to the core operations of the backend tensor engine 
-#' (e.g. TensorFlow, CNTK, Theano, etc.). 
-#' 
+#' lower level access to the core operations of the backend tensor engine
+#' (e.g. TensorFlow, CNTK, Theano, etc.).
+#'
 #' You can see a list of all available backend functions here:
-#' <https://keras.rstudio.com/articles/backend.html#backend-functions>.
-#' 
+#' <https://tensorflow.rstudio.com/reference/keras/index.html#backend>.
+#'