global-ish replace python -> Python

Author: t-kalinowski

NEWS.md

@@ -603,7 +603,7 @@ SavedModel format.
 - Don't re-export `install_tensorflow()` and `tf_config()` from tensorflow
   package.
 
-- `is_keras_available()` function to probe whether the Keras python
+- `is_keras_available()` function to probe whether the Keras Python
   package is available in the current environment.
 
 - `as.data.frame()` S3 method for Keras training history

R/install.R

@@ -1,4 +1,4 @@
-#' Install TensorFlow and Keras, including all python dependencies
+#' Install TensorFlow and Keras, including all Python dependencies
 #'
 #' This function will install Tensorflow and all Keras dependencies. This is a
 #' thin wrapper around [`tensorflow::install_tensorflow()`], with the only

R/preprocessing.R

@@ -213,10 +213,10 @@ text_one_hot <- function(input_text, n, filters = '!"#$%&()*+,-./:;<=>?@[\\]^_`{
 #'
 #' @param text Input text (string).
 #' @param n Dimension of the hashing space.
-#' @param hash_function if `NULL` uses python `hash` function, can be 'md5' or
-#'   any function that takes in input a string and returns a int. Note that
+#' @param hash_function if `NULL` uses the Python `hash()` function. Otherwise can be `'md5'` or
+#'   any function that takes in input a string and returns an int. Note that
 #'   `hash` is not a stable hashing function, so it is not consistent across
-#'   different runs, while 'md5' is a stable hashing function.
+#'   different runs, while `'md5'` is a stable hashing function.
 #' @param filters Sequence of characters to filter out such as
 #'   punctuation. Default includes basic punctuation, tabs, and newlines.
 #' @param lower Whether to convert the input to lowercase.
@@ -906,7 +906,7 @@ flow_images_from_directory <- function(
 #'   image data, which is useful to use in  `predict_generator()`).
 #'
 #' @note
-#' This functions requires that `pandas` (python module) is installed in the
+#' This functions requires that `pandas` (Python module) is installed in the
 #' same environment as `tensorflow` and `keras`.
 #'
 #' If you are using `r-tensorflow` (the default environment) you can install
@@ -928,7 +928,7 @@ flow_images_from_dataframe <- function(
   interpolation = "nearest", drop_duplicates = NULL) {
 
   if (!reticulate::py_module_available("pandas"))
-    stop("Pandas (python module) must be installed in the same environment as Keras.",
+    stop("Pandas (Python module) must be installed in the same environment as Keras.",
          'Install it using reticulate::virtualenv_install("pandas", envname = "r-tensorflow") ',
          'or reticulate::conda_install("pandas", envname = "r-tensorflow") depending on ',
          'the kind of environment you are using.')

R/utils.R

@@ -283,7 +283,7 @@ keras_array <- function(x, dtype = NULL) {
 
 #' Check if Keras is Available
 #'
-#' Probe to see whether the Keras python package is available in the current
+#' Probe to see whether the Keras Python package is available in the current
 #' system environment.
 #'
 #' @param version Minimum required version of Keras (defaults to `NULL`, no
@@ -429,7 +429,7 @@ capture_args <- function(cl, modifiers = NULL, ignore = NULL,
   if("..." %in% fn_arg_nms && !"..." %in% ignore) {
     assert_all_dots_named(envir, cl)
     # this might reorder args by assuming ... are last, but it doesn't matter
-    # since everything is supplied as a keyword arg to the python side anyway
+    # since everything is supplied as a keyword arg to the Python side anyway
     cl2 <- c(cl2, quote(...))
   }
 

vignettes/faq.Rmd

@@ -554,12 +554,12 @@ You can pass the `version` argument to check for a specific version of Keras.
 
 ### Keras Module
 
-Another consideration is gaining access to the underlying Keras python module. You might need to do this if you require lower level access to Keras than is provided for by the Keras R package. 
+Another consideration is gaining access to the underlying Keras Python module. You might need to do this if you require lower level access to Keras than is provided for by the Keras R package. 
 
 Since the Keras R package can bind to multiple different implementations of Keras (either the original Keras or the TensorFlow implementation of Keras), you should use the `keras::implementation()` function to obtain access to the correct python module. You can use this function within the `.onLoad` function of a package to provide global access to the module within your package. For example:
 
 ```{r}
-# Keras python module
+# Keras Python module
 keras <- NULL
 
 # Obtain a reference to the module from the keras R package

vignettes/new-guides/making_new_layers_and_models_via_subclassing.Rmd

@@ -70,7 +70,7 @@ y <- linear_layer(x)
 print(y)
 ```
 
-`Linear` behaves similarly to a layer present in the python interface to keras
+`Linear` behaves similarly to a layer present in the Python interface to keras
 (e.g., `keras$layers$Dense`).
 
 However, one additional step is needed to make it behave like the builtin layers
@@ -937,19 +937,19 @@ For more information, make sure to read the [Functional API guide](/guides/funct
 
 ## Defining custom layers and models in an R package
 
-Unfortunately you can't use anything that creates references to python objects, at the top-level of an R package.
+Unfortunately you can't use anything that creates references to Python objects, at the top-level of an R package.
 
 Here is why: when you build an R package, all the R files in the `R/` directory get sourced
 in an R environment (the package namespace), and then that environment is saved
 as part of the package bundle. Loading the package means restoring the saved R
 environment. This means that the R code only gets sourced once, at build time.
-If you create references to external objects (e.g., python objects) at package
+If you create references to external objects (e.g., Python objects) at package
 build time, they will be NULL pointers when the package is loaded, because the
 external objects they pointed to at build time no longer exist at load time.
 
-The solution is to delay creating references to python objects until run time.
+The solution is to delay creating references to Python objects until run time.
 Fortunately, `%py_class%`, `Layer()`, and `create_layer_wrapper(R6Class(...))`
-are all lazy about initializing the python reference, so they are safe to define
+are all lazy about initializing the Python reference, so they are safe to define
 and export in an R package.
 
 If you're writing an R package that uses keras and reticulate,