Merge pull request #1204 from rstudio/fix-note

Fix note: no visible binding for global variable 'object'

Author: dfalbel

R/Layer.R

@@ -109,13 +109,13 @@ Layer <- function(classname, initialize, build = NULL, call = NULL,
   layer$`__module__` <- classname
 
   # build the function to be used
-  f <- function() {
+  f <- function(object) {
     .args <- as.list(match.call())[-c(1)]
     .args <- .args[names(.args) != "object"]
     create_layer(layer, object, .args)
   }
   formals(f) <- append(
-    list(object = quote(expr=)),
+    formals(f),
     formals(initialize)
   )
   attr(f, "layer") <- layer

R/applications.R

@@ -238,7 +238,7 @@ inception_v3_preprocess_input <- function(x) {
 #' preprocessing.
 #' 
 #' @section Reference:
-#'  - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261)(http://arxiv.org/abs/1512.00567)
+#'  - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261)(https://arxiv.org/abs/1512.00567)
 #'
 #' @export
 application_inception_resnet_v2 <- function(include_top = TRUE, weights = "imagenet", input_tensor = NULL, input_shape = NULL,

R/callbacks.R

@@ -186,7 +186,7 @@ callback_early_stopping <- function(monitor = "val_loss", min_delta = 0, patienc
 #' @family callbacks
 #'
 #' @export
-callback_remote_monitor <- function(root = "http://localhost:9000", path = "/publish/epoch/end/", 
+callback_remote_monitor <- function(root = "https://localhost:9000", path = "/publish/epoch/end/", 
                                     field = "data", headers = NULL, send_as_json = FALSE) {
 
   if (!have_requests())
@@ -745,4 +745,4 @@ normalize_callbacks <- function(callbacks) {
   callbacks
 }
 
-empty_fun <- function(batch, logs = NULL) {}
+empty_fun <- function(batch, logs = NULL) {}

man/application_inception_resnet_v2.Rd

@@ -2,7 +2,7 @@
 
 This script can run on CPU in a few minutes.
 
-Results example: http://i.imgur.com/4nj4KjN.jpg
+Results example: https://i.imgur.com/4nj4KjN.jpg
 '''
 from __future__ import print_function
 

man/callback_remote_monitor.Rd

@@ -416,7 +416,7 @@ def train(run_name, start_epoch, stop_epoch, img_w):
         input_shape = (img_w, img_h, 1)
 
     fdir = os.path.dirname(get_file('wordlists.tgz',
-                                    origin='http://www.mythic-ai.com/datasets/wordlists.tgz', untar=True))
+                                    origin='https://www.mythic-ai.com/datasets/wordlists.tgz', untar=True))
 
     img_gen = TextImageGenerator(monogram_file=os.path.join(fdir, 'wordlist_mono_clean.txt'),
                                  bigram_file=os.path.join(fdir, 'wordlist_bi_clean.txt'),

vignettes/examples/conv_filter_visualization.py

@@ -35,10 +35,10 @@
 # Data download:
 
 English to French sentence pairs.
-http://www.manythings.org/anki/fra-eng.zip
+https://www.manythings.org/anki/fra-eng.zip
 
 Lots of neat sentence pairs datasets can be found at:
-http://www.manythings.org/anki/
+https://www.manythings.org/anki/
 
 # References:
 

vignettes/examples/image_ocr.py

@@ -24,7 +24,7 @@
 of the data, with the window length = "lahead".
 This is similar to sklearn's "view_as_windows"
 with "window_shape" being a single number
-Ref: http://scikit-image.org/docs/0.10.x/api/skimage.util.html#view-as-windows
+Ref: https://scikit-image.org/docs/0.10.x/api/skimage.util.html#view-as-windows
 
 When lahead < tsteps, only the stateful LSTM converges because its
 statefulness allows it to see beyond the capability that lahead

vignettes/examples/lstm_seq2seq.py

@@ -13,7 +13,7 @@
         Results have shown that HRNN outperforms standard
         RNNs and may play some role in more sophisticated generation tasks like
         summarization or question answering.
-    - [Hierarchical recurrent neural network for skeleton based action recognition](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298714)
+    - [Hierarchical recurrent neural network for skeleton based action recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298714)
         Achieved state-of-the-art results on skeleton based action recognition with 3 levels
         of bidirectional HRNN combined with fully connected layers.